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Models for estimating the metabolic syndrome biological age as the new index for evaluation and management of metabolic syndrome

PURPOSE: This study aims to propose a metabolic syndrome (MS) biological age model, through which overall evaluation and management of the health status and aging state in MS can be done easily. Through this model, we hope to provide a novel evaluation and management health index that can be utilize...

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Autores principales: Kang, Young Gon, Suh, Eunkyung, Chun, Hyejin, Kim, Sun-Hyun, Kim, Deog Ki, Bae, Chul-Young
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Dove Medical Press 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5295798/
https://www.ncbi.nlm.nih.gov/pubmed/28203066
http://dx.doi.org/10.2147/CIA.S123316
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author Kang, Young Gon
Suh, Eunkyung
Chun, Hyejin
Kim, Sun-Hyun
Kim, Deog Ki
Bae, Chul-Young
author_facet Kang, Young Gon
Suh, Eunkyung
Chun, Hyejin
Kim, Sun-Hyun
Kim, Deog Ki
Bae, Chul-Young
author_sort Kang, Young Gon
collection PubMed
description PURPOSE: This study aims to propose a metabolic syndrome (MS) biological age model, through which overall evaluation and management of the health status and aging state in MS can be done easily. Through this model, we hope to provide a novel evaluation and management health index that can be utilized in various health care fields. PATIENT AND METHODS: MS parameters from American Heart Association/National Heart, Lung, and Blood Institute guidelines in 2005 were used as biomarkers for the estimation of MS biological age. MS biological age model development was done by analyzing data of 263,828 participants and clinical application of the developed MS biological age was assessed by analyzing the data of 188,886 subjects. RESULTS: The principal component accounted for 36.1% in male and 38.9% in female of the total variance in the battery of five variables. The correlation coefficient between corrected biological age and chronological age in males and females were 0.711 and 0.737, respectively. Significant difference for mean MS biological age and chronological age between the three groups, normal, at risk and MS, was seen (P<0.001). CONCLUSION: For the comprehensive approach in MS management, MS biological age is expected to be additionally utilized as a novel evaluation and management index along with the traditional MS diagnosis.
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spelling pubmed-52957982017-02-15 Models for estimating the metabolic syndrome biological age as the new index for evaluation and management of metabolic syndrome Kang, Young Gon Suh, Eunkyung Chun, Hyejin Kim, Sun-Hyun Kim, Deog Ki Bae, Chul-Young Clin Interv Aging Original Research PURPOSE: This study aims to propose a metabolic syndrome (MS) biological age model, through which overall evaluation and management of the health status and aging state in MS can be done easily. Through this model, we hope to provide a novel evaluation and management health index that can be utilized in various health care fields. PATIENT AND METHODS: MS parameters from American Heart Association/National Heart, Lung, and Blood Institute guidelines in 2005 were used as biomarkers for the estimation of MS biological age. MS biological age model development was done by analyzing data of 263,828 participants and clinical application of the developed MS biological age was assessed by analyzing the data of 188,886 subjects. RESULTS: The principal component accounted for 36.1% in male and 38.9% in female of the total variance in the battery of five variables. The correlation coefficient between corrected biological age and chronological age in males and females were 0.711 and 0.737, respectively. Significant difference for mean MS biological age and chronological age between the three groups, normal, at risk and MS, was seen (P<0.001). CONCLUSION: For the comprehensive approach in MS management, MS biological age is expected to be additionally utilized as a novel evaluation and management index along with the traditional MS diagnosis. Dove Medical Press 2017-02-01 /pmc/articles/PMC5295798/ /pubmed/28203066 http://dx.doi.org/10.2147/CIA.S123316 Text en © 2017 Kang et al. This work is published and licensed by Dove Medical Press Limited The full terms of this license are available at https://www.dovepress.com/terms.php and incorporate the Creative Commons Attribution – Non Commercial (unported, v3.0) License (http://creativecommons.org/licenses/by-nc/3.0/). By accessing the work you hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed.
spellingShingle Original Research
Kang, Young Gon
Suh, Eunkyung
Chun, Hyejin
Kim, Sun-Hyun
Kim, Deog Ki
Bae, Chul-Young
Models for estimating the metabolic syndrome biological age as the new index for evaluation and management of metabolic syndrome
title Models for estimating the metabolic syndrome biological age as the new index for evaluation and management of metabolic syndrome
title_full Models for estimating the metabolic syndrome biological age as the new index for evaluation and management of metabolic syndrome
title_fullStr Models for estimating the metabolic syndrome biological age as the new index for evaluation and management of metabolic syndrome
title_full_unstemmed Models for estimating the metabolic syndrome biological age as the new index for evaluation and management of metabolic syndrome
title_short Models for estimating the metabolic syndrome biological age as the new index for evaluation and management of metabolic syndrome
title_sort models for estimating the metabolic syndrome biological age as the new index for evaluation and management of metabolic syndrome
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5295798/
https://www.ncbi.nlm.nih.gov/pubmed/28203066
http://dx.doi.org/10.2147/CIA.S123316
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