ArticleBMC endocrine disorders2025
Establishing optimal cut-offs of cardio-metabolic indices for diagnosing metabolic syndrome in type 2 diabetes.
Article in BMC endocrine disorders, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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9 authors.
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Abstract
backgroundThis study aimed to determine the optimal cut-off values of various cardio-metabolic indices for predicting MetS in Iranian adults with T2DM.
methodsThis cross-sectional analytical study included 400 Iranian adults with T2DM. Anthropometric and biochemical parameters were assessed, including body mass index (BMI), waist circumference (WC), waist-to-hip ratio (WHR), lipid profile, fasting blood glucose (FBG), and blood pressure. Several cardio-metabolic indices-including the cardio-metabolic index (CMI), lipid accumulation product (LAP), and atherogenic index of plasma (AIP)-were calculated. Receiver operating characteristic (ROC) curve analysis was used to determine optimal cut-off points, sensitivity, and specificity of these indices for MetS diagnosis.
resultsAll cardio-metabolic indices, including LCI, CMI, AI, AC, CHOL index, CRI, LAP, and AIP, were significantly associated with an increased risk of MetS across all three models (p < 0.05). Among them, LAP and CMI demonstrated the highest predictive accuracy, with area under the ROC curve (AUC) values of 0.90 and 0.88, respectively. The optimal cut-off point for LAP was 66.84 (sensitivity = 0.76, specificity = 0.93), while for CMI, it was 2.19 (sensitivity = 0.74, specificity = 0.88). Multivariable logistic regression analysis confirmed the strong association of these indices with MetS risk, with LAP showing the highest odds ratio (OR = 56.28, 95% CI: 26.10-121.34, p < 0.001). These associations remained significant across all three models: Model 1 (unadjusted), Model 2 (adjusted for age and gender), and Model 3 (adjusted for age, gender, race, education, occupation, disease duration, physical activity, and medications).
conclusionAll cardio-metabolic indices significantly predicted MetS risk, but LAP and CMI emerged as the most effective predictors of MetS in Iranian adults with T2DM, demonstrating high diagnostic performance. These indices can serve as valuable, cost-effective screening tools in clinical practice, enabling early intervention and risk reduction in high-risk populations. Future studies should validate these findings across diverse ethnic groups and assess their long-term predictive value for MetS-related complications.
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