Determining a New Formula for Calculating Low-Density Lipoprotein Cholesterol: Data Mining Approach

Abstract

Low-density lipoprotein cholesterol (LDL-C) is a risk factor of coronary heart diseases. The estimation of LDL-C (LDL-Cal) level was performed using Friedewald's equation for triglyceride (TG) level less than 400 mg/dL. Therefore, the aim of this study is to generate a new formula for LDL-Cal and validate the correlation coefficient between LDL-Cal and LDL-C directly measured (LDL-Direct). A data set of 1786 individuals receiving annual medical check-ups from the Faculty of Medical Technology, Mahidol University, Thailand in 2008 was used in this study. Lipid profiles including total cholesterol (TC), TG, high-density lipoprotein cholesterol (HDL-C) and LDL-C were determined using Roche/Hitachi modular system analyzer. The estimated LDL-C was obtained using Friedewald's equation and the homogenous enzymatic method. The level of TG was divided into 6 groups (TG<200, <300, <400, <500, <600 and < 1000 mg/dL) for constructing the LDL-Cal formula. The pace regression model was used to construct the candidate formula for the LDL-Cal and determine the correlation coefficient (r) with the LDL-Direct. The candidate LDL-Cal formula was generated for 6 groups of TG levels that displayed well correlation between LDL-Cal and LDL-Direct. Interestingly, The TG level was less than 1000 mg/dL, the regression model was able to generate the equation as shown as strong r of 0.9769 with LDL-Direct. Furthermore, external data set (n = 666) with TG measurement (36-1480 mg/dL) was used to validate new formula which displayed high r of 0.971 between LDL-Cal and LDL-direct. This study explored a new formula for LDL-Cal which exhibited higher r of 0.9769 and far beyond the limitation of TG more than 1000 mg/dL and potential used for estimating LDL-C in routine clinical laboratories.