[Biological prediction model of insulin resistance in obese adolescents based on LASSO-Logistic regression]
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View abstract on PubMed
Summary
This summary is machine-generated.This study identified key lipid indicators to predict insulin resistance in obese adolescents. A nomogram model using triglyceride, LDL cholesterol, and TC/HDL-C effectively screens for insulin resistance risk.
Area Of Science
- Endocrinology and Metabolism
- Pediatric Obesity Research
- Cardiovascular Risk Assessment
Context
- Obesity in adolescents is a growing concern, increasing the risk of metabolic complications like insulin resistance.
- Early identification of insulin resistance is crucial for timely intervention and prevention of long-term health issues.
- Lipid profiles play a significant role in metabolic health and can serve as indicators for insulin resistance.
Purpose
- To identify risk factors for insulin resistance in obese adolescents.
- To develop and validate a predictive nomogram model for insulin resistance risk.
- To enable early screening and intervention for insulin resistance in this vulnerable population.
Summary
- A Lasso-logistic regression model was developed using data from 404 obese adolescents (aged 10-17).
- Key lipid indicators identified include triglyceride, low-density lipoprotein cholesterol, and the total cholesterol/high-density lipoprotein cholesterol ratio.
- The resulting nomogram demonstrated good predictive accuracy (AUC=0.825) and internal validation (C-Index=0.804).
Impact
- Provides a validated tool for early detection of insulin resistance in obese children and adolescents.
- Facilitates targeted interventions to mitigate the risk of type 2 diabetes and cardiovascular disease.
- Contributes to personalized risk assessment strategies in pediatric metabolic health.
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