Ten metabolites-based algorithm predicts the future development of type 2 diabetes in Chinese

• Published in: Journal of advanced research 2024-10, Vol.64, p.131-142
• Main Authors: Su, Xiuli, Cheung, Chloe Y.Y., Zhong, Junda, Ru, Yi, Fong, Carol H.Y., Lee, Chi-Ho, Liu, Yan, Cheung, Cynthia K.Y., Lam, Karen S.L., Xu, Aimin, Cai, Zongwei
• Format: Article
• Language: English
• Subjects: Adult; Aged; Algorithms; Asian People; Basic and Biological Science; Biomarkers - blood; China; Diabetes Mellitus, Type 2 - blood; Diabetes Mellitus, Type 2 - metabolism; East Asian People; Female; Humans; Insulin Resistance; Insulin-Secreting Cells - metabolism; Male; Metabolome; Metabolomics; Metabolomics - methods; Middle Aged; Prediction; Prospective Studies; Risk Factors; Type 2 diabetes
• ISSN: 2090-1232; 2090-1224; 2090-1224
• DOI: 10.1016/j.jare.2023.11.026

[Display omitted] •Profound perturbation of metabolome preceding T2D onset was revealed in a prospective Chinese population-based cohort.•Insulin resistance rather than β-cell dysfunction was strongly associated with metabolic shifts before T2D.•The impact of metabolites on T2D risk could be mediated by insulin resistance.•Discovery of previously unknown independent associations between metabolites and incident T2D.•The inclusion of metabolites selected by machine learning improved the predictive power of traditional clinical models. Type 2 diabetes (T2D) is a heterogeneous metabolic disease with large variations in the relative contributions of insulin resistance and β-cell dysfunction across different glucose tolerance subgroups and ethnicities. A more precise yet feasible approach to categorize risk preceding T2D onset is urgently needed. This study aimed to identify potential metabolic biomarkers that could contribute to the development of T2D and investigate whether their impact on T2D is mediated through insulin resistance and β-cell dysfunction. A non-targeted metabolomic analysis was performed in plasma samples of 196 incident T2D cases and 196 age- and sex-matched non-T2D controls recruited from a long-term prospective Chinese community-based cohort with a follow-up period of ∼ 16 years. Metabolic profiles revealed profound perturbation of metabolomes before T2D onset. Overall metabolic shifts were strongly associated with insulin resistance rather than β-cell dysfunction. In addition, 188 out of the 578 annotated metabolites were associated with insulin resistance. Bi-directional mediation analysis revealed putative causal relationships among the metabolites, insulin resistance and T2D risk. We built a machine-learning based prediction model, integrating the conventional clinical risk factors (age, BMI, TyG index and 2hG) and 10 metabolites (acetyl-tryptophan, kynurenine, γ-glutamyl-phenylalanine, DG(18:2/22:6), DG(38:7), LPI(18:2), LPC(P-16:0), LPC(P-18:1), LPC(P-20:0) and LPE(P-20:0)) (AUROC = 0.894, 5.6% improvement comparing to the conventional clinical risk model), that successfully predicts the development of T2D. Our findings support the notion that the metabolic changes resulting from insulin resistance, rather than β-cell dysfunction, are the primary drivers of T2D in Chinese adults. Metabolomes as a valuable phenotype hold potential clinical utility in the prediction of T2D.