ArticleDiabetes, metabolic syndrome and obesity : targets and therapy2023
Optimal Anthropometric Indicators and Cut Points for Predicting Metabolic Syndrome in Chinese Patients with Type 2 Diabetes Mellitus by Gender.
Article in Diabetes, metabolic syndrome and obesity : targets and therapy, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed.
- Determination of the Optimal Cut-Off Point of Anthropometric Indices to Predict the Risk of Metabolic Syndrome in Iranian Adult Population With Type 2 Diabetes Mellitus: A Cross-Sectional-Analytical Study.Endocrinology, diabetes & metabolism · 2026Article
- Comparison of the association and discriminatory ability of CVAI, LAP, CI, and AVI for type 2 diabetes in Chinese adults aged ≥ 50 years: a sex-specific analysis.Frontiers in endocrinology · 2026Article
- Association of TyG-related indices with incident DM among NAFLD patients: a retrospective study.Hormones (Athens, Greece) · 2025Article
- Cardiometabolic risk assessment by anthropometric and biochemical indices in mexican population.Frontiers in endocrinology · 2025Article
- Assessing the predictive value of insulin resistance indices for metabolic syndrome risk in type 2 diabetes mellitus patients.Scientific reports · 2024Article
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Abstract
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 Conclusion: The best predictor of MetS in adults with T2DM is BMI with a cut point of 24.5500 kg/m
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