Personalized Profiling of Lipoprotein and Lipid Metabolism Based on 1018 Measures from Combined Quantitative NMR and LC-MS/MS Platforms

Applications of advanced omics methodologies are increasingly
popular
in biomedicine. However, large-scale studies aiming at clinical translation
are typically siloed to single technologies. Here, we present the
first comprehensive large-scale population data combining 209 lipoprotein
measures from a quantitative NMR spectroscopy platform and 809 lipid
classes and species from a quantitative LC-MS/MS platform. These data
with 1018 molecular measures were analyzed in two population cohorts
totaling 7830 participants. The association and cluster analyses revealed
excellent coherence between the methodologically independent data
domains and confirmed their quantitative compatibility and suitability
for large-scale studies. The analyses elucidated the detailed molecular
characteristics of the heterogeneous circulatory macromolecular lipid
transport system and the underlying structural and compositional relationships.
Unsupervised neural network analysisthe so-called self-organizing
maps (SOMs)revealed that these deep molecular and metabolic
data are inherently related to key physiological and clinical population
characteristics. The data-driven population subgroups uncovered marked
differences in the population distribution of multiple cardiometabolic
risk factors. These include, e.g., multiple lipoprotein lipids, apolipoprotein
B, ceramides, and oxidized lipids. All 79 structurally unique triglyceride
species showed similar associations over the entire lipoprotein cascade
and indicated systematically increased risk for carotid intima media
thickening and other atherosclerosis risk factors, including obesity
and inflammation. The metabolic attributes for 27 individual cholesteryl
ester species, which formed six distinct clusters, were more intricate
with associations both with highere.g., CE(16:1)and
lowere.g., CE(20:4)cardiometabolic risk. The molecular
details provided by these combined data are unprecedented for molecular
epidemiology and demonstrate a new potential avenue for population
studies.