A Non-invasive Model for Detection of Undiagnosed Metabolic Syndrome in School Children and Adolescents

Abstract Background: We aimed to develop a non-invasive model to detect the metabolic syndrome (MetS) in school children and adolescents. Methods: Participants were 7,330 children and adolescents aged 10–18 years, attending schools in eight Chinese localities. Participants had anthropometry measured and underwent fasting blood tests. MetS was defined as central obesity and a combination of abnormal glycaemia, hypertension, and/or dyslipidaemia. A prediction model for MetS was developed using non-invasive anthropometric and clinical parameters. Results: The prediction model had acceptable discrimination accuracy (AUROC 0.75) and 65.7% sensitivity. While its PPV was 36.5%, 72.2% of false-positives had one other metabolic abnormality beyond central adiposity. An alternative mixed process was developed: first, all children with central adiposity and hypertension were considered as cases; secondly, a prediction model was developed on remaining normotensive children with central adiposity, yielding possibly-helpful discrimination (AUROC 0.67). This combined approach yielded higher sensitivity (75.4%) but lower PPV (30.7%) with more false-positives, of whom 57.0% had one other metabolic abnormality beyond central adiposity. Conclusions: Most undiagnosed MetS cases could be detected in school children and adolescents with non-invasive methods. Importantly, most false-positive cases had metabolic abnormalities, so that the vast majority of cases identified by the models warranted medical follow-up.