Evidence map›Paper›PMID 41969614›Full record

ArticleHuman mutation2026

Genome-Wide Cross-Trait Analysis Dissects the Shared Genetic Architecture Between Type 2 Diabetes Mellitus and Metabolic Dysfunction-Associated Steatotic Liver Disease.

Zijun Zhu, Hailong Li, Xin Wang, Xinyu Chen, Liang Cheng

Abstract read
In one paragraph

Article in Human mutation, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
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

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

5 authors.

Zijun ZhuCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China, hrbmu.edu.cn.ORCID https://orcid.org/0009-0008-6488-2707
Hailong LiCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China, hrbmu.edu.cn.ORCID https://orcid.org/0009-0009-5271-3858
Xin WangCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China, hrbmu.edu.cn.ORCID https://orcid.org/0009-0005-2226-2073
Xinyu ChenCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China, hrbmu.edu.cn.ORCID https://orcid.org/0009-0003-1401-8877
Liang ChengCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China, hrbmu.edu.cn.ORCID https://orcid.org/0000-0002-6665-6710

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The observational studies confirmed the high prevalence of metabolic dysfunction-associated steatotic liver disease (MASLD) in patients with Type 2 diabetes mellitus (T2DM), but whether this reflects a shared genetic etiology and exists underlying causal relationships remains unknown. Here, we utilized the largest scale cross-trait analysis from genome-wide association studies (GWASs) to investigate the shared genetic architecture and found a significant genetic correlation between T2DM and MASLD. Subsequently, we identified 581 shared risk single-nucleotide polymorphisms (SNPs) and observed consistent patterns of tissue-specific heritability enrichment in embryonic stem cells, stomach, kidney, large and small intestine, and pancreas. Of the six highly shared risk SNPs (rs7203132, rs11642015, rs58542926, rs6857, rs10404726, and rs738408), we further systematically performed regional and functional analysis. Using Mendelian randomization (MR), we discovered significant evidence for a positive causal effect with no reverse causality of T2DM on MASLD and further explained what causes causality to occur. Finally, we used an orthogonal strategy to provide genetic evidence, highlighting 11 possible comorbidity targets, most of which are located on Chromosomes 19 or 22 with five on 19p13.11, such as NCAN, MAU2, GATAD2A, TM6SF2, and GMIP. Our study sheds insights into the informed biology of comorbidity and reveals their shared genetic factors and potential drug targets.

Indexed as

Diabetes Mellitus, Type 2Fatty LiverGenetic Predisposition to DiseaseGenome-Wide Association StudyHumansPolymorphism, Single Nucleotidegenome-wide association studiesnonalcoholic fatty liver diseaseshared genetic architectureType 2 diabetes mellitus

Identifiers

PMID41969614
PMCPMC13062659

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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.