A model for estimating body shape biological age based on clinical parameters associated with body composition
Authors Bae C, Kang YG, Suh, Han J, Kim S, Shim K
Received 17 September 2012
Accepted for publication 10 November 2012
Published 28 December 2012 Volume 2013:8 Pages 11—18
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 3
Chul-Young Bae,1 Young Gon Kang,2 Young-Sung Suh,3 Jee Hye Han,4 Sung-Soo Kim,5 Kyung Won Shim6
1MediAge Research Center, Seoul, Korea; 2Chaum Power Aging Center, College of Medicine, CHA University, Seoul, Korea; 3Health Promotion Center, Keimyung University Dongsam Medical Center, Daegu, Korea; 4Department of Family Medicine, College of Medicine, Eulji University, Seoul, Korea; 5Department of Family Medicine, College of Medicine, Chungnam National University, Daejeon, Korea; 6Department of Family Medicine, Ewha Womans University Mokdong Hospital, Ewha Womans University, Seoul, Korea
Background: To date, no studies have attempted to estimate body shape biological age using clinical parameters associated with body composition for the purposes of examining a person's body shape based on their age.
Objective: We examined the relations between clinical parameters associated with body composition and chronological age, and proposed a model for estimating the body shape biological age.
Methods: The study was conducted in 243,778 subjects aged between 20 and 90 years who received a general medical checkup at health promotion centers at university and community hospitals in Korea from 2004 to 2011.
Results: In men, the clinical parameters with the highest correlation to age included the waist-to-hip ratio (r = 0.786, P < 0.001), hip circumference (r = −0.448, P < 0.001), and height (r = −0.377, P < 0.001). In women, the clinical parameters with the highest correlation to age include the waist-to-hip ratio (r = 0.859, P < 0.001), waist circumference (r = 0.580, P < 0.001), and hip circumference (r = 0.520, P < 0.001). To estimate the optimal body shape biological age based on clinical parameters associated with body composition, we performed a multiple regression analysis. In a model estimating the body shape biological age, the coefficient of determination (R2) was 0.71 in men and 0.76 in women.
Conclusion: Our model for estimating body shape biological age might be a novel approach to variation in body shape that is due to aging. We assume that our estimation model would be used as an adjunctive measure in easily predicting differences in body shape with the use of clinical parameters that are commonly used to assess the status of obesity in a clinical setting.
Keywords: chronological age, body shape, biological age
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