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Causal Relationship between Gut Microbiota and Pulmonary Embolism: An Analysis Using Mendelian Randomization

LILAN CEN

LILAN CEN

Guangxi Academy of Medical Sciences, Department of Infectious Disease, the People’s Hospital of Guangxi Zhuang Autonomous RegionNanning, China
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LING QIN

LING QIN

Guangxi Academy of Medical Sciences, Department of Infectious Disease, the People’s Hospital of Guangxi Zhuang Autonomous RegionNanning, China
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WANLING CHEN

WANLING CHEN

Guangxi Academy of Medical Sciences, Department of Infectious Disease, the People’s Hospital of Guangxi Zhuang Autonomous RegionNanning, China
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LIHUA WEI

LIHUA WEI

Guangxi Academy of Medical Sciences, Department of Infectious Disease, the People’s Hospital of Guangxi Zhuang Autonomous RegionNanning, China
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CAIXIA TANG

CAIXIA TANG

Guangxi Academy of Medical Sciences, Department of Infectious Disease, the People’s Hospital of Guangxi Zhuang Autonomous RegionNanning, China
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XIANG TENG

XIANG TENG

Guangxi Academy of Medical Sciences, Department of Infectious Disease, the People’s Hospital of Guangxi Zhuang Autonomous RegionNanning, China
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ZHE TIAN

ZHE TIAN

  • Department of Pulmonary and Critical Care Medicine, Affiliated Hospital of Youjiang Medical University for NationalitiesBaise, China
  • Life Science and Clinical Medicine Research Center, Affiliated Hospital of Youjiang Medical University for NationalitiesBaise, China
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TIAN, ZHE
  
18 jun 2025
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Categoría del artículo: Original Paper
Publicado en línea: 18 jun 2025
Páginas: 153 - 164
Recibido: 16 dic 2024
Aceptado: 09 abr 2025
DOI: https://doi.org/10.33073/pjm-2025-013
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© 2025 LILAN CEN et al., published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Fig. 1.

Illustrates the principles of Mendelian randomization (MR) analysis and its three fundamental assumptions. The diagram was generated using Figdraw2.0 software, with the unique identifier RRSAAba96a.
Illustrates the principles of Mendelian randomization (MR) analysis and its three fundamental assumptions. The diagram was generated using Figdraw2.0 software, with the unique identifier RRSAAba96a.

Fig. 2.

The forest plot illustrates the causal relationships between four GM (Slackia, Oscillospira, Bacteroides, and Clostridium sensu stricto 1) and the risk of PE.
The forest plot illustrates the causal relationships between four GM (Slackia, Oscillospira, Bacteroides, and Clostridium sensu stricto 1) and the risk of PE.

Fig. 3.

The scatter plots were used to examine the correlation between four GM and the likelihood of developing PE disease: A) Slackia’s potential causal association with PE; B) potential causal relationship between Oscillospira and PE; C) the potential causal relationship between Bacteroides and PE; D) potential causal relationship between Clostridium sensu stricto1 and PE.
The scatter plots were used to examine the correlation between four GM and the likelihood of developing PE disease: A) Slackia’s potential causal association with PE; B) potential causal relationship between Oscillospira and PE; C) the potential causal relationship between Bacteroides and PE; D) potential causal relationship between Clostridium sensu stricto1 and PE.

Fig. 4.

The leave-one-out sensitivity analysis demonstrated that the exclusion of any single SNP did not significantly impact the outcomes.
The leave-one-out sensitivity analysis demonstrated that the exclusion of any single SNP did not significantly impact the outcomes.

Fig. 5.

The funnel plot analysis indicated the absence of heterogeneity in the experiments as confirmed by the IVW and MR-Egger tests.
The funnel plot analysis indicated the absence of heterogeneity in the experiments as confirmed by the IVW and MR-Egger tests.

MR results of causal links between gut microbiota and pulmonary embolism risk_

Classification SNP SE p-value OR (95%Cl) Pleiotropy Heterogeneity MR-PRESSO
Egger intercept SE p-value Method Q p-value
Slackia 9 0.0007 0.031 0.998 (0.997–1.000) 0.0003 0.0003 0.424 MR-Egger 5.228 0.632 0.630
IVW 5.948 0.653
Oscillospira 9 0.0006 0.038 0.998 (0.996–1.000) –7.75 × 10−5 0.0004 0.851 MR-Egger 7.609 0.368 0.494
IVW 7.650 0.468
Bacteroides 11 0.0012 0.032 0.997 (0.995–1.000) –0.0003 0.0004 0.537 MR-Egger 10.079 0.344 0.403
IVW 10.541 0.394
Clostridium sensu stricto 1 9 0.0009 0.049 0.998 (0.996–1.000) –0.0001 0.0002 0.634 MR-Egger 4.684 0.698 0.794
IVW 4.931 0.765
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Ciencias de la vida, Microbiología y virología
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