https://orcid.org/0000-0002-6463-8965
Figures
Abstract
For Alzheimer’s disease–a leading cause of dementia and global morbidity–improved identification of presymptomatic high-risk individuals and identification of new circulating biomarkers are key public health needs. Here, we tested the hypothesis that a polygenic predictor of risk for Alzheimer’s disease would identify a subset of the population with increased risk of clinically diagnosed dementia, subclinical neurocognitive dysfunction, and a differing circulating proteomic profile. Using summary association statistics from a recent genome-wide association study, we first developed a polygenic predictor of Alzheimer’s disease comprised of 7.1 million common DNA variants. We noted a 7.3-fold (95% CI 4.8 to 11.0; p < 0.001) gradient in risk across deciles of the score among 288,289 middle-aged participants of the UK Biobank study. In cross-sectional analyses stratified by age, minimal differences in risk of Alzheimer’s disease and performance on a digit recall test were present according to polygenic score decile at age 50 years, but significant gradients emerged by age 65. Similarly, among 30,541 participants of the Mass General Brigham Biobank, we again noted no significant differences in Alzheimer’s disease diagnosis at younger ages across deciles of the score, but for those over 65 years we noted an odds ratio of 2.0 (95% CI 1.3 to 3.2; p = 0.002) in the top versus bottom decile of the polygenic score. To understand the proteomic signature of inherited risk, we performed aptamer-based profiling in 636 blood donors (mean age 43 years) with very high or low polygenic scores. In addition to the well-known apolipoprotein E biomarker, this analysis identified 27 additional proteins, several of which have known roles related to disease pathogenesis. Differences in protein concentrations were consistent even among the youngest subset of blood donors (mean age 33 years). Of these 28 proteins, 7 of the 8 proteins with concentrations available were similarly associated with the polygenic score in participants of the Multi-Ethnic Study of Atherosclerosis. These data highlight the potential for a DNA-based score to identify high-risk individuals during the prolonged presymptomatic phase of Alzheimer’s disease and to enable biomarker discovery based on profiling of young individuals in the extremes of the score distribution.
Author summary
Alzheimer’s disease is a leading cause of dementia and global morbidity. Despite decades of research, disease modifying therapies remain elusive. One possible explanation for failed clinical trials is intervention too late in the disease process when therapies are unlikely to be effective. Here, we developed a genetic predictor for Alzheimer’s disease allowing us to identify asymptomatic individuals at increased risk of developing Alzheimer’s disease. We next measured the levels of 3,231 proteins in the blood of middle-aged, healthy individuals and found proteins whose levels were changed in individuals with a high genetic risk of developing Alzheimer’s disease. Several of these proteins have not previously been studied in Alzheimer’s. Our study suggests a method to identify high genetic risk individuals during the presymptomatic phase of disease, enabling us to discover new protein-based biomarkers in the early stages of disease progression.
Citation: Paranjpe MD, Chaffin M, Zahid S, Ritchie S, Rotter JI, Rich SS, et al. (2022) Neurocognitive trajectory and proteomic signature of inherited risk for Alzheimer’s disease. PLoS Genet 18(9): e1010294. https://doi.org/10.1371/journal.pgen.1010294
Editor: Zihuai He, Stanford University, UNITED STATES
Received: October 27, 2021; Accepted: June 14, 2022; Published: September 1, 2022
Copyright: © 2022 Paranjpe et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: Proteomics and genetics data used in this study were obtained by the authors from the study consortia. For the INTERVAL, MESA, Mass General Brigham Biobank cohorts, dataset access is subject to approval by an independent data access committee. The UK Biobank is available to qualified researchers via application to the data access committee as described online: https://www.ukbiobank.ac.uk/enable-your-research/apply-for-access. Co-authorship was not required to gain access to this dataset. J.I.R, S.S.R, R.G, X.G, S.H, R.T, are affiliated with the MESA cohort. J.D, M.I, and A.S.B are affiliated with the INTERVAL cohort. Protecting data stability and access is made possible through individual consortia data access committees. For the UK Biobank cohort, the data access committee can be contacted at: access@ukbiobank.ac.uk. For the MESA cohort, data access can be obtained by contacting Craig Johnson (wcraigj@uw.edu). For the INTERVAL cohort, data access can be obtained by contacting Lisa Salloway (ls768@medschl.cam.ac.uk). For the Mass General Brigham Biobank cohort, data access can be obtained by contacting biobank@partners.org.
Funding: Participants in the INTERVAL randomised controlled trial were recruited with the active collaboration of NHS Blood and Transplant England (www.nhsbt.nhs.uk), which has supported field work and other elements of the trial. DNA extraction and genotyping was co-funded by the National Institute for Health Research (NIHR), the NIHR BioResource (http://bioresource.nihr.ac.uk) and the NIHR (Cambridge Biomedical Research Centre at the Cambridge University Hospitals NHS Foundation Trust). Olink® Proteomics assays were funded by Biogen, Inc. (Cambridge, MA, US). SomaLogic assays were funded by Merck and the NIHR (Cambridge Biomedical Research Centre at the Cambridge University Hospitals NHS Foundation Trust). The academic coordinating centre for INTERVAL was supported by core funding from: NIHR Blood and Transplant Research Unit in Donor Health and Genomics (NIHR BTRU-2014- 10024), UK Medical Research Council (MR/L003120/1), British Heart Foundation (SP/09/002; RG/13/13/30194; RG/18/13/33946) and the NIHR (Cambridge Biomedical Research Centre at the Cambridge University Hospitals NHS Foundation Trust). This work was supported by Health Data Research UK, which is funded by the UK Medical Research Council, Engineering and Physical Sciences Research Council, Economic and Social Research Council, Department of Health and Social Care (England), Chief Scientist Office of the Scottish Government Health and Social Care Directorates, Health and Social Care Research and Development Division (Welsh Government), Public Health Agency (Northern Ireland), British Heart Foundation and Wellcome. This study was also supported by the Victorian Government’s Operational Infrastructure Support (OIS) program. Whole genome sequencing (WGS) for the Trans-Omics in Precision Medicine (TOPMed) program was supported by the National Heart, Lung and Blood Institute (NHLBI). MESA and the MESA SHARe projects are conducted and supported by the National Heart, Lung, and Blood Institute (NHLBI) in collaboration with MESA investigators. Support for MESA is provided by contracts 75N92020D00001, HHSN268201500003I, N01-HC-95159, 75N92020D00005, N01-HC-95160, 75N92020D00002, N01-HC-95161, 75N92020D00003, N01-HC-95162, 75N92020D00006, N01-HC-95163, 75N92020D00004, N01-HC-95164, 75N92020D00007, N01-HC-95165, N01-HC-95166, N01-HC-95167, N01-HC-95168, N01-HC-95169, UL1-TR-000040, UL1-TR-001079, and UL1-TR-001420. Funding for SHARe genotyping was provided by NHLBI Contract N02-HL-64278. R.E.G is supported by National Institutes of Health (awards NIH R01HL133870, R01HL132320; HHSN268201600034I, and NIH R01AG063507). J.D holds a British Heart Foundation Personal Chair and an NIHR Senior Investigator Award. A.V.K. was supported by the National Human Genome Research Institute (awards 1K08HG010155, 1U01HG01179), institutional grants from the Broad Institute of MIT and Harvard (Merkin Institute Fellowship and variant2function), and a Hassenfeld Scholar Award from Massachusetts General Hospital. The funders played no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: I have read the journal’s policy and the authors of this manuscript have the following competing interests: SK is an employee of Verve Therapeutics; holds equity in Verve Therapeutics, Maze Therapeutics, Catabasis, and San Therapeutics; has served on scientific advisory boards for Regeneron Genetics Center and Corvidia Therapeutics; has served as a consultant for Acceleron, Eli Lilly, Novartis, Merck, Novo Nordisk, Novo Ventures, Ionis, Alnylam, Aegerion, Haug Partners, Noble Insights, Leerink Partners, Bayer Healthcare, Illumina, Color Genomics, MedGenome, Quest, Pfizer, and Medscape; and has patents related to a method of identifying and treating a person having a predisposition to or afflicted with cardiometabolic disease (20180010185) and a genetics risk predictor (20190017119). ASB has received grants from AstraZeneca, Bayer, Biogen, Bioverativ, Novartis and Sanofi. A.V.K. is an employee and holds equity in Verve Therapeutics; has served as a scientific advisor to Amgen, Maze Therapeutics, Navitor Pharmaceuticals, Sarepta Therapeutics, Novartis, Silence Therapeutics, Korro Bio, Veritas International, Color Health, Third Rock Ventures, Illumina, Foresite Labs, and Columbia University (NIH); received speaking fees from Illumina, MedGenome, Amgen, and the Novartis Institute for Biomedical Research; received a sponsored research agreement from IBM Research, and is listed as a co-inventor on a patent application for use of imaging data in assessing body fat distribution and associated cardiometabolic risk.
Introduction
Alzheimer’s disease is a neurodegenerative disorder characterized by slowly progressive impairment in memory and executive function, with a lifetime risk of up to 10% [1]. Although clinical diagnosis typically occurs late in life, the pathologic hallmarks–including neuritic plaques and neurofibrillary tangles–begin to accumulate during a prolonged presymptomatic phase [2,3]. Risk stratification using advanced neuroimaging [4–7] or biomarker assessment from cerebrospinal fluid is possible [8–12], but is resource-intensive or invasive, and is unlikely to be useful when applied to asymptomatic individuals early in life [13]. Although some treatments can improve symptoms, no disease-modifying therapies are currently available [14,15].
For a range of conditions, patient stratification based on inherited DNA variation has proven useful in providing insights into disease biology or enabling targeted therapy [16]. The traditional approach has relied on rare, ‘monogenic’ variants of large effect that disrupt a specific physiologic pathway. For Alzheimer’s disease, causative variants in three key genes–amyloid precursor protein (APP) [17–19], presenilin 1 (PSEN1) [20], and presenilin 2 (PSEN2) [21]–were uncovered in studies of families enriched for early-onset cases. These observations have provided key insight into the role of amyloid precursor protein secretion and cleavage abnormalities that accelerate disease but are present in fewer than 5% of afflicted individuals [22].
A second approach to DNA-based risk stratification involves polygenic scoring, which integrates information from many variants that confer individually modest increases in risk via many different pathways. Advances in polygenic score development have demonstrated potential clinical utility for several important and preventable diseases, identifying–in some cases–individuals with risk equivalent to rare monogenic mutations [23–25].
Here, we set out to derive and validate a new polygenic score for Alzheimer’s disease to test two key hypotheses: (i) a polygenic score can stratify the population into differing trajectories of clinical and subclinical cognitive decline with age; (ii) proteomic profiling of asymptomatic individuals with high or low polygenic score may nominate new circulating biomarkers of disease (Fig 1).
Using previously published genome-wide association study summary association statistics [26] and a linkage disequilibrium reference panel of 503 European-ancestry participants from the 1000 Genomes study [27], we derived six candidate polygenic scores for Alzheimer’s disease using the LDPred computational algorithm [28]. The best performing polygenic score was selected based on maximal area-under-the curve in a validation dataset derived from the UK Biobank [29] (n = 119,248 European-ancestry participants) and subsequently calculated in an independent set of UK Biobank participants (n = 288,940). Associations with a clinical diagnosis of Alzheimer’s and performance on a neurocognitive test were determined in both overall and in age-stratified analyses. In an independent dataset derived from the INTERVAL study of healthy blood donors [30], we compared the levels of 3,231 circulating proteins between 636 participants in the top or bottom decile of the polygenic score. We sought to replicate proteins significantly associated with the polygenic score in the INTERVAL study in participants of the MESA study. IGAP: International Genomics of Alzheimer’s Project [26]; UKBB: United Kingdom Biobank [29]; MESA: Multi-Ethnic Study of Atherosclerosis [31].
Results
To create a polygenic score, we used summary association statistics from a previously published genome-wide association (GWAS) study involving 21,982 AD cases and 41,944 unaffected controls and analyzing 7,055,881 common DNA variants [26]. Importantly, individuals in the UK Biobank study were not included in this previous GWAS. Summary statistics from more recent studies were not used because–although they were larger–they included participants of the UK Biobank needed for our validation and testing strategy [32,33]. The summary statistics were used as input into the LDPred computational algorithm, which reweights each variant according to its effect size, strength of statistical significance, correlation with nearby variants, and a global tuning parameter that denotes the number of variants with non-zero effect size [29]. Because the optimal value of this global tuning parameter is difficult to know a priori, a range of six values was tested as previously recommended in order to create six candidate scores [29].
To select the global tuning parameter, we assessed our candidate scores in an independent validation set of 119,248 randomly-selected participants of European ancestry from the UK Biobank of whom 279 (0.2%) had been diagnosed with Alzheimer’s disease. Each of the 6 candidate scores was associated with disease in logistic regression models that included age, sex, and principal components of ancestry as covariates. Odds ratios per standard deviation higher polygenic score in these models ranged from 1.1 to 1.9 and area under the receiver operator curve (AUROC) ranged from 0.72 to 0.78 (S1 Table).We selected the score with the maximal AUROC (0.78) to carry forward into our testing set of 288,940 additional UK Biobank participants, all of whom were distinct from our validation set. Among these participants, mean age at enrollment was 57 years, 54% were female, and 651 (0.2%) had been diagnosed with Alzheimer’s disease. Results in the testing dataset were highly concordant with the validation dataset, with odds ratio per standard deviation higher polygenic score of 1.9 (95% CI 1.7 to 2.0; p = 4.6 x 10−69) and AUROC of 0.77, accounting for 3.4% of the observed variance. We estimate that 64% of this variance explained was contributed by variants near the gene encoding apolipoprotein E (APOE)–which include the well-known ApoE ε4 risk haplotype [34–36]–and 36% by variants in the remainder of the genome (see Methods). This model was well calibrated (calibration slope: 1.04; Hosmer-Lemeshow p value: 0.19; S1 Fig). As expected, the frequency of the ApoE ε4 risk haplotype varied substantially across polygenic score deciles–from an allele frequency of 0 for those in the lowest decile to 59% for those in the highest decile (S2 Fig).
The association between polygenic score for Alzheimer’s disease and disease was analyzed in a testing set of 288,940 UK Biobank participants, of whom 651 had been diagnosed with Alzheimer’s disease. Odds ratios were calculated by comparing those with high polygenic score to the middle quintile of the population in a logistic regression model adjusted for age, sex, genotyping array, and the first four principal components of ancestry.
Across the entire testing dataset, presence of Alzheimer’s disease ranged from 0.1% in the bottom decile to 0.7% in the top decile, corresponding to an adjusted odds ratio of 7.3 (95% CI 4.8 to 11.0; p = 4.5 x 10−21; Fig 2A). As noted for other diseases, increased risk was most pronounced for those in the extreme tail of the distribution [23–25]. As compared to those in the middle quintile, odds ratios for those in top 20%, 10%, 5%, and 1% of the score distribution were 3.1, 4.2, 5.1, and 6.2 respectively (Table 1).
a. Relationship of polygenic score decile to rates of Alzheimer’s disease diagnosis within the UK Biobank testing dataset. b. Age-stratified analysis of the relationship between polygenic score decile groupings and Alzheimer’s disease diagnosis within the UK Biobank testing dataset. Age is assigned based on age at diagnosis of Alzheimer’s disease for those affected or date of last follow-up for others. c. Age-stratified analysis of the relationship between polygenic score decile groupings and performance on a ‘digit recall test,’ a measure of cognitive function. Age is binned into groups corresponding to <50, ≥50–54, ≥55–59, ≥60–64, and ≥65 years at time of assessment. Error bars represent 95% confidence intervals.
Age dependent association of Alzheimer’s disease polygenic score with Alzheimer’s disease
Given that rates of Alzheimer’s disease are known to increase substantially with age, we next performed age-stratified analyses (Fig 2B). Among participants aged less than 50 years, almost none had been diagnosed with disease and there was no detectable gradient according to polygenic score (0% in the bottom decile, 0.01% for those in deciles 2–9 and 0% in top decile, p = 0.45). However, with increasing age, we noted progressively more pronounced gradients. Among individuals aged 65 years and older, the gradient had increased significantly– 0.1% versus 1.1% for those in the bottom versus top decile, respectively (p = 4.5 x 10−21). We replicated this age-dependent association of the polygenic score with Alzheimer’s disease among 30,541 participants of the Mass General Brigham Biobank, of whom 460 (1.5%) had a diagnosis of Alzheimer’s disease (S3 Fig). We again noted no significant differences at younger ages, but for those over 65 years we noted a prevalence of 2.0% versus 4.0% in the bottom versus top decile respectively, p = 0.002.
Alzheimer’s disease polygenic score is associated with cognitive function
Because a clinical diagnosis of overt Alzheimer’s disease occurs late in the disease process, we explored the existence of similar variability in disease trajectory using a subclinical measure of cognitive function. Among 30,853 participants with available genetic data who completed the assessment, mean number of digits recalled was 6.5 (standard deviation 1.7). As noted for disease diagnoses, we noted no significant difference for those less than 45 years but progressively larger differences among older participants (Fig 2C). For those aged 65 years or older, the mean number of digits remembered was 6.4 versus 6.0 digits among those in the bottom versus top decile respectively, p = 0.002). Results were nearly identical in a sensitivity analysis that removed 65 participants who had been previously diagnosed with Alzheimer’s disease.
A high polygenic score is associated with circulating proteins in asymptomatic individuals
Polygenic risk scores have important potential implications for biomarker discovery because they identify at-risk individuals before they experience symptoms. To test the hypothesis that circulating biomarkers would vary according to polygenic risk for Alzheimer’s disease among putatively unaffected individuals, we studied 3,231 circulating proteins using the Somalogic aptamer-based assay in the INTERVAL study of 3,175 blood donors in the UK [37,38]. We compared levels of each of the proteins for those in the bottom versus top decile of the polygenic score (n = 318 in each group). Among these 636 participants, mean age was 43 years and 47% were female without significant differences in age or sex according to the polygenic score (S2 Table).
Given a well-characterized role in amyloid plaque deposition [39–41], levels of apolipoprotein E served as a useful positive control. We noted significantly increased levels of apolipoprotein E in participants with a high polygenic score, mean values (expressed in terms of Z score as described previously) of -0.05 versus 0.28 for those in bottom versus top decile respectively (p = 2.3 x 10−9; Fig 3A and 3C) [37].
The levels of each of 3,231 plasma proteins quantified using an aptamer-based assay were compared between 636 participants from the INTERVAL study with top versus bottom decile of the polygenic score in models adjusted for age, sex, duration between blood draw and processing and the first three principal components of ancestry. The x-axis shows difference–in standardized units with mean 0 and standard deviation 1 –in concentration and the y-axis -log10 p-value for strength of association. The horizontal dashed line represents the Bonferroni-corrected threshold for statistical significance (P < 1.55 x 10−5). b. Boxplots show levels of the three most significantly associated proteins and apolipoprotein E, a known Alzheimer’s disease-related protein. c. The associations between 28 proteins with levels that significantly differed according to high vs low polygenic score. The x-axis refers to the difference in concentration in standardized units. Whiskers represent 1.5*IQR. TBCA: tubulin-specific chaperon protein A; S100A13: S100 calcium-binding protein A13; RUXF: Small Nuclear Ribonucleoprotein Polypeptide F; CUZD1: CUB and zona pellucida-like domain-containing protein 1; ARL1: ADP-ribosylation factor-like protein 1; CRP: C-reactive protein; VPS29: Vacuolar protein sorting-associated protein 29; SG1D2: Secretoglobin Family 1D Member 2; ZO1: Tight junction protein 1; MA2B2: Mannosidase Alpha Class 2B Member 2; CPBE: Choline binding protein E; ApoB: Apolipoprotein B; SYVC: Valyl-TRNA Synthetase 1; LCN10: Lipocalin 10; APOE: Apolipoprotein E; NRBP: Nuclear Receptor Binding Protein 1; MMP-3: matrix metalloproteinase-3; DCK: Deoxycytidine kinase; SNAB: Beta-soluble NSF attachment protein; MMP-8: matrix metalloproteinase-8; SELS: Selenoprotein S; GPR110: Adhesion G-protein coupled receptor F1; CA056: Protein MENT; PSD1: PH and SEC7 domain-containing protein 1; CEI: Protein CEI; LRRN1: Leucine-rich repeat neuronal protein 1
In addition to apolipoprotein E, there were 27 additional proteins whose levels varied according to low versus high polygenic score for Alzheimer’s disease at a Bonferroni corrected p-value 1.5 x 10−5 (0.05/ 3231; Fig 3A and 3C and S3 Table). For several proteins, the differences in levels were significantly more pronounced than for apolipoprotein E. The strongest associated biomarker was tubulin specific chaperone A (Fig 3B), a protein with a role in preventing neurotoxicity due to abnormal beta tubulin folding.42 Individuals with a high polygenic score had substantially lower circulating levels of this protein–mean score of 0.40 versus -1.2 for those in bottom versus top decile. For other proteins, such as S100 calcium binding protein A13 (a member of the S100 family known to interact with the advanced glycation end product pathway [42,43]) and leucine-rich repeat neuronal protein (known to regulate early neuronal progenitor cell signaling [44]), levels were substantially higher in those with higher inherited risk. Additional description of each of the 28 polygenic score-associated proteins is presented in S4 Table).
Among the 28 proteins associated with a high polygenic score, 20 proteins had at least one cis-pQTL or trans-pQTL in the INTERVAL cohort, consisting of 14 unique pQTLs. Several of the pQTLs were in known AD-risk genes including APOE, APOC4, APOC1, C7, CRP (S5 Table). Among the 14 pQTLs, 7 were significantly associated with the overall polygenic score.
As an additional sensitivity analysis, we restricted our proteomics analysis to younger participants from the INTERVAL study, in whom any meaningful clinical manifestation of Alzheimer’s disease is even less likely to have occurred. Among 334 participants aged less than 45 years (mean 33 years)– 163 with a polygenic score in the bottom decile versus 171 in the top decile–we note directionally consistent and nominally significant results (p <0.05 in a logistic model that included age, sex and the first four principal components of ancestry) for 25 out 28 proteins identified in the overall cohort (S4 Fig).
To assess the generalizability of our results to a multiethnic population, we computed the association between polygenic score and each of the 28 proteins in the multi-ethnic MESA cohort [31]. Of the 28 proteins associated with a high polygenic score in INTERVAL, 8 were measured in the MESA study: SNRPF, Moesin, MMP8, MMP3, APOE, APOB, CBPE, and CRP. We compared levels of each of the proteins for those in the bottom versus top decile of the polygenic score (n = 170 in each group). Among these 340 participants, mean age was 60 years and 53% were female. Seven proteins (all except MMP3), were also associated with a high versus low polygenic score in MESA (S5 Fig).
Discussion
In this study, we describe a systematic approach to identify a proteomic signature of an elevated genetic susceptibility to disease quantified through a polygenic score. Focusing on Alzheimer’s disease as a common disease with significant public health burden for which few circulating biomarkers exist, we first computed a polygenic score using previously published summary association statistics. In an independent testing cohort from the UK Biobank, we found a striking association between the polygenic score and diagnosis of Alzheimer’s disease and cognitive function, a finding that was replicated in the independent Mass General Brigham biobank. Interestingly, we found that an elevated polygenic score for Alzheimer’s disease is associated with levels of 28 circulating proteins in a group of 636 healthy, middle aged participants in the INTERVAL cohort. For 25 out of the 28 proteins, their association with a high polygenic score was present even among individuals <45 years of age, suggesting an early proteomic signature of disease that begins decades before clinical manifestation of Alzheimer’s disease.
Our analysis of the relationship between a polygenic score for Alzheimer’s disease with disease trajectories and potential new biomarkers has at least two implications:
First, one possible reason for failure of past Alzheimer’s trial may be intervention too late in the disease process [42]. These failures–which are costly and likely to have prevented additional investment in drug development–often occur even when a therapeutic target is believed to be pathophysiologically sound, as was the case for solanezumab, an antibody designed to clear amyloid-beta from the brain.47,48 While there have been examples of clinical trials aimed at rare genetic forms of early-onset Alzheimer’s disease [45–47], a primary prevention trial enrichment strategy focused on middle-aged asymptomatic individuals with high polygenic score might prove useful [48].
Second, molecular profiling of individuals with very high or very low inherited risk based on a polygenic score–but who remain unaffected–may provide a new approach to nominating new biomarkers or pathways for a given disease [38]. This strategy is different from the traditional approach of profiling individuals after symptom onset, where distinguishing whether changes are a cause or consequence of disease onset often proves challenging. Although differences in circulating biomarkers do not prove disease relevance, additional research into those nominated here may prove useful in uncovering new biology or serving as biomarkers of therapeutic efficacy or target engagement within drug development efforts.
In the current study, our finding that levels of APOE were increased in individuals with a high polygenic score served as a useful positive control, given the well-documented role of APOE in the pathophysiology of Alzheimer’s disease. Serum levels of APOE have been associated with increased risk of developing Alzheimer’s disease and cognitive impairment [49,50]. In addition to proteins known to play a pathophysiological role in Alzheimer’s disease such as APOE, numerous other proteins were associated with the polygenic score and replicated in the MESA cohort. Overall, we found 8 proteins whose levels were lower in the high polygenic score group and 20 proteins whose levels were higher in the high polygenic score group. Among the proteins whose levels were lower in the high polygenic score were a number of proteins critical for maintaining the integrity of endolysosomal-trans-golgi axis, an important mechanism for neuronal proteostasis [51]. For example, VPS29 is one such protein that is part of the retromer complex which functions in recycling protein cargoes from endosomes to the trans-golgi network. This process has been associated with amyloid beta trafficking and processing, and deficiency in retromer has been associated with neuronal loss and amyloid-beta aggregation in a mouse model of Alzheimer’s [52]. Another protein whose levels were lower the high polygenic score group is Arl1, whose downregulation leads to loss of trans-golgi cisternae [53]. Overall, these findings support the hypothesis of an early defect in the endolysosomal-trans-golgi network priming the brain for amyloid-beta accumulation. Among the proteins elevated in the high polygenic score group include MMP-8 and MMP-3, members of the metalloproteinase family. MMP-8 is known to play a role in macrophage [54] and microglia-mediated immune activation [55]. These results suggest a role for increased peripheral and central nervous system immune activation in Alzheimer’s disease, a finding that has been observed by others and validated through PET neuroimaging [56,57] and CSF studies [58–60]. Further, MMP-8 has been widely nominated as a therapeutic target in AD [61,62], suggesting the ability of proteomic profiling at the extremes of a polygenic score distribution to uncover therapeutic targets. Interestingly, other than APOE, none of the genes encoding the 28 polygenic score-associated proteins are near (<500kb) loci implicated in Alzheimer’s disease GWAS efforts [63]. This suggests the proteins identified using our approach would likely not have been identified in traditional GWAS studies.
Several limitations exist to the current study. Although we demonstrate here–and others have demonstrated previously [64–68]–that it is possible to create a polygenic score for Alzheimer’s disease, we urge caution prior to deployment outside of a research setting. First, as is the case with most polygenic scores developed to date, effect size is likely to be lower in non-European populations due to lack of training data [67,69]. Second, current clinical guidelines do not yet support assessment of genetic risk for Alzheimer’s’s disease outside of suspected rare monogenic forms, largely due to concerns about implications for long-term-care or disability insurance, inducing anxiety, and relative absence of efficacious preventive measures [64]. The polygenic score developed in the present study demonstrated an odds ratio per standard deviation increase of 1.90. Although this effect estimate is comparable to that noted with other recent polygenic scores [64–67]–with odds ratios per standard deviation increase ranging from 1.38 to 2.20–we did not directly compare them in the present study. Additional efforts to characterize the relationship between future polygenic scores, neurocognitive trajectory, and proteomic signatures are warranted in future studies. Additionally, several rare mutations of large effect have been associated with Alzheimer’s disease [17–21], our polygenic score was restricted to common DNA variants. Future efforts to develop an integrated risk model that includes both common and rare variants for Alzheimer’s disease is likely to be of significant utility. Another limitation of the current study is the lack of a multiethnic polygenic score, which is important given the reduction in performance when European-derived scores are applied to non-European populations [19,70,71]. A key additional limitation of the current study is limitation of the analysis to individuals of European ancestry. While these analysis provide important proof-of-concept for the potential value of polygenic scoring for risk stratification or clinical development, additional assessment in diverse ancestral populations or development of a multiethnic polygenic score are of major interest. Lastly, while we replicated proteins associated with a high versus low polygenic score in the MESA cohort, additional replication in large-scale studies will be of interest.
Methods
Ethics statement
This research was approved by the UK Biobank Application Committee (application number 7089) and by the Massachusetts General Hospital Institutional Review Board.
Informed consent and study approval
All participants provided written informed consent at the time of enrolling in the UK Biobank, INTERVAL, MESA and Mass General Brigham Biobank studies. Analysis for this study was approved by the Mass General Brigham Institutional Review Board (Boston, MA).
Study cohorts
The polygenic score was validated and tested in the UK Biobank, a large observational, longitudinal study that enrolled 502,505 participants aged 40–69 from centers across the United Kingdom starting in 2006[70]. A subset of participants completed a cognitive assessment, including the Forward Digit Span Test to assess working memory [71]. We selected participants who underwent genomic profiling using either of two genotyping arrays covering 800,000 common genetic markers [29]. Genotype imputation was performed previously by the UK Biobank using the Haplotype Reference Consortium panel version 1.1, the UK10K panel, and the 1000 Genomes panel. To minimize potential confounding related to genetic ancestry, analyses were restricted to participants of White British ancestry previously defined by the UK Biobank using a combination of self-reported ancestry and genetic confirmation. Quality control was performed as described previously [29]. In brief, participants were excluded based on quality control metrics, previously computed by the UK Biobank, including a high genotype missing rate, sex discordance, putative sex chromosome aneuploidy, and withdrawal of informed consent.
Within the UK Biobank, participants with Alzheimer’s disease were identified centrally using a combination of primary care, patient inpatient hospital records, and mortality records using the International Classification of Disease (ICD-10) diagnosis code of G30 and READ code F00 (UK Biobank Field ID 131036).
The INTERVAL BioResource involves ~50,000 blood donors recruited from 25 centres across England during 2012–2014[30]. Study enrollment criteria were consistent with standard blood donation criteria defined by National Health Service Blood and Transplant [72] and excluded individuals with history of major disease including heart disease, stroke, diabetes, atrial fibrillation, type 2 diabetes requiring medications, cancer and recent illness or infection [30,73]. Genotyping was performed using the Axiom UK Biobank genotyping array developed by Affymetrix (Santa Clara, California, US). Sample and variant quality control had been performed previously and involved exclusion based on sex mismatch, low genotype call rates, duplicate samples, extreme heterozygosity and non-European ancestry, as described earlier [37]. Genotyping imputation was performed previously [37] using the UK10K and 1000 Genomes reference panels.
The polygenic score was independently tested in a cohort of 30,541 European-ancestry participants of the Mass General Brigham Biobank who had previously undergone genomic profiling [74]. Among this cohort, 458 participants had been diagnosed with Alzheimer’s disease based on inclusion of the ICD-10 code G30.X in the electronic health record. Age of Alzheimer’s disease diagnosis or last follow-up for controls, sex and the first four principal components of ancestry were recorded for each participant. Samples were imputed to the Haplotype Reference Consortium panel version 1.1 using the Michigan Imputation Server [27,75].
Among the 45,263 blood donors originally recruited in the INTERVAL cohort, 3,562 underwent proteomic profiling in two batches using 4,034 SOMAscan aptamers developed by SomaLogic Inc. (Boulder, Colorado, US) as previously described [37]. In brief, the SOMAscan technology allows for the simultaneous measurement of thousands of proteins from small sample volumes (15 uL serum or plasma) with a lower detection limit compared to traditional methods such as immunoassays [76,77]. The SOMAscan aptamer panel measures both intracellular and extracellular proteins with a bias towards secreted proteins, reflecting the availability of purified protein targets and targets with a putative role in human disease [76,77].
The Multi-Ethnic Study of Atherosclerosis (MESA) cohort was used to replicate proteins significantly associated with a high versus low polygenic score. The design of the MESA study has been described previously and the protocol is available at www.mesa-nhlbi.org. In brief, MESA is a multiethnic prospective cohort that enrolled 6,814 participants in the United States free of cardiovascular disease between 2000 and 2002[31]. Whole genome sequencing was performed on a subset of 3,932 participants, of whom 3,761 were retained after application of sample and variant quality control criteria, as described previously [69].
Polygenic score derivation and validation
Polygenic scores quantify genetic risk across common variants (minor allele frequency ≥1%) by summing variants weighted by the strength of their association with a given trait. To derive a polygenic score for Alzheimer’s disease, we first divided the UK Biobank into a validation set of 119,248 participants and a test set of 288,940 non-overlapping participants. Within the validation set, we used the LDPred computational algorithm, summary statistics from a recent genome-wide association study for Alzheimer’s disease [26] and a reference panel of 503 European-ancestry participants from 1000 Genomes phase 3 version 5[27] to derive candidate polygenic scores.
The LDPred algorithm uses a Bayesian approach to calculate posterior mean effect sizes using genome wide association summary statistics by assuming priors for genetic architecture and linkage disequilibrium from a reference panel. A tuning parameter, ρ, is used to control the fraction of causal (ie. non-zero effect size) variants. Consistent with previous work [23], a range of tuning parameters– 1, 0.3, 0.1, 0.03, 0.01, 0.003 –was used to derive 6 candidate polygenic scores. Each candidate polygenic score was calculated in the validation set by multiplying the genotype dosage of each risk allele by its respective variant weight, and then summing across all variants in the score using PLINK279 software, as previously described [23]. To account for subtle variation in genetic ancestry that may confound the association between polygenic score and Alzheimer’s disease, we corrected our polygenic score for the effects of ancestry as described previously [23]. In brief, a linear regression model was used to predict polygenic score using the first four principal components of ancestry. The residual from this model was retained as an ancestry-corrected polygenic score for downstream analysis
The polygenic score with the best discriminative capacity was defined as the score with the maximal AUROC in a logistic regression model with Alzheimer’s disease as the outcome and the candidate ancestry-corrected polygenic score, age, sex, first four principal components of ancestry. The best polygenic score was applied to the test set.
Assessment of polygenic score in the UK Biobank test set
Within the UK Biobank testing dataset, we first assessed the risk of Alzheimer’s disease for participants in the top 1%, top 5%, top 10% and top 20% of the polygenic score distribution compared to those in the middle quintile. A logistic regression model was fit using covariates of an indicator variable for having a top polygenic score vs middle quintile score, age, sex, and the first four principal components of ancestry and Alzheimer’s disease as the outcome. For each model, we calculated the odds ratio conferred by having a high polygenic score.
To determine the relative contribution of variants near the APOE gene region to the predictive ability of our polygenic score in the UK Biobank testing dataset, we compared the proportion of variance explained–using the Nagelkerke’s pseudo-R2 metric–for two models: (i) a base logistic regression model that included only the covariates of age, sex, and the first four principal components of ancestry and (ii) the covariates plus the polygenic score.
We assessed the gradient in Alzheimer’s disease prevalence across polygenic score deciles. Individuals in the test set were split into polygenic score deciles and disease prevalence was calculated. An odds ratio for the top decile vs bottom decile was calculated using a logistic regression model with Alzheimer’s disease as the outcome and age, sex, and the first four principal components of ancestry as covariates. Calibration curves and intercepts were derived by fitting a linear regression model with observed Alzheimer’s prevalence as the outcome variable and predicted prevalence as the independent variable. Goodness of fit was evaluated using the Hosmer-Lemeshow test.
Age-stratified analyses were conducted by dividing the test set into age groups corresponding to <50, ≥50–54, ≥55–59, ≥60–64, and ≥65 years. Age was assigned based on age at diagnosis of Alzheimer’s disease for those affected or date of last follow-up for others based on the most recent available hospital inpatient record, mortality record, or primary care re cord. Participants were also characterized as belonging to the bottom decile, deciles 2–9, or top decile of polygenic score. For each age category, we compared the prevalence of Alzheimer’s disease among participants in the bottom decile to those in the top decile using a logistic regression model adjusted for sex and the first four principal components of ancestry.
To assess the association between Alzheimer’s disease polygenic score and working memory, we analyzed 30,853 participants who underwent cognitive testing in the UK Biobank. As part of the study protocol, UK Biobank participants completed a test of numeric short-term memory based on ability to recall strings of digits of various length (‘digit span test’) [71]. Polygenic score was associated with the number of digits recalled on the Digit Span Test using a linear regression model that included age, sex, and the first four principal components of ancestry as covariates. A sensitivity analysis conducted by removing participants diagnosed with Alzheimer’s disease yielded nearly identical results.
All statistical analyses were conducted using R version 3.6.1 (The R Foundation).
Assessment of polygenic score in the Mass General Brigham Healthcare Biobank
The age-dependent association between polygenic score and Alzheimer’s disease was independently tested in the Mass General Brigham Biobank [74]. As in the UK Biobank, the Mass General Brigham cohort was divided into age groups corresponding to <50, 50–54, 55–59, 60–64, and ≥65 years. Participants were also characterized as belonging to the bottom decile, middle 2nd-9th deciles, or top deciles of polygenic score. For each age category, we compared the prevalence of Alzheimer’s disease among participants in the bottom decile to those in the top decile using a logistic regression model with sex and first four principal components of ancestry as covariates.
Assessment for a proteomic signature of high versus low polygenic score
For participants in the INTERVAL cohort who underwent proteomic profiling, data processing and quality control were performed as described previously [30]. A multiplexed, aptamer-based approach (SomaLogic SOMAscan assay) was used to measure the relative levels of 3,622 plasma proteins or protein complexes, using 4,034 modified aptamers. Assayed proteins were selected based on the availability of purified protein targets, and screening of proteins that are likely to be involved in human disease. Quality control metrics for the SOMAscan platform have been described [30]. When multiple aptamers mapped to the same protein, we selected the aptamer with strongest binding affinity (Kd) measured using pulldown pull-down assays followed by mass spectrometry and SDS-based gel to assess the binding affinity of each SOMAmer for its target, as described.82 Following quality control, 3,231 proteins were retained for analysis.
To test the associations of plasma protein levels with a high polygenic score for Alzheimer’s disease, we first natural log-transformed the relative protein abundances. Log-transformed protein levels were then adjusted in a linear regression model for age, sex, duration between blood draw and processing (binary, ≤1 day/>1day) and the first three principal components of ancestry as described previously [37]. The protein residuals from this linear regression were then rank-inverse normalized and used as phenotypes for association testing. Participants in the INTERVAL cohort were dichotomized as belonging to the top polygenic score decile (high polygenic score) or bottom polygenic score decile (low polygenic score), the genotype dosage of each risk allele was multiplied by its respective variant weight, and then summed across all variants to yield a score using PLINK2[28] software. Adjusted protein levels were compared between high and low polygenic score participants using a two-sample t-test. A p value < 1.55 x 10−5 (0.05/3231) was deemed significant. A sensitivity analysis was conducted by restricting analysis to participants < 45 years of age at the time of plasma sampling.
Protein quantitative trait loci (pQTL) were identified for proteins significantly associated with the polygenic score. pQTLs were obtained using previously published summary statistics from the INTERVAL cohort [37]. Genetic associations were considered significant using a genome-wide threshold as previously described [37]. The association between pQTLs and AD PRS was examined using a linear regression model with AD PRS as the outcome and pQTL, age, sex, and principal components as covariates.
Replication of proteomic markers of proteomics signature of high versus low polygenic score in the MESA cohort
A subset of MESA participants underwent proteomic profiling using an older version of the SOMAscan platform–including 1,319 markers–using samples obtained at Exam 1 (2000–2002) as previously described [76]. Following quality control, 846 individuals who underwent both proteomic profiling and whole genome sequencing profiling were available for analysis. This cohort self-identified as White (n = 742, 44%), Asian (n = 108, 6%), Black (n = 338, 20%) and Hispanic (n = 512, 30%). To compute the AD polygenic score for Alzheimer’s disease in MESA, the genotype dosage of each risk allele was multiplied by its respective variant weight, and then summed across all variants to yield a score using PLINK [78]. To enable analysis across the four self-reported MESA ethnic/racial groups, an ancestry-corrected polygenic score was computed by retaining the residuals of a linear regression model in which the polygenic score was regressed against the first three principal components of ancestry. Participants in the MESA cohort were dichotomized as belonging to the top ancestry-corrected polygenic score decile (high polygenic score; n = 85) or bottom ancestry-corrected polygenic score decile (low polygenic score, n = 85).
For the subset of protein markers that were available in the MESA study participants, we sought to replicate results from the INTERVAL study. Relative protein abundances were first natural log-transformed. Log-transformed protein levels were then adjusted in a linear regression model for age, sex, and the first three principal components of ancestry. The protein residuals from this linear regression were then rank-inverse normalized and used as phenotypes for association testing. Adjusted protein levels were compared between high and low polygenic score individuals using a two-sample t-test. A nominal one-tailed p-value < 0.05 with the direction of effect prespecified based on the INTERVAL analysis was deemed statistically significant.
Supporting information
S1 Fig. Calibration plots in the testing cohort.
A logistic regression model that included the AD PRS, age, sex, and principal components of ancestry as covariates was well-calibrated in the test dataset. Slope of the calibration curve is displayed. Error bars represent 95% CI.
https://doi.org/10.1371/journal.pgen.1010294.s001
(DOCX)
S2 Fig. Distribution of the APOE ε4 allele among polygenic score deciles.
The distribution of APOE ε4 is presented for each polygenic score decile, ranging from 0.59 APOE ε4 allele frequency in the top decile to 0 in the bottom decile. Consistent with the 64% contribution of variants near the gene encoding apolipoprotein E (APOE) to the polygenic score, we observe significantly more APOE ε4/ε4 homozygous individuals in the top polygenic score decile (23%) compared to the bottom (0%).
https://doi.org/10.1371/journal.pgen.1010294.s002
(DOCX)
S3 Fig. Age-stratified relationship between polygenic score and Alzheimer’s disease diagnosis in the Mass General Brigham Biobank.
The Alzheimer’s disease polygenic score was independently validated in the Mass General Brigham Biobank. Age was assigned based on age at diagnosis of Alzheimer’s disease for those affected or date of last follow-up for others. Similar to the UK Biobank, we observe a significant gradient in Alzheimer’s disease prevalence across polygenic score deciles at later ages in a logistic regression model adjusted for sex and the first four genetic principal components. Error bars represent 95% confidence intervals.
https://doi.org/10.1371/journal.pgen.1010294.s003
(DOCX)
S4 Fig. Sensitivity analysis of circulating protein levels and polygenic score in individuals < 45 years.
To assess differences in protein levels among individuals <45 years (mean 32.6 years), when the onset of Alzheimer’s disease is even more unlikely, we analyzed standardized levels of the 28 proteins identified in the overall dataset. A low polygenic score indicates individuals in the first decile of the distribution and a high score indicates individuals tenth decile. * represent proteins with levels significantly different between high and low polygenic score individuals. In middle age, protein levels are consistently associated with polygenic score (p<0.05, two-tailed t-test). Whiskers represent 1.5*IQR.
https://doi.org/10.1371/journal.pgen.1010294.s004
(DOCX)
S5 Fig. Replication of proteomic signature of high polygenic score in the MESA cohort.
Boxplots are displayed comparing levels of 8 proteins in individuals with a high polygenic score for Alzheimer’s disease (top 10%) and a low polygenic score (bottom 10%) in the MESA cohort. Of the 28 proteins associated with a high polygenic score in the INTERVAL discovery cohort, 8 proteins were available in the MESA cohort. Among the 8 proteins assayed, 7 replicated their association with a high polygenic score for Alzheimer’s disease. P values computed using a two-sample one-tailed t-test using adjusted protein levels (see Methods). Whiskers represent 1.5*IQR.
https://doi.org/10.1371/journal.pgen.1010294.s005
(DOCX)
S1 Table. Association of candidate polygenic scores with Alzheimer’s Disease in UK Biobank validation set.
To select the global tuning parameter, six candidate scores were assessed in a validation set of 119,248 randomly-selected participants of European ancestry from the UK Biobank of whom 279 (0.2%) had been diagnosed with Alzheimer’s disease. Each candidate score was associated with disease in logistic regression models that included age, sex, and principal components of ancestry as covariates and odds ratio (OR) per standard deviation (SD) of polygenic score and area under the receiver operator curve (AUROC) was calculated. The tuning parameter refers to the LDpred ρ parameter used to control the proportion of variants assumed to be causal. Bold indicates polygenic score with maximal AUROC carried forward to the testing datasets. The calibration curves and intercepts were derived by fitting a linear regression model with observed Alzheimer’s prevalence as the outcome variable and predicted prevalence as the independent variable.
https://doi.org/10.1371/journal.pgen.1010294.s006
(XLSX)
S2 Table. INTERVAL cohort characteristics.
*P value defined using a two-sample t-test or Chi-squared test for categorical variables.
https://doi.org/10.1371/journal.pgen.1010294.s007
(XLSX)
S3 Table. AD Polygenic Score-Protein Associations.
Beta represents average change in protein level among individuals in 90% AD PRS compared to those in the 10%.
https://doi.org/10.1371/journal.pgen.1010294.s008
(XLSX)
S4 Table. Description and evidence for role in Alzheimer’s disease of each polygenic score-associated protein.
https://doi.org/10.1371/journal.pgen.1010294.s009
(XLSX)
S5 Table. Proteins with pQTL variants and their association with AD PRS.
pQTL- AD PRS assocation was ascertained in a linear regression model with AD PRS as the outcome and pQTL, age, sex, and principal components as covariates. Beta represents the average change in AD PRS for a 1 unit change in pQTL variant where the pQTL variant is encoded as 0,1,2. A P value < 0.05/14, where 14 is the number of unique pQTL variants, was considered significant. pQTL variants within 1Mb of an aptamer were considered as cis-pQTL with remaining variants being trans-pQTLs. A P value < 0.05/14, where 14 is the number of unique pQTL variants considered, was considered significant.”
https://doi.org/10.1371/journal.pgen.1010294.s010
(XLSX)
Acknowledgments
A complete list of the investigators and contributors to the INTERVAL trial is provided in reference [19]. The academic coordinating centre would like to thank blood donor centre staff and blood donors for participating in the INTERVAL trial.
WGS for “NHLBI TOPMed: Multi-Ethnic Study of Atherosclerosis (MESA)” (phs001416.v1.p1) was performed at the Broad Institute of MIT and Harvard (3U54HG003067-13S1).
Centralized read mapping and genotype calling, along with variant quality metrics and filtering were provided by the TOPMed Informatics Research Center (3R01HL-117626-02S1, contract HHSN268201800002I) (Broad RNA Seq, Proteomics HHSN268201600034I, UW RNA Seq HHSN268201600032I, USC DNA Methylation HHSN268201600034I, Broad Metabolomics HHSN268201600038I). Phenotype harmonization, data management, sample-identity QC, and general study coordination, were provided by the TOPMed Data Coordinating Center (3R01HL-120393; U01HL-120393; contract HHSN268180001I).
Genotyping was performed at Affymetrix (Santa Clara, California, USA) and the Broad Institute of Harvard and MIT (Boston, Massachusetts, USA) using the Affymetrix Genome-Wide Human SNP Array 6.0.
References
- 1. Seshadri S, Wolf PA, Beiser A, Au R, McNulty K, White R, et al. Lifetime risk of dementia and Alzheimer’s disease: The impact of mortality on risk estimates in the Framingham Study. Neurology. 1997 Dec 1;49(6):1498–504. pmid:9409336
- 2. Serrano-Pozo A, Frosch MP, Masliah E, Hyman BT. Neuropathological alterations in Alzheimer disease. Cold Spring Harb Perspect Med. 2011 Sep;1(1):a006189. pmid:22229116
- 3. Jack CR, Knopman DS, Jagust WJ, Petersen RC, Weiner MW, Aisen PS, et al. Tracking pathophysiological processes in Alzheimer’s disease: an updated hypothetical model of dynamic biomarkers. The Lancet Neurology. 2013 Feb;12(2):207–16. pmid:23332364
- 4. Klunk WE, Engler H, Nordberg A, Wang Y, Blomqvist G, Holt DP, et al. Imaging brain amyloid in Alzheimer’s disease with Pittsburgh Compound-B: Imaging Amyloid in AD with PIB. Ann Neurol. 2004 Mar;55(3):306–19. pmid:14991808
- 5. Mosconi L, Tsui WH, Herholz K, Pupi A, Drzezga A, Lucignani G, et al. Multicenter Standardized 18 F-FDG PET Diagnosis of Mild Cognitive Impairment, Alzheimer’s Disease, and Other Dementias. J Nucl Med. 2008 Mar;49(3):390–8. pmid:18287270
- 6. Killiany RJ, Gomez-Isla T, Moss M, Kikinis R, Sandor T, Jolesz F, et al. Use of structural magnetic resonance imaging to predict who will get Alzheimer’s disease. Ann Neurol. 2000 Apr;47(4):430–9. pmid:10762153
- 7. Jack CR, Lowe VJ, Senjem ML, Weigand SD, Kemp BJ, Shiung MM, et al. 11C PiB and structural MRI provide complementary information in imaging of Alzheimer’s disease and amnestic mild cognitive impairment. Brain. 2008 Mar;131(3):665–80. pmid:18263627
- 8. Hansson O, Zetterberg H, Buchhave P, Londos E, Blennow K, Minthon L. Association between CSF biomarkers and incipient Alzheimer’s disease in patients with mild cognitive impairment: a follow-up study. The Lancet Neurology. 2006 Mar;5(3):228–34. pmid:16488378
- 9. Johnson ECB, Dammer EB, Duong DM, Ping L, Zhou M, Yin L, et al. Large-scale proteomic analysis of Alzheimer’s disease brain and cerebrospinal fluid reveals early changes in energy metabolism associated with microglia and astrocyte activation. Nat Med. 2020 May;26(5):769–80. pmid:32284590
- 10. Perneczky R, Tsolakidou A, Arnold A, Diehl-Schmid J, Grimmer T, Forstl H, et al. CSF soluble amyloid precursor proteins in the diagnosis of incipient Alzheimer disease. Neurology. 2011 Jul 5;77(1):35–8. pmid:21700579
- 11. Olsson B, Lautner R, Andreasson U, Öhrfelt A, Portelius E, Bjerke M, et al. CSF and blood biomarkers for the diagnosis of Alzheimer’s disease: a systematic review and meta-analysis. The Lancet Neurology. 2016 Jun;15(7):673–84. pmid:27068280
- 12. Brier MR, Gordon B, Friedrichsen K, McCarthy J, Stern A, Christensen J, et al. Tau and Aβ imaging, CSF measures, and cognition in Alzheimer’s disease. Sci Transl Med. 2016 May 11;8(338):338ra66–338ra66. pmid:27169802
- 13. Valcárcel-Nazco C, Perestelo-Pérez L, Molinuevo JL, Mar J, Castilla I, Serrano-Aguilar P. Cost-Effectiveness of the Use of Biomarkers in Cerebrospinal Fluid for Alzheimer’s Disease. JAD. 2014 Sep 16;42(3):777–88. pmid:24916543
- 14. Cummings JL, Morstorf T, Zhong K. Alzheimer’s disease drug-development pipeline: few candidates, frequent failures. Alzheimers Res Ther. 2014;6(4):37. pmid:25024750
- 15. Mangialasche F, Solomon A, Winblad B, Mecocci P, Kivipelto M. Alzheimer’s disease: clinical trials and drug development. The Lancet Neurology. 2010 Jul;9(7):702–16. pmid:20610346
- 16. Claussnitzer M, Cho JH, Collins R, Cox NJ, Dermitzakis ET, Hurles ME, et al. A brief history of human disease genetics. Nature. 2020 Jan 9;577(7789):179–89. pmid:31915397
- 17. Goate A, Chartier-Harlin MC, Mullan M, Brown J, Crawford F, Fidani L, et al. Segregation of a missense mutation in the amyloid precursor protein gene with familial Alzheimer’s disease. Nature. 1991 Feb;349(6311):704–6. pmid:1671712
- 18. Suzuki N, Cheung T, Cai X, Odaka A, Otvos L, Eckman C, et al. An increased percentage of long amyloid beta protein secreted by familial amyloid beta protein precursor (beta APP717) mutants. Science. 1994 May 27;264(5163):1336–40. pmid:8191290
- 19. Chartier-Harlin MC, Crawford F, Houlden H, Warren A, Hughes D, Fidani L, et al. Early-onset Alzheimer’s disease caused by mutations at codon 717 of the β-amyloid precursor protein gene. Nature. 1991 Oct;353(6347):844–6. pmid:1944558
- 20. Sherrington R, Rogaev EI, Liang Y, Rogaeva EA, Levesque G, Ikeda M, et al. Cloning of a gene bearing missense mutations in early-onset familial Alzheimer’s disease. Nature. 1995 Jun;375(6534):754–60. pmid:7596406
- 21. Levy-Lahad E, Wijsman EM, Nemens E, Anderson L, Goddard KAB, Weber JL, et al. A Familial Alzheimer’s Disease Locus on Chromosome 1. Science. 1995 Aug 18;269(5226):970–3. pmid:7638621
- 22. Ferri CP, Prince M, Brayne C, Brodaty H, Fratiglioni L, Ganguli M, et al. Global prevalence of dementia: a Delphi consensus study. The Lancet. 2005 Dec;366(9503):2112–7. pmid:16360788
- 23. Khera AV, Chaffin M, Aragam KG, Haas ME, Roselli C, Choi SH, et al. Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations. Nat Genet. 2018 Sep;50(9):1219–24. pmid:30104762
- 24. Khera AV, Chaffin M, Wade KH, Zahid S, Brancale J, Xia R, et al. Polygenic Prediction of Weight and Obesity Trajectories from Birth to Adulthood. Cell. 2019 Apr;177(3):587–596.e9. pmid:31002795
- 25. Mavaddat N, Michailidou K, Dennis J, Lush M, Fachal L, Lee A, et al. Polygenic Risk Scores for Prediction of Breast Cancer and Breast Cancer Subtypes. The American Journal of Human Genetics. 2019 Jan;104(1):21–34. pmid:30554720
- 26. European Alzheimer’s Disease Initiative (EADI), Genetic and Environmental Risk in Alzheimer’s Disease (GERAD), Alzheimer’s Disease Genetic Consortium (ADGC), Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE), Lambert JC, Ibrahim-Verbaas CA, et al. Meta-analysis of 74,046 individuals identifies 11 new susceptibility loci for Alzheimer’s disease. Nat Genet. 2013 Dec;45(12):1452–8. pmid:24162737
- 27. The 1000 Genomes Project Consortium, Corresponding authors, Auton A, Abecasis GR, Steering committee, Altshuler DM, et al. A global reference for human genetic variation. Nature. 2015 Oct 1;526(7571):68–74. pmid:26432245
- 28. Vilhjálmsson BJ, Yang J, Finucane HK, Gusev A, Lindström S, Ripke S, et al. Modeling Linkage Disequilibrium Increases Accuracy of Polygenic Risk Scores. Am J Hum Genet. 2015 Oct 1;97(4):576–92. pmid:26430803
- 29. Bycroft C, Freeman C, Petkova D, Band G, Elliott LT, Sharp K, et al. The UK Biobank resource with deep phenotyping and genomic data. Nature. 2018 Oct;562(7726):203–9. pmid:30305743
- 30. Moore C, Sambrook J, Walker M, Tolkien Z, Kaptoge S, Allen D, et al. The INTERVAL trial to determine whether intervals between blood donations can be safely and acceptably decreased to optimise blood supply: study protocol for a randomised controlled trial. Trials. 2014 Sep 17;15:363. pmid:25230735
- 31. Bild DE. Multi-Ethnic Study of Atherosclerosis: Objectives and Design. American Journal of Epidemiology. 2002 Nov 1;156(9):871–81. pmid:12397006
- 32. Jansen IE, Savage JE, Watanabe K, Bryois J, Williams DM, Steinberg S, et al. Genome-wide meta-analysis identifies new loci and functional pathways influencing Alzheimer’s disease risk. Nat Genet. 2019 Mar;51(3):404–13. pmid:30617256
- 33. Wightman DP, Jansen IE, Savage JE, Shadrin AA, Bahrami S, Rongve A, et al. Largest GWAS (N = 1,126,563) of Alzheimer’s Disease Implicates Microglia and Immune Cells [Internet]. Genetic and Genomic Medicine; 2020 Nov [cited 2021 Oct 11]. Available from: http://medrxiv.org/lookup/doi/10.1101/2020.11.20.20235275
- 34. Genin E, Hannequin D, Wallon D, Sleegers K, Hiltunen M, Combarros O, et al. APOE and Alzheimer disease: a major gene with semi-dominant inheritance. Mol Psychiatry. 2011 Sep;16(9):903–7. pmid:21556001
- 35. Bonham LW, Geier EG, Fan CC, Leong JK, Besser L, Kukull WA, et al. Age-dependent effects of APOE ε4 in preclinical Alzheimer’s disease. Ann Clin Transl Neurol. 2016 Sep;3(9):668–77. pmid:27648456
- 36. Meyer MR, Tschanz JT, Norton MC, Welsh-Bohmer KA, Steffens DC, Wyse BW, et al. APOE genotype predicts when—not whether—one is predisposed to develop Alzheimer disease. Nat Genet. 1998 Aug;19(4):321–2. pmid:9697689
- 37. Sun BB, Maranville JC, Peters JE, Stacey D, Staley JR, Blackshaw J, et al. Genomic atlas of the human plasma proteome. Nature. 2018 Jun;558(7708):73–9. pmid:29875488
- 38. Ritchie SC, Lambert SA, Arnold M, Teo SM, Lim S, Scepanovic P, et al. Integrative analysis of the plasma proteome and polygenic risk of cardiometabolic diseases [Internet]. Genetics; 2019 Dec [cited 2021 Oct 11]. Available from: http://biorxiv.org/lookup/doi/10.1101/2019.12.14.876474
- 39. Castellano JM, Kim J, Stewart FR, Jiang H, DeMattos RB, Patterson BW, et al. Human apoE Isoforms Differentially Regulate Brain Amyloid- Peptide Clearance. Science Translational Medicine. 2011 Jun 29;3(89):89ra57–89ra57. pmid:21715678
- 40. Cramer PE, Cirrito JR, Wesson DW, Lee CYD, Karlo JC, Zinn AE, et al. ApoE-Directed Therapeutics Rapidly Clear β-Amyloid and Reverse Deficits in AD Mouse Models. Science. 2012 Mar 23;335(6075):1503–6. pmid:22323736
- 41. Deane R, Sagare A, Hamm K, Parisi M, Lane S, Finn MB, et al. apoE isoform–specific disruption of amyloid β peptide clearance from mouse brain. J Clin Invest. 2008 Dec 1;118(12):4002–13. pmid:19033669
- 42. Rani SG, Sepuru KM, Yu C. Interaction of S100A13 with C2 domain of receptor for advanced glycation end products (RAGE). Biochim Biophys Acta. 2014 Sep;1844(9):1718–28. pmid:24982031
- 43. Leclerc E, Fritz G, Vetter SW, Heizmann CW. Binding of S100 proteins to RAGE: An update. Biochimica et Biophysica Acta (BBA)—Molecular Cell Research. 2009 Jun;1793(6):993–1007. pmid:19121341
- 44. Andreae LC, Peukert D, Lumsden A, Gilthorpe JD. Analysis of Lrrn1 expression and its relationship to neuromeric boundaries during chick neural development. Neural Dev. 2007 Dec;2(1):22. pmid:17973992
- 45. Mills SM, Mallmann J, Santacruz AM, Fuqua A, Carril M, Aisen PS, et al. Preclinical trials in autosomal dominant AD: Implementation of the DIAN-TU trial. Revue Neurologique. 2013 Oct;169(10):737–43. pmid:24016464
- 46. Sperling RA, Rentz DM, Johnson KA, Karlawish J, Donohue M, Salmon DP, et al. The A4 Study: Stopping AD Before Symptoms Begin? Sci Transl Med [Internet]. 2014 Mar 19 [cited 2021 Oct 11];6(228). Available from: https://www.science.org/doi/10.1126/scitranslmed.3007941 pmid:24648338
- 47. Alexandra S. New Drug Trial Seeks to Stop Alzheimer’s’s Before It Starts. 2012;
- 48. Ballard C, Atri A, Boneva N, Cummings JL, Frölich L, Molinuevo JL, et al. Enrichment factors for clinical trials in mild-to-moderate Alzheimer’s disease. Alzheimers Dement (N Y). 2019;5:164–74. pmid:31193334
- 49. Wolters FJ, Koudstaal PJ, Hofman A, van Duijn CM, Ikram MA. Serum apolipoprotein E is associated with long-term risk of Alzheimer’s disease: The Rotterdam Study. Neurosci Lett. 2016 Mar 23;617:139–42. pmid:26876448
- 50. Taddei K, Clarnette R, Gandy SE, Martins RN. Increased plasma apolipoprotein E (apoE) levels in Alzheimer’s disease. Neurosci Lett. 1997 Feb 14;223(1):29–32. pmid:9058415
- 51. Winckler B, Faundez V, Maday S, Cai Q, Guimas Almeida C, Zhang H. The Endolysosomal System and Proteostasis: From Development to Degeneration. J Neurosci. 2018 Oct 31;38(44):9364–74. pmid:30381428
- 52. Ye H, Ojelade SA, Li-Kroeger D, Zuo Z, Wang L, Li Y, et al. Retromer subunit, VPS29, regulates synaptic transmission and is required for endolysosomal function in the aging brain. eLife. 2020 Apr 14;9:e51977. pmid:32286230
- 53. Ireland SC, Huang H, Zhang J, Li J, Wang Y. Hydrogen peroxide induces Arl1 degradation and impairs Golgi-mediated trafficking. Mol Biol Cell. 2020 Aug 1;31(17):1931–42. pmid:32583744
- 54. Pepys MB, Hirschfield GM. C-reactive protein: a critical update. J Clin Invest. 2003 Jun 15;111(12):1805–12. pmid:12813013
- 55. Lee EJ, Han JE, Woo MS, Shin JA, Park EM, Kang JL, et al. Matrix Metalloproteinase-8 Plays a Pivotal Role in Neuroinflammation by Modulating TNF-α Activation. JI. 2014 Sep 1;193(5):2384–93.
- 56. Cagnin A, Brooks DJ, Kennedy AM, Gunn RN, Myers R, Turkheimer FE, et al. In-vivo measurement of activated microglia in dementia. The Lancet. 2001 Aug;358(9280):461–7. pmid:11513911
- 57. Yasuno F, Kosaka J, Ota M, Higuchi M, Ito H, Fujimura Y, et al. Increased binding of peripheral benzodiazepine receptor in mild cognitive impairment–dementia converters measured by positron emission tomography with [11C]DAA1106. Psychiatry Research: Neuroimaging. 2012 Jul;203(1):67–74. pmid:22892349
- 58. Bettcher BM, Johnson SC, Fitch R, Casaletto KB, Heffernan KS, Asthana S, et al. Cerebrospinal Fluid and Plasma Levels of Inflammation Differentially Relate to CNS Markers of Alzheimer’s Disease Pathology and Neuronal Damage. JAD. 2018 Feb 6;62(1):385–97. pmid:29439331
- 59. Taipa R, das Neves SP, Sousa AL, Fernandes J, Pinto C, Correia AP, et al. Proinflammatory and anti-inflammatory cytokines in the CSF of patients with Alzheimer’s disease and their correlation with cognitive decline. Neurobiology of Aging. 2019 Apr;76:125–32. pmid:30711675
- 60. Janelidze S, Mattsson N, Stomrud E, Lindberg O, Palmqvist S, Zetterberg H, et al. CSF biomarkers of neuroinflammation and cerebrovascular dysfunction in early Alzheimer disease. Neurology. 2018 Aug 28;91(9):e867–77. pmid:30054439
- 61. Duits FH, Hernandez-Guillamon M, Montaner J, Goos JDC, Montañola A, Wattjes MP, et al. Matrix Metalloproteinases in Alzheimer’s Disease and Concurrent Cerebral Microbleeds. Mroczko B, editor. JAD. 2015 Oct 1;48(3):711–20. pmid:26402072
- 62. Rosenberg GA. Matrix metalloproteinases and their multiple roles in neurodegenerative diseases. The Lancet Neurology. 2009 Feb;8(2):205–16. pmid:19161911
- 63. Schwartzentruber J, Cooper S, Liu JZ, Barrio-Hernandez I, Bello E, Kumasaka N, et al. Genome-wide meta-analysis, fine-mapping and integrative prioritization implicate new Alzheimer’s disease risk genes. Nat Genet. 2021 Mar;53(3):392–402. pmid:33589840
- 64. Escott-Price V, Sims R, Bannister C, Harold D, Vronskaya M, Majounie E, et al. Common polygenic variation enhances risk prediction for Alzheimer’s disease. Brain. 2015 Dec;138(12):3673–84. pmid:26490334
- 65. Huq AJ, Fulton-Howard B, Riaz M, Laws S, Sebra R, Ryan J, et al. Polygenic score modifies risk for Alzheimer’s disease in APOE ε4 homozygotes at phenotypic extremes. Alzheimers Dement (Amst). 2021;13(1):e12226. pmid:34386572
- 66. Leonenko G, Baker E, Stevenson-Hoare J, Sierksma A, Fiers M, Williams J, et al. Identifying individuals with high risk of Alzheimer’s disease using polygenic risk scores. Nat Commun. 2021 Jul 23;12(1):4506. pmid:34301930
- 67. de Rojas I, Moreno-Grau S, Tesi N, Grenier-Boley B, Andrade V, Jansen IE, et al. Common variants in Alzheimer’s disease and risk stratification by polygenic risk scores. Nat Commun. 2021 Jun 7;12(1):3417. pmid:34099642
- 68. Escott-Price V, Shoai M, Pither R, Williams J, Hardy J. Polygenic score prediction captures nearly all common genetic risk for Alzheimer’s disease. Neurobiology of Aging. 2017 Jan;49:214.e7-214.e11. pmid:27595457
- 69. Khera AV, Chaffin M, Zekavat SM, Collins RL, Roselli C, Natarajan P, et al. Whole-Genome Sequencing to Characterize Monogenic and Polygenic Contributions in Patients Hospitalized With Early-Onset Myocardial Infarction. Circulation. 2019 Mar 26;139(13):1593–602. pmid:30586733
- 70. Sudlow C, Gallacher J, Allen N, Beral V, Burton P, Danesh J, et al. UK Biobank: An Open Access Resource for Identifying the Causes of a Wide Range of Complex Diseases of Middle and Old Age. PLoS Med. 2015 Mar 31;12(3):e1001779. pmid:25826379
- 71.
Wechsler Daniel. Wechsler Adult Intelligence Test: Fourth Edition Technical and Interpretive Manual. Pearson; 2008.
- 72.
Who can give blood [Internet]. NHS Blood Donation. [cited 2021 Oct 11]. Available from: https://www.blood.co.uk/who-can-give-blood/
- 73. Di Angelantonio E, Thompson SG, Kaptoge S, Moore C, Walker M, Armitage J, et al. Efficiency and safety of varying the frequency of whole blood donation (INTERVAL): a randomised trial of 45 000 donors. The Lancet. 2017 Nov;390(10110):2360–71. pmid:28941948
- 74. Karlson EW, Boutin NT, Hoffnagle AG, Allen NL. Building the Partners HealthCare Biobank at Partners Personalized Medicine: Informed Consent, Return of Research Results, Recruitment Lessons and Operational Considerations. J Pers Med. 2016 Jan 14;6(1):E2. pmid:26784234
- 75. Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MAR, Bender D, et al. PLINK: A Tool Set for Whole-Genome Association and Population-Based Linkage Analyses. The American Journal of Human Genetics. 2007 Sep;81(3):559–75. pmid:17701901
- 76. Gold L, Ayers D, Bertino J, Bock C, Bock A, Brody EN, et al. Aptamer-Based Multiplexed Proteomic Technology for Biomarker Discovery. Gelain F, editor. PLoS ONE. 2010 Dec 7;5(12):e15004. pmid:21165148
- 77. Rohloff JC, Gelinas AD, Jarvis TC, Ochsner UA, Schneider DJ, Gold L, et al. Nucleic Acid Ligands With Protein-like Side Chains: Modified Aptamers and Their Use as Diagnostic and Therapeutic Agents. Molecular Therapy—Nucleic Acids. 2014 Jan;3:e201. pmid:25291143
- 78. Goldman JS, Hahn SE, Catania JW, Larusse-Eckert S, Butson MB, Rumbaugh M, et al. Genetic counseling and testing for Alzheimer disease: Joint practice guidelines of the American College of Medical Genetics and the National Society of Genetic Counselors. Genet Med. 2011 Jun;13(6):597–605. pmid:21577118