Frontiers in public health · 2026
Nonlinear relationship between triglyceride glucose-body mass index and the risk of prediabetes: a multi-center retrospective cohort study.
СтатьяМногоцентровое исследованиеНазвание и аннотация: Перевод пока не подготовлен
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Peng Hui, Cai Zhimao, Zhan Yongqiang, Yang Wei, Zeng Xiaomei, Dai Yuan, Pan Junhong, Jia Rui, Luo Youlian, Cai Hualang, Chen Weifeng, Zhou Dan
Аннотация
BACKGROUND: The triglyceride-glucose body mass index (TyG-BMI), a novel composite measure integrating lipid-glucose metabolism and obesity, has shown promise in predicting diabetes risk. Nevertheless, its association with the onset of prediabetes remains insufficiently investigated, particularly concerning potential nonlinear relationships.
METHODS: Cox proportional hazards models were used to calculate hazard ratios (HRs) and 95% confidence intervals (CIs). Nonlinear relationships were evaluated through the use of smooth curve fitting and threshold effect models. Additionally, interaction tests and subgroup analyses were undertaken to examine the robustness of the associations found.
RESULTS: Following comprehensive adjustment, a 10-unit increment in TyG-BMI correlated with an 11% elevated risk of developing prediabetes (Hazard Ratio [HR] 1.11, 95% Confidence Interval [CI] 1.09-1.13). A significant nonlinear association was observed, with an inflection point identified at a TyG-BMI value of 189.37. Below the threshold, each unit increase in TyG-BMI was linked to a 2% rise in risk (HR 1.02, 95% CI 1.02-1.03), whereas above this point, the risk increase diminished to 1% per unit (HR 1.01, 95% CI 1.01-1.01).
CONCLUSION: This research identifies a significant, nonlinear association between TyG-BMI and the likelihood of developing prediabetes. Additional research is required to thoroughly examine their relationship and clarify the underlying mechanisms.
METHODS: Cox proportional hazards models were used to calculate hazard ratios (HRs) and 95% confidence intervals (CIs). Nonlinear relationships were evaluated through the use of smooth curve fitting and threshold effect models. Additionally, interaction tests and subgroup analyses were undertaken to examine the robustness of the associations found.
RESULTS: Following comprehensive adjustment, a 10-unit increment in TyG-BMI correlated with an 11% elevated risk of developing prediabetes (Hazard Ratio [HR] 1.11, 95% Confidence Interval [CI] 1.09-1.13). A significant nonlinear association was observed, with an inflection point identified at a TyG-BMI value of 189.37. Below the threshold, each unit increase in TyG-BMI was linked to a 2% rise in risk (HR 1.02, 95% CI 1.02-1.03), whereas above this point, the risk increase diminished to 1% per unit (HR 1.01, 95% CI 1.01-1.01).
CONCLUSION: This research identifies a significant, nonlinear association between TyG-BMI and the likelihood of developing prediabetes. Additional research is required to thoroughly examine their relationship and clarify the underlying mechanisms.