A comparative analysis of different methods for LDL cholesterol calculation

Abstract

Abstract Objectives Accurate estimation of low-density lipoprotein cholesterol (LDL-C) is essential for cardiovascular risk management. This study evaluated the performance of the Friedewald, Martin–Hopkins (MH), and National Institutes of Health Equation 2 (NIH2) methods compared with direct enzymatic measurement across different lipid profiles. Methods Fasting blood samples from 618 participants were analyzed for triglycerides (TG), total cholesterol, HDL-C, and LDL-C on the Roche Cobas c701 platform. LDL-C was calculated using the Friedewald, MH, and NIH2 methods. Participants were stratified by LDL-C clinical decision thresholds and TG levels (<400 and ≥400 mg/dL). Method performance was assessed using Passing–Bablok regression, Lin’s concordance correlation coefficient (CCC), Bland–Altman analysis, misclassification rates. Results All calculation methods underestimated LDL-C compared with direct measurement, with the smallest bias observed for the MH method. Across LDL-C thresholds, the MH method showed the closest agreement, particularly at low LDL-C levels, whereas Friedewald and NIH2 consistently yielded lower values (p<0.05). In the TG≥400 mg/dL subgroup, agreement declined for all methods, most notably for Friedewald and NIH2 (CCC=0.824 and 0.878), while MH maintained strong concordance (CCC=0.985). Misclassification analysis showed lower error rates for MH across TG subgroups. Conclusions The MH method provides more consistent and reliable LDL-C estimation across a wide range of lipid profiles, particularly at low LDL-C levels and in the presence of hypertriglyceridemia. Compared with the Friedewald and NIH2 equations, it demonstrates lower systematic bias, improved agreement with direct enzymatic measurement, and reduced misclassification, supporting its potential to enhance clinical decision-making.