Metabolomics as a Candidate for Endometriosis Biomarker: A Systematic Review

Background: To date, diagnosis is still a challenge in managing endometriosis. There is a need to develop a less invasive yet accurate approach, such as a biomarker, to help diagnose endometriosis earlier and more precisely. Metabolomics, the study of metabolites, are thought to provide a more accurate relation to phenotype compared to other omics technology and may help develop endometriosis biomarkers. This review aims to systematically summarize evidence regarding the use of metabolomics studies in the diagnosis of endometriosis. Methods: This review was conducted according to PRISMA 2020 guidelines. Including studies on endometriosis and metabolomics in Medline, Embase, and Cochrane Library to May 2022. Inclusion: Women of reproductive age, laparoscopically diagnosed — exclusion criteria: no full-text, languages other than English, and animal studies. Papers were screened and extracted using Covidence, appraised using Newcastle-Ottawa Scale (NOS), by two independent reviewers. Results: A total of 33 studies are included in this review, and 24 showed positive results regarding the metabolites identified and their association with endometriosis. Twenty-four studies reported good area under curve (AUC), sensitivity, and specificity scores in diagnosing endometriosis using a prediction model from the attained metabolites. However, the summary of these scores is not feasible due to the lack of standardization in reporting metabolomics studies. Both serum and endometriotic lesion microenvironments, such as peritoneal fluid, endometrioma, follicular fluid, and tissue lesions, give essential information on which metabolites are altered in endometriosis. Phosphatidylcholine (PC), acylcarnitines (AC), and sphingomyelins (SM) are these studies’ most frequent significant metabolites, but their levels vary. Although all studies had an overall good appraisal score, regarding confounders included, the variation in their methods of analysis and prediction model approach should be interpreted with caution. These predictions are also mostly done without using two different data groups for test and validation. Conclusions: Metabolomics studies may become an alternative for a less invasive approach to diagnosing endometriosis. This review revealed how encouraging results from metabolomics studies are. However, the need for more standardization in the study report and preliminary design for making a prediction model is still in the way of fully trusting metabolomics studies to account for endometriosis diagnostic biomarkers. Registration: PROSPERO CRD42022334916