Purpose The best predictors and cut points for metabolic syndrome (MetS) in Chinese patients with type 2 diabetes (T2DM) were determined by comparing six anthropometric measures: body mass index (BMI), triglyceride-glucose (TyG), the product of TyG and waist-to-hip ratio (TyG-WHpR), the product of TyG and waist-to-height ratio (TyG-WHtR), the product of TyG and waist circumference (TyG-WC), and the product of TyG and body mass index (TyG-BMI). Patients and Methods Sixteen hundred and sixty-five adult patients with T2DM were collected, and the ability and cut points of each index to predict MetS were compared by plotting the receiver operating characteristic (ROC) curve and calculating the area under the curve (AUC) values. Then, logistic regression analysis was used to adjust for confounders, including adjustment for menopause in women, to obtain the odds ratio (OR) and 95% confidence interval (CI). Results MetS was present in 71.60% of T2DM patients, 75.00% of men, and 67.02% of women. BMI was the best predictor of MetS in men with T2DM (AUC = 0.8646, 95% CI: 0.8379–0.8912), with a cut point of 24.5500 kg/m 2 (specificity: 0.7714; sensitivity: 0.7533), and TyG-WC was the best predictor of MetS in women with T2DM (AUC = 0.8362, 95% CI: 0.8034–0.8690), with a cut point of 154.1548 (specificity: 0.7455; sensitivity: 0.8076). Conclusion The best predictor of MetS in adults with T2DM is BMI with a cut point of 24.5500 kg/m 2 for men and TyG-WC with a cut point of 154.1548 for women.
To explore the correlation between Chinese visceral adipose index (CVAI) and urinary microalbumin/creatinine ratio (UACR) and urinary albumin, and whether there is any difference in correlation between Han and Tujia ethnicity. Methods: This cross-sectional study was conducted in Changde, Hunan, China from May 2021 to December 2021. Biochemical indicators including anthropometric parameters, blood pressure, blood glucose, blood lipids, and UACR of the participants were measured. Univariate analysis, multivariate analyses and multinomial logistic regression analysis were carried out to assess the association between CVAI and albuminuria. In addition, curve fitting and threshold effect analysis were used to explore the nonlinear association between CVAI and albuminuria, and to observe whether there were ethnic differences in this association. Results: A total of 2026 adult residents were enrolled in this study, 500 of whom had albuminuria. Population-standardized prevalence of albuminuria is 19.06%. In the multivariable model adjusted for confounding factors, the odds ratio (OR) of albuminuria for pre-unit increase of CVAI and pre-SD increase of CVAI were 1.007 (1.003-1.010) and 1.298 (1.127-1.496), respectively. Multinomial logistic regression analysis confirmed the robustness and consistency of the results.The generalized additive model showed that CVAI and albuminuria had a nonlinear relationship with inflection point at 97.201 using the threshold effect. Compared with Han ethnic groups, the threshold between CVAI and albuminuria in Tujia people moved backward. The thresholds were 159.785 and 98.527, respectively. Conclusion: There was a positive nonlinear dose-response relationship between increased CVAI and higher levels of albuminuria. Maintaining appropriate CVAI levels may be important for the prevention of albuminuria.
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