Evidence map›Paper›PMID 40888248›Full record

ArticleDiabetes, obesity & metabolism2025

Integrative proteomic analysis provides novel therapeutic insights for etiological subtypes of diabetes.

Jiahe Wei, Hanzhang Wu, Ningjian Wang, Jiahao Zhu, Ranjit Mohan Anjana, Aleksandr V Sokolov, Helgi B Schiöth, Viswanathan Mohan, Xiao Tan

Abstract read
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Article in Diabetes, obesity & metabolism, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1 · What the graph read from it

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3 · Its place in the literature

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1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

9 authors.

Jiahe WeiDepartment of Big Data in Health Science, Zhejiang University School of Public Health and Department of Psychiatry, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Hanzhang WuDepartment of Big Data in Health Science, Zhejiang University School of Public Health and Department of Psychiatry, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Ningjian WangDepartment of Endocrinology and Metabolism, Institute of Endocrinology and Metabolism, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.ORCID 0000-0001-9591-6991
Jiahao ZhuDepartment of Epidemiology and Health Statistics, School of Public Health, Hangzhou Medical College, Hangzhou, China.
Ranjit Mohan AnjanaMadras Diabetes Research Foundation, (ICMR - Collaborating Centre of Excellence and Dr. Mohan's Diabetes Specialities Centre (IDF Centre of Excellence in Diabetes Care), Chennai, India.
Aleksandr V SokolovDepartment of Surgical Sciences, Division of Functional Pharmacology and Neuroscience, Uppsala University, Uppsala, Sweden.
Helgi B SchiöthDepartment of Surgical Sciences, Division of Functional Pharmacology and Neuroscience, Uppsala University, Uppsala, Sweden.
Viswanathan MohanMadras Diabetes Research Foundation, (ICMR - Collaborating Centre of Excellence and Dr. Mohan's Diabetes Specialities Centre (IDF Centre of Excellence in Diabetes Care), Chennai, India.ORCID 0000-0001-5038-6210
Xiao TanDepartment of Big Data in Health Science, Zhejiang University School of Public Health and Department of Psychiatry, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China.ORCID 0000-0003-3992-5812

Funding

Development Project of Zhejiang Province 2025C01119National Natural Science Foundation of China 82570128Rut and Arvid Wolff Memorial Foundation 2023-02467
6 · The paper itself

Abstract

aimsType 2 diabetes (T2D) is a highly heterogeneous disease characterised by subtypes with variations in aetiology, disease progression, and risk of complications. However, potential drug targets for these subtypes have not been explored. This study aims to investigate potential drug targets by integrating proteomics. MATERIALS AND

methodsSummary-level data of circulating proteins were extracted from the UK Biobank and the deCODE Health Study. Genetic associations with five diabetes subtypes were obtained from Swedish All New Diabetics in Scania and Malmö Diet and Cancer cohort, including severe autoimmune diabetes (SAID), severe insulin-deficient diabetes (SIDD), severe insulin-resistant diabetes (SIRD), mild obesity-related diabetes (MOD), and mild age-related diabetes (MARD). The associations between circulating proteins and diabetes subtypes were assessed through Mendelian randomisation, followed by multiple sensitivity and colocalization analyses. Additionally, tissue-specific, pathway and functional enrichment analysis, assessment of protein druggability, and the protein-protein interaction (PPI) networks were used to further explore biological mechanisms and therapeutic potential.

resultsGenetically predicted levels of 2, 2, 9, 3, and 5 circulating proteins were associated with SIRD, SIDD, MARD, MOD, and SAID, respectively. Colocalization analyses further revealed links between GRN with MARD/SIRD, LILRB5 with SIDD/MARD, CR1 with MARD, TNFSF12 with MOD, and DAPK2 with SAID. Enrichment analysis suggested that these proteins were mainly enriched in blood and adipose tissues and involved in immune and inflammatory related pathways. PPI analysis revealed GRN, TNFSF12, and DAPK2 are associated with known T2D targets.

conclusionsOur study identified several potential drug targets for different subtypes of diabetes using an integrated genetic approach, yielding new insights for precision medicine of diabetes.

Indexed as

Diabetes Mellitus, Type 2ProteomicsAgedFemaleGenetic Predisposition to DiseaseHumansMaleMendelian Randomization AnalysisMiddle AgedProtein Interaction MapsSwedendiabetesdiabetes clustersmendelian randomisationprecision medicineproteomicstherapeutic target

Identifiers

PMID40888248
PMCPMC12587232

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