Symptomatology and Serum Nuclear Magnetic Resonance Metabolomics; Do They Predict Endometriosis in Fertile Women Undergoing Laparoscopic Sterilisation? A Prospective Cross-sectional Study
This study found that while some symptoms like heavy or prolonged periods may be more frequent in women with endometriosis, serum metabolomics and general symptomology did not reliably predict the condition in women undergoing laparoscopic sterilization.
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This prospective cross-sectional study recruited 102 fertile women undergoing laparoscopic sterilisation at a tertiary referral centre, assessing endometriosis-related symptomology via pre-operative questionnaires and performing serum nuclear magnetic resonance (NMR) metabolomics on blood collected immediately before surgery. Twelve women were incidentally diagnosed with endometriosis (11.7%), and while many classically attributed symptoms occurred at similar frequencies in women with versus without endometriosis, endometriosis was associated with higher reported rates of apparently persistent heavy periods, prolonged periods, and problems conceiving. Serum NMR metabolomic profiling did not identify distinguishable differences in the serum metabolome between groups. This paper does not explicitly discuss limitations in the provided text beyond emphasizing the difficulty of identifying clear symptoms or biomarkers for endometriosis in the general population, but it highlights that symptom and metabolomic signatures were not reliably predictive in this low-risk cohort. This paper is centrally about endometriosis — it evaluates whether symptom questionnaires and serum NMR metabolomics can predict incidentally diagnosed endometriosis in women undergoing laparoscopic sterilisation.
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Cited by (6)
- Metabolite changes in patients with endometriosis: new potential diagnostic and therapeutic targets 2025
- Presentation of the Disease and Diagnostic Strategy 2025
- Validation of a Saliva Micro–RNA Signature for Endometriosis 2025
- Metabolomic biomarkers of endometriosis: A systematic review 2024
- Clues to revising the conventional diagnostic algorithm for endometriosis 2024
- Omics-based novel strategies in the diagnosis of endometriosis 2023
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