Gut Microbiota, Circulating Metabolites and Risk of Endometriosis: A Two-Step Mendelian Randomization Study
Artikel-Kategorie: ORIGINAL PAPER
Online veröffentlicht: 13. Dez. 2024
Seitenbereich: 491 - 503
Eingereicht: 30. Juni 2024
Akzeptiert: 19. Sept. 2024
DOI: https://doi.org/10.33073/pjm-2024-041
Schlüsselwörter
© 2024 Hua Yang, published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Endometriosis (EMs) is characterized by the abnormal presence of endometrial tissue outside the uterus, which can lead to chronic pain, subfertility, and the formation of nodules or masses due to recurrent bleeding and inflammatory responses. This condition affects approximately 10% of women of reproductive age, equating to an estimated 196 million women worldwide (Allaire et al. 2023; Frayne et al. 2023; Garcia Garcia et al. 2023). Given its estrogen-dependent nature and the lack of curative treatment, current medical interventions are primarily symptomatic, with both hormonal therapies and surgical excision failing to prevent recurrences. Consequently, affected women may endure physical and mental health challenges until menopause, contributing to substantial social and economic burdens (Della et al. 2020).
The definitive diagnosis of EMs requires surgical intervention with histological confirmation, as there is no non-invasive diagnostic method available despite ongoing research into various biomarkers in preclinical studies (Ahn et al. 2017; Bjorkman and Taylor 2019; Anastasiu et al. 2020). The identification of EMs is often challenging, leading to delayed diagnoses. Despite extensive research efforts over the past several decades, the pathogenesis and etiology of EMs remain unclear (Czyzyk et al. 2017; Asghari et al. 2018; Shafrir et al. 2018). While the theory of retrograde menstruation has gained widespread acceptance (Guo et al. 2023), it still needs to explain the biological processes and characteristics of EMs fully. Alternative hypotheses have been proposed to supplement and refine the retrograde menstruation theory, including the concepts of abnormal remnants of embryonic Müllerian ducts (Lambrinoudaki et al. 2009), coelomic metaplasia (Vercellini et al. 2014), metastatic spread via vascular or lymphatic vessels (Jerman and Hey-Cunningham 2015), and determinism of the eutopic endometrium (Maia et al. 2012). However, none of these theories have conclusively elucidated the relationship with EMs. It is generally accepted that EMs are likely the result of multiple determinant factors involving various genetic and epigenetic modifications, hormonal influences, environmental factors, and immunological aspects (Laganà et al., 2019).
The gut microbiota (GM) naturally coexists with the host. It plays a pivotal role in physiological processes, such as nutrient absorption, metabolism, detoxification, maintaining the integrity of the intestinal mucous barrier, and regulating the immune and endocrine systems (Thaiss et al. 2016; Shi et al. 2017; Régnier et al. 2021; Wiertsema et al. 2021). These microbial communities are essential for maintaining host health. Dysbiosis of the GM, manifesting as alterations in composition and abundance, can compromise the mucosal barrier, facilitate bacterial and endotoxin translocation (Ha et al. 2020), trigger inflammation (Clemente et al. 2018), disrupt the immune environment (Hou et al. 2022), and alter the metabolome (Barko et al. 2018). Intestinal dysbacteriosis can affect not only the local gastrointestinal tract but also elicit systemic responses and has been associated with various diseases, including multiple sclerosis (Preiningerova et al. 2022), type-II diabetes (Del et al. 2022), Grave’s disease (Jiang et al. 2022), systemic lupus erythematosus (Zhang et al. 2021), reproductive disorders (Azpiroz et al. 2021), cancers (Tong et al. 2021; Zhao et al. 2021; Chen et al. 2022; Park et al. 2022), among others. Certain bacteria within the GM possess genes encoding estrogen-metabolizing enzymes, which may regulate circulating estrogen levels (Lephart and Naftolin 2022). Given the direct link between estrogen and the onset and progression of EMs, it is plausible that GM could contribute to the development of EMs.
Evidence from animal models and human samples increasingly suggests a correlation between the abundance of GM and the risk of EMs (Bailey and Coe 2002; Ata et al. 2019; Chadchan et al. 2019; Ni et al. 2020; Shan et al. 2021; Svensson et al. 2021). Concurrently, growing evidence points to potential links between metabolic disorders and the occurrence of EMs. With the etiological and risk factors for EMs still largely unknown, the direct impact and pathogenic mechanisms of GM and circulating metabolites on EMs remain elusive. The relationships between them have not been adequately addressed due to the limitations inherent in conventional observational studies, which are susceptible to confounding biases or reverse causation.
Mendelian randomization (MR) is a statistical approach designed to overcome the limitations of observational studies by minimizing bias risks due to residual confounding or reverse causation (Sekula et al. 2016; Bowden and Holmes 2019; Birney 2022). It employs germline single nucleotide polymorphisms (SNPs) as instrumental variables (IVs) to estimate causal effects between exposures and outcomes. To elucidate the association and pathogenic mechanisms between GM, circulating metabolites, and EMs, I employed a two-sample and two-step MR analysis in this study.
This study utilized the most comprehensive summary of GWAS data available: finn-b-N14-EMs (Liu et al. 2024), encompassing 16,377,306 SNPs. The cohort included 8,288 individuals with EMs and 68,969 non-gender-specific healthy controls, originating from the European population. Patient consent was obtained in the respective studies, and the present report adhered to the guidelines of the Strengthening the Reporting of Observational Studies in Epidemiology.
Data on GM abundance was retrieved from the IEU Open GWAS (MR Base) public database (
The GWAS data for 486 metabolites were sourced from the Metabolomics GWAS Server (
The selection of IVs should adhere to the key assumptions of MR: 1) IVs must be associated with exposures, 2) IVs must not be associated with confounders, and 3) IVs must affect outcomes exclusively through exposures, not via alternative pathways. To ensure the robustness and veracity of association inferences linking, circulating metabolites, and the risk of EMs, stringent quality control measures were employed for optimal IVs extraction. Initially, single nucleotide polymorphisms (SNPs) meeting the statistical significance threshold (
Horizontal pleiotropy was tested using MR-PRESSO (Verbanck et al. 2018) and MR-Egger regression (Burgess and Thompson 2017; Sang et al. 2022; Zeng et al. 2023). The MR-PRESSO outlier test calculated a
Two-sample univariate Mendelian randomization (UVMR) was employed to infer causal relationships between GM, circulating metabolites, and EMs. Additionally, two-step mediation Mendelian randomization (TSMR) was used to decompose the indirect effects of GM or circulating metabolites on EMs, assuming no interaction between exposure and mediator. Firstly, the effect of exposure on outcome (β0) was estimated via UVMR. Secondly, the effect of exposure on mediator (β1) was determined using UVMR. Thirdly, the effect of mediators significantly associated with exposure on the outcome was assessed using UVMR. Finally, mediators significantly related to the outcome were screened and multivariable Mendelian randomization (MVMR) was conducted to evaluate the mediator’s effect on the outcome after genetic effect adjustment (β2). The proportion of mediation in the association between exposure and outcome was computed as (β1 × β2)/β0 (Sanderson 2021).
Three mainstream Mendelian randomization methods were used for analyses involving multiple instrumental variables (IVs): the inverse-variance weighted (IVW) test, the weighted median test, and the MR-Egger test (Cao et al. 2022; Zhao et al. 2022). The IVW method is often more potent under certain conditions; therefore, results with multiple IVs primarily rely on IVW, with the other methods serving as complementary. The Wald ratio (Gnona and Stewart 2022) test was used for analyses with only one IV.
Several sensitivity analyses were conducted to assess the robustness of the association. A leave-one-out analysis (Cheng et al. 2017) evaluated whether the association was driven by a single SNP. The causal direction test compared the variance caused by IVs in both exposure and outcome, considering the identified causality directionally robust if IVs caused greater variance in exposure than in outcome.
Horizontal pleiotropy was tested using Egger regression, with IVs affecting the outcome through pathways other than exposure considered pleiotropic and violating MR assumptions. Cochran’s Q statistics and the two-sample MR package were used for heterogeneity testing. A Q value higher than the number of IVs minus one or a
All statistical analyses were performed using R software version 3.5.3 (R Core Team 2019), with MR analyses conducted using the “MendelianRandomization”, “TwosimpleMR”, “MVMR”, and “MR-PRESSO” packages. The Benjamini-Hochberg (B-H) corrected

The detailed flowchart of present mendelian randomization analysis.
The analysis was initiated by extracting 1 to 11 SNPs per GM taxon and 1 to 126 SNPs per circulating metabolite, adhering to a stringent significance threshold of
Within the set of instrumental variables (IVs) (

The forest plot summarized the causality of gut microbiota on the risk of endometriosis.
The causality of gut microbiota abundance on risk of endometriosis.
Exposure | n SNP | IVW | MR Egger | Weighted median | Horizontal pleiotropy | Heterogeneity | |||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
β | se | β | se | β | se | ERI | se | Q | |||||||
class Negativicutes | 7 | 0.002521 | 0.001072 | 0.01863 | 0.005405 | 0.003049 | 0.1364 | 0.002102 | 0.001445 | 0.1458 | –0.00022 | 0.00022 | 0.359 | 6.198 | 0.4014 |
genus |
2 | 0.003723 | 0.001718 | 0.03027 | – | – | – | – | – | – | – | – | – | 0.7413 | 0.3893 |
genus |
3 | 0.002015 | 0.0009595 | 0.03575 | 0.0005135 | 0.002386 | 0.8651 | 0.001771 | 0.001182 | 0.134 | 0.00026 | 0.00038 | 0.617 | 0.7286 | 0.6947 |
genus |
7 | 0.003385 | 0.000999 | 0.0007027 | –0.001805 | –0.001805 | 0.5773 | 0.003223 | 0.001374 | 0.01897 | 0.00044 | 0.00025 | 0.131 | 6.42 | 0.3778 |
genus |
3 | 0.001948 | 0.0007719 | 0.0007719 | 0.002244 | 0.003202 | 0.6108 | 0.001635 | 0.001025 | 0.1107 | –0.00005 | 0.00051 | 0.938 | 1.767 | 0.4134 |
order Selenomonadales | 7 | 0.002521 | 0.001072 | 0.01863 | 0.005405 | 0.003049 | 0.1364 | 0.1364 | 0.001445 | 0.1458 | –0.00022 | 0.00022 | 0.359 | 6.198 | 0.4014 |
genus |
7 | –0.003294 | 0.001028 | 0.001354 | –0.001902 | 0.003123 | 0.5692 | –0.002869 | 0.001362 | 0.03517 | –0.00011 | 0.00022 | 0.657 | 4.788 | 0.5713 |
genus |
2 | –0.003588 | 0.00158 | 0.02319 | – | – | – | – | – | – | – | – | – | 1.34 | 0.2471 |
SNP – single nucleotide polymorphisms, IVW – inverse variance weighted, MR – mendelian randomization, se – standard error, ERI – egger regression intercept
Within the defined set of instrumental variables (IVs) (

The forest plot summarized the causality of circulating metabolites on the risk of endometriosis.
The causality of circulating metabolites on risk of endometriosis.
Exposure | Subset | IVW/Wald ratio | MR Egger | Weighted median | Horizontal pleiotropy | Heterogeneity | |||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
α | SE | α | SE | α | SE | ERI | SE | Q | |||||||
1-Eicosatrienoylglycero-phosphocholine | lipid | 0.7316 | 0.001575 | 0.02416 | 2.538 | 0.7409 | 0.006486 | 0.005782 | 0.002841 | 0.04843 | –0.048 | 0.018 | 0.0253 | 8.367 | 0.593 |
1-Oleoylglycero-phosphocholine | lipid | 1.15 | 0.4893 | 0.01871 | –1.011 | 1.023 | 0.3685 | 1.157 | 0.652 | 0.07597 | 0.043 | 0.018 | 0.0658 | 1.725 | 0.8858 |
2-Aminooctanoic acid | amino acid | 0.3516 | 0.1602 | 0.02819 | 0.2772 | 0.262 | 0.3716 | 0.3716 | 0.1729 | 0.06623 | 0.004 | 0.011 | 0.72 | 10.64 | 0.301 |
3 - Phenylpropionate | amino acid | –0.6252 | 0.2425 | 0.009951 | –0.4805 | 0.3796 | 0.3331 | –0.5788 | 0.2893 | 0.04539 | –0.0095 | 0.019 | 0.669 | 0.7925 | 0.6729 |
Decanoylcarnitine | lipid | 0.4646 | 0.1842 | 0.01166 | 0.5798 | 0.3388 | 0.1307 | 0.5456 | 0.2251 | 0.01536 | –0.0055 | 0.013 | 0.697 | 4.314 | 0.743 |
Dihomo-linolenate | fatty acid | –0.7076 | 0.3451 | 0.04032 | 0.05512 | 0.9978 | 0.9569 | –0.4608 | 0.4847 | 0.3418 | –0.015 | 0.018 | 0.43 | 12.96 | 0.3723 |
Dimethylarginine (SDMA + ADMA) | amino acid | –1.255 | 0.4615 | 0.006548 | –2.798 | 1.317 | 0.04693 | –2.063 | 0.6368 | 0.001197 | 0.017 | 0.014 | 0.226 | 14.04 | 0.7814 |
Ergothioneine | xenobiotics | –1.13 | 0.4685 | 0.4685 | – | – | – | – | – | – | – | – | – | – | – |
Hexadecanedioate | lipid | 0.3527 | 0.128 | 0.005867 | 0.3823 | 0.1914 | 0.06711 | 0.4095 | 0.1608 | 0.01088 | –0.002 | 0.0095 | 0.834 | 17.36 | 0.1832 |
Hexanoylcarnitine | lipid | 0.4772 | 0.2379 | 0.04487 | 0.4677 | 0.4768 | 0.3523 | 0.4522 | 0.2656 | 0.08871 | 0.00032 | 0.014 | 0.982 | 12.08 | 0.2089 |
Octanoylcarnitine | lipid | 0.4604 | 0.2092 | 0.02779 | 0.4957 | 0.3655 | 0.2466 | 0.4395 | 0.2324 | 0.05855 | –0.0017 | 0.015 | 0.912 | 0.3964 | 0.9828 |
Palmitate (16:0) | fatty acid | –1.415 | 0.4277 | 0.0009344 | –2.996 | 0.9716 | 0.006741 | –0.9829 | 0.6468 | 0.1286 | 0.021 | 0.011 | 0.0878 | 11.85 | 0.809 |
Pseudouridine | nucleotide | –1.312 | 0.6122 | 0.03213 | 1.971 | 2.362 | 0.4164 | –1.942 | 0.8565 | 0.02334 | –0.035 | 0.024 | 0.17 | 16.41 | 0.4244 |
Stearate (18:0) | fatty acid | –0.7386 | 0.3701 | 0.04598 | –0.1354 | –0.1354 | 0.8967 | –0.3944 | 0.5247 | 0.4522 | –0.007 | 0.011 | 0.537 | 15.77 | 0.8962 |
Threonine | amino acid | 0.9348 | 0.4587 | 0.04153 | 0.04153 | 1.501 | 1.501 | 0.6212 | 0.5832 | 0.2868 | 0.011 | 0.022 | 0.635 | 8.823 | 0.6382 |
SNP – single nucleotide polymorphisms, IVW – inverse variance weighted, MR – mendelian randomization, se – standard error, ERI – egger regression intercept
Initially, the potential mediating role of circulating metabolites between GM and the risk of EMs was considered. It was observed that a higher abundance of the class Negativicutes corresponded to lower levels of hexadecanedioate (β = -0.1311, se = 0.05139,
Subsequently, the examination of GM as a potential mediator between circulating metabolites and EMs was undertaken. A positive association was discovered between the level of pseudouridine and the abundance of class Negativicutes (β = 1.206, se = 0.4339,

The forest plot summarized the causality between circulating metabolites and gut microbiota.
The present study constitutes the inaugural two-sample Mendelian randomization (TSMR) investigation into the interrelationships among GM, circulating metabolites, and EMs, revealing significant clinical relevance. Through the most extensive GWAS conducted to date on GM, circulating metabolites, and EMs, we identified robustly associated SNPs. Leveraging comprehensive genetic data from over 400,000 European individuals, we discovered a genetic predisposition to specific GM abundances that are causally linked with EMs. Similarly, we found a genetic liability to specific circulating metabolites associated with EMs. These findings suggest that circulating metabolites do not mediate the relationship between GM and EMs. Instead, GM may mediate between circulating metabolites and EMs, potentially guiding future mechanistic inquiries and clinical translational studies concerning EMs.
Endometriosis is a prevalent condition affecting women during their reproductive years, causing severe health and psychological distress. Symptoms such as chronic pelvic pain, infertility, and excessive bleeding are prevalent. Despite decades of research, the etiology and pathogenesis of EMs remain unclear, diagnosis is frequently delayed due to the requirement for invasive procedures, and curative treatments are elusive because the disease is estrogen dependent. Twin studies have estimated the heritability of EMs to be approximately 50% (Saha et al. 2015). Common genetic variations account for about 26% of the risk for EMs, making it an ideal candidate for MR studies (Lee et al. 2013). The functional elucidation of variants implicated by GWAS in the pathogenesis of EMs necessitates integrated analyses of genomic data with epigenomic, metabo-lomic, proteomic, and transcriptomic data. The current study aims to reveal potential connections between gut microbiota, circulating metabolites, and EMs.
Over the past few decades, researches have consistently reported alterations in the abundance of GM among animal models and women afflicted with EMs. In a mouse model of EMs, established through the intraperitoneal injection of endometrial fragments, Ni et al. (2020) observed a significant correlation between EMs and altered gut microbiome profiles. Furthermore, Chadchan et al. (2019) discovered that metronidazole and broad-spectrum antibiotics could mitigate the development of EMs in a surgical mouse model. Comparable findings were noted in studies with Rhesus monkeys affected by EMs; Bailey and Coe (2002) identified a markedly different gut microbiota composition compared to healthy controls, with a higher prevalence of Gram-negative bacteria and reduced levels of lactobacilli. Corresponding patterns have been evident in human studies as well. Shan et al. (2021) reported notable disparities in the alpha diversity of gut microbiota and the Firmicutes/Bacteroidetes ratio between individuals with stage III/IV EMs and those who are healthy. Ata et al. (2019) found an increased ratio of
The histopathological features of EMs were characterized by local inflammation. The inflammation and immune system dysfunction were crucial causes of EM pathogenesis. Recent studies presented strong evidence for an association between alteration of the gut microbiota and inflammatory bowel disease (Franzosa et al. 2019), neuropsychiatric diseases (Socała et al. 2021), psoriasis (Buhaş et al. 2022), arthritis (Xu et al. 2022), and some cancers. This was explained by the potential immunoregulation of gut microbiota on systemic inflammatory cellular responses. Since abnormal immune and inflammatory responses were thought to be involved in EM pathogenesis, the causality between microbiota and EMs was logically rational. The mouse model found that a fecal transplant from an EM mouse could alter EM progression and be accompanied by modulation of inflammatory and immune response. Lui et al. (2016) found that alteration of gut microbiota could influence mucosal T cell composition and function (TH1, TH17, TReg, etc.). Therefore, alteration of the gut microbiota might impact the mucosal immune balance, triggering inflammation and disease. Kogut et al. (2020) found that altering gut microbiota could cause elevated levels of systematic immune mediators. Macrophages played an essential role in EMs and were the predominant immune cell population in the ascites of EMs women. Elkabets et al. (2010), Lobo et al. (2018), and Rao et al. (2023) found that dysfunctional NK cells could down-regulate the phagocytic activity of macrophages and induce Treg lymphocytes, might promote ectopic endometrial cells to escape from immune surveillance. Recent studies have suggested that alteration of gut microbiota might cause the inappropriate activity of macrophages (Krishnan et al. 2018; Li et al. 2019; Sun et al. 2021), which might be involved in the EMs pathogenesis. Our MR study has revealed a significant association between specific GM taxa of the and the risk of developing EMs, thereby confirming the influence of the GM on the pathophysiology of EMs.
Numerous studies had yielded substantial evidence highlighting statistically significant differences in circulating metabolites between individuals with EMs and healthy controls. Dutta et al. (2012; 2018) observed that serum samples from EMs patients exhibited elevated levels of lactate, 3-hydroxybutyrate, alanine, leucine, valine, threonine, lysine, glycerophosphatidylcholine, succinic acid, and 2-hydroxybutyrate, as well as decreased levels of lipids, glucose, isoleucine, and arginine. Additionally, they identified several dysregulated lipids, including phosphatidylcholines, sphingomyelins, phosphatidylethanolamines, and triglycerides in a mouse model induced with EMs. Conversely, Jana et al. (2013) reported decreased serum leucine levels in patients with EMs. Vicente-Muñoz et al (2015) discovered that the EMs patients’ plasma metabolomic profile was characterized by increased concentrations of valine, fucose, choline-containing metabolites, lysine/arginine, and lipoproteins, along with decreased concentrations of creatinine. Vouk et al. (2012) found an association between the serum of EMs patients and elevated levels of sphingomyelins and phosphatidylcholines. Ghazi et al. (2016) noted significantly enhanced serum levels of 2-methoxyestron, 2-methoxy estradiol, dehydroepiandrosterone, androstenedione, aldosterone, and deoxycorticosterone in EMs, whereas cholesterol and primary bile acids were reduced. Letsiou et al. (2017) identified a panel of acylcarnitines that predicted the presence of endometriosis with 88.9% specificity and 81.5% sensitivity in human plasma, offering a positive predictive value of 75%. Present MR study revealed associations between specific circulating metabolites and the risk of EMs, encompassing lipids, amino acids, fatty acids, xenobiotics, and nucleotides. These findings align with previous research and underscore the influence of circulating metabolites on the pathophysiology of EMs.
The mechanisms through which gut microbiota may influence endometriosis largely remain unidentified. One potential hypothesis, as elaborated in a prior publication (Yang 2024), involves immunological dysfunction and alterations in estrogen homeostasis. Another hypothesis suggests that GM influences EMs through the action of circulating metabolites. However, illogical results were obtained when utilizing TSMR to evaluate the mediating effect of circulating metabolites linking GM and EMs. This led to the speculation that circulating metabolites are not the primary mediator. Instead, it is proposed that GM acts as the mediator between circulating metabolites and EMs. Notably, it was identified that pseudouridine and 3-phenylpropionate may mediate a small proportion (0.06–0.31%) of the GM’s effect on EMs, representing a novel finding in the field that could guide future mechanistic and clinical translational studies on EMs.
The main strengths of the present study included the pioneering application of TSMR analysis to elucidate the causal relationships between GM, circulating metabolites, and EMs, enrolling the largest sample size to date, and employing multiple testing corrections to ensure the robustness of our results. The MR approach helps eliminate confounders typically observed in epidemiological studies, attaining a level of evidence comparable to randomized controlled trials (RCTs). Furthermore, these selected SNPs showed strong associations with GM and circulating metabolites. Sensitivity analyses indicated no pleiotropy or heterogeneity, confirming the statistical robustness of our findings.
Present study provides valuable insights but is not without limitations. Firstly, the FDR for all causality assessments exceeded 0.05, indicating that they did not pass multiple testing corrections, which could undermine the robustness of results. Secondly, since the GWAS data originated from European populations, ethnic and geographical factors could influence GM abundance and the characteristics of circulating metabolites, possibly limiting the generalizability of findings to other populations. Thirdly, although MR is thought to mimic randomized controlled trials, the possibility of bias and confounding still exists. For example, if there is population stratification not properly controlled for, or if genetic markers associated with multiple biological pathways are used, it can lead to confounding. Fourthly, MR studies require large sample sizes to achieve sufficient statistical power, especially when estimating small effect sizes. Obtaining and analyzing large datasets while keeping type II errors (false negatives) low and controlling type I errors (false positives) is challenging. Fifth, the genotype and outcome measurements come from different populations, and selection bias may be introduced. Sixth, MR studies are limited to studying inheritable exposures and have limited capability to capture the effects of complex environmental exposures or behaviors.
Recent studies (Zheng et al. 2023) have revealed that histone deacetylase 8 (HDAC8) is progressively and aberrantly overexpressed in EMs, complementing the documented upregulation of HDAC1 in EMs. This dysregulation, alongside the promising therapeutic potential of histone deacetylase inhibitors (HDACIs), underscores the urgent need for a deeper investigation into the epigenetic alterations that drive EMs, mainly focusing on the roles of HDACs.
Melatonin, recognized for its ability to modulate crucial biochemical pathways involved in inflammation, oxidative stress, apoptosis, and energy metabolism, has been found in significantly higher concentrations in the gut than in the pineal gland. The GM, a critical player in multiple physiological functions, is increasingly implicated in various pathologies due to its imbalances. Emerging data (Iesanu et al. 2022; Sharifi et al. 2023; Ahmadi et al. 2024; Magadán-Corpas et al. 2024) suggest that melatonin may play a role in modulating the GM, offering potential therapeutic implications for these conditions. In the context of EMs, melatonin’s anti-proliferative, antioxidant, anti-inflammatory, and anti-invasive properties make it a promising agent in mitigating the disease’s progression. These protective effects involve multiple pathways, including antiestrogenic, antioxidant, anti-inflammatory, and anti-apoptotic mechanisms, as well as inhibiting the growth of E2-induced endometriotic tissue.
Concurrently, EMs patients exhibit altered HPA-axis function (van Aken et al. 2018), potentially stemming from heightened chronic stress levels. The interplay between pro-inflammatory and anti-inflammatory cytokines is also evident in EMs pathogenesis, where these cytokines significantly influence cellular proliferation and differentiation (Oală et al. 2024). Dysbiosis of GM and reduced serum levels of acetic and propionic acids may contribute to glycolipid metabolism disorders associated with EMs.
Future research should aim to enroll larger sample sizes from diverse racial and geographical backgrounds to explore more robust causalities. Concurrently, deeper mechanistic investigations are warranted to uncover the complex interplay between the gut microbiome, circulating metabolites, and EMs.
In summary, the present study has comprehensively assessed the association between GM, circulating metabolites, and EMs. The results indicate that eight GM taxa and fifteen circulating metabolites have significant associations with EMs. Additionally, pseudodo-uridine and 3-phenylpropionate have been identified as potential mediators of the GM’s influence on EMs. This research contributes novel candidate factors that could serve as a foundation for future mechanistic inquiries and clinical translational efforts.