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. 2025 Nov 14;104(46):e45790. doi: 10.1097/MD.0000000000045790

Exploring the causal relationship between gut microbiota and thromboembolism: A Mendelian randomization study

Zhiyi Wang a, Zhijun Zheng b,*
PMCID: PMC12622754  PMID: 41239641

Abstract

Although several studies have indicated potential associations between gut microbiota (GM) and thromboembolic diseases, the causative role of GM in thromboembolism remains unclear. We employed two-sample Mendelian randomization (MR) analysis to investigate the potential causal association between GM and 3 thromboembolic conditions: arterial embolism and thrombosis (AET), venous thromboembolism (VTE), and pulmonary embolism (PE). Genome-wide association study datasets from MiBioGen and FinnGen were utilized, with GM serving as the exposure and thromboembolism outcomes as endpoints. Using the inverse-variance weighted approach, under conditions without heterogeneity or horizontal pleiotropy, 16 gut microbial genera were significantly associated with thromboembolic risks. Specifically, Holdemanella and the Ruminococcus gnavus group exhibited protective effects against AET, whereas Parabacteroides, Subdoligranulum, and the Eubacterium hallii group were linked to elevated AET risk. Regarding VTE, protective associations were found for Ruminococcaceae NK4A214 group, Ruminococcaceae UCG002, Ruminococcaceae UCG004, Lachnospiraceae UCG010, Sutterella, and Christensenellaceae R.7 group, while the Eubacterium rectale group showed a positive correlation with increased VTE risk. Furthermore, Defluviitaleaceae UCG011, Ruminococcaceae UCG004, Turicibacter, Lachnospiraceae UCG010, Sutterella, and Collinsella demonstrated protective effects against PE, whereas Erysipelatoclostridium was positively associated with heightened PE risk. In conclusion, this two-sample MR study suggests potential associations between GM and thromboembolic disorders. However, as none of the associations remained significant after Bonferroni correction, these findings should be regarded as exploratory and hypothesis-generating, requiring further validation in future studies.

Keywords: arterial embolism and thrombosis, causal relationship, gut microbiota, Mendelian randomization analysis, pulmonary embolism, thromboembolism, venous thromboembolism

1. Introduction

Thromboembolism refers to a disease where a solid mass in the blood obstructs a blood vessel.[1] Thrombosis can develop in either arteries or veins,[1] and depending on the site and vessel type, thromboembolism can result in diverse clinical presentations and health complications. Arterial embolism and thrombosis (AET), which occurs within arteries, is commonly triggered by vascular endothelial injury, platelet activation, and the coagulation system’s activation due to atherosclerosis.[2,3] It primarily affects the coronary arteries and cerebral arteries, leading to myocardial infarction and ischemic stroke.[4] Venous thromboembolism (VTE) is characterized by the development of blood clots in the venous system, frequently presenting as deep vein thrombosis, primarily located in the DV of the legs. Virchow triad identifies venous stasis, endothelial injury, and hypercoagulability as the fundamental mechanisms driving the pathogenesis of VTE.[5,6] Importantly, deep vein thrombosis can progress to pulmonary embolism (PE), a severe complication characterized by acute chest pain, respiratory distress, and potential fatality.[7–9] Current clinical management strategies for thromboembolic disorders largely consist of anticoagulant pharmacotherapy and surgical interventions.[10] However, prolonged anticoagulant use may increase hemorrhagic risk and adversely affect organ function,[11] while surgical approaches involve inherent procedural risks. Consequently, ongoing medical research continues to prioritize improvements in both thromboembolism treatment and prevention.

The gut microbiota (GM) exerts a fundamental influence on host physiological homeostasis,[12,13] and accumulating evidence has associated GM alterations with various systemic disorders including AET,[14,15] VTE, and PE. GM dysbiosis may provoke systemic inflammatory responses, a known critical contributor to thrombosis.[16,17] Additionally, alterations in GM composition might elevate thrombosis risk via immune modulation and functional impairment of vascular endothelium.[18–22] Notably, observational clinical studies examining associations between GM and thromboembolic conditions are prone to confounding effects arising from environmental factors and dietary variations.[23]

Mendelian randomization (MR) denotes a genetic instrumental variable analysis that employs genetic variants, particularly single nucleotide polymorphisms (SNPs), as instruments linked to exposure factors. The fundamental concept of MR involves deducing causal connections between exposures and outcomes by utilizing the genetic association of instrumental SNPs with the outcomes being studied. SNPs are prevalent genetic variants distributed randomly across populations and inherited stably, thereby minimizing potential biases from confounding variables and bolstering the internal validity of causal inference studies.[24] Therefore, this investigation employed genome-wide association study (GWAS) datasets obtained from MiBioGen and FinnGen R10, selected SNPs associated with GM composition as genetic instruments, and applied MR analyses to elucidate causal associations between GM and thromboembolic disorders. This approach provides hypothesis-generating evidence and offers novel insights that may inform future mechanistic studies and potential therapeutic strategies.

2. Materials and methods

2.1. Study overview

The results of this study were reported in accordance with the recommendations made by the STROBE-MR.[25,26]

Figure 1 provides a schematic representation of the research workflow. Extensive GWAS data concerning GM and thromboembolic conditions were collected, with each microbial genus considered as a unique exposure variable, while the 3 types of thromboembolism were analyzed individually as outcome variables. A two-sample MR analysis was performed to assess possible causal relationships between GM and the 3 thromboembolic outcomes. To ensure reliable conclusions, 3 fundamental assumptions of MR needed to be fulfilled: Instrumental variable SNPs demonstrated a strong correlation with the exposure (GM); the chosen SNPs showed no relationship with confounding variables; and these SNPs influenced thromboembolism risk exclusively through their connection with GM, with no other causal pathways identified.[27,28] (Fig. 1).

Figure 1.

Figure 1.

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(A) Three fundamental assumptions underpinning MR: I – correlation assumption; II – independence assumption; III – exclusion restriction assumption; (B) Detailed workflow of the MR analysis used in this study. MR = Mendelian randomization.

2.2. Data sources

The MR analysis used data from 2 separate GWAS datasets. The MiBioGen collaboration, which included 18,340 people from a variety of ethnic backgrounds, was the source of the GM data.[29] Initially, 14,587 GM-related SNPs identified from human samples met the significance criterion (P < 1 × 10−5); following exclusion of SNPs corresponding to 12 unidentified genera, 119 genera remained eligible for inclusion.[29] Data concerning AET, VTE, and PE were obtained from the FinnGen R10 dataset.[30] Specifically, the AET cohort comprised 383,860 participants (1883 cases and 381,977 controls), encompassing 21,305,815 SNPs. The VTE cohort included 412,181 individuals (21,021 cases, 391,160 controls) with 21,306,349 SNPs, while the PE dataset encompassed 411,174 participants (10,046 cases, 401,128 controls), containing 23,106,331 SNPs. All participants were of European descent. Public GWAS summary data utilized herein had received prior ethical approval from their respective institutions. Table 1 provides further detailed descriptions (Table 1).

Table 1.

Details of the genome-wide association studies included in the Mendelian randomization analysis.

Trait Data type N_cases N_controls Consortium
GM Exposure 18,340 MiBioGen
AET Outcome 1883 381,977 FinnGen_R10
VTE Outcome 21,021 391,160 FinnGen_R10
PE Outcome 10,046 401,128 FinnGen_R10
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AET = arterial embolism and thrombosis, GM = gut microbiota, PE = pulmonary embolism, VTE = venous thromboembolism.

2.3. Selection of instrumental variables (IVs)

The inferred causal links between GM and thromboembolism were made more reliable with the implementation of strict quality control measures. At first, SNPs linked to GM were chosen as IVs using a strict significance threshold (P < 1 × 10−5).[31] Subsequently, linkage disequilibrium pruning using European genomic reference panels (kb = 10,000, r2 < 0.001) was conducted to ensure SNP independence.[32] Palindromic SNPs were removed to minimize potential allele-orientation bias. Moreover, SNPs associated with both exposure and outcome factors were matched, and effect alleles were aligned for data accuracy. Lastly, the strength and reliability of selected IVs were evaluated through F-statistics, calculated as F = β2_exposure/ SE2_exposure.[29] IVs demonstrating F-statistics ≤10 were excluded to mitigate weak instrument bias.

2.4. Statistical methods and sensitivity analysis

This study utilized 5 complementary MR analytical methods – inverse-variance weighted (IVW), Weighted median, Simple mode, MR-Egger, and Weighted mode – to systematically examine the potential causal relationships between GM and thromboembolic diseases.[33] Among these, the IVW analysis functioned as the primary analytical approach.[34] To account for multiple testing and reduce the likelihood of false-positive results, a rigorous Bonferroni correction was implemented, establishing a significance threshold of P = 4.20 × 10−4 (0.05/119).[35]

The evaluation of heterogeneity among MR estimates was conducted using Cochran Q test. A non-significant result (P > .05) suggested that significant heterogeneity was not present. Based on the outcomes of the heterogeneity assessment, a decision was made to utilize either a fixed-effects or random-effects model. The assessment of potential horizontal pleiotropy (HP) was conducted through MR-Egger intercept and MR-PRESSO analyses. A non-significant intercept (P > .05) suggested the absence of pleiotropy, thus confirming the robustness of the findings. Furthermore, leave-one-out (LOO) sensitivity analyses were performed to systematically exclude each SNP and assess its influence on the overall MR estimate, thereby improving the robustness of the results. Funnel plots and forest plots offered visual evaluations of the consistency and stability of the MR estimates. All statistical procedures were executed using R-4.3.2 and RStudio software, employing the TwoSampleMR package (version 0.5.7).

3. Results

3.1. IV selection

After applying stringent selection criteria for IVs as outlined in Section 2.3, we identified 1531 SNPs linked to 119 genera of GM, all demonstrating strong instrument strength with F-statistics exceeding 10,[36] thereby reducing the potential for weak instrument bias (Table S1, Supplemental Digital Content, https://links.lww.com/MD/Q624). Figure 2 illustrates the causal relationships deduced from MR analyses involving the 119 GM genera and 3 thromboembolic disorders (Tables S2–S4, Supplemental Digital Content, https://links.lww.com/MD/Q624)(Fig. 2).

Figure 2.

Figure 2.

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Circular plot summarizing MR analysis outcomes for all GM genera. GM = gut microbiota, MR = Mendelian randomization.

3.2. The causal relationship between GM and 3 types of thromboembolism

3.2.1. The causal relationship between GM and AET

The IVW MR analyses revealed connections between 5 GM genera and the likelihood of AET. In this analysis, Holdemanella and Ruminococcus gnavus group exhibited protective associations with AET risk. Conversely, Parabacteroides, Subdoligranulum, and Eubacterium hallii group were positively correlated with an increased susceptibility to AET (Fig. 3). Figure 4 illustrates scatter plots that represent the effect estimates of individual SNPs on the risk associated with these GM genera in relation to AET.

Figure 3.

Figure 3.

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Forest plot illustrating associations between genetically predicted GM genera and AET risk based on IVW analysis. AET = arterial embolism and thrombosis, GM = gut microbiota, IVW = inverse-variance weighted.

Figure 4.

Figure 4.

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Scatter plots for causal effects of GM on AET risk using 5 MR methods. AET = arterial embolism and thrombosis, GM = gut microbiota, MR = Mendelian randomization.

Furthermore, the findings from Cochran Q, MR-Egger, and MR-PRESSO tests revealed no indication of substantial heterogeneity or HP among the 5 genera linked to AET (Table 2). Additionally, the LOO sensitivity analyses, funnel plots, and forest plots consistently indicated the absence of significant outliers, thereby reinforcing the robustness of the MR findings (Fig. S1A–C, Supplemental Digital Content, https://links.lww.com/MD/Q623). Following the application of Bonferroni correction, it was observed that none of the identified associations attained statistical significance, indicating a need for careful interpretation of the causal relationships between GM and AET (Figs. 3 and 4, Table 2).

Table 2.

Sensitivity analysis of the MR analysis results of the GM and AET.

Exposure Heterogeneity Directional pleiotropy MR-PRESSO
Cochran Q P-value Egger intercept P-value P-value
Holdemanella 15.084 .237 −0.021 .598 .321
Ruminococcus gnavus group 3.241 .954 0.002 .970 .977
Parabacteroides 4.474 .613 −0.057 .434 .671
Subdoligranulum 10.424 .493 −0.044 .220 .449
Eubacterium hallii group 14.880 .248 0.047 .123 .158
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AET = arterial embolism and thrombosis, GM = gut microbiota, MR = Mendelian randomization.

3.2.2. The causal relationship between GM and VTE

The IVW analysis revealed 7 GM genera linked to the risk of VTE. Protective associations were identified for the Ruminococcaceae NK4A214 group, Ruminococcaceae UCG002, Ruminococcaceae UCG004, Lachnospiraceae UCG010, Sutterella, and Christensenellaceae R.7 group. Conversely, a heightened risk of VTE was linked to the Eubacterium rectale group (Fig. 5). Figure 6 displays scatter plots that illustrate the effect estimates of the GM-associated SNPs on the risk of VTE.

Figure 5.

Figure 5.

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Forest plot illustrating the IVW-based associations between genetically predicted GM genera and VTE risk. GM = gut microbiota, IVW = inverse-variance weighted, VTE = venous thromboembolism.

Figure 6.

Figure 6.

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Scatter plots for causal effects of GM on VTE risk using 5 MR methods. GM = gut microbiota, MR = Mendelian randomization, VTE = venous thromboembolism.

The findings from Cochran Q test, MR-Egger, and MR-PRESSO analyses revealed heterogeneity and HP concerning the association with the Eubacterium rectale group (P < .05), which may affect the validity of the results. Following the removal of 2 outlier SNPs (rs10248854 and rs3980709), the ensuing sensitivity analyses demonstrated a reduction in heterogeneity and HP (P < .05). The 6 genera that remain showed no signs of HP (Table 3). Additionally, LOO analyses, funnel plots, and forest plots indicated the absence of significant outliers, reinforcing the reliability of these MR findings (Fig. S2A–C, Supplemental Digital Content, https://links.lww.com/MD/Q623). Nonetheless, following the implementation of the Bonferroni correction, all associations between GM and VTE lost their statistical significance (Figs. 5 and 6, Table 3).

Table 3.

Sensitivity analysis of the MR analysis results of the GM and VTE.

Exposure Heterogeneity Directional pleiotropy MR-PRESSO
Cochran Q P-value Egger intercept P-value P-value
Ruminococcaceae NK4A214 group 13.411 .494 −0.009 .412 .521
Ruminococcaceae UCG002 21.381 .436 −0.002 .773 .542
Ruminococcaceae UCG004 15.826 .105 0.013 .636 .153
Eubacterium rectale group 20.183 .017 0.006 .717 .041
2.048 .957 0.009 .480 .966
Lachnospiraceae UCG010 6.713 .752 −0.001 .917 .814
Sutterella 10.838 .370 0.019 .248 .344
Christensenellaceae R.7 group 4.646 .795 −0.010 .546 .820
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GM = gut microbiota, MR = Mendelian randomization, VTE = venous thromboembolism.

3.2.3. The causal relationship between GM and PE

The IVW analysis identified 7 GM genera associated with PE risk. Among these genera, Defluviitaleaceae UCG011, Ruminococcaceae UCG004, Turicibacter, Lachnospiraceae UCG010, Sutterella, and Collinsella exhibited protective associations against PE. In contrast, Erysipelatoclostridium was positively correlated with increased PE risk (Fig. 7). Scatter plots illustrating the estimated effects of GM-related SNPs on PE risk are shown in Figure 8.

Figure 7.

Figure 7.

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Forest plot displaying IVW-based associations between genetically predicted GM genera and the risk of PE. GM = gut microbiota, IVW = inverse-variance weighted, PE = pulmonary embolism.

Figure 8.

Figure 8.

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Scatter plots for causal effects of GM on PE risk using 5 MR methods. GM = gut microbiota, MR = Mendelian randomization, PE = pulmonary embolism.

The Cochran Q test was employed to examine the heterogeneity present among SNPs, while MR-Egger intercept and MR-PRESSO analyses were performed to investigate HP.[33] The analyses conducted showed no signs of heterogeneity or HP concerning the 7 GM genera linked to PE (all P-values > .05; Table 4). Additionally, sensitivity assessments such as LOO analyses, funnel plots, and forest plots revealed no significant outliers,[33] thereby affirming the robustness and dependability of the MR results (Fig. S3A–C, Supplemental Digital Content, https://links.lww.com/MD/Q623). However, following the application of Bonferroni correction for multiple comparisons, none of the identified causal relationships between GM and PE achieved statistical significance (Figs. 7 and 8, Table 4).

Table 4.

Sensitivity analysis of the MR analysis results of the GM and PE.

Exposure Heterogeneity Directional pleiotropy MR-PRESSO
Cochran Q P-value Egger intercept P-value P-value
Defluviitaleaceae UCG011 4.162 .842 0.041 .134 .701
Ruminococcaceae UCG004 12.055 .281 0.012 .708 .365
Turicibacter 12.986 .294 −0.024 .367 .367
Lachnospiraceae UCG010 7.356 .691 −0.002 .921 .793
Sutterella 7.663 .662 0.028 .208 .611
Collinsella 3.152 .958 −0.011 .656 .986
Erysipelatoclostridium 9.193 .819 0.009 .612 .847
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GM = gut microbiota, MR = Mendelian randomization, PE = pulmonary embolism.

4. Discussion

This research marks the initial exploration employing MR analysis to investigate possible causal relationships between GM and 3 categories of thromboembolic conditions: AET, VTE, and PE. Through thorough MR and sensitivity analyses encompassing 119 bacterial genera, we identified 16 genera that showed suggestive associations with thromboembolism risk. Sensitivity analyses indicated no major heterogeneity or pleiotropy, supporting the robustness of these exploratory findings. These results provide hypothesis-generating evidence regarding the potential link between GM composition and thromboembolic disorders.

Specifically, our findings indicated that increased abundances of Parabacteroides, Subdoligranulum, and the Eubacterium hallii group appeared to be associated with a higher risk of AET. Previous evidence suggests Parabacteroides plays an essential role in inflammation, a fundamental trigger for embolic events.[37] Furthermore, observational data indicate that Subdoligranulum is implicated as a risk factor for vasculitis,[38] a condition linked to endothelial damage and subsequent arterial thrombosis. Similarly, increased presence of the Eubacterium hallii group has been observed among patients with cirrhosis complicated by portal vein thrombosis,[39] aligning with the elevated AET risk reported here. Although the context of the previous study was cirrhosis with portal vein thrombosis, this result indirectly suggests that the impact of the Eubacterium hallii group on thrombosis formation might not be limited to specific types of vessels or disease contexts. Therefore, the mechanism by which the Eubacterium hallii group promotes thrombosis formation warrants further investigation. Additionally, Holdemanella and the Ruminococcus gnavus group are protective factors against AET. They may enhance the host’s immune function, likely due to the production of butyrate and isovalerate.[40,41]

Our study found that the risk of VTE may be positively correlated with the Eubacterium rectale group. Currently, there are no reports in the literature linking the Eubacterium rectale group with VTE; however, the complex interactions between GM and the host may influence thrombus formation. Intriguingly, earlier research has shown that the Eubacterium rectale group may modulate host inflammation through metabolic interactions.[42] Enhanced abundance of this group can disrupt the Th17/Treg balance, driving systemic inflammation and potentially heightening VTE risk,[42] a mechanistic pathway warranting further investigation. In contrast, we observed protective associations against VTE for various genera, such as the Ruminococcaceae NK4A214 group, Ruminococcaceae UCG002, Ruminococcaceae UCG004, Lachnospiraceae UCG010, Sutterella, and Christensenellaceae R.7 group. These genera probably influence their protective mechanisms by generating short-chain fatty acids, including butyrate and acetate, which are recognized for their anti-inflammatory characteristics and their involvement in the metabolism of complex polysaccharides.[43–45]

Our analysis further indicated Erysipelatoclostridium showed a suggestive association with increased PE risk. This genus interacts with host Toll-like receptors, subsequently initiating signaling cascades that promote inflammatory cytokine release, including IL-6 and IL-1β, potentially exacerbating thromboembolic conditions.[44,46–48] Another clinical study found that Erysipelatoclostridium is positively correlated with vascular diseases, which can increase the risk of thrombosis.[49] Conversely, Defluviitaleaceae UCG011, Ruminococcaceae UCG004, Turicibacter, Lachnospiraceae UCG010, Sutterella, and Collinsella were found to reduce the risk of PE, suggesting that they may be protective factors. Microorganisms from the Ruminococcaceae and Lachnospiraceae families possess the capability to ferment dietary fibers, resulting in the production of short-chain fatty acids. These metabolites are acknowledged for their roles in diminishing systemic inflammation, strengthening the integrity of the gut barrier, and influencing immune responses.[43] Moreover, genera such as Turicibacter and Sutterella are implicated in immune regulation pathways, potentially mitigating excessive inflammation and maintaining immune homeostasis, thereby lowering the risk of PE.[43,50]

It is well established that PE is typically caused by a VTE that breaks free and travels through the bloodstream to the pulmonary vessels.[7] In our study, an interesting phenomenon was observed: Ruminococcaceae UCG004, Lachnospiraceae UCG010, and Sutterella were protective factors in both PE and VTE. This finding indicates that these bacterial groups may represent exploratory microbial markers relevant to thrombotic disease research, though further validation is needed before any preventive implications can be drawn.

Employing MR methodology, we elucidated potential causal links between GM composition and 3 thromboembolic conditions. This research offers several strengths. Firstly, the MR approach utilized genetic variants as IVs, effectively controlling for potential confounders such as lifestyle and environmental influences. Secondly, the latest data from the Finnish R10 database significantly enhanced the statistical robustness and precision of our conclusions. Lastly, comprehensive sensitivity analyses assured the reliability and consistency of our findings.

Nevertheless, several limitations should be acknowledged. First, the analyses exclusively involved individuals of European ancestry, which may limit the generalizability of the findings to other populations. Second, our investigation was confined to genus-level GM, lacking higher taxonomic resolution. Third, available GWAS data did not allow stratification according to demographic factors such as age or gender. Fourth, no formal power calculation was conducted, which may limit the ability to detect modest causal effects. Future MR studies with larger datasets should incorporate power assessments to ensure adequate sensitivity. Finally, after Bonferroni correction for multiple testing, none of the observed associations remained statistically significant; therefore, the results should be regarded as exploratory and hypothesis-generating. Furthermore, future studies should incorporate formal power calculations to ensure adequate sensitivity, expand the scope of investigation to include diverse populations beyond European ancestry, and conduct higher-resoluti.

5. Conclusion

In conclusion, this two-sample MR study explored potential associations between GM and 3 thromboembolic disorders, with several bacterial genera showing suggestive protective or risk effects. These findings should be regarded as exploratory and hypothesis-generating, requiring validation in future large-scale studies.

Acknowledgments

MiBioGen and FinnGen database were used in this study, and the authors would like to thank all those who contributed and participated in the data collection.

Author contributions

Data curation: Zhiyi Wang, Zhijun Zheng.

Investigation: Zhiyi Wang.

Methodology: Zhiyi Wang.

Resources: Zhijun Zheng.

Software: Zhiyi Wang, Zhijun Zheng.

Validation: Zhijun Zheng.

Writing – original draft: Zhiyi Wang.

Writing – review & editing: Zhijun Zheng.

Supplementary Material

medi-104-e45790-s001.xlsx (438.7KB, xlsx)

Abbreviations:

AET
arterial embolism and thrombosis
GM
gut microbiota
GWAS
genome-wide association study
HP
horizontal pleiotropy
IVW
inverse-variance weighted
LOO
leave-one-out
MR
Mendelian randomization
PE
pulmonary embolism
SNPs
single nucleotide polymorphisms
VTE
venous thromboembolism

The summary data used in the article is sourced from public data. Each GWAS mentioned in the article has received ethical approval from their respective institutions.

The authors have no funding and conflicts of interest to disclose.

The datasets generated during and/or analyzed during the current study are publicly available.

Supplemental Digital Content is available for this article.

How to cite this article: Wang Z, Zheng Z. Exploring the causal relationship between gut microbiota and thromboembolism: A Mendelian randomization study. Medicine 2025;104:46(e45790).

References

  • [1].Wendelboe AM, Raskob GE. Global burden of thrombosis: epidemiologic aspects. Circ Res. 2016;118:1340–7. [DOI] [PubMed] [Google Scholar]
  • [2].Xu YT, Yin QQ, Guo ZR. Common carotid artery thrombosis and malignant middle cerebral artery infarction following ovarian hyperstimulation syndrome: a case report. World J Clin Cases. 2022;10:9478–83. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [3].Annema W, von Eckardstein A, Kovanen PT. HDL and atherothrombotic vascular disease. Handb Exp Pharmacol. 2015;224:369–403. [DOI] [PubMed] [Google Scholar]
  • [4].Visonà A, Quere I, Mazzolai L, et al. ; European Society of Vascular Medicine (ESVM). Post-thrombotic syndrome. Vasa. 2021;50:331–40. [DOI] [PubMed] [Google Scholar]
  • [5].Bejjani A, Khairani CD, Krishnathasan D, et al. The link between venous thromboembolism and atherothrombosis: epidemiology, pathophysiology, and implications for clinical care. Eur Heart J Acute Cardiovasc Care. 2025;14:zuaf109. [DOI] [PubMed] [Google Scholar]
  • [6].Wenger N, Sebastian T, Engelberger RP, Kucher N, Spirk D. Pulmonary embolism and deep vein thrombosis: similar but different. Thromb Res. 2021;206:88–98. [DOI] [PubMed] [Google Scholar]
  • [7].Di Nisio M, van Es N, Büller HR. Deep vein thrombosis and pulmonary embolism. Lancet. 2016;388:3060–73. [DOI] [PubMed] [Google Scholar]
  • [8].Metz AK, Diaz JA, Obi AT, Wakefield TW, Myers DD, Henke PK. Venous thrombosis and post-thrombotic syndrome: from novel biomarkers to biology. Methodist Debakey Cardiovasc J. 2018;14:173–81. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [9].Cucuruz B, Kopp R, Pfister K, et al. Risk and protective factors for post-thrombotic syndrome after deep venous thrombosis. J Vasc Surg Venous Lymphat Disord. 2020;8:390–5. [DOI] [PubMed] [Google Scholar]
  • [10].Priya V, Viswanadh MK, Mehata AK, Jain D, Singh SK, Muthu MS. Targeted nanotherapeutics in the prophylaxis and treatment of thrombosis. Nanomedicine (Lond). 2021;16:1153–76. [DOI] [PubMed] [Google Scholar]
  • [11].Wells PS, Tritschler T, Khan F, et al. Predicting major bleeding during extended anticoagulation for unprovoked or weakly provoked venous thromboembolism. Blood Adv. 2022;6:4605–16. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [12].Barrio C, Arias-Sánchez S, Martín-Monzón I. The gut microbiota-brain axis, psychobiotics and its influence on brain and behaviour: a systematic review. Psychoneuroendocrinology. 2022;137:105640. [DOI] [PubMed] [Google Scholar]
  • [13].Wu P, Zhu T, Tan Z, Chen S, Fang Z. Role of gut microbiota in pulmonary arterial hypertension. Front Cell Infect Microbiol. 2022;12:812303. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [14].Hasselbalch HC, Elvers M, Schafer AI. The pathobiology of thrombosis, microvascular disease, and hemorrhage in the myeloproliferative neoplasms. Blood. 2021;137:2152–60. [DOI] [PubMed] [Google Scholar]
  • [15].Lippi G, Danese E, Mattiuzzi C, Favaloro EJ. The intriguing link between the intestinal microbiota and cardiovascular disease. Semin Thromb Hemost. 2017;43:609–13. [DOI] [PubMed] [Google Scholar]
  • [16].Olivera PA, Zuily S, Kotze PG, et al. International consensus on the prevention of venous and arterial thrombotic events in patients with inflammatory bowel disease. Nat Rev Gastroenterol Hepatol. 2021;18:857–73. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [17].Jackson SP, Darbousset R, Schoenwaelder SM. Thromboinflammation: challenges of therapeutically targeting coagulation and other host defense mechanisms. Blood. 2019;133:906–18. [DOI] [PubMed] [Google Scholar]
  • [18].Violi F, Pignatelli P, Cammisotto V, Carnevale R, Nocella C. COVID-19 and thrombosis: clinical features, mechanism of disease, and therapeutic implications. Kardiol Pol. 2021;79:1197–205. [DOI] [PubMed] [Google Scholar]
  • [19].ten Cate-Hoek AJ, Henke PK, Wakefield TW. The post thrombotic syndrome: ignore it and it will come back to bite you. Blood Rev. 2016;30:131–7. [DOI] [PubMed] [Google Scholar]
  • [20].Bochenek ML, Schütz E, Schäfer K. Endothelial cell senescence and thrombosis: ageing clots. Thromb Res. 2016;147:36–45. [DOI] [PubMed] [Google Scholar]
  • [21].Witkowski M, Weeks TL, Hazen SL. Gut microbiota and cardiovascular disease. Circ Res. 2020;127:553–70. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [22].Jansen VL, Gerdes VE, Middeldorp S, van Mens TE. Gut microbiota and their metabolites in cardiovascular disease. Best Pract Res Clin Endocrinol Metab. 2021;35:101492. [DOI] [PubMed] [Google Scholar]
  • [23].Li P, Wang H, Guo L, et al. Association between gut microbiota and preeclampsia-eclampsia: a two-sample Mendelian randomization study. BMC Med. 2022;20:443. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [24].Xu Q, Ni JJ, Han BX, et al. Causal relationship between gut microbiota and autoimmune diseases: a two-sample Mendelian randomization study. Front Immunol. 2021;12:746998. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [25].Skrivankova VW, Richmond RC, Woolf BAR, et al. Strengthening the reporting of observational studies in epidemiology using Mendelian randomisation (STROBE-MR): explanation and elaboration. BMJ. 2021;375:n2233. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [26].Skrivankova VW, Richmond RC, Woolf BAR, et al. Strengthening the reporting of observational studies in epidemiology using Mendelian randomization: the STROBE-MR statement. JAMA. 2021;326:1614–21. [DOI] [PubMed] [Google Scholar]
  • [27].Rasooly D, Peloso GM. Two-sample multivariable Mendelian randomization analysis using R. Curr Protoc. 2021;1:e335. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [28].Liu Y, Xu H, Zhao Z, Dong Y, Wang X, Niu J. No evidence for a causal link between Helicobacter pylori infection and nonalcoholic fatty liver disease: a bidirectional Mendelian randomization study. Front Microbiol. 2022;13:1018322. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [29].Xia D, Wang J, Zhao X, Shen T, Ling L, Liang Y. Association between gut microbiota and benign prostatic hyperplasia: a two-sample Mendelian randomization study. Front Cell Infect Microbiol. 2023;13:1248381. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [30].Kurki MI, Karjalainen J, Palta P, et al. ; FinnGen. FinnGen provides genetic insights from a well-phenotyped isolated population. Nature. 2023;613:508–18. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [31].Xiang K, Wang P, Xu Z, et al. Causal effects of gut microbiome on systemic lupus erythematosus: a two-sample Mendelian randomization study. Front Immunol. 2021;12:667097. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [32].Chen S, Zhou G, Han H, Jin J, Li Z. Causal effects of specific gut microbiota on bone mineral density: a two-sample Mendelian randomization study. Front Endocrinol (Lausanne). 2023;14:1178831. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [33].Li Y, Chen J, Yao H, et al. Gut microbiota’s role in lipoma development: evidence from Mendelian randomization. Front Genet. 2024;15:1430671. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [34].Lee CH, Cook S, Lee JS, Han B. Comparison of two meta-analysis methods: inverse-variance-weighted average and weighted sum of Z-scores. Genomics Inform. 2016;14:173–80. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [35].Li B, Han Y, Fu Z, et al. The causal relationship between gut microbiota and lymphoma: a two-sample Mendelian randomization study. Front Immunol. 2024;15:1397485. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [36].Yao Y, Hu H, Chen L, Zheng H. Association between gut microbiota and menstrual disorders: a two-sample Mendelian randomization study. Front Microbiol. 2024;15:1321268. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [37].Medawar E, Haange SB, Rolle-Kampczyk U, et al. Gut microbiota link dietary fiber intake and short-chain fatty acid metabolism with eating behavior. Transl Psychiatry. 2021;11:500. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [38].Yu B, Jin L, Chen Z, et al. The gut microbiome in microscopic polyangiitis with kidney involvement: common and unique alterations, clinical association and values for disease diagnosis and outcome prediction. Ann Transl Med. 2021;9:1286. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [39].Huang XY, Zhang YH, Yi SY, et al. Potential contribution of the gut microbiota to the development of portal vein thrombosis in liver cirrhosis. Front Microbiol. 2023;14:1217338. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [40].Zhang Q, Vasquez R, Yoo JM, Kim SH, Kang DK, Kim IH. Dietary supplementation of Limosilactobacillus mucosae LM1 enhances immune functions and modulates gut microbiota without affecting the growth performance of growing pigs. Front Vet Sci. 2022;9:918114. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [41].Liu K, Cai Y, Song K, Yuan R, Zou J. Clarifying the effect of gut microbiota on allergic conjunctivitis risk is instrumental for predictive, preventive, and personalized medicine: a Mendelian randomization analysis. EPMA J. 2023;14:235–48. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [42].Xin X, Wang Q, Qing J, et al. Th17 cells in primary Sjögren’s syndrome negatively correlate with increased Roseburia and Coprococcus. Front Immunol. 2022;13:974648. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [43].Vitetta L, Bambling M, Strodl E. Probiotics and commensal bacteria metabolites trigger epigenetic changes in the gut and influence beneficial mood dispositions. Microorganisms. 2023;11:1334. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [44].Radjabzadeh D, Bosch JA, Uitterlinden AG, et al. Gut microbiome-wide association study of depressive symptoms. Nat Commun. 2022;13:7128. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [45].Wang J, Ren C, Jin L, Batu W. Seabuckthorn Wuwei Pulvis attenuates chronic obstructive pulmonary disease in rat through gut microbiota-short chain fatty acids axis. J Ethnopharmacol. 2023;314:116591. [DOI] [PubMed] [Google Scholar]
  • [46].Chen D, Wang Y, Yang J, et al. Shenling Baizhu San ameliorates non-alcoholic fatty liver disease in mice by modulating gut microbiota and metabolites. Front Pharmacol. 2024;15:1343755. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [47].Ke H, Li F, Deng W, et al. Metformin exerts anti-inflammatory and mucus barrier protective effects by enriching Akkermansia muciniphila in mice with ulcerative colitis. Front Pharmacol. 2021;12:726707. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [48].Cheng XR, Guan LJ, Muskat MN, Cao CC, Guan B. Effects of Ejiao peptide-iron chelates on intestinal inflammation and gut microbiota in iron deficiency anemic mice. Food Funct. 2021;12:10887–902. [DOI] [PubMed] [Google Scholar]
  • [49].Li W, Jiang J, Yin X, et al. Mediation of regional cerebral blood flow in the relationship between specific gut microbiota and cognition in vascular cognitive impairment. J Alzheimers Dis. 2024;97:435–45. [DOI] [PubMed] [Google Scholar]
  • [50].Zhu J, Lyu J, Zhao R, Liu G, Wang S. Gut macrobiotic and its metabolic pathways modulate cardiovascular disease. Front Microbiol. 2023;14:1272479. [DOI] [PMC free article] [PubMed] [Google Scholar]

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