Human Blood Lipoprotein Predictions from H-1 NMR Spectra: Protocol, Model Performances, and Cage of Covariance

Lipoprotein subfractions are biomarkers for the early diagnosis of cardiovascular diseases. The reference method, ultracentrifugation, for measuring lipoproteins is time-consuming, and there is a need to develop a rapid method for cohort screenings. This study presents partial least-squares regression models developed using H-1 nuclear magnetic resonance (NMR) spectra and concentrations of lipoproteins as measured by ultracentrifugation on 316 healthy Danes. This study explores, for the first time, different regions of the H-1 NMR spectrum representing signals of molecules in lipoprotein particles and different lipid species to develop parsimonious, reliable, and optimal prediction models. A total of 65 lipoprotein main and subfractions were predictable with high accuracy, Q(2) of >0.6, using an optimal spectral region (1.4-0.6 ppm) containing methylene and methyl signals from lipids. The models were subsequently tested on an independent cohort of 290 healthy Swedes with predicted and reference values matching by up to 85-95%. In addition, an open software tool was developed to predict lipoproteins concentrations in human blood from standardized H-1 NMR spectral recordings.