Comparison of calculated versus directly-measured low-density lipoprotein-cholesterol: An evaluation of ten formulas for an HIV-positive population in Sub-Saharan Africa.

ABSTRACT

BACKGROUND: Low-density lipoprotein cholesterol (LDLC) is a modifiable risk factor for atherosclerotic cardiovascular disease, therefore needs to be assessed and monitored. Direct homogeneous assays and various formulas exist to determine LDLC. We aimed to compare the directly measured LDL (dLDLC) with ten formulas for estimating LDLC. MATERIALS AND METHODS: This was a 2-year retrospective study of fasting lipid profile results obtained from HIV-positive patients attending an outpatient clinic at the University of Nigeria Teaching Hospital, Enugu, Nigeria, using homogeneous direct assays. Estimated LDLC was determined using ten formulas. Pearson's correlation, Bland-Altman plots, and linear regression were performed. Statistical significance was P < 0.05. RESULTS: Three thousand four hundred and eighty-two lipid results with mean ± standard deviation (SD) dLDLC of 2.1 ± 1.1 mmol/L were included in this study. There was a strong, positive correlation between Friedewald's LDLC and dLDLC n = 3412, r = 0.84, P < 0.001, but linear regression demonstrated a proportional bias P = 0.005. Ahmadi's equation showed the worst correlation n = 3482, r = 0.35, P < 0.001, but when applied to samples with triglyceride (TG) <1.13 mmol/L (100 mg/dl), the correlation showed a strong, positive relationship n = 1395, r = 0.80, P < 0.001, and no proportional bias P = 0.86. Teerankanchana's equation was the only formula that showed no difference between its LDLC and dLDLC (n = 3482, P = 0.056). It also demonstrated strong, positive correlation (n = 3482, r = 0.84, P < 0.001) and had a mean difference ± SD of -0.68 ± 0.63. CONCLUSION: Teerankanchana's formula showed good correlation and minimal bias with dLDLC at all TG levels. Moreover, linear regression showed no difference in the two. It seems to be the most suitable formula for estimating LDLC in our HIV-positive population.