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

Xiuli Su; Chloe Y Y Cheung; Junda Zhong; Yi Ru; Carol H Y Fong; Chi-Ho Lee; Yan Liu; Cynthia K Y Cheung; Karen S L Lam; Aimin Xu; Zongwei Cai

doi:10.1016/j.jare.2023.11.026

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

J Adv Res. 2023 Nov 28:S2090-1232(23)00365-X. doi: 10.1016/j.jare.2023.11.026. Online ahead of print.

Authors

Xiuli Su¹, Chloe Y Y Cheung², Junda Zhong², Yi Ru¹, Carol H Y Fong², Chi-Ho Lee², Yan Liu², Cynthia K Y Cheung², Karen S L Lam³, Aimin Xu⁴, Zongwei Cai⁵

Affiliations

¹ State Key Laboratory of Environmental and Biological Analysis, Hong Kong Baptist University, Hong Kong, China.
² Department of Medicine, The University of Hong Kong, Hong Kong, China; State Key Laboratory of Pharmaceutical Biotechnology, The University of Hong Kong, Hong Kong, China.
³ Department of Medicine, The University of Hong Kong, Hong Kong, China; State Key Laboratory of Pharmaceutical Biotechnology, The University of Hong Kong, Hong Kong, China. Electronic address: ksllam@hku.hk.
⁴ Department of Medicine, The University of Hong Kong, Hong Kong, China; State Key Laboratory of Pharmaceutical Biotechnology, The University of Hong Kong, Hong Kong, China; Department of Pharmacology & Pharmacy, The University of Hong Kong, Hong Kong, China. Electronic address: amxu@hku.hk.
⁵ State Key Laboratory of Environmental and Biological Analysis, Hong Kong Baptist University, Hong Kong, China. Electronic address: zwcai@hkbu.edu.hk.

PMID: 38030128
DOI: 10.1016/j.jare.2023.11.026

Abstract

Introduction: 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.

Methods: 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.

Results: 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.

Conclusions: 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.

Keywords: Insulin resistance; Metabolomics; Prediction; Type 2 diabetes.