Evidence map›Paper›PMID 40128806›Full record

ArticleBioData mining2025

High-dimensional mediation analysis reveals the mediating role of physical activity patterns in genetic pathways leading to AD-like brain atrophy.

Hanxiang Xu, Shizhuo Mu, Jingxuan Bao, Christos Davatzikos, Haochang Shou, Li Shen

Abstract read
In one paragraph

Article in BioData mining, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed
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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Hanxiang XuDepartment of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.
Shizhuo MuDepartment of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.
Jingxuan BaoDepartment of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.
Christos DavatzikosDepartment of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.
Haochang ShouDepartment of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA. haochang.shou@pennmedicine.upenn.edu.
Li ShenDepartment of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA. li.shen@pennmedicine.upenn.edu.

Funding

Ultrascale Machine Learning to Empower Discovery in Alzheimers Disease BiobanksU01AG068057 · NIA · UNIVERSITY OF SOUTHERN CALIFORNIA · PI Christos Davatzikos, Heng Huang · 2020 to 2026
$20.7M
Machine Learning and Large-scale Imaging analytics for dimensional representations of brain trajectories in aging and preclinical Alzheimer's Disease: The brain aging chart and the iSTAGING consortiumRF1AG054409 · NIA · UNIVERSITY OF PENNSYLVANIA · PI DAVATZIKOS, CHRISTOS · 2017 to 2023
$6.3M
NIA NIH HHS RF1 AG054409NIA NIH HHS U01 AG068057NIH HHS RF1 AG054409NIH HHS U01 AG068057
6 · The paper itself

Abstract

backgroundAlzheimer's disease (AD) is a complex disorder that affects multiple biological systems including cognition, behavior and physical health. Unfortunately, the pathogenic mechanisms behind AD are not yet clear and the treatment options are still limited. Despite the increasing number of studies examining the pairwise relationships between genetic factors, physical activity (PA), and AD, few have successfully integrated all three domains of data, which may help reveal mechanisms and impact of these genomic and phenomic factors on AD. We use high-dimensional mediation analysis as an integrative framework to study the relationships among genetic factors, PA and AD-like brain atrophy quantified by spatial patterns of brain atrophy.

resultsWe integrate data from genetics, PA and neuroimaging measures collected from 13,425 UK Biobank samples to unveil the complex relationship among genetic risk factors, behavior and brain signatures in the contexts of aging and AD. Specifically, we used a composite imaging marker, Spatial Pattern of Abnormality for Recognition of Early AD (SPARE-AD) that characterizes AD-like brain atrophy, as an outcome variable to represent AD risk. Through GWAS, we identified single nucleotide polymorphisms (SNPs) that are significantly associated with SPARE-AD as exposure variables. We employed conventional summary statistics and functional principal component analysis to extract patterns of PA as mediators. After constructing these variables, we utilized a high-dimensional mediation analysis method, Bayesian Mediation Analysis (BAMA), to estimate potential mediating pathways between SNPs, multivariate PA signatures and SPARE-AD. BAMA incorporates Bayesian continuous shrinkage prior to select the active mediators from a large pool of candidates. We identified a total of 22 mediation pathways, indicating how genetic variants can influence SPARE-AD by altering physical activity. By comparing the results with those obtained using univariate mediation analysis, we demonstrate the advantages of high-dimensional mediation analysis methods over univariate mediation analysis.

conclusionThrough integrative analysis of multi-omics data, we identified several mediation pathways of physical activity between genetic factors and SPARE-AD. These findings contribute to a better understanding of the pathogenic mechanisms of AD. Moreover, our research demonstrates the potential of the high-dimensional mediation analysis method in revealing the mechanisms of disease.

Indexed as

Alzheimer’s diseaseDisease mechanismGenetic risk factorsHigh-dimensional mediation analysisPhysical activitySPARE-AD index

Identifiers

PMID40128806
PMCPMC11931790

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.