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

Young Gon Kang, Eunkyung Suh, Hyejin Chun, Sun Hyun Kim, Deog Ki Kim, Chul Young Bae

Research output: Contribution to journalArticlepeer-review

18 Scopus citations

Abstract

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.

Original languageEnglish
Pages (from-to)253-261
Number of pages9
JournalClinical Interventions in Aging
Volume12
DOIs
StatePublished - 1 Feb 2017

Bibliographical note

Publisher Copyright:
© 2017 Kang et al.

Keywords

  • Biological age
  • Biomarker
  • Health care
  • Index
  • Metabolic syndrome

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