[논문]Comparison of Waist-to-height Ratio (WHtR), Body Mass Index (BMI) and Waist Circumference (WC) as a Screening Tool for Prediction of Metabolic-related Diseases

Oh, Hyun Sook

doi:10.13160/ricns.2015.8.4.305

Comparison of Waist-to-height Ratio (WHtR), Body Mass Index (BMI) and Waist Circumference (WC) as a Screening Tool for Prediction of Metabolic-related Diseases 원문보기

Journal of the Chosun Natural Science = 조선자연과학논문집, v.8 no.4, 2015년, pp.305 - 312

Oh, Hyun Sook (Department of Applied Statistics, Gachon University)

Abstract ▼ AI-Helper

The present study showed WHtR to be significantly better than BMI and WC for prediction of metabolic-related diseases in the middle-aged and older people in Korea, based on Bayesian ordered probit model analysis. The variations of WC, BMI and WHtR were compared according to the number of metabolic-related diseases such as hypertension, dyslipidemia, stroke, myocardial infarction, angina pectoris and diabetes. It was found that the three measures showed the similar variation except a very few extreme cases for age less than 40. For subjects over the age of 40, WC was not significant and WHtR gave more influence in greater variability than BMI on the number of metabolic diseases. Also, the rate of change for WHtR was higher than for BMI as the number of metabolic-related diseases increased. Specifically, the difference of the marginal effect of WHtR between no disease and only one disease was 1.81 times higher than that of BMI. Moreover, it was pointed out that the threshold value of WHtR for obesity should be considered differently by age.

주제어

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문제 정의

However, BMI is still being used primarily in Korea while WHtR is rarely used for the prediction of metabolic-related diseases. The purpose of the present paper is to show that WHtR is better than BMI and WC by using the big data from Korean National Health and Nutrition Examination Survey (KNHANES) conducted in 2013 based on a model-based statistical analysis. The KNHANES has been conducted periodically since 1998 to assess the health and nutritional status of the civilian noninstitutionalized population of the Korea^[2].

제안 방법

The data contains information on whether each subject received a diagnosis from a doctor for metabolic-related diseases such as hypertension, dyslipidemia, stroke, myocardial infarction, angina pectoris, diabetes. Score one or zero was given depending on whether one has received a diagnosis or not from a doctor for each disease.

대상 데이터

There were total of 6113 respondents in the survey. Excluding data with one or more missing values, we used the remaining 4894 subjects for our analysis.
This study is based on data for adults (age 19 older) from the First Korean National Health and Nutrition Examination Survey (KNHANES) in 2013. There were total of 6113 respondents in the survey. Excluding data with one or more missing values, we used the remaining 4894 subjects for our analysis.
This study is based on data for adults (age 19 older) from the First Korean National Health and Nutrition Examination Survey (KNHANES) in 2013. There were total of 6113 respondents in the survey.

이론/모형

In addition to BMI, WC and WHtR, we also consider age, gender and metabolic risk factors such as systolic blood pressure (SBP), diastolic blood pressure (DBP), triglyceride (TG), high density lipoproteins (HDL), fasting plasma glucose (GLU) as a highly correlated variables with the number of metabolic-related diseases. Bayesian ordered probit model was used for the analysis.
For comparing the influence for WC, BMI and WHtR on metabolic-related diseases, Bayesian ordered probit model was applied. Bayesian ordered probit model is described as follows^[12].
It was found that the three measures showed the similar variation except a very few extreme cases for age less than 40. So the analysis was focused on subjects with older than 40 years old by applying Bayesian ordered probit model.

성능/효과

All marginal effects for age, SBP, BMI, WHtR, GLU and TG were significant for each probability P(Y=j),j= 0,1,2,3 and HDL showed significant marginal effect for P(Y=3) only.

참고문헌 (13)

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상세보기
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상세보기
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상세보기
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상세보기
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상세보기
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상세보기
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상세보기
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상세보기
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상세보기
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상세보기
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원문보기 상세보기
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