The genetic associations between gut microbiota and menstruation quality of life impacts in the East Asian women

doi:10.21203/rs.3.rs-4812423/v1

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The genetic associations between gut microbiota and menstruation quality of life impacts in the East Asian women

https://doi.org/10.21203/rs.3.rs-4812423/v1

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Background Menstruation affects the physiological and psychological health of East Asian women, imposing burdens on their daily lives and work. The gut microbiota has been found to improve various diseases. The study used Mendelian randomization to exploratorily discuss the causal effects of gut microbiota on the menstruation quality of life impacts.

Methods According to the predefined threshold, independent genetic variants significantly linked to 119 bacterial genera in the gut were extracted as instrumental variables. The inverse variance weighted (IVW) was utilized as the primary analysis method, supplemented by MR-Egger and Weighted median methods. Effect estimates were assessed primarily using the odds ratios (OR) and 95% confidence intervals (95%CI). In addition, sensitivity analyses (heterogeneity, horizontal pleiotropy, and outliers) were implemented to evaluate the robustness and accuracy of the findings.

Results Causal effects were found between 28 bacterial genera and 13 menstruation quality of life impacts. Based on OR, 18 bacterial genera were identified as risk factors, while 10 were identified as protective factors. Furthermore, sensitivity analysis did not detect heterogeneity, horizontal pleiotropy, or outliers.

Conclusions This study provides evidence for causal effects of gut microbiota on menstruation quality of life impacts. These findings will help elucidate how gut microbiota could affect physiological and psychological health issues during menstruation. Furthermore, they also offer valuable perspectives into the management of women's health during menstruation.

Gut microbiota

Menstruation

Causal inference

Genetic factors

Menstruation refers to the process during the menstruation where the uterine lining sheds monthly, accompanied by the discharge of blood and tissue fragments from the vagina [1]. Current research indicates that menstruation is regulated by various factors, including hormone levels, genetic factors, and environmental factors [2–4]. Meantime, menstruation also significantly influences women's physiological and psychological health. During different phases of the menstruation, women experience significant fluctuations in hormone levels, which can lead to physical discomforts such as dysmenorrhea, acne, and headache [5–7]. Additionally, hormonal fluctuations may trigger psychological health problems, such as depression, nervousness and loss of concentration [8]. In summary, menstruation serves as a significant indicator reflecting overall physiological health and psychological status in women. Therefore, improving menstrual management and providing support can effectively assist women in better managing the various impacts of the menstruation in their daily lives and work.

The gut microbiota, as a dynamic and evolving microbial community, is believed to exert significant influences on both the physiological and psychological processes of the host [9, 10]. Maintaining balance in the gut microbiota is vital for human health. An association has been observed between dysbiosis of the gut microbiota and deficiencies in estrogens [11]. This could be explained by the role of gut microbiota in estrogen metabolism [12]. Meanwhile, the brain-gut axis has garnered widespread attention in both preclinical and clinical research. Research has found that the gut has various bacteria capable of producing γ-aminobutyric acid (GABA) [13]. Furthermore, dysbiosis of gut microbiota results in decreased levels of serotonin, GABA, and dopamine within the human feces [14]. Emerging studies are beginning to consider gut microbiota as biomarkers for predicting susceptibility to diseases [15, 16]. In addition, clinical trials are extensively exploring the modulation of gut microbiota composition and diversity through fecal microbiota transplantation and probiotic supplementation to further improve disease states and drug efficacy [17, 18]. Therefore, investigating gut microbiota associated with the menstruation quality of life impacts will aid in predicting and mitigating various effects of the menstruation, offering multifaceted benefits to women's daily life and work.

Mendelian randomization (MR) is a method that uses genetic variants associated with specific exposure to investigate causal effects of exposure on outcome [19]. Compared to observational studies, MR offers a more reliable means to establish causality, mitigating biases introduced by confounding factors [20].

This study utilizes two-sample MR to explore the causal effects of gut microbiota on menstruation quality of life impacts. Subsequently, sensitivity analyses were conducted to ensure the robustness and accuracy of the study findings. Our findings aim to exploratorily discuss genetic associations of gut microbiota on menstruation quality of life impacts.

Study design

Figure 1 illustrates the application of two-sample MR, exploring the genetic associations of gut microbiota on menstruation quality of life impacts. In MR analysis, three assumptions must be followed: (1) robust statistical correlation exists between the exposure and instrumental variables (IVs), (2) IVs are unrelated to any potential confounding factors, (3) IVs affect outcomes solely through the exposure, without any other pathways intervening in this relationship. This study utilized publicly available genome-wide association study (GWAS) summary statistics, obviating the need for new data collection or additional ethical approval.

GWAS summary data for gut microbiota

The gut microbiota GWAS summary data from a large-scale meta-analysis conducted by the MiBioGen consortium [21]. The study analyzed whole-genome genotyping and 16S rRNA gut microbiota data from 18,340 individuals (24 cohorts from the USA, Canada, Israel, South Korea, Germany, Denmark, the Netherlands, Belgium, Sweden, Finland, and the UK). This study encompassed 211 bacterial taxa, including 9 phyla, 16 classes, 20 orders, 35 families, and 131 genera. However, after excluding 15 unidentified families and genera, the analysis focused on 196 bacterial traits. The GWAS data can be accessed at https://mibiogen.gcc.rug.nl/.

GWAS summary data for menstruation quality of life impacts

The data concerning menstruation quality of life impacts were obtained from a meta-analysis of GWAS involving 5,734 Japanese female volunteers [22]. The study identified 15 traits, including: stiff neck, fever, acne, joint pain, edema, dysmenorrhea, headache, bowel movement, pressure in breast, aggressiveness, nervousness, loss of concentration, sleepiness, increased appetite, and depression. To enhance comprehension, these traits were categorized into two major groups: physiological health and psychological health. Detailed informations about outcomes are presented in Table 1.

Table 1

Sources and characteristics of GWAS data for included outcomes
GWAS ID	Outcome	Sample size	Number of SNPs	Taxonomy
ebi-a-GCST006652	Menstruation quality of life impact (stiff neck)	5,734	533,137	Physiological health
ebi-a-GCST006638	Menstruation quality of life impact (fever)	5,734	551,913
ebi-a-GCST006640	Menstruation quality of life impact (acne)	5,734	533,138
ebi-a-GCST006647	Menstruation quality of life impact (joint pain)	5,734	533,137
ebi-a-GCST006645	Menstruation quality of life impact (edema)	5,734	533,138
ebi-a-GCST006636	Menstruation quality of life impact (dysmenorrhea)	5,734	533,138
ebi-a-GCST006646	Menstruation quality of life impact (headache)	5,734	533,138
ebi-a-GCST006643	Menstruation quality of life impact (bowel movement)	5,734	533,138
ebi-a-GCST006650	Menstruation quality of life impact (pressure in breast)	5,734	533,138
ebi-a-GCST006641	Menstruation quality of life impact (aggressiveness)	5,734	533,138	Psychological health
ebi-a-GCST006649	Menstruation quality of life impact (nervousness)	5,734	533,137
ebi-a-GCST006648	Menstruation quality of life impact (loss of concentration)	5,734	533,138
ebi-a-GCST006651	Menstruation quality of life impact (sleepiness)	5,734	533,138
ebi-a-GCST006642	Menstruation quality of life impact (increased appetite)	5,734	533,138
ebi-a-GCST006644	Menstruation quality of life impact (depression)	5,734	533,138

Selection of IVs

To ensure robustness and accuracy in building causal effects of gut microbiota on menstruation quality of life impacts, strict quality control measures are necessary to capture genetic variations correlated with gut microbiota. Initially, a genome-wide significance threshold of P < 5e-08 was established to identify single nucleotide polymorphisms (SNPs) closely related to gut microbiota. Due to the limited number of acquired SNPs insufficient for MR analysis, the genome-wide significance threshold was relaxed to 1e-05 [23]. Subsequently, to fulfill the assumptions of MR analysis, linkage disequilibrium analysis is required for the selected SNPs. Based on the European Thousand Genomes Project, a threshold (R2 < 0.001, clumping distance = 10,000 kb) was set to aid in filtering out SNPs that do not meet the criteria [24]. Additionally, to exclude weak IVs and mitigate potential biases, the F-statistic was employed to assess the strength of IVs. The F-value was calculated using the formula F = R² × (N−2)/1 - R² where R² = 2 × (1 - MAF) × MAF × (beta / sd)², R² represents the explained variance, N is the sample size, MAF is the minor allele frequency, and sd is the variance of the SNP [25]. IVs that exhibited an F-statistic exceeding 10 were deemed strongly associated with the exposures.

MR and sensitivity analysis

The inverse variance weighted (IVW), MR-Egger, and Weighted median methods were employed to assess causal effects of gut microbiota on menstruation quality of life impacts [26]. IVW is the primary method, with MR-Egger and Weighted median methods used as supplements. This will enhance the robustness of causal effects and minimize bias. In the MR analysis, a series of results involving only 2 or 3 SNPs were found. Due to the limited number of SNPs, this would be insufficient for conducting the MR-PRESSO and may constrain the statistical power of the MR analysis. This may lead to less reliability in the results. Therefore, these results were excluded before sensitivity analyses. For the remaining causal effects, the Cochran's Q test was employed to assess heterogeneity in IVW analysis [27]. The MR-Egger intercept test was employed to detect horizontal pleiotropy [28]. The MR-PRESSO was utilized to identify horizontal pleiotropy and outliers [29]. The "TwoSampleMR" (version 0.6.5) and "MR-PRESSO" (version 1.0) packages were utilized for the analysis. All analyses were conducted in the R environment (version 4.4.1). The results are presented as odds ratios (OR) along with their 95% confidence intervals (95%CI).

The included exposures and IVs

119 genera of bacteria were included in this study. Selection threshold based on IVs, 1531 SNPs were used as IVs for 119 genera of bacteria. Significantly, all F-statistics exceed 10, indicating the absence of weak IVs in this study. Table S1 provides comprehensive details concerning all IVs.

Physiological health

In the IVW method, genetic prediction identifies 18 causal effects (Fig. 2). When menstruation quality of life impact (acne) was the outcome, g_Anaerofilum (OR = 1.53, 95% CI = 1.07–2.21) and g_Ruminococcaceae NK4A214 group (OR = 1.96, 95% CI = 1.14–3.37) were identified as risk factors. There is a negative correlation between g_Blautia (OR = 0.50, 95% CI = 0.27–0.92) and menstruation quality of life impact (bowel movement), and the Weighted median method also demonstrated a causal effect between the two, indicating a stable result. In the results of menstruation quality of life impact (dysmenorrhea), g_Alistipes (OR = 0.57, 95% CI = 0.35–0.91) and g_Butyricimonas (OR = 0.69, 95% CI = 0.49–0.97) were both considered protective factors. In the results of menstruation quality of life impact (edema), both risk factors and protective factors were identified. Specifically, g_Lachnospiraceae FCS020 group (OR = 1.59, 95% CI = 1.03–2.45) and g_Lachnospiraceae UCG010 (OR = 1.52, 95% CI = 1.00-2.32) were positively associated with the outcome. In contrast, g_Ruminococcaceae UCG011 (OR = 0.71, 95% CI = 0.50–0.99) and g_Fusicatenibacter (OR = 0.66, 95% CI = 0.43–0.99) were negatively associated with the outcome. In menstruation quality of life impact (fever), g_Ruminococcaceae UCG002 (OR = 3.88, 95% CI = 1.10-13.63) and g_Lachnospiraceae UCG010 (OR = 4.41, 95% CI = 1.49–13.04) are considered risk factors. Notably, g_Lachnospiraceae UCG010 is also identified as a risk factor for menstruation quality of life impact (edema), suggesting this bacterial genus may be an important potential risk factor. Similarly, 2 protective factors associated with menstruation quality of life impact (fever) were identified: g_Butyricimonas (OR = 0.27, 95% CI = 0.09–0.80) and g_Oscillibacter (OR = 0.31, 95% CI = 0.10–0.97). The 3 bacterial genera associated with menstruation quality of life impact (headache) are all protective factors: g_Anaerotruncus (OR = 0.58, 95% CI = 0.38–0.90), g_Paraprevotella (OR = 0.75, 95% CI = 0.58–0.97), and g_Eubacterium coprostanoligenes group (OR = 0.55, 95% CI = 0.35–0.86). g_Ruminococcus torques group (OR = 2.28, 95% CI = 1.22–4.25) is significantly associated with high risk of menstruation quality of life impact (joint pain). Menstruation quality of life impact (stiff neck) is negatively associated only with g_Coprococcus1 (OR = 0.53, 95% CI = 0.29–0.97). Table S2 presents the results for all methods.

There was no observed evidence of heterogeneity across all IVs (Cochran’s Q test P > 0.05). The MR-Egger intercept test found no indication of horizontal pleiotropy (MR-Egger intercept test P > 0.05). The MR-PRESSO detected neither outliers nor horizontal pleiotropy (Global test P-value > 0.05). Table 2 displays detailed information from the sensitivity analysis.

Table 2

Sensitivity analysis of physiological response outcomes
Exposure	Outcome	IVW Q statistic (P-value)	MR-Egger intercept (P-value)	MR-PRESSO (Global test P-value)
g_Anaerofilum	Menstruation quality of life impact (acne)	0.232	0.938	0.310
g_Ruminococcaceae NK4A214 group	Menstruation quality of life impact (acne)	0.365	0.960	0.472
g_Blautia	Menstruation quality of life impact (bowel movement)	0.656	0.717	0.702
g_Alistipes	Menstruation quality of life impact (dysmenorrhea)	0.171	0.943	0.250
g_Butyricimonas	Menstruation quality of life impact (dysmenorrhea)	0.452	0.649	0.515
g_Lachnospiraceae FCS020 group	Menstruation quality of life impact (edema)	0.932	0.454	0.943
g_Ruminococcaceae UCG011	Menstruation quality of life impact (edema)	0.977	0.927	0.985
g_Lachnospiraceae UCG010	Menstruation quality of life impact (edema)	0.879	0.708	0.876
g_Fusicatenibacter	Menstruation quality of life impact (edema)	0.427	0.214	0.264
g_Butyricimonas	Menstruation quality of life impact (fever)	0.194	1.000	0.227
g_Ruminococcaceae UCG002	Menstruation quality of life impact (fever)	0.602	0.349	0.566
g_Lachnospiraceae UCG010	Menstruation quality of life impact (fever)	0.585	0.220	0.607
g_Oscillibacter	Menstruation quality of life impact (fever)	0.415	0.415	0.609
g_Anaerotruncus	Menstruation quality of life impact (headache)	0.678	0.404	0.767
g_Paraprevotella	Menstruation quality of life impact (headache)	0.714	0.821	0.817
g_Eubacterium coprostanoligenes group	Menstruation quality of life impact (headache)	0.343	0.396	0.448
g_Ruminococcus torques group	Menstruation quality of life impact (joint pain)	0.508	0.505	0.726
g_Coprococcus1	Menstruation quality of life impact (stiff neck)	0.604	0.952	0.640

Psychological health

In the gut microbiota's influence on psychological health, 10 potential causal effects were identified (Fig. 3). There exists a positive correlation between menstruation quality of life impact (aggressiveness) and g_Lachnospiraceae UCG010 (OR = 2.06, 95% CI = 1.30–3.25). 5 bacterial genera associated with menstruation quality of life impact (nervousness) were identified. Among these, only g_Eubacterium coprostanoligenes group (OR = 0.56, 95% CI = 0.34–0.94) exhibited a protective effect, while the remaining genera were identified as risk factors. These include g_Adlercreutzia (OR = 1.58, 95% CI = 1.09–2.29), g_Lachnospiraceae UCG001 (OR = 1.52, 95% CI = 1.04–2.21), g_Lachnospiraceae UCG010 (OR = 1.66, 95% CI = 1.16–2.39), and g_Lactococcus (OR = 1.26, 95% CI = 1.00-1.58). For menstruation quality of life impact (loss of concentration), g_Ruminiclostridium6 (OR = 1.66, 95% CI = 1.09–2.55) is identified as a risk factor. In bacterial genera associated with menstruation quality of life impact (sleepiness), both risk factors and protective factors were identified. Specifically, g_Lachnospiraceae UCG010 (OR = 1.66, 95% CI = 1.04–2.65) was identified as a risk factor, while g_Ruminococcaceae UCG011 (OR = 0.70, 95% CI = 0.52–0.94) was identified as a protective factor. The g_Adlercreutzia (OR = 1.68, 95% CI = 1.01–2.77) is a risk factor for menstruation quality of life impact (increased appetite). The results for all methods are shown in Table S3.

Cochran's Q test indicated no significant heterogeneity in the effects of IVs (P > 0.05). Furthermore, MR-Egger intercept test and MR-PRESSO suggested absence of outliers and horizontal pleiotropy (P > 0.05). Table 3 presents the results of all sensitivity analyses.

Table 3

Sensitivity analysis of psychological response outcomes
Exposure	Outcome	IVW Q statistic (P-value)	MR-Egger intercept (P-value)	MR-PRESSO (Global test P-value)
g_Lachnospiraceae UCG010	Menstruation quality of life impact (aggressiveness)	0.940	0.573	0.950
g_Adlercreutzia	Menstruation quality of life impact (nervousness)	0.434	0.259	0.486
g_Lachnospiraceae UCG001	Menstruation quality of life impact (nervousness)	0.865	0.508	0.815
g_Lachnospiraceae UCG010	Menstruation quality of life impact (nervousness)	0.703	0.878	0.786
g_Eubacterium coprostanoligenes group	Menstruation quality of life impact (nervousness)	0.166	0.306	0.269
g_Lactococcus	Menstruation quality of life impact (nervousness)	0.734	0.460	0.715
g_Ruminiclostridium6	Menstruation quality of life impact (loss of concentration)	0.501	0.306	0.659
g_Lachnospiraceae UCG010	Menstruation quality of life impact (sleepiness)	0.142	0.960	0.224
g_Ruminococcaceae UCG011	Menstruation quality of life impact (sleepiness)	0.794	0.885	0.819
g_Adlercreutzia	Menstruation quality of life impact (increased appetite)	0.603	0.439	0.672

This study employed MR analysis to exploratorily discuss the causal effects of gut microbiota on menstruation quality of life impacts. The study findings indicate that out of 119 screened bacterial genera, 28 genera showed potential causal effects with the aforementioned menstruation quality of life impacts. Through OR analysis, 15 bacterial genera were identified as risk factors, while 13 bacterial genera were considered potential protective factors. These findings suggest that the gut microbiota plays a significant role in menstruation quality of life impacts. This study represents the first exploration of the relationship of gut microbiota on menstruation quality of life impacts. This may provide potential therapeutic targets by modulating gut microbiota to improve menstruation quality of life impacts, suggesting public health interventions to alleviate the inconvenience of menstrual periods on daily life and work for women.

The gut microbiota, as a dynamic and evolving ecosystem within the human body, has been found in current research to influence the initiation and development of diseases through pathways such as the gut-brain axis, gut-gonadal axis, and gut-liver axis [30–32]. This understanding also opens up new therapeutic directions targeting gut microbiota for numerous diseases. In response to the physiological and psychological health of females during the menstruation, we believe that the gut microbiota primarily influences the levels of estrogen and progesterone within the body. Research indicates that members of the Ruminococcaceae family may influence the onset of puberty by regulating host sex hormone levels [33]. In particular, Oscillibacter has been found to be connected to elevated levels of estrogen [34]. Furthermore, gut microbiota such as members of the Eubacterium genus are capable of secreting β-glucuronidase [35], which can cleave conjugated metabolites of estrogens and phytoestrogens, facilitating their intestinal reabsorption [36]. In ovariectomized rats experiencing estrogen depletion-induced osteoporosis, several members of the Lachnospiraceae family were found to exhibit increased relative abundance [37]. These studies collectively indicate that female estrogen levels are influenced by the gut microbiota.

In addition to estrogen and progesterone, gut metabolites, inflammatory factors, and neurotransmitters also play significant roles in the physiological and psychological health of women during the menstruation [38–40]. Blautia, as an important butyrate-producing bacterium, can improve intestinal barrier and inflammatory functions by fermenting dietary fibers to produce butyrate. Primary dysmenorrhea may be attributed to increased production of prostaglandins (PG) via the cyclooxygenase pathway [41]. The anti-inflammatory properties of butyrate contribute to alleviating pain and inflammation caused by PG [42]. The metabolite of tryptophan, 5-hydroxytryptamine (5-HT), is believed to improve symptoms such as depression, loss of concentration, and sleepiness. Study has reported that Lactococcus increases the expression of tryptophan hydroxylase in enterochromaffin cells, thereby enhancing levels of 5-hydroxytryptophan (the precursor to 5-HT) [43]. In addition, gut microbiota actively participate in the synthesis and metabolism of dopamine. Dopamine can be O-methylation by the host's catechol O-methyltransferase (COMT) to form 3-methoxytyramine (3MT), thereby reducing dopamine levels. However, members of the genera Eubacterium and Blautia are capable of reversing this conversion [44].

Overall, these studies largely confirm our findings, although some of them contradict ours. Based on current research progress, we believe that future studies should further investigate whether there is dysbiosis in the gut microbiota of women experiencing physiological and psychological health issues during menstruation, and explore characteristic components. Meanwhile, given the complex relationship of gut microbiota on menstruation physiological and psychological health, further mediator MR analysis and mechanism research are needed to elucidate their causal effects and mechanisms.

Nevertheless, this study also has certain limitations. The gut microbiota GWAS data were derived from multi-ethnic cohorts, including East Asian and European populations, whereas the outcome GWAS data originated from Japanese population. This may lead to our findings not being applicable to women in other regions. Future research should encompass more diverse samples to ensure broader applicability to populations in other regions. Moreover, the gut microbiota GWAS data included samples from both males and females. Due to inherent compositional differences between male and female gut microbiota, our results may be influenced by gender disparities. GWAS data for the female cohort should be provided in the future to ensure that gender effects can be excluded in gynaecological studies to ensure robustness and accuracy of the results.

This study identifies causal effects of gut microbiota on menstruation quality of life impacts from a genetic perspective. This serves as a reference for future research into the pathogenic mechanisms connecting gut microbiota with physiological and psychological health issues during menstruation. Additionally, it offers valuable insights for preventing and treating menstrual-related health concerns, aiding women in managing the diverse impacts of the menstruation.

Ethics approval and consent to participate

Our analysis used publicly available genome-wide association study (GWAS) summary statistics. No new data were collected, and no new ethical approval was required.

Consent for publication

Not applicable.

Availability of data and materials

The datasets analyzed during the current study are available in the GWAS Catalog (https://www.ebi.ac.uk/gwas/home) and the MiBioGen consortium (https://mibiogen.gcc.rug.nl/menu/main/home).

Competing interests

The authors declare no competing interests.

Funding

This work was supported by National Nature Science Foundation of China (82373113, XJ), Shenyang Breast Cancer Clinical Medical Research Center (2020-48-3-1, ST), Shenyang Public Health R&D Special Project (22-321-31-04,ST), LiaoNing Revitalization Talents Program (XLYC1907160, XJ).

Authors' contributions

Yuhang Zhou, Tao Sun and Junnan Xu contributed to the concept and design of the study; Yuhang Zhou was responsible for statistical analysis and writing of the manuscript; Yuhang Zhou, Yue Wang and Yun Feng assisted with the statistical analysis. All authors have read and approved the final manuscript.

Acknowledgements

We want to acknowledge the participants and investigators of the MiBioGen consortium and the menstruation quality of life impacts study. We appreciate their contribution to the field.

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No competing interests reported.

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The genetic associations between gut microbiota and menstruation quality of life impacts in the East Asian women

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Abstract

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Background

Methods

Study design

GWAS summary data for gut microbiota

GWAS summary data for menstruation quality of life impacts

Selection of IVs

Results

The included exposures and IVs

Discussion

Conclusion

Declarations

References

Additional Declarations

Supplementary Files

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Version 1