A non-invasive tool for the early identification of children at risk of cardiometabolic dysfunction: data from the PODiaCar project.

Abstract

Early identification of children at risk for metabolic syndrome (MetS) can reveal traits linked to cardiometabolic disease. We aimed to develop a simple, user-friendly tool to detect pediatric cardiometabolic risk using clinical, nutritional, and lifestyle data. A total of 317 patients (11.35 ± 3.62) were assessed using clinical, dietary, and biochemical data. Metabolic risk was defined by a MetS z-score >0.75, and MetS diagnosis required at least three altered parameters (body composition, blood pressure, glucose, lipids). A 22-variable binary tool generated a cumulative risk score: ≥7 altered components indicated high risk; otherwise, low risk. A pathological MetS-score was found in 62.15% of subjects, while MetS was diagnosed in 39.4%. The MetS z-score was significantly correlated with MetS prevalence (r = 0.581). When considering a screening tool score ≥7, along with patients presenting at least 3 of 4 altered MetS parameters, the results demonstrated good sensitivity (0.768 [0.715, 0.835]), negative predictive value (0.775 [0.702, 0.848]), and accuracy (0.618 [0.564, 0.672]), though specificity (52.1% [0.420, 0.600]) and positive predictive value (0.511 [0.439, 0.582]) were moderate. A score ≥7 reliably identifies children at cardiometabolic risk, providing a sensitive, non-invasive tool that supports early detection, prevention, and personalized care while reducing time and healthcare costs. Early detection of at-risk children can uncover cardio-metabolic traits. A 22-noninvasive variable tool was developed to identify pediatric cardio-metabolic risk. A score ≥7 effectively identifies children at cardiometabolic risk. The proposed non-invasive tool achieves good sensitivity (76.8%) and moderate specificity (52.1%). The tool supports clinicians in prevention, monitoring, and personalized care.