{"paper_id":"cfddbe0c-ceef-4653-966a-b09fc6923af2","body_text":"Nearly a century after Sampson proposed the retrograde menstruation theory, accumulating evidence suggests that no single mechanism can fully explain the diverse clinical manifestations associated with endometrial tissue at ectopic sites. Many alternative hypotheses have been proposed to explain the development of endometriosis, yet its precise etiology remains unknown ( Abbott  et al. , 2024 ). Interestingly, recent findings have implicated microbiota dysfunctions, particularly bacterial infections, as potential causal factors. Thus, this research area has become particularly active. The link between endometriosis and microbiome alterations is supported by a few studies conducted on murine models of the disease ( Yuan  et al. , 2018 ;  Chadchan  et al. , 2023 ;  Hu  et al. , 2023 ;  Muraoka  et al. , 2023 ;  Wei  et al. , 2023 ). Among these, the most interesting breakthrough was reported by  Muraoka  et al.  (2023) , who showed that uterine tissue fragments infected with  Fusobacterium nucleatum , when injected into recipient mice, led to larger endometriotic lesions characterized by increased M2 macrophage infiltration, elevated expression of transforming growth factor (TGF)-β1, and a greater number of transgelin-positive myofibroblasts. Importantly, antibiotic treatment reduced lesion weight, underscoring a potential therapeutic avenue.\nEarlier murine studies linked endometriosis to alterations in the gut microbiome rather than the uterine microbiome ( Yuan  et al. , 2018 ;  Chadchan  et al. , 2023 ). Similarly, treatment with broad-spectrum antibiotics reduced the risk of endometriosis also in these models ( Chadchan  et al. , 2019 ). In human studies, four systematic reviews have synthesized evidence regarding gut, endometrial, and vaginal microbiome composition in patients with endometriosis ( Leonardi  et al. , 2020 ;  D’Alterio  et al. , 2021 ;  Colonetti  et al. , 2023 ;  Weber  et al. , 2024 ). These reviews, however, highlighted inconsistencies across studies, with no definitive trends indicating whether bacterial changes in patients with endometriosis were predominantly beneficial or harmful.\nSignificant knowledge gaps persist in this field. Understanding whether and how microbiome dysbiosis may influence the development and/or progression of endometriosis could guide or limit future interventions targeting bacterial colonization. To address these gaps, we chose to conduct a scoping review to systematically evaluate the existing literature, assess research methodologies with a particular emphasis on sampling and analytic techniques, and explore potential microbiome-related exposures in affected patients. This way, we have systematically and critically mapped the existing literature addressing the following primary questions:\nMicrobiome characterization: What data are available concerning gut and reproductive tract microbiomes in patients with endometriosis?\nMethodological assessment: What are the methodological design methods, sequencing platform, analytical pipelines employed in microbiome studies on endometriosis?\n\nThis scoping review was conducted in accordance with the PRISMA-ScR guidelines (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) ( Tricco  et al ., 2018 ), which provides a widely accepted framework to ensure transparency and methodological rigor in exploratory reviews. The methodological approach was based on the foundational framework originally proposed by  Arksey and O’Malley (2005)  and later refined by  Levac  et al . (2010 ) and the Joanna Briggs Institute Reviewers’ Manual ( Aromataris  et al . 2024 ). Specifically, the review process adhered to five key stages: identifying the research question, searching the literature, selecting relevant studies, charting the data, and synthesizing and reporting the results. These established protocols helped maintain consistency and reproducibility throughout the review process. The scoping review protocol was recorded  a priori  and is available at OSF registries (Registration doi: 10.17605/OSF.IO/X6HBT;  https://osf.io/registries ).\nThe PubMed, EMBASE, and Web of Science databases were searched to identify studies on endometriosis microbiome characterization, using a combination of MeSH terms (search string available in  Supplementary File S1 ). The scoping review included all studies published from January 2016 to November 2024. Papers published before 2016 were excluded because sequencing technologies were in developmental stages and had not yet been widely adopted. Starting in 2016, sequencing methods, particularly next-generation sequencing, became more commonly applied, improving consistency and reproducibility in microbiome analyses due to advancements in depth, accuracy, and standardization ( Wen  et al ., 2017 ).\nThe initial literature search was conducted on 5 February 2024, with the most recent update performed on 11 November 2024. Studies were included if they met the following criteria:\nthe study population consisted of women diagnosed with endometriosis and an appropriate control group;\nthe microbiome of the reproductive tract or gastrointestinal system was analyzed using molecular techniques; and\nthe study followed an experimental design and was published in English.\nThe screening process was conducted in two phases. First, titles and abstracts were assessed to exclude clearly irrelevant publications. Subsequently, full-text reviews were carried out to confirm final eligibility. Both stages were independently performed by two reviewers (G.D.S. and D.D.), and any disagreements were resolved by discussion with a third reviewer (M.C.) to ensure objectivity and consensus. The final list of included studies was then reviewed and discussed by the full research team to support the technical and methodological integrity of the selection process.\nA standardized data-charting form was developed to guide the extraction of key variables. Three co-authors (D.D., G.D.S., and M.C.) independently extracted and charted data from each included study. Discrepancies were resolved through discussion and consensus. The form was iteratively refined as needed to ensure consistency and completeness in data collection.\nFirst, the authors collected a combination of qualitative or quantitative data from the final list of publications in a ‘s-charting form’ ( Arksey and O’Malley, 2005 ), reporting: (i) study characteristics, including study design and sample size of study groups; (ii) sample location and (iii) sampling method; (iv) population studies characteristics; (v) inclusion and exclusion criteria; (vi) endometriosis stage/phenotype and diagnostic methods; (vii) technical characteristics of microbiome analysis; (viii) technical characteristics of bioinformatic analysis; and (ix) descriptive results, including alpha and beta diversity (a glossary of microbiome terminology is available in  Supplementary Table S1 ).\nTo ensure a structured evaluation of the quality of the studies included in this review, the widely recognized Newcastle–Ottawa Scale (NOS) was employed. This tool was used exclusively to assess the quality of patient selection and group comparability, and not to evaluate methodological or bioinformatic procedures such as sequencing or data analysis. The NOS consists of an 8-item checklist that focuses on three key domains: the selection of study participants, the comparability of groups, and the ascertainment of exposure or outcomes. In accordance with established guidance ( Stang, 2010 ), two independent reviewers applied the NOS criteria to each included study to ensure consistency and minimize subjective bias in the assessment.\n\nThe study selection process is summarized in the PRISMA flow diagram ( Fig. 1 ). A total of 2182 studies were initially retrieved from three databases: PubMed, EMBASE, and Web of Science. After removing 659 duplicates and excluding 509 studies published before 2016, 1014 records remained for title and abstract screening. Of these, 181 studies were selected for full-text review. Following the full-text assessment, 145 studies were excluded: 93 did not include a comparison between women with and without endometriosis, and 52 did not enable a molecular-level evaluation of the microbiome. Ultimately, 36 studies met the inclusion criteria and were included in the review. A summary of these studies is provided in  Table 1 .\nPRISMA flow diagram illustrating the study selection process .\nOverview of the articles included in the review.\nEutopic endometrium.\nPeritoneal fluid.\nFollicular or luteal. ( P =NS)\nStool.\nStool;\nVaginal fluid.\nVaginal fluid.\nDiverse phases ( P =NS)\nNo, and the use was in different rates between groups ( P =NR)\nStool;\nVaginal fluid;\nOropharyngeal fluid.\nVaginal fluid.\nStool.\nNo, but the frequency of antibiotics consumption in the last year was not statistically different between groups. ( P =NS)\nAnal fluid;\nVaginal fluid.\nStool;\nVaginal fluid;\nUterine fluid;\nEutopic endometrium;\nOropharyngeal fluid.\nNo ( P =NR)\nPeritoneal fluid;\nEndometrial fluid.\nEndometriotic tissue.\nMainly follicular (>90%) ( P =NR)\nStool.\nEndometriotic tissue;\nStool.\nCervical fluid;\nVaginal fluid.\nYes  (in the last 30 days)\nVaginal fluid;\nEutopic endometrium;\nEndometriotic tissue.\nFollicular or luteal. ( P =NR)\nYes  (in the last 30 days)\nStool.\nVaginal fluid.\nYes  (in the last 30 days)\nCervical fluid.\nPeritoneal fluid.\nFollicular or luteal. ( P =NS)\nStool;\nCervical fluid;\nPeritoneal fluid.\nFollicular or luteal. ( P =NR)\nAnal fluid;\nVaginal fluid.\nEutopic endometrium.\nFollicular, luteal, or menstrual. ( P =NS)\nEutopic endometrium.\nDiverse phases in similar rates between groups ( P =NR)\nVaginal fluid.\nDiverse phases ( P =NS)\nPeritoneal fluid’s EVs.\nStool.\nStool.\nOMA, SUP, and DIE\nNo, and the use of hormonal treatments was significantly higher in the endometriosis groups.  P  < 0.001\nNo, but its use was not statistically different between groups. ( P =NS)\nVaginal fluid;\nCervical mucus;\nPeritoneal fluid;\nEndometrial fluid.\nVaginal fluid;\nEutopic endometrium;\nEndometriotic tissue.\nAnal fluid;\nVaginal fluid.\nOMA, SUP, and DIE\nCervical mucus.\nFollicular or luteal, in the same proportion between groups. ( P =NS)\nStool;\nCervical fluid;\nVaginal fluid.\nOMA, SUP, and DIE\nFollicular or luteal. ( P =NS)\nPeritoneal fluid.\nCervical fluid;\nPeritoneal fluid;\nEndometriotic tissue;\nPeritoneal tissue.\nOMA, SUP, and DIE\nNo, the use of hormonal treatments was higher in the endometriosis group ( P =NR)\nFollicular or luteal. ( P =NS)\nEndometrial fluid (mixed with cells);\nOvarian cyst fluid.\nDiverse phases in different rates between groups ( P =NR)\nCCP, chronic pelvic pain; DIE, deep infiltrating endometriosis; Evs, extracellular vesicles; NR, not reported; ns, non-significant; OMA, ovarian endometriosis; rASRM, revised American Society of Reproductive Medicine classification; SUP, superficial endometriosis.\nOf the 36 studies included in this review ( Table 1 ), the types of biological samples analyzed were distributed as follows:\nstool/anal fluid: 15 studies (41.7%) ( Ata  et al. , 2019 ;  Perrotta  et al. , 2020 ;  Huang  et al. , 2021 ;  Le  et al. , 2021 ;  Shan  et al. , 2021 ;  Svensson  et al. , 2021 ;  Hu  et al. , 2023 ;  Pai  et al. , 2023 ;  Wei  et al. , 2023 ;  Do  et al. , 2024 ;  Guo  et al. , 2024 ;  Jimenez  et al. , 2024 ;  Marcos  et al. , 2024 ;  Pérez-Prieto  et al. , 2024 ;  Hicks  et al. , 2025 );\nvaginal fluid: 15 studies (41.7%) ( Ata  et al. , 2019 ;  Hernandes  et al. , 2020 ;  Perrotta  et al. , 2020 ;  Wei  et al. , 2020 ;  Chao  et al. , 2021 ;  Le  et al. , 2021 ;  Lu  et al. , 2022 ;  Muraoka  et al. , 2023 ;  Yang  et al. , 2023 ;  Do  et al. , 2024 ;  Jimenez  et al. , 2024 ;  MacSharry  et al. , 2024 ;  Marcos  et al. , 2024 ;  Sessa  et al. , 2024 ;  Hicks  et al. , 2025 );\ncervical mucus/fluid: seven studies (19.4%) ( Campos  et al. , 2018 ;  Akiyama  et al. , 2019 ;  Ata  et al. , 2019 ;  Wei  et al. , 2020 ;  Huang  et al. , 2021 ;  Chang  et al. , 2022 ;  Yang  et al. , 2023 );\nperitoneal fluid: eight studies (22.2%) ( Campos  et al. , 2018 ;  Wang  et al. , 2018 ;  Wei  et al. , 2020 ;  Huang  et al. , 2021 ;  Lee  et al. , 2021 ;  Yuan  et al. , 2022 ;  Zhu  et al. , 2024 ;  Malvezzi  et al. , 2025 );\nuterine/endometrial fluid: four studies (11.1%) ( Khan  et al. , 2016 ;  Wei  et al. , 2020 ;  Marcos  et al. , 2024 ;  Zhu  et al. , 2024 );\novarian cyst fluid: one study (2.7%) ( Khan  et al. , 2016 );\noropharyngeal fluid: two studies (5.6%) ( Marcos  et al. , 2024 ;  Hicks  et al. , 2025 );\neutopic endometrium: seven studies (19.4%) ( Hernandes  et al. , 2020 ;  Khan  et al. , 2021 ;  Wessels  et al. , 2021 ;  Muraoka  et al. , 2023 ;  Marcos  et al. , 2024 ;  Guo  et al. , 2025 );\nendometriotic tissue: five studies (13.9%) ( Campos  et al. , 2018 ;  Hernandes  et al. , 2020 ;  Hu  et al. , 2023 ;  Muraoka  et al. , 2023 ;  Chen  et al. , 2024 );\nThe majority of studies were conducted in East Asian populations: 14 in China ( Wang  et al. , 2018 ;  Wei  et al. , 2020 ,  2023 ;  Chao  et al. , 2021 ;  Huang  et al. , 2021 ;  Shan  et al. , 2021 ;  Lu  et al. , 2022 ;  Yuan  et al. , 2022 ;  Hu  et al. , 2023 ;  Yang  et al. , 2023 ;  Chen  et al. , 2024 ;  Guo  et al. , 2024 ,  2025 ;  Zhu  et al. , 2024 ), four in Japan ( Khan  et al. , 2016 ,  2021 ;  Akiyama  et al. , 2019 ;  Muraoka  et al. , 2023 ), two in Taiwan ( Chang  et al. , 2022 ;  Pai  et al. , 2023 ), and one in South Korea ( Lee  et al. , 2021 ). European-based studies included one in Ireland ( MacSharry  et al. , 2024 ), one in Italy ( Sessa  et al. , 2024 ), one in Sweden ( Svensson  et al. , 2021 ), one in Turkey ( Ata  et al. , 2019 ), and two in Spain, one involving a Spanish population ( Marcos  et al. , 2024 ) and the other an Estonian population ( Pérez-Prieto  et al. , 2024 ). Eight studies were from the Americas: four from Brazil ( Campos  et al. , 2018 ;  Hernandes  et al. , 2020 ;  Perrotta  et al. , 2020 ;  Malvezzi  et al. , 2025 ), three from the USA ( Le  et al. , 2021 ;  Do  et al. , 2024 ;  Jimenez  et al. , 2024 ), and one from Canada ( Wessels  et al. , 2021 ). Only one was conducted in Australia ( Hicks  et al. , 2025 ;  Table 1 ).\nThis review included only original studies that evaluated the gut or reproductive tract microbiome in women with endometriosis (cases) compared to women without the condition (controls). However, only a small number of included studies explicitly reported using a case-control design ( Hernandes  et al. , 2020 ;  Pai  et al. , 2023 ;  Pérez-Prieto  et al. , 2024 ;  Malvezzi  et al. , 2025 ). Four were reported as cross-sectional studies ( Perrotta  et al. , 2020 ;  Chao  et al. , 2021 ;  Wessels  et al. , 2021 ;  Sessa  et al. , 2024 ), and three as cohort studies ( Ata  et al. , 2019 ;  Marcos  et al. , 2024 ;  Hicks  et al. , 2025 ). Most studies, however, did not clearly report their study design ( Khan  et al. , 2016 ,  2021 ;  Campos  et al. , 2018 ;  Wang  et al. , 2018 ;  Akiyama  et al. , 2019 ;  Wei  et al. , 2020 ,  2023 ;  Huang  et al. , 2021 ;  Le  et al. , 2021 ;  Lee  et al. , 2021 ;  Shan  et al. , 2021 ;  Svensson  et al. , 2021 ;  Chang  et al. , 2022 ;  Lu  et al. , 2022 ;  Yuan  et al. , 2022 ;  Hu  et al. , 2023 ;  Muraoka  et al. , 2023 ;  Yang  et al. , 2023 ;  Chen  et al. , 2024 ;  Do  et al. , 2024 ;  Guo  et al. , 2024 ,  2025 ;  Jimenez  et al. , 2024 ;  MacSharry  et al. , 2024 ;  Zhu  et al. , 2024 ).\nSample sizes varied significantly across studies, ranging from as few as 21 participants ( Hernandes  et al. , 2020 ;  Wessels  et al. , 2021 ;  Marcos  et al. , 2024 ) to up to 1000 participants ( Pérez-Prieto  et al. , 2024 ). Notably, 30 out of 36 studies included 100 participants or fewer.\nThe composition of control groups was also highly heterogeneous. Only 10 studies ( Ata  et al. , 2019 ;  Chao  et al. , 2021 ;  Shan  et al. , 2021 ;  Svensson  et al. , 2021 ;  Chang  et al. , 2022 ;  Lu  et al. , 2022 ;  Wei  et al. , 2023 ;  Yang  et al. , 2023 ;  Guo  et al. , 2024 ;  Hicks  et al. , 2025 ) included at least a subgroup of healthy women as controls. In contrast, the remaining studies used control groups composed of women with other gynecological conditions unrelated to endometriosis, such as infertility, uterine fibroids, tubal obstruction, leiomyomas, ovarian cysts, and chronic pelvic pain.\nRegarding the type of endometriosis evaluated, five studies did not specify either the disease phenotype or the stages ( Chao  et al. , 2021 ;  Guo  et al. , 2024 ,  2025 ;  Marcos  et al. , 2024 ;  Pérez-Prieto  et al. , 2024 ). In contrast, only 11 studies provided complete information, reporting both the phenotype and stage of endometriosis ( Campos  et al. , 2018 ;  Ata  et al. , 2019 ;  Perrotta  et al. , 2020 ;  Khan  et al. , 2021 ;  Lee  et al. , 2021 ;  Yuan  et al. , 2022 ;  Hu  et al. , 2023 ;  Wei  et al. , 2023 ;  Yang  et al. , 2023 ;  Chen  et al. , 2024 ;  MacSharry  et al. , 2024 ). The methods used to diagnose endometriosis varied across the included studies. Most studies included only participants diagnosed through surgery followed by histological confirmation ( Khan  et al. , 2016 ,  2021 ;  Campos  et al. , 2018 ;  Akiyama  et al. , 2019 ;  Ata  et al. , 2019 ;  Hernandes  et al. , 2020 ;  Wei  et al. , 2020 ,  2023 ;  Huang  et al. , 2021 ;  Lee  et al. , 2021 ;  Shan  et al. , 2021 ;  Wessels  et al. , 2021 ;  Chang  et al. , 2022 ;  Lu  et al. , 2022 ;  Yuan  et al. , 2022 ;  Hu  et al. , 2023 ;  Pai  et al. , 2023 ;  Yang  et al. , 2023 ;  Chen  et al. , 2024 ;  Jimenez  et al. , 2024 ;  Zhu  et al. , 2024 ;  Guo  et al. , 2025 ;  Hicks  et al. , 2025 ;  Malvezzi  et al. , 2025 ).\nSeveral important confounders relevant to microbiome research were considered during this review, including the use of hormonal treatments, antibiotic usage, special dietary habits, and the menstrual phase at the time of sample collection ( Table 1 ). Notably, eight studies ( Campos  et al. , 2018 ;  Hernandes  et al. , 2020 ;  Chang  et al. , 2022 ;  Pai  et al. , 2023 ;  Guo  et al. , 2024 ;  Marcos  et al. , 2024 ;  Hicks  et al. , 2025 ;  Malvezzi  et al. , 2025 ) did not report whether hormonal treatment was used as an exclusion criterion or whether its use differed between groups. Similarly, nine studies either failed to report antibiotic use ( Khan  et al. , 2016 ;  Wang  et al. , 2018 ;  Le  et al. , 2021 ;  Wessels  et al. , 2021 ;  Chang  et al. , 2022 ) or did not clarify whether usage differed across groups ( Chen  et al. , 2024 ;  MacSharry  et al. , 2024 ;  Hicks  et al. , 2025 ;  Malvezzi  et al. , 2025 ).\nOnly three studies addressed dietary factors ( Table 1 ): two excluded participants with specific eating habits ( Pai  et al. , 2023 ;  Wei  et al. , 2023 ), while one prescribed a standardized diet prior to sampling ( Shan  et al. , 2021 ). In addition, several studies ( Hernandes  et al. , 2020 ;  Le  et al. , 2021 ;  Svensson  et al. , 2021 ;  Chang  et al. , 2022 ;  Lu  et al. , 2022 ;  Pai  et al. , 2023 ;  Do  et al. , 2024 ;  Guo  et al. , 2024 ,  2025 ;  Jimenez  et al. , 2024 ;  Pérez-Prieto  et al. , 2024 ;  Zhu  et al. , 2024 ;  Hicks  et al. , 2025 ) did not report the menstrual cycle phase during sample collection, or failed to clarify whether distribution across phases differed between groups ( Khan  et al. , 2016 ,  2021 ;  Huang  et al. , 2021 ;  Muraoka  et al. , 2023 ;  Chen  et al. , 2024 ). Specific differences among study groups will be addressed in the results for each sample type.\nTechnical characteristics of the 15 studies ( Ata  et al. , 2019 ;  Perrotta  et al. , 2020 ;  Huang  et al. , 2021 ;  Le  et al. , 2021 ;  Shan  et al. , 2021 ;  Svensson  et al. , 2021 ;  Hu  et al. , 2023 ;  Pai  et al. , 2023 ;  Wei  et al. , 2023 ;  Do  et al. , 2024 ;  Guo  et al. , 2024 ;  Jimenez  et al. , 2024 ;  Marcos  et al. , 2024 ;  Pérez-Prieto  et al. , 2024 ;  Hicks  et al. , 2025 ) providing comprehensive taxonomic evaluations of stool microbiome are detailed in  Table 2 .\nTechnical characteristics of microbiome analyses across stool/anal fluid samples in the included studies.\nWomen with endometriosis vs healthy controls\n18\nvs\n18\n32.23 ± 5.25\nvs\n31.40 ± 3.75  §\n( P =NR)\n22.53 ± 3.15\nvs\n21.14 ± 1.94  §\n( P =NR)\nTIANamp Stool DNA Kit—TianGen  (For extraction of high-quality genomic DNA from various stool samples)\nWomen with endometriosis vs women without endometriosis\n33\nvs\n15\n31.6 ± 0.8\nvs\n33.3 ± 1.8  §\n( P =NS)\n28.3 ± 1.3\nvs\n29.5 ± 1.9  §\n( P =NS)\nDNeasy PowerSoil Pro Kit—Qiagen  (For the isolation of microbial genomic DNA from all soil types)\nWomen with endometriosis vs (women with other gynecological diseases vs healthy controls)\n21\nvs\n(24 vs 19)\n35.9 ± 8.1\nvs\n(35.8 ± 7.3 vs 31.5 ± 3.5)  §\n( P =NS)\nPSP ®  Spin Stool DNA Basic Kit—Invitek  (For isolation of bacterial DNA and host DNA from stool samples)\nWomen with endometriosis vs women without endometriosis\n136\nvs\n864\n50.0 [40.8–57.9]\nvs\n45.0 [36.0–54.0]  †\n( P  = 0.005)\n25.1 [22.2–29.5]\nvs\n24.2 [21.6; 28.6]  †\n( P =NS)\nQIAamp Fast DNA Stool Mini Kit—Qiagen  (For isolation of gDNA from stool samples)\nWomen with chronic pelvic pain with endometriosis vs women with other gynecological disorders without chronic pelvic pain\n35\nvs\n15\n34.7 ± 8.8\nvs\n40.6 ± 8.2  §\n( P =NS)\nReported by three ranges of age ( P =NS).\nDNaeasy PowerSoil Pro Kit—Qiagen  (For the isolation of microbial genomic DNA from all soil types)\nInfertile women with endometriosis vs women with other infertility-related conditions\n8\nvs\n13\n42.7 ± 5.5\nvs\n39.4 ± 3.7  §\n( P =NS)\nQIAamp Fast DNA Tissue Kit—Qiagen  (For rapid isolation of genomic DNA from solid tissue samples)\nWomen with endometriosis vs women without endometriosis\n27\nvs\n24\n38.1 ± 1.0\nvs\n37.7 ± 1.3  §\n( P =NS)\n24.04 ± 0.87\nvs\n21.77 ± 0.73  §\n( P  = 0.051)\nQIAamp PowerFecal Pro DNA Kits—Qiagen  (For the isolation of microbial DNA from stool and gut samples)\nWomen with endometriotic cysts vs healthy controls\n14\nvs\n24\n30.6 [29.3–32.0]\nvs\n29.4 [28.4–30.4]  †\n( P =NS)\n20.29 [19.12–21.46]\nvs\n22.75 [21.23–24.27]  †\n( P  = 0.01)\nCetyltrimethylammonium bromide (CTAB)\n(Generic method for isolating genomic DNA from different tissues)\nInfertile women with endometriosis vs infertile and healthy women without endometriosis\n35\nvs\n(8 and 22)\n32.6 ± 5.7\nvs\n30.2 ± 5.6  §\n( P =NS)\n20.52 ± 2.02\nvs\n19.78 ± 1.55  §\n( P =NS)\nE.Z.N.A. ®  Soil DNA Kit—Omega Bio-Tek  (For isolation of DNA from soil samples)\nWomen with endometriosis vs healthy controls\n21\nvs\n20\n38.3 ± 7.88\nvs\n34.0 ± 10.8  §\n( P =NS)\n21.5 ± 2.79\nvs\n24.3 ± 8.16  §\n( P =NS)\nQuick-RNA Fecal/Soil Microbe Microprep Kit—Zymo Research  (For extract of RNA from various soil, fecal, and water samples)\nWomen with endometriosis vs women without endometriosis\n20\nvs\n9\n32.5 ± 1.1\nvs\n32.6 ± 2.0  §\n( P =NS)\n26.5 ± 1.5\nvs\n28.1 ± 2.4  §\n( P =NS)\nPowerMag Soil DNA Isolation Kit—MoBio  (For isolation of microbial DNA from all types of soil)\nWomen with endometriosis vs healthy controls\n12\nvs\n12\n32 ± 2\nvs\n32 ± 3  §\n( P =NS)\nE.Z.N.A. ®  Soil DNA Kit—Omega Bio-Tek  (For isolation of DNA from soil samples)\nWomen with endometriosis vs healthy controls\n66\nvs\n198\n37.8 [32.8–43.3]\nvs\n37.0 [32.0–44.0]  †\n( P =NS)\n37.8 [32.8–43.3]\nvs\n24.7 [22.1–27.5]  †\n( P =NS)\nQIAamp Fast DNA Stool Mini Kit—Qiagen  (For isolation of gDNA from stool samples)\nWomen with endometriosis vs women without endometriosis\n35\nvs\n24\n34.9 ± 6.8\nvs\n35.25 ± 6.9  §\n( P =NS)\n24.8 ± 4.5\nvs\n24.3 ± 2.7  §\n( P =NS)\nPowerMag Soil DNA Isolation Kit—MoBio  (For isolation of microbial DNA from all types of soil)\nWomen with endometriosis vs healthy controls\n14\nvs\n14\n28.5 [26.0–31.3]\nvs\n27.5 [25.8–30.0]  †\n( P =NS)\n23.0 [21.0–24.3]\nvs\n21.0 [20.1–24.2]  †\n( P =NS)\nQIAamp Fast DNA Stool Mini Kit—Qiagen  (For isolation of gDNA from stool samples)\nData reported as reported by the original papers, unless otherwise stated.\nBp, base pairs; NR, not reported; NS, non-significant; WGS, whole-genome sequencing.\nData are expressed as mean±SD.\nData are expressed as median [25th–75th percentile].\nSample sizes ranged from n = 8 to n = 136 for cases and n = 9 to n = 864 for controls. Only six studies ( Ata  et al. , 2019 ;  Shan  et al. , 2021 ;  Svensson  et al. , 2021 ;  Hu  et al. , 2023 ;  Wei  et al. , 2023 ;  Hicks  et al. , 2025 ) recruited healthy women as controls. Most studies reported comparable age between groups; however, the largest one ( Pérez-Prieto  et al. , 2024 ) showed a significant age difference ( P  = 0.005), and one study ( Guo  et al. , 2025 ) did not report this information. BMI was significantly different in  Hu  et al.  (2023 ;  P  = 0.01) and was unreported in four studies ( Shan  et al. , 2021 ;  Guo  et al. , 2024 ;  Marcos  et al. , 2024 ;  Hicks  et al. , 2025 ). All others reported BMI comparability between groups.\nEndometriosis diagnosis was surgical in nearly all studies, except for three that also included diagnoses based on imaging ( Perrotta  et al. , 2020 ;  Guo  et al. , 2024 ;  Marcos  et al. , 2024 ), generally indicating a focus on moderate to severe forms. Only six studies excluded participants on hormonal therapy ( Ata  et al. , 2019 ;  Perrotta  et al. , 2020 ;  Huang  et al. , 2021 ;  Shan  et al. , 2021 ;  Hu  et al. , 2023 ;  Wei  et al. , 2023 ) or on antibiotics therapy ( Ata  et al. , 2019 ;  Perrotta  et al. , 2020 ;  Huang  et al. , 2021 ;  Shan  et al. , 2021 ;  Hu  et al. , 2023 ;  Wei  et al. , 2023 ). Menstrual cycle phase was unreported in eight studies ( Le  et al. , 2021 ;  Svensson  et al. , 2021 ;  Pai  et al. , 2023 ;  Do  et al. , 2024 ;  Guo  et al. , 2024 ;  Jimenez  et al. , 2024 ;  Pérez-Prieto  et al. , 2024 ;  Hicks  et al. , 2025 ) and standardized to early follicular only in four ( Perrotta  et al. , 2020 ;  Shan  et al. , 2021 ;  Hu  et al. , 2023 ;  Wei  et al. , 2023 ). One study ( Huang  et al. , 2021 ) reported that the collection phase varied but whether there was a statistical difference between groups was not reported. Despite the known influence of diet on gut microbiota ( Flint  et al. , 2015 ), only two studies ( Pai  et al. , 2023 ;  Wei  et al. , 2023 ) excluded participants with specific dietary habits, and only one ( Shan  et al. , 2021 ) requested participants to follow a specific diet for 3 days before sample collection ( Table 1 ).\nMicrobiome analysis methods are summarized in  Table 2 . Notably, several studies used DNA extraction kits designed for soil ( Perrotta  et al. , 2020 ;  Le  et al. , 2021 ;  Shan  et al. , 2021 ;  Wei  et al. , 2023 ;  Do  et al. , 2024 ;  Jimenez  et al. , 2024 ) or for tissue samples ( Hu  et al. , 2023 ;  Marcos  et al. , 2024 ) rather than stool samples, potentially impacting microbial yield and composition.\nNearly all studies employed 16S rRNA sequencing on Illumina or Ion Torrent platforms, though the hypervariable regions targeted varied across studies. In contrast,  Pérez-Prieto  et al.  (2024)  employed shotgun metagenomic paired-end sequencing, allowing for broader and deeper taxonomic resolution through whole-genome profiling. Bioinformatics pipelines, including sequence filtering, chimera removal, operational taxonomic unit (OTU) clustering, and taxonomic assignment, differed widely across studies. A complete overview of the specific pipelines used is provided in  Supplementary Table S2 .\nAll studies, except one ( Perrotta  et al. , 2020 ), assessed alpha diversity, within-sample bacterial diversity, and beta diversity, between-sample bacterial composition differences ( Fig. 2 ). Significant differences in alpha diversity were observed in six studies ( Huang  et al. , 2021 ;  Svensson  et al. , 2021 ;  Hu  et al. , 2023 ;  Do  et al.  2024 ;  Hicks  et al.  2025;  Guo  et al. , 2024 ), and beta diversity differences were reported in another six ( Huang  et al. , 2021 ;  Shan  et al. , 2021 ;  Svensson  et al. , 2021 ;  Do  et al.  2024 ;  Hicks  et al.  2025 ;  Guo  et al. , 2024 ). However, each of these studies had at least one major methodologic or demographic variable unreported or significantly different between groups, limiting interpretation.\nAlpha diversity (within-sample bacterial diversity) and beta diversity (between-sample bacterial composition differences) comparisons between women with and without endometriosis across studies.  Red, diversity reported as statistically significant; green, diversity reported as not statistically significant; yellow, mixed findings, with some analyses showing statistical significance and others not; white, diversity not reported in the study.\nAmong the genera reported as significantly differing between women with and without endometriosis, only six genera were identified in more than one study, which were  Eubacterium dolichum  ( Huang  et al. , 2021 ;  Shan  et al. , 2021 ),  Haemophilus  sp. ( Marcos  et al. , 2024 ;  Hicks  et al. , 2025 ),  Phascolarctobacterium  sp. ( Jimenez  et al. , 2024 ;  Hicks  et al. , 2025 ),  Prevotella  sp. ( Shan  et al. , 2021 ;  Hu  et al. , 2023 ) with increased abundance in the endometriosis groups while  Fusicatenibacter  sp. ( Wei  et al. , 2023 ;  Guo  et al. , 2024 ), and  Lachnospira  sp. ( Shan  et al. , 2021 ;  Svensson  et al. , 2021 ;  Guo  et al. , 2024 ;  Hicks  et al. , 2025 ) were identified as being more abundant in the control groups.\nInterestingly, several other genera were reported as differentially abundant between groups but inconsistently since they were found to be increased in the endometriosis group in some studies and in the control group in others, indicating lack of consensus and highlighting variability in study design or populations ( Bacteroides  sp. ( Huang  et al. , 2021 ;  Svensson  et al. , 2021 ;  Hu  et al. , 2023 ;  Do  et al. , 2024 ;  Guo  et al. , 2024 ),  Bifidobacterium  sp. ( Shan  et al. , 2021 ;  Hu  et al. , 2023 ),  Blautia  sp. ( Huang  et al. , 2021 ;  Le  et al. , 2021 ;  Shan  et al. , 2021 ;  Guo  et al. , 2024 ),  Coprococccus  sp. ( Shan  et al. , 2021 ;  Svensson  et al. , 2021 ),  Dialister  sp. ( Le  et al. , 2021 ;  Wei  et al. , 2023 ;  Guo  et al. , 2024 ),  Dorea  sp. ( Huang  et al. , 2021 ;  Shan  et al. , 2021 ),  Escherichia  sp. ( Ata  et al. , 2019 ;  Hu  et al. , 2023 ),  Eubacterium  sp. ( Wei  et al. , 2023 ;  Jimenez  et al. , 2024 ;  Hicks  et al. , 2025 ),  Lactobacillus  sp. ( Jimenez  et al. , 2024 ;  Hicks  et al. , 2025 ),  Paraburkholderia  sp. ( Wei  et al. , 2023 ;  Guo  et al. , 2024 ),  Ruminococcus  sp. ( Ata  et al. , 2019 ;  Huang  et al. , 2021 ;  Guo  et al. , 2024 ;  Jimenez  et al. , 2024 ), and  Senegalimassilia  sp. ( Ata  et al. , 2019 ;  Jimenez  et al. , 2024 )) ( Fig. 3 ).\nBacterial genera identified across stool/anal fluid samples in the included studies.  E, genus’s abundance increased in endometriosis; C, genus’s abundance increased in controls; mid-pink, increased in endometriosis in two studies; light pink, increased in endometriosis in one study; grey, inconsistent findings across studies; light blue, decreased in endometriosis in one study; mid-blue, decreased in endometriosis in two studies; dark blue, decreased in endometriosis in ≥3 studies.\nAt higher taxonomic levels (phylum, class, order, family), 11 studies reported differential abundance ( Huang  et al. , 2021 ;  Le  et al. , 2021 ;  Shan  et al. , 2021 ;  Svensson  et al. , 2021 ;  Hu  et al. , 2023 ;  Pai  et al. , 2023 ;  Wei  et al. , 2023 ;  Guo  et al. , 2024 ;  Jimenez  et al. , 2024 ;  Marcos  et al. , 2024 ;  Hicks  et al. , 2025 ), when genus-level resolution was not achieved ( Supplementary Table S3 ).\nOverall, no consistent dysbiotic signature was identified across studies. Importantly, only  Pérez-Prieto  et al.  (2024)  applied Benjamini–Hochberg correction to account for multiple comparisons and reduce the likelihood of type I errors, underscoring a critical gap in statistical rigor in the current literature.\nFurthermore, among the studies that reported significant differences at the genus level between women with and without endometriosis, only four ( Huang  et al. , 2021 ;  Shan  et al. , 2021 ;  Hu  et al. , 2023 ;  Wei  et al. , 2023 ) were considered to be of moderate quality according to the NOS. The remaining studies ( Svensson  et al. , 2021 ;  Guo  et al. , 2024 ;  Jimenez  et al. , 2024 ;  Marcos  et al. , 2024 ;  Hicks  et al. , 2025 ) were assessed as low quality, indicating a higher risk of bias ( Supplementary Table S4 ).\nA total of 25 studies ( Khan  et al. , 2016 ;  Campos  et al. , 2018 ;  Wang  et al. , 2018 ;  Akiyama  et al. , 2019 ;  Ata  et al. , 2019 ;  Hernandes  et al. , 2020 ;  Perrotta  et al. , 2020 ;  Wei  et al. , 2020 ;  Chao  et al. , 2021 ;  Huang  et al. , 2021 ;  Le  et al. , 2021 ;  Lee  et al. , 2021 ;  Chang  et al. , 2022 ;  Lu  et al. , 2022 ;  Yuan  et al. , 2022 ;  Muraoka  et al. , 2023 ;  Yang  et al. , 2023 ;  Do  et al. , 2024 ;  Jimenez  et al. , 2024 ;  MacSharry  et al. , 2024 ;  Marcos  et al. , 2024 ;  Sessa  et al. , 2024 ;  Zhu  et al. , 2024 ;  Hicks  et al. , 2025 ;  Malvezzi  et al. , 2025 ) investigated the microbiome composition in various biological fluids to explore associations with endometriosis. Fluids analyzed included vaginal, cervical, peritoneal, uterine, ovarian cyst, and oropharyngeal samples (see  Tables 3–8  for study-specific details). Unlike fecal samples, biological fluids are complex matrices with inherently lower microbial biomass and, typically, their collection is more complex. Protocols for DNA extraction and microbiome analysis from these fluids are so far, less standardized, contributing to methodological heterogeneity across studies.\nTechnical characteristics of microbiome analyses across vaginal fluid samples in the included studies.\nWomen with endometriosis vs women without endometriosis\n33\nvs\n15\n31.6 ± 0.8\nvs\n33.3 ± 1.8  §\n( P =NS)\n28.3 ± 1.3\nvs\n29.5 ± 1.9  §\n( P =NS)\nDNeasy PowerSoil Pro Kit—Qiagen  (For the isolation of microbial genomic DNA from all soil types)\n(Women with mild/minimal endometriosis vs women with moderate/severe endometriosis) vs women without endometriosis\n(11 vs 10)\nvs\n19\n(35 [33–37] vs 33 [30–35])\nvs\n38 [35–40]  †\n( P =NR)\n(23.3 [22.6–24.1] vs 25.6 [22.2–26.8])\nvs\n23.0 [21.1–28.0]  †\n( P =NR)\nQIAamp UCP Pathogen Mini Kit—Qiagen  (For microbial DNA purification from whole blood, swabs, cultures, and body fluids)\nWomen with endometriosis vs (women with other gynecological diseases vs healthy controls)\n21\nvs\n(24 vs 19)\n35.9 ± 8.1\nvs\n(35.8 ± 7.3 vs 31.5 ± 3.5)  §\n( P =NS)\nQIAamp DNA Kit—Qiagen  (For isolation of genomic, mitochondrial, bacterial, parasite or viral DNA from tissues, swabs, CSF, blood, body fluids or washed cells from urine)\nFertile women with endometriosis vs fertile women without endometriosis\n24\nvs\n99\n27.4 ± 3.2\nvs\n25 ± 5.7  §\n( P =NS)\n22.5 ± 3.3\nvs\n22.7 ± 4.5  §\n( P =NS)\nDNeasy Blood and Tissue Kit—Qiagen  (For extraction of total DNA from animal blood and tissues and from cells, yeast, bacteria, or viruses)\nWomen with chronic pelvic pain with endometriosis vs women with other gynecological disorders without chronic pelvic pain\n35\nvs\n15\n34.7 ± 8.8\nvs\n40.6 ± 8.2  §\n( P =NS)\nReported by three ranges of age\n( P =NS)\nDNeasy PowerSoil Pro Kit—Qiagen  (For the isolation of microbial genomic DNA from all soil types)\nInfertile women with endometriosis vs women with other infertility-related conditions\n8\nvs\n13\n42.7 ± 5.5\nvs\n39.4 ± 3.7  §\n( P =NS)\nQIAamp Fast DNA Tissue Kit—Qiagen  (For rapid isolation of genomic DNA from solid tissue samples)\nWomen with endometrioma vs healthy controls\n19\nvs\n21\n29 [28–37]\nvs\n37 [34–40]  †\n( P =NR)\nDNeasy PowerLyzer PowerSoil Kit—Qiagen  (For isolation of DNA from tough soil microbes)\nWomen with endometriosis vs women without endometriosis\n10\nvs\n10\n34.5 [31.0–39.0]\nvs\n34.5 [32.0–37.0]  †\n( P =NR)\nQIAamp DNA Microbiome Kit—Qiagen  (For isolation of bacterial microbiome DNA from swab and body fluids)\nWomen with endometriosis vs healthy controls\n16\nvs\n18\n36.75 ± 7.11\nvs\n35 ± 6.61  §\n( P =NS)\n20.64 ± 3.04\nvs\n19.75 ± 1.47  §\n( P =NS)\nTIANamp Bacteria DNA Kit—TianGen  (For genomic DNA extraction from Gram-negative, Gram-positive bacteria, and pathogenic bacteria of food)\nWomen with endometriosis vs women with other benign gynecological indications\n20\nvs\n9\n32.5 ± 1.1\nvs\n32.6 ± 2.0  §\n( P =NS)\n26.5 ± 1.5\nvs\n28.1 ± 2.4  §\n( P =NS)\nPowerMag Soil DNA Isolation Kit—MoBio  (For isolation of microbial DNA from all types of soil)\nWomen with CPP with endometriosis vs (women with CPP without endometriosis vs healthy control)\n37\nvs\n(25 vs 66)\n39.89 ± 6.24\nvs\n( 37.56 ± 5.480 vs 38.23 ± 7.80)  §\n( P =NR)\nCetyltrimethylammonium bromide (CTAB)  (Generic method for isolating genomic DNA from different tissues)\nWomen with endometriosis vs women without endometriosis with other benign gynecological conditions\n36\nvs\n14\nQIAamp DNA Kit—Qiagen  (For isolation of genomic, mitochondrial, bacterial, parasite or viral DNA from tissues, swabs, CSF, blood, body fluids or washed cells from urine)\nWomen with endometriosis vs women without endometriosis with other benign gynecological conditions\n10\nvs\n11\nQIAamp DNA Blood Kit—Qiagen  (For purification of genomic, mitochondrial or viral DNA from blood and other body fluids)\nWomen with endometriosis vs women without endometriosis\n35\nvs\n24\n34.9 ± 6.8\nvs\n35.25 ± 6.9  §\n( P =NS)\n24.8 ± 4.5\nvs\n24.3 ± 2.7  §\n( P =NS)\nPowerMag Soil DNA Isolation Kit—MoBio  (For isolation of microbial DNA from all types of soil)\nWomen with endometriosis vs healthy controls\n14\nvs\n14\n28.5 [26–31.3]\nvs\n27.5 [25.8–30]  †\n( P =NS)\n23 (21–24.3)\nvs\n21 (20.1–24.2)  †\n( P =NS)\nQuickGene DNA Extraction Tissue Kit S—Biotec  (For isolation of genomic DNA)\nData reported as reported by the original papers, unless otherwise stated.\nbp = base pairs; CPP = chronic pelvic pain syndrome; NR = not reported; NR = not reported; NS = non-significant; nt = nucleotides;.\nLVFX, levofloxacin; qRT-PCR, quantitative real time-PCR; WGS, whole-genome sequencing.\nData are expressed as mean±SD.\nData are expressed as median [25th–75th percentile].\nTechnical characteristics of microbiome analyses across cervical fluid samples in the included studies.\nWomen with endometrioma vs healthy controls\n19\nvs\n21\n29 [28–37]\nvs\n37 [34–40]  †\n( P =NR)\nDNeasy PowerLyzer PowerSoil Kit—Qiagen  (For isolation of DNA from tough soil microbes)\nWomen with endometriosis vs healthy controls\n23\nvs\n10\n35 [30–39]  †\nvs\nNR\nTC Genomic DNA Isolation Kit—Fair Biotech  (For genomic DNA isolation from tissue samples)\nWomen with endometriosis vs women without endometriosis\n21\nvs\n20\n38.3 ± 7.88\nvs\n34.0 ± 10.8  §\n( P =NS)\n21.5 ± 2.79\nvs\n24.3 ± 8.16  §\n( P =NS)\nQuick-RNA Fecal/Soil Microbe Microprep Kit—Zymo Research  (For extract of RNA from various soil, fecal, and water samples)\nWomen with endometriosis vs women without endometriosis with other benign gynecological conditions\n36\nvs\n14\nQIAamp DNA Kit—Qiagen  (For isolation of genomic, mitochondrial, bacterial, parasite or viral DNA from tissues, swabs, CSF, blood, body fluids or washed cells from urine)\nWomen with endometriosis vs women without endometriosis\n30\nvs\n39\n33.9 ± 5.7\nvs\n32.5 ± 6.0  §\n( P =NS)\n21.3 ± 3.2\nvs\n20.5 ± 2.8  §\n( P =NS)\nNucleoSpin Microbial DNA Mini kit—Macherey‐Nagel  (For Isolation of total DNA from Gram-positive and -negative bacteria, yeast, and fungi)\nWomen with endometriosis vs healthy controls\n14\nvs\n14\n28.5 [26–31.3]\nvs\n27.5 [25.8–30]  †\n( P =NS)\n23 (21–24.3)\nvs\n21 (20.1–24.2)  †\n( P =NS)\nQuickGene DNA Extraction Tissue Kit S—Biotec  (For isolation of genomic DNA)\nWomen with endometriosis vs women without endometriosis\n73\nvs\n31\n36 [15–49]\nvs\n39 [26–51]  †\n( P =NS)\nReported by three ranges of age ( P =NS)\nPureLink Genomic DNA Mini Kit—Invitrogen  (For genomic DNA purification from blood, tissues, cells, bacteria, swabs, and blood spots)\nData reported as reported by the original papers, unless otherwise stated.\nBp, base pairs; NR, not reported; NS, non-significant; qRT-PCR, quantitative real time-PCR.\nData are expressed as mean±SD.\nData are expressed as median [25th–75th percentile].\nTechnical characteristics of microbiome analyses across peritoneal fluid samples in the included studies.\nWomen with endometriosis vs women without endometriosis\n27\nvs\n23\n34[31–41]\nvs\n42[34–46]  †\n( P  = 0.029)\n23[21–28]\nvs\n26[23–29]  †\n( P =NS)\nQIAamp DNA Kit—Qiagen  (For isolation of genomic, mitochondrial, bacterial, parasite or viral DNA from tissues, swabs, CSF, blood, body fluids or washed cells from urine)\n(Infertile women with endometriosis stage I/II vs stage III/IV) vs women with tubal obstruction-related infertility\n(8 vs 18)\nvs\n31\n(28.8 ± 4.4 vs 31.1 ± 5.6)\nvs\n31.0 ± 5.3  §\n( P =NS)\n(20.88 ± 2.05 vs 20.60 ± 2.83)\nvs\n23.14 ± 2.98  §\n( P  = 0.007)\nMagPure Soil DNA Kit—Magen  (For isolation of high-quality genomic DNA from various soil, stool, and other environmental samples)\nWomen with endometriosis vs women without endometriosis\n36\nvs\n25\n35.28 ± 7.24\nvs\n33.32 ± 8.04  §\n( P =NS)\n20.9 ± 2.11\nvs\n21.4 ± 2.03  §\n( P =NS)\nChloroform/Isoamyl Alcohol  (Generic method for purifying DNA from cells and soft tissues)\nWomen with endometriosis vs women without endometriosis\n21\nvs\n20\n38.3 ± 7.88\nvs\n34.0 ± 10.8  §\n( P =NS)\n21.5 ± 2.79\nvs\n24.3 ± 8.16  §\n( P =NS)\nQuick-RNA Fecal/Soil Microbe Microprep Kit—Zymo Research  (For extract of RNA from various soil, fecal, and water samples)\nWomen with endometriosis vs women without endometriosis\n45\nvs\n45\n36.2 ± 1.3\nvs\n39.4 ± 1.1  §\n( P =NS)\n36.2 ± 1.3\nvs\n39.4 ± 1.1  §\n( P =NS)\nPowerMag Soil DNA Isolation Kit—MoBio  (For isolation of microbial DNA from all types of soil)\nWomen with endometriosis vs women without endometriosis with other benign gynecological conditions\n36\nvs\n14\nQIAamp DNA Kit—Qiagen  (For isolation of genomic, mitochondrial, bacterial, parasite or viral DNA from tissues, swabs, CSF, blood, body fluids or washed cells from urine)\nInfertile women with endometriosis vs infertile women without endometriosis\n55\nvs\n30\n37.2 ± 8.2\nvs\n37.7 ± 7.4  §\n( P =NS)\n22.5 ± 2.3\nvs\n22.9 ± 2.1  §\n( P =NS)\nMagicPure Soil and Stool Genomic DNA Kit–TransGen Biotech  (For DNA purification from various types of soil and stool samples)\nWomen with endometriosis vs women without endometriosis\n54\nvs\n24\nPureLink Genomic DNA Mini Kit—Invitrogen  (For genomic DNA purification from blood, tissues, cells, bacteria, swabs, and blood spots)\nData reported as reported by the original papers, unless otherwise stated.\nBp, base pairs; NR, not reported; NS, non-significant; qRT-PCR, quantitative real time-PCR.\nData are expressed as mean±SD.\nData are expressed as median [25th–75th percentile].\nTechnical characteristics of microbiome analyses across uterine fluid samples in the included studies.\nInfertile women with endometriosis vs women with other infertility-related conditions\n8\nvs\n13\n42.7 ± 5.5\nvs\n39.4 ± 3.7  §\n( P =NS)\nQIAamp Fast DNA Tissue Kit—Qiagen  (For rapid isolation of genomic DNA from solid tissue samples)\n(Infertile women with endometriosis stage I/II vs stage III/IV) vs women with tubal obstruction-related infertility\n(8 vs 18)\nvs\n31\n(28.8 ± 4.4 vs 31.1 ± 5.6)\nvs\n31.0 ± 5.3  §\n( P =NS)\n(20.88 ± 2.05 vs 20.60 ± 2.83)\nvs\n23.14 ± 2.98  §\n( P  = 0.007)\nMagPure Soil DNA Kit—Magen  (For isolation of high-quality genomic DNA from various soil, stool, and other environmental samples)\nWomen with endometriosis vs women without endometriosis with other benign gynecological conditions\n36\nvs\n14\nQIAamp DNA Kit—Qiagen  (For isolation of genomic, mitochondrial, bacterial, parasite or viral DNA from tissues, swabs, CSF, blood, body fluids or washed cells from urine)\n(Women with endometriosis using GnRHa vs not using GnRHa) vs (women without endometriosis using GnRH analogue vs not using GnRH analogue)\n(16 vs 16)\nvs\n(16 vs 16)\n(37.5 ± 5.6 vs 35.7 ± 8.3)\nvs\n(42.1 ± 8.6 vs 33.6 ± 8.9;  P  < 0.01)  §\n( P =NR)\nUltraClean ®  Soil DNA Isolation Kit—MoBio  (For isolate cellular, PCR quality DNA from soil)\nData reported as reported by the original papers, unless otherwise stated.\nBp, base pairs; NR, not reported; NS, non-significant.\nData are expressed as mean±SD.\nTechnical characteristics of microbiome analyses across ovarian cyst fluid samples in the included studies.\nWomen with endometrioma not using GnRH analogue vs women with serous/mucinous cyst adenoma not using GnRH analogue\n8\nvs\n8\nUltraClean ®  Soil DNA Isolation Kit—MoBio  (For isolate cellular, PCR quality DNA from soil)\nData reported as reported by the original papers, unless otherwise stated.\nNR, not reported.\nTechnical characteristics of microbiome analyses across oropharyngeal fluid samples in the included studies.\nWomen with endometriosis vs (women with other gynecological diseases vs healthy controls)\n21\nvs\n(24 vs 19)\n35.9 ± 8.1\nvs\n(35.8 ± 7.3 vs 31.5 ± 3.5)  §\n( P =NS)\nQIAamp DNA Kit—Qiagen  (For isolation of genomic, mitochondrial, bacterial, parasite or viral DNA from tissues, swabs, CSF, blood, body fluids or washed cells from urine)\nInfertile women with endometriosis vs women with other infertility-related conditions\n8\nvs\n13\n42.7 ± 5.5\nvs\n39.4 ± 3.7  §\n( P =NS)\nQIAamp Fast DNA Tissue Kit—Qiagen  (For rapid isolation of genomic DNA from solid tissue samples)\nData reported as reported by the original papers, unless otherwise stated.\nBp, base pairs; NR, not reported; NS, non-significant.\nData are expressed as mean±SD.\nConsequently, it is not surprising that DNA extraction methods varied substantially. The majority of studies ( Khan  et al. , 2016 ;  Wang  et al. , 2018 ;  Hernandes  et al. , 2020 ;  Perrotta  et al. , 2020 ;  Chao  et al. , 2021 ;  Huang  et al. , 2021 ;  Lee  et al. , 2021 ;  Chang  et al. , 2022 ;  Lu  et al. , 2022 ;  Yuan  et al. , 2022 ;  Yang  et al. , 2023 ;  Do  et al. , 2024 ;  Jimenez  et al. , 2024 ;  Marcos  et al. , 2024 ;  Sessa  et al. , 2024 ;  Zhu  et al. , 2024 ) employed DNA extraction kits not specifically designed for fluid samples, using kits optimized for soil, feces, or tissues. For instance, one study ( Chao  et al. , 2021 ) used cetyltrimethylammonium bromide (CTAB), a general-purpose method traditionally used for plants and tissues, to isolate DNA from vaginal fluid. Similarly, another study ( Yuan  et al. , 2022 ) extracted DNA from peritoneal fluid using chloroform/isoamyl alcohol, a standard method for DNA purification from soft tissues and cells.\nDespite the use of extraction methods not tailored for low-biomass fluid samples or microbiome-specific applications, most studies reported adequate DNA recovery, enabling successful sequencing and subsequent microbiome analysis.\nTechnical characteristics of the 15 studies ( Ata  et al. , 2019 ;  Hernandes  et al. , 2020 ;  Perrotta  et al. , 2020 ;  Wei  et al. , 2020 ;  Chao  et al. , 2021 ;  Le  et al. , 2021 ;  Lu  et al. , 2022 ;  Muraoka  et al. , 2023 ;  Yang  et al. , 2023 ;  Jimenez  et al. , 2024 ;  MacSharry  et al. , 2024 ;  Marcos  et al. , 2024 ;  Sessa  et al. , 2024 ;  Hicks  et al. , 2025 ) analyzing the vaginal fluid microbiome in women with endometriosis are detailed in  Table 3 .\nSample sizes were generally small, ranging from n = 8 to n = 37 for endometriosis cases and n = 9 to n = 99 for controls. Only five studies recruited healthy women as controls ( Ata  et al. , 2019 ;  Lu  et al. , 2022 ;  Yang  et al. , 2023 ) or as a subgroup within the control population ( Chao  et al. , 2021 ;  Hicks  et al. , 2025 ). Some studies further limited their scope to specific subtypes of endometriosis. For instance, while  Muraoka  et al.  (2023)  enrolled 144 participants, only n = 10 cases of ovarian endometriosis were included for vaginal fluid analysis. Similarly,  Yang  et al.  (2023)  and  Hernandes  et al.  (2020)  focused solely on deep endometriosis, analyzing n = 19 and n = 10 vaginal fluid samples, respectively.\nAge was reported and found to be comparable between groups in most studies, though four ( Chao  et al. , 2021 ;  Muraoka  et al. , 2023 ;  Yang  et al. , 2023 ;  MacSharry  et al. , 2024 ) did not report whether significant differences existed, and two ( Hernandes  et al. , 2020 ;  Wei  et al. , 2020 ) did not report age data at all. BMI was not reported in seven studies ( Hernandes  et al. , 2020 ;  Wei  et al. , 2020 ;  Chao  et al. , 2021 ;  Muraoka  et al. , 2023 ;  Yang  et al. , 2023 ;  Marcos  et al. , 2024 ;  Hicks  et al. , 2025 ).\nDiagnosis of endometriosis was primarily surgical, although three studies ( Perrotta  et al. , 2020 ;  Marcos  et al. , 2024 ;  Sessa  et al. , 2024 ) also accepted imaging-based diagnoses, suggesting a focus on moderate to severe cases. However, eight studies ( Wei  et al. , 2020 ;  Chao  et al. , 2021 ;  Le  et al. , 2021 ;  Lu  et al. , 2022 ;  Do  et al. , 2024 ;  Jimenez  et al. , 2024 ;  Marcos  et al. , 2024 ;  Hicks  et al. , 2025 ) did not specify the phenotype of endometriosis and two of them ( Chao  et al. , 2021 ;  Marcos  et al. , 2024 ) also omitted information on disease stage.\nHormonal treatment was an exclusion criterion in seven studies ( Ata  et al. , 2019 ;  Perrotta  et al. , 2020 ;  Wei  et al. , 2020 ;  Lu  et al. , 2022 ;  Muraoka  et al. , 2023 ;  Yang  et al. , 2023 ;  MacSharry  et al. , 2024 ) while two studies did not report hormonal treatment status at all ( Hernandes  et al. , 2020 ;  Hicks  et al. , 2025 ). Among the remaining studies, several reported hormone usage, but only four ( Chao  et al. , 2021 ;  Le  et al. , 2021 ;  Do  et al. , 2024 ;  Jimenez  et al. , 2024 ) confirmed balanced distribution across groups. The other two ( Marcos  et al. , 2024 ;  Sessa  et al. , 2024 ) did not clarify this distribution.\nRegarding antibiotic use, only three studies ( Le  et al. , 2021 ;  MacSharry  et al. , 2024 ;  Hicks  et al. , 2025 ) did not consider antibiotic treatments as an exclusion criterion; furthermore, none of these clarify whether its use was similar between groups.\nThere was marked inconsistency in accounting for the menstrual cycle phase at the time of vaginal fluid collection. Six studies ( Hernandes  et al. , 2020 ;  Le  et al. , 2021 ;  Lu  et al. , 2022 ;  Do  et al. , 2024 ;  Jimenez  et al. , 2024 ;  Hicks  et al. , 2025 ) did not report this information. One study ( Perrotta  et al. , 2020 ) collected samples during both menstrual and follicular phases. Among the others, either the distribution of phases was similar between groups ( Ata  et al. , 2019 ;  Chao  et al. , 2021 ;  Muraoka  et al. , 2023 ;  MacSharry  et al. , 2024 ), or samples were collected during a uniform phase across participants, through the chosen phase differed across studies: ovulatory phase ( Marcos  et al. , 2024 ;  Sessa  et al. , 2024 ); early follicular phase ( Wei  et al. , 2020 ); or follicular phase ( Yang  et al. , 2023 ).\nAs with stool and anal fluid studies, dietary factors were not thoroughly considered. No study excluded participants based on special diets (such as vegetarianism), which could influence microbiome composition ( Table 1 ).\nMicrobiome analysis details are summarized in  Table 3 . The majority of studies utilized 16S rRNA gene sequencing on Illumina or Ion Torrent platforms ( Ata  et al. , 2019 ;  Hernandes  et al. , 2020 ;  Perrotta  et al. , 2020 ;  Wei  et al. , 2020 ;  Chao  et al. , 2021 ;  Le  et al. , 2021 ;  Lu  et al. , 2022 ;  Yang  et al. , 2023 ;  Do  et al. , 2024 ;  Jimenez  et al. , 2024 ;  Marcos  et al. , 2024 ;  Sessa  et al. , 2024 ;  Hicks  et al. , 2025 ). Only one study ( MacSharry  et al. , 2024 ) conducted shotgun metagenomic paired-end sequencing, a technique based on whole-genome sequencing.\nIn one case ( Muraoka  et al. , 2023 ), the analysis involved bioinformatic reanalysis of previously deposited datasets (The European Nucleotide Archive: PRJEB16013 and PRJEB21098), followed by quantitative real-time polymerase chain reaction (qRT-PCR) to detect a specific bacterial species ( Supplementary Table S2 ). Bioinformatics pipelines, including sequence filtering, chimera removal, OTU clustering, and taxonomic assignment, varied widely across studies. A detailed overview is provided in  Supplementary Table S2 .\nAlpha and beta diversity metrics were reported in nearly all studies ( Fig. 2 ), with the exception of four ( Perrotta  et al. , 2020 ;  Wei  et al. , 2020 ;  Muraoka  et al. , 2023 ;  Marcos  et al. , 2024 ). Significant differences in alpha diversity between endometriosis cases and controls were consistently reported in only two studies ( Yang  et al. , 2023 ;  MacSharry  et al. , 2024 ). Four additional studies observed differences only in specific sub-analyses ( Chao  et al. , 2021 ;  Le  et al. , 2021 ;  Do  et al. , 2024 ;  Sessa  et al. , 2024 ).\nBeta diversity findings were similarly variable. Five studies ( Ata  et al. , 2019 ;  Le  et al. , 2021 ;  Yang  et al. , 2023 ;  Jimenez  et al. , 2024 ;  Hicks  et al. , 2025 ) reported no significant difference in community composition between groups, while one study ( Lu  et al. , 2022 ) found a significant overall difference. Five other studies ( Hernandes  et al. , 2020 ;  Chao  et al. , 2021 ;  Do  et al. , 2024 ;  MacSharry  et al. , 2024 ;  Sessa  et al. , 2024 ) reported differences only within subgroup analyses.\nAmong the genera found to differ significantly between endometriosis and control groups, only nine taxa were reported in two or more studies with consistent findings ( Fig. 4A ).  Alloscardovia  sp. ( Chao  et al. , 2021 ;  Lu  et al. , 2022 ;  MacSharry  et al. , 2024 ),  Anaerococcus  sp. ( Jimenez  et al. , 2024 ;  MacSharry  et al. , 2024 ),  Clostridium  sp. ( Chao  et al. , 2021 ;  Le  et al. , 2021 ),  Corynebacterium  sp. ( Jimenez  et al. , 2024 ;  MacSharry  et al. , 2024 ),  Escherichia  sp. ( Ata  et al. , 2019 ;  Sessa  et al. , 2024 ;  Hicks  et al. , 2025 ),  Fusobacterium  sp. ( Muraoka  et al. , 2023 ;  Jimenez  et al. , 2024 ),  Streptococcus  sp. ( Yang  et al. , 2023 ;  Jimenez  et al. , 2024 ), and  Veillonella  sp. ( Chao  et al. , 2021 ;  Yang  et al. , 2023 ;  MacSharry  et al. , 2024 ) had increased abundance in the endometriosis groups while  Pseudomonas  sp. ( Sessa  et al. , 2024 ;  Hicks  et al. , 2025 ) was identified as being more abundant in the control groups. Other genera exhibited inconsistent trends, showing increased abundance in endometriosis in some studies and in controls in others, such as  Atopobium  sp. ( Ata  et al. , 2019 ;  Le  et al. , 2021 ;  Lu  et al. , 2022 ),  Bifidobacterium  ( Yang  et al. , 2023 ;  Jimenez  et al. , 2024 ;  Sessa  et al. , 2024 ),  Gardnerella  sp. ( Ata  et al. , 2019 ;  Hernandes  et al. , 2020 ;  Le  et al. , 2021 ;  Lu  et al. , 2022 ;  Yang  et al. , 2023 ;  Marcos  et al. , 2024 ),  Lactobacillus  sp. ( Hernandes  et al. , 2020 ;  Wei  et al. , 2020 ;  Chao  et al. , 2021 ;  Le  et al. , 2021 ;  Lu  et al. , 2022 ;  Yang  et al. , 2023 ;  MacSharry  et al. , 2024 ;  Sessa  et al. , 2024 ),  Limosilactobacillus  sp. ( Yang  et al. , 2023 ;  Jimenez  et al. , 2024 ;  Sessa  et al. , 2024 ),  Megasphaera  sp. ( Yang  et al. , 2023 ;  Sessa  et al. , 2024 ),  Prevotella  ( Hernandes  et al. , 2020 ;  Wei  et al. , 2020 ;  Le  et al. , 2021 ;  Yang  et al. , 2023 ;  Jimenez  et al. , 2024 ;  Sessa  et al. , 2024 ;  Hicks  et al. , 2025 ),  Sneathia  sp. ( Chao  et al. , 2021 ;  Yang  et al. , 2023 ;  Sessa  et al. , 2024 ;  Hicks  et al. , 2025 ). One study ( Perrotta  et al. , 2020 ) conducted a subgroup analysis by endometriosis stage and found that  Anaerococcus  sp. was significantly increased in stage III-IV disease compared to stage I-II. Furthermore, among studies that reported significant differences at the genus level between women with and without endometriosis, half of them ( Le  et al. , 2021 ;  Lu  et al. , 2022 ;  Muraoka  et al. , 2023 ;  Jimenez  et al. , 2024 ;  Hicks  et al. , 2025 ) were considered to be of low or moderate quality according to the NOS. In contrast, the remaining studies ( Chao  et al. , 2021 ;  Yang  et al. , 2023 ;  MacSharry  et al. , 2024 ;  Sessa  et al. , 2024 ) were assessed as high quality, indicating a low risk of bias ( Supplementary Table S4 ).\nBacterial genera identified across vaginal and cervical samples in the included studies.  E, genus’s abundance increased in endometriosis; C, genus’s abundance increased in controls; dark pink, increased in endometriosis in ≥3 studies; mid-pink, increased in endometriosis in two studies; light pink, increased in endometriosis in one study; grey, inconsistent findings across studies; light blue, decreased in endometriosis in one study; mid-blue, decreased in endometriosis in two studies. ( A ) Vaginal fluid. ( B ) Cervical fluid.\nSeven studies also reported microbial differences at higher taxonomic ranks (phylum, class, order, or family) ( Wei  et al. , 2020 ;  Chao  et al. , 2021 ;  Le  et al. , 2021 ;  Lu  et al. , 2022 ;  Marcos  et al. , 2024 ;  Sessa  et al. , 2024 ;  Hicks  et al. , 2025 ) ( Supplementary Table S3 ).\nTechnical characteristics of the five studies investigating the microbiome of cervical fluid ( Campos  et al. , 2018 ;  Ata  et al. , 2019 ;  Huang  et al. , 2021 ;  Chang  et al. , 2022 ;  Yang  et al. , 2023 ) and the two in the cervical mucus ( Akiyama  et al. , 2019 ;  Wei  et al. , 2020 ) are detailed in  Table 4 .\nThe sample sizes varied considerably: the number of endometriosis cases ranged from n = 14 to n = 73, and controls from n = 10 to n = 39. Only three studies ( Ata  et al. , 2019 ;  Chang  et al. , 2022 ;  Yang  et al. , 2023 ) included healthy women as controls, whereas the others used symptomatic women with other gynecological conditions.\nDemographic data such as age and BMI were inconsistently reported. Age was found to be comparable between groups in most studies, except for one ( Yang  et al. , 2023 ) that did not report whether differences were significant, and two studies ( Wei  et al. , 2020 ;  Chang  et al. , 2022 ), which did not report age data for at least one group. BMI was not reported in three studies ( Wei  et al. , 2020 ;  Chang  et al. , 2022 ;  Yang  et al. , 2023 ), while others confirmed no significant difference between groups.\nAll studies confirmed endometriosis diagnosis via surgery and histological analysis, and most included women with moderate to severe disease. However, four studies ( Akiyama  et al. , 2019 ;  Wei  et al. , 2020 ;  Huang  et al. , 2021 ;  Chang  et al. , 2022 ) did not specify the phenotype of endometriosis. Only  Yang  et al.  (2023)  limited their analysis to endometrioma cases ( Table 1 ).\nRegarding hormonal treatments, five studies ( Akiyama  et al. , 2019 ;  Ata  et al. , 2019 ;  Wei  et al. , 2020 ;  Huang  et al. , 2021 ;  Yang  et al. , 2023 ) excluded participants receiving hormonal therapy. One study ( Chang  et al. , 2022 ) did not report on hormonal treatment, while another ( Campos  et al. , 2018 ) allowed inclusion of participants undergoing treatment, with reported use in 30.1% of cases and 16.1% of controls, though statistical significance of this difference was not provided.\nOnly one study ( Chang  et al. , 2022 ) did not consider antibiotic use as an exclusion criterion and did not report whether usage differed between groups.\nMenstrual cycle phase at sample collection was not standardized: two studies ( Chang  et al. , 2022 ;  Yang  et al. , 2023 ) did not report the cycle phase; one ( Wei  et al. , 2020 ) collected samples in the early follicular phase; another ( Yang  et al. , 2023 ) consistently used the follicular phase.The other studies collected samples during either the follicular or luteal phase. Some reported no significant differences between phases within groups ( Campos  et al. , 2018 ;  Akiyama  et al. , 2019 ;  Ata  et al. , 2019 ) while one study did not specify whether a difference existed ( Huang  et al. , 2021 ).\nNo study in this group considered dietary habits as an exclusion criterion.\nMicrobiome analysis details are summarized in  Table 4 . All studies used 16S rRNA sequencing on Illumina or Ion Torrent platforms, except for one ( Campos  et al. , 2018 ), who applied qRT-PCR to detect specific bacterial species ( Supplementary Table S2 ). However, the 16S regions targeted were not consistent across studies. Bioinformatics pipelines, including sequence filtering, chimera removal, OTU clustering, and taxonomic assignment, varied widely across studies. A detailed overview is provided in  Supplementary Table S2 .\nAlpha diversity ( Fig. 2 ) was reported by five studies ( Akiyama  et al. , 2019 ;  Ata  et al. , 2019 ;  Huang  et al. , 2021 ;  Chang  et al. , 2022 ;  Yang  et al. , 2023 ), but only three ( Akiyama  et al. , 2019 ;  Chang  et al. , 2022 ;  Yang  et al. , 2023 ) found statistically significant differences in bacterial diversity between cases and controls.\nBeta diversity was evaluated in six studies ( Campos  et al. , 2018 ;  Akiyama  et al. , 2019 ;  Ata  et al. , 2019 ;  Huang  et al. , 2021 ;  Chang  et al. , 2022 ;  Yang  et al. , 2023 ), but only two ( Campos  et al. , 2018 ;  Chang  et al. , 2022 ) reported a statistically significant difference in microbial composition between women with and without endometriosis.\nMicrobiome composition at the genus level was reported in all studies ( Fig. 4B ). Only four genera were consistently identified in two or more studies as being more abundant in endometriosis, which were  Bifidobacterium  sp. ( Chang  et al. , 2022 ;  Yang  et al. , 2023 ),  Pseudomonas  sp. ( Akiyama  et al. , 2019 ;  Wei  et al. , 2020 ),  Streptococcus  sp. ( Akiyama  et al. , 2019 ;  Ata  et al. , 2019 ;  Chang  et al. , 2022 ;  Yang  et al. , 2023 ), and  Veillonella  sp. ( Wei  et al. , 2020 ;  Yang  et al. , 2023 ).  Megasphaera  sp. , Prevotella  sp. , Sneathia  sp. which were found to be more abundant in endometriosis cases in  Yang  et al.  (2023) , were more abundant in controls in  Ata  et al.  (2019) . Three studies ( Akiyama  et al. , 2019 ;  Wei  et al. , 2020 ;  Huang  et al. , 2021 ) also reported higher-level taxonomic data at the phylum, class, order, and/or family level ( Supplementary Table S3 ).\nFurthermore, among studies that reported significant differences at the genus level between women with and without endometriosis, two ( Akiyama  et al. , 2019 ;  Wei  et al. , 2020 ) were considered to be of moderate quality according to the NOS, while the other three ( Ata  et al. , 2019 ;  Chang  et al. , 2022 ;  Yang  et al. , 2023 ) were assessed as high quality, indicating a low risk of bias ( Supplementary Table S4 ).\nTechnical characteristics of the eight studies ( Campos  et al. , 2018 ;  Wang  et al. , 2018 ;  Wei  et al. , 2020 ;  Huang  et al. , 2021 ;  Lee  et al. , 2021 ;  Yuan  et al. , 2022 ;  Zhu  et al. , 2024 ;  Malvezzi  et al. , 2025 ) investigating the microbiome of peritoneal fluid in women with endometriosis are detailed in  Table 5 .\nThe number of endometriosis cases ranged from n = 21 to n = 55. Control groups included n = 14 to n = 45 participants, composed exclusively of women undergoing laparoscopy for gynecological conditions unrelated to endometriosis. In two studies ( Wang  et al. , 2018 ;  Zhu  et al. , 2024 ), controls also included infertile women. Most studies reported comparable age between groups; however, one study ( Malvezzi  et al. , 2025 ) observed a statistically significant age difference ( P  = 0.029). Two studies ( Campos  et al. , 2018 ;  Wei  et al. , 2020 ) did not report age data. BMI was significantly different in one study ( Zhu  et al. , 2024 ), unreported in two ( Campos  et al. , 2018 ;  Wei  et al. , 2020 ), and comparable across groups in the remaining studies.\nThe diagnosis of endometriosis was confirmed by surgery and histological examination in all studies, except for one ( Wang  et al. , 2018 ), which relied on surgical findings alone. An unusual methodological choice was made by  Lee  et al.  (2021) , who analyzed the microbiome in extracellular vesicles isolated from peritoneal fluid, rather than the fluid itself. The phenotypes of endometriosis investigated varied widely across studies, although all the studies included severe presentations of the disease.\nRegarding hormonal therapy, almost all studies considered it an exclusion criterion, except for  Malvezzi  et al.  (2025)  and  Campos  et al.  (2018) , who did not even report whether its distribution differed significantly between groups. Similarly, while most studies excluded participants with recent antibiotic use, two ( Wang  et al. , 2018 ;  Malvezzi  et al. , 2025 ) did not report data on this aspect.\nSignificant heterogeneity was observed in the menstrual cycle phase during which peritoneal fluid samples were collected. Some studies collected samples exclusively during the early follicular phase ( Wang  et al. , 2018 ;  Wei  et al. , 2020 ); while one study ( Lee  et al. , 2021 ) collected exclusively during the follicular phase. One study ( Zhu  et al. , 2024 ) did not report the timing of sample collection. Other studies collected samples at various phases of the menstrual cycle. Among them, three studies ( Campos  et al. , 2018 ;  Yuan  et al. , 2022 ;  Malvezzi  et al. , 2025 ) reported that the distribution of cycle phases was similar between study groups. In contrast, two studies ( Huang  et al. , 2021 ;  Zhu  et al. , 2024 ) did not indicate whether the phase distribution was comparable between cases and controls. No study accounted for dietary habits as an exclusion criterion.\nMicrobiome sequencing methodologies are summarized in  Table 5 . All studies used 16S rRNA gene sequencing via Illumina or Ion Torrent platforms, except for one ( Campos  et al. , 2018 ), who used qRT-PCR to target specific bacterial species. However, the 16S regions amplified differed between studies, and there was no standardized approach to bioinformatic processing. Details on pipelines used for filtering, chimeric sequence removal, identification of OTUs, etc, varied significantly. Notably, some studies provided minimal ( Wei  et al. , 2020 ;  Zhu  et al. , 2024 ) or no ( Wang  et al. , 2018 ) bioinformatics details ( Supplementary Table S2 ).\nAlpha diversity ( Fig. 2 ) was assessed in four studies ( Huang  et al. , 2021 ;  Lee  et al. , 2021 ;  Yuan  et al. , 2022 ;  Zhu  et al. , 2024 ). Only one ( Zhu  et al. , 2024 ) reported a significant difference, observed only between women with stage III-IV endometriosis and controls.\nBeta diversity was reported by five studies ( Campos  et al. , 2018 ;  Huang  et al. , 2021 ;  Lee  et al. , 2021 ;  Yuan  et al. , 2022 ;  Zhu  et al. , 2024 ). Significant differences in microbial community structure between endometriosis and control groups were consistently found in three studies ( Campos  et al. , 2018 ;  Lee  et al. , 2021 ;  Yuan  et al. , 2022 ), and in one study ( Zhu  et al. , 2024 ) only when comparing stage III-IV patients with both stage I-II and controls combined.\nConsidering the genera identified in the peritoneal fluid samples, significant differences in microbial composition were observed between women with and without endometriosis. Notably, an increased abundance of  Streptococcus  sp. was found in the endometriosis groups in two studies ( Lee  et al. , 2021 ;  Yuan  et al. , 2022 ).  Pseudomonas  was another genus consistently reported with higher abundance in endometriosis cases across multiple studies ( Wei  et al. , 2020 ;  Huang  et al. , 2021 ;  Lee  et al. , 2021 ;  Zhu  et al. , 2024 ;  Malvezzi  et al. , 2025 ), emerging as a predominant and recurring finding. This latter association was further supported by unsupervised analyses using random forest classifiers ( Huang  et al. , 2021 ). In contrast,  Lactobacillus iners  was reported to be more abundant in the control groups in two studies ( Wei  et al. , 2020 ;  Huang  et al. , 2021 ;  Fig. 5A ), suggesting a potential protective or non-pathogenic role.\nBacterial genera identified across peritoneal, uterine, and oropharyngeal fluid samples in the included studies.  E, genus’s abundance increased in endometriosis; C, genus’s abundance increased in controls; dark pink, increased in endometriosis in ≥3 studies; mid-pink, increased in endometriosis in two studies; light pink, increased in endometriosis in one study; grey, inconsistent findings across studies; light blue, decreased in endometriosis in one study; mid-blue, decreased in endometriosis in two studies. ( A ) Peritoneal fluid. ( B ) Uterine fluid. ( C ) Oropharyngeal fluid.\nDespite these consistent findings, notable inconsistencies were also observed. For instance,  Enhydrobacter  species ( Lee  et al. , 2021 ;  Zhu  et al. , 2024 ;  Malvezzi  et al. , 2025 ) and  Staphylococcus  sp. ( Zhu  et al. , 2024 ;  Malvezzi  et al. , 2025 ) were found to be more abundant in endometriosis cases in some studies, whereas other investigations reported higher levels in the control group. These divergent results may reflect methodological differences or population-specific factors across studies.\nIn addition to genus-level analysis, several studies also provided bacterial identification at higher taxonomic levels, including phylum, class, order, and family ( Supplementary Table S3 ).\nFurthermore, among studies that reported significant differences at the genus level between women with and without endometriosis, four ( Wei  et al. , 2020 ;  Huang  et al. , 2021 ;  Yuan  et al. , 2022 ;  Zhu  et al. , 2024 ) were considered to be of moderate quality according to the NOS. The other two ( Lee  et al. , 2021 ;  Malvezzi  et al. , 2025 ) were assessed as high quality, indicating a low risk of bias ( Supplementary Table S4 ).\nTechnical characteristics of the four studies ( Khan  et al. , 2016 ;  Wei  et al. , 2020 ;  Marcos  et al. , 2024 ;  Zhu  et al. , 2024 ) analyzing the uterine fluid microbiome in women with endometriosis are detailed in  Table 6 .\nThe sample sizes across these investigations were generally small, ranging from as few as n = 8 to a maximum of n = 36 cases, and from n = 14 to n = 32 controls ( Table 6 ). In all studies, the control groups comprised women with other gynecological conditions. Age was reported to be comparable between groups in two studies ( Marcos  et al. , 2024 ;  Zhu  et al. , 2024 ), whereas one study ( Khan  et al. , 2016 ) did not clarify whether age distributions were similar, and another ( Wei  et al. , 2020 ) did not provide age data at all. BMI was reported only by one study ( Zhu  et al. , 2024 ), which found a statistically significant difference between groups ( P  = 0.007).\nEndometriosis diagnosis was confirmed by surgery and histology in most of the studies, all of which included patients across different disease stages. Notably, the most recent study ( Marcos  et al. , 2024 ) based the diagnosis on imaging or surgical findings but did not report even the disease stage.\nRegarding hormonal treatment, there was considerable variation. In two studies ( Wei  et al. , 2020 ;  Zhu  et al. , 2024 ), hormonal treatments were an exclusion criterion. In contrast, in one ( Khan  et al. , 2016 ), half of the participants in each group were receiving GnRH analogues as part of the study design.  Marcos  et al.  (2024)  did not report whether hormonal therapy was an exclusion criterion or whether its use differed between groups. Most studies excluded participants with recent antibiotic use; however, one study ( Khan  et al. , 2016 ) did not report any information on this aspect.\nThere was also substantial heterogeneity regarding the menstrual cycle phase during which uterine fluid samples were collected.  Marcos  et al.  (2024)  collected samples during ovulation (Days 12–16 of the menstrual cycle),  Wei  et al.  (2020)  exclusively during the early follicular phase, while  Zhu  et al.  (2024)  did not report the timing of sample collection. In  Khan  et al.  (2016) , samples were collected across different phases of the cycle, but it was not specified whether this variability was distributed similarly between groups. Consistent with the majority of studies included in this review, none of the investigations evaluating the uterine fluid microbiome considered special dietary habits as an exclusion criterion.\nMicrobiome analysis details are presented in  Table 6 . All studies employed 16S rRNA sequencing using Illumina or Ion Torrent platforms; however, the amplified regions varied ( Wei  et al. , 2020 ;  Zhu  et al. , 2024 ), or were not reported at all ( Khan  et al. , 2016 ;  Marcos  et al. , 2024 ). Likewise, comprehensive details regarding bioinformatic analyses, such as pipelines used, filtering criteria, chimeric sequence removal, identification of OTUs, etc, were absent in most studies, with the exception of  Marcos  et al.  (2024) , who reported nearly all steps of the computational workflow ( Supplementary Table S2 ).\nOnly one study ( Zhu  et al. , 2024 ) assessed microbial diversity measures. In this study, alpha diversity was found to be similar between groups, while beta diversity differed significantly between women with stages III-IV endometriosis and those with stages I-II combined with controls ( Fig. 2 ). Regarding the taxonomic composition, only two studies ( Wei  et al. , 2020 ;  Zhu  et al. , 2024 ) reported the identification of bacterial genera in uterine or endometrial fluid samples. However, no specific taxa were consistently observed between these two studies ( Fig. 5B ).\nThe microbiome of ovarian cyst fluid in women with endometriosis has been examined in only a single study to date ( Khan  et al. , 2016 ), as detailed in  Table 1 . This study included a limited sample size, comprising just n = 8 cases and n = 8 controls ( Table 7 ). Information regarding patient selection, sample collection, as well as bioinformatic methods and pipelines, is summarized in  Tables 1  and  7  and  Supplementary Table S2 .\nNotably, the study did not evaluate alpha or beta diversity metrics ( Fig. 2 ). At the family level, ovarian cyst fluid from endometriosis patients showed an enrichment of  Streptococcaceae  and  Moraxellaceae , alongside a relative decrease of  Lactobacillaceae ,  Enterobacteriaceae , and  Staphylococcaceae , when compared to cyst fluid from women with other benign ovarian conditions ( Supplementary Table S3 ). These findings, while suggestive, remain preliminary and underscore the need for further confirmatory studies with larger cohorts and standardized methodologies.\nThe microbiome of oropharyngeal fluid in women with endometriosis has been investigated by only two studies to date ( Marcos  et al. , 2024 ;  Hicks  et al. , 2025 ), with technical characteristics summarized in  Table 8 .  Marcos  et al.  (2024)  included a small sample of n = 8 endometriosis cases and n = 13 infertile women without endometriosis as controls, while  Hicks  et al.  (2025)  evaluated a larger cohort comprising n = 21 women with endometriosis, n = 24 women with other gynecological conditions, and n = 19 healthy controls. As observed in other microbiome studies included in this review, critical variables that could influence microbial composition, such as hormonal use and dietary patterns, were not considered exclusion criteria in either study. Moreover, BMI was not reported, and  Hicks  et al.  (2025)  did not provide information on the timing of sample collection within the menstrual cycle or on the distribution of recent antibiotic use across groups.\nMicrobiome sequencing methods are detailed in  Table 8 , while the approaches to bioinformatic analyses, including pipelines, filtering, chimeric sequence removal, identification of OTUs, etc, varied widely and are presented in  Supplementary Table S2 . Of the two studies, only  Hicks  et al.  (2025)  evaluated alpha and beta diversity. While alpha diversity did not differ significantly between groups, beta diversity was found to be statistically different between them ( Fig. 2 ).\nRegarding microbial composition, certain taxa appeared to differ between women with and without endometriosis.  Haemophilus  sp. ( Marcos  et al. , 2024 ) and  Veillonella  sp. ( Hicks  et al. , 2025 ) were reported in greater abundance in the oropharyngeal fluid of women with endometriosis. Conversely, several genera, including  Actinobacillus  sp. , Bifidobacterium  sp. , Butyrivibrio  sp. , Lactobacillus  sp. , Lactococcus  sp. , Lautropia  sp. , Megasphaera  sp. , Neisseria  sp. , Prevotella  sp. ( Hicks  et al. , 2025 ), as well as  Streptococcus  sp. ( Marcos  et al. , 2024 ) were found to be more abundant in controls ( Fig. 5C ). At the family level ( Supplementary Table S3 ), endometriosis cases were associated with increased abundance of  Actinomycetaceae  ( Hicks  et al. , 2025 ) and  Enterobacteriaceae  ( Marcos  et al. , 2024 ), along with a reduction in  Peptostreptococcaceae  and  Neisseriaceae  ( Hicks  et al. , 2025 ). Despite these findings, inconsistencies across studies highlight the need for further research to confirm whether these microbial alterations are reproducible and relevant to the pathophysiology of endometriosis ( Supplementary Table S3 ).\nThe microbiome of eutopic endometrial tissue in women with endometriosis has been investigated in six studies ( Hernandes  et al. , 2020 ;  Khan  et al. , 2021 ;  Wessels  et al. , 2021 ;  Muraoka  et al. , 2023 ;  Marcos  et al. , 2024 ;  Guo  et al. , 2025 ), with technical characteristics summarized in  Table 9 .\nTechnical characteristics of microbiome analyses across eutopic endometrium samples in the included studies.\nSymptomatic women with endometriosis vs symptomatic women without endometriosis\n30\nvs\n13\n37.10 ± 7.30\nvs\n40.92 ± 7.41  §\n( P =NS)\n21.27 ± 1.94\nvs\n23.63 ± 3.37  §\n( P  < 0.01)\nQIAamp DNA Kit—Qiagen  (For isolation of genomic, mitochondrial, bacterial, parasite or viral DNA from tissues, swabs, CSF, blood, body fluids or washed cells from urine)\nInfertile women with endometriosis vs women with other infertility-related conditions\n8\nvs\n13\n42.7 ± 5.5\nvs\n39.4 ± 3.7  §\n( P =NS)\nQIAamp Fast DNA Tissue Kit—Qiagen  (For rapid isolation of genomic DNA from solid tissue samples)\nWomen with endometriosis vs women with other gynecological conditions\n42\nvs\n42\n34.5 [31.0–39.0]\nvs\n34.5 [32.0–37.0]  †\n( P =NR)\nWomen with pelvic pain with endometriosis vs women with pelvic pain without endometriosis\n12\nvs\n9\n33.8 ± 5.8\nvs\n35.1 ± 3.3  §\n( P =NS)\nRNeasy Kit—Qiagen  (For purification of total RNA from cells, tissues, and yeast)\n(Women with endometriosis receiving different treatments: untreated vs GnRHa vs LVFX vs GnRHa+ LVFX) vs (fertile women with uterine fibroids receiving different treatments: untreated vs GnRHa vs LVFX vs GnRHa+ LVFX)\n(21 vs 11 vs 15 vs 6)\nvs\n(11 vs 12 vs 10 vs 14)\n( P =NR)\n(36.3 ± 7.7 vs 38.7 ± 5.2 vs 38.2 ± 8.2 vs 35.5 ± 5.6)\nvs\n(41.2 ± 8.1 vs 37.5 ± 5.3 vs 43.0 ± 4.5 vs 36.7 ± 4.5)  §\n( P =NR)\nUltraClean ®  Soil DNA Isolation Kit—MoBio  (For isolate cellular, PCR quality DNA from soil)\nWomen with endometriosis vs women without endometriosis with other benign gynecological conditions\n10\nvs\n11\nDNeasy PowerSoil Pro Kit—Qiagen  (For the isolation of microbial genomic DNA from all soil types)\nData reported as reported by the original papers, unless otherwise stated.\nBp, base pairs; NR, not reported; NS, non-significant; nt, nucleotides; LVFX, levofloxacin; qRT-PCR, quantitative real time-PCR.\nData are expressed as mean±SD.\nData are expressed as median [25th–75th percentile].\nSample sizes varied considerably, ranging from as few as n=8 to a maximum of n=53 endometriosis cases, with one study further subdividing its few cases (n=53) based on treatment exposure, including GnRH analogues or levofloxacin. Control groups, generally composed of symptomatic or infertile women, or those with other gynecological conditions, ranged from n=9 to n=51 participants, with similar subgrouping applied in the latter.\nOnly half of the studies reported comparisons of age between cases and controls. One study ( Hernandes  et al. , 2020 ) did not provide age-related data, while two ( Khan  et al. , 2021 ;  Muraoka  et al. , 2023 ) reported ages without clarifying whether groups were statistically comparable. BMI was reported in only one study, which found a statistically significant difference between groups.\nThe diagnostic criteria for endometriosis were relatively consistent across studies, with most confirming diagnosis through both surgery and histological examination. While two studies ( Khan  et al. , 2021 ;  Muraoka  et al. , 2023 ) focused exclusively on ovarian endometriosis and one ( Hernandes  et al. , 2020 ) on deep disease, the remaining did not specify disease phenotype. Only one ( Wessels  et al. , 2021 ) reported that all the stages of endometriosis were included.\nThe use of hormonal treatment varied. In three studies ( Wessels  et al. , 2021 ;  Muraoka  et al. , 2023 ;  Guo  et al. , 2025 ), its use was an exclusion criterion. In contrast, in  Khan  et al.  (2021) , hormonal treatment with GnRH analogue was integrated into the study design, with participants evenly distributed across treatment groups.  Marcos  et al.  (2024)  did not clarify the compatibility of treatment usage between groups, and  Hernandes  et al.  (2020)  omitted this information entirely. Regarding use of antibiotics, it was excluded in nearly all studies, except one ( Wessels  et al. , 2021 ), who did not report it as an exclusion criterion, and another ( Khan  et al. , 2021 ), who incorporated levofloxacin use as part of the intervention. As with other sample types, none of the studies considered dietary habits as an exclusion criterion.\nDespite the known influence of hormonal cycles on endometrial physiology and microbial communities, the menstrual cycle phase at the time of sample collection was inconsistently addressed.  Marcos  et al.  (2024)  were the only authors to collect all the samples during a uniform menstrual phase, the time of ovulation.  Muraoka  et al.  (2023)  and  Wessels  et al.  (2021)  collected samples at different phases but reported that time did not differ significantly between groups.  Khan  et al.  (2021)  documented cycle phases but did not report on their statistical comparability.  Guo  et al.  (2025) , and  Hernandes  et al.  (2020)  did not mention the menstrual phase at all.\nThe sampling methods also varied across studies. Curettage was used in three studies ( Hernandes  et al. , 2020 ;  Wessels  et al. , 2021 ;  Guo  et al. , 2025 ), a seed swab in  Khan  et al.  (2021) , and an endometrial sampler in  Marcos  et al.  (2024) . In the study by  Muraoka  et al.  (2023) , samples were obtained during surgical removal of the uterus, although the methodology was not described in detail.\nMethodological heterogeneity was also reflected in DNA extraction protocols. Some studies employed kits originally intended for soil samples (such as UltraClean ®  Soil DNA Isolation Kit—MoBio ( Khan  et al. , 2021 ) and DNeasy PowerSoil Pro Kit—Qiagen ( Hernandes  et al. , 2020 )), raising concerns about methodological appropriateness for tissue microbiome profiling. Sequencing platforms were consistent, with nearly all studies employing 16S rRNA sequencing on Illumina or Ion Torrent systems; however, the regions of the 16S gene analyzed varied widely, ranging from the V3 region alone ( Wessels  et al. , 2021 ) to V3–V4 ( Hernandes  et al. , 2020 ;  Guo  et al. , 2025 ) and V5–V6 ( Khan  et al. , 2021 ), while  Marcos  et al.  (2024)  did not specify the region amplified.  Muraoka  et al.  (2023)  took a distinct approach, conducting a bioinformatic reanalysis of publicly available datasets (European Nucleotide Archive studies PRJEB16013 and PRJEB21098), followed by targeted qRT-PCR validation. Bioinformatic analyses, including sequence filtering, chimera removal, and OTU assignment, varied across studies, with further details presented in  Supplementary Table S2 .\nAlpha diversity findings were inconsistent ( Fig. 2 ). While  Hernandes  et al.  (2020)  reported no differences in microbial richness or evenness between women with and without endometriosis,  Guo  et al.  (2025) ,  Wessels  et al.  (2021) , and  Khan  et al.  (2021)  observed significant differences.  Khan  et al.  (2021)  additionally found that alpha diversity varied among treated and untreated endometriosis patients.\nBeta diversity analyses yielded similarly inconsistent results. Three studies ( Hernandes  et al. , 2020 ;  Khan  et al. , 2021 ;  Wessels  et al. , 2021 ) found no significant differences between cases and controls. In contrast,  Guo  et al.  (2025)  observed a significant separation in microbial community structure between the groups. Notably,  Marcos  et al.  (2024)  and  Muraoka  et al.  (2023)  did not report any diversity metrics for eutopic endometrium tissue in their analyses.\nIn terms of taxonomic composition, the genera identified as differing significantly between women with and without endometriosis were not consistent across studies ( Fig. 6A ). No bacterial genus was consistently found to be significantly altered across studies, highlighting the lack of reproducibility and the overall weakness of evidence for an endometriosis-associated microbial signature in eutopic endometrial tissue. These inconsistencies underscore the challenges in identifying a coherent dysbiosis profile for endometriosis. Three studies ( Wessels  et al. , 2021 ;  Marcos  et al. , 2024 ;  Guo  et al. , 2025 ) extended their analyses to higher taxonomic levels, including phylum, class, order, and family ( Supplementary Table S3 ).\nBacterial genera identified across tissue sample types in the included studies.  E, genus’s abundance increased in endometriosis; C, genus’s abundance increased in controls; mid-pink, increased in endometriosis in two studies; light pink, increased in endometriosis in one study; grey, inconsistent findings across studies; light blue, decreased in endometriosis in one study. ( A ) Eutopic endometrium. ( B ) Endometriotic tissue.\nTechnical characteristics of the five studies ( Campos  et al. , 2018 ;  Hernandes  et al. , 2020 ;  Hu  et al. , 2023 ;  Muraoka  et al. , 2023 ;  Chen  et al. , 2024 ) that analyzed the microbiome composition of ectopic endometrial tissue compared to control tissues are detailed in  Table 10 .\nTechnical characteristics of microbiome analyses across endometriotic tissue samples in the included studies.\nOvarian endometriotic tissue from women with endometriosis vs eutopic endometrium from non-endometriosis women with uterine fibroids\n23\nvs\n22\n34.8 ± 6.8\nvs\n37.2 ± 8.2  §\n( P =NS)\nMagPure Soil DNA Kit—Magen  (For isolation of high-quality genomic DNA from various soil, stool, and other environmental samples)\nEndometriotic tissue vs eutopic endometrium from the same women with endometriosis\n14\nvs\n14\nOvarian endometriotic tissue from women with endometriosis vs eutopic endometrium from the same women\n42\nvs\n42\nEndometriotic tissue vs eutopic endometrium from the same women with endometriosis\n10\nvs\n11\nDNeasy PowerSoil Pro Kit—Qiagen  (For the isolation of microbial genomic DNA from all soil types)\nEndometriotic tissue vs healthy peritoneum from women without endometriosis\n68\nvs\n30\nPureLink Genomic DNA Mini Kit—Invitrogen  (For genomic DNA purification from blood, tissues, cells, bacteria, swabs, and blood spots)\nData reported as reported by the original papers, unless otherwise stated.\nBp, base pairs; NA, not applicable; NR, not reported; NS, non-significant; nt, nucleotides; qRT-PCR, quantitative real time-PCR.\nData are expressed as mean±SD.\nSample sizes ranged from n=10 to n=68 for endometriosis cases and from n=11 to n=42 controls. When evaluating the microbiome of endometriotic tissues, the choice of control tissue is critical. Microbial profiles are expected to differ depending on whether the comparison is made to the eutopic endometrium from the same affected women, from a woman without endometriosis, or from a different tissue altogether. In three studies ( Hernandes  et al. , 2020 ;  Hu  et al. , 2023 ;  Muraoka  et al. , 2023 ), ectopic tissue microbiomes were compared to eutopic endometrial tissue from the same woman. In contrast,  Chen  et al.  (2024)  used eutopic endometrium from women without endometriosis but with uterine fibroids as the control tissue. These control subjects were age-matched to the cases, although BMI data were not reported. Similarly,  Campos  et al.  (2018)  used peritoneal tissue from symptomatic women without endometriosis as controls, but data on age and BMI were not reported. In all studies, both eutopic and ectopic tissue samples were obtained intraoperatively ( Table 1 ).\nHistological confirmation of endometriosis was conducted in all studies except one ( Muraoka  et al. , 2023 ), which relied on surgical identification alone ( Table 10 ). Three studies ( Hu  et al. , 2023 ;  Muraoka  et al. , 2023 ;  Chen  et al. , 2024 ) focused exclusively on ovarian endometriosis, while  Hernandes  et al.  (2020)  analyzed deep endometriosis.  Campos  et al.  (2018)  included multiple phenotypes and stages of the disease.\nHormonal treatment varied across the studies. In three studies ( Hu  et al. , 2023 ;  Muraoka  et al. , 2023 ;  Chen  et al. , 2024 ), participants were not on hormonal therapies.  Hernandes  et al.  (2020)  did not report hormonal use and did not list it as an exclusion criterion.  Campos  et al.  (2018)  included participants with varying hormonal use across groups but did not indicate whether differences were statistically significant.\nMenstrual cycle phase at the time of sample collection was not consistently controlled.  Hernandes  et al.  (2020)  did not report this information.  Campos  et al.  (2018)  noted similar phases between groups, while  Chen  et al.  (2024)  stated that over 90% of samples were in the follicular phase. Regarding antibiotic use, most studies considered it an exclusion criterion, except for  Chen  et al.  (2024) , who did not specify its distribution between groups. None of the studies reported any exclusion criteria regarding dietary habits.\nTechnical aspects and microbiome analysis methods are summarized in  Table 10 . Only  Campos  et al.  (2018)  reported using a tissue-specific DNA extraction kit (Purelink Genomic DNA Mini Kit, Invitrogen). Other studies used adapted soil DNA extraction kits ( Hernandes  et al. , 2020 ;  Chen  et al. , 2024 ), and two ( Hu  et al. , 2023 ;  Muraoka  et al. , 2023 ) did not report their extraction methods. Three studies used 16S rRNA sequencing on Illumina platforms, targeting the V3-V4 regions.  Muraoka  et al.  (2023)  and  Campos  et al.  (2018)  did not conduct 16S sequencing but instead used qRT-PCR to detect specific taxa. As previously mentioned,  Muraoka  et al.  (2023)  conducted a bioinformatic reanalysis of publicly available datasets followed by targeted qRT-PCR validation. Bioinformatic analyses, including sequence filtering, chimera removal, and OTU assignment, varied across studies, with further details presented in  Supplementary Table S2 .\nAlpha and beta diversity were evaluated by three studies ( Hernandes  et al. , 2020 ;  Hu  et al. , 2023 ;  Chen  et al. , 2024 ) ( Fig. 2 ). All studies reported no significant differences in alpha diversity between ectopic and control tissues. Similarly, beta diversity did not differ between groups, except in the study by  Hernandes  et al.  (2020) , which reported significant differences when using weighted UniFrac distances, but not when using Bray–Curtis dissimilarity.\nRegarding the taxonomic composition, the results were heterogeneous. Among the genera identified as significantly differing between ectopic and control tissues ( Fig. 6B ), only  Pseudomonas  was reported by more than one study ( Hernandes  et al. , 2020 ;  Hu  et al. , 2023 ) as being significantly increased in ectopic tissue compared to eutopic tissue. This limited concordance reflects the weak evidence base, lack of reproducibility, and the absence of evidence of a distinct microbial signature in endometriotic lesions.\nFurthermore, the two studies ( Hernandes  et al. , 2020 ;  Hu  et al. , 2023 ) that reported significant differences at the genus level between women with and without endometriosis were assessed as being of moderate quality according to the NOS, indicating a moderate risk of bias ( Supplementary Table S4 ).\n\nThe association between endometriosis and microbiota is emerging as a promising area of research for understanding pathophysiological mechanisms underlying the disease and for identifying potential biomarkers for diagnosis and management. A healthy microbial balance across different anatomical sites is essential for maintaining mucosal integrity, protecting against pathogens, and regulating physiological processes. Dysbiosis, or an imbalance in the microbiota, can compromise the intestinal barrier, allowing bacteria or endotoxins to pass through, triggering inflammation responses that may contribute to the onset or progression of various diseases. Additionally, some gut bacteria produce enzymes involved in estrogen metabolism, potentially influencing the estrobolome, the collection of microbial genes involved in estrogen metabolism and estrogen-related disorders such as endometriosis ( Pai  et al. , 2023 ). In line with these concepts, a quite large number of narrative reviews have been published over the past few years, speculating on the causal relationship between microbiota and endometriosis ( Leonardi  et al. , 2020 ;  D’Alterio  et al. , 2021 ;  Colonetti  et al. , 2023 ;  Weber  et al. , 2024 ). Yet, despite these efforts, there remains a critical gap in the literature including a thorough review of study designs and methodologies employed to investigate microbiota alterations in endometriosis.\nScoping reviews are particularly valuable in such contexts, enabling researchers to map existing evidence systematically and to discern patterns across studies. Indeed, several studies have utilized scoping reviews to synthesize knowledge in various areas of microbiome research ( Gáspár  et al. , 2024 ;  Lianos  et al. , 2024 ;  Nagamine, 2024 ;  Sessa  et al. , 2025 ). A scoping review is often more suitable for a microbiome research compared to a systematic review due to several reasons: (i) it allows a broad overview of available evidence, including different approaches, trends, and knowledge gaps, rather than answering a specific, narrow question like a systematic review; (ii) contrary to a systematic review which requires strict inclusion/exclusion criteria, it embraces a wider diversity among studies; (iii) it is more appropriate if there is not enough homogeneous data to conduct a quantitative synthesis; microbiome studies vary widely in sample types, sequencing platform, analytical pipelines, and study designs (cross-sectional, longitudinal, interventional). Importantly in our context, scoping reviews also illuminate methodological shortcomings and highlight critical areas where further investigation is needed. Such reviews are particularly well-suited for identifying research gaps and shaping future research agendas, offering a roadmap for addressing limitations and improving the rigor of studies in our field.\nRecently, multiple studies have focused on characterizing a distinct endometriosis microbiome signature in both humans and animal models. As shown in the results section of this review, several bacterial species have been identified as being associated with endometriosis pathogenesis. However, consistency in the type of bacterial species present and their relative abundances compared to control subjects continues to be elusive for all the anatomical districts investigated. In eutopic endometrial tissue investigated by six studies, no bacterial genus was consistently found to be significantly different between groups across the studies. Some consistency was found for the reduced presence of  Lachnospira  sp. in stool/anal fluid of endometriosis patients according to 4 of the 15 studies investigating this district microbiome and for the higher presence of  Streptococcus  sp. in four of the seven studies on cervical samples. Similarly, a higher abundance in peritoneal fluid of patients with endometriosis was found for  Pseudomonas  sp. in four of the eight studies. Among the genera reported as significantly differing between women with and without endometriosis in vaginal microbiome, three genera ( Alloscardovia  sp. , Escherichia  sp., and  Veillonella  sp.) were identified as more abundant in endometriosis in 3 of the 15 studies. Several studies even reported contradicting results between women affected and controls, specifically with regard to abundance of  Blautia  sp. and  Ruminococcus  sp. in the stool microbiome and  Gardnerella  sp.,  Prevotella  sp., and  Sneathia  sp. in the vaginal district. As shown in  Fig. 2 , inconsistencies across studies were also found for alpha and beta diversity for all the districts investigated. Overall, these data hint at weak evidence supporting an endometriosis-associated microbial signature.\nOne recurring issue in microbiome research is the challenge of ensuring adequate sample sizes. Studies have suggested that statistical analyses of microbiome datasets require specialized considerations that differ from traditional sample size calculation methods ( Ferdous  et al. , 2022 ), since these studies are exposed to high risks of type I errors. Thus, in the context of case-control studies investigating differences in microbial composition, robust study design requires an  a priori  assessment of sample size to ensure sufficient statistical power to detect hypothesized effects.\nRecent guidelines aiming to improve the quality and consistency of microbiome research underscore the importance of power calculations and adequate sample sizes as essential criteria for rigorous study design ( Kelly  et al. , 2015 ). Despite this, none of the studies reviewed in this analysis performed an  a priori  sample size calculation, which represents a significant methodological limitation that compromises the reliability of the findings.\nA significant limitation in comparing microbiome composition between women with and without endometriosis across available studies is the insufficient attention given to the phase of the menstrual cycle during sample collection. While shifts in microbiome composition throughout the menstrual cycle are described as subtle in fecal samples, they are pronounced in vaginal samples ( Krog  et al. , 2022 ). For instance, studies have reported an increase in microbial diversity in the vaginal microbiome during menstruation, followed by a marked dominance of  Lactobacillus  spp. during the follicular and luteal phases, which strongly correlates with serum estradiol levels. Menstrual-phase samples tend to show a relatively higher abundance of taxa from the phyla  Fusobacteria ,  Proteobacteria ,  Bacteroidetes , and  Actinobacteria . In contrast, follicular-phase samples exhibit a notable reduction or absence of these phyla, with an increased prevalence of  Firmicutes  taxa ( Kaur  et al. , 2020 ).\nSimilarly, hormone-driven variations also affect the gut microbiome. Differences in microbial composition have been linked to testosterone levels in men and estradiol levels in women. Elevated estradiol levels, in particular, have been associated with altered gut microbial communities, characterized by increased abundance of  Bacteroidetes  and decreased  Firmicutes  ( Shin  et al. , 2019 ). These findings underscore the significant influence of hormonal fluctuations on microbiome composition.\nTherefore, the lack of menstrual phase standardization across the included studies poses a major methodological concern. Notably, 10 out of 15 studies comparing vaginal fluid microbiome in women with and without endometriosis did not report the menstrual cycle phase during sampling or failed to collect samples consistently within the same phase. This issue severely compromises the validity and comparability of the results obtained. The same problem applies to gut microbiomes, as hormone-related variations in microbial composition prevent reliable aggregation of findings without rigorous control for menstrual cycle phases across cases and controls. Addressing this critical confounding factor in future research is essential to enhance the interpretability and robustness of microbiome studies in endometriosis.\nDifferences in vaginal microbiome composition based on contraception methods have been well-documented ( Shin  et al. , 2019 ;  Balle  et al. , 2023 ). Women who do not use hormonal contraceptives exhibit significantly greater shifts in vaginal microbiome composition across the menstrual cycle compared to those using oral contraceptives. Studies investigating the influence of oral contraceptives on the vaginal microbiome have primarily reported increased levels of  Lactobacillus  species ( Balle  et al. , 2023 ). On the other hand, long-acting progestin-only contraceptives, such as levonorgestrel-implants, have not been shown to significantly alter the vaginal mucosal microbial environment ( Balle  et al. , 2023 ).\nThe impact of exogenous sex hormones extends beyond the vaginal microbiome, influencing the gut microbiome as well. Hormonal treatments, including oral contraceptives, have been associated with reduced bacterial richness and diversity in the gut ( Kheloui  et al. , 2023 ). Specifically, users of oral contraceptives demonstrate decreased alpha diversity but no significant changes in beta diversity compared to non-users.\nGiven these findings, the inclusion of participants undergoing hormonal treatments alongside those who are not, presents a critical confounding factor in studies comparing the vaginal fluid and gut microbiota between women with and without endometriosis. Standardized protocols controlling for hormonal treatment status are therefore essential to improve the reliability and validity of microbiome research.\nStudying low-biomass microbial niches such as the endometrium requires meticulously designed experiments to minimize contamination, which can lead to data misinterpretation ( Molina  et al. , 2021 ;  Odendaal  et al. , 2024 ). Contaminant DNA can originate from various sources, including endometrial sampling techniques, the laboratory environment, plastic consumables, researchers, and reagents ( Molina  et al. , 2021 ). Furthermore, cross-contamination during microbiome sample processing, such as from other samples or sequencing runs, is a significant concern ( Molina  et al. , 2021 ). Recently, methodological considerations and good practice recommendations for studying the endometrial microbiome have been proposed ( Molina  et al. , 2021 ).  Molina  et al.  (2021)  emphasized that the primary challenge in sampling for endometrial microbiota analysis lies in the high risk of contamination from the lower genital tract. Most studies reviewed utilized conventional endometrial sampling devices. While some authors reported measures to avoid contact with the cervical and vaginal walls, the insertion of sampling devices through the high-microbial biomass cervicovaginal canal without a sheathed tool inevitably increases the likelihood of contamination. None of the studies employed double-sheathed catheters, which are widely used in embryo transfers and are expected to significantly reduce the risk of contamination from cervical or vaginal microbiota ( Reschini  et al. , 2022 ).\nOne study ( Muraoka  et al. , 2023 ) collected samples during hysterectomy, effectively avoiding contamination from the vaginal or cervical microbiota. However, details of the sampling procedure were not provided, and the study involved peri- and postmenopausal women, limiting the applicability of the findings to women of reproductive age. Additionally, there was a borderline statistical difference in age between women with and without endometriosis in the study, potentially introducing an additional confounding factor.\nWomen with endometriosis often adopt lifestyle changes, including dietary modifications to help manage symptoms. In an online survey of 4087 Italian women with endometriosis,  Mazza  et al.  (2023)  reported that 66% of respondents altered their eating habits following diagnosis. Common dietary choices included gluten-free, anti-inflammatory, Mediterranean, and ketogenic diets, indicating that women explore diverse dietary strategies to alleviate symptoms and improve quality of life. Since dietary patterns can significantly influence gut microbiota composition ( Mirhosseini  et al. , 2024 ) changes in gut microbial diversity in women with endometriosis may reflect these self-management practices rather than being a direct cause of the disease itself. Studies investigating the gut microbiome should account for dietary factors as potential confounders. However, few studies reviewed in this context controlled these dietary influences, highlighting a critical gap in the existing research.\nIn recent years, substantial efforts have been devoted to developing standardized methods for microbiome research. Initiatives such as the Human Microbiome Project and various methodological guidelines have emphasized the importance of ensuring reliable, reproducible, and robust microbiome data ( Bruggeling  et al. , 2021 ;  Tourlousse  et al. , 2021 ;  Szóstak  et al. , 2022 ).\nLarge-scale studies of human gut microbiomes have demonstrated significant compositional differences across geographically distinct populations. However, most studies on the relationship between microbiome composition and health outcomes focus on single populations. This raises questions about whether geographical variations in microbiome composition translate to differences in disease susceptibility. Factors such as lifestyle, dietary habits, and antibiotic usage profoundly influence microbiome composition, complicating cross-country comparisons. In this review, most of the included studies were conducted in China ( Wang  et al. , 2018 ;  Wei  et al. , 2020 ,  2023 ;  Chao  et al. , 2021 ;  Huang  et al. , 2021 ;  Shan  et al. , 2021 ;  Lu  et al. , 2022 ;  Yuan  et al. , 2022 ;  Hu  et al. , 2023 ;  Yang  et al. , 2023 ;  Chen  et al. , 2024 ;  Zhu  et al. , 2024 ).\nThe reviewed studies employed diverse sampling, DNA extraction, and sequencing techniques. DNA extraction was predominantly performed using commercial extraction kits while a smaller subset of studies used alternative methods ( Chao  et al. , 2021 ;  Yuan  et al. , 2022 ;  Hu  et al. , 2023 ). Even among studies using commercial kits, subtle differences in protocols across laboratories can introduce variability, particularly in low-abundance samples such as mucus, tissues, or fluids ( Sinclair  et al. , 2023 ). Commercial kits, not optimized for a specific tissue or fluid, may lead to bias in the metagenomic analysis due to inefficient extraction, alteration in taxa abundance, and alpha and beta diversities ( Galla  et al. , 2024 ;  Seethalakshmi  et al. , 2024 ).\nFor taxonomic identification, most studies employed 16s rRNA gene sequencing for bacterial detection, while others PCR targeting specific genes of interest ( Campos  et al. , 2018 ;  Muraoka  et al. , 2023 ). Only the two more recent studies used shotgun metagenomics ( MacSharry  et al. , 2024 ;  Pérez-Prieto  et al. , 2024 ) that provides a broader compositional overview of multiple components of human microbiomes ( Joos  et al. , 2025 ). To analyze microbial functionality, shotgun sequencing has been reported as ‘ a preferable analytic approach, as it allows for direct functional annotation and analysis of metagenomes compared with amplicon sequencing ’ ( Joos  et al. , 2025 ).\nThe choice of sequencing platform often reflects the availability of instruments at the time of the study, which adds another dimension of variability.\nIn downstream statistical analyses, the reviewed studies varied widely in their choice of software tools and, frequently, the specific versions of those tools. Multiple approaches were exploited for filtering of the data, OTU identification and taxonomy and functional annotation, as well as for the computation of alpha and beta diversity scores. However, in many cases, the studies did not specify the versions of the tools used, adding another layer of inconsistency. These methodological discrepancies make reproducibility challenging, and complicate efforts to compare findings across studies. Standardized bioinformatic pipelines and transparent reporting of methods, including software versions, are essential for advancing microbiome research and ensuring comparability of results across different populations and research contexts. The best way to compare the results would be to reanalyze all the data using the same bioinformatic procedure together with a batch effect correction step. However, this is not possible presently since not all the studies uploaded their data and metadata on a repository to make them publicly available.\nA recent perspective paper published in  Nature Reviews Microbiology  highlights critical limitations in microbiome research, emphasizing challenges such as defining a ‘healthy’ microbiome and the need to account for confounding factors, niche-specific variables, and population-specific geographical findings ( Joos  et al. , 2025 ). These limitations directly impact the reliability and relevance of microbiome studies and may explain the inconsistencies observed in the studies reviewed here. Specifically, the changes in gut, vaginal, or endometrial microbiomes among patients with endometriosis compared to controls were found to be highly variable.\nMicrobiome research in endometriosis often mirrors approaches used in other fields without adequately addressing previously identified limitations. A notable example is the investigation of  Fusobacterium nucleatum  ( F. nucleatum ) which has been found to have a role in various diseases, particularly those linked to active inflammation and malignancies. Whether  F. nucleatum  acts as a driver (causative agent) or a passenger (opportunistic colonizer) remains a subject of debate. For instance,  F. nucleatum  is implicated in placental infections and preterm births, with evidence from human umbilical endothelial cell studies and mouse models suggesting its pathogenic potential. However, its presence in healthy placental microbiota raises concerns about sampling bias, as diseased tissues are more frequently studied. This suggests that  F. nucleatum  may only become pathogenic under specific conditions or involve particular strains, paralleling its role in oral health.\nThe association between  F. nucleatum  and colorectal cancer further illustrates the challenges of translating microbiome findings into clinical practice. While multiple studies report its presence across various stages of colorectal cancer, reproducibility issues persist due to differences in detection methods. Prevalence rates range from 3% to 56%, depending on the methodology employed ( Brennan and Garrett, 2019 ). Diagnostic applications are further hindered by the lack of standardization cutoff values and biomarker analysis methods ( Lișcu  et al. , 2024 ). In preclinical models, antibiotics targeting  F. nucleatum  reduced tumor growth and bacterial burden in xenografts ( Brennan and Garrett, 2019 ). However, these findings have seen limited clinical applications, as antibiotics may negatively impact immunotherapy efficacy or exacerbate microbial imbalances, complicating their therapeutic use ( González  et al. , 2024 ).\nOverall, lessons from these studies underline the importance of addressing heterogeneity in: (i) specimen collection, (ii) sequencing methods, (iii) population demographics, and (iv) data analysis tools. Only a single study adjusted for multiple comparisons ( Pérez-Prieto  et al. , 2024 ). When the first paper on endometriosis and the gut microbiome was published, experts in other fields underscored that ‘ robust experimental approaches, be it within human cohorts or preclinical models, and reproducible results across microbiota studies are pivotal to bridge the translational gap and ensure that data are neither lost in translation nor mistranslated clinically ’ ( Brennan and Garrett, 2019 ). It seems that we missed the opportunities to follow this suggestion and in the absence of robust data, we are risking being lost in translation.\n\nAfter analyzing the studies assessing microbiome composition in various districts of women with endometriosis, and according to the information provided by the literature that addresses technical confounders of microbiome analysis, the main conclusion of this review was the mandatory need to standardize study designs. The calculation of power and sample size, the recruiting of an adequate number of participants and samples, the reduction of interpersonal confounders and the use of adequate computational methodological approaches are critical for the advancement of knowledge in this field. Alternatively, all attempts to interpret the tremendously huge and different dimensions of a microbiome will fail to approach its full potential. This review aimed to highlight the existing research on this subject and provide insights that may help guide future directions to enhance the applicability of obtained results in the future.","source_license":"CC-BY-4.0","license_restricted":false}