Models for estimating the metabolic syndrome biological age as the new index for evaluation and management of metabolic syndrome
Authors Kang YG, Suh EK, Chun HJ, Kim SH, Kim DK, Bae CY
Received 27 September 2016
Accepted for publication 22 December 2016
Published 1 February 2017 Volume 2017:12 Pages 253—261
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 4
Editor who approved publication: Dr Richard Walker
Young Gon Kang,1 Eunkyung Suh,2 Hyejin Chun,3 Sun-Hyun Kim,4 Deog Ki Kim,5 Chul-Young Bae1
1MediAge Research Center, Seongnam-si, Gyeonggi-do, 2Department of Family Medicine, College of Medicine, CHA University, Chaum, Seoul, 3Department of Family Medicine, College of Medicine, CHA University, Bundang CHA Medical Center, Seongnam-si, Gyeonggi-do, 4Department of Family Medicine, International St Mary’s Hospital, College of Medicine, Catholic Kwandong University, Incheon, 5Pharmicell Clinical Research Center, Seoul, South Korea
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.
Keywords: metabolic syndrome, biological age, biomarker, index, health care
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