Lines within boxes indicate mean; boxes, the first and third quartile; lines extending beyond the boxes, values within 1.5 times the interquartile range; and points, outliers. A, EOAD cases have higher plasma LDL-C levels compared with aged controls after adjusting for copies of APOE E4, educational attainment, sex, and study center (n = 267), and the adjusted mean (SD) LDL-C difference between EOAD cases and controls was 22.0 (4.5) mg/dL. B, To remove the effect of the apolipoprotein E gene (APOE), we performed a stratified analysis among participants with the APOE E3/3 genotype only. EOAD was associated with higher LDL-C levels after adjusting for educational attainment, sex, and study center (n = 153). The adjusted mean (SD) LDL-C difference between EOAD cases and controls was 26.0 (5.7) mg/dL. C, Likewise, among participants with APOE E3/4 genotype only, EOAD cases had higher LDL-C levels relative to controls after adjusting for the same covariates (n = 82).
eMethods. Participants and Data Analysis
eFigure. Normalized LDL-C for Carriers of APOB Variants
eTable 1. Known Pathogenic Mutations Identified
eTable 2. Results of Gene-Based Burden t Test for APP
eTable 3. Results of Gene-Based Burden t Test for PSEN1
eTable 4. Results of Gene-Based Burden t Test for PSEN2
eTable 5. Variants Identified in APOB
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Wingo TS, Cutler DJ, Wingo AP, et al. Association of Early-Onset Alzheimer Disease With Elevated Low-Density Lipoprotein Cholesterol Levels and Rare Genetic Coding Variants of APOB. JAMA Neurol. Published online May 28, 201976(7):809–817. doi:10.1001/jamaneurol.2019.0648
Is circulating cholesterol level associated with early-onset Alzheimer disease, and if so, what is the underlying genetic mechanism?
This case series of plasma samples from 2125 cases and controls found an association between early-onset Alzheimer disease and higher levels of low-density lipoprotein cholesterol independent of the effects of APOE, as well as demonstrated enrichment of rare coding variants of APOB, a gene known to influence plasma cholesterol levels, in samples from patients with early-onset Alzheimer disease.
Two parallel lines of evidence suggest that circulating low-density lipoprotein cholesterol levels may play a role in the pathogenesis of early-onset Alzheimer disease, and future work should elucidate whether APOB influences the risk for early-onset Alzheimer disease by acting through plasma low-density lipoprotein cholesterol levels or through other mechanisms.
Early-onset Alzheimer disease (EOAD) is a rare form of Alzheimer disease (AD) with a large genetic basis that is only partially understood. In late-onset AD, elevated circulating cholesterol levels increase AD risk even after adjusting for the apolipoprotein E ε4 (APOE E4) allele, a major genetic factor for AD and elevated cholesterol levels; however, the role of circulating cholesterol levels in EOAD is unclear.
To investigate the association between circulating cholesterol levels and EOAD and to identify genetic variants underlying this possible association.
Design, Setting, and Participants
In this case series, plasma cholesterol levels were directly measured in 267 samples from the AD research centers (ADRCs) of Emory University and University of California, San Francisco, collected from January 21, 2009, through August 21, 2014. The association between cholesterol and EOAD was examined using multiple linear regression. To determine the underlying genetic variants, APOB, APP, PSEN1, and PSEN2 were sequenced in samples from 2125 EOAD cases and controls recruited from 29 ADRCs from January 1, 1984, through December 31, 2015. Data were analyzed from November 23, 2016, through April 10, 2018.
Clinical diagnosis, age at clinical diagnosis, plasma cholesterol measures (total cholesterol, low-density lipoprotein cholesterol [LDL-C], high-density lipoprotein cholesterol, triglycerides, and apolipoprotein B), and genetic variants in APOE, APP, PSEN1, PSEN2, and APOB.
Main Outcomes and Measures
The primary outcome was the association between EOAD and plasma cholesterol measures. The secondary outcome was the association between EOAD and the burden of genetic variants in APOB.
Of the 2125 samples that underwent genetic sequencing, 1276 were from women (60.0%) and 654 (30.8%) were from patients with EOAD (mean [SD] ages, 55.6 [4.3] years for cases and 72.0 [9.6] years for controls). APOE E4 explained 10.1% of the variance of EOAD. After controlling for APOE E4, EOAD cases had higher levels of total cholesterol (mean difference [SE], 21.9 [5.2] mg/dL; P = 2.9 × 10−5), LDL-C (mean difference [SE], 22.0 [4.5] mg/dL; P = 1.8 × 10−6), and ApoB (mean difference [SE], 12.0 [2.4] mg/dL; P = 2.0 × 10−6) than controls in 267 frozen samples. Approximately 3% of EOAD cases carried known AD-causing mutations. Gene-based rare variant burden testing in 2066 samples showed that rare APOB coding variants were significantly more abundant in EOAD cases after adjusting for sex, APOE E4, genetic principal components, ADRC center, and batch (effect size, 0.20; P = 4.20 × 10−4).
Conclusions and Relevance
Elevated LDL-C levels were associated with higher probability of having EOAD, and EOAD cases were enriched for rare coding variants in APOB, which codes for the major protein of LDL-C. Collectively, these novel findings highlight the important role of LDL-C in EOAD pathogenesis and suggest a direct link of APOB variants to AD risk.
Early-onset Alzheimer disease (EOAD) is a rare form of Alzheimer disease (AD) that manifests before 65 years of age and has a large genetic basis with heritability of 91% to 100%.1 Studies of families with EOAD led to the discovery of AD-causing mutations in 3 genes—amyloid precursor protein (APP [NCBI 351]), presenilin 1 (PSEN1 [NCBI 5663]), and presenilin 2 (PSEN2 [NCBI 5664])—that proved pivotal in establishing the amyloid hypothesis in AD.2 The precise contribution of APP, PSEN1, and PSEN2 to EOAD is unknown but is likely less than 10% of all incident EOAD cases,1-3 leaving approximately 90% of EOAD cases unexplained.1
To identify new causes of EOAD, we investigated the role of circulating cholesterol because elevated cholesterol levels have been linked to increased risk of late-onset AD (LOAD) in multiple lines of evidence. First, as with LOAD, EOAD is associated with the apolipoprotein E ε4 allele (APOE E4) [OMIM 107741]),4 and APOE E4 is known to raise circulating cholesterol levels, primarily by raising low-density lipoprotein cholesterol (LDL-C) levels.5 Second, epidemiologic studies of LOAD have found elevated midlife cholesterol levels increase the risk of AD and cognitive decline,6,7 even after adjusting for APOE E4.8-12 Third, several large studies reported that use of drugs to lower cholesterol levels is associated with a reduced AD risk,13-18 and the largest of these studies found that this effect was independent of APOE E4.18 These data suggest that cholesterol is a risk factor for AD independent of APOE; however, the role of cholesterol in EOAD and of other cholesterol-related genes in EOAD is not known.
Herein, we present a 3-part study to identify new causes of EOAD. First, we examined the contribution of known AD-causing mutations—APP, PSEN1, PSEN2, and APOE E4—in 2125 patients with EOAD and controls recruited from across the United States. Second, we investigated whether different fractions of circulating plasma lipoproteins were associated with EOAD after adjusting for APOE E4 in 267 EOAD cases and controls from the Alzheimer’s Disease Research Centers (ADRCs) of Emory University, Atlanta, Georgia, and University of California, San Francisco (UCSF). Third, on finding that EOAD was associated with higher levels of total cholesterol, low-density lipoprotein cholesterol (LDL-C), and plasma apolipoprotein B (ApoB), we aimed to understand the mechanistic link between cholesterol and AD. To that end, we performed a large-scale targeted sequencing of the 2125 EOAD cases and controls for APOB because APOB is known to harbor rare variants that have strong effects on lowering or raising LDL-C levels.19-21
Additional details about the study are provided in the eMethods in the Supplement. All individuals were recruited from US ADRCs using established comprehensive research evaluations and diagnostic criteria.22-24 Patients with EOAD were defined as having probable or definite AD, using the National Institute of Neurological and Communicative Diseases and Stroke–Alzheimer’s Disease and Related Disorders Association Work Group criteria,24 with symptoms beginning at 65 years or younger, and controls were cognitively normal individuals older than 60 years. The Emory University ADRC samples constituted the discovery data set, and the UCSF ADRC samples and the ADRC samples obtained from the National Cell Repository for Alzheimer’s Disease (NCRAD) constituted 2 separate replication data sets. Samples from the Emory University and UCSF ADRCs provided frozen plasma, and all samples had genetic material available. Samples were restricted to white research volunteers because only this group had sufficient sample size for meaningful testing. For the samples obtained from NCRAD, we obtained demographic data and APOE genotyping on available samples from the National Alzheimer’s Coordinating Center. All participants or their legal guardians gave written informed consent. Samples from each center were obtained using research protocols approved by the respective institutional review boards. All genetic analyses for this work were approved by the institutional review board of Emory University.
Plasma samples that had not previously been thawed were available for measurement of LDL-C levels in 267 of 667 of the Emory University and UCSF samples (40.0%). Samples were randomized with respect to sex, EOAD status, and age at draw for the Emory University and UCSF data sets, which were analyzed together. Lipid levels were analyzed by colorimetric method on a chemistry analyzer (AU480; Beckman Coulter) at the Emory Lipid Research Laboratory. Total cholesterol and triglyceride levels were assayed using reagents from Beckman Diagnostics, and LDL-C and high-density lipoprotein cholesterol (HDL-C) levels were determined by the homogeneous assay methods using reagents from Sekisui Diagnostics. Levels of ApoB were determined by immunoturbidometric methods using reagents from Sekisui Diagnostics.
Multiplex polymerase chain reaction primers were designed for the Access Array System by using the MPD software25 that targeted APOB, APP, PSEN1, PSEN2, 400 ancestrally informative markers, and 25 common X chromosome markers. The PEMapper and PECaller computational package26 was used for mapping and base calling, respectively, against the Genome Reference Consortium human genome build 38. Variants were annotated and filtered using Bystro,27 and quality control procedures are detailed in eMethods in the Supplement.
Data were analyzed from November 23, 2016, through April 10, 2018. Estimates of the liability to AD were generated using age at onset for patients with EOAD and age at enrollment for controls, sex, and AD presence or absence by linear interpolation, as described previously.1 Linear regression was used to test for association between EOAD and copies of APOE E4 alleles, adjusted for sex and sample source.
Linear regression was used to test for association between each plasma cholesterol measure (ie, total cholesterol level and directly measured ApoB, LDL-C, HDL-C, and triglyceride levels) and EOAD, adjusted for sex, batch, educational attainment, and copies of APOE E4 alleles. In a separate model, we included the aforementioned covariates plus age because age and diagnosis are correlated (ie, affected individuals are younger by definition). To further control for the APOE effect, a stratified analysis was performed on samples within each of the 2 most common genotypes, APOE E3/3 and APOE E3/4, adjusting for the same covariates.
Statistical testing of sequencing data was performed with a burden-based variable threshold test28 as implemented in RAREMETALWORKER (version 4.13.8) in the discovery data set and separately in each replication data set. Meta-analysis of all 3 data sets was performed using RAREMETAL (version 4.13.8).29 Based on the premise that sites of interest are likely to be rare (or would have been identified by genome-wide association study) and of at least moderate effect size (or we would not have power to detect), we focused our analysis on those in which the coding sequence is altered (ie, missense or nonsense mutations) with a Combined Annotation Dependent Depletion Phred scale score of greater than 20.30 We used a variable-threshold gene-based burden test with an adaptive frequency that selects a minor allele threshold to maximize the burden statistic. Analysis in each individual data set (ie, 1 discovery and 2 replication data sets) was adjusted for sex and the first 4 genetic principal components. The final meta-analysis was adjusted for sex, the first 4 genetic principal components, copies of the APOE E4 allele, study center, and sequencing batch. Our primary analysis was to test for enrichment of rare APOB coding variants in EOAD. A secondary analysis was to test for enrichment of rare coding variants in known AD-causing genes in EOAD.
A total of 2125 cases and controls from 29 ADRCs contributed to this study (1276 women [60.0%] and 849 men [40.0%]; mean [SD] ages, 55.6 [4.3] years for cases and 72.0 [9.6] years for controls) and were divided into the following 3 data sets: a discovery data set consisting of samples from the Emory University ADRC (n = 381), a first replication data set consisting of samples from the UCSF ADRC (n = 181), and a second replication data set consisting of samples from the 27 other ADRCs (excluding samples from the Emory University and UCSF ADRCs) (n = 1563). Table 1 provides demographic details for each data set. Patients with EOAD were younger than controls (by design), had a higher burden of APOE E4 alleles (as expected), were more likely to be male in the Emory and other ADRCs, and tended to have fewer years of formal education for all three groups (Table 1). In the NCRAD data set, educational attainment data were present for all control subjects but were missing for 69% of patients with EOAD, making statistical adjustment for educational attainment unfeasible.
Deep sequencing of APP, PSEN1, and PSEN2 in all 2125 samples revealed 13 previously described AD-causing mutations in 23 individuals (3.4% of all samples), including 2 sites in APP (n = 2), 10 sites in PSEN1 (n = 18), and 2 sites in PSEN2 (n = 3) (eTable 1 in the Supplement). A gene-based burden analysis of rare (minor allele frequency <1%) coding variants in APP, PSEN1, and PSEN2 (including known pathogenic sites) was performed. Only PSEN1 was significantly associated with EOAD, although well-described pathogenic mutations were found in all 3 genes (eTables 2-4 in the Supplement). For PSEN1, we found no evidence of enrichment in the Emory University (effect size, −0.11; P = .70) or UCSF (effect size, 0.42; P = .06) data set, but there was an association with the larger replication data set of 1563 samples (effect size, 0.41; P = 1.6 × 10−5) that was even stronger in a meta-analysis of all 3 data sets (effect size, 0.37; P = 1.1 × 10−5). To account for potential selection bias of known AD mutation carriers in the data sets, we performed a fully adjusted meta-analysis that accounted for number of APOE E4 alleles and study center. This analysis revealed a weaker association between PSEN1 and EOAD in 2088 samples (effect size, 0.21; P = 2.60 × 10−3) (eTable 3 in the Supplement) and no association between APP or PSEN2 and EOAD. Moreover, to determine whether known pathogenic mutations in APP, PSEN1, or PSEN2 were responsible for the association between PSEN1 and EOAD, we excluded these known pathogenic mutations and repeated the gene-based rare variant burden analyses. They revealed no significant association between EOAD and APP, PSEN1, or PSEN2 (eTables 2-4 in the Supplement). These findings suggest that known AD-causing mutations account for the association between PSEN1 and EOAD in our data. Going forward, for our primary analysis described below, all known AD mutation carriers were excluded to reduce the genetic heterogeneity of EOAD and improve our power to detect new associations in the remaining samples.
As expected, we found APOE E4 highly associated with EOAD in 2083 samples (APOE E4 allele frequency of 75.1% in cases vs 30.5% in controls; P < 2 × 10−16). We used sex-specific AD prevalence estimates and linear interpolation to find that APOE explains approximately 10.1% of the variance in liability to EOAD after adjusting for sex and study center.
Given the strong associations between APOE E4 and EOAD and between APOE E4 and elevated circulating LDL-C levels, we expected individuals with EOAD to have elevated LDL-C levels. In this analysis of 267 frozen samples from the Emory University and UCSF data sets, we found that even after accounting for the effect of APOE E4, EOAD cases had higher mean (SD) levels of total cholesterol (203.0 [42.7] vs 177.7 [37.2] mg/dL; P = 2.9 × 10−5), LDL-C (131.6 [41.9] vs 104.4 [30.6] mg/dL; P = 1.8 × 10−6), and plasma ApoB (82.2 [20.8] vs 69.3 [16.6] mg/dL; P = 2.0 × 10−6) compared with controls (to convert cholesterol to millimoles per liter, multiply by 0.0259; ApoB to grams per liter, multiply by 0.01). Because total cholesterol largely consists of LDL-C and ApoB is the main lipoprotein of LDL-C, these findings are consistent with one another. On the other hand, EOAD showed no association with HDL-C (mean [SD], 62.7 [15.6] vs 65.1 [20.5] mg/dL; P = .39) and was only nominally associated with higher triglyceride levels (mean [SD], 141.3 [70.2] vs 127.3 [66.4] mg/dL; P = .05) (to convert triglycerides to millimoles per liter, multiply by 0.0113) (Table 2). All results were adjusted for study center, batch, sex, educational attainment, and copies of APOE E4. From these data, we estimated that LDL-C explains 7.6% of the variance in liability to EOAD independently of APOE E4.
To further demonstrate that the association between EOAD and LDL-C is independent of APOE, we performed 2 separate regression analyses stratified by APOE E3/3 or APOE E3/4 genotype. Both analyses confirmed that EOAD cases have significantly higher LDL-C levels compared with controls independently of APOE E3/3 in 153 samples (mean difference [SE], 25.6 [5.8] mg/dL; P = 2.0 × 10−5) or APOE E3/4 in 82 samples (mean difference [SE], 19.8 [8.4] mg/dL; P = .02) (Figure, B and C).
Age is well known to influence LDL-C; however, adjusting for age is potentially problematic in our models because age and diagnosis were significantly correlated (Spearman ρ = −0.34; P = 1.3 × 10−9). Nevertheless, adjusting for age gives similar results in both the adjusted analysis (mean difference [SE] for LDL-C level, 21.3 [5.4] mg/dL; P = 1.2 × 10−4) and stratified analyses for APOE E3/3 (mean difference [SE] for LDL-C level, 25.3 [7.0] mg/dL; P = 4.3 × 10−4) and APOE E3/4 (mean difference [SE] for LDL-C level, 17.8 [10.1] mg/dL; P = .08). Age was not significantly associated with LDL-C in any of these models. Together, these results demonstrate that elevated levels of LDL-C (and ApoB) were significantly associated with increased EOAD risk, and this effect was only partially mediated by APOE E4 genotype.
To identify genes underlying the association between LDL-C and EOAD independently of APOE, we performed deep resequencing of APOB because APOB is known to have variants that raise or lower LDL-C levels independently of APOE genotype,19-21 and ApoB is the main lipoprotein of LDL-C. Thus, we hypothesized that rare genetic variants in APOB partially underlie the association between elevated LDL-C levels and EOAD observed above. To test this hypothesis, we examined APOB variants associated with coding changes (ie, missense or nonsense variants) with a scaled Combined Annotation Dependent Depletion score of greater than 20. This cutoff was selected to focus on variants that likely alter structure, function, or expression.
In the 381 samples in the Emory University discovery data set, we found significant enrichment of APOB variants in EOAD after adjusting for sex and genetic principal components (effect size, 0.26; P = 9.75 × 10−3) (Table 3). This association was replicated in the 1548 samples in the ADRC replication data set after adjusting for sex and genetic principal components (effect size, 0.21; P = .02) (Table 3) but not in 173 samples in the UCSF replication data set, possibly owing to small sample size (effect size, −0.07; P = .78) (Table 3). Results of the meta-analysis of 2102 samples in all 3 data sets showed a stronger association between APOB variants and EOAD (effect size, 0.23; P = 6.40 × 10−4) (Table 3) than in individual data set analyses. A final meta-analysis that was additionally adjusted for study center and APOE E4, in addition to sex and genetic principal components, also showed a strong association between APOB and EOAD independent of APOE E4 in 2066 samples (effect size, 0.20; P = 4.20 × 10−4) (Table 3).
Fifty-seven rare APOB coding variants were included in the final meta-analysis, with 33 variants in 31 EOAD cases, 24 variants in 24 controls, and no shared variants (eTable 5 in the Supplement). Five percent of patients with EOAD carried an allele, in comparison with 1.7% of controls (P = 3.72 × 10−5 by Fisher exact test). Two affected individuals from the Emory University and combined ADRC data sets were compound heterozygotes, with the remainder being heterozygotes. Each compound heterozygote case was heterozygous for 2 different rare coding sites: p.150P>S and p.999T>I in one case and p.3074Y>H and p.3919S>T in the other case. Of these 4 variants, only p.150P>S has been previously described (minor allele frequency of 7.41 × 10−6) in the Genome Aggregation Database.31 Twenty-six variants were described in the Genome Aggregation Database with a mean allele frequency of 6.27 × 10−4 (range, 0.11 × 10−6 to 4.06 × 10−6). Because APOB mutations can cause hyperlipidemia, we queried the ClinVar database,32 a database of genomic variants and human health, for these APOB variants and found 11 annotated sites, of which 3 are labeled as “conflicting interpretations of pathogenicity” and 8 as “of uncertain significance” for “familial hypercholesterolemia.” The sites with conflicting interpretations of pathogenicity were identified in 1 EOAD case (p.3294S>P [rs12720855]) and 2 controls (p.532R>W [rs13306194] and p.3594W>R [rs61744288]). One of those, rs13306194, was found to be associated with hypercholesterolemia in a Malaysian data set.33
We found no evidence that a particular ApoB region or domain has a clustering of variants found in EOAD cases or controls in any of the 5 known domains by using the Fisher exact test. To determine whether carriers of rare coding APOB variants accounted for the difference in LDL-C observed for EOAD in our data, we examined plasma lipid and sequencing data from samples obtained in 221 subjects with such variants. Among carriers of rare coding APOB variants, we found no statistically significant difference in LDL-C levels between cases and controls, likely owing to small sample size and thus low power (n = 21) (mean difference [SE] for LDL-C level, 18.4 [16.5] mg/dL; P = .28). Among noncarriers of APOB variants, we continued to see higher LDL-C levels among patients with EOAD (n = 200) (mean difference [SE], 18.1 [4.9] mg/dL; P = 2.6 × 10−4) after adjusting for APOE variants and sex. This finding suggests that additional factors contribute to differences in LDL-C levels in patients with EOAD (eFigure in the Supplement).
Early-onset AD is an extreme phenotype of AD that accounts for approximately 10% of AD cases. In this study, we first examined the role of known AD-causing genes (ie, APP, PSEN1, and PSEN2) and the largest single genetic risk factor for EOAD (ie, APOE mutations) in a large data set including patients with EOAD (n = 654) and controls (n = 1471) from 29 US ADCs/ADRCs regardless of family history. In our data set, we found approximately 3.4% of all patients with EOAD carried a known AD-causing variant; however, only rare coding variants in PSEN1 were associated with EOAD, although pathogenic variants were found in all 3 genes. The association between EOAD and PSEN1 was accounted for by known pathogenic mutations.
As expected, we confirmed the strong association between APOE E4 and EOAD, with APOE E4 accounting for approximately 10.1% of the liability to EOAD. The association between APOE E4 and EOAD prompted us to examine the role of LDL-C in EOAD, given the known association between APOE E4 and elevated LDL-C levels. This association has not been previously examined, to our knowledge. Using frozen plasma samples from 267 patients with EOAD and controls from the Emory University and UCSF ADRCs, we found a robust association between elevated LDL-C levels and EOAD independent of APOE E4. This finding led us to hypothesize that genes with genetic variants that are known to be associated with higher LDL-C likely increase EOAD risk. To test this hypothesis, we performed large-scale targeted sequencing of APOB, a gene known to harbor rare variants that alter LDL-C, and found a novel genetic association between rare APOB coding variants and EOAD. Approximately 5.0% of patients with EOAD and 1.7% of controls were found to harbor a rare coding polymorphism in APOB that is likely to disrupt the structure, functions, or abundance of ApoB protein. Finally, we showed that the association between higher LDL-C levels and EOAD was not fully explained by APOE E4 or APOB, suggesting that additional genes and/or mechanisms contribute to those findings.
To place the genetic association between APOB and EOAD in context, we note that only 3.4% of all EOAD cases in our combined data set showed a known pathogenic mutation, and we found a stronger association between EOAD and rare coding variants in APOB compared with PSEN1 in our fully adjusted analysis. A likely explanation of this finding is ascertainment bias, that is, carriers with PSEN1 mutations are more likely to be recruited by an ADC because of their mutation status. In this case, we would expect to see attenuation in the association between known AD-causing mutations and EOAD after adjusting for ADC site, which is precisely what we observed.
Our finding of elevated LDL-C levels in EOAD is consistent with epidemiologic studies of LOAD.34 Likewise, our estimate that APOE variants account for 10.1% of the variance in EOAD is comparable with the reported estimate that approximately 13% of the genetic variance in LOAD is explained by APOE.35 Our finding of a significant association between rare coding variants in APOB and EOAD independently of APOE is novel, important, and consistent with multiple genome-wide association studies that revealed strong associations between LOAD and common intron markers of genes involved in brain cholesterol metabolism (eg, ABCA7, BIN1, CLU, SORL1).36-38 Also consistent with our findings is the report that rare loss-of-function variants in ABCA7 were enriched in LOAD.39 Furthermore, mice overexpressing ApoB show hyperlipidemia, neurodegeneration, increases in APP, accumulation of amyloid plaques, and cognitive impairment similar to mice overexpressing wild-type human APP.40,41 Collectively, these studies and our findings suggest an important role of cholesterol metabolism in AD pathogenesis.
A notable strength of the study is that samples were recruited by 29 US ADRCs that have standardized research and diagnosis protocols, which enables greater generalizability of our findings and mitigates the influence of the recruitment strategy of any given center. Our findings generated a number of questions, including whether APOB may harbor protective and deleterious variants and the role of APOB variants in LOAD. Our data showed that controls who carried specific APOB variants continued to have lower LDL-C levels. In particular, APOB p.154R>X was associated with an exceptionally low LDL-C level (39 mg/dL). Thus, APOB may harbor protective variants similar to the common APOE E2 allele. With regard to LOAD, the rarity of these APOB variants and the reduced heritability of LOAD (compared with EOAD) would make identifying risk and protective variants unlikely, given the power of current LOAD sequencing efforts.42
Genetic studies that are designed to probe for associations between a trait or disease (ie, EOAD) and a potential biological intermediate (ie, LDL-C) commonly infer causality vs genetic pleiotropy using mendelian randomization.43 In this study, the rarity of the alleles tested and the limited number of cases that included plasma LDL-C measurement made this process unfeasible. Thus, we were unable to conclude that the observed association is causal and not due to genetic pleiotropy; however, both APOB and APOE act on plasma circulating cholesterol, suggesting a connection, perhaps through effects on the brain vasculature. Moreover, APOE is a key gene in brain cholesterol homeostasis, whereas APOB is not expressed in the brain. Thus, variants in APOE that alter cholesterol metabolism may act both centrally and peripherally, whereas APOB variants may act predominantly if not exclusively through the periphery.
Another potential limitation of this study is that the LDL-C analysis may be confounded by unavailable data (eg, severity of AD, smoking, or use of drugs to lower cholesterol levels). However, the genetic association between APOB and EOAD is unlikely to be confounded by these factors; its statistical significance substantially tempers this concern.
Our APOB findings are not of exome-wide significance, but neither were the PSEN1 findings. The low prevalence of EOAD presents a challenge for obtaining enough samples to perform well-powered whole-exome or whole-genome sequencing studies. The existing LOAD sequencing efforts have excluded individuals with EOAD. Furthermore, our findings in plasma assays and in genetic sequencing were mutually consistent and complementary, strongly supporting an overall association between APOB and EOAD.
Our study provides evidence that circulating cholesterol is associated with EOAD independently of APOE E4. Furthermore, we have identified novel rare genetic coding changes in APOB that are associated with EOAD independently of APOE. The APOB variants we found do not fully explain the association between elevated LDL-C levels and EOAD, indicating that further studies are needed to identify additional genetic variants underlying the contribution of lipid metabolism to AD pathogenesis.
Accepted for Publication: January 18, 2019.
Corresponding Author: Thomas S. Wingo, MD, Department of Neurology, Emory University School of Medicine, 505K Whitehead Bldg, 615 Michael St NE, Atlanta, GA 30322 (firstname.lastname@example.org).
Published Online: May 28, 2019. doi:10.1001/jamaneurol.2019.0648
Author Contributions: Dr T. Wingo had full access to all the data in the study and takes responsibility for the integrity of the data and accuracy of the data analysis.
Concept and design: T. Wingo, Cutler, Miller, Lah, Levey.
Acquisition, analysis, or interpretation of data: T. Wingo, Cutler, A. Wingo, Le, Rabinovici, Lah, Levey.
Drafting of the manuscript: T. Wingo, Cutler, Lah.
Critical revision of the manuscript for important intellectual content: All authors.
Statistical analysis: T. Wingo, Cutler, A. Wingo.
Obtained funding: T. Wingo, Lah, Levey.
Administrative, technical, or material support: Lah, Levey.
Supervision: T. Wingo, Miller, Lah, Levey.
Conflict of Interest Disclosures: Dr T. Wingo reports receiving grants from the National Institutes of Health (NIH), the National Institute of Aging (NIA), and the Veterans Administration (VA) during conduct of the study. Dr Cutler reported receiving grants from the NIH during the conduct of the study. Dr A. Wingo reported receiving grants from the VA during conduct of the study. Dr Rabinovici reported receiving grants from Avid Radiopharmaceuticals, Eli Lilly, Piramal Imaging, and GE Healthcare and personal fees from Eisai, Axon Neuroscience, Merck, Genentech, and Roche outside the submitted work. Dr Miller reported receiving grants from the NIH, NIA, Quest Diagnostics Dementia Pathway Collaboration, Cornell University Subcontract, and the Bluefield Project to cure frontotemporal dementia during the conduct of the study. Dr Lah reported receiving grants from the NIH, Avanir Pharmaceuticals, Genentech, AbbVie, Novartis, Biogen, Lilly, Cognito Therapeutics, and Roche during the conduct of the study. Dr Levey reported serving as a webinar speaker for Future Health LLC, and receiving grants from the NIH, the NIA, AbbVie, Genentech, Cognito Therapeutics, Biogen, Merck, Esai, Novartis, and Takeda outside the submitted work. No other disclosures were reported.
Funding/Support: This work was supported by grants IK2 BX001820 and IK2 CX000601 from the Veterans Health Administration; grants P50 AG025688, P50 AG023501, R01 AG056533, and R01 AG045611 from the NIH; the Emory Integrated Genomics Core, which is subsidized by the Emory University School of Medicine and is one of the Emory Integrated Core Facilities; the To Remember Foundation; the Douglas French Alzheimer’s Foundation; and contract 04-33516 from the State of California Department of Health Services, Alzheimer’s Disease Research Center of California.
Role of the Funder/Sponsor: The funding sources had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.
Disclaimer: The content is solely the responsibility of the authors and does not necessarily represent the official views of the Veterans Health Administration or the NIH.
Additional Contributions: We thank contributors who collected samples used in this study, as well as patients and their families, whose help and participation made this work possible. Zoe White, BS, and Se Min Canon, BS, Wingo Laboratory, provided technical assistance, for which they were compensated.
Additional Information: Samples from the National Cell Repository for Alzheimer’s Disease (NCRAD), which receives government support under cooperative agreement grant U24 AG21886 from the NIA, were used in this study. The National Alzheimer’s Coordinating Center (NACC) database is funded by grant U01 AG016976 from the NIA/NIH. The NACC data are contributed by Alzheimer disease centers with the following grants from the NIA: P30 AG019610 (principal investigator [PI], Eric Reiman, MD), P30 AG013846 (PI, Neil Kowall, MD), P50 AG008702 (PI, Scott Small, MD), P50 AG025688 (PI, Allan Levey, MD, PhD), P50 AG047266 (PI, Todd Golde, MD, PhD), P30 AG010133 (PI, Andrew Saykin, PsyD), P50 AG005146 (PI, Marilyn Albert, PhD), P50 AG005134 (PI, Bradley Hyman, MD, PhD), P50 AG016574 (PI, Ronald Petersen, MD, PhD), P50 AG005138 (PI, Mary Sano, PhD), P30 AG008051 (PI, Thomas Wisniewski, MD), P30 AG013854 (PI, M. Marsel Mesulam, MD), P30 AG008017 (PI, Jeffrey Kaye, MD), P30 AG010161 (PI, David Bennett, MD), P50 AG047366 (PI, Victor Henderson, MD, MS), P30 AG010129 (PI, Charles DeCarli, MD), P50 AG016573 (PI, Frank LaFerla, PhD), P50 AG005131 (PI, James Brewer, MD, PhD), P50 AG023501 (PI, Bruce Miller, MD), P30 AG035982 (PI, Russell Swerdlow, MD), P30 AG028383 (PI, Linda Van Eldik, PhD), P30 AG053760 (PI, Henry Paulson, MD, PhD), P30 AG010124 (PI, John Trojanowski, MD, PhD), P50 AG005133 (PI, Oscar Lopez, MD), P50 AG005142 (PI, Helena Chui, MD), P30 AG012300 (PI, Roger Rosenberg, MD), P30 AG049638 (PI, Suzanne Craft, PhD), P50 AG005136 (PI, Thomas Grabowski, MD), P50 AG033514 (PI, Sanjay Asthana, MD, FRCP), P50 AG005681 (PI, John Morris, MD), and P50 AG047270 (PI, Stephen Strittmatter, MD, PhD).
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