Quantitative Lipoprotein Subclass Analysis in Pig Plasma by 1H NMR Spectroscopy and Stability Assessment

This study explores, for the first time, the accurate prediction of lipoprotein subclasses in pig plasma by a partial least-squares regression (PLSR) model based on the optimization of the H-1 NMR detection method. The H-1 NMR-based detection method of plasma metabolites first was optimized and evaluated. The coefficients of variation for intraday and interday detection were less than 5%, and there were no obvious metabolic differences among repeated tests of plasma samples. The variability of plasma metabolite detection based on H-1 NMR revealed the consistent and stable performance of NMR methods, and the prediction models for a total of 116 subclasses in four lipoprotein classes including plasma very-low-density lipoprotein, low-density lipoprotein, intermediate-density lipoprotein, and high-density lipoprotein were established combining H-1 NMR spectra and PLSR models using the data from the ultracentrifugation method. The cross-validation results showed that the PLSR prediction models for 107 lipoproteins' main and subfractions performed excellently (R-2 > 0.5), which met the method requirements. The PLSR models for the remaining nine lipoprotein major components and subcomponents performed well (0.2 < R-2 < 0.5), which basically met the method requirements. According to the PLSR models based on H-1 NMR, the concentrations of lipoprotein subclasses were predicted, including APO A1 (277.84-731.4 mu g/mL), APO B (11.97-431.5 mu g/mL), PL (142.36-1100.76 ng/mL), TG (70.21-915.35 mu mol/L), CH (1313.56-6761.79 mu mol/L), FC (6.37-93.06 mu mol/L), and CE (1319.93-6854.85 mu mol/L). Therefore, the H-1 NMR-based method for the detection of lipoprotein subclasses in pig plasma was successfully established and could provide the methodological basis for the research on molecular mechanism, function, and application of lipoprotein subclasses.