Introduction

Endometriosis is a chronic gynecological disease whose course is strongly influenced by estrogen [1]. It affects a significant proportion of women of reproductive age globally, ranging from 5 to 15% [2]. While traditionally considered benign, endometriosis shares certain characteristics with malignant tumors, including aggressive growth, recurrence, and metastasis [3]. As a result, endometriosis can lead to a reduced quality of life for patients and places a substantial economic burden on society [4]. At present, the etiology and pathogenesis of endometriosis are unclear, which poses a primary challenge to the development of clinical interventions to cure endometriosis [5].

In recent years, the relationship between human microbiota and female reproductive health has garnered significant attention [6, 7], and numerous studies have demonstrated variations in the diversity of gut microbiomes between women with endometriosis and those without [8,9,10]. Huang et al. reported an increase in the number of harmful Eggerthella lenta and Eubacterium dolichum in the gut of women with endometriosis and a significant decrease in the number of other protective microorganisms, particularly Clostridia, Ruminococcus, and Lachnospiraceae [8]. Chadchan et al. conducted a study on autograft-induced EMS mice, using antibiotics to inhibit lesion growth and fecal gavage to disrupt the gut microbiome. Their findings revealed that both lesion growth and associated inflammation were restored [11]. Despite these findings, the available evidence does not establish a causal relationship between the gut microbiome and endometriosis. Moreover, the susceptibility of the gut microbiome to various factors complicates the elucidation of their relationship.

Some studies have demonstrated that the increased risk of endometriosis may be related to abnormal lipid metabolism [12, 13]. Epidemiological studies, animal experiments, and Mendelian randomization (MR) Studies have demonstrated an association between gut microbiome and lipids [14,15,16]. Therefore, blood lipids may be a potential mediator between the gut microbiome and endometriosis.

In this work, we conducted an MR study, aiming to evaluate the causal effect of gut microbiome on the risk of endometriosis, as well as the potential mediating role of lipids. The results might shed light on the etiology of endometriosis and help make strategies for the clinical screening and prevention of endometriosis.

Methods

Study design

This work encompassed two stages of analysis. In stage I, we investigated the causal association between gut microbiome and endometriosis through a bidirectional MR. In stage II, we performed a two-step MR to establish a causal pathway from the gut microbiome to endometriosis, containing two analysis steps with lipids acting as mediators. In step 1, we identified the causative lipids in endometriosis by integrating univariable MR and MR based on Bayesian model averaging (MR-BMA). In step 2, we investigated the causal associations between the identified gut microbiota and lipids. Then we calculated the mediating effect of lipids in the association of gut microbiota with endometriosis. Figure 1 presents the study design. The selection of instrumental variables (IVs) was based on three assumptions: [1] IVs should be significantly associated with exposure [2], IVs are not associated with confounders that affect the relationship between exposure and outcome, and [3] IVs should affect the outcome only through the exposure.

Fig. 1
figure 1

The flowchart of the study based on three assumptions: [1] Instrumental variables are significantly linked to exposure [2], Instrumental variables are not linked to confounders that affect the relationship between exposure and outcome, and [3] Instrumental variables are only linked to outcome through exposure

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Data source

The genetic IVs for the gut microbiome were obtained from the research conducted by Kurilshikov et al. in the MiBioGen Consortium [17]. The data included the 16s rRNA gene sequencing profiles and whole genome genotyping data of 18,473 individuals from 12 countries, including the United Kingdom, the United States, and the Netherlands. The dataset also encompassed 122,110 genetic variation loci corresponding to 211 gut microbiome taxa at five hierarchical levels: phyla, kingdom, order, family, and genus. For our study, the genus was selected as the lowest classification level, and a total of 131 genera with an average abundance greater than 1% were identified, of which 12 could not be identified [17]. Therefore, we analyzed 119 genus-level taxa. The GWAS for blood lipids was obtained from the study conducted by Richardson et al., containing over 440,000 participants from the UK Biobank [18]. We obtained genetic associations with each lipid trait, including apolipoprotein A-1 (ApoA-1) (n = 393,193), apolipoprotein B (ApoB) (n = 439,214), high-density lipoprotein (HDL) cholesterol (n = 403,943), low-density lipoprotein (LDL) cholesterol (n = 440,546), and triglycerides (TG) (n = 441,016). The endometriosis GWAS data was derived from FinnGen, which was deposited in the IEU GWAS database (https://gwas.mrcieu.ac.uk/) with the GWAS ID “finn-b-N14_ENDOMETRIOSIS” [19]. Specifically, there were 8,288 cases diagnosed with endometriosis and 68,969 controls, and 16,377,306 single nucleotide polymorphisms (SNPs) were tested. There was no sample overlapping among the GWAS data of the exposure, outcome, or mediators.

Stage I analysis

In stage I, we evaluated the causal effect of gut microbiome on endometriosis using univariable MR analysis. For the identified gut microbiome, we also conducted a reverse MR analysis to investigate the causal effect of endometriosis on them.

IVs selection

We implemented a series of quality-control steps in selecting the genetic IVs associated with the gut microbiome to ensure the precision and effectiveness of the study’s conclusions. First, we extracted the significant IVs for gut microbiome with a threshold of P < 1 × 10− 5 as a limited number of SNPs available at genome-wide significance cutoff (P < 5 × 10− 8) [20]. Second, we excluded SNPs exhibiting linkage disequilibrium (LD) with an r2 ≥ 0.001 to ensure the included SNPs were independent. Then the SNPs were extracted from the endometriosis dataset and harmonization was performed to align the effect allele and remove palindromic SNPs with intermediate minor allele frequency (maf > 0.42). We finally used the following formula to assess weak instrumental variable bias: F = R2 (n-k-1)/k(1-R2), where n represents the number of samples, k represents the number of SNPs, and R2 signifies the exposure variance. IVs with F-statistics < 10 were excluded [21]. When conducting reverse MR analysis, we used genetic IVs associated with endometriosis at P < 5 × 10− 8, and the consequent processing steps were as mentioned above. Supplementary Tables S1 and S2 present the characteristics of SNPs included in this study.

MR analysis

We tested the bidirectional effects between gut microbiome and endometriosis using several MR models, including inverse variance weighted (IVW), weighted median, simple mode, weighted mode, and MR-Egger regression. Given the IVW method is more efficient than other MR methods, this study used the IVW method as the primary model to test the causal effect [22]. Besides, considering the random-effects model for IVW was more conservative than the fix-effects model when existing heterogeneity, this MR study applied the random-effect model IVW as the major approach.

For sensitivity analysis, we initially assessed heterogeneity using Cochran’s Q test. The Cochran’s Q with P < 0.05 indicated that heterogeneity was detected. We then performed the “leave-one-out” analysis by omitting each instrumental SNP in turn. We also used the intercept term of the Egger regression to judge horizontal pleiotropy. The intercept with P < 0.05 suggested that horizontal pleiotropy could not be ruled out. Finally, the MR pleiotropy residual sum and outlier (MR-PRESSO) method was applied for testing and calculating the causal estimate following the removal of the identified outliers, once heterogeneity was detected in Cochran’s Q test [23].

In addition, to identify the independent effect of gut microbiome on endometriosis, we further conducted a multivariable MR (MVMR) analysis with the detected gut microbiome included in an MVMR IVW model.

Stage II analysis

The stage II analysis was predominantly based on a two-step MR analysis containing two steps. In step 1, we first evaluated the causal effects of several lipid traits on endometriosis (β2); in step 2, we evaluated the causal associations between the identified gut microbiome and lipids (β1). Then the mediating effects of lipids were calculated.

Step1: evaluating the effect of lipids on endometriosis

To identify potential lipid traits associated with endometriosis, we first conducted an univariable MR analysis. Genetic IVs for several lipid traits, including ApoA-1, ApoB, HDL, LDL, and TG, were extracted with a P threshold at 5 × 10− 8. The processing steps for IVs and statistical analyses were as mentioned above. For the detected lipids in univariable MR analysis, we further conducted MR-BMA to prioritize them at the risk of endometriosis. The MR-BMA is the extension of multivariable MR, which allows for the analysis of high-throughput experiments. Zuber et al. found that in a realistic simulation study, MR-BMA could detect true causal risk factors even when the candidate risk factors were highly correlated [24]. Specifically, MR-BMA takes all possible combinations of the covariates into account and generates posterior probability for each combination model. The marginal inclusion probability (MIP) was determined by summing the posterior probabilities of all models, providing a ranking of causal associations with the outcome. Finally, model-averaged causal effects (MACE) were analyzed and the P-value of each lipid was calculated using the permutation method. The P-values were adjusted by several tests through the False Discovery Rate (FDR) procedure [28]. In addition, we also conducted an LD score regression (LDSC) analysis to investigate the genetic correlation among the lipids.

Step 2: evaluating the causal associations between the detected microbiome and lipids

Based on the analyses above, gut microbiome and lipids associated with endometriosis were identified. We then conducted a univariable MR analysis to detect levels of blood lipids attributed to gut microbiome, with the statistical methods mentioned above.

Calculating the mediating effect and proportion of lipids

The indirect effect mediated by lipids was calculated as β1 × β2, and the proportion of the mediating effect was calculated using β1 × β2 / β3, where β1 is the MR effect of the gut microbiome on lipids, β2 is the MR effect of lipids on endometriosis, and β3 represents the MR effect of the gut microbiome on endometriosis. The delta method was used to analyze 95% confidence intervals (CIs) [25]. MR estimates from MR-PRESSO were used to calculate the mediating effect when existing heterogeneity.

This analysis was performed using the R software (version 4.2.3). The “TwoSampleMR” and “MR-PRESSO” packages were used for statistical analysis. The MR-BMA was conducted based on the R-code deposited in GitHub (https://github.com/mglev1n/mrbma). The MR study was reported according to the MR-STROBE.

Result

The IVs of 119 bacterial genera are presented in Supplementary Table S1. No weak IVs were included as F-statistics were over 10 for all the SNPs. For binary outcomes (endometriosis), the results were presented as odds ratio (OR); whereas for continuous outcomes (gut microbiome and lipids), the results were presented as beta. The corresponding 95% CIs were presented.

Causal effects of the gut microbiome on endometriosis

We identified four possible intestinal bacteria genera through the IVW method (Figs. 2 and 3). Eubacterium ruminantium group exhibited a negative causal relationship with endometriosis (OR = 0.881, 95% CI: 0.795–0.976, P = 0.015), whereas the remaining three bacteria genera Anaerotruncus (OR = 1.252, 95% CI: 1.028–1.525, P = 0.025), Olsenella (OR = 1.110, 95% CI: 1.007–1.223, P = 0.036), and Oscillospira (OR = 1.215, 95% CI: 1.014–1.456, P = 0.035) were positively associated with the risk of endometriosis. Similar results were derived from other MR models, including weighted median, MR-Egger regression, simple mode, and weighted mode (Fig. 3).

Fig. 2
figure 2

The forest plot shows the causal estimates between gut microbiome and endometriosis. The odds ratio and 95% confidence interval were obtained using the inverse–variance weighted method. OR, odds ratio; CI, confidence interval

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Fig. 3
figure 3

Scatter plots of significant causality between gut microbiome genera and endometriosis. (A) Anaerotruncus; (B) Eubacteriumruminantiumgroup; (C) Oscillospira; (D) Olsenella

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Cochran’s IVW Q test indicated no marked heterogeneity among these IVs (Supplementary Table S3). Additionally, the P-values of the MR-Egger regression intercept all exceeded 0.05, suggesting the absence of obvious horizontal pleiotropy. The leave-one-out analysis also indicated that no SNPs altered the causal association signal (Fig. 4). Besides, the MR estimates were further supported by replicative analyses after removing outliers identified in MR-PRESSO (Supplementary Table S3).

Fig. 4
figure 4

Leave-one-out analysis for the impact of individual SNPs on the association between GM and EMS risk. (A) Anaerotruncus; (B) Eubacterium ruminantium group; (C) Oscillospira; (D) Olsenella

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Further MVMR analysis showed that Eubacterium ruminantium group (OR = 0.85, 95% CI: 0.77–0.94, P = 0.001), Olsenella (OR = 1.10, 95% CI: 1.01–1.20, P = 0.03), and Oscillospira (OR = 1.25, 95% CI: 1.08–1.46, P = 0.003) remained an independent effect on endometriosis, whereas the effect of Anaerotruncus was slightly attenuated (OR = 1.21, 95% CI: 0.99–1.47, P = 0.057).

Reverse MR analysis of the above four gut microbiomes revealed that endometriosis had a suggestive causal effect on Anaerotruncus (Beta = -0.061, 95% CI: -0.120 – -0.002, P = 0.041), whereas no bidirectional causal relationship was observed between the remaining three gut microbiome and endometriosis (Supplementary Table S4).

Causal effect of blood lipids on endometriosis

Univariable MR analysis indicated that among the five included lipid traits, ApoA-1 (OR = 0.893, 95% CI: 0.817–0.975, P = 0.013), HDL cholesterol (OR = 0.899, 95% CI: 0.824–0.981, P = 0.018) and TG (OR = 1.159, 95% CI: 1.054–1.275, P = 0.002) had causal effects on endometriosis. The Cochran’s Q test showed considerable heterogeneity in some MR estimates (Table 1). However, the MR-Egger intercept test indicated no evidence of pleiotropy.

Table 1 Mendelian randomization analysis on the causal effect of blood lipids on endometriosis
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Using MR-BMA, TG was identified as the top-ranked lipid most strongly associated with endometriosis (MIP = 0.839, MACE = 0.117, PFDR = 0.039) (Tables 2 and 3). Through the LDSC, we determined that there were significantly strong genetic correlations among TG, ApoA-1, and HDL cholesterol (Fig. 5).

Table 2 Ranking of models for endometriosis according to their posterior probability after exclusion of outlying and influential variants in MR-BMA analysis
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Table 3 Prioritization of causal blood lipids of endometriosis using the MR-BMA method
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Fig. 5
figure 5

Heat map show the genetic correlation estimates from linkage disequilibrium score regression among selected metabolites. The red indicated a positive, while the blue indicated a negative correlation. The number marked in the upper right part of the figure is the correlation coefficient, and the lower left part is the P values of the LDSC test. ApoA1, apolipoprotein A1; HDL-C, high-density lipoprotein cholesterol; TG, triglycerides

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Causal effect of gut microbiome on lipid traits

We further conducted a univariable MR analysis to investigate the causal effects of Eubacterium ruminantium group, Anaerotruncus, Olsenella, and Oscillospira on TG, The IVW showed that Olsenella exhibited a causal effect on TG (Beta = 0.023, 95% CI: 0.005–0.041, P = 0.011). No causal associations were observed in the other three microbiomes.

Mediation analysis

Resultantly, we analyzed TG as the mediator of the Olsenella-endometriosis pathway (Fig. 6). Using the product of coefficients, we found the mediating effect of TG in the Olsenella-endometriosis pathway was 0.003, with a mediating proportion of 3.3% (95% CI = 1.5 − 5.1%, P = 0.048).

Fig. 6
figure 6

The triglycerides mediated the causal effect of Olsenella on endometriosis. The β value between Olsenella and triglycerides and endometriosis are the MR estimates using the inverse variance weighted method. The β value between triglycerides and endometriosis are the Mendelian randomization estimates using the Mendelian randomization pleiotropy residual sum and outlier method

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Discussion

In this study, we found that genetic liability for a higher abundance of Eubacterium ruminantium group was associated with a lower risk of endometriosis, whereas genetic liability for a higher abundance of Anaerotruncus, Olsenella, and Oscillospira, was associated with a higher risk of endometriosis. Further mediation analysis revealed that TG partially mediated the detrimental effect of Olsenella on endometriosis. The findings in this MR work shed light on the screening and prevention of endometriosis by examining the gut microbiome. On the one hand, examining fecal bacteria might be a promising strategy to screen populations with a higher risk of endometriosis; on the other hand, regulating the intestinal flora microenvironment might be a novel strategy to prevent endometriosis in the future. In addition, this MR work also implies the involvement of lipid traits (TG) in the gut microbiome-endometriosis pathway, suggesting modulating TG could also be a promising strategy for endometriosis prevention.

Animal studies have demonstrated that gut microbiome plays a crucial pro-inflammatory role in the progression of endometriosis and results in higher levels of IL-1β, TNF-α, IL-6, and TGF-β1 and an increase in macrophages at the lesion site [26]. Short-chain fatty acids, like acetate, propionate, and n-butyrate, are bioactive metabolites produced through the processing of indigestible nutrients by the gut microbiome [27, 28]. Butyrate is the main energy source for colon epithelial cells and plays a crucial role in maintaining the integrity of the intestinal barrier [29, 30]. Research findings indicate that n-butyrate inactivates the pro-growth RAP1 signaling pathway by activating GPR43 and GPR109A, inhibiting histone deacetylase activity and activating the expression of RAP1GAP. This process then inhibits the development of endometriosis lesions and inflammatory cell infiltration [11]. Therefore, once the gut microbiome is disturbed, the immune integrity of the intestinal wall will be decreased, and the permeability will be increased, causing bacterial endotoxins in the intestinal tract to enter the blood and consequently trigger various inflammatory reactions in the body to induce the occurrence of epithelial-mesenchymal transition (EMT) [31,32,33,34,35,36,37,38]. Studies have demonstrated that the pathogenesis of EMT is consistent with the pathogenesis of endometriosis. It has been proven that EMT plays a crucial role in the invasion of ectopic endometrium and is a key step in the successful invasion of ectopic endometrium into the external position of the uterine cavity [39].

PD-1 and PD-L1 are overexpressed in endometriosis, and their overactivation promotes the development of endometriosis [40]. Additionally, studies have demonstrated that the immune factor IL-27 in endometriosis accelerates the development of endometriosis by triggering the production of IL-10 by Th17 and promoting the proliferation and implantation of ectopic lesions [41]. Therefore, the successful implantation and transfer of ectopic endometrium outside the uterus is closely related to immune tolerance, especially the immune tolerance caused by PD-1/PD-L1 and IL-27 overexpression [42,43,44]. Numerous studies have shown that endometriosis patients exhibit immunological tolerance of T cells, which includes immunosuppression and the depletion of helper T cells or cytotoxic T cell [45, 46]. This lowers the immune clearance ability and promotes the emergence of immune escape [47, 48]. In our study, Olsenella exhibited a causal association with an increased risk of endometriosis. However, studies have demonstrated that Olsenella can enhance the activity of anti-PD-1 antibodies by producing the metabolite inosine to activate antitumor T cells [49]. Therefore, the association between Olsenella and endometriosis deserves further study.

In addition, estrogen metabolism might be another potential mechanism worth discussing. Endometriosis is an estrogen-driven disease, and high estrogen levels are considered to be an important pathogenic factor in endometriosis [50, 51]. The metabolism of estrogen mainly occurs in the liver, which can produce sex hormone-binding globulin (SHBG). Estrogen loses its biological activity after binding to SHBG [52]. Conjugated estrogens are excreted in the bile and enter the intestine, uncoupled by beta-glucuronidase secreted by the gut microbiome, reabsorbed through the intestinal mucosa, and enter the circulatory system through the portal vein [53]. Ervin et al. reported that beta-glucuronidase secreted by the gut microbiome can affect intestinal estrogen metabolism and the growth of estrogen-dependent tumors [54]. An imbalance of the gut microbiome leads to a large production of beta-glucuronidase, causing the levels of circulating estrogen metabolites to increase, stimulating the growth of endometrial tissue, and promoting the occurrence and development of endometriosis [55, 56].

Except for the potential immunomodulatory and estrogen metabolism mechanisms proposed above, our study further identified TG partially mediated the causal association between Olsenella and endometriosis, suggesting blood lipid metabolism might be the underlying mechanism linking Olsenella with an increased risk of endometriosis. Specifically, the role of lipids in mediating the effect of gut microbiome on health outcomes has been investigated in previous studies. For example, Dai et al. found that ApoB accounted for nearly 10% of the effect of species B. dorei on heart failure [57]. As mentioned above, we predominantly proposed the mediating role of TG in the pathway from gut microbiome to endometriosis. Despite this, other potential pathways warrant further investigation. Except for TG, previous studies have reported enriched ApoB mRNA Editing Enzyme Catalytic Subunit (APOBEC) signature mutations were higher in endometriosis patients, indicating APOBEC-mediated mutagenesis might drive genomic heterogeneity in endometriosis [58]. In addition, the coagulation function might be another mechanism that should be noted. One GWAS study has reported that LDL and TC were associated with an increased risk of pulmonary embolism, suggesting the lipids traits were involved in coagulation pathways [59]. Another MR study has demonstrated the causal associations between ADAMTS13/vWF and the risk of endometriosis, suggesting the involvement of coagulation factors in endometriosis development [60].

The study had some limitations. First, our study was limited to European patients, and this demographic bias limits our study and its applicability to other ethnic groups [61]. Second, this study was limited to patients diagnosed with ovarian endometriosis and did not consider intestinal endometriosis, peritoneal endometriosis, and pelvic endometriosis. For the above two limitations, further study is needed to expand the investigation to diverse populations and other forms of endometriosis to enhance generalizability when future GWAS data containing various subtypes of endometriosis from different ancestries is available. Third, the current study could not rule out the bias from the complex interaction between gut microbiome and host metabolism. Fourth, given that only summary-level statistics are publicly available, stratification analysis like sex stratification is unavailable. Though the MR design is less likely prone to confounders (including gender and age), future investigation focusing on stratification analysis when stratified GWAS data is available would improve the reliability of the results. Fifth, in this exploratory MR work, we did not conduct multiple testing corrections for p values that might miss out on certain significant genera potentially associated with the occurrence of endometriosis. Instead, we used P < 0.05 as the threshold to determine the significant estimates. This strategy might help preliminarily screen potential genera involved in endometriosis development. Sixth, we used a relaxed P threshold to extract IVs for gut microbiome, which has been widely used in previous MR studies. This might be prone to including weak instruments, but the F-statistics for each of the SNPs suggested no weak IVs were included (all F > 10). Seventh, the leave-one-out analysis showed some high influence points driving the overall estimates, which would potentially make the MR results over-estimated. This might be owing to the relatively limited number of SNPs included in the MR work, though a relaxed significance threshold for the associations between SNPs and gut microbiome was used in this work. Further investigation is warranted using gut microbiome GWAS with greater sequencing depth to validate our results. Eighth, our study suggests that a small part of the causal effect of the gut microbiome on endometriosis is mediated by blood lipids. Therefore, further research is needed to quantify other potential mediators. Finally, several issues should be also noted in terms of the inherent nature of the MR approach, including pleiotropy, measurement error in GWAS data, and the assumption that genetic variants accurately reflect the lifetime exposure to microbiome composition or lipid levels.

Conclusion

Our study shows evidence for gut microbiome involved in endometriosis development and identified TG might partially mediate the effect of Olsenella on endometriosis risk. This study provides new insights into the etiology of endometriosis and might help set out strategies for endometriosis screening and prevention.