Human blood lipoprotein predictions from ¹H NMR spectra: protocol, model performances and cage of covariance

Abstract

Lipoprotein subfractions are biomarkers for 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. Here we present partial least squares regression models developed using 1H-NMR spectra and concentrations of lipoproteins as measured by ultracentrifugation on 316 healthy Danes. Different regions of the 1H-NMR spectra representing signals of the lipoproteins and different lipid species were investigated to develop parsimonious, reliable and best performing prediction models. 65 LP main and subfractions were predictable with an accuracy Q2 of > 0.6 on test set samples. The models were tested on an independent cohort of 290 healthy Swedes with predicted and reference values matching by up to 85-95%. The software was developed to predict lipoproteins in human blood using 1H-NMR spectra and made freely available to be applied for future cohort screenings.

Gut microbiome and its cofactors are linked to lipoprotein distribution profiles

“…Subjects are representatives of community dwelling and apparently healthy adults living in the Danish Capital Region. In this study, we included 1 H NMR spectroscopy based quantified lipoproteins from human blood plasma 6 , short-chain fatty acids profiling and GM composition on fecal samples based on 16S rRNA-gene amplicon sequencing and shotgun metagenome sequencing for a subset of samples (Figure 1C).…”

“…LPD profiles of the study subjects were predicted from 1 H NMR measurements of blood plasma. A total of 55 lipoproteins-subfractions were quantified including cholesterol, triglycerides (TG), cholesterol ester (CE), free cholesterol, phospholipids, apolipoprotein A (ApoA1) and apolipoprotein B (ApoB) content in all or in some of lipoprotein in plasma (VLDL, IDL, HDL, LDL) and/or in lipoprotein subfractions (HDL2a, HDL2b, HDL3, LDL1, LDL2, LDL3, LDL4, LDL5, LDL6) 6 . Linking host covariates and LPD profiles, redundancy analysis (RDA) of LPD profiles showed a significant ( p ≤ 0.01) effect of age, BMI and sex on LPD profiles (Figure 2B) with a combined size effect of up to 24.6% (Figure 2B-C).…”

“…The human blood plasma lipoproteins were quantified using SigMa LP software 17 . The SigMa LP quantifies lipoproteins from blood plasma or serum using optimized partial least squares (PLS) regression models developed for each lipoprotein variable using one-dimensional (1D) 1 H NMR spectra of blood plasma or serum and ultracentrifugation based quantified lipoproteins as response variables as determined in Khakimov et al 6 .…”

“…Recent advances in metabolomics research have allowed large-scale and high-throughput profiling of lipoprotein distribution’s (LPD) in human blood plasma based upon their composition and concentration 4–6 . It has been hypothesized that numerous medical conditions such as glucose intolerance, type-2 diabetes, myocardial infarction, ischemic stroke and intracerebral hemorrhage, might be associated with lower blood levels of larger HDL particles (e.g.…”

“…However, the relationship between GM and LPD distribution is still far from being understood. Thus, with the aim of gaining a deeper understanding of the relationship between GM and LPD in blood, we carried out a detailed compositional analysis of GM, its metabolic functions, and studied its associations with blood lipoproteins quantified using a recently developed method based on proton ( 1 H) nuclear magnetic resonance (NMR) spectroscopy 6 . We determined covariations between larger HDL subclasses and lower total cholesterol with a several Clostridiales (Ruminococcaceae and Lachnospiraceae) and Eggerthelalles members, whose metabolic potential is linked to biosynthesis of cofactors essential for carrying out lipid metabolism.…”