Metabolomic signatures support the diagnostics of peritoneal endometriosis using generalised linear models.

Cecil A, Alexander Cecil, Katja Vouk, Vouk K, Maja Pušić, Novak Pušić M, Vogler A, Andrej Vogler, Wenzl R, R Wenzl, Cornelia Prehn, Prehn C, Jerzy Adamski, Tea Lanišnik Rižner, Lanišnik Rižner T, Lanisnik Rizner T
In: bioRxiv · 2026 · doi:10.64898/2026.07.05.736551 · W7167504669
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This study identified specific metabolite ratios in plasma, analyzed using generalized linear models, that can distinguish peritoneal endometriosis from other conditions with 76% sensitivity and 81% specificity.

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Abstract

Endometriosis, a common inflammatory gynecological disorder affecting up to 10% of women worldwide, is characterized by the presence of endometrium-like tissue outside the uterus. Current diagnostic methods, such as ultrasound and MRI, effectively detect ovarian and deep endometriosis but fail to detect more common peritoneal type. Diagnosing peritoneal endometriosis currently necessitates invasive laparoscopy and histological confirmation. Despite numerous efforts, no new reliable biomarkers have successfully transitioned into routine clinical use. This study aimed to investigate the use of targeted metabolomics to discover metabolite ratios capable of identifying endometriosis in plasma samples. We analyzed a discovery population of 235 patients and a validation population of 278 patients. All cases and controls in both populations were diagnosed by laparoscopy. Control subjects included individuals presenting with symptoms such as pain, dysmenorrhea, infertility, or other benign conditions, but who had no laparoscopic evidence of endometriosis. Using generalized linear models (GLMs) and machine learning, the study identified specific metabolite ratios as potential biomarkers that can distinguish different types of endometriosis and enable mass spectrometry-based diagnostics for peritoneal endometriosis. The best-validated GLM, derived from the concentration ratios of amino acids, acylcarnitines, sphingomyelins, and phosphatidylcholines, consisted of Thr/SM(OH) C22:2 + PC aa C40:5/SFA_PC + lysoPC a C16:0/SM(OH) C16:1. This model yielded an AUC of 0.82 (95% CI 0.619-0.891, with 76% sensitivity and 81% specificity) for peritoneal endometriosis. This innovative approach offers a robust diagnostic model, addressing an unmet medical need by facilitating earlier detection of peritoneal endometriosis and improving overall clinical management.
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Abstract Endometriosis, a common inflammatory gynecological disorder affecting up to 10% of women worldwide, is characterized by the presence of endometrium-like tissue outside the uterus. Current diagnostic methods, such as ultrasound and MRI, effectively detect ovarian and deep endometriosis but fail to detect more common peritoneal type. Diagnosing peritoneal endometriosis currently necessitates invasive laparoscopy and histological confirmation. Despite numerous efforts, no new reliable biomarkers have successfully transitioned into routine clinical use. This study aimed to investigate the use of targeted metabolomics to discover metabolite ratios capable of identifying endometriosis in plasma samples. We analyzed a discovery population of 235 patients and a validation population of 278 patients. All cases and controls in both populations were diagnosed by laparoscopy. Control subjects included individuals presenting with symptoms such as pain, dysmenorrhea, infertility, or other benign conditions, but who had no laparoscopic evidence of endometriosis. Using generalized linear models (GLMs) and machine learning, the study identified specific metabolite ratios as potential biomarkers that can distinguish different types of endometriosis and enable mass spectrometry-based diagnostics for peritoneal endometriosis. The best-validated GLM, derived from the concentration ratios of amino acids, acylcarnitines, sphingomyelins, and phosphatidylcholines, consisted of Thr/SM(OH) C22:2 + PC aa C40:5/SFA_PC + lysoPC a C16:0/SM(OH) C16:1. This model yielded an AUC of 0.82 (95% CI 0.619-0.891, with 76% sensitivity and 81% specificity) for peritoneal endometriosis. This innovative approach offers a robust diagnostic model, addressing an unmet medical need by facilitating earlier detection of peritoneal endometriosis and improving overall clinical management. Competing Interest Statement J.A. is co-founder and acts as CSO at Metaron Diagnostics. The paper reflects the view of the authors, and not the company. Metaron Diagnostics had no role in the design of the study; in the collection, analyses; or interpretation of data; in the writing of the manuscript, or in the decision to publish the results. The patent WO 2023/170045 [123] and its international family resulting from the work reported in this manuscript has been invented by A.C., K.V., C.P., R.W., T.K., A.V., J.A. and T.L.R. Footnotes Correct error in labeling of panels in the supplementary figures. Abbreviations - 95%CI - range of confidence interval at 95% - AUC - area under the curve - BMI - body mass index - CV - coefficient of variation - FIA - flow injection - GLM - generalised linear models - HMDB - Human Metabolome Data Bank - LC-ESI-MS/MS - liquid chromatography-electrospray ionisation tandem mass spectrometry - LR - Logistic regression - LLOQ - lower limit of quantification - LOD - limit of detection - NA - not available - NAFLD - non-alcoholic fatty liver disease - MRI - magnetic resonance imaging - OHC - oral hormonal contraception - PLS-R - partial least squares - regression - PLS-DA - partial least squares – discriminant analysis - QC - quality control - RF - random forest - rASRM - revised American Society for Reproductive Medicine - RMSE - root mean square error - RMSEE - root mean square error of estimations - ROC - receiver operator characteristic - SP - specificity - SE - sensitivity - sMRM - scheduled multiple reaction monitoring - MRM - multiple reaction monitoring - SOPs - standard operating procedures

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