Predicting Insulin Resistance in a Pediatric Population With Obesity.

ABSTRACT

OBJECTIVES: Insulin resistance (IR) affects children and adolescents with obesity and early diagnosis is crucial to prevent long-term consequences. Our aim was to identify predictors of IR and develop a multivariate model to accurately predict IR. METHODS: We conducted a cross-sectional analysis of demographical, clinical, and biochemical data from a cohort of patients attending a specialized Paediatric Nutrition Unit in Portugal over a 20-year period. We developed multivariate regression models to predict IR. The participants were randomly divided into 2 groups a model group for developing the predictive models and a validation group for cross-validation of the study. RESULTS: Our study included 1423 participants, aged 3-17 years old, randomly divided in the model (n = 879) and validation groups (n = 544). The predictive models, including uniquely demographic and clinical variables, demonstrated good discriminative ability [area under the curve (AUC) 0.834-0.868; sensitivity 77.0%-83.7%; specificity 77.0%-78.7%] and high negative predictive values (88.9%-91.6%). While the diagnostic ability of adding fasting glucose or triglycerides/high density lipoprotein cholesterol index to the models based on clinical parameters did not show significant improvement, fasting insulin appeared to enhance the discriminative power of the model (AUC 0.996). During the validation, the model considering demographic and clinical variables along with insulin showed excellent IR discrimination (AUC 0.978) and maintained high negative predictive values (90%-96.3%) for all models. CONCLUSION: Models based on demographic and clinical variables can be advantageously used to identify children and adolescents at moderate/high risk of IR, who would benefit from fasting insulin evaluation.