Evidence map›Paper›PMID 42118257›Full record

ArticleDiabetologia2026

Integrating immune-cell transcriptomic data with Mendelian randomisation reveals novel causal genes for type 2 diabetes and its complications.

Yilan Ding, Haoyu Liu, Lei Jiang, Qian Yang, Shuangyuan Wang, Hong Lin, Mian Li, Zhiyun Zhao, Tiange Wang, Min Xu and 6 more

Abstract read
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In one paragraph

Article in Diabetologia, 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

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

16 authors.

Yilan Ding *Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Haoyu Liu *Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Lei Jiang *Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Qian YangDepartment of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Shuangyuan WangDepartment of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Hong LinDepartment of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Mian LiDepartment of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Zhiyun ZhaoDepartment of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Tiange WangDepartment of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Min XuDepartment of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Yu XuDepartment of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Guang NingDepartment of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Weiqing WangDepartment of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China. wqingw61@163.com.ORCID http://orcid.org/0000-0001-6027-3084
Yufang BiDepartment of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China. byf10784@rjh.com.cn.ORCID http://orcid.org/0000-0002-9536-2682
Jie ZhengDepartment of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China. Jie.Zheng@bristol.ac.uk.ORCID http://orcid.org/0000-0002-6623-6839
Jieli LuDepartment of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China. ljl11319@rjh.com.cn.ORCID http://orcid.org/0000-0003-1317-0896

Funding

Clinical Research Project of Shanghai Municipal Health Commission 20214Y0002Clinical Research Project of Shanghai Municipal Health Commission grant nos. 20224Y0087Ministry of Science and Technology of China 2022YFC2505202Ministry of Science and Technology of China grant no. 2021YFA1301103National Key Research and Development Program of China 2022YFC2505203National Natural Science Foundation of China 82088102National Natural Science Foundation of China 82170819National Natural Science Foundation of China 91857205National Natural Science Foundation of China grant nos. 82370810Noncommunicable Chronic Diseases-National Science and Technology Major Project 2023ZD050Noncommunicable Chronic Diseases-National Science and Technology Major Project 2023ZD0508401Noncommunicable Chronic Diseases-National Science and Technology Major Project grant nos. 2023ZD0508402Science and Technology Committee of Shanghai 23JS140090Science and Technology Committee of Shanghai 23Y11908400Science and Technology Committee of Shanghai grant nos. 22Y31900300
6 · The paper itself

Abstract

aims/hypothesisThe aim of this study was to facilitate the fine classification of diabetic complications and the prioritisation of drug targets via immune-cell-related gene expression data, distinguishing cell-type-related pleiotropy.

methodsWe analysed the combined clinical, genetic and immune-related features of diabetic complications, generating distinct clusters. Using 18,611 immune expression quantitative trait loci (eQTL) of 4487 genes, we investigated the causal effects of immune-cell-related gene expression on the risks of type 2 diabetes and its complications through Mendelian randomisation (MR) and colocalisation approaches. We then explored cell-type-related enrichment and specificity, and attenuated the cell-type-related pleiotropy for the top MR findings via multivariable MR methods.

resultsClustering analyses identified diabetic neuropathy as a distinct cluster of diabetes complications, with distinct immune-related features. MR and colocalisation analyses revealed the expression of 425 and 123 unique genes associated with type 2 diabetes and its complications, respectively, with external validation performed using single-cell RNA-seq data. We further quantified the impacts of cell-type-related pleiotropy, demonstrating that the percentage of pleiotropic genes increased from 40.0% (classic pleiotropy) to 71.1% (classic and/or cell-type-related pleiotropy). Applying six multivariable MR (MVMR) methods substantially attenuated the cell-type-related pleiotropy for the top findings. Finally, we integrated clinical trial evidence with genetic evidence and prioritised ten immune-related drug targets for diabetic complications. CONCLUSIONS/

interpretationOur study supports a key role for immune mechanisms in diabetic complications and highlights promising therapeutic targets by distinguishing and minimising the influence of cell-type-related pleiotropy.

Indexed as

Diabetes ComplicationsDiabetes Mellitus, Type 2TranscriptomeCluster AnalysisDiabetic NeuropathiesGenetic PleiotropyGenetic Predisposition to DiseaseGenome-Wide Association StudyHumansMendelian Randomization AnalysisQuantitative Trait LociCell-type-related pleiotropyClusteringDiabetic complicationsGeneticsMendelian randomisationType 2 diabetes

Identifiers

PMID42118257

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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.