Two-sample Mendelian randomization to analyze the association between depression and osteoarthritis risk

Two-sample Mendelian randomization to analyze the association between depression and osteoarthritis risk | 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 Two-sample Mendelian randomization to analyze the association between depression and osteoarthritis risk Sidian Yang, Dezhi Yan, Xiangpeng Wang, JIguang Yin, Fulin Yan, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4624689/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract BACKGROUND: Clinical experience has shown that psychiatric disorders are likely to be risk factors for osteoarthritis, and the incidence of depression, as an important category of psychiatric disorders, has been increasing year by year, but the association between depression and osteoarthritis has not yet been established. , as an important category of psychiatric disorders, has been increasing year by year, but the association between depression and osteoarthritis has not been clearly investigated. OBJECTIVE: To select a dataset of depression and osteoarthritis from the GWAS database and explore the relationship between the two by Mendelian randomisation analysis. RESEARCH METHODS: A database of depression and osteoarthritis was selected to explore the relationship between depression and osteoarthritis using the inverse variance weighting (IVW) method of the Mendelian randomised random-effects model, and the results of the study were supplemented with the the inverse variance weighting (IVW) method of the Mendelian randomized random-effects model, and the results of the study were supplemented with the use of the MR-Egger method, among others. The results of the study were supplemented with the use of the MR-Egger method, among others. RESULTS AND CONCLUSION: IVW results found a causal association between depression and osteoarthritis and that depression is a risk factor for osteoarthritis. mendelian randomization depression osteoarthritis single nucleotide polymorphism causal association Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Osteoarthritis (OA) mainly includes Knee Osteoarthritis (KOA) and Hip Osteoarthritis (HOA) [1] , it is a common chronic degenerative joint disease, and more than half a billion people around the world suffer from osteoarthritis. Osteoarthritis accounts for about 7% of the world's total population [2] , and its clinical manifestations mainly include joint pain, joint deformity, and unfavourable movement. Currently, the incidence of osteoarthritis is on the rise, and the mainstream view is that it is related to ageing [3] , joint injuries [4] , pre-existing joint diseases [5] and obesity [6-7] . In recent years, the impact of psychiatric factors on osteoarthritis has gradually entered the public eye, causing widespread discussion. Depression, as an important component of mental illness and also a major global public health problem, has been a widespread concern, and by 2020 it will have become the second largest cause of disease burden [8] . Some studies have shown that 20.6% of adult osteoarthritis patients also suffer from depression [9] , but there have been conflicting views on the specific relationship between depression and osteoarthritis, with some saying that depression occurs due to osteoarthritis, others saying that there is a positive correlation between the two sides, and some scholars suggesting that the relationship between depression and osteoarthritis is bidirectional [10] , and there are contradictions in the views of several parties, Zheng S et al [11] in 2021 The relationship between depression and osteoarthritis has been studied and analysed, but the causal relationship between the two has not been elaborated, so further exploration of the causal relationship between the two has become the main direction of this study, and Mendelian Randomization (MR) is a reliable tool for this study. Mendelian randomisation is a method of taking genetic variation as an instrumental variable to study the causal relationship between exposure factors and outcomes, in which single nucleotide polymorphisms (SNPs) are the most common genetic variations, which are innately acquired and usually not associated with the rest of the variables, because genetics is innate and disease is subsequent. diseases occur later, the order of innate and acquired variants cannot be reversed, so MR can exclude the interference of reverse causality [12] , at the same time, MR can also reduce the influence of confounding factors to a certain extent.Jiawen Xu et al. [13] applied MR to study the relationship between iron death and osteoarthritis, Ziqin Cao et al. [14] applied MR to explore the relationship between childhood obesity and osteoarthritis, etc., and an increasing number of cases prove the feasibility of MR. The present study used MR principles with the aim of investigating the causal relationship between depression and the risk of osteoarthritis in the knee and hip joints, and providing a new scientific basis and method for the clinical prevention and treatment of osteoarthritis. Materials And Methods 1.1 Study design The current study used two-sample Mendelian randomisation to explore the relationship between depression and risk of osteoarthritis, with depression as the exposure and osteoarthritis as the outcome. 1.2 Data sources The data of depression and osteoarthritis selected for this study were obtained from GWAS database, in which the data of depression as the exposure factor of the study were derived from the data of Naomi R Wray et al [15] in 2018, applying 135,458 cases with 344,901 controls for a gene-wide analysis of depression. Osteoarthritis as an outcome with data derived from Ioanna Tachmazidou et al [16] in 2019, applying 77052 cases versus 378169 controls for a whole gene analysis of osteoarthritis. Both data sets were publicly available and did not require special approval. The study populations for both data sets were of European ancestry and there was no bias due to ethnic genetic differences. （ Table 1 for details) （Table 1：Information about GWAS data on depression and osteoarthritis） 1.3 Research methodology The causal relationship between depression and osteoarthritis was analysed using Mendelian randomisation based on the GWAS database, and single nucleotide polymorphisms (SNPS) were used as the basis of the study instrumental variables for the analysis and comparison. Throughout the study, three basic conditions should be met ① The selected SNPS should be strongly correlated with the exposure factors, in line with the association hypothesis ② The selected SNPS should not be strongly correlated with the outcome, ruling out the exclusion hypothesis ③ The selected SNPS should not be able to affect the outcome by influencing other factors related to the outcome, ruling out the independence hypothesis. （ Table 2 for details） (Table 2:GWAS data processing screening flowchart) Preliminary findings of the inverse variance weighted (IVW) method of the Mendelian randomised random effects model to explore the relationship between depression and osteoarthritis using the MR-Egger method, Weighted median (WME), Simple median ( Simple median, SM), and Weighted median method (Weighed mode, WM) to supplement the results of the study validation, using the PhenoScanner database to remove SNPs directly related to the outcome, setting the parameters P < 5 × 10-8, r2 < 0.01, and kb = 10000, and based on the selected conditions for depression and osteoarthritis SNPS were screened, the data of exposure factors were extracted, the chain imbalance was removed, binary variables were performed to calculate the OR value, and then the calculation results were analysed for heterogeneity, and the selected SNPS that met the screening conditions were brought into the osteoarthritis database for analysis to exclude the rest of the interfering factors, and those SNPS with statistic >10 were retained.The data were presented in terms of odds ratio (OR) and 95% confidence intervals (95%), and the data were presented in the following way. The data were recorded as odds ratio (OR) and 95% confidence interval (95% CI), and the obtained data were further processed using R language (version 4.3.1). Results 2.1 Instrumental variables According to the screening criteria of the research methodology, 36 SNPs were screened from the exposure factor depression that were closely associated with the outcome osteoarthritis without chain imbalance, and each of the 36 SNPs was brought into the PhenScanner database for searching, and two SNPs that were directly associated with osteoarthritis were deleted, and the process of conducting the analyses was carried out by removing two outlier SNPs ( rs4074723, rs2005864), a total of 31 SNPs were screened and re-analysed to investigate the association between depression and osteoarthritis. 2.2 Effect of depression on osteoarthritis The validity of the study was demonstrated by applying the IVW method of analysis to produce a result (OR=1.178, P=0.0013, 95% CI=1.066-1.301) and using the MR Egger method of results (OR=1.166, 95% CI=0.711-1.911, P=0.547); using the Weighted median method of results (OR=1.182, 95% CI=1.035-1.350, P=0.013); using the Simple mode method results (OR=1.0733, 95% CI=0.817-1.411, P=0.615); and using the Weighed mode method results (OR=1.106, 95% CI=0.868 -1.410, P=0.422). Among the results, the WME method and the IVW method yielded strongly similar results, and although the MR Egger, WM and SM methods did not support a strong causal association between depression and osteoarthritis, the results obtained from data analysis by the five methods were highly consistent in direction (OR > 1). All results with F > 10 are shown in Table 3 .In addition, scatter plot, forest plot and funnel plot ( Figures 1, 2 and 3 ) indicate the validity of the results of the present study, further suggesting that not only is there a causal relationship between depression and osteoarthritis, but also that depression is a risk factor for osteoarthritis. (Table 3: The P-values ,OR-values，95%CI obtained by the five methods of MR analysis) (Fig 1:Scatter plot using different MR methods. The slopes of line represent the causal effect of each method.) (Fig 2: Forest plot of the causal effects of depression associated SNPs on OA.) (Fig 3：The horizontal coordinate is the effect value (β) of the instrumental variable (IV) versus OA; the vertical coordinate is the inverse of the standard error (1/SE) of the instrumental variable (IV) versus OA. The distribution of causal effects presented in all funnel plots had a basic symmetry and no significant bias was seen.) 2.3 Sensitivity analysis The P-value of Cochran's Q-test was used to determine whether there was heterogeneity in the analyses. The results of the analyses using the IVW and WME methods showed that there was heterogeneity in the analyses (P < 0.05) ( Table 3 ), and the sensitivity analysis of the leave-one-out method indicated that after excluding each depressed SNP, the un-excluded SNPs did not affect the results ( Figure 4 ) （Fig 4：The robustness and reliability of assessing the causal effect of depressive genetic variants on osteoarthritis was tested by excluding each individual individually, with no significant bias seen.） Discussion The main pathological manifestations of osteoarthritis are changes in cartilage, generation of bone redundancy, and inflammation of synovial membrane etc. [17-19] , with the aging of the population and the rising obesity rate globally, it has brought about a very great impact on the quality of life of the majority of middle-aged and elderly people [20] , which not only becomes a shackle to the happiness of the elderly life, but also brings about an impact on the health care security and social security [9] . Depression, as a common mental disorder, has also been high in incidence. This MR study investigates the causal relationship between depression and osteoarthritis. Unlike previous clinical studies that collected samples, we obtained the relevant information from a database to conduct the MR study, which not only avoids the influence of interfering factors on the accuracy of the results, but also obtains results that are even better than those obtained from a randomised controlled trial of cases and controls. This study is also the first to investigate the correlation between depression and osteoarthritis, and the most intuitive conclusion from the results of the study is that there is a causal association between depression and osteoarthritis, and that depression is a risk factor for osteoarthritis. Regarding the association between depression and osteoarthritis, previous clinical studies have shown that the effects of depression on osteoarthritis are mainly reflected in the negative impact on pain and deterioration. Patients with depression combined with osteoarthritis have more pronounced impaired mobility, faster disease progression, and stronger pain compared to patients with osteoarthritis alone [21] . The correlation can be elaborated in the following aspects: ① The pain symptoms of osteoarthritis are mainly joint pain, but studies have shown that non-inflammatory pain brought by depression such as neurofibromyalgia [22] neuropathic pain can also stimulate osteoarthritis and bring serious negative effects on its pain symptoms. ② The development of osteoarthritis is closely related to a variety of inflammatory factors, such as interleukin-1, interleukin-6, and tumour necrosis factor [23-25] . The induction of multiple inflammatory factors not only negatively affects osteochondral cartilage, but some inflammatory factors, especially interleukin-1, stimulate the production and delivery of other inflammatory factors such as interleukin-8 [26] . Previous studies have shown that depression is also a disease related to chronic inflammation. In the course of depression, there is a significant increase in inflammatory factors such as interleukin, tumour necrosis factor and C-reactive protein [27] , of which the increase in interleukin 6 is associated with a decrease in serotonin metabolites, and a decrease in serotonin metabolites is an important factor in the production of depression [28] . Overseas studies have also shown that patients suffering from long-term inflammatory diseases are more prone to depression than the general population. Patients with long-term inflammatory diseases are more prone to depression than general patients. (3) In patients suffering from arthritis, most of the patients, although arthritis will not affect the life expectancy, but suffers from long-term pain, seriously affecting the quality of life [29-30] , coupled with persistent monetary costs and continuous medication, patients will develop anxiety and boredom over time, to the already depressed patients is even worse, seriously destroying the physical and mental health of patients with depression and arthritis co-morbidities. ④ Long-term depressed patients have significantly lower sleep quality [31] , the decline in sleep quality by affecting the inflammatory cell pathway and increase sympathetic nerve tone and so on, contributing to the occurrence of high blood pressure, heart disease and other diseases [32-33] , and cardiovascular disease is one of the important causes of osteoarthritis [34] . Of course, there are some limitations in this study, because the sample base used for the analysis is from the GWAS database, and there is no way to explore the basic physical condition of each sample, which can not completely exclude the individualised differences, and may lead to a certain degree of bias, but based on the analysis of a large sample of data, the unavoidable variability has been reduced to a minimum; the study shows that there is a correlation between depression and osteoarthritis, but its Although the association between depression and osteoarthritis has been demonstrated, further clinical observation and practice are needed to establish specific prevention and treatment options; the present study was limited to the European population, and it cannot be claimed that it is applicable to all races, and experiments should continue to be carried out to investigate the suitability and effectiveness of the treatment for all populations around the world. Overall, Mendelian randomisation analysis is a powerful tool for studying disease associations, and the results of the present study reveal a causal association between depression and osteoarthritis and that depression is a risk factor for osteoarthritis. These depressed groups suffer from psychiatric factors, and osteoarthritis as a complication would be a quick knife to the throat of the depressed patients. This study provides a potential basis for the development of osteoarthritis due to depression, provides new ideas and research support for the prevention and treatment of osteoarthritis in the future, and opens the door for future research on the association between psychiatric disorders and osteoarthritis. Declarations Funding Declaration: This work was supported by the Natural Science Foundation of Shandong Province, China (Grant No.ZR2022QH208) Author Contribution SidianYang completed major data processing and thesis writing work.Dezhi Yan provided ideas for writing.XiangPeng Wang producted of in-text tables.Jiguang Yin calibrated article logic.Fulin Yan writed the abstract section of an article.Weishan Wu provided modifications Data Availability declaration : The data that support the findings of this study are openly available in IEU OpenGWAS project (mrcieu.ac.uk) Competing Interest declaration : The authors declare no conflicts of interest. References Katz JN, Arant KR, Loeser RF. Diagnosis and Treatment of Hip and Knee Osteoarthritis: A Review. JAMA. 2021;325(6):568–78. 10.1001/jama.2020.22171 . PMID: 33560326; PMCID: PMC8225295. Hunter DJ, March L, Chew M. Osteoarthritis in 2020 and beyond: a Lancet Commission. Lancet. 2020;396(10264):1711–1712. 10.1016/S0140-6736 (20)32230-3. 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Rahman MM, Kopec JA, Cibere J. The relationship between osteoarthritis and cardiovascular disease in a population health survey: a cross-sectional study. BMJ Open. 2013;3(5):e002624. 10.1136/bmjopen-2013-002624 . PMID: 23674445; PMCID: PMC3657665. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 03 Jul, 2024 Editor assigned by journal 01 Jul, 2024 Submission checks completed at journal 01 Jul, 2024 First submitted to journal 23 Jun, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4624689","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":324025404,"identity":"9621bc01-d447-4d8a-bcb6-a94ebcf3468e","order_by":0,"name":"Sidian Yang","email":"","orcid":"","institution":"Shandong University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Sidian","middleName":"","lastName":"Yang","suffix":""},{"id":324025405,"identity":"5f0ab8ad-4c3a-4fd4-9376-26a374179494","order_by":1,"name":"Dezhi Yan","email":"","orcid":"","institution":"Shandong University of Traditional Chinese 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2","display":"","copyAsset":false,"role":"figure","size":47247,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMR forest plot\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Picture2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4624689/v1/2ac86756320e913e90eca520.jpg"},{"id":61031958,"identity":"a3989efe-2664-43d6-b97c-89b87be79c9d","added_by":"auto","created_at":"2024-07-24 19:45:48","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":22228,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMR funnel plot\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Picture3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4624689/v1/11778fc63c07ee920b3e3a22.jpg"},{"id":61032261,"identity":"2a9ac3b1-cf41-48a8-b1da-33a10e6607a5","added_by":"auto","created_at":"2024-07-24 19:53:48","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":51235,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMR Leave-one-out sensitivity analysis\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Picture4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4624689/v1/208a9f43bb59f9df6974828d.jpg"},{"id":61032262,"identity":"4de39fbc-829e-4a4b-bbba-08026c345f95","added_by":"auto","created_at":"2024-07-24 19:53:53","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":630190,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4624689/v1/397f1ec2-4826-483d-80b6-429f01f3df54.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Two-sample Mendelian randomization to analyze the association between depression and osteoarthritis risk","fulltext":[{"header":"Introduction","content":"\u003cp\u003eOsteoarthritis\u0026nbsp;(OA) mainly includes Knee Osteoarthritis (KOA) and Hip Osteoarthritis (HOA)\u003csup\u003e[1]\u003c/sup\u003e , it is a common chronic degenerative joint disease, and more than half a billion people around the world suffer from osteoarthritis. Osteoarthritis accounts for about 7% of the world\u0026apos;s total population\u003csup\u003e[2]\u003c/sup\u003e , and its clinical manifestations mainly include joint pain, joint deformity, and unfavourable movement. Currently, the incidence of osteoarthritis is on the rise, and the mainstream view is that it is related to ageing\u003csup\u003e[3]\u003c/sup\u003e , joint injuries\u003csup\u003e[4]\u003c/sup\u003e , pre-existing joint diseases\u003csup\u003e[5]\u003c/sup\u003e and obesity\u003csup\u003e[6-7]\u003c/sup\u003e .\u003c/p\u003e\n\u003cp\u003eIn recent years, the impact of psychiatric factors on osteoarthritis has gradually entered the public eye, causing widespread discussion. Depression, as an important component of mental illness and also a major global public health problem, has been a widespread concern, and by 2020 it will have become the second largest cause of disease burden\u003csup\u003e[8]\u003c/sup\u003e . Some studies have shown that 20.6% of adult osteoarthritis patients also suffer from depression\u003csup\u003e[9]\u003c/sup\u003e , but there have been conflicting views on the specific relationship between depression and osteoarthritis, with some saying that depression occurs due to osteoarthritis, others saying that there is a positive correlation between the two sides, and some scholars suggesting that the relationship between depression and osteoarthritis is bidirectional\u003csup\u003e[10]\u003c/sup\u003e , and there are contradictions in the views of several parties,\u0026nbsp;Zheng S et al\u003csup\u003e[11]\u003c/sup\u003e in 2021 The relationship between depression and osteoarthritis has been studied and analysed, but the causal relationship between the two has not been elaborated,\u0026nbsp;so further exploration of the causal relationship between the two has become the main direction of this study, and\u0026nbsp;Mendelian Randomization (MR)\u0026nbsp;is a reliable tool for this study.\u003c/p\u003e\n\u003cp\u003eMendelian randomisation is a method of taking genetic variation as an instrumental variable to study the causal relationship between exposure factors and outcomes, in which single nucleotide polymorphisms (SNPs)\u0026nbsp;are the most common genetic variations, which are innately acquired and usually not associated with the rest of the variables, because genetics is innate and disease is subsequent. diseases occur later, the order of innate and acquired variants cannot be reversed, so MR can exclude the interference of reverse causality\u003csup\u003e[12]\u003c/sup\u003e , at the same time, MR can also reduce the influence of confounding factors to a certain extent.Jiawen Xu et al.\u003csup\u003e[13]\u003c/sup\u003e applied MR to study the relationship between iron death and osteoarthritis, Ziqin Cao et al.\u003csup\u003e[14]\u003c/sup\u003e applied MR to explore the relationship between childhood obesity and osteoarthritis, etc., and an increasing number of cases prove the feasibility of MR. The present study used MR principles with the aim of investigating the causal relationship between depression and the risk of osteoarthritis in the knee and hip joints, and providing a new scientific basis and method for the clinical prevention and treatment of osteoarthritis.\u003c/p\u003e"},{"header":"Materials And Methods","content":"\u003cp\u003e1.1 Study design\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; The current study used two-sample Mendelian randomisation to explore the relationship between depression and risk of osteoarthritis, with depression as the exposure and osteoarthritis as the outcome.\u003c/p\u003e\n\u003cp\u003e1.2 Data sources\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; The data of depression and osteoarthritis selected for this study were obtained from GWAS database, in which the data of depression as the exposure factor of the study were derived from the data of Naomi R Wray et al\u003cstrong\u003e\u003csup\u003e[15]\u003c/sup\u003e\u003c/strong\u003e in 2018, applying 135,458 cases with 344,901 controls for a gene-wide analysis of depression. Osteoarthritis as an outcome with data derived from Ioanna Tachmazidou et al\u003cstrong\u003e\u003csup\u003e[16]\u003c/sup\u003e\u003c/strong\u003e in 2019, applying 77052 cases versus 378169 controls for a whole gene analysis of osteoarthritis. Both data sets were publicly available and did not require special approval. The study populations for both data sets were of European ancestry and there was no bias due to ethnic genetic differences. （\u003cstrong\u003eTable 1\u003c/strong\u003e for details)\u003c/p\u003e\n\u003cp\u003e（Table 1：Information about GWAS data on depression and osteoarthritis）\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\"\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e1.3 Research methodology\u003c/p\u003e\n\u003cp\u003eThe causal relationship between depression and osteoarthritis was analysed using Mendelian randomisation based on the GWAS database, and single nucleotide polymorphisms (SNPS) were used as the basis of the study instrumental variables for the analysis and comparison. Throughout the study, three basic conditions should be met\u0026nbsp;①\u0026nbsp;The selected SNPS should be strongly correlated with the exposure factors, in line with the association hypothesis\u0026nbsp;②\u0026nbsp;The selected SNPS should not be strongly correlated with the outcome, ruling out the exclusion hypothesis\u0026nbsp;③\u0026nbsp;The selected SNPS should not be able to affect the outcome by influencing other factors related to the outcome, ruling out the independence hypothesis.\u0026nbsp;（\u003cstrong\u003eTable 2\u0026nbsp;\u003c/strong\u003efor details）\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e(Table 2:GWAS data processing screening flowchart)\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\"\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003ePreliminary findings of the inverse variance weighted (IVW) method of the Mendelian randomised random effects model to explore the relationship between depression and osteoarthritis using the MR-Egger method, Weighted median (WME), Simple median ( Simple median, SM), and Weighted median method (Weighed mode, WM) to supplement the results of the study validation, using the PhenoScanner database to remove SNPs directly related to the outcome, setting the parameters P \u0026lt; 5 \u0026times; 10-8, r2 \u0026lt; 0.01, and kb = 10000, and based on the selected conditions for depression and osteoarthritis SNPS were screened, the data of exposure factors were extracted, the chain imbalance was removed, binary variables were performed to calculate the OR value, and then the calculation results were analysed for heterogeneity, and the selected SNPS that met the screening conditions were brought into the osteoarthritis database for analysis to exclude the rest of the interfering factors, and those SNPS with statistic \u0026gt;10 were retained.The data were presented in terms of odds ratio (OR) and 95% confidence intervals (95%), and the data were presented in the following way. The data were recorded as odds ratio (OR) and 95% confidence interval (95% CI), and the obtained data were further processed using R language (version 4.3.1).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e2.1 Instrumental variables\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAccording to the screening criteria of the research methodology, 36 SNPs were screened from the exposure factor depression that were closely associated with the outcome osteoarthritis without chain imbalance, and each of the 36 SNPs was brought into the PhenScanner database for searching, and two SNPs that were directly associated with osteoarthritis were deleted, and the process of conducting the analyses was carried out by removing two outlier SNPs ( rs4074723, rs2005864), a total of 31 SNPs were screened and re-analysed to investigate the association between depression and osteoarthritis.\u003c/p\u003e\n\u003cp\u003e2.2 Effect of depression on osteoarthritis\u003c/p\u003e\n\u003cp\u003eThe validity of the study was demonstrated by applying the IVW method of analysis to produce a result (OR=1.178, P=0.0013, 95% CI=1.066-1.301) and using the MR Egger method of results (OR=1.166, 95% CI=0.711-1.911, P=0.547); using the Weighted median method of results (OR=1.182, 95% CI=1.035-1.350, P=0.013); using the Simple mode method results (OR=1.0733, 95% CI=0.817-1.411, P=0.615); and using the Weighed mode method results (OR=1.106, 95% CI=0.868 -1.410, P=0.422). Among the results, the WME method and the IVW method yielded strongly similar results, and although the MR Egger, WM and SM methods did not support a strong causal association between depression and osteoarthritis, the results obtained from data analysis by the five methods were highly consistent in direction (OR \u0026gt; 1). All results with F \u0026gt; 10 are shown in \u003cstrong\u003eTable 3\u003c/strong\u003e.In addition, scatter plot, forest plot and funnel plot (\u003cstrong\u003eFigures 1, 2 and 3\u003c/strong\u003e) indicate the validity of the results of the present study, further suggesting that not only is there a causal relationship between depression and osteoarthritis, but also that depression is a risk factor for osteoarthritis.\u003c/p\u003e\n\u003cp\u003e(Table 3:\u0026nbsp;The P-values ,OR-values，95%CI obtained by the five methods of MR analysis)\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\"\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e(Fig 1:Scatter plot using different MR methods. The slopes of line represent the causal effect of each method.)\u003c/p\u003e\n\u003cp\u003e(Fig 2: Forest plot of the causal effects of depression associated SNPs on OA.)\u003c/p\u003e\n\u003cp\u003e(Fig 3：The horizontal coordinate is the effect value (\u0026beta;) of the instrumental variable (IV) versus OA; the vertical coordinate is the inverse of the standard error (1/SE) of the instrumental variable (IV) versus OA. The distribution of causal effects presented in all funnel plots had a basic symmetry and no significant bias was seen.)\u003c/p\u003e\n\u003cp\u003e2.3 Sensitivity analysis\u003c/p\u003e\n\u003cp\u003eThe P-value of Cochran\u0026apos;s Q-test was used to determine whether there was heterogeneity in the analyses. The\u0026nbsp;results of the analyses using the IVW and WME methods showed that there was heterogeneity in the analyses (P \u0026lt; 0.05) (\u003cstrong\u003eTable 3\u003c/strong\u003e), and the sensitivity analysis of the leave-one-out method indicated that after excluding each depressed SNP, the un-excluded SNPs did not affect the results (\u003cstrong\u003eFigure 4\u003c/strong\u003e)\u003c/p\u003e\n\u003cp\u003e（Fig 4：The robustness and reliability of assessing the causal effect of depressive genetic variants on osteoarthritis was tested by excluding each individual individually, with no significant bias seen.）\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe main pathological manifestations of osteoarthritis are changes in cartilage, generation of bone redundancy,\u0026nbsp;and inflammation of synovial membrane etc.\u003csup\u003e[17-19]\u003c/sup\u003e , with the aging of the population and the rising obesity rate globally, it has brought about a very great impact on the quality of life of the majority of middle-aged and elderly people\u003csup\u003e[20]\u003c/sup\u003e , which not only becomes a shackle to the happiness of the elderly life, but also brings about an impact on the health care security and social security\u003csup\u003e[9]\u003c/sup\u003e . Depression, as a common mental disorder, has also been high in incidence. This MR study investigates the causal relationship between depression and osteoarthritis. Unlike previous clinical studies that collected samples, we obtained the relevant information from a database to conduct the MR study, which not only avoids the influence of interfering factors on the accuracy of the results, but also obtains results that are even better than those obtained from a randomised controlled trial of cases and controls. This study is also the first to investigate the correlation between depression and osteoarthritis, and the most intuitive conclusion from the results of the study is that there is a causal association between depression and osteoarthritis, and that depression is a risk factor for osteoarthritis.\u003c/p\u003e\n\u003cp\u003eRegarding the association between depression and osteoarthritis, previous clinical studies have shown that the effects of depression on osteoarthritis are mainly reflected in the negative impact on pain and deterioration. Patients with depression combined with osteoarthritis have more pronounced impaired mobility, faster disease progression, and stronger pain compared to patients with osteoarthritis alone\u003csup\u003e[21]\u003c/sup\u003e . The correlation can be elaborated in the following aspects:\u0026nbsp;①\u0026nbsp;The pain symptoms of osteoarthritis are mainly joint pain, but studies have shown that non-inflammatory pain brought by depression such as neurofibromyalgia\u003csup\u003e[22]\u003c/sup\u003e neuropathic pain can also stimulate osteoarthritis and bring serious negative effects on its pain symptoms.\u0026nbsp;②\u0026nbsp;The development of osteoarthritis is closely related to a variety of inflammatory factors, such as interleukin-1, interleukin-6, and tumour necrosis factor\u003csup\u003e[23-25]\u003c/sup\u003e . The induction of multiple inflammatory factors not only negatively affects osteochondral cartilage, but some inflammatory factors, especially interleukin-1, stimulate the production and delivery of other inflammatory factors such as interleukin-8\u003csup\u003e[26]\u003c/sup\u003e . Previous studies have shown that depression is also a disease related to chronic inflammation. In the course of depression, there is a significant increase in inflammatory factors such as interleukin, tumour necrosis factor and C-reactive protein\u003csup\u003e[27]\u003c/sup\u003e , of which the increase in interleukin 6 is associated with a decrease in serotonin metabolites, and a decrease in serotonin metabolites is an important factor in the production of depression\u003csup\u003e[28]\u003c/sup\u003e . Overseas studies have also shown that patients suffering from long-term inflammatory diseases are more prone to depression than the general population. Patients with long-term inflammatory diseases are more prone to depression than general patients. (3) In patients suffering from arthritis, most of the patients, although arthritis will not affect the life expectancy, but suffers from long-term pain, seriously affecting the quality of life\u003csup\u003e[29-30]\u003c/sup\u003e ,\u0026nbsp;coupled with persistent monetary costs and continuous medication, patients will develop anxiety and boredom over time, to the already depressed patients is even worse, seriously destroying the physical and mental health of patients with depression and arthritis co-morbidities.\u0026nbsp;④\u0026nbsp;Long-term depressed patients have significantly lower sleep quality\u003csup\u003e[31]\u003c/sup\u003e , the decline in sleep quality by affecting the inflammatory cell pathway and increase sympathetic nerve tone and so on, contributing to the occurrence of high blood pressure, heart disease and other diseases\u003csup\u003e[32-33]\u003c/sup\u003e , and cardiovascular disease is one of the important causes of osteoarthritis\u003csup\u003e[34]\u003c/sup\u003e .\u003c/p\u003e\n\u003cp\u003eOf course, there are some limitations in this study, because the sample base used for the analysis is from the GWAS database, and there is no way to explore the basic physical condition of each sample, which can not completely exclude the individualised differences, and may lead to a certain degree of bias, but based on the analysis of a large sample of data, the unavoidable variability has been reduced to a minimum; the study shows that there is a correlation between depression and osteoarthritis, but its Although the association between depression and osteoarthritis has been demonstrated, further clinical observation and practice are needed to establish specific prevention and treatment options; the present study was limited to the European population, and it cannot be claimed that it is applicable to all races, and experiments should continue to be carried out to investigate the suitability and effectiveness of the treatment for all populations around the world.\u003c/p\u003e\n\u003cp\u003eOverall, Mendelian randomisation analysis is a powerful tool for studying disease associations, and the results of the present study reveal a causal association between depression and osteoarthritis and that depression is a risk factor for osteoarthritis. These depressed groups suffer from psychiatric factors, and osteoarthritis as a complication would be a quick knife to the throat of the depressed patients. This study provides a potential basis for the development of osteoarthritis due to depression, provides new ideas and research support for the prevention and treatment of osteoarthritis in the future, and opens the door for future research on the association between psychiatric disorders and osteoarthritis.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eDeclaration: This work was supported by the Natural Science Foundation of Shandong Province, China (Grant No.ZR2022QH208)\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eSidianYang completed major data processing and thesis writing work.Dezhi Yan provided ideas for writing.XiangPeng Wang producted of in-text tables.Jiguang Yin calibrated article logic.Fulin Yan writed the abstract section of an article.Weishan Wu provided modifications\u003c/p\u003e\u003ch2\u003eData Availability declaration :\u003c/h2\u003e \u003cp\u003e The data that support the findings of this study are openly available in IEU OpenGWAS project (mrcieu.ac.uk)\u003c/p\u003e\u003cp\u003eCompeting Interest declaration : The authors declare no conflicts of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eKatz JN, Arant KR, Loeser RF. 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BMJ Open. 2013;3(5):e002624. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1136/bmjopen-2013-002624\u003c/span\u003e\u003cspan address=\"10.1136/bmjopen-2013-002624\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. PMID: 23674445; PMCID: PMC3657665.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"bmc-musculoskeletal-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmsd","sideBox":"Learn more about [BMC Musculoskeletal Disorders](http://bmcmusculoskeletdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://author-welcome.nature.com/12891","title":"BMC Musculoskeletal Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"mendelian randomization, depression, osteoarthritis, single nucleotide polymorphism, causal association","lastPublishedDoi":"10.21203/rs.3.rs-4624689/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4624689/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBACKGROUND: \u003c/strong\u003eClinical experience has shown that psychiatric disorders are likely to be risk factors for osteoarthritis, and the incidence of depression, as an important category of psychiatric disorders, has been increasing year by year, but the association between depression and osteoarthritis has not yet been established. , as an important category of psychiatric disorders, has been increasing year by year, but the association between depression and osteoarthritis has not been clearly investigated.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOBJECTIVE: \u003c/strong\u003eTo select a dataset of depression and osteoarthritis from the GWAS database and explore the relationship between the two by Mendelian randomisation analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRESEARCH METHODS: \u003c/strong\u003eA database of depression and osteoarthritis was selected to explore the relationship between depression and osteoarthritis using the inverse variance weighting (IVW) method of the Mendelian randomised random-effects model, and the results of the study were supplemented with the the inverse variance weighting (IVW) method of the Mendelian randomized random-effects model, and the results of the study were supplemented with the use of the MR-Egger method, among others. The results of the study were supplemented with the use of the MR-Egger method, among others.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRESULTS AND CONCLUSION: \u003c/strong\u003eIVW results found a causal association between depression and osteoarthritis and that depression is a risk factor for osteoarthritis.\u003c/p\u003e","manuscriptTitle":"Two-sample Mendelian randomization to analyze the association between depression and osteoarthritis risk","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-24 19:45:43","doi":"10.21203/rs.3.rs-4624689/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-07-03T18:40:53+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-07-01T13:59:06+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-07-01T13:57:56+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Musculoskeletal Disorders","date":"2024-06-23T09:39:39+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-musculoskeletal-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmsd","sideBox":"Learn more about [BMC Musculoskeletal Disorders](http://bmcmusculoskeletdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://author-welcome.nature.com/12891","title":"BMC Musculoskeletal Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b23f2089-97b7-408d-8486-2f8347b3ebb0","owner":[],"postedDate":"July 24th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2024-07-24T19:45:44+00:00","versionOfRecord":[],"versionCreatedAt":"2024-07-24 19:45:43","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4624689","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4624689","identity":"rs-4624689","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}