Serum metabolomic profiling of incident type 2 diabetes mellitus in the Multi-Ethnic Study of Atherosclerosis and Rotterdam Study

  • Xuanwei Jiang
  • , Fang Zhu
  • , Gonçalo Graça
  • , Xihao Du
  • , Jinjun Ran
  • , Fariba Ahmadizar
  • , Alexis C Wood
  • , Yanqiu Zhou
  • , Denise M Scholtens
  • , Ali Farzaneh
  • , M Arfan Ikram
  • , Alan Kuang
  • , Carel Le Roux
  • , Meghana D Gadgil
  • , Marilyn C Cornelis
  • , Kent D Taylor
  • , Xiuqing Guo
  • , Mohsen Ghanbari
  • , Laura J Rasmussen-Torvik
  • , Russell P Tracy
  • Alain G Bertoni, Jerome I Rotter, David M Herrington, Philip Greenland*, Maryam Kavousi*, Victor W Zhong*
*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

Objective This study aimed to investigate serum metabolomic biomarkers associated with incident type 2 diabetes mellitus (T2DM) and evaluate their performance in improving T2DM risk prediction. Methods Untargeted proton nuclear magnetic resonance (1H NMR) spectroscopy-based metabolomics analyses were conducted in the Multi-Ethnic Study of Atherosclerosis (MESA; n = 3460; discovery cohort) and The Rotterdam Study (RS; n = 1556; replication cohort). Multivariable cause-specific hazards models were used to analyze the associations between 23 571 serum metabolomic spectral variables and incident T2DM. Replicated metabolites required an false discovery rate-adjusted P <. 01 in MESA, P <. 05 in RS, and consistent direction of association. Pathway and network analyses were conducted to elucidate biological mechanisms underlying T2DM development. The utility of the replicated metabolites in improving T2DM risk prediction was assessed based on the Framingham Diabetes Risk Score. A 2-sample Mendelian randomization was conducted to assess causal associations. Results Nineteen metabolites were significantly associated with incident T2DM. Pathway analyses revealed disturbances in aminoacyl-tRNA biosynthesis, metabolism of branched-chain amino acids (BCAAs), glycolysis/gluconeogenesis, and glycerolipid metabolism. Network analyses identified interactions with upstream regulators including p38 mitogen-activated protein kinases, c-Jun N-terminal kinase, and mammalian target of rapamycin signaling pathways. Adding replicated metabolites to the Framingham Diabetes Risk Score showed modest to moderate improvements in prediction performance in MESA and RS, with ΔC-statistic of 0.05 [95% confidence interval (CI), 0.04-0.07] in MESA and 0.03 (95% CI, 0.01-0.05) in RS. Genetically increased BCAAs and mannose were associated with T2DM. Conclusion 1H NMR measured metabolites involved in aminoacyl-tRNA biosynthesis, BCAA metabolism, glycolysis/gluconeogenesis, and glycerolipid metabolism were significantly associated with incident T2DM and provided modest to moderate predictive utility beyond traditional risk factors.

Original languageEnglish
Pages (from-to)e2700-e2710
JournalThe Journal of clinical endocrinology and metabolism
Volume110
Issue number8
Early online date20 Nov 2024
DOIs
Publication statusPublished - 1 Aug 2025

Keywords

  • metabolomics
  • prediction
  • type 2 diabetes mellitus

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