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. Author manuscript; available in PMC: 2016 Apr 22.
Published in final edited form as: Int J Geriatr Psychiatry. 2015 Aug 6;31(3):316–322. doi: 10.1002/gps.4339

Amyloid-associated depression and ApoE4 allele: longitudinal follow-up for the development of Alzheimer’s disease

Wei Qiao Qiu 1,2,3, Haihao Zhu 2, Michael Dean 2, Zhiheng Liu 2,6, Linh Vu 2, Guanguang Fan 2,6, Huajie Li 2,7, Mkaya Mwamburi 5, David C Steffens 8, Rhoda Au 4
PMCID: PMC4840849  NIHMSID: NIHMS776100  PMID: 26250797

Abstract

Background

Amyloid-associated depression is associated with cognitive impairment cross sectionally. This follow-up study was to determine the relationship between amyloid-associated depression and the development of Alzheimer’s disease (AD).

Methods

Two hundred and twenty three subjects who did not have dementia at baseline were given a repeat cognitive evaluation for incident AD. Depression was defined by having a Center for Epidemiological Studies Depression (CES-D) score ≥ 16, and non-amyloid vs. amyloid-associated depression by having a low vs. high plasma amyloid-β peptide 40 (Aβ40)/Aβ42 ratio. Apolipoprotein E (ApoE) genotype was determined, and antidepressant usage was documented.

Results

Fifteen subjects developed AD (7%) after an average follow-up time of 6.2 years. While none of those with non-amyloid depression developed AD, 9% of those with amyloid-associated depression developed AD. Further, among those with amyloid-associated depression, ApoE4 carriers tended to have a higher risk of AD than ApoE4 non-carriers (40% vs. 4%, p=0.06). In contrast, 8% of those who did not have depression at baseline developed AD, but ApoE4 carriers and non-carriers did not show a difference in the AD risk. After adjusting for age, the interaction between ApoE4 and amyloid-associated depression (β=+0.113, SE=0.047, P=0.02) and the interaction between ApoE4 and antidepressant use (β=+0.174, SE=0.064, P=0.007) were associated with the AD risk.

Conclusions

Amyloid-associated depression may be prodromal depression of AD especially in the presence of ApoE4. Future studies with a larger cohort and a longer follow-up are warranted to further confirm this conclusion.

Keywords: amyloid-associated depression, Alzheimer’s disease, apolipoprotein E4 allele (ApoE4)

Introduction

Depression increases the risk or is an early symptom of Alzheimer’s disease (AD) in the elderly (Ownby et al., 2006; Saczynski et al., 2010). Late life depression is a clinical syndrome with different subtypes and not all depressed elderly will develop dementia (Steffens et al., 1997; Dal Forno et al., 2005). Thus, it is important for clinical practice to identify depression subtypes that are predictive of the onset of AD. Our cross-sectional study showed that depression with a high amyloid-β peptide 40 (Aβ40)/Aβ42 ratio determined by high Aβ40 and low Aβ42 in plasma is associated with cognitive impairment, especially memory domain, suggesting a distinct depression subtype, which we have termed “amyloid-associated depression” (Sun et al., 2008). Yet, the longitudinal relationship between amyloid-associated depression and AD has not been previously determined.

Plasma Aβ42 declines significantly at a pre-clinical or early stage of AD (Pomara et al., 2005), suggesting a biomarker for a prodrome of the disease. In Amyloid Precursor Protein (APP) transgenic mice, plasma Aβ42 declines significantly before the formation of AD pathology in the brain (Kawarabayashi et al., 2001; DeMattos et al., 2002). Elevated plasma Aβ40 is correlated with cerebral microvascular pathology, which is linked with both late life depression and AD (van Dijk et al., 2004; Gurol et al., 2006). Thus, it is possible that the combination of high Aβ40 and low Aβ42 in plasma can be utilized as a biomarker of pathological change in the brain to determine prodromal depression of AD. The Apolipoprotein E4 (ApoE4) allele is the major genetic risk factor of late-onset AD (Strittmatter et al., 1993). ApoE4 is associated with higher amyloid plaque burden composed mainly of Aβ42 in AD brains compared to those carrying ApoE2 and E3 (Gearing et al., 1996). The interaction between ApoE4 allele and amyloid-associated depression increases the AD risk, however, is yet unknown.

We hypothesized that a high plasma Aβ40/Aβ42 ratio plus clinical symptoms of depression may represent a prodromal depression signaling imminent cognitive deterioration leading to development of AD, especially in the presence of ApoE4. In order to test this hypothesis, we conducted a second cognitive evaluation of subjects who were not demented at baseline and examined the longitudinal relationships between amyloid-associated depression and the development of incident AD.

Methods

Study population and recruitment

The Nutrition, Aging and Memory in the Elderly (NAME) study is population-based study of homecare elderly from four homecare agencies in the Boston area that were recruited between 2003 and 2006 for baseline assessment (Scott et al., 2006). For an original sample (n=1248), 223 participants who did not have dementia at baseline were invited for a repeat cognitive evaluation between 2009 and 2013. The Boston University Institutional Review Board approved the study. All participants provided written informed consent to participate in this follow-up study. Each subject engaged in two home visits conducted by a trained examiner, who administered the cognitive battery during the first visit, and by a research physician who drew a fasting blood sample and collected data on medical conditions during the second visit.

Depression status at baseline

At the study’s inception, participants were given the Center for Epidemiological Studies Depression Scale (CES-D) to measure presence of depressive symptoms at baseline (Radloff, 1977). A CES-D score of ≥16 was used as the cut-off point for clinical depression (Fuhrer and Rouillon, 1989). Of the 106 subjects with baseline CES-D scores greater than the cut-off, there was a sensitivity of 0.90 and a specificity of 0.83 for the DSM-IV diagnosis of major depression by a board-certified psychiatrist.

Amyloid-associated depression

Those subjects with a CES-D score ≥ 16 and a plasma Aβ40/Aβ42 ratio > median (7.1) were defined as having amyloid-associated depression. Those with a CES-D score ≥ 16 and a plasma Aβ40/Aβ42 ratio ≤ median were defined as having non-amyloid depression (Sun et al., 2008).

ApoE4 genotyping and clinical information

A 244 bp of the ApoE gene, which included the two polymorphic sites, was amplified by PCR using a robotic Thermal Cycler (ABI 877, Perkin-Elmer/Applied Biosystems). The PCR products were digested with five units of Hha I, and the fragments were separated by electrophoresis on 8% polyacrylamide non-denaturing gel. The specific allelic fragments were: E2, E3 and E4. ApoE4 was defined by E4/4, E3/4 or E2/4 (Lahoz et al., 1996).

Subjects were classified as having cardiovascular disease (CVD) according to whether they had been previously informed by a doctor that they had congestive heart failure, coronary heart disease, angina pectoris or a heart attack. Diabetes was defined as the use of anti-diabetic medication or fasting glucose greater than 126 mg/dl (available on 96% of the samples). Self-reported stroke history was also recorded.

Diagnoses of dementia and Alzheimer’s disease

Research assistants, trained by a neuropsychologist, administered the cognitive tests including WMS-III Logical Memory, WMS-III Word List Learning, Digit Symbol, WAIS-III Block Design, Trails A and B at both baseline and repeat evaluations. Based on these neuropsychological scores and clinical information, consensus diagnosis was made for each participant during a consensus diagnosis meeting attended by the psychiatrist, neuropsychologist and research assistants. The diagnosis of dementia was based on the DSM-IV criteria. NINCDS-ADRDA guidelines (McKhann et al., 1984) were used to determine whether criteria were met for a diagnosis of possible or probable AD.

Statistical analysis

Statistical analysis was performed using SAS (version 9.1). For analyses of baseline characteristics, the Chi-Square test (X2 test) was used to compare proportions for binary and categorical variables. Continuous variables were presented as mean ± SD and compared using T-tests or ANOVA analyses. To account for non-independence of repeated measures in the longitudinal analyses, generalized estimation equations (GEE) logistic regression with first order autoregression (AR(1)) covariance matrix structure was used to examine associations between presence of AD at the end of the interval vs. presence of ApoE4 or amyloid-associated depression or antidepressant use while adjusting for age. The interactions between ApoE4 and amyloid-associated depression or antidepressant use were explored in the logistic regression models. Because the number of incident AD cases was small, we only controlled for age for each multivariate analysis. For all analyses, the two-tailed alpha level of 0.05 was used.

Results

The average follow-up time was 6.2 years, and 15 (7%) of the participants developed AD (Table 1). Among those who developed AD, 12 subjects did not have depression, three subjects had amyloid-associated depression and none of them had non-amyloid depression at baseline. Seven out of 15 who developed AD had been taking antidepressants. In the presence of amyloid-associated depression, two subjects who were ApoE4 carriers developed AD (6.3%), while one subject was ApoE4 non-carriers developed AD (3.1%) (p=0.06) (Figure 1). In contrast, in the absence of depression there was no statistical difference in the AD development between ApoE4 carriers and non-carriers.

Table 1.

Demographic, medical and depression status in the subgroups with and without the development of Alzheimer’s disease

Follow-up No AD
n = 208		 AD
n = 15		 P value
Follow-up, year, mean ± SD 6.2 ± 0.6 6.2 ± 0.5 0.69
Age, year, mean ± SD 71.3 ± 7.5 78.0 ± 5.5 0.0005
Female, n/total (%) 171/208 (82%) 10/15 (67%) 0.17
High school and above, n/total (%) 160/208 (77%) 11/15 (73%) 0.09
African Americans, n/total (%) 62/208 (30%) 2/15 (13%) 0.56
ApoE4, n/total (%) 44/206 (21%) 5/14 (36%) 0.32
Diabetes, n/total (%) 58/205 (28%) 4/15 (27%) 0.99
CVD, n/total (%) 73/204 (36%) 7/12 (58%) 0.13
Stroke, n/total (%) 25/206 (12%) 2/14 (14%) 0.68
MMSE score, mean ± SD 27.0 ± 2.4 26.1 ± 2.5 0.16
CES-D score, mean ± SD 11.3 ± 9.8 10.6 ± 6.9 0.87
Antidepressant usage, n/total (%) 71/207 (34%) 7/15 (47%) 0.40
Depression status 0.18
 No depression 143/208 (69%) 12/15 (80%) —
 Non-amyloid depression 36/208 (17%) 0/15 (0%) —
 Amyloid-associated depression 29/208 (14%) 3/15 (20%) —
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Mean ± SD with t-Test or n/total (%) with Chi Square test are presented. The values between those with and without the development of AD are compared, and p values for statistical significance are shown. CVD = Cardiovascular disease; MMSE = Mini-Mental State Exam; CES-D = the Center for Epidemiological Studies Depression Scale.

Figure 1.

Figure 1

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Longitudinal study on ApoE4 allele, the depression subtypes and the development of Alzheimer’s disease. Subjects were divided into three groups based on the baseline depression status: those without depression, those with non-amyloid depression and those with amyloid-associated depression. Within each group, ApoE4 non-carriers (ApoE4−) and carriers (ApoE4+) were further divided. Frequencies and numbers of subjects who developed AD with statistical significance are shown.

Compared with those who did not develop AD, those who developed AD were older (mean ± SD: 71.3 ± 7.5 vs. 78.0 ±5.5, p=0.0005), had less education and tended to be men and having a higher rate of cardiovascular disease (Table 1). At baseline, 155 subjects did not have depression, 36 had non-amyloid depression and 32 had amyloid-associated depression (Table 2). There were no differences in the demographic except age (mean ± SD) among those without depression (72.6 ±7.7), those with non-amyloid depression (68.0 ± 6.6) and those with amyloid-associated depression (72.3 ± 8.0) (p=0.002). There were no differences between the two depressed and non-depressed groups in clinical variables including diabetes, cardiovascular disease and stroke. There was also no difference in the frequency of ApoE4 carriers or average MMSE scores among the three subgroups. Because of the sample size, and because age was shown to be a major risk for AD in this study (Tables 1 and 2), we used multivariate regression analysis to study the relationships between different factors and the risk of developing AD after adjusting for age. We found that the interaction between ApoE4 allele and amyloid associated depression was associated with an increased risk of AD (β=+0.113, SE=0.047, P=0.02) after adjusting for age (Table 3), but there were no relationship between ApoE4 or amyloid-associated depression alone and the AD development in this model.

Table 2.

Baseline comparisons among three subgroups based on depression status

Depression status None
n = 155		 Non-amyloid
n = 36		 Amyloid-associated
n = 32		 P value
Follow-up, year, mean ± SD 6.2 ± 0.7 6.1 ± 0.5 6.2 ± 0.6 0.67
Age, year, mean ± SD 72.6 ± 7.7 68.0 ± 6.6 72.3 ± 8.0 0.002
Female, n/total (%) 125/155 (81%) 29/36 (81%) 27/32 (84%) 0.88
High school and above, n/total (%) 121/155 (78%) 24/36 (67%) 26/32 (81%) 0.13
African Americans, n/total (%) 44/155 (28%) 12/36 (33%) 8/32 (25%) 0.78
ApoE4, n/total (%) 37/153 (24%) 7/36 (19%) 5/31 (16%) 0.55
Diabetes, n/total (%) 41/153 (27%) 13/35 (37%) 8/32 (25%) 0.42
Cardiovascular disease, n/total (%) 57/150 (38%) 11/35 (31%) 12/31 (38%) 0.75
Stroke, n/total (%) 19/153 (12%) 5/36 (13%) 3/31 (10%) 0.87
Antidepressant usage, n/total (%) 45/154 (28%) 21/36 (58%) 14/32 (44%) 0.002
MMSE score, mean ± SD 27.0 ± 2.3 26.4 ± 3.1 26.6 ± 3.1 0.43
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No depression was defined by having a CES-D score < 16, non-amyloid depression was defined by having a CES-D score ≥ 16 and plasma Aβ40/Aβ42 ratio ≤ 7.1 and amyloid-associated depression was defined by having a CES-D score ≥ 16 and plasma Aβ40/Aβ42 ratio > 7.1. Mean ± SD with ANOVA test or n/total (%) with Chi Square test are presented. P values for statistical significance are shown. MMSE = Mini-Mental State Exam.

Table 3.

Influences of ApoE4 and amyloid-associated depression on the development of Alzheimer’s disease

Development of Alzheimer’s disease (n = 15) Model I
n = 219
 Model II
n = 222
 Model III
n = 219
β estimate (SE) P value β estimate (SE) P value β estimate SE) P value
Age, years +0.007 (0.002) 0.001 +0.007 (0.002) 0.001 +0.007 (0.002) 0.001
ApoE4 +0.044 (0.039) 0.26 — — — —
Amyloid-associated depression (0, 1 and 2) — — −0.001 (0.022) 0.96 — —
ApoE4 × amyloid-associated depression — — — — +0.113 (0.047) 0.02
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Each regression model is adjusted for age. Amyloid-associated depression variables were defined as no depression = 0 (CES-D score < 16), non-amyloid depression = 1 (CES-D score ≥ 16 and plasma Aβ40/Aβ42 ratio ≤ 7.1) and amyloid-associated depression = 2 (CES-D score ≥ 16 and plasma Aβ40/Aβ42 ratio > 7.1). The interaction between ApoE4 allele and amyloid-associated depression variables was used. P value < 0.05 is considered significant.

Twenty-eight percent of those without depression, 58% of those with non-amyloid depression and 44% of those with amyloid-associated depression had been taking antidepressants (p=0.002). Because there were significant different rates of taking antidepressants among the three subgroups (Table 2), we next studied the relationship between antidepressant usage and the AD development (Table 4). After adjusting for age, antidepressant usage tended to be associated with the risk of developing AD (Model I, p=0.07), and the interaction between ApoE4 allele and antidepressant usage was significantly associated with the development of AD (Model II: β=+0.174, SE=0.064, P=0.007) (Table 4). Among ApoE4 carriers, 20% of taking antidepressants vs. 6% of those who did not take this class of mediation developed AD. Among ApoE4 non-carriers, 5% developed AD regardless of antidepressant usage.

Table 4.

Influence of ApoE4 on the development of Alzheimer’s disease among those without and with depression at baseline

Development of Alzheimer’s
disease		 Model I
n = 221
 Model II
n = 218
β estimate (SE) P value β estimate (SE) P value
Age, years +0.008 (0.002) 0.0003 +0.008 (0.002) 0.0003
Antidepressant use +0.064 (0.035) 0.07 — —
ApoE4 × antidepressant use — — +0.174 (0.064) 0.007
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Each regression model is adjusted for age. The interaction between ApoE4 allele and antidepressant use was used. P value < 0.05 is considered significant.

Discussion

There is growing evidence in the literature that depression can be either a risk factor (Devanand et al., 1996; Geerlings et al., 2000; Paterniti et al., 2002; Kumar et al., 2006; Ownby et al., 2006) or an early symptom of AD (Steffens et al., 1997; Berger et al., 1999; Cervilla et al., 2000; Ritchie et al., 2000; Wilson et al., 2002; Lopez et al., 2003). However, not all depressed elderly developed dementia, and thus clinically it is necessary and likely important for a physician to make differential diagnoses of depression subtypes and predict prognoses in the elderly. We proposed that plasma Aβ might be useful for differentiating different depression subtypes and predictive of a prodromal stage of AD.

This longitudinal study provided some evidence supportive of our hypothesis that amyloid-associated depression increased the risk of developing AD in the presence of ApoE4 allele (Figure 1 and Table 3), although it did not reach statistical significance because of the lack of a large number of subjects. Several studies suggest that amyloid deposits in the brain are associated with late life depression, but not with early onset depression (Butters et al., 2008; Pomara and Sidtis, 2010; Tateno et al., 2014; Chung et al., 2015; Pietrzak et al., 2015). The brain amyloid imaging plus mood symptoms including depression and anxiety predict the risk of AD development (Pietrzak et al., 2015). There is ample evidence suggesting that CSF Aβ and tau proteins (Perrin et al., 2009; Pomara et al., 2012; Vanderstichele et al., 2012; Weiner et al., 2012) enhance diagnostic accuracy and likely are surrogate markers of AD pathology in the brains of elderly depressed patients. In contrast to the Aβ biomarkers in central nervous system (CNS), peripheral Aβ biomarkers for AD prognosis in late life depression are variable among different studies (Osorio et al., 2014). For example, while some study samples show that high plasma Aβ42 is associated with late life depression (Pomara and Murali Doraiswamy, 2003; Blasko et al., 2010), others show that low plasma Aβ42 or high Aβ40/42 ratio is associated with late life depression in some elders (Qiu et al., 2007; Baba et al., 2012; Namekawa et al., 2013). Despite that the brain amyloid imaging and CSF reflect the brain pathology of AD better than peripheral Aβ, the cost of using PET scan to screen for the AD prognosis among all depressed elderly will be considerably high and that depressed patients tend to refuse lumbar puncture. Thus it is still necessary to develop some blood tests, probably by using other peripheral biomarkers or the combination of peripheral biomarkers including Aβ, for the prognosis and diagnosis of AD.

Our cross-sectional study found that ApoE4 carriers had lower plasma Abeta42 and higher Abeta40/Abeta42 ratio than ApoE4 non-carriers regardless the depression status (Sun et al., 2009). Results from this longitudinal study suggested that ApoE4 carriers with these Aβ biomarkers plus depressive symptoms indicate an imminent prodromal stage of AD (Figure 1 and Table 2). The ApoE4 allele is the major genetic risk factor of late-onset AD (Strittmatter et al., 1993), and is associated with higher amyloid plaque burden (Gearing et al., 1996), probably through reduced clearance or accelerated aggregation of the peptide. Previous studies reported that among the elderly, aged 80 and older, who are at much higher risk of AD and suffer from compared to those who are younger, a relationship between ApoE4 and depression has been observed (Krishnan et al., 1996; Steffens et al., 2003). However, some other studies with younger elders have shown that late life depression is not generally associated with ApoE4 (Mauricio et al., 2000; Butters et al., 2003). While late-life depression has different subtypes, it is probable that only the prodromal depression of AD was associated with the ApoE4 allele. Our study suggests that both plasma Aβ and ApoE genotyping to predict cognitive prognoses of late life depression.

Amyloid-associated vs. non-amyloid depression are probably two depression subtypes of late life depression (Sun et al., 2008). While amyloid-associated depression was linked with a prodromal stage of AD, non-amyloid depression was associated with a cognitive pattern of vascular depression, visuospatial and executive dysfunction (Sun et al., 2008) and did not lead to increased risk in the development of AD (Figure 1). A post-mortem study has shown that history of depression is linked with increased amyloid plaques and neurofibrillary tangles, which are the neuropathological hallmarks of AD (Rapp et al., 2006). Antidepressants, especially SSRI, are effective drugs against mood symptoms of depression including late life depression (Bruce et al., 2002; Kuzuya et al., 2006). Our longitudinal study showed that usage of antidepressants alone tended to increase the AD risk; however, in the presence of ApoE4, antidepressant use was significantly associated with increased risk for AD (Table 4). For our study, use of antidepressants likely reflected greater severity of depressive symptoms, as many patients with milder depressive symptoms in community are often not prescribed or refuse to take antidepressants. We would thus suggest that severe depression, but not the drug use per se, was related to incident AD in our study. Consistently, it is shown that antidepressant use does not modify cognitive decline during aging process (Saczynski et al., 2015). Studies including ours show that there was no relationship between the antidepressants use and plasma Aβ42 (Pomara et al., 2006; Sun et al., 2007), which is the major component of neuritic plaques in the AD brain. While it is possible that antidepressants may not be effective to delay cognitive decline in AD prodromal depression, one study shows that fluoxetine treatment improves cognition in vascular dementia probably through increasing brain derived neurotrophic factor (BDNF) in the brain (Liu et al., 2014).

The major limitation of this study was the sample size and the small number of incident AD cases (7%) during a relatively short follow-up. Although our longitudinal study found that amyloid-associated depression increased the risk of AD in the presence of ApoE4, the longitudinal relationship between amyloid-associated depression alone and AD was not statistically significant. We will need a large cohort with a longer follow-up period to confirm these initial findings. Other limitations include: (1) depression was based on the CES-D score rather than the DSM-IV criteria, and we had no information about disease onset and its course; (2) although our assays for plasma Aβ had high sensitivity and specificity (Qiu et al., 2007), the field lacks standard assays of measuring Aβ40 vs. Aβ42 in plasma for inter-laboratory comparisons in order to be applied in clinical practice. Further studies are needed to examine whether amyloid-associated depression is related to brain pathology, and is causally linked to the onset of cognitive decline and AD in other populations. Nevertheless, our cross-sectional and prospective discoveries have pointed out the existence of a depression subtype that precedes onset of AD and by using plasma Aβ prodromal depression of AD plus ApoE genotyping could differentiate it from other late-life depression subtypes.

Key points.

  • Late life depression may have different subtypes such as vascular depression and prodromal depression of AD. It is important to identify the biomarkers for different depression subtypes.

  • Amyloid-associated depression is associated with cognitive impairment cross-sectionally. This follow-up study showed that amyloid-associated depression may be prodromal depression of AD especially in the presence of ApoE4 allele.

Acknowledgements

We especially thank Dr. Marshal Folstein who had the vision to establish the NAME study more than a decade ago. We also thank the NAME study staff and the Boston homecare agencies for their hard work and acquisition of subjects for the baseline study, which is the foundation for this follow-up study. This work was supported by grant from the NIA, AG-022476 for W.Q.Q.

Footnotes

Conflict of interest

None declared.

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