REVIEW

The vascular depression hypothesis: mechanisms linking vasculardisease with depressionWD Taylor1, HJ Aizenstein2 and GS Alexopoulos3

The ‘Vascular Depression’ hypothesis posits that cerebrovascular disease may predispose, precipitate or perpetuate some geriatricdepressive syndromes. This hypothesis stimulated much research that has improved our understanding of the complexrelationships between late-life depression (LLD), vascular risk factors, and cognition. Succinctly, there are well-establishedrelationships between LLD, vascular risk factors and cerebral hyperintensities, the radiological hallmark of vascular depression.Cognitive dysfunction is common in LLD, particularly executive dysfunction, a finding predictive of poor antidepressant response.Over time, progression of hyperintensities and cognitive deficits predicts a poor course of depression and may reflect underlyingworsening of vascular disease. This work laid the foundation for examining the mechanisms by which vascular disease influencesbrain circuits and influences the development and course of depression. We review data testing the vascular depression hypothesiswith a focus on identifying potential underlying vascular mechanisms. We propose a disconnection hypothesis, wherein focalvascular damage and white matter lesion location is a crucial factor, influencing neural connectivity that contributes to clinicalsymptomatology. We also propose inflammatory and hypoperfusion hypotheses, concepts that link underlying vascular processeswith adverse effects on brain function that influence the development of depression. Testing such hypotheses will not only informthe relationship between vascular disease and depression, but also provide guidance on the potential repurposing ofpharmacological agents that may improve LLD outcomes.

Molecular Psychiatry advance online publication, 26 February 2013; doi:10.1038/mp.2013.20

Keywords: cerebrovascular; cognition; depression; geriatrics; neuroimaging; review

INTRODUCTIONThere is a substantial heterogeneity in the biological factors thatinfluence the risk of developing depression across the lifespan.Genetic and epigenetic factors influence gene expression orprotein function across a variety of molecular systems, resulting ina vulnerability to develop depression in context of psychosocialadversity.1 This heterogeneity is even more apparent indepression occurring in older adults, or late-life depression(LLD), as aging-related changes across multiple organ systemsalso contribute to depression. Aging-related changes in theneurological, immune and endocrine systems have all beenimplicated in depression, but one of the best studied changes inLLD is the role of vascular disease.

The ‘Vascular Depression’ hypothesis proposed that ‘cerebro-vascular disease may predispose, precipitate or perpetuate somegeriatric depressive syndromes’.2 The hypothesis wasaccompanied by two proposals on the core characteristics ofand how to define a vascular depression subtype. First,Alexopoulos and colleagues2,3 proposed a working definitionbased on the presence of vascular risk factors. The clinicalpresentation of ‘vascular depression’ was characterized bycognitive deficits, psychomotor retardation, lack of insight anddisability disproportional to the depression severity. Investigatorssubsequently focused on the cognitive dysfunction occurring inLLD and its relationship to response to antidepressants.4 Second,Krishnan and colleagues5 proposed a magnetic resonance

imaging (MRI)-based definition, requiring evidence of vascularchanges on neuroimaging, referred to as MRI hyperintensities.Both definitions are important as subsequent work demonstratedthe internal validity of a vascular depression diagnostic subtypecharacterized by MRI findings and executive dysfunction.6

WHITE MATTER LESIONS, WHITE MATTER HYPERINTENSITIESAND MRI-DEFINED VASCULAR DEPRESSIONThe hallmark of MRI-defined vascular depression is the presenceof white matter lesions (WMLs) identified as white matterhyperintensities (WMH) on T2-weighted or fluid-attenuatedinversion recovery MRI. Throughout this review we refer to theradiological findings as WMHs, while reserving the term WMLwhen discussing the underlying changes in the cerebral whitematter.

WMHs are associated with advanced age7 and cerebrovascularrisk factors, including diabetes, cardiac disease andhypertension.8–12 Vascular dysregulation contributes to WMHdevelopment, as white matter is sensitive to transientischemia,13 and many larger WMHs are ischemic in origin.14,15

Hypertension and blood pressure variability are associated withLLD16–18 and also contribute to WMH development,19,20

particularly when accompanied by impaired cerebral vasomotorreactivity and altered autoregulatory processes.21,22 Such deficitsreduce cerebral blood flow (CBF) and may lead to WMHs.23,24

1Center for Cognitive Medicine, Department of Psychiatry, Vanderbilt University, Nashville, TN, USA; 2Department of Psychiatry, University of Pittsburgh Medical Center,Pittsburgh, PA, USA and 3Department of Psychiatry, Weill-Cornell Medical College, New York, NY, USA. Correspondence: Dr WD Taylor, Center for Cognitive Medicine, Departmentof Psychiatry, Vanderbilt University, 1601 23rd Avenue South, Nashville, TN 37212, USA.E-mail: Warren.d.taylor@vanderbilt.eduReceived 23 October 2012; revised 9 January 2013; accepted 18 January 2013

Molecular Psychiatry (2013), 1–12& 2013 Macmillan Publishers Limited All rights reserved 1359-4184/13

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Article

Association of Gene Variants of the Renin-AngiotensinSystem With Accelerated Hippocampal Volume Loss

and Cognitive Decline in Old Age

Anthony S. Zannas, M.D., M.Sc.

Douglas R. McQuoid, M.P.H.

Martha E. Payne, Ph.D., M.P.H.

James R. MacFall, Ph.D., M.S.

Allison Ashley-Koch, Ph.D.

David C. Steffens, M.D., M.H.Sc.

Guy G. Potter, Ph.D.

Warren D. Taylor, M.D., M.H.Sc.

Objective: Genetic factors confer risk forneuropsychiatric phenotypes, but the poly-genic etiology of these phenotypes makesidentification of genetic culprits challenging.An approach to this challenge is to examinethe effects of genetic variation on relevantendophenotypes, such as hippocampalvolume loss. A smaller hippocampus isassociated with gene variants of the renin-angiotensin system (RAS), a system impli-cated in vascular disease. However, nostudies to date have investigated longitu-dinally the effects of genetic variation ofRAS on the hippocampus.

Method: The authors examined the ef-fects of polymorphisms of AGTR1, the geneencoding angiotensin-II type 1 receptor ofRAS, on longitudinal hippocampal volumesof older adults. In all, 138 older adults (age$60 years) were followed for an average ofabout 4 years. The participants underwentrepeated structural MRI and comprehen-sive neurocognitive testing, and they weregenotyped for four AGTR1 single-nucleotide

polymorphisms (SNPs) with low pairwiselinkage disequilibrium values and apoli-poprotein E (APOE) genotype.

Results: Genetic variants at three AGTR1SNPs (rs2638363, rs1492103, and rs2675511)were independently associated with accel-erated hippocampal volume loss over the4-year follow-up period in the right but notleft hemisphere. Intriguingly, these AGTR1risk alleles also predicted worse episodicmemory performance but were not re-lated to other cognitivemeasures. Two riskvariants (rs2638363 and rs12721331) inter-acted with the APOE4 allele to accelerateright hippocampal volume loss.

Conclusions: Risk genetic variants of theRAS may accelerate memory decline inolder adults, an effect that may be con-ferred by accelerated hippocampal vol-ume loss. Molecules involved in thissystem may hold promise as early thera-peutic targets for late-life neuropsychiatricdisorders.

Am J Psychiatry Zannas et al.; AiA:1–8

Genetic factors have long been hypothesized to conferrisk for neuropsychiatric phenotypes, but it has been chal-lenging to identify genetic culprits. The failure to identify riskgenes may be in large part a result of the polygenic etiologyand phenotypic heterogeneity of neuropsychiatric disor-ders (1). These disorders result from complex interactionsamongmultiple genes, tissue-specific epigenetic regulation,and environmental influences, which make identifying therelationships between single genes and distal phenotypeschallenging.An alternative approach is to examine endophenotypes,

measurable constructs that confer risk for complex dis-orders and are thought to lie in greater etiological prox-imity to genetic factors (2). One promising endophenotypefor late-life neuropsychiatric disorders is small hippocam-pal volume (3). Smaller hippocampal volume has beenassociated with treatment resistance in late-life depression(4) and predicts progressive cognitive decline in elderly in-dividuals (5). Therefore, efforts to link hippocampal volumereduction with risk genes may be particularly relevant forlate-life neuropsychiatric syndromes.

A novel and biologically plausible gene to investigate inthese relationships isAGTR1, the gene encoding angiotensin-II type 1 (AT1) receptor in humans. AT1 receptors are theprimary effector of the renin-angiotensin system (RAS) inseveral organs, including the brain. The RAS is an im-portant regulator of the stress response, and AT1 receptorsare expressed in brain regions that modulate stress andemotion, including the hypothalamus, amygdala, and hip-pocampus (6, 7). In animal studies, RAS activation leads tohyperactivity of the stress system and heightened anxiousbehavior, whereas blockade of AT1 receptors dampensstress responses and ameliorates anxious and depressivebehavior (6, 8). As the RAS also plays a central role in bloodpressure regulation and has been implicated in vasculardisease (9), examining this system may be particularly rel-evant for older adults with depression, since late-life de-pression is often characterized by vascular comorbidity (10).Despite these theoretical implications, few studies have

examined the relationship between genetic variation inAGTR1 and either neuropsychiatric phenotypes or hippo-campalmorphology. Studies on the common rs5186AGTR1

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(A-to-C) polymorphism have reported antidepressant re-sponse differences between genotypes in elderly depressedindividuals (11–13). In abroader analysis of single-nucleotidepolymorphisms (SNPs) in AGTR1 (14), our group reportedthat allele frequency differences in two AGTR1 SNPs in-creased the odds of late-life depression.We also found cross-sectional associations between right hippocampal volumeand fourAGTR1 intronic SNPs, namely rs2638363, rs1492103,rs2675511, and rs12721331 (14). To our knowledge, no stud-ies have examined the effects of AGTR1 polymorphisms onlongitudinal changes in hippocampal morphology in olderadults. This is particularly important as reduction in hip-pocampal volume is associated with subsequent cognitivedecline (5).

To extend our previous findings demonstrating cross-sectional relationships between AGTR1 and hippocampalmorphology (14), we examined the effects of AGTR1 geno-type on longitudinal change in hippocampal volume. Oura priori hypothesis was that the gene variants we previouslyassociated with smaller cross-sectional hippocampus vol-ume would also be associated with greater hippocampalvolume loss over time. We also sought to determine if thesegene variants were associated with differences in cognitivefunction over time, particularly in domains involving thehippocampus, such as episodic memory. To control forthe effects of depression that have been shown to lead tosmaller hippocampal volumes via chronic hyperactivity ofthe stress system (15), we examined two cohorts of elderlydepressed and nondepressed individuals. In secondaryanalyses, we examined whether depression diagnosis orapolipoprotein E (APOE) genotype had synergistic effectswith AGTR1 genotypes on hippocampal volume change.

Method

Study Participants and Clinical Care

All participants were age 60 years or older and participated inthe Conte Center for the Neuroscience of Depression in Late Lifeand the Neurocognitive Outcomes of Depression in the Elderlystudies conducted at Duke University Medical Center. The DukeUniversity Medical Center institutional review board approvedthese studies, and eligible individuals provided written informedconsent.

The two cohorts were made up of depressed patients and non-depressed comparison subjects. Depressed patients were recruitedprimarily by clinical referral and secondarily by advertisements,and they met DSM-IV criteria for major depressive disorder. Di-agnosis was based on the NIMH Diagnostic Interview Schedule(DIS) (16) and was confirmed by a geriatric psychiatrist during base-line clinical evaluation. Nondepressed individuals were communitydwelling older adults recruited either from the Duke UniversityAging Center Subject Registry or through advertisements. All par-ticpants underwent cognitive screening with the Mini-Mental StateExamination (MMSE) (17). Medical comorbidity was assessed witha previously used self-report questionnaire (18).

Exclusion criteria for all individuals were as follows: 1) presenceof other major psychiatric disorder such as schizophrenia orbipolar disorder; 2) history of substance abuse or dependence; 3)presence of neurological disease; 4) metal in the body or other

contraindication for MRI; and 5) a screening MMSE score lowerthan 25. Furthermore, nondepressed individuals were excluded ifthey had evidence of past psychiatric disorder based on the DIS.

Antidepressant treatment followed the Duke Somatic Treat-ment Algorithm for Geriatric Depression (19), which allows step-wise use of commercially available antidepressant modalities.The majority of depressed patients were prescribed sertraline onstudy entry, but treatment differed among participants and wasguided by previous medication trials and depression severity.Following failed trials, switches to other antidepressant agentsand augmentation strategies were allowed as clinically indicated.Treatment alternatives included psychotherapy and electrocon-vulsive therapy.

The current longitudinal study extends our past research (14),where in a separate cohort we found a relationship between fourAGTR1 SNPs and right hippocampal volumes. Although therewas some overlap in participants, the present sample is muchlarger (138 compared with 70 individuals) and imaging data differfrom our previous study. The previous study used 3-T MRI data;the present study used 1.5-T MRI data, as longitudinal 3-T datawere not available for the majority of participants. Similar to ourprevious report and based on research showing racial differencesin AGTR1 allele frequencies (14, 20), we limited the currentanalyses to Caucasian individuals who had genotype data for allfour AGTR1 SNPs and at least two hippocampal volume measure-ments (a baseline and at least one follow-up measurement).

Neuropsychological Assessments

Neuropsychological testing was administered to study partic-ipants at baseline and then annually, regardless of the presenceor absence of depressive symptoms. The battery is described fullyelsewhere (21) and has been successfully employed in a numberof clinical and epidemiological settings (22). Testing was ad-ministered by a trained psychometric technician supervised bya licensed clinical psychologist.

We created composite variables from the broader neuro-psychological test battery that represented cognitive domainsthat may be adversely affected by aging. This was achieved bygrouping neuropsychological tests into rational constructs,similar to previously published studies (23). We created Z scoresfor each measure based on the performance of all participantsand summed the Z scores for all tests within each domain. Internalconsistency for each domain was assured using Cronbach’scoefficient alpha. Following this approach, we created fourcomposite neurocognitive measures: 1) episodic memory (logicalmemory, Benton visual retention test, immediate word learning,and word list recall; Cronbach’s coefficient alpha=0.88); 2) executivespeed (speed in Trails A and Trails B and symbol-digit modalitytest; Cronbach’s coefficient alpha=0.86); 3) verbal fluency (verbalfluency test and controlled oral word association test; Cronbach’scoefficient alpha=0.74); and d) working memory (digits forward,digits backward, and digits ascending; Cronbach’s coefficientalpha=0.74).

Genotyping and Genetic Analyses

All genotyping was performed on DNA from whole peripheralblood using the Gentra PureGene system (Qiagen, Valencia,Calif.). Assays employed quality control procedures, whichincluded serial genotyping of blinded duplicate samples. Qualityrequirements for each assay were met only if duplicate samplesmatched 100%, and efficiency of 95% was further required foreach assay before statistical analyses. Deviations from Hardy-Weinberg equilibrium were previously tested for all SNPsseparately in the depressed and nondepressed cohorts (14) usingexact tests per the Genetic Data Analysis program (24).Genotyped SNPs were identified using Linkage DisequilibriumSelect (25). Selected SNPs had linkage disequilibrium of r2,0.64

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GENE VARIANTS OF THE RENIN-ANGIOTENSIN SYSTEM

and minor allele frequency of at least 0.10 in the HapMap project(www.hapmap.org), which was based on Utah residents ofEuropean ancestry and provided genetic coverage of the entireAGTR1. In this study, we focused on the four SNPs that displayedsignificant cross-sectional relationships with hippocampal vol-umes among the 10 SNPs tested in our previous report (14). Toexamine whether these four SNPs could be treated as indepen-dent signals in our analyses, we estimated the pairwise linkagedisequilibrium between all SNPs in our study population. Allpairwise r2 values were found to be below 0.5. APOE genotype wasdetermined using previously published methods (26).

MRI Acquisition and Analysis

Each participant was screened for contraindications andscanned with a 1.5-T whole-body MRI system (Signa, GE MedicalSystems, Milwaukee) approximately every 2 years. All acquisitionswere performed with the standard head (volumetric) radiofre-quency coil. Using a previously described MRI acquisition protocol(27, 28), we first confirmed alignment by a rapid sagittal localizerscan and then obtained two dual-echo fast spin-echo acquisitions:one in the axial plane for cerebral morphometry and one in acoronal oblique plane for hippocampal morphometry.

The images were then analyzed at the Duke NeuropsychiatricImaging Research Laboratory. Segmentation of tissue and mea-surement of total cerebral volume, which included total whiteand gray matter and CSF volumes in both hemispheres, were per-formed as previously described (27). Image analysts received ex-tensive training, and reliability was established before any dataprocessing by repeated measurements on multiple MRIs sepa-rated by at least a week. Intraclass correlation coefficients wereas follows: left hippocampus=0.8; right hippocampus=0.7; andtotal cerebral volume=0.997.

Delineation of the hippocampus was based on previouslydescribed methods (28). Analysts began with the most posteriorcoronal slice and moved anteriorly, measuring the hippocampuswhere the pulvinar nucleus of the thalamus obscured the crurafornicis on each side. The fimbria and the thin strip of graymatter along the medial border of the hippocampus weretransected at their narrowest points. Tracing continued aroundthe hippocampal body to the starting point. The anterior borderof the hippocampus was defined as the slice on which theinferolateral ventricle appeared horizontally without any body ofgray matter visible below it. The amygdala-hippocampal transi-tion zone, which was transected at its narrowest point, appearedas a diffuse area of gray matter between the anterior portion ofthe hippocampus and the posterior portion of the amygdala.

Statistical Analyses

All statistical tests were performed with SAS, version 9.2 (Cary,N.C.). The level of statistical significance was set a priori at a=0.05and all tests were two tailed. To maximize power in our analyses,we dichotomized each SNP into two genotype groups, the majorallele homozygotes and the minor allele carriers. This furtherincreased comparability with our previous report (14), where thesame genotype groups were used. The two diagnostic cohorts(depressed and nondepressed) were compared at baseline fordifferences in demographic variables and baseline measures.Categorical variables were compared with chi-square tests, equal-variance continuous variables with pooled two-sample t tests,and unequal-variance continuous variables with Satterthwaitet tests.

We next examined the longitudinal effects of AGTR1 poly-morphisms on hippocampal volumes and composite cognitivemeasures. We used linear mixed-effects models (29) using thePROC MIXED command in SAS 9.3 to analyze these longitudinaldata. Analyses included the maximum number of participantswith data for longitudinal time points and for all variables

included in the models. In these models, each individual was theindependent sampling unit, and each measurement at a partic-ular time point was the observation unit. Separate models werecreated for the right and left hippocampus and for each compositecognitive measure, with each AGTR1 SNP genotype group, time(as a continuous variable), and genotype-by-time interaction asthe main independent variables. In order to account for between-subject variability in brain volume, we included cerebral volumeas a covariate in models that used hippocampal volume as thedependent variable. Other covariates included in all models weresex, age, diagnostic group (depressed or nondepressed), and therespective baseline hippocampal volume or cognitive measure-ment. The primary effects of interest were the genotype maineffect, which examines the genotype effect irrespective of time,and the genotype-by-time interaction, which examines the rateof change in the mean hippocampal volume or cognitive mea-sure over time between the two genotype groups for each AGTR1SNP. Secondary analyses tested the three-way depression-by-genotype-by-time interaction and the AGTR1 genotype-by-APOEgenotype-by-time interaction. All models were initially run withthe interaction terms, and these terms remained in the model ifsignificant, but were excluded if nonsignificant.

Results

Sample Demographics and Baseline Measures

The primary sample included 138 elderly individuals (79depressed and 59 nondepressed) with genotype data forAGTR1 SNPs and at least two MRI scans (a baseline andone follow-up assessment). The demographic character-istics and clinical data are summarized in Table 1. Theirages ranged from 60 to 84 years for the depressed cohortand from 60 to 82 years for the nondepressed cohort. Thetwo groupswere similar in genotype frequencies, age,MMSEscores, and baseline left and right hippocampal volumes.However, the percentage of female participants and edu-cational levels were higher in the nondepressed group, andthe percentage of patients who reported hypertension washigher in the depressed group. Finally, we found that thenumber of patients who reported a history of either car-diac complaints or hypertension did not significantly differbetween the four AGTR1 or APOE genotype groups (datanot shown). The maximum number of MRI measures perparticipant was five, and no significant difference wasfound for length of study follow-up between diagnosticcohorts (a mean of 1,435.3 days [SD=502.5] for depressedpatients and a mean of 1,495.9 days [SD=785.8] for non-depressed patients; Satterthwaite t test=130, df=2, t=0.55,p=0.5855).

Longitudinal Effects of AGTR1 SNPs on HippocampalVolumes

We created mixed models examining hippocampusvolume in the left or right hemisphere as repeatedly mea-sured dependent variables. Independent variables includeddiagnosis (depressed or nondepressed), age, sex, cerebralvolume, baseline hippocampal volume, time, and AGTR1SNP genotype. To test the hypothesis that SNPs woulddifferentially affect hippocampal volume over time, we

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included a SNP-by-time interaction term. Table 2 summa-rizes the effects of the four AGTR1 SNPs examined andtheir interactions with time on left and right hippocampalvolumes. When examining models of right hippocampalvolume, three of the four SNPs (rs2638363, rs1492103,rs2675511) showed both a statistically significant primaryeffect and an interactive effect with time (Table 2). Theprimary effect of AGTR1 SNPs replicated our previousfindings (14), despite nearly double the sample size anddifferent MRI field strength. Again, individuals homozy-gous for the vascular risk alleles (rs2638363 GG, rs1492103TT, and rs2675511 AA) exhibited smaller hippocampal vol-umes when compared with individuals who were het-erozygous or homozygous for the alternate allele. TheSNP-by-time interaction tested for differences betweenSNP alleles on change in hippocampal volume over time.When examining these interactions, individuals homozy-gous for the risk alleles exhibited an accelerated decreasein right hippocampal volumeover time relative to individuals

with alternate genotypes. However, the four SNPs exhib-ited neither a significant primary effect nor a significantgene-by-time interaction on left hippocampal volume. Inother words, the effect of all three SNPs was selective andlateralized to the right hemisphere.In secondary analyses we tested whether AGTR1 genetic

variation has synergistic effects with depression or APOEgenotype on the rate of hippocampal volume change. Thethree-way depression-by-AGTR1-by-time interactions didnot reach significance for any of the SNPs for either the leftor right hippocampus. However, we did observe signifi-cant gene-gene interactions. The APOE genotype hadstatistically significant epistatic effects with rs2638363 andrs12721331 for the right hippocampus volume over time(Table 2). In both cases, the presence of both the riskAGTR1allele and the APOE4 allele was associated with acceleratedhippocampal volume loss. We observed a similar trend thatdid not achieve statistical significance for the interactionbetween rs1492103, APOE, and time (Table 2).

TABLE 1. Demographic Variables and Genotype Frequencies in a Study of the Renin-Angiotensin System, AcceleratedHippocampal Volume Loss, and Cognitive Decline in Old Agea

Variable Depressed Patients (N=79) Nondepressed Comparison Subjects (N=59) Analysis

Mean SD Mean SD t df pAge (years) 69.6 6.7 69.7 5.8 0.05 136 0.9611Education (years) 14.5 2.1 15.7 1.6 4.08 134 0.0004Mini-Mental State Examination score 28.8 1.3 29.1 1.1 1.35 132 0.1808Baseline right hippocampal volume, ml 3.08 0.39 3.13 0.42 0.85 136 0.3968Baseline left hippocampal volume, ml 2.97 0.4 3.00 0.45 0.47 136 0.6395Cerebrum, ml 1,164.8 128.3 1,140.6 117.2 1.12 136 0.2645

N N x2

Sex 4.08 1 0.0433Female 52 48Male 27 11

History of cardiac complaint 0.2 1 0.6517Yes 14 9No 63 50

History of hypertension 5.82 1 0.0158Yes 29 11No 48 48

rs2638363 (A/G) 2.39 1 0.1220GG 58 36AA/AG 21 23

rs1492103 (C/T) 0.87 1 0.3522TT 58 39CC/CT 21 20

rs12721331 (C/T) 1.43 1 0.2312TT 70 48CC/CT 9 11

rs2675511 (A/G) 1.03 1 0.3107AA 40 35GG/GA 39 24

APOE 0.46 1 0.4994APOE4 carriers 20 18No APOE4 59 41

a All continuous variables had equal variances and were tested with pooled t test, except for education, which exhibited unequal variancesbetween the two diagnostic groups and was thus tested using Satterthwaite t test. Reported p values are two-tailed.

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Longitudinal Effects of AGTR1 SNPs on CompositeCognitive Measures

We next tested whether AGTR1 variants confer risk forcognitive decline. A total of 138 elderly participants (63depressed and 75 nondepressed) had both genotype andneurocognitive assessment data andwere included in theseanalyses. This sample partially overlaps with the sampleexamining hippocampal volumes, with 81 participants (36depressed and 45 nondepressed) included in both analyses.As expected, depressed individuals performed significantlyworse than nondepressed individuals on univariate compar-isons of the four composite cognitive measures of episo-dic memory (t=5.14, df=106, p,0.0001), executive speed(t=4.36, df=67, p,0.0001), verbal fluency (t=5.18, df=136,p,0.0001), and working memory (t=2.97, df=114, p=0.0037).We used mixed models to examine the effect of AGTR1

genotypes and their interaction with time on each of thecomposite cognitive measures. All models controlled fordiagnosis (depressed or nondepressed), age, sex, educa-tion, time, and baseline cognitive measure score. In thesemodels, only rs2638363 displayed a significant main effectof genotype on composite episodic memory performance(F=4.59, df=1, 126, p=0.0340), but the genotype-by-time in-teraction did not reach significance. Similarly, rs1492103exhibited a trend for a direct effect on episodic memory(F=3.85, df=1, 126, p=0.0519), but this did not reach sta-tistical significance. Finally, rs2675511 exhibited a gene-by-time interaction (F=5.61, df=1, 125, p=0.0194) predictingworsening episodic memory performance. In accordancewith SNP effects on hippocampal volumes, individualshomozygous for the vascular risk alleles (rs2638363 GG,rs1492103 TT, and rs2675511 AA) exhibited lower episodicmemory scores. None of the four genetic variants exhibitedeither a statistically significant direct effect or interactionwith time effect on executive speed, verbal fluency, orwork-ing memory.

Discussion

Replicating and extending our previous findings ofAGTR1 effects on right hippocampal morphology (14),three of the same SNPs predicted longitudinal hippocam-pal volume change in the right but not the left hemispherein elderly individuals. The direction of the observed re-lationships was similar to results from the previous cross-sectional study, that is, each of the previously identified“risk” genotypes (rs2638363 GG, rs1492103 TT, and rs2675511AA) predicted longitudinally greater shrinkage of the righthippocampus when compared with the alternate geno-types. Intriguingly, these AGTR1 risk alleles also predictedpoorer performance in episodicmemory, a neurocognitivemeasuremediated by the hippocampus, but had no effectson other cognitive measures. Additionally, two risk variants(rs2638363 and rs12721331) showed epistatic effects withthe APOE4 allele, a known risk factor for dementia (26), onright hippocampal volume loss over time. Importantly, theSNPs examined are in low pairwise linkage disequilibrium(r2,0.5) and thus represent independent signals. Thepresence of several signals within the AGTR1 locus thatare independently associated with hippocampal volumechanges and memory decline lends further support to thereliability of these associations. Taken together, ourfindingsstrongly support that AGTR1 gene variants may confervulnerability via both direct and epistatic effects for pro-gressive memory decline by accelerating hippocampalvolume loss. The main findings and the supported modelare schematically summarized in Figure 1.Our study offers novel insights into the role of the renin-

angiotensin system (RAS) in hippocampal atrophy and

cognitive decline. Hippocampal atrophy has been shown to

predict cognitive decline in late life and time-to-conversion

to Alzheimer’s disease (5, 30). Previous animal and human

studies support involvementof theRAS incognitive syndromes

TABLE 2. Longitudinal Effects of AGTR1 Single-Nucleotide Polymorphisms (SNPs) on Hippocampal Volumesa

Genotype

Right Hippocampal Volume Left Hippocampal Volume

F p F p

rs2638363 5.96 0.0160 0.81 0.3693rs2638363 by time 6.43 0.0124 0.11 0.7400rs2638363 by APOE by time 4.80 0.0303 0.14 0.7115rs1492103 5.01 0.0269 0.72 0.3967rs1492103 by time 5.90 0.0166 0.10 0.7558rs1492103 by APOE by time 3.78 0.0541 0.01 0.9100rs12721331 0.20 0.6564 0.96 0.3297rs12721331 by time 1.29 0.2574 0.09 0.7709rs12721331 by APOE by time 6.45 0.0123 0.07 0.7982rs2675511 4.06 0.0459 0.30 0.5842rs2675511 by time 4.62 0.0335 0.23 0.6352rs2675511 by APOE by time 0.75 0.3890 0.3 0.5876a Analyses conducted using linear mixed-effects models. These models tested for the effect of AGTR1 SNP genotype on hippocampal volumeand also examined AGTR1-by-time and AGTR1-by-APOE-by-time interactions. All models included 138 participants and controlled for sex, age,diagnostic group (depressed or nondepressed), baseline hippocampal volume, and total cerebral volume. Values in bold denote statisticallysignificant parameters, defined as p,0.05. Reported p values are two-tailed.

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(6, 14, 31). RAS activation modulates cerebral blood flow,increases brain vulnerability to ischemia, and promotesbrain inflammation (6, 32). These effects render this systemparticularly relevant in late life, where vascular and in-flammatory processes are hypothesized to contribute todepressive and cognitive syndromes (10, 33). On the otherhand, AT1 receptor blockade dampens stress responses,ameliorates anxious and depressive behaviors, and reducesbrain inflammation and vulnerability to ischemia (6, 8, 32).Thus, it is plausible that perturbations in the RAS couldheighten the risk for developing dementia by acceleratingage-related changes in brain morphology, particularly inbrain regionsmost vulnerable to the effects of aging, such asthe hippocampus. Although RAS activitymay bemodulatedby functional variants at the AGTR1 locus, the moleculareffects of the risk AGTR1 SNPs are currently not clear.Despite their intronic location, these SNPs could havefunctional effects. For instance, intronic SNPs may be locatedin enhancer regions that influence the three-dimensionalchanges in chromatin conformation necessary for transcrip-tion regulation (34). Alternately, these SNPs may be taggingother functional variants in nearby regions of the AGTR1locus. Finally, as supported by the epistatic effects withAPOE observed in our study, these SNPs may havefunctional effects via interaction with other genes that

lie in common biological pathways central for the develop-ment of brain pathology and dementia. Future studies on thefunctional effects of these genetic variants are warrantedand may shed light on the mechanisms linking AGTR1and cognitive syndromes.Intriguingly, the effects of AGTR1 variation were later-

alized to the right hippocampus, which is in accordancewith our previous cross-sectional study (14). Although thesignificance of this lateralization is unclear, hippocampalasymmetry may be an important endophenotype that hasbeen underemphasized by previous studies. Hippocampalasymmetry has been observed in patients with severedepression and in nondepressed relatives of depressedindividuals (35, 36). Furthermore, unilateral hippocampalvolume changes may be associated with decline in specificcognitive outcomes (5, 37) and with decreased likelihoodof achieving antidepressant remission (4). Thus, hippo-campal asymmetry may be an early step in the pathogen-esis of some late-life mood and cognitive syndromes. Thisasymmetry could be induced by differential hemisphericeffects of the RAS on the hippocampus. Notably, activity ofangiotensinase, an enzyme that metabolizes angiotensin,has been found to be distributed asymmetrically betweenthe left and right hippocampi of the rat brain (38, 39).This may also reflect differential activity of angiotensin and

FIGURE 1. Graphic Summary of AGTR1 Single-Nucleotide Polymorphism (SNP) Associations With Accelerated RightHippocampal Volume Loss and Episodic Memory Decline in Old Agea

rs2638363

rs2675511

rs1492103

rs2638363

Episodic memorydecline

Right hippocampalvolume loss

Aging

rs12721331+ ApoE4

+ ApoE4

F=4.59p=0.03F=4.80

p=0.03

F=4.62p=0.03

F=5.90p=0.02

F=5.61p=0.02

F=6.43p=0.01

F=6.45p=0.01

a Genotype-by-time interactions are shown as arrows connecting aging and outcomes (right hippocampal volume loss and episodic memorydecline) after moderation by encircled SNPs. Direct genotype effects are shown on the right side of the figure as arrows directly connectingSNPs with episodic memory decline. The encircled numbers on each arrow represent the F values and p values as estimated from therespective mixed regression models. Further details are provided in text and Table 2.

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potentially asymmetric effects of AGTR1 genetic variantsbetween the two hemispheres. Whether this mechanismholds true, and the clinical significance of these asymme-tries, remains to be determined.Several limitations should be considered when inter-

preting our findings. Althoughwe controlled for the effectsof depression diagnosis and basic demographic factors onhippocampal morphology, we did not control for antide-pressant treatments thatmay have neurotrophic effects onthe hippocampus (40) and may even reverse hippocampalvolume loss in some cases of depression (37). Depressedparticipants were treated based on an algorithm ratherthan a rigid clinical trial. Although this makes our ap-proach comparable to clinical practice, it makes it chal-lenging to elucidate the effects of antidepressants. Data onantidepressant use prior to enrollment, as well as specifictreatment modalities, duration, and dosages used over thestudy period, were not known for each participant. Thus, itis possible that variable treatments between genotypegroups may have influenced our results. However, theassignment of treatment modalities occurred randomlyand treating clinicians were blind to genotype groups, andthus systematic errors cannot account for our findings.Another limitation was our focus on variation at a singlegenetic locus and a single brain region. This was based onour sample size and the a priori plausible involvement ofAGTR1 and hippocampal pathology in late-life neuropsy-chiatric syndromes. Larger longitudinal studies are war-ranted for a more systematic examination of the multiplebrain regions, genes, and biological pathways involved inthe pathogenesis of these syndromes. Our study showsthat the RAS is one such pathway that should be includedin future pathogenetic models.In summary, this is the first study to our knowledge to

explore the longitudinal effects of AGTR1 genetic variationon hippocampalmorphology and cognitive decline. In con-trast with cross-sectional approaches that cannot establishtemporal relationships, the present study reveals that olderadults homozygous for AGTR1 risk variants exhibit accel-erated hippocampal volume loss and memory decline. Ourstudy exemplifies how examining the longitudinal effects ofbiologically plausible genotypes on relevant endopheno-types may provide novel insights into the pathogenesis ofcomplex phenotypes. It further suggests that moleculesinvolved in the RAS may serve as early therapeutic targetsfor late-life neuropsychiatric syndromes.

Received Nov. 23, 2013; revision received Apr. 21, 2014; acceptedMay 6, 2014 (doi: 10.1176/appi.ajp.2014.13111543). From theDepartment of Psychiatry and Behavioral Sciences, the Neuropsychi-atric Imaging Research Laboratory, the Department of Radiology, theCenter for Human Genetics, and the Department of Medicine, DukeUniversity Medical Center, Durham, N.C.; the Department of Trans-lational Research in Psychiatry, Max Planck Institute of Psychiatry,Munich, Germany; the Department of Psychiatry, University ofConnecticut Health Sciences Center, Farmington, Conn.; and theDepartment of Psychiatry, Vanderbilt University Medical Center andThe Geriatric Research, Education, and Clinical Center (GRECC), Department

of Veterans Affairs Medical Center, Tennessee Valley Healthcare System,Nashville, Tenn. Address correspondence to Dr. Zannas (aszannas@gmail.com).The authors report no financial relationships with commercial

interests.The authors acknowledge Dr. Torsten Klengel for helpful discus-

sions on interpreting the single-nucleotide polymorphism data.Supported by research grants R01 MH077745, R01 MH054846, and

K24 MH070027.

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ORIGINAL RESEARCH

APOE ε4 associated with preserved executive functionperformance and maintenance of temporal and cingulate brainvolumes in younger adults

Warren D. Taylor1,2 & Brian Boyd2 & Rachel Turner2 & Douglas R. McQuoid3 &

Allison Ashley-Koch4 & James R. MacFall5 & Ayman Saleh1 & Guy G. Potter3

# Springer Science+Business Media New York 2016

Abstract The APOE ε4 allele is associated with cognitivedeficits and brain atrophy in older adults, but studies in youn-ger adults are mixed.We examined APOE genotype effects oncognition and brain structure in younger adults and whether ge-notype effects differed by age and with presence of depression.157 adults (32 % ε4 carriers, 46 % depressed) between 20 and50 years of age completed neuropsychological testing, 131 ofwhich also completed 3 T cranial MRI. We did not observe adirect effect of APOE genotype on cognitive performance orstructural MRI measures. A significant genotype by age interac-tion was observed for executive function, where age had less ofan effect on executive function in ε4 carriers. Similar interactionswere observed for the entorhinal cortex, rostral and caudal

anterior cingulate cortex and parahippocampal gyrus, where theeffect of age on regional volumes was reduced in ε4 carriers.There were no significant interactions between APOE genotypeand depression diagnosis. The ε4 allele benefits younger adultsby allowing them to maintain executive function performanceand volumes of cingulate and temporal cortex regions with ag-ing, at least through age fifty years.

Keywords Aging . Depression . Cognition .MRI . Apoe

Background

There is a need to identify early biomarkers indicative ofAlzheimer’s disease risk as the presentation of clinically evi-dent symptoms lag behind the development of neuropatholo-gy (Jack et al. 2013, Risacher and Saykin 2013). Supportingthis goal, it is important to understand how genetic differencesthat increase Alzheimer’s disease risk may influence cognitiveperformance and brain structure in pre-clinical populations.This is particularly relevant to younger adult populationswho could benefit from preventive strategies. When consider-ing genotypic effects, it is important to test not only for directrelationships but also whether genetic influences alter the ef-fect of age on clinical or imaging measures. Similarly, it isimportant to examine interactive effects of common condi-tions that are themselves associated with an increased risk ofdementia, such as depression (Byers et al. 2012).

Apolipoprotein E (apoE) is a very-low-density lipoproteininvolved in transporting cholesterol from the blood. In thecentral nervous system, the apoE lipoprotein is involved inmobilization of cholesterol and fatty acids, aids in recoveryfrom injury and promotes synaptic plasticity (Mahley andHuang 2012; Nathan et al. 1994). The human APOE genecoding for this protein is located on chromosome 19 and has

Preliminary data were previously presented at the 53rd Annual Meetingof the American College of Neuropsychopharmacology.

Electronic supplementary material The online version of this article(doi:10.1007/s11682-016-9522-9) contains supplementary material,which is available to authorized users.

* Warren D. Taylorwarren.d.taylor@vanderbilt.edu

1 The Geriatric Research, Education, and Clinical Center (GRECC),Department of Veterans Affairs Medical Center, Tennessee ValleyHealthcare System, Nashville, TN 37212, USA

2 The Center for Cognitive Medicine, Department of Psychiatry,Vanderbilt University Medical Center, 1601 23rd Avenue South,Nashville, TN 37212, USA

3 Department of Psychiatry and Behavioral Sciences, Duke UniversityMedical Center, Durham, NC 27710, USA

4 Center for Human Disease Modeling and Department of Medicine,Duke University Medical Center, Durham, NC 27710, USA

5 Department of Radiology, Duke University Medical Center,Durham, NC 27710, USA

Brain Imaging and BehaviorDOI 10.1007/s11682-016-9522-9

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3 allelic variants: ε2, ε3, and ε4. A large body of work dem-onstrates that the APOE ε4 allele is the primary genetic riskfactor for the development of Alzheimer’s disease in later life(Corder et al. 1993, Strittmatter et al. 1993) and, comparedwith non-carriers, even healthy older ε4 carriers exhibit dif-ferences on cognitive testing (Caselli et al. 2004). Similarly,healthy older ε4 carriers exhibit smaller volumes and greaterrates of atrophy in multiple cortical and subcortical regions(Donix et al. 2010, Honea et al. 2009, Risacher et al. 2010,Taylor et al. 2014, Wishart et al. 2006). In contrast, the APOEε2 allele may have protective effects, as it is associated withreduced age-related cognitive decline and lower risk for de-veloping Alzheimer’s disease (Suri et al. 2013).

Studies investigating the APOE ε4 allele’s effects on cog-nition in younger adult carriers are less consistent. Comparedwith noncarriers, several reports found that younger adult ε4carriers exhibited better cognitive performance (Schultz et al.2008, Yu et al. 2000) on tasks of attention (Rusted et al. 2013),verbal memory (delayed recall) (Mondadori et al. 2007), ver-bal fluency (Alexander et al. 2007, Marchant et al. 2010), andprospective memory (Evans et al. 2014, Marchant et al. 2010).In context with observations in geriatric populations, thesefindings support a theory that APOE ε4 exhibits antagonisticpleiotropy, or different effects on cognition at different ages(Han and Bondi 2008). However, other studies failed to ob-serve differences on cognitive performance based on APOEgenotype (Bunce et al. 2011, Ihle et al. 2012, Jorm et al. 2007,Matura et al. 2014, Richter-Schmidinger et al. 2011), raisingquestions about this theory and whether APOE genotype hasany effect on cognition in younger adults.

There is less work in younger adults examining the effectsof APOE genotype on brain structure. Studies examiningsmall samples report that young adult and midlife ε4 carriersexhibit smaller hippocampal volumes (Lind et al. 2006,O’Dwyer et al. 2012) while others report that younger adultcarriers exhibit increased entorhinal cortex volumes(DiBattista et al. 2014). Analyses utilizing voxel-based ap-proaches are mixed: some find no difference in cortical graymatter volume (Dowell et al. 2013, Matura et al. 2014) whileothers report thinner cortex in frontal regions (Fennema-Notestine et al. 2011). Such differences may be reconciledby observations that the ε4 allele may be associated with re-gionally different effects, as a study examining gray matternetworks in younger carriers found the ε4 allele was associat-ed with gray matter reductions in frontal and cingulate re-gions, but relative increases in other regions including thehippocampus (Alexander et al. 2012).

Just as aging is a risk factor for developing Alzheimer’sdisease, so is depression (Byers et al. 2012, Diniz et al.2013). Depression increases the risk of dementia even occur-ring over a decade before dementia onset, although the effectof earlier life depression is primarily in APOE ε4 carriers(Karlsson et al. 2015). Early work concluded that APOE

genotype is not a risk factor for depression (Steffens et al.2003), but recent longitudinal population-based studies foundthat the ε4 allele was associated with prospectively identifieddepressive symptoms (Skoog et al. 2015). Studies in de-pressed older populations report that ε4 carriers exhibit greaterdepression severity, poorer cognitive performance, and small-er frontotemporal volumes (Corsentino et al. 2009, Kim et al.2002, Lavretsky et al. 2003, Niti et al. 2009, Qiu et al. 2009,Rajan et al. 2014, Yuan et al. 2010). This work suggests thereis an interactive or additive effect of depression and APOEgenotype on cognitive decline, brain atrophy and risk of de-mentia (Rajan et al. 2014). It is unclear if such relationshipsexist in younger adult populations where depression is asso-ciated with structural alterations in frontal, temporal, and cin-gulate regions (Kempton et al. 2011, Pizzagalli 2011) andneurocognitive differences (McClintock et al. 2010). Giventhe overlap in depressive and cognitive symptoms in olderadults, it is important to examine if depression and APOEgenotype may have interactive effects in younger adults.

The purpose of the study was to examine influences ofAPOE genotype on cognition and gray matter structure in ayoung to midlife adult sample. Given the nature of the rela-tionships between APOE ε4 genotype and pathological aging,we hypothesized we would observe an interactive effect be-tween APOE genotype and age on cognition and brain struc-ture. We also sought to determine whether APOE genotype’seffects on cognition and brain structure differs in individualswith current major depressive disorder.

Methods

Participants

Participants between the ages of 20 and 50 years were enrolledat Duke University and Vanderbilt University. Depressed par-ticipants had a DSM-IV diagnosis of recurrent MDD, asassessed by the Mini-International NeuropsychiatricInterview (MINI, version 5.0) (Sheehan et al. 1998) and inter-view with a study psychiatrist. Additional entry criteria in-cluded onset of first depressive episode before age 35 yearsand a Montgomery-Asberg Depression Rating Scale(MADRS) (Montgomery and Asberg 1979) of 15 or greater.Entry criteria specified no antidepressant use in the last month;however, most subjects reported no antidepressant use for atleast three months or longer. Eligible control subjects hadneither any lifetime history of psychiatric disorders nor anyhistory of psychotropic medication use.

Exclusion criteria included other lifetime DSM-IV Axis Idisorders including substance abuse or dependence. Subjectswere excluded for Axis II disorders determined by theStructured Clinical Interview for DSM-IV Axis IIPersonality Disorders (SCID-II) (First et al. 1997).

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Additional exclusion criteria included: history of psychosis,acute suicidality, use of illicit substances in the last month,ECT in the last 6 months, a family history of bipolar disorder,any unstable medical condition, any history of neurologicalillness or head injury, or MRI contraindications.

Both the Duke University Medical Center InstitutionalReview Board and the Vanderbilt University InstitutionalReview Board approved this study. All study participants pro-vided informed consent.

Neuropsychological testing

Participants completed a battery of neuropsychological teststhat covered cognitive domains relevant to depression. Atrained psychometric technician supervised by a licensed clin-ical psychologist administered neuropsychological testing.

Similar to our past approach in late-life depression (Shelineet al. 2010), we created rationally constructed composite do-main variables from a broad test battery. To combine tasks, wecreated Z-scores for each measure based on the performanceof all participants and averaged the Z-scores for all tests withineach domain for each individual. Internal consistency for eachdomain was assessed using Cronbach’s coefficient alpha(CoA). This resulted in four composite neuropsychologicalmeasures: a) episodic memory (Logical Memory 1 and 2;Benton Visual Retention Test, number correct; Rey’s VerbalLearning Test, total I-V and total VII, CoA = 0.87); b) execu-tive function (Controlled Oral Word Association [COWA] test(total score); Trails B time (reverse scored time to comple-tion); verbal fluency (total phonological and semantic);Stroop Color-Word interference condition (number complet-ed); CoA = 0.75); c) processing speed (Symbol-DigitModality (number completed); Trails A (reverse scored timeto completion); Stroop color naming condition (number com-pleted); CoA = 0.70); and d) working memory (Digit spanforward (number of trials correctly completed); digit spanbackward (number of trials correctly completed);CoA = 0.75).

MRI acquisition

To simplify data analyses across two sites using different MRIscanners by different manufacturers, only subjects with MRIdata acquired at Duke University was included in these anal-yses. Cranial MRI was performed using the eight-channelparallel imaging head coil on a whole-body MRI system(Trio, Siemens Medical Systems, Malvern, PA). Parallel im-aging was employed with an acceleration factor of 2.Duplicate T1-weighted image sets were acquired during thescan session using a sagittal MPRAGE sequence with TR/TE = 2300/3.46 msec, a 240 Hz/pixel bandwidth, a256 × 256matrix, a 240 mmdiameter field-of-view, 160 slices

with a 1.2 mm slice thickness, yielding an image with voxelsizes of 0.9 × 0.9 × 1.2 mm.

Analyses of volumetric and cortical thickness MRI data

All volumetric measures were calculated using FreeSurfer(version 5.1) software running in a high-performance Linuxcluster environment. The FreeSurfer methods used to derivecortical and subcortical brain volumes have been previouslydescribed. (Dale et al. 1999, Fischl et al. 2002, Fischl et al.2004a, Fischl et al. 2004b) Cortical parcellation used theDesikan-Killiany Atlas; (Desikan et al. 2006) in each hemi-sphere, this method identified 33 cortical and 7 subcorticalgray matter regions (nucleus accumbens, amygdala, caudate,hippocampus, pallidum, putamen, and thalamus). Intracranialvolume was assessed using the method implemented inFreeSurfer. We visually inspected the data by overlaying thesurfaces and subcortical segmentations over the T1 data.Individual slices in each orientation were assessed for errors.No manual corrections were needed.

We also tested for differences in cortical thickness usingFreesurfer’s QDEC module. This was an atheoretical ap-proach to test for differences not captured in atlas-based com-parisons. In this method, cortical thickness is computed as theshortest distance between any point on the pial surface and thegray/white boundary and vice-versa; these two values are av-eraged (Fischl and Dale 2000).Mapswere smoothed using thestandard Gaussian kernel of 10 mm. We used a general linearmodel (GLM) to test for differences in cortical thickness whilecorrecting for multiple comparisons using the Monte Carlosimulation method. Data were tested against an empirical nulldistribution of maximum cluster size by running 10,000 syn-thesized Gaussian noise simulations with an initial thresholdof p < 0.01. Right and left hemispheres were tested separately.

Selection of a priori regions of interest

Selection of a priori volumetric regions for primary analyseswas based on work that identified APOE genotype effects onregional volumes. These included: the amygdala, hippocam-pus, entorhinal cortex, parahippocampal gyrus, precuneus,and cingulate cortex (Donix et al. 2010, Honea et al. 2009,Hostage et al. 2014, O’Dwyer et al. 2012, Reinvang et al.2013, Risacher et al. 2010, Taylor et al. 2014). For the cingu-late cortex, we examined anterior divisions due to its implica-tion in depression (Pizzagalli 2011) and posterior divisionsdue to APOE genotype’s influence on metabolism in midlifepopulations (Protas et al. 2013).

Genotyping

Genotyping was conducted on blood samples. APOE alleles(corresponding to allele combinations at single nucleotide

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polymorphism (SNP) +3937/rs429358 and SNP + 4075/rs7412) were genotyped using the ABI 7900 TaqMan(Applied Biosystems, Foster City, California, USA) system.The two APOE single nucleotide polymorphisms exist at ami-no acid 112 and 158, which are targeted by the TaqManprobes. The individual genotypes at the two sites are thencombined to create a single standard APOE genotype.

Analytic plan

Our a priori hypotheses involved interactive effects betweenAPOE ε4 genotype, age, and depression diagnosis, so we tooka stepwise approach. All analyses were conducted with SAS9.4 (Cary, NC) using PROC MIXED. Our primary analysesincluded all participants regardless of APOE genotype, how-ever as the APOE ε2 allele may have protective effects (Suriet al. 2013) we also conducted follow-up analyses where ε2carriers were excluded.

First, we created initial models that did not include interac-tion terms but instead tested for a direct effect of APOE ε4genotype. In these models, either cognitive domain z-scores orMRI measures were the dependent variable, with APOE ε4genotype (presence or absence of an ε4 allele), diagnosis (de-pressed/never depressed), age, sex, and race (white or minor-ity). Analyses of cognitive data included the additional covar-iate of years of education, while analyses of imaging dataincluded total intracranial volume and hemisphere. After test-ing for initial effects, we subsequently created additionalmodels that included either an APOE by age interaction termor an APOE by diagnosis interaction term. Final models testedfor a three-way age by APOE by depression diagnosisinteraction.

For analyses of cognition, we focused on our a priori planof examining the z-transformed summary measures of pro-cessing speed, working memory, episodic memory, and exec-utive function. For domains with a statistically significant ge-notype effect or interaction term, we planned for subsequentanalyses that would examine the individual z-transformedtests within those domains.

For analyses of volumetric MRI data, we initially focusedon our a priori regions as described above. Subsequent analy-ses also considered other brain regions not specified in our apriori hypotheses but quantified by our image analysis meth-od. Given the large number of regions identified, for analysesof regions not included in our a priori hypotheses we correctedfor multiple comparisons using the false discovery rate (FDR)method. When testing for differences in cortical thicknessusing FreeSurfer’s QDEC module, we used a general linearmodel (GLM) to test for differences between genotypegroups, including age as an additional independent variable.This allowed us to examine the direct effects of APOE geno-type on cortical thickness as well as test for statistical interac-tions between genotype and age.

To elucidate the relationship between cognition and struc-ture, we examined cognitive domains and brain regions iden-tified as exhibiting statistically significant relationship in theabove planned analyses. In these analyses, we examined the z-transformed cognitive domain score as the dependent variable.Independent variables included age, sex, diagnosis, race, andeducation. We also included brain regions as independent var-iables, utilizing the mean volume across the hemispheres, nor-malized for total intracranial volume.

Results

APOE genotype effects on cognitive performance

The sample for cognitive analyses included participants re-cruited at both the Duke and Vanderbilt sites. This consistedof 107 APOE ε4 negative individual (48 % depressed,N = 51)and 50 APOE ε4 positive individuals (42 % depressed,N = 21; Table 1). There was no significant difference inAPOE ε4 frequency between diagnostic groups (χ2 = 0.44,p = 0.5070). The complete genotypic breakdown consisted of:2/2, N = 2; 2/3, N = 14; 2/4, N = 4; 3/3, N = 87; 3/4, N = 45;4/4, N = 5). The depressed cohort was significantly older andexhibited greater severity of depressive symptoms byMADRS than the nondepressed cohort, but there was no sig-nificant difference in age or MADRS score within each diag-nostic cohort between those who were and were not ε4 car-riers. After controlling for demographic covariates, we did notobserve any statistically significant differences in the z-transformed cognitive domain scores between individualsidentified by both genotype and diagnosis (Table 1).

In primary models without interaction terms, we did notfind direct effects of either APOE genotype or diagnosis onany cognitive domain score. We also did not observe anystatistically significant interactions between depression diag-nosis and genotype on cognitive domain scores. Similarly, wedid not find statistically significant three-way interactions be-tween APOE genotype, age, and depression.

On testing for an interaction between age and APOE geno-type, we found a significant interaction for executive function(F1,149 = 4.23, p = 0.0415) and a trend for episodic memory(F1,149 = 3.16, p = 0.0779) but no difference for other domains(working memory: F1,149 = 1.90, p = 0.1707; processingspeed: F1,149 = 0.01, p = 0.9038). In analysis of executivefunction, APOE ε4 noncarriers exhibited worse executivefunction with increasing age than did the ε4 carriers (Fig. 1).In other words, presence of the APOE ε4 allele reduced theeffect of age on executive function.

We conducted additional analyses for the executive func-tion and episodic memory domains, examining the z-transformed individual test items included in the compositedomain scores. Controlling for the same covariates as in

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primary models, for the executive function domain we foundsignificant interactions between age and genotype for Trails B(time to completion; F1,149 = 4.39, p = 0.0378) and COWAtotal score (F1,149 = 8.97, p = 0.0032), but not verbal fluency(F1,149 = 0.48, p = 0.4880) or Stroop color-word interference(F1,149 = 0.08, p = 0.7722). For the episodic memory domain,there was a significant interaction only for the Benton VisualRetention Test (number correct; F1,149 = 7.00, p = 0.0090).

The interactions were similar to those observed with the ex-ecutive function composite score, where ε4 carriers exhibitedless reduction in performance with age (Fig. 1). In the absenceof the age by genotype interaction term, APOE genotype didnot have a direct effect on test measures.

Secondary analyses where we excluded the twenty ε2 car-riers were largely concordant with the results described above.We continued to observe a significant interaction between age

Table 1 Demographic and cognitive domain score differences by APOE genotype and diagnostic group

APOE ε4 –Depressed(N = 51)

APOE ε4 –Nondepressed(N = 56)

APOE ε4 + Depressed(N = 21)

APOE ε4 + Nondepressed(N = 29)

TestStatistic

p value

Age (y) 36.5 (8.8) 29.2 (8.2) 36.8 (8.8) 31.4 (10.1) F = 7.69 p < 0.0001

Sex (% female) 66.7 % (34) 58.9 % (33) 76.2 % (16) 68.9 % (20) χ2 = 2.32 p = 0.5088

Race (% Caucasian) 64.7 % (33) 51.8 % (29) 61.9 % (13) 51.7 % (15) χ2 = 2.39 p = 0.4955

Education (y) 15.5 (2.5) 15.8 (1.9) 14.7 (2.1) 16.1 (2.4) F = 1.83 p = 0.1434

MADRS 23.9 (4.2) 0.6 (0.9) 24.4 (5.2) 1.31 (1.4) F = 692.83 p < 0.0001

Episodic memory - 0.76 (4.16) 0.92 (4.02) - 1.35 (4.22) 0.62 (3.40) F = 0.38 p = 0.7675

Executive function - 0.95 (3.07) 0.49 (3.12) 0.13 (2.97) 0.40 (3.06) F = 1.67 p = 0.1762

Processing speed - 0.76 (2.35) 0.57 (2.39) - 0.28 (1.89) 0.30 (2.46) F = 1.21 p = 0.3094

Working memory - 0.28 (1.68) 0.48 (1.75) - 0.10 (1.68) - 0.15 (1.72) F = 1.13 p = 0.3383

Continuous measures presented as mean (standard deviation). Group differences in demographic variables (age, education, MADRS score) tested usinganalysis of variance (ANOVA) where the model had a total 156 degrees of freedom. Categorical variables presented as percentage (number), differencestested using chi-square with 3 degrees of freedom. Cognitive domain data presented as z-transformed scores, mean (SD). These cognitive domainmodelstested for 4-way group differences and included 147 degrees of freedom, controlling for age, sex, education, and race

Fig. 1 Differential effect of age between APOE ε4 carriers andnoncarriers on z-transformed cognitive performance. Data presented arethe domain and tests exhibiting statistically significant age by genotypeinteractive effects on performance, after controlling for depressiondiagnosis, sex, race, and education. The executive function domain isthe averaged z-score over multiple neuropsychological tests, including

Trails Part B, COWA, verbal fluency and the Stroop color-wordcondition. The individual tests presented are those with statisticallysignificant interactions between genotype and age, and included BVRTfrom the episodic memory domain. APOE ε4 noncarriers are triangles/solid line, APOE ε4 carriers are squares/dashed line. Z-transformed testperformance score on the y-axis, age in years on x-axis

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and genotype for executive function (F1129 = 5.36,p = 0.0222), but there was no support for a trend with episodicmemory (F1129 = 2.63, p = 0.1074). Analyses of specific cog-nitive tests resulted in findings similar to those reportedabove, aside from Trails B time to completion that nolonger achieved statistical significance (F1129 = 3.84,p = 0.0523). Again, ε4 carriers exhibited less reductionin performance with age and in the absence of the ageby genotype interaction term, APOE genotype did nothave a direct effect on any test measure.

APOE genotype effects on regional brain volumes

To simplify analyses of MRI data across sites with differentMRI manufacturers, we only included MRI data from theDuke site. Notably, 9 participants recruited at Duke eithercould not complete MRI or had MRI data of poor qualityand so could not be included in analyses. The sample withMRI and APOE genotype data thus included 58 individualswith MDD and 73 never-depressed comparison subjects.There was no difference in APOE ε4 frequency between de-pressed and nondepressed groups in the MRI sample (ε4 –,MDDN = 41; ε4 –, noMDDN = 47; ε4 +,MDDN = 17; ε4 +,no MDD N = 26; χ2 = 0.58, p = 0.4452). Other demographiccomparisons were comparable to the larger sample(Supplemental Table 1).

In primary models examining a priori defined regionalbrain volumes, we did not find direct effects of either APOEgenotype or depression diagnosis on any region. Wealso did not observe any statistically significant interac-tions between depression diagnosis and genotype on re-gional volumes. Similarly, we did not find statisticallysignificant three-way interactions between APOE geno-type, age, and depression.

In analyses of our a priori defined regions, we observedstatistically significant interactions between APOE genotypeand age for the entorhinal cortex, rostral and caudal cingulatecortex (Table 2). Similar to what is observed in the cognitiveanalyses, the APOE ε4 allele reduced the effect of ageon regional volumes (Fig. 2). In other words, the rela-tionship between age and volume exhibits a more neg-ative slope in APOE ε4 noncarriers compared with ε4carriers. These findings persisted when removing the seventeenAPOE ε2 allele carriers, and we additionally observed an inter-action between age and genotype for the parahippocampalgyrus.

Additional analyses examined other regions measured byFreeSurfer that were not part of our a priori hypotheses, wherewe again created similar models testing for direct and interac-tive effects of APOE genotype. After FDR correction for mul-tiple comparisons, we observed neither statistically significantprimary effects nor interactive effects of APOE genotype.

APOE genotype effects on cortical thickness

We found no statistically significant effects where APOE ge-notype was directly associated with differences in corticalthickness. When examining age by genotype interactions,we did not observe any regions where the effect of age oncortical thickness differed significantly between the APOEgenotype groups. Similarly, we observed no differences whentesting for different effects of genotype between depressed andnondepressed participants.

Relationship between cognitive domains and regionalbrain volumes

Finally we sought to examine the relationship between cognitivedomains and regional volumes identified in the above analyses.These analyses focused on the z-transformed executive functiondomain score, examining the entorhinal cortex, parahippocampalgyrus, and caudal and rostral anterior cingulate cortex volumes.After controlling for demographic differences, normalizedparahippocamapal gyrus (F1,124 = 7.66, p = 0.0065) and rostralanterior cingulate cortex volumes (F1,124 = 6.18, p = 0.0143)were significantly associated with executive function. Therewas also a trend for the caudal anterior cingulate cortex volume(F1,124 = 3.86, p = 0.0518) but no relationship with entorhinalcortex volume (F1,124 = 2.06, p = 0.1536).

Discussion

Our major finding is that in a younger adult population, theAPOE ε4 allele reduces the effect of aging on executive func-tion and regional brain volumes in temporal and anterior cin-gulate regions. Overall APOE ε4 carriers exhibited less reduc-tion in executive function and regional volumes with age thandid APOE ε4 noncarriers. We did not observe a direct effect ofAPOE genotype on cognition or brain morphology. We alsodid not find evidence to support that depression diagnosisinteracted with APOE genotype to result in greater deficitsin cognition or altered brain morphology. These findings werepersistent across the entire sample, then also when we limitedthe sample to individuals who did not carry the ε2 allele.

Integration with aging literature

Our analyses of APOE genotype’s effect in younger adults areconcordant with and extend past work. These findings supportthe theory of antagonistic pleiotropy (Han and Bondi 2008),or that the ε4 allele has different effects on cognition at differ-ent ages. This may represent superior compensatory functionduring midlife. Had our sample’s age extended past age50 years, we anticipate that we would observe a downwardcurve in the age-cognition relationship in older ε4 carriers.

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Recent work suggests this more rapid rate of cognitive declinein ε4 carriers occurs around age 70 years (Jack et al. 2015),well beyond our current sample.

There is still uncertainty about what cognitive domains maybe differentially affected by APOE genotype. We found thatgenotype influenced the effect of age in our executive functiondomain (specifically performance on the Trails B and COWA)and visual memorymeasured with the BVRT. These findings aresupported by past studies (Marchant et al. 2010), althoughAPOEeffects on attention (Rusted et al. 2013) may also be influencingperformance on our executive function tests.

Our neuroimaging findings follow a pattern similar to ourcognitive findings. These results are discrepant with a previ-ous study that did not observe interactive effects of APOEgenotype and age on structural measures (Filippini et al.2011). However, that study examined a smaller sample withtwo distinct age populations rather than a young to midlifeadult sample with a continuous age range.

This work supports that aging is associated with poorer cog-nitive performance and smaller brain volumes, even in young tomidlife adults. Our observations occur before ages where signif-icant increases in amyloidosis occur (Jack et al. 2015) and suchaging effects are reduced in APOE ε4 carriers. Brain aging inyounger adults may be due tomultiple causes, including vascularrisk factors, early tauopathy (Crary et al. 2014, Jack et al. 2013),or occur in the absence of any specific pathology (Jagust 2013).Further work is needed to better understand what processes aremodified in APOE ε4 allele carriers and how that benefit tocognition and brain structure is obtained prior to later amyloiddeposition and cognitive decline.

Integration with depression literature

It is clear that neuropathology related to cognitive decline canoccur decades before the onset of clinical symptoms (Jack

et al. 2013) and that earlier-life onset is associated with in-creased dementia risk (Byers et al. 2012). However, there arefew studies examining the relationship between depressionand risk factors for Alzheimer’s disease in younger adult pop-ulations. We observed neither differential effects of APOEgenotype nor different genotype by age interactions betweendepressed and nondepressed individuals. This does not sup-port a theory that cognitive or morphological differences re-lated to APOE genotype may adversely affect neural circuitryin such a way that it would increase a predisposition to de-pression. Overall this suggests that the ultimate pathways bywhich depression increases dementia risk does not occurthrough apolipoprotein E mediated mechanisms in youngeradults. However, the underlying nature of the relationship be-tween depression and dementia remains an important questionand alternative pathways should be considered, such as therelationship between depression and proinflammatoryprocesses.

Study strengths and limitations

The study has several strengths, including a large sample sizeand comprehensive neuropsychological testing battery.Moreover image analyses utilized a combination ofhypothesis-testing and hypothesis-generating approaches.These methods are complementary, as we found no re-lationship between study measures and cortical thick-ness, but did find volumetric differences in cingulatecortex regions and subcortical structures. Finally, inpost-hoc analyses, we demonstrated a relationship be-tween the identified cognitive domain (executive func-tion) and identified brain regions (parahippocampal gy-rus and rostral anterior cingulate cortex).

The study also has limitations. Although we have a broadcognitive battery, specific domains such as attention (Rusted

Table 2 Interactive effects ofAPOE genotype and age on apriori brain region volumes

All MRI Subjects (N = 131) APOE ε3 and ε4 carriers only (N = 114)

Region F value p value F value p value

Amygdala F = 3.37 p = 0.0685 F = 2.09 p = 0.1514

Cingulate, Caudal Anterior F = 5.94 p = 0.0161 F = 4.18 p = 0.0434

Cingulate, Rostral Anterior F = 5.08 p = 0.0260 F = 3.94 p = 0.0496

Cingulate, Isthmus F = 0.14 p = 0.7057 F = 0.62 p = 0.4338

Cingulate, Posterior F = 0.86 p = 0.3565 F = 1.16 p = 0.2838

Entorhinal cortex F = 6.64 p = 0.0111 F = 5.35 p = 0.0226

Hippocampus F = 3.10 p = 0.0808 F = 2.71 p = 0.1025

Parahippocampal gyrus F = 3.06 p = 0.0828 F = 3.92 p = 0.0503

Precuneus F = 0.45 p = 0.5028 F = 0.15 p = 0.7009

Variables for the entire sample analyses had 1129 df. Variables in analyses where ε2 carriers were excluded had1112 df. Mixed models included the independent variables of APOE ε4 genotype, diagnosis (depressed/neverdepressed), age, sex, race, intracranial volume, and hemisphere

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et al. 2013) were not assessed. Additionally, this was a well-educated sample and it is unclear if these results would gen-eralize to populations with lower education. Moreover,although we examine age effects, this remains a cross-sectional study. Ultimately longitudinal studies are nec-essary to examine change in such measures and agingtrajectories. Finally, there are notable limitations relatedto the sample, including a lower number of APOE ε4allele carriers than ε4 noncarriers, although this limita-tion applies to much of the existent literature. The agedifference between the diagnostic cohorts is also impor-tant. The strong effect of age on both cognitive andbrain aging may have negatively affected our ability todetect a diagnosis by genotype interaction.

It should be emphasized that despite examining a relativelylarge sample, we made numerous statistical comparisons. For

our a priori hypotheses, we did not control for multiple com-parisons, thus increasing the risk for a type 1 error. Given thesignificance levels observed, correcting for multiple compar-isons would have resulted in null findings. Thus while ourfindings fit within past literature, they should be viewedcautiously.

Despite these limitations, this study advances our un-derstanding of the aging process by demonstrating thatthe APOE ε4 allele mitigates the effect of age on cog-nitive processes and brain structure in early- to midlife-adulthood. This occurs prior to significant increases in amy-loid deposition as detected by PETscanning (Jack et al. 2015),suggesting that the ε4 allele may modify other processesinfluencing brain aging. More work is needed to determinewhat those processes are, but also to determine how depres-sion may increase risk of dementia.

Fig. 2 Differences in age effectson regional brain volumesbetween APOE ε4 positive andnegative individuals. Datapresented are those regionsexhibiting statistically significantage by genotype interactiveeffects on regional volumes, aftercontrolling for depressiondiagnosis, sex, race, intracranialvolume and hemisphere. Similarfindings were observedunilaterally for the hippocampus,parahippocampus, and amygdala(figures not shown). APOE ε4noncarriers are triangles/solidline, APOE ε4 carriers aresquares/dashed line. Z-transformed test performancescore on the y-axis, age in yearson x-axis. Regional volume inmilliliters on the y-axis, age inyears on x-axis

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Acknowledgments This project was supported by National Institute ofMental Health (NIMH) grant R01 MH077745. It was further supportedby CTSA award UL1TR000445 from the National Center for AdvancingTranslational Sciences (NCATS) and conducted in part using the re-sources of the Advanced Computing Center for Research andEducation at Vanderbilt University, Nashville, TN.

Compliance with ethical standards

Disclosures All authors (including Dr. Taylor, Mr. Boyd, Ms. Turner,Mr. McQuoid, Dr. Ashley-Koch, Dr. MacFall, Dr. Saleh, and Dr. Potter)declare they have no conflict of interest.

Informed consent statement All procedures followed were in accor-dance with the ethical standards of the responsible committee on humanexperimentation (institutional and national) and with the HelsinkiDeclaration of 1975, and the applicable revisions at the time of the inves-tigation. Informed consent was obtained from all patients for being in-cluded in the study.

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Risacher, S. L., & Saykin, A. J. (2013). Neuroimaging and otherbiomarkers for alzheimer’s disease: the changing landscape ofearly detection. Annual Review of Clinical Psychology, 9,621–648.

Risacher, S. L., Shen, L., West, J. D., Kim, S., McDonald, B. C., Beckett,L. A., et al. (2010). Longitudinal MRI atrophy biomarkers: relation-ship to conversion in the ADNI cohort. Neurobiology of Aging,31(8), 1401–1418.

Rusted, J. M., Evans, S. L., King, S. L., Dowell, N., Tabet, N., & Tofts, P.S. (2013). APOE e4 polymorphism in young adults is associatedwith improved attention and indexed by distinct neural signatures.NeuroImage, 65, 364–373.

Schultz, M. R., Lyons, M. J., Franz, C. E., Grant, M. D., Boake,C., Jacobson, K. C., et al. (2008). Apolipoprotein E genotypeand memory in the sixth decade of life. Neurology, 70(19 Pt2), 1771–1777.

Sheehan, D. V., Lecrubier, Y., Sheehan, K. H., Amorim, P., Janavs, J.,Weiller, E., et al. (1998). The Mini-International NeuropsychiatricInventory (M.I.N.I.): the development and validation of a structureddiagnostic interview for DSM-IV and ICD-10. The Journal ofClinical Psychiatry, Suppl 20, 22–33.

Sheline, Y. I., Pieper, C. F., Barch, D. M., Welsh-Boehmer, K.,McKinstry, R. C., MacFall, J. R., et al. (2010). Support for thevascular depression hypothesis in late-life depression: results of a2-site, prospective, antidepressant treatment trial. Archives ofGeneral Psychiatry, 67(3), 277–285.

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WMHs in LLDLLD is consistently associated with greater WMH severity25–28 andgreater measured WMH volumes.10,29–31 These observationsguided proposals for MRI-defined vascular depression diagnosticcriteria that include a lesion severity criterion.32,33 In suchconceptualizations, age at the initial depressive episode is animportant consideration. Compared with early-onset depression,individuals with a later onset (for example, after 50 years) exhibitgreater WMH severity5,18,31,34–38 and cognitive impairment.39,40

However, ‘vascular depression’ as a potential diagnostic entitymay not be limited to late-onset patients. Individuals with anearlier onset are at increased vascular risk, as precedentdepression is associated with increased risk of vascular diseaseand stroke.41–45 Depression in early and midlife may promoteinflammation46–48 or epigenetic modifications of genes related tovascular homeostasis.49 Thus, some individuals with early-onsetdepression may be prone to ‘vascular depression’ later in life.

Localization of WMHsAlthough many studies do not specify lesion location, distinctionsin WMH location are important in understanding their role in LLD.In nondepressed samples, periventricular WMHs are more closelyassociated with cognitive impairment than are deep WMHs.50–52 Itis possible that this is due to anatomical differences, as theperiventricular region has a high density of long associating fiberswith cortical–subcortical connections, whereas subcortical deepwhite matter has a high density of shorter U-fibers connectingadjacent cortical regions.52,53 However, as WMH severity acrossregions is highly correlated, distinctions between periventricularand deep WMH may be arbitrary and reflect total WMH disease.54

Nonetheless, others have localized WMHs associated withdepression to the frontal55–58 and temporal lobe.59 Morerecently several groups reported that LLD is associated withgreater WMH severity in specific white matter fiber tracts,including the cingulum bundle, uncinate fasciculus and superiorlongitudinal fasciculus.60–62

WMHs and cerebral functionA small number of studies suggest that greater WMH burden isassociated with disrupted function in motor, cognitive and affectivecircuits. During a simple motor task, high WMH burden is associatedwith low BOLD functional signal,63 suggesting that WMH diseaseinterferes with the neural circuit mediating the task. Otherstudies64,65 associated low BOLD activity with poorer performanceon executive tasks, supporting the model of altered white matterintegrity leading to cognitive network dysfunction. Greater WMHvolume is further associated with altered default mode networkconnectivity at rest between the posterior cingulate cortex andmedial prefrontal cortex (PFC).66 During affective tasks in LLD, WMHsare associated with altered functional activity with an exaggerationof the functional ‘depression’ fMRI patterns: individuals with highWMH burden showed hyperactivation in limbic regions whenpresented with fearful faces.67

Neuropathological studiesStudies utilizing both neuroimaging and neuropathologicaltechniques demonstrate that WMHs represent a wide range ofpathological processes, including perivascular demyelination,arteriosclerosis, ischemia, gliosis, or partial loss of myelin andaxons.15,68,69 Generally, confluent deep WMH but notperiventricular WMH appear to be related to ischemic processes.70

There are also regional differences in WMH etiology. Seminalwork by Thomas and colleagues14 showed that deep WMH andpunctate lesions of depressed older adults are most likely to haveischemic origins. In LLD ischemic lesions are also more likely tooccur in the dorsolateral prefrontal cortex, instead of the anterior

cingulate cortex or occipital lobe. Similarly, depressed eldersexhibit increased expression of cellular adhesion molecules in thedorsolateral prefrontal cortex, but not the anterior cingulate cortexor occipital lobe.71–73 Cellular adhesion molecules are inflam-matory markers whose expression is increased by ischemia,supporting a role for ischemia in LLD and highlighting therelationship between vascular and inflammatory processes.Although ischemic pathology is thus consistently localized tothe dorsolateral prefrontal cortex, other frontal regions may alsobe involved. Depressed elders exhibit decreased density ofpyramidal neurons in the orbitofrontal cortex, which may be theresult of vascular processes74 in arterioles and medium arteries,75

or alterations in astrocyte-associated immune function.76

Neuropathological studies also demonstrate that LLD candevelop in the absence of significant vascular abnormalities. In astudy of late-onset depressed elders, depression was not relatedto either lacunes or microvascular lesions.77 Similarly, in apopulation-based study in which depression was ascertained ina pre-mortem diagnostic interview, depression was associatedneither with cerebrovascular nor Alzheimer pathology.78 However,in contrast to prior studies,79 depression was associated with thepresence of Lewy bodies.78 Such findings highlight theheterogeneity of pathologies that present as depression.

THE DEPRESSION-EXECUTIVE DYSFUNCTION SYNDROME ANDCOGNITIVE DEFICITS IN LLDA ‘depression-executive dysfunction syndrome’4 wasconceptualized as the clinical expression of frontal networkimpairment caused by vascular and other age-related factors.Accordingly, this syndrome describes depressed patients withvascular disease and evidence of impairment in networks relatedto mood and executive function.80

Executive function is a frontally mediated domain81–83 thatencompasses cognitive processes including selective attention,response inhibition and performance monitoring. It is clinicallyexpressed as difficulty with planning, sequencing, organizing andabstracting. These deficits are common in LLD,84–87 particularly inlate-onset depression.40,86,88 Efforts to characterize thepresentation of the depression-executive dysfunction syndromehave dichotomized samples by their performance on executivefunction tasks. When compared with depressed elders withoutexecutive dysfunction, patients with depression-executivedysfunction exhibit reduced fluency, impaired visual naming,suspiciousness, anhedonia, psychomotor retardation andsignificant disability.89–91 As discussed later, studies across eightdifferent samples identified executive dysfunction as a predictorof poor antidepressant response.92–99

Broader cognitive deficits in LLD: relationship with vascular riskfactorsIndividuals with LLD exhibit executive dysfunction and deficitsacross other cognitive domains, including episodic memory,working memory, visuospatial ability and processingspeed.84,87,100–105 Processing speed deficits may influence othercognitive deficits103–105 and mediate in part executive taskperformance.106 Although cognitive performance improves withsuccessful antidepressant treatment, such deficits can persist andperformance may not be restored to a normal level.100,107–109

Cognitive deficits are associated with vascular dysfunction. Innondepressed cohorts, there are associations between episodicmemory, psychomotor speed and executive function, andmeasures of vascular dysfunction, such as hypertension, heartrate variability and orthostasis.110–112 In LLD, Framingham VascularRisk scores113 are associated with deficits in processing speed,executive function and episodic memory.98,104 Greater WMHburden is associated not only with executive dysfunction,

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perseveration and slowed processing speed12,61,114–117 but alsowith episodic and visuospatial memory deficits.102,110,118,119 Thesecross-sectional observations are supported by longitudinal studies,demonstrating that progression of WMH severity parallelscognitive decline.120–122

WMHs localized to specific fiber tracts are associated withspecific cognitive deficits. Executive function deficits are asso-ciated with WMHs in the uncinate fasciculus, superior longitudinalfasciculus and the cingulum bundle.123 Similarly, episodic memorydeficits are associated with WMHs in the cingulum bundle andinferior longitudinal fasciculus.123,124

VASCULAR DEPRESSION AND COURSE OF DEPRESSIONCognitive dysfunction and antidepressant outcomesIn LLD, the relationship between treatment outcome andexecutive dysfunction is well established. Multiple studiesdemonstrate that executive dysfunction predicts poor acuteresponse of LLD to antidepressants.92,94,96,97,99,125 Executivedysfunction is also associated with high relapse and recurrencerates,126 although not all studies found this association.127 Much ofthis work used the Mattis Dementia Rating Scale Initiation-Perseveration (I/P) subtest, which yields a composite score ofseveral executive skills.128 Notably, a recent meta-analysisexamined the relationship between antidepressant response andperformance across multiple executive tests, including the I/Psubtest, the Wisconsin Card Sort Test, the Stroop Color-Word testand others. The authors concluded that the I/P subtest was theonly test providing reliable discrimination between antidepressantresponders and nonresponders.129 Further analysis of I/P subtestcomponents showed that semantic strategy (semantic clustering)while performing the I/P verbal fluency task explained most of thevariance in predicting remission.130 Effective semantic strategyappears to be associated with treatment response regardless ofthe probing task; effective semantic strategy during verballearning task was also associated with high LLD remissionrate.131 These observations suggest that preserved semanticorganization, rather than fluency or verbal learning, is critical forremission during antidepressant treatment. However, impairedresponse inhibition is another aspect of executive dysfunction thatinfluences antidepressant response.92,95,125,132

Corroborating the role of the cognitive control network in LLDare reports examining resting state functional connectivity (FC).Low-resting FC in the cognitive control network, but not in thedefault network, predict poor, acute antidepressant remissionrates along with persistence of depression, apathy and dys-executive behavior.133

Other cognitive deficits can influence outcomes. Poorerperformance on tests of episodic memory, language processingand processing speed are associated with poorer acute anti-depressant response,98 and may predict poorer long-term courseof depression.134 Further analysis is needed to identify asuperordinate cognitive dysfunction that accounts for therelationship of these tests to antidepressant response.

WMHs as a predictor of antidepressant outcomesAlthough greater WMH severity is often associated with pooreracute antidepressant response,36,98,99,135–138 results areinconsistent.97,139–142 Importantly, these studies assess totalcerebral WMH severity, while the effect of WMLs on theantidepressant response may depend on lesion location.138

Unfortunately, methodological differences in assessing WMHseverity make it difficult to reconcile discrepant findings acrossstudies.143,144

Most studies used cross-sectional WMH measures to predictoutcome. However, WMHs are a progressive rather than a staticprocess,11,145 and rate of change may be a more important

predictor. The few studies in LLD cohorts examining this questionfound that greater increases in WMH volume over 2- and 4-yearintervals are associated with nonremission or relapse.146,147

Some have examined both WMH severity and cognitivedysfunction as predictors of outcome. In these studies cognitivemeasures are generally a better predictor of antidepressantresponse than are WMH measures. The largest published studyexamining a 12-week response to sertraline found that poorerresponse was independently associated with greater WMHseverity and poorer performance in cognitive domains of episodicmemory, language processing, processing speed and executivefunction. However, in models controlling for baseline depressionseverity, WMH severity was no longer associated with response.98

Smaller studies have found similar patterns, wherein initialassociations between WMH severity and response were not statis-tically significant in models incorporating cognitive measures.97,99

Other measures of white matter integrity are associated withantidepressant response. In voxel-wide analysis, microstructuralwhite matter abnormalities (low fractional anisotropy, FA) inmultiple frontolimbic brain areas, including the rostral and dorsalanterior cingulate, dorsolateral PFC, genu of the corpus callosum,hippocampus and posterior cingulate, were associated withnonremission.148 In region of interest analyses, low FA in thecorpus callosum, left superior corona radiata and right inferiorlongitudinal fasciculum, was associated with lower remissionrates.149 In contrast, others found that higher prefrontal whitematter FA was associated with a failure to remit toantidepressants,150 a finding that parallels reports in depressionof increased FC to a dorsal nexus.151 Notably, depressed elderly5-HTTLPR polymorphism short allele carriers had lower FA thanlong allele homozygotes in frontolimbic areas, including theanterior cingulate, posterior cingulate, dorsolateral and medialprefrontal regions.152 Such differences in FA can be caused byseveral pathologies, not all of which are vascular in nature.

VASCULAR RISK GENES IN DEPRESSIONThere is a wide literature examining genetic influences inLLD, including reports examining polymorphisms in BDNF,APOE and serotonin transporter (5-HTTLPR) genes, amongothers (see recent reviews by Lotrich153 and Benjamin andTaylor154). Within this field, there are an increasing number ofreports associating LLD with genetic polymorphisms, increasingvascular risk.

One example of this literature includes studies examininggenetic variation of the renin-angiotensin system (RAS). The RASregulates fluid homeostasis and blood pressure, and is a target fortwo classes of antihypertensive drugs. Beyond influencing vascularfunction, RAS polymorphisms may also increase the risk ofdepression via modulation of monoamines155 or contributing tohypothalamic–pituitary–adrenal axis dysregulation.156 Resultsfrom studies examining the common angiotensin-convertingenzyme (ACE) insertion/deletion polymorphism are mixed inadult depression157–162 and suicidality,162–164 although other ACEpolymorphisms may be important.161,165,166 Despite its key role inthe RAS, the gene encoding for the angiotensin II type 1 receptor(AGTR1) is less well studied. The CC genotype of the A1166C(rs5186) polymorphism is associated with increased responsive-ness to Ang-II.167 However, despite one study associating theCC genotype with increased risk of major depressive disorder,159

this was not replicated in an elderly sample.168

RAS variation is further related to differences in brain structureand function. Several studies in older adults have now associatedvariants in ACE and AGTR1 with altered cerebral frontotemporalstructure169–172 and abnormal default mode network activity.173

AGTR1 variants are associated with greater WMH progression,particularly in men,168 and this effect may be localized to specifictracts.60

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RAS polymorphisms may also influence antidepressant out-comes. In a general adult population, the ACE D variant and theAGTR1 C1166 allele are associated with better acute antidepres-sant response.174,175 However, this contrasts with a study of LLD,wherein AGTR1 C1166 allele homozygous individuals exhibited apoorer antidepressant response over a longer period.176 Such agedifferences could be important, reflecting stress reactivity in earlierlife and allostatic effects of an overly active RAS later in life.

MECHANISTIC HYPOTHESESAccording to a model by Alexopoulos,1 the clinical expression ofLLD is mediated by altered brain activity in cognitive and affectivecircuits, characterized as hypometabolism of dorsal corticalregions and hypermetabolism of ventral limbic structures. Age-,disease- and stress-related neurobiological changes serve asetiological factors, contributing to alterations in circuitsregulating mood and cognition. The impact of theseneurobiological contributors can be increased by vulnerabilityfactors originating from preexisting differences in mood circuitry.

Arguably, such etiological factors may lead to depression onlyafter crossing a certain threshold, above which they acquire adose-effect relationship (Figure 1). In this conceptualization, singleor multiple potential ‘etiological factors’ may reach an initialseverity threshold and contribute to LLD ‘vulnerability’ due tofrontolimbic compromise evident through imaging or abnormalperformance on cognitive or affective tasks. When etiologicalfactors further increase in severity and cross a second threshold,they may have a greater and direct effect on mood circuitfunction, leading to affective and cognitive symptomatology. Ofcourse, instead of threshold effects such relationships could becumulative following linear or curvilinear patterns.

Such a threshold model can account for multifactorialcontributions to LLD. It also explains why our best clinical andbiological measures predict only a modest part of the variance indistinguishing LLD from controls and in predicting treatmentresponse as any individual measure may itself not cross a crucialthreshold. This model allows for mixed syndromes of multipleetiological contributors, if each disturbance alone or in combina-tion is strong enough to cross the threshold required to induceneural circuit changes mediating LLD.

Given the central role of vascular disease and WMHs in much ofthe literature, it is worth examining their role within this model.WMHs can be conceptualized as visible markers of ischemicdamage, although vascular compromise may result in insults to

white and/or gray matter that are not visible on conventionalneuroimaging.177 In this context, greater WMH severity representsa state of vascular compromise and ischemic injury in specificregions. Depending on the severity of the underlying WMLs andhow strongly they affect affective and cognitive circuitry, suchdamage may itself predispose individuals to depressive episodes.However, WMHs may also be an index of the severity ofunderlying vascular disease affecting neural circuits. In this case,the direct effect of WMHs on neural circuits may be modestcompared with that of the underlying vascular process.

The disconnection hypothesisBuilding on the concept of ‘disconnection syndromes,’ ischemiaand WMLs may contribute to depression by disrupting neuralconnections among regions regulating mood and cognition.178 Inthis model, global cerebral WMH severity is less germane to LLDthan is focal damage to specific fiber tracts and neural circuits.Such focal damage would adversely affect the tract’s structuraland functional connectivity. In turn, this state adversely affects thefunction of connected regions at rest and during cognitive tasks,and contribute to neural circuitry alterations that mediate clinicalsymptoms and influence antidepressant response.

This view is supported by studies examining WMH location.Recent work demonstrates that LLD is associated with greaterWMH severity in specific tracts including the cingulum bundle,uncinate fasciculus and superior longitudinal fasciculus.60–62

In addition, greater WMH severity in the uncinate andsuperior longitudinal fasciculi is associated with executivedysfunction61,123,124 and greater depression severity.179

These macrostructural MR findings are supported by diffusiontensor imaging (DTI) studies, examining white matter microstruc-ture. DTI changes reflect various pathologies leading to decreasedmyelin integrity, including demyelination secondary to cerebro-vascular and inflammatory changes. WMLs occurring in fiber tractsare also associated with DTI changes.177,180 LLD is associated withdifferences on DTI measures of the uncinate fasciculus, cingulumbundle, anterior thalamic radiation and superior longitudinalfasciculus.181,182 The uncinate fasciculus and cingulum bundle areparticularly relevant to LLD183–188 because of their role incognition and emotion processing,188,189 although other tractsare also associated with antidepressant response in LLD.149

Importantly, differences in the uncinate fasciculus and cingulumbundle are also observed in younger, depressed adults.190,191

Although those younger adult studies do not necessarily suggest avascular cause in that population, they support that structuralalterations in these fiber tracts are associated with depression.

Structural differences have functional effects. Fiber tractstructural connectivity is positively correlated with resting stateFC of connected regions.192–196 These data warrant acomprehensive examination of the relationships amongischemic focal WML injury, damage to tract microstructure,regional connectivity and circuit function. Examination of such a‘lesion model’ can identify neural circuit deficits contributing todepression and predictive of poor outcomes.

The inflammation hypothesisAging- and disease-related processes promote proinflammatorystates in older individuals.197,198 Further, immune activation canbe a characteristic of depression48,199 and precipitate depressivesymptoms.200 Alexopoulos and Morimoto201 recently proposedthat immune dysregulation may promote the development ofaffective and cognitive symptoms in LLD. This view is supportedby studies in midlife adult populations. Administration ofcytokines or induction of peripheral inflammation results in aninflammatory response, which in turn is correlated with fatigue,slowed reaction time202 and mood reduction.200,203 Even withoutmedical illness, depressed individuals exhibit increased levels of

Figure 1. Proposed progression of etiological processes affectingfrontolimbic function. After crossing an initial threshold, vascular orother processes may serve as one of many factors that increasevulnerability to depression through compromise of affective andcognitive brain circuits. As the underlying disease process pro-gresses, its adverse effects on the affective and cognitive circuitswould likewise increase, resulting in the circuit dysfunctions thatcharacterize depression.

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proinflammatory cytokines204,205 and reduced anti-inflammatorycytokine levels.206,207

Several mechanisms may explain these relationships. Proin-flammatory cytokines affect monoamine neurotransmitter path-ways,208 including indoleamine 2,3-dioxygenase upregulation andkynurenine pathway activation.209–211 This results in decreasedtryptophan and serotonin and increased synthesis of detrimentaltryptophan catabolites that promote hippocampal damage andapoptosis.211,212 Cytokines, including interleukin (IL)-1b, alsoreduce extracellular serotonin levels by activating the serotonintransporter.213 In addition, proinflammatory cytokines disruptglucocorticoid receptor function214 and reduce neurotrophicsupport.215

Peripheral inflammation is associated with altered brainfunction in areas mediating cognition and mood. In healthyindividuals, typhoid-induced IL-6 levels correlate with lower mood,increased subgenual anterior cingulate cortex activity duringaffective processing and altered subgenual cingulate connectiv-ity.203 Laboratory-administered social stressors increaseinflammatory maker levels that in turn are associated withactivation of the dorsal anterior cingulate cortex and anteriorinsula.216 In bereaved women undergoing grief elicitation,inflammatory markers were positively associated with subgenualcingulate and orbitofrontal cortex activation.217

Supporting this work is a growing literature demonstrating thatgenetic polymorphisms promoting proinflammatory processesmay contribute to depression,218,219 and in LLD may be related toage of onset.220 Such relationships are likely moderated throughan effect on cytokine levels that in turn are associated with sadmood221 and altered emotion processing.222

Regarding LLD, the aging process disrupts immune function,223

increasing peripheral immune activity and shifting the CNS into aproinflammatory state.198 Elevated peripheral cytokine levels areassociated with depressive symptoms in older adults, with the mostconsistent finding being for IL-6, but also implicating IL-1b, IL-8 andtumor necrosis factor-a.224–228 Proinflammatory states in older adultsare associated with cognitive deficits, including poorer executivefunction, poorer memory performance, worse global cognition andsteeper decline in cognition.229–231 Finally, greater IL-6 and C-reactiveprotein levels are associated with greater WMH burden.232–235

Successful antidepressant treatment may reduce proinflamma-tory markers.48 This action may be a direct effect as in vitro studiesdemonstrate that antidepressants reduce proinflammatory markerswhile increasing levels of anti-inflammatory cytokines.236–240 Resultsin clinical populations are inconsistent, but a recent meta-analysisconcluded that antidepressants, particularly SSRIs, reduce IL-6, IL-1band tumor necrosis factor-a.241 Additional evidence supports thatsome anti-inflammatory drugs may have antidepressantproperties,242–244 particularly in individuals with elevated pre-treatment proinflammatory markers.245

Importantly, proinflammatory processes also contribute toneurodegenerative processes. Increased peripheral inflammatorymarker levels are associated with increased risk for all-causedementia,246 and central inflammatory processes significantlycontribute to the pathogenesis of Alzheimer’s disease.247 How-ever, nonsteroidal anti-inflammatory drugs have not consistentlydemonstrated clinical benefit in Alzheimer’s disease,247 and in arecent consensus statement were considered but not recom-mended as a priority for further investigation.248

The hypoperfusion hypothesisVascular dysregulation is common in LLD249–254 and CBFreductions can impair regional brain function, contributing toaffective and cognitive symptoms. Regional cerebral metabolicactivity is tightly correlated with CBF, which is regulated by localinteractions between neurons, glia and the vasculature.255,256

Blood flow to the brain is influenced by systemic hemodynamics

and cerebrovascular autoregulation, with cerebral arteriescontracting or dilating as arterial pressure changes. Theseprocesses interact to maintain stable perfusion. However, theseprocesses are impaired in the context of vascular disease:256

hypertension, diabetes and atherosclerosis leading to vascular wallhypertrophy, reduced arterial lumen diameter, reduced arterialdistensibility and endothelial cell dysfunction.256–258 Such vascularchanges, including increased intima media thickness, increasedarterial stiffness and endothelial dysfunction are pronounced inLLD populations,249–254 and endothelial function may beparticularly poor in antidepressant nonresponders.259 Suchvascular pathology results in reduced blood flow velocities anddecreased vasomotor reactivity,260,261 thus adversely affecting CBF.

Perfusion deficits do not need to cause ischemia in order toinfluence brain function. Reduced CBF impairs protein synthesisthat is262 crucial for cognitive processing263,264 and formaintaining the integrity of cortical functional maps.265 Thus,mild CBF reduction may impair cognitive and affective processes,while greater CBF reduction in the context of autoregulatorydeficits may cause ischemic injury. The subcortical white matteris particularly sensitive to these changes because it is suppliedby terminal arterioles with limited collateral flow,266 and sosusceptible to infarction due to impaired autoregulation.267

Greater WMH severity may be a marker of broader deficits inperfusion and autoregulation as individuals with greater WMHseverity exhibit reduced CBF in both white matter and gray matterregions.268–270 Depressed elders with greater WMH severity alsoexhibit decreased perfusion in the cingulate gyrus,271 a regioninvolved in cognitive and affective processing.272

Positron emission tomography, single-photon emission com-puted tomography and MRI arterial spin labeling have identifiedCBF differences in midlife adult depressed populations.273–276

Advanced age is itself associated with decreased frontotemporalCBF,277 an effect mediated by vascular risk factors.278 In LLD,perfusion deficits are more severe, particularly in the medial andlateral PFC, subcortical and temporal structures.269,279–283 Deficitsin the dorsolateral PFC may improve with antidepressantresponse, while other perfusion deficits may not.281 Othersreport no change in CBF in antidepressant nonresponders,284–288

suggesting that persistently reduced regional CBF may be abiomarker of nonresponse.

Cerebral hypoperfusion is associated not only with Alzheimer’sdisease and mild cognitive impairment,289–291 but also withabnormal performance on specific cognitive tasks.291–294 Forexample, resting hippocampal blood flow is positively correlatedwith spatial memory performance.292 Although not specifically ameasure of perfusion, reduced arterial CBF predicts slowerprocessing speed,295 a domain that may mediate other deficitsin LLD.103 Perfusion deficits may thus contribute to the affectiveand cognitive symptoms observed in LLD. Intriguingly, these datasupport investigating the utility of agents that may improveperfusion or restore endothelial function.296,297

ROLE OF VASCULAR RISK FACTORS IN LLDFifteen years after the formulation of the ‘vascular depression’hypothesis,2 a confluence of findings suggests that:

1 Vascular risk factors and radiological findings are oftenassociated with LLD.

2 Cognitive deficits across multiple domains are common in LLDand associated with vascular risk factors and greater WMHseverity. Such deficits, particularly executive dysfunction,predict poor response to antidepressants.

3 WMHs, although associated with cognitive dysfunction, do notconsistently predict poor antidepressant response. However,progressive WMH disease is associated with poorer depressioncourse.

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The link between progressive vascular disease and thedevelopment and persistence of LLD invites study of theunderlying mechanisms by which vascular disease alters brainfunction in such a way to mediate the development of clinicalsymptoms. The ultimate goal of this line of study is to developnovel treatments or repurpose existent agents to improve LLDoutcomes.

We propose three mechanistic paths to ‘vascular depression.’Although the processes of these pathways could proceedindependently, they are complementary and interconnected andso may all contribute to LLD (Figure 2). For example, inflammationparticipates in vascular remodeling, accelerates vasculardamage298 and can contribute to perfusion deficits. In turn,regional perfusion deficits adversely affect gray matter functionand contribute to the development of WMLs that disrupt regionalstructural and FC.

We are not proposing a single mechanism underlying LLD.Nonvascular factors clearly contribute to LLD. The same genetic,epigenetic and environmental factors that contribute to depres-sion in younger adults continue to confer vulnerability todepression in later life. Moreover, although alterations in immuneand endocrine regulation affect vascular risk, they may alsoincrease the risk of depression through independent mechanisms.

Just as there are nonvascular factors contributing to LLD, theremay be additional mechanisms linking vascular disease with LLD.Altered homocysteine metabolism is associated with LLD,299 withhyperhomocysteinuria potentially contributing to vascular risk byincreasing atherosclerosis and endothelial dysfunction.300 Another

example is altered sympatho–adrenomedullary system andhypothalamic-pituitary-adnenal axis function301 that, amongother activities, affects inflammation.302 These and othermechanisms may contribute to the development orperpetuation of LLD both through vascular and nonvascularpathways and require further study. Such studies would benefitfrom the incorporation of genomic approaches along withmeasures of systemic function, thus linking molecularmechanisms with clinically salient measures and outcomes.

Demonstrating that such mechanisms affect neural circuitfunction, it opens the door for intervention studies examiningwhether modulation of these mechanisms can improve depres-sion outcomes. There is already preliminary support for using anti-inflammatory drugs in depression. Etanercept, a soluble tumornecrosis factor-a receptor, and celecoxib, a cyclo-oxygenase-2inhibitor, may reduce depressive symptoms in patients withinflammatory diseases242,243 and infliximab may improvedepression in patients with greater pre-treatment inflam-mation.245 Similarly, omega-3 fatty acids are anti-inflammatoryand also affect monoamine neurotransmission,303 suggesting anantidepressant role for omega-3 supplementation. However,recent meta-analyses have not provided strong support foromega-3 supplementation for depression, although larger welldesigned trials may be warranted.304,305 Finally, drugs thatmodulate the RAS improve cerebral perfusion297 and mayimprove cognition,306–308 although they have not been wellstudied in depression. We suggest that study of these agents inLLD may produce a novel, targeted pharmacology for the largegroup of LLD patients who are unresponsive to conventionalantidepressants.

CONFLICT OF INTERESTDr. Alexopoulos reports serving as a consultant for Lilly and on the speakers’ bureaufor Astra Zeneca, Forest, Merck, Avanir and Lundbeck. Dr. Alexopoulos also is astockholder of Johnson and Johnson. The remaining authors declare no conflict ofinterest.

ACKNOWLEDGEMENTSThis project was supported by NIH grants R01 MH077745, R01 MH076079, R01MH079414 and P30 MH085943. Dr. Alexopoulos reports research grant support fromForest.

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Figure 2. This model links the disconnection, inflammation andhypoperfusion processes proposed in late-life depression. Vasculardisease may contribute to altered brain function characteristic ofdepression (dorsal hypometabolism, ventral hypermetabolism)either through structural damage adversely affecting connectivity,through perfusion deficits altering regional function, or both.Proinflammatory processes increase vascular risk, but may alsoaffect brain function through independent processes. In this model,vascular disease is an important and central contributor to LLD. Notonly is vascular disease common and perhaps unavoidable in laterlife, but it also interacts with other pathological processes related todepression. However, vascular disease is not the only contributor toLLD. Other biological and environmental factors can increase therisk for depression by alterating neural circuit structure and function.Similarly, genetic influences may increase risk of depression byacting at multiple points, such altering function of neural circuits,changing the biological response to stress, adding to vascular risk,or predisposing to proinflammatory states.

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