Genetic Insights into the Gut-Lung Axis: Mendelian Randomization Analysis on Gut Microbiota, Lung Function, and COPD

Abstract

Background

Chronic obstructive pulmonary disease (COPD) is a respiratory disorder with a complex etiology involving genetic and environmental factors. The dysbiosis of gut microbiota has been implicated in COPD. Mendelian Randomization (MR) provides a tool to investigate causal links using genetic variants as instrumental variables. This study aims to employ MR analysis to explore the causal relationship between gut microbiota, lung function, and COPD.

Methods

We utilized genome-wide association study (GWAS) data from MiBioGen, UK Biobank and FinnGen, which were related to gut microbial taxa, lung function parameters including forced vital capacity in one second (FEV₁), forced vital capacity (FVC), and percentage of predicted FEV₁ (FEV₁%pred), as well as GWAS data for COPD. MR analysis was conducted to assess the causal effects of gut microbiota on lung function and the risk of COPD. Sensitivity analysis was utilized to examine the stability of the causal relationships. Multiple testing and reverse analysis were employed to evaluate the robustness of these relationships.

Results

Using the IVW method, 64 causal correlations were identified. Through conducting sensitivity analysis, multiple testing, and reverse analysis, we identified 14 robust and stable causal relationships. The bacterial taxa that showed a positive association with lung function included Desulfovibrionaceae, Erysipelotrichales, Desulfovibrionales, Clostridiales, Clostridia, Deltaproteobacteria and Erysipelotrichia, while Selenomonadales and Negativicutes showed a negative association with lung function. The abundance of Holdemanella were positively correlated with the risk of COPD, while FamilyXIII exhibited a negative correlation with the risk of COPD.

Conclusion

Several microbial taxa were discovered to have a positive causal correlation with lung function, offering potential insights into the development of probiotics. The presence of microbial taxa negatively correlated with lung function and positively correlated with COPD emphasized the potential impact of gut microbiota dysbiosis on respiratory health.

Keywords:

• COPD
• lung function
• gut-lung axis
• gut microbiota
• Mendelian randomization analysis

Abbreviations

COPD, Chronic obstructive pulmonary disease; ICS, inhaled corticosteroids; MR, Mendelian Randomization; IV, instrumental variables; GWAS, Genome-Wide Association Study; FEV₁, forced expiratory volume in 1 second; FVC, forced vital capacity; LD, linkage disequilibrium; IVW, inverse variance weighted; BH, Benjamini-Hochberg; SCFA, short-chain fatty acid; IL, interleukin.

Data Sharing Statement

The datasets supporting the conclusions of this article are available in the MiBioGen [https://mibiogen.gcc.rug.nl/], FinnGen [https://www.finngen.fi/en/access_results] and UK biobank [https://www.nealelab.is/uk-biobank] repository.

Ethical Approval

Summary statistics for the studies used for analysis were composed and obtained from published studies. All studies have received prior approval from their respective institutional review boards (IRBs). The institutional Review Board of Zhongshan Hospital approved the protocol for this study, and as per their guidelines, this study exclusively utilized publicly available data without using any individual-level data. Therefore, no additional IRB approval was necessary.

Acknowledgments

We would like to extend our appreciation to the participants and investigators involved in the MiBioGen Consortium, UK biobank, and FinnGen study. Their invaluable contributions to the large-scale GWAS studies have significantly advanced our knowledge of the gut microbiome and its connection to lung function and COPD. We would also like to extend appreciation to Dr. Xicheng Gu from Huashan Hospital, Fudan University, for providing invaluable advices on code writing.

Disclosure

The authors declare that they have no competing interests in this work.

Additional information

Funding

This work is supported by the Shanghai Science and Technology Committee (Project number 19DZ1920104).