Improving Prediction Algorithms for Cardiometabolic Risk in Children and Adolescents
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...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2013-01-01
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| Series: | Journal of Obesity |
| Online Access: | http://dx.doi.org/10.1155/2013/684782 |
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| author | Ulla Sovio Aine Skow Catherine Falconer Min Hae Park Russell M. Viner Sanjay Kinra |
| author_facet | Ulla Sovio Aine Skow Catherine Falconer Min Hae Park Russell M. Viner Sanjay Kinra |
| author_sort | Ulla Sovio |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-7f5cf5dd80ec442580db7741323ee75b |
| institution | OA Journals |
| issn | 2090-0708 2090-0716 |
| language | English |
| publishDate | 2013-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Obesity |
| spelling | doaj-art-7f5cf5dd80ec442580db7741323ee75b2025-08-20T02:08:42ZengWileyJournal of Obesity2090-07082090-07162013-01-01201310.1155/2013/684782684782Improving Prediction Algorithms for Cardiometabolic Risk in Children and AdolescentsUlla Sovio0Aine Skow1Catherine Falconer2Min Hae Park3Russell M. Viner4Sanjay Kinra5Department of Non-Communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT, UKDepartment of Non-Communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT, UKDepartment of Non-Communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT, UKDepartment of Non-Communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT, UKGeneral and Adolescent Paediatrics Unit, Institute of Child Health, University College London, 30 Guilford Street, London WC1N 1EH, UKDepartment of Non-Communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT, UKClustering 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.http://dx.doi.org/10.1155/2013/684782 |
| spellingShingle | Ulla Sovio Aine Skow Catherine Falconer Min Hae Park Russell M. Viner Sanjay Kinra Improving Prediction Algorithms for Cardiometabolic Risk in Children and Adolescents Journal of Obesity |
| title | Improving Prediction Algorithms for Cardiometabolic Risk in Children and Adolescents |
| title_full | Improving Prediction Algorithms for Cardiometabolic Risk in Children and Adolescents |
| title_fullStr | Improving Prediction Algorithms for Cardiometabolic Risk in Children and Adolescents |
| title_full_unstemmed | Improving Prediction Algorithms for Cardiometabolic Risk in Children and Adolescents |
| title_short | Improving Prediction Algorithms for Cardiometabolic Risk in Children and Adolescents |
| title_sort | improving prediction algorithms for cardiometabolic risk in children and adolescents |
| url | http://dx.doi.org/10.1155/2013/684782 |
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