Genetic Stratification to Identify Risk Groups for Alzheimer's Disease

Brain change trajectories in healthy adults correlate with Alzheimer's related genetic variation and memory decline across life

Throughout adulthood and ageing our brains undergo structural loss in an average pattern resembling faster atrophy in Alzheimer's disease (AD). Using a longitudinal adult lifespan sample (aged 30-89; 2-7 timepoints) and four polygenic scores for AD, we show that change in AD-sensitive brain features correlates with genetic AD-risk and memory decline in healthy adults. We first show genetic risk links with more brain loss than expected for age in early Braak regions, and find this extends beyond APOE genotype. Next, we run machine learning on AD-control data from the Alzheimer's Disease Neuroimaging Initiative using brain change trajectories conditioned on age, to identify AD-sensitive features and model their change in healthy adults. Genetic AD-risk linked with multivariate change across many AD-sensitive features, and we show most individuals over age ~50 are on an accelerated trajectory of brain loss in AD-sensitive regions. Finally, high genetic risk adults with elevated brain change showed more memory decline through adulthood, compared to high genetic risk adults with less brain change. Our findings suggest quantitative AD risk factors are detectable in healthy individuals, via a shared pattern of ageing- and AD-related neurodegeneration that occurs along a continuum and tracks memory decline through adulthood.

Network dynamics-based subtyping of Alzheimer's disease with microglial genetic risk factors

BACKGROUND

The potential of microglia as a target for Alzheimer's disease (AD) treatment is promising, yet the clinical and pathological diversity within microglia, driven by genetic factors, poses a significant challenge. Subtyping AD is imperative to enable precise and effective treatment strategies. However, existing subtyping methods fail to comprehensively address the intricate complexities of AD pathogenesis, particularly concerning genetic risk factors. To address this gap, we have employed systems biology approaches for AD subtyping and identified potential therapeutic targets.

METHODS

We constructed patient-specific microglial molecular regulatory network models by utilizing existing literature and single-cell RNA sequencing data. The combination of large-scale computer simulations and dynamic network analysis enabled us to subtype AD patients according to their distinct molecular regulatory mechanisms. For each identified subtype, we suggested optimal targets for effective AD treatment.

RESULTS

To investigate heterogeneity in AD and identify potential therapeutic targets, we constructed a microglia molecular regulatory network model. The network model incorporated 20 known risk factors and crucial signaling pathways associated with microglial functionality, such as inflammation, anti-inflammation, phagocytosis, and autophagy. Probabilistic simulations with patient-specific genomic data and subsequent dynamics analysis revealed nine distinct AD subtypes characterized by core feedback mechanisms involving SPI1, CASS4, and MEF2C. Moreover, we identified PICALM, MEF2C, and LAT2 as common therapeutic targets among several subtypes. Furthermore, we clarified the reasons for the previous contradictory experimental results that suggested both the activation and inhibition of AKT or INPP5D could activate AD through dynamic analysis. This highlights the multifaceted nature of microglial network regulation.

CONCLUSIONS

These results offer a means to classify AD patients by their genetic risk factors, clarify inconsistent experimental findings, and advance the development of treatments tailored to individual genotypes for AD.

Deep learning-based polygenic risk analysis for Alzheimer's disease prediction

BACKGROUND

The polygenic nature of Alzheimer's disease (AD) suggests that multiple variants jointly contribute to disease susceptibility. As an individual's genetic variants are constant throughout life, evaluating the combined effects of multiple disease-associated genetic risks enables reliable AD risk prediction. Because of the complexity of genomic data, current statistical analyses cannot comprehensively capture the polygenic risk of AD, resulting in unsatisfactory disease risk prediction. However, deep learning methods, which capture nonlinearity within high-dimensional genomic data, may enable more accurate disease risk prediction and improve our understanding of AD etiology. Accordingly, we developed deep learning neural network models for modeling AD polygenic risk.

METHODS

We constructed neural network models to model AD polygenic risk and compared them with the widely used weighted polygenic risk score and lasso models. We conducted robust linear regression analysis to investigate the relationship between the AD polygenic risk derived from deep learning methods and AD endophenotypes (i.e., plasma biomarkers and individual cognitive performance). We stratified individuals by applying unsupervised clustering to the outputs from the hidden layers of the neural network model.

RESULTS

The deep learning models outperform other statistical models for modeling AD risk. Moreover, the polygenic risk derived from the deep learning models enables the identification of disease-associated biological pathways and the stratification of individuals according to distinct pathological mechanisms.

CONCLUSION

Our results suggest that deep learning methods are effective for modeling the genetic risks of AD and other diseases, classifying disease risks, and uncovering disease mechanisms.

Explainable machine learning aggregates polygenic risk scores and electronic health records for Alzheimer's disease prediction

Alzheimer's disease (AD) is the most common late-onset neurodegenerative disorder. Identifying individuals at increased risk of developing AD is important for early intervention. Using data from the Alzheimer Disease Genetics Consortium, we constructed polygenic risk scores (PRSs) for AD and age-at-onset (AAO) of AD for the UK Biobank participants. We then built machine learning (ML) models for predicting development of AD, and explored feature importance among PRSs, conventional risk factors, and ICD-10 codes from electronic health records, a total of > 11,000 features using the UK Biobank dataset. We used eXtreme Gradient Boosting (XGBoost) and SHapley Additive exPlanations (SHAP), which provided superior ML performance as well as aided ML model explanation. For participants age 40 and older, the area under the curve for AD was 0.88. For subjects of age 65 and older (late-onset AD), PRSs were the most important predictors. This is the first observation that PRSs constructed from the AD risk and AAO play more important roles than age in predicting AD. The ML model also identified important predictors from EHR, including urinary tract infection, syncope and collapse, chest pain, disorientation and hypercholesterolemia, for developing AD. Our ML model improved the accuracy of AD risk prediction by efficiently exploring numerous predictors and identified novel feature patterns.

Polygenic resilience scores capture protective genetic effects for Alzheimer's disease

Polygenic risk scores (PRSs) can boost risk prediction in late-onset Alzheimer's disease (LOAD) beyond apolipoprotein E (APOE) but have not been leveraged to identify genetic resilience factors. Here, we sought to identify resilience-conferring common genetic variants in (1) unaffected individuals having high PRSs for LOAD, and (2) unaffected APOE-ε4 carriers also having high PRSs for LOAD. We used genome-wide association study (GWAS) to contrast "resilient" unaffected individuals at the highest genetic risk for LOAD with LOAD cases at comparable risk. From GWAS results, we constructed polygenic resilience scores to aggregate the addictive contributions of risk-orthogonal common variants that promote resilience to LOAD. Replication of resilience scores was undertaken in eight independent studies. We successfully replicated two polygenic resilience scores that reduce genetic risk penetrance for LOAD. We also showed that polygenic resilience scores positively correlate with polygenic risk scores in unaffected individuals, perhaps aiding in staving off disease. Our findings align with the hypothesis that a combination of risk-independent common variants mediates resilience to LOAD by moderating genetic disease risk.

Association between polygenic risk for Alzheimer's disease, brain structure and cognitive abilities in UK Biobank

Previous studies testing associations between polygenic risk for late-onset Alzheimer's disease (LOAD-PGR) and brain magnetic resonance imaging (MRI) measures have been limited by small samples and inconsistent consideration of potential confounders. This study investigates whether higher LOAD-PGR is associated with differences in structural brain imaging and cognitive values in a relatively large sample of non-demented, generally healthy adults (UK Biobank). Summary statistics were used to create PGR scores for n = 32,790 participants using LDpred. Outcomes included 12 structural MRI volumes and 6 concurrent cognitive measures. Models were adjusted for age, sex, body mass index, genotyping chip, 8 genetic principal components, lifetime smoking, apolipoprotein (APOE) e4 genotype and socioeconomic deprivation. We tested for statistical interactions between APOE e4 allele dose and LOAD-PGR vs. all outcomes. In fully adjusted models, LOAD-PGR was associated with worse fluid intelligence (standardised beta [β] = -0.080 per LOAD-PGR standard deviation, p = 0.002), matrix completion (β = -0.102, p = 0.003), smaller left hippocampal total (β = -0.118, p = 0.002) and body (β = -0.069, p = 0.002) volumes, but not other hippocampal subdivisions. There were no significant APOE x LOAD-PGR score interactions for any outcomes in fully adjusted models. This is the largest study to date investigating LOAD-PGR and non-demented structural brain MRI and cognition phenotypes. LOAD-PGR was associated with smaller hippocampal volumes and aspects of cognitive ability in healthy adults and could supplement APOE status in risk stratification of cognitive impairment/LOAD.

Characterization of Brain Iron Deposition Pattern and Its Association With Genetic Risk Factor in Alzheimer's Disease Using Susceptibility-Weighted Imaging

The presence of iron is an important factor for normal brain functions, whereas excessive deposition of iron may impair normal cognitive function in the brain and lead to Alzheimer’s disease (AD). MRI has been widely applied to characterize brain structural and functional changes caused by AD. However, the effectiveness of using susceptibility-weighted imaging (SWI) for the analysis of brain iron deposition is still unclear, especially within the context of early AD diagnosis. Thus, in this study, we aim to explore the relationship between brain iron deposition measured by SWI with the progression of AD using various feature selection and classification methods. The proposed model was evaluated on a 69-subject SWI imaging dataset consisting of 24 AD patients, 21 mild cognitive impairment patients, and 24 normal controls. The identified AD progression-related regions were then compared with the regions reported from previous genetic association studies, and we observed considerable overlap between these two. Further, we have identified a new potential AD-related gene (MEF2C) closely related to the interaction between iron deposition and AD progression in the brain.

Omics Data and Their Integrative Analysis to Support Stratified Medicine in Neurodegenerative Diseases

Molecular and clinical heterogeneity is increasingly recognized as a common characteristic of neurodegenerative diseases (NDs), such as Alzheimer's disease, Parkinson's disease and amyotrophic lateral sclerosis. This heterogeneity makes difficult the development of early diagnosis and effective treatment approaches, as well as the design and testing of new drugs. As such, the stratification of patients into meaningful disease subgroups, with clinical and biological relevance, may improve disease management and the development of effective treatments. To this end, omics technologies-such as genomics, transcriptomics, proteomics and metabolomics-are contributing to offer a more comprehensive view of molecular pathways underlying the development of NDs, helping to differentiate subtypes of patients based on their specific molecular signatures. In this article, we discuss how omics technologies and their integration have provided new insights into the molecular heterogeneity underlying the most prevalent NDs, aiding to define early diagnosis and progression markers as well as therapeutic targets that can translate into stratified treatment approaches, bringing us closer to the goal of personalized medicine in neurology.

Genetic risk for Alzheimer's disease, cognition, and mild behavioral impairment in healthy older adults

BACKGROUND

The neuropsychiatric syndrome mild behavioral impairment (MBI) describes an at-risk state for dementia and may be a useful screening tool for sample enrichment. We hypothesized that stratifying a cognitively normal sample on MBI status would enhance the association between genetic risk for Alzheimer's disease (AD) and cognition.

METHODS

Data from 4458 participants over age 50 without dementia was analyzed. A cognitive composite score was constructed and the MBI Checklist was used to stratify those with MBI and those without. Polygenic scores for AD were generated using summary statistics from the IGAP study.

RESULTS

AD genetic risk was associated with worse cognition in the MBI group but not in the no MBI group (MBI: β = -0.09, 95% confidence interval: -0.13 to -0.03, P = 0.002, R2  = 0.003). The strongest association was in those with more severe MBI aged ≥65.

CONCLUSIONS

MBI is an important feature of aging; screening on MBI may be a useful sample enrichment strategy for clinical research.

Polygenic Score Models for Alzheimer's Disease: From Research to Clinical Applications

The high prevalence of Alzheimer's disease (AD) among the elderly population and its lack of effective treatments make this disease a critical threat to human health. Recent epidemiological and genetics studies have revealed the polygenic nature of the disease, which is possibly explainable by a polygenic score model that considers multiple genetic risks. Here, we systemically review the rationale and methods used to construct polygenic score models for studying AD. We also discuss the associations of polygenic risk scores (PRSs) with clinical outcomes, brain imaging findings, and biochemical biomarkers from both the brain and peripheral system. Finally, we discuss the possibility of incorporating polygenic score models into research and clinical practice along with potential challenges.

Genetic risk for Alzheimer's disease influences neuropathology via multiple biological pathways

Alzheimer’s disease is a highly heritable, common neurodegenerative disease characterized neuropathologically by the accumulation of β-amyloid plaques and tau-containing neurofibrillary tangles. In addition to the well-established risk associated with the APOE locus, there has been considerable success in identifying additional genetic variants associated with Alzheimer’s disease. Major challenges in understanding how genetic risk influences the development of Alzheimer’s disease are clinical and neuropathological heterogeneity, and the high level of accompanying comorbidities. We report a multimodal analysis integrating longitudinal clinical and cognitive assessment with neuropathological data collected as part of the Brains for Dementia Research study to understand how genetic risk factors for Alzheimer’s disease influence the development of neuropathology and clinical performance. Six hundred and ninety-three donors in the Brains for Dementia Research cohort with genetic data, semi-quantitative neuropathology measurements, cognitive assessments and established diagnostic criteria were included in this study. We tested the association of APOE genotype and Alzheimer’s disease polygenic risk score—a quantitative measure of genetic burden—with survival, four common neuropathological features in Alzheimer’s disease brains (neurofibrillary tangles, β-amyloid plaques, Lewy bodies and transactive response DNA-binding protein 43 proteinopathy), clinical status (clinical dementia rating) and cognitive performance (Mini-Mental State Exam, Montreal Cognitive Assessment). The APOE ε4 allele was significantly associated with younger age of death in the Brains for Dementia Research cohort. Our analyses of neuropathology highlighted two independent pathways from APOE ε4, one where β-amyloid accumulation co-occurs with the development of tauopathy, and a second characterized by direct effects on tauopathy independent of β-amyloidosis. Although we also detected association between APOE ε4 and dementia status and cognitive performance, these were all mediated by tauopathy, highlighting that they are a consequence of the neuropathological changes. Analyses of polygenic risk score identified associations with tauopathy and β-amyloidosis, which appeared to have both shared and unique contributions, suggesting that different genetic variants associated with Alzheimer’s disease affect different features of neuropathology to different degrees. Taken together, our results provide insight into how genetic risk for Alzheimer’s disease influences both the clinical and pathological features of dementia, increasing our understanding about the interplay between APOE genotype and other genetic risk factors.

Module Analysis Using Single-Patient Differential Expression Signatures Improves the Power of Association Studies for Alzheimer's Disease

The causal mechanism of Alzheimer's disease is extremely complex. Achieving great statistical power in association studies usually requires a large number of samples. In this work, we illustrated a different strategy to identify AD risk genes by clustering AD patients into modules based on their single-patient differential expression signatures. The evaluation suggested that our method could enrich AD patients with similar clinical manifestations. Applying this to a cohort of only 310 AD patients, we identified 174 AD risk loci at a strict threshold of empirical p < 0.05, while only two loci were identified using all the AD patients. As an evaluation, we collected 23 AD risk genes reported in a recent large-scale meta-analysis and found that 18 of them were rediscovered by association studies using clustered AD patients, while only three of them were rediscovered using all AD patients. Functional annotation suggested that AD-associated genetic variants mainly disturbed neuronal/synaptic function. Our results suggested module analysis helped to enrich AD patients affected by the common risk variants.

Association of Alzheimer's Disease Genetic Risk Loci with Cognitive Performance and Decline: A Systematic Review

The association of Apolipoprotein E (APOE) with late-onset Alzheimer's disease (LOAD) and cognitive endophenotypes of aging has been widely investigated. There is increasing interest in evaluating the association of other LOAD risk loci with cognitive performance and decline. The results of these studies have been inconsistent and inconclusive. We conducted a systematic review of studies investigating the association of non-APOE LOAD risk loci with cognitive performance in older adults. Studies published from January 2009 to April 2018 were identified through a PubMed database search using keywords and by scanning reference lists. Studies were included if they were either cross-sectional or longitudinal in design, included at least one genome-wide significant LOAD risk loci or a genetic risk score, and had one objective measure of cognition. Quality assessment of the studies was conducted using the quality of genetic studies (Q-Genie) tool. Of 2,466 studies reviewed, 49 met inclusion criteria. Fifteen percent of the associations between non-APOE LOAD risk loci and cognition were significant. However, these associations were not replicated across studies, and the majority were rendered non-significant when adjusting for multiple testing. One-third of the studies included genetic risk scores, and these were typically significant only when APOE was included. The findings of this systematic review do not support a consistent association between individual non-APOE LOAD risk and cognitive performance or decline. However, evidence suggests that aggregate LOAD genetic risk exerts deleterious effects on decline in episodic memory and global cognition.

From Polygenic Scores to Precision Medicine in Alzheimer's Disease: A Systematic Review

BACKGROUND

Late-onset Alzheimer's disease (AD) is highly heritable. The effect of many common genetic variants, single nucleotide polymorphisms (SNPs), confer risk. Variants are clustered in areas of biology, notably immunity and inflammation, cholesterol metabolism, endocytosis, and ubiquitination. Polygenic scores (PRS), which weight the sum of an individual's risk alleles, have been used to draw inferences about the pathological processes underpinning AD.

OBJECTIVE

This paper aims to systematically review how AD PRS are being used to study a range of outcomes and phenotypes related to neurodegeneration.

METHODS

We searched the literature from July 2008-July 2018 following PRISMA guidelines.

RESULTS

57 studies met criteria. The AD PRS can distinguish AD cases from controls. The ability of AD PRS to predict conversion from mild cognitive impairment (MCI) to AD was less clear. There was strong evidence of association between AD PRS and cognitive impairment. AD PRS were correlated with a number of biological phenotypes associated with AD pathology, such as neuroimaging changes and amyloid and tau measures. Pathway-specific polygenic scores were also associated with AD-related biologically relevant phenotypes.

CONCLUSION

PRS can predict AD effectively and are associated with cognitive impairment. There is also evidence of association between AD PRS and other phenotypes relevant to neurodegeneration. The associations between pathway specific polygenic scores and phenotypic changes may allow us to define the biology of the disease in individuals and indicate who may benefit from specific treatments. Longitudinal cohort studies are required to test the ability of PGS to delineate pathway-specific disease activity.

Analysis of the B cell receptor repertoire in six immune-mediated diseases

B cells are important in the pathogenesis of many, and perhaps all, immune-mediated diseases (IMDs). Each B cell expresses a single B cell receptor (BCR)¹, with the diverse range of BCRs expressed by an individual’s total B cell population being termed the “BCR repertoire”. Our understanding of the BCR repertoire in the context of IMDs is incomplete, and defining this could reveal new insights into pathogenesis and therapy. We therefore compared the BCR repertoire in systemic lupus erythematosus (SLE), ANCA-associated vasculitis (AAV), Crohn’s disease (CD), Behçet’s disease (BD), eosinophilic granulomatosis with polyangiitis (EGPA) and IgA vasculitis (IgAV), analysing BCR clonality, and immunoglobulin heavy chain gene (IGHV) and, in particular, isotype usage. An IgA-dominated increased clonality in SLE and CD, together with skewed IGHV gene usage in these and other diseases, suggested a microbial contribution to pathogenesis. Different immunosuppressive treatment had specific and distinct impacts on the repertoire; B cells persisting after rituximab were predominately isotype-switched and clonally expanded, the inverse of those persisting after mycophenolate mofetil. A comparative analysis of the BCR repertoire in immune-mediated disease reveals a complex B cell architecture, providing a platform for understanding pathological mechanisms and designing treatment strategies.

Genetic resilience to Alzheimer's disease in APOE ε4 homozygotes: A systematic review

INTRODUCTION

Individuals with homozygosity for the apolipoprotein E (APOE) ε4 allele are in the highest risk category for late-onset Alzheimer's disease (LOAD). However, some individuals in this category do not develop LOAD beyond the age of 75 years, despite being at elevated genetic risk. These "resilient" individuals may carry protective genetic factors.

METHODS

This study aimed to systematically review any previous studies that involved resilient APOE ε4 homozygotes and to identify possible modifying or protective genetic factors.

RESULTS

Fifteen studies met our inclusion criteria and reported genetic factors contributing to reduced risk. We found that only two single nucleotide polymorphisms, CASP7 rs10553596 and SERPINA3 rs4934-A/A, had strong evidence.

DISCUSSION

We found a paucity of studies adequately designed to discover protective genetic factors against LOAD. Many studies combined APOE ε4 homozygotes and heterozygotes together because of small sample sizes and used control populations too young to be clearly defined as controls for LOAD.

Use of an Alzheimer's disease polygenic risk score to identify mild cognitive impairment in adults in their 50s

Early identification of younger, non-demented adults at elevated risk for Alzheimer's disease (AD) is crucial because the pathological process begins decades before dementia onset. Toward that end, we showed that an AD polygenic risk score (PRS) could identify mild cognitive impairment (MCI) in adults who were only in their 50s. Participants were 1176 white, non-Hispanic community-dwelling men of European ancestry in the Vietnam Era Twin Study of Aging (VETSA): 7% with amnestic MCI (aMCI); 4% with non-amnestic MCI (naMCI). Mean age was 56 years, with 89% <60 years old. Diagnosis was based on the Jak-Bondi actuarial/neuropsychological approach. We tested six P-value thresholds (0.05-0.50) for single nucleotide polymorphisms included in the ADPRS. After controlling for non-independence of twins and non-MCI factors that can affect cognition, higher PRSs were associated with significantly greater odds of having aMCI than being cognitively normal (odds ratios (ORs) = 1.36-1.43 for thresholds P < 0.20-0.50). The highest OR for the upper vs. lower quartile of the ADPRS distribution was 3.22. ORs remained significant after accounting for APOE-related SNPs from the ADPRS or directly genotyped APOE. Diabetes was associated with significantly increased odds of having naMCI (ORs = 3.10-3.41 for thresholds P < 0.05-0.50), consistent with naMCI having more vascular/inflammation components than aMCI. Analysis of sensitivity, specificity, and negative and positive predictive values supported some potential of ADPRSs for selecting participants in clinical trials aimed at early intervention. With participants 15+ years younger than most MCI samples, these findings are promising with regard to efforts to more effectively treat or slow AD progression.

A Network of Genetic Effects on Non-Demented Cognitive Aging: Alzheimer's Genetic Risk (CLU + CR1 + PICALM) Intensifies Cognitive Aging Genetic Risk (COMT + BDNF) Selectively for APOEɛ4 Carriers

BACKGROUND

Trajectories of complex neurocognitive phenotypes in preclinical aging may be produced differentially through selective and interactive combinations of genetic risk.

OBJECTIVE

We organize three possible combinations into a "network" of genetic risk indices derived from polymorphisms associated with normal and impaired cognitive aging, as well as Alzheimer's disease (AD). Specifically, we assemble and examine three genetic clusters relevant to non-demented cognitive trajectories: 1) Apolipoprotein E (APOE), 2) a Cognitive Aging Genetic Risk Score (CA-GRS; Catechol-O-methyltransferase + Brain-derived neurotrophic factor), and 3) an AD-Genetic Risk Score (AD-GRS; Clusterin + Complement receptor 1 + Phosphatidylinositol-binding clathrin assembly protein).

METHOD

We use an accelerated longitudinal design (n = 634; age range = 55-95 years) to test whether AD-GRS (low versus high) moderates the effect of increasing CA-GRS risk on executive function (EF) performance and change as stratified by APOE status (ɛ4+ versus ɛ4-).

RESULTS

APOEɛ4 carriers with high AD-GRS had poorer EF performance at the centering age (75 years) and steeper 9-year decline with increasing CA-GRS but this association was not present in APOEɛ4 carriers with low AD-GRS.

CONCLUSIONS

APOEɛ4 carriers with high AD-GRS are at elevated risk of cognitive decline when they also possess higher CA-GRS risk. Genetic risk from both common cognitive aging and AD-related indices may interact in intensification networks to differentially predict (1) level and trajectories of EF decline and (2) potential selective vulnerability for transitions into impairment and dementia.

Neurogenetic contributions to amyloid beta and tau spreading in the human cortex

Tau and amyloid beta (Aβ) proteins accumulate along neuronal circuits in Alzheimer's disease. Unraveling the genetic background for the regional vulnerability of these proteinopathies can help in understanding the mechanisms of pathology progression. To that end, we developed a novel graph theory approach and used it to investigate the intersection of longitudinal Aβ and tau positron emission tomography imaging of healthy adult individuals and the genetic transcriptome of the Allen Human Brain Atlas. We identified distinctive pathways for tau and Aβ accumulation, of which the tau pathways correlated with cognitive levels. We found that tau propagation and Aβ propagation patterns were associated with a common genetic profile related to lipid metabolism, in which APOE played a central role, whereas the tau-specific genetic profile was classified as 'axon related' and the Aβ profile as 'dendrite related'. This study reveals distinct genetic profiles that may confer vulnerability to tau and Aβ in vivo propagation in the human brain.

Investigating the relationship between DNA methylation age acceleration and risk factors for Alzheimer's disease

Introduction

The “epigenetic clock” is a DNA methylation–based estimate of biological age and is correlated with chronological age—the greatest risk factor for Alzheimer's disease (AD). Genetic and environmental risk factors exist for AD, several of which are potentially modifiable. In this study, we assess the relationship between the epigenetic clock and AD risk factors.

Methods

Multilevel models were used to assess the relationship between age acceleration (the residual of biological age regressed onto chronological age) and AD risk factors relating to cognitive reserve, lifestyle, disease, and genetics in the Generation Scotland study (n = 5100).

Results

We report significant associations between age acceleration and body mass index, total cholesterol to high-density lipoprotein cholesterol ratios, socioeconomic status, high blood pressure, and smoking behavior (Bonferroni-adjusted P < .05).

Discussion

Associations are present between environmental risk factors for AD and age acceleration. Measures to modify such risk factors might improve the risk profile for AD and the rate of biological ageing. Future longitudinal analyses are therefore warranted.

Combining Polygenic Hazard Score With Volumetric MRI and Cognitive Measures Improves Prediction of Progression From Mild Cognitive Impairment to Alzheimer's Disease

Improved prediction of progression to Alzheimer's Disease (AD) among older individuals with mild cognitive impairment (MCI) is of high clinical and societal importance. We recently developed a polygenic hazard score (PHS) that predicted age of AD onset above and beyond APOE. Here, we used data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) to further explore the potential clinical utility of PHS for predicting AD development in older adults with MCI. We examined the predictive value of PHS alone and in combination with baseline structural magnetic resonance imaging (MRI) data on performance on the Mini-Mental State Exam (MMSE). In survival analyses, PHS significantly predicted time to progression from MCI to AD over 120 months (p = 1.07e-5), and PHS was significantly more predictive than APOE alone (p = 0.015). Combining PHS with baseline brain atrophy score and/or MMSE score significantly improved prediction compared to models without PHS (three-factor model p = 4.28e-17). Prediction model accuracies, sensitivities and area under the curve were also improved by including PHS in the model, compared to only using atrophy score and MMSE. Further, using linear mixed-effect modeling, PHS improved the prediction of change in the Clinical Dementia Rating-Sum of Boxes (CDR-SB) score and MMSE over 36 months in patients with MCI at baseline, beyond both APOE and baseline levels of brain atrophy. These results illustrate the potential clinical utility of PHS for assessment of risk for AD progression among individuals with MCI both alone, or in conjunction with clinical measures of prodromal disease including measures of cognitive function and regional brain atrophy.

The association of 5HT2A and 5HTTLPR polymorphisms with Alzheimer’s disease susceptibility: a meta-analysis with 6945 subjects

Alzheimer’s disease (AD) is a progressive neurodegenerative disease. Relationships of 5HT2A and 5HTTLPR polymorphisms and AD risk have been widely investigated previously, whereas results derived from these studies were inconclusive and controversial. The aim of this study was to investigate the association of the 5-HT2A and 5HTTLPR polymorphisms and AD using a meta-analysis of existing literatures. Studies were collected using PubMed, Web of Science, the Cochrane Library databases, Chinese National Knowledge Infrastructure (CNKI) and Embase. Pooled odds ratios (ORs) with 95% confidence intervals (CIs) were used to assess associations. As a result, a total of 7 publications for 5-HT2A T102C and 16 publications for 5HTTLPR (L/S) comprised 3255 cases and 3690 controls fulfilled the inclusion criteria. Significant association was covered between allelic and recessive models of 5-HT2A T102C and AD (allelic model: p = 0.003, OR [95% CI] = 1.23 [1.07, 1.40]; recessive model: p = 0.03, OR [95% CI] = 1.28 [1.02, 1.59]). Subsequently, we conducted subgroup analysis for 5-HT2A T102C polymorphism based on ethnicities and APOE ε4, and identified a significantly increased risk for the allelic and dominant models of 5-HT2A T102C and AD in Asian subgroup (allelic model: p = 0.002, OR [95% CI] = 1.42 [1.14, 1.78]; dominant model: p = 0.02, OR [95% CI] = 1.60 [1.09, 2.35]) and subgroup without APOE ε4 (allelic model: p = 0.02, OR [95% CI] = 1.44 [1.05, 1.99]; dominant model: p = 0.0008, OR [95% CI] = 2.49 [1.46, 4.25]). Nevertheless, the pooled analyses suggested no significant association between allelic, dominant, and recessive models of 5HTTLPR (L/S) and AD (p > 0.05). In conclusion, our meta-analysis demonstrates that 5HT2A C10T, but not 5HTTLPR (L/S), might increase risk for AD.

Polygenic hazard score: an enrichment marker for Alzheimer's associated amyloid and tau deposition

There is an urgent need for identifying nondemented individuals at the highest risk of progressing to Alzheimer's disease (AD) dementia. Here, we evaluated whether a recently validated polygenic hazard score (PHS) can be integrated with known in vivo cerebrospinal fluid (CSF) or positron emission tomography (PET) biomarkers of amyloid, and CSF tau pathology to prospectively predict cognitive and clinical decline in 347 cognitive normal (CN; baseline age range = 59.7-90.1, 98.85% white) and 599 mild cognitively impaired (MCI; baseline age range = 54.4-91.4, 98.83% white) individuals from the Alzheimer's Disease Neuroimaging Initiative 1, GO, and 2. We further investigated the association of PHS with post-mortem amyloid load and neurofibrillary tangles in the Religious Orders Study and Memory and Aging Project (ROSMAP) cohort (N = 485, age at death range = 71.3-108.3). In CN and MCI individuals, we found that amyloid and total tau positivity systematically varies as a function of PHS. For individuals in greater than the 50th percentile PHS, the positive predictive value for amyloid approached 100%; for individuals in less than the 25th percentile PHS, the negative predictive value for total tau approached 85%. High PHS individuals with amyloid and tau pathology showed the steepest longitudinal cognitive and clinical decline, even among APOE ε4 noncarriers. Among the CN subgroup, we similarly found that PHS was strongly associated with amyloid positivity and the combination of PHS and biomarker status significantly predicted longitudinal clinical progression. In the ROSMAP cohort, higher PHS was associated with higher post-mortem amyloid load and neurofibrillary tangles, even in APOE ε4 noncarriers. Together, our results show that even after accounting for APOE ε4 effects, PHS may be useful in MCI and preclinical AD therapeutic trials to enrich for biomarker-positive individuals at highest risk for short-term clinical progression.

Polygenic hazard scores in preclinical Alzheimer disease

Identifying asymptomatic older individuals at elevated risk for developing Alzheimer disease (AD) is of clinical importance. Among 1,081 asymptomatic older adults, a recently validated polygenic hazard score (PHS) significantly predicted time to AD dementia and steeper longitudinal cognitive decline, even after controlling for APOE ɛ4 carrier status. Older individuals in the highest PHS percentiles showed the highest AD incidence rates. PHS predicted longitudinal clinical decline among older individuals with moderate to high Consortium to Establish a Registry for Alzheimer's Disease (amyloid) and Braak (tau) scores at autopsy, even among APOE ɛ4 noncarriers. Beyond APOE, PHS may help identify asymptomatic individuals at highest risk for developing Alzheimer neurodegeneration. Ann Neurol 2017;82:484-488.