Diabetologia · 2026
Plasma metabolite association profiles for type 2 diabetes genetic clusters in Finnish men.
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Peng Ruyi, Liu Lei, Chu Xiaomeng, Xia Zhijie, Fu Qi, Silva Lilian Fernandes, Ji Xiaolong, Hu Xinxian, Liang Yuxi, Li Jack, Ryan Brady, Bose Debraj, Stringham Heather M, Morrison Jean, Wen Xiaoquan, Scott Laura J, Burant Charles F, Fauman Eric, Yang Tao, Boehnke Michael, Laakso Markku, Yin Xianyong
Аннотация
AIMS/HYPOTHESIS: A recent study has suggested eight clusters of genetic variants associated with type 2 diabetes. We aimed to characterise metabolite associations for these eight clusters.
METHODS: We 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.
RESULTS: We 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/INTERPRETATION: This 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).
METHODS: We 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.
RESULTS: We 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/INTERPRETATION: This 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).