Genomics of post-prandial lipidomic phenotypes in the Genetics of Lipid Lowering Drugs and Diet Network (GOLDN) study

Marguerite R. Irvin, Degui Zhi, Stella Aslibekyan, Steven A. Claas, Devin M. Absher, Jose M. Ordovas, Hemant K. Tiwari, Steve Watkins, Donna K. Arnett

Research output: Contribution to journalArticlepeer-review

18 Scopus citations


Background: Increased postprandial lipid (PPL) response to dietary fat intake is a heritable risk factor for cardiovascular disease (CVD). Variability in postprandial lipids results from the complex interplay of dietary and genetic factors. We hypothesized that detailed lipid profiles (eg, sterols and fatty acids) may help elucidate specific genetic and dietary pathways contributing to the PPL response. Methods and Results: We used gas chromatography mass spectrometry to quantify the change in plasma concentration of 35 fatty acids and 11 sterols between fasting and 3.5 hours after the consumption of a high-fat meal (PPL challenge) among 40 participants from the GOLDN study. Correlations between sterols, fatty acids and clinical measures were calculated. Mixed linear regression was used to evaluate associations between lipidomic profiles and genomic markers including single nucleotide polymorphisms (SNPs) and methylation markers derived from the Affymetrix 6.0 array and the Illumina Methyl450 array, respectively. After the PPL challenge, fatty acids increased as well as sterols associated with cholesterol absorption, while sterols associated with cholesterol synthesis decreased. PPL saturated fatty acids strongly correlated with triglycerides, very low-density lipoprotein, and chylomicrons. Two SNPs (rs12247017 and rs12240292) in the sorbin and SH3 domain containing 1 (SORBS1) gene were associated with b-Sitosterol after correction for multiple testing (P≤4.5*10-10). SORBS1 has been linked to obesity and insulin signaling. No other markers reached the genome-wide significance threshold, yet several other biologically relevant loci are highlighted (eg, PRIC285, a co-activator of PPARa). Conclusions: Integration of lipidomic and genomic data has the potential to identify new biomarkers of CVD risk.

Original languageEnglish
Article numbere99509
JournalPLoS ONE
Issue number6
StatePublished - Jun 6 2014

ASJC Scopus subject areas

  • General


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