Evidence map›Paper›PMID 41848902›Full record

ArticleDiabetologia2026

Plasma metabolite association profiles for type 2 diabetes genetic clusters in Finnish men.

Ruyi Peng, Lei Liu, Xiaomeng Chu, Zhijie Xia, Qi Fu, Lilian Fernandes Silva, Xiaolong Ji, Xinxian Hu, Yuxi Liang, Jack Li and 12 more

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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

22 authors.

Ruyi Peng *Department of Epidemiology, School of Public Health, Lianyungang Medical-Education Innovation and Research Center, Nanjing Medical University, Nanjing, China.ORCID http://orcid.org/0009-0000-9892-7637
Lei Liu *Department of Epidemiology, School of Public Health, Lianyungang Medical-Education Innovation and Research Center, Nanjing Medical University, Nanjing, China.ORCID http://orcid.org/0009-0008-1786-0740
Xiaomeng Chu *Department of Epidemiology, School of Public Health, Lianyungang Medical-Education Innovation and Research Center, Nanjing Medical University, Nanjing, China.ORCID http://orcid.org/0000-0001-6396-8578
Zhijie XiaDepartment of Epidemiology, School of Public Health, Lianyungang Medical-Education Innovation and Research Center, Nanjing Medical University, Nanjing, China.ORCID http://orcid.org/0009-0008-3449-0286
Qi FuDepartment of Endocrinology and Metabolism, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.ORCID http://orcid.org/0000-0002-2463-3123
Lilian Fernandes SilvaInstitute of Clinical Medicine, Internal Medicine, University of Eastern Finland, Kuopio, Finland.ORCID http://orcid.org/0000-0003-0225-842X
Xiaolong JiDepartment of Epidemiology, School of Public Health, Lianyungang Medical-Education Innovation and Research Center, Nanjing Medical University, Nanjing, China.ORCID http://orcid.org/0009-0000-5930-041X
Xinxian HuDepartment of Epidemiology, School of Public Health, Lianyungang Medical-Education Innovation and Research Center, Nanjing Medical University, Nanjing, China.ORCID http://orcid.org/0009-0002-6185-3173
Yuxi LiangDepartment of Epidemiology, School of Public Health, Lianyungang Medical-Education Innovation and Research Center, Nanjing Medical University, Nanjing, China.ORCID http://orcid.org/0009-0007-2127-8377
Jack LiDepartment of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA.ORCID http://orcid.org/0000-0001-6126-0701
Brady RyanDepartment of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA.ORCID http://orcid.org/0009-0009-4164-2871
Debraj BoseDepartment of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA.ORCID http://orcid.org/0000-0002-3177-6608
Heather M StringhamDepartment of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA.ORCID http://orcid.org/0000-0002-2991-6392
Jean MorrisonDepartment of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA.ORCID http://orcid.org/0000-0003-4829-8283
Xiaoquan WenDepartment of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA.ORCID http://orcid.org/0000-0001-8990-2737
Laura J ScottDepartment of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA.ORCID http://orcid.org/0000-0002-4886-5084
Charles F BurantDepartment of Internal Medicine, University of Michigan, Ann Arbor, MI, USA.ORCID http://orcid.org/0000-0001-9189-5003
Eric FaumanInternal Medicine Research Unit, Pfizer Worldwide Research, Development and Medical, Cambridge, MA, USA.ORCID http://orcid.org/0000-0002-9739-0249
Tao YangDepartment of Endocrinology and Metabolism, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.ORCID http://orcid.org/0000-0001-6375-3622
Michael BoehnkeDepartment of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA. boehnke@umich.edu.ORCID http://orcid.org/0000-0002-6442-7754
Markku LaaksoInstitute of Clinical Medicine, Internal Medicine, University of Eastern Finland, Kuopio, Finland. markku.laakso@uef.fi.ORCID http://orcid.org/0000-0002-3394-7749
Xianyong YinDepartment of Epidemiology, School of Public Health, Lianyungang Medical-Education Innovation and Research Center, Nanjing Medical University, Nanjing, China. xianyongyin@njmu.edu.cn.ORCID http://orcid.org/0000-0001-6454-2384

Funding

Resolving Methodological Challenges in Genomics Research: Causality, Risk Prediction, and ReproducibilityR35GM138121 · UNIVERSITY OF MICHIGAN AT ANN ARBOR · 2025 to 2025
$381k
American Diabetes Association Postdoctoral Fellowship (1-19-PDF-061American Diabetes Association X. Y.)CLC NIH HHS R01 DK119380CLC NIH HHS R35 GM138121CLC NIH HHS U01 DK062370Nanjing Medical University Award NMUR20230003National Natural Science Foundation of China 82574183(X. Y.)National Science and Technology Major Project 2025ZD0550702Research Council of Finland Grant no. 321428 (M. L.)University of Michigan Precision Health Scholarship (X. Y.)
6 · The paper itself

Abstract

aims/hypothesisA recent study has suggested eight clusters of genetic variants associated with type 2 diabetes. We aimed to characterise metabolite associations for these eight clusters.

methodsWe constructed type 2 diabetes overall and cluster-partitioned polygenic risk scores (PRSs) in 10,015 Finnish men with 979 named plasma metabolites measured in Metabolon HD4 mass spectrometry platform. We evaluated metabolite-PRS associations using linear regression. We also performed a mediation analysis to examine whether metabolites statistically accounted for part of the association between genetic risk and incident type 2 diabetes that developed in a mean of 13.6 years' follow-up.

resultsWe identified 337 metabolites significantly associated with type 2 diabetes genetic risk, including 242 exclusive to cluster-partitioned PRSs. Of the significant metabolites, 26 exhibited significantly heterogeneous associations across clusters. We identified significant enrichment for 33 metabolic pathways among the cluster-associated metabolites. Notably, metabolites for the two pancreatic beta cell-related clusters exhibited enrichment in distinct pathways: the beta cell + proinsulin (PI) cluster in fructose, mannose and galactose metabolism; and the beta cell - PI cluster in branched-chain amino acid metabolism. Mediation analysis suggested that >50% of the associated metabolites showed patterns statistically consistent with a mediating role in the associations between PRSs and incident type 2 diabetes. CONCLUSIONS/

interpretationThis study underscores the value of type 2 diabetes clustering and highlights metabolic heterogeneity across clusters. The findings have the potential to guide personalised interventions. DATA AVAILABILITY: The datasets generated during and/or analysed in the current study are available in dbGaP (accession ID: phs000743.v4.p1 and phs004033.v1.p1).

Indexed as

Diabetes Mellitus, Type 2AdultFinlandGenetic Predisposition to DiseaseGenetic Risk ScoreHumansMaleMetabolomeMetabolomicsMiddle AgedPolymorphism, Single NucleotideGenetic clusterMetabolomicsPolygenic risk scoreType 2 diabetes

Identifiers

PMID41848902
PMCPMC13236824

What Socratic holds

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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 Socratic graph.