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Size-Resolved Lipoprotein Fatty Acid Content as a Novel Nuclear Magnetic Resonance-Derived Trait Specifically Associates with Genetic Variants That Control Fatty Acid Metabolism

delete2026-06-28
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OA
AI
A
Aziz Belkadi *
G
Gaurav Thareja
N
Nisha Stephan
A
Anna Halama
K
Karsten Suhre *
DOI:10.1021/acs.jproteome.6c00107delete
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Abstract

Abstract

En 中文
Population-level nuclear magnetic resonance (NMR)-based lipoprotein profiling is a key tool for investigating dysregulated lipoprotein metabolism and its role in cardiovascular disorders. However, associations with size-resolved lipoprotein composition readouts are difficult to dissect, as these traits are often highly correlated. Derived variables can therefore be more relevant to biological interpretation. Here, we show that the total fatty acid (FA) content of the lipoproteins derived from their lipid headgroup concentrations using the formula FA = 3TG + 2PL + CE strongly correlates (Spearman rho = 0.98) with the independently measured total fatty acid content. This observation is not self-evident since these variables are determined using different portions of the NMR spectrum. Using NMR data acquired on the Nightingale platform for 274,303 UK Biobank (UKB) participants and genetic associations as a readout, we then show that this relationship also holds at the size-resolved lipoprotein level. We identified eight gene loci where the proposed FA variables display a significantly stronger genetic association signal than that of the corresponding lipid headgroup variables. Five of these loci (LIPC, LIPG, PLB1, LPL, and APOC3) have a direct function in FA metabolism. Including computed size-resolved FA variables may therefore improve the biological interpretation of future studies based on Nightingale’s data.
Keywords:
Biomarkers
Genetics
Lipids
Metabolism
Peptides and proteins
lipoprotein composition
nuclear magnetic resonance spectroscopy
fatty acid metabolism
triglycerides
cholesteryl ester
UK Biobank
Nightingale platform

Journal

Journal of Proteome Research cover
Journal of Proteome Research
IF:
3.6
Papers:
9.3K
Citations:
2.3W

Organization

W
weill cornell medicine-qatar
Scholars:
29
Papers: 11
Citations: 0