Prediction Model for Insulin Resistance and Implications for MASLD in Youth: A Novel Marker; the Pediatric Insulin Resistance Assessment Score

Abstract

Purpose: Insulin resistance (IR) is a condition closely associated with cardiovascular risk factors and metabolic dysfunction-associated steatotic liver disease (MASLD) is emerging as a significant IR-related complication. We aimed to develop a predictive model for IR in youths and implicate this model for MASLD.

Materials and methods: A total of 1588 youths from the population-based data were included in the training set. For the test sets, 121 participants were included for IR and 50 for MASLD from real-world clinic data. Logistic regression analysis, random forest, extreme gradient boosting (XGBoost), light gradient boosting machine (GBM), and deep neural network (DNN) were used to develop the models. A nomogram scoring system was constructed based on a model used to predict the probability of IR and MASLD.

Results: After stepwise selection, age, body mass index (BMI) standard deviation score (SDS), waist circumference (WC), systolic blood pressure, HbA1c, high-density lipoprotein cholesterol, triglyceride, and alanine aminotransferase levels were included in the model. A nomogram scoring system was constructed based on a multivariable logistic regression model. The areas under the curves (AUCs) of the models for IR prediction in external validation were 0.75 (logistic regression), 0.78 (random forest), 0.72 (XGBoost), 0.71 (light GBM), and 0.71 (DNN). For MASLD prediction, the AUCs were 0.93 (logistic regression), 0.95 (random forest), 0.90 (XGBoost), 0.91 (light GBM), and 0.85 (DNN). BMI SDS and WC SDS were the most important contributors to IR prediction in all models.

Conclusion: The Pediatric Insulin Resistance Assessment Score is a novel scoring system for predicting IR and MASLD in youths.