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. 2024 Sep;13(3):348-357.
doi: 10.12997/jla.2024.13.3.348. Epub 2024 Jul 15.

Comparison of Multiple Equations for Low-Density Lipoprotein Cholesterol Calculation Against the Direct Homogeneous Method

Rawaa E K Alsadig  1   2 , Adel N Morsi  1
Affiliations

Comparison of Multiple Equations for Low-Density Lipoprotein Cholesterol Calculation Against the Direct Homogeneous Method

Rawaa E K Alsadig et al. J Lipid Atheroscler. 2024 Sep.

Erratum in

  • Corrigendum to Previously Published Articles.
    Editorial Office of the Journal of Lipid and Atherosclerosis. Editorial Office of the Journal of Lipid and Atherosclerosis. J Lipid Atheroscler. 2025 Jan;14(1):132-133. doi: 10.12997/jla.2025.14.1.132. Epub 2025 Jan 14. J Lipid Atheroscler. 2025. PMID: 39911960 Free PMC article.

Abstract

Objective: Several equations have been proposed as alternatives for the reference method of measuring low-density lipoprotein cholesterol (LDL-C). This study aimed to evaluate these alternatives in comparison to the homogeneous method and validate their clinical utility.

Methods: Data on the lipid profiles of 1,006 Sudanese individuals were analyzed. The paired t-test was used to compare the results of direct and calculated LDL-C. Bland-Altman plots were used to demonstrate the differences between the measured and calculated LDL-C against the mean values. Linear regression was conducted, using the correlation coefficient (r) to quantify the relationship between methods. The bias between measured and calculated LDL-C was compared to the National Cholesterol Education Program Laboratory Standardization Panel criteria (i.e., accuracy within ±4% of expected values).

Results: The Martin and Anandaraja equations showed no significant difference compared to directly measured LDL-C (p>0.05). The DeLong equation indicated an insignificant difference only with a 99% confidence interval (p>0.01). The Martin, DeLong, and Teerakanchana equations exhibited the smallest limits of agreement, with data points concentrated closely around the mean difference line. Linear regression analysis revealed strong positive correlations (r>0.8) for most equations, except for the Ahmadi equation. The DeLong, Rao, and Martin equations demonstrated superior performance for LDL cutoff points (bias within ± 4%). The DeLong formula also showed superior performance at different lipid levels, closely followed by the Martin equation (bias within ±4%).

Conclusion: The DeLong and Martin equations outperformed others, such as the widely used Friedewald equation, in calculating LDL-C. Further validation studies are needed.

Keywords: Coronary heart disease; Low density lipoprotein cholesterol; Methodological study.

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Conflict of interest statement

Conflict of Interest: The authors have no conflicts of interest to declare.

Figures

Fig. 1
Fig. 1. Bland Altman plotting for calculated LDL-C values against directly measured LDL-C values.
(A) Friedewald, (B) De Cordova, (C) Hattori, (D) Anandaraja, (E) Chen, (F) Teerakanchana, (G) Ahmadi, (H) DeLong, (I) Rao, and (J) Martin formulas. LDL-C, low-density lipoprotein cholesterol.
Fig. 2
Fig. 2. Linear regression plots for calculated LDL-C values using 10 equations against directly measured LDL-C values.
(A) Friedewald, (B) De Cordova, (C) Hattori, (D) Anandaraja, (E) Chen, (F) Teerakanchana, (G) Ahmadi, (H) DeLong, (I) Rao, and (J) Martin formulas. LDL-C, low-density lipoprotein cholesterol.
See this image and copyright information in PMC

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