Fig. 1: Study overview.
a, To identify blood metabolites associated with incident T2D, we analyzed 469 harmonized metabolites in up to 23,634 participants from ten prospective cohort studies. At baseline, participants were free of T2D and other chronic diseases; and blood metabolome was profiled using the metabolomic platforms at Broad Institute or Metabolon Inc. A metabolome-wide association study (MWAS) for incident T2D was conducted in each cohort; and results from the ten cohorts were combined using meta-analysis, identifying 235 metabolites associated with T2D risk. b, We curated meta-analyzed genome-wide association studies (GWASs) for each metabolite using data of up to 18,590 people from eight cohorts, followed by functional analyses, colocalization analyses and Mendelian randomization analyses. c, We conducted MWASs for major modifiable risk factors in up to 16,883 participants from five cohorts, identifying metabolites that potentially mediated the associations between risk factors and T2D risk. d, We used machine learning analyses to develop a metabolomic signature reflecting the complex metabolic states predictive of long-term T2D risk, which may facilitate the identification of high-risk individuals and precision prevention.
