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Comparison of Two Nuclear Magnetic Resonance Spectroscopy Methods for the Measurement of Lipoprotein Particle Concentrations.

Martin RiefReinhard RaggamPeter RiefPhilipp MetnitzTatjana StojakovicMarkus ReinthalerMarianne BrodmannWinfried MärzHubert ScharnaglGünther Silbernagel
Published in: Biomedicines (2022)
Background: Measuring lipoprotein particle concentrations may help to improve cardiovascular risk stratification. Both the lipofit (Numares) and lipoprofile (LabCorp) NMR methods are widely used for the quantification of lipoprotein particle concentrations. Objective: The aim of the present work was to perform a method comparison between the lipofit and lipoprofile NMR methods. In addition, there was the objective to compare lipofit and lipoprofile measurements of standard lipids with clinical chemistry-based results. Methods: Total, LDL, and HDL cholesterol and triglycerides were measured with ß-quantification in serum samples from 150 individuals. NMR measurements of standard lipids and lipoprotein particle concentrations were performed by Numares and LabCorp. Results: For both NMR methods, differences of mean concentrations compared to ß-quantification-derived measurements were ≤5.5% for all standard lipids. There was a strong correlation between ß-quantification- and NMR-derived measurements of total and LDL cholesterol and triglycerides (all r > 0.93). For both, the lipofit (r = 0.81) and lipoprofile (r = 0.84) methods, correlation coefficients were lower for HDL cholesterol. There was a reasonable correlation between LDL and HDL lipoprotein particle concentrations measured with both NMR methods (both r > 0.9). However, mean concentrations of major and subclass lipoprotein particle concentrations were not as strong. Conclusions: The present analysis suggests that reliable measurement of standard lipids is possible with these two NMR methods. Harmonization efforts would be needed for better comparability of particle concentration data.
Keyphrases
  • low density lipoprotein
  • magnetic resonance
  • high resolution
  • solid state
  • machine learning