Vascular Risk Factors and Depression in Later Life: A Systematic Review and Meta-Analysis

REVIEW

Vascular Risk Factors and Depression in Later Life: A Systematic Review and Meta-Analysis Vyara Valkanova and Klaus P. Ebmeier Reports of the association between cardiovascular risk factors and depression in later life are inconsistent; to establish the nature of their association seems important for prevention and treatment of late-life depression. We searched MEDLINE, EMBASE, and PsycINFO for relevant cohort or case control studies over the last 22 years; 1097 were retrieved; 26 met inclusion criteria. Separate meta-analyses were performed for Risk Factor Composite Scores (RFCS) combining different subsets of risk factors, Framingham Stroke Risk Score, and single factors. We found a positive association (odds ratio [OR]: 1.49; 95% confidence interval [CI]: 1.27–1.75) between RFCS and late-life depression. There was no association between Framingham Stroke Risk Score (OR: 1.25; 95% CI: .99–1.57), hypertension (OR: 1.14; 95% CI: .94–1.40), or dyslipidemia (OR: 1.08; 95% CI: .91–1.28) and late-life depression. The association with smoking was weak (OR: 1.35; 95% CI: 1.00–1.81), whereas positive associations were found with diabetes (OR: 1.51; 95% CI: 1.30–1.76), cardiovascular disease (OR: 1.76; 95% CI: 1.52–2.04), and stroke (OR: 2.11; 95% CI: 1.61–2.77). Moderate to high heterogeneity was found in the results for RFCS, smoking, hypertension, dyslipidemia, and stroke, whereas publication bias was detected for RFCS and diabetes. We therefore found convincing evidence of a strong relationship between key diseases and depression (cardiovascular disease, diabetes, and stroke) and between composite vascular risk and depression but not between some vascular risk factors (hypertension, smoking, dyslipidemia) and depression. More evidence is needed to be accumulated from large longitudinal epidemiological studies, particularly if complemented by neuroimaging.

Key Words: Cardiovascular, major depressive disorder, metaanalysis, old age, risk, stroke

L

ate-life depression (LLD) and particularly late-onset depression (LOD) have been conceptualized as distinct from depression with early onset (EOD) (1–4). Compared with EOD, LOD is more often associated with no family history of depression and depressive ideation but more psychomotor retardation (5,6), cognitive impairment (especially executive dysfunction [7–9]), lack of insight, poor response to treatment (1), and a greater chance of progression to dementia (10,11). In addition, magnetic resonance imaging studies have demonstrated higher rates and greater severity of white matter hyperintensities in LOD compared with healthy volunteers (12–15) and with EOD patients (15,16). The differences between early and late-life depression might be due to different underlying pathophysiological mechanisms (17). The term vascular (or subcortical ischemic) depression postulates a link between cerebrovascular disease and later life depression (18–20). It implies that micro-damage to small vessels compromises the integrity of the frontal-subcortical circuits involved in mood regulation (6,16,21–25). The vascular depression hypothesis can explain increased risk of depression after stroke and myocardial infarction (26–28) and the association of LLD with brain scans suggestive of subclinical cerebrovascular disease (12,21,23). However, studies of common cardiovascular risk factors—such as smoking, hypertension, dyslipidemia and diabetes, and depression—have yielded mixed results. Some studies provide support for an association (29–35), whereas others fail to do so (36–46). There is also strong evidence for a From the Department of Psychiatry, University of Oxford, Oxford, United Kingdom. Address correspondence to Klaus P. Ebmeier, M.D., Department of Psychiatry, University of Oxford, Warneford Hospital, Oxford OX3 7JX, United Kingdom; E-mail: [email protected]. Received Jul 6, 2012; revised Oct 22, 2012; accepted Oct 31, 2012.

0006-3223/$36.00 http://dx.doi.org/10.1016/j.biopsych.2012.10.028

reciprocal relationship (47–52). Recent meta-analyses report that depression predicts incident myocardial infarction and earlier death, coronary artery disease, stroke, other cardiovascular diseases (CVDs), and diabetes; apart from common causes of both CVD and depression, potential mechanisms for depression causing CVD span the depressive stress response, lifestyle factors such as exercise and food intake, as well as aspects of the treatments used (47,53). The importance of vascular risks and diseases preceding depression might not be greater than that of other chronic diseases. Vascular diseases might be associated with depression, not because of associated pathology (i.e., small or large brain vessel disease) but because of their effect on function and the resulting poor quality of life. Consistent with the chronic illness hypothesis, the relationship between vascular risks or diseases and depression was significantly attenuated after controlling for presence of chronic illness (37,54,55), although attribution of variability to one (chronic illness) or the other (vascular risk and disease) will be arbitrary or at least uncertain in most cases. Depression seems associated with poor general health (56), chronic obstructive pulmonary disease (27), chronic renal disease (57), arthritis (27), and loss of hearing or vision (27). Further support for a causal relationship between general chronic illness and depression is provided by a recent prospective cohort study that found an equally strong association between long-term nonvascular conditions and risk of depressive symptoms (46). Although several systematic reviews have focused on the vascular depression hypothesis (13,20,58–62), the relationship between vascular risk factors (VRFs) or vascular diseases and depression has not been quantified. This systematic review and meta-analysis aims to provide an overview of the literature to date, to quantify the extent to which VRFs or vascular diseases might be associated with or might be risk factors for depression in late life, and to consider the contribution of the associated disability. If vascular risks and pathological changes are etiological factors for depression, we expect to find significant associations even after controlling for the complex effects of chronic illness and disability. Establishing the nature of the relationship BIOL PSYCHIATRY 2013;73:406–413 & 2013 Society of Biological Psychiatry

V. Valkanova and K.P. Ebmeier between VRFs or diseases and LLD is important both in terms of prevention and treatment of depression.

Methods and Materials Search Strategy We systematically searched for studies that investigated the association between VRFs and depression in late life. Studies considering vascular diseases such as coronary heart disease together with VRFs were included, because it is a common feature of risk scales to include previous disease. The MEDLINE, EMBASE, and PsycINFO databases were searched for publications in all languages between 1990 and May 2012. The search terms were: [“depress*”] AND [“late life” OR “late onset” OR “older adults” OR “geriatric”]; and second: [“depress*”] AND [“vascular diseases” OR “vascular risk factors” OR “cerebrovascular risk factors” OR “vascular”]. Additional studies were identified from reference lists of relevant reviews and studies. Unpublished literature was identified from the DART Europe E-thesis Portal (dissertations and thesis), ZeTOC (conference proceedings), and Open Grey (Grey Literature) databases. A total of 1097 results were retrieved. After screening of titles and abstracts 140 studies were considered potentially relevant. The inclusion criteria were: cohort or case control studies, age ‡50 years, and frequency or new cases of depression reported with and without VRFs, respectively. After review of the full text, 26 studies met the inclusion criteria. Common reasons for exclusion were review articles, dual publications, or insufficient data to calculate outcome measures. Further reasons for exclusion were “exposure to vascular risk factors not reported” and “depression not reported as an outcome” (Figure 1). Where there was an overlap in samples between studies, the study of higher quality or the one providing stronger evidence was included (e.g., more participants, longitudinal design) (Figure 1). The quality of the studies was assessed by scoring on a selfdevised checklist (Table S1 in Supplement 1) that included the following parameters: sample representativeness, study design, quality of reporting, VRFs measurement, outcome measurement,

Figure 1. Identification and attrition of studies. VRF, vascular risk factor.

BIOL PSYCHIATRY 2013;73:406–413 407 and confounding factors (Supplement 1). Following the recommendations of the Meta-Analysis of Observational Studies in Epidemiology guidelines, we performed a sensitivity analysis excluding studies with a score below 8 (Table 1). Depression in studies was defined as: 1) diagnosis of major depression, minor depression, or dysthymia according to the DSMIII R, DSM-IV, or other standard psychiatric diagnostic criteria; 2) depressive disorder or depressive symptoms, as defined by scores above a cutoff point on a standard mood rating scale (Centers for Epidemiologic Studies Depression Scale, Hamilton Rating Scale for Depression, or Geriatric Depression Scale). Of the studies included, three did not use these criteria. In two studies depressive symptoms were identified through a single question from a questionnaire (31,33), and one study used data recorded by general practitioners in problem lists of patients (44). Data Extraction Data were extracted in a systematic fashion as follows: 1) study characteristics (name, authors, publication year); 2) study design; 3) sample source; 4) sample characteristics (e.g., age, gender); 5) inclusion and exclusion criteria; 6) definition and measures of exposure; 7) definition and measures of outcome; and 8) analysis strategy (statistical models, measures of effect size, confounders that were controlled). Data were extracted independently by both authors, and inconsistencies were resolved by consent. Data Analysis A meta-analysis was performed for studies that use a composite measure of vascular risk (Risk Factor Composite Score [RFCS]). The RFCSs included different subsets of risk factors, and different studies used different RFCS groupings (e.g., two, three, or four groups; the low-risk group in some studies comprised participants without risk factors, whereas in other studies it included participants with one risk factor). To make studies comparable, the data were organized into two categories representing low vascular risk (0 or 1 risk factor) and high vascular risk (2 or more risk factors). A separate analysis was performed for studies using the Framingham Stroke Risk Score (FSRS), because it has been specifically developed for assessing the risk of cerebrovascular disease (especially stroke). The FSRS is also well-validated and widely used (63,64), although whether it predicts incident depression in later life is not known. Most importantly, the studies using the FSRS used the same subset and definition of risk factors, thus increasing the reliability of the results. Separate meta-analyses were also conducted for the single factors smoking, hypertension, diabetes, dyslipidemia, CVD, and stroke. Data were analyzed with Comprehensive Meta-Analysis, version 2.2 (65). First, odds ratios (ORs) with confidence intervals (CIs) were extracted or calculated from the available data (e.g., percentages or fractions, w2 with 1 df). When it was not possible to compute OR directly, standardized mean differences (Cohen’s d) were computed from means and SDs or from regression coefficient and transformed to ORs with conventional formulae (66). A random-effects model was used to calculate the pooled mean effect size. The random-effects model was preferred over a fixed effect model, because the included studies are heterogeneous in terms of population characteristics, definition and measurement of vascular risk, and outcomes (implicating that the true effect size varies from one study to another) and also to allow generalization of the results (67). Heterogeneity across studies was assessed with the Cochrane Q statistic (p ⬍ .10 was considered to indicate statistically significant heterogeneity) and the I2 statistic (25%, 50%, and 75% were considered to represent low, medium, and high heterogeneity, respectively). Publication bias was www.sobp.org/journal

408 BIOL PSYCHIATRY 2013;73:406–413

V. Valkanova and K.P. Ebmeier

Table 1. Weighted Mean Effect Sizes and Measures of Heterogeneity and Bias Vascular Risk Factor

Number of Studies

D HTN Smoking DM DMa CVD CVDa Stroke Strokea RFCS RFCSa FSRS

10 14 10 15 5 10 6 10 5 18 8 5

Begg & Mazumdar Rank Correlation

Egger’s Regression Intercept

Odds Ratio

95% CI (lower and upper limits)

p

Q

p

I2

Kendall‘s t

p (2-tail)

t

df

p (2-tail)

1.08 1.14 1.35 1.51 1.46 1.76 1.40 2.11 1.80 1.49 1.15 1.25

.91–1.28 .94–1.40 1.00–1.81 1.30–1.76 1.14–1.86 1.52–2.04 1.08–1.80 1.61–2.77 1.24–2.62 1.27–1.75 1.02–1.28 .99–1.57

.40 .19 .05 ⬍.0005 .003 ⬍.0005 .01 ⬍.0005 .002 ⬍.0005 .02 .06

15.3 32.7 27.1 18.7 6.3 12.1 10.6 21.9 7.3 71.3 10.2 7.2

.08 .002 .001 .18 .18 .21 .06 .01 .12 ⬍.0005 .18 .12

41.0 60.3 66.8 25.0 36.5 25.7 53.0 58.9 45.4 76.2 31.2 44.6

.07 .21 .16 .42 .20 .16 .07 .11 .4 .10 .07 .2

.79 .30 .53 .03 .62 .53 .85 .65 .33 .57 .80 .62

.37 .80 .20 2.07 .82 .27 .09 .40 2.10 3.94 1.10 1.98

8 12 8 13 3 8 4 8 3 16 6 3

.72 .44 .85 .06 .47 .80 .93 .70 .13 .001 .32 .14

Heterogeneity

CI, confidence interval; CVD, cardiovascular disease; D, dyslipidemia; DM, diabetes mellitus; HTN, hypertension; FSRS, Framingham Stroke Risk Score; RFCS, Risk Factor Composite Score. a Adjusted for chronic illness/disability.

assessed with funnel plots with the Duval and Tweedie trim-and-fill method, Begg and Mazumdar’s rank correlations, and Egger’s regression intercept test (65). Finally, sensitivity and subgroup analyses were performed to estimate the effect of study and participant characteristics on the results, such as mean age, study design (cross sectional vs. longitudinal), source of sample (community vs. hospital), measure of vascular risk (self-report vs. clinical examination), definition of vascular risk (patient with stroke included or excluded), and outcome measure (rating scale vs. clinical interview). In addition, to assess the influence of chronic illness and disability on the relationship between VRFs and LLD, subgroup analyses including studies that controlled for these factors were performed for diabetes, CVD, stroke, and RFCS.

The literature search identified 1097 studies. Twenty-six studies met the inclusion criteria (29–33,35,37–39,41,44,46,54,55,68–79). Their baseline characteristics are summarized in Tables S2 (20 crosssectional studies) and S3 (6 longitudinal studies) in Supplement 1.

vascular risk was associated with greater odds of depression in all subgroups, particularly in studies with cross-sectional design (OR: 1.54; 95% CI: 1.27–1.87; p ⬍ .0005), noncommunity samples (OR: 1.60; 95% CI: 1.20–2.12; p ¼ .001), self-report of vascular risk (OR: 1.75; 95% CI: 1.50–2.04; p ⬍ .0005), clinical diagnosis of depression (OR: 1.63; 95% CI: 1.19–2.25; p ¼ .003), and studies that excluded patients with stroke (OR: 1.53; 95% CI: 1.21–1.93; p ⬍ .0005). When the analysis was confined to the five studies that used FSRS, the mean effect size decreased greatly, resulting in a nonsignificant relationship between FSRS and LLD (OR: 1.25; 95% CI: .99–1.57; p ¼ .06). The strength of the association between vascular risk and LLD weakened when only studies that adjusted for sociodemographic variables were considered (OR: 1.37; 95% CI: 1.15–1.63; p ⬍ .0005). It was further attenuated when the analysis was confined to the eight studies that controlled for chronic illness but remained statistically significant (OR: 1.15; 95% CI: 1.02–1.28; p ¼ .02). Sensitivity analysis limited to the studies with quality score ‡8 (30,35,37,39,41,46,54,71, 75,77,79) also showed an attenuated but significant association (OR: 1.34; 95% CI: 1.12–1.16; p ⬍ .0005).

Composite Measure of Vascular Risk The RFCS were available from 18 studies, yielding a total sample of 17,899 participants (29–33,35,37,39,41,44,46,54,68,69, 71,75,77,79). The random-model pooled OR showed that the odds of LLD are 1.49 greater in participants with high vascular risk compared with participants with low vascular risk (95% CI: 1.27–1.75; p ⬍ .0005) (Figure S1 in Supplement 1). The studies were highly heterogeneous (Q ¼ 71.3; p ⬍ .0005; I2 ¼ 76.2), suggesting that the variability of effect sizes is caused not by sampling error but by systematic differences between studies. Begg and Mazumdar rank correlation showed no significant publication bias (Kendall’s t ¼ .10, two-tailed p ¼ .57), but the more sensitive Egger’s regression intercept detected bias (t(16) ¼ 3.94; two-tailed p ⬍ .0005), and analysis with Duval & Tweedie’s trim-and-fill method (65) trimmed eight studies and resulted in a lower but still significant pooled OR (OR: 1.10; 95% CI: 1.05 to 1.16) (Table 1; Figure S2 in Supplement 1). The pooled effect sizes in subgroups defined by different study characteristics are summarized in Table 2. Generally, higher

Individual VRFs/Vascular Diseases Smoking. Ten studies reported data on the association between smoking and LLD (30,35,38,39,55,74–77,79). In total there were 20,120 participants. The pooled random-model OR was 1.35 (95% CI: 1.00–1.81; p = .05). Studies were significantly heterogeneous (Q = 27.1; p = .001; I2 = 66.8) (Table 1). Hypertension. Fourteen studies compared the prevalence or incidence of LLD between individuals with and without hypertension (30,35,38,39,44,70,72–79). This yielded a total sample of 20,197. After pooling these 14 studies, the random-model OR was ⬎ 1 but statistically nonsignificant (OR: 1.14; 95% CI: .94–1.40; p = .19). The effects were moderately heterogeneous (Q = 32.7; p = .002; I2 = 60.3) (Table 1). Dyslipidemia. Data on the association between dyslipidemia and LLD were available from 10 studies, with 17,957 participants in total (30,39,55,72–75,77–79). After pooling these 10 studies, the random-model OR was 1.08 (95% CI: .91–1.28; p = .4). A moderate heterogeneity was found with an I2 = 41.0 (Q = 15.26; p = .08) (Table 1).

Results

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BIOL PSYCHIATRY 2013;73:406–413 409

V. Valkanova and K.P. Ebmeier Table 2. Vascular Risk and Likelihood of Late-Life Depression in Different Subgroups Studies Included

n

OR (95% CI)

p

All Studies Study Design Longitudinal studies (30,35,37,46,75) Cross-sectional studies (29,31–33,39,41,44,54,68,69,71,77,79) Age: Studies that Include Persons ‡65 yrs (30–32,35,39,46,54,69,71) Source of Sample Studies that used community-based samples (30–33,35,39,41,46,68,71,75,79) Studies that used non community-based samples (29,37,44,54,69,77) Measures of Exposure Self-report (31–33,35,68) Examination and blood tests (29,30,37,39,41,44,46,54,69,71,75,77,79) Measures of Outcome Studies that measured outcome with rating scale:(30,31,33,35,39,41,44,46,69,71) Studies that measured outcome with clinical interview (29,32,37,54,68,75,77,79) Studies that Controlled for SDV (30,31,35,37,39,41,46,54,71,75,77,79) Studies that Controlled for Chronic Illness/Disability (30,35,37,39,44,46,71,75) Studies that Used FSRS (37,46,54,75,77) Studies that Exclude Patients with Stroke (29–31,33,39,44,46,69,77)

18

1.49 (1.27–1.75)

⬍.0005

5 13 9

1.38 (1.06–1.79) 1.54 (1.27–1.87) 1.47 (1.17–1.85)

.02 ⬍.0005 .001

12 6

1.45 (1.19–1.75) 1.60 (1.20–2.12)

⬍.0005 .001

5 13

1.75 (1.50–2.04) 1.38 (1.15–1.65)

⬍.0005 ⬍.0005

10 8 12 8 5 9

1.42 1.63 1.37 1.15 1.25 1.53

⬍.0005 .003 ⬍.0005 .02 .06 ⬍.0005

(1.18–1.71) (1.19–2.25) (1.15–1.63) (1.02–1.28) (.99–1.57) (1.21–1.93)

Odds ratios (ORs) with 95% confidence intervals (CIs). FSRS, Framingham Stroke Risk Score; SDV, sociodemographic variables.

Diabetes mellitus. Fifteen studies, with a total sample of 24,466 participants, reported data on prevalence or incidence of LLD in individuals with and without diabetes (30,35,38,39,44, 55,70,72–79). The pooled effect sizes revealed that the likelihood of LLD was 1.51 greater in individuals diagnosed with diabetes (OR: 1.51; 95% CI: 1.30–1.76; p ⬍ .0005; random-effects model). The studies were homogeneous (Q = 18.7; p = .18; I2 = 25.0). When the analysis was confined to the five studies that controlled for chronic illness (30,44,55,75,76), the overall OR was 1.46 (95% CI: 1.14–1.86; p = .003) (Table 1). CVD. Ten studies compared the prevalence or incidence of LLD between individuals with and without CVD (30,35,44,55, 70,73–76,78). In total there were 21,841 participants. The randommodel pooled OR showed 1.76 greater odds of LLD in individuals diagnosed with CVD (95% CI: 1.52–2.04; p ⬍ .0005). The effects were homogeneous (Q = 12.1, p = .21, I2 = 25.7). When the analysis was confined to the six studies that control for chronic illness (30,44,55,73,75,76), the overall effect size decreased but remained statistically significant (OR: 1.40; 95% CI: 1.08–1.80; p = .01) (Table 1) Stroke. Ten included studies compared the prevalence or incidence of LLD between individuals with and without stroke (35,38,39,41,55,69,71,73,74,78). The total sample consisted of 16,221 participants. A diagnosis of stroke was associated with 2.11 greater likelihood of LLD (OR: 2.11; 95% CI: 1.61–2.77; p ⬍ .0005, random-effects model). Measures of heterogeneity were significant (Q = 21.9; p = .01; I2 = 58.9). Five studies controlled for chronic illness (38,39,55,71,73) with a pooled random-model OR of 1.80 (95% CI: 1.24–2.62; p = .002) (Table 1). Publication Bias There was no evidence of publication bias except for the studies examining the association between diabetes and LLD, in which there was significant bias as measured by Begg and Mazumdar rank correlation (Kendall’s t ¼ .42; two-tailed p ¼ .03) and Egger’s regression intercept (t ¼ 2.07; two-tailed p ¼ .06) (Table 1).

Discussion The central question of this review was whether VRFs are directly related to LLD or whether the observed relationship is nonspecific. A significant association was found between the composite measure of vascular risk and depression in later life. The positive association also persisted and remained statistically significant across several subgroups stratified by study characteristics, such as study design, source of sample, measures of exposure, and measures of outcome. These results support the vascular—or subcortical ischemic—hypothesis (18–20). However, although the association remained significant, its strength was considerably attenuated when only studies controlling for chronic illnesses were included. A recent review reported an association between LLD and nonvascular as well as vascular chronic conditions (27). If nonspecific chronic illness can, to a great extent, account for the association between vascular disorders and LLD, this challenges the vascular hypothesis. The relationship between chronic illness and vascular health is complex. Vascular disease might be only one of several pathophysiological steps leading to depression. Causes or consequences of many chronic nonbrain illnesses might affect behavior and might promote neurohumoral (immune or endocrine) responses, influencing vascular integrity and ultimately the cortico-limbic circuits implicated in depression. Patients with high medical burden are more likely to have vascular disease, and therefore controlling for overall medical burden might remove a significant part of the impact of vascular disease. We found a lack of association between the FSRS and LLD. In a risk score like the FSRS acute vascular diseases are less likely to be considered. This implies that vascular risk without acute vascular disease might not precede depression and is in keeping with the other results of this meta-analysis. Alternatively, this negative finding—in the context of a robust relationship between CVD and depression—might simply suggest that the FSRS is not suitable for assessing the relationship between peripheral VRFs and depression, as opposed to stroke, and a www.sobp.org/journal

410 BIOL PSYCHIATRY 2013;73:406–413 better instrument is still needed. The use of the FSRS made it more likely that risks were measured in a systematic way, and thus studies were more comparable, leading to a moderate variability in effect size. This contrasts with the highly heterogeneous results for the RFCS on the basis of different subsets of risk factors. When individual risk factors were considered, no association was found between hypertension and LLD. Dyslipidemia was also not associated with LLD, whereas the effects for smoking just reached significance. Diabetes, heart disease, and stroke were strongly associated with LLD, and the association was still significant after adjustment for chronic illnesses. Therefore, the meta-analysis demonstrates a clear effect of current vascular disease on the rates of depression, which is in agreement with earlier meta-analyses (26–28,80). The strong association with diabetes, heart disease, and stroke is consistent with a vascular mechanism for LLD, but the weak association with smoking and the lack of association with hypertension and dyslipidemia are not supportive. A number of explanations could account for the discrepant findings. First, it is possible that certain risk factors are more closely linked to LLD than others. However, all the aforementioned risk factors are associated with small-vessel brain disease (81), which has been implicated in the pathogenesis of vascular depression (6,16,21–25). The negative finding for hypertension generates particular challenges for the vascular depression hypothesis. Hypertension is strongly related to small-vessel brain disease (82); has greater predictive power for atherothrombotic disease in the cerebrovascular territory compared with other risk factors (e.g., smoking) (83); and increases the risk of white matter hyperintensities (60,84–87), which have been linked to LLD (12–16). Second, LLD might result only from severe vascular pathology, which is typically associated with overt vascular disease. The significant association found with the composite score where participants with high risk (two or more risk factors) were compared with participants with low risk (no or one risk factors) and the lack of association with some individual risk factors implies that the risk of depression increases when the vascular burden increases. The relationship between severity of vascular lesions and vascular symptoms, however, is not straightforward. Krishnan et al. (21) found that only 16% of those with severe vascular lesions demonstrated vascular symptoms, whereas in a later study they found that participants with and without magnetic resonance imaging-defined subcortical ischemic depression can have similar vascular scores (19). These findings suggest that the risk of depression might not be linearly related to the severity of vascular pathology but that vascular changes are only associated with depression, if they become severe enough to compromise organ function. This might explain the absence of associations with hypertension, dyslipidemia, and the weak association with smoking as single factors. In addition to severity of lesions, the important factor might be lesion location. In this case, vascular pathology in the frontal-subcortical circuits that are related to mood regulation would be predictive of depression (23,88–90). Third, risk-factor studies assume that brain changes correspond with peripheral changes. Atherosclerosis, the main cause of vascular disease, is a systemic disease that affects arteries simultaneously in different vascular territories, but it might develop at different rates of progression in different locations (83). Although there is evidence that cerebral and extra-cerebral atherosclerosis are associated (91,92), the degree of cerebral atherosclerosis might not always correlate with the degree of extra-cerebral atherosclerosis. Furthermore, much evidence supporting the vascular www.sobp.org/journal

V. Valkanova and K.P. Ebmeier depression hypothesis comes from neuroimaging studies, in which cerebral atherosclerosis is measured as the presence and severity of white matter lesions (12–15,21,90). Therefore, it is possible that only cerebral atherosclerosis increases the risk of depression in old age. For instance, Lee et al. (93) found that white matter hyperintensities in patients with ischemic stroke were associated with intracranial rather than extra-cranial atherosclerosis, whereas in a large longitudinal study measures of extra-cerebral atherosclerosis did not increase the risk of LLD (45). Finally, it is possible that depression in later-life is a heterogeneous condition, so patients with vascular depression represent a small subgroup of elders with depressive disorders. Not only can different neuropathological pathways lead to illness, but VRFs themselves are linked to depression through varying pathophysiological mechanisms. There is evidence for the role of structural and functional disconnection, chronic low-grade inflammation, and hypoperfusion (94–97). These processes develop to a different degree in patients with one or more risk factors and might explain the weakness of associations of depression with individual risk factors. Methodological Limitations Definitional problems made comparison between studies difficult. There is disagreement about the age cutoff (50, 60, or 65 years) to differentiate between early- and late-onset depression, so participants classified as LOD in one study would be considered EOD in another. Furthermore, many studies did not control for a history of depression. Even though a substantial number of participants with LLD might have a late age of onset, some of them might have suffered from a recurrent depressive episode. The interpretation of results was further complicated, because aspects of each individual risk factor—such as duration, appropriate treatment, and adherence to treatment—were not considered, which might result in measurement error. Not all studies controlled for chronic illness, and some of the studies that did included vascular disease within the overall medical burden, thus minimizing the relationship between vascular disease and depression. There were inconsistencies between studies in the definitions of VRFs. Vascular risk in some studies was conceptualized as the presence of one or a number of VRFs, such as smoking or hypertension, whereas others included in the RFCS definite vascular diseases, such as stroke. Moreover, VRFs were grouped together and regarded as having an equal impact on depression. However, other studies (42,73,75,76) and this meta-analysis found that the effects of VRFs on depression were heterogeneous, challenging the use of nonweighted composite scores to quantify risk. Because many studies considered only a limited range of risk factors, undetected vascular conditions in the low-risk group that would attenuate a true relationship between vascular risk and LLD cannot be excluded. Finally, there was moderate-to-high heterogeneity in the effects for RFCS, smoking, hypertension, dyslipidemia, and stroke, which might be due to study design, sample size, participant characteristics, and definitions or measurement of risk factors and LLD. In addition, publication bias was detected for RFCS and diabetes. The results of the meta- analysis therefore have to be interpreted with caution.

Conclusions We found convincing evidence of a strong relationship between key diseases—such as CVD, diabetes, and stroke—and between composite vascular risk and depression but not between some VRFs (hypertension, smoking, dyslipidemia) and

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