# Multi-omics integration reveals shared genetic architecture between metabolic markers and gray matter atrophy in Alzheimer’s Disease

## TL;DR

This study finds shared genetic links between brain structure changes in Alzheimer's and metabolic issues like glucose and cholesterol levels, suggesting new treatment targets.

## Contribution

The study is the first to integrate multi-omics data to reveal shared genetic architecture between Alzheimer’s gray matter atrophy and metabolic traits.

## Key findings

- ConjFDR analysis identified 20–87 shared genes per metabolic trait between AD-related GMV atrophy and five metabolic markers.
- Six genes were shared across all five metabolic markers, suggesting a common genetic basis.
- Functional analysis linked these genes to Aβ aggregation and phosphatidylinositol metabolism.

## Abstract

•Multi-omics Integration: Integrated voxel-based morphometry (VBM) meta-analysis, transcriptome-neuroimaging association analysis, and GWAS to identify pleiotropic genes.•Shared Genetic Architecture: ConjFDR analysis revealed significant genetic overlap between AD-associated GMV atrophy and five metabolic markers, identifying 20–87 shared genes per metabolic trait.•Molecular Mechanisms: Functional enrichment analysis elucidated the molecular interplay between metabolic dysregulation and neurodegenerative pathology, thereby identifying potential genetic targets for developing metabolism-focused targeted therapies for AD.

Multi-omics Integration: Integrated voxel-based morphometry (VBM) meta-analysis, transcriptome-neuroimaging association analysis, and GWAS to identify pleiotropic genes.

Shared Genetic Architecture: ConjFDR analysis revealed significant genetic overlap between AD-associated GMV atrophy and five metabolic markers, identifying 20–87 shared genes per metabolic trait.

Molecular Mechanisms: Functional enrichment analysis elucidated the molecular interplay between metabolic dysregulation and neurodegenerative pathology, thereby identifying potential genetic targets for developing metabolism-focused targeted therapies for AD.

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder characterized by widespread gray matter volume (GMV) reductions. Emerging evidence links glucose and lipid metabolic dysregulation to AD pathophysiology. However, the extent to which AD-related GMV alterations and metabolic traits share a common genetic basis remains poorly understood.

To explore the shared genetic architecture between GMV alterations in AD and metabolites related to glucose and lipid metabolism, aiming to provide biological insights into the prevention and treatment of AD.

This is a multimodal, cross-disciplinary study combining neuroimaging meta-analysis, transcriptome-neuroimaging association analysis, conjunctional false discovery rate (conjFDR) analysis, and functional enrichment analysis to identify the shared genetic architecture between AD-related brain structural alterations and metabolic traits.

Public databases and European populations.

The meta-analysis included 49 studies (1945 CE patients and 2598 controls). The largest genome-wide association study (GWAS) summary statistics were used for AD (Ncase = 39,918; Ncontrol =358,140), two glycemic traits—glucose (GLU, N = 459,772) and glycated hemoglobin (HbA1c, N = 146,864), and three lipid traits (N = 1320,016)—high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), and triglycerides (TG).

We conducted a voxel-based morphometric meta-analysis of GMV in AD by systematically reviewing 49 neuroimaging studies, identified through a literature search in PubMed and Web of Science using a predefined search strategy. Building upon these neuroanatomical findings, we performed a transcriptome-neuroimaging association analysis using data from the Allen Human Brain Atlas to identify genes spatially correlated with GMV alterations. To further explore the shared genetic architecture, we integrated GWAS summary statistics for AD and five metabolic markers using conjFDR analysis. Finally, functional enrichment analyses were performed to elucidate the biological relevance of the identified genes through this integrative framework.

Consistent GMV reductions in AD were observed in the bilateral middle temporal gyrus, right superior temporal gyrus, and other key subcortical regions. The conjFDR analysis identified 20, 17, 78, 87, and 82 genes shared between AD-related GMV reductions and GLU, HbA1c, HDL-C, LDL-C, and TG, respectively. Notably, 6 genes were shared across all five metabolic markers. Enrichment analysis implicated these genes in biological processes related to Aβ aggregation and phosphatidylinositol metabolism.

This study reveals a convergent genetic architecture underlying AD-related GMV atrophy and metabolic dysfunction. These findings may offer novel insights into the molecular interplay between systemic metabolism and neurodegeneration in AD and highlight potential targets for therapeutic strategies.