Identification of metabolic biomarkers associated with the onset of type 2 diabetes based on a nested case-control study.
10.3760/cma.j.cn112150-20220315-00239
- VernacularTitle:基于巢式病例对照研究识别2型糖尿病发病相关代谢标志物
- Author:
Yun QIAN
1
;
Jia LIU
1
;
Lu WANG
1
;
Yun Qiu DONG
1
;
Hai CHEN
1
;
Qian SHEN
1
;
Zhi Jie YANG
1
Author Information
1. Department of Health Promotion, Wuxi Center for Disease Control and Prevention, Wuxi 214023, China.
- Publication Type:Journal Article
- MeSH:
Humans;
Male;
Female;
Diabetes Mellitus, Type 2/epidemiology*;
Case-Control Studies;
Risk Factors;
Triglycerides;
Biomarkers
- From:
Chinese Journal of Preventive Medicine
2022;56(12):1784-1788
- CountryChina
- Language:Chinese
-
Abstract:
Objective: To explore metabolic biomarkers associated with the onset of type 2 diabetes. Methods: Cluster random sampling method was used to select 10 867 local residents aged ≥ 20 years in Liangxi district of Wuxi City, Jiangsu Province in 2007. The baseline survey and physical examination were conducted to collect participants' information, including demographic characteristics, behavior and lifestyles, disease history, family history of diabetes, height, weight, waist circumference and blood pressure, etc. Blood samples were collected and biochemical indexes (high density lipoprotein cholesterol, total cholesterol, triglyceride, fasting blood glucose, etc.) were tested. By June 30, 2020, 220 newly diagnosed patients with type 2 diabetes during the follow-up were selected as cases, and 220 healthy individuals were matched as controls with age (±5 years) and the same sex. High performance liquid chromatography mass spectrometer was used to detect and identify metabolites in serum samples of two groups at baseline. Lasso regression and multivariate conditional logistic regression were used to explore the metabolites associated with the onset of type 2 diabetes. Results: The age of participants at baseline was (53±7) years, and 41.82% were male. 25 out of 1 579 metabolites were selected to be potentially associated with the onset of type 2 diabetes in the lasso regression model. The multivariable conditional logistic regression analysis showed that only 7-Methylxanthine had an independent effect on type 2 diabetes (P=0.019). The area under the receiver operating characteristic curve (AUC) (95%CI) of the prediction model of type 2 diabetes based on traditional risk factors was 0.80 (0.76-0.85). After the 7-methylxanthine in the model, the AUC (95%CI) increased to 0.92 (0.89-0.95) (P<0.001). From the second year, 7-methylxanthine could improve the prediction performance (P=0.007). Conclusion: The level of 7-methylxanthine is related to the onset of type 2 diabetes, and can be used as a biomarker to predict its incidence risk.
