title: Improving prediction algorithms for cardiometabolic risk in children and adolescents.
abstract: Clustering of abnormal metabolic traits, the Metabolic Syndrome (MetS), has been associated with an increased cardiovascular disease (CVD) risk. Several algorithms including the MetS and other risk factors exist for adults to predict the risk of CVD. We discuss the use of MetS scores and algorithms in an attempt to predict later cardiometabolic risk in children and adolescents and offer suggestions for developing clinically useful algorithms in this population. There is little consensus in how to define the MetS or to predict future CVD risk using the MetS and other risk factors in children and adolescents. The MetS scores and prediction algorithms we identified had usually not been tested against a clinical outcome, such as CVD, and they had not been validated in other populations. This makes comparisons of algorithms impossible. We suggest a simple two-step approach for predicting the risk of adult cardiometabolic disease in overweight children. It may have advantages in terms of cost-effectiveness since it uses simple measurements in the first step and more complex, costly measurements in the second step. It also takes advantage of the continuous distributions of the metabolic features. We suggest piloting and validating any new algorithms.
authors.affiliation: Department of Non-Communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT, UK. ulla.sovio@lshtm.ac.uk
authors.forename: Ulla
authors.initials: U
authors.lastname: Sovio
authors.forename: Aine
authors.initials: A
authors.lastname: Skow
authors.forename: Catherine
authors.initials: C
authors.lastname: Falconer
authors.forename: Min Hae
authors.initials: MH
authors.lastname: Park
authors.forename: Russell M
authors.initials: RM
authors.lastname: Viner
authors.forename: Sanjay
authors.initials: S
authors.lastname: Kinra
mesh_terms: D000293:Adolescent; D000328:Adult; D000367:Age Factors; D000465:Algorithms; D000886:Anthropometry; D015415:Biomarkers / Q000097:blood; D002318:Cardiovascular Diseases / Q000097:blood / Q000175:diagnosis / Q000209:etiology*; D002648:Child; D002675:Child, Preschool; D003661:Decision Support Techniques; D005260:Female; D006801:Humans; D008019:Life Style; D008297:Male; D024821:Metabolic Syndrome / Q000097:blood / Q000175:diagnosis / Q000209:etiology*; D063766:Pediatric Obesity / Q000097:blood / Q000150:complications* / Q000175:diagnosis; D011379:Prognosis; D015203:Reproducibility of Results; D018570:Risk Assessment; D012307:Risk Factors; D055815:Young Adult
chemical_list: D015415:Biomarkers