Author:
Dalle Céline,Tournayre Jérémy,Mainka Malwina,Basiak-Rasała Alicja,Pétéra Mélanie,Lefèvre-Arbogast Sophie,Dalloux-Chioccioli Jessica,Deschasaux-Tanguy Mélanie,Lécuyer Lucie,Kesse-Guyot Emmanuelle,Fezeu Léopold,Hercberg Serge,Galan Pilar,Samieri Cécilia,Zatońska Katarzyna,Calder Philip C.,Hjorth Mads Fiil,Astrup Arne,Mazur André,Bertrand-Michel Justine,Schebb Nils H.,Szuba Andrzej,Touvier Mathilde,Newman John W.,Gladine Cécile
Abstract
AbstractBackgroundMetabolic syndrome (MetS) is a complex condition encompassing a constellation of cardiometabolic abnormalities. Integratively phenotyping the molecular pathways involved in MetS would help to deeply characterize its pathophysiology and to better stratify the risk of cardiometabolic diseases. Oxylipins are a superfamilly of lipid mediators regulating most biological processes involved in cardiometabolic health.MethodsA high-throughput validated mass spectrometry method allowing the quantitative profiling of over 130 oxylipins was applied to identify and validate the oxylipin signature of MetS in two independent case/control studies involving 476 participants.ResultsWe have uncovered and validated an oxylipin signature of MetS (coined OxyScore) including 23 oxylipins and having high performances of classification and replicability (cross-validated AUCROCof 89%, 95% CI: 85%-93% and 78%, 95% CI: 72%-85% in the Discovery and Replication studies, respectively). Correlation analysis and comparison with a classification model incorporating both the oxylipins and the MetS criteria showed that the oxylipin signature brings consistent and complementary information supporting its clinical utility. Moreover, the OxyScore provides a unique mechanistic signature of MetS regarding the activation and/or negative feedback regulation of crucial molecular pathways that may help identify patients at higher risk of cardiometabolic diseases.ConclusionOxylipin profiling identifies a mechanistic signature of metabolic syndrome that may help to enhance MetS phenotyping and ultimately to better predict the risk of cardiometabolic diseasesviaa better patient stratification.
Publisher
Cold Spring Harbor Laboratory