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. 2023 Jul 7;14(3):405–416. doi: 10.1007/s13167-023-00332-6

Genetically predicted the causal relationship between gut microbiota and infertility: bidirectional Mendelian randomization analysis in the framework of predictive, preventive, and personalized medicine

Yujia Xi 1,2,#, Chenwei Zhang 2,#, Yiqian Feng 3, Shurui Zhao 3,4, Yukai Zhang 5, Guosheng Duan 2, Wei Wang 1, Jingqi Wang 1,✉
PMCID: PMC10439866  PMID: 37605651

Abstract

Objective

Several studies have reported the association between gut microbiota and infertility; however, the causal association between them remains unclear. This study aimed to explore the causal relationship between gut microbiota and infertility and evaluate how specific gut microbiota can support early monitoring and prevention of infertility in the context of predictive, preventive, and personalized medicine (PPPM/3PM).

Methods

The gut microbiota GWAS data included 18,340 individuals. Female infertility (6481 cases and 68,969 controls) and male infertility data (680 cases and 72,799 controls) were obtained from the FinnGen consortium. The inverse variance weighting (IVW), MR-Egger, weighted median (WM), Cochran Q tests, MR-PRESSO, and leave-one-out were used as a supplement to Mendelian randomization (MR) results and sensitivity analysis.

Results

The results of MR analysis indicated a significant causal association between Eubacterium oxidoreducens (OR = 2.048, P = 0.008), Lactococcus (OR = 1.445, P = 0.042), Eubacterium ventriosum (OR = 0.436, P = 0.018), Eubacterium rectale (OR = 0.306, P = 0.002), and Ruminococcaceae NK4A214 (OR = 0.537, P = 0.045) and male infertility. Genetically predicted Eubacterium ventriosum (OR = 0.809, P = 0.018), Holdemania (OR = 0.836, P = 0.037), Lactococcus (OR = 0.867, P = 0.020), Ruminococcaceae NK4A214 (OR = 0.830, P < 0.050), Ruminococcus torques (OR = 0.739, P = 0.022), and Faecalibacterium (OR = 1.311, P = 0.007) were associated with female infertility. Sensitivity analysis did not detect heterogeneity and pleiotropy (P > 0.05).

Conclusions

Our results provided evidence for the causal relationship between some gut microbiota and male and female infertility. These findings might be valuable in providing personalized treatment options for preventing infertility and improving reproductive function by monitoring and regulating the gut microbiota of infertility patients in the context of PPPM. Moreover, detecting the abundance of microbiota in feces can support preventive and personalized strategies, which may benefit more infertility patients.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13167-023-00332-6.

Keywords: Predictive preventive personalized medicine (PPPM/3PM), Mendelian randomization, Infertility, Gut microbiota, Causal relationship

Introduction

Exploring the potential influences of infertility is significant in the context of prediction, prevention, and personalized medicine

Infertility, defined as a couple’s inability to conceive after a year of regular unprotected intercourse, represents an important and growing health problem [1]. Studies have indicated that infertility tortures 10–16% of couples worldwide and couples in developing countries more often suffered from secondary infertility, whereas in developed countries primary infertility is more common [2]. The reasons for infertility exist in men, women, and even in both, including genetic, psychological, immune, environmental factors, and so on [3]. Infertility can affect a considerable part of humanity. It is estimated that 72.4 million people suffer from infertility, 4.5 million of whom seek infertility treatment. Therefore, it is necessary to identify more potential causal factors for infertility to provide preventive and predictive strategies for the development of the disease in the context of predictive, preventive, and personalized medicine (PPPM/3PM) [4].

The gut microbiome is causally associated with infertility and is an emerging tool for PPPM

The gut microbiota is a dynamic and complex creature that surrounds human natural motion. Growing evidence shows that changes in the microbiological intestinal structure are closely linked to the reproductive system [5, 6]. Polycystic ovary syndrome (PCOS) is one of the most popular diseases that can cause infertility in women. Recent studies have suggested differences in specific gut microbiota between PCOS patients and controls, including Bacteroidaceae, Bacteroides, and Lactobacillus [7]. Endometriosis is also a common reason for female infertility. Irene Jiang et al. found that endometriosis was associated with a reduced lactobacillus community and an increased abundance of opportunistic pathogens [6]. For male infertility, research showed that Prevotella abundance was negatively correlated with sperm concentration, while Pseudomonas was directly correlated with total motile sperm count [8].

The biggest way to improve health is through early prevention, and probiotics happen to have an extremely high potential for prevention with sufficient evidence. Therefore, within the framework of PPPM, the use of certain probiotics and prebiotics may improve host health by altering the structure and function of microbial communities, all of which provide enormous potential for the use of prebiotics to develop comprehensive strategies for the treatment and prevention of healthy diets and infertility related diseases [9, 10]. Moreover, exploring causal relationships is also significant for expanding the potential mechanism between gut microbiota and infertility. Metabolic health is highly correlated with infertility. For example, Changing the intestinal microbiota, changing the metabolism of bile acid, and increasing the level of IL-22 may be valuable for the treatment of PCOS [11]. Polyamine metabolism may be one of the mechanisms associated with gut microbiota and testicular dysfunction [12]. Exploring the potential mechanism of gut microbiota affecting fertility can be of great significance for the research and prevention of infertility in the context of PPPM.

Genomics and Mendelian randomization are fundamental considerations for PPPM

Previous studies have shown the potential of gut microbiota as a novel biomarker for predicting and preventing infertility. However, the association between gut microbiota and infertility is easily affected by age, environment, mental state, lifestyle, and other confounding factors in observational studies. These conditions limit the causal inference between gut microbiota and infertility. Therefore, we attempted to predict the causal relationship between them at the genetic level using Mendelian randomization (MR) method and to identify biomarkers for disease prevention and improvement at the gut microbial level in infertile patients in the context of PPPM [13]. MR is a novel and suitable approach using genetic instrumental variables (IVs) to explore the causal association between gut microbiota and infertility [14]. The development of the PPPM framework, which combines predictive medicine, preventive medicine, and personalized medicine. The new 3P medical model emphasizes early intervention, proactive predictive prevention, and the interaction of health and disease [15]. This strategy with clarity of causal relationship may provide new evidence from the perspective of PPPM that surveillance and modulation of gut microbiota play a key role in preventing and improving poor reproductive function [16].

Working hypothesis and anticipated impact in the framework of PPPM

So far, the role of gut microbiota in predicting the causality and incidence rate of infertility has never been studied in infertile patients. To make more accurate recommendations for focused prevention and unique therapies for reproductive problems, this study aimed to determine whether the absolute abundance of gut microbiota in infertility can provide new criteria to personalize the risk of infertility using the MR. If there is a causal relationship between some specific gut microbiota and infertility, monitoring the gut microbiota of pregnant women may become a new target for PPPM. More importantly, fecal detection of intestinal microbiota has the characteristic of being non-invasive and convenient, and has been widely used in intestinal microbiota detection. We hope to achieve personalized prediction and prevention of infertility under the PPPM framework by detecting certain microbiota in individual feces that are causal related to fertility and provide a high level of evidence-based medicine theoretical basis for establishing a comprehensive service model for reproductive health prediction, diagnosis, treatment, prevention, and health promotion [17].

Materials and methods

Study design

A TSMR model was used to evaluate the causal effect between gut microbiota and infertility, including female infertility and male infertility. The flowchart of this study was displayed in Fig. 1.

Fig. 1.

Fig. 1

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The flowchart of Mendelian randomization (MR)

Data sources

Genetic variation data for the gut microbiota was obtained from the most comprehensive genome-wide meta-analysis of gut microbiota composition published by the MiBioGen consortium. This study included and coordinated 18,340 individuals from 24 cohorts, 17 of whom were of European ancestry. 16S rRNA gene sequencing data were used as microbiome trait locus (mbTL) mapping analysis to identify the relative abundance of gut microbial genera [18]. Age, sex, technical covariates, and genetic principal components were adjusted during the study. The genus was the lowest level of biological classification in this study. A total of 131 genera were identified, including 12 unknown genera, and thus, 119 genera microbiota were included in subsequent analyses.

Genome-Wide Association Studies (GWAS) data on infertility was obtained from the FinnGen consortium R5 release data (https://www.finngen.fi/en), including male infertility (680 cases and 72,799 controls) and female infertility (6481 cases and 68,969 controls). The individuals’ sex, age, top 10 principal components, and genotyping batches were corrected during the study.

Generation of instrumental variables

The selection of IVs adopted the following criteria. Since there were too few IVs when P < 5 × 10−8, the standard (P < 1 × 10−5) was appropriately relaxed to increase the number of MR and sensitivity analysis SNPs [19]. The IVs with r2 < 0.001 and window size = 10,000 kb were set as the constraint to eliminate SNPs with strong linkage disequilibrium (LD) and thus reduce the occurrence of biased results. SNPs with moderate palindrome sequences that cannot be adjusted were eliminated [20]. PhenoScanner (http://www.phenoscanner.medschl.cam.ac.uk/), which used to eliminate confounding factors, is a tool for screening the second phenotype of IVs [21, 22].

Statistical analysis

Three default and complementary MR methods were used to estimate the effect between gut microbiota and infertility, including inverse variance weighting (IVW), MR-Egger regression, and weighted median (WM). The main results were the IVW results because IVW results were unbiased if no level of pleiotropy existed [23–25]. To assess the causal relationship between gut microbiota and infertility, bidirectional MR analysis was performed for gut microbiota found to be causally associated with infertility in forward MR analysis [26].

The presence of heterogeneity and horizontal pleiotropy within IVs might lead to bias in MR results. Heterogeneity was quantified by Cochran IVW and MR-Egger Q tests [27]. The visual funnel plots and leave-one-out were used to identify potentially heterogeneous SNPs. Leave-one-out analysis meant gradually eliminating each SNP, calculating the meta-effect of the remaining SNPs, and observing how the results changed, thereby reducing the bias caused by individual SNPs [28]. MR-PRESSO was used to correct potential horizontal by selecting and deleting anomalous SNPs [28]. Moreover, intercept terms are used to assess the horizontal pleiotropy of IVs in MR-Egger’s hypothesis.

All analyses were performed using R Version 4.2.0 with the R package “TwosampleMR” and “MRPRESSO.”

Results

Associations of gut microbiota with male and female infertility

A total of 2465 SNPs were used as IVs for 119 gut microbiota genera (Table S1). The IVW results showed significant causal associations between the five bacterial genera and male infertility, of which Eubacterium oxidoreducens (OR = 2.048, 95%CI = 1.203–3.486, P = 0.008) and Lactococcus (OR = 1.445, 95%CI = 1.013–2.061, P = 0.042) were risk factors for the development of male infertility, while Eubacterium ventriosum (OR = 0.436, 95%CI = 0.219–0.869, P = 0.018), Eubacterium rectale (OR = 0.306, 95%CI = 0.147–0.637, P = 0.002), and Ruminococcaceae NK4A214 (OR = 0.537, 95%CI = 0.292–0.987, P = 0.045) were protective factors for male infertility. For female infertility, IVW results suggested that there were six bacterial genera with significant causal associations, of which Eubacterium ventriosum (OR = 0.809, 95%CI = 0.678–0.964, P = 0.018), Holdemania (OR = 0.836, 95%CI = 0.707–0.990, P = 0.037), Lactococcus (OR = 0.867, 95%CI = 0.768–0.978, P = 0.020), Ruminococcaceae NK4A214 (OR = 0.830, 95%CI = 0.690–1.000, P < 0.050), and Ruminococcus torques (OR = 0.739, 95%CI = 0.571–0.957, P = 0.022) had a protective effect on female infertility, while Faecalibacterium (OR = 1.311, 95%CI = 1.076–1.597, P = 0.007) was a risk factor for female infertility. Notably, Ruminococcaceae NK4A214 acted as a protective factor in both male and female infertility, while Lactococcus was the opposite in different gender (Fig. 2 and Fig. 3).

Fig. 2.

Fig. 2

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The forest plot showed primary results of the causal associations between gut microbiota and infertility

Fig. 3.

Fig. 3

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Scatter plots for the causal association between gut microbiota and infertility (female infertility: a–f; male infertility: g–k)

Sensitivity analysis and exclusion of confounding factors

No pleiotropy existed using MR-PRESSO and MR-Egger intercept analysis. The heterogeneity was not found either using Cochran’s Q tests (Tables 1 and 2). Funnel plots were basic symmetry (Fig. 4). No significant change was observed in the estimate of the association between gut microbiota and infertility after one SNP was excluded on a case-by-case basis (Fig. 5). By searching for the second phenotype of IVs in the positive results, we did not find any potential confounding factors that could lead to bias in the results, indicating that the results are not affected by confounding factors.

Table 1.

MR estimates for the association between gut microbiota and female infertility

Exposure Heterogeneity MR-Egger regression MR-PRESSO
MR-Egger IVW Intercept SE P P
Eubacterium ventriosum 0.693 0.670 0.033 0.030 0.296 0.775
Faecalibacterium 0.216 0.281  − 0.008 0.021 0.734 0.432
Holdemania 0.103 0.139 0.006 0.025 0.829 0.181
Lactococcus 0.921 0.957 0.005 0.036 0.898 0.949
Ruminococcaceae NK4A214 0.483 0.508 0.019 0.023 0.416 0.396
Ruminococcus torques 0.440 0.548  − 0.002 0.026 0.944 0.462
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Table 2.

MR estimates for the association between gut microbiota and male infertility

Exposure Heterogeneity MR-Egger regression MR-PRESSO
MR-Egger IVW Intercept SE P P
Eubacterium ventriosum 0.693 0.778 0.034 0.118 0.781 0.858
Eubacterium oxidoreducens 0.767 0.681 0.117 0.109 0.361 0.226
Eubacterium rectale 0.428 0.517 0.043 0.087 0.641 0.417
Lactococcus 0.603 0.672  − 0.060 0.107 0.594 0.483
Ruminococcaceae NK4A214 0.241 0.234 0.076 0.075 0.335 0.274
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Fig. 4.

Fig. 4

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Funnel plots for the causal association between gut microbiota and infertility (female infertility: a–f; male infertility: g–k)

Fig. 5.

Fig. 5

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Leave-one-out plots for the causal association between gut microbiota and infertility (female infertility: a–f; male infertility: g–k)

Bidirectional Mendelian randomization analysis

A bidirectional MR analysis was conducted if the forward MR analysis results were positive. A causal relationship between male infertility and Lactococcus was observed (OR = 1.052, 95%CI = 1.000–1.106, P = 0.048, while no reverse causal association was observed in other gut microbiota genera. Sensitivity analyses indicated no heterogeneity (P = 0.362) and pleiotropy (P = 0.680) (Fig. 6).

Fig. 6.

Fig. 6

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The reverse MR used to estimate the effect of male infertility on Lactococcus levels: a scatter plot; b leave-one-out plot; c funnel plot; d forest plot

Discussion

Summary of research findings

Prediction of individuals’ health at the genetic level is one of the fundamental steps in implementing PPPM. The advantage of predictive medicine lies in early scientific judgment and intervention to maximize the protection of the population’s health and in the field of reproductive health [29]. In this study, we performed bidirectional TSMR analyses to assess the causal relationship between gut microbiota and male and female infertility respectively, and hope to provide prevention and personalized treatment strategies for reproductive health by regulating the gut microbiota.

The results indicated that Eubacterium venereum, Lactococcus, and Ruminococcaceae NK4A214 were protective factors for male infertility, while redox fungi and Lactococcus were risk factors. For female infertility, there was a potential protective effect on Eubacterium ventriosum, Holdemania, Lactococcus, Ruminococcaceae NK4A214, and Ruminococcus torques, in addition to the opposite for Faecalibacterium. The reverse TSMR result suggested a bidirectional causal relationship between Lactococcus and male infertility.

Major gut microbiota causally related to men and female infertility: performance of PPPM strategy

The difference in the absolute abundance of the gut microbiota might lead to different results in men and women. When exploring the relationship between gut microbiota and gestational weight gain (GWG) in healthy pregnant women in late pregnancy, the abundance of Eubacterium ventriosum was found lower in both GWG deficient and excess populations, which seemed to confirm that Eubacterium ventriosum was one of the protective factors for female fertility [30]. Lactococcus spp., one of nine genera of core microbiota, has been shown a protective effect on female reproductive health [31, 32]. Lactic acid bacteria, of which Lactococcus spp. are abundant, can break down the glycogen of vaginal epithelial cells to produce lactic acid, creating a weakly acidic environment that provides the soil for the growth of the dominant vaginal bacteria, while producing antibacterial substances to inhibit the growth of harmful bacteria and forming a dominant bacterial film [33]. Moreover, Lactococcus lactis is recognized as a safe food-grade microorganism, and in the future, we will investigate the specific mechanisms of its role in female reproductive health through clinical trials based on the background of PPPM with the hope of achieving protection and prevention of female fertility [34].

Many factors affect male reproductive health, including sperm quality and quantity, long-term urinary tract infections, orchitis, etc. [35–38]. Lactococcus was a risk factor for male infertility, contrary to the result of female infertility. The unique manifestation of this pathogen was urinary tract infections (UTIs) in males [39]. For Ruminococcaceae NK4A214, a recent study revealed that vitamin A uptake in the intestine was eliminated due to significantly lower bile acid levels using metabolomic analysis and 16S rDNA-seq, which was significantly associated with reduced abundance of the Ruminococcaceae NK4A214 group [40]. Moreover, the abnormal metabolism of vitamin A can be transferred to testicular cells through circulating blood, which may lead to abnormal spermatogenesis, and the results of fecal microbiota transplantation (FMT) confirm it [41]. FMT as a potential innovative treatment for male infertility directly alters the recipient’s gut microbiota and obtains therapeutic effects, and it has been shown that FMT promotes the expression of spermatogenesis-related genes in the testis by enhancing the protein levels of the antioxidant enzyme GPX1 and increases the protein levels of the genes most essential for spermatogenesis, while a new hypothesis has been proposed in the effective and sustained treatment of female PCOS, in which the combination of FMT and curcumin has a much lower remission rate [42, 43].

It is also important to consider the concept of certain microbiota targets outside the gut [44]. Some studies suggest that the gut microbiome is considered an endocrine organ that can affect distant organs and related biological pathways [45]. Among them, metabolic factors are most commonly associated with infertility, which can involve the gut testis axis, liver gallbladder gut axis, and so on [11, 46, 47]. Furthermore, the characteristics of the microbiota itself can help the microbiota to play a role in the gastrointestinal tract, for example, the anti-gastric juice and anti-bile acid characteristics of some probiotics can help them survive in the gastrointestinal tract, thus benefiting the bacteria host interaction [48].

Based on the PPPM model, these findings could provide an innovative potential preventive and therapeutic approach to modulate the intestinal testicular axis through improved gut microbiota (protective factors) to achieve semen quality and male fertility in infertility patients [47]. Meanwhile, for some probiotics, we can enhance their activity through the use of prebiotics or use certain bacteria as drugs to play a role in combating infertility [9]. For other gut microbes, few references provided support for the relationship between them and infertility, and more clinical practice and trials are needed to be conducted to predict their potential association in a future dominated based on the PPPM model [49].

The advantages and innovations of using the MR method to predict causal relationships for PPPM

The strength of PPPM lies in making early scientific judgments and proposing appropriate interventions to maximize the protection of the health of the population, a concept that is shared in the field of reproductive health [50]. The application of genetic information is an effective tool for implementing PPPM, and how to integrate genetic information with clinical practice is a challenge. Our study successfully linked the gut microbiota with infertility by using MR to regard genetic information as IVs and predicted the relationship between them at the genetic level. Our research has the following advantages. The essence of our research in statistics regards SNPs as IVs to investigate causal effects between exposures and outcomes avoiding bias and reverse causality [51, 52]. We also had advantages over the relatively high level of evidence in RCT studies while saving significant time, effort, and money [53]. MR analysis can eliminate the interference of some confounding factors. In the context of the close relationship between metabolism and infertility, liver fibrosis may lead to the occurrence of infertility. We use PhenoScanner to search for the second phenotype related to IVs to exclude confounding factors that may cause bias in the results. Furthermore, the good results of sensitivity analysis proved the reliability of MR analysis. Through the method of MR, we could identify the microbiota that caused reproductive dysfunction and then predict the risk of infertility through changes in the number of gut microbiotas.

Probiotics have been suggested as a potential tactic to improve reproductive health and lower the risk of illness by altering the balance of the gut microbiota. The goal of the current study, which focuses on the gut-reproductive axis, is to improve the content of beneficial bacteria in the gut to control the host’s metabolic and reproductive health by influencing the neurological, immunological, and endocrine systems. And we can enhance the activity of probiotics through the use of potential prebiotics, thereby strengthening their application in the reproductive field. In the future, we may develop personalized and functional probiotic strains for different populations by combining MR methods with multi-omics technologies and establishing rapid and accurate screening methods and functional prediction technologies at multiple scales to give safe and efficient individualized probiotic interventions, balance the human microecology, realize a new model of personalized treatment, and maintain the reproductive health of the population [54]. In addition, with the emergence of new technologies, we expect that both fecal microbiota transplantation (FMT) and targeted drug technology will contribute to our findings and personalize our gut microbial interventions for therapeutic purposes [55, 56].

Overall, it is crucial to make attempts to find novel dietary therapies, microbial supplements, or FMT-regulated bacteria for the therapeutically meaningful treatment of infertile patients. An in-depth investigation of microbial-microbial-host interactions is key to linking alterations in the gut microbiota to microbial regulation of host processes and will be key to optimally realizing the potential for improving the gut microbiota to combat infertility disorders.

Limitations

The outcome was performed by sex, although SNPs located on sex chromosomes were not used during analysis and GWAS data of gut microbiota has adjusted for sex, a potential bias due to sex cannot be excluded completely. Additionally, most subjects in the GWAS meta-analysis of gut microbiota data were of European origin, such that the findings cannot be applied to other populations [18]. Future studies on the causal relationship between gut microbiota and infertility should be considered on a global scale to complement and refine our findings.

Conclusions and expert recommendations

Gut microbiota is a large number of microorganisms present in the human gut, which can assist hosts in completing various physiological and biochemical functions. Studying the differences in gut microbiome between normal individuals and infertile patients has profound significance for comprehensively understanding changes in reproductive ability, developing new biomarkers, and precise prevention and treatment models. We innovatively considered gut microbiota as indicators for predicting the risk of infertility using genetic information as IVs. It is particularly important that our results supported the causal relationship between certain specific microbiota and infertility, thus providing a novel and evidence-based medical method for predicting the risk of infertility in the context of PPPM. From a personalized perspective, gut microbiota can be easily obtained through feces and non-invasive, so applying gut microbiota to detect infertility may benefit most people.

Finally, although we have predicted the causal relationship between gut microbiota and infertility at the genetic level, its biological mechanisms need to be further explored, and our results may provide a theoretical basis for exploring the mechanisms of specific gut microbiomes in infertility patients. In subsequent clinical work, we can detect the abundance of gut microbiota in feces to predict the risk of infertility and prevent and treat infertility by regulating gut microbiota, thus achieving the original intention of PPPM.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (XLSX 275 KB) (275.4KB, xlsx)

Acknowledgements

We would like to express our gratitude to the participants and investigators of the FinnGen study. Thanks also to the MiBioGen consortium for publishing the GWAS summary statistics of the gut microbiota.

Abbreviations

PPPM/3PM

Predictive, preventive, and personalized medicine

PCOS

Polycystic ovary syndrome

MR

Mendelian randomization

IVs

Instrumental variables

TSMR

Two-sample Mendelian randomization

mbTL

Microbiome trait locus

GWAS

Genome-Wide Association Studies

SNPs

Single-nucleotide polymorphisms

LD

Linkage disequilibrium

IVW

Inverse variance weighting

WM

Weighted median

GWG

Gestational weight gain

UTIs

Urinary tract infections

FMT

Fecal microbiota transplantation

Author contribution

Y. J. X. and C. W. Z. conceived and designed the study. S. R. Z., Y. Q. F., and Y. K. Z. contributed to obtaining the data and analysis. Y. K. Z., G. S. D., and C. W. Z. provided support in the visualization of results. Y. J. X., C. W. Z., and Y. Q. F. drafted the manuscript. J. Q. W., W. W. and Y. J. X. critically revised this article. All authors gave final approval for the version to be published.

Funding

This study was supported by Wu Jieping Medical Foundation (No. 320.6750.2022–02-39).

Data availability

All data could be found in the IEU OpenGWAS project (https://gwas.mrcieu.ac.uk/).

Code availability

The software and code used in the study could be obtained through the corresponding author.

Declarations

Consent to participate

Not applicable to this study.

Competing interests

The authors declare no competing interests.

Footnotes

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Yujia Xi and Chenwei Zhang contributed equally to this work.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary file1 (XLSX 275 KB) (275.4KB, xlsx)

Data Availability Statement

All data could be found in the IEU OpenGWAS project (https://gwas.mrcieu.ac.uk/).

The software and code used in the study could be obtained through the corresponding author.

Articles from The EPMA Journal are provided here courtesy of Springer

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