CAO Rudai 1,2 , WANG Shihong 1,2 , WU Shufa 1,2 , LI Mingzi 1,2,3 , DING Haifeng 1 , YU Haibing 1,2
  • 1. Provincial and Ministerial Co-construction State Key Laboratory of the Etiology and Prevention of Central Asian High Incidence Diseases, Dongguan First People's Hospital Affiliated to Guangdong Medical University, Dongguan, Guangdong 523808, P. R. China;
  • 2. Dongguan Key Laboratory of Chronic Disease Prevention and Control, Dongguan, Guangdong 523808, P. R. China;
  • 3. School of Public Health, Xiamen University, Xiamen, Fujian 361102, P. R. China;
DING Haifeng, Email: 1065301447@qq.com; YU Haibing, Email: hby616688@163.com
Hypertension; diabetes mellitus; comorbidity; insulin resistance; triglyceride glucose- body mass index; China Health and Retirement Longitudinal Study
10.7507/1002-0179.202504034
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Objective  To explore the relationship between the triglyceride glucose- body mass index (TyG-BMI) and hypertension, type 2 diabetes, as well as their comorbidity, aiming to provide a scientific basis for the early identification and precise prevention of these three diseases.Methods  This research collected data from subjects in the China Health and Retirement Longitudinal Study (CHARLS) database. According to the quartiles of TyG BMI, the included subjects were divided into Q1 group, Q2 group, Q3 group, and Q4 group. Logistic regression was used to analyze the association between the TyG-BMI and the three diseases separately. Further, a restricted cubic spline model was employed to investigate the potential non-linear dose-response relationship between the TyG-BMI index and the three diseases. Subgroup analysis was conducted using interaction tests to investigate whether there was an interaction between TyG BMI and subgroup factors such as age and gender. Results  A total of 4 847 participants were included. There were 1 212 cases in Q1 group, 1 212 cases in Q2 group, 1 211 cases in Q3 group, and 1 212 cases in Q4 group. The logistic regression results indicate that, after adjusting for all confounding factors, participants in the Q4 group have a higher risk of developing type 2 diabetes, hypertension, and comorbidity of hypertension and type 2 diabetes in Model 3 (P<0.05). The results from the restricted cubic spline model demonstrated a linear relationship between the TyG-BMI index and the risk of type 2 diabetes (P for non-linearity >0.05), while a non-linear relationship was observed with hypertension (P for non-linearity <0.05) and the comorbidity of hypertension and type 2 diabetes (P for non-linearity <0.05). Subgroup analysis using interaction tests showed that compared to the Q1 group, factors such as age, gender, smoking, alcohol consumption, and dyslipidemia in the Q2, Q3, and Q4 groups did not significantly alter the relationship between TyG-BMI and type 2 diabetes, hypertension, and their comorbidity. Overall, there was no significant interaction between TyG-BMI and factors like age, gender, smoking, alcohol consumption, and dyslipidemia (P for interaction >0.05). Conclusions  In middle-aged and elderly populations, the higher the TyG-BMI, the greater the risk of hypertension, type 2 diabetes, and their comorbidity. The TyG-BMI could be considered an important indicator for the early identification of hypertension, type 2 diabetes, and their comorbidities.

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