Gut microbiota and risk of ovarian diseases: a two-sample Mendelian randomization study

doi:10.21203/rs.3.rs-4188266/v1

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Gut microbiota and risk of ovarian diseases: a two-sample Mendelian randomization study

https://doi.org/10.21203/rs.3.rs-4188266/v1

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Background Previous studies have reported an association between gut microbial dysbiosis and ovarian diseases, however, it is not clear whether a causal association exists.

Methods Two-sample Mendelian randomization (MR) analysis was performed to genetically predict the causal effects of the gut microbiota on polycystic ovary syndrome (PCOS), premature ovarian failure (POF), ovarian endometriosis, and malignant and benign ovarian neoplasms. The inverse variance weighted (IVW) method was used as the primary statistical method. A series of sensitivity analyses, including weighted median, MR-Egger, simple mode, weighted mode methods, MR pleiotropy residual sum and outlier (MR-PRESSO) and leave-one-out analysis, were also conducted to assess the robustness of the MR analysis results. Reverse MR analysis was implemented to explore whether ovarian diseases have any causal impact on the bacterial genera. Additionally, the Cochran’s Q test was used to evaluate heterogeneity among instrumental variables.

Results IVW analysis revealed that several bacteria were associated with decreased risk of PCOS, POF, ovarian endometriosis, and benign and malignant ovarian neoplasm. Moreover, several bacteria were the causes of increased risks for POF, ovarian endometriosis, and benign and malignant ovarian neoplasm, respectively. Reverse MR analysis did not reveal a significant causal effect of these ovarian diseases on the gut microbiota. These findings were robust according to extensive sensitivity analyses.

Conclusion Our results provide genetic evidence to support the causal relationship between specific gut microbiota taxa and ovarian diseases; thus, the gut microbiota should be considered a preventative strategy for ovarian diseases.

gut microbiota

polycystic ovary syndrome

premature ovarian failure

ovarian endometriosis

ovarian neoplasm

Mendelian randomization

The ovary is essential for establishing and maintaining secondary sexual characteristics and fertility in females. However, ovarian diseases, such as polycystic ovary syndrome (PCOS), premature ovarian failure (POF), ovarian endometriosis and ovarian neoplasm, negatively influence reproductive health and induce disorders of ovarian function. PCOS is one of the most common reproductive endocrine and metabolic disorders, and common signs of PCOS are ovulatory dysfunction, excess androgen exposure and the presence of polycystic ovaries, which can impair fertility [1]. POF refers to the exhaustion of the ovarian reserve before the age of 40 years. Given that the chance of spontaneous conception is 5%-10% [2],adoption or in vitro fertilization and embryo transfer using donor oocytes are considered effective fertility treatments for women with POF. Radiotherapy, chemotherapy, and pelvic surgery for malignant and benign conditions, including ovarian endometriomas and ovarian cancer, can negatively affect the ovarian reserve [2, 3] and even lead to POF [4]. Unfortunately, the potential causes and molecular mechanisms of these ovarian diseases have not yet been elucidated.

More recently, the gastrointestinal tract, which hosts ten trillion diverse symbionts (50 bacterial phyla and approximately 100–1000 bacterial species), has been extensively studied owing to its basic functions in the immunological, metabolic, structural and neurological landscapes in humans [5]. Recent research has shown that the interaction of the female reproductive endocrine system with estrogen, androgens, insulin, and other hormones appears to be crucial for successful pregnancy [6]. Several observational studies have suggested that an imbalance in gut microbiota stabilization may induce PCOS [7–10], endometriosis [11, 12], ovarian dysfunction [13] and ovarian cancer [14] in women. However, less is known about the exact role of the gut microbiota in ovarian physiology, and few studies have explored the causal relationship between the gut microbiota and certain diseases [15].

Establishing the causal relationship between specific gut bacteria and certain ovarian diseases would be highly valuable for the prevention and treatment of this disease. Although randomized controlled trials (RCTs) are the gold standard for establishing causal relationships, they can be costly, time-consuming and even impractical [16]. On the other hand, observational studies may not robustly reflect causal relationships owing to many potential biases, confounders and reverse causation [17]. Mendelian randomization (MR) is an approach that uses genetic variants associated with an exposure as instrumental variables (IVs) to examine the causality of exposure–outcome associations. MR can minimize potential confounders and reverse causality as genetic variants segregate randomly and independently and precede the outcome of interest [16]. Furthermore, during the last decade, the publication of a large volume of genome-wide association studies (GWASs) has led to the conduction of MR studies without the need to recruit new patients. Therefore, MR offers a suitable means to infer the causal effect between the gut microbiota and the risk of ovarian disease. Here, we implemented bidirectional MR analyses to explore the causal relationship between the gut microbiota and ovarian diseases.

We assessed the causal links between the gut microbiota and five ovarian diseases using two-sample MR. An overview of the analytical approach is shown in Fig. 1a.

2.1 Exposure data

We obtained genetic variant information related to the human gut microbiome composition from the latest large-scale genome-wide meta-analysis conducted by the MiBioGen consortium (https://mibiogen.gcc.rug.nl/.) based on European-dominated participants [18]. This study analyzed genome-wide genotypes and 16S fecal microbiome data from 18,340 individuals from 24 cohorts. Accordingly, the genus level was the lowest. A total of 131 genera with a mean abundance greater than 1% were identified, 12 of which were unknown genera [18]. As a result, we included 119 genus-level taxa in the present study.

2.2 Outcome data

We obtained genetic variants related to the human gut microbiome composition from the latest large-scale genome-wide meta-analysis conducted by the MiBioGen consortium. https//mibiogen.gcc.rug.nl/.) based on European-dominated participants [18]. This study analyzed genome-wide genotype and 16S fecal microbiome data from 18,340 individuals from 24 cohorts. Accordingly, the genus level was the lowest. A total of 131 genera with a mean abundance greater than 1% were identified, 12 of which were unknown genera [18]. As a result, we included 119 genus-level taxa in the present study.

2.3 Instrumental variable selection

Single-nucleotide polymorphisms (SNPs) are used as IVs in MR analysis to provide evidence of causality between an exposure and outcome. To ensure the accuracy and robustness of the causal link, SNPs must satisfy three core assumptions to be used as IVs (Fig. 1b) [19]. Therefore, the following steps were conducted. First, we selected independent SNPs (linkage disequilibrium R² < 0.001 and clumping distance = 10,000 kb, based on the European-based 1000 Genome Projects reference panel) associated with each genus at a locus-wide threshold of significance (P < 1× 10^− 5). Second, the minor allele frequency (MAF) threshold of the variants of interest was 0.01. Third, allele frequency information was used to infer that palindromic SNPs were aligned in the same direction for exposure and outcome.

2.4 Statistical analysis

The inverse variance weighted (IVW) method was used as the primary statistical method and can provide the most accurate causal estimates provided that the pleiotropic effect is balanced and that all IVs meet the MR assumptions [10]. Since it is difficult to verify that IVs influence the outcome only through the exposure of interest, we performed a series of sensitivity analyses with different assumptions to assess the robustness of the associations and to examine horizontal pleiotropy for exposures, including weighted median, MR‒Egger simple mode weighted mode and MR pleiotropy residual sum and outlier (MR-PRESSO) analyses. The weighted median of SNP-specific estimates provides valid estimates when more than 50% of the information is contributed from the IVs [20]. MR-Egger regression provides a valid estimate of causal estimates under the instrument strength independent of direct effect (InSIDE) assumption [21]. However, this approach was used to detect and adjust for unbalanced horizontal pleiotropy rather than to produce causal estimates due to the low statistical power of MR-Egger. A MR-Egger intercept significantly different from 0 (P < 0.05) indicated the occurrence of directional pleiotropy and a potentially biased IVW estimate. In addition, horizontal pleiotropy was also assessed by employing the MR-PRESSO global test, and where evident, the MR‐PRESSO outlier test was used to exclude outlying SNPs and correct the IVW estimate [22]. To further test the robustness of our results, Cochran’s Q test was used to evaluate heterogeneity among the SNPs included in each analysis. Q statistics significant at P < 0.05 provide evidence for heterogeneity between individual genetic variants and the existence of invalid instruments [23]. In addition, leave-one-out analysis was performed to assess whether an outcome was driven by a single outlying SNP [24], indicating the presence of heterogeneous SNPs. Furthermore, if the genetic variants do not explain enough of the variance, there will be significant weak instrumental bias toward the confounded estimate [25]. To address this concern, SNP–specific F-statistics, approximated by the square of the beta divided by the variance for the SNP–exposure association, were calculated to evaluate the strength of the instruments used, and values exceeding the standard threshold of 10 are indicative of strong genetic instruments [25]. To explore whether ovarian diseases have any causal impact on the bacterial genera that were found to be causally associated with ovarian diseases in forward MR analysis, we also performed a reverse MR analysis using SNPs that are associated with each ovarian disease at a threshold smaller than the locus-wide significance level (5 × 10^− 6) as IVs.

All tests were two-sided and performed using R Version 4.2.1 with the R packages “TwoSampleMR”, “MendelianRandomization” and “MR-PRESSO”. A P value < 0.05 indicated statistical significance of the MR effect estimate. No ethical approval was required since we used publicly available summary data.

3.1 SNP selection

There were 1508 SNPs selected for the MR analyses according to the IV selection criteria. The detailed information and F-statistic for the selected instruments are shown in Supplementary Table 2. The overall instrument had a high F-statistic (> 10), indicating the good strength of the genetic instruments used.

3.2 Causal effects of the gut microbiome on ovarian diseases

Figure 2 shows causal effect estimates of the gut microbiota on PCOS, POF, ovarian endometriosis, and ovarian neoplasm from the IVW MR analyses. Associations for individual SNPs using the different MR methods are presented in Supplementary Table 3–7. Scatter and forest plots of the SNP-outcome associations against the SNP-exposure associations are shown in Supplementary Figs. 1–10, allowing visualization of the causal effect estimate for each individual SNP on PCOS, POF, ovarian endometriosis, and ovarian neoplasm. Leave-one-out plots are shown in Supplementary Fig. 11–15 to evaluate the influential outliers.

3.2.1 PCOS

MR analysis via the IVW method showed that Barnesiella, Bilophila, and Holdemania were negatively associated with the risk of PCOS odds ratio (OR) = 0.55, 95%Cl, 0.36–0.84, P = 0.005; OR = 0.58, 95%Cl, 0.37–0.92, P = 0.021; OR = 0.64, 95%Cl, 0.47–0.88, P = 0.005 (Fig. 2). According to the reverse MR analysis by the IVW method, no significant causal association was found between PCOS and these three bacterial genera (Supplementary Table 18).

3.2.2 POF

MR analysis via the IVW method revealed that Eubacterium (hallii group) and Eubacterium (ventriosum group) were negatively associated with the risk of POF (OR = 0.49, 95% Cl, 0.26–0.90, P = 0.022; OR = 0.51, 95% Cl, 0.27–0.97, P = 0.040), while Adlercreutzia, Intestinibacter, Lachnospiraceae (UCG008), and Terrisporobacter were positively associated with the risk of POF (OR = 3.01, 95% Cl, 1.38–6.60, P = 0.006; OR = 1.82, 95% Cl, 1.04–3.20, P = 0.037; OR = 1.73, 95% Cl, 1.08–2.76, P = 0.023; OR = 2.47, 95% Cl, 1.14–5.36, P = 0.022) (Fig. 2). According to the reverse MR analysis by the IVW method, no significant causal association was found between POF and these six bacterial genera (Supplementary Table 19).

3.2.3 Ovarian endometriosis

MR analysis via the IVW method showed that Intestinimonas was positively associated with the risk of ovarian endometriosis (OR = 1.21, 95% Cl, 1.03–1.42, P = 0.018), while Rikenellaceae (RC9gut group) and Ruminococcaceae (UCG013) were negatively associated with the risk of ovarian endometriosis (OR = 0.88, 95% Cl, 0.80–0.98, P = 0.017; OR = 0.78, 95% Cl, 0.63–0.98, P = 0.030) (Fig. 2). According to the reverse MR analysis by the IVW method, no significant causal association was found between ovarian endometriosis and these three bacterial genera (Supplementary Table 20).

3.2.4 Benign ovarian neoplasm

MR analysis via the IVW method revealed that Eubacterium (nodatum group) was positively associated with the risk of benign ovarian neoplasm (OR = 1.15, 95% Cl, 1.01–1.31, P = 0.039), while Ruminococcus (torques group), Barnesiella, and Blautia were negatively associated with the risk of benign ovarian neoplasm (OR = 0.65, 95% Cl, 0.48–0.88, P = 0.006; OR = 0.79, 95% Cl, 0.63–0.99, P = 0.038; OR = 0.78, 95% Cl, 0.62–1.00, P = 0.046) (Fig. 2). According to the reverse MR analysis by the IVW method, no significant causal associations were found between benign ovarian neoplasms and these four bacterial genera (Supplementary Table 21).

3.2.5 Malignant ovarian neoplasms

MR analysis via the IVW method revealed that Lachnospiraceae (UCG008) and Ruminococcaceae (UCG011) were positively associated with the risk of malignant ovarian neoplasms (OR = 1.44, 95% Cl, 1.10–1.90, P = 0.009; OR = 1.35, 95% Cl, 1.01–1.80, P = 0.039), while Paraprevotella, Ruminococcaceae (UCG005), Senegalimassilia, and Slackia were negatively associated with the risk of malignant ovarian neoplasms (OR = 0.72, 95% Cl, 0.54–0.95, P = 0.022; OR = 0.64, 95% Cl, 0.45–0.92, P = 0.016; OR = 0.59, 95% Cl, 0.36–0.94, P = 0.029; OR = 0.66, 95% Cl, 0.44–0.98, P = 0.039) (Fig. 2). According to the reverse MR analysis by the IVW method, no significant causal association was found between malignant ovarian neoplasms and these six bacterial genera (Supplementary Table 22).

3.2.6 Sensitivity analyses

The observed causal associations were consistent in sensitivity analyses. There was no evidence of significant heterogeneity or directional pleiotropy using Cochran’s Q test, MR Egger intercepts or MR-PRESSO in our study (Table 1 and Supplementary Table 8–12). No outliers were visually inspected in either the scatter (Supplementary Figs. 1–5) or forest plots (Supplementary Figs. 6–10). Furthermore, the leave-one-out analysis suggested that the observed associations remained consistent after eliminating each single SNP at a time (Supplementary Figs. 11–15), suggesting the robustness of the results.

Table 1

Heterogeneity and directional pleiotropy tests from MR analysis of the gut microbiota and risk of ovarian diseases.
Outcome	Exposure	Heterogeneity		MR‒Egger			MR-PRESSO
Outcome	Exposure	Cochrane’s Q	P	Egger Intercept	SE	P_intercept	Causal Estimate	Global Test P
PCOS	Barnesiella	4.80	0.941	-0.01	0.07	0.837	-0.60	0.940
	Bilophila	15.61	0.210	-0.03	0.09	0.704	-0.54	0.225
	Holdemania	7.94	0.847	-0.01	0.05	0.821	-0.44	0.864
POF	Eubacterium (hallii group)	9.01	0.773	-0.01	0.05	0.916	-0.72	0.795
	Eubacterium (ventriosum group)	10.02	0.761	-0.02	0.11	0.858	-0.68	0.764
	Adlercreutzia	9.55	0.215	0.28	0.14	0.085	1.10	0.271
	Intestinibacter	12.90	0.535	-0.08	0.08	0.307	0.60	0.558
	Lachnospiraceae (UCG008)	10.44	0.491	0.10	0.12	0.446	0.55	0.505
	Terrisporobacter	2.93	0.570	0.05	0.12	0.703	0.90	0.603
Ovarian endometriosis	Intestinimonas	10.96	0.756	0.02	0.02	0.388	0.19	0.756
	Rikenellaceae (RC9gut group)	11.01	0.443	0.08	0.04	0.092	-0.12	0.465
	Ruminococcaceae (UCG013)	9.09	0.523	-0.01	0.02	0.730	-0.24	0.542
Benign ovarian neoplasm	Eubacterium (nodatum group)	13.97	0.174	-0.05	0.04	0.269	0.14	0.200
	Ruminococcus (torques group)	5.10	0.647	-0.03	0.03	0.351	-0.43	0.703
	Barnesiella	12.03	0.362	0.04	0.03	0.289	-0.24	0.376
	Blautia	10.85	0.456	0.03	0.02	0.269	-0.24	0.446
Malignant ovarian neoplasm	Lachnospiraceae (UCG008)	7.33	0.772	0.14	0.07	0.074	0.37	0.785
	Paraprevotella	14.63	0.262	0.02	0.06	0.719	-0.33	0.292
	Ruminococcaceae (UCG005)	11.76	0.547	-0.02	0.04	0.682	-0.44	0.592
	Ruminococcaceae (UCG011)	9.64	0.210	-0.05	0.10	0.668	0.30	0.256
	Senegalimassili	1.64	0.801	0.07	0.09	0.478	-0.54	0.813
	Slackia	3.52	0.620	-0.07	0.13	0.643	-0.42	0.656

PCOS, polycystic ovary syndrome; POF, premature ovarian failure; MR, Mendelian randomization.

We conducted MR analyses by using the largest GWAS dataset to systematically investigate the causal relationship between the gut microbiota and the risk of ovarian diseases. Our results showed that there were causal effects of several genetically predicted genera of the gut microbiota on the risk of PCOS, POF, ovarian endometriosis and ovarian neoplasm. The positive or negative relationships between the gut microbiota and certain ovarian diseases were further verified by sensitivity analyses. This study could provide important insight into the genetic relationship between the gut microbiome and ovarian diseases and shed new light on the potential causes and therapeutic strategies for ovarian diseases.

4.1 The potential role of the gut microbiota in PCOS

Recent studies have demonstrated that gut microbes play vital roles in the etiology of PCOS [26]. In this study, we found that Barnesiella, Bilophila and Holdemania had protective effects on PCOS, which has not been demonstrated previously [7–9]. Evidence from other studies has demonstrated that the gut microbiome and its metabolites are involved in the pathogenesis of hyperandrogenaemia and insulin resistance, which are salient features of PCOS [27–29]. Barnesiella was reported to be short-chain fatty acid (SCFA)-producing bacteria [27, 30]. SCFAs, including propionate, acetate and butyrate, are the main products of the fermentation of dietary fiber by the intestinal microbiota [31]. Butyrate can enhance the expression of tight-junction proteins and mucin to maintain the intestinal epithelial barrier [32], which is the first line of defense in the intestine. On the other hand, SCFAs beneficially affect the metabolic process of the host, especially insulin resistance. Data from human and animal studies have demonstrated that acetate promotes host metabolism and improves insulin sensitivity through the secretion of a gut hormone, which inhibits appetite and reduces lipolysis and systemic proinflammatory cytokine levels [33]. Furthermore, a study suggested that propionate and butyrate activate intestinal gluconeogenesis via complementary mechanisms [34]. Hence, Barnesiella may reduce the risk of PCOS through the biological functions of SCFAs. Barrett et al. [35, 36] reported that Holdemania abundance was positively associated with the intake of dietary fiber, overall polyunsaturated fat (PUFA) and both ω-3 and ω-6 PUFAs. In addition, it has been documented that adequate intake of dietary fiber and supplementation with PUFAs are both conducive to improving glucose metabolism and lipid profiles [37, 38]. Holdemania may exert important effects on metabolic status in PCOS patients through the beneficial influence of dietary fiber and PUFAs, but further studies are needed to prove this assumption. Although Bilophila has been shown to mitigate cardiovascular disease by metabolizing both trimethylamine and its precursors without the production of trimethylamine-N-oxide [39], no evidence has been reported for the positive effect of Bilophila on PCOS, and additional in-depth studies are needed to explore the underlying mechanism involved.

4.2 The potential role of the gut microbiota in POF

Altered gut microbial profiles have been abserved in women with POF [13]. Additionally, Elgart et al. [40] reported that the gut bacteria of Drosophila can affect oogenesis and maternal-to-zygotic transition during embryo development. In this study, we found that Eubacterium (hallii group) and Eubacterium (ventriosum group) had protective effects on POF. Eubacterium produces SCFAs. The abundance of Eubacterium in the gut is strongly correlated with SCFA levels and the beneficial effects of SCFAs under a range of clinical conditions [41].Several studies have shown that SCFAs play a major role in the modulation of inflammation through the inhibition of proinflammatory cytokines, such as interferon (IFN)-γ, interleukin (IL)-1β, IL-6, IL-8, and tumor necrosis factor receptor-α (TNF-α), while upregulating the expression of anti-inflammatory cytokines, such as IL-10 and transforming growth factor-β (TGF-β) [42, 43]. The human ovary is a ubiquitous target for autoimmune attack, leading to the consequent occurrence of POF [44]. Autoimmunity is responsible for approximately 4–30% of POF cases [45, 46]. E. hallii and E. ventriosum may act as anti-inflammatory agents to protect the ovary from inflammation. On the other hand, we found that Adlercreutzia, Intestinibacter, Lachnospiraceae (UCG008), and Terrisporobacter increased the risk of POF. Other studies have shown that these 4 gut microbiome taxa are correlated with the risk of diabetic retinopathy, male infertility, periodontitis, and sepsis [47–50]. However, there is a lack of corresponding research evidence to clarify the underlying mechanism by which these gut microbiome taxa contribute to POF, thus providing new directions for future studies.

4.3 The potential role of the gut microbiota in ovarian endometriosis

Endometriosis has been associated with several factors, including unopposed estrogen signaling, resistance to progesterone, altered immune function and epigenetic modifications [51]. Because the gut microbiota can affect estrogen metabolism and inflammation, a growing body of recent evidence suggests that the gut microbiota may be closely involved in the onset and progression of endometriosis. Microbially secreted β-glucuronidases are functional members of the estrobolome [52]. β-glucuronidases can deconjugate estrogen and enable it to bind to estrogen receptors, leading to its subsequent downstream effects [52]. Ata et al. [11] observed differences in microbiota composition at the genus level between the stage 3/4 endometriosis group and the control group. Patients with endometriosis may harbor more β-glucuronidase-producing bacteria to increase the level of deconjugate estrogen and therefore drive endometriosis [53]. On the other hand, Khan et al. [54] indicated that higher levels of Escherichia coli in menstrual blood may contribute to higher levels of endotoxin in the menstrual fluid and peritoneal fluid, which may promote the Toll-like receptor 4-mediated growth of endometriosis. E. coli or endotoxin from the gut may translocate into the pelvis through enterocytes [54]. In this study, we demonstrated that Intestinimonas increased the risk of ovarian endometriosis, while Rikenellaceae (RC9gut group) and Ruminococcaceae (UCG013) decreased the risk. It is unclear whether Intestinimonas, Rikenellaceae (RC9gut group) and Ruminococcaceae (UCG013) are involved in estrogen metabolism and inflammation modulation and thus participate in the onset and progression of endometriosis. These findings should be validated in the future to help develop therapeutic interventions for endometriosis.

4.4 The potential role of the gut microbiota in ovarian neoplasms

In recent years, microbial roles in cancer formation, diagnosis, prognosis and treatment have emerged as debated issues. The gut microbiota can accelerate cell proliferation or cellular death, disturb the immune system and alter metabolism within host cells [55]; however, the exact mechanism through which bacteria influence tumorigenesis has not been elucidated. The gut microbiome of patients with epithelial ovarian cancer differs from that of healthy women, and the abundance of beneficial bacteria, including Bifidobacterium and Ruminococcus, decreases. The gut microbiota can promote ovarian tumor progression through activation of Hedgehog signaling mediated by TLR4/NF-κB signaling [14]. In addition, animal study have shown that intestinal dysbiosis can significantly provoke the activation of macrophages and consequently increase the production of TNF-α and IL-6, and ultimately contributes to the development of epithelial-mesenchymal transition, resulting in the development of advanced ovarian cancer [56]. In this study, we found that Lachnospiraceae (UCG008) and Ruminococcaceae (UCG011) were positively associated with the risk of malignant ovarian neoplasm, while Paraprevotella, Ruminococcaceae (UCG005), Senegalimassilia and Slackia were negatively associated with the risk of malignant ovarian neoplasm. A study showed that Lachnospiraceae (UCG008) was positively correlated with the levels of proinflammatory cytokines, such as IL-6, high-sensitivity C-reactive protein and TNF-α [57], in the peripheral blood, possibly leading to tumor progression through its proinflammatory effect. On the other hand, Gebeyew et al. [58] demonstrated that the relative abundance of Ruminococcaceae (UCG005) was positively associated with propionate, iso-butyrate acetate, butyrate, and valeric acid. These SCFAs exert anti-inflammatory effects by regulating cytokine production and immune cell functions and may inhibit tumor progression through their anti-inflammatory effects [59]. Since there is currently no research exploring the relationship between these gut microbiota and ovarian neoplasms, the exact mechanism connecting these four bacterial genera with ovarian neoplasms has been unclear until recently and warrants additional study.

To conclude, we first provide evidence to support the causal relationship between specific gut microbiota taxa and ovarian diseases through MR analyses, thus providing effective preventive and predictive measures for ovarian diseases. However, further research is necessary to validate these findings and explore the underlying mechanisms in clinical trials and animal models.

Ethics approval and consent to participate

Not applicable. Only publicly available summary statistics were used.

Consent for publication

Not applicable.

Availability of data and materials

Data generated in the present study are provided within the main text and supplementary materials. Genetic instrumental variables and data sources are presented in the supplementary files. The exposure and outcome data can be obtained via www.mibiogen.org and https://www.r8.finngen.fi/.

Competing Interests

The authors have no relevant financial or non-financial interests to disclose.

Funding

This research was funded by National Key Research and Development Program of China (No. 2022YFC2703803, No.2022YFC2703000, No.2021YFC2700603), National Natural Science Foundation of China (No.82088102, No.82171613, No.82171688), Collaborative Innovation Program of Shanghai Municipal Health Commission (No.2020CXJQ01), Shanghai Frontiers Science Center of Reproduction and Development, CAMS Innovation Fund for Medical Sciences (No.2019-I2M-5-064), Clinical Research Plan of SHDC (No.SHDC2020CR1008A), Shanghai Clinical Research Center for Gynecological Diseases (22MC1940200), Shanghai Urogenital System Diseases Research Center (2022ZZ01012), Shanghai Frontiers Science Research Center of Reproduction and Development. Key Discipline Construction Project (2023-2025) of Three-Year Initiative Plan for Strengthening Public Health System Construction in Shanghai (GWVI-11.1-35), Zhejiang Province College Student Science and Technology Innovation Program (Xinmiao Plan) (2023R401210).

Authors' contributions

H.H., J.S. and J.P. designed the study. X.L. and Z.L. conducted statistical analysis and drafted the manuscript. K.Z., R.H., Z.J., H.W., J.Y. and Q.L. assisted in statistical analysis and manuscript drafting. J.P. and H.H. revised the manuscript critically. All authors have read and agreed to the published version of the manuscript.

Acknowledgments

We express our gratitude to the participants of the FinnGen study and the MiBioGen consortium for releasing the gut microbiota GWAS summary statistics.

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Gut microbiota and risk of ovarian diseases: a two-sample Mendelian randomization study

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Abstract

Figures

1. Introduction

2. Materials and Methods

2.1 Exposure data

2.2 Outcome data

2.3 Instrumental variable selection

2.4 Statistical analysis

3. Results

3.1 SNP selection

3.2 Causal effects of the gut microbiome on ovarian diseases

3.2.1 PCOS

3.2.2 POF

3.2.3 Ovarian endometriosis

3.2.4 Benign ovarian neoplasm

3.2.5 Malignant ovarian neoplasms

3.2.6 Sensitivity analyses

4. Discussion

4.1 The potential role of the gut microbiota in PCOS

4.2 The potential role of the gut microbiota in POF

4.3 The potential role of the gut microbiota in ovarian endometriosis

4.4 The potential role of the gut microbiota in ovarian neoplasms

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