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:	Sovio, Ulla, Skow, Aine, Falconer, Catherine, Park, Min Hae, Viner, Russell M., Kinra, Sanjay
Format:	Online
Language:	English
Published:	Hindawi Publishing Corporation 2013
Online Access:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3703718/

id	pubmed-3703718
recordtype	oai_dc
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 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.
repository_type	Open Access Journal
institution_category	Foreign Institution
institution	US National Center for Biotechnology Information
building	NCBI PubMed
collection	Online Access
language	English
format	Online
author	Sovio, Ulla Skow, Aine Falconer, Catherine Park, Min Hae Viner, Russell M. Kinra, Sanjay
spellingShingle	Sovio, Ulla Skow, Aine Falconer, Catherine Park, Min Hae Viner, Russell M. Kinra, Sanjay Improving Prediction Algorithms for Cardiometabolic Risk in Children and Adolescents
author_facet	Sovio, Ulla Skow, Aine Falconer, Catherine Park, Min Hae Viner, Russell M. Kinra, Sanjay
author_sort	Sovio, Ulla
title	Improving Prediction Algorithms for Cardiometabolic Risk in Children and Adolescents
title_short	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_sort	improving prediction algorithms for cardiometabolic risk in children and adolescents
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.
publisher	Hindawi Publishing Corporation
publishDate	2013
url	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3703718/
_version_	1611992533868478464

Improving Prediction Algorithms for Cardiometabolic Risk in Children and Adolescents

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