Association of distinct γ-glutamyltransferase trajectories with incident hyperglycemia using latent class growth mixture modeling: A longitudinal cohort study of Chinese adults

Aims: To elucidate the association between distinct latent.-glutamyltransferase (GGT) increasing trajectories in life time and incident hyperglycemia. Methods: 4547 subjects were followed up for 3 years (January 2016-December 2019), and data regarding fasting plasma glucose, Hb Lambda(1c), GGT, and other indices were recorded. Latent class growth mixed modeling (LCGMM) was used to analyze GGT latent trajectories. Results: A three-class quadratic model was selected as the best fit by LCGMM. Subjects were categorized into three latent classes: high-increasing (n = 98, 2.16%), low-increasing (n = 364, 8.01%), and stable (n = 4085, 89.83%) classes. Adjusted hazard ratios of hyperglycemia for the high-increasing and low-increasing classes were 1.341 (1.076-2.051) and 1.264 (1.048-1.525) when compared with stable class, respectively. Odds Ratios (ORs) of GGT slopes were confirmed in the 28-57-year age group, shaped like an "M", ranging from 1.144 (1.059, 1.237) to 2.502 (1.384-3.862). Significant differences in the associations between model-estimated GGT values and hyperglycemia incidence were observed from 28 to 51 years of age, with ORs ranging from 1.011 (1.011, 1.012) to 1.014 (1.012, 1.019). Conclusions: Our study demonstrated that subjects in GGT increasing classes exhibited higher risks of developing hyperglycemia. A steeper GGT slope is a more effective predictor of hyperglycemia than the GGT value.