Sophia Letsiou1, Dirkje P Peterse2, Amelie Fassbender2, Margriet M Hendriks3, Niels J van den Broek1, Rudolf Berger3, Dorien F O2, Arne Vanhie2, Alexandra Vodolazkaia2, Anne Van Langendonckt4, Jacques Donnez4, Amy C Harms5, Rob J Vreeken6, Patrick G Groothuis7, Marie-Madeleine Dolmans4, Arjan B Brenkman1, Thomas M D'Hooghe8. 1. University Medical Centre Utrecht, Department of Metabolic Diseases, and Netherlands Metabolomics Centre (NMC), Utrecht, the Netherlands. 2. Catholic University (KU) Leuven, Department of Development and Regeneration, Organ Systems, Leuven, Belgium. 3. Leiden University, NMC, Leiden, the Netherlands. 4. Département de Gynécologie, Cliniques Universitaires Saint Luc, Institut de Recherche Clinique et Expérimentale, Université Catholique de Louvain, Brussels, Belgium. 5. Leiden University, Department of Analytical Biosciences, Leiden Academic Centre for Drug Research (LACDR), Leiden, the Netherlands. 6. Leiden University, NMC, Leiden, the Netherlands; Leiden University, Department of Analytical Biosciences, Leiden Academic Centre for Drug Research (LACDR), Leiden, the Netherlands. 7. Department of Women's Health and Endocrine, Merck Sharpe Dome, Oss, the Netherlands. 8. Catholic University (KU) Leuven, Department of Development and Regeneration, Organ Systems, Leuven, Belgium. Electronic address: thomas.dhooghe@kuleuven.be.
Abstract
OBJECTIVE: To identify metabolites that are associated with and predict the presence of endometriosis. DESIGN: Metabolomics study using state-of-the-art mass spectrometry approaches. SETTING: University hospital and universities. PATIENT(S): Twenty-five women with laparoscopically confirmed endometriosis (cases) and 19 women with laparoscopically documented absence of endometriosis (controls). None of the women included in this study had received oral contraception or GnRH agonists for a minimum of 1 month before blood collection. INTERVENTION(S): Plasma collection. MAIN OUTCOME MEASURE(S): Metabolite profiles were generated and interrogated using multiple mass spectrometry methods, that is, high performance liquid chromatography coupled with negative mode electrospray ionization tandem mass spectrometry, UPLC-MS/MS, and ultra performance liquid chromatography-electroSpray ionization-quadrupole time-of-flight (UPLC-ESI-Q-TOF). Metabolite groups investigated included phospholipids, glycerophospholipids, ether-phospholipids, cholesterol-esters, triacylglycerol, sphingolipids, free fatty acids, steroids, eicosanoids, and acylcarnitines. RESULT(S): A panel of acylcarnitines predicted the presence of endometriosis with 88.9% specificity and 81.5% sensitivity in human plasma, with a positive predictive value of 75%. However, due to data limitations the outcome of the receiver operating characteristic curve analysis was not significant. CONCLUSION(S): A diagnostic model based on acylcarnitines has the potential to predict the presence and stage of endometriosis.
OBJECTIVE: To identify metabolites that are associated with and predict the presence of endometriosis. DESIGN: Metabolomics study using state-of-the-art mass spectrometry approaches. SETTING: University hospital and universities. PATIENT(S): Twenty-five women with laparoscopically confirmed endometriosis (cases) and 19 women with laparoscopically documented absence of endometriosis (controls). None of the women included in this study had received oral contraception or GnRH agonists for a minimum of 1 month before blood collection. INTERVENTION(S): Plasma collection. MAIN OUTCOME MEASURE(S): Metabolite profiles were generated and interrogated using multiple mass spectrometry methods, that is, high performance liquid chromatography coupled with negative mode electrospray ionization tandem mass spectrometry, UPLC-MS/MS, and ultra performance liquid chromatography-electroSpray ionization-quadrupole time-of-flight (UPLC-ESI-Q-TOF). Metabolite groups investigated included phospholipids, glycerophospholipids, ether-phospholipids, cholesterol-esters, triacylglycerol, sphingolipids, free fatty acids, steroids, eicosanoids, and acylcarnitines. RESULT(S): A panel of acylcarnitines predicted the presence of endometriosis with 88.9% specificity and 81.5% sensitivity in human plasma, with a positive predictive value of 75%. However, due to data limitations the outcome of the receiver operating characteristic curve analysis was not significant. CONCLUSION(S): A diagnostic model based on acylcarnitines has the potential to predict the presence and stage of endometriosis.
Authors: Naoko Sasamoto; Oana A Zeleznik; Allison F Vitonis; Stacey A Missmer; Marc R Laufer; Julian Avila-Pacheco; Clary B Clish; Kathryn L Terry Journal: Fertil Steril Date: 2022-03-30 Impact factor: 7.490
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