Abstract

Obesity alters the composition of plasma and tissue fatty acids (FAs), which contributes to inflammation and insulin resistance. Several studies have reported obesity and insulin resistance are associated with altered levels of linoleic (18:2), dihomo‐gamma linolenic (20:3), arachidonic (20:4), and nervonic (24:1) acid in total plasma phospholipids (PPLs). However, few researchers have investigated whether these FA changes are specific to individual phospholipid (PL) classes (e.g., phosphatidylcholines [PCs], sphingomyelins [SMs]). The objective of this study was to determine whether specific PPL species containing 18:2, 20:3, 20:4, and 24:1 in plasma are associated with obesity. At time of enrollment, blood samples were collected from fasting subjects (males ages 48–65). Plasma was separated and stored at −80ºC until analysis. Crude plasma lipid extracts were analyzed using UPLC‐MS on a QTof mass spectrometer using reversed phase UPLC, electrospray ionization, and non‐selective multiplexed collision‐induced dissociation in negative‐ion mode. Data were processed using MarkerLynx version 4.1 which identified 410 lipids. Orthogonal partial least square discriminant analysis (OPLS‐DA) was used to compare obese (n=52) to lean (n=28) individuals and to generate an S‐plot of PL variables of importance (VIP) that distinguish obese from lean PL profiles. VIPs >2 were tested for statistical differences between lean and obese individuals using a Mann‐Whitney nonparametric U‐test. Obesity was inversely associated with the following PL species: lysoPC(18:2) p<0.0005, lysophosphatidylethanolamine(18:2) p<0.01, PC(plasmenyl‐16:0/18:2) p<0.0001, and SM(d18:2/24:1) p<0.05. Obesity was positively associated with PC(16:0/20:3) p<0.0001, PC(18:0/20:3) p<0.0001, and PC(16:0/20:4) p<0.0005. Therefore, we suggest that the previously reported changes in PPL 18:2, 20:3, 20:4, and 24:1 likely occur in these specific PL species. Future studies should investigate whether and how altered levels of these PL species influence obesity‐associated pathologies and examine their usefulness as predictive biomarkers of metabolic dysregulation.Support or Funding InformationResearch Support: NIH R03CA142000 and MSU CTSI

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