Causal Effect of Tobacco Exposure on Acute Respiratory Distress Syndrome: A Mendelian Randomization Study

Causal Effect of Tobacco Exposure on Acute Respiratory Distress Syndrome: A 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 Effect of Tobacco Exposure on Acute Respiratory Distress Syndrome: A Mendelian Randomization Study Yunfeng Wang, Zhihui Cheng, Dongwei Xu, Kan Shen, Jun Li, Shenghua Yan, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4806401/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: Previous studies have reported increased heterogeneity in acute respiratory distress syndrome (ARDS), but the causal relationship between ARDS and tobacco exposure is uncertain. Considering that tobacco exposure is relatively common, it can be used as an easily accessible indicator and is closely related to respiratory diseases. We examined the causal effect of tobacco exposure on ARDS-related phenotypes using a Mendelian randomization (MR) approach. Methods: In this investigation, we obtained tobacco exposure data from the most recent genome-wide association studies (GWASs) conducted by the GWAS and Sequencing Consortium of Alcohol and Nicotine Use (GSCAN). Moreover, summary statistics data for lifetime smoking behavior (SmkIndex) were obtained from the UK Biobank. Furthermore, the present study utilized ARDS GWAS data from the Finngen database. This study used two-sample MR (TSMR) to investigate the causal relationship between tobacco exposure and ARDS. We performed extensive sensitivity analyses to confirm the robustness, heterogeneity, and potential multibiological effects of the study results. Additionally, to control for false positive results during multiple hypothesis testing, we adopted a false discovery rate (FDR) to control for statistical bias due to multiple comparisons. Results: After FDR correction, tobacco exposure had no statistically significant effect on ARDS incidence. Several phenotypes with unadjusted low P values are worth mentioning, including cigarettes smoked daily (CigDay) (OR = 3.11, 95% CI 1.19-8.14, p = 0.020, FDR- p = 0.051) and age of initiation of regular smoking (AgeSmk) (OR = 0.01, 95% CI 0.00-0.45, p = 0.016, FDR- p = 0.051). In contrast, no causal links were identified for other measures of tobacco exposure with unadjusted p values, including smoking cessation (SmkCes) (OR = 1.33, 95% CI 0.19-9.43, p = 0.773), lifetime smoking behavior (SmkIndex) (OR = 3.02, 95% CI 0.59-15.30, p = 0.183), and smoking initiation (SmkInit) (OR = 1.86, 95% CI 0.74-4.70, p = 0.189). Conclusion: This study revealed a causal link between CigDay and AgeSmk and the risk of ARDS. However, no genetic associations were found between SmkCes, SmkInit, or SmkIndex and ARDS, suggesting heterogeneity in the impact of smoking exposure on the disease. Further research is required to clarify the causes of this heterogeneity. Tobacco exposure Genetics ARDS Mendelian Randomization SNPs Figures Figure 1 Figure 2 Figure 3 1. Introduction Acute respiratory distress syndrome (ARDS) is a severe lung condition characterized by bilateral radiographical opacities and noncardiogenic pulmonary edema, which leads to significant hypoxemia[ 1 ]. It is typically caused by factors such as severe infections, trauma, tobacco use, alcohol use, hypoalbuminemia, chemotherapy within the previous six months, and exposure to ambient air pollutants[ 2 – 6 ]. The COVID-19 pandemic has caused an increase in ARDS incidence and highlighted the challenges associated with this syndrome, including its unacceptably high mortality and lack of effective pharmacotherapy[ 7 , 8 ]. As a major global public health problem, the fatality rate for ARDS can reach 40%, and ARDS is also a primary reason for the long-term mechanical ventilation required by critically ill patients[ 9 ]. At present, the treatment of ARDS is still mainly focused on preclinical studies, which are associated with the high heterogeneity of the disease[ 10 ]. Tobacco use, primarily through smoking, is a major preventable contributor to global morbidity and mortality and is significantly associated with a variety of diseases[ 11 ]. Extensive laboratory, clinical, and epidemiological studies have consistently demonstrated a strong link between smoking and the development of pulmonary conditions such as bronchial asthma, chronic obstructive pulmonary disease (COPD), emphysema, interstitial lung disease, lung fibrosis, and lung cancer[ 12 ]. Multiple studies underscore the complexity and heterogeneity in the relationship between smoking and ARDS. A 15-year cohort study by Iribarren et al. showed a correlation with a clear dose‒response effect[ 13 ]. Calfee et al. reported an increased risk of ARDS associated with smoking in sepsis patients but not in those with other ARDS risk factors[ 4 ]. Moazed et al. reported that both active and passive smoking increase the risk of ARDS in patients with sepsis; however, among ARDS patients, smokers exhibit less systemic inflammation and milder illness severity[ 14 ], indicating biological differences within the ARDS population. Interestingly, Iriyama et al. reported that current smokers had a lower risk of developing ARDS than did never smokers[ 15 ], and Balfanz et al. observed a similar trend among COVID-19 patients, suggesting a possible protective effect of smoking on severe respiratory conditions[ 16 ]. These findings highlight the need for further research to understand the nuanced link between smoking and ARDS risk. Mendelian randomization (MR) refers to a statistical method based on genome-wide association studies (GWASs) that use genetic variation as an instrumental variable (IV) to assess the causality of observed associations between modifiable exposures or risk factors and clinically relevant outcomes[ 17 ]. MR minimizes traditional confounding and reverses causation because genetic variants are randomly distributed during meiosis and are independent of the environment, disease onset, and progression[ 18 ]. Therefore, MR is not affected by the confounding biases found in traditional observational studies[ 19 , 20 ]. Based on this knowledge, we applied a two-sample MR analysis to comprehensively investigate the genetic association of tobacco exposure with ARDS. The results of this study may provide new strategies for the risk assessment of ARDS. 2. Materials and Methods 2.1. Study design Our study employs the MR design, which is based on three key assumptions: (1) the IVs are associated with the exposure, (2) the IVs are not related to the outcome through a confounding pathway, and (3) the IVs do not directly affect the outcome, except perhaps indirectly through the exposure[ 21 ]. In this study, the exposure factor was cigarette smoking, the IVs were single nucleotide polymorphisms (SNPs) strongly associated with cigarette smoking, and the outcome variable was ARDS. We utilized two-sample MR (TSMR) analysis to determine the causal relationships between cigarette smoking exposure and ARDS. Inverse variance weighted (IVW) MR analysis was used as the primary method for causal analysis. A further sensitivity analysis was performed to ensure the robustness of the results (Fig. 1 ). 2.2. Data Source All data utilized in this study are publicly accessible from the respective GWAS databases. Ethical approval was not required for this study because it involved the use of anonymized summary-level data that had been made publicly available. The genetic data of tobacco exposure were derived from the most recent GWAS conducted by GSCAN with a sample size of 3.4 million individuals of multiethnicity, focusing on various aspects of cigarette smoking and exposure to cigarette smoke in European populations[ 22 ]. Smoking initiation traits included a continuous phenotype (age of initiation of regular smoking, AgeSmk) and a binary phenotype (smoking initiation [yes or no], SmkInit). In total, 11 and 240 SNPs were significantly associated with these two smoking initiation traits in European descendants ( p < 5 × 10 − 8 ), achieving independence at a linkage disequilibrium (LD) threshold of r 2 = 0.001 and a clumping distance of 10,000 kb[ 22 ]. Comparing current versus former smokers, 21 SNPs were identified to be associated with smoking cessation (SmkCes) at genome-wide significance in European descendants[ 22 ]. To assess the degree of smoking, the average number of cigarettes smoked per day (CigDay) was measured among both current and former smokers, and 53 SNPs were identified at genome-wide significance in European descendants[ 22 ]. In addition, Wootton et al. conducted another GWAS of lifetime smoking behavior (SmkIndex), which is a synthetic index based on combined information on smoking intensity (number of cigarettes per day), smoking duration, and ever/never regular smoking status in a sample of 462,690 European individuals from the UK Biobank, and 125 SNPs were identified at genome-wide significance[ 23 ]. Furthermore, the present study utilized ARDS GWAS data from the Finngen database—a Finnish genetic resource integrating genotypic, phenotypic, diagnostic, and prescription information. This platform primarily focuses on identifying gene–disease associations to drive advancements in disease management. It also provides data analysis tools facilitating global collaboration and knowledge sharing. This study analyzed 406,536 samples (including 21,306,261 SNPs) from Finngen to investigate ARDS[ 24 ]. 2.3. IV Selection TSMR analysis requires three core assumptions: the SNPs as IVs must be closely associated with the exposure factor. We selected SNPs associated with smoking that were genome-wide significant ( p < 5× 10 − 8 ). We removed SNPs in linkage disequilibrium (r 2 < 0.001, clumping distance = 10,000 kb)[ 25 ] and excluded SNPs associated with confounding factors such as alcohol consumption, trauma, and blood transfusion. All SNPs were screened using the F-statistic to avoid weak instrument bias, with a value > 10 indicating the absence of weak instrument variables. The F-statistic is calculated as F = R 2 (N-K-1)/[K(1-R 2 )][ 26 – 28 ]. In this equation, R 2 refers to the cumulative explained variance of the selected SNP during exposure, K is the number of SNPs for the final analysis, and N is the number of samples of the selected GWAS. To exclude potential pleiotropy effects, we examined the secondary phenotypes associated with each SNP using the LDlink tool ( https://ldlink.nih.gov/?tab=ldtrait )[ 29 ] and subsequently removed SNPs associated with confounding factors ( Supplementary Table S2 ). 2.4. Statistical and sensitivity analysis The R version 4.3.3 program ( http://www.Rproject.org ) was used in all studies. To specifically assess the causal relationship between tobacco exposure and ARDS, we performed median-based weighted analyses[ 30 ], weighted mode analyses[ 31 ], and inverse variance weighted analyses (IVW)[ 31 ] using the ”TwoSampleMR” R package (version 0.5.10)[ 32 ]. Instrumental heterogeneity between variables was assessed using Cochran’s Q statistic and its p value (IV) and combined with the MR‒Egger method for horizontal multidimensionality, which was recognized if the intercept term was large[ 33 ]. Meanwhile, in the MR-PRESSO package, we used the technique of robust MR multidirectional entropy residuals and outliers (MR-PRESSO)[ 34 ] to find and remove horizontal multidirectional entropy outliers that may seriously affect the estimation results. Finally, we used funnel plots and scatter plots. Scatter plots showed that outliers had minimal effects on the data, whereas funnel plots showed a high degree of association and a lack of heterogeneity. 3. Results TSMR analysis was conducted to investigate the causal relationships between various measures of tobacco exposure and the risk of ARDS. The analysis included five different exposure measures: AgeSmk, CigDay, SmkCes, SmkIndex, and SmkInit. After harmonizing the SNPs with the outcome data from FinnGen, the final number of SNPs used as IVs in the analysis was as follows: 10 for AgeSmk; 45 for CigDay; 21 for SmkCes; 118 for SmkIndex; and 221 for SmkInit. The F values for all SNPs used in the analysis were greater than 10, ranging from 13.32 to 1,138.56, ensuring the absence of weak instrumental variables ( Supplementary Table S1 ). Before FDR correction, the IVW method indicated a significant inverse association between AgeSmk and ARDS (odds ratio [OR] = 0.01, 95% confidence interval [CI] = 0.00-0.45, p = 0.016). However, after FDR correction, the p values exceeded the threshold for significance (FDR- p = 0.051). MR‒Egger regression, with a p value for the intercept test of 0.657, suggested no significant horizontal pleiotropy. (Fig. 3 and Supplementary Table S3 ). Before FDR correction, the IVW method indicated a significant inverse association between CigDay and ARDS (OR = 3.11, 95% CI = 1.19–8.14; p = 0.020). Consistently, the MR-weighted median and MR-weighted mode methods supported this association, with ORs of 6.59 (95% CI = 1.72–25.22, p = 0.006) and 6.69 (95% CI = 1.65–27.04, p = 0.007), respectively. However, after FDR correction for the IVW method, the p value increased to 0.051, which exceeded the conventional threshold for significance ( p < 0.05). Additionally, MR‒Egger regression analysis yielded an OR of 9.58 (95% CI = 1.71–53.61, p = 0.014). The p value for the intercept test was 0.132, which does not provide evidence for significant horizontal pleiotropy (Fig. 2 B, Fig. 3 and Supplementary Table S3 ). In our MR analysis, no significant associations were found between ARDS risk and three measures of tobacco exposure: SmkCes, SmkIndex, or SmkInit (Fig. 2 C -Figure 2E ). These findings suggest that, contrary to CigDay and AgeSmk, these measures of smoking behavior may not directly influence the risk of ARDS, or their effects may be mediated through other unmeasured factors. Additionally, after conducting the “leave-one-out” analysis and progressively excluding SNPs ( Supplementary Fig. 2 ), the results indicated that no single SNP significantly influenced the robustness of the results, ensuring the study’s stability and reliability. Furthermore, the application of the PRESSO method has been instrumental in enhancing the validity of our findings ( Table 1 and Supplementary Fig. 1 ). By partitioning the sum of effect sizes into components attributable to individual SNPs and adjusting for outliers, we have effectively mitigated the potential influence of spurious associations that could arise from extreme values or outliers in the data. 4. Discussion Our study utilized TSMR analysis to provide evidence for a causal relationship between tobacco exposure and the risk of ARDS. Our findings indicate a potential causal link between daily tobacco exposure, measured by CigDay, and ARDS, which is biologically plausible given the known detrimental effects of cigarette smoke on lung health. Similarly, the age of initiation of regular smoking, AgeSmk, also demonstrated an association with ARDS, suggesting that early-life exposures may have lasting impacts on respiratory health. However, after applying the FDR correction to adjust for multiple comparisons, the statistical significance of the associations for both CigDay and AgeSmk did not reach the conventional threshold, highlighting the need for cautious interpretation of these results. While FDR correction is a robust method for controlling the rate of false positives, it also has the potential to mask true associations when multiple comparisons are made. Despite the FDR-adjusted p values, the observed associations for CigDay and AgeSmk are noteworthy and align with the existing biological understanding of tobacco's impact on respiratory function. The lack of significant associations for other tobacco exposure metrics, such as SmkCes, SmkIndex, and SmkInit, suggests a complex and heterogeneous relationship between different aspects of tobacco use and the risk of ARDS. Numerous studies have confirmed that cigarette smoke contributes to the development of ARDS through multiple pathological mechanisms. The oxidative stress and proinflammatory properties of cigarette smoke can increase alveolar epithelial permeability[ 35 ], disrupt immune responses[ 36 , 37 ], and cause vascular endothelial damage[ 38 , 39 ]. This leads to a heightened risk of ARDS, particularly in patients with extrapulmonary factors such as sepsis and trauma. Smoke also impairs alveolar epithelial cell integrity and ion channel expression, exacerbating the likelihood of severe edema during ARDS[ 37 , 40 ]. Furthermore, cigarette smoke accelerates cellular aging and autophagy impairment in lung cells, priming the lungs for intense inflammatory reactions[ 41 , 42 ]. The causal relationship between both CigDay and AgeSmk with ARDS suggests a potential dose‒response relationship where increased cigarette smoke exposure escalates the risk of ARDS. This finding is corroborated by biological studies that revealed the harmful effects of tobacco on lung health. However, the absence of significant associations with other metrics of tobacco exposure, such as SmkCes, SmkInit, and SmkIndex, could be indicative of the complex interplay between tobacco use and ARDS pathogenesis. This complexity necessitates a refined understanding of the multifaceted impact of tobacco on ARDS risk. As Chugh et al.[ 43 ] systematically reviewed, the global implementation of tobacco control policies has shown that multifaceted initiatives, particularly those involving taxation, are linked to substantial reductions in smokeless tobacco use. This underscores the importance of a strategic, policy-driven approach to tobacco control, which could be instrumental in mitigating ARDS risk factors. Future research endeavors should be directed toward dissecting the intricate interactions between various smoking behaviors and ARDS subtypes within a more granular framework, taking into account etiological differences, coexisting health conditions, and individual patient characteristics. The MR approach utilized in this study presents significant advantages, particularly in minimizing biases associated with reverse causation and confounding that are prevalent in traditional observational research. Nonetheless, this study has its limitations. The assumption that selected SNPs exclusively influence ARDS risk through tobacco exposure might overlook potential pleiotropic effects, where genetic variants impact a spectrum of traits[ 25 ]. While MR‒Egger regression intercept tests offer some validation of our instrumental variables, the possibility of unmeasured confounding factors cannot be discounted. Our findings, based on a European population, may not be directly generalizable to other ethnicities. The genetic and behavioral variations across diverse populations necessitate further investigation in various settings to substantiate these outcomes and elucidate the underlying biological mechanisms[ 24 ]. 5. Conclusion In conclusion, our MR study provides genetic support for a causal association of both AgeSmk and CigDay with ARDS, underscoring the importance of smoking reduction in its prevention. However, no causal relationship was identified for other tobacco exposure factors in the genesis and progression of ARDS, highlighting the complexity of tobacco exposure and the heterogeneity of ARDS. This suggests a need for a nuanced understanding of how different dimensions of tobacco exposure may interact with specific ARDS subtypes to influence risk. Abbreviations ARDS Acute Respiratory Distress Syndrome GWAS Genome-wide association studies GSCAN GWAS and Sequencing Consortium of Alcohol and Nicotine use SNPs single nucleotide polymorphisms COPD Chronic Obstructive Pulmonary Disease MR Mendelian randomization IVs instrumental variables OR Odds Ratio IVW inverse-varianceweighted MR-IVW MR-inverse-varianceweighted CI Confidence Interval AgeSmk Age of initiation of regular smoking SmkInit Smoking initiation (yes or no) SmkCes Smoking cessation CigDay Average number of cigarettes smoked per day SmkIndex Smoking index Declarations Author Contributions The study was conceived and designed by Y Wang, Y Li and X Deng . GWAS summary data collection and IV selection were conducted by Y Wang , Z Cheng and D Xu . MR analysis was performed by K Shen, J Li and S Yan . The manuscript was drafted by M Zhou, Y Qi, H Yu, H Ni and L Li . The manuscript was revised by Y Wang and Y Li . Funding This research was self-funded by the authors. Conflict of interest statement No conflict of interest exists in the submission of this manuscript. We declare that all the authors listed meet the authorship criteria according to the latest guidelines of the International Committee of Medical Journal Editors, and all the authors are in agreement with the manuscript. All the authors declare that the work has not been published previously and is not under consideration for publication elsewhere, in whole or in part. Ethics Statement Ethical approval has been given for the use of data relating to humans from public datasets. Based on a publicly accessible database, this study did not require ethical approval or informed consent[19]. Availability of data and materials The GWAS for tobacco smoking can be obtained through the GSCAN data portal (https://conservancy.umn.edu/handle/11299/241912) and the dataset on the website of the University of Bristol (https://data.bris.ac.uk/data/dataset/10i96zb8gm0j81yz0q6ztei23d). The GWAS for ARDS can be obtained form the FinnGen database. We want to acknowledge the participants and investigators of the FinnGen study. References Meyer, N.J., L. Gattinoni and C.S. Calfee, Acute respiratory distress syndrome. Lancet, 2021. 398(10300): p. 622-637. Bellani, G., et al., Epidemiology, Patterns of Care, and Mortality for Patients With Acute Respiratory Distress Syndrome in Intensive Care Units in 50 Countries. JAMA, 2016. 315(8): p. 788-800. Trillo-Alvarez, C., et al., Acute lung injury prediction score: derivation and validation in a population-based sample. Eur Respir J, 2011. 37(3): p. 604-9. Calfee, C.S., et al., Cigarette Smoke Exposure and the Acute Respiratory Distress Syndrome. Crit Care Med, 2015. 43(9): p. 1790-7. Moss, M. and E.L. Burnham, Chronic alcohol abuse, acute respiratory distress syndrome, and multiple organ dysfunction. Crit Care Med, 2003. 31(4 Suppl): p. S207-12. Reilly, J.P., et al., Low to Moderate Air Pollutant Exposure and Acute Respiratory Distress Syndrome after Severe Trauma. Am J Respir Crit Care Med, 2019. 199(1): p. 62-70. Wang, D., et al., Clinical Characteristics of 138 Hospitalized Patients With 2019 Novel Coronavirus-Infected Pneumonia in Wuhan, China. JAMA, 2020. 323(11): p. 1061-1069. Wu, Z. and J.M. McGoogan, Characteristics of and Important Lessons From the Coronavirus Disease 2019 (COVID-19) Outbreak in China: Summary of a Report of 72 314 Cases From the Chinese Center for Disease Control and Prevention. JAMA, 2020. 323(13): p. 1239-1242. Bellani, G., et al., Epidemiology, Patterns of Care, and Mortality for Patients With Acute Respiratory Distress Syndrome in Intensive Care Units in 50 Countries. JAMA, 2016. 315(8): p. 788-800. Xu, Z., et al., Current Status of Cell-Based Therapies for COVID-19: Evidence From Mesenchymal Stromal Cells in Sepsis and ARDS. Front Immunol, 2021. 12: p. 738697. Smoking prevalence and attributable disease burden in 195 countries and territories, 1990-2015: a systematic analysis from the Global Burden of Disease Study 2015. Lancet, 2017. 389(10082): p. 1885-1906. Ishii, Y., [Smoking and respiratory diseases]. Nihon Rinsho, 2013. 71(3): p. 416-20. Iribarren, C., et al., Cigarette smoking, alcohol consumption, and risk of ARDS: a 15-year cohort study in a managed care setting. Chest, 2000. 117(1): p. 163-8. Moazed, F., et al., Cigarette Smoke Exposure and Acute Respiratory Distress Syndrome in Sepsis: Epidemiology, Clinical Features, and Biologic Markers. Am J Respir Crit Care Med, 2022. 205(8): p. 927-935. Iriyama, H., et al., Risk modifiers of acute respiratory distress syndrome in patients with non-pulmonary sepsis: a retrospective analysis of the FORECAST study. J Intensive Care, 2020. 8: p. 7. Balfanz, P., et al., Early risk markers for severe clinical course and fatal outcome in German patients with COVID-19. PLoS One, 2021. 16(1): p. e0246182. Sekula, P., et al., Mendelian Randomization as an Approach to Assess Causality Using Observational Data. J Am Soc Nephrol, 2016. 27(11): p. 3253-3265. Lawlor, D.A., et al., Mendelian randomization: using genes as instruments for making causal inferences in epidemiology. Stat Med, 2008. 27(8): p. 1133-63. Hartwig, F.P., et al., Inflammatory Biomarkers and Risk of Schizophrenia: A 2-Sample Mendelian Randomization Study. JAMA Psychiatry, 2017. 74(12): p. 1226-1233. Deng, M.G., et al., Association between frailty and depression: A bidirectional Mendelian randomization study. Sci Adv, 2023. 9(38): p. eadi3902. Burgess, S., D.S. Small and S.G. Thompson, A review of instrumental variable estimators for Mendelian randomization. Stat Methods Med Res, 2017. 26(5): p. 2333-2355. Saunders, G., et al., Genetic diversity fuels gene discovery for tobacco and alcohol use. Nature, 2022. 612(7941): p. 720-724. Wootton, R.E., et al., Evidence for causal effects of lifetime smoking on risk for depression and schizophrenia: a Mendelian randomisation study. Psychol Med, 2020. 50(14): p. 2435-2443. Kurki, M.I., et al., FinnGen provides genetic insights from a well-phenotyped isolated population. Nature, 2023. 613(7944): p. 508-518. Hemani, G., J. Bowden and S.G. Davey, Evaluating the potential role of pleiotropy in Mendelian randomization studies. Hum Mol Genet, 2018. 27(R2): p. R195-R208. Sekula, P., et al., Mendelian Randomization as an Approach to Assess Causality Using Observational Data. J Am Soc Nephrol, 2016. 27(11): p. 3253-3265. Pierce, B.L., H. Ahsan and T.J. Vanderweele, Power and instrument strength requirements for Mendelian randomization studies using multiple genetic variants. Int J Epidemiol, 2011. 40(3): p. 740-52. Burgess, S. and S.G. Thompson, Avoiding bias from weak instruments in Mendelian randomization studies. Int J Epidemiol, 2011. 40(3): p. 755-64. Lin, S.H., R. Thakur and M.J. Machiela, LDexpress: an online tool for integrating population-specific linkage disequilibrium patterns with tissue-specific expression data. BMC Bioinformatics, 2021. 22(1): p. 608. Bowden, J., et al., Consistent Estimation in Mendelian Randomization with Some Invalid Instruments Using a Weighted Median Estimator. Genet Epidemiol, 2016. 40(4): p. 304-14. Hartwig, F.P., S.G. Davey and J. Bowden, Robust inference in summary data Mendelian randomization via the zero modal pleiotropy assumption. Int J Epidemiol, 2017. 46(6): p. 1985-1998. Hemani, G., et al., The MR-Base platform supports systematic causal inference across the human phenome. Elife, 2018. 7. Cho, Y., et al., Exploiting horizontal pleiotropy to search for causal pathways within a Mendelian randomization framework. Nat Commun, 2020. 11(1): p. 1010. Verbanck, M., et al., Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases. Nat Genet, 2018. 50(5): p. 693-698. Xu, L., et al., Cigarette smoke triggers inflammation mediated by autophagy in BEAS-2B cells. Ecotoxicol Environ Saf, 2019. 184: p. 109617. Lugg, S.T., et al., Cigarette smoke exposure and alveolar macrophages: mechanisms for lung disease. Thorax, 2022. 77(1): p. 94-101. Zhang, Y., et al., Cigarette smoke-inactivated SIRT1 promotes autophagy-dependent senescence of alveolar epithelial type 2 cells to induce pulmonary fibrosis. Free Radic Biol Med, 2021. 166: p. 116-127. Shin, I.S., et al., Melatonin attenuates neutrophil inflammation and mucus secretion in cigarette smoke-induced chronic obstructive pulmonary diseases via the suppression of Erk-Sp1 signaling. J Pineal Res, 2015. 58(1): p. 50-60. Kaminski, T.W., et al., Lung microvascular occlusion by platelet-rich neutrophil-platelet aggregates promotes cigarette smoke-induced severe flu. JCI Insight, 2024. 9(2). Marwick, J.A., et al., Cigarette smoke-induced oxidative stress and TGF-beta1 increase p21waf1/cip1 expression in alveolar epithelial cells. Ann N Y Acad Sci, 2002. 973: p. 278-83. Hsieh, S.J., et al., Prevalence and impact of active and passive cigarette smoking in acute respiratory distress syndrome. Crit Care Med, 2014. 42(9): p. 2058-68. Sang, S., et al., Learning From Past Respiratory Infections to Predict COVID-19 Outcomes: Retrospective Study. J Med Internet Res, 2021. 23(2): p. e23026. Chugh, A., et al., The global impact of tobacco control policies on smokeless tobacco use: a systematic review. Lancet Glob Health, 2023. 11(6): p. e953-e968. Table 1 Table 1 is available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files Table1.xlsx Table 1. Cochran's Q test for heterogeneity among MR analyses *The Q statistic has a chi-squared distribution on N-1 degrees of freedom (df) under the null hypothesis that all genetic variants are valid IVs and that the same causal effect is identified by all variants. N is the number of genetic variants. SupplementaryFigure1.pptx SupplementaryFigure2.pptx SupplementaryTableS1.xlsx SupplementaryTableS2.xlsx SupplementaryTableS3.xlsx 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-4806401","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":336449093,"identity":"8cd647c8-e658-493f-92f1-f23dd28c7e60","order_by":0,"name":"Yunfeng Wang","email":"","orcid":"","institution":"Shanghai Pudong New Area Zhoupu Hospital, Shanghai University of Medicine \u0026 Health Sciences Affiliated Zhoupu Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yunfeng","middleName":"","lastName":"Wang","suffix":""},{"id":336449094,"identity":"aecee5ff-0428-4030-8ec4-83549158ec81","order_by":1,"name":"Zhihui Cheng","email":"","orcid":"","institution":"Shanghai Pudong New Area Zhoupu Hospital, Shanghai University of Medicine \u0026 Health Sciences Affiliated Zhoupu Hospital","correspondingAuthor":false,"prefix":"","firstName":"Zhihui","middleName":"","lastName":"Cheng","suffix":""},{"id":336449095,"identity":"49dcff4b-70eb-4ed1-93ed-54e76c52b3dc","order_by":2,"name":"Dongwei Xu","email":"","orcid":"","institution":"Shanghai Pudong New Area Zhoupu Hospital, Shanghai University of Medicine \u0026 Health Sciences Affiliated Zhoupu Hospital","correspondingAuthor":false,"prefix":"","firstName":"Dongwei","middleName":"","lastName":"Xu","suffix":""},{"id":336449096,"identity":"00ad2945-a2bc-4989-a4d5-5fcd1b84dc26","order_by":3,"name":"Kan Shen","email":"","orcid":"","institution":"Shanghai Pudong New Area Zhoupu Hospital, Shanghai University of Medicine \u0026 Health Sciences Affiliated Zhoupu Hospital","correspondingAuthor":false,"prefix":"","firstName":"Kan","middleName":"","lastName":"Shen","suffix":""},{"id":336449097,"identity":"7f48ae4a-45f6-4f45-a483-e377645d983a","order_by":4,"name":"Jun Li","email":"","orcid":"","institution":"Shanghai Pudong New Area Zhoupu Hospital, Shanghai University of Medicine \u0026 Health Sciences Affiliated Zhoupu Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jun","middleName":"","lastName":"Li","suffix":""},{"id":336449098,"identity":"2dba848b-1633-4aad-a9c8-7d93546e5904","order_by":5,"name":"Shenghua Yan","email":"","orcid":"","institution":"Shanghai University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Shenghua","middleName":"","lastName":"Yan","suffix":""},{"id":336449099,"identity":"10509a11-46d2-4f0b-a1bc-aea7018ec210","order_by":6,"name":"Maofeng Zhou","email":"","orcid":"","institution":"Shanghai Pudong New Area Zhoupu Hospital, Shanghai University of Medicine \u0026 Health Sciences Affiliated Zhoupu Hospital","correspondingAuthor":false,"prefix":"","firstName":"Maofeng","middleName":"","lastName":"Zhou","suffix":""},{"id":336449100,"identity":"a29b6711-1cd2-464d-a71f-875f70335235","order_by":7,"name":"Yingchao Qi","email":"","orcid":"","institution":"Shanghai Pudong New Area Zhoupu Hospital, Shanghai University of Medicine \u0026 Health Sciences Affiliated Zhoupu Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yingchao","middleName":"","lastName":"Qi","suffix":""},{"id":336449101,"identity":"fb334108-6197-458c-8400-264f14535ca4","order_by":8,"name":"Hua Yu","email":"","orcid":"","institution":"Shanghai Pudong New Area Zhoupu Hospital, Shanghai University of Medicine \u0026 Health Sciences Affiliated Zhoupu Hospital","correspondingAuthor":false,"prefix":"","firstName":"Hua","middleName":"","lastName":"Yu","suffix":""},{"id":336449102,"identity":"c50bbd59-3bb9-4998-aa50-bcfbec100f3e","order_by":9,"name":"Hui Ni","email":"","orcid":"","institution":"Shanghai Pudong New Area Zhoupu Hospital, Shanghai University of Medicine \u0026 Health Sciences Affiliated Zhoupu Hospital","correspondingAuthor":false,"prefix":"","firstName":"Hui","middleName":"","lastName":"Ni","suffix":""},{"id":336449103,"identity":"d0bb430a-2367-442f-9b29-420552680b2e","order_by":10,"name":"Lijun Liao","email":"","orcid":"","institution":"Tongji University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Lijun","middleName":"","lastName":"Liao","suffix":""},{"id":336449104,"identity":"b37d0c95-f781-49c7-b085-f22bb90d9a2e","order_by":11,"name":"Yuling Li","email":"","orcid":"","institution":"Putuo Hospital, Shanghai University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Yuling","middleName":"","lastName":"Li","suffix":""},{"id":336449105,"identity":"7a64a595-6822-4e19-a972-7c0ce0c45405","order_by":12,"name":"Xingqi Deng","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABA0lEQVRIiWNgGAWjYDCCA0DM2ABi8YAIGwY2UrWkka7lMGF38R3vPfzi5w6bPHn/swcfF/w6b88n3fyA4UfFNpxaJM+cS7PsPZNWbHgjL9l4Zt/txDaZYwaMPWdu49RicCPHzJix7XDixhk8ZtK8PbcT2CQSDJgZ2/Bouf8GpOV/4sb+M+a/eXvO2bNJpH/Ar+UGj/FjxrYDifMZcsyYeX4cYGyTyMFvi+SZHDPG3rbkxA0SOcbSvA3JiUAtBQfx+YXv+BnjDz/b7BLn958x/Mzzx85efkb6xgc/KnBrAQI2CbALDwAJxjaI0AF86oGA+QOIlG8AkX8IqB0Fo2AUjIIRCQDBNl0krOsm5AAAAABJRU5ErkJggg==","orcid":"","institution":"Shanghai Pudong New Area Zhoupu Hospital, Shanghai University of Medicine \u0026 Health Sciences Affiliated Zhoupu Hospital","correspondingAuthor":true,"prefix":"","firstName":"Xingqi","middleName":"","lastName":"Deng","suffix":""}],"badges":[],"createdAt":"2024-07-26 08:10:39","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4806401/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4806401/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":63338769,"identity":"c5981eb8-09b8-4adc-97c8-05523ccab0d0","added_by":"auto","created_at":"2024-08-27 06:15:48","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":381124,"visible":true,"origin":"","legend":"\u003cp\u003eThe analysis process of our research. Assumption 1: The instrumental variables (IVs) are significantly associated with the tobacco exposure; Assumption 2: The IVs are not related to the confounders; Assumption 3: The IVs affect the ARDS solely through the tobacco exposure.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4806401/v1/6820dfeeeaffad53d606dc9b.png"},{"id":63338368,"identity":"813b0d4a-8523-4215-8510-eb80bb65d3f2","added_by":"auto","created_at":"2024-08-27 06:07:48","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":527997,"visible":true,"origin":"","legend":"\u003cp\u003eScatter plot of the causal relationships between exposure and outcomes using different MR methods. \u003cstrong\u003e(A)\u003c/strong\u003e Scatter plot of the causal relationships between Smk and ARDS. (B) Scatter plot of the causal relationships between SmkInit and ARDS. The slope of each line corresponds to the causal estimates for each method. \u003cstrong\u003e(C) \u003c/strong\u003eScatter plot of the causal relationships between SmkIndex and ARDS. \u003cstrong\u003e(D) \u003c/strong\u003eScatter plot of the causal relationships between SmkCes and ARDS. \u003cstrong\u003e(E)\u003c/strong\u003e Scatter plot of the causal relationships between SmkInit and ARDS. The individual SNP effect on the outcome (point and vertical line) against its effect on the exposure (point and horizontal line) was delineated in the background. ARDS: acute respiratory distress syndrome; AgeSmk, age of initiation of regular smoking; CigDay, average number of cigarettes smoked per day.\u003c/p\u003e","description":"","filename":"Figure2.pptxRepaired.png","url":"https://assets-eu.researchsquare.com/files/rs-4806401/v1/c1f7117e5bb3691c14e07353.png"},{"id":63337761,"identity":"c85c0d8a-d87f-466c-b8d9-59c9233457c0","added_by":"auto","created_at":"2024-08-27 05:59:48","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":54885,"visible":true,"origin":"","legend":"\u003cp\u003eOdds ratios and 95% confidence intervals for the effect of tobacco exposure on ARDS incidence was estimated by MR-IVW analyses.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-4806401/v1/e11bacc26069b275256c49ac.png"},{"id":63452945,"identity":"64b5961f-cc32-456f-9177-4d0ac57f6228","added_by":"auto","created_at":"2024-08-28 09:44:19","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1335698,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4806401/v1/881f6aae-647d-473a-9bf1-97f8eb8c92af.pdf"},{"id":63337765,"identity":"ea9ae901-97e5-4ab3-8a09-d03ea63674a2","added_by":"auto","created_at":"2024-08-27 05:59:48","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":10900,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTable 1. Cochran's Q test for heterogeneity among MR analyses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e*\u003c/strong\u003eThe Q statistic has a chi-squared distribution on N-1 degrees of freedom (df) under the null hypothesis that all genetic variants are valid IVs and that the same causal effect is identified by all variants. N is the number of genetic variants.\u003c/p\u003e","description":"","filename":"Table1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4806401/v1/43e6ea8402239bfd11c59cbe.xlsx"},{"id":63338371,"identity":"30a38649-7430-4a53-bb9a-4481ca68fb92","added_by":"auto","created_at":"2024-08-27 06:07:48","extension":"pptx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":137389,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigure1.pptx","url":"https://assets-eu.researchsquare.com/files/rs-4806401/v1/23cbdafa2e941b44b34de925.pptx"},{"id":63338370,"identity":"2385c907-d120-42e7-af4a-47ad2d876229","added_by":"auto","created_at":"2024-08-27 06:07:48","extension":"pptx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":445254,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigure2.pptx","url":"https://assets-eu.researchsquare.com/files/rs-4806401/v1/6fedcefd6cd18a7c2346af38.pptx"},{"id":63338770,"identity":"a7d33221-d338-454d-96a9-5b9f29024f9c","added_by":"auto","created_at":"2024-08-27 06:15:48","extension":"xlsx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":62291,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTableS1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4806401/v1/25cc04274ff8c1597fc43427.xlsx"},{"id":63337762,"identity":"7d62db6e-bc74-40b4-ad7e-aea57480cb9d","added_by":"auto","created_at":"2024-08-27 05:59:48","extension":"xlsx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":11396,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTableS2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4806401/v1/2bd3a49b9c7c82aa51e8fa31.xlsx"},{"id":63337767,"identity":"75ab35e4-72ba-4235-b4eb-d60e01993816","added_by":"auto","created_at":"2024-08-27 05:59:48","extension":"xlsx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":11984,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTableS3.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4806401/v1/9cac01892d2f56e7ec0dcce8.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Causal Effect of Tobacco Exposure on Acute Respiratory Distress Syndrome: A Mendelian Randomization Study","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eAcute respiratory distress syndrome (ARDS) is a severe lung condition characterized by bilateral radiographical opacities and noncardiogenic pulmonary edema, which leads to significant hypoxemia[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. It is typically caused by factors such as severe infections, trauma, tobacco use, alcohol use, hypoalbuminemia, chemotherapy within the previous six months, and exposure to ambient air pollutants[\u003cspan additionalcitationids=\"CR3 CR4 CR5\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. The COVID-19 pandemic has caused an increase in ARDS incidence and highlighted the challenges associated with this syndrome, including its unacceptably high mortality and lack of effective pharmacotherapy[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. As a major global public health problem, the fatality rate for ARDS can reach 40%, and ARDS is also a primary reason for the long-term mechanical ventilation required by critically ill patients[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. At present, the treatment of ARDS is still mainly focused on preclinical studies, which are associated with the high heterogeneity of the disease[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTobacco use, primarily through smoking, is a major preventable contributor to global morbidity and mortality and is significantly associated with a variety of diseases[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Extensive laboratory, clinical, and epidemiological studies have consistently demonstrated a strong link between smoking and the development of pulmonary conditions such as bronchial asthma, chronic obstructive pulmonary disease (COPD), emphysema, interstitial lung disease, lung fibrosis, and lung cancer[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Multiple studies underscore the complexity and heterogeneity in the relationship between smoking and ARDS. A 15-year cohort study by Iribarren et al. showed a correlation with a clear dose‒response effect[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Calfee et al. reported an increased risk of ARDS associated with smoking in sepsis patients but not in those with other ARDS risk factors[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Moazed et al. reported that both active and passive smoking increase the risk of ARDS in patients with sepsis; however, among ARDS patients, smokers exhibit less systemic inflammation and milder illness severity[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], indicating biological differences within the ARDS population. Interestingly, Iriyama et al. reported that current smokers had a lower risk of developing ARDS than did never smokers[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], and Balfanz et al. observed a similar trend among COVID-19 patients, suggesting a possible protective effect of smoking on severe respiratory conditions[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. These findings highlight the need for further research to understand the nuanced link between smoking and ARDS risk.\u003c/p\u003e \u003cp\u003eMendelian randomization (MR) refers to a statistical method based on genome-wide association studies (GWASs) that use genetic variation as an instrumental variable (IV) to assess the causality of observed associations between modifiable exposures or risk factors and clinically relevant outcomes[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. MR minimizes traditional confounding and reverses causation because genetic variants are randomly distributed during meiosis and are independent of the environment, disease onset, and progression[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Therefore, MR is not affected by the confounding biases found in traditional observational studies[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Based on this knowledge, we applied a two-sample MR analysis to comprehensively investigate the genetic association of tobacco exposure with ARDS. The results of this study may provide new strategies for the risk assessment of ARDS.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Study design\u003c/h2\u003e \u003cp\u003eOur study employs the MR design, which is based on three key assumptions: (1) the IVs are associated with the exposure, (2) the IVs are not related to the outcome through a confounding pathway, and (3) the IVs do not directly affect the outcome, except perhaps indirectly through the exposure[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. In this study, the exposure factor was cigarette smoking, the IVs were single nucleotide polymorphisms (SNPs) strongly associated with cigarette smoking, and the outcome variable was ARDS. We utilized two-sample MR (TSMR) analysis to determine the causal relationships between cigarette smoking exposure and ARDS. Inverse variance weighted (IVW) MR analysis was used as the primary method for causal analysis. A further sensitivity analysis was performed to ensure the robustness of the results (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\u003e2.2. Data Source\u003c/h2\u003e \u003cp\u003eAll data utilized in this study are publicly accessible from the respective GWAS databases. Ethical approval was not required for this study because it involved the use of anonymized summary-level data that had been made publicly available.\u003c/p\u003e \u003cp\u003eThe genetic data of tobacco exposure were derived from the most recent GWAS conducted by GSCAN with a sample size of 3.4\u0026nbsp;million individuals of multiethnicity, focusing on various aspects of cigarette smoking and exposure to cigarette smoke in European populations[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Smoking initiation traits included a continuous phenotype (age of initiation of regular smoking, AgeSmk) and a binary phenotype (smoking initiation [yes or no], SmkInit). In total, 11 and 240 SNPs were significantly associated with these two smoking initiation traits in European descendants (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;5 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e), achieving independence at a linkage disequilibrium (LD) threshold of r\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.001 and a clumping distance of 10,000 kb[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Comparing current versus former smokers, 21 SNPs were identified to be associated with smoking cessation (SmkCes) at genome-wide significance in European descendants[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. To assess the degree of smoking, the average number of cigarettes smoked per day (CigDay) was measured among both current and former smokers, and 53 SNPs were identified at genome-wide significance in European descendants[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. In addition, Wootton et al. conducted another GWAS of lifetime smoking behavior (SmkIndex), which is a synthetic index based on combined information on smoking intensity (number of cigarettes per day), smoking duration, and ever/never regular smoking status in a sample of 462,690 European individuals from the UK Biobank, and 125 SNPs were identified at genome-wide significance[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Furthermore, the present study utilized ARDS GWAS data from the Finngen database\u0026mdash;a Finnish genetic resource integrating genotypic, phenotypic, diagnostic, and prescription information. This platform primarily focuses on identifying gene\u0026ndash;disease associations to drive advancements in disease management. It also provides data analysis tools facilitating global collaboration and knowledge sharing. This study analyzed 406,536 samples (including 21,306,261 SNPs) from Finngen to investigate ARDS[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. IV Selection\u003c/h2\u003e \u003cp\u003eTSMR analysis requires three core assumptions: the SNPs as IVs must be closely associated with the exposure factor. We selected SNPs associated with smoking that were genome-wide significant (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;5\u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e). We removed SNPs in linkage disequilibrium (r\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, clumping distance\u0026thinsp;=\u0026thinsp;10,000 kb)[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] and excluded SNPs associated with confounding factors such as alcohol consumption, trauma, and blood transfusion. All SNPs were screened using the F-statistic to avoid weak instrument bias, with a value\u0026thinsp;\u0026gt;\u0026thinsp;10 indicating the absence of weak instrument variables. The F-statistic is calculated as F\u0026thinsp;=\u0026thinsp;R\u003csup\u003e2\u003c/sup\u003e(N-K-1)/[K(1-R\u003csup\u003e2\u003c/sup\u003e)][\u003cspan additionalcitationids=\"CR27\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. In this equation, R\u003csup\u003e2\u003c/sup\u003e refers to the cumulative explained variance of the selected SNP during exposure, K is the number of SNPs for the final analysis, and N is the number of samples of the selected GWAS. To exclude potential pleiotropy effects, we examined the secondary phenotypes associated with each SNP using the LDlink tool (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ldlink.nih.gov/?tab=ldtrait\u003c/span\u003e\u003cspan address=\"https://ldlink.nih.gov/?tab=ldtrait\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] and subsequently removed SNPs associated with confounding factors (\u003cb\u003eSupplementary Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e\u003c/b\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Statistical and sensitivity analysis\u003c/h2\u003e \u003cp\u003eThe R version 4.3.3 program (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.Rproject.org\u003c/span\u003e\u003cspan address=\"http://www.Rproject.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was used in all studies. To specifically assess the causal relationship between tobacco exposure and ARDS, we performed median-based weighted analyses[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], weighted mode analyses[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], and inverse variance weighted analyses (IVW)[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] using the \u0026rdquo;TwoSampleMR\u0026rdquo; R package (version 0.5.10)[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Instrumental heterogeneity between variables was assessed using Cochran\u0026rsquo;s Q statistic and its \u003cem\u003ep\u003c/em\u003e value (IV) and combined with the MR‒Egger method for horizontal multidimensionality, which was recognized if the intercept term was large[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Meanwhile, in the MR-PRESSO package, we used the technique of robust MR multidirectional entropy residuals and outliers (MR-PRESSO)[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] to find and remove horizontal multidirectional entropy outliers that may seriously affect the estimation results. Finally, we used funnel plots and scatter plots. Scatter plots showed that outliers had minimal effects on the data, whereas funnel plots showed a high degree of association and a lack of heterogeneity.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003eTSMR analysis was conducted to investigate the causal relationships between various measures of tobacco exposure and the risk of ARDS. The analysis included five different exposure measures: AgeSmk, CigDay, SmkCes, SmkIndex, and SmkInit. After harmonizing the SNPs with the outcome data from FinnGen, the final number of SNPs used as IVs in the analysis was as follows: 10 for AgeSmk; 45 for CigDay; 21 for SmkCes; 118 for SmkIndex; and 221 for SmkInit. The F values for all SNPs used in the analysis were greater than 10, ranging from 13.32 to 1,138.56, ensuring the absence of weak instrumental variables (\u003cb\u003eSupplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e).\u003c/p\u003e \u003cp\u003eBefore FDR correction, the IVW method indicated a significant inverse association between AgeSmk and ARDS (odds ratio [OR]\u0026thinsp;=\u0026thinsp;0.01, 95% confidence interval [CI]\u0026thinsp;=\u0026thinsp;0.00-0.45, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.016). However, after FDR correction, the \u003cem\u003ep\u003c/em\u003e values exceeded the threshold for significance (FDR-\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.051). MR‒Egger regression, with a \u003cem\u003ep\u003c/em\u003e value for the intercept test of 0.657, suggested no significant horizontal pleiotropy. (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e and \u003cb\u003eSupplementary Table \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e\u003c/b\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eBefore FDR correction, the IVW method indicated a significant inverse association between CigDay and ARDS (OR\u0026thinsp;=\u0026thinsp;3.11, 95% CI\u0026thinsp;=\u0026thinsp;1.19\u0026ndash;8.14; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.020). Consistently, the MR-weighted median and MR-weighted mode methods supported this association, with ORs of 6.59 (95% CI\u0026thinsp;=\u0026thinsp;1.72\u0026ndash;25.22, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.006) and 6.69 (95% CI\u0026thinsp;=\u0026thinsp;1.65\u0026ndash;27.04, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.007), respectively. However, after FDR correction for the IVW method, the \u003cem\u003ep\u003c/em\u003e value increased to 0.051, which exceeded the conventional threshold for significance (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Additionally, MR‒Egger regression analysis yielded an OR of 9.58 (95% CI\u0026thinsp;=\u0026thinsp;1.71\u0026ndash;53.61, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.014). The \u003cem\u003ep\u003c/em\u003e value for the intercept test was 0.132, which does not provide evidence for significant horizontal pleiotropy (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003eB, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e and \u003cb\u003eSupplementary Table \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e\u003c/b\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn our MR analysis, no significant associations were found between ARDS risk and three measures of tobacco exposure: SmkCes, SmkIndex, or SmkInit (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003eC\u003cb\u003e-Figure 2E\u003c/b\u003e). These findings suggest that, contrary to CigDay and AgeSmk, these measures of smoking behavior may not directly influence the risk of ARDS, or their effects may be mediated through other unmeasured factors.\u003c/p\u003e \u003cp\u003eAdditionally, after conducting the \u0026ldquo;leave-one-out\u0026rdquo; analysis and progressively excluding SNPs (\u003cb\u003eSupplementary Fig.\u0026nbsp;2\u003c/b\u003e), the results indicated that no single SNP significantly influenced the robustness of the results, ensuring the study\u0026rsquo;s stability and reliability. Furthermore, the application of the PRESSO method has been instrumental in enhancing the validity of our findings (\u003cb\u003eTable\u0026nbsp;1\u003c/b\u003e and \u003cb\u003eSupplementary Fig.\u0026nbsp;1\u003c/b\u003e). By partitioning the sum of effect sizes into components attributable to individual SNPs and adjusting for outliers, we have effectively mitigated the potential influence of spurious associations that could arise from extreme values or outliers in the data.\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eOur study utilized TSMR analysis to provide evidence for a causal relationship between tobacco exposure and the risk of ARDS. Our findings indicate a potential causal link between daily tobacco exposure, measured by CigDay, and ARDS, which is biologically plausible given the known detrimental effects of cigarette smoke on lung health. Similarly, the age of initiation of regular smoking, AgeSmk, also demonstrated an association with ARDS, suggesting that early-life exposures may have lasting impacts on respiratory health.\u003c/p\u003e \u003cp\u003eHowever, after applying the FDR correction to adjust for multiple comparisons, the statistical significance of the associations for both CigDay and AgeSmk did not reach the conventional threshold, highlighting the need for cautious interpretation of these results. While FDR correction is a robust method for controlling the rate of false positives, it also has the potential to mask true associations when multiple comparisons are made. Despite the FDR-adjusted \u003cem\u003ep\u003c/em\u003e values, the observed associations for CigDay and AgeSmk are noteworthy and align with the existing biological understanding of tobacco's impact on respiratory function. The lack of significant associations for other tobacco exposure metrics, such as SmkCes, SmkIndex, and SmkInit, suggests a complex and heterogeneous relationship between different aspects of tobacco use and the risk of ARDS.\u003c/p\u003e \u003cp\u003eNumerous studies have confirmed that cigarette smoke contributes to the development of ARDS through multiple pathological mechanisms. The oxidative stress and proinflammatory properties of cigarette smoke can increase alveolar epithelial permeability[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], disrupt immune responses[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e], and cause vascular endothelial damage[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. This leads to a heightened risk of ARDS, particularly in patients with extrapulmonary factors such as sepsis and trauma. Smoke also impairs alveolar epithelial cell integrity and ion channel expression, exacerbating the likelihood of severe edema during ARDS[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Furthermore, cigarette smoke accelerates cellular aging and autophagy impairment in lung cells, priming the lungs for intense inflammatory reactions[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe causal relationship between both CigDay and AgeSmk with ARDS suggests a potential dose‒response relationship where increased cigarette smoke exposure escalates the risk of ARDS. This finding is corroborated by biological studies that revealed the harmful effects of tobacco on lung health. However, the absence of significant associations with other metrics of tobacco exposure, such as SmkCes, SmkInit, and SmkIndex, could be indicative of the complex interplay between tobacco use and ARDS pathogenesis. This complexity necessitates a refined understanding of the multifaceted impact of tobacco on ARDS risk. As Chugh et al.[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e] systematically reviewed, the global implementation of tobacco control policies has shown that multifaceted initiatives, particularly those involving taxation, are linked to substantial reductions in smokeless tobacco use. This underscores the importance of a strategic, policy-driven approach to tobacco control, which could be instrumental in mitigating ARDS risk factors. Future research endeavors should be directed toward dissecting the intricate interactions between various smoking behaviors and ARDS subtypes within a more granular framework, taking into account etiological differences, coexisting health conditions, and individual patient characteristics.\u003c/p\u003e \u003cp\u003eThe MR approach utilized in this study presents significant advantages, particularly in minimizing biases associated with reverse causation and confounding that are prevalent in traditional observational research. Nonetheless, this study has its limitations. The assumption that selected SNPs exclusively influence ARDS risk through tobacco exposure might overlook potential pleiotropic effects, where genetic variants impact a spectrum of traits[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. While MR‒Egger regression intercept tests offer some validation of our instrumental variables, the possibility of unmeasured confounding factors cannot be discounted. Our findings, based on a European population, may not be directly generalizable to other ethnicities. The genetic and behavioral variations across diverse populations necessitate further investigation in various settings to substantiate these outcomes and elucidate the underlying biological mechanisms[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eIn conclusion, our MR study provides genetic support for a causal association of both AgeSmk and CigDay with ARDS, underscoring the importance of smoking reduction in its prevention. However, no causal relationship was identified for other tobacco exposure factors in the genesis and progression of ARDS, highlighting the complexity of tobacco exposure and the heterogeneity of ARDS. This suggests a need for a nuanced understanding of how different dimensions of tobacco exposure may interact with specific ARDS subtypes to influence risk.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eARDS Acute Respiratory Distress Syndrome\u003c/p\u003e\n\u003cp\u003eGWAS Genome-wide association studies\u003c/p\u003e\n\u003cp\u003eGSCAN GWAS and Sequencing Consortium of Alcohol and Nicotine use\u003c/p\u003e\n\u003cp\u003eSNPs single nucleotide polymorphisms\u003c/p\u003e\n\u003cp\u003eCOPD Chronic Obstructive Pulmonary Disease\u003c/p\u003e\n\u003cp\u003eMR Mendelian randomization\u003c/p\u003e\n\u003cp\u003eIVs instrumental variables\u003c/p\u003e\n\u003cp\u003eOR Odds Ratio\u003c/p\u003e\n\u003cp\u003eIVW inverse-varianceweighted\u003c/p\u003e\n\u003cp\u003eMR-IVW MR-inverse-varianceweighted\u003c/p\u003e\n\u003cp\u003eCI Confidence Interval\u003c/p\u003e\n\u003cp\u003eAgeSmk Age of initiation of regular smoking\u003c/p\u003e\n\u003cp\u003eSmkInit Smoking initiation (yes or no)\u003c/p\u003e\n\u003cp\u003eSmkCes Smoking cessation\u003c/p\u003e\n\u003cp\u003eCigDay Average number of cigarettes smoked per day\u003c/p\u003e\n\u003cp\u003eSmkIndex Smoking index\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch1\u003eAuthor Contributions\u003c/h1\u003e\n\u003cp\u003eThe study was conceived and designed by \u003cstrong\u003eY Wang, Y Li and X Deng\u003c/strong\u003e.\u0026nbsp;GWAS summary data collection and IV selection were conducted by \u003cstrong\u003eY Wang\u003c/strong\u003e,\u0026nbsp;\u003cstrong\u003eZ Cheng and D Xu\u003c/strong\u003e.\u0026nbsp;MR analysis was performed by\u0026nbsp;\u003cstrong\u003eK Shen,\u003c/strong\u003e\u003cstrong\u003eJ Li and S Yan\u003c/strong\u003e.\u0026nbsp;The manuscript was drafted by\u0026nbsp;\u003cstrong\u003eM Zhou, Y Qi, H Yu, H Ni and L Li\u003c/strong\u003e.\u0026nbsp;The manuscript was revised by\u0026nbsp;\u003cstrong\u003eY Wang and Y Li\u003c/strong\u003e.\u003c/p\u003e\n\u003ch1\u003eFunding\u003c/h1\u003e\n\u003cp\u003eThis research was self-funded by the authors.\u003c/p\u003e\n\u003ch1\u003eConflict of interest statement\u003c/h1\u003e\n\u003cp\u003eNo conflict of interest exists in the submission of this manuscript. We declare that all the authors listed meet the authorship criteria according to the latest guidelines of the International Committee of Medical Journal Editors, and all the authors are in agreement with the manuscript. All the authors declare that the work has not been published previously and is not under consideration for publication elsewhere, in whole or in part.\u003c/p\u003e\n\u003ch1\u003eEthics Statement\u003c/h1\u003e\n\u003cp\u003eEthical approval has been given for the use of data relating to humans from public datasets. Based on a publicly accessible database, this study did not require ethical approval or informed consent[19].\u003c/p\u003e\n\u003ch1\u003eAvailability of data and materials\u003c/h1\u003e\n\u003cp\u003eThe GWAS for tobacco smoking can be obtained through the GSCAN data portal (https://conservancy.umn.edu/handle/11299/241912) and the dataset on the website of the University of Bristol (https://data.bris.ac.uk/data/dataset/10i96zb8gm0j81yz0q6ztei23d). The GWAS for ARDS can be obtained form the FinnGen database. We want to acknowledge the participants and investigators of the FinnGen study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eMeyer, N.J., L. Gattinoni and C.S. Calfee, Acute respiratory distress syndrome. Lancet, 2021. 398(10300): p. 622-637.\u003c/li\u003e\n\u003cli\u003eBellani, G., et al., Epidemiology, Patterns of Care, and Mortality for Patients With Acute Respiratory Distress Syndrome in Intensive Care Units in 50 Countries. JAMA, 2016. 315(8): p. 788-800.\u003c/li\u003e\n\u003cli\u003eTrillo-Alvarez, C., et al., Acute lung injury prediction score: derivation and validation in a population-based sample. Eur Respir J, 2011. 37(3): p. 604-9.\u003c/li\u003e\n\u003cli\u003eCalfee, C.S., et al., Cigarette Smoke Exposure and the Acute Respiratory Distress Syndrome. Crit Care Med, 2015. 43(9): p. 1790-7.\u003c/li\u003e\n\u003cli\u003eMoss, M. and E.L. Burnham, Chronic alcohol abuse, acute respiratory distress syndrome, and multiple organ dysfunction. Crit Care Med, 2003. 31(4 Suppl): p. S207-12.\u003c/li\u003e\n\u003cli\u003eReilly, J.P., et al., Low to Moderate Air Pollutant Exposure and Acute Respiratory Distress Syndrome after Severe Trauma. Am J Respir Crit Care Med, 2019. 199(1): p. 62-70.\u003c/li\u003e\n\u003cli\u003eWang, D., et al., Clinical Characteristics of 138 Hospitalized Patients With 2019 Novel Coronavirus-Infected Pneumonia in Wuhan, China. JAMA, 2020. 323(11): p. 1061-1069.\u003c/li\u003e\n\u003cli\u003eWu, Z. and J.M. McGoogan, Characteristics of and Important Lessons From the Coronavirus Disease 2019 (COVID-19) Outbreak in China: Summary of a Report of 72 314 Cases From the Chinese Center for Disease Control and Prevention. JAMA, 2020. 323(13): p. 1239-1242.\u003c/li\u003e\n\u003cli\u003eBellani, G., et al., Epidemiology, Patterns of Care, and Mortality for Patients With Acute Respiratory Distress Syndrome in Intensive Care Units in 50 Countries. JAMA, 2016. 315(8): p. 788-800.\u003c/li\u003e\n\u003cli\u003eXu, Z., et al., Current Status of Cell-Based Therapies for COVID-19: Evidence From Mesenchymal Stromal Cells in Sepsis and ARDS. Front Immunol, 2021. 12: p. 738697.\u003c/li\u003e\n\u003cli\u003eSmoking prevalence and attributable disease burden in 195 countries and territories, 1990-2015: a systematic analysis from the Global Burden of Disease Study 2015. Lancet, 2017. 389(10082): p. 1885-1906.\u003c/li\u003e\n\u003cli\u003eIshii, Y., [Smoking and respiratory diseases]. Nihon Rinsho, 2013. 71(3): p. 416-20.\u003c/li\u003e\n\u003cli\u003eIribarren, C., et al., Cigarette smoking, alcohol consumption, and risk of ARDS: a 15-year cohort study in a managed care setting. Chest, 2000. 117(1): p. 163-8.\u003c/li\u003e\n\u003cli\u003eMoazed, F., et al., Cigarette Smoke Exposure and Acute Respiratory Distress Syndrome in Sepsis: Epidemiology, Clinical Features, and Biologic Markers. Am J Respir Crit Care Med, 2022. 205(8): p. 927-935.\u003c/li\u003e\n\u003cli\u003eIriyama, H., et al., Risk modifiers of acute respiratory distress syndrome in patients with non-pulmonary sepsis: a retrospective analysis of the FORECAST study. J Intensive Care, 2020. 8: p. 7.\u003c/li\u003e\n\u003cli\u003eBalfanz, P., et al., Early risk markers for severe clinical course and fatal outcome in German patients with COVID-19. PLoS One, 2021. 16(1): p. e0246182.\u003c/li\u003e\n\u003cli\u003eSekula, P., et al., Mendelian Randomization as an Approach to Assess Causality Using Observational Data. J Am Soc Nephrol, 2016. 27(11): p. 3253-3265.\u003c/li\u003e\n\u003cli\u003eLawlor, D.A., et al., Mendelian randomization: using genes as instruments for making causal inferences in epidemiology. Stat Med, 2008. 27(8): p. 1133-63.\u003c/li\u003e\n\u003cli\u003eHartwig, F.P., et al., Inflammatory Biomarkers and Risk of Schizophrenia: A 2-Sample Mendelian Randomization Study. JAMA Psychiatry, 2017. 74(12): p. 1226-1233.\u003c/li\u003e\n\u003cli\u003eDeng, M.G., et al., Association between frailty and depression: A bidirectional Mendelian randomization study. Sci Adv, 2023. 9(38): p. eadi3902.\u003c/li\u003e\n\u003cli\u003eBurgess, S., D.S. Small and S.G. Thompson, A review of instrumental variable estimators for Mendelian randomization. Stat Methods Med Res, 2017. 26(5): p. 2333-2355.\u003c/li\u003e\n\u003cli\u003eSaunders, G., et al., Genetic diversity fuels gene discovery for tobacco and alcohol use. Nature, 2022. 612(7941): p. 720-724.\u003c/li\u003e\n\u003cli\u003eWootton, R.E., et al., Evidence for causal effects of lifetime smoking on risk for depression and schizophrenia: a Mendelian randomisation study. Psychol Med, 2020. 50(14): p. 2435-2443.\u003c/li\u003e\n\u003cli\u003eKurki, M.I., et al., FinnGen provides genetic insights from a well-phenotyped isolated population. Nature, 2023. 613(7944): p. 508-518.\u003c/li\u003e\n\u003cli\u003eHemani, G., J. Bowden and S.G. Davey, Evaluating the potential role of pleiotropy in Mendelian randomization studies. Hum Mol Genet, 2018. 27(R2): p. R195-R208.\u003c/li\u003e\n\u003cli\u003eSekula, P., et al., Mendelian Randomization as an Approach to Assess Causality Using Observational Data. J Am Soc Nephrol, 2016. 27(11): p. 3253-3265.\u003c/li\u003e\n\u003cli\u003ePierce, B.L., H. Ahsan and T.J. Vanderweele, Power and instrument strength requirements for Mendelian randomization studies using multiple genetic variants. Int J Epidemiol, 2011. 40(3): p. 740-52.\u003c/li\u003e\n\u003cli\u003eBurgess, S. and S.G. Thompson, Avoiding bias from weak instruments in Mendelian randomization studies. Int J Epidemiol, 2011. 40(3): p. 755-64.\u003c/li\u003e\n\u003cli\u003eLin, S.H., R. Thakur and M.J. Machiela, LDexpress: an online tool for integrating population-specific linkage disequilibrium patterns with tissue-specific expression data. BMC Bioinformatics, 2021. 22(1): p. 608.\u003c/li\u003e\n\u003cli\u003eBowden, J., et al., Consistent Estimation in Mendelian Randomization with Some Invalid Instruments Using a Weighted Median Estimator. Genet Epidemiol, 2016. 40(4): p. 304-14.\u003c/li\u003e\n\u003cli\u003eHartwig, F.P., S.G. Davey and J. Bowden, Robust inference in summary data Mendelian randomization via the zero modal pleiotropy assumption. Int J Epidemiol, 2017. 46(6): p. 1985-1998.\u003c/li\u003e\n\u003cli\u003eHemani, G., et al., The MR-Base platform supports systematic causal inference across the human phenome. Elife, 2018. 7.\u003c/li\u003e\n\u003cli\u003eCho, Y., et al., Exploiting horizontal pleiotropy to search for causal pathways within a Mendelian randomization framework. Nat Commun, 2020. 11(1): p. 1010.\u003c/li\u003e\n\u003cli\u003eVerbanck, M., et al., Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases. Nat Genet, 2018. 50(5): p. 693-698.\u003c/li\u003e\n\u003cli\u003eXu, L., et al., Cigarette smoke triggers inflammation mediated by autophagy in BEAS-2B cells. Ecotoxicol Environ Saf, 2019. 184: p. 109617.\u003c/li\u003e\n\u003cli\u003eLugg, S.T., et al., Cigarette smoke exposure and alveolar macrophages: mechanisms for lung disease. Thorax, 2022. 77(1): p. 94-101.\u003c/li\u003e\n\u003cli\u003eZhang, Y., et al., Cigarette smoke-inactivated SIRT1 promotes autophagy-dependent senescence of alveolar epithelial type 2 cells to induce pulmonary fibrosis. Free Radic Biol Med, 2021. 166: p. 116-127.\u003c/li\u003e\n\u003cli\u003eShin, I.S., et al., Melatonin attenuates neutrophil inflammation and mucus secretion in cigarette smoke-induced chronic obstructive pulmonary diseases via the suppression of Erk-Sp1 signaling. J Pineal Res, 2015. 58(1): p. 50-60.\u003c/li\u003e\n\u003cli\u003eKaminski, T.W., et al., Lung microvascular occlusion by platelet-rich neutrophil-platelet aggregates promotes cigarette smoke-induced severe flu. JCI Insight, 2024. 9(2).\u003c/li\u003e\n\u003cli\u003eMarwick, J.A., et al., Cigarette smoke-induced oxidative stress and TGF-beta1 increase p21waf1/cip1 expression in alveolar epithelial cells. Ann N Y Acad Sci, 2002. 973: p. 278-83.\u003c/li\u003e\n\u003cli\u003eHsieh, S.J., et al., Prevalence and impact of active and passive cigarette smoking in acute respiratory distress syndrome. Crit Care Med, 2014. 42(9): p. 2058-68.\u003c/li\u003e\n\u003cli\u003eSang, S., et al., Learning From Past Respiratory Infections to Predict COVID-19 Outcomes: Retrospective Study. J Med Internet Res, 2021. 23(2): p. e23026.\u003c/li\u003e\n\u003cli\u003eChugh, A., et al., The global impact of tobacco control policies on smokeless tobacco use: a systematic review. Lancet Glob Health, 2023. 11(6): p. e953-e968.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table 1","content":"\u003cp\u003eTable 1 is available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"Tobacco exposure, Genetics, ARDS, Mendelian Randomization, SNPs","lastPublishedDoi":"10.21203/rs.3.rs-4806401/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4806401/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Previous studies have reported increased heterogeneity in acute respiratory distress syndrome (ARDS), but the causal relationship between ARDS and tobacco exposure is uncertain. Considering that tobacco exposure is relatively common, it can be used as an easily accessible indicator and is closely related to respiratory diseases. We examined the causal effect of tobacco exposure on ARDS-related phenotypes using a Mendelian randomization (MR) approach.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eIn this investigation, we obtained tobacco exposure data from the most recent genome-wide association studies (GWASs) conducted by the GWAS and Sequencing Consortium of Alcohol and Nicotine Use (GSCAN). Moreover, summary statistics data for lifetime smoking behavior (SmkIndex) were obtained from the UK Biobank. Furthermore, the present study utilized ARDS GWAS data from the Finngen database. This study used two-sample MR (TSMR) to investigate the causal relationship between tobacco exposure and ARDS. We performed extensive sensitivity analyses to confirm the robustness, heterogeneity, and potential multibiological effects of the study results. Additionally, to control for false positive results during multiple hypothesis testing, we adopted a false discovery rate (FDR) to control for statistical bias due to multiple comparisons.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eAfter FDR correction, tobacco exposure had no statistically significant effect on ARDS incidence. Several phenotypes with unadjusted low P values are worth mentioning, including cigarettes smoked daily (CigDay) (OR = 3.11, 95% CI 1.19-8.14, \u003cem\u003ep\u003c/em\u003e = 0.020, FDR-\u003cem\u003ep\u003c/em\u003e = 0.051) and age of initiation of regular smoking (AgeSmk) (OR = 0.01, 95% CI 0.00-0.45, \u003cem\u003ep\u003c/em\u003e = 0.016, FDR-\u003cem\u003ep\u003c/em\u003e= 0.051). In contrast, no causal links were identified for other measures of tobacco exposure with unadjusted\u003cem\u003e p\u003c/em\u003e values, including smoking cessation (SmkCes) (OR = 1.33, 95% CI 0.19-9.43, \u003cem\u003ep\u003c/em\u003e = 0.773), lifetime smoking behavior (SmkIndex) (OR = 3.02, 95% CI 0.59-15.30, \u003cem\u003ep\u003c/em\u003e = 0.183), and smoking initiation (SmkInit) (OR = 1.86, 95% CI 0.74-4.70, \u003cem\u003ep\u003c/em\u003e = 0.189).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eThis study revealed a causal link between CigDay and AgeSmk and the risk of ARDS. However, no genetic associations were found between SmkCes, SmkInit, or SmkIndex and ARDS, suggesting heterogeneity in the impact of smoking exposure on the disease. Further research is required to clarify the causes of this heterogeneity.\u003c/p\u003e","manuscriptTitle":"Causal Effect of Tobacco Exposure on Acute Respiratory Distress Syndrome: A Mendelian Randomization Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-27 05:59:43","doi":"10.21203/rs.3.rs-4806401/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":"99a4341a-667a-42e5-98ed-a23a8747be03","owner":[],"postedDate":"August 27th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-08-28T09:36:11+00:00","versionOfRecord":[],"versionCreatedAt":"2024-08-27 05:59:43","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4806401","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4806401","identity":"rs-4806401","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}