Literature DB >> 24655488

Adiposity measurements in association with metabolic syndrome in older men have different clinical implications.

Chun-Hsien Hsu1, Jiunn-Diann Lin2, Chang-Hsun Hsieh3, Shu Chuen Lau1, Wei-Yong Chiang4, Yen-Lin Chen5, Dee Pei6, Jin-Biou Chang7.   

Abstract

Obesity is a major public health problem, and measuring adiposity accurately and predicting its future comorbidities are important issues. Therefore, we hypothesized that 4 adiposity measurements, body mass index (BMI), waist circumference (WC), waist-to-height ratio, and body fat percentage, have different physiological meanings and distinct associations with adverse health consequences. This study aimed to investigate the relationship of these 4 measurements with metabolic syndrome (MetS) components and identify the most associated factor for MetS occurrence in older, non-medicated men. Cross-sectional data from 3004 men, all 65 years of age and older, were analyzed. The correlation and association between adiposity measurements and MetS components were evaluated by Pearson correlation and multiple linear regression. Based on multivariate logistic regression, BMI and WC were significantly associated with MetS and were selected to build a combined model of receiver operating characteristic curves to increase the diagnosis accuracy for MetS. The results show that BMI is independently associated with systolic and diastolic blood pressure; WC and body fat percentage are associated with fasting plasma glucose and log transformation of triglyceride; BMI and WC are negatively associated with high-density lipoprotein cholesterol (HDL-C); and WC is a better discriminate for MetS than BMI, although the combined model (WC + BMI) is not significantly better than WC alone. Based on these results, we conclude that the 4 adiposity measurements have different clinical implications. Thus, in older men, BMI is an important determinant for blood pressure and HDL-C. Waist circumference is associated with the risk of fasting plasma glucose, HDL-C, triglyceride, and MetS occurrence. The combined model did not increase the diagnosis accuracy.
Copyright © 2014 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  body fat percentage; body mass index; men; metabolic syndrome; receiver operating characteristic; waist circumference; waist-to-height ratio

Mesh:

Substances:

Year:  2014        PMID: 24655488     DOI: 10.1016/j.nutres.2014.01.004

Source DB:  PubMed          Journal:  Nutr Res        ISSN: 0271-5317            Impact factor:   3.315


  4 in total

1.  Comparison of adiposity indices in relation to prehypertension by age and gender: A community-based survey in Henan, China.

Authors:  Shuaibing Wang; Rui Peng; Shuying Liang; Kaiyan Dong; Wei Nie; Qian Yang; Nan Ma; Jianying Zhang; Kaijuan Wang; Chunhua Song
Journal:  Clin Cardiol       Date:  2018-12-05       Impact factor: 2.882

2.  Changes in weight and weight distribution across the lifespan among HIV-infected and -uninfected men and women.

Authors:  Kristine M Erlandson; Long Zhang; Jordan E Lake; Jennifer Schrack; Keri Althoff; Anjali Sharma; Phyllis C Tien; Joseph B Margolick; Lisa P Jacobson; Todd T Brown
Journal:  Medicine (Baltimore)       Date:  2016-11       Impact factor: 1.889

3.  Associations between metabolic risk factors and body mass index, waist circumference, waist-to-height ratio and waist-to-hip ratio in a Chinese rural population.

Authors:  Xin Guan; Guozhe Sun; Liqiang Zheng; Wenyu Hu; Wenna Li; Yingxian Sun
Journal:  J Diabetes Investig       Date:  2015-12-26       Impact factor: 4.232

Review 4.  Advanced body composition assessment: from body mass index to body composition profiling.

Authors:  Magnus Borga; Janne West; Jimmy D Bell; Nicholas C Harvey; Thobias Romu; Steven B Heymsfield; Olof Dahlqvist Leinhard
Journal:  J Investig Med       Date:  2018-03-25       Impact factor: 2.895

  4 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.