Microbiome dysbiosis and endometriosis: a systematic scoping review of current literature and knowledge gaps
Bibliographic record
Abstract
STUDY QUESTION: What is the evidence available concerning gut and reproductive tract microbiomes in patients with endometriosis and what are the methodological approaches employed in microbiome studies on endometriosis? SUMMARY ANSWER: The taxonomic profiles exhibited pronounced heterogeneity within women with and also within women without endometriosis across reviewed studies for all the anatomical districts evaluated. WHAT IS KNOWN ALREADY: Both human and animal studies support differences in the microbiome composition of individuals with and without endometriosis. Endometriosis onset occurs with variable symptoms and manifestations. The microbiome composition at different sites may contribute to this variability. STUDY DESIGN SIZE DURATION: We used the scoping review methodology. Systematic searches of studies from the PubMed, EMBASE, and Web of Science databases published between 1 January 2016 and 1 November 2024 addressing endometriosis microbiome characterization in: (i) gut, (ii) vaginal fluid, (iii) cervical fluid, (iv) peritoneal fluid, (v) uterine fluid, (vi) ovarian cyst fluid, (vii) oropharyngeal fluid, and (viii) eutopic and (ix) ectopic tissues were performed using a combination of MeSH terms. References from relevant publications were systematically screened. PARTICIPANTS/MATERIALS SETTING METHODS: Results were reported in accordance with the PRISMA-ScR guidelines. Studies that did not report original data, not written in English or providing a review of the field were excluded. From the 2182 publications retrieved, 36 papers were selected and analyzed, focusing on sample characterization (patients, controls, tissues, and fluids) and methodologies used. MAIN RESULTS AND THE ROLE OF CHANCE: sp. in stool/anal fluid of endometriosis patients. However, these findings may be explained by confounders or by intrinsic patient or population characteristics. We appraised the limitations of the studies and proposed suggestions for optimizing sequencing techniques and experimental designs. LIMITATIONS REASONS FOR CAUTION: The number of participants per study greatly varied and, with few exceptions, was typically low. Incomplete information on methodological approaches was broadly observed. The impact of participants' menstrual cycle phase, diet, and drug assumption was frequently not considered. WIDER IMPLICATIONS OF THE FINDINGS: Standardization of research protocols to allow reproducibility is required, as well as collaborations to harmonize data analysis, interpretation, and, more importantly, health outcome prediction or improvement. STUDY FUNDING/COMPETING INTERESTS: The review was funded by the Italian Ministry of Health: RF-2019-12369460, and Current Research IRCCS. P.Vi. serves as co-editor in Chief of Journal of Endometriosis and Uterine Disorders. E.S. serves as Editor in Chief of Human Reproduction Open and discloses research grants from Ferring, Ibsa, Gedeon Richter, and Theramex, and honoraria from Ibsa and Gedeon Richter. P.Ve. serves as Associate Editor for Human Reproduction Open; is a member of the Editorial Board of the Journal of Obstetrics and Gynaecology Canada, of the Italian Journal of Obstetrics and Gynaecology, and of the International Editorial Board of Acta Obstetricia et Gynecologica Scandinavica; has received royalties from Wolters Kluwer for chapters on endometriosis management in the clinical decision support resource UpToDate; and maintains both a public and private gynecological practice. All other authors declare they have no conflict of interest. REGISTRATION NUMBER: 10.17605/OSF.IO/X6HBT at https://osf.io/registries.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.021 | 0.085 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.009 |
| Bibliometrics | 0.031 | 0.026 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".