title: Plasma lipidome is independently associated with variability in metabolic syndrome in Mexican American families.
abstract: Plasma lipidome is now increasingly recognized as a potentially important marker of chronic diseases, but the exact extent of its contribution to the interindividual phenotypic variability in family studies is unknown. Here, we used the rich data from the ongoing San Antonio Family Heart Study (SAFHS) and developed a novel statistical approach to quantify the independent and additive value of the plasma lipidome in explaining metabolic syndrome (MS) variability in Mexican American families recruited in the SAFHS. Our analytical approach included two preprocessing steps: principal components analysis of the high-resolution plasma lipidomics data and construction of a subject-subject lipidomic similarity matrix. We then used the Sequential Oligogenic Linkage Analysis Routines software to model the complex family relationships, lipidomic similarities, and other important covariates in a variance components framework. Our results suggested that even after accounting for the shared genetic influences, indicators of lipemic status (total serum cholesterol, TGs, and HDL cholesterol), and obesity, the plasma lipidome independently explained 22% of variability in the homeostatic model of assessment-insulin resistance trait and 16% to 22% variability in glucose, insulin, and waist circumference. Our results demonstrate that plasma lipidomic studies can additively contribute to an understanding of the interindividual variability in MS.
authors.affiliation: Department of Genetics, Texas Biomedical Research Institute, San Antonio, TX 78227.
authors.forename: Hemant
authors.initials: H
authors.lastname: Kulkarni
authors.forename: Peter J
authors.initials: PJ
authors.lastname: Meikle
authors.forename: Manju
authors.initials: M
authors.lastname: Mamtani
authors.forename: Jacquelyn M
authors.initials: JM
authors.lastname: Weir
authors.forename: Marcio
authors.initials: M
authors.lastname: Almeida
authors.forename: Vincent
authors.initials: V
authors.lastname: Diego
authors.forename: Juan Manuel
authors.initials: JM
authors.lastname: Peralta
authors.forename: Christopher K
authors.initials: CK
authors.lastname: Barlow
authors.forename: Claire
authors.initials: C
authors.lastname: Bellis
authors.forename: Thomas D
authors.initials: TD
authors.lastname: Dyer
authors.forename: Laura
authors.initials: L
authors.lastname: Almasy
authors.forename: Michael C
authors.initials: MC
authors.lastname: Mahaney
authors.forename: Anthony G
authors.initials: AG
authors.lastname: Comuzzie
authors.forename: Harald H H
authors.initials: HH
authors.lastname: Göring
authors.forename: Joanne E
authors.initials: JE
authors.lastname: Curran
authors.forename: John
authors.initials: J
authors.lastname: Blangero
mesh_terms: D000328:Adult; D019295:Computational Biology; D005260:Female; D006801:Humans; D008055:Lipids / Q000097:blood*; D008297:Male; D024821:Metabolic Syndrome / Q000097:blood* / Q000453:epidemiology* / Q000235:genetics; D017058:Mexican Americans / Q000235:genetics / Q000706:statistics & numerical data*; D010375:Pedigree; D010641:Phenotype; D025341:Principal Component Analysis
chemical_list: D008055:Lipids
keywords: genetics; insulin resistance; lipidomics; lipids; obesity; plasma lipidomics; variance components