Instantiated mixed effects modeling of Alzheimer's disease markers

The assessment and prediction of a subject's current and future risk of developing neurodegenerative diseases like Alzheimer's disease are of great interest in both the design of clinical trials as well as in clinical decision making. Exploring the longitudinal trajectory of markers related to neurodegeneration is an important task when selecting subjects for treatment in trials and the clinic, in the evaluation of early disease indicators and the monitoring of disease progression. Given that there is substantial intersubject variability, models that attempt to describe marker trajectories for a whole population will likely lack specificity for the representation of individual patients. Therefore, we argue here that individualized models provide a more accurate alternative that can be used for tasks such as population stratification and a subject-specific prognosis. In the work presented here, mixed effects modeling is used to derive global and individual marker trajectories for a training population. Test subject (new patient) specific models are then instantiated using a stratified “marker signature” that defines a subpopulation of similar cases within the training database. From this subpopulation, personalized models of the expected trajectory of several markers are subsequently estimated for unseen patients. These patient specific models of markers are shown to provide better predictions of time-to-conversion to Alzheimer's disease than population based models

Systems modeling of white matter microstructural abnormalities in Alzheimer's disease

INTRODUCTION: Microstructural abnormalities in white matter (WM) are often reported in Alzheimer's disease (AD). However, it is unclear which brain regions have the strongest WM changes in presymptomatic AD and what biological processes underlie WM abnormality during disease progression. METHODS: We developed a systems biology framework to integrate matched diffusion tensor imaging (DTI), genetic and transcriptomic data to investigate regional vulnerability to AD and identify genetic risk factors and gene subnetworks underlying WM abnormality in AD. RESULTS: We quantified regional WM abnormality and identified most vulnerable brain regions. A SNP rs2203712 in CELF1 was most significantly associated with several DTI-derived features in the hippocampus, the top ranked brain region. An immune response gene subnetwork in the blood was most correlated with DTI features across all the brain regions. DISCUSSION: Incorporation of image analysis with gene network analysis enhances our understanding of disease progression and facilitates identification of novel therapeutic strategies for AD

Accurate risk estimation of β-amyloid positivity to identify prodromal Alzheimer's disease: Cross-validation study of practical algorithms

INTRODUCTION: The aim was to create readily available algorithms that estimate the individual risk of β-amyloid (Aβ) positivity. METHODS: The algorithms were tested in BioFINDER (n = 391, subjective cognitive decline or mild cognitive impairment) and validated in Alzheimer's Disease Neuroimaging Initiative (n = 661, subjective cognitive decline or mild cognitive impairment). The examined predictors of Aβ status were demographics; cognitive tests; white matter lesions; apolipoprotein E (APOE); and plasma Aβ₄₂/Aβ₄₀, tau, and neurofilament light. RESULTS: Aβ status was accurately estimated in BioFINDER using age, 10-word delayed recall or Mini–Mental State Examination, and APOE (area under the receiver operating characteristics curve = 0.81 [0.77–0.85] to 0.83 [0.79–0.87]). When validated, the models performed almost identical in Alzheimer's Disease Neuroimaging Initiative (area under the receiver operating characteristics curve = 0.80–0.82) and within different age, subjective cognitive decline, and mild cognitive impairment populations. Plasma Aβ₄₂/Aβ₄₀ improved the models slightly. DISCUSSION: The algorithms are implemented on http://amyloidrisk.com where the individual probability of being Aβ positive can be calculated. This is useful in the workup of prodromal Alzheimer's disease and can reduce the number needed to screen in Alzheimer's disease trials

Predicting sporadic Alzheimer's disease progression via inherited Alzheimer's disease‐informed machine‐learning

Introduction Developing cross‐validated multi‐biomarker models for the prediction of the rate of cognitive decline in Alzheimer's disease (AD) is a critical yet unmet clinical challenge. Methods We applied support vector regression to AD biomarkers derived from cerebrospinal fluid, structural magnetic resonance imaging (MRI), amyloid‐PET and fluorodeoxyglucose positron‐emission tomography (FDG‐PET) to predict rates of cognitive decline. Prediction models were trained in autosomal‐dominant Alzheimer's disease (ADAD, n = 121) and subsequently cross‐validated in sporadic prodromal AD (n = 216). The sample size needed to detect treatment effects when using model‐based risk enrichment was estimated. Results A model combining all biomarker modalities and established in ADAD predicted the 4‐year rate of decline in global cognition (R2 = 24%) and memory (R2 = 25%) in sporadic AD. Model‐based risk‐enrichment reduced the sample size required for detecting simulated intervention effects by 50%–75%. Discussion Our independently validated machine‐learning model predicted cognitive decline in sporadic prodromal AD and may substantially reduce sample size needed in clinical trials in AD

Whole-exome rare-variant analysis of Alzheimer's disease and related biomarker traits

INTRODUCTION: Despite increasing evidence of a role of rare genetic variation in the risk of Alzheimer's disease (AD), limited attention has been paid to its contribution to AD-related biomarker traits indicative of AD-relevant pathophysiological processes. METHODS: We performed whole-exome gene-based rare-variant association studies (RVASs) of 17 AD-related traits on whole-exome sequencing (WES) data generated in the European Medical Information Framework for Alzheimer's Disease Multimodal Biomarker Discovery (EMIF-AD MBD) study (n = 450) and whole-genome sequencing (WGS) data from ADNI (n = 808). RESULTS: Mutation screening revealed a novel probably pathogenic mutation (PSEN1 p.Leu232Phe). Gene-based RVAS revealed the exome-wide significant contribution of rare coding variation in RBKS and OR7A10 to cognitive performance and protection against left hippocampal atrophy, respectively. DISCUSSION: The identification of these novel gene-trait associations offers new perspectives into the role of rare coding variation in the distinct pathophysiological processes culminating in AD, which may lead to identification of novel therapeutic and diagnostic targets

Type 2 diabetes mellitus, brain atrophy, and cognitive decline.

ObjectiveTo study longitudinal relationships between type 2 diabetes mellitus (T2DM), cortical thickness, and cognitive function in older people with normal cognition, mild cognitive impairment, and Alzheimer disease (AD).MethodsThe sample was derived from the Alzheimer's Disease Neuroimaging Initiative cohort who underwent brain MRI and cognitive tests annually for 5 years. Presence of T2DM was based on fasting blood glucose ≥7.0mml/L or the use of glucose-lowering agents. We used latent growth curve modeling to explore longitudinal relationships between T2DM, cortical thickness, and cognitive function, adjusting for relevant covariates and testing for interactions.ResultsThere were 124 people with T2DM (mean age 75.5 years, SD 6.2) and 693 without T2DM (mean age 75.1 years, SD 6.9) with at least 1 MRI available. AD and lower cortical thickness at study entry was associated with a lower chance of having a MRI available at each follow-up phase (all p < 0.001). T2DM was associated with lower baseline cortical thickness (p = 0.01). We found no direct effect of T2DM on decline in cortical thickness or cognitive function, but there was an indirect pathway linking T2DM and cognitive decline via baseline cortical thickness (β = -0.17, p = 0.022). There was an interaction between T2DM and education whereby the negative effect of T2DM on baseline cortical thickness was reduced in those with greater education (β = 0.34, p = 0.037). These associations changed minimally when adjusted for baseline cognitive diagnosis.ConclusionsIn an older cohort with low cerebrovascular disease burden, T2DM contributes to cognitive decline via neurodegeneration. Prior brain and cognitive reserve may protect against this effect

Translating Alzheimer's disease-associated polymorphisms into functional candidates: a survey of IGAP genes and SNPs.

The International Genomics of Alzheimer's Project (IGAP) is a consortium for characterizing the genetic landscape of Alzheimer's disease (AD). The identified and/or confirmed 19 single-nucleotide polymorphisms (SNPs) associated with AD are located on non-coding DNA regions, and their functional impacts on AD are as yet poorly understood. We evaluated the roles of the IGAP SNPs by integrating data from many resources, based on whether the IGAP SNP was (1) a proxy for a coding SNP or (2) associated with altered mRNA transcript levels. For (1), we confirmed that 12 AD-associated coding common SNPs and five nonsynonymous rare variants are in linkage disequilibrium with the IGAP SNPs. For (2), the IGAP SNPs in CELF1 and MS4A6A were associated with expression of their neighboring genes, MYBPC3 and MS4A6A, respectively, in blood. The IGAP SNP in DSG2 was an expression quantitative trait loci (eQTL) for DLGAP1 and NETO1 in the human frontal cortex. The IGAP SNPs in ABCA7, CD2AP, and CD33 each acted as eQTL for AD-associated genes in brain. Our approach for identifying proxies and examining eQTL highlighted potentially impactful, novel gene regulatory phenomena pertinent to the AD phenotype