Establishment and Validation of a New Predictive Model for Insulin Resistance based on 2 Chinese Cohorts: A Cross-Sectional Study. (17th October 2022)

• Record Type: Journal Article
• Authors: Zhang, Shi; Wang, Xin-Cheng; Li, Jing; Wang, Xiao-He; Wang, Yi; Zhang, Yan-Ju; Du, Mei-Yang; Zhang, Min-Ying; Lin, Jing-Na; Li, Chun-Jun
• Other Names: Rajpal Aman Academic Editor.
• Is Part Of: International journal of endocrinology. Volume 2022(2022)
• Publication Date: 2022-10-17
• Subjects: Endocrinology -- Periodicals; Endocrinology; Endocrinology -- Periodicals; Endocrine System Diseases -- Periodicals; Periodicals; 616.4
• DOI: 10.1155/2022/8968793
• Languages: English
• ISSNs: 1687-8337

Abstract

Abstract : Background . Visceral adiposity plays a key role in the development of insulin resistance (IR), so surrogate index that can indicate visceral obesity may have higher predictive value for IR. This study aimed to establish and validate a new predictive model including indicator of visceral obesity for IR. Methods . The study population consisted of two cohorts. The derivation cohort was a group of 667 patients with newly diagnosed type 2 diabetes and the population undergoing a routine health checkup was the validation cohort. The predictive model was established by the logistic regression analysis. Its value for predicting IR was compared with other surrogate indices by the receiver operating characteristic curve. Results . The odds ratio (OR) of age, visceral fat area (VFA), triglyceride (TG), fasting plasma glucose (FPG), and alanine aminotransferase (ALT) for IR was 1.028 (95% CI, 1.008–1.048) (P < 0.01 ), 1.016 (95% CI, 1.009–1.023) (P < 0.001 ), 1.184 (95% CI, 1.005–1.396) (P < 0.05 ), 1.334 (95% CI, 1.225–1.451) (P < 0.001 ), and 1.021 (95% CI, 1.001–1.040) (P < 0.05 ). The formula of the predictive model was (0.0293 × age + 1.4892 × Ln VFA + 0.4966 × Ln TG + 2.784 × Ln FPG + 0.6906 × Ln ALT)/2. The area under the curve was the largest among all the previously reported predictors. Conclusions . This study established and validated a predicting model for IR and confirmed its predictive value in comparison with other surrogate indicators, which will offer a simple and effective tool to measure IR in future large population studies.