Potential Novel Serum Metabolic Markers Associated With Progression of Prediabetes to Overt Diabetes in a Chinese Population

Background: Identifying the metabolite profile of individuals with prediabetes who progressed to type 2 diabetes may give novel insights into early type 2 diabetes interception. The purpose of this study was to identify metabolic markers that predict the development of type 2 diabetes from prediabetes in a Chinese population. Methods: We used an untargeted metabolomics approach to investigate the associations between serum metabolites and risk of prediabetes who progressed to overt T2D (n=153, mean follow up 5 years) in a Chinese population (REACTION study). Results were compared with matched controls who had prediabetes at baseline (age: 56.32±7.00, BMI: 24.22±2.75) and at a 5-year follow-up (age: 61.86±6.96, BMI: 24.51±11.75). Confounding factors were adjusted and the associations between metabolites and diabetes risk were evaluated with multivariate logistic regression analysis. A 10-fold cross-validation random forest classification (RFC) model was used to select the optimal metabolites panels for predicting the development of diabetes, and to internally validate the discriminatory capability of the selected metabolites beyond conventional clinical risk factors. Findings: Metabolic alterations, including those associated with amino acid and lipid metabolism, were associated with an increased risk of prediabetes progressing to diabetes. The most important metabolites were inosine (odds ration [OR] = 21.97; 95% confidence interval [CI]: 4.98-96.84) and carvacrol (OR = 16.03; 95% CI: 5.09-50.51). Thirteen metabolites were found to improve type 2 diabetes risk prediction beyond conventional type 2 diabetes risk factors (AUC was 0.74 for risk factors vs. 0.97 for risk factors + metabolites, P < 0.05). Interpretations: Use of the metabolites identified in this study may help determine patients with prediabetes who are at highest risk of progressing to diabetes. Funding Information: This work was supported by grants from the National Natural Science Foundation of China (81870571,81770827). Declaration of Interests: No potential conflicts of interest relevant to this article were reported. Ethics Approval Statement: The Institutional Review Boards at each study site approved the study protocol, and all participants provided written informed consent.