1H NMR- based metabolomics approaches as non-invasive tools for diagnosis of endometriosis Original paper
Researched by:
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Karen Pendergrass
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Karen Pendergrass
Karen Pendergrass is a microbiome researcher specializing in microbiome-targeted interventions (MBTIs). She systematically analyzes scientific literature to identify microbial patterns, develop hypotheses, and validate interventions. As the founder of the Microbiome Signatures Database, she bridges microbiome research with clinical practice. In 2012, based on her own investigative research, she became the first documented case of FMT for Celiac Disease—four years before the first published case study.
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Karen Pendergrass
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Karen Pendergrass
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Women’s Health
Women’s Health
Women’s health, a vital aspect of medical science, encompasses various conditions unique to women’s physiological makeup. Historically, women were often excluded from clinical research, leading to a gap in understanding the intricacies of women’s health needs. However, recent advancements have highlighted the significant role that the microbiome plays in these conditions, offering new insights and potential therapies. MicrobiomeSignatures.com is at the forefront of exploring the microbiome signature of each of these conditions to unravel the etiology of these diseases and develop targeted microbiome therapies.
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Endometriosis
Endometriosis
Endometriosis involves ectopic endometrial tissue causing pain and infertility. Validated and Promising Interventions include Hyperbaric Oxygen Therapy (HBOT), Low Nickel Diet, and Metronidazole therapy.
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Metabolomic Signature
Metabolomic Signature
Metabolomic signatures are unique metabolite patterns linked to specific biological conditions, identified through metabolomics. They reveal underlying biochemical activities, aiding in disease diagnosis, biomarker development, and personalized medicine. The microbiome significantly affects these signatures, influencing health and disease outcomes through metabolic interactions.
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Karen Pendergrass
Researched by:
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Karen Pendergrass
ID
Karen Pendergrass
Fact-checked by:
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Karen Pendergrass
ID
Karen Pendergrass
Microbiome Signatures identifies and validates condition-specific microbiome shifts and interventions to accelerate clinical translation. Our multidisciplinary team supports clinicians, researchers, and innovators in turning microbiome science into actionable medicine.
Karen Pendergrass
What was studied?
This study investigated the application of metabolomics, specifically through proton nuclear magnetic resonance (¹H-NMR) spectroscopy, to identify non-invasive biomarkers for diagnosing endometriosis. The researchers developed computational models using Quadratic Discriminant Analysis (QDA) and Artificial Neural Networks (ANNs) to analyze metabolic changes in serum samples and assess their utility in early diagnosis of the disease.
Who was studied?
The study analyzed serum samples from 31 infertile women diagnosed with stage II or III endometriosis confirmed via laparoscopy and 15 healthy women without any signs of endometriosis. The participants were aged 22–44 years and were recruited from an infertility center in Iran. Exclusion criteria included recent medical or hormonal treatments, prior gynecological surgeries, or other pelvic inflammatory conditions.
What were the most important findings?
The study revealed significant metabolic differences between women with endometriosis and healthy controls. Key findings included elevated levels of 2-methoxyestrone, 2-methoxyestradiol, androstenedione, aldosterone, dehydroepiandrosterone, and deoxycorticosterone in the endometriosis group, alongside decreased cholesterol and primary bile acids. These metabolic changes are linked to disruptions in steroid hormone biosynthesis and bile acid metabolism, indicating underlying hyperestrogenism and impaired hepatic estrogen clearance. The QDA model achieved a correct classification rate of 76%, with 71% positive predictive value and 78% negative predictive value, outperforming the ANN model, which had lower sensitivity and specificity. Metabolic pathway analyses highlighted altered steroid hormone and bile acid biosynthesis, which are critical in the pathophysiology of endometriosis.
What are the greatest implications of this study?
This study underscores the potential of ¹H-NMR-based metabolomics as a minimally invasive diagnostic tool for endometriosis, reducing reliance on invasive laparoscopy.The identification of specific biomarkers and disrupted pathways could facilitate earlier diagnosis, improved patient stratification, and targeted therapeutic interventions. The findings also demonstrate the utility of computational modeling, particularly QDA, in translating complex metabolomics data into clinically actionable insights. This approach represents a significant advancement in bridging diagnostic gaps for endometriosis.
Endometriosis
Endometriosis involves ectopic endometrial tissue causing pain and infertility. Validated and Promising Interventions include Hyperbaric Oxygen Therapy (HBOT), Low Nickel Diet, and Metronidazole therapy.