BACKGROUND: Current non-invasive diagnostic methods for endometriosis lack sensitivity and specificity. In search for new diagnostic biomarkers for ovarian endometriosis, we used a hypothesis-generating targeted metabolomics approach. METHODS: In a case-control study, we collected plasma of study participants and analysed their metabolic profiles. We selected a group of 40 patients with ovarian endometriosis who underwent laparoscopic surgery and a control group of 52 healthy women who underwent sterilization at the University Clinical Centre Ljubljana, Slovenia. Over 140 targeted analytes included glycerophospholipids, sphingolipids and acylcarnitines. The analytes were quantified by electrospray ionization tandem mass spectrometry. For assessing the strength of association between the metabolite or metabolite ratios and the disease, we used crude and adjusted odds ratios. A stepwise logistic regression procedure was used for selecting the best combination of biomarkers. RESULTS: Eight lipid metabolites were identified as endometriosis-associated biomarkers due to elevated levels in patients compared with controls. A model containing hydroxysphingomyelin SMOH C16:1 and the ratio between phosphatidylcholine PCaa C36:2 to ether-phospholipid PCae C34:2, adjusted for the effect of age and the BMI, resulted in a sensitivity of 90.0%, a specificity of 84.3% and a ratio of the positive likelihood ratio to the negative likelihood ratio of 48.3. CONCLUSIONS: Our results suggest that endometriosis is associated with elevated levels of sphingomyelins and phosphatidylcholines, which might contribute to the suppression of apoptosis and affect lipid-associated signalling pathways. Our findings suggest novel potential routes for therapy by specifically blocking highly up-regulated isoforms of phosphpolipase A2 and lysophosphatidylcholine acyltransferase 4.
BACKGROUND: Current non-invasive diagnostic methods for endometriosis lack sensitivity and specificity. In search for new diagnostic biomarkers for ovarian endometriosis, we used a hypothesis-generating targeted metabolomics approach. METHODS: In a case-control study, we collected plasma of study participants and analysed their metabolic profiles. We selected a group of 40 patients with ovarian endometriosis who underwent laparoscopic surgery and a control group of 52 healthy women who underwent sterilization at the University Clinical Centre Ljubljana, Slovenia. Over 140 targeted analytes included glycerophospholipids, sphingolipids and acylcarnitines. The analytes were quantified by electrospray ionization tandem mass spectrometry. For assessing the strength of association between the metabolite or metabolite ratios and the disease, we used crude and adjusted odds ratios. A stepwise logistic regression procedure was used for selecting the best combination of biomarkers. RESULTS: Eight lipid metabolites were identified as endometriosis-associated biomarkers due to elevated levels in patients compared with controls. A model containing hydroxysphingomyelin SMOH C16:1 and the ratio between phosphatidylcholine PCaa C36:2 to ether-phospholipid PCae C34:2, adjusted for the effect of age and the BMI, resulted in a sensitivity of 90.0%, a specificity of 84.3% and a ratio of the positive likelihood ratio to the negative likelihood ratio of 48.3. CONCLUSIONS: Our results suggest that endometriosis is associated with elevated levels of sphingomyelins and phosphatidylcholines, which might contribute to the suppression of apoptosis and affect lipid-associated signalling pathways. Our findings suggest novel potential routes for therapy by specifically blocking highly up-regulated isoforms of phosphpolipase A2 and lysophosphatidylcholine acyltransferase 4.
Authors: Signe Altmäe; Francisco J Esteban; Anneli Stavreus-Evers; Carlos Simón; Linda Giudice; Bruce A Lessey; Jose A Horcajadas; Nick S Macklon; Thomas D'Hooghe; Cristina Campoy; Bart C Fauser; Lois A Salamonsen; Andres Salumets Journal: Hum Reprod Update Date: 2013-09-29 Impact factor: 15.610
Authors: Vicki Nisenblat; Patrick M M Bossuyt; Rabia Shaikh; Cindy Farquhar; Vanessa Jordan; Carola S Scheffers; Ben Willem J Mol; Neil Johnson; M Louise Hull Journal: Cochrane Database Syst Rev Date: 2016-05-01