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 had 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.
Objective To investigate the relationship between estimated glucose disposal rate (eGDR) and the incidence of cardiovascular disease (CVD) in individuals without diabetes and those with diabetes. Methods Participants were drawn from the China Health and Retirement Longitudinal Study from 2011 to 2018. Participants were divided into four subgroups based on quartiles of baseline eGDR. In this study, data were analyzed using Kaplan-Meier survival curves, Cox proportional hazards models, restricted cubic spline curves, subgroup analyses, and receiver operator characteristic curves. Results A total of 6 283 participants were included. Among them, 47.2% are male, with an average age of (59.6±9.5) years; 285 cases (4.5%) had diabetes; there were 1 571 cases in Q1 group, 1 572 cases in Q2 group, 1 583 cases in Q3 group, and 1 557 cases in Q4 group. A total of 761 CVD events occurred. According to the multivariate-adjusted model, baseline eGDR levels were significantly associated with the risk of CVD events (P<0.05). Baseline eGDR was associated with the risk of CVD events in individuals without diabetes (P<0.05), but the results were not entirely consistent for those with diabetes [CVD: hazard ratio (HR)=0.85, 95% confidence interval (CI) (0.75, 0.96), P=0.012; heart disease: HR=0.91, 95%CI (0.78, 1.06), P=0.211; stroke: HR=0.74, 95%CI (0.58, 0.93), P=0.012]. Restricted cubic spline curves revealed significant negative linear relationships between baseline eGDR and CVD, heart disease, and stroke. Subgroup analyses with interaction testing revealed that the association between baseline eGDR and CVD was not modified by age, sex, smoking status, alcohol consumption, or dyslipidemia. Receiver operator characteristic curves further demonstrated that baseline eGDR exhibited significantly better predictive performance than the triglyceride-glucose (TyG) index, obesity indices, and the TyG index-obesity composite. Conclusions Low level baseline eGDR is associated with an increased risk of CVD in individuals without diabetes. This finding may help improve risk stratification to guide preventive measures and enhance the prognosis of CVD.
Objective To analyze the effects of hyperuricemia (HUA) on the prevalence of dyslipidemia in the elderly. MethodsA total of 5 990 elderly people with complete and important variables from the China Health and Retirement Longitudinal Study (CHARLS) public database in 2015 were extracted. Their blood lipids, related physiological and biochemical indices, and basic demographic information were collected. The effects of HUA on the prevalence of dyslipidemia in the elderly were analyzed using the probit model, and empirical analysis was performed using the propensity score matching method (PSM). Results Among the 5 990 subjects, 13.6% of the elderly had HUA and the prevalence of dyslipidemia was 37.5%. After correcting the endogeneity among variables, the probability of dyslipidemia in elderly patients with HUA increased by 9.5%-11.7% (P<0.01), in which the probability of high triglyceridemia (TG), high total cholesterol (TC), high low-density lipoprotein cholesterol (LDL-C), and low high-density lipoprotein cholesterol (HDL-C) increased by 10.4%-11.5% (P<0.01), 2.7%-3.8% (P<0.01), 1.7%-2.3% (P<0.05), and 4.3%-4.9% (P<0.05), respectively. Conclusion HUA is associated with various types of dyslipidemia, among which its relationship with high TG and low HDL-C is strong. Targeted interventions should be taken for elderly HUA patients, aiming to reduce the rate of dyslipidemia and promote the goal of "healthy ageing" in China.