Causal relationships between smoking, alcohol consumption frequency, coffee intake and osteomyelitis: a univariable and multivariable Mendelian randomization study

Causal relationships between smoking, alcohol consumption frequency, coffee intake and osteomyelitis: a univariable and multivariable 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 relationships between smoking, alcohol consumption frequency, coffee intake and osteomyelitis: a univariable and multivariable Mendelian randomization study Tianxuan Feng, Peisheng Chen, Dongze Lin, Ke Zheng, Jiajie Liu, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5305235/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 This study aimed to evaluate the causal relationships between smoking, alcohol consumption and coffee intake with osteomyelitis through Mendelian randomization (MR) analysis. Methods Data on smoking, alcohol consumption, coffee intake, and osteomyelitis-related single nucleotide polymorphisms (SNPs) were obtained from the open Genome-Wide Association Study (GWAS) database of the Integrated Epidemiology Unit. We employed univariable Mendelian randomization (MR) methods, including MR‒Egger, weighted median, inverse variance weighted (IVW), simple mode, and weighted mode, to investigate the causal relationships between the three exposures (smoking initiation, alcohol consumption frequency, and coffee intake) and osteomyelitis. Sensitivity analyses, including heterogeneity tests, horizontal pleiotropy tests, and leave-one-out (LOO) analyses, were subsequently conducted. Furthermore, multivariable MR (MVMR) analyses were performed to simultaneously consider the effects of the three exposure factors on osteomyelitis. Results On the basis of the results of the univariable Mendelian randomization analysis via the inverse variance weighted method, a significant causal relationship was found between smoking initiation and alcohol consumption frequency with osteomyelitis, whereas no significant association was detected for coffee intake. Specifically, smoking initiation (p ≤ 0.001, odds ratio (OR) = 1.642, 95% confidence interval (CI): 1.321–2.041) and alcohol consumption frequency (p = 0.003, OR = 1.384, 95% CI: 1.116–1.716) were identified as risk factors for osteomyelitis. With respect to the sensitivity analysis for MR results, there was no heterogeneity or horizontal pleiotropy. Leave-one-out (LOO) analysis confirmed the robustness of the univariable MR results. Additionally, multivariable MR analysis revealed that smoking (p ≤ 0.001, OR = 1.573, 95% CI: 1.259–1.965) and alcohol consumption (p = 0.011, OR = 1.312, 95% CI: 1.064–1.618) remained significant risk factors for osteomyelitis when all three factors were considered simultaneously, whereas coffee intake was not statistically significant (p = 0.528). Conclusion This Mendelian randomization study revealed that smoking and alcohol consumption are significant risk factors for osteomyelitis, whereas coffee intake is not significantly associated with osteomyelitis. These findings provide important insights for osteomyelitis prevention and public health strategies. Mendelian randomization Osteomyelitis Smoking Alcohol consumption Lifestyle factors Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Background Osteomyelitis is a progressive inflammatory process caused by pathogens, resulting in bone destruction and sequestration. [ 1 ] Patients with osteomyelitis often suffer from persistent bone pain, localized swelling, and fever, which may lead to long-term complications such as bone necrosis and joint deformity if left untreated. [ 2 ] The incidence of osteomyelitis varies by region and population demographics, making accurate estimation of its global prevalence challenging. However, studies suggest an increasing trend, with an estimated incidence rate ranging from 20 to 100 cases per 100,000 individuals annually, depending on the healthcare setting and geographic location. [ 3 ] The disease is more commonly observed in males than in females, and individuals with underlying conditions such as diabetes, vascular insufficiency, and immune suppression are at greater risk. [ 4 ] Despite advancements in medical technologies, the treatment of osteomyelitis remains challenging. Thus, identifying modifiable lifestyle risk factors and emphasizing prevention are critically important. Smoking, alcohol consumption frequency, and coffee intake are prevalent lifestyle factors known to influence various health outcomes [ 5 ] . Some studies have suggested that smoking may increase the risk of osteomyelitis, although these studies are often subject to residual confounding, which may result in inaccurate risk estimates. [ 6 ] Mendelian randomization (MR) is an innovative approach that uses genetic variants as instrumental variables to infer causal relationships between exposures and outcomes. [ 7 , 8 ] This method largely overcomes the limitations of traditional observational studies, as genetic variants are randomly allocated during meiosis and remain unaffected by environmental factors. [ 9 , 10 ] Given the limitations of previous research and the strengths of the Mendelian randomization approach, we performed an MR analysis using summary data from large-scale genome-wide association studies (GWASs) to explore the causal relationships among smoking, alcohol consumption, coffee intake, and the risk of osteomyelitis. Methods Study design This study utilized both univariable and multivariable Mendelian randomization (MR) analyses, leveraging summary data from GWASs to explore the causal relationships between smoking initiation, alcohol consumption frequency, or coffee intake, and osteomyelitis. Univariable MR aimed to assess the effect of each exposure individually on osteomyelitis, whereas multivariable MR evaluated the independent causal effects of these exposures when considered together. The MR design adhered to three core principles of genetic instruments: (1) they are strongly associated with smoking initiation, alcohol consumption frequency, or coffee intake; (2) they are related to osteomyelitis only through these exposures, with no alternative pathways; and (3) they are not associated with any confounding factors in the exposure‒outcome relationship. An overview of the MR analysis design is provided in Fig. 1 . Data sources and summary The datasets used in this study were obtained from the Integrative Epidemiology Unit (IEU) OpenGWAS database ( https://gwas.mrcieu.ac.uk/ ). The osteomyelitis data were sourced from the UK Biobank and included 486,484 controls. All participants were of European ancestry, and informed consent was obtained from each participant. The summary-level GWAS data on smoking initiation were retrieved from the IEU database and included 607,291 controls. Alcohol consumption frequency data were extracted from the UK Biobank, which included 462,346 participants, and coffee intake data were also sourced from the UK Biobank, which included 428,860 participants. Detailed information about these datasets is presented in Table 1 . Table 1 Detailed information about these datasets Trait year population GWAS ID Sample size snp consortium Smoking initiation 2019 European ieu-b-4877 607,291 11,802,365 GSCAN Alcohol intake frequency 2018 European ukb-b-5779 462,346 9,851,867 UKB Biobank Coffee intake 2018 European ukb-b-5237 428,860 9,851,867 UK Biobank Osteomyelitis 2021 European ieu-b-4975 486,484 12,243,512 UK Biobank Data preprocessing The “extract_instruments” function in the R package “TwoSampleMR” (v.0.5.6) was employed to extract exposure factors and screen instrumental variables (IVs), identifying SNPs significantly associated with the exposure factors as IVs (P < 5 × 10 − 8). SNPs in linkage disequilibrium were then removed (clump = TRUE; r2 = 0.001; kb = 100). Following this, the “harmonize data” function in “TwoSampleMR” was used to harmonize effect alleles with effect sizes after excluding SNPs that were strongly associated with the outcome. In this study, smoking, alcohol consumption, and coffee intake served as the exposure factors, and osteomyelitis was the outcome in both the univariable and multivariable Mendelian randomization analyses. Univariate and multivariate Mendelian randomization analysis The “mr” function in the “TwoSampleMR” package was utilized to conduct both univariable and multivariable Mendelian randomization (MR) analyses, applying five algorithms: MR Egger, simple mode, weighted mode, weighted median, and inverse variance weighted (IVW). Notably, the IVW method was crucial in determining the causal relationships between smoking initiation, alcohol consumption frequency, coffee intake and osteomyelitis outcome. Odds ratios (ORs) were calculated, where an OR > 1 suggests that exposure is a risk factor, and an OR < 1 indicates a protective factor. The results were visualized through scatter plots, forest plots, and funnel plots. Various sensitivity analyses were conducted to ensure the robustness of the univariable and multivariable MR results. Cochran’s Q test was used to assess SNP heterogeneity (p > 0.05 indicating no heterogeneity), and the MR‒Egger intercept was used to evaluate horizontal pleiotropy. A leave-one-out analysis was used to check for SNPs that disproportionately influenced the results, and MR-PRESSO detected outlier SNPs. A two-sided p value < 0.05 was deemed statistically significant, with Bonferroni correction applied for multiple outcomes. For the primary analysis, p < 0.01 was considered significant. MR analyses were conducted via TwoSampleMR (v.0.5.6) and MVMR (v.0.3) in R (v.4.2.2). Results Instrumental variables After rigorous analysis, 86 instrumental variables were identified for smoking initiation, 95 SNPs for alcohol consumption frequency, and 39 SNPs for coffee intake. (Supplementary Table 1–3) Causal Relationships between the Three Exposure Factors and Osteomyelitis The results of the analysis suggested a causal association between genetically predicted smoking, alcohol consumption and an increased risk of osteomyelitis. Specifically, as shown in Fig. 2 , the IVW method revealed a significant causal relationship between smoking initiation (p < 0.001, OR = 1.450), alcohol consumption frequency (p = 0.01, OR = 1.3), and osteomyelitis, with both exposure factors showing positive slopes in the scatter plot (Fig. 3 ), indicating that they are risk factors. However, coffee intake (p > 0.05) was not significantly associated with the incidence of osteomyelitis. Furthermore, forest plots (Fig. 4 ) were generated on the basis of the findings of this MR study. Heterogeneity and sensitivity tests The heterogeneity of the causal estimates for smoking initiation, alcohol consumption frequency, and coffee intake was assessed via Cochran’s Q test. The funnel plot in Fig. 5 indicates that heterogeneity did not affect the overall results (Table 2 ). Furthermore, all p values in the MR‒Egger analysis were greater than 0.05, suggesting that there was no horizontal pleiotropy between the three exposure factors and osteomyelitis (p > 0.05) (Table 3 ). In addition, leave-one-out (LOO) analysis revealed no substantial bias, confirming the robustness of the results (Fig. 6 ). Therefore, exposure to smoking and alcohol consumption was significantly associated with the occurrence of osteomyelitis. Table 2 Heterogeneity test between eight exposure factors and osteomyelitis Outcome exposure Q Q_df Q_pval om Smoking 82.15990 85 0.567 om Alcohol intake frequency 119.254 93 0.037 om Coffee intake 35.46452 38 0.5872879 Table 3 Horizontal pleiotropy test between exposure factors and osteomyelitis Outcome exposure Egger_intercept se Q_pval om smoking -0.004072797 0.01296636 0.754 om Alcohol intake frequency 0.004 0.006 0.503 om Coffee intake 0.008 0.008 0.276 Multivariable MR analysis (MVMR) After screening, 163 SNPs were selected from the three exposure factors as instrumental variables (IVs) for MVMR analysis. The MVMR results revealed that, when all three factors were considered simultaneously, smoking (p ≤ 0.001, OR = 1.478, 95% CI: 1.191–1.829) and alcohol consumption (p ≤ 0.001, OR = 1.458, 95% CI: 1.177–1.806) remained significant risk factors for osteomyelitis. In contrast, coffee consumption did not demonstrate statistical significance in the MVMR analysis (p = 0.318, OR = 1.067, 95% CI: 0.472–1.277). These findings suggest that smoking and alcohol consumption are significantly associated with osteomyelitis, whereas coffee intake is not significantly related to the risk of osteomyelitis. (Supplementary Table 4–5) Discussion In this study, we employed a multi-sample causal analysis method based on large-scale genome-wide association study (GWAS) data to investigate the potential causal relationships between smoking, alcohol consumption frequency, coffee intake, and osteomyelitis. Mendelian randomization (MR) analysis revealed that genetic predispositions to smoking and excessive alcohol consumption frequency were significantly associated with an increased risk of osteomyelitis, whereas coffee intake was not associated with osteomyelitis. Interventions targeting these risk factors could reduce the incidence and severity of osteomyelitis. Specifically, smoking cessation and alcohol moderation may play critical roles in reducing the occurrence of osteomyelitis and slowing its progression. These findings offer new insights for the prevention and management of osteomyelitis. A growing body of clinical research suggests that smoking and alcohol consumption may be associated with an increased risk of osteomyelitis. One observational cohort study, which analyzed data from 1,186 hospitalized osteomyelitis patients between 2004 and 2015, revealed a significant association between smoking history and the likelihood of lower limb amputation. Specifically, former smokers had a relative risk (RR) of 1.38 (p < 0.01), highlighting the negative impact of smoking on the severity of osteomyelitis [ 11 ] . Additionally, a multivariate analysis of 38 dental implant candidates revealed that smokers had an osteomyelitis risk ratio ranging from 47–64.7%, indicating that smoking significantly increases the incidence of osteomyelitis [ 12 ] . Related literature also reports that patients with chronic osteomyelitis should avoid smoking and alcohol consumption, as these habits can adversely affect the treatment and progression of the disease [ 13 ] . Although observational studies have not established a causal relationship between smoking, alcohol consumption, and osteomyelitis, these behaviors often coexist with other pathological conditions, such as impaired immune function, metabolic disorders, and inflammatory responses. These comorbidities may contribute to the observed positive correlation between smoking, alcohol consumption, and osteomyelitis [ 14 ] . Genome-wide association studies, powerful tools for investigating complex diseases, have been widely used to identify single genes or groups of genes, surpassing the limitations of traditional single-gene association studies. GWASs not only validate existing research but also open new directions for exploration [ 15 ] . On the basis of large-scale GWAS data, our Mendelian randomization study extracted genetic evidence of causal relationships between smoking, alcohol consumption, and osteomyelitis, further highlighting the importance of targeting these behaviors in public health interventions aimed at prevention and control. The negative effects of smoking and alcohol consumption on the immune system are well documented, with key physiological mechanisms including increased oxidative stress, heightened inflammatory responses, and impaired vascular function—all of which may contribute to the development of osteomyelitis [ 16 , 17 ] . Smoking inhibits bone formation primarily by reducing the expression of bone morphogenetic proteins (BMPs) and osteocalcin, thereby slowing bone growth. Additionally, smoking increases the expression of RANKL, stimulating osteoclast activity and promoting bone resorption [ 18 ] . Cigarette exposure also inhibits the osteogenic potential of mesenchymal stem cells by suppressing the activities of catalase and glutathione reductase, leading to impaired bone formation [ 19 ] . Similarly, alcohol consumption hinders bone formation by reducing BMP-2 expression and inhibiting the Wnt signaling pathway, further compromising bone health [ 20 ] . Studies further indicate that the combined effect of smoking and alcohol consumption may weaken the body's immune defense mechanisms, providing an opportunity for pathogens to invade bone tissue [ 21 ] . The proinflammatory properties of smoking are closely linked to oxidative stress, where smoking induces chronic inflammation and oxidative stress, inhibiting cytokine secretion and the cytotoxic functions of immune cells [ 22 ] . This weakens the immune system and increases the risk of infection. Alcohol consumption is associated with immune dysregulation, with chronic alcohol consumption impairing the functions of monocytes and macrophages, weakening microbial responses and delaying wound healing, thus increasing the risk of infection [ 23 ] . Smoking generates reactive oxygen species (ROS) and nitrogen species (RNS), which damage cellular and subcellular targets such as lipids, proteins, and nucleic acids, triggering oxidative stress and inflammation [ 24 ] . Oxidative stress induces inflammation, which in turn produces more ROS, leading to further oxidative damage. Alcohol similarly promotes systemic inflammation, exacerbates various chronic conditions, and is thus considered a risk factor for immune-related diseases [ 25 ] . This study has several strengths. First, this study is the first to utilize both univariable and multivariable Mendelian randomization (MR) analyses combined with large-scale GWAS data, effectively overcoming the limitations of observational studies. The univariable MR approach allowed us to explore the individual effects of smoking and alcohol consumption on osteomyelitis, whereas multivariable MR enabled the assessment of these exposures simultaneously, providing a more comprehensive understanding of their independent causal relationships. The instrumental variables were rigorously selected, ensuring robust and reliable results. Furthermore, by leveraging these MR methods, we minimized confounding factors and reverse causality, thereby strengthening the validity of the observed causal relationships. However, some limitations should be acknowledged. All participants in this study were of European ancestry, which limits the generalizability of the findings to other ethnic groups. Additionally, we were unable to conduct stratified analyses on the basis of other demographic variables. In the future, large-scale studies involving more diverse populations, along with stratified analyses, will be necessary to confirm the applicability of these findings across different racial and ethnic groups. Conclusion Through univariable and multivariable Mendelian randomization (MR) analyses, we identified a potential causal relationship between smoking, alcohol consumption, and the incidence of osteomyelitis. Both behaviors appear to increase the risk of osteomyelitis. Our study provides genetic evidence supporting the causal link between smoking, alcohol consumption, and osteomyelitis, underscoring the need for public health interventions. However, the precise mechanisms underlying this relationship remain unclear. Future research should focus on further investigating these mechanisms and developing more effective prevention strategies. Abbreviations MR Mendelian randomization GWAS Genome-Wide Association Studies IVW Inverse-variance weighting OR Odds ratio CI Confidence interval IVs instrumental variables SNPs single-nucleotide polymorphisms BMP bone morphogenetic protein RANKL receptor Activator of Nuclear Factor-κB Ligand ROS reactive oxygen species RNS reactive nitrogen species Declarations Ethics approval and consent to participate Because the study was based on a public database, did not involve animal or human studies, and was available in the form of open access and anonymous data, Institutional Review Board approval was not required. Consent for publication Not applicable Availability of data and material s The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. Competing interests The authors declare that they have no competing interests. Funding This study was supported by grants from Center for First Aid and Rehabilitation in Orthopedic Trauma (2020Y2014); Science and Technology Planning Project of Fuzhou (2022-R-009). Authors' contributions Tianxuan Feng: designed research, performed research, collected data, analyzed data, wrote paper. Ps C, Dz L, KZ, and Jj L: collected data and verification results. Ff L: designed research and revised article. Authors' information 1.Tianxuan Feng, Fujian University of Traditional Chinese Medicine, Fuzhou 350007, Fujian Province, China 2. Peisheng Chen、Dongze Lin、Ke Zheng, Jiajie Liu China Department of Orthopedics, Fuzhou Second General Hospital, School of Clinical Medicine of Fujian Medical University, Fujian Provincial Clinical Medical Research Center for First Aid and Rehabilitation in Orthopedic Trauma, Fuzhou 350007, Fujian, China Acknowledgements Not applicable Consent to Participate Consent to Participate declaration: not applicable Clinical trial number Clinical trial number: not applicable References Panteli M, Giannoudis PV. Chronic osteomyelitis: what the surgeon needs to know. EFORT Open Rev. 2017;1(5):128–35. 10.1302/2058-5241.1.000017 . Published 2017 Mar 13. Bury DC, Rogers TS, Dickman MM. Osteomyelitis: Diagnosis and Treatment. Am Fam Physician. 2021;104(4):395–402. Kremers HM, Nwojo ME, Ransom JE, Wood-Wentz CM, Melton LJ 3rd. Huddleston PM 3rd. Trends in the epidemiology of osteomyelitis: a population-based study, 1969 to 2009. J Bone Joint Surg Am. 2015;97(10):837–45. 10.2106/JBJS.N.01350 . 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Supplementary Files supplementarytable1.csv supplementarytable2.csv supplementarytable3.csv supplementarytable4.csv supplementarytable5.csv 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-5305235","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":372054270,"identity":"e30cbd21-1738-48d9-b9c3-2f486575766b","order_by":0,"name":"Tianxuan Feng","email":"","orcid":"","institution":"Fujian University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Tianxuan","middleName":"","lastName":"Feng","suffix":""},{"id":372054272,"identity":"5448a479-c4b7-4f0f-a193-ccc21b6b8ce1","order_by":1,"name":"Peisheng Chen","email":"","orcid":"","institution":"Fuzhou Second General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Peisheng","middleName":"","lastName":"Chen","suffix":""},{"id":372054273,"identity":"9e882043-e527-4dac-a647-aef11a21c37b","order_by":2,"name":"Dongze Lin","email":"","orcid":"","institution":"Fuzhou Second General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Dongze","middleName":"","lastName":"Lin","suffix":""},{"id":372054274,"identity":"010d0eec-7e24-4579-950c-b640b939c7c7","order_by":3,"name":"Ke Zheng","email":"","orcid":"","institution":"Fuzhou Second General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Ke","middleName":"","lastName":"Zheng","suffix":""},{"id":372054275,"identity":"34368c6b-fbdd-4719-8703-fc6509a2d09e","order_by":4,"name":"Jiajie Liu","email":"","orcid":"","institution":"Fuzhou Second General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jiajie","middleName":"","lastName":"Liu","suffix":""},{"id":372054276,"identity":"36295b99-bc7b-438b-81e1-8e20ed57bc37","order_by":5,"name":"Fengfei Lin","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAy0lEQVRIiWNgGAWjYBAC+/bm459/VNgwM7Y3EKnFgOdYGjPDmTR25p4DxGqRyFFjZmw7xM8+I4FILeYSOWyPC84ckOad+XjjDYYam2iCWix73h43nlFxx1hydlqxBcOxtNwGgnqO5yVI8Jx5lmw4O8dMgrHhMBFaDuQYSPC2Ha7ff/MMkVoMTuSYSQO1MDPO4CFSi2TPsWTDGWfSmBl7gH5JIMYv/OzNBx98AEfl4Y03PtTYEOEXZEdKJJCiHKKFVB2jYBSMglEwMgAANctFa8z/xPQAAAAASUVORK5CYII=","orcid":"","institution":"Fuzhou Second General Hospital","correspondingAuthor":true,"prefix":"","firstName":"Fengfei","middleName":"","lastName":"Lin","suffix":""}],"badges":[],"createdAt":"2024-10-21 14:23:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5305235/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5305235/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":68351593,"identity":"35f5986a-adde-48d1-86e3-700039a58aa1","added_by":"auto","created_at":"2024-11-06 10:48:44","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":74708,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eOverview of the study design for the univariable and multivariable Mendelian randomization (MR) analysis.\u003c/strong\u003e MR estimates were calculated via five algorithms. The results of the inverse variance weighted (IVW) method are crucial for determining the causal relationships among smoking, alcohol consumption, coffee intake, and osteomyelitis. Both univariable and multivariable MR analyses were employed to assess the individual and combined effects of the exposures. Sensitivity analyses, including heterogeneity tests, horizontal pleiotropy evaluation, and leave-one-out (LOO) analysis, were conducted to ensure the reliability of the MR results.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-5305235/v1/248cab535eaaf579107db3f4.png"},{"id":68352950,"identity":"4023258c-0c24-474e-82d3-0f76a71eaba5","added_by":"auto","created_at":"2024-11-06 10:56:44","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":175404,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot of the study evaluating the causal relationship between exposure and osteomyelitis via the IVW MR method.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5305235/v1/5469c2dff69f23b039bcf8f7.png"},{"id":68351595,"identity":"1c46bc3d-df25-4e63-a25f-34ac8d0ba2f2","added_by":"auto","created_at":"2024-11-06 10:48:44","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":247881,"visible":true,"origin":"","legend":"\u003cp\u003eScatter plots from MR analysis (A) Scatter plot illustrating the causal relationship between smoking initiation and osteomyelitis (B) Scatter plot illustrating the causal relationship between alcohol intake frequency and osteomyelitis (C) Scatter plot illustrating the causal relationship between coffee intake and osteomyelitis\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-5305235/v1/76a555738cb26ba9020a5e92.png"},{"id":68351599,"identity":"120d1593-c10b-4e66-b7fd-4268dd0a72b8","added_by":"auto","created_at":"2024-11-06 10:48:44","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":334860,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot from MR analysis. (A) Relationship between smoking initiation and osteomyelitis. (B) Relationship between alcohol intake frequency and osteomyelitis. (C) Relationship between coffee intake and osteomyelitis.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-5305235/v1/82c5716e4f1e6bc1a21656a3.png"},{"id":68351602,"identity":"300076b3-4426-4888-81bf-202da833c3ef","added_by":"auto","created_at":"2024-11-06 10:48:44","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":190198,"visible":true,"origin":"","legend":"\u003cp\u003eFunnel plot t of MR analysis. (A–C) Funnel plots showing the overall heterogeneity of MR analysis for osteomyelitis: (A) smoking initiation, (B) alcohol intake frequency, and (C) coffee intake.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-5305235/v1/956c72f8bbd7650a3abe2ecb.png"},{"id":68351603,"identity":"0477bc7f-3ea9-43f3-adcd-90b864fab076","added_by":"auto","created_at":"2024-11-06 10:48:44","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":303761,"visible":true,"origin":"","legend":"\u003cp\u003eLeave-one-out analysis of MR data. (A) Smoking initiation, (B)alcohol intake frequency, and (C) coffee intake\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-5305235/v1/b6389d5a5b2942c390ef1eb2.png"},{"id":69088023,"identity":"41fe04d3-e9be-4dda-9a97-7879d6c82dfa","added_by":"auto","created_at":"2024-11-15 13:02:08","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1710149,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5305235/v1/d327b7b1-4e3b-42bc-813e-7db88dafe820.pdf"},{"id":68351594,"identity":"04508c53-b124-4095-a242-4f8006efe0dc","added_by":"auto","created_at":"2024-11-06 10:48:44","extension":"csv","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":38031,"visible":true,"origin":"","legend":"","description":"","filename":"supplementarytable1.csv","url":"https://assets-eu.researchsquare.com/files/rs-5305235/v1/b71862dfbadc63bafbde0dc6.csv"},{"id":68352949,"identity":"c0c3cde5-5f09-4005-a100-79124daa1c13","added_by":"auto","created_at":"2024-11-06 10:56:44","extension":"csv","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":15212,"visible":true,"origin":"","legend":"","description":"","filename":"supplementarytable2.csv","url":"https://assets-eu.researchsquare.com/files/rs-5305235/v1/008232678ecb99cf21404ef1.csv"},{"id":68353415,"identity":"66107242-ad41-4bd1-b314-35501ce940b4","added_by":"auto","created_at":"2024-11-06 11:04:44","extension":"csv","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":32881,"visible":true,"origin":"","legend":"","description":"","filename":"supplementarytable3.csv","url":"https://assets-eu.researchsquare.com/files/rs-5305235/v1/8874c3742256cf6caf247f96.csv"},{"id":68351597,"identity":"50ff9a50-6725-498b-a18c-386b40ef1fc1","added_by":"auto","created_at":"2024-11-06 10:48:44","extension":"csv","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":794,"visible":true,"origin":"","legend":"","description":"","filename":"supplementarytable4.csv","url":"https://assets-eu.researchsquare.com/files/rs-5305235/v1/3c488f2a83aca902ce079096.csv"},{"id":68351601,"identity":"b8b12300-e4ad-4e6b-a8e5-8726dc48d86f","added_by":"auto","created_at":"2024-11-06 10:48:44","extension":"csv","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":34063,"visible":true,"origin":"","legend":"","description":"","filename":"supplementarytable5.csv","url":"https://assets-eu.researchsquare.com/files/rs-5305235/v1/d9ad137d03a465b722f7d223.csv"}],"financialInterests":"No competing interests reported.","formattedTitle":"Causal relationships between smoking, alcohol consumption frequency, coffee intake and osteomyelitis: a univariable and multivariable Mendelian randomization study","fulltext":[{"header":"Background","content":"\u003cp\u003eOsteomyelitis is a progressive inflammatory process caused by pathogens, resulting in bone destruction and sequestration.\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e Patients with osteomyelitis often suffer from persistent bone pain, localized swelling, and fever, which may lead to long-term complications such as bone necrosis and joint deformity if left untreated.\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e The incidence of osteomyelitis varies by region and population demographics, making accurate estimation of its global prevalence challenging. However, studies suggest an increasing trend, with an estimated incidence rate ranging from 20 to 100 cases per 100,000 individuals annually, depending on the healthcare setting and geographic location.\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e The disease is more commonly observed in males than in females, and individuals with underlying conditions such as diabetes, vascular insufficiency, and immune suppression are at greater risk.\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e Despite advancements in medical technologies, the treatment of osteomyelitis remains challenging. Thus, identifying modifiable lifestyle risk factors and emphasizing prevention are critically important.\u003c/p\u003e \u003cp\u003eSmoking, alcohol consumption frequency, and coffee intake are prevalent lifestyle factors known to influence various health outcomes \u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. Some studies have suggested that smoking may increase the risk of osteomyelitis, although these studies are often subject to residual confounding, which may result in inaccurate risk estimates. \u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eMendelian randomization (MR) is an innovative approach that uses genetic variants as instrumental variables to infer causal relationships between exposures and outcomes.\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e This method largely overcomes the limitations of traditional observational studies, as genetic variants are randomly allocated during meiosis and remain unaffected by environmental factors.\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e Given the limitations of previous research and the strengths of the Mendelian randomization approach, we performed an MR analysis using summary data from large-scale genome-wide association studies (GWASs) to explore the causal relationships among smoking, alcohol consumption, coffee intake, and the risk of osteomyelitis.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design\u003c/h2\u003e \u003cp\u003eThis study utilized both univariable and multivariable Mendelian randomization (MR) analyses, leveraging summary data from GWASs to explore the causal relationships between smoking initiation, alcohol consumption frequency, or coffee intake, and osteomyelitis. Univariable MR aimed to assess the effect of each exposure individually on osteomyelitis, whereas multivariable MR evaluated the independent causal effects of these exposures when considered together. The MR design adhered to three core principles of genetic instruments: (1) they are strongly associated with smoking initiation, alcohol consumption frequency, or coffee intake; (2) they are related to osteomyelitis only through these exposures, with no alternative pathways; and (3) they are not associated with any confounding factors in the exposure‒outcome relationship. An overview of the MR analysis design is provided in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eData sources and summary\u003c/h3\u003e\n\u003cp\u003eThe datasets used in this study were obtained from the Integrative Epidemiology Unit (IEU) OpenGWAS database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://gwas.mrcieu.ac.uk/\u003c/span\u003e\u003cspan address=\"https://gwas.mrcieu.ac.uk/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The osteomyelitis data were sourced from the UK Biobank and included 486,484 controls. All participants were of European ancestry, and informed consent was obtained from each participant. The summary-level GWAS data on smoking initiation were retrieved from the IEU database and included 607,291 controls. Alcohol consumption frequency data were extracted from the UK Biobank, which included 462,346 participants, and coffee intake data were also sourced from the UK Biobank, which included 428,860 participants. Detailed information about these datasets is presented 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\u003eDetailed information about these datasets\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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=\"left\" 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=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrait\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\u003epopulation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGWAS ID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSample size\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003esnp\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003econsortium\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking\u003c/p\u003e \u003cp\u003einitiation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eieu-b-4877\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e607,291\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e11,802,365\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eGSCAN\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlcohol intake frequency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eukb-b-5779\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e462,346\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9,851,867\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eUKB Biobank\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoffee intake\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eukb-b-5237\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e428,860\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9,851,867\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eUK Biobank\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOsteomyelitis\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\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eieu-b-4975\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e486,484\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e12,243,512\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eUK Biobank\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eData preprocessing\u003c/h3\u003e\n\u003cp\u003eThe \u0026ldquo;extract_instruments\u0026rdquo; function in the R package \u0026ldquo;TwoSampleMR\u0026rdquo; (v.0.5.6) was employed to extract exposure factors and screen instrumental\u003c/p\u003e \u003cp\u003evariables (IVs), identifying SNPs significantly associated with the exposure factors as IVs (P\u0026thinsp;\u0026lt;\u0026thinsp;5 \u0026times; 10\u0026thinsp;\u0026minus;\u0026thinsp;8). SNPs in linkage disequilibrium were then removed (clump\u0026thinsp;=\u0026thinsp;TRUE; r2\u0026thinsp;=\u0026thinsp;0.001; kb\u0026thinsp;=\u0026thinsp;100). Following this, the \u0026ldquo;harmonize data\u0026rdquo; function in \u0026ldquo;TwoSampleMR\u0026rdquo; was used to harmonize effect alleles with effect sizes after excluding SNPs that were strongly associated with the outcome. In this study, smoking, alcohol consumption, and coffee intake served as the exposure factors, and osteomyelitis was the outcome in both the univariable and multivariable Mendelian randomization analyses.\u003c/p\u003e \u003cp\u003eUnivariate and multivariate Mendelian randomization analysis\u003c/p\u003e \u003cp\u003eThe \u0026ldquo;mr\u0026rdquo; function in the \u0026ldquo;TwoSampleMR\u0026rdquo; package was utilized to conduct both univariable and multivariable Mendelian randomization (MR) analyses, applying five algorithms: MR Egger, simple mode, weighted mode, weighted median, and inverse variance weighted (IVW). Notably, the IVW method was crucial in determining the causal relationships between smoking initiation, alcohol consumption frequency, coffee intake and osteomyelitis outcome. Odds ratios (ORs) were calculated, where an OR\u0026thinsp;\u0026gt;\u0026thinsp;1 suggests that exposure is a risk factor, and an OR\u0026thinsp;\u0026lt;\u0026thinsp;1 indicates a protective factor. The results were visualized through scatter plots, forest plots, and funnel plots. Various sensitivity analyses were conducted to ensure the robustness of the univariable and multivariable MR results. Cochran\u0026rsquo;s Q test was used to assess SNP heterogeneity (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05 indicating no heterogeneity), and the MR‒Egger intercept was used to evaluate horizontal pleiotropy. A leave-one-out analysis was used to check for SNPs that disproportionately influenced the results, and MR-PRESSO detected outlier SNPs. A two-sided p value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was deemed statistically significant, with Bonferroni correction applied for multiple outcomes. For the primary analysis, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01 was considered significant. MR analyses were conducted via TwoSampleMR (v.0.5.6) and MVMR (v.0.3) in R (v.4.2.2).\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eInstrumental variables\u003c/h2\u003e \u003cp\u003eAfter rigorous analysis, 86 instrumental variables were identified for smoking initiation, 95 SNPs for alcohol consumption frequency, and 39 SNPs for coffee intake. (Supplementary Table\u0026nbsp;1\u0026ndash;3)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eCausal Relationships between the Three Exposure Factors and Osteomyelitis\u003c/h2\u003e \u003cp\u003eThe results of the analysis suggested a causal association between genetically predicted smoking, alcohol consumption and an increased risk of osteomyelitis. Specifically, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, the IVW method revealed a significant causal relationship between smoking initiation (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, OR\u0026thinsp;=\u0026thinsp;1.450), alcohol consumption frequency (p\u0026thinsp;=\u0026thinsp;0.01, OR\u0026thinsp;=\u0026thinsp;1.3), and osteomyelitis, with both exposure factors showing positive slopes in the scatter plot (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), indicating that they are risk factors. However, coffee intake (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05) was not significantly associated with the incidence of osteomyelitis. Furthermore, forest plots (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) were generated on the basis of the findings of this MR study.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eHeterogeneity and sensitivity tests\u003c/h3\u003e\n\u003cp\u003eThe heterogeneity of the causal estimates for smoking initiation, alcohol consumption frequency, and coffee intake was assessed via Cochran\u0026rsquo;s Q test. The funnel plot in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e indicates that heterogeneity did not affect the overall results (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Furthermore, all p values in the MR‒Egger analysis were greater than 0.05, suggesting that there was no horizontal pleiotropy between the three exposure factors and osteomyelitis (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). In addition, leave-one-out (LOO) analysis revealed no substantial bias, confirming the robustness of the results (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Therefore, exposure to smoking and alcohol consumption was significantly associated with the occurrence of osteomyelitis.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eHeterogeneity test between eight exposure factors and osteomyelitis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" 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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOutcome\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eexposure\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eQ_df\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eQ_pval\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eom\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSmoking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e82.15990\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.567\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eom\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAlcohol intake frequency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e119.254\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.037\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eom\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoffee intake\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e35.46452\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.5872879\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eHorizontal pleiotropy test between exposure factors and osteomyelitis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" 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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOutcome\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eexposure\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEgger_intercept\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ese\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eQ_pval\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eom\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003esmoking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.004072797\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.01296636\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.754\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eom\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAlcohol intake frequency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.503\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eom\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoffee intake\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.276\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \n\u003ch3\u003eMultivariable MR analysis (MVMR)\u003c/h3\u003e\n\u003cp\u003eAfter screening, 163 SNPs were selected from the three exposure factors as instrumental variables (IVs) for MVMR analysis. The MVMR results revealed that, when all three factors were considered simultaneously, smoking (p\u0026thinsp;\u0026le;\u0026thinsp;0.001, OR\u0026thinsp;=\u0026thinsp;1.478, 95% CI: 1.191\u0026ndash;1.829) and alcohol consumption (p\u0026thinsp;\u0026le;\u0026thinsp;0.001, OR\u0026thinsp;=\u0026thinsp;1.458, 95% CI: 1.177\u0026ndash;1.806) remained significant risk factors for osteomyelitis. In contrast, coffee consumption did not demonstrate statistical significance in the MVMR analysis (p\u0026thinsp;=\u0026thinsp;0.318, OR\u0026thinsp;=\u0026thinsp;1.067, 95% CI: 0.472\u0026ndash;1.277). These findings suggest that smoking and alcohol consumption are significantly associated with osteomyelitis, whereas coffee intake is not significantly related to the risk of osteomyelitis. (Supplementary Table\u0026nbsp;4\u0026ndash;5)\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we employed a multi-sample causal analysis method based on large-scale genome-wide association study (GWAS) data to investigate the potential causal relationships between smoking, alcohol consumption frequency, coffee intake, and osteomyelitis. Mendelian randomization (MR) analysis revealed that genetic predispositions to smoking and excessive alcohol consumption frequency were significantly associated with an increased risk of osteomyelitis, whereas coffee intake was not associated with osteomyelitis. Interventions targeting these risk factors could reduce the incidence and severity of osteomyelitis. Specifically, smoking cessation and alcohol moderation may play critical roles in reducing the occurrence of osteomyelitis and slowing its progression. These findings offer new insights for the prevention and management of osteomyelitis.\u003c/p\u003e \u003cp\u003eA growing body of clinical research suggests that smoking and alcohol consumption may be associated with an increased risk of osteomyelitis. One observational cohort study, which analyzed data from 1,186 hospitalized osteomyelitis patients between 2004 and 2015, revealed a significant association between smoking history and the likelihood of lower limb amputation. Specifically, former smokers had a relative risk (RR) of 1.38 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), highlighting the negative impact of smoking on the severity of osteomyelitis \u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. Additionally, a multivariate analysis of 38 dental implant candidates revealed that smokers had an osteomyelitis risk ratio ranging from 47\u0026ndash;64.7%, indicating that smoking significantly increases the incidence of osteomyelitis\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. Related literature also reports that patients with chronic osteomyelitis should avoid smoking and alcohol consumption, as these habits can adversely affect the treatment and progression of the disease \u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAlthough observational studies have not established a causal relationship between smoking, alcohol consumption, and osteomyelitis, these behaviors often coexist with other pathological conditions, such as impaired immune function, metabolic disorders, and inflammatory responses. These comorbidities may contribute to the observed positive correlation between smoking, alcohol consumption, and osteomyelitis \u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. Genome-wide association studies, powerful tools for investigating complex diseases, have been widely used to identify single genes or groups of genes, surpassing the limitations of traditional single-gene association studies. GWASs not only validate existing research but also open new directions for exploration \u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. On the basis of large-scale GWAS data, our Mendelian randomization study extracted genetic evidence of causal relationships between smoking, alcohol consumption, and osteomyelitis, further highlighting the importance of targeting these behaviors in public health interventions aimed at prevention and control.\u003c/p\u003e \u003cp\u003eThe negative effects of smoking and alcohol consumption on the immune system are well documented, with key physiological mechanisms including increased oxidative stress, heightened inflammatory responses, and impaired vascular function\u0026mdash;all of which may contribute to the development of osteomyelitis\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e. Smoking inhibits bone formation primarily by reducing the expression of bone morphogenetic proteins (BMPs) and osteocalcin, thereby slowing bone growth. Additionally, smoking increases the expression of RANKL, stimulating osteoclast activity and promoting bone resorption\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. Cigarette exposure also inhibits the osteogenic potential of mesenchymal stem cells by suppressing the activities of catalase and glutathione reductase, leading to impaired bone formation\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e. Similarly, alcohol consumption hinders bone formation by reducing BMP-2 expression and inhibiting the Wnt signaling pathway, further compromising bone health \u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eStudies further indicate that the combined effect of smoking and alcohol consumption may weaken the body's immune defense mechanisms, providing an opportunity for pathogens to invade bone tissue\u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e. The proinflammatory properties of smoking are closely linked to oxidative stress, where smoking induces chronic inflammation and oxidative stress, inhibiting cytokine secretion and the cytotoxic functions of immune cells \u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e. This weakens the immune system and increases the risk of infection. Alcohol consumption is associated with immune dysregulation, with chronic alcohol consumption impairing the functions of monocytes and macrophages, weakening microbial responses and delaying wound healing, thus increasing the risk of infection \u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e. Smoking generates reactive oxygen species (ROS) and nitrogen species (RNS), which damage cellular and subcellular targets such as lipids, proteins, and nucleic acids, triggering oxidative stress and inflammation \u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e. Oxidative stress induces inflammation, which in turn produces more ROS, leading to further oxidative damage. Alcohol similarly promotes systemic inflammation, exacerbates various chronic conditions, and is thus considered a risk factor for immune-related diseases\u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThis study has several strengths. First, this study is the first to utilize both univariable and multivariable Mendelian randomization (MR) analyses combined with large-scale GWAS data, effectively overcoming the limitations of observational studies. The univariable MR approach allowed us to explore the individual effects of smoking and alcohol consumption on osteomyelitis, whereas multivariable MR enabled the assessment of these exposures simultaneously, providing a more comprehensive understanding of their independent causal relationships. The instrumental variables were rigorously selected, ensuring robust and reliable results. Furthermore, by leveraging these MR methods, we minimized confounding factors and reverse causality, thereby strengthening the validity of the observed causal relationships. However, some limitations should be acknowledged. All participants in this study were of European ancestry, which limits the generalizability of the findings to other ethnic groups. Additionally, we were unable to conduct stratified analyses on the basis of other demographic variables. In the future, large-scale studies involving more diverse populations, along with stratified analyses, will be necessary to confirm the applicability of these findings across different racial and ethnic groups.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThrough univariable and multivariable Mendelian randomization (MR) analyses, we identified a potential causal relationship between smoking, alcohol consumption, and the incidence of osteomyelitis. Both behaviors appear to increase the risk of osteomyelitis. Our study provides genetic evidence supporting the causal link between smoking, alcohol consumption, and osteomyelitis, underscoring the need for public health interventions. However, the precise mechanisms underlying this relationship remain unclear. Future research should focus on further investigating these mechanisms and developing more effective prevention strategies.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMendelian randomization\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGWAS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGenome-Wide Association Studies\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIVW\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInverse-variance weighting\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eOR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eOdds ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eConfidence interval\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIVs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003einstrumental variables\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSNPs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003esingle-nucleotide polymorphisms\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBMP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ebone morphogenetic protein\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRANKL\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ereceptor Activator of Nuclear Factor-κB Ligand\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eROS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ereactive oxygen species\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRNS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ereactive nitrogen species\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003eEthics approval and consent to participate\u003c/p\u003e\n\u003cp\u003eBecause the study was based on a public database, did not involve animal or human studies, and was available in the form of open access and anonymous data, Institutional Review Board approval was not required.\u003c/p\u003e\n\u003cp\u003eConsent for publication\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003eAvailability of data and material\u003cem\u003es\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003eCompeting interests\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThis study was supported by grants from\u0026nbsp;Center for First Aid and Rehabilitation in Orthopedic Trauma (2020Y2014); Science and Technology Planning Project of Fuzhou (2022-R-009).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTianxuan Feng: designed research, performed research, collected data, analyzed data, wrote paper. Ps C, Dz L, KZ, and Jj L: collected data and verification results. Ff L: designed research and revised article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e1.Tianxuan Feng, Fujian University of Traditional Chinese Medicine,\u0026nbsp;Fuzhou 350007, Fujian Province, China\u003c/p\u003e\n\u003cp\u003e2.\u0026nbsp;Peisheng Chen、Dongze Lin、Ke Zheng, Jiajie Liu China Department of\u0026nbsp;Orthopedics, Fuzhou Second General Hospital, School of Clinical Medicine of Fujian Medical University, Fujian Provincial Clinical Medical Research Center for First Aid and Rehabilitation in Orthopedic Trauma, Fuzhou 350007, Fujian, China\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003eConsent to Participate\u003c/p\u003e\n\u003cp\u003eConsent to Participate declaration: not applicable\u003c/p\u003e\n\u003cp\u003eClinical trial number\u003c/p\u003e\n\u003cp\u003eClinical trial number: not applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003ePanteli M, Giannoudis PV. 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Gut Microbes. 2021;13(1):1916278. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1080/19490976.2021.1916278\u003c/span\u003e\u003cspan address=\"10.1080/19490976.2021.1916278\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\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":"Mendelian randomization, Osteomyelitis, Smoking, Alcohol consumption, Lifestyle factors","lastPublishedDoi":"10.21203/rs.3.rs-5305235/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5305235/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThis study aimed to evaluate the causal relationships between smoking, alcohol consumption and coffee intake with osteomyelitis through Mendelian randomization (MR) analysis.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eData on smoking, alcohol consumption, coffee intake, and osteomyelitis-related single nucleotide polymorphisms (SNPs) were obtained from the open Genome-Wide Association Study (GWAS) database of the Integrated Epidemiology Unit. We employed univariable Mendelian randomization (MR) methods, including MR‒Egger, weighted median, inverse variance weighted (IVW), simple mode, and weighted mode, to investigate the causal relationships between the three exposures (smoking initiation, alcohol consumption frequency, and coffee intake) and osteomyelitis. Sensitivity analyses, including heterogeneity tests, horizontal pleiotropy tests, and leave-one-out (LOO) analyses, were subsequently conducted. Furthermore, multivariable MR (MVMR) analyses were performed to simultaneously consider the effects of the three exposure factors on osteomyelitis.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eOn the basis of the results of the univariable Mendelian randomization analysis via the inverse variance weighted method, a significant causal relationship was found between smoking initiation and alcohol consumption frequency with osteomyelitis, whereas no significant association was detected for coffee intake. Specifically, smoking initiation (p\u0026thinsp;\u0026le;\u0026thinsp;0.001, odds ratio (OR)\u0026thinsp;=\u0026thinsp;1.642, 95% confidence interval (CI): 1.321\u0026ndash;2.041) and alcohol consumption frequency (p\u0026thinsp;=\u0026thinsp;0.003, OR\u0026thinsp;=\u0026thinsp;1.384, 95% CI: 1.116\u0026ndash;1.716) were identified as risk factors for osteomyelitis. With respect to the sensitivity analysis for MR results, there was no heterogeneity or horizontal pleiotropy. Leave-one-out (LOO) analysis confirmed the robustness of the univariable MR results. Additionally, multivariable MR analysis revealed that smoking (p\u0026thinsp;\u0026le;\u0026thinsp;0.001, OR\u0026thinsp;=\u0026thinsp;1.573, 95% CI: 1.259\u0026ndash;1.965) and alcohol consumption (p\u0026thinsp;=\u0026thinsp;0.011, OR\u0026thinsp;=\u0026thinsp;1.312, 95% CI: 1.064\u0026ndash;1.618) remained significant risk factors for osteomyelitis when all three factors were considered simultaneously, whereas coffee intake was not statistically significant (p\u0026thinsp;=\u0026thinsp;0.528).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThis Mendelian randomization study revealed that smoking and alcohol consumption are significant risk factors for osteomyelitis, whereas coffee intake is not significantly associated with osteomyelitis. These findings provide important insights for osteomyelitis prevention and public health strategies.\u003c/p\u003e","manuscriptTitle":"Causal relationships between smoking, alcohol consumption frequency, coffee intake and osteomyelitis: a univariable and multivariable Mendelian randomization study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-06 10:48:39","doi":"10.21203/rs.3.rs-5305235/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":"7fcaf42a-f91d-4b65-83ed-5d096d3f836b","owner":[],"postedDate":"November 6th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-11-15T12:54:00+00:00","versionOfRecord":[],"versionCreatedAt":"2024-11-06 10:48:39","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5305235","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5305235","identity":"rs-5305235","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}