An XGBoost machine learning model integrating body roundness index and other non-invasive indicators achieved an AUC of 0.94 and sensitivity of 0.96 for predicting metabolic syndrome in an external validation cohort.
Observational (n=329,741)
Yes
Do machine learning models integrating body roundness index and basic demographics accurately predict metabolic syndrome?
Machine learning models incorporating body roundness index provide a highly accurate, non-invasive method for large-scale screening and prediction of metabolic syndrome.
BACKGROUND: Metabolic syndrome (MetS) is strongly associated with increased cardiovascular morbidity and mortality. Traditional invasive diagnostic methods are costly, inconvenient, and unsuitable for large-scale screening. Developing a non-invasive, accurate prediction model is clinically significant for early MetS detection and prevention. OBJECTIVE: To develop a non-invasive prediction model for MetS by integrating body roundness index (BRI), gender, age, height, and waist circumference with machine learning algorithms, and to validate its generalizability across different ethnic groups. METHODS: We trained and validated machine learning models using a retrospective health examination dataset from Central South University (D1, n = 268,942) and externally validated them on a European cohort (D2, n = 60,799). Five non-invasive features-BRI, waist circumference, height, age, and gender-were used as predictors. MetS was diagnosed based on the IDF criteria. Ten machine learning algorithms were evaluated using 10-fold cross-validation. Model performance was assessed via accuracy, specificity, sensitivity, F1-score, precision, ROC AUC and Brier score. Additional analyses included threshold sensitivity testing (0.1-0.9) and ablation of central adiposity features to evaluate robustness and generalizability. RESULTS: In the D1, the prevalence of MetS was 18.26%. In the D2, the prevalence was 9.07%. Among all predictors, BRI demonstrated the strongest correlation with MetS (r = 0.585 in D1; r = 0.426 in D2; both P < 0.01). Compared with a rule-based baseline (F1 score = 0.50), all machine learning models achieved superior performance. At the default 0.5 threshold, XGBoost attained an AUC of 0.94, sensitivity of 0.96, and F1 score of 0.53 in D2. Further threshold adjustment improved XGBoost's precision from 0.36 to 0.43, while maintaining high sensitivity (0.87). Model performance declined markedly when central adiposity features (BRI and WC) were removed, highlighting the critical predictive value of BRI. These findings support the utility of BRI-integrated ML approaches for scalable, non-invasive MetS screening. CONCLUSIONS: The combination of BRI and machine learning provides a non-invasive and effective method for predicting MetS and offers a promising strategy for its early prevention. Machine learning models demonstrate significant advantages over baseline rule-based models in terms of sensitivity and predictive performance.
He et al. (Fri,) conducted a observational in Metabolic Syndrome (n=329,741). Machine learning models (XGBoost) integrating body roundness index (BRI), gender, age, height, and waist circumference vs. Rule-based baseline model (IDF criteria) was evaluated on Area Under the Curve (AUC) for predicting Metabolic Syndrome in external validation. An XGBoost machine learning model integrating body roundness index and other non-invasive indicators achieved an AUC of 0.94 and sensitivity of 0.96 for predicting metabolic syndrome in an external validation cohort.