Development of a User-Friendly Self-Screening Tool for Assessing Metabolic Syndrome Risk in Youths from Economically Challenged Regions

TL;DR

Researchers created a self-screening tool to help youths in low-income areas assess their risk of metabolic syndrome using easy-to-answer questions.

Contribution

The study introduces three nomograms using self-reported data to predict metabolic syndrome risk in economically disadvantaged youth.

Findings

Ten variables were identified as predictors of abdominal obesity in young people.

Three nomograms showed acceptable predictive performance for metabolic syndrome risk.

External validation confirmed the A and B scores had strong predictive capability.

Abstract

Background: Metabolic syndrome increases the risk of heart disease and diabetes. Early identification and management are crucial, especially in economically challenged regions with limited healthcare access. Aims: To develop nomograms for individualized risk estimation for metabolic syndrome in young people from low-income regions. Methods: We assessed 496 college students from two Brazilian cities with Gini indices ≤0.56. Of these, 69.9% were female, 65.1% were younger than 20 years, 71.8% were non-white, and 64.3% were enrolled in health-related courses. For external validity, we assessed metabolic syndrome in a subset of 375 students. Results: We found 10 variables associated with abdominal obesity by logistic regression: age, biological sex, physical education facilities, enrollment in sports competitions during elementary school, grade retention, physical education as the preferred subject, physical education classes per week, and enrollment in sports training in secondary school (score A); adherence to 24 h movement behaviors (B score); and body weight (score C). We designed three nomograms (for scores A, B, and C), all of which showed acceptable performance according to the area under the receiver operating characteristic curve (≥0.70) and calibration (Hosmer–Lemeshow test, p > 0.05). In the external validation, we observed higher predictive capability for the A and B scores, while the C score had lower but still acceptable predictive ability. Conclusions: User-friendly self-reported data accurately predict metabolic syndrome among youths from economically challenging areas.

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