Causal relationship between chronic obstructive pulmonary disease and respiratory tuberculosis susceptibility: A two-sample Bayesian weighted Mendelian randomization study

Causal relationship between chronic obstructive pulmonary disease and respiratory tuberculosis susceptibility: A two-sample Bayesian weighted Mendelian randomization study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Causal relationship between chronic obstructive pulmonary disease and respiratory tuberculosis susceptibility: A two-sample Bayesian weighted Mendelian randomization study Abulikemu Aili, Yan Zhang, Xiaomin Wang, Baofeng Wen, Junan Wang, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4176361/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background The causal relationship between COPD and respiratory TB is still unclear due to limited evidence from prospective studies. Therefore, the present study aimed to assess the causal relationship between COPD and respiratory TB using this two-sample Bayesian weighted Mendelian randomization (BWMR) study. Methods The genetic instrumental variants (IVs) for COPD and respiratory TB were obtained from the IEU Open GWAS project in 2021. The inverse variance weighted (IWV) method was used as the main statistical analysis method and was supplemented with weighted median and BWMR methods. Pleiotropy was tested using the MR-PRESSO global test and MR-Egger regression. Heterogeneity was analyzed using Cochran's Q statistics. The robustness of the results was tested using the leave-one-out sensitivity analysis method. Results In our two-sample BWMR analysis, we found that patients with COPD had a higher risk of respiratory TB based on IVW (OR = 1.259, 95% CI for OR: 1.011–1.568; p = 0.040), weighted median (OR = 1.287, 95% CI for OR: 0.981–1.688; p = 0 .069) and BWMR (OR = 1.240, 95% CI for OR: 1.013–1.518; p = 0.037). Reverse BWMR analysis showed that respiratory TB has no causal effect on COPD. We found no significant pleiotropy or heterogeneity in all selected IVs. The results were stable when removing the SNPs one by one. Conclusion This two-sample BWMR study provided compelling evidence that individuals with COPD are at a higher risk of respiratory TB at the genetic level, while respiratory TB has no causal effect on COPD. Chronic Obstructive Pulmonary Disease Tuberculosis Mendelian Randomization Study Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Tuberculosis (TB) is a chronic infectious disease caused by infection with Mycobacterium tuberculosis (MTB) and mainly affects the lung and other organs [1, 2]. In recent decades, the incidence and mortality of TB have gradually decreased, but it has become one of the top ten causes of death globally and poses a serious threat to public health [3–5]. In TB cases, the respiratory system acts as the primary entry point and is frequently affected during active TB infections [6]. Besides, emerging research has found that there may be a mutual causal relationship between TB and several diseases, including lung cancer, hypertension and chronic obstructive pulmonary disease (COPD) [7–9]. Among the diseases that may have mutual affect with TB, COPD is a common and burdensome disease globally that shares some common risk factors with TB [10–12]. In a study, Inghammar et al. found that the coexistence and interaction of COPD and TB are increasing the burden of disease and the risk of patients’ incidence rate [9]. Although there is increasing evidence that both COPD and TB cause mechanical damage to the lung and are related to each other, evidence for the relationship between them is based only on observational studies in humans [9, 13–15]. Consequently, it is still unclear whether there is a causal relationship between COPD and TB, especially COPD and respiratory TB. Mendelian randomization (MR), conceptually similar to randomized controlled trials (RCTs) and increasingly used to strengthen causal evidence in observational studies, is based on the principle of randomly assigning genetic variants to meiotic chromosomes, making these genetic variants independent of confounding factors in observational studies, thereby enabling the inference of causal relationships between exposures and outcomes [16–18]. In addition, the MR-based Bayesian weighted method, called Bayesian weighted Mendelian randomization (BWMR), is considered robust in terms of Type I error control and statistical power, and outperforms other MR methods in terms of estimation accuracy [19]. Therefore, the present study aimed to assess the causal relationship between COPD and respiratory TB by conducting this two-sample BWMR study. Methods Study design In two-sample MR analysis, single nucleotide polymorphisms (SNPs) used as instrumental variables (IVs) should meet three core assumptions [20]. Therefore, this two-sample BWMR analysis also requires IVs that satisfy the following three core assumptions. (1) Relevance assumption: IVs should be strongly associated with exposure. (2) Independence assumption: IVs should be independent of confounding factors in the association between exposure and outcome. (3) Exclusion restriction: IVs should affect the outcome only through exposure and not through other pathways. In present study, aimed to assess the causal effect of COPD on respiratory TB, COPD was considered as the exposure, respiratory TB was the outcome, and SNPs significantly associated with COPD were used as IVs. The study design is shown in Fig. 1 . Data source The genetic IVs for COPD and respiratory TB were obtained from the IEU Open GWAS project in 2021. The summary statistics of COPD GWAS and respiratory TB GWAS are available on IEU Open GWAS project at https://gwas.mrcieu.ac.uk/datasets/finn-b-J10_COPD and https://gwas.mrcieu.ac.uk/datasets/finn-b-AB1_RESP_TUBERCU respectively. The summary information about the COPD GWAS and respiratory TB GWAS are shown in Table 1 . Table 1 Genome-wide association study (GWAS) for COPD and respiratory TB GWAS ID Year Trait Sample size N case N control N SNPs Population Sex finn-b-J10_COPD 2021 COPD 193,638 6,915 186,723 16,380,382 European Males and females finn-b-AB1_RESP_TUBERCU 2021 Respiratory TB 218,448 849 217,599 16,380,466 European Males and Females Abbreviations: GWAS ID, Genome wide association study identified; COPD, chronic obstructive pulmonary disease; N case, the number of cases; N control, the number of the controls; N SNPs, the number of single‐nucleotide polymorphisms. IVs selection To meet the three core assumptions of MR, we selected IVs from the COPD GWAS summary statistics using several criteria: (1) SNPs displaying significant correlations with COPD (genome-wide significance threshold <5×10-6); (2) Without linkage disequilibrium (r 2 10,000 kb) (3) without effects on other potential risk factors. R 2 is the proportion of COPD variance explained for each independent SNP and estimated based on beta, standard error and sample size [21]. To avoid bias caused by weak IVs, we used the F statistic to assess the strength of the relationship between IVs and the phenotype. The formula for the its calculation is as follows: F= (R 2 /K) / ([1−R 2 ] /[N−K−1]), where R 2 is the proportion of COPD variance, K is the number of instruments used model and N is the sample size [22]. We ensured that there was no weak instrumental bias when the F statistic for each selected IV was >10 [23]. After genetic IVs were selected, we extracted independent COPD genetic IVs from respiratory TB GWAS for MR analysis. BWMR analysis We selected IVW as the main MR analysis method and supplemented the weighted median method. In addition, we also selected Bayesian weighted Mendelian randomization (BWMR) as a robust method to infer the causal effect of COPD on respiratory TB. A P value < 0.05 was considered statistically significant and represented the causal relationship between COPD and respiratory TB. The R packages “Two Sample MR” and “BWMR” were used to perform all statistical tests of the MR analysis. Sensitivity analysis To test for horizontal pleiotropy, we used MR‐Egger intercept and MR‐pleiotropy residual sum and outlier (MR-PRESSO) global test methods [24]. If the P value is > 0.05 and the MR Egger intercept term tends to zero as the sample size increases, this indicates the absence of horizontal pleiotropy [25]. Besides, we used MR-Egger and inverse variance weighted (IVW) in Cochran's Q statistic to test the heterogeneity of genetic IVs and provide evidence of heterogeneity due to pleiotropy or other causes [26]. Finally, we used a “leave‐one‐out” sensitivity analysis, in which each SNP was excluded in turn and conducted IVW on the remaining SNPs to identify the potential influence of outlying and pleiotropic SNPs on causal estimation [27, 28]. The R package “Two Sample MR” was used to perform the statistical tests. Reverse BWMR analysis To demonstrate whether COPD is causally affected by respiratory TB, we performed this reverse BWMR analysis and selected genetic IVs from the respiratory TB GWAS summary statistics using criteria same as COPD-associated IVs selection. Then all selected respiratory TB genetic IVs were extracted from the COPD GWAS and used to perform this reverse BWMR analysis. The reverse BWMR analysis was performed using IVW as the main MR analysis method and supplemented the weighted median and BWMR methods. To test for horizontal pleiotropy, we used the MR-Egger intercept and MR-PRESSO methods to assess the pleiotropy of independent respiratory TB genetic IVs. Besides, we used the MR-Egger and inverse variance weighted (IVW) in Cochran's Q statistic to assess the heterogeneity of independent respiratory TB genetic IVs. Finally, we used a “leave‐one‐out” sensitivity analysis to identify the potential influence of outlying and pleiotropic SNPs on causal estimation. Results COPD genetic IVs selection Based on the above selection criteria for genetic IVs, 29 SNPs were identified to be significantly associated with COPD and the F-statistics for all selected genetic IVs were almost greater than 10, indicating no evidence of weak instrumental bias (Supplementary Information: Table 1). All 29 independent COPD genetic IVs were successfully extracted from respiratory TB GWAS, and used to perform MR analysis（Supplementary Information: Table 2）. COPD increases the risk of respiratory TB In our BWMR analysis, we found that patients with COPD had a higher risk of respiratory TB based on the IVW (OR = 1.259, 95% CI for OR: 1.011–1.568; p = 0.040), weighted median (OR = 1.287, 95% CI for OR: 0.981–1.688; p =0 .069) and BWMR (OR = 1.240, 95% CI for OR: 1.013–1.518; p = 0.037) (Figure 2). Moreover, the individual MR estimates demonstrated that as the effect of a single SNP on COPD increased, the promoting effect of a single SNP on respiratory TB increased, as determined using two sample BWMR analysis methods including IVW, weighted median and BWMR (Figure 3). As shown in the forest plot, effect size analysis suggested that each effect of COPD SNPs on respiratory TB were similar (Figure 4). Based on the above facts, our BWMR analysis indicated an increased risk of respiratory TB in patients with COPD. Sensitivity analysis Both MR-Egger intercept and MR-PRESSO global test methods showed on significant pleiotropy of COPD genetic IVs in respiratory TB GWAS (Table 2). And both MR-Egger and inverse variance weighted (IVW) in Cochran's Q statistic also showed no significant heterogeneity of COPD genetic IVs in respiratory TB GWAS (Table 3). MR leave‐one‐out sensitivity analysis showed that removing SNP of COPD in turn did not change the results and the single COPD SNP effect on respiratory TB did not have an obvious bias (Figure 5). In addition, the symmetry of the funnel plot also confirmed the absence of heterogeneity (Figure 6). Therefore, all selected COPD genetic IVs could be considered as effective IVs and support an increased risk of respiratory TB in patients with COPD. Table 2 Pleiotropy test of COPD and respiratory TB genetic instrumental variants (IVs) Pleiotropy MR-Egger MR-PRESSO global test p-value intercept SE p-value COPD genetic IVs 0.006 0.039 0.884 0.903 Respiratory TB genetic IVs -0.007 0.023 0.758 0.462 Abbreviations: SE, standard error; MR-PRESSO global test, MR-pleiotropy residual sum and outlier global test; COPD, chronic obstructive pulmonary disease. Table 3 Heterogeneity test of COPD and respiratory TB genetic instrumental variants (IVs) Heterogeneity MR-Egger IVW Q value p-value Q value p-value COPD genetic IVs 40.330 0.048 40.362 0.061 Respiratory TB genetic IVs 7.798 0.351 7.913 0.442 Abbreviations: COPD, chronic obstructive pulmonary disease; IVs, instrumental variants; IVW, inverse variance weighted; Q value, Cochran's Q statistic value. Respiratory TB has no causal effect on COPD Based on the above selection criteria for genetic IVs, 10 SNPs significantly associated with respiratory tuberculosis without weak instrumental bias were identified (Supplementary Information: Table 3). All selected respiratory TB genetic IVs were successfully extracted from the COPD GWAS (Supplementary Information: Table 4), and used to perform this reverse BWMR analysis. The reverse BWMR analysis showed that respiratory TB has no causal effect on COPD using IVW as the main MR analysis method and supplementing the weighted median and BWMR methods. The summary result of the reverse MR analysis is shown in Figure 2.Both MR-Egger intercept and MR-PRESSO global test methods showed on significant pleiotropy of respiratory TB genetic IVs in COPD GWAS (Table 2). And both MR-Egger and inverse variance weighted (IVW) in Cochran's Q statistic also showed no significant heterogeneity of respiratory TB genetic IVs in COPD GWAS (Table 3). Therefore, all selected respiratory TB genetic IVs could be considered as the effective IVs and support the result that respiratory TB has no causal effect on COPD. Discussion Previous observational studies have shown a strong association between COPD and TB, suggesting that both may be risk factors for each other [9, 13, 29, 30]. Furthermore, inflammation and damage to the respiratory system caused by COPD can promote the infection of TB, and respiratory infections and inflammation may also play a role in the development and progression of COPD [31] [32]. However, our current two sample BWMR analysis suggests that COPD increases the risk of respiratory TB and respiratory TB does not have a causal effect on COPD. A 2010 study using data from 115,867 elderly hospitalized patients in Sweden during 1987–2003 found that the relative risk of developing active tuberculosis was 3.14 times higher in patients with COPD than in controls (HR = 3.14, 95% CI for HR: 2.42-4.08) and suggested that COPD may be associated with the development of active TB [9] . While the above and other studies have shown that patients with COPD are at a higher risk of developing TB, the causal relationship between COPD and the development of active TB is still unclear [33]. And some hypotheses suggested that the increased risk of developing TB may be due to impaired cellular immunity and macrophage function associated with smoking in patients with COPD, because of smoking was well known as an important risk factor for both COPD and TB [14, 34]. Whatever the mechanism, it was obvious that people with COPD may be at increased risk of developing TB, and further research needs to be done to confirm this association. Therefore, we planned to demonstrate the causal effect of COPD on respiratory TB by using BWMR study. We used the largest publicly available COPD GWAS consisting of 193,638 participants (6915 cases and 186,723 controls) to perform the two sample BWMR analysis, and successfully extracted 29 independent genetic IVs from COPD GWAS summary statistics. Subsequently, we identified a causal effect of COPD on respiratory TB using the IVW (OR = 1.259, 95% CI for OR: 1.011–1.568; p = 0.040), weighted median (OR = 1.287, 95% CI for OR: 0.981–1.688; p =0 .069) and BWMR (OR = 1.240, 95% CI for OR: 1.013–1.518; p = 0.037) (Figure 2). Thus, our findings provided conclusive evidence of an increased risk of respiratory TB in patients with COPD. In a study, which was designed to investigate the impact of history of TB on the long-term course of COPD, revealed that patients with history of TB were diagnosed with COPD four years earlier and died five years earlier comparing with the patients without TB, and conclude that history of TB has an important role in the natural course of COPD [15]. In another study, Katarina et al. found that previous tuberculosis disease is a significant risk factor for COPD and poor lung function in low and middle-income countries (LMICs) [35]. While the above studies have shown that history of TB has an important role in the natural course of COPD, the causal relationship between TB and the course of COPD is still unclear. Therefore, we used reverse BWMR analysis to demonstrate whether COPD is causally affected by respiratory TB. However, our reverse BWMR analysis showed that respiratory TB has no causal effect on COPD (Figure 2). To ensure the robustness of our findings, we performed several sensitivity analyses, including the MR-PRESSO global test, the MR-Egger intercept test and Cochran's Q test [24, 26]. Both the MR-PRESSO global test and the MR-Egger intercept test provided evidence for the absence of pleiotropy of COPD genetic IVs and respiratory TB IVs (Table 2). The results of the Cochran's Q test showed no heterogeneity of COPD genetic IVs and respiratory TB IVs (Table 3). Meanwhile, leave-one-out analysis and funnel plots showed that the estimates were not biased by any single SNP, further proving the robustness of our results (Figure 5 and Figure 6). Therefore, all selected genetic IVs could be considered as the effective IVs and our results provided conclusive evidence of an increased risk of respiratory TB in patients with COPD and respiratory TB having no causal effect on COPD. This MR study has the following advantages compared with previous studies. This is the first MR study that clarified the causal relationship between COPD and respiratory TB. The innovation of this study is the BWMR design, which is considered to be robust in type I error control and statistical power, and outperforms other MR methods in terms of estimation accuracy [19]. Second, the MR study minimized the influence of reverse causality and bias from confounding factors [36]. Third, genetic variants proved to be effective genetic IVs for identifying the causal effect between COPD and respiratory TB. Fourth, all F statistics > 10 indicated that there is no weak IVs bias in this MR study. Fifth, four independent statistical methods demonstrated no significant pleiotropy or heterogeneity of COPD genetic IVs and respiratory TB IVs. Sixth, the populations of both GWAS datasets are European ancestry, which effectively avoids racial differences bias. Seventh, two traditional MR analytical methods and BWMR prove a causal effect of COPD on respiratory TB. Finally, reverse BWMR analysis using two traditional MR analytical methods and BWMR method demonstrated that respiratory TB has no causal effect on COPD. Our study also has some limitations. First, the causal relationship between COPD and respiratory TB was demonstrated by this two sample BWMR analysis, but the mechanism of bidirectional influence between them is still unclear. Second, this MR study only clarified the causal relationship between COPD and respiratory TB, and further MR studies need to focus on the causal relationship between COPD and other tuberculosis types. Third, the GWAS datasets for COPD and respiratory TB are based on European ancestry. Therefore, our findings need to be confirmed in other ancestries. Fourth, background factors such as age and duration of disease could not be obtained and analyzed as stratification factors. Therefore, studies in different populations and ethnic groups with large sample sizes and different stratification factors are needed to further substantiate the present findings. Finally, previous observational studies mainly focus on the association between COPD and TB, and more RCTs are needed to research the association between COPD and respiratory TB. Conclusions This is the first BWMR analysis that revealed a causal relationship between COPD and respiratory TB in the European population. The study provides convincing evidence that individuals with COPD are at a higher risk of respiratory TB at the genetic level, while respiratory TB has no causal effect on COPD. Abbreviations SNPs: single-nucleotide polymorphisms; IVs, instrumental variants; COPD: chronic obstructive pulmonary disease; GWAS: genome wide association study; GWAS ID: Genome wide association study identity; TB, tuberculosis; EA, effect allele; OA, other allele; EAF, effect allele frequency; SE, standard error; MR-PRESSO global test, MR‐pleiotropy residual sum and outlier global test; Q value, Cochran's Q statistic value. BWMR, Bayesian weighted Mendelian randomization. IVW, inverse variance weighted; WM, weighted median; BWMR, Bayesian weighted Mendelian randomization. Declarations Ethics approval and consent to participate The data used in this study were obtained from publicly available sources, which have been approved by the corresponding ethical review board. Therefore, ethical approval was not required. Consent for publication Not applicable. Availability of data and materials The datasets analyzed in the current study are available in IEU Open GWAS project at https://gwas.mrcieu.ac.uk/datasets/finn-b-J10_COPD and https://gwas.mrcieu.ac.uk/datasets/finn-b-AB1_RESP_TUBERCU . R packages for MR analysis can be found at https://github.com/MRCIEU/TwoSampleMR and https://github.com/jiazao97/BWMR . Conflict of interest The authors declare that they have no conflict of interest. Funding This study was supported by the National Natural Science Foundation of China (Grant no: 82060622). Authors’ contributions Study concept and design: AA and MQC. Data collection and statistical analysis: AA, YZ and XMW. Figures and supplementary material: AA, BFW and JAW. Writing-original draft: AA. Writing—review and editing: YZ and MQC. All authors contributed to the interpretation of the results and critical revision of the manuscript, and approved it for publication. Acknowledgments We thank the IEU Open GWAS project (https://gwas.mrcieu.ac.uk/datasets/ ) for providing summary results data for these analyzes and the Figdraw 2.0 online platform (https://www.figdraw.com/ ) for providing drawing materials and tools for flowchart. Contributor Information Abulikemu Aili, Email: [email protected] Mingqin Cao, Email: [email protected] Yan Zhang, Email: [email protected] Xiaomin Wang, Email: [email protected] Baofeng Wen, Email: [email protected] Junan Wang, Email: [email protected] References Wang W, Cai Y, Deng G, Yang Q, Tang P, Wu M, Yu Z, Yang F, Chen J, Werz O et al : Allelic-Specific Regulation of xCT Expression Increases Susceptibility to Tuberculosis by Modulating microRNA-mRNA Interactions. mSphere 2020, 5(2):e00263-00220.https://doi.org/10.1128/mSphere.00263-20. 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Thorax 2022, 77(11):1088–1097.https://doi.org/10.1136/thoraxjnl-2020-216500. Nitsch D, Molokhia M, Smeeth L, DeStavola BL, Whittaker JC, Leon DA: Limits to causal inference based on Mendelian randomization: a comparison with randomized controlled trials. Am J Epidemiol 2006, 163(5):397–403.https://doi.org/10.1093/aje/kwj062. Additional Declarations No competing interests reported. Supplementary Files SupplementaryInformation.pdf Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4176361","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":286443183,"identity":"c0d5a4d2-83f3-4b09-9a25-9519c2bb619f","order_by":0,"name":"Abulikemu Aili","email":"","orcid":"","institution":"Xinjiang Medical University, Xinjiang Uygur Autonomous Region","correspondingAuthor":false,"prefix":"","firstName":"Abulikemu","middleName":"","lastName":"Aili","suffix":""},{"id":286443184,"identity":"221689ba-744e-465d-a37a-ff84c4f8c19d","order_by":1,"name":"Yan Zhang","email":"","orcid":"","institution":"Xinjiang Medical University, Xinjiang Uygur Autonomous Region","correspondingAuthor":false,"prefix":"","firstName":"Yan","middleName":"","lastName":"Zhang","suffix":""},{"id":286443185,"identity":"46a1f898-dde6-477c-875e-f14f5d22d817","order_by":2,"name":"Xiaomin Wang","email":"","orcid":"","institution":"Xinjiang Medical University, Xinjiang Uygur Autonomous Region","correspondingAuthor":false,"prefix":"","firstName":"Xiaomin","middleName":"","lastName":"Wang","suffix":""},{"id":286443186,"identity":"d5c2ab87-9c1c-4d0d-a92e-2830f02e0cd4","order_by":3,"name":"Baofeng Wen","email":"","orcid":"","institution":"Xinjiang Medical University, Xinjiang Uygur Autonomous Region","correspondingAuthor":false,"prefix":"","firstName":"Baofeng","middleName":"","lastName":"Wen","suffix":""},{"id":286443187,"identity":"512fc66c-0d30-48d8-971c-363f8f6f9217","order_by":4,"name":"Junan Wang","email":"","orcid":"","institution":"Xinjiang Medical University, Xinjiang Uygur Autonomous Region","correspondingAuthor":false,"prefix":"","firstName":"Junan","middleName":"","lastName":"Wang","suffix":""},{"id":286443188,"identity":"76a4d848-9298-4aab-9d33-b6c972d6028b","order_by":5,"name":"Mingqin Cao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAo0lEQVRIiWNgGAWjYHACxgcfKiTk+EnRwmw444yFsWQDCVrYhDnbKhI3EK2Ff9rhbcyM8yQYNzAwP3x0gxgtErfTyh4XbpNgNmdgMzbOIUaLgXSOufHMbRJslg08bNLEajGT5p0jwWNwgDQtDRISxGsB+qXYcMYxCQPJZmL9wj87eeODDzV19f3szQ8fE6UF5DYIxUykciQto2AUjIJRMApwAQCcbStco3z61QAAAABJRU5ErkJggg==","orcid":"","institution":"Xinjiang Medical University, Xinjiang Uygur Autonomous Region","correspondingAuthor":true,"prefix":"","firstName":"Mingqin","middleName":"","lastName":"Cao","suffix":""}],"badges":[],"createdAt":"2024-03-27 13:17:27","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4176361/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4176361/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":54037280,"identity":"24fd5c7f-be30-4358-8d10-46c5eec05db7","added_by":"auto","created_at":"2024-04-03 17:11:01","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":128712,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of three core assumptions for Mendelian randomization. (1) Relevance assumption: IVs should be strongly associated with exposure. (2) Independence assumption: IVs should be independent of confounding factors in the association between exposure and outcome. (3) Exclusion restriction: IVs should affect the outcome only through exposure and not through other pathways. Abbreviations: SNPs, single-nucleotide polymorphisms; COPD, chronic obstructive pulmonary disease.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4176361/v1/5def8b5fb358b55d6b43e113.png"},{"id":54037318,"identity":"911d3483-a280-49d1-9926-e57e443ce508","added_by":"auto","created_at":"2024-04-03 17:11:04","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":24195,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot for the effect of COPD on respiratory TB and the effect of respiratory TB on COPD. Abbreviations: COPD, chronic obstructive pulmonary disease; IVW, inverse variance weighted; WM, weighted median; BWMR, Bayesian weighted Mendelian randomization.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4176361/v1/31101d089fcc7e4afe729714.png"},{"id":54037288,"identity":"d05d1912-f74f-429c-8352-ca75d933cc2f","added_by":"auto","created_at":"2024-04-03 17:11:04","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":30698,"visible":true,"origin":"","legend":"\u003cp\u003eScatter plot for the effect of COPD on respiratory TB. The black dots represent instrumental variables. The X-axis represents the effect of SNPs on exposure (COPD). The Y-axis represents the effect of SNPs on the outcome (respiratory TB). Colored lines represent the results of MR analysis based on BWMR, Weighted median, IVW methods. Abbreviations: COPD, chronic obstructive pulmonary disease; SNP, single-nucleotide polymorphism; IVW, inverse variance weighted; BWMR, Bayesian weighted Mendelian randomization.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-4176361/v1/cc541e30d67c8fb22ebc3c51.png"},{"id":54037317,"identity":"ff0420d0-f5a2-436a-bdd0-a01daaa74e0d","added_by":"auto","created_at":"2024-04-03 17:11:04","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":36429,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot for the effect of all selected COPD‐associated SNPs on respiratory TB risk. The black line represents the effect produced by a single SNP and the red line shows the causal estimate using all IVs. If the solid line lies completely to the left of 0, the result estimated by this SNP is that COPD can reduce the risk of respiratory TB. If the solid line lies completely to the right of 0, the result estimated by this SNP is that COPD may increase the risk of respiratory TB. The result is not significant if the solid line crosses 0. Abbreviations: COPD, chronic obstructive pulmonary disease; SNP, single-nucleotide polymorphism; IVs, instrumental variants.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-4176361/v1/4413d2de13239847bfd2648a.png"},{"id":54037287,"identity":"5156abfe-b3a8-4efe-b62c-49f748c508fc","added_by":"auto","created_at":"2024-04-03 17:11:03","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":46283,"visible":true,"origin":"","legend":"\u003cp\u003eMR leave‐one‐out sensitivity analysis for the effect of all selected COPD‐associated SNPs on respiratory TB. The position of the red dot is greater than zero. The black dots are on the right side of the invalid line. This indicates that removing any of the SNPs does not have a significant impact on the results. Abbreviations: COPD, chronic obstructive pulmonary disease; SNP, single-nucleotide polymorphism.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-4176361/v1/67f2a3044b043ee4d8c721c3.png"},{"id":54037245,"identity":"e59eb3d2-cbe4-4ff5-b053-75208e9ad1be","added_by":"auto","created_at":"2024-04-03 17:10:58","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":29495,"visible":true,"origin":"","legend":"\u003cp\u003eFunnel plot for the overall heterogeneity test of the effect of COPD on respiratory TB. Black dots represent SNPs and the distribution of dots is symmetrical to the inverse variance weighted and MR-Egger line.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-4176361/v1/989d4eb66e24d706891ff056.png"},{"id":77326195,"identity":"ebeb550c-3aec-4e93-960f-dea844cf4e6b","added_by":"auto","created_at":"2025-02-27 12:32:28","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":993260,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4176361/v1/e5315687-27e2-4a48-8b76-dbe4b1cafe87.pdf"},{"id":54037289,"identity":"a7d53072-8510-4f9b-9139-78d7ae2e6648","added_by":"auto","created_at":"2024-04-03 17:11:04","extension":"pdf","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":230891,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryInformation.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4176361/v1/aed93fbbcb81482266f0c45d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Causal relationship between chronic obstructive pulmonary disease and respiratory tuberculosis susceptibility: A two-sample Bayesian weighted Mendelian randomization study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eTuberculosis (TB) is a chronic infectious disease caused by infection with Mycobacterium tuberculosis (MTB) and mainly affects the lung and other organs [1, 2]. In recent decades, the incidence and mortality of TB have gradually decreased, but it has become one of the top ten causes of death globally and poses a serious threat to public health [3\u0026ndash;5].\u003c/p\u003e \u003cp\u003eIn TB cases, the respiratory system acts as the primary entry point and is frequently affected during active TB infections [6]. Besides, emerging research has found that there may be a mutual causal relationship between TB and several diseases, including lung cancer, hypertension and chronic obstructive pulmonary disease (COPD) [7\u0026ndash;9]. Among the diseases that may have mutual affect with TB, COPD is a common and burdensome disease globally that shares some common risk factors with TB [10\u0026ndash;12]. In a study, Inghammar et al. found that the coexistence and interaction of COPD and TB are increasing the burden of disease and the risk of patients\u0026rsquo; incidence rate [9]. Although there is increasing evidence that both COPD and TB cause mechanical damage to the lung and are related to each other, evidence for the relationship between them is based only on observational studies in humans [9, 13\u0026ndash;15]. Consequently, it is still unclear whether there is a causal relationship between COPD and TB, especially COPD and respiratory TB.\u003c/p\u003e \u003cp\u003eMendelian randomization (MR), conceptually similar to randomized controlled trials (RCTs) and increasingly used to strengthen causal evidence in observational studies, is based on the principle of randomly assigning genetic variants to meiotic chromosomes, making these genetic variants independent of confounding factors in observational studies, thereby enabling the inference of causal relationships between exposures and outcomes [16\u0026ndash;18]. In addition, the MR-based Bayesian weighted method, called Bayesian weighted Mendelian randomization (BWMR), is considered robust in terms of Type I error control and statistical power, and outperforms other MR methods in terms of estimation accuracy [19]. Therefore, the present study aimed to assess the causal relationship between COPD and respiratory TB by conducting this two-sample BWMR study.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design\u003c/h2\u003e \u003cp\u003eIn two-sample MR analysis, single nucleotide polymorphisms (SNPs) used as instrumental variables (IVs) should meet three core assumptions [20]. Therefore, this two-sample BWMR analysis also requires IVs that satisfy the following three core assumptions. (1) Relevance assumption: IVs should be strongly associated with exposure. (2) Independence assumption: IVs should be independent of confounding factors in the association between exposure and outcome. (3) Exclusion restriction: IVs should affect the outcome only through exposure and not through other pathways. In present study, aimed to assess the causal effect of COPD on respiratory TB, COPD was considered as the exposure, respiratory TB was the outcome, and SNPs significantly associated with COPD were used as IVs. The study design is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eData source\u003c/h2\u003e \u003cp\u003eThe genetic IVs for COPD and respiratory TB were obtained from the IEU Open GWAS project in 2021. The summary statistics of COPD GWAS and respiratory TB GWAS are available on IEU Open GWAS project at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://gwas.mrcieu.ac.uk/datasets/finn-b-J10_COPD\u003c/span\u003e\u003cspan address=\"https://gwas.mrcieu.ac.uk/datasets/finn-b-J10_COPD\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://gwas.mrcieu.ac.uk/datasets/finn-b-AB1_RESP_TUBERCU\u003c/span\u003e\u003cspan address=\"https://gwas.mrcieu.ac.uk/datasets/finn-b-AB1_RESP_TUBERCU\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e respectively. The summary information about the COPD GWAS and respiratory TB GWAS are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGenome-wide association study (GWAS) for COPD and respiratory TB\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGWAS ID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYear\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTrait\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSample size\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eN case\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eN control\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eN SNPs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003ePopulation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003efinn-b-J10_COPD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCOPD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e193,638\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6,915\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e186,723\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e16,380,382\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eMales and females\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003efinn-b-AB1_RESP_TUBERCU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRespiratory TB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e218,448\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e849\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e217,599\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e16,380,466\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eMales and Females\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003cp\u003eAbbreviations: GWAS ID, Genome wide association study identified; COPD,\u0026nbsp;chronic obstructive pulmonary disease;\u0026nbsp;N case, the number of cases; N control, the number of the controls; N SNPs, the number of single‐nucleotide polymorphisms.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIVs selection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo meet the three core assumptions of MR, we selected IVs from the COPD GWAS summary statistics using several criteria: (1) SNPs displaying significant correlations with COPD (genome-wide significance threshold \u0026lt;5\u0026times;10-6); (2) Without linkage disequilibrium (r\u003csup\u003e2\u003c/sup\u003e \u0026lt; 0.001 and clumping distance \u0026gt; 10,000 kb) (3) without effects on other potential risk factors. R\u003csup\u003e2\u003c/sup\u003e is the proportion of COPD variance explained for each independent SNP and estimated based on beta, standard error and sample size [21]. To avoid bias caused by weak IVs, we used the F statistic to assess the strength of the relationship between IVs and the phenotype. The formula for the its calculation is as follows: F= (R\u003csup\u003e2\u003c/sup\u003e/K) / ([1\u0026minus;R\u003csup\u003e2\u003c/sup\u003e] /[N\u0026minus;K\u0026minus;1]), where R\u003csup\u003e2\u003c/sup\u003e is the proportion of COPD variance, K is the number of instruments used model and N is the sample size [22]. We ensured that there was no weak instrumental bias when the F statistic for each selected IV was \u0026gt;10 [23]. After genetic IVs were selected, we extracted independent COPD genetic IVs from respiratory TB GWAS for MR analysis.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBWMR analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe selected IVW as the main MR analysis method and supplemented the weighted median method. In addition, we also selected Bayesian weighted Mendelian randomization (BWMR) as a robust method to infer the causal effect of COPD on respiratory TB.\u0026nbsp;A P value \u0026lt; 0.05 was considered statistically significant and represented the causal relationship between COPD and respiratory TB. The R packages \u0026ldquo;Two Sample MR\u0026rdquo; and \u0026ldquo;BWMR\u0026rdquo; were used to perform all statistical tests of the MR analysis.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSensitivity analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo test for horizontal pleiotropy, we used MR‐Egger intercept and MR‐pleiotropy residual sum and outlier (MR-PRESSO) global test methods [24]. If the P value is \u0026gt; 0.05 and the MR Egger intercept term tends to zero as the sample size increases, this indicates the absence of horizontal pleiotropy [25]. Besides, we used MR-Egger and inverse variance weighted (IVW) in Cochran\u0026apos;s Q statistic to test the heterogeneity of genetic IVs and provide evidence of heterogeneity due to pleiotropy or other causes [26]. Finally, we used a \u0026ldquo;leave‐one‐out\u0026rdquo; sensitivity analysis, in which each SNP was excluded in turn and conducted IVW on the remaining SNPs to identify the potential influence of outlying and pleiotropic SNPs on causal estimation [27, 28]. The R package \u0026ldquo;Two Sample MR\u0026rdquo; was used to perform the statistical tests.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eReverse BWMR analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo demonstrate whether COPD is causally affected by respiratory TB, we performed this reverse BWMR analysis and selected genetic IVs from the respiratory TB GWAS summary statistics using criteria same as COPD-associated IVs selection. Then all selected respiratory TB genetic IVs were extracted from the COPD GWAS and used to perform this reverse BWMR analysis.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe reverse BWMR analysis was performed using IVW as the main MR analysis method and supplemented the weighted median and BWMR methods. To test for horizontal pleiotropy, we used the MR-Egger intercept and MR-PRESSO methods to assess the pleiotropy of independent respiratory TB genetic IVs. Besides, we used the MR-Egger and inverse variance weighted (IVW) in Cochran\u0026apos;s Q statistic to assess the heterogeneity of independent respiratory TB genetic IVs. Finally, we used a \u0026ldquo;leave‐one‐out\u0026rdquo; sensitivity analysis to identify the potential influence of outlying and pleiotropic SNPs on causal estimation.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eCOPD genetic IVs selection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBased on the above selection criteria for genetic IVs, 29 SNPs were identified to be significantly associated with COPD and the F-statistics for all selected genetic IVs were almost greater than 10, indicating no evidence of weak instrumental bias (Supplementary Information: Table 1). All 29 independent COPD genetic IVs were successfully extracted from respiratory TB GWAS, and used to perform MR analysis（Supplementary Information: Table 2）.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCOPD increases the risk of respiratory TB\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn our BWMR analysis, we found that patients with COPD had a higher risk of respiratory TB based on the IVW (OR = 1.259, 95% CI for OR: 1.011\u0026ndash;1.568; p = 0.040), weighted median (OR = 1.287, 95% CI for OR: 0.981\u0026ndash;1.688; p =0 .069) and BWMR (OR = 1.240, 95% CI for OR: 1.013\u0026ndash;1.518; p = 0.037) (Figure 2). Moreover, the individual MR estimates demonstrated that as the effect of a single SNP on COPD increased, the promoting effect of a single SNP on respiratory TB increased, as determined using two sample BWMR analysis methods including IVW, weighted median and BWMR (Figure 3). As shown in the forest plot, effect size analysis suggested that each effect of COPD SNPs on respiratory TB were similar (Figure 4). Based on the above facts, our BWMR analysis indicated an increased risk of respiratory TB in patients with COPD.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSensitivity analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBoth MR-Egger intercept and MR-PRESSO global test methods showed on significant pleiotropy of COPD genetic IVs in respiratory TB GWAS (Table 2). And both MR-Egger and inverse variance weighted (IVW) in Cochran\u0026apos;s Q statistic also showed no significant heterogeneity of COPD genetic IVs in respiratory TB GWAS (Table 3). MR leave‐one‐out sensitivity analysis showed that removing SNP of COPD in turn did not change the results and the single COPD SNP effect on respiratory TB did not have an obvious bias (Figure 5). In addition, the symmetry of the funnel plot also confirmed the absence of heterogeneity (Figure 6). Therefore, all selected COPD genetic IVs could be considered as effective IVs and support an increased risk of respiratory TB in patients with COPD.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"char\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cdiv class=\"SimplePara\"\u003ePleiotropy test of COPD and respiratory TB genetic instrumental variants (IVs)\u003c/div\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth rowspan=\"2\" align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003ePleiotropy\u003c/div\u003e\n \u003c/th\u003e\n \u003cth colspan=\"3\" align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eMR-Egger\u003c/div\u003e\n \u003c/th\u003e\n \u003cth rowspan=\"2\" align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eMR-PRESSO global test p-value\u003c/div\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eintercept\u003c/div\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eSE\u003c/div\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003ep-value\u003c/div\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eCOPD genetic IVs\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0.006\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0.039\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0.884\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0.903\u003c/div\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eRespiratory TB genetic IVs\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e-0.007\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0.023\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0.758\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0.462\u003c/div\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003eAbbreviations: SE, standard error; MR-PRESSO global test, MR-pleiotropy residual sum and outlier global test; COPD, chronic obstructive pulmonary disease.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"char\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eHeterogeneity test of COPD and respiratory TB genetic instrumental variants (IVs)\u003c/div\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth rowspan=\"2\" align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eHeterogeneity\u003c/div\u003e\n \u003c/th\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eMR-Egger\u003c/div\u003e\n \u003c/th\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eIVW\u003c/div\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eQ value\u003c/div\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003ep-value\u003c/div\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eQ value\u003c/div\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003ep-value\u003c/div\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eCOPD genetic IVs\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e40.330\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0.048\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e40.362\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0.061\u003c/div\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eRespiratory TB genetic IVs\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e7.798\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0.351\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e7.913\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0.442\u003c/div\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003eAbbreviations: COPD, chronic obstructive pulmonary disease; IVs, instrumental variants; IVW, inverse variance weighted; Q value, Cochran\u0026apos;s Q statistic value.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRespiratory TB has no causal effect on COPD\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBased on the above selection criteria for genetic IVs, 10 SNPs significantly associated with respiratory tuberculosis without weak instrumental bias were identified (Supplementary Information: Table 3). All selected respiratory TB genetic IVs were successfully extracted from the COPD GWAS (Supplementary Information: Table 4), and used to perform this reverse BWMR analysis.\u003c/p\u003e\n\u003cp\u003eThe reverse BWMR analysis showed that respiratory TB has no causal effect on COPD using IVW as the main MR analysis method and supplementing the weighted median and BWMR methods. The summary result of the reverse MR analysis is shown in Figure 2.Both MR-Egger intercept and MR-PRESSO global test methods showed on significant pleiotropy of respiratory TB genetic IVs in COPD GWAS (Table 2). And both MR-Egger and inverse variance weighted (IVW) in Cochran\u0026apos;s Q statistic also showed no significant heterogeneity of respiratory TB genetic IVs in COPD GWAS (Table 3). Therefore, all selected respiratory TB genetic IVs could be considered as the effective IVs and support the result that respiratory TB has no causal effect on COPD.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003ePrevious observational studies have shown a strong association between COPD and TB, suggesting that both may be risk factors for each other [9, 13, 29, 30]. Furthermore, inflammation and damage to the respiratory system caused by COPD can promote the infection of TB, and respiratory infections and inflammation may also play a role in the development and progression of COPD [31] [32]. However, our current two sample BWMR analysis suggests that COPD increases the risk of respiratory TB and respiratory TB does not have a causal effect on COPD.\u003c/p\u003e\n\u003cp\u003eA 2010 study using data from 115,867 elderly hospitalized patients in Sweden during 1987\u0026ndash;2003 found that the relative risk of developing active tuberculosis was 3.14 times higher in patients with COPD than in controls (HR = 3.14, 95% CI for HR: 2.42-4.08) and suggested that COPD may be associated with the development of active TB [9] . While the above and other studies have shown that patients with COPD are at a higher risk of developing TB, the causal relationship between COPD and the development of active TB is still unclear [33]. And some hypotheses suggested that the increased risk of developing TB may be due to impaired cellular immunity and macrophage function associated with smoking in patients with COPD, because of smoking was well known as an important risk factor for both COPD and TB [14, 34]. Whatever the mechanism, it was obvious that people with COPD may be at increased risk of developing TB, and further research needs to be done to confirm this association. Therefore, we planned to demonstrate the causal effect of COPD on respiratory TB by using BWMR study. We used the largest publicly available COPD GWAS consisting of 193,638 participants (6915 cases and 186,723 controls) to perform the two sample BWMR analysis, and successfully extracted 29 independent genetic IVs from COPD GWAS summary statistics. Subsequently, we identified a causal effect of COPD on respiratory TB using the IVW (OR = 1.259, 95% CI for OR: 1.011\u0026ndash;1.568; p = 0.040), weighted median (OR = 1.287, 95% CI for OR: 0.981\u0026ndash;1.688; p =0 .069) and BWMR (OR = 1.240, 95% CI for OR: 1.013\u0026ndash;1.518; p = 0.037) (Figure 2). Thus, our findings provided conclusive evidence of an increased risk of respiratory TB in patients with COPD.\u003c/p\u003e\n\u003cp\u003eIn a study, which was designed to investigate the impact of history of TB on the long-term course of COPD, revealed that patients with history of TB were diagnosed with COPD four years earlier and died five years earlier comparing with the patients without TB, and conclude that history of TB has an important role in the natural course of COPD [15]. In another study, Katarina et al. found that previous tuberculosis disease is a significant risk factor for COPD and poor lung function in low and middle-income countries (LMICs) [35]. While the above studies have shown that history of TB has an important role in the natural course of COPD, the causal relationship between TB and the course of COPD is still unclear. Therefore, we used reverse BWMR analysis to demonstrate whether COPD is causally affected by respiratory TB. However, our reverse BWMR analysis showed that respiratory TB has no causal effect on COPD (Figure 2).\u003c/p\u003e\n\u003cp\u003eTo ensure the robustness of our findings, we performed several sensitivity analyses, including the MR-PRESSO global test, the MR-Egger intercept test and Cochran's Q\u003c/p\u003e\n\u003cp\u003etest [24, 26]. Both the MR-PRESSO global test and the MR-Egger intercept test provided evidence for the absence of pleiotropy of COPD genetic IVs and respiratory TB IVs (Table 2). The results of the Cochran's Q test showed no heterogeneity of COPD genetic IVs and respiratory TB IVs (Table 3). Meanwhile, leave-one-out analysis and funnel plots showed that the estimates were not biased by any single SNP, further proving the robustness of our results (Figure 5 and Figure 6). Therefore, all selected genetic IVs could be considered as the effective IVs and our results provided conclusive evidence of an increased risk of respiratory TB in patients with COPD and respiratory TB having no causal effect on COPD.\u003c/p\u003e\n\u003cp\u003eThis MR study has the following advantages compared with previous studies. This is the first MR study that clarified the causal relationship between COPD and respiratory TB. The innovation of this study is the BWMR design, which is considered to be robust in type I error control and statistical power, and outperforms other MR methods in terms of estimation accuracy [19]. Second, the MR study minimized the influence of reverse causality and bias from confounding factors [36]. Third, genetic variants proved to be effective genetic IVs for identifying the causal effect between COPD and respiratory TB. Fourth, all F statistics \u0026gt; 10 indicated that there is no weak IVs bias in this MR study. Fifth, four independent statistical methods demonstrated no significant pleiotropy or heterogeneity of COPD genetic IVs and respiratory TB IVs. Sixth, the populations of both GWAS datasets are European ancestry, which effectively avoids racial differences bias. Seventh, two traditional MR analytical methods and BWMR prove a causal effect of COPD on respiratory TB. Finally, reverse BWMR analysis using two traditional MR analytical methods and BWMR method demonstrated that respiratory TB has no causal effect on COPD.\u003c/p\u003e\n\u003cp\u003eOur study also has some limitations. First, the causal relationship between COPD and respiratory TB was demonstrated by this two sample BWMR analysis, but the mechanism of bidirectional influence between them is still unclear. Second, this MR study only clarified the causal relationship between COPD and respiratory TB, and further MR studies need to focus on the causal relationship between COPD and other tuberculosis types. Third, the GWAS datasets for COPD and respiratory TB are based on European ancestry. Therefore, our findings need to be confirmed in other ancestries. Fourth, background factors such as age and duration of disease could not be obtained and analyzed as stratification factors. Therefore, studies in different populations and ethnic groups with large sample sizes and different stratification factors are needed to further substantiate the present findings. Finally, previous observational studies mainly focus on the association between COPD and TB, and more RCTs are needed to research the association between COPD and respiratory TB.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis is the first BWMR analysis that revealed a causal relationship between COPD and respiratory TB in the European population. The study provides convincing evidence that individuals with COPD are at a higher risk of respiratory TB at the genetic level, while respiratory TB has no causal effect on COPD.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eSNPs: single-nucleotide polymorphisms;\u003c/p\u003e\n\u003cp\u003eIVs, instrumental variants;\u003c/p\u003e\n\u003cp\u003eCOPD: chronic obstructive pulmonary disease;\u003c/p\u003e\n\u003cp\u003eGWAS: genome wide association study;\u003c/p\u003e\n\u003cp\u003eGWAS ID: Genome wide association study identity;\u003c/p\u003e\n\u003cp\u003eTB, tuberculosis;\u003c/p\u003e\n\u003cp\u003eEA, effect allele;\u003c/p\u003e\n\u003cp\u003eOA, other allele;\u003c/p\u003e\n\u003cp\u003eEAF, effect allele frequency;\u003c/p\u003e\n\u003cp\u003eSE, standard error;\u003c/p\u003e\n\u003cp\u003eMR-PRESSO global test, MR‐pleiotropy residual sum and outlier global test;\u003c/p\u003e\n\u003cp\u003eQ value, Cochran's Q statistic value.\u003c/p\u003e\n\u003cp\u003eBWMR, Bayesian weighted Mendelian randomization.\u003c/p\u003e\n\u003cp\u003eIVW, inverse variance weighted;\u003c/p\u003e\n\u003cp\u003eWM, weighted median;\u003c/p\u003e\n\u003cp\u003eBWMR, Bayesian weighted Mendelian randomization.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data used in this study were obtained from publicly available sources, which have been approved by the corresponding ethical review board. Therefore, ethical approval was not required.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets analyzed in the current study are available in IEU Open GWAS project at https://gwas.mrcieu.ac.uk/datasets/finn-b-J10_COPD and https://gwas.mrcieu.ac.uk/datasets/finn-b-AB1_RESP_TUBERCU . R packages for MR analysis can be found at https://github.com/MRCIEU/TwoSampleMR\u0026nbsp; and https://github.com/jiazao97/BWMR .\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the National Natural Science Foundation of China (Grant no: 82060622).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStudy concept and design: AA and MQC. Data collection and statistical analysis: AA, YZ and XMW. Figures and supplementary material: AA, BFW and JAW. Writing-original draft: AA. Writing\u0026mdash;review and editing: YZ and MQC. All authors contributed to the interpretation of the results and critical revision of the manuscript, and approved it for publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank the IEU Open GWAS project (https://gwas.mrcieu.ac.uk/datasets/ ) for providing summary results data for these analyzes and the Figdraw 2.0 online platform (https://www.figdraw.com/ ) for providing drawing materials and tools for flowchart.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContributor Information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAbulikemu Aili, Email: [email protected]\u003c/p\u003e\n\u003cp\u003eMingqin Cao, Email: [email protected]\u003c/p\u003e\n\u003cp\u003eYan Zhang, Email: [email protected]\u003c/p\u003e\n\u003cp\u003eXiaomin Wang, Email: [email protected]\u003c/p\u003e\n\u003cp\u003eBaofeng Wen, Email: [email protected]\u003c/p\u003e\n\u003cp\u003eJunan Wang, Email: [email protected]\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003e Wang W, Cai Y, Deng G, Yang Q, Tang P, Wu M, Yu Z, Yang F, Chen J, Werz O \u003cem\u003eet al\u003c/em\u003e: Allelic-Specific Regulation of xCT Expression Increases Susceptibility to Tuberculosis by Modulating microRNA-mRNA Interactions. \u003cem\u003emSphere\u003c/em\u003e 2020, 5(2):e00263-00220.https://doi.org/10.1128/mSphere.00263-20.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e Li Z, Lai K, Li T, Lin Z, Liang Z, Du Y, Zhang J: Factors associated with treatment outcomes of patients with drug-resistant tuberculosis in China: A retrospective study using competing risk model. \u003cem\u003eFront Public Health\u003c/em\u003e 2022, 10:906798.https://doi.org/10.3389/fpubh.2022.906798.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e Tanner L, Mashabela GT, Omollo CC, de Wet TJ, Parkinson CJ, Warner DF, Haynes RK, Wiesner L: Intracellular 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\u003cem\u003eet al\u003c/em\u003e: Previous tuberculosis disease as a risk factor for chronic obstructive pulmonary disease: a cross-sectional analysis of multicountry, population-based studies. \u003cem\u003eThorax\u003c/em\u003e 2022, 77(11):1088\u0026ndash;1097.https://doi.org/10.1136/thoraxjnl-2020-216500.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e Nitsch D, Molokhia M, Smeeth L, DeStavola BL, Whittaker JC, Leon DA: Limits to causal inference based on Mendelian randomization: a comparison with randomized controlled trials. \u003cem\u003eAm J Epidemiol\u003c/em\u003e 2006, 163(5):397\u0026ndash;403.https://doi.org/10.1093/aje/kwj062.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Chronic Obstructive Pulmonary Disease, Tuberculosis, Mendelian Randomization Study","lastPublishedDoi":"10.21203/rs.3.rs-4176361/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4176361/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe causal relationship between COPD and respiratory TB is still unclear due to limited evidence from prospective studies. Therefore, the present study aimed to assess the causal relationship between COPD and respiratory TB using this two-sample Bayesian weighted Mendelian randomization (BWMR) study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e The genetic instrumental variants (IVs) for COPD and respiratory TB were obtained from the IEU Open GWAS project in 2021. The inverse variance weighted (IWV) method was used as the main statistical analysis method and was supplemented with weighted median and BWMR methods. Pleiotropy was tested using the MR-PRESSO global test and MR-Egger regression. Heterogeneity was analyzed using Cochran's Q statistics. The robustness of the results was tested using the leave-one-out sensitivity analysis method.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e In our two-sample BWMR analysis, we found that patients with COPD had a higher risk of respiratory TB based on IVW (OR = 1.259, 95% CI for OR: 1.011–1.568; p = 0.040), weighted median (OR = 1.287, 95% CI for OR: 0.981–1.688; p = 0 .069) and BWMR (OR = 1.240, 95% CI for OR: 1.013–1.518; p = 0.037). Reverse BWMR analysis showed that respiratory TB has no causal effect on COPD. We found no significant pleiotropy or heterogeneity in all selected IVs. The results were stable when removing the SNPs one by one.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e This two-sample BWMR study provided compelling evidence that individuals with COPD are at a higher risk of respiratory TB at the genetic level, while respiratory TB has no causal effect on COPD.\u003c/p\u003e","manuscriptTitle":"Causal relationship between chronic obstructive pulmonary disease and respiratory tuberculosis susceptibility: A two-sample Bayesian weighted Mendelian randomization study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-03 17:10:49","doi":"10.21203/rs.3.rs-4176361/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"18e8e5ea-7917-47d2-bae0-625509ee4623","owner":[],"postedDate":"April 3rd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-02-27T12:24:15+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-03 17:10:49","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4176361","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4176361","identity":"rs-4176361","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}