Improving prediction algorithms for cardiometabolic risk in children and adolescents

Ulla Sovio, Aine Skow, Catherine Falconer, Min Hae Park, Russell M Viner, Sanjay Kinra

Research output: Contribution to journalArticle (Academic Journal)peer-review

18 Citations (Scopus)


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.
Original languageEnglish
Pages (from-to)684782
JournalJournal of Obesity
Publication statusPublished - 2013


  • Adolescent
  • Adult
  • Age Factors
  • Algorithms
  • Anthropometry
  • Biological Markers
  • Cardiovascular Diseases
  • Child
  • Child, Preschool
  • Decision Support Techniques
  • Female
  • Humans
  • Life Style
  • Male
  • Metabolic Syndrome X
  • Pediatric Obesity
  • Prognosis
  • Reproducibility of Results
  • Risk Assessment
  • Risk Factors
  • Young Adult


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