ArticleRisk management and healthcare policy2022
Construction of a Personalized Insulin Resistance Risk Assessment Tool in Xinjiang Kazakhs Based on Lipid- and Obesity-Related Indices.
Article in Risk management and healthcare policy, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Reassessing of the incremental predictive value of triglyceride-glucose index beyond traditional cardiovascular risk factors: a cohort study based on the China health and nutrition survey and meta-analysis.Frontiers in cardiovascular medicine · 2026Article
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Authors and funding
10 authors.
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Abstract
Purpose: This study aimed to explore the relationship between obesity- and lipid-related indices and insulin resistance (IR) and construct a personalized IR risk model for Xinjiang Kazakhs based on representative indices. Methods: This cross-sectional study was performed from 2010 to 2012. A total of 2170 Kazakhs from Xinyuan County were selected as research subjects. IR was estimated using the homeostasis model assessment of insulin resistance. Multivariable logistic regression analysis, least absolute shrinkage and selection operator penalized regression analysis, and restricted cubic spline were applied to evaluate the association between lipid- and obesity-related indices and IR. The risk model was developed based on selected representative variables and presented using a nomogram. The model performance was assessed using the area under the ROC curve (AUC), the Hosmer-Lemeshow goodness-of-fit test, and decision curve analysis (DCA). Results: After screening out 25 of the variables, the final risk model included four independent risk factors: smoking, sex, triglyceride-glucose (TyG) index, and body mass index (BMI). A linear dose-response relationship was observed for the BMI and TyG indices against IR risk. The AUC of the risk model was 0.720 based on an independent test and 0.716 based on a 10-fold cross-validation. Calibration curves showed good consistency between actual and predicted IR risks. The DCA demonstrated that the risk model was clinically effective. Conclusion: The TyG index and BMI had the strongest association with IR among all obesity- and lipid-related indices, and the developed model was useful for predicting IR risk among Kazakh individuals.
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