Hornseth, Jean M

Abstract Background Insulin resistance leads to changes in lipid and lipoprotein metabolism prior to developing prediabetes, including an increase in the concentration of large very-low-density lipoprotein particles (LVLDL-p), the mean size of VLDL (VLDL-s), and the concentration of small low-density lipoprotein particles (sLDL-p). Conversely, there is a decrease in the concentration of large high-density lipoprotein particles (LHDL-p), mean size of HDL (HDL-s), and mean size of LDL (LDL-s). Clinically validated studies show that proton nuclear magnetic resonance (NMR) can measure these six parameters and derive a lipoprotein insulin resistance (LP-IR) score, ranging from 0 to 100, with higher scores indicating greater risk for insulin resistance. The reference method for calculating LP-IR relies on a proprietary NMR spectrometer, spectral deconvolution method, and algorithm. This study seeks to determine if lipoprotein subfraction parameters measured from a commercially available NMR system can produce LP-IR scores comparable to the reference method. Methods Serum lipoprotein NMR spectra were collected using a Bruker Ascend 600 MHz magnet, Bruker Avance NEO 500 spectrometer, and the Numares AXINON lipoFIT System to report the six LP-IR score parameters between January 2023 and January 2025 (Cohort A, n = 2,474). During the same period, LP-IR score and parameters were collected using the Vantera Clinical Analyzer (400 MHz) with the LipoProfile LP4 method (Cohort B, n = 2,959). Linear regression of decile-matched data for each parameter yielded adjustment equations to align lipoFIT values with the LipoProfile method. LP-IR scores were then calculated for both adjusted and unadjusted values using a previously published LP-IR scoring algorithm. Finally, LP-IR scores were compared for sera with measures from both instruments was (Cohort C, n = 108). Results The median concentrations of sLDL-p, LDL-s, VLDL-s, HDL-S matched between Cohort A and B (<1% difference). Significant differences were noted for LVLDL-p and LHDL-p, with percent differences of 21.5% and 23.5%, respectively. Despite closely matching medians, the distributions varied greatly, with a mean interquartile range (IQR) percent difference across measures of 31.2% (Table 1). After distribution matching, all parameter median and mean IQR percent differences fell to <5% and <10%, respectively, between Cohort A and Cohort B. In Cohort C the LP-IR score matched well (y=1.1x-15, r=0.92; 95%CI 0.88, 0.94). Following distribution matching adjustment of lipoFIT values for Cohort C, new calculated LP-IR scores matched with reduced bias (y=0.94x-1.2) while correlation was slightly reduced (r=0.88 [0.83, 0.92]). By comparison, adjustments made via scaling lipoFIT values proportionally by the ratio of the medians between Cohort A and Cohort B yielded calculated LP-IR scores in Cohort C that maintained a similar bias prior to adjustments (y=1.2x-21), highlighting the ability of distribution matching to reduce bias. Conclusion Adjustments to measured lipoprotein parameters from a commercially available NMR spectrometer can be achieved via distribution matching. These adjustments greatly reduce the bias between a commercially available method and algorithm compared to the reference method, allowing for wider adoption of LP-IR score reporting.

Abstract Background Low-density lipoprotein cholesterol (LDL-C) is the primary biomarker for assessing risk for atherosclerotic cardiovascular disease (ASCVD). However, multiple large studies have demonstrated greater accuracy using Apolipoprotein B (apoB). One obstacle to its widespread use is the lack of guideline endorsed apoB treatment targets. A recently published solution has proposed using population percentiles to calculate LDL-C using apoB values (PMID: 36366949). The LDL-C concentration correlated to an individual’s ApoB percentile may more appropriately reflect ASCVD risk compared to lipid panel LDL-C. Here we evaluated this approach using a cohort where LDL-C was measured with the gold-standard beta-quantification method (LDL-CBQ). Method Results from clinically ordered apoB, LDL-CBQ, and lipid panel were included (n = 18 852). Samples with triglycerides >1000 mg/dL or LpX were excluded. Total cholesterol, high-density lipoprotein cholesterol (HDL-C), triglycerides (TG), and apoB were measured using Roche Cobas c501 analyzers. LDL-CBQ was performed using Beckman LE-80K ultracentrifuge and dextran sulfate-Mg/Cl precipitation. ApoB equivalent LDL-C (eqLDL-CapoB) was calculated according to linear regression of LDL-C percentiles against apoB percentiles described by the equation eqLDL-CapoB = 1.45(apoB)-25. Concordance was determined based on clinical thresholds for LDL-CBQ and eqLDL-CapoB. Results Median (IQR) apoB and LDL-CBQ was 98 (79−119) mg/dL and 120 (94−150) mg/dL, respectively. LDL-CBQ and eqLDL-CapoB were concordant in 60% of samples overall. Discordance between eqLDL-CapoB and LDL-CBQ shifted from primarily overestimating to underestimating as LDL-CBQ increased. Discordant eqLDL-CapoB was directly associated with TG and inversely associated with HDL-C. Conclusion The eqLDL-CapoB calculation is a useful tool to present apoB results in a more familiar context of LDL-C-equivalent units. The concordance findings largely confirm previously published results using eqLDL-CapoB. Furthermore, the pattern of HDL-C and TG suggests that a discordantly elevated eqLDL-CapoB is an indicator of a higher risk lipid profile. More investigation is needed to determine if eqLDL-CapoB is appropriate for widespread adoption.