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

28 Pages Posted: 3 May 2021

See all articles by Meng Ren

Meng Ren

Sun Yat-Sen University (SYSU) - Department of Endocrinology

Diao zhu Lin

Sun Yat-Sen University (SYSU) - Department of Endocrinology

Zhi peng Liu

Biotree-Shanghai

Kan Sun

Sun Yat-Sen University (SYSU) - Department of Endocrinology

Chuan Wang

Sun Yat-Sen University (SYSU) - Department of Endocrinology

Guo juan Lao

Sun Yat-Sen University (SYSU) - Department of Endocrinology

Yan qun Fan

Biotree-Shanghai

Xiao Yi Wang

Sun Yat-Sen University (SYSU) - Department of Endocrinology

Jing Liu

Sun Yat-Sen University (SYSU) - Department of Endocrinology

Jie Du

Biotree-Shanghai

Guo bin Zhu

Biotree-Shanghai

Jia huan Wang

Sun Yat-Sen University (SYSU) - Department of Endocrinology

Li Yan

Sun Yat-Sen University (SYSU) - Department of Endocrinology

More...

Abstract

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.

Keywords: prediabetes, diabetes, metabolites, prediction

Suggested Citation

Ren, Meng and Lin, Diao zhu and Liu, Zhi peng and Sun, Kan and Wang, Chuan and Lao, Guo juan and Fan, Yan qun and Wang, Xiao Yi and Liu, Jing and Du, Jie and Zhu, Guo bin and Wang, Jia huan and Yan, Li, Potential Novel Serum Metabolic Markers Associated With Progression of Prediabetes to Overt Diabetes in a Chinese Population. Available at SSRN: https://ssrn.com/abstract=3829652 or http://dx.doi.org/10.2139/ssrn.3829652

Meng Ren (Contact Author)

Sun Yat-Sen University (SYSU) - Department of Endocrinology ( email )

Guangzhou, Guangdong 510120
China

Diao zhu Lin

Sun Yat-Sen University (SYSU) - Department of Endocrinology ( email )

Guangzhou, Guangdong 510120
China

Zhi peng Liu

Biotree-Shanghai ( email )

Kan Sun

Sun Yat-Sen University (SYSU) - Department of Endocrinology ( email )

Guangzhou, Guangdong 510120
China

Chuan Wang

Sun Yat-Sen University (SYSU) - Department of Endocrinology

Guangzhou, Guangdong 510120
China

Guo juan Lao

Sun Yat-Sen University (SYSU) - Department of Endocrinology ( email )

Guangzhou, Guangdong 510120
China

Yan qun Fan

Biotree-Shanghai ( email )

Xiao Yi Wang

Sun Yat-Sen University (SYSU) - Department of Endocrinology

Guangzhou, Guangdong 510120
China

Jing Liu

Sun Yat-Sen University (SYSU) - Department of Endocrinology

Guangzhou, Guangdong 510120
China

Jie Du

Biotree-Shanghai

Guo bin Zhu

Biotree-Shanghai ( email )

Jia huan Wang

Sun Yat-Sen University (SYSU) - Department of Endocrinology ( email )

Guangzhou, Guangdong 510120
China

Li Yan

Sun Yat-Sen University (SYSU) - Department of Endocrinology ( email )

Guangzhou, Guangdong 510120
China