Evidence map›Paper›PMID 42443709›Full record

ArticleDiabetes, obesity & metabolism2026

Immune Cell-Stratified Regulatory Contexts Associated With BMI-Related Multi-System Disease Risk: A Cell-Stratified Mendelian Randomization Study Using Single-Cell eQTL Data.

Kai Cui, Yitong Shen, Meishan Lu, Jiashuo Teng, Qiuming Zou, Lili Liu, Yu Yan, Qi Qi

Abstract read
In one paragraph

Article in Diabetes, obesity & metabolism, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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

1 citing paper in PubMed.

  1. Article
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

8 authors.

Kai CuiDepartment of Pharmacology, School of Medicine, Jinan University, Guangzhou, China.
Yitong ShenDepartment of Pharmacology, School of Medicine, Jinan University, Guangzhou, China.ORCID 0009-0003-6931-2846
Meishan LuDepartment of Pharmacology, School of Medicine, Jinan University, Guangzhou, China.
Jiashuo TengDepartment of Pharmacology, School of Medicine, Jinan University, Guangzhou, China.
Qiuming ZouDepartment of Pharmacology, School of Medicine, Jinan University, Guangzhou, China.
Lili LiuDepartment of Pharmacology, School of Medicine, Jinan University, Guangzhou, China.
Yu YanFunctional Experimental Teaching Center, School of Medicine, Jinan University, Guangzhou, China.
Qi QiDepartment of Pharmacology, School of Medicine, Jinan University, Guangzhou, China.

Funding

Basic and Applied Basic Research Foundation of Guangdong Province 2024A1515013108National Natural Science Foundation of China 82573391
6 · The paper itself

Abstract

aimsBody mass index (BMI) is associated with multisystem disease risk, but the immune cell-specific regulatory contexts underlying BMI-related genetic associations with disease outcomes remain unclear.

methodsWe applied a cell-stratified Mendelian randomization framework integrating European-ancestry BMI GWAS data, GWAS datasets for 33 disease outcomes across five disease systems, single-cell cis-eQTL data from 28 peripheral blood immune cell types, and dynamic CD4

resultsAcross 28 immune cell types, 1326 colocalized variants regulating 1426 genes were identified. In primary MR analyses, genetically predicted BMI showed Bonferroni-significant associations with 26 disease outcomes. Cell-stratified analyses identified 87 Bonferroni-significant associations across 17 disease outcomes. Cardiovascular diseases showed the broadest cell-stratified associations, followed by respiratory and metabolic diseases. CD4

conclusionThese findings prioritize CD4

Indexed as

Body Mass IndexQuantitative Trait LociCardiovascular DiseasesCD4-Positive T-LymphocytesGenetic Predisposition to DiseaseGenome-Wide Association StudyHumansMendelian Randomization AnalysisPolymorphism, Single NucleotideSingle-Cell Analysisbody mass indexCD4+ T cellscell‐stratified Mendelian randomizationimmune cellssingle‐cell eQTL

Identifiers

PMID42443709
PMCPMC13538758

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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.