Objective: To characterize the most relevant changes in the lipidome of endometrial fluid aspirate (EFA) in non-implantative cycles.
Design: Lipidomics in a prospective cohort study.
Settings: Reproductive unit of a university hospital.
Patients: Twenty-nine women undergoing an IVF cycle. Fifteen achieved pregnancy and 14 did not.
Intervention: Endometrial fluid aspiration immediately before performing embryo transfer.
Main outcome measures: Clinical pregnancy rate and lipidomic profiles obtained on an ultra-high performance liquid chromatography coupled to time-of-flight mass spectrometry (UHPLC-ToF-MS)-based analytical platform.
Results: The comparative analysis of the lipidomic patterns of endometrial fluid in implantative and non-implantative IVF cycles revealed eight altered metabolites: seven glycerophospholipids and an omega-6 polyunsaturated fatty acid. Then, women with a non-implantative cycle were accurately classified with a support vector machine algorithm including these eight lipid metabolites. The diagnostic performances of the algorithm showed an area under the receiver operating characteristic curve, sensitivity, specificity, and accuracy of 0.893 ± 0.07, 85.7%, 80.0%, and 82.8%, respectively.
Conclusion: A predictive lipidomic signature linked to the implantative status of the endometrial fluid has been found.
Keywords: Assisted reproduction; Endometrial fluid; Implantation; In vitro fertilization; Lipidomics; Machine learning algorithms; Pregnancy.