Observations Of Cumulene Carbenes, H2cccc And H2ccc, In Tmc-1

BACKGROUND: Although obesity is a recognized cardiometabolic risk determinant, debate persists over the most appropriate anthropometric technique. This study evaluated different anthropometric techniques for predicting cardiometabolic risk in Turkish adolescents and aimed to develop and temporally validate a machine learning-based prediction model, reported in accordance with the TRIPOD statement. METHODS: Adolescents from the Türkiye Nutrition and Health Survey in 2010 ( RESULTS: In ROC analysis, VAI showed the highest discrimination (AUC = 0.747, CONCLUSIONS: Machine learning approaches, together with the developed model and simplified formula, may be useful tools for estimating cardiometabolic risk in adolescents and could support a stepwise screening strategy, pending recalibration and prospective validation.