Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Sovio, Ulla, Skow, Aine, Falconer, Catherine, Park, Min Hae, Viner, Russell M., Kinra, Sanjay
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi Publishing Corporation 2013
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
_version_ 1782275938567847936
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
work_keys_str_mv AT sovioulla improvingpredictionalgorithmsforcardiometabolicriskinchildrenandadolescents
AT skowaine improvingpredictionalgorithmsforcardiometabolicriskinchildrenandadolescents
AT falconercatherine improvingpredictionalgorithmsforcardiometabolicriskinchildrenandadolescents
AT parkminhae improvingpredictionalgorithmsforcardiometabolicriskinchildrenandadolescents
AT vinerrussellm improvingpredictionalgorithmsforcardiometabolicriskinchildrenandadolescents
AT kinrasanjay improvingpredictionalgorithmsforcardiometabolicriskinchildrenandadolescents