Literature DB >> 20519812

Body mass index can similarly predict the presence of multiple cardiovascular risk factors in middle-aged Japanese subjects as waist circumference.

Hiroki Satoh1, Reiko Kishi, Hiroyuki Tsutsui.   

Abstract

OBJECTIVE: Adiposity is closely associated with the clustering of metabolic risk factors such as high blood pressure, dyslipidemia, and glucose intolerance. Waist circumference and body mass index (BMI) are the established markers of abdominal adiposity and general adiposity, respectively. However, it has not been examined whether these two markers can detect the clustering of metabolic risk factors in Japanese subjects. METHODS AND
RESULTS: We studied 5,796 Japanese middle-aged subjects aged 40-60 years (4,344 males and 1,452 females). Metabolic risk factors including high blood pressure, dyslipidemia, and glucose intolerance were identified according to the diagnostic criteria for metabolic syndrome in Japan. The number of metabolic risk factors was significantly associated with the BMI values in both male and female subjects. The prevalence of subjects with multiple (two or more) metabolic risk factors was 29.4% and 7.6% in males and females, respectively. According to receiver operating characteristic (ROC) analysis, the area under curve values of BMI and waist circumference did not differ in male (0.658 vs. 0.671, p=n.s.) and female (0.776 vs. 0.790, p=n.s.) subjects, indicating that the waist circumference as well as the BMI could be useful in detecting the occurrence of multiple metabolic risk factors. The appropriate cut-off values of BMI to predict the presence of multiple metabolic risk factors were 24.7 and 23.4 kg/m(2) in males and females, respectively. The sensitivity and specificity using these cut-off values were 58 and 65% in males and 65 and 77% in females, respectively.
CONCLUSION: The BMI values can similarly predict the presence of multiple metabolic risk factors just as the waist circumference in Japanese middle-aged subjects.

Entities:  

Mesh:

Year:  2010        PMID: 20519812     DOI: 10.2169/internalmedicine.49.3006

Source DB:  PubMed          Journal:  Intern Med        ISSN: 0918-2918            Impact factor:   1.271


  13 in total

1.  Evaluation of the National Kidney Foundation of Hawai'i's Kidney Early Detection Screening program.

Authors:  Merle R Kataoka-Yahiro; Kamomilani Anduha Wong; Jill Tamashiro; Victoria Page; Julaine Ching; Dongmei Li
Journal:  Hawaii J Med Public Health       Date:  2012-07

2.  BMI, waist circumference, and clustering of cardiovascular risk factors in Japanese adults.

Authors:  Machi Suka; Yuichi Miwa; Yoshiki Ono; Hiroyuki Yanagisawa
Journal:  Environ Health Prev Med       Date:  2010-08-11       Impact factor: 3.674

3.  Obesity phenotype and coronary heart disease risk as estimated by the Framingham risk score.

Authors:  Yong Soon Park; Jun-Su Kim
Journal:  J Korean Med Sci       Date:  2012-02-23       Impact factor: 2.153

4.  Anthropometric measurements of general and central obesity and the prediction of cardiovascular disease risk in women: a cross-sectional study.

Authors:  Louise G H Goh; Satvinder S Dhaliwal; Timothy A Welborn; Andy H Lee; Phillip R Della
Journal:  BMJ Open       Date:  2014-02-06       Impact factor: 2.692

5.  Relationship between sarcopenic obesity and cardiovascular disease risk as estimated by the Framingham risk score.

Authors:  Jeong-Hyeon Kim; Jung Jin Cho; Yong Soon Park
Journal:  J Korean Med Sci       Date:  2015-02-16       Impact factor: 2.153

6.  Correlation of adiposity indices with cardiovascular disease risk factors in healthy adults of Singapore: a cross-sectional study.

Authors:  Xinyan Bi; Siew Ling Tey; Claudia Leong; Rina Quek; Yi Ting Loo; Christiani Jeyakumar Henry
Journal:  BMC Obes       Date:  2016-07-07

7.  Association between obesity indices and type 2 diabetes mellitus among middle-aged and elderly people in Jinan, China: a cross-sectional study.

Authors:  Shukang Wang; Wei Ma; Zhongshang Yuan; Shu-Mei Wang; Xiangren Yi; Hongying Jia; Fuzhong Xue
Journal:  BMJ Open       Date:  2016-11-03       Impact factor: 2.692

8.  Including selective metabolic components in current diagnostic criteria does not improve discriminative validity for metabolic syndrome: a risk score approach.

Authors:  Huan-Cheng Chang; Sheng-Pyng Chen; Hao-Jan Yang
Journal:  J Int Med Res       Date:  2019-01-24       Impact factor: 1.573

9.  High level of fatty liver index predicts new onset of diabetes mellitus during a 10-year period in healthy subjects.

Authors:  Yukimura Higashiura; Masato Furuhashi; Marenao Tanaka; Satoko Takahashi; Masayuki Koyama; Hirofumi Ohnishi; Keita Numata; Takashi Hisasue; Nagisa Hanawa; Norihito Moniwa; Kazufumi Tsuchihashi; Tetsuji Miura
Journal:  Sci Rep       Date:  2021-06-18       Impact factor: 4.379

10.  Discriminatory Capacity of Anthropometric Indices for Cardiovascular Disease in Adults: A Systematic Review and Meta-Analysis.

Authors:  Mitra Darbandi; Yahya Pasdar; Shima Moradi; Hamid Jan Jan Mohamed; Behrooz Hamzeh; Yahya Salimi
Journal:  Prev Chronic Dis       Date:  2020-10-22       Impact factor: 2.830

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.