Elevated triglyceride-glucose (TyG) index predicts incidence of Prediabetes: a prospective cohort study in China

• Published in: Lipids in health and disease Vol. 19; no. 1; pp. 1 - 226
• Main Authors: Wen, Jing, Wang, Anping, Liu, Guangxu, Wang, Meiping, Zuo, Yingting, Li, Wei, Zhai, Qi, Mu, Yiming, Gaisano, Herbert Y., He, Yan, Dou, Jingtao
• Format: Journal Article
• Language: English
• Published: London BioMed Central 15-10-2020; BMC
• Subjects: Alcohol; Blood pressure; Body mass index; Cardiovascular disease; Cholesterol; Cohort analysis; Diabetes; Diabetes mellitus; Education; Fasting; Glucose; Glucose tolerance; Hypertension; Impaired glucose tolerance; Insulin; Insulin resistance; Lipid metabolism; Lipids; Lipoproteins; Metabolism; Obesity; Pandemics; Phenotypes; Prediabetes; Predictive value; Questionnaires; Reclassification; Risk factors; Smoking; Software; Triglyceride-glucose index; Beijing China; China
• ISSN: 1476-511X; 1476-511X
• DOI: 10.1186/s12944-020-01401-9

Abstract
Background
Prediabetes has become a pandemic. This study aimed to identify a better predictor for the incidence of prediabetes, which we hypothesize to be the triglyceride-glucose (TyG) index, a simplified insulin resistance index. We compared its predictive value with the other common risk factors of prediabetes.
Methods
The participants of this analysis were derived from the Risk Evaluation of cAncers in Chinese diabeTic Individuals: a lONgitudinal (REACTION) study. A total of 4543 participants without initial prediabetes or diabetes were followed up for 3.25 years. Using multivariate logistic regression model, the associations between baseline obesity, lipid profiles and non-insulin-based insulin resistance indices with the incidence of prediabetes were analyzed. To assess which is better predictor for the incidence of prediabetes, the area under curves (AUCs) calculated from the receiver operating characteristic curve analyses were used to evaluate and compare with the predictive value of the different indices.
Results
During the 3.25 years, 1071 out of the 4543 participants developed prediabetes. Using the logistic regression analysis adjusted for some potential confounders, the risk of incidence of prediabetes increased 1.38 (1.28–1.48) fold for each 1–SD increment of TyG index. The predictive ability (assessed by AUCs) of TyG index for predicting prediabetes was 0.60 (0.58–0.62), which was superior to the indices of obesity, lipid profiles and other non-insulin-based insulin resistance indices. Although the predictive ability of the TyG index was overall similar to fasting plasma glucose (FPG) (
P
 = 0.4340), TyG index trended higher than FPG in females (0.62 (0.59–0.64) vs. 0.59 (0.57–0.61),
P
 = 0.0872) and obese subjects (0.59 (0.57–0.62) vs. 0.57 (0.54–0.59),
P
 = 0.1313). TyG index had superior predictive ability for the prediabetic phenotype with isolated impaired glucose tolerance compared with FPG (
P
 <  0.05) and other indices. Furthermore, TyG index significantly improved the C statistic (0.62 (0.60–0.64)), integrated discrimination improvement (1.89% (1.44–2.33%)) and net reclassification index (28.76% (21.84–35.67%)) of conventional model in predicting prediabetes than other indices.
Conclusions
TyG could be a potential predictor to identify the high risk individuals of prediabetes.