ISSN 1470-3947 (print) | ISSN 1479-6848 (online)

Endocrine Abstracts (2019) 63 GP64 | DOI: 10.1530/endoabs.63.GP64

Untargeted lipidomics profiling by high-performance liquid chromatography/time-of-flight mass spectrometry in polycystic ovary syndrome: candidate biomarkers and association to disease traits

Carmen Emanuela Georgescu1, Camelia Vonica2, Ioana Rada Ilie1, Corina Moraru3, Dana Pop4, Georgeta Hazi5, Oana Pinzariu1 & Carmen Socaciu3,6


1Department of Endocrinology, Iuliu Hatieganu University of Medicine and Pharmacy, Cluj-Napoca, Romania; 2Department of Diabetes, Nutrition and Metabolic Diseases, Iuliu Hatieganu University of Medicine and Pharmacy, Cluj-Napoca, Romania; 3BIODIATECH Center, Cluj-Napoca, Romania Department; 4Department of Cardiology, Iuliu Hatieganu University of Medicine and Pharmacy, Cluj-Napoca, Romania; 5Central Laboratory, Cluj County Emergency Hospital, Romania; 6Department of Biochemistry, University of Agricultural Sciences and Veterinary Medicine, Cluj-Napoca, Romania.


Background: Metabolomics profiling of bio-fluids suggests altered signatures in patients with the polycystic ovary syndrome (PCOS).

Subjects and methods: A high-performance liquid chromatography/time-of-flight mass spectrometry (HPLC/TOF-MS)-based metabolomics approach was developed to characterize the untargeted lipidomics signature associated with PCOS status in a discovery cohort of 30 plasma samples (15 PCOS, 15 controls, age-matched), followed in a second step by evaluation of another cohort of 46 samples (27 PCOS, 19 controls, age-matched). Rotterdam criteria were applied to confirm the subjects’ eligibility. Of note, all patients with PCOS presented hyperandrogenic phenotypes. Blood samples were processed for extraction of lipids with Bligh & Dyer method, and subjected to HPLC-ESI(+)TOF-MS measurements. To analyze matrix data, Profile Analysis 2.0 (Bruker) and Metabolyst 4.0 bioinformatics tools were used. All participants were referred to body composition assessment by dual X-ray absorptiometry (DXA); in addition to vascular evaluation, including flow-mediated vasodilation (FMD) and arterial stiffness indices in a subset.

Results: Separation of more than 150 low-molecular-weight metabolites between groups resulted by untargeted lipidomics approach, with very good data clustering confirmed in multivariate analysis (PCA, PLS-DA, Cluster Analysis). ROC curves (AUC between 0.834–1) showed that a group of 18 lipid metabolites efficiently discriminated between controls and PCOS, among them, carnitines (decanoylcarnitine, L-octanoylcarnitine, heptadecanoylcarnitine) that presented highly significant elevated levels (P<0.0001), in addition to monoacylglycerols (MG20:3/0:0, MG0:0/18:0/0:0), with up to more than 5-fold higher levels (P<0.0001). On the contrary, lysophosphocholines were markedly decreased in women with PCOS vs. controls (P<0.0001). Tetranor-12R-HETE, released by endothelial cells upon stimulation and directly linked to inflammation, increased more than 3-fold in PCOS (P<0.0001). Furthermore, correlations were described. Decanoylcarnitine (C10:1), a fatty ester lipid molecule was strongly associated to a high number of metabolites, including monoacylglycerols and other acylcarnitines, tetranor-12R-HETE, lipoxin B4 and leukotriene. Within the PCOS group, acylcarnitines species correlated with body fat and the lipid profile (P≤0.01). Moreover, an inverse association between carnitines and FMD was revealed (P<0.05).

Conclusion: Metabolomics is useful in identifying novel potential biomarkers of PCOS. Alterations in fatty acid metabolism, with decreased fatty acid oxidation may contribute to the cardio metabolic profile in PCOS. Candidate biomarkers are involved in specific metabolomic networks affected by pathological processes and may discriminate between healthy women and PCOS.