Identifying underlying patterns in Alzheimer's disease trajectory: a deep learning approach and Mendelian randomization analysis
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ABSTRACT:
SUBMITTER: Yi F
PROVIDER: S-EPMC10556591 | biostudies-literature | 2023 Sep
REPOSITORIES: biostudies-literature
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Project description:ObjectiveEfforts to prevent depression, the leading cause of disability worldwide, have focused on a limited number of candidate factors. Using phenotypic and genomic data from over 100,000 UK Biobank participants, the authors sought to systematically screen and validate a wide range of potential modifiable factors for depression.MethodsBaseline data were extracted for 106 modifiable factors, including lifestyle (e.g., exercise, sleep, media, diet), social (e.g., support, engagement), and environmental (e.g., green space, pollution) variables. Incident depression was defined as minimal depressive symptoms at baseline and clinically significant depression at follow-up. At-risk individuals for incident depression were identified by polygenic risk scores or by reported traumatic life events. An exposure-wide association scan was conducted to identify factors associated with incident depression in the full sample and among at-risk individuals. Two-sample Mendelian randomization was then used to validate potentially causal relationships between identified factors and depression.ResultsNumerous factors across social, sleep, media, dietary, and exercise-related domains were prospectively associated with depression, even among at-risk individuals. However, only a subset of factors was supported by Mendelian randomization evidence, including confiding in others (odds ratio=0.76, 95% CI=0.67, 0.86), television watching time (odds ratio=1.09, 95% CI=1.05, 1.13), and daytime napping (odds ratio=1.34, 95% CI=1.17, 1.53).ConclusionsUsing a two-stage approach, this study validates several actionable targets for preventing depression. It also demonstrates that not all factors associated with depression in observational research may translate into robust targets for prevention. A large-scale exposure-wide approach combined with genetically informed methods for causal inference may help prioritize strategies for multimodal prevention in psychiatry.
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Project description:Stroke and Alzheimer's disease (AD) are common neurological diseases. Several exiting studies indicated that late onset-AD and ischemic stroke have shared genetic links. Different kinds of stroke have different mechanisms. However, it remains unclear whether there is a causal relationship between different types of strokes, including any stroke (AS), any ischemic stroke (AIS), large-artery atherosclerotic stroke (LAS), and cardio-embolic stroke (CES), and AD. Herein, we conducted several Mendelian randomization (MR) studies to explore genetically causal link of different kinds of strokes and AD. The results for inverse-variance weighted (IVW) meta-analysis (β = -0.039, OR = 0.9618, and P-value = 0.750) and weighted median regression (WMR) (β = -0.156, OR = 0.8556, and P-value = 0.274) demonstrated that AS is not causally associated with AD risk. The result of MR-Egger regression (β = -1.312, P-value = 0.098) and intercept term (P-value = 0.105) illustrated no pleiotropy in this MR study. According to the results for IVW (P-value = 0.305, β = -0.103, and OR = 0.9021) and WMR (P-value = 0.487, β = -0.092, and OR = 0.9121) in the MR study between AIS and AD, there is no causal association between AIS and AD risk. In addition, the MR-Egger regression (P-value = 0.290 and β = -0.512) and intercept term (P-value = 0.387) showed no potential pleiotropy. LAS is not causally associated with AD risk according to the MR results (IVW: P-value = 0.568, β = 0.037, and OR = 1.0377; WMR: P-value = 0.793, β = -0.022, and OR = 0.9782). Additionally, the results of MR-Egger regression (P-value = 0.122 and β = -1.220) and intercept term (P-value = 0.110) showed no potential pleiotropy. Our results [IVW: P-value = 0.245, β = -0.064, and OR = 0.938; WMR: P-value = 0.331, β = -0.057, and OR = 0.9446; MR-Egger: P-value = 0.673 and β = -0.062, and intercept term (P-value = 0.985)] further demonstrated there is no causal link between CES and AD and no pleiotropy in this MR study. In conclusion, different types of stroke, including AS, AIS, LAS, and CES, would not be causally associated with AD risk.
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Project description:Background The convolutional neural network (CNN) is a mainstream deep learning (DL) algorithm, and it has gained great fame in solving problems from clinical examination and diagnosis, such as Alzheimer's disease (AD). AD is a degenerative disease difficult to clinical diagnosis due to its unclear underlying pathological mechanism. Previous studies have primarily focused on investigating structural abnormalities in the brain's functional networks related to the AD or proposing different deep learning approaches for AD classification. Objective The aim of this study is to leverage the advantages of combining brain topological features extracted from functional network exploration and deep features extracted by the CNN. We establish a novel fMRI-based classification framework that utilizes Resting-state functional magnetic resonance imaging (rs-fMRI) with the phase synchronization index (PSI) and 2D-CNN to detect abnormal brain functional connectivity in AD. Methods First, PSI was applied to construct the brain network by region of interest (ROI) signals obtained from data preprocessing stage, and eight topological features were extracted. Subsequently, the 2D-CNN was applied to the PSI matrix to explore the local and global patterns of the network connectivity by extracting eight deep features from the 2D-CNN convolutional layer. Results Finally, classification analysis was carried out on the combined PSI and 2D-CNN methods to recognize AD by using support vector machine (SVM) with 5-fold cross-validation strategy. It was found that the classification accuracy of combined method achieved 98.869%. Conclusion These findings show that our framework can adaptively combine the best brain network features to explore network synchronization, functional connections, and characterize brain functional abnormalities, which could effectively detect AD anomalies by the extracted features that may provide new insights into exploring the underlying pathogenesis of AD.
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Project description:Epidemiological studies have implicated systemic inflammation in the development of Alzheimer's disease (AD). However, these observations have been subject to residual confounding and reverse causation. We applied Mendelian randomization approaches to address this. We did not identify any causal associations between serum interleukin (IL)-18, IL-1ra, IL-6, or erythrocyte sedimentation rate (ESR) concentrations and AD. Our findings are limited by the low number of available instruments, though some of those identified (e.g., IL-6) were of sufficient power to indicate true negative results. Taken together, it appears there is no evidence for a causal association between these serum inflammatory cytokines and AD.
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Project description:The goal of the study was to identify genes whose aberrant expression can contribute to diabetic retinopathy. We determined differential response in gene expression to high glucose in lymphoblastoid cell lines derived from matched type 1 diabetic individuals with and without retinopathy. Those genes exhibiting the largest difference in glucose response between diabetic subjects with and without retinopathy were assessed for association to diabetic retinopathy utilizing genotype data from a meta-genome-wide association study. All genetic variants associated with gene expression (expression QTLs; eQTLs) of the glucose response genes were tested for association with diabetic retinopathy. We detected an enrichment of the glucose response gene eQTLs among small association p-values for diabetic retinopathy. Among these, we identified FLCN as a susceptibility gene for diabetic retinopathy. Expression of FLCN in response to glucose is greater in individuals with diabetic retinopathy compared to diabetic individuals without retinopathy. Three large, independent cohorts of diabetic individuals revealed an enhanced association of FLCN eQTL to diabetic retinopathy. Mendelian randomization confirmed a direct positive effect of increased FLCN expression on retinopathy in diabetic individuals. Together, our studies integrating genetic association and gene expression implicate FLCN as a disease gene in diabetic retinopathy.
2020-03-10 | GSE146615 | GEO