심박변이도를 통한 폐경 전 한국인 비만 여성의 비만 관련 대사체 농도 예측을 위한 회귀분석 (Predicting the Concentration of Obesity-related Metabolites via Heart Rate Variability for Korean Premenopausal Obese Women: Multiple Regression Analysis)

서지정보

· 발행기관 : 한방재활의학과학회
· 수록지 정보 : 한방재활의학과학회지 / 24권 / 4호 / 155 ~ 162페이지
· 저자명 : 김종연, 양요찬, 이운섭, 김제인, 맹태호, 유덕주, 심재우, 조우영, 송미연, 이종수

초록

Objectives Advanced researches on the relationship between obesity and heart rate variability (HRV), heretofore, focused on characteristics of HRV depending on the state of obesity. However, the previous researches have not quantified predictive power of HRV toward the obesity-related variables, which is rather more meaningful for clinicians who regularly treat obese patients. Hence, we designed a research to investigate whether HRV could predict serum levels of obesity-related metabolites.
Methods Ninety obese premenopausal women meeting the inclusion criteria were recruited. The HRV test, blood sampling, and measurement of physical traits were conducted. Multiple regression analysis of the measurement data was carried out, putting obesity-related metabolites (insulin, glucose, triglyceride, hs-CRP, HDL, LDL, total cholesterol) as outcome variables and the others as predictors. To select appropriate predictive variables, the Akaike's Information Criterion (AIC) was applied. Normality and homoskedasticity of residuals for each model were tested to identify if there were any violations of the regression analysis's basic assumption. Logarithm transformation was used for the values of the concentration of metabolites and the HRV.
Results The regression model including Total Power (TP) value and BMI had significant predictive power for serum insulin concentration (F(2, 88)=835.7, p＜0.001, R2=0.95). The regression coefficient of ln (TP) was -0.1002. However, it was not sure if the HRV could predict concentrations of other metabolites.
Conclusions The results suggest that the Total Power (TP) value of the HRV can predict the level of serum insulin. If the BMI could be assumed as being constant, when the TP value is multiplied by n, the predicted change of insulin could be drawn by multiplying n−0.1002.
The uncertainty of this model can be assumed as approximately 5%.

영어초록

Objectives Advanced researches on the relationship between obesity and heart rate variability (HRV), heretofore, focused on characteristics of HRV depending on the state of obesity. However, the previous researches have not quantified predictive power of HRV toward the obesity-related variables, which is rather more meaningful for clinicians who regularly treat obese patients. Hence, we designed a research to investigate whether HRV could predict serum levels of obesity-related metabolites.
Methods Ninety obese premenopausal women meeting the inclusion criteria were recruited. The HRV test, blood sampling, and measurement of physical traits were conducted. Multiple regression analysis of the measurement data was carried out, putting obesity-related metabolites (insulin, glucose, triglyceride, hs-CRP, HDL, LDL, total cholesterol) as outcome variables and the others as predictors. To select appropriate predictive variables, the Akaike's Information Criterion (AIC) was applied. Normality and homoskedasticity of residuals for each model were tested to identify if there were any violations of the regression analysis's basic assumption. Logarithm transformation was used for the values of the concentration of metabolites and the HRV.
Results The regression model including Total Power (TP) value and BMI had significant predictive power for serum insulin concentration (F(2, 88)=835.7, p＜0.001, R2=0.95). The regression coefficient of ln (TP) was -0.1002. However, it was not sure if the HRV could predict concentrations of other metabolites.
Conclusions The results suggest that the Total Power (TP) value of the HRV can predict the level of serum insulin. If the BMI could be assumed as being constant, when the TP value is multiplied by n, the predicted change of insulin could be drawn by multiplying n−0.1002.
The uncertainty of this model can be assumed as approximately 5%.

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