Verification of Low-Density Lipoprotein Cholesterol Levels Measured by Anion-Exchange High-Performance Liquid Chromatography in Comparison with Beta Quantification Reference Measurement Procedure

Evaluation of low-density lipoprotein cholesterol equations by cross-platform assessment of accuracy-based EQA data against SI-traceable reference value

OBJECTIVES

Low-density lipoprotein cholesterol (LDLC) is the primary cholesterol target for the diagnosis and treatment of cardiovascular disease (CVD). Although beta-quantitation (BQ) is the gold standard to determine LDLC levels accurately, many clinical laboratories apply the Friedewald equation to calculate LDLC. As LDLC is an important risk factor for CVD, we evaluated the accuracy of Friedewald and alternative equations (Martin/Hopkins and Sampson) for LDLC.

METHODS

We calculated LDLC based on three equations (Friedewald, Martin/Hopkins and Sampson) using the total cholesterol (TC), triglycerides (TG), and high-density lipoprotein cholesterol (HDLC) in commutable serum samples measured by clinical laboratories participating in the Health Sciences Authority (HSA) external quality assessment (EQA) programme over a 5 years period (number of datasets, n=345). LDLC calculated from the equations were comparatively evaluated against the reference values, determined from BQ-isotope dilution mass spectrometry (IDMS) with traceability to the International System of Units (SI).

RESULTS

Among the three equations, Martin/Hopkins equation derived LDLC had the best linearity against direct measured (y=1.141x - 14.403; R2=0.8626) and traceable LDLC (y=1.1692x - 22.137; R2=0.9638). Martin/Hopkins equation (R2=0.9638) had the strongest R2 in association with traceable LDLC compared with the Friedewald (R2=0.9262) and Sampson (R2=0.9447) equation. The discordance with traceable LDLC was the lowest in Martin/Hopkins (median=-0.725%, IQR=6.914%) as compared to Friedewald (median=-4.094%, IQR=10.305%) and Sampson equation (median=-1.389%, IQR=9.972%). Martin/Hopkins was found to result in the lowest number of misclassifications, whereas Friedewald had the most numbers of misclassification. Samples with high TG, low HDLC and high LDLC had no misclassification by Martin/Hopkins equation, but Friedewald equation resulted in ∼50% misclassification in these samples.

CONCLUSIONS

The Martin/Hopkins equation was found to achieve better agreement with the LDLC reference values as compared to Friedewald and Sampson equations, especially in samples with high TG and low HDLC. Martin/Hopkins derived LDLC also enabled a more accurate classification of LDLC levels.

Label-free and selective cholesterol detection based on multilayer functional structure coated fiber fabry-perot interferometer probe

A reflective fiber-optic Fabry-Perot cavity probe sensor is proposed to selectively measure cholesterol concentration by insert single mode fiber into ceramic tube and immobilize epoxy resin (ER)/graphene oxide (GO)/beta-cyclodextrin (β-CD) multi-layer film onto end face of ceramic tube. EDC/NHS activated GO is selected to form chemical binding with β-CD, and β-CD is the sensitive materials to bind with cholesterol molecules. With multi-layer film assisted, the sensitivity of sensor to cholesterol concentration can reach 3.92 nm/mM and the limit of detection reaches 3.48 μ M. In addition, 4 mM hemoglobin, glucose and ascorbic acid are doped into a set cholesterol sample and verified the highly selectivity of sensing cholesterol. Furthermore, the reproducibility was proved by measure the spectrum of four sensors with same fabrication process, and the reusability was also proved by repeated measurements. Overall, the sensor features with high mechanical strength, ease of fabrication, real-time monitoring, low cost and ease for measurement that given by probe structure. Therefore, the sensor provides a remarkable analytical platform for biosensing applications.

Distinct Differences in Lipoprotein Particle Number Evaluation between GP-HPLC and NMR: Analysis in Dyslipidemic Patients Administered a Selective PPARα Modulator, Pemafibrate

Aim: We established a method to evaluate the lipid concentrations, size and particle numbers (PNs) of lipoprotein subclasses by gel permeation chromatography (GP-HPLC). Nuclear magnetic resonance (NMR) is widely used to analyze these parameters of lipoprotein subclasses, but differences of the two methods are unknown. Current study compared the PNs of each lipoprotein subclass measured by GP-HPLC and NMR, and assessed the effect of a selective PPARα modulator, pemafibrate.

Methods: Lipoprotein profiles of 212 patients with dyslipidemia who participated in the phase 2 clinical trial of a selective PPARα modulator, pemafibrate, were analyzed by two methods, GP-HPLC and NMR, which were performed with LipoSEARCH (Skylight Biotech) and LipoProfile 3 (LabCorp), respectively. GP-HPLC evaluated the PNs of 18 subclasses, consisting of CM, VLDL1-5, LDL1-6, and HDL1-6. NMR evaluated the PNs of 9 subclasses, consisting of large VLDL & CM, medium VLDL, small VLDL, IDL, large LDL, small LDL, large HDL, medium HDL and small HDL.

Results: Three major classes, total CM&VLDL, total LDL and total HDL were obtained by grouping of corresponding subclasses in both methods and PNs of these classes analyzed by GP-HPLC were correlated positively with those by NMR. The correlation coefficients in total CM&VLDL, total LDL and total HDL between GP-HPLC and NMR was 0.658, 0.863 and 0.798 (all p ＜0.0001), respectively. The PNs of total CM&VLDL, total LDL and total HDL analyzed by GP-HPLC was 249.5±51.7nM, 1,679±359 nM and 13,273±1,564 nM, respectively, while those by NMR was 124.6±41.8 nM, 1,514±386 nM and 31,161±4,839 nM, respectively. A marked difference in the PNs between the two methods was demonstrated especially in total HDL.

The number of apolipoprotein (Apo) B molecule per one ApoB-containing lipoprotein particle, total CM&VLDL plus total LDL, was 1.10±0.05 by GP-HPLC, while 1.32±0.18 by NMR. The number of ApoA-I per one HDL particle was 3.40±0.17 by GP-HPLC, but only 1.46±0.15 by NMR, much less than reported previously.

From the phase 2 clinical trial, randomizing 212 patients to pemafibrate 0.025-0.2 mg BID, fenofibrate 100 mg QD, or placebo groups, pemafibrate reduced the PNs of CM, large VLDL1-VLDL3 and medium VLDL4, but not small VLDL5 by GP-HPLC. It significantly decreased the PNs of smaller LDL and larger HDL particles, but increased those of larger LDL and smaller HDL particles. In contrast, NMR showed marked variations in the effect of pemafibrate on lipoprotein PNs, and no significant size-dependent changes.

Conclusions: GP-HPLC evaluates the lipoprotein PNs more accurately than NMR and can be used for assessing the effects of lipid-lowering drugs on lipoprotein subclasses.