Home Uncategorized Literature Review Resources

Uncategorized

Literature Review Resources

Read at least 10 empirical articles in your general dissertation field that you have not read previously.

In the “Literature Review Resources” document that you submitted in RES-811, provide the following for each source that you are adding to the document:

The APA-formatted citation.
A brief annotation of the key points of the source. on the Literature Review Resource tool document provided 150 words minimum
An indication of whether the source has been added to (Y) or excluded from (N) your RefWorks list.

Highlight the additions to the document so your instructor can readily identify them.

Dementia and Geriatric Cognitive Disorders
About this Publication

Index
coverage:

February 1, 2013 – Current

Full-text
coverage:

January 1, 2015 – Current
Current denotes Gale’s entitled coverage based on our agreement with the publisher, though delays in availability due
to changing publication schedules, embargos or content delivery may occur.
(365 day delay due to publisher embargo period)

Title: Dementia and Geriatric Cognitive Disorders
Place of
Publication:
ISSN: 1420-8008
eISSN: 1421-9824
Format: Magazine/Journal
Publication
Frequency:

Monthly

Language: English
Audience: Academic
Peer-
Reviewed:

Yes

Gale Subject
Headings:

Alzheimer’s disease; Gerontology and geriatric care; Diseases and conditions; Medical specialties; Family medicine

Description:

As a unique forum devoted exclusively to the study of cognitive dysfunction, Dementia and Geriatric Cognitive
Disorders concentrates on Alzheimer s and Parkinson s disease, Huntington s chorea and other
neurodegenerative diseases. The journal draws from diverse related research disciplines such as psychogeriatrics,
neuropsychology, clinical neurology, morphology, physiology, genetic molecular biology, pathology, biochemistry,
immunology, pharmacology and pharmaceutics.

Publisher: S. Karger AG
Address: Allschwilerstrasse 10

P.O. Box Postfach Case Postale
CH-4009 BASEL
Switzerland

Sample APA Annotated Bibliography
Sample Title: Annotated Bibliography

Barrett, C. K. (1978). The Gospel according to St. John: An introduction with commentary and notes on the Greek text (2nd ed.). Westminster John Knox Press.
This commentary contains detailed exegesis of the Greek text that is worth considering regardless of whether one agrees with all of Barrett’s conclusions. Author provides a lengthy introduction (146 pages), including discussions on the historical, theological, and linguistic aspects of this book. Barrett is one of the greatest English-language commentary writers of the 20th century. However, he follows some older views on John’s gospel regarding source and background. Thus, it may be less useful than more modern commentaries, as much critical thought has moved beyond it.

Brown, O., & Robinson, J. (2012). Resilience in remarried families. South African Journal of Psychology, 42(1), 114–126.

Article reports on a salient research study in which the target was to identify and explore the resiliency factors that enable blended families to adjust and adapt. It involved a total of 35 participants: 19 parents and 16 adults. Descriptive statistics were used to analyze the biographical information. Correlation analysis was used to analyze the quantitative data, and content analysis was used to analyze the qualitative data. The research found that family hardiness, problem-solving, communication, family time, and routines showed a positive correlation for both parties. Common themes between the teen and parents such as spirituality, boundaries, communication, flexibility, and tolerance also had a strong positive correlation between both. The journal is a peer-reviewed journal. Ottilia Brown is also the author of other academic journal articles on learning disabilities

and ADD. Both of the author’s affiliations are with the Department of Psychology, Nelson Mandela Metropolitan University, South Africa. The article contains chart and graphs of the research study performed to aid in comprehension of the study.

Lamott, A. (1995). Bird by bird: Some instructions on writing and life. Anchor Books.
Taking a humorous approach to being a writer, this book is wry and anecdotal and offers advice on the writing life from plot development to jealousy, from perfectionism to struggling with one’s own internal critic. In the process, Lamott includes fun and productive writing exercises. She offers sane advice for those struggling with the anxieties of writing, but her main goal seems to be offering readers a reality check regarding writing, publishing, and struggling with one’s imperfections in the process. It is not a dry handbook of writing and/or publishing, but it is indispensable because of its honest perspective, down-to-earth humor, and encouraging approach. Parts of it could easily be included in the curriculum for a writing class. Several chapters in Part 1 address the writing process and would serve to generate discussion on students’ own drafting and revising processes. Some writing exercises would also be appropriate for generating classroom writing exercises. Students should find Lamott’s style both engaging and enjoyable.

International Review of Psychiatry, August 2008; 20(4): 382–388

Depression in Alzheimer’s disease: Phenomenology, clinical correlates
and treatment

SERGIO E. STARKSTEIN
1,2

, ROMINA MIZRAHI
3,4

, & BRIAN D. POWER
1,2

1
School of Psychiatry and Clinical Neurosciences, University of Western Australia,

2
Fremantle Hospital,

Western Australia, Australia,
3
Faculty of Medicine, University of Toronto, Ontario, Canada, and

4
Center for Addiction and Mental Health, PET centre, Toronto, Ontario, Canada

Abstract
Depression is one of the most frequent comorbid psychiatric disorders in Alzheimer’s disease and other dementias, and is
associated with worse quality of life, greater disability in activities of daily living, a faster cognitive decline, a high rate of
nursing home placement, relatively higher mortality, and a higher

frequency of depression and burden in caregivers.

Depression in Alzheimer’s disease is markedly under-diagnosed, and most patients with depression are either not treated or
are on subclinical doses of antidepressants. This is related to the lack of validated diagnostic criteria and specific instruments
to assess depression in dementia. Apathy and pathological affect-crying are the main differential diagnoses of depression in
Alzheimer’s disease. Left untreated, major depression in Alzheimer’s disease may last for about 12 months. Recent
randomized controlled trials demonstrated the efficacy of sertraline and moclobemide to treat depression in Alzheimer’s
disease. Other psychoactive compounds may be useful as well, but careful consideration must be given to potentially serious
side-effects.

Introduction

Depression is increasingly recognized as one of the

most frequent psychiatric disorders of Alzheimer’s

disease (AD). For the past ten years several studies

have examined the epidemiology, mechanism, clin-

ical correlates and treatment of

depression in AD

(Lyketsos & Olin, 2002). Nevertheless, important

issues regarding the phenomenon of depression in

AD remain to be properly clarified. First, there is no

general consensus on the most valid method to assess

and diagnose depression in AD. There is a major

overlap between symptoms of depression and symp-

toms of AD, and there are few instruments that

were specifically designed to assess depression in

dementia. These diagnostic limitations may at least

partially account for the high variability on the

frequency of depression in AD reported in several

studies (Lyketsos & Olin, 2002). Second, patients

with AD often show a variety of psychological and

behavioural problems such as anxiety, apathy, dis-

inhibition, irritability, and poor insight, among

others. Depression in AD rarely occurs in isolation

and is usually comorbid with one or more of the

above. Finally, there is a paucity of treatment studies

for depression in AD, although recent controlled

studies suggest that several antidepressant com-

pounds and psychological treatments may be

effective.

The aim of the present review is to provide a

critical analysis of the methods currently used to

diagnose depression in AD, to review the most

important clinical and psychiatric comorbidities of

this condition, and to examine the efficacy of

different treatment modalities

for depression in AD.

Diagnostic approaches to depression

in dementia

There are four main approaches to diagnose depres-

sion in chronic degenerative neurological conditions.

For the ‘inclusive approach’ (Cohen-Cole &

Stoudemire, 1987) symptoms which may or may

not be related to the physical illness are counted

towards the diagnostic criteria. For the ‘exclusive

approach’ symptoms specifically associated with the

neuropsychiatric disorder are counted but symptoms

that the interviewer feels are related to the physical

illness are not counted (Gallo, Rabins, Lyketsos,

Tien, & Anthony, 1997). For the ‘substitutive

Correspondence: Professor Sergio E. Starkstein, Education Building T-7, Fremantle Hospital, Fremantle, 6959 WA, Australia. Tel: 61 8 9431 2013.

E-mail: ses@cyllene.uwa.edu.au

ISSN 0954–0261 print/ISSN 1369–1627 online � 2008 Informa Healthcare USA, Inc.
DOI: 10.1080/09540260802094480

approach’ non-overlapping symptoms of depression

(e.g. psychological symptoms) are substituted for the

overlapping diagnostic criteria (Olin, Katz, Meyers,

Schneider, & Lebowitz, 2002a). Finally, the ‘specific

symptom approach’ only considers those symptoms

which are significantly more frequent in patients with

sad mood as compared to those without sad mood,

and the diagnostic criteria are modified to include

only specific symptoms (Starkstein, Jorge, Mizrahi,

& Robinson, 2005b). The question now arises as to

the best strategy to diagnose depression in AD.

A work group convened by the National Institutes

of Mental Health (NIMH) suggested that depression

in AD should be diagnosed using the inclusive

approach, which may minimize the rate of false

negatives (Olin et al., 2002a; Olin et al., 2002b).

This is an important suggestion given that depression

in AD largely goes unrecognized. On the other hand,

the inclusive strategy will maximize the rate of

false positives, with the concomitant over-diagnosis

and unnecessary treatment of depression. Therefore,

the ‘specific symptom approach’ is clinically more

appropriate, although its implementation will depend

on appropriate validation studies of depressive

symptoms in AD.

Diagnostic criteria for depression in AD

One of the main limitations to a valid diagnosis of

depression in dementia is the overlap between

symptoms of depression and symptoms of cognitive

and functional decline. For instance, insomnia,

psychomotor retardation, loss of energy, loss of

libido, and poor appetite are common among

patients with dementia, but are also clinical criteria

for a DSM-IV diagnosis of major depression. Several

studies examined the specificity of symptoms of

depression in AD. Chemerinski and co-workers

(2001) assessed a series of 233 patients with AD,

47 patients with depression without dementia, and

20 age-comparable healthy individuals for the fre-

quency of depressive symptoms. The main finding

was that the presence of sad mood was associated

with significantly higher scores on the HAM-D items

rating guilt, suicide, insomnia, loss of interest,

retardation, agitation, worry, anxiety, loss of

energy, loss of libido, and hypochondriasis, but not

loss of appetite. This finding demonstrates that both

physical and psychological symptoms of depression

are frequent among AD patients with

sad mood.

Another important finding of this study was that

AD patients without sad mood had no more

symptoms of depression than did age-comparable

healthy individuals, demonstrating that physical

and psychological symptoms of depression should

not be necessarily construed as mere epiphenomena

of a chronic neurological disease, but may be specific

to depression in AD. Furthermore, only 2% of

the AD patients met the DSM-IV criteria for

major depression without having sad mood or loss

of interest, confirming that symptoms of depression

are uncommon among AD patients without

sad mood.

Lyketsos and co-workers (2001) suggested an

empirically based taxonomy of psychiatric disorders

in AD. They suggested that the ‘individual symptom

approach’ may ignore the high comorbidity of

psychiatric symptoms and syndromes in AD and

should not be used. The authors examine a large

series of AD patients living in the community,

and using latent class analysis they identified a

group (27% of the participants) who exhibited

affective symptoms of depression, anxiety, irritability

and apathy. Based on these findings, they proposed

specific diagnostic criteria for AD-associated

affective disorder (Table I).

The NIMH workgroup proposed standardized

diagnostic criteria for depression in AD (Olin et al.,

2002a, 2002b) (Table II). These are similar to the

DSM-IV criteria for major depression, but with the

inclusion of irritability and social isolation replacing

loss of libido, and with loss of pleasure in response to

social contact replacing loss of interest. The NIMH

criteria require three symptoms for the diagnosis

of depression instead of the five required by the

DSM-IV criteria for a major depressive episode, and

symptoms are not required to be present nearly

every day (Table II).

Table I. Diagnostic criteria for AD-associated neuropsychiatric

disturbance (adapted from Lyketsos et al., 2001).

A. Meeting NINCDS/ADRDA criteria for probable AD.

B. A prominent disturbance of affect, disruptive to the patient

or the care environment and representing a change from

the patient’s baseline, as evidenced by the presence of one

or more of the following symptoms:
1. Depression

2. Iritability

3. Anxiety

4. Euphoria

C. One or more of the following associated symptoms must

be present:

1. Aggression

2. Psychomotor agitation

3. Delusions

4. Hallucinations

5. Sleep disturbance

6. Appetite disturbance

D. Symptoms from B and C co-occur most days, and

the disturbance has a duration of at least 2 weeks.

E. The disturbance has its first onset within two years or after

the onset of dementia.

F. The disturbance cannot be explained in its entirety by

another cause (e.g. a general medical condition,

medication, life stressors).

Depression in Alzheimer’s disease 383

Few studies have examined the validity of the

NIMH criteria. Starkstein and co-workers (2005b)

found that 41% of depressed AD patients in the stage

of severe dementia had no sad mood (i.e. depression

was diagnosed based on the presence of loss of

interest/anhedonia) suggesting that the NIMH

criteria may have low specificity for depression in

the late stages of dementia. A recent study by Vilalta-

Franch et al. (2006) compared the frequencies of

depression resulting from using 4 different diagnostic

schemes for depression in a sample of 491 patients

with AD. Frequencies of major or severe depression

were 5% for the International Classification of

Diseases, 10th Revision (ICD-10) criteria, 10% for

the Cambridge Examination for Mental Disorder of

the Elderly (CAMDEX) diagnostic criteria, 13% for

the DSM-IV criteria for major depression, and 27%

for the NIMH work group criteria. The authors

stressed that the requirements of loss of confidence/

self-esteem and irritability accounted for a large

variance of the discrepancies.

In a recent study Starkstein and co-workers

(2005c) examined the temporal stability of symptoms

of depression in a series of 65 AD patients with

depression at baseline that were re-assessed for

depression an average of 17 months later. At

follow-up about half of the sample had no depression

and showed a significant improvement in the

symptoms of sadness, guilt, suicidal ideation, dis-

ruption in sleep, loss of interest, loss of energy,

thoughts of death, social withdrawal, psychomotor

changes, changes in appetite/weight, and symptoms

of anxiety. On the other hand, no significant

between-group changes were found on scores of

irritability or apathy. The finding that symptoms

of anxiety co-varied over time with the presence of

depression suggests that anxiety is attributable

to depression rather than to dementia. On the other

hand, the lack of changes on scores of irritability

among patients with remission of depression suggests

that irritability should not be construed as a criterion

for depression in AD.

One limitation to assess mood changes in demen-

tia is that patients with AD may under-report

depressive symptoms due to poor awareness of

their behavioural and emotional changes.

Chemerinski and colleagues (2001) examined dis-

crepancies between patients’ and caregivers’ reports

of depressive symptoms, and found that AD patients

under-rated the severity of their depressive

symptoms as compared to reports provided by their

respective caregivers. Another confounder is that

depression in caregivers may influence depression

ratings of patients. However, while some studies

found a significant influence of caregivers’ depres-

sion on patients’ own ratings of depression (Teri &

Truax, 1994) other studies showed no significant

impact (Loewenstein et al., 2001). In a recent

12-week study on the efficacy of sertraline among

AD patients with depression, Rosenberg and cow-

orkers (2005) found that both caregiver depression

and burden decreased during the three months of

the study, although these changes were not asso-

ciated with improvement in patient mood as rated

by the caregivers.

Taken together, these findings suggest that both

physical and psychological symptoms of depression

are frequent in AD. However, in the absence of

sad mood, symptoms of depression are no more

prevalent in AD than in age-comparable individuals

without dementia, suggesting that physical and

psychological symptoms of depression are not

epiphenomena of a chronic neurological disease,

but constitute specific symptoms of a mood disorder.

The diagnosis of depression in dementia should be

based on a systematic mental status examination

leading to a psychiatric diagnosis based on the

presence of a syndromic symptom cluster using the

inclusive approach, until specific criteria are properly

validated. Future studies will provide specific

Table II. Provisional diagnostic criteria for depression of AD

(adapted from (Olin et al., 2002b).

A. Three or more of the following symptoms have been present

during the same 2-week period and represent a change from

previous functioning: at least one of the symptoms is either

(1) depressed mood or (2) decreased positive affect or

pleasure.
1. Clinically significant depressed mood

2. Decreased positive affect or pleasure in response to social

contacts and usual activities

3. Social isolation or withdrawal

4. Disruption in appetite

5. Disruption in sleep

6. Psychomotor changes

7. Irritability

8. Fatigue or loss of energy

9. Feelings of worthlessness, hopelessness, or excessive or

inappropriate guilt

10. Recurrent thoughts of death, suicidal ideation, plan or

attempt

B. All criteria met for dementia of the Alzheimer type.

C. The symptoms cause clinically significant distress or disrup-

tion in functioning.

D. The symptoms do not occur exclusively during the course of

a delirium.

E. The symptoms are not due to the direct physiological effects

of a substance.

F. The symptoms are not better accounted for by other

psychiatric conditions.

Specify if: Co-occurring onset: if onset antedates or co-occurs with

the AD symptoms

Post-AD onset: if onset occurs after AD symptoms

Specify: With psychosis of AD

With other significant behavioral signs or symptoms

With past history of mood disorders

384 S. E. Starkstein et al.

diagnostic criteria for a valid (i.e. clinically mean-

ingful) diagnosis of depression in AD.

Depression in AD: Diagnostic instruments

The presence of psychiatric signs and symptoms

should be assessed with a structured psychiatric

interview that includes questions for a variety of

behavioural and emotional symptoms, as well as

specific items to rate the presence and severity of

observed abnormal behaviours. The Structured

Clinical Interview for DSM-IV (SCID) is a semi-

structured psychiatric interview for making the major

Axis I diagnoses (Spitzer, Williams, Gibbon, & First

1992). This instrument includes an overview of the

present psychiatric complaint and past episodes of

psychopathology. This is followed by specific sec-

tions with open-ended questions to obtain symptom

description from the patient and caregivers. The

examiner should use all sources of information

available at the time of the evaluation, and use her/

his own judgement about the presence of a given

symptom. The full SCID usually takes from 60 to 90

minutes to complete and has been validated for use

in AD (Chemerinski, Petracca, Sabe, Kremer, &

Starkstein, 2001).

Depression rating scales are useful to rate the

severity of depressive disorders and may also be used

as screening instruments to determine the likelihood

of the presence or absence of mood disorders in

dementia. The Hamilton Depression Rating Scale

(HAM-D) is a 17-item interviewer-rated scale that

measures psychological and autonomic symptoms of

depression (Hamilton, 1960). This instrument

assesses the individual’s mood, self-esteem, suicidal

ideation and interest in daily life activity and work

productivity. Other HAM-D items, such as those

rating sleep problems, psychomotor retardation,

poor concentration, loss of energy, and hypochon-

driasis may be difficult to assess in patients with AD.

The Geriatric Depression Scale (GDS) is a short

screening instrument for depression in the elderly

that focuses on psychosocial aspects of depression,

avoiding symptoms that may overlap with medical

disorders or aging (Yesavage et al., 1982). One

limitation of the GDS is that it is a self-report

instrument, and some of the questions may be

difficult for patients with moderate or severe demen-

tia to answer reliably. The Cornell Scale for

Depression in Dementia (CSDD) was developed to

specifically assess depressive symptoms in dementia

and is based on information provided by a caregiver

and the patient (Alexopoulos, Abrams, Young, &

Shamoian, 1988). If the examiner considers that

some of the symptoms are secondary to the cognitive

deficits, those symptoms should not be considered

for the final score.

Frequency of depression in Alzhiemer’s

disease

Estimates of depression in AD depend on sampling

issues, diagnostic methods, and clinical manifesta-

tions. The prevalence of major and minor depression

has been estimated to range between 30% to 50%

(Olin et al., 2002a). Population studies reported a

prevalence of dysphoria of 20% and an 18-month

incidence of 18% (Lyketsos & Olin, 2002).

Clinical correlates of depression in

Alzheimer’s disease

Depression in AD has been associated with worse

quality of life, greater disability in activities of

daily living (ADLs), a faster cognitive decline,

and relatively higher mortality (Kales, Chen, Blow,

Welsh, & Mellow, 2005; Lee & Lyketsos, 2003).

Kales and coworkers recently demonstrated that

demented patients with coexistent depression have

significantly higher rates of nursing home placement

than patients with either depression or dementia

alone (Kales et al., 2005). Starkstein and coworkers

(2005b) examined the clinical correlates of major

and minor depression in a consecutive series of 670

patients with AD. They found that patients meeting

DSM-IV criteria for either major or minor depres-

sion had more severe social dysfunction and greater

impairment in activities of daily living than AD

patients without depression, suggesting that even

mild levels of depression are significantly associated

with more functional impairment in AD. Patients

with major depression also showed more severe

anxiety, apathy, delusions and Parkinsonism than

those with minor depression, demonstrating that

the severity of depression is significantly associated

with increased psychopathology and neurological

impairments.

Differential diagnosis of depression in AD

Apathy

Apathy is defined as diminished activity due to lack

of motivation (Starkstein, Ingram, Garau, & Mizrahi,

2005a). Among patients with AD, apathy is mani-

fested as diminished drive to perform their daily

chores, low interest about family activities, and

emotional indifference to positive or negative

events. AD patients with apathy put little effort into

their usual chores and need help from a caregiver to

structure their routines. Depression is frequently

associated with apathy in AD, an expected finding

given that loss of interest and motivation is a cardinal

symptom of both apathy and depression. For

instance, key symptoms of apathy such as loss of

interest and psychomotor retardation are specific

Depression in Alzheimer’s disease 385

DSM-IV diagnostic criteria for depression, and

about two thirds of AD patients with apathy also

have depression.

In a recent study Starkstein and co-workers

examined the association between apathy and

depression in the context of a longitudinal study

that included 247 patients with AD (Starkstein,

Jorge, Mizrahi, & Robinson, 2006). In a cross-

sectional analysis about one quarter of patients

without depression had apathy, demonstrating that

depression is not necessary for apathy in AD. On the

other hand, about half of the patients with depression

had apathy, demonstrating that depression is not

sufficient for apathy in AD. After a mean follow-up

period of 18 months there was a significant increase

on apathy scores over time, but syndromal depres-

sion at baseline (i.e. major or minor depression) was

not significantly associated with more severe

apathy at follow-up. On the other hand, the presence

of apathy at baseline was a significant predictor of

increasing depression during follow-up, suggesting

that apathy may be an early marker or a prodromal

stage of depression in AD.

Pathological affective display

Pathological affective display is another important

differential diagnosis of depression in AD (Starkstein

et al., 1995). Patients with dementia may present

with sudden episodes of crying that may be classified

into two different categories: (1) ‘Emotional lability’,

which is defined as the sudden onset of crying that

the patient is unable to suppress, which generally

occurs in appropriate situations and is accompanied

by a congruent emotion (e.g. sadness); and (2)

‘Pathological crying’, which is defined as the sudden

onset of crying episodes that do not correspond

to an underlying congruent emotional change

(e.g. crying episodes in the context of normal

mood). Pathological affective display-crying was

present in about 50% of patients with AD

(Starkstein et al., 1995). Patients with pathological

affective display-crying showed significantly higher

depression and anxiety scores and a significantly

higher frequency of major and minor depression than

patients with no pathological affect. Taken together,

these findings suggest that pathological affect-crying

in AD may be a marker of an underlying depression.

Longitudinal evolution of depression in

Alzheimer’s disease

A personal history of psychiatric disorders is a strong

predictor of major depression in AD (Migliorelli

et al., 1995; Garre-Olmo et al., 2003). Left

untreated, major depression in AD may last for

about 12 months, whereas minor depression has a

shorter course (Starkstein et al., 1997; Garre-Olmo

et al., 2003). Longitudinal studies suggest that the

incidence (i.e. new cases) of depression in AD is

about 20% during a 12-month period, suggesting

that most patients with AD will develop depression at

some stage of their illness (Starkstein et al., 1997).

Treatment of depression in Alzheimer’s

disease

Pharmacological treatments

Most randomized control trials (RCT) and a recent

meta-analysis have shown that antidepressants are

superior to placebo for both treatment response and

remission of depression (Thompson, Herrmann,

Rapoport, & Lanctot, 2007).

Randomized controlled trials using selective ser-

otonergic re-uptake inhibitors (SSRIs) demonstrated

a significant efficacy over placebo for citalopram and

sertraline, but not for fluoxetine (Lyketsos & Lee,

2004) (Petracca, Chemerinski, & Starkstein, 2001).

Whereas SSRIs are better tolerated than tricyclics,

they may induce agitation, anxiety, tremor and

sleep problems. Autonomic changes such as dry

mouth, sweating, loss of weight and diarrhoea may

also occur. Concurrent administration of opiates or

monoamine oxidase inhibitors increases the risk

of serotonin syndrome and the concomitant use of

these medications should be avoided.

Tricyclics are rarely used to treat depression in AD

given their relatively frequent side-effects among

elderly individuals, important contra-indications and

high lethality index. A randomized controlled trial

using the tricyclic clomipramine demonstrated a

greater efficacy of the active compound over placebo

(Petracca, Teson, Chemerinski, Leiguarda, &

Starkstein, 1996). On the other hand, other studies

found no significant differences between the tricyc-

lics maprotiline or imipramine over placebo

(Lyketsos & Lee, 2004). Side-effects are frequently

reported in elderly individuals on tricyclics. The

most dangerous adverse event is a delay in cardiac

conduction and a potential heart block. Orthostatic

hypotension is a frequent problem with tricyclics,

and other anticholinergic side-effects include dry

mouth, reduced tear flow, impaired visual accom-

modation, constipation, urinary retention and cog-

nitive changes characterized by confusion or overt

delirium. Sedation and weight gain are other

frequent problems. Contraindications to the use of

tricyclics are myocardial infarction within the past

6 months, first- or second-degree heart block or

life threatening arrhythmias, history of prostatic

hypertrophy, difficult to treat seizures and glaucoma.

A randomized controlled trial using the reversible

monoamine oxidase inhibitor (MAOI) moclobemide

386 S. E. Starkstein et al.

demonstrated a significant efficacy of this medication

over placebo (Roth, Mountjoy, & Amrein, 1996).

Side effects for moclobemide were mild and mostly

included restlessness, dizziness, nausea and

constipation.

In summary, the current strategy to treat depres-

sion in AD is to start with a SSRI (Lyketsos & Olin,

2002), although the patient’s psychiatric and phar-

macological history (e.g. positive response to tricyc-

lics in the past) may suggest other therapeutic

alternatives. Medically compromised elderly patients

may not be suitable for either tricyclics or MAOIs.

In addition, the patient’s living situation should be

carefully evaluated (e.g. nursing homes with reduced

staff may warrant the use of treatments that would

require less nursing needs).

Non-pharmacological treatments for

depression in AD

Psychotherapy

A recent systematic review of psychological

approaches to the management of neuropsychiatric

symptoms of dementia (Livingston, Johnston,

Katona, Paton, & Lyketsos, 2005) suggests that

behaviour management therapies, and specific types

of caregiver and residential care staff education are

among the most effective to treat neuropsychiatric

symptoms in patients with dementia. A recent study

has demonstrated the effectiveness of specific treat-

ment guidelines for AD delivered through a collab-

orative care model, which included active screening

for cognitive impairment, active case finding and

treatment for depression, psychoses, behavioural

disturbances, and active monitoring and support

of the caregiver’s emotional and physical health

(Callahan et al., 2006).

Electroconvulsive therapy (ECT)

There is anecdotal evidence that ECT may be a

useful treatment for demented patients with major

depression that are refractory to medication (Rao &

Lyketsos, 2000). On the other hand, confusion

post-ECT is very frequent among depressed

patients with dementia undergoing ECT, and the

severity of confusion is significantly associated

with the severity of pre-ECT cognitive deficits

(Rao & Lyketsos, 2000).

Conclusions

Depression is one of the most frequent comorbid

psychiatric disorders in AD.

The diagnosis of depression in AD should be

made after a thorough mental status examination

with a specific evaluation for the signs and symptoms

of mood disorders. The best strategy to diagnose

depression in dementia is to use a standardized

psychiatric interview and structured diagnostic

criteria.

Depression rating scales are useful to screen

patients for depressive disorders, to determine

the relative severity of depressive symptoms, and to

quantify changes in depression after specific

treatment.

Depressive symptoms are not rampant among

patients with dementia, but are specific to the

presence of sad mood.

Depression in AD is associated with worse quality

of life, greater disability in activities of living, a faster

cognitive decline, a high rate of nursing home

placement, relatively higher mortality, and a higher

frequency of depression and burden in caregivers.

Depression in Alzheimer’s disease is markedly

under-diagnosed, and most patients with depression

are either not treated or are on subclinical doses of

antidepressants.

Several antidepressants are effective to treat

depression in AD. SSRIs are currently the first

choice, given their acceptable efficacy and relatively

low rate of side-effects.

Acknowledgements

This study was partially supported with grants

from the National Health and Medical Research

Council.

Declaration of interest: The authors report no

conflicts of interest. The authors alone are respon-

sible for the content and writing of the paper.

References

Alexopoulos, G. S., Abrams, R. C., Young, R. C., &

Shamoian, C. A. (1988). Cornell Scale for Depression in

Dementia. Biological Psychiatry, 23, 271–284.

Callahan, C. M., Boustani, M. A., Unverzagt, F. W., Austrom,

M. G., Damush, T. M., Perkins, A. J., et al. (2006).

Effectiveness of collaborative care for older adults with

Alzheimer disease in primary care: A randomized controlled

trial. JAMA, 295, 2148–2157.

Chemerinski, E., Petracca, G., Sabe, L., Kremer, J., &

Starkstein, S. E. (2001). The specificity of depressive symptoms

in patients with Alzheimer’s disease. American Journal of

Psychiatry, 158, 68–72.

Cohen-Cole, S. A., & Stoudemire, A. (1987). Major depression

and physical illness. Psychiatric Clinics of North America, 10,

1–17.

Gallo, J. J., Rabins, P. V., Lyketsos, C. G., Tien, A. Y., &

Anthony, J. C. (1997). Depression without sadness: Functional

outcomes of nondysphoric depression in later life. Journal of the

American Geriatrics Society, 45, 570–578.

Depression in Alzheimer’s disease 387

Garre-Olmo, J., López-Pousa, S., Vilalta-Franch, J., Turon-

Estrada, A., Hernàndez-Ferràndiz, M., Lozano-Gallego, M.,

et al. (2003). Evolution of depressive symptoms in Alzheimer

disease: one-year follow-up [Comment]. Alzheimer Disease &

Associated Disorders, 17, 77–85.

Hamilton, M. (1960). A rating scale for depression. Journal of

Neurology Neurosurgery and Psychiatry, 23, 56–62.

Kales, H. C., Chen, P., Blow, F. C., Welsh, D. E., &

Mellow, A. M. (2005). Rates of clinical depression diagnosis,

functional impairment, and nursing home placement in

coexisting dementia and depression. American Journal of

Geriatric Psychiatry, 13, 441–449.

Lee, H. B., & Lyketsos, C. G. (2003). Depression in Alzheimer’s

disease: Heterogeneity and related issues. Biological Psychiatry,

54, 353–362.

Livingston, G., Johnston, K., Katona, C. L., Paton, J., &

Lyketsos, C. G. (2005). Systematic review of psychological

approaches to the management of neuropsychiatric symptoms

of dementia. American Journal of Psychiatry, 162, 1996–2021.

Loewenstein, D. A., Arguelles, S., Bravo, M., Freeman, R. Q.,

Arguelles, T., Acevedo, A., et al. (2001). Caregivers’ judgments

of the functional abilities of the Alzheimer’s disease patient: A

comparison of proxy reports and objective measures. Journals of

Gerontology Series B-Psychological Sciences & Social Sciences, 56,

P78–P84.

Lyketsos, C. G., Breitner, J. C., & Rabins, P. V. (2001). An

evidence-based proposal for the classification of neuropsychia-

tric disturbance in Alzheimer’s disease [Comment].

International Journal of Geriatric Psychiatry, 16, 1037–1042.

Lyketsos, C. G., & Lee, H. B. (2004). Diagnosis and treatment of

depression in Alzheimer’s disease. A practical update for the

clinician. Dementia & Geriatric Cognitive Disorders, 17, 55–64.

Lyketsos, C. G., & Olin, J. (2002). Depression in Alzheimer’s

disease: Overview and treatment. Biological Psychiatry, 52,

243–252.

Migliorelli, R., Teson, A., Sabe, L., Petrachi, M., Leiguarda, R.,

& Starkstein, S. E. (1995). Prevalence and correlates of

dysthymia and major depression among patients with

Alzheimer’s disease. American Journal of Psychiatry, 152, 37–44.

Olin, J. T., Katz, I. R., Meyers, B. S., Schneider, L. S., &

Lebowitz, B.D. (2002a). Provisional diagnostic criteria for

depression of Alzheimer disease: Rationale and background.

American Journal of Geriatric Psychiatry, 10, 129–141.

Olin, J. T., Schneider, L. S., Katz, I. R., Meyers, B. S.,

Alexopoulos, G. S., Breitner, J. C., et al. (2002b). Provisional

diagnostic criteria for depression of Alzheimer disease.

American Journal of Geriatric Psychiatry, 10, 125–128.

Petracca, G., Teson, A., Chemerinski, E., Leiguarda, R., &

Starkstein, S. E. (1996). A double-blind placebo-controlled

study of clomipramine in depressed patients with Alzheimer’s

disease. Journal of Neuropsychiatry & Clinical Neurosciences, 8,

270–275.

Petracca, G. M., Chemerinski, E., & Starkstein, S. E. (2001).

A double-blind, placebo-controlled study of fluoxetine in

depressed patients with Alzheimer’s disease. International

Psychogeriatrics, 13, 233–240.

Rao, V., & Lyketsos, C. G. (2000). The benefits and risks of ECT

for patients with primary dementia who also suffer from

depression [Comment]. International Journal of Geriatric

Psychiatry, 15, 729–735.

Rosenberg, P. B., Mielke, M. M., & Lyketsos, C. G. (2005).

Caregiver assessment of patients’ depression in Alzheimer

disease: Longitudinal analysis in a drug treatment study.

American Journal of Geriatric Psychiatry, 13, 822–826.

Roth, M., Mountjoy, C. Q., & Amrein, R. (1996). Moclobemide

in elderly patients with cognitive decline and depression:

An international double-blind, placebo-controlled trial.

British Journal of Psychiatry, 168, 149–157.

Spitzer, R. L., Williams, J. B., Gibbon, M., & First, M. B. (1992).

The Structured Clinical Interview for DSM-III-R (SCID).

I: History, rationale, and description. Archives of General

Psychiatry, 49, 624–629.

Starkstein, S. E., Chemerinski, E., Sabe, L., Kuzis, G.,

Petracca, G., Teson, A., et al. (1997). Prospective longitudinal

study of depression and anosognosia in Alzheimer’s disease.

British Journal of Psychiatry, 171, 47–52.

Starkstein, S. E., Ingram, L., Garau, M. L., & Mizrahi, R.

(2005a). On the overlap between apathy and depression

in dementia. Journal of Neurology, Neurosurgery & Psychiatry,

76, 1070–1074.

Starkstein, S. E., Jorge, R., Mizrahi, R., & Robinson, R. G.

(2005b). The construct of minor and major depression in

Alzheimer’s disease. American Journal of Psychiatry, 162,

2086–2093.

Starkstein, S. E., Jorge, R., Mizrahi, R., & Robinson, R. G.

(2006). A prospective longitudinal study of apathy in

Alzheimer’s disease. Journal of Neurology Neurosurgery &

Psychiatry, 77, 8–11.

Starkstein, S. E., Migliorelli, R., Teson, A., Petracca, G.,

Chemerinsky, E., Manes, F., et al. (1995). Prevalence and

clinical correlates of pathological affective display in

Alzheimer’s disease. Journal of Neurology, Neurosurgery &

Psychiatry, 59, 55–60.

Starkstein, S. E., Mizrahi, R., & Garau, L. M. (2005c). Specificity

of symptoms of depression in Alzheimer disease: A longitudinal

analysis. American Journal of Geriatric Psychiatry, 13, 803–807.

Teri, L., & Truax, P. (1994). Assessment of depression in

dementia patients: Association of caregiver mood with depres-

sion ratings. Gerontologist, 34, 231–234.

Thompson, S., Herrmann, N., Rapoport, M. J., & Lanctot, K. L.

(2007). Efficacy and safety of antidepressants for treatment of

depression in Alzheimer’s disease: A metaanalysis. Canadian

Journal of Psychiatry – Revue Canadienne de Psychiatrie, 52,

248–255.

Vilalta-Franch, J., Garre-Olmo, J., Lopez-Pousa, S., Turon-

Estrada, A., Lozano-Gallego, M., Hernandez-Ferrandiz, M.,

et al. (2006). Comparison of different clinical diagnostic criteria

for depression in Alzheimer disease. American Journal of

Geriatric Psychiatry, 14, 589–597.

Yesavage, J. A., Brink, T. L., Rose, T. L., Lum, O., Huang, V.,

Adey, M., et al. (1982). Development and validation of a

geriatric depression screening scale: A preliminary report.

Journal of Psychiatric Research, 17, 37–49.

388 S. E. Starkstein et al.

College of Doctoral Studies

Literature Review Resources Tool

Annotation

Number	Article Information	Added to RefWorks? ( Y or N)
1.	Bibliographic Information Chiu, P., Steffens, D., Chen, P., Hsu, Y., Huang, H., & Lai, T. (2012). Depression in Taiwanese patients with Alzheimer’s disease determined by the national institutes of mental health provisional criteria. International Psycho-geriatrics, 24(8), 1299-305.	Y
Link http://dx.doi.org.lopes.idm.oclc.org/10.1017/S1041610211002262
			Annotation
2.	Bibliographic Information Ringman, J. M., MD, Ross, L. K., PhD, Mulnard, Ruth A,RN, DNSc, Dick, M. B., PhD, Bartzokis, G., MD, Davies, Helen D, MS,APRN, BC, Galasko, D., MD, Hewett, L., PsyD, Mungas, D., PhD, Reed, B. R., PhD, Schneider, L. S., MD, Segal-Gidan, Freddi,PA, PhD, Yaffe, K., MD, & Cummings, J. L., MD. (2008). Diagnosing depression in Alzheimer disease with the national institute of mental health provisional criteria. The American Journal of Geriatric Psychiatry, 16(6), 469-77			Y
Link https://lopes.idm.oclc.org/login?url=https://www-proquest-com.lopes.idm.oclc.org/scholarly-journals/diagnosing-depression-alzheimer-disease-with/docview/195986540/se-2?accountid=7374
3.	Bibliographic Information Gómez-Gallego, M., Gómez-García, J., & Ato-Lozano, E. (2017). The mediating role of depression in the association between disability and quality of life in Alzheimer’s disease. Aging & Mental Health, 21(2), 163-172.
Link 10.1080/13607863.2015.1093603
4.	Bibliographic Information Tsuno, N., & Homma, A. (2009). What is the association between depression and Alzheimer’s disease? Expert Review of Neurotherapeutics, 9(11), 1667-76
Link http://dx.doi.org.lopes.idm.oclc.org/10.1586/ern.09.106
5.	Bibliographic Information Ohanna, I., Golander, H., & Barak, Y. (2011). Does late onset depression predispose to dementia? A retrospective, case-controlled study.Comprehensive Psychiatry, 52(6), 659-661
Link http://dx.doi.org.lopes.idm.oclc.org/10.1016/j.comppsych.2010.10.016
6.	Bibliographic Information Starkstein, S. E., Mizrahi, R., & Power, B. D. (2008). Depression in Alzheimer’s disease: Phenomenology, clinical correlates and treatment.International Review of Psychiatry, 20(4), 382-388
Link 10.1080/09540260802094480
7.	Bibliographic Information Bulloch, A. G. M., Fiest, K. M., Williams, J. V. A., Lavorato, D. H., Berzins, S. A., Jetté, N., Pringsheim, T. M., & Patten, S. B. (2015). Depression–a common disorder across a broad spectrum of neurological conditions: a cross-sectional nationally representative survey.General Hospital Psychiatry, 37(6), 507-512
Link 10.1016/j.genhosppsych.2015.06.007
8.	Bibliographic Information Kuring, J. K., Mathias, J. L., & Ward, L. (2020). Risk of Dementia in persons who have previously experienced clinically-significant Depression, Anxiety, or PTSD: A Systematic Review and Meta-Analysis. Journal of Affective Disorders, 274, 247-261
Link https://doi-org.lopes.idm.oclc.org/10.1016/j.jad.2020.05.020
9.	Bibliographic Information Bos, I., Vos, S. J., Frölich, L., Kornhuber, J., Wiltfang, J., Maier, W., Peters, O., Rüther, E., Engelborghs, S., Niemantsverdriet, E., De Roeck, E. E., Tsolaki, M., Freund-Levi, Y., Johannsen, P., Vandenberghe, R., Lleó, A., Alcolea, D., Frisoni, G. B., Galluzzi, S., . . . Visser, P. J. (2017). The frequency and influence of dementia risk factors in prodromal Alzheimer’s disease. Neurobiology of Aging, 56, 33-40
Link https://doi-org.lopes.idm.oclc.org/10.1016/j.neurobiolaging.2017.03.034
10.	Bibliographic Information Burke, S. L., Cadet, T., Alcide, A., O’Driscoll, J., & Maramaldi, P. (2018). Psychosocial risk factors and Alzheimer’s disease: the associative effect of depression, sleep disturbance, and anxiety. Aging & Mental Health, 22(12), 1577-1584.
Link 10.1080/13607863.2017.1387760

Available online at www.sciencedirect.com

Comprehensive Psychiatry 52 (2011) 659–661
www.elsevier.com/locate/comppsych

Does late onset depression predispose to dementia? A retrospective,
case-controlled study

Irit Ohanna, Hava Golander, Yoram Barak⁎

Sackler School of Medicine, Tel-Aviv University, 59100 Israel

Abstract

Background: Recent research suggests that there are clinical and biologic characteristics typical of late onset depression (LOD).
Furthermore, evidence has been put forward that LOD may be a prodrome of dementia.
Objective: This study aims to assess the association between LOD and the development of dementia.
Setting: The study was conducted in a tertiary care, university-affiliated mental health center providing services for an urban catchment
population of 800 000 subjects.
Method: A retrospective, case-controlled study was used.
Results: Fifty-one patients with LOD who developed dementia at least 1 year after diagnosis of LOD were defined as the index group: 18
males and 33 females, with a mean age of 75.4 ± 9.2 years. These were compared with 51 patients with LOD who did not develop dementia
during a 10-year follow-up period. Dementia types were as follows: 73% Alzheimer disease, 24% vascular and mixed dementia, and 3%
Parkinson dementia.

Patients with LOD who developed dementia were significantly characterized by having longer hospitalization for their first depressive
episode (P = .048), having a family history of dementia (P = .022), and having been exposed to the Holocaust as young adults (P = .013).
Conclusions: Patients with a history of significant traumatic experience in early life and a prolonged onset of depression may be at particular
risk of developing dementia. This issue requires further long-term prospective studies.
© 2011 Elsevier Inc. All rights reserved.

1. Introduction

Affective disorders, particularly depression and cognitive
dysfunction, are among the mental health problems adverse-
ly impacting the lives of elderly people. Both have severe
consequences, including decreased quality of life, functional
disability, increased use of general medical as well as mental
health services, and associated direct and indirect mortality.
Late onset depression (LOD) is closely associated with
cognitive impairment. However, it is not known whether
depression leads to cognitive decline or, in more severe
cases, to dementia [1].

Conflict of interest declaration: none
Description of authors’ roles: I Ohanna designed the study, collected the

data, and helped in writing the paper. H Golander supervised the designing
of this study and assisted with writing the article. Y Barak was responsible
for the statistical design of the study and wrote the paper.

⁎ Corresponding author. Abarbanel Mental Health Center, Bat-Yam,
Israel. Tel.: +972 3 5552738; fax: +972 3 5552738.

E-mail address: mdybarak@netvision.net.il (Y. Barak).

0010-440X/$ – see front matter © 2011 Elsevier Inc. All rights reserved.
doi:10.1016/j.comppsych.2010.10.016

Although studies have shown that depression and
dementia frequently coexist [2], causality remains contro-
versial. Several case-control studies have tested the associ-
ation between dementia and depression as early as 1986.
Speck et al reported that patients with Alzheimer disease
(AD) were more likely than nondemented patients to have a
history of depression, with later reports suggesting doubling
of dementia risk for patients with a history of depression [3].

Despite the published studies reporting a higher risk of
dementia for patients with a history of LOD, there are several
observations that do not complement this hypothesis. First,
only depressive episodes developing for the first time in
close temporal proximity to dementia onset were positively
associated with dementia risk [4,5]. Thus, depression may be
only a prodromal feature of dementia rather than a risk
factor. Other studies, however, have found the opposite [6],
reporting that the length of the interval between the
diagnoses of depression and AD was positively associated
with an increased risk of developing AD, concluding that
depression was a risk factor rather than a prodrome of

http://www.sciencedirect.com/science/journal/0010440X

http://dx.doi.org/10.1016/j.comppsych.2010.10.016

mailto:mdybarak@netvision.net.il

http://dx.doi.org/10.1016/j.comppsych.2010.10.016

660 I. Ohanna et al. / Comprehensive Psychiatry 52 (2011) 659–661

dementia. Because these contrasting views are not reconciled
in the literature, we aimed to approach this question using a
unique database of all tertiary care patients in a specific
urban catchment area in Israel.

2. Method

The present work was designed as a retrospective, case-
controlled study based on medical charts review. All medical
charts are computerized at the Abarbanel Mental Health
Center, Bat-Yam, Israel, and these served as the sample
source. This tertiary care center is a large, urban, university-
affiliated psychiatric hospital. The hospital has no selective
admission policy and is the only tertiary care psychogeriatric
facility in the area. At our center, there are 330 inpatient beds
and 60 day patients as well as a large outpatient clinic. The
center serves an urban catchment area of approximately 850
000 people of which 14.3% are 65 years or older and
possibly includes most potential incidence cases in our area.

The study was approved by the local institutional
review board.

The index group was defined as that encompassing
patients who were 50 years or older at the first-ever diagnosis
of a major depressive episode (Diagnostic and Statistical
Manual of Mental Disorders, Fourth Edition, criteria) as
well as being diagnosed as having dementia [7]. Dementia
had to be diagnosed a minimum of 12 months after onset of
the depressive episode. The comparison group was that of
patients with LOD (onset of first-ever depressive episode
after the age of 50 years) who were followed up for a period
of 10 years during which no dementia had developed. The
groups were matched for year of birth, sex, and education.
The study covered the period from January 1990 to
December 2001.

Each medical record was assessed for the following
parameters: (a) sociodemographic (age, sex, country of birth,
year of immigration, education, marital status, no. of
children, family history of any neuropsychiatric disorder,
and history of Holocaust experience), (b) health (physical
disorders, current medication regimen, and history of head
trauma), (c) depression (age at onset, duration of episodes,
hospitalizations, cognitive status, and treatment) and (d)
dementia ( age at onset, type, and treatment).

2.1. Statistical analysis

Data were analyzed using the independent-samples
approach. The 2-tailed t and nonparametric (Wilcoxon)
tests were undertaken to test for differences between the
evaluations for quantitative parameters. Examination of
differences between the categorical parameters was based
on the Pearson and Fisher exact tests. The analyses were a
test of univariate association of independent variables. All
tests applied were 2-tailed, and a P value of 5% or less was
considered statistically significant.

The data were analyzed using the Statistical Analysis
System software (SAS Institute, Cary, NC) [8].

3. Results

The Abarbanel Mental Health Center computerized files
provided a preliminary database of 1500 patients having
their first-ever major depressive episode after the age of 50
years. Of these, 51 fulfilled the inclusion criteria for the
index group. Fifty-one age-, sex-, and education-matched
subjects were randomly selected as the comparison group.

For the index group, the mean age was 75.5 ± 9.2 years
(range, 54-96 years); there were 33 female (65%) and 18
male (35%) patients. The mean years of education for the
group was 8.9 ± 3.2. Duration of first episode of depression
was 5.8 ± 11.1 months, and mean severity of the depressive
episode as reflected by the Clinical Global Impression–
Severity scale was 5.2 ± 1.4. Most patients, 46 (90%), had
intact cognitive functioning during the depressive episode.
There were 24 (47%) Holocaust survivors in this group.
Family history of dementia was established in 5 (10%) of the
patients. The minority of patients in the index group, 2 (4%),
was born in Israel.

The distribution of dementias in this group was as
follows: 37 (73%) cases, AD; 12 (24%), vascular and mixed
dementia; and 2 (3%), Parkinson disease dementia. Duration
of time elapsed between diagnosis of LOD and dementia was
6.2 ± 4.8 years (range, 1-25 years).

For the comparison group, the mean age was 74.8 ± 9.8
years (range, 52-96 years); there were 33 female (65%) and
18 male (35%) patients. The mean years of education for the
group was 9.1 ± 3.6. Duration of first episode of depression
was 2.8 ± 2.9 months, and mean severity of the depressive
episode as reflected by the Clinical Global Impression–
Severity scale was 5.3 ± 1.0. Most patients, 50 (98%), had
intact cognitive functioning during the depressive episode.
There were 14 (28%) Holocaust survivors in this group.
Family history of dementia was negative for all patients. The
minority of patients in the comparison group, 9 (18%), was
born in Israel.

There are 3 statistically significant differences between
the index and comparison groups: (a) family history of
dementia, χ21 = 53, P = .022; (b) duration of first-ever LOD
episode, χ2101 = 1.8, P = .048; and (c) exposure to the
Holocaust, χ22 = 8.6, P = .013.

4. Discussion

A meta-analysis of the data on history of depression as a
risk factor for dementia as well as the available evidence on
the hypotheses proposed to explain the association between
history of depression and dementia was undertaken by Jorm
[3] in 2001. The meta-analysis found evidence to support an
association from both case-control (relative risk, 2.01) and

661I. Ohanna et al. / Comprehensive Psychiatry 52 (2011) 659–661

prospective (relative risk, 1.87) studies. However, the
evidence did not clearly support any one hypothesis
explaining the association. The 2 most likely hypotheses
were that (1) depression may be a prodrome of dementia and
(2) depression brings forward the clinical manifestation of
dementing diseases. The possibility that history of depres-
sion is a risk factor for dementia need be considered
seriously, and explanations of the association need be
researched [3]. More specifically, close relationship between
the depressions of late life (LOD) and dementia has been
postulated. Schweitzer et al [9] argue that studies of LOD
and dementia offer persuasive evidence that LOD is often a
prodromal disorder for dementia.

The present study was a retrospective, case-control
attempt to add to the growing evidence on the possible
relationship between LOD and the development of dementia.
Our findings are that family history of dementia in patients
with LOD was associated with greater risk of developing
dementia. This is in line with many well-established
publications. However, 2 findings are novel; the duration
of the first LOD episode was associated with dementia
development and exposure to the Holocaust. We may
tentatively suggest that the duration of the first LOD episode
may indirectly reflect a more severe onset of depression—
especially because we analyzed cases from a tertiary care
psychiatric service. Severity of this episode may have been
more likely to bring forward the clinical manifestation of
dementing diseases as put forward by Jorm [3]. The exposure
to the Holocaust is a unique finding in this specific sample
that has many possible explanations such as lack of
protective factors (education, nutrition, and more), head
injuries, infections, and the traumatic experience developing
into posttraumatic stress disorder (PTSD). Recent evidence
suggests that PTSD adversely affects memory [10].
Furthermore, Yaffe et al [11] found that older veterans
with PTSD had nearly a 2-fold increased risk for dementia
compared with their counterparts without PTSD. This did
not appear to be associated with a particular dementia type
but rather had an “across-the-board effect” for all dementias,
including vascular dementia and AD. To examine the
question of whether PTSD might carry an increased risk
for dementia, these researchers used data from the
Department of Veterans Affairs National Patient Care
Database. The retrospective cohort study included 181 093
veterans 55 years and older without dementia at baseline and
compared rates of newly diagnosed dementia or cognitive

impairment in 53 155 subjects with a diagnosis of PTSD and
127 938 subjects without PTSD. The subjects’ mean age at
baseline was 68.8 years, and most were males. After
adjustment for demographics and medical and psychiatric
comorbidities, patients with PTSD were still nearly twice as
likely to develop incident dementia (hazard ratio, 1.77; 95%
confidence interval, 1.7-1.9). The results were similar when
investigators excluded subjects with a history of traumatic
brain injury, substance abuse, or depression [11].

In conclusion, despite the limitations inherent in a
retrospective analysis of a small and specific sample, the
present study offers further support to the association
between LOD and risk of developing dementia. We also
tentatively suggest that traumatic experiences need be
studied as confounders in the complex interplay between
depression and dementia.

References

[1] Brommelhoff JA, Gatz M, Johansson B, McArdle JJ, Fratiglioni L,
Pedersen NL. Depression as a risk factor or prodromal feature for
dementia? Findings in a population-based sample of Swedish twins.
Psychol Aging 2009;24:373-84.

[2] Vinkers DJ, Gussekloo J, Stek ML, Westendorp RG, van der Mast RC.
Temporal relation between depression and cognitive impairment in old
age: prospective population based study. BMJ 2004;329:7471-881.

[3] Jorm AF. History of depression as a risk factor for dementia: an
updated review. Australian New Zealand J Psychiatry 2001;35:776-81.

[4] Chen P, Ganguli M, Mulsant BH, DeKosky ST. The temporal
relationship between depressive symptoms and dementia: a commu-
nity-based prospective study. Arch Gen Psychiatry 1999;56:261-6.

[5] Wetherell JL, Gatz M, Johansson B, Pedersen NL. History of
depression and other psychiatric illness as risk factors for Alzheimer
disease in a twin sample. Alzheimer Dis Assoc Disord 1999;13
(1):47-52.

[6] Ownby RL, Crocco E, Acevedo A, John V, Loewenstein D.
Depression and risk for Alzheimer disease: systematic review, meta-
analysis, and metaregression analysis. Arch Gen Psychiatry
2006;63:530-8.

[7] Rush AJ. The varied clinical presentations of major depressive
disorder. J Clin Psychiatry 2007;68(Suppl 8):4-10.

[8] SAS Institute. SAS/STAT users’ guide, version 6, vols. 1 and 2. 4th ed.
Cary (NC): SAS Institute; 1990.

[9] Schweitzer I, Tuckwell V, O’Brien J, Ames D. Is late onset depression
a prodrome to dementia? Int J Geriatr Psychiatry 2002;17:997-1005.

[10] Yehuda R, Tischler L, Golier JA, Grossman R, Brand SR, Kaufman S,
et al. Longitudinal assessment of cognitive performance in Holocaust
survivors with and without PTSD. Biol Psychiatry 2006;60:714-21.

[11] Yaffe K, Vittinghoff E, Lindquist K, Barnes D, Covinsky KE, Neylan
T, et al. Posttraumatic stress disorder and risk of dementia among US
veterans. Arch Gen Psychiatry 2010;67:608-13.

Does late onset depression predispose to dementia? A retrospective, �case-controlled study

Introduction
Method
Statistical analysis
Results
Discussion
References

lable at ScienceDirect

Neurobiology of Aging 56 (2017) 33e40

Contents lists avai

Neurobiology of Aging

journal homepage: www.elsevier .com/locate/neuaging

The frequency and influence of dementia risk factors in prodromal
Alzheimer’s disease

Isabelle Bos a,*, Stephanie J. Vos a, Lutz Frölich b,c, Johannes Kornhuber b,d,
Jens Wiltfang b,e, Wolfgang Maier b,f, Oliver Peters b,g, Eckhart Rüther b,h,
Sebastiaan Engelborghs i, j, Ellis Niemantsverdriet j, Ellen Elisa De Roeck j,k,
Magda Tsolaki l, Yvonne Freund-Levi m,n, Peter Johannsen o, Rik Vandenberghe p,q,
Alberto Lleó r, Daniel Alcolea r, Giovanni B. Frisoni s,t,u, Samantha Galluzzi u,
Flavio Nobili s,v, Silvia Morbelli s,w, Alexander Drzezga s,x, Mira Didic s,y,z,
Bart N. van Berckel s,aa, Eric Salmon bb,cc, Christine Bastin cc, Solene Dauby bb,
Isabel Santana bb, Inês Baldeiras dd, Alexandre de Mendonça ee, Dina Silva ee,
Anders Wallin ff, Arto Nordlund ff, Preciosa M. Coloma gg, Angelika Wientzek hh,ii,
Myriam Alexander hh, Gerald P. Novak jj, Mark Forrest Gordon kk, the Alzheimer’s
Disease Neuroimaging Initiative1, Åsa K. Wallin ll, Harald Hampelmm, Hilkka Soininen nn,
Sanna-Kaisa Herukka nn, Philip Scheltens oo, Frans R. Verhey a, Pieter Jelle Visser a,oo

aDepartment of Psychiatry and Neuropsychology, Maastricht University, School for Mental Health and Neuroscience, Alzheimer Center Limburg,
Maastricht, Netherlands
bOn behalf of German Dementia Competence Network
cDepartment of Geriatric Psychiatry, Zentralinstitut für Seelische Gesundheit, University of Heidelberg, Mannheim, Germany
dDepartment of Psychiatry and Psychotherapy, Friedrich-Alexander University of Erlangen-Nürnberg, Erlangen, Germany
eDepartment of Psychiatry and Psychotherapy, University Medical Center (UMC), Georg-August-University, Göttingen, Germany
fDepartment of Psychiatry and Psychotherapy, University of Bonn, German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
gDepartment of Psychiatry and Psychotherapy, Charité Berlin, Berlin, Germany
hDepartment of Psychiatry and Psychotherapy, University of Göttingen, Göttingen, Germany
iDepartment of Neurology and Memory Clinic, Hospital Network Antwerp (ZNA) Middelheim and Hoge Beuken, Antwerp, Belgium
jReference Center for Biological Markers of Dementia (BIODEM), University of Antwerp, Antwerp, Belgium
kDepartment of Clinical and Lifespan Psychology, Vrije Universiteit Brussel, Brussels, Belgium
l 3rd Department of Neurology, Aristotle University of Thessaloniki, Memory and Dementia Center, “G Papanicolau” General Hospital,
Thessaloniki, Greece
mDivision of Clinical Geriatrics, Department of Neurobiology, Caring Sciences and Society (NVS), Karolinska Institutet, Huddinge, Sweden
nDepartment of Geriatric Medicine, Karolinska University Hospital Huddinge, Stockholm, Sweden
oDanish Dementia Research Centre, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
pDepartment of Neurology, University of Hospital Leuven, Leuven, Belgium
q Laboratory for Cognitive Neurology, Department of Neurosciences, KU Leuven, Belgium
rHospital de la Santa Creu i Sant Pau, Barcelona, Spain
sOn behalf of the EADC-PET consortium
tGeneva Neuroscience Center, University Hospital and University of Geneva, Geneva, Switzerland
u IRCCS San Giovanni di Dio Fatebenefratelli, Brescia, Italy
vClinical Neurology, Department of Neurosciences (DINOGMI), University of Genoa and IRCCS AOU San Martino-IST, Genoa, Italy
wNuclear Medicine, Department of Health Science (DISSAL), University of Genoa IRCCS AOU San Martino-IST, Genoa, Italy
xDepartment of Nuclear Medicine, University of Cologne, Cologne, Germany
yAP-HM Hôpitaux de la Timone, Service de Neurologie et Neuropsychologie, Marseille, France
zAix-Marseille Université, INSERM, Institut de Neurosciences des Systèmes, Marseille, France
aaDepartment of Radiology and Nuclear Medicine, VU University Medical Center, Amsterdam, the Netherlands
bbDepartment of Neurology and Memory Clinic, CHU Liège, Liège, Belgium
ccGIGA-CRC in vivo Imaging, University of Liège, Liège, Belgium
ddCenter for Neuroscience and Cell Biology, Faculty of Medicine, Department of Neurology, Centro Hospitalar e Universitário de Coimbra, Coimbra,
Portugal
ee Institute of Molecular Medicine and Faculty of Medicine, University of Lisbon, Portugal

* Corresponding author at: Department of Psychiatry & Neuropsychology, School for Mental Health and Neuroscience, Alzheimer Center Limburg, Maastricht University
Universiteitssingel 40, Box 34, P.O. Box 616, 6200 MD Maastricht, the Netherlands. Tel.: þ31 (0)43 38 84113; fax: þ31 (0)43 38 75444.

0197-4580/$ e see front matter

http://dx.doi.org/10.1016/j.neurobiolaging.2017.03.034

Delta:1_given name

Delta:1_surname

Delta:1_given name

Delta:1_surname

Delta:1_given name

Delta:1_surname

Delta:1_given name

Delta:1_surname

Delta:1_given name

Delta:1_surname

Delta:1_given name

Delta:1_surname

Delta:1_given name

Delta:1_surname

Delta:1_given name

Delta:1_surname

Delta:1_given name

Delta:1_surname

Delta:1_given name

Delta:1_surname

Delta:1_given name

Delta:1_surname

Delta:1_given name

Delta:1_surname

Delta:1_given name

Delta:1_surname

Delta:1_given name

Delta:1_surname

Delta:1_given name

Delta:1_surname

Delta:1_given name

Delta:1_surname

Delta:1_given name

Delta:1_surname

Delta:1_given name

Delta:1_surname

Delta:1_given name

Delta:1_surname

Delta:1_given name

Delta:1_surname

Delta:1_given name

Delta:1_surname

Delta:1_given name

Delta:1_surname

Delta:1_given name

http://crossmark.crossref.org/dialog/?doi=10.1016/j.neurobiolaging.2017.03.034&domain=pdf

www.sciencedirect.com/science/journal/01974580

http://www.elsevier.com/locate/neuaging

http://dx.doi.org/10.1016/j.neurobiolaging.2017.03.034

I. Bos et al. / Neurobiology of Aging 56 (2017) 33e4034

ffDepartment of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg,
Gothenburg, Sweden
ggReal World Data Science (RWD-S) Neuroscience and Established Products, F. Hoffmann-La Roche Ltd. Pharmaceuticals Division, Basel, Switzerland
hh PDB RWD (Real World Data) Team, Roche Products Limited, Welwyn Garden City, UK
ii Epidemiologische Beratung und Literatur-Recherche “conepi”, Herrsching, Germany
jj Janssen Pharmaceutical Research and Development, Titusville, NJ, USA
kkBoehringer Ingelheim Pharmaceuticals, Inc, Ridgefield, CT, USA
llDepartment of Clinical Sciences Malmö, Lund University, Clinical Memory Research Unit, Lund, Sweden
mm Sorbonne Universités, Université Pierre et Marie Curie, Paris 06, AXA Research Fund & UPMC Chair, Institut de la Mémoire et de la Maladie
d’Alzheimer (IM2A) & Institut du Cerveau et de la Moelle épinière (ICM), Département de Neurologie, Hôpital de la Pitié-Salpétrière, 47 Boulevard de
l’Hôpital, Paris, CEDEX 13, France
nn Institute of Clinical Medicine, Neurology, University of Eastern Finland and Neurocenter, Neurology, Kuopio University Hospital, Kuopio, Finland
ooAlzheimer Center & Department of Neurology, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, Netherlands

a r t i c l e i n f o

Article history:
Received 9 November 2016
Received in revised form 29 March 2017
Accepted 31 March 2017
Available online 8 April 2017

Keywords:
Alzheimer’s disease
Risk factors
IWG-2 criteria
NIA-AA criteria
Biomarkers
Prognosis

1 Data used in preparation of this article were
Alzheimer’s Disease Neuroimaging Initiative (ADNI) da
such, the investigators within the ADNI contributed to
tion of ADNI and/or provided data but did not participat
report. A complete listing of ADNI investigators can be
edu/wpcontent/uploads/ how_to_apply/ADNI_Acknow

a b s t r a c t

We investigated whether dementia risk factors were associated with prodromal Alzheimer’s disease (AD)
according to the International Working Group-2 and National Institute of Aging-Alzheimer’s Association
criteria, and with cognitive decline. A total of 1394 subjects with mild cognitive impairment from 14
different studies were classified according to these research criteria, based on cognitive performance and
biomarkers. We compared the frequency of 10 risk factors between the subgroups, and used Cox-
regression to examine the effect of risk factors on cognitive decline. Depression, obesity, and hyper-
cholesterolemia occurred more often in individuals with low-AD-likelihood, compared with those with a
high-AD-likelihood. Only alcohol use increased the risk of cognitive decline, regardless of AD pathology.
These results suggest that traditional risk factors for AD are not associated with prodromal AD or with
progression to dementia, among subjects with mild cognitive impairment. Future studies should validate
these findings and determine whether risk factors might be of influence at an earlier stage (i.e., pre-
clinical) of AD.

1. Introduction

Various risk factors have been associated with an increased risk
for Alzheimer’s disease (AD; Breteler, 2000; de Bruijn and Ikram,
2014). Recently, research criteria have been proposed to identify
AD in subjects with mild cognitive impairment (MCI) by their
biomarker status, referred to as prodromal AD by international
working group-2 (IWG-2; Dubois et al., 2014) and MCI due to AD by
the National Institute of Aging-Alzheimer Association (NIA-AA;
Albert et al., 2011). It remains uncertain whether risk factors are
associated with prodromal AD/MCI due to AD, and whether they
influence the rate of cognitive decline. This information could
improve early diagnosis and lead to new targets for secondary
prevention strategies.

Among the best-validated risk factors for AD are atherosclerosis,
depression, diabetes mellitus, hypercholesterolemia, hypertension,
lacunar infarcts, stroke, obesity, smoking, and alcohol consumption
(Breteler, 2000; de Bruijn and Ikram, 2014; Deckers et al., 2015).
Diabetes mellitus, depression, hypertension, stroke, and cardio-
vascular diseases have also been associated with an increased risk
of progressing from cognitively normal to MCI (Pankratz et al.,
2015; Roberts et al., 2015). Moreover, an association with cogni-
tive decline has been found in both cognitively normal and MCI
subjects (Jefferson et al., 2015; Kaffashian et al., 2013). Therefore,
we hypothesize that risk factors will occur more frequently in in-
dividuals with prodromal AD/MCI due to AD. We also expect that
risk factors will increase the risk of progression to dementia.

partially obtained from the
tabase (adni.loni.usc.edu). As
the design and implementa-
e in analysis orwriting of this
found at: http://adni.loni.usc.
ledgement_List .

We aim to investigate the frequency of several risk factors in
individuals with prodromal AD/MCI due to AD, classified according
to the IWG-2 and NIA-AA criteria, relative to subjects who do not
meet these criteria. Secondly, we aim to examine whether risk
factors influence the rate of cognitive decline.

2. Methods

2.1. Subjects

Subjects were recruited from 5 multicenter memory-clinic
based studies: DESCRIPA (Visser et al., 2008), German Dementia
Competence Network (Kornhuber et al., 2009), EDAR (www.
edarstudy.eu), the European Alzheimer’s Disease Consortium
(EADC)-PET study (Morbelli et al., 2012), and American Alzheimer’s
Disease Neuroimaging Initiative (ADNI-1) study (Mueller et al.,
2005; Supplemental Text 1); and 9 centers of the EADC and/or
EuropeanMedical Information Framework (EMIF)eAD: Amsterdam
(van der Flier et al., 2014), Antwerp (Somers et al., 2016), Barcelona
(Alcolea et al., 2014), Brescia (Frisoni et al., 2009), Coimbra
(Baldeiras et al., 2008), Gothenburg (Wallin et al., 2016), Kuopio
(Seppala et al., 2011), Liège (Bastin et al., 2010), and Lisbon (Maroco
et al., 2011). For subjects who participated in more than one study,
we used data from the study with the longest follow-up.

Inclusion criteria consisted of baseline diagnosis of MCI ac-
cording to the criteria of Petersen (Petersen, 2004), and at least one
of the following biomarkers available at baseline: amyloid-beta
(Ab) 1-42 and tau (total tau and/or phosphorylated tau) in CSF,
hippocampal volume on magnetic resonance imaging (MRI), or
cerebral glucose metabolism on [18F]FDG-PET of the brain. More-
over, baseline data had to be available on at least one of the selected
risk factors, as well as information on educational level and at least
one clinical follow-up assessment. Exclusion criteria were diagnosis
of dementia at baseline.

http://www.edarstudy.eu

http://adni.loni.usc.edu

http://adni.loni.usc.edu/wpcontent/uploads/%20how_to_apply/ADNI_Acknowledgement_List

Table 1
Classification of subjects according to IWG-2 and NIA-AA criteria

IWG-2 groups Amyloid marker:
CSF Ab 1-42

Neuronal injury marker:
CSF t-tau or p-tau

No prodromal AD Abnormal Normal
Normal Abnormal
Normal Normal

Prodromal AD Abnormal Abnormal

NIA-AA groups Amyloid Marker:
CSF Ab 1-42

Neuronal injury markers:
CSF t-tau or p-tau/MTA on
MRI/FDG-PET

Low-AD-likelihood Normal All normal
High-AD-likelihood Abnormal At least one abnormal
IAP Abnormal All normal
SNAP Normal At least one abnormal
Intermediate-AD-likelihood Unknown At least one abnormal
Inconclusive/uninformative Unknown All normal

Key: Ab, amyloid-beta; AD, Alzheimer’s disease; CSF, cerebrospinal fluid; FDG-PET,
fluorodeoxyglucose-positron emission tomography; IAP, isolated amyloid pathology;
IWG, International Working Group; MRI, magnetic resonance imaging; MTA, medial
temporal lobe atrophy;NIA-AA,National InstituteofAging-Alzheimer’sAssociation; p-
tau, phosphorylated tau; SNAP, non-Alzheimer pathophysiology; t-tau, total tau.

I. Bos et al. / Neurobiology of Aging 56 (2017) 33e40 35

2.2. Clinical assessment

The clinical assessment is described in detail by Vos et al. (2015).
In short, clinical assessment was performed at each site according
to local routine protocol. Cognitive impairment was defined as Z-
score<�1.5 SD on at least one neuropsychological test, which could be a memory or nonmemory test.

2.3. Outcome at follow-up

Cognitive decline was defined as progression to dementia ac-
cording to the Diagnostic and Statistical Manual of Mental Disor-
ders (APA,1994), or a decline on theMini-Mental State Examination
(MMSE) of at least 3 points at follow-up. We used a combination of
these 2 measures, as for a subgroup (n¼ 17), no clinical diagnosis at
follow-up was available. For sub analyses, diagnosis of AD-type
dementia at follow-up was made according to the National
Institute of Neurological and Communicative Disorders and
StrokeeAlzheimer’s Disease and Related Disorders Association
criteria (McKhann et al., 1984).

The medical ethics committee at each site approved the study.
All subjects provided informed consent.

2.4. Biomarker assessment

Biomarker assessment was performed according to the routine
protocol at each site and center-specific cut-offs were used to define
abnormality, as described elsewhere (Vos et al., 2015). Examination
of medial temporal lobe atrophy on MRI and cerebral glucose
metabolism on FDG-PET were performed through visual
assessment.

2.5. Subject classification

Subjects were classified as having prodromal AD according to
the IWG-2 criteria using CSF Ab1-42 and tau biomarkers (Table 1).
The NIA-AA criteria distinguish between 6 groups that indicate the
likelihood that MCI is due to AD, based on combinations of amyloid
and neuronal injury markers. We used CSF Ab1-42 as amyloid
marker and CSF total tau, CSF phosphorylated tau, cerebral glucose
metabolism on FDG-PET, hippocampal volume, or medial temporal
lobe atrophy on MRI as neuronal injury markers (Table 1).

2.6. Risk factors

We assessed the following risk factors at baseline: atheroscle-
rotic disease, depression, diabetes, hypercholesterolemia, hyper-
tension, lacunar infarct, obesity, stroke, current smoking, and
current alcohol use. Not all risk factors were available for each
subject. Supplemental Table 1 provides an overview of the available
risk factors for each center. The risk factor definitions are described
by center in Supplemental Table 2. For all risk factors occurrence in
medical history was used as a standard. For some risk factors, we
used additional definitions based on rating scales, physical mea-
surements or medication use, based on availability (Supplemental
Table 2).

2.7. Statistical analyses

Baseline differences between the biomarker profile groups were
analyzed using ANOVA for continuous variables and c2 test for
categorical variables. The relation of risk factors with prodromal
AD/MCI due to AD was tested with logistic regression (IWG-2
criteria) or multinomial regression (NIA-AA criteria). Cox propor-
tional hazards models were used to test the effect of each risk factor

on the rate of cognitive decline in the total sample, and for the IWG-
2 and NIA-AA biomarker subgroups. All analyses were adjusted for
age, sex, education, and center. Statistical analyses were performed
using SPSS version 22.0 with the significance level set at p < 0.05. We corrected for multiple comparisons, using the false discovery rate (FDR) adjustment (Benjamini and Hochberg, 1995), taking into account the testing of 10 risk factors. In tables, we reported un- corrected p-values and we indicated which associations were sig- nificant after correction for multiple comparisons in tables and the text.

3. Results

3.1. Subject characteristics

We included 1394 individuals (mean age ¼ 69.7, SD 8.3; 51%
female). Seven hundred and fifty-eight subjects had data available
on both amyloid and neuronal injury markers, whereas 6

sub-
jects only had data on a neuronal injury marker (medial temporal
lobe atrophy n ¼ 528, FDG-PET n ¼ 108). Five hundred and eighty
individuals (42%) showed cognitive decline after an average follow-
up time of 2.3 (SD 1.2) years. Table 2 shows the characteristics of the
subjects classified according to the IWG-2 and the NIA-AA criteria.

3.2. Analyses in subjects with both amyloid and neuronal injury
markers

Based on the IWG-2 criteria, 302 subjects (40%) were classified
as prodromal AD. Individuals with prodromal AD were older (p < 0.001), showed a lower score on the MMSE at follow-up (p< 0.001) and were more likely to progress to AD-type dementia at follow-up (p < 0.001) compared with subjects without prodromal AD. Ac- cording to the NIA-AA criteria, 142 individuals (10%) were classified in the low-AD-likelihood group, 356 (26%) in the high-AD- likelihood group, 54 (4%) in the isolated amyloid pathology group, and 206 (15%) in the Suspected Non-AD Pathophysiology group. Subjects in the high-AD-likelihood group were older and most likely to progress to AD-type dementia, compared with all other groups (Table 2).

3.2.1. Frequency of risk factors
Table 3 shows the frequency of AD risk factors for the NIA-AA

groups with both amyloid and neuronal injury data. Compared

Table 2
Demographics and clinical outcome according to IWG-2 and NIA-AA criteria

Characteristics IWG-2 criteria NIA-AA criteriadamyloid and neuronal injury markers NIA-AA criteriadonly neuronal injury markers

No prodromal
AD (N ¼ 456)

Prodromal
AD (N ¼ 302) Low-AD-likelihood

(N ¼ 142)
High-AD-likelihood
(N ¼ 356)

IAP
(N ¼ 54) SNAP

(N ¼ 206)

Uninformative/Inconclusive
(N ¼ 286)

Intermediate-AD-likelihood
(N ¼ 350)

Age, y 67.3 (8.6) 71.3 (7.5)e 63.4 (8.9)g,i 71.3 (7.5)f,h,i 66.2 (7.5)g 69.2 (8.0)f,g 67.8 (8.4) 73.1 (7.2)j

Female, n 207 (45%) 148 (49%) 64 (45%) 170 (48%) 25 (46%) 96 (46%) 183 (64%) 177 (50%)j

Education, y 10.4 (3.8) 11.7 (4.3)e 10.3 (3.2)g 11.6 (4.3)f,i 11.0 (4.0) 10.2 (3.9)g 9.9 (4.6) 10.7 (4.2)j

Follow-up, y 2.3 (1.2) 2.3 (1.1) 2.1 (0.9) 2.3 (1.2) 2.3 (1.2) 2.4 (1.3) 2.6 (1.4) 2.3 (1.3)j

APOE-ε4a 138 (35%) 184 (68%)e 37 (30%)g 205 (65%)f,i 22 (47%) 58 (32%)g 81 (36%) 141 (52%)j

MMSE at baseline 27.0 (2.3) 26.1 (2.5)e 27.5 (2.9)g,i 26.2 (2.5)f,h 27.3 (2.4)g 26.7 (2.3)f 27.5 (2.3) 26.6 (2.2)j

Decline on MMSE at
follow-upb

58 (25%) 114 (51%)e 8 (13%)g 127 (49%)f,h,i 6 (16%)g 31 (30%)g 329 (38%) 106 (45%)j

Progression to AD-type
dementia at follow-upc

86 (20%) 172 (59%)e 6 (5%)g,i 193 (56%)f,h,i 10 (20%)f,g 49 (24%)f,g 56 (19%) 167 (47%)j

Progression to non-AD
dementia at follow-upd

38 (9%) 6 (2%)e 12 (9%) 11 (3%) 1 (2%)i 20 (10%)h 10 (4%) 22 (6%)

Results are mean (SD) or continuous variables or frequency (%).
Key: AD, Alzheimer’s disease; APOE, apolipoprotein E; IAP, isolated amyloid pathology; IWG, International Working Group; MMSE, MinieMental State Exanimation (range 0e30); NIA-AA, National Institute of Aging and
Alzheimer’s Association; SNAP, Suspected non-Alzheimer Pathophysiology.

a APOE genotype was only available in a subgroup of the sample: IWG-2 no prodromal AD n ¼ 397, prodromal AD n ¼ 271; NIA-AA low-AD-likelihood n ¼ 123, high-AD-likelihood n ¼ 316, IAP n ¼ 47, SNAP n ¼ 182,
uninformative/inconclusive n ¼ 226, intermediate-AD-likelihood n ¼ 271.

b Decline on MMSE at follow-up was defined as a difference of 3 points or more and was available in a subgroup of the samples: IWG-2 no prodromal AD n ¼ 237, prodromal AD n ¼ 223; NIA-AA low-AD-likelihood n ¼ 61,
high-AD-likelihood n ¼ 259, IAP n ¼ 37, SNAP ¼ 103, uninformative/inconclusive n ¼ 184, intermediate-AD-likelihood n ¼ 233.

c Progression to AD-type dementia at follow-upwas available in a subgroup of the sample: IWG-2 no prodromal AD n¼ 435, prodromal AD n¼ 293, NIA-AA low-AD-likelihood n¼ 134, high-AD-likelihood n¼ 344, IAP n¼ 49,
SNAP n ¼ 201, uninformative/inconclusive n ¼ 286, intermediate-AD-likelihood n ¼ 350.

d Progression to non-AD dementia at follow-upwas available in a subgroup of the samples: IWG-2 no prodromal AD n¼ 433, prodromal AD n¼ 293, NIA-AA low-AD-likelihood n¼ 133, high-AD-likelihood n¼ 343, IAP n¼ 50,
SNAP n ¼ 200, uninformative/inconclusive n ¼ 286, intermediate-AD-likelihood n ¼ 350.

e p < 0.05 compared with the no prodromal Alzheimer’s disease after FDR correction. f p < 0.05 compared with low-AD-likelihood after FDR correction. g p < 0.05 compared with high-AD-likelihood after FDR correction. h p < 0.05 compared with IAP after FDR correction. i p < 0.05 compared with SNAP after FDR correction. j p < 0.05 compared with the uninformative/inconclusive after FDR correction.

I.Bos
et

al./
N
eurobiology

of
A
ging

56
(2017)

33
e
40

Table 3
Frequency of risk factors for NIA-AA groups

Risk factors Low-AD- likelihood
N ¼ 142

High-AD- likelihood
N ¼ 356

IAP
N ¼ 54

SNAP
N ¼ 206

p-value,
low vs. high

Atherosclerotic disease (n ¼ 1002) 4% 10% 5% 9% 0.277
Depression (n ¼ 1129) 46% 17%a 27% 29% 0.004
Diabetes (n ¼ 914) 8% 9% 14% 15% 0.960
Hypercholesterolemia (n ¼ 1001) 43% 27%a 38% 38% 0.009
Hypertension (n ¼ 1346) 50% 47% 54% 47% 0.038
Lacunar infarct (n ¼ 497) 29% 23% 18% 30% 0.133
Stroke (n ¼ 1013) 3% 4% 6% 5% 0.787
Obesity (n ¼ 993) 21% 8%a 8% 18% 0.004
Smoking (n ¼ 1195) 53% 36% 40% 42% 0.076
Alcohol use (n ¼ 973) 42% 50% 50% 42% 0.352

Comparisons were corrected for baseline age, gender, years of education and center.
Key: AD, Alzheimer’s disease; IAP, isolated amyloid pathology; SNAP, suspected non-Alzheimer pathophysiology.

a p < 0.05 after FDR correction.

I. Bos et al. / Neurobiology of Aging 56 (2017) 33e40 37

with the high-AD-likelihood group, subjects in the low-AD-
likelihood group had a higher frequency of depression (46% vs.
17%, p ¼ 0.004, FDR p ¼ 0.020), obesity (21% vs. 8%, p ¼ 0.004, FDR
p ¼ 0.020), and hypercholesterolemia (43% vs. 27%, p ¼ 0.009, FDR
p ¼ 0.030). No differences were found between the groups for the
other risk factors (Table 3).

Supplemental Table 3 shows the frequency of risk factors for the
groups according to the IWG-2 criteria. In the group without pro-
dromal AD, we found higher frequencies of depression (34% vs. 16%,
p ¼ 0.009, FDR p ¼ 0.045) and obesity (17% vs. 8%, p ¼ 0.007, FDR
p ¼ 0.045) compared with the group with prodromal AD
(Supplemental Table 3).

3.2.2. Effect of risk factors on cognitive decline
In the total group of subjects with both amyloid and neuronal

injury markers, alcohol use was associated with a higher risk of
cognitive decline (HR ¼ 1.5, p ¼ 0.003, FDR p ¼ 0.030, Table 4).
There were no significant interactions between risk factors and
NIA-AA group classification, indicating that the effect of risk factors
was similar for all groups. Using the IWG-2 classification, the effects
of depression, hypercholesterolemia, and smoking were different
between the 2 groups, but these differences were no longer sta-
tistically significant after adjusting for multiple testing (Table 4).

3.3. Analyses in subjects with only neuronal injury markers

Table 2 shows the characteristics of the 258 (21%) subjects
classified as uninformative/inconclusive and the 350 (25%) included
in the intermediate-AD-likelihood group according to the NIA-AA

Table 4
Effects of risk factors on cognitive decline

Risk factors Main effect risk factors Inter

HR 95% CI p-value HR

Atherosclerotic disease 1.1 0.7e1.7 0.825 0.5
Depression 0.7 0.5e0.9 0.022 0.5
Diabetes 1.0 0.7e1.6 0.907 1.1
Hypercholesterolemia 0.8 0.6e1.0 0.080 2.2
Hypertension 0.7 0.4e1.1 0.103 0.7
Lacunar infarct 0.8 0.3e2.1 0.603 0.8
Stroke 1.0 0.6e1.9 0.899 0.5
Obesity 0.7 0.4e1.1 0.111 1.0
Smoking 1.2 0.9e1.5 0.214 1.8
Alcohol use 1.5 1.2e2.0 0.003a 0.8

Cognitive decline is defined as progression to dementia or 3 points decline onMMSE at fol
corrected for baseline age, gender, years of education and center.
Key: CI, confidence interval; HR, hazard ratio; IWG, International Working Group; NIA-A

a p < 0.05 after FDR correction.

criteria. The subjects in the intermediate-AD-likelihood differed
on all characteristics from the uninformative/inconclusive group.

The frequency of risk factors for the subjects who had only
neuronal injury markers available is described in Supplemental
Table 4. In the intermediate-AD-likelihood-group lacunar infarcts
occurred more frequently (40%), compared with the uninformative/
inconclusive group (16%, p < 0.001). There were no differences for the other risk factors (Supplemental Table 4).

In the subjects with only neuronal injury markers available,
none of the risk factors increased the risk of cognitive decline. Also,
there was no difference between the 2 NIA-AA groups in the rate of
cognitive decline (Supplemental Table 5).

3.4. Post-hoc analysesdprogression to AD-type dementia

When we repeated the analyses with only progression to AD-
type dementia as an outcome in subjects with both amyloid and
neuronal injury markers available (n ¼ 725), alcohol use was no
longer associated with an increased risk of progression (HR ¼ 1.3,
95% CI: 0.9e1.8, p ¼ 0.164).

Since the ADNI cohort excluded subjects with depressive
symptoms (GDS >6), we repeated the analyses concerning
depression without ADNI subjects. This did not influence the
results.

4. Discussion

We examined the frequency of vascular and lifestyle risk
factors in prodromal AD/MCI due to AD, and the influence of
these factors on cognitive decline, in subjects with MCI. We

action with IWG-2 groups Interaction with NIA-AA groups

95% CI p-value HR 95% CI p-value

0.2e1.3 0.159 0.6 0.4e1.1 0.119
0.3e0.9 0.048 0.8 0.6e1.2 0.253
0.4e2.8 0.922 1.2 0.7e2.3 0.501
1.2e3.9 0.010 1.1 0.8e1.5 0.624
0.5e1.1 0.103 0.8 0.6e1.1 0.118
0.3e2.1 0.603 0.8 0.4e1.6 0.535
0.1e1.5 0.201 0.7 0.4e1.3 0.263
0.4e2.6 0.993 1.1 0.6e2.1 0.771
1.1e3.0 0.017 1.2 0.9e1.7 0.233
0.5e1.4 0.478 0.9 0.7e1.3 0.624

low-up. Hazard ratios and 95% CIs were calculated using Cox-regression analyses and

A, National Institute of Aging Alzheimer’s Association.

I. Bos et al. / Neurobiology of Aging 56 (2017) 33e4038

found that the frequencies of depression, hypercholesterolemia
and obesity were higher in the group without AD pathology
compared with the group with AD pathology. Only alcohol
increased the risk of cognitive decline, regardless of
AD-pathology.

4.1. Frequency of risk factors

Contrary to our hypothesis, we found higher frequencies of
depression, hypercholesterolemia, and obesity, in the group
without prodromal AD and in the low-AD-likelihood group. This
suggests that subjects without prodromal AD or low-AD-likelihood
had cognitive impairment due to other causes than AD, such as
depression or vascular disorders (DeCarli, 2003; Gorelick et al.,
2011). It is also possible that low cholesterol or low body mass in-
dex are risk factors for prodromal AD in this elderly sample.
Although obesity and hypercholesterolemia in middle age have
been shown to be predictive for AD (Deckers et al., 2015; Kivipelto
et al., 2005), other studies showed that this association is reversed
at older age, such that low body mass index and low cholesterol
increase the risk for AD (Anstey et al., 2008; Johnson et al., 2006).
When we compared the frequencies observed in the present study
to the prevalence of risk factors reported in meta-analyses and
population-based cohort studies (Supplemental Table 6), we found
that the frequencies of obesity and hypercholesterolemia in the
high-AD-likelihood group were decreased, whereas frequencies
were similar in subjects with a low-AD-likelihood. On the contrary,
the frequency of depression in the high-AD-likelihood group was
similar to that in the general population, whereas it was higher in
the low-AD-likelihood group compared with the population-based
studies. This would suggest that the difference in frequencies of
obesity and hypercholesterolemia between the low and high-AD-
likelihood in our study results from a decrease of obesity and hy-
percholesterolemia in the high-AD-likelihood, rather than from an
increase in the low-AD-likelihood. Conversely, the difference in
frequency in depression between groups could result from an in-
crease in frequency of depression in the low-AD-likelihood. This
indicates that depression can be a possible cause of MCI in the
studied population (DeCarli, 2003; Defrancesco et al., 2009). Clearly
there are methodological differences in the inclusion of subjects,
definition, and method of ascertainment of risk factors and age
range between the present study and the population-based studies.
Studies that directly compare frequency of risk factors in prodromal
AD to cognitively normal subjects are needed to further clarify this.

4.2. Influence of risk factors on cognitive decline

Alcohol consumption was associated with an increased risk of
cognitive decline, independent of AD-pathology. Although this
finding is in line with several previous studies (Deckers et al., 2015;
Jauhar et al., 2014) that identified alcohol as a risk factor for
cognitive decline, other studies have reported a protective or no
relation to alcohol consumption with incident AD (Anstey et al.,
2009; Ruitenberg et al., 2002). These conflicting results could be
explained by differences in study population (MCI vs. cognitively
normal), definitions of alcohol consumption (dichotomous vs. cat-
egories based on the amount of alcohol use) and the type of alcohol.
When conversion to AD-type dementia was used as outcome
instead of conversion to dementia or a decline on the MMSE, we
found that the effect of alcohol was no longer significant. This
suggests that alcohol consumption mainly has an effect on pro-
gression to non-AD types of dementia or cognitive decline in
general.

4.3. IWG-2 versus NIA-AA criteria

The results on frequency of risk factors were comparable for the
IWG-2 and NIA-AA criteria. Also, when comparing the effect of risk
factors on cognitive decline, we found similar outcomeswhen using
the 2 sets of criteria. This shows that eventhough the IWG-2 criteria
only classify neuronal injury based on tau in CSF, whereas the NIA-
AA criteria also include other neuronal injury markers, this did not
influence the results. Although the outcomes were similar for the 2
sets of criteria, the NIA-AA criteria provided more insight into
which specific biomarker profile was associated with a higher fre-
quency of a certain risk factor, which could be useful to give a more
refined diagnosis and prognosis of early AD and age-related
comorbidities.

4.4. Strengths and limitations of the study

Strengths of the study are the large sample size, the broad
spectrum of assessed risk factors, and longitudinal data on clinical
outcome. There are also limitations to this study that should be
mentioned. Some of the biomarker subgroups were small and some
risk factors were only available in a subgroup or had a low fre-
quency, which limited statistical power. The data used in this study
were contributed by different centers and data were not collected
using the same protocol. This might have led to variability, although
it does reflect current clinical practice. Furthermore, the use of in-
direct measures (e.g., medical history) of risk factors could have
introduced heterogeneity in classification. Wewere unable to study
the potential interactions between risk factors, as not all centers
contributed data on all risk factors. We had only limited data
available on medication use, which did not allow us to control for
this. Also, we could not correct for the duration and the severity of a
risk factor, as we had no information on this. Although the mean
follow-up was 2.3 years, some individuals likely would have shown
cognitive decline at a later stage. Since these findings are based on
clinical research populations, they may not be generalizable to
other settings.

5. Conclusion

In summary, we found that dementia risk factors were not
associated with prodromal AD/MCI due to AD in subjects with MCI,
and only alcohol increased the risk of cognitive decline, regardless
of underlying pathology. Moreover, we found that a lower fre-
quency of hypercholesterolemia or obesity may be indicative of
early AD in an elderly population. Although our findings should be
validated in future studies, they could have implications for clinical
practice, future scientific studies, as well as for the selection of in-
dividuals for participation in clinical trials. Different risk factor
profiles in subjects with MCI could be related to distinct etiologies
of cognitive dysfunction, and therefore may have different prog-
nostic values. Management of alcohol habits could possibly lessen
or prevent further cognitive decline. Future studies should focus on
the role of risk factors in even earlier stages of AD (e.g., preclinical
AD), examine longitudinal biomarkers values, and consider the
duration and severity of risk factors. Also, co-occurrence of risk
factors and possible synergistic effects on biomarkers should be a
topic for future research as we were unable to study this in the
current sample.

Acknowledgements

The research leading to these results has received support from
the Innovative Medicines Initiative Joint Undertaking under EMIF
grant agreement no 115372, resources of which are composed of

I. Bos et al. / Neurobiology of Aging 56 (2017) 33e40 39

financial contribution from the European Union’s Seventh Frame-
work Programme (FP7/2007-2013) and EFPIA companies’ in kind
contribution. The present study was conducted as part of the
Project VPH-DARE@IT funded by the European Union Seventh
Framework Programme (FP7-ICT-2011-9-601055) under grant
agreement no 601055. The Dementia Competence Network (DCN)
has been supported by a grant from the German Federal Ministry
of Education and Research (BMBF): Kompetenznetz Demenzen
(01GI0420). Additional funding related to the randomized clinical
trials came from Janssen-Cilag and Merz Pharmaceuticals. The
latter funds were exclusively used for personnel, pharmaceuticals,
blistering and shipment of medication, monitoring, and as capi-
tation fees for recruiting centers. The DESCRIPA study was funded
by the European Commission within the 5th framework program
(QLRT-2001-2455).

The EDAR study was funded by the European Commission
within the 5th framework program (contract # 37670). The
Coimbra center was funded by Project PIC/IC/83206/2007 da
Fundação para a Ciência e TecnologiaePortugal. Research of the
VUmc Alzheimer center is part of the neurodegeneration research
program of the Neuroscience Campus Amsterdam. The VUmc
Alzheimer Center is supported by Alzheimer Nederland and
Stichting VUmc fonds. The clinical database structure was
developed with funding from Stichting Dioraphte. The Alz-
heimer’s Disease Neuroimaging Initiative (ADNI; National In-
stitutes of Health grant U01 AG024904 and DOD ADNI
Department of Defense award number W81XWH-12-2-0012) was
funded by the National Institute on Aging, the National Institute
of Biomedical Imaging and Bioengineering, and through generous
contributions from the following: Alzheimer’s Association; Alz-
heimer’s Drug Discovery Foundation; BioClinica, Inc; Biogen Idec
Inc; Bristol-Myers Squibb Company; Eisai Inc; Elan Pharmaceu-
ticals, Inc; Eli Lilly and Company; F. Hoffmann-La Roche Ltd and
its affiliated company Genentech, Inc; GE Healthcare; Innoge-
netics, N.V.; IXICO Ltd.; Janssen Alzheimer Immunotherapy
Research & Development, LLC.; Johnson & Johnson Pharmaceu-
tical Research & Development LLC.; Medpace, Inc; Merck & Co,
Inc; Meso Scale Diagnostics, LLC.; NeuroRx Research; Novartis
Pharmaceuticals Corporation; Pfizer Inc; Piramal Imaging; Serv-
ier; Synarc Inc; and Takeda Pharmaceutical Company. The Cana-
dian Institutes of Health Research is providing funds to Rev
December 5, 2013 support ADNI clinical sites in Canada. Private
sector contributions are facilitated by the Foundation for the
National Institutes of Health (www.fnih.org). The grantee orga-
nization is the Northern California Institute for Research and
Education, and the study is coordinated by the Alzheimer’s Dis-
ease Cooperative Study at the University of California, San Diego.
ADNI data are disseminated by the Laboratory for Neuro Imaging
at the University of Southern California.

Statistical analysis and drafting the manuscript and study
concept and design is done by Isabelle Bos, Stephanie J. Vos, and
Pieter Jelle Visser. All authors contributed to acquisition and/or
interpretation of data and critical revision of final draft of
manuscript.

Appendix A. Supplementary data

Supplementary data associated with this article can be found, in
the online version, at http://dx.doi.org/10.1016/j.neurobiolaging.
2017.03.034.

Disclosure statement

The authors have no conflicts of interest to disclose.

References

Albert, M.S., DeKosky, S.T., Dickson, D., Dubois, B., Feldman, H.H., Fox, N.C., Gamst, A.,
Holtzman, D.M., Jagust, W.J., Petersen, R.C., Snyder, P.J., Carrillo, M.C., Thies, B.,
Phelps, C.H., 2011. The diagnosis of mild cognitive impairment due to Alz-
heimer’s disease: recommendations from the National Institute on Aging-Alz-
heimer’s Association workgroups on diagnostic guidelines for Alzheimer’s
disease. Alzheimer’s Dement. 7, 270e279.

Alcolea, D., Carmona-Iragui, M., Suarez-Calvet, M., Sanchez-Saudinos, M.B., Sala, I.,
Anton-Aguirre, S., Blesa, R., Clarimon, J., Fortea, J., Lleo, A., 2014. Relationship
between beta-Secretase, inflammation and core cerebrospinal fluid biomarkers
for Alzheimer’s disease. J. Alzheimer’s Dis. 42, 157e167.

Anstey, K.J., Lipnicki, D.M., Low, L.F., 2008. Cholesterol as a risk factor for dementia
and cognitive decline: a systematic review of prospective studies with meta-
analysis. Am. J. Geriatr. Psychiatry 16, 343e354.

Anstey, K.J., Mack, H.A., Cherbuin, N., 2009. Alcohol consumption as a risk factor for
dementia and cognitive decline: meta-analysis of prospective studies. Am. J.
Geriatr. Psychiatry 17, 542e555.

APA, 1994. Diagnostic and Statistical Manual of Mental Disorders: DSM-iv. American
Psychiatric Association, Washington, DC.

Baldeiras, I., Santana, I., Proenca, M.T., Garrucho, M.H., Pascoal, R., Rodrigues, A.,
Duro, D., Oliveira, C.R., 2008. Peripheral oxidative damage in mild cognitive
impairment and mild Alzheimer’s disease. J. Alzheimer’s Dis. 15, 117e128.

Bastin, C., Kerrouche, N., Lekeu, F., Adam, S., Guillaume, B., Lemaire, C., Aerts, J.,
d’Ydewalle, G., Collette, F., Salmon, E., 2010. Controlled memory processes in
questionable Alzheimer’s disease: a view from neuroimaging research.
J. Alzheimer’s Dis. 20, 547e560.

Benjamini, Y., Hochberg, Y., 1995. Controlling the false discovery rate: a practical and
powerful approach to multiple testing. J. R. Stat. Soc. Ser. B (Methodological) 57,
289e300.

Breteler, M.M., 2000. Vascular risk factors for Alzheimer’s disease: an epidemiologic
perspective. Neurobiol. Aging 21, 153e160.

de Bruijn, R.F., Ikram, M.A., 2014. Cardiovascular risk factors and future risk of
Alzheimer’s disease. BMC Med. 12, 130.

DeCarli, C., 2003. Mild cognitive impairment: prevalence, prognosis, aetiology, and
treatment. Lancet Neurol. 2, 15e21.

Deckers, K., van Boxtel, M.P., Schiepers, O.J., de Vugt, M., Munoz Sanchez, J.L.,
Anstey, K.J., Brayne, C., Dartigues, J.F., Engedal, K., Kivipelto, M., Ritchie, K.,
Starr, J.M., Yaffe, K., Irving, K., Verhey, F.R., Kohler, S., 2015. Target risk factors for
dementia prevention: a systematic review and Delphi consensus study on the
evidence from observational studies. Int. J. Geriatr. Psychiatry 30, 234e246.

Defrancesco, M., Marksteiner, J., Deisenhammer, E.A., Hinterhuber, H., Weiss, E.M.,
2009. Association of mild cognitive impairment (MCI) and depression. Neuro-
psychiatry 23, 144e150.

Dubois, B., Feldman, H.H., Jacova, C., Hampel, H., Molinuevo, J.L., Blennow, K.,
DeKosky, S.T., Gauthier, S., Selkoe, D., Bateman, R., Cappa, S., Crutch, S.,
Engelborghs, S., Frisoni, G.B., Fox, N.C., Galasko, D., Habert, M.O., Jicha, G.A.,
Nordberg, A., Pasquier, F., Rabinovici, G., Robert, P., Rowe, C., Salloway, S.,
Sarazin, M., Epelbaum, S., de Souza, L.C., Vellas, B., Visser, P.J., Schneider, L.,
Stern, Y., Scheltens, P., Cummings, J.L., 2014. Advancing research diagnostic
criteria for Alzheimer’s disease: the IWG-2 criteria. Lancet Neurol. 13, 614e629.

Frisoni, G.B., Prestia, A., Zanetti, O., Galluzzi, S., Romano, M., Cotelli, M.,
Gennarelli, M., Binetti, G., Bocchio, L., Paghera, B., Amicucci, G., Bonetti, M.,
Benussi, L., Ghidoni, R., Geroldi, C., 2009. Markers of Alzheimer’s disease in a
population attending a memory clinic. Alzheimer’s Dement. 5, 307e317.

Gorelick, P.B., Scuteri, A., Black, S.E., Decarli, C., Greenberg, S.M., Iadecola, C.,
Launer, L.J., Laurent, S., Lopez, O.L., Nyenhuis, D., Petersen, R.C., Schneider, J.A.,
Tzourio, C., Arnett, D.K., Bennett, D.A., Chui, H.C., Higashida, R.T., Lindquist, R.,
Nilsson, P.M., Roman, G.C., Sellke, F.W., Seshadri, S. American Heart Association
Stroke Council, Council on Epidemiology and Prevention Council on Cardio-
vascular Nursing, Council on Cardiovascular Radiology and Intervention and
Council on Cardiovascular Surgery and Anesthesia, 2011. Vascular contributions
to cognitive impairment and dementia: a statement for healthcare professionals
from the American Heart Association/American Stroke Association. Stroke 42,
2672e2713.

Jauhar, S., Marshall, E.J., Smith, I.D., 2014. Alcohol and cognitive impairment. Adv.
Psychiatr. Treat. 20, 304e313.

Jefferson, A.L., Hohman, T.J., Liu, D., Haj-Hassan, S., Gifford, K.A., Benson, E.M.,
Skinner, J.S., Lu, Z., Sparling, J., Sumner, E.C., Bell, S., Ruberg, F.L., 2015. Adverse
vascular risk is related to cognitive decline in older adults. J. Alzheimer’s Dis. 44,
1361e1373.

Johnson, D.K., Wilkins, C.H., Morris, J.C., 2006. Accelerated weight loss may precede
diagnosis in Alzheimer disease. Arch. Neurol. 63, 1312e1317.

Kaffashian, S., Dugravot, A., Elbaz, A., Shipley, M.J., Sabia, S., Kivimaki, M., Singh-
Manoux, A., 2013. Predicting cognitive decline: a dementia risk score vs. the
Framingham vascular risk scores. Neurology 80, 1300e1306.

Kivipelto, M., Ngandu, T., Fratiglioni, L., Viitanen, M., Kareholt, I., Winblad, B.,
Helkala, E.L., Tuomilehto, J., Soininen, H., Nissinen, A., 2005. Obesity and
vascular risk factors at midlife and the risk of dementia and Alzheimer disease.
Arch. Neurol. 62, 1556e1560.

Kornhuber, J., Schmidtke, K., Frolich, L., Perneczky, R., Wolf, S., Hampel, H., Jessen, F.,
Heuser, I., Peters, O., Weih, M., Jahn, H., Luckhaus, C., Hull, M., Gertz, H.J.,
Schroder, J., Pantel, J., Rienhoff, O., Seuchter, S.A., Ruther, E., Henn, F., Maier, W.,
Wiltfang, J., 2009. Early and differential diagnosis of dementia and mild

Home

http://dx.doi.org/10.1016/j.neurobiolaging.2017.03.034

http://refhub.elsevier.com/S0197-4580(17)30117-3/sref1

http://refhub.elsevier.com/S0197-4580(17)30117-3/sref2

http://refhub.elsevier.com/S0197-4580(17)30117-3/sref3

http://refhub.elsevier.com/S0197-4580(17)30117-3/sref4

http://refhub.elsevier.com/S0197-4580(17)30117-3/sref5

http://refhub.elsevier.com/S0197-4580(17)30117-3/sref6

http://refhub.elsevier.com/S0197-4580(17)30117-3/sref7

http://refhub.elsevier.com/S0197-4580(17)30117-3/sref8

http://refhub.elsevier.com/S0197-4580(17)30117-3/sref9

http://refhub.elsevier.com/S0197-4580(17)30117-3/sref10

http://refhub.elsevier.com/S0197-4580(17)30117-3/sref11

http://refhub.elsevier.com/S0197-4580(17)30117-3/sref12

http://refhub.elsevier.com/S0197-4580(17)30117-3/sref13

http://refhub.elsevier.com/S0197-4580(17)30117-3/sref14

http://refhub.elsevier.com/S0197-4580(17)30117-3/sref15

http://refhub.elsevier.com/S0197-4580(17)30117-3/sref16

http://refhub.elsevier.com/S0197-4580(17)30117-3/sref17

http://refhub.elsevier.com/S0197-4580(17)30117-3/sref18

http://refhub.elsevier.com/S0197-4580(17)30117-3/sref19

http://refhub.elsevier.com/S0197-4580(17)30117-3/sref20

http://refhub.elsevier.com/S0197-4580(17)30117-3/sref21

http://refhub.elsevier.com/S0197-4580(17)30117-3/sref22

I. Bos et al. / Neurobiology of Aging 56 (2017) 33e4040

cognitive impairment: design and cohort baseline characteristics of the German
Dementia Competence Network. Demen. Geriatr. Cogn. Disord. 27, 404e417.

Maroco, J., Silva, D., Rodrigues, A., Guerreiro, M., Santana, I., de Mendonca, A., 2011.
Data mining methods in the prediction of Dementia: a real-data comparison of
the accuracy, sensitivity and specificity of linear discriminant analysis, logistic
regression, neural networks, support vector machines, classification trees and
random forests. BMC Res. Notes 4, 299.

McKhann, G., Drachman, D., Folstein, M., Katzman, R., Price, D., Stadlan, E.M., 1984.
Clinical diagnosis of Alzheimer’s disease: report of the NINCDS-ADRDA Work
Group under the auspices of Department of Health and Human Services Task
Force on Alzheimer’s disease. Neurology 34, 939e944.

Morbelli, S., Drzezga, A., Perneczky, R., Frisoni, G.B., Caroli, A., van Berckel, B.N.,
Ossenkoppele, R., Guedj, E., Didic, M., Brugnolo, A., Sambuceti, G., Pagani, M.,
Salmon, E., Nobili, F., 2012. Resting metabolic connectivity in prodromal Alz-
heimer’s disease. A European Alzheimer Disease Consortium (EADC) project.
Neurobiol. Aging 33, 2533e2550.

Mueller, S.G., Weiner, M.W., Thal, L.J., Petersen, R.C., Jack, C.R., Jagust, W.,
Trojanowski, J.Q., Toga, A.W., Beckett, L., 2005. Ways toward an early diagnosis
in Alzheimer’s disease: the Alzheimer’s Disease Neuroimaging Initiative (ADNI).
Alzheimer’s Demen. 1, 55e66.

Pankratz, V.S., Roberts, R.O., Mielke, M.M., Knopman, D.S., Jack Jr., C.R., Geda, Y.E.,
Rocca, W.A., Petersen, R.C., 2015. Predicting the risk of mild cognitive impair-
ment in the Mayo clinic study of aging. Neurology 84, 1433e1442.

Petersen, R.C., 2004. Mild cognitive impairment as a diagnostic entity. J. Intern. Med.
256, 183e194.

Roberts, R.O., Cha, R.H., Mielke, M.M., Geda, Y.E., Boeve, B.F., Machulda, M.M.,
Knopman, D.S., Petersen, R.C., 2015. Risk and protective factors for cognitive
impairment in persons aged 85 years and older. Neurology 84, 1854e1861.

Ruitenberg, A., van Swieten, J.C., Witteman, J.C., Mehta, K.M., van Duijn, C.M.,
Hofman, A., Breteler, M.M., 2002. Alcohol consumption and risk of dementia:
the Rotterdam Study. Lancet 359, 281e286.

Seppala, T.T., Koivisto, A.M., Hartikainen, P., Helisalmi, S., Soininen, H., Herukka, S.K.,
2011. Longitudinal changes of CSF biomarkers in Alzheimer’s disease.
J. Alzheimer’s Dis. 25, 583e594.

Somers, C., Struyfs, H., Goossens, J., Niemantsverdriet, E., Luyckx, J., De Roeck, N., De
Roeck, E., De Vil, B., Cras, P., Martin, J.J., De Deyn, P.P., Bjerke, M., Engelborghs, S.,
2016. A decade of cerebrospinal fluid biomarkers for Alzheimer’s disease in
Belgium. J. Alzheimer’s Dis. 54, 383e395.

van der Flier, W.M., Pijnenburg, Y.A., Prins, N., Lemstra, A.W., Bouwman, F.H.,
Teunissen, C.E., van Berckel, B.N., Stam, C.J., Barkhof, F., Visser, P.J., van
Egmond, E., Scheltens, P., 2014. Optimizing patient care and research: the
Amsterdam dementia cohort. J. Alzheimer’s Dis. 41, 313e327.

Visser, P.J., Verhey, F.R., Boada, M., Bullock, R., De Deyn, P.P., Frisoni, G.B., Frolich, L.,
Hampel, H., Jolles, J., Jones, R., Minthon, L., Nobili, F., Olde Rikkert, M.,
Ousset, P.J., Rigaud, A.S., Scheltens, P., Soininen, H., Spiru, L., Touchon, J.,
Tsolaki, M., Vellas, B., Wahlund, L.O., Wilcock, G., Winblad, B., 2008. Develop-
ment of screening guidelines and clinical criteria for predementia Alzheimer’s
disease. The DESCRIPA Study. Neuroepidemiology 30, 254e265.

Vos, S.J., Verhey, F., Frolich, L., Kornhuber, J., Wiltfang, J., Maier, W., Peters, O.,
Ruther, E., Nobili, F., Morbelli, S., Frisoni, G.B., Drzezga, A., Didic, M., van
Berckel, B.N., Simmons, A., Soininen, H., Kloszewska, I., Mecocci, P., Tsolaki, M.,
Vellas, B., Lovestone, S., Muscio, C., Herukka, S.K., Salmon, E., Bastin, C.,
Wallin, A., Nordlund, A., de Mendonca, A., Silva, D., Santana, I., Lemos, R.,
Engelborghs, S., Van der Mussele, S., Freund-Levi, Y., Wallin, A.K., Hampel, H.,
van der Flier, W., Scheltens, P., Visser, P.J., 2015. Prevalence and prognosis of
Alzheimer’s disease at the mild cognitive impairment stage. Brain. 138 (Pt 5),
1327e1338.

Wallin, A., Nordlund, A., Jonsson, M., Blennow, K., Zetterberg, H., Ohrfelt, A.,
Stalhammar, J., Eckerstrom, M., Carlsson, M., Olsson, E., Gothlin, M., Svensson, J.,
Rolstad, S., Eckerstrom, C., Bjerke, M., 2016. Alzheimer’s diseasedsubcortical
vascular disease spectrum in a hospital-based setting: overview of results from the
Gothenburg MCI and dementia studies. J. Cereb. Blood FlowMetab. 36, 95e113.

http://refhub.elsevier.com/S0197-4580(17)30117-3/sref22

http://refhub.elsevier.com/S0197-4580(17)30117-3/sref23

http://refhub.elsevier.com/S0197-4580(17)30117-3/sref24

http://refhub.elsevier.com/S0197-4580(17)30117-3/sref25

http://refhub.elsevier.com/S0197-4580(17)30117-3/sref26

http://refhub.elsevier.com/S0197-4580(17)30117-3/sref27

http://refhub.elsevier.com/S0197-4580(17)30117-3/sref28

http://refhub.elsevier.com/S0197-4580(17)30117-3/sref29

http://refhub.elsevier.com/S0197-4580(17)30117-3/sref30

http://refhub.elsevier.com/S0197-4580(17)30117-3/sref31

http://refhub.elsevier.com/S0197-4580(17)30117-3/sref32

http://refhub.elsevier.com/S0197-4580(17)30117-3/sref33

http://refhub.elsevier.com/S0197-4580(17)30117-3/sref34

http://refhub.elsevier.com/S0197-4580(17)30117-3/sref35

http://refhub.elsevier.com/S0197-4580(17)30117-3/sref36

The frequency and influence of dementia risk factors in prodromal Alzheimer’s disease

1. Introduction
2. Methods
2.1. Subjects
2.2. Clinical assessment
2.3. Outcome at follow-up
2.4. Biomarker assessment
2.5. Subject classification
2.6. Risk factors
2.7. Statistical analyses
3. Results
3.1. Subject characteristics
3.2. Analyses in subjects with both amyloid and neuronal injury markers
3.2.1. Frequency of risk factors
3.2.2. Effect of risk factors on cognitive decline
3.3. Analyses in subjects with only neuronal injury markers
3.4. Post-hoc analyses—progression to AD-type dementia
4. Discussion
4.1. Frequency of risk factors
4.2. Influence of risk factors on cognitive decline
4.3. IWG-2 versus NIA-AA criteria
4.4. Strengths and limitations of the study
5. Conclusion
Acknowledgements
Appendix A. Supplementary data
Disclosure statement
References

General Hospital Psychiatry 37 (2015) 507–512

Contents lists available at ScienceDirect

General Hospital Psychiatry

j ourna l homepage: ht tp : / /www.ghp journa l .com

Depression — a common disorder across a broad spectrum of

neurological conditions: a cross-sectional nationally
representative survey☆

Andrew G.M. Bulloch, Ph.D. a,b,d,⁎, Kirsten M. Fiest, Ph.D. a,d, Jeanne V.A. Williams, M.Sc. a,
Dina H. Lavorato, M.Sc. a, Sandra A. Berzins, Ph.D. a,d, Nathalie Jetté, M.D. a,c,
Tamara M. Pringsheim, M.D. c,d, Scott B. Patten, M.D., Ph.D. a,b,d,⁎
a Department of Community Health Sciences, University of Calgary, Canada
b Department of Psychiatry, University of Calgary, Canada
c Department of Clinical Neurosciences, Hotchkiss Brain Institute and Institute for Public Health, University of Calgary, Canada
d Mathison Centre for Mental Health Research & Education, Hotchkiss Brain Institute, University of Calgary, Canada

a b s t r a c ta r t i c l e i n f o

☆ Disclaimer: This research and analysis were based on
the opinions expressed do not represent the views of Stat
⁎ Corresponding author. Department of Community He

Hospital Drive NW, Calgary, Canada, T2N 4Z6. Tel.: +1
210-8840.

E-mail address: bulloch@ucalgary.ca (A.G.M. Bulloch).

http://dx.doi.org/10.1016/j.genhosppsych.2015.06.007
0163-8343/

Article history:

Received 4 March 2015
Revised 19 May 2015
Accepted 8 June 2015

Keywords:
Neurological conditions
Depressive disorder
Depression rating scales
Prevalence
Community-based study

Objective: To estimate the prevalence of depression across a range of neurological conditions in a nationally rep-
resentative sample.
Methods: The data source was the Survey of Living with Neurological Conditions in Canada (SLNCC), which ac-
crued its sample by selecting participants from the Canadian Community Health Survey. The point prevalence
of depression was estimated by assessment of depressive symptoms with the Patient Health Questionnaire,
Brief (Patient Health Questionnaire, 9-item).
Results: A total of n=4408 participated in the SLNCC. The highest point prevalence of depression (N30%) was
seen in those with traumatic brain injury and brain/spinal cord tumors. Depression was also highly prevalent
(18–28%) in those with (listed from highest to lowest) Alzheimer’s disease/dementia, dystonia, multiple sclero-
sis, Parkinson’s disease, stroke, migraine, epilepsy and spina bifida. The odds ratios for depression, with the ref-

erent group being the general population, were significant (fromhighest to lowest) formigraine, traumatic brain
injury, stroke, dystonia and epilepsy.
Conclusions: All neurological conditions included in this study are associated with an elevated prevalence of de-
pression in community populations. The conditions with the highest prevalence are traumatic brain injury and
brain/spinal cord tumors.
© 2015 Elsevier Inc. All rights reserved.

1. Introduction

Major depression is frequently comorbid with a diverse range of
chronic medical conditions. Examples of such conditions include pain
[1], diabetes [2], heart disease [3], rheumatoid arthritis [4] and
Parkinson’s disease [5]. The relationship between depression and
chronic conditions is reciprocal. Although depression is often assumed
to be a consequence of chronic conditions, depression can increase the
risk of a number of chronic conditions including heart disease, arthritis,
asthma, back pain, bronchitis, hypertension and migraines [6]. Further-
more, higher levels of depression predict faster progression of

data from Statistics Canada, but
istics Canada.
alth Sciences, TRW 4D67, 3280
-403-220-4586; fax: +1-403-

Parkinson’s disease [7]. Accurate assessment of depression is therefore
pertinent both before and after the onset of chronic conditions.

Depression is known to be elevated in of neurological conditions in-
cluding epilepsy, multiple sclerosis (MS), Parkinson’s disease and trau-
matic brain injury, a finding that has been substantiated by recent
reviews [8–11]. However, these systematic reviews have identified con-
siderable heterogeneity in the estimates. Such heterogeneity is likely
due to different sampling and measurement procedures. For example,
different depression assessment tools have been applied, and many of
the samples are from clinical populations (see Discussion). This consid-
eration leads to uncertainty about the relative prevalence in different
neurological populations. Recently, a study called the Survey of Living
with Neurological Conditions in Canada (SLNCC) was launched. This
study explores a broad range of experiences and outcomes addressing
Canadians’ experiences with chronic neurological conditions in a
population-based sample. Among a number of important outcomes
(i.e., the economic impact of having a neurological condition), the
study provides an opportunity to estimate depression in persons with
a range of neurological conditions with consistent sampling and

http://crossmark.crossref.org/dialog/?doi=10.1016/j.genhosppsych.2015.06.007&domain=pdf

http://dx.doi.org/10.1016/j.genhosppsych.2015.06.007

mailto:bulloch@ucalgary.ca

Journal logo

http://dx.doi.org/10.1016/j.genhosppsych.2015.06.007

Imprint logo

http://www.sciencedirect.com/science/journal/01638343

508 A.G.M. Bulloch et al. / General Hospital Psychiatry 37 (2015) 507–512

measurement procedures. This is unique in that this is, to our knowledge,
the first time estimates of depression can be generated and compared
across many neurological conditions and in a community-based cohort,
using a validated depression questionnaire, the Patient Health Question-
naire, Brief [Patient Health Questionnaire, 9-item (PHQ-9)] [12].

The PHQ-9 is a commonly used instrument for assessing depressive
symptoms and can be scored both categorically and dimensionally. This
instrumentmaps directly onto the depressive symptomsof theDiagnos-
tic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV)
that has been validated in a number of settings including the general
population [13,14], in patients with epilepsy [15] and in patients with
MS [16]. Due to overlap of symptoms between depression and neuro-
logical disorders, concerns have been expressed about possible contam-
ination of responses to the PHQ-9when used in neurological conditions.
For example, for patients with Parkinson’s disorder, the Geriatric De-
pression Scale-15 showed a higher sensitivity than the PHQ-9 in
assessing depressive symptoms due to its reduced focus on somatic
symptoms [17]. In contrast, a study of patients with MS found that ex-
clusion of the fatigue and concentration items on the PHQ-9 did not im-
pact prevalence estimates [18]. Taken together, these studies suggest
that overlap of symptoms may impact depression estimates in some,
but not all, neurological conditions.

The importance of understanding the prevalence of depression in
neurological disorders has several aspects. For example, depression
can exacerbate neurological symptoms [19,20], reduce treatment ad-
herence [21,22], erode quality of life [23–26] and interfere with self-
management, leading to accelerated disease progression [20]. Of great
concern is that depression contributes significantly to elevated suicide
rates in neurological patients [27–30].

The objective of the current study was to establish the prevalence of
depression across a range of neurological conditions in the general pop-
ulation, using the same sampling and assessment methods and using
data drawn from the SLNCC.

2. Methods

2.1. Surveys

The SLNCC is a cross-sectional study that adopted a sampling strate-
gy linked to a large general health survey called the Canadian Commu-
nity Health Survey (CCHS) [31]. The CCHS selected a probability sample
of approximately 286,000 residents from 130,000 households in
2010–2011 and used a complex multistage sampling procedure to ob-
tain a representative sample of the Canadian population. First, geo-
graphical clusters are selected, then households are selected within
the clusters and finally one respondent per household is selected. The
CCHS includes questions about professionally diagnosed long-term (at
least 6 months) medical conditions, as well as a measure of major de-
pression (see below). In order to support the SLNCC, the CCHS interview
included questions about 18 neurological conditions and participants
with affirmative responses were invited to participate in the SLNCC.
Survey respondents were also asked whether there were household
members with one or more of the same list of conditions, and those
identified were also asked to participate. In order to produce the most
reliable estimates possible, every household that contained at least
one personwith a neurological condition, except for the twomost prev-
alent conditions (stroke and migraine), was selected. Then a sample of
households containing only persons reported to have either the effects of
stroke or migraine headaches was also selected. It was possible that more
than one person in a household reported being diagnosedwith a neurolog-
ical condition, and it was also possible that some respondents had more
than one condition. However, only one person per householdwas selected,
giving a higher chance of being selected (i.e., oversampling) to thosewith a
more rare condition than to those with stroke or migraine headaches.
Oversampling was required to yield sufficiently large samples of those
with rare conditions so that reasonably precise estimates could be made.

Individuals who reported having multiple neurological conditions were
also given a higher chance of being selected. Exclusions included living
in the three territories (Nunavut, Northwest Territories and the Yukon),
living on an aboriginal reserve or settlement, being a full-time member
of the Canadian Armed Forces, living in certain remote regions (Région
du Nunavik and Région des Terres-Cries-de-la-Baie-James) and residing
in an institution. In total, these exclusion apply to approximately 3% of
the Canadian population 15 years of age and older in the 10 provinces.
Data collection interviews for the SLNCC were conducted between
September to October 2011 and February to March 2012 and included a
total of 8200 (raw sample size) people 15 years of age and older living
with neurological conditions. The estimated response rate for the 2011
SLNCCwas 81.6% [31]. Subjects found to be dead, to havemoved to an in-
stitution, to havemoved outside Canada or to not actually have the condi-
tion reported were classified as “out of scope” and were not included in
the calculation of this response rate.

2.1.1. Selected neurological conditions
Eighteen neurological conditions were included in the SLNCC, but

some of these could not be included in the analysis reported here due
to limited sample size. The excluded conditionswere cerebral palsy, hy-
drocephalus (both of which mainly affect young children), muscular
dystrophy, Tourette’s syndrome, amyotrophic lateral sclerosis and
Huntington’s disease.We excluded spinal cord injurywhose prevalence
(0.4%) is likely overestimated perhaps due to inclusion of other spinal
conditions such as lower back pain, e.g., see estimate of 0.2% [32]. We
also excluded brain injury, which is an imprecise term that respondents
would likely include both traumatic brain injury and stroke. The 10 con-
ditions that were included were migraine, MS, epilepsy, dystonia,
Parkinson’s disease, spina bifida, Alzheimer’s disease and related demen-
tia, stroke, brain/spinal cord tumor and traumatic brain injury (Table 1).

The SLNCC included the PHQ-9, a widely scale use to assess depres-
sive symptoms [12–14]. This scale asks questions about depressive
symptoms in the preceding 2 weeks (i.e., point prevalence) and func-
tions as a symptom severity measure; however, it is closely aligned to
the DSM-IV definition of major depression. For purposes of prevalence
estimation, a cut point of 10 on the PHQ-9 is usually interpreted as indi-
cating the presence of depression, scores of 8–11 all showing similar
sensitivity and specificity [13]. A metaanalysis showed that, at a cut
point of 10, the pooled sensitivity and specificity of the PHQ-9 for de-
tecting depression is 85% and 89%, respectively [13]. It is important to
note that we cannot conclude that patients have major depression per
se without a diagnostic interview; rather, we use the term “depression”
to indicate the presence of significant depressive symptoms. The de-
pression data and other data collection elements were collected by
computer-assisted telephone interviewing after extensive qualitative
testing by Statistics Canada’s Questionnaire Design Resource Centre.

Other data collected in the SLNCC included age, sex, province, pro-
vincial health care number, preferred language, age at first diagnosis,
reasons the neurological disorder is better, years livedwith the disorder,
education, formal assistance received, general health, income, health
utility index, informal assistance received, medication use for neurolog-
ical conditions (not depression), out-of pocket expenses, restriction of
activities, stigma, social support and work activities.

2.1.2. Statistical analysis
The analysis reported here consisted of estimating the overall prev-

alence of depression with 95% confidence intervals (95% CI) in those
with the 10 neurological conditions noted above. In order to account
for design effects (includingundersampling of stroke andmigraine), ini-
tial samplingweights from the CCHSwere refined for the SLNCC by Sta-
tistics Canada and provided to researchers as a set of bootstrapweights.
Employment of a bootstrapping procedure results in estimates weight-
ed to the general household population and that provide an accurate es-
timate of variance.

Table 1
Prevalence of conditions in the CCHS 2010/2011 and demographics of the SLNCC sample.

Condition Prevalencea, % Men, %, 95% CI, N=2000b Women, %, 95% CI, N=2400b Age mean, 95% CI (range)

Migraine 8.3 18.0 82.0 43.4
13.0–22.7 77.3–86.7 41.6–45.2 (15–90)

Stroke 1.0 51.6 48.4 66.0
44.6–58.6 41.4–55.4 63.8–68.1 (17–98)

Alzheimer’s disease/dementiac 0.6 43.4 56.6 78.7 b

36.5–50.2 49.8–63.5 77.6–79.9 (45–98)
Epilepsy 0.4 41.0 59.0 45.3

33.2–48.7 51.3–66.8 42.3–48.3 (15–91)
Brain injury 0.4 54.7 45.3 47.2

44.9–64.5 35.5–55.1 43.4–51.0 (16–96)
MS 0.3 26.7 73.3 51.8

19.5–33.8 66.2–80.5 49.3–54.3 (15–95)
Parkinson’s disease 0.2 65.2 34.8 72.8

56.5–73.9 26.1–43.5 71.0–74.6 (43–94)
Spina bifida 0.1 38.3 61.7 38.8

24.2–52.3 47.7–75.8 33.7–43.9 (16–79)
Brain/spinal cord tumor 0.1 38.2 61.8 51.6

27.2–49.3 50.7–72.8 47.9–55.2 (21–89)
Dystonia b0.1 42.4 57.6 54.5

22.8–62.1 37.9–77.2 47.1–62.0 (17–94)
Traumatic brain injuryd N/A 53.1 46.9 46.1

42.4–63.8 36.2–57.6 42.0–50.2 (16–96)

N.B.: Estimates reflect those with at least one neurological disorder (not independent).
a Sample size reflects added household members with a neurological condition. Statistics Canada— cansim reference – table 105-1300 “Neurological conditions, by age group and sex,

household population aged 0 and over, 2010/2011”, data from the CCHS.
b Rounded estimates.
c Among those aged 35 years and older.
d Not documented as a separate group from brain injury in Statistics Canada table 105-1300.

Table 2
Prevalence of depression by neurological condition.

Condition Prevalence 95% CI

Traumatic brain injury 33.2 24.4–42.0
Brain/spinal cord tumor 32.0 20.8–43.1
Alzheimer’s disease/dementia 28.2 16.6–39.8
Dystonia 27.7 12.9–42.6
MS 26.0 18.9–33.0
Parkinson’s disease 23.4 14.9–31.8
Stroke 22.7 16.9–28.6
Migraine 21.9 16.5–27.3
Epilepsy 21.3 13.3–29.4
Spina bifida 18.6 9.1–28.0

509A.G.M. Bulloch et al. / General Hospital Psychiatry 37 (2015) 507–512

Additionally, we estimated the odds ratios (ORs) with 95% CI of de-
pression for each condition with the baseline group being the general
population of the CCHS sample. We also use logistic regression models
to produce age and sex adjusted ORs. Since the SLNCC did not include
a control group, we restricted this analysis to the CCHS participants
that did not report a neurological condition since they were adminis-
tered the short form for major depression [Composite International Di-
agnostic Interview Short Form for Major Depression (CIDI-SFMD)] [33].
The CIDI-SFMD was not included in all provinces, but this analysis still
included 35,544 participants. The CIDI-SFMD has sensitivity and speci-
ficity for major depression of 90% and 94% when compared to the
CIDI/Diagnostic and Statistical Manual of Mental Disorders, Revised Third
Edition diagnoses [33]. This research was approved by the ethics review
board of the University of Calgary.

3. Results

Table 1 shows the prevalence of the 10 neurological conditions in
the general population as estimated from full CCHS 2010/2011 survey
[34] in column 2. As explained in the methods section, oversampling
of those with rare conditions (i.e., excluding migraine or stroke)
meant that general population estimates could not be extrapolated
from the SLNCC sample. The other columns of Table 1 show the demo-
graphics of the SLNCC sample. The final sample for the SLNCC analysis
included 4400 participants (out-of-scope individuals were removed as
described in Discussion) and included a range from 110 to 730 individ-
uals with rare conditions, which is sufficient for reasonably precise esti-
mates of 95% CI values to be estimated. The SLNCC sample consisted of
45.5% men and 54.5% women with an overall mean age of 47.6 years.
As judged by nonoverlap of 95% CI values, the proportion of females
was higher for those with migraine, MS, epilepsy and brain/spinal
cord tumors. In contrast, the majority of those with Parkinson’s disease
were male. The youngest mean age was seen in migraine, epilepsy and
traumatic brain injury, whereas the highest mean ages were seen in
Alzheimer’s disease/dementia and Parkinson’s disease.

Before estimating depression prevalence, we examined the internal
consistency of the 9 individual PHQ-9 ratings in each neurological con-
dition. The internal consistency was very good for each condition

(Cronbach’s alpha≥0.82 for every condition). We also examined item-
total correlations for each of the 9 PHQ-9 items due to concerns that
common symptoms of neurological conditions (e.g., fatigue and cogni-
tive difficulties) might correlate poorly with the other items. However,
the PHQ-9 items for fatigue and cognitive difficulties consistently
displayed item-total correlations between 0.50 and 0.70, comparable
to other items. The only item with low item-total correlation was the
suicidal ideation item, for which most correlations were between 0.40
and 0.50 with that for epilepsy being the lowest at 0.38.

Complete PHQ-9 ratings were provided by 77.9% of the SLNCC sam-
ple. The overall prevalence of depression estimate for all 18 conditions
was 25.2% (95% CI: 23.8–26.7). Among the 10 selected conditions, the
prevalence was 25.1% (95% CI: 23.5–26.7). The estimated prevalence
of depression in the 10 neurological conditions is ranked from highest
to lowest in Table 2. The highest prevalence estimates (N30%) were ob-
served for traumatic brain injury and brain/spinal cord tumors, respec-
tively. All the other 8 conditions had prevalence estimates of N18%.

In the next step of the analysis, we estimated both crude as well as
age and sex adjusted ORs of depression in each conditionwith the base-
line group being the general population of the CCHS sample. As noted
above, the measure of depression in this part of the analysis was the
CIDI-SFMD. Significantly elevatedORswere observed formigraine, trau-
matic brain injury, stroke and epilepsy (Table 3). The adjusted ORswere
not significantly different from the crude ones; i.e., no evidence of

510 A.G.M. Bulloch et al. / General Hospital Psychiatry 37 (2015) 507–512

confounding by age or sex was evident. In some cases (e.g., stroke), the
adjusted point estimates differed from the crude ones, but the 95% CI
values of the two estimates overlapped. The ORs for the other 6 neurolog-
ical conditions are all elevated, but the 95% CI values include 1.0 so these
data do not provide evidence against the null hypothesis (i.e., that the
odds of depression are not different between those with and without a
neurological condition). Due to the smaller number of subjects in this
part of the analysis, some of the estimates were imprecise.

4. Discussion

The estimated prevalence of depression from this study in those
with neurological disorders ranged from 18.6% to 33.2% (Table 2).
These estimates derived from the PHQ-9 using the cutoff scoringmeth-
od are considerably higher than our previous estimate from a general
population survey conducted in Calgary, Canada, i.e., 8.4% [18]. Our find-
ings confirm previous data (see below) indicating a significantly elevat-
ed burden of depression in people with neurological conditions but
provide an overall comparative perspective for 10 conditions, some of
which have received little attention in the literature. Further, we used
the same depression symptom assessment scale in the same sample of
the general population. When ORs were calculated, the referent group
being the general population of Canada as sampled in the CCHS, signif-
icantly elevated ORswere observed formigraine, traumatic brain injury,
stroke, dystonia and epilepsy (Table 3).

It is important to consider our results from the general population in
the context of other disease-specific studies sincemost of these are from
clinical samples. For example, the PHQ-9 was used to estimate depres-
sion prevalence in a clinical sample (n=173) of thosewithMS in South-
ern Alberta [18]. Our estimate of 21.4% depression prevalence in this
sample falls into to the range estimated in the current study (Table 2);
see also Ref. [9]. In terms of epilepsy, a recent metaanalysis estimated
a current or past year depression prevalence of 23.1% [8], i.e., close to
our estimated point prevalence of depression in the current study. In a
study of 201 patients admitted to long-term care with dementia,
19.9% of patients had depression on admission [35], within the range es-
timated in the current study (Table 2).

In terms of movement disorders, two previous studies of
community-based clinical populations have estimated depression,
using the Beck Depression Inventory (BDI) (that assesses past week de-
pression), in those with dystonia at 37% (n=83) and 30% (n=329), re-
spectively [36,37]. The former study included Parkinson’s disease
patients (n=354), 48% of whom were found to be depressed, a higher
proportion than we observed. In another study of n=173 patients
with Parkinson’s disease attending a movement disorders clinic, 30.0%
were found to have major depression based on the nine symptom-
based DSM-IV criteria [38], a proportion within the range of our study.
A systematic review of both population and community samples using
Diagnostic and Statistical Manual of Mental Disorders criteria estimated

Table 3
ORs of CIDI Short-Form depression by neurological condition.

Condition OR 95% CI OR, age/sex
adjusted

95% CI, age/sex
adjusted

Migraine 6.1 4.0–9.1 6.0 4.0–9.0
Traumatic brain injury 6.1 3.5–11.0 6.0 3.9–12.3
Spina bifida 3.2 1.0–10.9 3.0 0.9–10.3
Stroke 3.1 1.6–6.0 4.7 2.4–9.4
Dystonia 2.8 0.6–12.1 2.8 0.6–12.3
Epilepsy 2.6 1.4–4.8 2.7 1.4–5.0
Brain/spinal cord tumor 2.3 0.8–6.6 2.5 0.9–7.1
Alzheimer’s disease/dementia 1.8 0.4–7.5 2.7 0.6–11.4
Parkinson’s disease 1.5 0.5–4.8 2.4 0.7–7.8
MS 1.3 0.6–3.0 1.3 0.6–2.9

As assessed by the CIDI-SFMD in a subset of study participants thatwere also CCHS partic-
ipants and (in the denominator) the odds of major depressive episode according to the
CIDI-SFMD in CCHS participants without a neurological condition.

the point prevalence of depression in Parkinson’s disease as 19% [10],
close to our point estimate.

Traumatic brain injury and stroke have received significant attention
in the literature. Major depression was found in 6–77% of patients fol-
lowing traumatic brain injury as assessed with a variety of methods
[39]. In a longitudinal study of traumatic brain injury patients, 26% of
participants were found to developmajor orminor depression between
1 and 2 years of follow-up as assessed with the PHQ-9 [40]. In terms of
stroke, a review of a large number of studies of clinical samples showed
that nearly 30% of stroke patients develop depression in the early or late
stages after stroke [41]. These data suggest that our population esti-
mates for point prevalence of depression in traumatic brain injury and
stroke are reasonable.

Brain tumors have been reported to be associatedwith a range of de-
pression prevalence. For example, in a study of n=77 patients assessed
before brain tumor surgery in Finland, a relatively small proportion
(16%) had depression according to the BDI [42]. Use of the same instru-
ment in n=73 patients attending a tertiary cancer center in Canada
showed 38% scoring in the depressed range using the BDI [43], and a
study in the USA in a similar clinical setting found 28% of depressed pa-
tients using DSM-IV criteria [44]. The latter proportions are in the range
of the current Canadian study that combined brain and spinal cord tu-
mors. To the best of our knowledge, no data are available regarding de-
pression in patients with spinal cord tumors as a stand-alone group.

An original contribution of the current study lies in its inclusion of a
rare condition (spina bifida) for which no data on depression are avail-
able to the best of our knowledge. Although the point estimate for the
prevalence of depression is patients with spina bifida that is the lowest
of the 11 conditionswe examined, it is still more than 2-fold higher than
for the general population (Table 2).

It might be expected that estimates of the prevalence of depression
in the general community would be lower than those obtained from
clinical samples. However, comparison of Table 2 with the estimates
discussed above show that most of the estimates from clinical popula-
tions fall within the 95% CI values of our estimates.

Another expectation might be that a difference in the prevalence of
depressionmight be evident between disorders characterized bymove-
ment impairment versus executive dysfunction. Inspection of Tables 2
and 3, however, does not support a clear demarcation between the dif-
ferent neurological disorders in this regard.

An important question that arises is why individuals with such a
wide range of different neurological conditions have a high prevalence
of depression. Depression is a heterogeneous condition with a number
of biopsychosocial determinants; however, neuroinflammation may
be a common biological link between depression and a number of neu-
rological conditions. The inflammatory (or cytokine) hypothesis for de-
pression is increasingly recognized as an important component in the
etiology of this condition [45]. A number of studies have shown that
neuroinflammation increases neurodegeneration, and it has been pro-
posed inflammation may be the biological mechanism that links de-
mentia with depression [46]. In terms of stroke, cytokine-driven
induced factors appear in the brain following ischaemic damage and in-
crease the likelihood of depression [47]. Traumatic brain injury is
known to be followed by an extended period of neuroinflammation,
and a recent preclinical study showed that brain injury primesmicroglia
that, when reactivated, are associatedwith the generation of depressive
symptoms [48]. Inflammation in the brain contributes to the generation
of seizures via cytokines that are released by glial cells [49], although a
connection to depression has not been established to date. Beyond neu-
rological conditions, it is thought that inflammation contributes to both
depression and adverse cardiac outcomes in patients with coronary
heart disease [50]. A complete review of the literature in this area is be-
yond the scope of this paper, but this discussion puts our findings into
the context of some of the current biological research in this area.

The principle strengths of this study lie in its use of a population-
based cohort and assessment of depression across many neurological

511A.G.M. Bulloch et al. / General Hospital Psychiatry 37 (2015) 507–512

conditions using consistent sampling andmeasurement procedures;we
are not aware of any comparable study. A limitation of the SLNCC is a
higher than expected out-of-scope proportion within the sample.
There are several reasons a person selected for the SLNCC could become
out of scope including if they had died, moved to an institution or
moved outside Canada. The most common reason a participant was
out of scope was that the respondent reported not having any of the
18 neurological conditions of interest, contrary to the CCHS report.
This group accounted for 28.7% of all resolved SLNCC cases, the apparent
reason being that householdmembersmay not have had accurate infor-
mation about the neurological health of other householdmembers. Due
to this high number of out-of-scope cases on the SLNCC, Statistics
Canada has advised against using the SLNCC for direct estimation of
neurological condition prevalence. Adjusted prevalence data have,
however, beenmade available by Statistics Canada after appropriate ad-
justments were made; see Table 1.

A limitation of the survey is that we do not have information on
treatment of depression. This may have led to underestimation of the
proportions of patients that had received a depression diagnosis. Anoth-
er limitation relates to the issue of symptom overlap between depres-
sion and neurological disorders, as well as the potential contamination
of PHQ-9 results. The literature reviewed in Introduction on this issue
suggests that this may be pertinent to some, but not all, neurological
disorders. An additional limitation lies in the reliance of the SLNCC
data on self-report. However, as an example, in the context of epilepsy,
self-reported disease status is commonly used in epidemiological stud-
ies of epilepsy [51], and self-reported lifetime epilepsy is highly sensi-
tive (84.2%) and specific (99.2%) when compared to medical record
diagnosis [52].

5. Conclusions

In conclusion, we estimate the point prevalence of significant de-
pressive symptoms in patients with neurological disorders to be in the
range from 33.2% to 18.6%, ranked from highest to lowest in prevalence
as traumatic brain injury, brain/spinal cord tumor, Alzheimer’s disease/
dementia, dystonia, MS, Parkinson’s disease, stroke, migraine, epilepsy
and spina bifida respectively.

Conflicts of interests

None.

Acknowledgements

This projectwas supported by a grant from theHotchkiss Brain Insti-
tute at the University of Calgary. This Institute had no role in study de-
sign, in the collection, analysis and interpretation of data, in the
writing of the report or in the decision to submit the article for publica-
tion. Scott Patten, Nathalie Jette and Kirsten Fiestwere supported by sal-
ary awards by Alberta Innovates — Health Solutions. Nathalie Jette also
holds a Canada Research Chair Tier 2 in Neuroscience Health Services
Research. Sandy Berzins was supported by an operating grant from
the Alberta Addiction andMental Health Research Partnership Program
and by an endMS Studentship.

References

[1] Walker AK, Kavelaars A, Heijnen CJ, Dantzer R. Neuroinflammation and comorbidity
of pain and depression. Pharmacol Rev 2014;66(1):80–101.

[2] Oladeji BD, Gureje O. The comorbidity between depression and diabetes. Curr Psy-
chiatry Rep 2013;9:390–8.

[3] Moller-Leimkuhler AM. Higher co-morbidity of depression and cardiovascular dis-
ease in women: a biopsychosocial perspective. World J Biol Psychiatry 2010;11(8):
922–33.

[4] Bruce TO. Comorbid depression in rheumatoid arthritis: pathophysiology and clini-
cal implications. Curr Psychiatry Rep 2008;10(3):258–64.

[5] Soh S-E, Morris ME, McGinley J. Determinants of health-related quality of life in
Parkinson’s disease: a systematic review. Parkinsonism Relat Disord 2011;17:1–9.

[6] Patten SB, Williams JVA, Lavorato DH, Mogdill G, Jette N, Eliasziw M. Major depres-
sion as a risk factor for chronic disease incidence: longitudinal analyses in a general
population cohort. Gen Hosp Psychiatry 2008;30:407–13.

[7] Starkstein SE, Merello M. Psychiatric and cognitive disorders in Parkinson’s disease.
Cambridge: Cambridge University Press; 2002.

[8] Fiest KM, Dykeman J, Patten SB, Wiebe S, Kaplan GG, Maxwell CJ, et al. Depression in
epilepsy: a systematic review and meta-analysis. Neurology 2013;80:590–9.

[9] Siegert RJ, Abernethy DA. Depression in multiple sclerosis: a review. J Neurol
Neurosurg Psychiatry 2005;76:469–75.

[10] Reijnders JS, Ehrt U, Weber WE, Aarsland D, Leentjens AF. A systematic review of
prevalence studies of depression in Parkinson’s disease. Mov Disord 2008 Jan 30;
23:183–9.

[11] Jorge RE, Starkstein SE. Pathophysiologic aspects of major depression following trau-
matic brain injury. J Head Trauma Rehabil 2005;6:475–87.

[12] Kroenke K, Spitzer RL. The PHQ-9: a new depression diagnostic and severity mea-
sure. Psychiatr Ann 2002;32:1–7.

[13] Manea L, Gilbody S, McMillan D. Optimal cut-off score for diagnosing depression
with the Patient Health Questionnaire (PHQ-9); a meta-analysis. CMAJ 2012;
184(3):E191–6.

[14] Kroenke K, Spitzer RL, Williams JB, Lowe B. The Patient Health Questionnaire Somat-
ic, Anxiety, and Depressive Symptom Scales: a systematic review. Gen Hosp Psychi-
atry 2010 Jul;32:345–59.

[15] Fiest KM, Patten SB, Wiebe S, Bulloch AG, Maxwell CJ, Jette N. Validating screening
tools for depression in epilepsy. Epilepsia 2014;55(10):1642–50.

[16] Patten SB, Fiest KM, Wiebe S, Bulloch AG, Koch M, Dobson KS, et al. The validity of
four screenings scales for depression in MS. Mult Scler 2015 [in press].

[17] Thompson AW, Liu H, Hays RD, Katon WJ, Rausch R, Diaz N, et al. Diagnostic accura-
cy and agreement across three depression assessment measures for Parkinson’s dis-
ease. Parkinsonism Relat Disord 2011;17:40–5.

[18] Sjonnesen K, Berzins S, Fiest KM, Bulloch AGM, Metz LM, Thombs BD, et al. Evalua-
tion of the 9-item Patient Health Questionnaire (PHQ-9) as an assessment instru-
ment for symptoms of depression in patients with Multiple Sclerosis. Postgrad
Med 2012;124(5):69–77.

[19] SullivanM, KatonW, Russo J, Dobie R, Sakai C. A randomized trial of nortriptyline for
severe chronic tinnitus. Effects on depression, disability, and tinnitus symptoms.
Arch Intern Med 1993;153(19):2251–9.

[20] Williams LS. Depression and stroke: cause or consequence? Semin Neurol 2005;
25(4):396–409.

[21] Mohr DC, Goodkin DE, Likosky W, Gatto N, Baumann KA, Rudick RA. Treatment of
depression improves adherence to interferon beta-1b therapy for multiple sclerosis.
Arch Neurol 1997;54(5):531–3.

[22] DiMatteo MR, Lepper HS, Croghan TW. Depression is a risk factor for noncompliance
with medical treatment: meta-analysis of the effects of anxiety and depression on
patient adherence. Arch Intern Med 2000;160(14):2101–7.

[23] Wang JL, Reimer MA, Metz LM, Patten SB. Major depression and quality of life in in-
dividuals with multiple sclerosis. Int J Psychiatry Med 2000;30(4):309–17.

[24] Jau-Shin L, Reeves A, Benice T, Sexton G. Fatigue and depression are associated with
poor quality of life in ALS. Neurology 2003;60:122–3.

[25] Ben-Shlomo Y, Camfield L, Warner T. What are the determinants of quality of life in
people with cervical dystonia? J Neurol Neurosurg Psychiatry 2002;72(5):608–14.

[26] Boylan LS, Flint LA, Labovitz DL, Jackson SC, Starner K, Devinsky O. Depression but
not seizure frequency predicts quality of life in treatment-resistant epilepsy. Neurol-
ogy 2004;62(2):258–61.

[27] Bronnum-Hansen H, Stenager E, Nylev Stenager E, Koch-Henriksen N. Suicide
among Danes with multiple sclerosis. J Neurol Neurosurg Psychiatry 2005;76(10):
1457–9.

[28] Viner R, Patten SB, Berzins S, Bulloch AGM, Fiest KM. Prevalence and risk factors for
suicidal ideation in a multiple sclerosis population. J Psychosom Res 2014;76:312–6.

[29] Jones JE, Hermann BP, Barry JJ, Gilliam FG, Kanner AM, Meador KJ. Rates and risk fac-
tors for suicide, suicidal ideation, and suicide attempts in chronic epilepsy. Epilepsy
Behav 2003;4(Suppl. 3):S31–8.

[30] Farrer LA. Suicide and attempted suicide in Huntington disease: implications for pre-
clinical testing of persons at risk. Am J Med Genet 1986;24(2):305–11.

[31] Statistics Canada. Survey of Living with Neurological Conditions in Canada (SLNCC).
http://www23.statcan.gc.ca/imdb/p2SV.pl?Function=getSurvey&SDDS=5182&Item_
Id=845. [Last updated: 12-11-2012, Date accessed: 8-23-2013].

[32] Noonan VK, FingasM, Farry A, Baxter D, Singh A, FehlingsMG, et al. Incidence and prev-
alence of spinal cord injury inCanada: a national perspective.Neuroepidemiology2012;
38:219–26.

[33] Kessler RC, Andrews G, Mroczek D, Ustun B, Wittchen H-U, et al. The World Health
Organization Composite International Diagnostic Interview Short-Form (CIDI-SF).
Int J Methods Psychiatr Res 1998;7:171–85.

[34] Statistics Canada. Table 105-1300. Neurological conditions, by age group and sex,
household population aged 0 and over, 2010/2011. http://www5.statcan.gc.ca/
cansim/pick-choisir?lang=eng&p2=33&id=1051300. [Last updated: 9-18-2012,
Date accessed: 8-23-2013].

[35] Payne JL, Sheppard J-M E, Steinberg M, Warren A, Baker A, Steele C, et al. Incidence,
prevalence, outcomes of depression in residents of a long-term care facility with de-
mentia. Int J Geriatr Psychiatry 2002;17:245–53.

[36] Miller KM, Okun MS, Fernandez HF, Jacobson CE, Rodriguez RL, Bowers D. Depres-
sion symptoms in movement disorders: comparing Parkinson’s disease, dystonia,
and essential tremor. Mov Disord 2007;22(5):666–72.

[37] Lewis L, Butler A, Jahanshahi M. Depression in focal, segmental and generalized dys-
tonia. J Neurol 2008;255:1750–5.

http://refhub.elsevier.com/S0163-8343(15)00144-9/rf0005

http://refhub.elsevier.com/S0163-8343(15)00144-9/rf0010

http://refhub.elsevier.com/S0163-8343(15)00144-9/rf0015

http://refhub.elsevier.com/S0163-8343(15)00144-9/rf0020

http://refhub.elsevier.com/S0163-8343(15)00144-9/rf0025

http://refhub.elsevier.com/S0163-8343(15)00144-9/rf0230

http://refhub.elsevier.com/S0163-8343(15)00144-9/rf0030

http://refhub.elsevier.com/S0163-8343(15)00144-9/rf0035

http://refhub.elsevier.com/S0163-8343(15)00144-9/rf0040

http://refhub.elsevier.com/S0163-8343(15)00144-9/rf0045

http://refhub.elsevier.com/S0163-8343(15)00144-9/rf0050

http://refhub.elsevier.com/S0163-8343(15)00144-9/rf0055

http://refhub.elsevier.com/S0163-8343(15)00144-9/rf0060

http://refhub.elsevier.com/S0163-8343(15)00144-9/rf0065

http://refhub.elsevier.com/S0163-8343(15)00144-9/rf0070

http://refhub.elsevier.com/S0163-8343(15)00144-9/rf0235

http://refhub.elsevier.com/S0163-8343(15)00144-9/rf0075

http://refhub.elsevier.com/S0163-8343(15)00144-9/rf0080

http://refhub.elsevier.com/S0163-8343(15)00144-9/rf0085

http://refhub.elsevier.com/S0163-8343(15)00144-9/rf0090

http://refhub.elsevier.com/S0163-8343(15)00144-9/rf0095

http://refhub.elsevier.com/S0163-8343(15)00144-9/rf0100

http://refhub.elsevier.com/S0163-8343(15)00144-9/rf0105

http://refhub.elsevier.com/S0163-8343(15)00144-9/rf0110

http://refhub.elsevier.com/S0163-8343(15)00144-9/rf0115

http://refhub.elsevier.com/S0163-8343(15)00144-9/rf0120

http://refhub.elsevier.com/S0163-8343(15)00144-9/rf0125

http://refhub.elsevier.com/S0163-8343(15)00144-9/rf0130

http://refhub.elsevier.com/S0163-8343(15)00144-9/rf0135

http://refhub.elsevier.com/S0163-8343(15)00144-9/rf0140

http://www23.statcan.gc.ca/imdb/p2SV.pl?Function=getSurvey&SDDS=5182&Item_Id=845

http://refhub.elsevier.com/S0163-8343(15)00144-9/rf0145

http://refhub.elsevier.com/S0163-8343(15)00144-9/rf0150

http://www5.statcan.gc.ca/cansim/pick-choisir?lang=eng&p2=33&id=1051300

http://refhub.elsevier.com/S0163-8343(15)00144-9/rf0155

http://refhub.elsevier.com/S0163-8343(15)00144-9/rf0160

http://refhub.elsevier.com/S0163-8343(15)00144-9/rf0165

512 A.G.M. Bulloch et al. / General Hospital Psychiatry 37 (2015) 507–512

[38] Starkstein SE, Merello M, Jorge R, Brockman S, Bruce D, Petracca G, et al. A validation
study of depressive symptoms in Parkinson’s disease. Mov Disord 2008;23(4):
546–83.

[39] Jorge RE, Starkstein SE. Pathophysiologic aspects of major depression following trau-
matic brain injury. J Head Trauma Rehabil 2005;20(6):475–87.

[40] Hart T, Hoffman JM, Pretz C, Kennedy R, Clark AN, Brenner LA. A longitudinal study
of major and minor depression following traumatic brain injury. Arch Phys Med
Rehabil 2012;83:1343–9.

[41] Paolucci S. Epidemiology and treatment of post-stroke depression. Neuropsychiatr
Dis Treat 2008;4(1):145–54.

[42] Mainio A, Hakko H, Niemela A, Koivukangas J, Rasanen P. Depression in relation to
anxiety, obsessionality and phobia among neurosurgical patients with a primary
tumor: a 1-year follow-up study. Clin Neurol Neurosurg 2011;113:649–53.

[43] Pelletier G, Verhoef MJ, Khatri N, Hagen N. Quality of life in brain tumor patients: the
relative contributions of depression, fatigue, emotional distress, and existential is-
sues. J Neurooncol 2002;57:41–9.

[44] Wellisch DK, Kalieta TA, Freeman D, Cloughesy T, Goldman J. Predicting major de-
pression in brain tumor patients. Psycho-Oncology 2002;11:230–8.

[45] Zunzain PA, Hepgul N, Pariante CM. Inflammation and depression. Curr Topics Behav
Neurosci 2013;14:135–51.

[46] Hermida AP, McDonaldWM, Steenland K, Levey A. The association between late-life
depression, mild cognitive impairment and dementia: is inflammation the missing
link? Expert Rev Neurother 2012;12(11):1339–56.

[47] Pascoe MC, Crewther SG, Carey LM, Crewther DP. Inflammation and depression: why
poststrokedepressionmaybe thenormandnot theexception. Int J Stroke2011;6:128–35.

[48] Fenn AM, Gensel JC, Huang Y, Popovich PG, Lifshitz J, Godbout JP. Immune activation
promotes depression 1 month after diffuse brain injury: a role for primed microglia.
Biol Psychiatry 2014;76(7):575–84.

[49] Vezzani A. Epilepsy and inflammation in the brain: overview and pathophysiology.
Curr Rev Clin Sci 2014;14((1) Suppl.):3–7.

[50] Poole L, Dickens C, Steptoe A. The puzzle of depression and acute coronary syn-
drome: reviewing the role of acute inflammation. J Psychosom Res 2011;71:61–8.

[51] Tellez-Zenteno JF, Pondal-SordoM, Matijevic S,Wiebe S. National and regional prev-
alence of self-reported epilepsy in Canada. Epilepsia 2004;45:1623–9.

[52] Brooks DR, Avetisyan R, Jarrett KM, et al. Validation of self-reported epilepsy for pur-
poses of community surveillance. Epilepsy Behav 2012;23:57–63.

http://refhub.elsevier.com/S0163-8343(15)00144-9/rf0250

http://refhub.elsevier.com/S0163-8343(15)00144-9/rf0170

http://refhub.elsevier.com/S0163-8343(15)00144-9/rf0175

http://refhub.elsevier.com/S0163-8343(15)00144-9/rf0180

http://refhub.elsevier.com/S0163-8343(15)00144-9/rf0185

http://refhub.elsevier.com/S0163-8343(15)00144-9/rf0190

http://refhub.elsevier.com/S0163-8343(15)00144-9/rf0195

http://refhub.elsevier.com/S0163-8343(15)00144-9/rf0200

http://refhub.elsevier.com/S0163-8343(15)00144-9/rf0205

http://refhub.elsevier.com/S0163-8343(15)00144-9/rf0210

http://refhub.elsevier.com/S0163-8343(15)00144-9/rf0261

http://refhub.elsevier.com/S0163-8343(15)00144-9/rf0260

http://refhub.elsevier.com/S0163-8343(15)00144-9/rf0215

http://refhub.elsevier.com/S0163-8343(15)00144-9/rf0220

http://refhub.elsevier.com/S0163-8343(15)00144-9/rf0225

Depression’— a common disorder across a broad spectrum of neurological conditions: a cross-„sectional nationally representa…

1. Introduction
2. Methods
2.1. Surveys
2.1.1. Selected neurological conditions
2.1.2. Statistical analysis

3. Results
4. Discussion
5. Conclusions
Conflicts of interests
Acknowledgements
References

The mediating role of depression in the association between disability and quality of life

in Alzheimer’s disease

Mar�ıa G�omez-Gallegoa*, Juan G�omez-Garc�ıab and Ester Ato-Lozanoc

aDepartment of Psychology, Faculty of Health Sciences, Catholic University of Murcia, Murcia, Spain; bDepartment of Quantitative
Methods, Faculty of Economics, University of Murcia, Murcia, Spain; cDepartment of developmental psychology, Faculty of

Psychology, University of Murcia, Murcia, Spain

(Received 18 March 2015; accepted 5 July 2015)

Background: An understanding of the determinants of quality of life in Alzheimer’s disease (AD) is required in order to
develop effective interventions to promote patients’ well-being. Most studies have pointed out depression, functional
ability and environmental factors. However, unmeasured confounders can jeopardize the interpretation of the results.
Objectives: To explore the mediating role of depression in the association between functional status and QoL, and
establish a procedure to detect confounding variables.
Methods: A sample of 192 AD patients and their respective caregivers were recruited from day centers and health care
centers in the region of Murcia (Spain). The mediating effect was evaluated using causal mediation analysis. Covariates
were introduced into the model in a stepwise fashion and sensitivity analyses were performed to assess the influence of
potential confounders.

Results

: Self-rated depression acted as a partial mediator between functional status and quality of life. The mediating effect
was positive and significant even after including both patient- and caregiver-related covariates. Only if confounders
explained more than 80% of the residual variance in the mediator or in the outcome, the mediating effects would not be

positive.

Conclusions

: The effect of lack of autonomy on the QoL is mostly explained by the negative consequences on mood status.
The sensitivity analysis confirms the robustness of this finding.

Keywords: Alzheimer’s disease; depression; quantitative methods and statistics

Introduction

Alzheimer’s disease (AD) is a neurodegenerative disorder

characterized by deterioration of cognitive and functional

abilities that usually affects both patients and caregivers’

psychological well-being. The world prevalence of AD is

thought to increase in the next decades as life expectancy

does (Rizz, Rosset, & Roriz-Cruz, 2014). Spain has an

aging population and a high prevalence of AD (Pedro-

Cuesta et al., 2009; Serrano, Latorre, & Gatz, 2014). The

care of AD patients has been mainly provided by their

family. Nevertheless, recent changes in the role of

women, a lower number of children per family and an

increase in the number of elderly living alone have led to

alternative solutions, such as ‘rotation’ and cohabitation

arrangement or the use of respite care services (Rivera,

Bermejo, Franco, Morales-Gonz�alez, & Benito-Le�on,
2009). In Spain, contrary to other European countries,

nursing homes are usually rejected by the family care-

givers and considered only as a last resource (Lopez, Los-

ada, Romero-Moreno, M�arquez-Gonz�alez, & Mart�ınez-
Mart�ın, 2012). This is not the case of day-care centers that
offer both physical and cognitive stimulation to patients

and also respite and support to caregivers (Kwok, Young,

Yip, & Ho, 2013). Current therapies are mainly aimed at

alleviating symptoms and improving quality of life (QoL)

and well-being (Keating & Gaudet, 2012; Moniz-Cook

et al., 2008; Moyle, Fetherstonhaugh, Greben, Beattie, &

AusQoL group, 2015). The assessment of QoL of patients

with AD has presented conceptual and practical chal-

lenges for the past decades (Rabin & Black, 2007). Nowa-

days, there are several instruments for measuring QoL in

AD including patients and proxy versions, and generic

and dementia-specific scales, most of them with good psy-

chometric properties (Bowling et al., 2015; Sch€olzel-Dor-
enbos, van der Steen, Engels, & Olde Rikkert, 2007).

Most of the studies on QoL in AD are focused on

ascertaining which factors have higher influence on it.

Overall, the main predictors of QoL are functional

impairment, depressive symptoms, behavioral disturban-

ces and caregivers’ burden (Gomez-Gallego, Gomez-

Amor, & Gomez-Garcia, 2012; Mjørud, Kirkevold,

Røsvik, Selbæk, & Engedal, 2014; Sheehan et al., 2012).

Although in some studies, collinearity among explanatory

variables has been controlled, there can be unmeasured

confounding variables that make difficult the interpreta-

tion of the estimated regression coefficients. In addition,

some of the proposed predictors could be mediating the

relationship between other factors and QoL. Thus, it

seems necessary to explore the relationships between pre-

dictors and QoL and detect the presence of mediation

together with the implementation of methods that increase

confidence in results.

*Corresponding author. Email: Mggallego@ucam.edu

� 2015 Taylor & Francis

Aging & Mental Health, 2017

Vol. 21, No. 2, 163�172, http://dx.doi.org/10.1080/13607863.2015.1093603

mailto:Mggallego@ucam.edu

http://dx.doi.org/10.1080/13607863.2015.1093603

In this study, the QoL of patients has been considered

as the outcome variable, functional status as the predictor

variable and depressive symptoms as the mediator vari-

able in the relation. There is some agreement in that QoL

implies, besides objective elements, a subjective evalua-

tion of important aspects of one’s life (Brod, Stewart,

Sands, & Walton, 1999; World Health Organization Qual-

ity of Life, 1995). These factors are related with physical

and mental health, functional abilities, participation

in activities, social relationships and financial status

(Lawton, 1994). Depression is a central component of

QoL, not only because is related to psychological well-

being, but also because is associated with more negative

self-perception (health, personal performance and capabil-

ities), and also with more personal and environmental

unmet needs (Houtjes, van Meijel, Deeg, & Beekman,

2011). Besides, depressive AD patients are likely to mis-

interpret neutral faces as sad which could lead to a nega-

tive bias (Weiss et al., 2008). In fact, depression is found

to be the main predictor of QoL at every stage of the dis-

ease in both transversal and longitudinal studies (Gomez-

Gallego et al., 2012; Hoe et al., 2009; Missoten et al.,

2007; Naglie et al., 2011; Tatsumi et al., 2009). Functional

disability mainly results from the progressive cognitive

decline (Brown, Devanand, Liu, & Caccappolo, 2011;

Sut, Ju, Yeon, & Shah, 2004) and predicts caregiver-rated

QoL and, to some extent, QoL self-ratings (Bruvik,

Ulstein, Ranhoff, & Engedal, 2012; Giebel, Sutcliffe, &

Challis, 2015). However, there is some controversy about

the relation between functional disability and depression.

Although, most papers show a weak influence of the

improvement in disability and depressive symptoms

(Bostr€om et al., 2014; Schieman & Plickert, 2007), others
suggest a negative effect of depression on functional capa-

bilities, especially in cognitively healthy elderly (Rog

et al., 2014; Zahodne & Tremont, 2013) and in relation

with psychosocial activities (Cipher & Clifford, 2004).

We have two objectives: to test the mediating effect of

depression on the association between functional status

and QoL and to establish a method for the detection of

confounding in linear models. We suggest using sensitiv-

ity analysis coupled with a hierarchical regression analy-

sis to examine the influence of some demographic

covariates in the mediation relation and detect the pres-

ence of unmeasured confounders.

Materials and methods

Sample

Patients and caregivers were recruited from several day-

care centers and health centers in the area of Murcia. The

inclusion criteria for the patients were as follows: (1)

diagnosis of possible or probable AD consistent with the

National Institute of Neurological and Communicative

Disorders and Stroke – Association of Alzheimer Disease

and Related Disorders (NINCDS-ADRDA) criteria

(McKhann et al., 1984); (2) non-severe dementia, defined

as a stage in Global Deterioration Scale (GDS) (Reisberg,

Ferris, De Leon, & Crook, 1982) lower than 6; and (3)

living with a caregiver in the community. Patients with

severe language disturbances were excluded. Caregivers

were defined as the main people providing day-to-day

care. Informed consent was obtained from both patients

and caregivers. The Bioethics Committee of the Univer-

sity of Murcia approved the study.

The final sample consisted of 192 patients and their

caregivers. A majority of the patients were female (n D
118) and their mean age was 75.8 years with standard

deviation (SD) of 6.14. Patients have 5.07 (SD D 2.95)
years of education, 104 were married and the rest were

widowed. The stage of the dementia using the GDS was 3

for 61 patients, 4 for 98 patients and 5 for 33 patients. The

mean scores of Geriatric Depression Scale (GDS-15), Bar-

thel Index (BI) and Mini-Mental State Examination

(MMSE) were 5.71 (SD D 2.98), 69.09 (SD D 25.4) and
20.41 (SD D 3.7), respectively. The mean age for the
caregivers was 55.9 (SD D 5.24), and 133 of them were
women. In 89 of the cases, caregivers were adult children,

in 77 of the cases were spouses and in the rest were no rel-

atives. The attrition rate for this study was zero, and there

were no incomplete data for any patient.

Instruments

Global Deterioration Scale (GDS)

This is a global staging instrument that incorporates

both cognitive and functional aspects of aging and

dementia (Reisberg et al., 1982). It has seven ordinal

stages (1�7) on a scale starting with Stage 1 (no cogni-
tive decline) and ending with Stage 7 (very severe cog-

nitive decline). Stages 3�5 correspond to mild
cognitive impairment, mild dementia and moderate

dementia, respectively.

Quality of Life in Alzheimer’s Disease Scale (QOL-AD)

This is a 13-item instrument designed to obtain ratings

of patient’s QoL from both patients and caregivers

(Logsdon, Gibbons, McCurry, & Teri, 2002). The scale

reflects the perception of important domains of QoL in

older adults, such as physical health, mood, functional

abilities, family, interpersonal relationships and living

situation. There is also a global item ‘life a as whole.’

Items are scored on a four-point scale, with 1 being

poor and 4 being excellent. Total scores range from 13

(the poorest QoL) to 52 (the highest QoL). The scale

has good reliability and validity when administered to

both patients and caregivers in different cultural set-

tings (G�omez-Gallego, G�omez-Amor, & G�omez-Garc�ıa,
2011; Lin Kiat Yap et al., 2008). Two factors have

been identified: ‘perceive physical health’ and

‘perceived psychological health’ (Gomez-Gallego,

Gomez-Garcia, & Ato-Garcia, 2014). However, most

papers about the psychometric properties of this instru-

ment have been done with mild-to-moderate dementia

patients. Hence, only for this population of patients are

guaranteed the reliability and validity of the scale. In

this study, patients rated their own QoL.

164 M. G�omez-Gallego et al.

Barthel Index (BI)

This is a 10-item scale frequently used to measure perfor-

mance in basic ADL (Mahoney & Barthel, 1965). Items

refer to basic ADL (e.g. grooming, feeding, transfer and

others) and are rated at two to four levels (0, 5, 10, 15

points), being 0 the score corresponding to the highest

dependence in that item (Mahoney & Barthel, 1965). BI is

normally used as a unidimensional instrument, and the

total score is obtained by summing each item score. Total

scores range from 0 (the highest dependence) to 100

(independence). Dependence is classified into five levels

(Shah, Vanclay, & Cooper, 1989): 0�20 (total depen-
dence), 21�60 (severe dependence), 61�90 (moderate
dependence), 91�99 (mild dependence) and 100 (inde-
pendence). The instrument has good internal consistency

and both test�retest and interrater reliability (Formiga,
Mascaro, & Pujol, 2005; Sainsbury, Seebass, Bansal, &

Young, 2005).

Geriatric Depression Scale (GDS-15)

GDS is an instrument that could be completed either by

the patient or by the caregiver (Gerety et al., 1994; Yesav-

age, Bronk, Rose, & Lum, 1983). It has been reported to

be valid and reliable when administered to institutional-

ized elders (Conradsson, Rosendahl, et al., 2013). In this

study, a brief version of the scale (15-item version) has

been administered to patients. Several papers note a high

correlation between this version and the original scale

(Alden, Austin, & Sturgeon, 1989; Lesher & Berryhill,

1994). Possible scores range from 0 to 15, with higher

scores indicating more depressive symptoms. A cutoff

point of 5 is used for the diagnosis of a major depressive

episode. In a previous work, we observed a good internal

consistency of GDS-15 in a sample of mild-to-moderate

dementia patients (G�omez-Gallego et al., 2012).

Statistical analysis

There are two main approaches for statistical mediation

analysis, the classical structural equation modeling (SEM)

approach and the causal inference approach. In the classi-

cal approach, the mediation model is a structural model

with two regression equations: one for explaining a medi-

ator M of a predictor X (i.e. X ! M) and another to
explain the outcome Y of a predictor X, given the mediator

M (i.e. X ! Y j M). The model may incorporate one or
more covariates (Ci) (Baron & Kenny, 1986):

mi D g0 C g1 x1 C g2ci C ei1 (1)

g i D b0 C b1 Xi C b2mi C b3ci C ei2 (2)

The effect of mediation (indirect effect) represents the

changes that X produces on Y transmitted through M. It is

estimated by the product of the coefficients b2 and g1.
The direct effect represents the changes that X produces

on Y at a fixed level of the M and is estimated by the

coefficient b1.

The causal inference approach uses the theory of

potential outcomes (Rubin, 2005), which defines a causal

mechanism as a process by which an exposure causes an

outcome in the presence of a mediator. Following Pearl

(2009), identifying a causal mechanism in this approach is

also formulated as a decomposition of the averaged total

effect (ATE) into indirect (average causal mediation

effect, ACME) and direct (average direct effect, ADE)

effects. The identification of ACME and ADE effects

requires sequential ignorability (SI) assumption, which

implies that two ignorability assumptions are made

sequentially. The first assumption implies that, given the

observed pretreatment confounders (e.g. age, gender), the

predictor assignment (e.g. functional disability) is

assumed to be ignorable. Ignorable means statistically

independent of potential outcomes and potential media-

tors. That is to say, there should be no latent confounders

in the X ! Y path (all of the variables that cause both X
and Y must be included in the model). In randomized

experimental studies, this assumption is expected to hold.

However, this is not the case of observational research

designs (Ho et al., 2007) like the present study. In this

case, it is necessary to adjust for many pretreatment con-

founders so that the ignorability of predictor assignment

is more credible. The second assumption (ignorability of

the mediator) implies that the observed mediator (e.g.

depression) is independent of all the potential values of

the outcome (QoL) given the actual predictor (e.g. func-

tional status) and pretreatment observed confounders.

This assumption may not hold even in randomized studies

(Imai, Keele, Tingley, & Yamamoto, 2011). In our study,

this assumption implies that among patients who share the

same functional disability and the same pretreatment

covariates (age, gender and others), the depression can be

regarded as if it were randomized. It is not possible to

know for certain if the ignorability of the mediator holds

even after controlling for as many pretreatment confound-

ers as possible (Manski, 2007; Imai, Keele, & Tingley,

2010). The sensitivity analyses are aimed to quantify the

degree to which their empirical findings are robust to a

potential departure of the SI assumption. In other words,

the objective is to assess the sensitivity of the results to an

unobserved confounder that influences both the mediator

and the outcome.

Under the SI assumption, Imai, Keele, and Yamamoto

(2013) proved that the ACME is non-parametrically iden-

tified and provides a valid estimate of the causal media-

tion effect if mediator and outcome are normally

distributed variables. Moreover, they showed that the

causal effects could be estimated as function of the sensi-

tivity parameter r, which represents the correlation

between the residuals of Equations (1) and (2) (Imai et al.,

2013). The SI assumption implies r D 0. Values of r dif-
ferent of zero imply deviations of SI assumption pointing

that some confounders are biasing the mediation effect

estimation (Imai et al., 2010). The values of r range from

¡0.9 to 0.9. We can examine how the value of the ACME
varies as a function of r and calculate the value of r for

the ACME to be zero or its confidence interval to include

zero (Imai, Keele, Tingley, & Yamamoto, 2010). In these

Aging & Mental Health 165

situations, there is no mediation effect. Another way to

denote the influence of the unobserved confounders is

based on the following decomposition of the error terms

in Equations (1) and (2):

eij D λ1Ui C e;ij

for j D 1, 2, where Ui is an unobserved pretreatment con-
founder and the SI is assumed given Ui and Xi. Then, we

can obtain the following coefficients of determination rep-

resenting the percent of residual variance that is explained

by the unmeasured confounders in the mediator (R2�M ) or
in the outcome (R2�Y ) (Imbens, 2003):

R2�M D 1¡
Varðe;i1Þ
Varðei1Þ D

Varðei1Þ¡Varðe;i1Þ
Varðei1Þ

Another interpretation is based on the proportion of the

original variance that is explained by the unobserved con-

founder in the mediator (~R2�M ) or in the outcome (~R
2�
Y )

(Imai et al., 2010):

~R2�M D
Varðei1Þ¡Varðe;i1Þ

VarðMiÞ D
�
1¡R2M

�
R2�M

~R2�Y D
Varðei2Þ¡Varðe;i2Þ

VarðYiÞ D
�
1¡R2Y

�
R2�Y

Since there is a relation between r and the coefficients

of determination,

r2 DR2�MR2�Y D~R2M~R2Y=fð1¡R2MÞð1¡R2Y Þg

where R2M and R
2
Y represent the coefficients of determina-

tion from the two regressions (1) and (2). We can obtain

values of ACME for a given value of (R2�M and R
2�
Y ) or

(~R2�M and ~R
2�
Y ). It is important to know what percent of

variance is explained by the confounder for the ACME to

be zero. The degree in which SI is satisfied is expressed in

terms of the importance of unobserved confounders in

explaining the variance in the mediator and outcome vari-

ables. The variables that were thought to be possible con-

founders should be included in the sensitivity analysis as

covariates.

In this study, we have performed a non-parametric

estimation of the causal effects as described in Imai et al.

(2011). We performed a stepwise hierarchical regression

procedure in order to examine the potential influence of

several covariates on the mediation effects. After the

inclusion of each set of variables, a sensitivity analysis

was performed. First, we introduced a set of covariates

related to patients (gender, marital status, years of educa-

tion and GDS), and after, a set of covariates related to

caregivers (gender and age). We examined the changes in

the direct and mediation effects, and the values of r for

the ACME to be zero. In addition, we calculated the coef-

ficients of determination from the regressions (1) and (2)

and their magnitude for the ACME to be zero.

The causal approach to mediation and sensibility anal-

yses can be applied with Stata (Hicks & Tingley, 2011), R

(Imai et al., 2010) and Mplus (Muthen & Asparouhov,

2015). In this paper, we used the R mediation package

(Tingley, Yamamoto, Hirose, Keele, & Imai, 2014) used

with 1000 Monte Carlo simulations to generate quasi-

Bayesian confidence intervals based on a normal approxi-

mation, as suggested by Imai et al. (2010), and robust esti-

mation of covariance matrix using consistent

heteroskedasticity estimator of R sandwich package

(Lumley & Zeieis, 2013).

Results

The first step of a hierarchical regression analysis revealed

a possible effect of partial mediation among the three vari-

ables with an adjusted R2 of 0.545. The indirect effect was

0.160 (p D 0.000), the direct effect was 0.112 and the pro-
portion of mediation was 58.5% (Table 1). The inclusion

of the covariates related to patients increased the adjusted

determination coefficient 0.077 units with respect to the

first model. As seen in Table 1, the mediation effect was

significant and positive (ACME D 0.122; p D 0.010)
though the proportion of mediation decreased (39.9%).

Finally, the inclusion of covariates related to caregiver

increased the adjusted R2 to 0.755. We found an indirect

effect of 0.119 (p D 0.010), similar to the estimated value
in the model 2 (Table 1). The estimation of direct effect

was ADE D 0.121 (p D 0.000) and the proportion of
mediation was nearly 50%.

The results of sensitivity analyses for these models are

summarized in Table 2 and represented in Figures 1�3.
The solid line represents the estimated ACME for the

depression mediator for differing values of sensitivity

parameterr. The gray region represents the 95% confi-

dence bands. The dashed line represents the estimated

Table 1. Causal mediation analyses.

Model 1 Model 2

Model 3

Estimate 95% CI p Estimate 95% CI p Estimate 95% CI p

ACME 0.160 0.068�0.261 0.000 0.122 0.026�0.229 0.010 0.119 0.021�0.218 0.010
ADE 0.112 0.004�0.221 0.040 0.182 0.086�0.277 0.000 0.121 0.036�0.207 0.000
TE 0.272 0.128�0.413 0.000 0.305 0.169�0.447 0.000 0.241 0.115�0.371 0.000
PM 0.585 0.307�0.976 0.000 0.399 0.121�0.649 0.010 0.497 0.124�0.795 0.010
ACME: average causal mediation effect; ADE: average direct effect; TE: total effect; PM: proportion of mediation; CI: confidence interval.

166 M. G�omez-Gallego et al.

mediation effect under the SI assumption. This effect is, in

all the models, positive. In the model 1, ACME is zero

whenr is ¡0.7. As shown in Figure 1, the direction of
ACME would remain positive unless the value of r was

lower than ¡0.7 (solid line) or if considering the sample
variability, this result holds unless r is lower than ¡0.6
(gray region). The products of the determination coeffi-

cients at which ACME is zero are R2�MR
2�
Y D 0.490 and

~R2M~R
2
Y D 0.192. That is, ACME would be positive unless

the confounders explained more than 70% of residual var-

iance (square root of 0.490) in the mediator and in the out-

come, though other combinations are possible. Similarly,

ACME would be positive unless the confounders

explained more than 44% of residual variance (square root

of 0.192) in the mediator or in the outcome.

The results of the sensitivity analysis for the

model 2 are shown in Figure 2. The mean value of

r when the ACME is zero is ¡0.75 (confidence interval
¡0.70 to ¡0.80). Similarly, in the model 2, the products
of the determination coefficients at which ACME is

zero are R2�MR
2�
Y D 0:562 and~R2M~R2Y D 0.176. The ACME

would be positive unless the confounders explained

more than 75% of the residual variance in the mediator

and in the outcome, though other combinations are pos-

sible. Similar reasoning may be applied for the total

variance.

In the final model, the value of r at which ACME is
zero is within the interval ranging from ¡0.75 to ¡0.85
(Figure 3). The ACME is guaranteed to be positive unless

r is lower than ¡0.85. In addition, the proportion of the

Table 2. Mediation sensitivity analysis for average causal
mediation effect: sensitivity region.

r ACME 95% CI R2�MR
2�
Y ~R

2
M~R

2
Y

Model 1

[1] ¡0.75 ¡0.031 ¡0.068�0.005 0.562 0.220
[2] ¡0.70 ¡0.005 ¡0.039�0.027 0.490 0.192
[3] ¡0.65 0.015 ¡0.019�0.049 0.422 0.165
[4] ¡0.60 0.033 ¡0.004�0.360 0.070 1412

Model 2

[1] ¡0.80 ¡0.031 ¡0.064�0.002 0.640 0.201
[2] ¡0.75 ¡0.007 ¡0.031�0.016 0.562 0.176
[3] ¡0.70 0.010 0.014�0.034 0.490 0.033
[4] ¡0.65 0.024 0.005�0.054 0.422 0.132

Model 3

[1] ¡0.80 ¡0.006 ¡0.026�0.120 0.640 0.139
[2] ¡0.75 0.012 ¡0.009�0.033 0.562 0.122
[3] ¡0.70 0.026 ¡0.002�0.055 0.490 0.106

r: sensitivity parameter; ACME: average causal mediation effect; CI:
confidence interval; R2�M : proportion of the variance explained by the
unobserved confounder in the mediator; R2�Y : proportion of the variance
explained by the unobserved confounder in the outcome;~R2M : proportion
of unexplained variance that is explained by the unobserved confounder
in the mediator;~R2Y : proportion of unexplained variance that is explained
by the unobserved confounder in the outcome.

Figure 1. Sensitivity analysis for the model 1.

Figure 2. Sensitivity analysis for the model 2.

Figure 3. Sensitivity analysis for the model 3.

Aging & Mental Health 167

unexplained variance that is explained by unobserved

confounders at which ACME equals zero is 0.640 and the

proportion of the original variance that is explained by

confounders at which ACME is zero is 0.139. Conse-

quently, only if the unmeasured confounders explained

more than 80% of the unexplained variance in the

mediator and in the outcome, the ACME would not be

positive.

Discussion

The present study used a theoretical model to explain the

relation between QoL and two important clinical predic-

tors: depression and functional disability. Moreover, the

validity of the results was assessed using a technique to

detect the presence of unmeasured confounders. Accord-

ing our results, the impact of the loss of autonomy on

patients’ QoL is mostly explained by the consequences on

mood. The mediation effect is positive since higher BI

scores imply lower GDS-15 scores and both changes

imply higher QoL scores. This effect remains significant

and notable regardless patient’s gender, marital status,

educational level, and stage of the disease and caregiver’s

gender and age. Mood and disability interact in a complex

way, depending on several factors as cognitive function,

social support and resilience (Conradsson, Littbrand,

et al., 2013; Hybels, Pieper, & Blazer, 2009; Monaci &

Morris, 2012). In dementia patients, depression mediates

the effect of awareness of dependence on QoL (Woods

et al., 2014). Similarly, depression and social relationships

mediate the relation between perceived health and QoL

(Livingston, Cooper, Woods, Milne, & Katona, 2008).

Furthermore, other personal factors as the individual’s

preferences and needs may mediate the effect of depen-

dence and the social support on QoL (Graff et al., 2007;

Miranda-Castillo et al., 2010). Depression, unmet needs

and social relationships are interrelated (Ball et al., 2010;

Houtjes, van Meijel, Deeg, & Beekman, 2011; Miranda-

Castillo et al., 2010; Miranda-Castillo, Woods, & Orrell,

2010). It is important to improve patients’ social support

and participation in activities in order to increase their

possibilities to live well with the disease (Clare et al.,

2014). Having a good family relationship and being inte-

grated in a supportive social network could strengthen

patients’ resilience to cope with the changes associated to

the disease (Clare, Kinsella, Logsdon, Whitlatch, & Zarit,

2011). There is a reciprocal relationship between the par-

ticipation in social activities and depression in older peo-

ple (Chiao, Weng, & Botticello, 2011; Liu, Leung, & Chi,

2011; Schwarzbac et al., 2013; Siedlecki et al., 2009).

Interestingly, there is an interrelationship between depres-

sion and coping resources (Bjørkløf et al., 2015; de Boer

et al., 2007). There are few studies about strategies aimed

to improve the patients’ capacity to cope with difficulties

in spite of being reduced (Preston, Marshall, & Bucks,

2007; Rickenbach, Condeelis, & Haley, 2015). At least in

mild and moderate stages, AD patients are aware of their

condition and can actively participate in the treatment

(Clare et al., 2014; Johansson, Marcusson, & Wressle,

2015). Recently, promising results have been reported

about the efficacy of the problem adaptation therapy on

depression and disability (Alexopoulos et al., 2011; Kio-

sses et al., 2015). This therapy focuses in enhancing

patients’ coping skills and helps to bypass functional limi-

tations. Other psychological interventions as social sup-

port groups may also be useful to reduce patients’

depression and improve QoL (Leung, Orrell, & Orgeta,

2015). We think that a better understanding of the factors

influencing patients’ QoL should contribute to develop

more efficacious therapies. The model to explain patients’

QoL is highly complex and encompasses multiple predic-

tors and should be tested on longitudinal studies (Clare et

al., 2014). Statistical methods to control confounding vari-

ables as sensitivity analysis are highly recommendable

(Lynch et al., 2008). Notwithstanding, our findings are rel-

evant and add to the literature suggesting that patients’

mood is the main determinant of QoL and mediates the

effect of other predictors. For that reason, an effort should

be done to perform an early detection of depression in AD

and improve the strategies to prevent and treat it.

Methodological connotations

We have tested the consistency of the estimated mediation

effect following the approach proposed by Imai et al.

(2010) of the sensitivity analysis. As shown in the

Figures 1�3 this consistency is high and the existence of
confounding is unlikely. The sensitivity coefficient r

reflects the magnitude of bias due to unobserved con-

founders. Although it is interpreted in terms of a range

and has a high degree of subjectivity, it is useful to assess

the degree to which confounding might bias results (Imai

et al., 2011). In this study, we note that the conclusion

about the original direction of the ACME (positive) under

SI is consistent and holds even when considering the sam-

pling variability. Although there is no cutoff value for the

r coefficients that would indicate a problematic level of

susceptibility of the results to confounding, we should

note that the value of r for the ACME to be zero (r D
¡0.7) is very high and the likelihood of the ACME be of
the opposite sign is low. That is to say, the conclusion

about the sign of the ACME is plausible given even fairly

large departures from the ignorability of the mediator

(Imai et al., 2010). On the other hand, in order to over-

come the difficulty to interpret the value of r, we also

used the alternative approach recommended by Imai,

Keele, and Yamamoto (2013) to detect the relevance of

confounders. Overall, our results pointed out that an unob-

served confounder should explain more than 80% of the

unexplained residual variance in the GDS-15 (mediator)

and in the QoL (outcome) for the ACME to change the

sign (be negative). According the criteria of Cohen for the

effect size in regression multiple, this is a very high per-

cent (Cohen, Cohen, West, & Aiken, 2003). Since this sit-

uation is unlikely, we must acknowledge that the

estimation of the ACME is very robust to confounding

due to unmeasured confounders. Moreover, the inclusion

of both sets of covariates hardly causes variation in the

percent of variance explained by the unmeasured

168 M. G�omez-Gallego et al.

confounders, which suggests that these do not confound

the mediation relation.

Limitations

This study has several limitations. First, although its

cross-sectional design is used to explain the factors associ-

ated to QoL (Woods et al., 2014; Wilks & Croom, 2008;

Zhang et al., 2014), it does not allow us to establish causal

associations. Second, patients were selected from daycare

centers and health centers. For this reason, they are repre-

sentative only of the community dwelling patients that

use these services. The level of patients’ cognition,

depression and disability, and caregivers’ data are similar

to those reported in other studies with ambulatory patients

and caregivers in Spain (Conde-Sala, Garre-Olmo, Turr�o-
Garriga, Vilalta-Franch, & L�opez-Pousa, 2010; Lucas-
Carrasco, G�omez-Benito, Rejas, & Brod, 2011).

Conclusions

Unfortunately, dependence is inherent to the progressive

character of the AD. However, this study shows that func-

tional disability does not necessarily lead to reduced QoL.

Its effect highly depends on its consequences on patients’

mood. Strategies to improve patients’ coping skills, social

support and participation in the community may counter-

act the consequences of the dependence. Regarding the

methodological aspects, we note that, to our knowledge,

this is the first clinical research study applying the method

proposed by Imai et al. (2010) to detect confounders.

Regression models applied to non-experimental data are

likely to produce biased estimates of mediation effects.

Consequently, we suggest the use of hierarchical regres-

sion analysis together with sensitivity analysis to assess,

interpret and validate the results of causal mediation anal-

yses performed in these research designs.

Acknowledgements

The authors thank the centers, professionals, patients and care-
givers who participated in this study.

Disclosure statement

No potential conflict of interest was reported by the authors.

Refer

ences

Alden, D., Austin, C., & Sturgeon, R. (1989). A correlation
between the Geriatric Depression Scale long and short
forms. Journal of Gerontology, 44, 124�125. doi:10.1093/
geronj/44.4.P124

Alexopoulos, G.S., Raue, P.J., Kiosses, D.N., Mackin, R.S.,
Kanellopoulos, D., McCulloch, C., … Are�an, P.A. (2011).
Problem-solving therapy and supportive therapy in older
adults with major depression and executive dysfunction:
Effect on disability. Archives of General Psychiatry, 68,
33�41. doi:10.1001/archgenpsychiatry.2010.177

Ball, V., Snow, A.L., Steele, A.B., Morgan, R.O., Davila, J.A.,
Wilson, N., … Kunik, M.E. (2010). Quality of relationships
as a predictor of psychosocial functioning in patients with

dementia. Journal of Geriatric, Psychiatry & Neurology, 23,
109�114. doi:10.1177/0891988710363709

Baron, J., & Kenny, D.A. (1986). The moderator-mediator vari-
able distinction in social psychological research conceptual,
strategic and statistical considerations. Journal of Personal-
ity & Social Psychology, 51, 1173�1182. doi:10.1037//
0022-3514.51.6.1173

Bjørkløf, G.H., Engedal, K., Selbæk, G., Maia, D.B., Coutinho,
E.S., … Helvik, A.S. (2015). Locus of control and coping
strategies in older persons with and without depression.
Aging & Mental Health, 8, 1�9. doi:10.1080/13607863
.2015.1040722

Bostr€om, G., Conradsson, M., Rosendahl, E., Nordstr€om, P.,
Gustafson, Y., & Littbrand, H. (2014). Functional capacity
and dependency in transfer and dressing are associated with
depressive symptoms in older people. Clinical & Interven-
tional Aging, 9, 249�256. doi:10.2147/CIA.S57535

Bowling, A., Rowe, G., Adams, S., Sands, P., Samsi, K., Crane,
M., … Manthorpe, J. (2015). Quality of life in dementia, a
systematically conducted narrative review of dementia-spe-
cific measurement scales. Aging and Mental Health, 19,
13�31. doi:10.1080/13607863.2014.915923

Brod, M., Stewart, A.L., Sands, L., & Walton, P. (1999). Con-
ceptualization and measurement of quality of life in demen-
tia: The dementia quality of life instrument (DQoL).
Gerontologist, 39, 25�35. doi:10.1093/geront/39.1.25

Brown, P.J., Devanand, D.P., Liu, X., & Caccappolo, E. (2011).
Alzheimer’s disease neuroimaging initiative. Functional
impairment in elderly patients with mild cognitive
impairment and mild Alzheimer disease. Archives of Gen-
eral Psychiatry, 68, 617�626. doi:10.1001/
archgenpsychiatry.2011.57

Bruvik, F.K., Ulstein, I.D., Ranhoff, A.H., & Engedal, K. (2012).
The quality of life of people with dementia and their family
carers. Dementia & Geriatric Cognitive Disorders, 34,
7�14. doi:10.1159/000341584

Chiao, C., Weng, L.J., & Botticello, A. L. (2011). Social partici-
pation reduces depressive symptoms among older adults: An
18-year longitudinal analysis in Taiwan. BMC Public
Health, 11, 292. doi:10.1186/1471-2458-11-292

Cipher, D.J., & Clifford, A. (2004). Dementia, pain, depression,
behavioral disturbances, and ADLs: Toward a comprehen-
sive conceptualization of quality of life in long-term care.
International Journal of Geriatric Psychiatry, 19, 741�748.
doi:10.1002/gps.1155

Clare, L., Kinsella, G.J., Logsdon, R., Whitlatch, C., & Zarit, S.
H. (2011). Building resilience in mild cognitive impairment
and early-stage dementia: Innovative approaches to inter-
vention and outcome evaluation. In B. Resnick, L.P.
Gwyther, & K.A. Roberto (Eds.), Resilience in aging
(pp. 245�259). Springer ScienceCBusiness Media.
doi:10.1007/978-1-4419-0232-0

Clare, L., Nelis, S.M., Quinn, C., Martyr, A., Henderson, C.,
Hindle, J.V., … Victor, C.R. (2014). Improving the experi-
ence of dementia and enhancing active life – living well with
dementia: Study protocol for the IDEAL study. Health and
Quality of Life Outcomes, 12, 164. doi:10.1186/s12955-014-
0164-6

Cohen, J., Cohen, P., West, S.G., & Aiken, L.S. (2003). Applied
multiple regression/correlation analysis for the behavioral
sciences (3rd ed.). Mahwah, NJ: Erlbaum.

Conde-Sala, J. L, Garre-Olmo, J., Turr�o-Garriga, O., Vilalta-
Franch, J., & L�opez-Pousa, S. (2010). Differential features
of burden between spouse and adult child caregivers of
patients with Alzheimer’s disease: An exploratory compara-
tive design. International Journal of Nursing Studies, 47:
1262�1273. doi:10.1016/j.ijnurstu.2010.03.001

Conradsson, M., Littbrand, H., Bostr€om, G., Lindel€of, N., Gus-
tafson, Y., & Rosendahl, E. (2013). Is a change in functional
capacity or dependency in activities of daily living associ-
ated with a change in mental health among older people

Aging & Mental Health 169

http://dx.doi.org/10.1093/geronj/44.4.P124

http://dx.doi.org/10.1001/archgenpsychiatry.2010.177

http://dx.doi.org/10.1177/0891988710363709

http://dx.doi.org/10.1037//0022-3514.51.6.1173

http://dx.doi.org/10.1080/13607863.2015.1040722

http://dx.doi.org/10.2147/CIA.S57535

http://dx.doi.org/10.1080/13607863.2014.915923

http://dx.doi.org/10.1093/geront/39.1.25

http://dx.doi.org/10.1001/archgenpsychiatry.2011.57

http://dx.doi.org/10.1159/000341584

http://dx.doi.org/10.1186/1471-2458-11-292

http://dx.doi.org/10.1002/gps.1155

http://dx.doi.org/10.1007/978-1-4419-0232-0

http://dx.doi.org/10.1186/s12955-014-0164-6

http://dx.doi.org/10.1016/j.ijnurstu.2010.03.001

living in residential care facilities? Clinical Interventions in
Aging, 8, 1561�1568. doi:10.2147/CIA.S53270

Conradsson, M., Rosendahl, E., Littbrand, H., Gustafson, Y.,
Olofsson, B., & L€ovheim, H. (2013). Usefulness of the
Geriatric Depression Scale 15-item version among very old
people with and without cognitive impairment. Aging &
Mental Health, 17, 638�645. doi:10.1080/13607863.2012
.758231

de Boer, M.E., Hertogh, C.M., Dr€oes, R.M., Riphagen, I.I.,
Jonker, C., & Eefsting, J.A. (2007). Suffering from dementia
� the patient’s perspective: A review of the literature. Inter-
national Psychogeriatrics, 19, 1021�1039. doi:http://dx.
doi.org/10.1017/S1041610207005765

Formiga, F., Mascaro, J., & Pujol, R. (2005). Inter-rater reliabil-
ity of the Barthel index. Age and Ageing, 34, 655�656.
doi:10.1093/ageing/afi209

Gerety, M.B., Williams, J.W., Mulrow, C.D., Cornell, J.E.,
Kadri, A.A., Rosenberg, J., … Long, M. (1994). Perfor-
mance of case-finding tools for depression in the nursing
home: Influence of clinical and functional characteristics
and selection of optimal threshold scores. Journal of Ameri-
can Geriatrics Society, 42, 1103�1109. doi:10.1111/j.1532-
5415.1994.tb06217.x

Giebel, C.M., Sutcliffe, C., & Challis, D. (2015). Activities of
daily living and quality of life across different stages of
dementia, a UK study. Aging & Mental Health, 19, 63�71.
doi:10.1080/13607863.2014.915920

G�omez-Gallego, M., Gomez-Garc�ıa, J., & Ato-Garc�ıa, M. (2014)
Confirmatory factor analysis of the quality of life in
Alzheimer’s disease scale in patients with Alzheimer’s dis-
ease. Experimental Aging Research, 40, 266�279.
doi:10.1080/0361073X.2014.896664

G�omez-Gallego, M., G�omez-Amor, J., & G�omez-Garc�ıa, J.
(2011) Validaci�on de la versi�on espa~nola de la escala QoL-
AD en pacientes con enfermedad de Alzheimer, cuidadores
y profesionales sanitarios [Validation of the Spanish version
of the QoL-AD Scale in Alzheimer disease patients, their
carers, and health professionals]. Neurologia, 27, 4�10.
doi:10.1016/j.nrl.2011.03.006

G�omez-Gallego, M., G�omez-Amor, J., & G�omez-Garc�ıa, J.
(2012). Determinants of quality of life in Alzheimer’s dis-
ease perspective of patients, informal caregivers, and profes-
sional caregivers. International Psychogeriatrics, 24,
1805�1815. doi:10.1017/S1041610212001081

Graff, M.J., Vernooij-Dassen, M.J., Thijssen, M., Dekker, J.,
Hoefnagels, W.H., & Olderikkert, M.G. (2007). Effects of
community occupational therapy on quality of life, mood,
and health status in dementia patients and their caregivers:
A randomized controlled trial. Journal of Gerontology: Bio-
logical and Medical Sciences, 62, 1002�1009. doi:10.1186/
1745-6215-15-74

Hicks, R., & Tingley, D. (2011). Causal mediation analysis. The
Stata Journal, 11, 609�615.

Ho, D.E., Imai, K., King, G., & Stuart, E.A. (2007). Matching as
nonparametric preprocessing for reducing model depen-
dence in parametric causal inference. Political Analysis,
15, 199�236. doi:10.1093/pan/mpl013

Hoe, J., Hancock, G., Livingston, G., Woods, B., Challis, D., &
Orrell, M. (2009). Changes in the quality of life of people
with dementia living in care homes. Alzheimer Disease and
Associated Disorders, 23, 285�290. doi:10.1097/
WAD.0b013e318194fc1e

Houtjes, W., van Meijel, B., Deeg, D.J., & Beekman, A.T.
(2011). Unmet needs of outpatients with late-life depression:
A comparison of patient, staff and carer perceptions. Journal
of Affective Disorders, 134, 242�248. doi:10.1016/j.
jad.2011.05.052

Hybels, C.F., Pieper, C.F., & Blazer, D.G. (2009). The complex
relationship between depressive symptoms and functional
limitations in community dwelling older adults: The impact

of subthreshold depression. Psychological Medicine, 39,
1677�1688. doi:10.1017/S0033291709005650

Imai, K., Keele, L., & Tingley, D. (2010). A general approach to
causal mediation analysis. Psychological Methods, 15,
309�334. doi:10.1037/a0020761

Imai, K., Keele, L., Tingley, D., & Yamamoto, T. (2010).
Causal mediation analysis using R. In H.D. Vinod (Ed.),
Advances in social science research using R
(pp. 129�154). New York, NY: Springer.

Imai, K., Keele, L., Tingley, D., & Yamamoto, T. (2011).
Unpacking the black box of causality learning about causal
mechanisms from experimental and observational studies.
American Political Science Review, 105, 765�789.
doi:10.1017/S0003055411000414

Imai, K., Keele, L., & Yamamoto, T. (2013). Experimental
designs for identifying causal mechanisms. Journal of the
Royal Statistical Society, 176, 5�51. doi:10.1111/j.1467-
985X.2012.01032

Imbens, G.W. (2003). Sensitivity to exogeneity assumptions in
program evaluation. American Economic Review, 93,
126�132. doi:10.1257/000282803321946921

Johansson, M.M., Marcusson, J., & Wressle, E. (2015).Cognitive
impairment and its consequences in everyday life: Experien-
ces of people with mild cognitive impairment or mild
dementia and their relatives. International Psychogeriatrics,
27, 949�958. doi:10.1017/S1041610215000058

Keating, N., & Gaudet, N. (2012). Quality of life of persons with
dementia. Journal of Nutrition & Health Aging, 16,
454�456. doi:10.1007/s12603-011-0346-4

Kiosses, D.N., Ravdin, L.D., Gross, J.J., Raue, P., Kotbi, N., &
Alexopoulos, G.S. (2015). Problem adaptation therapy for
older adults with major depression and cognitive
impairment: A randomized clinical trial. JAMA Psychiatry,
72, 22�30. doi:10.1001/jamapsychiatry.2014.1305

Kwok, T., Young, D., Yip, A., & Ho, F. (2013) Effectiveness of
day care services for dementia patients and their caregivers.
Asian Journal of Gerontology & Geriatrics, 8, 9�15.

Lawton, M.P. (1994). Quality of life in Alzheimer disease. Alz-
heimer Disease and Associated Disorders, 8, 138�150.

Lesher, E.L., & Berryhill, J.S. (1994). Validation of the geriatric
depression scale � short form among inpatients. Journal of
Clinical Psychology, 50, 256�260. doi:10.1002/1097-4679
(199403)50:23.0.CO;2-E

Leung, P., Orrell, M., & Orgeta, V. (2015). Social support group
interventions in people with dementia and mild cognitive
impairment: A systematic review of the literature. Interna-
tional Journal of Geriatric Psychiatry, 30, 1�9.
doi:10.1002/gps.4166

Lin Kiat Yap, P., Yen Ni Goh, J., Henderson, L. M., Min Han,
P., Shin Ong, K., & Si Ling Kwek, S. (2008). How do
Chinese patients with dementia rate their own quality of
life? International Psychogeriatrics, 20, 482�493.
doi:10.1017/S1041610207006096

Liu, C.P., Leung, D.S., & Chi, I. (2011). Social functioning, pol-
ypharmacy and depression in older Chinese primary care
patients. Aging & Mental Health, 15, 732�741.
doi:10.1080/13607863.2011.562174

Livingston, G., Cooper, C., Woods, J., Milne, A., & Katona, A.
(2008). Successful ageing in adversity: The LASER-AD lon-
gitudinal study. Journal of Neurology, Neurosurgery
and Psychiatry, 79, 641�645. doi:10.1136/jnnp.2007.
126706

Logsdon, R.G., Gibbons, L. E., McCurry, S.M., & Teri, L.
(2002). Assessing quality of life in older adults with cogni-
tive impairment. Psychosomatic Medicine, 64, 510�519.
doi:10.1097/00006842-200205000-00016

L�opez, J., Losada, A., Romero-Moreno, R., M�arquez-Gonz�alez,
M., & Mart�ınez-Mart�ın, P. (2012). Factors associated with
dementia caregivers’ preference for institutional care. Neu-
rologia, 27, 83�89. doi:10.1016/j.nrl.2011.04.003

170 M. G�omez-Gallego et al.

http://dx.doi.org/10.2147/CIA.S53270

http://dx.doi.org/10.1080/13607863.2012.758231

http://dx.doi.org/10.1017/S1041610207005765

http://dx.doi.org/10.1093/ageing/afi209

http://dx.doi.org/10.1111/j.1532-5415.1994.tb06217.x

http://dx.doi.org/10.1080/13607863.2014.915920

http://dx.doi.org/10.1080/0361073X.2014.896664

http://dx.doi.org/10.1016/j.nrl.2011.03.006

http://dx.doi.org/10.1017/S1041610212001081

http://dx.doi.org/10.1186/1745-6215-15-74

http://dx.doi.org/10.1093/pan/mpl013

http://dx.doi.org/10.1097/WAD.0b013e318194fc1e

http://dx.doi.org/10.1016/j.jad.2011.05.052

http://dx.doi.org/10.1017/S0033291709005650

http://dx.doi.org/10.1037/a0020761

http://dx.doi.org/10.1017/S0003055411000414

http://dx.doi.org/10.1111/j.1467-985X.2012.01032

http://dx.doi.org/10.1257/000282803321946921

http://dx.doi.org/10.1017/S1041610215000058

http://dx.doi.org/10.1007/s12603-011-0346-4

http://dx.doi.org/10.1001/jamapsychiatry.2014.1305

http://dx.doi.org/10.1002/1097-4679(199403)50:23.0.CO;2-E

http://dx.doi.org/10.1002/gps.4166

http://dx.doi.org/10.1017/S1041610207006096

http://dx.doi.org/10.1080/13607863.2011.562174

http://dx.doi.org/10.1136/jnnp.2007.126706

http://dx.doi.org/10.1097/00006842-200205000-00016

http://dx.doi.org/10.1016/j.nrl.2011.04.003

Lucas-Carrasco, R., G�omez-Benito, J., Rejas, J., & Brod, M.
(2011). The Spanish version of the dementia quality of life
questionnaire: A validation study. Aging & Mental Health,
15, 482�489. doi:10.1080/13607863.2010.543658

Lumley, T., & Zeieis, A. (2013). Sandwich package. R package
version 2.3.0. Vienna: R Foundation for Statistical Comput-
ing. Retrieved from http//www.R-project.org/

Lynch, K.G., Cary, M., Gallop, R., & Ten Have, T.R. (2008).
Causal mediation analyses for randomized trials. Health
Services & Outcomes Research Methodology, 8, 57�76.
doi:10.1007/s10742-008-0028-9

Mahoney, F., & Barthel, D. (1965). Functional evaluation: The
Barthel Index.Maryland State Medical Journal, 14, 61�65.

Manski, C.F. 2007. Identification for prediction and decision.
Cambridge, MA: Harvard University Press.

McKhann, G., Drachman, D., Folstein, M., Katzman, R., Price,
D., & Stadlan, E.M. (1984). Clinical diagnosis of
Alzheimer’s disease report of the NINCDS-ADRDA work
group under the auspices of Department of Health and
Human Services Task Force on Alzheimer’s disease. Neu-
rology, 34, 939�944. doi:10.1212/wnl.34.7.939

Miranda-Castillo, C., Woods, B., Galboda, K., Oomman, S.,
Olojugba, C., & Orrell, M. (2010). Unmet needs, quality of
life and support networks of people with dementia living at
home. Health and Quality of Life Outcomes, 8, 132.
doi:10.1186/1477-7525-8-132.

Miranda-Castillo, C., Woods, R.T., & Orrell, M. (2010). People
with dementia living alone: What are their needs and what
kind of support are they receiving? International Psycho-
geriatrics, 22, 607�617. doi:http://dx.doi.org/10.1017/
S104161021000013X

Missotten, P., Ylieff, M., Di Notte, D., Paquay, L., De Lepeleire,
J., & Fontaine, O. (2007). Quality of life in dementia: A 2-
year follow-up study. International Journal of Geriatric
Psychiatry, 22, 1201�1207. doi:10.1002/gps.1814

Mjørud, M., Kirkevold, M., Røsvik, J., Selbæk, G., & Engedal, K.
(2014). Variables associated to quality of life among nursing
home patients with dementia. Aging & Mental Health, 18,
1013�1021. doi:10.1080/13607863.2014.903468

Monaci, L., & Morris, R.G. (2012). Neuropsychological screen-
ing performance and the association with activities of daily
living and instrumental activities of daily living in dementia:
Baseline and 18- to 24-month follow-up. International Jour-
nal of Geriatric Psychiatry, 27, 197�204. doi:10.1002/
gps.2709

Moniz-Cook, E., Vernooij-Dassen, M., Woods, R., Verhey, F.,
Chattat, R., De Vugt M.,… Interdem group. (2008). A Euro-
pean consensus on outcome measures for psychosocial inter-
vention research in dementia care. Aging & Mental Health,
12, 14�29. doi:10.1080/13607860801919850

Moyle, W., Fetherstonhaugh, D., Greben, M., Beattie, E., &
AusQoL group. (2015). Influencers on quality of life as
reported by people living with dementia in long-term care:
A descriptive exploratory approach. BMC Geriatrics, 15, 50.
doi:10.1186/s12877-015-0050-z

Muthen, B., & Asparouhov, T. (2015). Causal effects in mediation
modeling: An introduction with applications to latent varia-
bles. Structural Equation Modeling: A Multidisciplinary Jour-
nal, 22, 12�23. doi:10.1080/10705511.2014.935843

Naglie, G., Hogan, D.B., Krahn, M., Beattie, B.L., Black, S.E.,
MacKnight, C., & Tomlinson, G. (2011). Predictors of
patient self-ratings of quality of life in Alzheimer’s disease:
Cross-sectional results from the Canadian Alzheimer’s Dis-
ease Quality of Life (CADQOL) Study. The American Jour-
nal of Geriatric Psychiatry, 19, 891�901. doi:10.1097/
JGP.0b013e3182006a7f

Pearl, J. (2009). Causal inference in statistics: An overview.
Statistics Surveys, 3, 96�146. doi:10.1214/09-SS057

Pedro-Cuesta, J., Viru�es-Ortega, J., Vega, S., Seijo-Mart�ınez, M.,
Saz, P., Rodr�ıguez, F.,… del Barrio, J.L. (2009). Prevalence of
dementia and major dementia subtypes in Spanish populations:

A reanalysis of dementia prevalence surveys, 1990�2008.
BMC Neurology, 9, 55. doi:10.1186/1471-2377-9-55.

Preston, L., Marshall, A., & Bucks, R.S. (2007). Investigating
the ways that older people cope with dementia: A qualitative
study. Aging & Mental Health, 11, 131�143. doi:10.1080/
13607860600844572

Rabins, P.V., & Black, B.S. (2007). Measuring quality of life in
dementia purposes, goals, challenges and progress. Interna-
tional Psychogeriatrics, 719, 401�407. doi:10.1017/
S1041610207004863

Reisberg, B., Ferris, S.H., De Leon, M.J., & Crook, T. (1982).
The Global Deterioration Scale for assessment of primary
degenerative dementia. American Journal of Psychiatry,
139, 1136�1139. doi:10.1176/ajp.139.9.1136

Rickenbach, E.H., Condeelis, K.L., & Haley, W.E. (2015).
Daily stressors and emotional reactivity in individuals
with mild cognitive impairment and cognitively healthy
controls. Psychology & Aging, 30, 420�431.
doi:10.1037/a0038973

Rivera, J., Bermejo, F., Franco, M., Morales-Gonz�alez, J.M., &
Benito-Le�on, J. (2009). Understanding care of people with
dementia in Spain: Cohabitation arrangements, rotation and
rejection to long term care institution. International Journal
of Geriatric Psychiatry, 24, 142�148. doi:10.1002/gps.2081

Rizzi, L., Rosset, I., & Roriz-Cruz, M. (2014). Global epidemiol-
ogy of dementia: Alzheimer’s and vascular types. Biomedi-
cal Research International. Advance online publication.
doi:10.1155/2014/908915

Rog, L.A., Park, L.Q., Harvey, D.J., Huang, C.J., Mackin, S., &
Farias, S.T. (2014). The independent contributions of cogni-
tive impairment and neuropsychiatric symptoms to everyday
function in older adults. Clinical Neuropsychology, 28,
215�236. doi:10.1080/13854046.2013.876101

Rubin, D.B. (2005). Causal inference using potential outcomes:
Design, modeling, decisions. Journal of the American
Statistical Association, 100, 322�331. doi:10.1198/
016214504000001880

Sainsbury, A., Seebass, G., Bansal, A., & Young, J. (2005). Reli-
ability of the Barthel Index when used with older people.
Age and Ageing, 34, 228�232. doi:10.1093/ageing/afi063

Schieman, S., & Plickert, G. (2007). Functional limitations and
changes in levels of depression among older adults: A multi-
ple-hierarchy stratification perspective. Journal of Gerontol-
ogy: Social Sciences, 62, S36�S42. doi:10.1093/geronb/
62.1.S36

Sch€olzel-Dorenbos, C.J., van der Steen, M.J., Engels, L.K., &
Olde Rikkert, M.G. (2007). Assessment of quality of life as
outcome in dementia and MCI intervention trials: A system-
atic review. Alzheimer Disease & Associated Disorders,
21, 172�178. doi:10.1097/WAD.0b013e318047df4c

Schwarzbach, M., Luppa, M., Sikorski, C., Fuchs, A., Maier, W.,
van den Bussche, H., … Riedel-Heller, S.G. (2013). The
relationship between social integration and depression in
non-demented primary care patients aged 75 years and older.
Journal of Affective Disorders, 145, 172�178. doi:10.1016/
j.jad.2012.07.025

Serrano, J.P., Latorre, J.M., & Gatz, M. (2014). Spain: Promoting
the welfare of older adults in the context of population aging.
Gerontologist, 54, 733�740. doi:10.1093/geront/gnu010

Shah, S., Vanclay, F., & Cooper B. (1989). Improving the sensi-
tivity of the Barthel Index for stroke rehabilitation. Journal
of Clinical Epidemiology, 42, 703�709.

Sheehan, B.D., Lall, R., Stinton, C., Mitchell, K., Gage, H., Hol-
land, C., … Katz, J. (2012). Patient and proxy measurement
of quality of life among general hospital in-patients with
dementia. Aging & Mental Health, 16, 603�607.
doi:10.1080/13607863.2011.653955

Siedlecki, K.L., Tatarina, O., Sanders, L., Albert, M., Blacker,
D., Dubois, B., … Stern, Y. (2009). Comparison of patient
and caregiver reports of patient activity participation and its
relationship to mental health in patients with Alzheimer’s

Aging & Mental Health 171

http://dx.doi.org/10.1080/13607863.2010.543658

http://www.R-project.org/

http://dx.doi.org/10.1007/s10742-008-0028-9

http://dx.doi.org/10.1212/wnl.34.7.939

http://dx.doi.org/10.1186/1477-7525-8-132

http://dx.doi.org/10.1017/S104161021000013X

http://dx.doi.org/10.1002/gps.1814

http://dx.doi.org/10.1080/13607863.2014.903468

http://dx.doi.org/10.1002/gps.2709

http://dx.doi.org/10.1080/13607860801919850

http://dx.doi.org/10.1186/s12877-015-0050-z

http://dx.doi.org/10.1080/10705511.2014.935843

http://dx.doi.org/10.1097/JGP.0b013e3182006a7f

http://dx.doi.org/10.1214/09-SS057

http://dx.doi.org/10.1186/1471-2377-9-55

http://dx.doi.org/10.1080/13607860600844572

http://dx.doi.org/10.1017/S1041610207004863

http://dx.doi.org/10.1176/ajp.139.9.1136

http://dx.doi.org/10.1037/a0038973

http://dx.doi.org/10.1002/gps.2081

http://dx.doi.org/10.1155/2014/908915

http://dx.doi.org/10.1080/13854046.2013.876101

http://dx.doi.org/10.1198/016214504000001880

http://dx.doi.org/10.1093/ageing/afi063

http://dx.doi.org/10.1093/geronb/62.1.S36

http://dx.doi.org/10.1097/WAD.0b013e318047df4c

http://dx.doi.org/10.1016/j.jad.2012.07.025

http://dx.doi.org/10.1093/geront/gnu010

http://dx.doi.org/10.1080/13607863.2011.653955

disease. The Journals of Gerontology Series B: Psychologi-
cal Sciences & Social Sciences, 64B, 687�695.
doi:10.1093/geronb/gbp071

Suh, G.H., Ju, Y.S., Yeon, B.K., & Shah, A.A. (2004). Longitu-
dinal study of Alzheimer’s disease: Rates of cognitive and
functional decline. International Journal of Geriatric Psy-
chiatry, 19, 817�824. doi:10.1002/gps.1168

Tatsumi, H., Nakaaki, S., Torii, K., Shinagawa, Y., Watanabe,
N., Murata, Y., … Furukawa, T.A. (2009). Neuropsychiatric
symptoms predict change in quality of life of Alzheimer dis-
ease patients: A two-year follow-up study. Psychiatry &
Clinical Neuroscience, 63, 374�384. doi:10.1111/j.1440-
1819.2009.01955.x

Tingley, D., Yamamoto, T., Hirose, K., Keele, L., & Imai, K.
(2014). Mediation R package for causal mediation analysis.
Journal of Statistical Software, 59, 1�40.

Weiss, E.M., Kohler, C.G., Vonbank, J., Stadelmann, E., Kemm-
ler, G., Hinterhuber, H., … Marksteiner, J. (2008).
Impairment in emotion recognition abilities in patients with
mild cognitive impairment, early and moderate Alzheimer
disease compared with healthy comparison subjects. Ameri-
can Journal of Geriatrics Psychiatry, 6, 974�980.
doi:10.1097/JGP.0b013e318186bd53

Wilks, S.E., & Croom, B. (2008). Perceived stress and resilience
in Alzheimer’s disease caregivers: Testing moderation and

mediation models of social support. Aging & Mental Health,
12, 357�365. doi:10.1080/13607860801933323

Woods, R.T., Nelis, S.M., Martyr, A., Roberts, J., Whitaker, C.J.,
& Markova, I. (2014). What contributes to a good quality of
life in early dementia? Awareness and the QoL-AD, a cross-
sectional study. Health & Quality of Life Outcomes, 11.
doi:10.1186/1477-7525-12-94

World Health Organization Quality of Life. (1995). The World
Health Organization Quality of Life assessment (WHO-
QOL): Position paper from the World Health Organization.
Social Science & Medicine, 41, 1403�1409. doi:10.1016/
0277-9536(95)00112-K

Yesavage, J.A., Brink, T.L., Rose, T.L., & Lum, O. (1983).
Development and validation of a geriatric depression scale a
preliminary report. Journal of Psychiatric Research, 17,
37�49. doi:10.1016/0022-3956(82)90033-4

Zahodne, L.B., & Tremont G. (2013). Unique effects of apathy
and depression signs on cognition and function in amnestic
mild cognitive impairment. International Journal of Geriat-
ric Psychiatry, 28, 50�56. doi:10.1002/gps.3789

Zhang, S., Edwards, H., Yates, P., Li, C., & Guo, Q. (2014).
Self-efficacy partially mediates between social support and
health-related quality of life in family caregivers for demen-
tia patients in Shanghai. Dementia & Geriatric Cognitive
Disorders, 37, 34�44. doi:10.1159/000351865

172 M. G�omez-Gallego et al.

http://dx.doi.org/10.1093/geronb/gbp071

http://dx.doi.org/10.1002/gps.1168

http://dx.doi.org/10.1111/j.1440-1819.2009.01955.x

http://dx.doi.org/10.1097/JGP.0b013e318186bd53

http://dx.doi.org/10.1080/13607860801933323

http://dx.doi.org/10.1186/1477-7525-12-94

http://dx.doi.org/10.1016/0277-9536(95)00112-K

http://dx.doi.org/10.1016/0022-3956(82)90033-4

http://dx.doi.org/10.1002/gps.3789

http://dx.doi.org/10.1159/000351865

Copyright of Aging & Mental Health is the property of Routledge and its content may not be
copied or emailed to multiple sites or posted to a listserv without the copyright holder’s
express written permission. However, users may print, download, or email articles for
individual use.

Abstract

Introduction
Materials and methods
Sample
Instruments
Global Deterioration Scale (GDS)
Quality of Life in Alzheimer’s Disease Scale (QOL-AD)
Barthel Index (BI)
Geriatric Depression Scale (GDS-15)
Statistical analysis
Results
Discussion
Methodological connotations
Limitations
Conclusions
Acknowledgements
Refer

Psychosocial risk factors and Alzheimer’s disease: the associative effect of
depression, sleep disturbance, and anxiety

Shanna L. Burke a, Tamara Cadet b, Amary Alcidea, Janice O’Driscolla and Peter Maramaldic,d,e

aSchool of Social Work, Robert Stempel College of Public Health and Social Work, Florida International University, Miami, FL, USA; bOral Health
Policy and Epidemiology, Simmons College School of Social Work, Harvard School of Dental Medicine, Boston, MA, USA; cHartford Faculty Scholar
& National Mentor, Simmons College School of Social Work, Boston, MA, USA; dOral Health Policy and Epidemiology, Harvard School of Dental
Medicine, Boston, MA, USA; eDepartment of Social and Behavioral Sciences, Harvard T.H. Chan School of Public Health, Boston, MA, USA

ARTICLE HISTORY
Received 6 June 2016
Accepted 12 September 2017

ABSTRACT
Objectives: Alzheimer’s disease (AD) dementia is a neurodegenerative condition, which leads to
impairments in memory. This study predicted that sleep disturbance, depression, and anxiety increase
the hazard of AD, independently and as comorbid conditions.

Methods

: Data from the National Alzheimer’s Coordinating Center was used to analyze evaluations of
12,083 cognitively asymptomatic participants. Survival analysis was used to explore the longitudinal
effect of depression, sleep disturbance, and anxiety as predictors of AD. The comorbid risk posed by
depression in the last two years coupled with sleep disturbance, lifetime depression and sleep
disturbance, clinician-verified depression and sleep disturbance, sleep disturbance and anxiety,
depression in the last two years and anxiety, lifetime depression and anxiety, and clinician-verified
depression and anxiety were also analyzed as predictors of AD through main effects and additive
models.

Results

: Main effects models demonstrated a strong hazard of AD development for those reporting
depression, sleep disturbance, and anxiety as independent symptoms. The additive effect remained
significant among comorbid presentations.
Conclusion: Findings suggest that sleep disturbance, depression, and anxiety are associated with AD
development among cognitively asymptomatic participants. Decreasing the threat posed by
psychological symptoms may be one avenue for possibly delaying onset of AD.

KEYWORDS
Alzheimer’s disease;
dementia; anxiety;
depression; sleep
disturbance

Introduction

Alzheimer’s disease (AD) research has identified several risk
factors that may be associated with to the development of
AD including advancing age, the apolipoprotein (APOE) e4
gene, female sex, family history of AD, mild cognitive
impairment, smoking, obesity at midlife, diabetes, midlife
hypertension, midlife high cholesterol, moderate and severe
traumatic brain injury, fewer years of formal education, and
lower levels of social and cognitive engagement (Albert et al.,
2011; Alzheimer’s Association, 2015a; Di Marco et al., 2014;
Green et al., 2002; He, Zhang, & Zhang, 2000 Lautensclager
et al., 1996; Loy, Schofield, Turner, & Kwok, 2014; Sando et al.,
2008; Vincent & Velkof, 2010; Wang, Xu, & Pei, 2012; Wilson
et al., 2013). Cardiovascular factors, such as high cholesterol,
high blood pressure, and heart disease, are known to contrib-
ute to dementia risk (Diniz, Butters, Albert, Dew, & Reynolds,
2013). However, what is less established in the current litera-
ture are definitive conclusions on the relationships between
the contribution of other factors such as depression, anxiety,
and sleep disturbance, and eventual AD development. There
is also limited information on whether these factors are symp-
toms of AD or if they contribute directly to the development
of AD.

To date, depression has not been causally connected to
AD, though many studies have yielded results that demon-
strate a positive relationship between the two conditions
(Andersen, Lolk, Kragh-Sorensen, Petersen, & Green, 2005;

Burke et al., 2016a, 2016b; Chen, Ganguli, Mulsant, & DeKosky,
1999; Gracia-Garcia et al., 2015; Li, Meyer, & Thornby, 2001;
Steffens et al., 2004). Contradictory evidence about the role of
depression remains. Findings from Ownby, Crocco, Acevedo,
John, and Loewenstein (2006) suggest depression may be a
prodromal symptom to AD, as opposed to a suggested cause.
Furthermore, findings from Gatz, Tyas, St. John, and Mont-
gomery (2005) indicate that depression may act as a predictor
of AD development among individuals over the age of 65.
Psychosocial factors, including depression symptoms, have
been linked to future cognitive decline and development of
AD (Burke et al., 2016; Green et al., 2003; Kessing & Nilsson,
2003; Speck et al., 1995; Yaffe et al., 1999).

Some studies have suggested amyloid deposits and atro-
phy in frontal brain regions are related and contribute to AD
development in a linear or even synchronous fashion (Becker
et al., 2011; Ch�etelat et al., 2010; Oh, Habeck, Madison, & Jag-
ust, 2014). Very low levels of dysphoria, apathy, and anhedo-
nia may be indicators to neurodegeneration in areas of the
brain that are associated with AD, but this may be indepen-
dent of amyloid burden (Donovan et al., 2015). Further,
patients in a three-year prospective cohort study experiencing
mild cognitive impairment and depression were at twice the
risk of developing dementia (Alzheimer’s type) than those
without depression. Of the depressed patients studied, 85%
developed dementia as opposed to 32% of the non-
depressed patients (Modrego & Ferr�andez, 2004).

CONTACT Shanna L. Burke sburke@fiu.edu

https://doi.org/10.1080/13607863.2017.1387760

AGING & MENTAL HEALTH, 2018
VOL. 22, NO. 12, 1577–1584

http://crossmarksupport.crossref.org/?doi=10.1080/13607863.2017.1387760&domain=pdf

http://orcid.org/0000-0001-6969-3536

http://orcid.org/0000-0002-4660-8009

mailto:sburke@fiu.edu

https://doi.org/10.1080/13607863.2017.1387760

http://www.tandfonline.com

Psychological distress, such as that experienced by individ-
uals with anxiety, may also be associated with an increased
risk of AD development (Russ, Hamer, Stamatakis, Starr, &
Batty, 2011). Russ et al. (2011) indicated that higher levels of
psychological distress may be correlated with AD-related
deaths. The study results remained the same even when other
risk factors were taken into account, such as age, diabetes,
smoking, and cardiovascular disease. Recently, Pietrzak et al.
(2015) found that elevated anxiety symptoms (measured by
the Hospital Anxiety and Depression Scale) moderated the
effect of amyloid-b on cognitive decline in a preclinical AD
study sample (n = 333). That is, the presence of anxiety symp-
toms led to more rapid decline in verbal memory, language,
and executive function. These domains suggest that
impairment first occurred in the temporal and prefrontal corti-
cal regions.

In addition, poor sleeping habits may also be a contribut-
ing factor to the risk of AD development (Burke et al., 2016;
Xie et al., 2013). This risk may be further increased by homozy-
gous or heterozygous APOE-e4 carrier status, which is known
to increase risk of AD in a dose-dependent fashion (Yoshizawa
et al., 1994). Xie et al. (2013) found that cerebrospinal and
interstitial fluid exchange increases substantially during sleep,
which aids in b-amyloid protein removal. However, when
sleep disturbance occurs (defined by less than five hours of
sleep or frequent night fits), Ab-amyloid deposition may
increase, which may exacerbate the potential to develop AD
(Spira et al., 2013). Lim et al. (2013) indicated that APOE-e4
carriers with sleep disturbance may also have an increased
chance of developing AD compared to e4 carriers who sleep
for the recommended duration of 7–8 hours a night. It has
not been definitively concluded that the sleep disturbance
instigates AD development, but the aforementioned results
provide a platform for further investigation into this correla-
tion. Given the continued debate in the scientific literature,
further investigation of the role of psychosocial risk factors in
predicting AD risk in a cognitively normal population is
warranted.

It is important to not only view the concepts of depression,
anxiety, and sleep disturbance as distinct and independent
risk factors for AD, but to consider their interacting effect in
AD development. For this purpose, the authors selected the
syndemic perspective as the theoretical framework guiding
the design and analysis of this study. While comorbidity is
defined as the presence of multiple, but usually independent,
diseases or disorders, a syndemic perspective involves an
interaction between two or more diseases or disorders that
result in additional negative health consequences (Singer,
2009). However, the syndemic perspective reaches beyond a
person’s biology, and takes account of stress, inequality, the
community, and the environment, all over time, as potential
cofactors in the exacerbation of illnesses (Singer, 2009). The
syndemic perspective has often been used in the study of
medical problems, such as HIV/AIDS and its co-infections like
tuberculosis or malaria. A search of the Medline and PsycInfo
databases provided no evidence that the syndemic has been
used to explain AD development, suggesting that this may be
a novel application of the syndemic perspective.

This study hypothesized that the presence of sleep distur-
bance, depression, and anxiety as individual factors increases
the likelihood of meeting the criteria for AD diagnosis. It was
also hypothesized that the synergistic effect (examined
through additive effects) of two psychological factors, that

induce stress, will increase the hazard of AD development
beyond the hazard of possessing the presence of one psycho-
social effect alone.

Methods

This study examined psychosocial risk factors for late-onset
AD through a secondary data analysis of the National Alz-
heimer’s Coordinating Center’s (NACC) Uniform Data Set
(UDS). A prospective cohort design was utilized with individu-
als who were cognitively asymptomatic at the time of their
initial visit (n = 12,053). The analysis sought to determine who
among those initially unaffected demonstrated clinical signs
of AD dementia by their last visit.

Participants

The participants volunteered from one of the 34 AD Centers
(ADCs) in the United States. Data was collected between Sep-
tember 2005 and September 2015. Participants with demen-
tia, mild cognitive impairment, and cognitively intact
individuals were recruited by individual ADCs throughout the
United States (n = 33,610). The UDS is not a nationally repre-
sentative sample of the United States population with respect
to AD or dementia, as these participants self-select and volun-
tarily present for an examination at one of the participating
NIH/NIA sponsored ADCs. Written informed consent was
obtained from all participants and/or self-designated inform-
ants by the individual ADC site (NACC, 2010). Participants eli-
gible for this study were cognitively asymptomatic at visit one
(n = 12,053). Of this initial sample, 9,184 participants were eli-
gible for analysis as a result of their participation in the origi-
nal examination and at least one follow-up visit. Therefore, all
participants had at least two total visits to the ADC. Partici-
pants with more than one dementia-type diagnosis were
excluded. This study received approval from the [blinded]
University Institutional Review Board.

Measures

Data are collected particiants or provided by a trusted infor-
mant by trained clinicians. These interviews acquire a wide
range of demographic information; family history; medica-
tions used; health history; a physical; and imaging and labs.
Participants respond to several neuropsychological rating
scales, functional assessments, and from these measures a
diagnosis regarding dementia status is determined through
the Clinical Rating Scale (Morris, 1993). Probable AD was diag-
nosed within the UDS using criteria set forth by the National
Institute of Neurological and Communicative Disorders and
Stroke and the Alzheimer’s Disease and Related Disorders
Association (McKhann et al., 1984, 2011). Diagnoses arising
from these interviews are assigned either by a consensus
team or the examining physician (NACC, 2010). Individuals
that began the study as cognitively normal, but were diag-
nosed with AD at a later visit are defined in this study as ‘con-
verters’ to AD; their counterparts that did not develop AD
during the course of the study were defined as ‘non-convert-
ers’ (Table 2).

The variables utilized for this study include self-reported
depression in the last two years, other episodes of depression
before the two-year timeframe, clinician-verified depression
at the time of visit, self-reported sleep disturbance, and self-

1578 S. L. BURKE ET AL.

reported anxiety. Sporadic late-onset AD (probable AD) is the
outcome of interest. Three depression measurements were
used in the study. The first variable included active depression
in the last two years self-reported by the participant. This
includes depressive disorders for which a clinician was con-
sulted, even if treatment or medication was not received.
Depression includes major depressive disorder, situational
depression, bipolar disorders, dysthymic disorders, and other
mood disorders. The second measure of depression, depres-
sion: other episodes, includes self-reported depression epi-
sodes prior to the last two years and is considered to be the
measure of lifetime depression. The third depression variable
is the clinician’s judgment of symptoms and measures the
presence or absence of depression at the time of the visit.
The Diagnostic and Statistical Manual of Mental Disorders
(American Psychiatric Association, 2013) was utilized to
inform the clinician’s diagnosis of depression.

Sleep disturbance was measured by the self-reported pres-
ence or absence of nighttime behaviors. These behaviors
include awakening during the night, rising too early in the
morning, or taking excessive naps during the day. If any of
the three symptoms were reported to occur in the past
30 days, participants were considered to have a sleep distur-
bance. Anxiety was also measured by self-report, specifically
whether the person in question was ‘very nervous, worried, or
frightened for no apparent reason’ and seemed ‘very tense or
fidgety’ or is ‘afraid to be apart from’ the informant (Cum-
mings, 2009, p. 12). The variables of anxiety and sleep distur-
bance were measured utilizing different questions of the
Neuropsychiatric Inventory Questionnaire (NPI-Q), which
measures the presence or absence of each individual psychi-
atric symptom (Cummings, 1977/2009). The NPI-Q has dem-
onstrated adequate test–retest reliability and convergent
validity for the individual symptomology with which the cur-
rent study is concerned (Kaufer et al., 2000).

Survival analysis was used to estimate and interpret hazard
functions (Kleinbaum & Klein, 2012). The primary goal was to
assess the effect of psychosocial predictor variables on the
eventual diagnosis of AD dementia. An event (outcome vari-
able) was defined as a diagnosis of probable AD among indi-
viduals with normal cognition at baseline; a diagnosis of
probable AD was the failure point and the participant’s
remaining observations were removed from the study once
AD diagnosis was delivered. Right censoring (Kleinbaum &
Klein, 2012) was utilized to account for the fact that a partici-
pant was not necessarily diagnosed with AD prior to their last
observation. As a result, true survival time is unobserved
unless a participant is diagnosed with AD on or prior to their
last recorded observation. In order to exclude missing values
from the analysis, but retain all other information from a par-
ticipant’s visit, missing information was recoded and excluded
in a listwise fashion.

The statistical software, STATA (StataCorp, Release 14,
2015), was utilized for the analyses, and a p-value of <0.05 was considered statistically significant for the analysis. Partici- pants’ observations ranged from 1 to 10 visits. Observation intervals were measured in days to account for the staggered nature of the visits.

A descriptive analysis of the baseline sample was con-
ducted and included distributions across predictor variables,
percentages for categorical variables, and means and stan-
dard deviation for continuous variables. Baseline survival
function was determined prior to the addition of predictors

and covariates (age, race, education, e4 carrier status, the
presence of hypertension and hypercholesterolemia) in the
model. We also controlled for APOE as 30% of the sample
were e4 carriers. Log-rank tests for equality were used to test
for significant differences in survival curves between the vari-
ous response categories within each predictor variable (i.e.
those reporting ‘yes’ to anxiety versus those reporting ‘no’ to
the same question). Chi-square and t-tests were conducted to
assess whether there was a statistically significant difference
between those who eventually developed AD and those who
did not with respect to demographic and predictor variables.

The relationships of certain predictor variables were exam-
ined relative to the outcome variable using the Cox propor-
tional hazards model (Cox, 1972). The time variable used for
the cox analysis was number of days from the first visit until
the first occasion of AD diagnosis. Regression modeling
included simultaneous control of multiple predictors and
covariates. Four models were developed to explore the main
effects of the predictor variables. In the first model, unad-
justed main effects of each individual predictor were exam-
ined. In the second model, the covariates, sex, age, education,
and race were controlled. In the third model, probable AD
was examined in relation to the aforementioned controlled
variables with the addition of APOE genotypes. In the final
model, all previous covariates were controlled, including the
presence of hypertension and hypercholesterolemia. A similar
structure was applied to the exploration of synergistic effects.
The assumption of proportionality was examined through
inspection of Schoenfeld residuals in order to determine
whether the proportional hazards assumption had been met.

Results

The minimum amount of time participants were under obser-
vation was 208 days until the first occasion of AD diagnosis.
The maximum observation interval was 3458 days (M =
1549.3; Mdn: 1456 days). The mean number of visits was 3.26
(SD: 2.12), with a range of 1 to 10 visits. There were 361 diag-
noses of AD dementia by the end of the observation period
among older adults who presented for at least an initial visit
and at least one follow-up visit. The mean age of subjects
with normal cognition at visit one was 71 years (SD: 10.86
years). At visit one, 80.5% of the sample population were
White, 13.55% were African American, and 5.95% were from
other ethnic groups. Almost 6% of the sample reported His-
panic origin. Approximately 35% of subjects reported that
their mother had been diagnosed with dementia, while
16.45% reported that their father had been diagnosed with
dementia. Almost 18% of subjects reported depression in the
last two years or lifetime depression, 10.05% of subjects were
diagnosed with clinician-verified depression, 10.55% reported
a sleep disturbance, and 8.69% reported the presence of anxi-
ety. Percentages, means, and standard deviations (where
applicable) are displayed in Table 1.

Preliminary log-rank tests for equality of survivor functions
revealed that those who reported depression in the last two
years, clinician verified depression at the time of the visit,
sleep disturbance, and anxiety expressed statistically signifi-
cant (p < 0.001) different survival curves than those who did not. The same was true for those reporting lifetime depression versus those who did not (p < 0.05). Chi-square and t-tests examined differences between participants who ultimately were diagnosed with AD and those who did not demonstrate

AGING & MENTAL HEALTH 1579

a statistically significant difference on all psychosocial predic-
tors and most demographic factors. There was not a signifi-
cant difference between converters and non-converters with
regard to race.

There was a significant association (p< 0.001) between individuals reporting depression in the last two years and the occurrence (diagnosis) of AD. Specifically, in model one, those who self-reported depression in the last two years experi- enced a significantly higher risk of AD dementia diagnosis (HR = 2.32 [95% CI, 1.87–2.88]) compared to those who had not. The hazard remained stable even when adjusted for covariates in model two, adjusting further for APOE-e4 carrier status, and finally adjusting for high blood pressure and cho- lesterol in the fourth model.

The main effect of lifetime depression episodes, occurring
more than two years earlier, presented a significant risk of AD
diagnosis within the follow-up period compared to those
who did not report such episodes (HR = 1.32 [1.04–1.68], p
< .05). Similarly, depression verified by a clinician was signifi- cantly associated with the diagnosis of AD during the follow- up period as compared to those without verified depression symptoms in model one (HR = 2.72 [2.15–3.43], p < .001), and remained relatively similar when the effect of APOE-e4 carrier status was controlled as well as demographic confounders (HR = 2.89 [2.24–3.72], p < .001).

The presence of sleep disturbance was also significantly
associated with the eventual diagnosis of AD. The unadjusted
hazard (HR = 2.86 [2.25–3.63], p < .001) was similar to that of clinician-verified depression (HR = 2.72 [2.15–3.43], p < .001).

Finally, anxiety symptoms were significantly associated
with an increased hazard of eventual AD development (HR =
3.50 [2.77–4.44], p < .001), presenting the strongest associa- tion out of the aforementioned psychosocial predictors. Main effects for all primary predictors are displayed in Table 3.

Additive interactions

The hazard of eventual AD diagnosis for those experiencing
both recent depression symptoms and sleep disturbance was
statistically significant (HR = 4.95 [95% CI 3.53–6.94], p
< .001), as compared to those experiencing neither symptom. The effect of lifetime depression and sleep disturbance in rela- tion to AD indicated a strong positive relationship (HR = 3.26Ta

bl
e
1.

D
em

og
ra
ph

ic
ov
er
vi
ew

of
sa
m
pl
e
an
d
pr
ed
ic
to
rv
ar
ia
bl
es
.

D
ep
re
ss
io
n
–
la
st
tw
o
ye
ar
s

D
ep
re
ss
io
n
–
lif
et
im
e

D
ep
re
ss
io
n
–
cl
in
ic
ia
n
ve
rifi

ed
Sl
ee
p
di
st
ur
ba
nc
e

An
xi
et
y

At
vi
si
to

ne
–
ba
se
lin
e

Pr
es
en
t

Ab
se
nt

Pr
es
en
t
Ab
se
nt
Pr
es
en
t
Ab
se
nt
Pr
es
en
t
Ab
se
nt
Pr
es
en
t
Ab
se
nt

N
or
m
al
co
gn

iti
on

12
,0
83

21
63

(1
7.
97
%
)

98
71

(8
2.
03
%
)

21
60

(1
8.
09
%
)

97
82

(8
1.
91
%
)

12
14

(1
0.
05
%
)

10
,8
69

(8
9.
95
%
)

11
80

(1
0.
55
%
)

10
,0
00

(8
9.
45
%
)

97
1
(8
.6
9%

)
10
,2
09

(9
1.
31
%
)

Fe
m
al
e

7,
86
5
(6
5.
09
%
)

15
86

(2
0.
17
%
)

62
45

(7
9.
40
%
)

15
90

(2
0.
22
%
)

61
83

(7
8.
61
%
)

90
8
(1
1.
54
%
)

69
57

(8
8.
46
%
)

73
4
(9
.3
3%

)
65
42

(8
3.
18
%
)

64
4
(8
.1
9%

)
66

32
(8
4.
32
%
)

Ag
e

X
=
71
.0
5
(S
D
=
10
.8
6)

X
=
68
.6
5
(S
D
=
10
.7
8)

X
=
68
.1
4
(S
D
=
10
.5
9)

X
=
68
.5
9
(S
D
=
10
.7
9)

X
=
72
.1
3
(S
D
=
11
.1
8)

X
=
72
.2
4
(S
D
=
11
.4
3)

Ra
ce W
hi
te

96
53

(8
0.
5%

)
18
65

(1
9.
32
%
)

77
51

(8
0.
30
%
)

18
46

(1
9.
12
%
)

76
99

(7
9.
76
%
)

10
01

(1
0.
37
%
)

86
52

(8
9.
63
%
)

99
4
(1
0.
30
%
)

79
44

(8
2.
30
%
)

82
1
(8
.5
1%

)
81
17

(8
4.
09
%
)

Af
ric
an

Am
er
ic
an

16
25

(1
3.
55
%
)

17
1
(1
0.
52
%
)

14
88

(9
1.
57
%
)

18
5
(1
0.
02
%
)

14
18

(8
7.
26
%
)

11
1
(6
.8
3%

)
15
14

(9
3.
17
%
)

10
3
(6
.3
4%

)
13
97

(8
5.
97
%
)

80
(4
.9
2%

)
14
20

(8
7.
38
%
)

H
is
pa
ni
c

71
4
(5
.9
3%

)
16
6
(2
3.
24
%
)

54
5
(7
6.
33
%
)

13
5
(1
9.
12
%
)

57
1
(8
0.
88
%
)

12
8
(1
7.
93
%
)

58
6
(8
2.
07
%
)

11
2
(1
7.
5%

)
52
8
(8
2.
5%

)
66

(1
8.
86
%
)

28
4
(8
1.
14
%
)

O
th
er

71
3
(5
.9
5%

)
10
4
(1
4.
58
%
)

60
4
(8
4.
71
%
)

10
2
(1
4.
31
%
)

60
2
(8
4.
43
%
)

77
(1
0.
80
%
)

63
6
(8
9.
20
%
)

75
(1
0.
52
%
)

59
1
(8
2.
89
%
)

59
(8
.2
7%

)
60
7
(8
5.
13
%
)

N
ot
e:
Ro
w
pe
rc
en
ta
ge
s
ar
e
di
sp
la
ye
d.

Table 2. Demographic overview of converters and non-converters.
Converters to AD

(n = 712)
Non-Converters
(n = 1039) t or x2 Statistica

Age (years) 84.64 (SD:8.49)b 81.93 (SD: 9.44) –6.18, df = 1763, p =
0.00

Female 476 (62.58%)c 642 (57.42%) 4.27, df = 1, p = 0.039
Education (years) 16.36 (SD: 4.66) 15.62 (SD: 9.84) –2.12, df = 1763, p =

0.03
Race 1.77, df = 2, p = 0.412

White 614 (86.24%) 872 (83.93%)
African-American 75 (10.53%) 129 (12.42%)
Other 23 (3.23%) 38 (3.66%)
Hispanic 41 (5.75%) 28 (2.69%) 10.42, df = 1, p = 0.001

e4 Carrier 277 (38.90%) 289 (27.82%) 25.69, df = 1, p = 0.00
Depressed 2 Years 277 (38.90%) 275 (26.47%) 31.09, df = 1, p = 0.00
Depression –
Lifetime

206 (28.93%) 238 (22.91%) 8.68, df = 1, p = 0.003

Clinician-Verified
Depression

205 (28.79%) 210 (20.21%) 16.86, df = 1, p = 0.00

Anxiety 196 (27.53%) 163 (15.69%) 38.64, df = 1, p = 0.00
Sleep Disturbance 185 (25.98%) 163 (15.69%) 30.00, df = 1, p = 0.00
a x2 test statistics are displayed for categorical variables, t test statistics for
continuous variables.

b Continuous variables are described with mean and standard deviation.
c Categorical variables are described with sample size and percentage.

1580 S. L. BURKE ET AL.

[2.24–4.75] p < 0.001), compared to those who did not endorse these symptoms. Clinician-verified depression and sleep disturbance was also strongly correlated (p < 0.001) with eventual AD diagnosis; yielding a hazard three times greater than those without these symptoms (HR = 3.82 [2.47– 5.90]). The findings for additive effects of those reporting anxi- ety symptoms are similar to participants with recent depres- sion (HR = 5.09 [3.68–7.03], p < .001), lifetime depression (HR = 3.41 [2.30–5.05], p < .001), and clinician-verified depression (HR = 4.14 [2.86–6.01], p < .001) as compared to those who do not report anxiety nor depression symptoms. The additive effect of sleep disturbance and anxiety was also statistically significant (HR = 4.67 [3.23–6.75], p < .001), compared to those without sleep disturbance nor anxiety. These figures are displayed in Table 4.

Discussion

This study examined correlations between depression, sleep
disturbance, and anxiety with eventual AD dementia develop-
ment, both as individual factors as well as comorbid condi-
tions. Previous empirical literature on the association
between mental health symptoms and AD and/or dementia
focuses on the synchronous relationship between the two.
Specifically, studies have examined the correlation between
the level of psychiatric symptoms and dementia stage (Lopez
et al., 2003; Verkaik, Nuyen, Schellevis, & Francke, 2007), the
incidence of psychiatric comorbidity (Steinburg et al., 2008),
and psychiatric symptoms impacting dementia caregiver bur-
den and the risk of subsequent nursing home placement
(Buhr, Kuchibhatla, & Clipp, 2006).

Few studies have focused on psychological symptoms,
such as depression (Richard et al., 2013), anxiety, and sleep
disturbance as independent and combined predictors of AD
dementia. Similar studies often sample or examine an already
impaired population, tracking the progression from MCI to
dementia to understand the association between psychiatric

symptoms and progression rate (Gabryelewicz et al., 2007). In
these studies, the assumptions and hypotheses indicate that
psychosocial or psychological symptoms are behavioral mani-
festations or challenging behaviors associated with dementia
as opposed to independent mental health symptoms. Symp-
toms which precede dementia diagnosis are often assumed
to be prodromal symptoms and perhaps a signal of advancing
AD pathology (Jorm, 2001).

An estimated 50–70 million Americans suffer chronically
from a sleep disorder, impacting their health across daily
functioning and lifetime longevity. The Centers for Disease
Control and Prevention (2015) reports that 10% of the US
population reports chronic insomnia, which is just one facet
of the sleep disturbance described in the current study. There
are approximately 90 unique sleep disorders, many of which
share the following symptoms ‘excessive daytime sleepiness,
difficulty initiating or maintaining sleep, and abnormal events
occurring during sleep’ (Naismith et al., 2011). In the fifth edi-
tion of the Diagnostic and Statistical Manual (DSM-V), the char-
acteristics of various sleep disorders were taken into
consideration and organized into 11 sleep–wake disorder
diagnostic groups (American Psychiatric Association, 2013).
Likewise, diagnostic indicators of depression as outlined in
the DSM-V include the following: depressed mood daily or
nearly daily, decreased interest in activities most of the day,
significant weight and appetite change, change in sleep,
change in activity, fatigue, guilt/worthlessness, diminished
capacity for concentration, and thoughts of death or suicide
(American Psychiatric Association, 2013). Sleep disturbance
among the aging may be a prognostic indicator for decline in
cognitive functioning. Among older adults, sleep disturbance
may predict depression. Sleep disturbance in depressed
patients can represent the presence of a residual mood disor-
der. Its presence can linger and may be a precursor for a later
depressive episode (Lee et al., 2013).

A recent study of Manhattan residents (n = 2160) over the
age of 65 (Richard et al., 2013) found depression to be

Table 3. Main effects (Probable Alzheimer’s Disease Dementia as Outcome Variable).

Predictor Model 1 Hazard Ratio (95% CI) Model 2 Hazard Ratio (95% CI) Model 3Hazard Ratio (95% CI) Model 4Hazard Ratio

(95% CI)

Depression – last 2 years 2.32 (1.87–2.88)** 2.51 (2.02–3.12)** 2.53 (2.00–3.21)** 2.53 (1.99–3.21)**
Depression – lifetime 1.32 (1.04–1.68)* 1.50 (1.18–1.90)** 1.53 (1.19–1.98)** 1.53 (1.19–1.98)**
Depression – clinician verified 2.72 (2.15–3.43)** 2.67 (2.10–3.37)** 2.89 (2.24–3.72)** 2.88 (2.23–3.71)**
Sleep disturbance 2.86 (2.25–3.63)** 2.57 (2.01–3.27)** 2.57 (1.98–3.34)** 2.56 (1.97–3.33)**
Anxiety 3.50 (2.77–4.44)** 3.25 (2.56–4.12)** 3.17 (2.45–4.10)** 3.16 (2.44–4.09)**

Model 1: Main effect unadjusted.
Model 2: Main effects adjusted for sex, education, age, and race.
Model 3: Main effects adjusted for adjusted for sex, age, education, race, e4 carrier status.
Model 4: Main effects adjusted for adjusted for sex, age, education, race, e4 carrier status, the presence of hypertension and hypercholesterolemia.
* indicates statistical significance at p < 0.05, ** indicates p < 0.001.

Table 4. Additive effects among psychosocial predictor variables (Probable Alzheimer’s disease as outcome variable).

Predictor
Model 1Hazard ratio

(95% CI)
Model 2 Hazard ratio

(95% CI)
Model 3Hazard ratio

(95% CI)
Model 4Hazard ratio

(95% CI)

Depressed 2 years + sleep disturbance 4.95 (3.53–6.94)** 4.81 (3.42–6.75)** 4.66 (3.22–6.75)** 4.65 (3.21–6.74)**
Depression lifetime + sleep disturbance 3.26 (2.24–4.75)** 3.24 (2.22–4.74)** 3.32 (2.20–5.00)** 3.31 (2.19–5.00)**
Clinician-verified depression + sleep disturbance 3.82 (2.47–5.90)** 3.76 (2.43–5.81)** 4.13 (2.58–6.59)** 4.10 (2.56–6.55)**
Sleep Disturbance + anxiety 4.67 (3.23–6.75)** 4.27 (2.94–6.21)** 4.21 (2.79–6.35)** 4.19 (2.77–6.32)**
Depressed 2 years + anxiety 5.09 (3.68–7.03)** 5.09 (3.67–7.05)** 4.75 (3.31–6.80)** 4.75 (3.31–6.82)**
Depression lifetime + anxiety 3.41 (2.30–5.05)** 3.47 (2.34–5.15)** 3.26 (2.10–5.07)** 3.26 (2.10–5.08)**
Clinician-verified depression + anxiety 4.14 (2.86–6.01)** 4.03 (2.78–5.85)** 4.28 (2.88–6.38)** 4.26 (2.86–6.35)**

Model 1: Main effect unadjusted
Model 2: Main effects adjusted for sex, education, age, and race.
Model 3: Main effects adjusted for adjusted for sex, education, age, race, and e4 carrier status.
Model 4: Main effects adjusted for adjusted for sex, education, age, race, e4 carrier status, the presence of hypertension and hypercholesterolemia.
* indicates statistical significance at p < 0.05, ** indicates p < 0.001.

AGING & MENTAL HEALTH 1581

associated with an increased risk of dementia diagnosis, but
that depression did not precede dementia development.
Alternatively, a study focusing on diabetes and depression,
both as independent and combined factors, found depression
to be independently associated with risk of dementia devel-
opment; the hazard of which was further increased for people
diagnosed with diabetes. The study included a wide age
range (30–75) and a large sample (n = 19,239). These investi-
gations suggest that ongoing research is examining the rela-
tionships between depression and AD but not yet conclusive
on how depression may act as an independent risk factor of
AD. Similarly, anxiety was found to be a risk factor dementia
in men (n = 1481) 17 years after baseline anxiety assessments
(Gallacher et al., 2009).

Furthermore, a recent study found that daytime sleepiness
was associated with increased incidence of dementia (Tsapa-
nou, Gu, Scarmeas, & Stern, 2015). In a study of Swedish men
followed from age 50 to 88 between 1970 and 2010 for the
Uppsala Longitudinal Study of Adult Men, researchers found
self-reported sleep disturbance (as evidenced by difficulty fall-
ing asleep, inability to fall back asleep if waking too early, or the
use of sleeping pills three or more times per week) increased
the risk of dementia development (+33%) and AD development
(+51%; Benedict et al., 2015). Using data from the survey of
Health, Aging and Retirement in Europe, researchers found that
self-reported sleep disturbance measured as ‘sleeping problems
in the past 6 months’, ‘recent trouble sleeping or change in pat-
tern’, and ‘restless sleep’ were also significantly related to the
development of dementia (Theou, Rusak, & Rockwood, 2004).
Finally, Jorm’s (2001) comprehensive meta-analysis indicate
that affective symptomology, such as depression, anxiety, and
sleep disturbance are worthy risk factors for exploration in rela-
tion to AD risk and development.

The findings from the current investigation support the
hypothesis that the presences of psychosocial factors of sleep
disturbance, depression, and anxiety as individual factors (which
may induce stress), increasing the likelihood ofmeeting the crite-
ria for AD diagnosis. When these factors were combined to cre-
ate hypothetical scenarios in which a participant experienced
two of these symptoms simultaneously, the synergistic effect
remained significant, growing beyond the simple sum of the
main effects when covariates were taken into account. These
finding provide partial support for the study’s second hypothesis;
that the co-occurrence of two psychosocial factors will increase
the hazard of AD development beyond the hazard of possessing
the presence of one psychosocial effect alone. The mechanism
behind such increasing risk, however, is unknown. This, in turn,
provides evidence of a syndemic framework or perspective in
which the interaction between some of the factors of interest in
this study exacerbates AD development.

The strength of this novel study is the inclusion of psycho-
social symptoms and cognitive status related to the onset
and progression of AD. All participants in this study were
determined by a physician or a consensus team to possess
normal cognition at visit one. The symptoms explored in this
study were reported at baseline.

This study has several limitations. It cannot be confirmed
without neuroimaging results that the subjects did not pos-
sess underlying AD pathology. It is unlikely that they would
be diagnosed with ‘normal cognition’ and not some form of
cognitive impairment if this was the case, however, previous
studies have shown that up to a third of subjects categorized
as not-demented (Storandt, Grant, Miller, & Morris, 2006) or

with pre-MCI (Storandt et al., 2006) during their lifetimes dem-
onstrate AD pathology at autopsy (Storandt et al., 2006; Jack
et al., 2002). In addition, we now know that AD pathology
likely begins developing 10–20 years prior to any observable
manifestations (Sperling et al., 2011). Future studies of this
nature would benefit from confirmation of normal cognition
through neuroimaging at baseline. Also, the sample was not
randomly selected, but it was comprised of individuals volun-
teering themselves to at least two visits: the initial visit plus
one follow-up. Therefore, the results of this study cannot be
generalized to the aging population at large, though it pro-
vides preliminary evidence that could inform future research
with a nationally representative sample. This study also did
not compare cognitively normal participants with at least two
visits to those with less than two visits (also known as those
not eligible for survival analysis).

Additionally, the measures used in the UDS measure
depression in three ways – past two years, depression in the
lifetime outside of the past two years, and clinician verified
depression at the time of visit. Lifetime depression is difficult
to interpret without additional treatment data, and it does
not distinguish between a chronic mood disorder and an
acute episode of depression. The measures also do not define
depression by more explicit DSM diagnoses; for example
bipolar depressive episodes are not distinguished from clini-
cal depression or dysthymia. Finally, while this study gives
insight into the relationships between self-reported depres-
sion, lifetime depression, clinician verified depression at the
time of visit, anxiety, sleep disturbance, and AD development,
more research is needed to refine the causal pathways. Specif-
ically, utilizing the syndemic perspective requires further
investigation with societal, political, and economic inequality
factors that may be relevant to survival modeling.

The total estimated worldwide costs of Alzheimer’s and
dementia in 2015 was $818 billion in US dollars (Prince et al.,
2015). The World Alzheimer’s Institute estimates that total
dementia costs will reach $1 trillion worldwide per year by
2018 and $2 trillion per year worldwide by 2030 (Prince et al.,
2015). In the United States, total health care, long-term care,
and hospice care costs for AD in 2016 are estimated to be
$236 billion, with Medicare and Medicaid paying for approxi-
mated 68% of these costs (Alzheimer’s Association, 2016).
Without any changes, Alzheimer’s costs are expected to sur-
pass $1 trillion in the United States alone by 2050, presenting
a nearly 500% increase in Medicare and Medicaid spending
(Alzheimer’s Association, 2016).

The Alzheimer’s Association (2015b) estimates that if treat-
ment providing a delay in AD diagnosis becomes available by
2025, costs of the disease have the potential to drastically
decrease over the subsequent five years. Within the first year
alone, savings could amount to about $3 billion in Medicare
costs and by 2035 this amount could increase to $67 billion
(Alzheimer’s Association, 2015b). Even further, they predict that
Medicaid savings could amount to $1 billion and $38 billion by
2035 (Alzheimer’s Association, 2015b). Cumulatively, these gov-
ernment-funded programs could have a total savings of $535
billion over a 10-year period and $935 billion for payer sources
that are not government funded (Alzheimer’s Association,
2015b). Additionally, the out-of-pocket expenditures for
patients and their families have the potential to decrease by
$2 billion by 2026 (Alzheimer’s Association, 2015b).

Given the current state of knowledge regarding possible
risks of AD development, it is essential to identify and develop

1582 S. L. BURKE ET AL.

ways to reduce occurrence before the pathophysiological dis-
ease progression begins. This may be beneficial to those at
risk but also to the national economy and overall healthcare
costs. The Centers for Disease Control and Prevention’s Syn-
demics Prevention Network suggest the use of system
dynamics models to identify the course and development of
health ailments, then to use those models to create a more
balanced system of health protection (Milstein, 2008). In this
scenario, screening and treatment for depression, sleep dis-
turbance and anxiety are relatively low-cost and potentially
preventive measures to be considered in a more holistic
approach to AD prevention. This point of view for treating AD
broadens the focus from the individual’s biology at the
moment of AD diagnosis to the individual within his or her
environment over time before AD development. Seeing these
factors as modifiable ailment ties in a syndemic system, versus
comorbidities or prodromal symptoms of AD, may provide an
avenue of treatment that delays the onset of AD
development.

Disclosure statement

No potential conflict of interest was reported by the authors.

ORCID

Shanna L. Burke http://orcid.org/0000-0001-6969-3536
Tamara Cadet http://orcid.org/0000-0002-4660-8009

References

Albert, M. S., DeKosky, S. T., Dickson, D., Dubois, B., Feldman, H. H., Fox, N.,
… Phelps, C. H. (2011). The diagnosis of mild cognitive impairment
due to Alzheimer’s disease: Recommendations from the National Insti-
tute on Aging–Alzheimer’s Association workgroups on diagnostic
guidelines for Alzheimer’s disease. Alzheimer’s & Dementia, 7(3), 270–
279.

Alzheimer’s Association. (2015a). 2015 Alzheimer’s disease facts and fig-
ures. Retrieved from https://www.alz.org/facts/downloads/facts_fig
ures_2015 .

Alzheimer’s Association. (2015b). Changing the trajectory of Alzheimer’s
disease: How a treatment by 2025 saves lives and dollars. Retrieved
from https://www.alz.org/documents_custom/trajectory .

Alzheimer’s Association. (2016). 2016 Alzheimer’s disease facts and fig-
ures. Retrieved from http://www.alz.org/documents_custom/2016-
facts-and-figures .

American Psychiatric Association. (2013). Diagnostic and statistical manual
of mental disorders (5th ed.). Washington, DC: Author.

Andersen, K., Lolk, A., Kragh-Sørensen, P., Petersen, N. E., & Green, A.
(2005). Depression and the risk of Alzheimer disease. Epidemiology, 16
(2), 233–238.

Becker, J. A., Hedden, T., Carmasin, J., Maye, J., Rentz, D. M., Putcha, D., …
Johnson, K. A. (2011). Amyloid-b associated cortical thinning in clini-
cally normal elderly. Annals of Neurology, 69(6), 1032–1042.

Benedict, C., Byberg, L., Cedernaes, J., Hogenkamp, P. S., Giedratis, V.,
Kilander, L., … Schioth, H. B. (2015). Self-reported sleep disturbance is
associated with Alzheimer’s disease risk in men. Alzheimer’s & Demen-
tia, 11, 1090–1097.

Buhr, G. T., Kuchibhatla, M., & Clipp, E. C. (2006). Caregivers’ reasons for
nursing home placement: Clues for improving discussions with fami-
lies prior to the transition. The Gerontologist, 46(1), 52–61.

Burke, S. L., Maramaldi, P., Cadet, T., & Kukull, W. (2016a). Associations
between depression, sleep disturbance, and apolipoprotein E in the
development of Alzheimer’s disease: dementia. International Psycho-
geriatrics, 28(9), 1409–1424.

Burke, S. L., Maramaldi, P., Cadet, T., & Kukull, W. (2016b). Neuropsychiatric
symptoms and Apolipoprotein E: Associations with eventual Alzheim-
er’s disease development. Archives of gerontology and geriatrics, 65,
231–238.

Centers for Disease Control and Prevention. (2015). Sleep and sleep disor-
ders. Retrieved from http://www.cdc.gov/sleep/index.html.

Chen, P., Ganguli, M., Mulsant, B. H., & DeKosky, S. T. (1999). The temporal
relationship between depressive symptoms and dementia: A commu-
nity-based prospective study. Archives of General Psychiatry, 56, 261–
266.

Ch�etelat, G., Villemagne, V. L., Bourgeat, P., Pike, K. E., Jones, G., Ames, D.,
… Rowe, C. C. (2010). Relationship between atrophy and beta-amyloid
deposition in Alzheimer disease. Annals of Neurology, 67(3), 317–324.

Cox, D. R. (1972). Regression models and life-tables. Journal of the Royal
Statistical Society: Series B (Statistical Methodology), 342: 187–220.

Cummings, J. L. (1977). The Neuropsychiatric Inventory: Assessing psycho-
pathology in dementia patients. Neurology, 48, S10–S16.

Cummings, J. L. (2009). The neuropsychiatric inventory: Comprehensive
assessment of psychopathology in patients with dementia. Retrieved
from http://www.dementia-assessment.com.au/behavioural/npi

Di Marco, L. Y., Marzo, A., Mu~noz-Ruiz, M., Ikram, M. A., Kivipelto, M., Ruefe-
nacht, D., … Frangi, A. F. (2014). Modifiable lifestyle factors in demen-
tia: A systematic review of longitudinal observational cohort studies.
Journal of Alzheimer’s Disease, 42(1), 119–135.

Diniz, B. S., Butters, M. A., Albert, S. M., Dew, M. A., & Reynolds, C. F. (2013).
Late-life depression and risk of vascular dementia and Alzheimer’s dis-
ease: Systematic review and meta-analysis of community-based
cohort studies. The British Journal of Psychiatry, 202(5), 329–335.

Donovan, N. J., Hsu, D. C., Dagley, A. S., Schultz, A. P., Amariglio, R. E., Mor-
mino, E. C., … Marshall, G. A. (2015). Depressive symptoms and bio-
markers of Alzheimer’s disease in cognitively normal older adults.
Journal of Alzheimer’s Disease, 46(1), 63–73.

Gabryelewicz, T., Styczynska, M., Luczywek, E., Barczak, A., Pfeffer, A.,
Androsiuk, W., … Barcikowska, M. (2007). The rate of conversion of
mild cognitive impairment to dementia: Predictive role of depression.
International Journal of Geriatric Psychiatry, 22(6), 563–567.

Gallacher, J., Bayer, A., Fish, M., Pickering, J., Pedro, S., Dunstan, F., … Ben-
Shlomo, Y. (2009). Does anxiety affect risk of dementia? Findings from the
Caerphilly Prospective Study. Psychosomatic Medicine, 71(6), 659–666.

Gatz, J. L., Tyas, S. L., St. John, P., & Montgomery, P. (2005). Do depressive
symptoms predict Alzheimer’s disease and dementia ? Journal of Ger-
ontology, 60(6), 744–747.

Gracia-Garcia, P., de la-Camara, C., Santabarbara, J., Lopez-Anton, R., Quin-
tanilla, M. A., Ventura, T., … Lobo, A. (2015). Depression and incident
Alzheimer disease: The impact of disease severity. The American Jour-
nal of Geriatric Psychiatry, 23(2), 119–129.

Green, R. C., Cupples, L. A., Go, R., Benke, K. S., Edeki, T., Griffith, P. A., …
Ferrer, L. A. (2002). Risk of dementia among white and African Ameri-
can relatives of patients with Alzheimer disease. JAMA, 287(3), 329–
336.

Green, R. C., Cupples, L. A., Kurz, A., Auerbach, S., Go, R., Sandovnick, D., …
Farrer, L. (2003). Depression as a risk factor for Alzheimer disease: The
MIRAGE study. Archives of Neurology, 60, 753–759.

He, Y. L., Zhang, X. K., & Zhang, M. Y. (2000). Psychosocial risk factors for
Alzheimer’s disease. Hong Kong Journal of Psychiatry, 10(2), 2–7.

Jack, C. R., Dickson, D. W., Parisi, J. E., Xu, Y. C., Cha, R. H., O’brien, P. C., …
Kokmen, E. (2002). Antemortem MRI findings correlate with hippo-
campal neuropathology in typical aging and dementia. Neurology, 58
(5), 750–757.

Jorm, A. F. (2001). History of depression as a risk factor for dementia: An
updated review. Australian and New Zealand Journal of Psychiatry, 35
(6), 776–781.

Kaufer, D. I., Cummings, J. L., Ketchel, P., Smith, V., MacMillan, A., Shelley,
T., … DeKosky, S. T. (2000). Validation of the NPI-Q: A brief clinical
form of the neuropsychiatric inventory. The Journal of Neuropsychiatry
and Clinical Neurosciences, 12(2), 233–239.

Kessing, L. V., & Nilsson, F. M. (2003). Increased risk of developing demen-
tia in patients with major affective disorders compared to patients
with other medical illnesses. Journal of Affect Disorders, 73, 261–269.

Kleinbaum, D. G. & Klein, M. (2012). Survival analysis: A self-learning text
3rd ed. New York: Springer.

Lautenschlager, N. T., Cupples, L. A., Rao, V. S., Auerbach, S. A., Becker, R.,
Burke, J., … & Green, R. C. (1996). Risk of dementia among relatives of
Alzheimer’s disease patients in the MIRAGE study What is in store for
the oldest old?. Neurology, 46(3), 641–650.

Lee, E., Cho, H. J., Olmstead, R., Levin, M. J., Oxman, M. N., & Irwin, M. R.
(2013). Persistent sleep disturbance: A risk factor for recurrent depres-
sion in community-dwelling older adults. Sleep, 36(11), 1685–1691.
doi:10.5665/sleep.3128

AGING & MENTAL HEALTH 1583

http://orcid.org/0000-0001-6969-3536

http://orcid.org/0000-0002-4660-8009

https://www.alz.org/facts/downloads/facts_figures_2015

https://www.alz.org/documents_custom/trajectory

http://www.alz.org/documents_custom/2016-facts-and-figures

http://www.cdc.gov/sleep/index.html

http://www.dementia-assessment.com.au/behavioural/npi

https://doi.org/10.5665/sleep.3128

Li, Y. S., Meyer, J. S., & Thornby, J. (2001). Longitudinal follow-up of depres-
sive symptoms among normal versus cognitively impaired elderly.
International Journal of Geriatric Psychiatry, 16, 718–727.

Lim, A. S., Yu, L., Kowgier, M., Schneider, J. A., Buchman, A. S., & Bennett, D.
A. (2013). Modification of the relationship of the apolipoprotein E e4
allele to the risk of Alzheimer disease and neurofibrillary tangle den-
sity by sleep. JAMA Neurology, 70(12), 1544–1551. doi:10.1001/
jamaneurol.2013.4215

Lopez, O. L., Becker, J. T., Sweet, R. A., Klunk, W., Kaufer, D. I., Saxton, J., …
DeKosky, S. T. (2003). Psychiatric symptoms vary with the severity of
dementia in probable Alzheimer’s disease. The Journal of Neuropsychi-
atry and Clinical Neurosciences, 15(3), 346–353.

Loy, C. T., Schofield, P. R., Turner, A. M., & Kwok, J. J. (2014). Genetics of
dementia. Lancet, 383, 828–840.

McKhann, G., Drachman, D., Folstein, M., Katzman, R., Price, D., & Stadlan,
E. M. (1984). Clinical diagnosis of Alzheimer’s disease: Report of the
NINCDS-ADRDA Work Group under the auspices of Department of
Health and Human Services Task Force on Alzheimer’s disease. Neurol-
ogy, 34(7), 939–944.

McKhann, G., Knopman, D. S., Chertkow, H., Hyman, B. T., Jack Jr C. R.,
Kawas, C. H., … Phelps, C. H. (2011). The diagnosis of dementia due to
Alzheimer’s disease: Recommendations from the National Institute on
Aging-Alzheimer’s Association workgroups on diagnostic guidelines
for Alzheimer’s disease. Alzheimer’s & Dementia, 7(3), 263–269.
doi:10.1016/j.jalz.2011.03.005

Milstein, B. (2008). Hygeia’s constellation: Navigating health futures in a
dynamic and democratic world. Atlanta, GA: The Centers for Disease
Control and Prevention, Syndemics Prevention Network.

Modrego, P. J., & Ferr�andez, J. (2004). Depression in patients with mild
cognitive impairment increases the risk of developing dementia of
Alzheimer type: A prospective cohort study. Archives of Neurology, 61
(8), 1290–1293.

Morris, J. C. (1993). The Clinical Dementia Rating (CDR): Current version
and scoring rules. Neurology, 43(11), 2412–2414.

Naismith, S. L., Rogers, N. L., Lewis, S. J. G., Diamond, K., Terpening, Z., Nor-
rie, L., & Hickie, I. B. (2011). Sleep disturbance in mild cognitive
impairment: Differential effects of current and remitted depression.
Acta Neuropsychiatrica, 23(4), 167–172. doi:10.1111/j.1601-
5215.2011.00555.x

National Alzheimer’s Coordinating Center. (2010). The UDS study popula-
tion. Retrieved from https://www.alz.washington.edu/WEB/study_pop.
html

Oh, H., Habeck, C., Madison, C., & Jagust, W. (2014). Covarying alterations
in Ab deposition, glucose metabolism, and gray matter volume in cog-
nitively normal elderly. Human Brain Mapping, 35(1), 297–308.
doi:10.1002/hbm.22173

Ownby, R. L., Crocco, E., Acevedo, A., John, V., & Loewenstein, D. (2006).
Depression and risk for Alzheimer disease: Systematic review, meta-
analysis, and metaregression analysis. Archives of General Psychiatry,
63(5), 530–538.

Pietrzak, R. H., Lim, Y. Y., Neumeister, A., Ames, D., Ellis, K. A., Harrington, K.,
… Villemagne, V. L. (2015). Amyloid-b, anxiety, and cognitive decline
in preclinical Alzheimer disease: A multicenter, prospective cohort
study. JAMA Psychiatry, 72(3), 284–291.

Prince, M., Wimo, A., Guerchet, M., Ali, G., Wu, Y., & Prina, M. (2015). World
Alzheimer report 2015 of the global impact of dementia: An analysis
of prevalence, incidence, costs and trends. Retrieved from http://
www.alz.co.uk/research/WorldAlzheimerReport2015 .

Richard, E., Reitz, C., Honig, L. H., Schupf, N., Tang, M. X., Manly, J. J., …
Luchsinger, J. A. (2013). Late-life depression, mild cognitive
impairment, and dementia. JAMA Neurology, 70(3), 383–389.

Russ, T. C., Hamer, M., Stamatakis, E., Starr, J. M., & Batty, D. (2011). Psycho-
logical distress as a risk factor for dementia death. Archives of Internal
Medicine, 171(20), 1859.

Sando, S. B., Melquist, S., Cannon, A., Hutton, M., Sletvold, O., Saltvedt, I.,…
Aasly, J. (2008). Risk-reducing effect of education in Alzheimer’s dis-
ease. International Journal of Geriatric Psychiatry, 23(11), 1156–1162.

Singer, M. (2009). Introduction to syndemics: A critical systems approach to
public and community health. San Francisco, CA: Jossey-Bass.

Speck, C. E., Kukull, W. A., Brenner, D. E., Bowen, J. D., McCormick, W. C.,
Teri, L. P., … Larson, E. B. (1995). History of depression as a risk factor
for Alzheimer’s disease. Epidemiology, 6, 366–369.

Sperling, R. A., Aisen, P. S., Beckett, L. A., Bennett, D. A., Craft, S., Fagan, A.
M., … Phelps, C. H. (2011). Toward defining the preclinical stages of
Alzheimer’s disease: Recommendations from the National Institute on
Aging-Alzheimer’s Association workgroups on diagnostic guidelines
for Alzheimer’s disease. Alzheimer’s & Dementia, 7(3), 280–292.
doi:10.1016/j.jalz.2011.03.003

Spira, A. P., Gamaldo, A. A., An, Y., Wu, M. N., Simonsick, E. M., Bilgel, M., …
Resnick, S. M. (2013). Self-reported sleep and b-amyloid deposition in
community-dwelling older adults. JAMA Neurology, 70(12), 1537–1543.

StataCorp. (2015). Stata statistical software: Release 14. College Station, TX:
StataCorp LP.

Steffens, D. C., Welsh-Bohmer, K. A., Burke, J. R., Plassman, B. L., Beyer, J. L.,
Gersing, K. R., & Potter, G. G. (2004). Methodology and preliminary
results from the neurocognitive outcomes of depression in the elderly
study. Journal of Geriatric Psychiatry & Neurology, 17, 202–211.

Steinberg, M., Shao, H., Zandi, P., Lyketsos, C. G., Welsh-Bohmer, K. A., Nor-
ton, M. C., … & Tschanz, J. T. (2008). Point and 5-year period preva-
lence of neuropsychiatric symptoms in dementia: the Cache County
Study. International journal of geriatric psychiatry, 23(2), 170–177.

Storandt, M., Grant, E. A., Miller, J. P., & Morris, J. C. (2006). Longitudinal course
and neuropathologic outcomes in original vs revised MCI and in pre-MCI.
Neurology, 67(3), 467–473. doi: 10.1212/01.wnl.0000228231.26111.6e

Theou, O., Rusak, B., & Rockwood, K. (2004). Sleep disturbance predicts
incident dementia and mortality. Alzheimer’s & Dementia: The Journal
of the Alzheimer’s Association, 9(4), 541.

Tsapanou, A., Gu, Y., Scarmeas, N., & Stern, Y. (2015). Self-reported sleep
problems and incidence of dementia. Neurology, 84(14 Suppl), P2–173.

Verkaik, R., Nuyen, J., Schellevis, F., & Francke, A. (2007). The relationship
between severity of Alzheimer’s disease and prevalence of comorbid
depressive symptoms and depression: A systematic review. Interna-
tional Journal of Geriatric Psychiatry, 22(11), 1063–1086.

Vincent, G. K., & Velkoff, V. A. (2010). The next four decades: The older popu-
lation in the United States: 2010 to 2050 (No. 1138), Washington, D.C.:
US Department of Commerce, Economics and Statistics Administra-
tion, US Census Bureau.

Wang, H., Xu, W., & Pei, J. (2012). Leisure activities, cognition and demen-
tia. BBA – Molecular Basis of Disease, 1822(3), 482–491.

Wilson, R. S., Boyle, P. A., Yu, L., Barnes, L. L., Schneider, J. A., & Bennett, D.
A. (2013). Life-span cognitive activity, neuropathologic burden, and
cognitive aging. Neurology, 81(4), 314–321.

Xie, L., Kang, H., Xu, Q., Chen, M. J., Liao, Y., Thiyagarajan, M., … Neder-
gaard, M. (2013). Sleep drives metabolite clearance from the adult
brain. Science, 42(6156), 373–377.

Yaffe, K., Blackwell, T., Gore, R., Sands, L., Reus, V., & Browner, W. S. (1999).
Depressive symptoms and cognitive decline in nondemented elderly
women: A prospective study. Archives of General Psychiatry, 56, 425–430.

Yoshizawa, T., Yamakawa�Kobayashi, K., Komatsuzaki, Y., Arinami, T.,
Oguni, E. I., Mizusawa, H., … Hamaguchi, H. (1994). Dose�dependent
association of apolipoprotein e allele e4 with late -onset, sporadic Alz-
heimer’s disease. Annals of Neurology, 36(4), 656–659.

1584 S. L. BURKE ET AL.

https://doi.org/10.1001/jamaneurol.2013.4215

https://doi.org/10.1016/j.jalz.2011.03.005

https://doi.org/10.1111/j.1601-5215.2011.00555.x

https://www.alz.washington.edu/WEB/study_pop.html

https://doi.org/10.1002/hbm.22173

http://www.alz.co.uk/research/WorldAlzheimerReport2015

https://doi.org/10.1016/j.jalz.2011.03.003

https://doi.org/10.1212/01.wnl.0000228231.26111.6e

Abstract

Introduction
Methods
Participants
Measures
Results
Additive interactions
Discussion
Disclosure statement
References

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.

Contents lists available at ScienceDirect

Journal of Affective Disorders

journal homepage: www.elsevier.com/locate/jad

Review article

Risk of Dementia in persons who have previously experienced clinically-
significant Depression, Anxiety, or P

SD: A Systematic Review and Meta-
Analysis

J.K. Kuring, J.L. Mathias⁎, L. Ward
School of Psychology, Faculty of Health & Medical Sciences, University of Adelaide, Adelaide, Australia

A R T I C L E I N F O

Keywords:
Risk
depression
anxiety
PTSD
dementia
Alzheimer’s

A B S T R A C T

Background: Depression, anxiety and PTSD appear to be linked to dementia, but it is unclear whether they are
risk factors (causal or prodromal) for, comorbid with, or sequelae to (secondary effect of) dementia. Existing
meta-analyses have examined depression or anxiety in all-cause dementia, Alzheimer’s disease (AD) and vascular
dementia (VaD), but have not considered post-traumatic stress disorder (PTSD), dementia with Lewy bodies
(DLB), or frontotemporal dementia (FTD). The current meta-analysis examined the risk of developing dementia
(AD, VaD, DLB, FTD, all-cause) in people with and without a history of clinically-significant depression, anxiety
or PTSD in order to better understand the link between mental illness and dementia (PROSPERO number:
CRD42018099872).
Methods: PubMed, EMBASE, PsycINFO and CINAHL searches identified 36 eligible studies.
Results: There is a higher risk of developing all-cause dementia and AD in people with previous depression, and a
higher risk of all-cause dementia in people with prior anxiety, than in persons without this history. Prior PTSD
was not associated with a higher risk of later being diagnosed with dementia.
Limitations: The data for anxiety, PTSD, DLB and FTD were limited.
Conclusions: Depression and anxiety appear to be risk factors for dementia, but longitudinal studies across
adulthood (young adult/mid-life/older adult) are needed to evaluate the likely causal or prodromal nature of
this risk. The link between PTSD and dementia remains unclear. Regular screening for new onset mental illness
and for cognitive changes in older adults with a history of mental illness may assist with earlier identification of
dementia.

1. Introduction

As the population ages, the number of people with dementia and the
associated personal and health care costs are predicted to rise, with
over 130 million people worldwide estimated to be affected by 2050
(Alzheimer’s Disease International, 2015). Although the search for a
cure continues, research has increasingly attempted to identify poten-
tially modifiable risk factors in order to prevent or slow the onset of
dementia using early interventions (Deckers et al., 2015;
Livingstone et al., 2017). The recent Lancet Commission into dementia
prevention, intervention and care calculated that more than a third of
dementia cases could be prevented by eliminating reversible risk factors
(Livingstone et al., 2017). Even if not eliminated entirely, it is estimated
that approximately three million cases of Alzheimer’s disease (AD)
could be prevented by 2050 if modifiable risk factors were reduced by

as little as 10% per decade (Barnes & Yaffe, 2011; Norton et al., 2014).
There is now a growing body of research to suggest that these

modifiable risk factors may include depression, anxiety and post-trau-
matic stress disorder (PTSD). This research has been examined in a
number of recent meta-analyses, which have reported a higher risk of
dementia in people with a history of depression (Cherbuin et al., 2015;
Diniz et al., 2013; Ford et al., 2018; Xu et al., 2015) and anxiety
(Becker et al., 2018; Ford et al., 2018; Gulpers et al., 2016;
Santabarbara et al., 2019). A recent systematic review has additionally
reported that there is a greater risk of dementia in military veterans
who have a history of PTSD (Rafferty et al., 2017). Most recently, a
meta-analysis also reported that there is an association between the
four most common types of dementia (AD, vascular dementia [VaD],
dementia with Lewy bodies [DLB], frontotemporal dementia [FTD])
and depression, anxiety and PTSD (Kuring et al., 2018). However, it

https://doi.org/10.1016/j.jad.2020.05.020
Received 27 August 2019; Received in revised form 16 April 2020; Accepted 10 May 2020

⁎ Corresponding author.
E-mail address: psyj-mat@psychology.adelaide.edu.au (J.L. Mathias).

Journal of Affective Disorders 274 (2020) 247–

261

http://www.sciencedirect.com/science/journal/01650327

https://www.elsevier.com/locate/jad

https://doi.org/10.1016/j.jad.2020.05.020

mailto:psyj-mat@psychology.adelaide.edu.au

https://doi.org/10.1016/j.jad.2020.05.020

http://crossmark.crossref.org/dialog/?doi=10.1016/j.jad.2020.05.020&domain=pdf

remains unclear whether depression, anxiety and PTSD are causal risk
factors, prodromal symptoms of, or comorbid with dementia
(Kuring et al., 2018).

There are multiple reasons why dementia may be linked to de-
pression, anxiety, and PTSD. One possibility is that mental illness plays
a causal role in the development of dementia, with the inflammatory
processes that are thought to be associated with depression, anxiety,
and PTSD hypothesized to underpin the subsequent neurodegeneration
that leads to dementia (Leonard, 2017). Specifically, hypothalamic-pi-
tuitary-adrenal (HPA) axis hyperactivity, dysfunction in the trypto-
phan-kynurenine pathway, and dysfunctional brain glucose metabolism
have all been suggested to lead to neurodegeneration as a consequence
of the stress that is associated with mental illness (Rafferty et al., 2017;
Leonard, 2017). Alternatively, depression and anxiety may be pro-
dromal symptoms of dementia, such that the underlying disease pro-
cesses that cause dementia also lead to depression and anxiety (en-
dogenous cause), with these mental illnesses potentially manifesting
earlier (or identified earlier) than the cognitive and functional symp-
toms required for a diagnosis of dementia (Stella et al., 2014). Another
possibility is that depression and anxiety are comorbid with, or the
sequelae to (secondary effect of), dementia; with dementia indirectly
resulting in the secondary symptoms of depression and anxiety (exo-
genous/reactive cause) due to the cognitive and other forms of decline
that are experienced (Kales et al., 2015; Lawlor, 1996). Although en-
dogenous and exogenous causes do not readily account for the link
between PTSD and dementia, it has been suggested that dementia may
act as a trigger for a relapse of PTSD in people with a history of this
mental illness (Hiskey et al., 2008).

If depression, anxiety and PTSD are risk factors (causal or pro-
dromal) for dementia, it would be expected that prior mental illnesses
would be associated with an increased likelihood of dementia at a later
time, when compared to persons with no history of mental illness.
Alternatively, if these mental illnesses are either comorbidities of, or
sequelae to, dementia then persons with and without a prior history of
depression, anxiety or PTSD should not differ in their risk of later de-
veloping dementia.

Several meta-analyses have examined the temporal relationship
between depression and/or anxiety that has been experienced prior to a
diagnosis of dementia; all of which have reported a positive association
between depression or anxiety and subsequent dementia (Becker et al.,
2018; Cherbuin et al., 2015; Diniz et al., 2013; Ford et al., 2018;
Gulpers et al., 2016; Jorm et al., 1991; Jorm, 2000; Jorm, 2001;
Ownby et al., 2006; Santabarbara et al., 2019; Xu et al., 2015). How-
ever, there are several important limitations to these meta-analyses.
First, none of them confined their examination to clinically-significant
symptoms of depression and anxiety, instead combining persons with
clinically significant mental illness with others who were experiencing
milder/subclinical symptoms. This prevents an evaluation of whether it
is only clinically-significant mental illness that is a risk factor for de-
mentia, as suggested by the inflammatory neurodegeneration hypoth-
esis (Leonard, 2017), rather than any level of depression or anxiety
symptoms (including subclinical levels). Second, some examined both
state and trait anxiety (Becker et al., 2018; Cherbuin et al., 2015;
Santabarbara et al., 2019), potentially confounding mental illness with
more stable personality traits. Third, others failed to consistently in-
clude healthy controls (Cherbuin et al., 2015; Jorm, 2001; Jorm, 2000;
Jorm et al., 1991) or separately report the findings for those with de-
mentia and those with milder cognitive decline (Diniz et al., 2013).
Some are also now quite dated (Jorm, 2001; Jorm, 2000; Jorm et al.,
1991) and consequently fail to include recent research. Finally, none of
them have examined the temporal relationship between PTSD and de-
mentia, or DLB and FTD when examining depression or anxiety as
possible risk factors for dementia.

The current meta-analysis therefore sought to build on these ex-
isting studies in order to further examine whether depression, anxiety
and PTSD are likely to be risk factors for (causal or prodromal),

comorbidities of, or sequelae to, dementia. Unlike existing studies, the
current meta-analysis: (1) only examined clinically-significant levels of
depression/anxiety/PTSD; (2) excluded studies that assessed trait (ra-
ther than state) anxiety; (3) ensured that the groups were cognitively
‘normal’ at baseline; (4) ensured that dementia was identified on the
basis of published diagnostic/research criteria and that persons with
milder cognitive decline were not included; and (5) investigated de-
pression, anxiety and PTSD, along with the four most common types of
dementia (AD, VaD, DLB, FTD) and all-cause dementia. The latter ca-
tegory was included because many studies examine dementia, more
generally, rather than specific subtypes; the assumption being that ‘all-
cause dementia’ primarily comprises AD, VaD, DLB and FTD.

2. Method

This research adhered to two sets of guidelines: the Preferred
Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)
(Moher et al., 2009) and the Meta-analysis Of Observational Studies in
Epidemiology (MOOSE) (Stroup et al., 2000). The PROSPERO regis-
tration number for this systematic review and meta-analysis is:
CRD42018099872.

2.1. Search strategy

PubMed, EMBASE, PsycINFO, and CINAHL electronic databases
were all searched, under the guidance of an expert research librarian,
using multiple terms for dementia, mental illness (depression, anxiety,
and PTSD), and risk (see Online Resource 1 for detailed search strate-
gies). The search was confined to publications dated from 1980, which
is when ‘major depressive disorder’ first appeared in the DSM-III
(American Psychiatric Association, 1980). The final search was com-
pleted on 03 January 2019 and all references were managed in EndNote
X9.

2.2. Selection criteria

Studies were eligible for inclusion if they: (1) diagnosed dementia,
or a subtype of dementia, on the basis of published diagnostic criteria
(e.g., DSM-III) or a diagnostic tool (e.g., Cambridge Mental Disorders of
the Elderly Examination); (2) evaluated clinically-significant levels of
depression, anxiety and/or PTSD, which was defined on the basis of a
clinical diagnosis made using published diagnostic criteria (e.g., DSM-
IV) or a diagnostic tool (e.g., Geriatric Mental State Schedule –
Automated Geriatric Examination for Computer Assisted Taxonomy), or
using a cut-off score from a published screening tool (e.g. Geriatric
Depression Scale) that has been demonstrated to identify clinically-
significant levels of symptoms; (3) measured depression/anxiety/PTSD
prior (Time 1) to the diagnosis of dementia (Time 2); (4) examined
adults (≥ 18 years); (5) reported original, peer-reviewed research that
was published in English; (6) used a longitudinal study design and in-
cluded a healthy control group, the latter being defined as one that was
cognitively normal and not experiencing clinically-significant levels of
depression, anxiety or PTSD at Time 1; (7) screened for dementia at
Time 1 (unless it was a young sample, where cognitive decline was less
likely: mean age + 2 SD < 60 years); and (8) provided data that would enable the calculation of an odds ratio (OR) to estimate the risk of dementia (Time 2) in people with a prior diagnosis (or clinically-sig- nificant symptoms) of depression/anxiety/PTSD (Time 1). These data could be in the form of ORs or raw cross-tabulated data, from which an OR could be calculated (i.e. number of persons with and without de- pression/anxiety/PTSD at Time 1, and the number from each of these groups who had dementia at Time 2).

Studies were excluded if they: (1) assessed trait (i.e. a stable per-
sonality characteristic) rather than state (i.e. transient mood symptoms)
anxiety; (2) reported a case study or case series; or (3) recruited par-
ticipants from highly selected groups with medical conditions known to

J.K. Kuring, et al. Journal of Affective Disorders 274 (2020) 247–261

248

be independent risk factors for dementia (e.g. hypertension, diabetes,
high cholesterol, Mild Cognitive Impairment). Where there were mul-
tiple publications that examined the same or overlapping samples (i.e.
non-independent data), only those data from the paper that used the
most stringent diagnostic criteria were included or, where equal, the
study with the largest sample size or, where equal, the most recent
study. In two cases, the data from two non-independent publications
were included to maximize the sample size (Lin et al.,
2017 + Wang et al., 2016; Byers et al., 2012 + Yaffe et al., 2010), with
depression data taken from the former study of each pair (largest
sample) and PTSD data from the latter (largest sample). Published re-
views, systematic reviews/meta-analyses and conference abstracts were
all excluded; however, their bibliographies, together with those of the
included studies, were examined to identify any potentially relevant
studies that were not captured by the above searches. None of the ad-
ditional references that were identified via this method (N = 139)

proved to be eligible for inclusion, indicating that the original search
criteria were effective. The first author (JKK) screened all studies, first
by title and abstract, after which the full-texts of all potentially relevant
studies underwent detailed review (see Figure 1 for details). In the
event of any ambiguity, the second and third authors (JLM, LW) ad-
ditionally reviewed the study and a consensus reached regarding elig-
ibility. The authors of six papers were emailed to request additional
data to enable the calculation of ORs (Becker et al., 2009; de Bruijn
et al., 2014; Mawanda et al., 2017; Meziab et al., 2014; Saczynski et al.,
2010; Singh-Manoux et al., 2017). Unfortunately, none of them re-
sponded, necessitating their exclusion.

2.3. Data extraction and quality assessment

The following data was extracted (JKK) from all studies using a
standardized template: (1) study characteristics (author, publication

Fig. 1. PRISMA flow chart documenting the literature search and screening process.

J.K. Kuring, et al. Journal of Affective Disorders 274 (2020) 247–261

249

year, sample size, length of follow-up, recruitment setting, geographical
region), (2) sample demographics (sex, age, education, ethnicity, Mini
Mental Status Examination [MMSE] score, percentages living in in-
stitutions & living alone, socioeconomic status), (3) dementia details
(type of dementia, criteria/tool used to diagnose dementia), (4) mental
illness details (type of mental illness [depression, anxiety, PTSD], cri-
teria/tool/clinical cut-off score used to diagnose mental illness), and (5)
OR for the risk of dementia associated with prior depression/anxiety/
PTSD or raw data to calculate an OR. If studies reported multiple
measures for the same mental illness (e.g., multiple measures of de-
pression, such as DSM-III criteria and Neuropsychiatric Inventory
[NPI]), the OR that was based on the most stringent measure/criteria
or, where similar, the largest sample was used. If studies reported
multiple subtypes of a mental illness, only the more severe subtype was
included (e.g., depression, rather than dysthymia, included).

The reporting quality of the included studies was assessed using an
adaptation of the National Institute of Health (NIH) Quality Assessment
Tool for Observational Cohort and Cross-Sectional Studies
(National Institute of Health, 2014). Each study was rated by the first
author (JKK) in terms of whether they met (score = 2), partially met
(score = 1), did not meet (score = 0), or did not report (score = 0)
each of the 17 criteria (see Figure 2). The intra-rater reliability of these
scores was assessed by having the same rater (JKK) re-rate a random
sample of 15 studies after 6 to 8 weeks. An intra-class correlation (ICC)
measured the consistency between the two total scores for each study
(Rankin & Stokes, 1998) and a percentage score assessed the agreement
between the scores assigned to the 17 individual criteria on the two
occasions.

2.4. Effect size and statistical analysis

Summary descriptive statistics and the ICC were calculated using
SPSS (Version 25). All other analyses were completed using
Comprehensive Meta-Analysis (CMA; Version 3). Funnel plots were
generated by CMA and forest plots were created using Microsoft Excel.

Odds ratios (ORs) (and 95% Confidence Intervals [CIs]) were used
to calculate the risk of developing dementia (at Time 2), having pre-
viously experienced clinically-significant depression, anxiety or PTSD
(Time 1). ORs were calculated for all dementia types combined and,
where possible, separately for AD, VaD, DLB and FTD (random-effects
model). Although ORs are less intuitive to interpret than risk ratios
(RRs), they are the preferred statistic for meta-analyses that are ana-
lyzing cross-tabulated dichotomous data due to their superior statistical
properties (Chen et al., 2010; Lipsey & Wilson, 2001). ORs can range in
value from 0 to infinity. An OR = 1 indicates there is no relationship
between depression/anxiety/PTSD at Time 1 and a subsequent diag-
nosis of dementia (Time 2). ORs that fall between 0 and 1 indicate a
negative association, such that depression/anxiety/PTSD at Time 1 is
associated with a lower risk of subsequently being diagnosed with de-
mentia (Time 2). ORs above 1 indicate a positive relationship, with
depression/anxiety/PTSD at Time 1 being associated with a higher risk
of later dementia (Lipsey & Wilson, 2001). As an example, an OR = 2
indicates the odds of developing dementia having previously experi-
enced depression/anxiety/PTSD are 2 to 1. In other words, for every 2
people who have previously suffered from depression/anxiety/PTSD
and gone on to develop dementia, there will be 1 person who did not
have a history of depression/anxiety/PTSD but will develop dementia.

Between-study heterogeneity in the ORs was analyzed using both Q
(to test for significant heterogeneity) and I2 (to measure the magnitude
of heterogeneity: higher values indicate greater heterogeneity) statis-
tics, and T2 was used to calculate the 95% prediction intervals (range
within which we would expect to find the true effect/OR). Duval and
Tweedies’ (1998) trim-and-fill procedure was used to assess publication
bias, based on a random-effects model and looking for studies with
lower ORs (left of the mean effect). This procedure adjusts for pub-
lication bias by estimating the number of missing studies that may exist,

but were not published (or not captured by the searches), and calcu-
lating the impact that these studies may have had on the current
findings (Sutton et al., 2000).

Where possible, subgroup analyses were conducted to determine
whether reporting quality or recruitment setting contributed to the
observed heterogeneity in the ORs reported by different studies. Where
there was missing data for one or more variables, these studies were
excluded from the subgroup analyses. Studies were divided into higher
and lower reporting quality (total score), based on a median split (be-
cause this tool does not have a cut-off score to define high quality
studies). Recruitment setting was additionally examined in the sub-
group analyses because healthcare samples (e.g. health databases, in-
patient/outpatient medical settings) are likely to have higher base rates
of dementia than community samples, potentially impacting on ORs.
Where possible, meta-regression (random-effects, method of moments,
with z-distribution and log OR) was also used for moderator analyses to
examine whether specific explanatory variables (mean age, percentage
female) and covariates (mean length of follow-up) contributed to the
observed heterogeneity. Age and sex were used as explanatory variables
because the risk of dementia is known to increase with age and is higher
in females (Podcasy & Epperson, 2016). Length of follow-up was ad-
ditionally examined because studies that followed participants for re-
latively short intervals (e.g. 1 to 5 years) would not capture later-de-
veloping cases of dementia, particularly in younger cohorts (e.g. mean
age: 60 to 70 vs 80 to 90s), potentially impacting on the findings.

3. Results

3.1. Study screening and summary details

As seen in Figure 1, a total of 29,963 references were identified
through the literature searches, with an additional 139 references lo-
cated from the bibliographies of review articles and included studies. Of
these, 7,695 were duplicates which, when removed, reduced the
number to 22,268 references. Preliminary screening of the titles and
abstracts of these references against the eligibility criteria further re-
duced the number to 249, all of which then underwent full-text review.
A further 213 articles were excluded at this stage, leaving 36 studies
that were eligible for inclusion in this meta-analysis.

Table 1 summarizes key demographic information for the 36 studies
that were meta-analyzed (see Table 2 for specific details for each
study). As expected, participants were elderly and, on average, had
some high school education. Although depression and anxiety are
usually more prevalent in females (Seedat et al., 2009), the inclusion of
two large scale studies of military veterans (comprising ≥ 96% males;
Byers et al., 2012, Nparticipants = 279,368; Yaffe et al., 2010,
Nparticipants = 181,093), resulted in the overall sample comprising
marginally more males than females. Although most studies recruited
from community settings, the vast majority of participants were from
healthcare settings. Less than half of the studies reported their mean
length of follow-up (Nstudies = 16), with the mean interval between
assessing depression/anxiety/PTSD (Time 1) and dementia (Time 2)
ranging from 3.1 to 20.2 years (overall M = 6.3, SD = 4.0). A further
19 studies reported the minimum and/or maximum follow up interval,
with the midrange interval varying between 1 to 13 years (M = 5.64,
SD = 2.53), but did not provide a median from which to estimate the
mean length of follow-up (Hozo et al., 2005). Very few studies provided
information about socio-economic status (Nstudies = 4) and ethnicity
(Nstudies = 6).

Most studies investigated all-cause dementia (Nstudies = 26), with
only nine providing data for AD and two studies for VaD. Depression
was the most commonly examined mental illness (Nstudies = 31), with
four studies each evaluating the risk of dementia in relation to previous
anxiety or PTSD. Thirty-three of the 36 studies reported controlling for
confounding variables, the most common of which were age
(Nstudies = 28), education (Nstudies = 24) sex (Nstudies = 23), and

J.K. Kuring, et al. Journal of Affective Disorders 274 (2020) 247–261

250

vascular risk factors (Nstudies = 21). Fewer adjusted for baseline cog-
nition (Nstudies = 13) or reported controlling for comorbid mental ill-
ness (Nstudies = 8), lifestyle factors (e.g. smoking; Nstudies = 6), or
psychotropic medication (Nstudies = 6). Consideration was given to
conducting a subgroup analysis to compare studies that controlled for
specific confounding variables with those that did not. However, too
few studies adjusted for the same combination of variables (Nstudies ≤
4), which is much lower than the recommended minimum of 10 studies
for a subgroup analysis (Deeks, Higgins, & Altman, 2019).

3.2. Study reporting quality

Figure 2 shows the percentage of studies that met each of the 17

criteria from the adapted NIH Quality Assessment Tool for Observa-
tional Cohort and Cross-Sectional Studies (National Institute of
Health, 2014). Most studies at least partially met more than half of the
17 criteria, with the average reporting quality score being 62%
(SD = 7.9%). The criteria with the lowest ratings related to: study
power, the severity and subtype of mental illness under investigation
(e.g. major depressive disorder vs dysthymia), whether mental illness
was assessed on multiple occasions prior to a dementia diagnosis or
across age groups (e.g. young adults/mid-life/older adults), and whe-
ther the outcome (dementia) assessors were blinded to baseline results.
The first three items were likely to have a limited impact on the current
meta-analysis because meta-analyses are designed to address the pro-
blem of underpowered single studies. Second, the inclusion criteria

Fig. 2. Percentage of studies meeting each of the adapted NIH study quality criteria.

J.K. Kuring, et al. Journal of Affective Disorders 274 (2020) 247–261

251

ensured that the mental illness met published diagnostic criteria or
exceeded clinical cut-offs. Third, depression and anxiety subtypes were
not being examined. Finally, older adults were the only group relevant
to this study. If outcome (dementia) assessors were not blinded to
baseline results (presence or absence of mental illness), there is a po-
tential for selection bias because some symptoms of mental illness
mimic those of dementia (and vice versa), consequently participants
with mental illness at Time 1 may have been more likely (than those
without mental illness) to be screened for dementia. The majority of
included studies did not report whether the outcome (dementia) as-
sessors were blinded to a person’s baseline results, making it difficult to
know whether this had an impact on the current findings.

An intra-rater reliability analysis yielded an ICC of 0.81 (95% CIs
0.54 – 0.93), with 96% agreement between the reporting quality scores
assigned to the 17 individual criteria on the two separate occasions (6-8
weeks apart). Thus, these ratings showed very good intra-rater relia-
bility.

3.3. Depression

Thirty-one studies examined the risk of dementia in those with a
history of depression. As seen in Figure 3, the majority of these studies
had statistically significant ORs (Nstudies = 22) that ranged from 1.21
(Luppa et al., 2013) to 6.37 (Bae et al., 2015), with two additional
outliers of 16.00 (Bartolini et al., 2004) and 130.73 (Lauriola et al.,
2018). Most of the studies were prospective (Nstudies = 26), but there

was no obvious difference between the prospective and retrospective
studies in terms of range of ORs or whether they were statistically
significant. Notably the four studies with exceptionally large 95%
confidence intervals (see Figure 3) were all prospective; however, this
within study heterogeneity is unlikely to be explained by the pro-
spective design because the other 22 prospective studies all had much
narrower 95% confidence intervals. The average length of follow-up
was only reported by 14 of these 31 studies and ranged from 2.4
(Kim et al., 2011) to 20.2 years (Zilkens et al., 2014).

The unadjusted pooled OR for a diagnosis of dementia (all types) in
people who previously had depression was 1.91 (Nstudies = 31)
(z=12.05, p<0.001) (see Figure 3). Thus, persons with a history of depression had almost twice the odds of developing dementia than those who did not have this history. Moreover, this OR did not change markedly (OR = 1.87) when the analyses were re-run after excluding the two outliers (ORs = 16.00 & 130.73). When AD was examined (Nstudies = 8), alone, the unadjusted pooled OR was 2.23 (95% CIs = 1.46 to 3.41, z=3.71, p<0.001), indicating that people with depression at Time 1 had more than twice the odds of later developing AD than those who had not suffered from depression (see Figure 4). There was one notable outlier in the ORs for AD (OR = 130.73; Lauriola et al., 2018), which was so large that it could not be shown graphically. However, when this study was excluded from the analysis, the OR proved to be only slightly lower (OR = 1.91). Unfortunately, the data for the other types of dementia were extremely limited, with a single study of VaD reporting an OR of 4.23 (95% CI 2.94 - 6.08;

Table 1
Summary demographic and study information for the mental illness and control groups.

All Participants Mental Illnessa Controlsa

Nstudies Nparticipants M SD Nstudies Nparticipants M SD Nstudies Nparticipants M SD

Sample size 36 828,767 23,021 39,715 35c 102,885 2,940 9,849 35c 725,116 20,718 54,233
Age (at baseline) 31 721,689 74.1 5.8
Education (years) 11 20,434 9.0 4.1
MMSE score 10 21,356 25.4 3.1
Length of follow-up (years) 16 247,190 6.3 4.0

%
Sex: Female 34 809,169 47.9%b

Ethnicity 6 202,718
Caucasian 3 196,768 69.0%b

Black 5 200,786 6.8%b

Hispanic 5 191,234 7.8%b

Asian 2 190,573 10.8%b

Socioeconomic Status 4 516,792
Low 33.3%b

Medium 33.2%b

High 30.0%b

Nstudies Nparticipants %studiesd %particip.e Nstudies Nparticipants %studiesd %particip.e Nstudies Nparticipants %studiesd %particip.e

Dementia 36 828,767 100.0 100.0 36 102,885f 100.0 100.0 36 725,116f 100.0 100.0
All-cause 26 761,689 72.2 91.9 26 90,762f 72.2 88.2 26 670,161f 72.2 92.4
Alzheimer’s disease 9 17,827 25.0 2.2 9 2,836 25.0 2.8 9 14,991 25.0 2.1
Vascular dementia 2 51,091 5.6 6.2 2 10,035 5.6 9.8 2 41,056 5.6 5.7
Dementia with Lewy bodies 1 1,136 2.8 0.1 1 44 2.8 <0.1 1 1,092 2.8 0.2 Frontotemporal dementia 1 1,136 2.8 0.1 1 44 2.8 <0.1 1 1,092 2.8 0.2

Mental Illness 36 828,767 100.0 100.0 36 102,885f 100.0 100.0 36 725,116f 100.0 100.0
Depression 31 446,669 86.1 53.9 31 45,423f 86.1 44.1 31 400,480f 86.1 55.2
Anxiety 4 17,226 11.1 2.1 4 2,294 11.1 2.2 4 14,932 11.1 2.1
PTSD 4 379,620 11.1 45.8 4 56,096 11.1 54.5 4 323,524 11.1 44.6

Recruitment Setting 36 828,767 100.0 100.0 36 102,885f 100.0 100.0 36 725,116f 100.0 100.0
Community 19 58,173 52.8 7.0 19 4,502f 52.8 4.4 19 53,671f 52.8 7.4
Health 17 774,086 47.2 93.4 17 98,383 47.2 95.6 17 675,703 47.2 93.2

MMSE = Mini Mental Status Examination. M = mean. SD = standard deviation.
a Summary demographic and background data (age, education, MMSE, follow-up interval, sex, ethnicity, socioeconomic status) were not available for the mental

illness and control groups because studies did not routinely provide it. Only data for the combined samples was available.
b Percentage based on total participant data available for that variable (not entire sample).
c One study only provided the total sample size (n=766) and did not report the sample size for the mental illness and control groups separately.
d Percentage based on total number of studies (n=36).
e Percentage based on total available participant numbers (102,885 for mental illness and 725,116 for control).
f Figure based on Nstudies – 1 because one study did not report separate sample sizes for those with mental illness and controls.

J.K. Kuring, et al. Journal of Affective Disorders 274 (2020) 247–261

252

Table 2
Summary details for individual studies that examined the risk of dementia (OR) associated with depression, anxiety and/or PTSD.

Study Dementia
group

Dementia
measure

Mental illness Mental
illness
measure

n Odds Ratio Age (years)
M(SD) (all
groups)

Female %
(total)

Follow-up
length
(years) M
(SD)

Setting Region

Almeida et al 2017 All-cause ICD-10 Depression GDS-15 294 1.70 77.2 (3.7) 0.0 8.9 (-) community Australia/NZ
Controls 4628

Bae et al 2015 AD N-ADRDA Depression GDS short
form

29 6.37 71.7 (5.1) 55.2 3.5 (0.3) community Asia

Controls 87
Baldwin et al 2006 All-cause ICD-10 Depression DSM-IV 32 5.37 73.3 (6.6) 63.5 3.1 (-) health Western

EuropeControls 30
Bartolini et al 2004 AD N-ADRDA Depression DSM-III-R 124 16.00 69.2 (4.8) 63.5 – health Western

EuropeControls 98
Billioti de Gage et al

2014
AD ICD-9 Depression ICD-9 224 1.22 – 67.0 – healthbc North

America/
Canada

Controls 8756
Anxiety ICD-9 1467 1.53
Controls 7513

Bonanni et al 2018 All-cause (AD/VaD/
DLB/FTD
combined)

PTSD DSM-IV-TR 44 4.09 72.2 (4.1) 53.4 – healthc Western
Europe

AD N-ADRDA Controls 1092
VaD N-AIREN
DLB McKeith et al

1992
FTD McKhann et al

2001
Brommelhoff et al

2009
All-cause DSM-IV Depression ICD-7/8/9/

10
317 1.60 73.0 (6.4) 56.2 – communitybc Western

Europe
Controls 12363

Buntinx et al 1996 All-cause ICHPPC-2 Depression ICHPPC-2 489 2.38 – – – healthbc Western
EuropeControls 18614

Burke et al 2017 AD N-ADRDA Depression DSM-V 415 1.60 83.0 (9.2) 63.9 4.2 (-) healthb North
America/
Canada

Controls 1336

Byers et al 2012 All-cause ICD-9-CM Depression ICD-9-CM 27594 1.85 69.7 (8.2) 4.0 – healthb North
America/
Canada

Controls 251774

Dal Forno et al 2005 AD N-ADRDA Depression CES-D 149 2.03 65.5 (12.0) 45.5 6.1 (-) community North
America/
Canada

Controls 1083

Flatt et al 2018 All-cause ICD-9 PTSD ICD-9 1147 0.33 71.1 (7.9) 54.7 8.0 (4.6) healthb North
America/
Canada

Controls 187494

Fuhrer et al 2003 All-cause DSM-III-R Depression CES-D 527 2.00 75.2 (6.9) 58.3 – community Western
EuropeControls 3250

Ganguli et al 2006 All-cause DSM-III-R Depression modified
CES-D

128 1.38 74.6 (5.3) 60.8 7.4 (-) community North
America/
CanadaControls 1137

Gatz et al 2005 All-cause DSM-III-R Depression CES-D 766a 2.37 74.5 (6.0) 61.7 – community North
America/
Canada

Controls

Geerlings et al 2008 All-cause N-ADRDA Depression CES-D 35 3.42 73.5 (7.9) 49.0 5.9 (1.6) community Western
EuropeControls 451

Goveas et al 2011 All-cause DSM-IV Depression CES-D short
form

508 2.19 70.1 (3.8) 100.0 5.4 (1.6) community North
America/
CanadaControls 5868

Gracia-Garcia et al
2015

AD DSM-IV Depression GMS-
AGECAT

452 1.74 71.9 (9.0) 54.4 – community Western
Europe

Controls 3412
Heser et al 2016 All-cause SIDAM Depression GDS-15 311 1.76 81.1 (3.5) 65.2 – health Western

EuropeControls 2391
Irie et al 2008 All-cause DSM-III-R Depression CES-D 11

items
169 2.35 76.3 (3.6) 0.0 – community North

America/
CanadaControls 1763

Kassem et al 2018 All-cause DSM-IV Depression GDS 121 2.07 82.8 (3.1) 100.0 4.9 (0.6) community North
America/
Canada

Controls 1304
Anxiety Goldberg

Anxiety
Scale

190 1.09

Controls 1235
Kim et al 2011 All-cause DSM-IV Depression GMS-

AGECAT
57 2.12 71.8 (5.0) 54.4 2.4 (0.3) community Asia

Controls 461
Lauriola et al 2018 AD DSM-V Depression DSM-V 115 130.73 74.5 (7.5) 59.7 – health Western

EuropeControls 66

(continued on next page)

J.K. Kuring, et al. Journal of Affective Disorders 274 (2020) 247–261

253

Lin et al., 2017) and no studies examining DLB or FTD.
The Q statistics provided in Table 3 indicate that there was sig-

nificant between-study heterogeneity in the ORs when all dementia
types were combined and for AD alone. The I2 statistics also show a
moderate degree of between-study variability for all dementia types
combined and slightly more variability for AD, as evidenced by the 95%
prediction intervals (see Table 3). Thus, dementia type does not appear
to be the main source of variability in the ORs reported by different
studies because significant variability remained when AD was ex-
amined alone.

A subgroup analysis that examined the impact of reporting quality
(median split: lower vs higher ratings) on the ORs for depression and all
dementia types combined revealed that there was no significant dif-
ference (Q=0.008, df=1, p=0.930) between the findings from studies
with lower (OR = 1.93, 95% CI 1.56 – 2.39, z=6.08, p<0.001) or higher quality ratings (OR=1.91, 95% CI 1.77 – 2.07, z=16.59, p<0.001). Thus, reporting quality did not explain the variability in ORs. Similarly, the pooled OR for studies that examined community samples (OR=1.88, 95%CI 1.70 – 2.09, z=11.79, p<0.001) did not differ significantly (Q=0.121, df=1, p=0.727) from those that re- cruited in healthcare settings (OR=1.96, 95% CI 1.63 – 2.34, z=7.33, p<0.001), indicating that recruitment setting did not account for the

between-study variability in the estimates of the risk of dementia as-
sociated with previous depression.

A meta-regression (see Table 4), which examined whether the age
and sex of the sample contributed to the risk of developing dementia,
having previously suffered from depression, was not significant
(Nstudies = 25, Qmodel=0.56, df=2, p=0.7555). Although we intended
to examine a third covariate – the mean length of time between the
depression assessment and diagnosis of dementia – the available data
did not permit this (i.e., the subject/study [Nstudies = 14] to variable [3:
age, sex, time] ratio was too low). Similarly, subgroup and meta-re-
gression analyses could not be conducted for AD, due to insufficient
data.

The potential impact of publication bias was assessed using
Duval and Tweedies’ (1998) trim-and-fill procedure. This analysis re-
vealed that the current estimate of the risk of developing dementia (all
types combined) associated with prior depression was likely to be
missing 10 studies (shown as solid dots in Online Resource 2) and that
the imputed OR after adjusting for these missing studies was 1.73 (95%
CI 1.54 – 1.95), which was only marginally lower than the aforemen-
tioned OR of 1.91 (95% CI 1.72 – 2.12).

Table 2 (continued)

Study Dementia
group
Dementia
measure
Mental illness Mental
illness
measure
n Odds Ratio Age (years)
M(SD) (all
groups)
Female %
(total)
Follow-up
length
(years) M
(SD)
Setting Region

Lenoir et al 2011 All-cause DSM-IV Depression CES-D 1053 2.10 74.0 (5.4) 61.3 – community Western
EuropeControls 6936

Li et al 2011 All-cause DSM-IV Depression CES-D 11
items

321 1.79 74.9 (6.2) 59.9 7.1 (-) community North
America/
CanadaControls 3089

Lin et al 2017 VaD ICD-9-CM Depression ICD-9-CM 9991 4.23 – 61.2 – healthbc Asia
Controls 39964

Lugtenburg et al 2016 All-cause GMS-AGECAT Depression GMS-
AGECAT

186 2.73 – 62.3 – health Western
Europe

Controls 1725
Luppa et al 2013 All-cause DSM-IV Depression DSM-III-R 128 1.21 81.3 (4.7) 73.4 4.3 (2.4) community Western

EuropeControls 760
Santabarbara et al

2018
All-cause DSM-IV Anxiety GMS-

AGECAT
54 2.61 72.1 (9.1) 54.9 – community Western

Europe
Controls 2424

Vilalta-Franch et al
2013

AD DSM-IV Depression DSM-IV 41 2.89 76.7 (5.4) 63.7 – community Western
EuropeControls 304

Wallin et al 2013 All-cause DSM-IV Depression DSM-IV 50 2.82 88.8 (4.1) 67.5 3.4 (1.6) community Western
EuropeControls 162

Wang et al 2016 All-cause ICD-9-CM PTSD ICD-9-CM 1750 6.24 55.4 (9.2) 76.6 6.5 (2.7) healthbc Asia
Controls 7000

Yaffe et al 2010 All-cause ICD-9-CM PTSD ICD-9-CM 53155 5.49 68.8 (8.6) 3.5 – healthbc North
America/
Canada

Controls 127938

Zahodne et al 2016 All-cause DSM-III-R Depression CES-D 10
items

394 1.24 76.0 (6.1) 68.7 – healthb North
America/
CanadaControls 2199

Zalsman et al 2000 All-cause DSM-IV Depression DSM-IV 164 1.94 77.3 (-) – – healthbc Asia
Controls 338

Zilkens et al 2014 All-cause ICD-10-AM Depression ICD-10-AM 1005 1.92 – 56.6 20.2 (10.4) healthbc Australia/NZ
Controls 26131
Anxiety ICD-10-AM 583 1.88
Controls 26553

a Total sample size indicated; separate sample sizes for those with mental illness and healthy controls were not reported.
b Study based on registry data.
c Retrospective study design. CES-D = Center for Epidemiologic Studies – Depression Scale. DSM III-R/IV/IV-TR/V = Diagnostic and Statistical Manual of Mental

Disorders. GDS= Geriatric Depression Scale. GMS-AGECAT= Geriatric Mental State – Automated Geriatric Examination for Computer Assisted Taxonomy. ICD 7/8/
9/9AM/9CM/10/10AM = International Classification of Diseases ICHPPC-2 = International Classification of Health Problems in Primary Care. PTSD = post
traumatic stress disorder. AD = Alzheimer’s disease, VaD = vascular dementia. DLB = dementia with Lewy bodies. FTD = fronto-temporal dementia. N-
ADRDA= National Institute of Neurological and Communicative Disorders and Stroke – Alzheimer’s Disease and Related Disorders Association. N-AIREN= National
Institute of Neurological and Communicative Disorders and Stroke – Association Internationale pur la Recherche et l’Enseignement en Neurosciences.
SIDAM = Structured Interview for Diagnosis of Dementia of Alzheimer type, Multi-infarct Dementia and Dementia of other Aetiology according to DSM-III-R, DSM-
IV, and ICD-

J.K. Kuring, et al. Journal of Affective Disorders 274 (2020) 247–261

254

3.4. Anxiety

Four studies examined the risk of dementia in elderly persons who
had previously suffered from an anxiety disorder or clinically-sig-
nificant levels of symptoms (see Table 1 for participant numbers). Three
of them had significant ORs, ranging from 1.09 (Kassem et al., 2018) to
2.61 (Santabarbara et al., 2018) (see Figure 5). The average length of
follow-up for the two studies that provided this information varied
considerably, ranging from 4.9 (Kassem et al., 2018) to 20.2
(Zilkens et al., 2014) years.

As seen in Figure 5, the pooled OR for a dementia diagnosis (Time 2)
in those with previous anxiety (Time 1) was 1.60 (95% CI = 1.29 to
2.00, z=4.211, p<0.001) for the combined dementia types (Nstudies = 4). Therefore, persons with clinically-significant anxiety had a 1.6 greater odds of being diagnosed with dementia at a later time, compared to those with no history of anxiety. However, it should be noted that, although there were a large number of participants, the

number of studies was small. Only one study (Billioti de Gage et al.,
2014) reported an OR for AD (1.53; 95% CI 1.35 – 1.75), and no studies
reported on VaD, DLB or FTD. No subgroup or meta-regression analyses
could be conducted due to the small number of studies, preventing the
planned exploration of some of the variables that may have impacted
on these results (e.g. reporting quality, recruitment setting, age, sex,
length of follow-up).

As with depression, the Q statistics indicated that there was sig-
nificant between-study heterogeneity in the ORs for the combined de-
mentia types (see Table 3) and the I2 statistics indicated moderate
heterogeneity. However, it is important to note that these analyses are
likely to be underpowered due to the small number of studies
(Nstudies = 4). Although not feasible to test formally, it is possible that
the sex of the participants may have contributed to the observed het-
erogeneity. Whereas the one study with a non-significant OR included
only females, the three studies that had significant ORs (ranging from
1.53 to 2.61) included both males and females (55% to 67% female).

Fig. 3. Forest plot for the risk (OR) of subsequent dementia diagnosis (all types) associated with previous clinically-significant symptoms of depression.
*p<0.05 aLauriola et al 2018 OR could not be plotted because it exceeded the scale. Note: OR=1 indicates no association between dementia and previous depression. OR>1 indicates greater odds of a subsequent dementia diagnosis of dementia in
people with previously diagnosed depression compared to people with no history of depression. OR<1 indicates lower odds of a subsequent diagnosis of dementia in people with previously diagnosed depression compared to people with no history of depression.

J.K. Kuring, et al. Journal of Affective Disorders 274 (2020) 247–261

255

There were no other obvious differences between the four studies – in
terms of age, length of follow-up, recruitment setting, reporting quality,
or whether prospective or retrospective data was used – that might
assist in explaining the observed heterogeneity.

An examination of the potential impact of publication bias, using
Duval and Tweedies’ (1998) trim-and-fill procedure, indicated that data
from one study was likely to be missing (shown as a solid dot in Online
Resource 3). The imputed OR after adjusting for this missing study was
1.55 (95% CI 1.24 – 1.93), which was only marginally lower than the
aforementioned 1.60 (95% CI 1.29 – 2.00). However, with fewer than
10 studies this analysis was likely underpowered (Sterne et al., 2011).

3.5. PTSD

Just four studies examined PTSD and the risk of subsequently de-
veloping dementia (see Table 1 for participant numbers), three of which
were found to have significant ORs (see Figure 6). The ORs for these
studies were very divergent, ranging from 0.33 (Flatt et al., 2018) –
which indicates that persons who had previously suffered from PTSD
were less likely to later develop dementia than persons who had no
history of PTSD – to 6.24 (Wang et al., 2016), indicating that persons
with PTSD were much more likely to subsequently develop dementia
than those without such a history. The average length of follow-up for
the two studies that provided this information ranged from 6.5
(Wang et al., 2016) to 8.0 years (Flatt et al., 2018) years.

As shown in Figure 6, the unadjusted pooled OR for a subsequent
dementia diagnosis (all types combined) in people with PTSD was non-

significant (OR = 2.55, 95% CI 0.43 – 15.12, z = 1.302, p = 0.302),
reflecting the small number of studies and divergent individual ORs.
There were no obvious differences between the studies that might ex-
plain the disparate findings (e.g. sample size, age, sex, length of follow-
up, recruitment setting), except for the use of prospective or retro-
spective data. More specifically, the single study that reported an OR
indicating a lower odds of developing dementia in people with a history
of PTSD was conducted prospectively. In contrast, the three remaining
studies were conducted retrospectively (reviewed prior medical data to
identify persons with PTSD) and had ORs (one non-significant) sug-
gesting a higher odds of later developing dementia in people with a
history of PTSD.

4. Discussion

This study built on existing meta-analyses by examining whether a
history of clinically-significant depression, anxiety or PTSD is a risk
factor (causal or prodromal) for a later diagnosis of one of the four most
common types of dementia, namely AD, VaD, DLB, and FTD. The focus
on clinically-significant symptoms is important because previous meta-
analyses have examined a much broader range of symptoms, including
subclinical and very mild levels that are commonly experienced in the
general community and unlikely to pose a serious risk to health. All-
cause dementia was also investigated because many studies do not
identify the specific type, consequently it was assumed that this broader
group primarily comprised the four most common subtypes. A total of
36 studies were eligible for inclusion; 31 examined depression, with an

Fig. 4. Forest plot for the risk (OR) of subsequent Alzheimer’s disease (AD) diagnosis associated with previous clinically-significant symptoms of depression.
*p<0.05 aLauriola et al 2018 OR could not be plotted because it exceeded the scale. Note: OR=1 indicates no association between dementia and previous depression. OR>1 indicates greater odds of a subsequent diagnosis of dementia in people with
previously diagnosed depression compared to people with no history of depression. OR<1 indicates lower odds of a subsequent diagnosis of dementia in people with previously diagnosed depression compared to people with no history of depression.

Table 3
Heterogeneity results for depression and anxiety as risk factors (OR) for dementia.

Depression
Q df(Q) p I2 T2 T 95% Prediction Interval

All dementia 84.951 30 <0.001 64.686 0.035 0.188 1.28 – 2.85 AD 25.448 7 0.001 72.493 0.193 0.439 0.67 – 7.40

Anxiety
Q df(Q) p I2 T2 T 95% Prediction Interval

All dementia 8.676 3 0.034 65.421 0.027 0.165 0.68 – 3.78

J.K. Kuring, et al. Journal of Affective Disorders 274 (2020) 247–261

256

additional four studies each for anxiety and PTSD. The majority in-
vestigated all-cause dementia (Nstudies = 26), with eight for AD, 1 for
VaD, and none for DLB and FTD. One study provided data for all de-
mentia types (combined) as well as separate results for AD, VaD, DLB
and FTD; however, only the former data were analyzed to avoid using a
single control group in multiple comparisons.

Older persons who had previously experienced clinically-significant
depression were at a higher risk of subsequently being diagnosed with
all-cause dementia (OR = 1.91, p < 0.001) and AD (OR = 2.23, p < 0.001), with approximately twice the odds of developing dementia compared to those with no history of depression. Although there was significant heterogeneity in the ORs from individual studies, subgroup analyses indicated that neither reporting quality (higher vs lower rat- ings) nor recruitment setting (healthcare vs community) adequately accounted for this variability. Similarly, a meta-regression found that neither age nor sex (percentage female) contributed to the findings. Unfortunately, it was not possible to determine whether the amount of time that had elapsed between the depression (earlier) and dementia (later) diagnoses affected the findings because the subject/study to variable ratio was too low. An examination of potential publication bias revealed a marginally lower result (OR = 1.73, 95% CI 1.54 – 1.95) after adjusting for imputed studies, while still indicating that there is a greater risk for developing dementia in persons who had previously suffered from depression.

A prior history of clinically-significant anxiety was additionally as-
sociated with a higher risk of developing all-cause dementia at a later
time (OR = 1.60, p < 0.001), compared with those with no such history. However, caution should be exercised when interpreting these results because of the small number of studies (Nstudies = 4) and sig- nificant heterogeneity in the ORs. It is possible that sampling differ- ences may have contributed to the observed heterogeneity: unlike the other studies, the one study with a non-significant OR examined an all- female sample. Publication bias is unlikely to have played a major role, with the OR only marginally lower after adjusting for imputed studies (OR = 1.55, 95% CI 1.24 – 1.93). Thus, both the unadjusted and ad- justed ORs indicate that there is a greater risk of being diagnosed with dementia in persons who had previously suffered from anxiety, than persons without such a history.

Unlike depression and anxiety, a prior diagnosis of PTSD was not
associated with a subsequent dementia diagnosis (OR = 2.55, p > .05).

As with anxiety, this result should be interpreted cautiously because it
is likely to have been affected by the small number of studies
(Nstudies = 4) and the opposing findings. The one notable difference
between the single study that had an OR indicating a lower risk of de-
mentia in persons who had previously suffered from PTSD, and the
three that indicated a higher risk, was that it used a prospective design;
which is methodologically stronger.

These findings expand on those of previous meta-analyses by de-
monstrating that clinically-significant levels of depression or anxiety in
cognitively normal persons are associated with a greater risk of devel-
oping dementia at a later time. Multiple explanations have been pro-
posed to explain this link, including the causal inflammatory neuro-
degeneration hypothesis, which asserts that chronic major depression
(rather than mild/subclinical levels) leads to several pro-inflammatory
reactions within the brain and, ultimately, the degenerative changes
that characterize dementia (Leonard, 2017). Although a plausible ex-
planation for the findings, the fact that the data are observational mean
that it is not possible to rule out a competing explanation: namely, that
depression and anxiety are prodromal symptoms of dementia, which
share the same underlying disease processes, but manifest (or are
identified) earlier than dementia. Importantly, these results indicate
that depression and anxiety are not merely comorbidities or sequelae to
dementia, because cognitive decline or dementia were not evident
when these mental illnesses were assessed. Overall, the results support
the hypothesis that previous depression and anxiety are risk factors for
later development of dementia; however, the causal or prodromal
nature of this risk remains to be determined. The relationship between
PTSD and dementia remains unclear with existing studies reporting
very divergent results.

Unlike other meta-analyses that have examined this topic
(Becker et al., 2018; Cherbuin et al., 2015; Diniz et al., 2013; Ford et al.,
2018; Gulpers et al., 2016; Jorm et al., 1991; Jorm, 2000; Jorm, 2001;
Ownby et al., 2006; Santabarbara et al., 2019; Xu et al., 2015), the
present study focused solely on clinically-significant mental illness (de-
pression and anxiety), thus excluding mild/subclinical symptoms. This
resulted in 14% to 45% overlap in the studies that were meta-analyzed
here and elsewhere. Despite this, there were notable similarities in the
findings, with the current findings for depression (OR = 1.91 for all-
cause dementia, OR = 2.23 for AD) being comparable to those of
earlier meta-analyses (OR = 1.96 all-cause dementia, OR = 1.85 AD,

Table 4
Random effects model meta-regression results for the impact of covariates age and sex on the risk of dementia associated with previous depression.

Random effects (method of moments), z-distribution, log odds ratio (OR)
Covariate Coefficient Standard Error 95% lower 95% upper Z-value 2 sided p-value

Depression
All dementia groups
Intercept 1.4111 1.0496 -0.6461 3.4684 1.34 0.1788
Mean age -0.0106 0.0143 -0.0387 0.0175 -0.74 0.4593
Percentage female 0.0004 0.0026 -0.0046 0.0054 0.15 0.8785

Fig. 5. Forest plot for the risk (OR) of sub-
sequent dementia diagnosis (all types) asso-
ciated with previous clinically-significant
symptoms of anxiety.
*p<0.05 Note: OR=1 indicates no association between dementia and previous depression. OR>1 in-
dicates greater odds of a subsequent diagnosis
of dementia in people with previously diag-
nosed depression compared to people with no
history of depression. OR<1 indicates lower odds of a subsequent diagnosis of dementia in people with previously diagnosed depression compared to people with no history of de- pression.

J.K. Kuring, et al. Journal of Affective Disorders 274 (2020) 247–261

257

Diniz et al., 2013; OR = 1.64 all-cause dementia, OR = 1.40 AD,
Ford et al., 2018). This was also true for anxiety (OR = 1.60, for the
current study; OR = 2.05, Ford et al., 2018). Thus, when milder sub-
clinical cases of depression, anxiety and cognitive decline are excluded,
there remains a significant relationship between prior clinically-sig-
nificant depression and anxiety and a subsequent diagnosis of de-
mentia. However, the similarity between the ORs reported by meta-
analyses that did and did not include mild/subclinical symptoms of
depression or anxiety also raises the possibility that the risk of later
dementia is not restricted to those who suffer from clinically-significant
mental illnesses. If the risk of dementia (be it causal or prodromal) is
associated with all symptoms of depression and anxiety (including
mild/subclinical), regardless of severity, this may argue against the
inflammatory neurodegeneration hypothesis (Leonard, 2017). It may
also suggest that mental illness is only a partially modifiable risk factor
because, although clinically-significant depression and anxiety can be
reduced through targeted screening, treatment and preventative stra-
tegies, milder symptoms usually represent normal responses to major
life events and are, therefore, more difficult to prevent (Anusic et al.,
2014).

Modifiability aside, the knowledge that depression and anxiety (and
potentially PTSD) are risk factors – be they causal or prodromal in
nature – for dementia highlights the importance of healthcare profes-
sionals regularly screening for cognitive changes in older people who
have a history of these mental illnesses. Regular screening for new-
onset mental illness in older adults is also important, not only from a
mental health perspective, but also because they may be prodromal
symptoms of dementia. These screens may improve the early detection
of cognitive decline and possible dementia, with the potential for ear-
lier treatment and intervention. Earlier diagnosis and symptom treat-
ment can help maintain functional independence for longer and reduce
carer burden, thereby improving the quality of life for persons living
with dementia and their carers, and reducing healthcare costs (de Vugt
& Verhey, 2013).

There were several limitations to the current meta-analysis. First, it
was necessary to exclude six studies after unsuccessful attempts to
contact the corresponding authors in order to obtain data that would
enable the calculation of ORs. Additionally, there was a further risk of
omitting relevant studies because only one reviewer conducted the
screening process. Reassuringly, the current publication bias analyses
confirmed that missing studies are likely to have had a minimal impact
on the ORs.

Second, less than half of the studies reported the mean length of
follow-up, with most of the remainder only reporting ranges, which
meant that its impact could not be examined statistically. For those that
did report this information, the majority had an average of 6 years or

less. A longer interval between the onset of mental illness (e.g. mid-life)
and later dementia diagnosis would provide more credibility for the
hypothesis that depression/anxiety/PTSD are potentially causal risk
factors, rather than merely prodromal symptoms; but would not rule
out the possibility that mental illness and dementia share the same
underlying risk factors.

Third, there was considerable missing data for several other de-
mographic variables (i.e. education, ethnicity, socioeconomic status),
again preventing a statistical examination of their impact on the ORs
and making it difficult to comment on the generalizability of these re-
sults.

Fourth, very few studies examined anxiety and PTSD. Although the
findings for anxiety are based on a total of 2,294 participants, they
came from only four studies and the majority (Nparticipants = 2,050)
were recruited from healthcare settings, limiting the generalizability of
the findings. In the case of PTSD, the majority of them used retro-
spective data. Retrospective studies are typically thought to be more
biased than prospective studies because the data collection is un-
planned and, therefore, reliant on what is available (Norvell, 2010).
Notably, the one PTSD study that reported an OR that was contrary to
expectations (i.e. a lower risk of later dementia) used a more rigorous
prospective design. It is therefore possible that study design sig-
nificantly impacted on the ORs and the validity of the findings.

Fifth, it is possible that people with more (i.e. frequent relapses) or
longer episodes of depression, anxiety or PTSD have a greater risk of
subsequent dementia diagnosis due to their greater exposure to neu-
roinflammation, as suggested by the inflammatory neurodegeneration
hypothesis (Leonard 2017). This hypothesis could not be examined due
to insufficient data.

Finally, having only one reviewer assess the quality of the included
studies increased the potential for information bias. An intra-rater re-
liability analysis was conducted to partially address this risk, which
revealed 96% agreement across the two separate occasions indicating
that the reviewer was highly consistent in their decision-making when
assessing study quality. Further bias may also have resulted from the
inclusion of 12 studies that used registry data, 9 of which were retro-
spective, with one sourcing their data via a review of medical records
(see Table 2). Registry and retrospective data are likely to have come
from multiple clinicians, with varying expertise, who may have used
slightly different criteria to diagnose mental illness and dementia, po-
tentially reducing the reliability of the data obtained from these
sources. Moreover, retrospective studies are less likely than prospective
studies to have systematically followed-up all participants in order to
determine whether they developed dementia, potentially leading to an
underestimate of the number of dementia cases. Unfortunately, it was
not possible to determine whether the number of dementia cases

Fig. 6. Forest plot for the risk (OR) of subsequent dementia diagnosis (all types) associated with previous diagnosis of PTSD.
*p<0.05 Note: OR=1 indicates no association between dementia and previous depression. OR>1 indicates greater odds of a subsequent diagnosis of dementia in people with
previously diagnosed depression compared to people with no history of depression. OR<1 indicates lower odds of a subsequent diagnosis of dementia in people with previously diagnosed depression compared to people with no history of depression.

J.K. Kuring, et al. Journal of Affective Disorders 274 (2020) 247–261

258

differed between retrospective/registry and prospective studies because
there were fewer than 10 retrospective/registry studies for each type of
mental illness (depression/anxiety/PTSD); 10 being the minimum re-
commended number for subgroup analyses (Deeks et al., 2019).

Future research should endeavor to better understand the likely
causal or prodromal nature of the increased risk of dementia associated
with prior depression and anxiety, and seek to clarify whether prior
PTSD is also a risk factor. The necessary observational nature of this
research prevents causal inferences and is further complicated by the
knowledge that the disease processes underlying dementia (e.g. amy-
loid β deposition and tau protein accumulation) may begin decades
prior to clinical diagnosis (Bateman et al., 2012). However, there are
several hypotheses that could be explored to add weight to the possi-
bility of a causal link between mental illness and dementia. First, pro-
spective, longitudinal studies that repeatedly assess for the presence of
clinically-significant depression, anxiety or PTSD across the adult age
range (young adult/ mid-life/ older adult) would help to determine
whether the risk of dementia differs depending on the age at which
these disorders occur. A prodromal link would be more likely if only
late-life mental illness is associated with an increased risk of dementia;
whereas a causal link may be more likely if this risk was present, re-
gardless of the age at which the mental illness was experienced. Second,
an evaluation of whether the severity and number of episodes of de-
pression/anxiety/PTSD influence the risk of later dementia would assist
in clarifying whether greater exposure to the potential neuro-in-
flammatory sequelae of these mental illnesses increases the risk of de-
mentia. Although there is evidence to suggest that greater duration and
frequency of depression is associated with an increased risk of later
developing dementia (Dal Forno et al., 2005; Dotson, Beydoun &
Zonderman, 2010), further research is needed to examine whether this
dose-dependent relationship also applies to anxiety and PTSD. Ad-
ditionally, it is important to examine whether the risk of dementia only
increases in cases of clinically-significant depression, anxiety or PTSD,
or whether milder/subclinical symptoms also increase a person’s risk of
dementia. Even if these milder symptoms are risk factors, it is unlikely
that they could be avoided entirely, although some modification to the
frequency and duration of these milder symptoms may be possible.
Similarly, the impact of different diagnostic approaches to mental ill-
ness (e.g. DSM-IV vs ICD-10) and dementia (e.g. DSM-IV vs N-ADRDA)
across studies should be explored as a potential confounding variable to
ensure the findings reflect the disorder, rather than the measurement
tool. Future research should also investigate the risk of developing the
four most common types of dementia (AD, VaD, DLB and FTD), having
previously experienced depression, anxiety or PTSD in order to de-
termine whether the risk differs between dementia-types. Finally,
traumatic brain injury (TBI) is a known risk factor for depression, an-
xiety, and PTSD (Rogers and Read, 2007) as well as dementia
(Fann et al., 2018). As a result, TBIs may artificially inflate estimates of
the extent to which mental illness increases the risk of dementia. Re-
searchers should therefore provide separate risk estimates for in-
dividuals with and without a TBI in order to evaluate its impact.

In conclusion, depression and anxiety appear to be risk factors
(causal or prodromal) for a later diagnosis of dementia, and are not just
comorbidities or sequelae to dementia. This highlights the importance
of healthcare professionals screening for cognitive impairment in older
persons with a history of these mental illnesses in order to assist in the
early identification of cognitive decline and dementia. The causal or
prodromal nature of the risk between depression and anxiety with de-
mentia remains to be clarified. Additionally, the relationship between
prior PTSD and subsequent dementia requires further examination. A
prospective, longitudinal study, ideally examining clinically-significant
depression, anxiety and PTSD at multiple time points (young adult,
mid-life and older adults) and their association with later dementia
diagnosis is needed to further explore the nature of the relationship.

Author Declaration

We wish to confirm that there are no known conflicts of interest
associated with this publication.

Contributors

We confirm that the manuscript has been read and approved by all
named authors and that there are no other persons who satisfied the
criteria for authorship but are not listed. All authors were involved in
the design, interpretation, and report preparation. The first and second
authors were additionally involved in the data collection and analysis.
We further confirm that the order of authors listed in the manuscript
has been approved by all of us.

We confirm that we have given due consideration to the protection
of intellectual property associated with this work and that there are no
impediments to publication, including the timing of publication, with
respect to intellectual property. In so doing we confirm that we have
followed the regulations of our institutions concerning intellectual
property.

We understand that the Corresponding Author is the sole contact for
the Editorial process (including Editorial Manager and direct commu-
nications with the office). He/she is responsible for communicating
with the other authors and progress, submissions of revisions and final
approval of proofs. We confirm that we have provided a current, correct
email address which is accessible by the Corresponding Author.

Role of Funding Source

We confirm that there has been no significant financial support for
this work that could have influenced its outcome. This research is
supported by an Australian Government Research Training Program
(RTP) Scholarship. The funding source had no involvement in the de-
sign, data collection, data analysis and interpretation, report writing, or
the decision to submit the article for publication.

Declaration of Competing Interest

None

Acknowledgements

The authors would like to thank M. Bell (Research Librarian,
Research and Reference Services, Barr Smith Library, University of
Adelaide) for her expert assistance with developing the search grids for
the literature searches.

Supplementary materials

Supplementary material associated with this article can be found, in
the online version, at doi:10.1016/j.jad.2020.05.020.

References

Almeida, O.P., Hankey, G.J., Yeap, B.B., Golledge, J., Flicker, L., 2017. Depression as a
modifiable factor to decrease the risk of dementia. Transl Psychiatry 7 (5), e1117.
https://doi.org/10.1038/tp.2017.90.

Alzheimer’s Disease International. (2015). World Alzheimer Report 2015 The Global
Impact of Dementia: an analysis of prevalence, incidence, cost and trends. Retrieved
from London, UK: https://www.alz.co.uk/research/WorldAlzheimerReport2015 .

American Psychiatric Association, 1980. Diagnostic and statistical manual of mental
disorders (3rd Ed.). Author, Washington, DC.

Anusic, I., Yap, S.C.Y., Lucas, R.E., 2014. Does personality moderate reaction and adap-
tation to major life events? Analysis of life satisfaction and affect in an Australian
national sample. Journal of Research in Personality 51, 69–77. http://dx.doi.org/10.
1016/j.jrp.2014.04.009.

. . .Bae, J.B., Kim, Y.J., Han, J.W., Kim, T.H., Park, J.H., Lee, S.B., Kim, K.W., 2015.
Incidence of and risk factors for Alzheimer’s disease and mild cognitive impairment in
Korean elderly. Dementia and Geriatric Cognitive Disorders 39 (1-2), 105–115.

J.K. Kuring, et al. Journal of Affective Disorders 274 (2020) 247–261

259

https://doi.org/10.1016/j.jad.2020.05.020

https://doi.org/10.1038/tp.2017.90

http://https://www.alz.co.uk/research/WorldAlzheimerReport2015

http://refhub.elsevier.com/S0165-0327(19)32303-1/opt6TrUKM6d6x

http://http://dx.doi.org/10.1016/j.jrp.2014.04.009

http://refhub.elsevier.com/S0165-0327(19)32303-1/sbref0003

Baldwin, R.C., Gallagley, A., Gourlay, M., Jackson, A., Burns, A., 2006. Prognosis of late
life depression: A three-year cohort study of outcome and potential predictors.
International Journal of Geriatric Psychiatry 21 (1), 57–63. http://dx.doi.org/10.
1002/gps.1424.

Barnes, D.E., Yaffe, K., 2011. The projected impact of risk factor reduction on Alzheimer’s
disease prevalence. Lancet Neurology 10, 819–828. https://doi.org/10.1016/S1474-
4422(11)70072-2.

Bartolini, M., Coccia, M., Luzzi, S., Provinciali, L., Ceravolo, M., 2004. Motivational
Symptoms of Depression Mask Preclinical Alzheimer’s Disease in Elderly Subjects.
Dementia and Geriatric Cognitive Disorders 19 (1), 31–36. http://dx.doi.org/10.
1159/000080968.

Bateman, R.J., Xion, C., Benzinger, T.L.S., Fagan, A.M., Goate, A., Fox, N.C., … Morris,
J.C., 2012. Clinical and biomarker changes in dominantly inherited Alzheimer’s
disease. The New England Journal of Medicine 367, 795–804.

. . .Becker, J.T., Chang, Y.-F., Lopez, O.L., Dew, M.A., Sweet, R.A., Barnes, D., Reynolds
III, C.F., 2009. Depressed mood is not a risk factor for incident dementia in a com-
munity-based cohort. The American Journal of Geriatric Psychiatry 17 (8), 653–663.
http://dx.doi.org/10.1097/JGP.0b013e3181aad1fe.

Becker, E., Orellana Rios, C.L., Lahmann, C., Rucker, G., Bauer, J., Boeker, M., 2018.
Anxiety as a risk factor of Alzheimer’s disease and vascular dementia. Br J Psychiatry
213 (5), 654–660. https://doi.org/10.1192/bjp.2018.173.

. . .Billioti de Gage, S., Moride, Y., Ducruet, T., Kurth, T., Verdoux, H., Tournier, M.,
Begaud, B., 2014. Benzodiazepine use and risk of Alzheimer’s disease: case-control
study. BMJ 349, g5205. https://doi.org/10.1136/bmj.g5205.

. . .Bonanni, L., Franciotti, R., Martinotti, G., Vellante, F., Flacco, M.E., Di Giannantonio,
M., Onofrj, M., 2018. Post Traumatic Stress Disorder Heralding the Onset of Semantic
Frontotemporal Dementia. Journal of Alzheimers Disease 63, 203–215. https://doi.
org/10.3233/jad-171134.

Brommelhoff, J.A., Gatz, M., Johansson, B., McArdle, J.J., Fratiglioni, L., Pedersen, N.L.,
2009. Depression as a risk factor or prodromal feature for dementia? Findings in a
population-based sample of Swedish twins. Psychology and Aging 24 (2), 373–384.
http://dx.doi.org/10.1037/a0015713.

Buntinx, F., Kester, A., Bergers, J., Knottnerus, J.A., 1996. Is depression in elderly people
followed by dementia? A retrospective cohort study based in general practice. Age
Ageing 25 (3), 231–233.

Burke, S.L., Cadet, T., Alcide, A., O’Driscoll, J., Maramaldi, P., 2017. Psychosocial risk
factors and Alzheimer’s disease: the associative effect of depression, sleep dis-
turbance, and anxiety. Aging & Mental Health 1–8. https://doi.org/10.1080/
13607863.2017.1387760.

Byers, A.L., Covinsky, K.E., Barnes, D.E., Yaffe, K., 2012. Dysthymia and depression in-
crease risk of dementia and mortality among older veterans. The American Journal of
Geriatric Psychiatry 20, 664–672. http://dx.doi.org/10.1097/JGP.
0b013e31822001c1.

Chen, H., Cohen, P., Chen, S., 2010. How big is a big odds ratio? Interpreting the mag-
nitudes of odds ratios in epidemiological studies. Communications in Statistics –
Simulation and Computation 39, 860–864.

Cherbuin, N., Kim, S., Anstey, K.J., 2015. Dementia risk estimates associated with mea-
sures of depression: A systematic review and meta-analysis. BMJ Open 5 (12).

Dal Forno, G., Palermo, M.T., Donohue, J.E., Karagiozis, H., Zonderman, A.B., Kawas,
C.H., 2005. Depressive Symptoms, Sex, and Risk for Alzheimer’s Disease. Annals of
Neurology 57 (3), 381–387. http://dx.doi.org/10.1002/ana.20405.

de Bruijn, R.F., Direk, N., Mirza, S.S., Hofman, A., Koudstaal, P.J., Tiemeier, H., Ikram,
M., 2014. Anxiety is not associated with the risk of dementia or cognitive decline: The
rotterdam study. The American Journal of Geriatric Psychiatry 22 (12), 1382–1390.
http://dx.doi.org/10.1016/j.jagp.2014.03.001.

Deckers, K., van Boxtel, M.P., Schiepers, O.J., de Vugt, M., Munoz Sanchez, J.L., Anstey,
K.J., … Kohler, S., 2015. Target risk factors for dementia prevention: a systematic
review and Delphi consensus study on the evidence from observational studies.
International Journal of Geriatric Psychiatry 30, 234–246. https://doi.org/10.1002/
spgs.4245.

Deeks, J.J., Higgins, J.P.T., Altman, D.G., 2019. Chapter 10: Analysing data and under-
taking meta-analyses. In: Higgins, J.P.T., Thomas, J., Chandler, J., Cumpston, M., Li,
T., Page, M.J., Welch, V.A. (Eds.), Cochrane Handbook for Systematic Reviews of
Interventions, version 6.0 (updated July 2019). Cochrane, 2019. Available from.
www.training.cochrane.org/handbook.

De Vugt, M.E., Verhey, F.R.J., 2013. The impact of early dementia diagnosis and inter-
vention on informal caregivers. Progress in Neurobiology 110, 54–62.

Diniz, B.S., Butters, M.A., Albert, S.M., Dew, M.A., Reynolds III, C.F., 2013. Late-life
depression and risk of vascular dementia and Alzheimer’s disease: Systematic review
and meta-analysis of community-based cohort studies. The British Journal of
Psychiatry 202 (5), 329–335. http://dx.doi.org/10.1192/bjp.bp.112.118307.

Dotson, V.M., Beydoun, M.A., Zonderman, A.B., 2010. Recurrent depressive symptoms
and the incidence of dementia and mild cognitive impairment. Neurology 75, 27–34.

Duval, S., Tweedie, R., 1998. Practical estimates of the effect of publication bias in meta-
analysis. Australasian Epidemiologist 5, 14–17.

Fann, J.R., Riisgaard Ribe, A., Schou Pedersen, H., Fenger-Gron, M., Christensen, J.,
Eriksen Benros, M., Vestergaard, M., 2018. Long-term risk of dementia among people
with traumatic brain injury in Denmark: a population-based observational cohort
study. Lancet Psychiatry 5, 424–431. http://dx.doi.org/10.1016/S2215-0366(18)
30065-8.

Flatt, J.D., Gilsanz, P., Quesenberry, C.P., Albers, K.B., Whitmer, R.A., 2018. Post-trau-
matic stress disorder and risk of dementia among members of a health care delivery
system. Alzheimer’s and Dementia 14 (1), 28–34. https://doi.org/10.1016/j.jalz.
2017.04.014.

. . .Ford, E., Greenslade, N., Paudyal, P., Bremner, S., Smith, H.E., Banerjee, S., Cassell, J.,
2018. Predicting dementia from primary care records: A systematic review and meta-

analysis. PLoS ONE 13, e0194735. https://doi.org/10.1371/journal.pone.0194735.
Fuhrer, R., Dufouil, C., Dartigues, J.F., 2003. Exploring Sex Differences in the

Relationship Between Depressive Symptoms and Dementia Incidence: Prospective
Results From the PAQUID Study. Journal of the American Geriatrics Society 51 (8),
1055–1063. http://dx.doi.org/10.1046/j.1532-5415.2003.51352.x.

Ganguli, M., Du, Y., Dodge, H.H., Ratcliff, G.G., Chang, C.-C.H., 2006. Depressive
symptoms and cognitive decline in late life. A prospective epidemiological study.
Archives of General Psychiatry 63 (2), 153–160. http://dx.doi.org/10.1001/
archpsyc.63.2.153.

Gatz, J.L., Tyas, S.L., St. John, P., Montgomery, P., 2005. Do Depressive Symptoms
Predict Alzheimer’s Disease and Dementia. The Journals of Gerontology: Series A:
Biological Sciences and Medical Sciences 60 (6), 744–747. http://dx.doi.org/10.
1093/gerona/60.6.744.

Geerlings, M., den Heijer, T., Koudstaal, P., Hofman, A., Breteler, M., 2008. History of
depression, depressive symptoms, and medial temporal lobe atrophy and the risk of
Alzheimer disease. Neurology 70 (15), 1258–1264. http://dx.doi.org/10.1212/01.
wnl.0000308937.30473.d1.

Goveas, J.S., Espeland, M.A., Woods, N.F., Wassertheil-Smoller, S., Kotchen, J.M., 2011.
Depressive symptoms and incidence of mild cognitive impairment and probable de-
mentia in elderly women: The Women’s Health Initiative Memory Study. Journal of
the American Geriatrics Society 59 (1), 57–66. http://dx.doi.org/10.1111/j.1532-
5415.2010.03233.x.

. . .Gracia-Garcia, P., de-la-Camara, C., Santabarbara, J., Lopez-Anton, R., Quintanilla,
M.A., Ventura, T., Lobo, A., 2015. Depression and incident alzheimer disease: The
impact of disease severity. The American Journal of Geriatric Psychiatry 23 (2),
119–129. http://dx.doi.org/10.1016/j.jagp.2013.02.011.

Gulpers, B., Ramakers, I., Hamel, R., Kohler, S., Oude Voshaar, R., Verhey, F., 2016.
Anxiety as a predictor for cognitive decline and dementia: A systematic review and
meta-analysis. The American Journal of Geriatric Psychiatry 24 (10), 823–842.
http://dx.doi.org/10.1016/j.jagp.2016.05.015.

. . .Heser, K., Bleckwenn, M., Wiese, B., Mamone, S., Riedel-Heller, S.G., Stein, J., Wagner,
M., 2016. Late-life depressive symptoms and lifetime history of major depression:
Cognitive deficits are largely due to incipient dementia rather than depression.
Journal of Alzheimer’s Disease 54 (1), 185–199.

Hiskey, S., Luckie, M., Davies, S., Brewin, C.R., 2008. The emergence of posttraumatic
distress in later life: A review. Journal of Geriatric Psychiatry and Neurology 21,
232–241.

Hozo, S.P., Djulbegovic, B., Hozo, I., 2005. Estimating the mean and variance from the
median, range, and the size of a sample. BMC Medical Research Methodology 5, 13.
https://doi.org/10.1186/1471-2288-5-13.

. . .Irie, F., Masaki, K.H., Petrovitch, H., Abbott, R.D., Ross, G., Taaffe, D.R., White, L.R.,
2008. Apolipoprotein E epsilon4 allele genotype and the effect of depressive symp-
toms on the risk of dementia in men: The Honolulu-Asia Aging Study. Archives of
General Psychiatry 65 (8), 906–912. http://dx.doi.org/10.1001/archpsyc.65.8.906.

. . .Jorm, A.F., van Duijn, C.M., Chandra, V., Fratiglioni, L., Graves, A.B., Heyman, A.,
Hoffman, A., for the EURODEM Risk Factors Research Group, 1991. Psychiatric
history and related exposures as risk factors for Alzheimer’s disease: a collaborative
re-analysis of case-control studies. EURODEM Risk Factors Research Group.
International Journal of Epidemiology 20, S43–S47.

Jorm, A., 2000. Is depression a risk factor for dementia or cognitive decline? A review.
Gerontology 46 (4), 219–227. http://dx.doi.org/10.1159/000022163.

Jorm, A.F., 2001. History of depression as a risk factor for dementia: An updated review.
Australian and New Zealand Journal of Psychiatry 35 (6), 776–781. http://dx.doi.
org/10.1046/j.1440-1614.2001.00967.x.

Kales, H.C., Gitlin, L.N., Lyketsos, C.G., 2015. Assessment and management of behavioral
and psychological symptoms of dementia. BMJ 350, h369. https://doi.org/10.1136/
bmj.h369.

. . .Kassem, A.M., Ganguli, M., Yaffe, K., Hanlon, J.T., Lopez, O.L., Wilson, J.W., Cauley,
J.A., 2018. Anxiety symptoms and risk of dementia and mild cognitive impairment in
the oldest old women. Aging & Mental Health 22 (4), 474–482. https://doi.org/10.
1080/13607863.2016.1274370.

. . .Kim, J.M., Stewart, R., Kim, S.Y., Kim, S.W., Bae, K.Y., Yang, S.J., Yoon, J.S., 2011.
Synergistic associations of depression and apolipoprotein E genotype with incidence
of dementia. Int J Geriatr Psychiatry 26 (9), 893–898. https://doi.org/10.1002/gps.
2621.

Kuring, J.K., Mathias, J.L., Ward, L., 2018. Prevalence of Depression, Anxiety and PTSD in
People with Dementia: a Systematic Review and Meta-Analysis. Neuropsychology
Review 28, 393–416. https://doi.org/10.1007/s11065-018-9396-2.

. . .Lauriola, M., Mangiacotti, A., D’Onofrio, G., Cascavilla, L., Paris, F., Ciccone, F., Greco,
A., 2018. Late-Life Depression versus Amnestic Mild Cognitive Impairment:
Alzheimer’s Disease Incidence in 4 Years of Follow-Up. Dementia and Geriatric
Cognitive Disorders 46 (3-4), 140–153. https://doi.org/10.1159/000492489.

Lawlor, B.A., 1996. Environmental and social aspects of behavioural disturbances in
dementia. International Psychogeriatrics 8, 259–261.

Lenoir, H., Dufouil, C., Auriacombe, S., Lacombe, J.M., Dartigues, J.F., Ritchie, K.,
Tzourio, C., 2011. Depression history, depressive symptoms and incident dementia:
The 3c study. Alzheimer’s and Dementia 7 (4), S355–S356.

Leonard, B.E., 2017. Major Depression as a Neuroprogressive Prelude to Dementia: What
Is the Evidence. Mod Trends Pharmacopsychiatry 31, 56–66. https://doi.org/10.
1159/000470807.

. . .Li, G., Wang, L.Y., Shofer, J.B., Thompson, M.L., Peskind, E.R., McCormick, W., Larson,
E.B., 2011. Temporal relationship between depression and dementia: Findings from a
large community-based 15-year follow-up study. Archives of General Psychiatry 68
(9), 970–977. http://dx.doi.org/10.1001/archgenpsychiatry.2011.86.

Lin, W.-C., Hu, L.-Y., Tsai, S.-J., Yang, A.C., Shen, C.-C., 2017. Depression and the risk of
vascular dementia: a population-based retrospective cohort study. International

J.K. Kuring, et al. Journal of Affective Disorders 274 (2020) 247–261

260

http://http://dx.doi.org/10.1002/gps.1424

https://doi.org/10.1016/S1474-4422(11)70072-2

http://http://dx.doi.org/10.1159/000080968

http://refhub.elsevier.com/S0165-0327(19)32303-1/sbref0007

http://http://dx.doi.org/10.1097/JGP.0b013e3181aad1fe

https://doi.org/10.1192/bjp.2018.173

https://doi.org/10.1136/bmj.g5205

https://doi.org/10.3233/jad-171134

http://http://dx.doi.org/10.1037/a0015713

http://refhub.elsevier.com/S0165-0327(19)32303-1/sbref0013

https://doi.org/10.1080/13607863.2017.1387760

http://http://dx.doi.org/10.1097/JGP.0b013e31822001c1

http://refhub.elsevier.com/S0165-0327(19)32303-1/sbref0016

http://refhub.elsevier.com/S0165-0327(19)32303-1/sbref0017

http://http://dx.doi.org/10.1002/ana.20405

http://http://dx.doi.org/10.1016/j.jagp.2014.03.001

https://doi.org/10.1002/spgs.4245

http://www.training.cochrane.org/handbook

http://refhub.elsevier.com/S0165-0327(19)32303-1/sbref0023

http://http://dx.doi.org/10.1192/bjp.bp.112.118307

http://refhub.elsevier.com/S0165-0327(19)32303-1/sbref0025

http://refhub.elsevier.com/S0165-0327(19)32303-1/sbref0026

http://http://dx.doi.org/10.1016/S2215-0366(18)30065-8

https://doi.org/10.1016/j.jalz.2017.04.014

https://doi.org/10.1371/journal.pone.0194735

http://http://dx.doi.org/10.1046/j.1532-5415.2003.51352.x

http://http://dx.doi.org/10.1001/archpsyc.63.2.153

http://http://dx.doi.org/10.1093/gerona/60.6.744

http://http://dx.doi.org/10.1212/01.wnl.0000308937.30473.d1

http://http://dx.doi.org/10.1111/j.1532-5415.2010.03233.x

http://http://dx.doi.org/10.1016/j.jagp.2013.02.011

http://http://dx.doi.org/10.1016/j.jagp.2016.05.015

http://refhub.elsevier.com/S0165-0327(19)32303-1/sbref0037

http://refhub.elsevier.com/S0165-0327(19)32303-1/sbref0038

http://https://doi.org/10.1186/1471-2288-5-13

http://http://dx.doi.org/10.1001/archpsyc.65.8.906

http://refhub.elsevier.com/S0165-0327(19)32303-1/sbref0041

http://http://dx.doi.org/10.1159/000022163

http://http://dx.doi.org/10.1046/j.1440-1614.2001.00967.x

https://doi.org/10.1136/bmj.h369

https://doi.org/10.1080/13607863.2016.1274370

https://doi.org/10.1002/gps.2621

http://https://doi.org/10.1007/s11065-018-9396-2

https://doi.org/10.1159/000492489

http://refhub.elsevier.com/S0165-0327(19)32303-1/sbref0049

http://refhub.elsevier.com/S0165-0327(19)32303-1/sbref0050

https://doi.org/10.1159/000470807

http://http://dx.doi.org/10.1001/archgenpsychiatry.2011.86

Journal of Geriatric Psychiatry 32 (5), 556–563. https://doi.org/10.1002/gps.4493.
Lipsey, M.W., Wilson, D.B., 2001. Applied social research methods series: Vol. 49.

Practical Meta-Analysis. Sage Publications Inc, CA, US.
Livingstone, G., Sommerlad, A., Orgeta, V., Costafreda, S.G., Huntley, J., Ames, D., …

Mukadam, N., 2017. Dementia prevention, intervention, and care. Lancet 390,
2673–2734. http://dx.doi.org/10.1016/S0140-6736(17)31363-6.

Lugtenburg, A., Zuidersma, M., Voshaar, R.C., Schoevers, R.A., 2016. Symptom dimen-
sions of depression and 3-year incidence of dementia: Results from the Amsterdam
study of the elderly. Journal of Geriatric Psychiatry and Neurology 29 (2), 99–107.
http://dx.doi.org/10.1177/0891988715606235.

Luppa, M., Luck, T., Ritschel, F., Angermeyer, M.C., Villringer, A., Riedel-Heller, S.G.,
2013. Depression and incident dementia. An 8-year population-based prospective
study. PLoS ONE 8 (3) 2013, ArtID e59246, 8(3). http://dx.doi.org/10.1371/journal.
pone.0059246.

Mawanda, F., Wallace, R.B., McCoy, K., Abrams, T.E., 2017. PTSD, psychotropic medi-
cation use, and the risk of dementia among US veterans: a retrospective cohort study.
Journal of the American Geriatrics Society 65, 1043–1050. https://doi.org/10.1111/
jgs.14756.

Meziab, O., Kirby, K.A., Williams, B., Yaffe, K., Byers, A.L., Barnes, D.E., 2014. Prisoner of
war status, posttraumatic stress disorder, and dementia in older veterans. Alzheimer’s
& Dementia: The Journal of the Alzheimer’s Association 10, S236–S241. http://dx.
doi.org/10.1016/j.jalz.2014.04.004.

Moher, D., Liberati, A., Tetzlaff, J., Altman, D.G., The PRISMA Group, 2009. Preferred
reporting items for systematic reviews and meta-analyses: the PRISMA statement.
PLoS Medicine 6, e1000097. https://doi.org/10.1371/journal.pmed1000097.

National Institute of Health. (2014). Quality Assessment Tool for Observational Cohort
and Cross-Sectional Studies. Retrieved from https://www.nhlbi.nih.gov/health-
topics/study-quality-assessment-tools.

Norton, S., Matthews, F.E., Barnes, D.E., Yaffe, K., Brayne, C., 2014. Potential for primary
prevention of Alzheimer’s disease: an analysis of population-based data. The Lancet,
Neurology 13, 788–794. https://doi.org/10.1016/s1474-4422(14)70136-x.

Norvell, D.C., 2010. Study types and bias – don’t judge a study by the abstract’s conclusion
alone. Evidence-Based Spine-Care Journal 1, 7–10. https://doi.org/10.1055/s-0028-
1100908.

Ownby, R.L., Crocco, E., Acevedo, A., John, V., Loewenstein, D., 2006. Depression and
Risk for Alzheimer Disease: Systematic Review, Meta-analysis, and Metaregression
Analysis. Archives of General Psychiatry 63 (5), 530–538. http://dx.doi.org/10.
1001/archpsyc.63.5.530.

Podcasy, J.L., Epperson, C.N., 2016. Considering sex and gender in Alzheimer disease and
other dementias. Dialogues in Clinical Neuroscience 18, 437–446.

Rafferty, L.A., Cawkill, P.E., Stevelink, S.A.M., Greenberg, K., Greenberg, N., 2017.
Dementia, post-traumatic stress disorder and major depressive disorder: a review of
the mental health risk factors for dementia in the military veteran population.
Psychological Medicine 1–10. https://doi.org/10.1017/s0033291717001386.

Rankin, G., Stokes, M., 1998. Reliability of assessment tools in rehabilitation: an illus-
tration of appropriate statistical analyses. Clinical Rehabilitation 12, 187–199.

Rogers, J.M., Read, C.A., 2007. Psychiatric comorbidity following traumatic brain injury.
Brain Injury 21, 1321–1333. https://doi.org/10.1080/02699050701765700.

Saczynski, J.S., Beiser, A., Seshadri, S., Auerbach, S., Wolf, P.A., Au, R., 2010. Depressive
symptoms and risk of dementia: the Framingham Heart Study. Neurology 75 (1),
35–41. https://doi.org/10.1212/WNL.0b013e3181e62138.

. . .Santabarbara, J., Lopez-Anton, R., de la Camara, C., Lobo, E., Gracia-Garcia, P.,
Villagrasa, B., Lobo, A., 2018. Clinically significant anxiety as a risk factor for de-
mentia in the elderly community. Acta Psychiatrica Scandinavica 1–9. https://doi.
org/10.1111/acps.12966.

Santabárbara, J., Lipnicki, D.M., Villagrasa, B., Lobo, E., Lopez-Anton, R., 2019. Anxiety
and risk of dementia: Systematic review and meta-analysis of prospective cohort
studies. Maturitas 119, 14–20. https://doi.org/10.1016/j.maturitas.2018.10.014.

. . .Seedat, S., Scott, K.M., Angermeyer, M.C., Berglund, P., Bromet, E.J., Brugha, T.S.,
Kessler, R.C., 2009. Cross-national associations between gender and mental disorders
in the WHO World Mental Health Surveys. Archives of General Psychiatry 66,
785–795.

Singh-Manoux, A., Dugravot, A., Fournier, A., Abell, J., Ebmeier, K., Kivimaki, M., Sabia,
S., 2017. Trajectories of Depressive Symptoms Before Diagnosis of Dementia: A 28-
Year Follow-up Study. JAMA Psychiatry 74 (7), 712–718. https://doi.org/10.1001/
jamapsychiatry.2017.0660.

Stella, F., Radanovic, M., Balthazar, M.L.F., Canineu, P.R., de Souza, L.C., Forlenza, O.V.,
2014. Neuropsychiatric symptoms in the prodromal stages of dementia. Current
Opinions in Psychiatry 27, 230–235. https://doi.org/10.1097/YCO.
0000000000000050.

Sterne, J.A.C., Sutton, A.J., Ioannidis, J.P.A., Terrin, N., Jones, D.R., Lau, J., … Higgins,
J.P.T., 2011. Recommendations for examining and interpreting funnel plot asym-
metry in meta-analyses of randomized controlled trials. BMJ 343, d4002.

Stroup, D.F., Berlin, J.A., Morton, S.C., Olkin, I., Williamson, G.D., Rennie, D., … the
Meta-analysis Of Observational Studies in Epidemiology (MOOSE) Group, 2000.
Meta-analysis of observational studies in epidemiology; a proposal for reporting.
Journal of the American Medical Association 283, 2008–2012.

Sutton, A.J., Duval, S.J., Tweedie, R.L., Abrams, K.R., Jones, D.R., 2000. Empirical as-
sessment of effect of publication bias on meta-analyses. British Medical Journal 10,
1574–1577.

Vilalta-Franch, J., Lopez-Pousa, S., Llinas-Regla, J., Calvo-Perxas, L., Merino-Aguado, J.,
Garre-Olmo, J., 2013. Depression subtypes and 5-year risk of dementia and
Alzheimer’s disease in patients aged 70 years. International Journal of Geriatric
Psychiatry 28 (4), 341–350. http://dx.doi.org/10.1002/gps.3826.

Wallin, K., Bostrom, G., Kivipelto, M., Gustafson, Y., 2013. Risk factors for incident de-
mentia in the very old. International Psychogeriatrics 25 (7), 1135–1143. http://dx.
doi.org/10.1017/S1041610213000409.

. . .Wang, T.-Y., Wei, H.-T., Liou, Y.-J., Su, T.-P., Bai, Y.-M., Tsai, S.-J., Chen, M.-H., 2016.
Risk for developing dementia among patients with posttraumatic stress disorder: A
nationwide longitudinal study. Journal of Affective Disorders 205, 306–310. http://
dx.doi.org/10.1016/j.jad.2016.08.013.

. . .Xu, W., Tan, L., Wang, H.-F., Jiang, T., Tan, M.-S., Tan, L., Yu, J.-T., 2015. Meta-
analysis of modifiable risk factors for Alzheimer’s disease. Journal of Neurology,
Neurosurgery & Psychiatry 86 (12), 1299–1306.

. . .Yaffe, K., Vittinghoff, E., Lindquist, K., Barnes, D., Covinsky, K.E., Neylan, T., Marmar,
C., 2010. Posttraumatic stress disorder and risk of dementia among US veterans.
Archives of General Psychiatry 67 (6), 608–613. http://dx.doi.org/10.1001/
archgenpsychiatry.2010.61.

Zahodne, L.B., Schupf, N., Brickman, A.M., Mayeux, R., Wall, M.M., Stern, Y., Manly, J.J.,
2016. Dementia Risk and Protective Factors Differ in the Context of Memory
Trajectory Groups. Journal of Alzheimer’s Disease 52 (3), 1013–1020.

Zalsman, G., Aizenberg, D., Sigler, M., Nahshony, E., Karp, L., Weizman, A., 2000.
Increased risk for dementia in elderly psychiatric inpatients with late-onset major
depression. Journal of Nervous and Mental Disease 188 (4), 242–243. http://dx.doi.
org/10.1097/00005053-200004000-00010.

Zilkens, R.R., Bruce, D.G., Duke, J., Spilsbury, K., Semmens, J.B., 2014. Severe psychiatric
disorders in mid-life and risk of dementia in late-life (age 65-84 years): A population
based case-control study. Current Alzheimer Research 11 (7), 681–693. http://dx.doi.
org/10.2174/1567205011666140812115004.

J.K. Kuring, et al. Journal of Affective Disorders 274 (2020) 247–261
261

https://doi.org/10.1002/gps.4493

http://refhub.elsevier.com/S0165-0327(19)32303-1/sbref0054

http://http://dx.doi.org/10.1016/S0140-6736(17)31363-6

http://http://dx.doi.org/10.1177/0891988715606235

http://http://dx.doi.org/10.1371/journal.pone.0059246

https://doi.org/10.1111/jgs.14756

http://http://dx.doi.org/10.1016/j.jalz.2014.04.004

https://doi.org/10.1371/journal.pmed1000097

http://https://www.nhlbi.nih.gov/health-topics/study-quality-assessment-tools

https://doi.org/10.1016/s1474-4422(14)70136-x

https://doi.org/10.1055/s-0028-1100908

http://http://dx.doi.org/10.1001/archpsyc.63.5.530

http://refhub.elsevier.com/S0165-0327(19)32303-1/sbref0064

https://doi.org/10.1017/s0033291717001386

http://refhub.elsevier.com/S0165-0327(19)32303-1/sbref0066

https://doi.org/10.1080/02699050701765700

https://doi.org/10.1212/WNL.0b013e3181e62138

https://doi.org/10.1111/acps.12966

https://doi.org/10.1016/j.maturitas.2018.10.014

http://refhub.elsevier.com/S0165-0327(19)32303-1/sbref0072

https://doi.org/10.1001/jamapsychiatry.2017.0660

https://doi.org/10.1097/YCO.0000000000000050

http://refhub.elsevier.com/S0165-0327(19)32303-1/sbref0075

http://refhub.elsevier.com/S0165-0327(19)32303-1/sbref0076

http://refhub.elsevier.com/S0165-0327(19)32303-1/sbref0077

http://http://dx.doi.org/10.1002/gps.3826

http://http://dx.doi.org/10.1017/S1041610213000409

http://http://dx.doi.org/10.1016/j.jad.2016.08.013

http://refhub.elsevier.com/S0165-0327(19)32303-1/sbref0081

http://http://dx.doi.org/10.1001/archgenpsychiatry.2010.61

http://refhub.elsevier.com/S0165-0327(19)32303-1/sbref0083

http://http://dx.doi.org/10.1097/00005053-200004000-00010

http://http://dx.doi.org/10.2174/1567205011666140812115004

Risk of Dementia in persons who have previously experienced clinically-significant Depression, Anxiety, or PTSD: A Systematic Review and Meta-Analysis

Introduction
Method
Search strategy
Selection criteria
Data extraction and quality assessment
Effect size and statistical analysis
Results
Study screening and summary details
Study reporting quality
Depression
Anxiety
PTSD
Discussion
Author Declaration
Contributors
Role of Funding Source
Declaration of Competing Interest
Acknowledgements
Supplementary materials
References

Turn in your highest-quality paper
Get a qualified writer to help you with

“ Literature Review Resources ”

Get high-quality paper

NEW! AI matching with writer

Hire a Writer

Client Reviews

4.9

Sitejabber

4.6

Trustpilot

4.8

Our Guarantees

100% Confidentiality

Information about customers is confidential and never disclosed to third parties.

Original Writing

We complete all papers from scratch. You can get a plagiarism report.

Timely Delivery

No missed deadlines – 97% of assignments are completed in time.

Money Back

If you're confident that a writer didn't follow your order details, ask for a refund.

New to Your Trusted Assignment Help Service? Sign up & Save

Calculate the price of your order

Type of paper needed:

Pages:

You will get a personal manager and a discount.

Academic level:

We'll send you the first draft for approval by at

Total price:

$0.00

Power up Your Academic Success with the
Team of Professionals. We’ve Got Your Back.

Power up Your Study Success with Experts We’ve Got Your Back.

Order Now Order Now