Evidence map›Paper›PMID 42656980›Full record

ArticleOsteoarthritis and cartilage open2026

Unraveling the pathogenic mechanisms of osteoarthritis and obesity: An integration of GWAS, cellular specificity, and spatial transcriptomics.

Zehong Lin, Jihu Wei, Honghai Zhou

Abstract read
In one paragraph

Article in Osteoarthritis and cartilage open, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Zehong LinGuangxi University of Chinese Medicine, Nanning, Guangxi, 530000, China.
Jihu WeiThe Second Affiliated Hospital of Guangxi University of Chinese Medicine, Nanning, Guangxi, 530000, China.
Honghai ZhouGuangxi University of Chinese Medicine, Nanning, Guangxi, 530000, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: This study aims to systematically elucidate the shared and specific genetic basis of osteoarthritis (OA) and obesity by integrating large-scale genome-wide association study (GWAS) summary statistics, cross-tissue quantitative trait loci (QTLs), and single-cell and spatial transcriptomic data. Method: The research employed a multi-omics integrative analysis pipeline. First, a meta-analysis was conducted on GWAS data for OA and obesity. Next, tissue- and spatial-specific enrichment analyses were performed using methods such as QTLEnrich, MAGMA, and gsMap. Key steps included the application of single-cell analysis, Cell-stratified mendelian randomization (csMR), and the ECLIPSER/CELLECT framework to identify specific cell types. Finally, hub genes were identified using hdWGCNA. Results: The results revealed significant enrichment of genetic risk signals for OA and obesity in brain tissues, including the cortex and pituitary gland. At the cellular level, T cells were identified as the highest-priority shared cell type for both diseases. Hub genes-GSN, CALD1, EBF1, LHFPL6, and TIMP3-were identified through co-expression network analysis. Spatial transcriptomic analysis further mapped the genetic risk signals to brain regions during embryonic development. Conclusion: This study precisely anchors the genetic risk of OA and obesity to specific brain regions, cell types, and developmental time windows, providing a novel perspective for understanding the pathological mechanisms of OA.

Indexed as

GWASObesityOsteoarthritisQTLEnrichSingle-cell spatial transcriptomics

Identifiers

PMID42656980
PMCPMC13508628

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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.