Determining a new formula for calculating low-density lipoprotein cholesterol: Data mining approach
© 2014, Leibniz Research Centre for Working Environment and Human Factors. All rights Reserved. Low-density lipoprotein cholesterol (LDL-C) is a risk factor of coronary heart diseases. The estimation of LDLC (LDL-Cal) level was performed using Friedewald’s equation for triglyceride (TG) level less t...
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th-mahidol.351852018-11-23T16:44:45Z Determining a new formula for calculating low-density lipoprotein cholesterol: Data mining approach Prabhop Dansethakul Lalin Thapanathamchai Sarawut Saichanma Apilak Worachartcheewan Phannee Pidetcha Mahidol University Huachiew Chalermprakiet University Agricultural and Biological Sciences Biochemistry, Genetics and Molecular Biology © 2014, Leibniz Research Centre for Working Environment and Human Factors. All rights Reserved. Low-density lipoprotein cholesterol (LDL-C) is a risk factor of coronary heart diseases. The estimation of LDLC (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. 2018-11-23T09:31:41Z 2018-11-23T09:31:41Z 2015-03-26 Article EXCLI Journal. Vol.14, (2015), 478-483 10.17179/excli2015-162 16112156 2-s2.0-84925869845 https://repository.li.mahidol.ac.th/handle/123456789/35185 Mahidol University SCOPUS https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84925869845&origin=inward |
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Agricultural and Biological Sciences Biochemistry, Genetics and Molecular Biology Prabhop Dansethakul Lalin Thapanathamchai Sarawut Saichanma Apilak Worachartcheewan Phannee Pidetcha Determining a new formula for calculating low-density lipoprotein cholesterol: Data mining approach |
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© 2014, Leibniz Research Centre for Working Environment and Human Factors. All rights Reserved. Low-density lipoprotein cholesterol (LDL-C) is a risk factor of coronary heart diseases. The estimation of LDLC (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. |
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Mahidol University |
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Mahidol University Prabhop Dansethakul Lalin Thapanathamchai Sarawut Saichanma Apilak Worachartcheewan Phannee Pidetcha |
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Article |
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Prabhop Dansethakul Lalin Thapanathamchai Sarawut Saichanma Apilak Worachartcheewan Phannee Pidetcha |
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Prabhop Dansethakul |
title |
Determining a new formula for calculating low-density lipoprotein cholesterol: Data mining approach |
title_short |
Determining a new formula for calculating low-density lipoprotein cholesterol: Data mining approach |
title_full |
Determining a new formula for calculating low-density lipoprotein cholesterol: Data mining approach |
title_fullStr |
Determining a new formula for calculating low-density lipoprotein cholesterol: Data mining approach |
title_full_unstemmed |
Determining a new formula for calculating low-density lipoprotein cholesterol: Data mining approach |
title_sort |
determining a new formula for calculating low-density lipoprotein cholesterol: data mining approach |
publishDate |
2018 |
url |
https://repository.li.mahidol.ac.th/handle/123456789/35185 |
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1763491434975985664 |