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KMID : 0895820140240040155
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Journal of Oriental Rehabilitation Medicine
2014 Volume.24 No. 4 p.155 ~ p.162
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Predicting the Concentration of Obesity-related Metabolites via Heart Rate Variability for Korean Premenopausal Obese Women: Multiple Regression Analysis
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Kim Jong-Yeon
Yang Yo-Chan
Yi Woon-Sup
Kim Je-In
Maeng Tae-Ho
Yoo Duk-Joo
Shim Jae-Woo
Cho Woo-Young
Song Mi-Yeon
Lee Jong-Soo
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Abstract
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Objectives : Advanced researches on the relationship between obesity and heart rate var-iability (HRV), heretofore, focused on characteristics of HRV depending on the state of obesity. However, the previous researches have not quantified predictive power of HRV to-ward the obesity-related variables, which is rather more meaningful for clinicians who regu-larly 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 choles-terol) as outcome variables and the others as predictors. To select appropriate predictive variables, the Akaike``s Information Criterion (AIC) was applied. Normality and homo-skedasticity 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 val-ues 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 pre-dict 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 val-ue 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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KEYWORD
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Obesity, Heart rate variability, Metabolite, Multiple regression, Premeno-pausal women
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