ESPE2019 Poster Category 2 Fat, Metabolism and Obesity (38 abstracts)
A Non-invasive Model for Detection of the Metabolic Syndrome in Children and Adolescents
Hu Lin 1 , José Derraik 1,2,3,4 , Ye Hong 1 , Li Liang 5 , ChunXiu Gong 6 , FeiHong Luo 7 , GeLi Liu 8 , Feng Xiong 9 , ShaoKe Chen 10 , Guanping Dong 1 , Ke Huang 1 , Chunlin Wang 5 , Xuefeng Chen 1 , Jinna Jinna Yuan 1 & Junfen Fu 1
1Endocrinology Department, Children's Hospital of Zhejiang University School of Medicine, Hangzhou, China. 2Liggins Institute, University of Auckland, Auckland, New Zealand. 3Department of Women's and Children's Health, Uppsala University, Uppsala, Sweden. 4A Better Start National Science Challenge, Auckalnd, New Zealand. 5Pediatric Department of the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China. 6Beijing Children's Hospital of Capital Medical University, Beijing, China. 7Children's Hospital of Shanghai Fudan University, Shanghai, China. 8General Hospital of Tianjin Medical University, Tianjin, China. 9Children's Hospital of Chongqing Medical University, Chongqing, China. 10Maternal and Child Health Hospital of Guangxi Zhuang Autonomous Region, Nanjing, China
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 10–18 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.
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