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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...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi Publishing Corporation
2013
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3703718/ https://www.ncbi.nlm.nih.gov/pubmed/23862055 http://dx.doi.org/10.1155/2013/684782 |
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author | Sovio, Ulla Skow, Aine Falconer, Catherine Park, Min Hae Viner, Russell M. Kinra, Sanjay |
author_facet | Sovio, Ulla Skow, Aine Falconer, Catherine Park, Min Hae Viner, Russell M. Kinra, Sanjay |
author_sort | Sovio, Ulla |
collection | PubMed |
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 | Online Article Text |
id | pubmed-3703718 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2013 |
publisher | Hindawi Publishing Corporation |
record_format | MEDLINE/PubMed |
spelling | pubmed-37037182013-07-16 Improving Prediction Algorithms for Cardiometabolic Risk in Children and Adolescents Sovio, Ulla Skow, Aine Falconer, Catherine Park, Min Hae Viner, Russell M. Kinra, Sanjay J Obes Review Article 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. Hindawi Publishing Corporation 2013 2013-06-19 /pmc/articles/PMC3703718/ /pubmed/23862055 http://dx.doi.org/10.1155/2013/684782 Text en Copyright © 2013 Ulla Sovio et al. https://creativecommons.org/licenses/by/3.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Review Article Sovio, Ulla Skow, Aine Falconer, Catherine Park, Min Hae Viner, Russell M. Kinra, Sanjay Improving Prediction Algorithms for Cardiometabolic Risk in Children and Adolescents |
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 |
topic | Review Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3703718/ https://www.ncbi.nlm.nih.gov/pubmed/23862055 http://dx.doi.org/10.1155/2013/684782 |
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