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Metabolomics profiling predicts SORT1 LDL‐cholesterol locus in a fit, young adult population
Author(s) -
Kirtiadi Lawrence,
Gnatiuk Elizabeth,
Karlos Angie,
Connors Kimberly,
Vogel Hans J,
Shearer Jane,
Hittel Dustin S
Publication year - 2013
Publication title -
the faseb journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.709
H-Index - 277
eISSN - 1530-6860
pISSN - 0892-6638
DOI - 10.1096/fasebj.27.1_supplement.1073.13
Subject(s) - metabolomics , confounding , locus (genetics) , genotype , medicine , population , metabolite , genetics , genome wide association study , biology , endocrinology , bioinformatics , single nucleotide polymorphism , gene , environmental health
SORT1 locus was originally identified by genome wide association studies of LDL‐cholesterol (LDL‐C) in older adults. We hypothesized that a younger population would show a greater genetic effect due to fewer confounding variables. As such, we investigated the association between the SORT1 locus and LDL‐C in a group of healthy, young adults. Subjects (n=80, age=23) were recruited. Lipid measures and genomic DNA were collected from blood after an overnight fast. Blood pressure, body fat (%BF), V02 max, and metabolomics profile (LC‐MS) were measured. Associations between genotype and LDL‐C were investigated using linear regression. 21.7% of male subjects had %BF that was above a healthy range, while 25% had non‐optimal LDL‐C values. A significant association was observed between the SORT1 locus (GG: 2.46±0.11 mmol/L versus TG/TT: 2.06±0.12 mmol/L, p=0.016) and LDL‐C in male subjects with genotype explaining 3.0% of the variability in LDL‐C. LC‐MS metabolite profiling was able to further discriminate between genotypes independent of other clinical parameters. Differentially affected metabolites included sphingomyelins, phosphatidylcholines and acylcarnitines. The marriage of metabolomics and genomics represents a powerful means to identify the earliest biomarkers associated with cardiovascular disease that could be used to identify individuals who would most benefit from early interventions. Grant Funding Source : Metabolomics Research Center