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Combined effect of body mass index and body size perception on metabolic syndrome in South Korea: results of the fifth Korea National Health and Nutrition Examination Surveys (2010-2012)

Overview of attention for article published in BMC Public Health, June 2015
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Title
Combined effect of body mass index and body size perception on metabolic syndrome in South Korea: results of the fifth Korea National Health and Nutrition Examination Surveys (2010-2012)
Published in
BMC Public Health, June 2015
DOI 10.1186/s12889-015-1839-6
Pubmed ID
Authors

Sook Hee Yoon, Kyu-Tae Han, Sun Jung Kim, Tae Yong Sohn, Byungyool Jeon, Woorim Kim, Eun-Cheol Park

Abstract

Body mass index (BMI) has been used as an indirect predictor for the risk of metabolic syndrome. However, there are challenges in evaluating the risk of metabolic syndrome using BMI in certain parts of the world. Therefore, it is worth exploring additional factors that could supplement BMI to predict the risk of metabolic syndrome. In this study, we assessed the combined effect of BMI and perception for predicting metabolic syndrome. We used the fifth Korea National Health and Nutrition Examination Surveys (KNHANES V, 2010-12, N = 16,537) in this study. Multivariable logistic regression analysis was performed to examine the association while controlling for potential confounding variables. We also performed an analysis for the combined effect of BMI and perception of body size, and subgroup analysis by age group or moderate physical activity. Data from 16,537 participants were analyzed in this study (males: 6,978, females: 9,559). Among them, metabolic syndrome was diagnosed in 1,252 (17.9 %) males and 2,445 (25.6 %) females, respectively. The combination of BMI and body size perception had a positive relation with the presence of metabolic syndrome. People who perceived themselves to be overweight for their body size had a higher risk for metabolic syndrome even if they have the same BMI. Our findings suggest that the combination of body size perception and BMI is useful in predicting the risk of metabolic syndrome. The use of complementary predictors could reduce the risk for inaccurate prediction of metabolic syndrome.

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Mendeley readers

The data shown below were compiled from readership statistics for 27 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 27 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 3 11%
Student > Bachelor 3 11%
Student > Ph. D. Student 3 11%
Professor 2 7%
Student > Postgraduate 2 7%
Other 4 15%
Unknown 10 37%
Readers by discipline Count As %
Nursing and Health Professions 6 22%
Psychology 2 7%
Medicine and Dentistry 2 7%
Biochemistry, Genetics and Molecular Biology 2 7%
Social Sciences 2 7%
Other 2 7%
Unknown 11 41%