Evidence map›Paper›PMID 41636648›Full record

ArticleDiabetes care2026

Plasma Metabolite Associations for Risk and Laboratory Measures of Type 2 Diabetes in a Large-Scale Finnish Prospective Cohort.

Lei Liu, Ruyi Peng, Xiaomeng Chu, Zhijie Xia, Qi Fu, Lilian Fernandes Silva, Heather M Stringham, Xinxian Hu, Yuxi Liang, Xiaolong Ji and 10 more

Abstract read
In one paragraph

Article in Diabetes care, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

20 authors.

Lei LiuDepartment of Epidemiology, School of Public Health, Lianyungang Medical-Education Innovation and Research Center, Nanjing Medical University, Nanjing, China.
Ruyi PengDepartment of Epidemiology, School of Public Health, Lianyungang Medical-Education Innovation and Research Center, Nanjing Medical University, Nanjing, China.
Xiaomeng ChuDepartment of Epidemiology, School of Public Health, Lianyungang Medical-Education Innovation and Research Center, Nanjing Medical University, Nanjing, China.
Zhijie XiaDepartment of Epidemiology, School of Public Health, Lianyungang Medical-Education Innovation and Research Center, Nanjing Medical University, Nanjing, China.
Qi FuDepartment of Endocrinology and Metabolism, First Affiliated Hospital of Nanjing Medical University, Nanjing, China.ORCID 0000-0002-2463-3123
Lilian Fernandes SilvaInstitute of Clinical Medicine, Internal Medicine, University of Eastern Finland, Kuopio, Finland.
Heather M StringhamDepartment of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI.
Xinxian HuDepartment of Epidemiology, School of Public Health, Lianyungang Medical-Education Innovation and Research Center, Nanjing Medical University, Nanjing, China.
Yuxi LiangDepartment of Epidemiology, School of Public Health, Lianyungang Medical-Education Innovation and Research Center, Nanjing Medical University, Nanjing, China.
Xiaolong JiDepartment of Epidemiology, School of Public Health, Lianyungang Medical-Education Innovation and Research Center, Nanjing Medical University, Nanjing, China.
Jack LiDepartment of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI.
Tao YangDepartment of Endocrinology and Metabolism, First Affiliated Hospital of Nanjing Medical University, Nanjing, China.ORCID 0000-0001-6375-3622
Jean MorrisonDepartment of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI.
Xiaoquan WenDepartment of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI.
Eric FaumanInternal Medicine Research Unit, Pfizer Worldwide Research, Development and Medical, Cambridge, MA.
Charles F BurantDepartment of Internal Medicine, University of Michigan, Ann Arbor, MI.
Laura J ScottDepartment of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI.
Michael BoehnkeDepartment of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI.
Markku LaaksoInstitute of Clinical Medicine, Internal Medicine, University of Eastern Finland, Kuopio, Finland.ORCID 0000-0002-3394-7749
Xianyong YinDepartment of Epidemiology, School of Public Health, Lianyungang Medical-Education Innovation and Research Center, Nanjing Medical University, Nanjing, China.ORCID 0000-0001-6454-2384

Funding

Regional Pilot And Feasibility Study Grants ProgramP30DK020572 · NIDDK · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI DAVID P OLSON · 2013 to 2026
$24.3M
Academy of Finland no. 321428 (M. L.)American Diabetes Association Postdoctoral Fellowship 1-19-PDF-061 (X. Y.)China National Science and Technology Major Project 2025ZD0550702CLC NIH HHS R01 DK119380 (X. Q. W.)CLC NIH HHS R35 GM138121 (X. Q. W.)CLC NIH HHS U01 DK062370 (M. B.)Jiangsu Professorship (X. Y.)Nanjing Medical University NMUR20230003 (X. Y.)National Natural Science Foundation of China X. Y., 82574183NIDDK NIH HHS P30 DK020572Sigrid Juselius Foundation (M. L.)University of Michigan Precision Health Scholarship (X. Y.)
6 · The paper itself

Abstract

objectiveWe aimed to identify plasma metabolites significantly associated with existing type 2 diabetes (T2D), further characterize their associations with T2D-related laboratory measures and lifestyle-related risk factors, and evaluate their potential mediating and predictive roles for incident T2D. RESEARCH DESIGN AND

methodsWe performed metabolomic profiling in 10,163 Finnish men in the Metabolic Syndrome in Men (METSIM) study at baseline. Of these participants, 1,412 had prevalent T2D and 1,234 developed incident T2D during an average follow-up period of 13.6 years. We tested for associations of 979 named metabolites with prevalent T2D. For significant metabolites, we further evaluated their associations with 24 T2D-related laboratory measures of five groups and five T2D lifestyle-related risk factors. We performed an exploratory mediation analysis to examine the mediation effects of metabolites on incident T2D for the five risk factors. We evaluated metabolite associations with incident T2D and built exploratory metabolite predictive models for incident T2D.

resultsWe identified 193 plasma metabolites significantly associated with prevalent T2D at baseline, including 71 previously unreported. Of these 193 metabolites, 88 were associated with incident T2D. In participants with prevalent T2D, the 193 metabolites showed associations with zero to 24 (mean 10.2) T2D-related laboratory measures. Eighty-one metabolites partially mediated the associations of the five risk factors with T2D onset under standard mediation assumptions. The significant metabolites showed improved predictive ability for future T2D.

conclusionsThis study identifies T2D plasma metabolic biomarkers for further investigation in women and other populations. The findings enhance our understanding of T2D biology.

Indexed as

Diabetes Mellitus, Type 2AdultFinlandHumansMaleMiddle AgedProspective StudiesRisk Factors

Identifiers

PMID41636648
PMCPMC13006812

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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.