Objectives: We aimed to develop a non-invasive model for the detection of metabolic syndrome (MetS) in school children and adolescents.
Methods: Participants were 7,330 children and adolescents aged 1018 years attending schools in eight Chinese cities. Participants had anthropometry measured by research nurses and underwent fasting blood tests. MetS was defined as central obesity (waist-to-height ratio ≥0.46 for boys and ≥0.48 for girls), and a combination of abnormal glycaemia, hypertension, and/or dyslipidaemia. A prediction model for MetS was developed using multivariable logistic regression using non-invasive anthropometric and clinical parameters.
Results: Overall, MetS prevalence was 3.9%. The prediction model included age, waist-to-height ratio, hypertension, acanthosis nigricans, and sex as independent variables, had acceptable discrimination accuracy (AUROC 0.75) and 65.7% sensitivity (190/289 MetS cases). Its PPV was 36.5%, but 72.2% of false-positives (231/320) had one other metabolic abnormality beyond central adiposity. An alternative mixed process was also 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 (n=493), of whom 57.0% (n=281) had one other metabolic abnormality beyond central adiposity.
Conclusions: It is possible to detect most undiagnosed MetS cases in school children and adolescents with non-invasive methods. Importantly, a large proportion of false-positive cases had metabolic abnormalities, so that the vast majority of youth identified by the model warranted medical follow-up.
19 Sep 2019 - 21 Sep 2019