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Assessing the predictive value of insulin resistance indices for metabolic syndrome risk in type 2 diabetes mellitus patients.
Bazyar, Hadi; Zare Javid, Ahmad; Masoudi, Mahmood Reza; Haidari, Fatemeh; Heidari, Zeinab; Hajializadeh, Sohrab; Aghamohammadi, Vahideh; Vajdi, Mahdi.
Affiliation
  • Bazyar H; Student Research Committee, Sirjan School of Medical Sciences, Sirjan, Iran.
  • Zare Javid A; Department of Public Health, Sirjan School of Medical Sciences, Sirjan, Iran.
  • Masoudi MR; Nutrition and Metabolic Diseases Research Center, Clinical Sciences Research Institute, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran.
  • Haidari F; Department of Nutrition, School of Allied Medical Sciences, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran.
  • Heidari Z; Sirjan School of Medical Sciences, Sirjan, Iran.
  • Hajializadeh S; School of Health, Medical and Applied Sciences, Central Queensland University, Brisbane, Australia.
  • Aghamohammadi V; Student Research Committee, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran.
  • Vajdi M; Student Research Committee, Sirjan School of Medical Sciences, Sirjan, Iran.
Sci Rep ; 14(1): 8917, 2024 04 18.
Article in En | MEDLINE | ID: mdl-38632455
ABSTRACT
Limited research has explored the effectiveness of insulin resistance (IR) in forecasting metabolic syndrome (MetS) risk, especially within the Iranian population afflicted with type 2 diabetes mellitus (T2DM). The present investigation aimed to assess the efficacy of IR indices in predicting the risk of MetS among T2DM patients. Convenient sampling was utilized to select four hundred subjects with T2DM. Metabolic factors and IR indices, including the Waist Circumference-Triglyceride Index (WTI), Triglyceride and Glucose Index (TyG index), the product of TyG index and abdominal obesity indices, and the Metabolic Score for Insulin Resistance (METS-IR), were evaluated. Logistic regression, coupled with modeling, was employed to explore the risk of MetS. The predictive performance of the indices for MetS stratified by sex was evaluated via receiver operating characteristic (ROC) curve analysis and estimation of the area under the curve (AUC) values. The TyG-Waist Circumference (TyG-WC) index exhibited the largest AUCs in both males (0.91) and females (0.93), while the TyG-Body Mass Index (TyG-BMI) demonstrated the smallest AUCs (0.77 in males and 0.74 in females). All indices significantly predicted the risk of MetS in all subjects before and after adjustment (p < 0.001 for all). The TyG-WC index demonstrated the highest odds ratios for MetS (8.06, 95% CI 5.41-12.00). In conclusion, all IR indices assessed in this study effectively predicted the risk of MetS among Iranian patients with T2DM, with the TyG-WC index emerging as the most robust predictor across both genders.
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Insulin Resistance / Metabolic Syndrome / Diabetes Mellitus, Type 2 Limits: Female / Humans / Male Country/Region as subject: Asia Language: En Journal: Sci Rep Year: 2024 Document type: Article Affiliation country: Iran Country of publication: United kingdom

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Insulin Resistance / Metabolic Syndrome / Diabetes Mellitus, Type 2 Limits: Female / Humans / Male Country/Region as subject: Asia Language: En Journal: Sci Rep Year: 2024 Document type: Article Affiliation country: Iran Country of publication: United kingdom