The causal relationship between depression and low back pain: a two-sample Mendelian randomized study

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This analysis utilizes genetic variants as instrumental variables to help establish a causal relationship. By leveraging the genetic variants associated with depression as the exposure factor, the study aimed to investigate whether there is a causal effect on the development or severity of low back pain. This approach provides valuable insights into the potential causal association between these two conditions. Methods The entire Gene Association Study database (GWAS) was utilized for data mining in a study aiming to investigate the relationship between depression as the exposure factor and low back pain as the outcome factor. Mendelian randomization analysis was performed using regression models such as inverse variance weighting (IVW), the MR‒Egger method, the simple mode method, the weighted median method, and the weighted mode method. The objective was to uncover any potential causal relationship between the exposure factors and the outcome. Results In this study, a total of 25 single nucleotide polymorphism (SNP) loci were utilized as instrumental variables to assess the causal association between depression and low back pain. The inverse variance weighting method estimated that individuals with depression had a 1.71 times higher risk (OR) of experiencing low back pain than the healthy population (95% CI: 1.258 to 2.332, p=0.0006). Similarly, the weighted median method also supported a causal effect between depression and low back pain (95% CI: 1.180 to 2.789, p = 0.0007). Tests for heterogeneity using the inverse variance weighting and MR‒Egger regression methods indicated no significant heterogeneity. Furthermore, the MR‒Egger regression intercept terms and MRPRESSO method tests suggested that the results were less likely to be influenced by genetic pleiotropy. Leave-one-out analyses did not identify any nonspecific SNPs that unduly influenced the results. Overall, these findings provide stronger evidence for a causal relationship between depression and low back pain. Conclusions There may be a positive causal association between depression and low back pain. Mendelian randomization depression low back pain Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Lower back pain is characterized by pain located between the lower ribs and the transverse gluteal line. It is often accompanied by pain in one or both lower extremities and may present with neurological symptoms in some patients[1]. According to an epidemiological survey, lower back pain was one of the top five causes of disability worldwide from 1990 to 2016[2]. This condition not only causes physical and psychological suffering for patients but also places a significant economic burden on families and society. In the United States, a majority of people experience low back pain, with approximately 25% of adults reporting at least one day of lower back pain in the past three months[3]. In 2018, the Lancet published a series of articles focusing on lower back pain, emphasizing the need for global attention to the physical, psychological, and social consequences of this condition[4]. The articles highlighted the existing gaps between evidence and medical practice across different income populations and the lack of appropriate evidence-based guidelines for low- and middle-income individuals[5]. With the increasing burden of lower back pain due to population growth and aging [6], finding ways to address effectiveness and cost conflict has become an urgent task. Depression is a common psychological disorder characterized by persistent feelings of sadness, loss of interest or pleasure in activities, and cognitive and behavioral impairments. As of 2019, approximately 280 million people worldwide were affected by depression[7], making it a significant global health issue[8]. Multiple factors, including psychological, genetic, biochemical, and social-environmental factors, contribute to the development of depression[9-11]. When depression becomes recurrent and reaches a moderate or severe level, it can cause significant distress and interfere with daily functioning, and in severe cases, it can even lead to suicide[12]. Research suggests a potential association between depression and low back pain[13]. It has been observed that the incidence of depression may impact the occurrence of low back pain[13]. Additionally, early detection and comprehensive treatment of depressive symptoms may benefit individuals with low back pain[14]. However, it is important to note that the causal relationship between depression and low back pain is still not fully understood and requires further investigation. Managing depression and low back pain typically involves a multidisciplinary approach, including psychological therapy, medication, physical therapy, and lifestyle modifications. It is essential for individuals experiencing symptoms of depression or low back pain to seek professional medical help for an accurate diagnosis and appropriate treatment. Mendelian randomization (MR) analyses are a useful tool in determining whether there is a causal relationship between observed modifiable risk factors or exposures and clinical outcomes[15]. Traditional randomized controlled trials might not always be feasible or ethical to investigate causality, and observational studies may suffer from confounding or reverse causality biases[16]. In this context, MR analysis can provide valuable insights. It utilizes genetic variants as instrumental variables to mimic a randomized controlled trial-like design. These genetic variants should be associated with the exposure of interest (in this case, depression) but not associated with confounders or competing risk factors. By examining the genetic variants' association with the outcome of interest (low back pain), it is possible to establish a causal relationship. This particular study collected published data and conducted a two-sample MR analysis to investigate whether there is a causal relationship between depression and low back pain. The findings of the study can help clarify whether depression has a direct impact on the occurrence of low back pain. However, it is important to note that MR analysis has limitations, including the assumptions that need to be met for valid causal inference and potential pleiotropy (genetic variants affecting multiple outcomes). Therefore, additional research is needed to confirm and validate these findings. Methods Study design The Mendelian randomization study design for the three hypotheses is as follows (Figure 1): ① Hypothesis of association: The genetic instrumental variable used in the study is expected to have a strong association with the exposure factor, which in this case is depression. This means that the selected genetic variants are chosen based on their known association with depression.② Hypothesis of independence: The genetic instrumental variable needs to be independent of potential confounders. This means that the selected genetic variants should not be associated with any other factors that could affect the outcome (low back pain) or introduce bias into the analysis. ③ Hypothesis of exclusion: The genetic instrumental variable should influence the outcome factor (low back pain) exclusively through the exposure factor (depression). This means that the selected genetic variants should only affect low back pain through their effect on depression and not through any other pathways. To conduct Mendelian randomization analysis, relevant genome-wide association study (GWAS) datasets were collected from the website https://gwas.mrcieu.ac.uk. The GWAS dataset for depression (ID: ebi-a-GCST003769) included 122,210 subjects of European ancestry and 6,019,632 single nucleotide polymorphisms (SNPs). The GWAS dataset for low back pain (ID: finn-b-M13_LOWBACKPAIN) included 13,178 patients and 164,682 control subjects of European ancestry (Table 1). These datasets provide the necessary genetic information to analyze the causal relationship between depression and low back pain using the Mendelian randomization approach. Instrumental variable selection Instrumental variables should meet the following requirements: ① All instrumental variables should have genome-wide significance (P<5×10 -6 ); ② Chain disequilibrium parameter (R 2 10 is an indicator of statistical stability[18], which suggests that there is no weak instrumental variable bias; ④ SNPs related to confounding factors need to be excluded. SNPs associated with confounding factors need to be excluded, and SNPs associated with sex or age were excluded by examining the PhenoScanner database. The SNPs that passed the screening were used as instrumental variables in this study. MR analysis R4.3.1 version and R packages, such as TwoSampleMR and MR-PRESSO, were utilized for conducting statistical analysis. To ensure that the effects of SNPs on exposure and clinical outcomes corresponded to the same alleles, the summary statistics of the exposure and clinical outcome datasets were adjusted. Two-sample MR analysis employs various methods, including inverse variance weighting (IVW), weighted median, MR‒Egger regression, simple mode, and weighted mode, to estimate causality. IVW, as the primary MR analysis method, combines the MR effect estimates of each SNP with weighted averaging to obtain an overall estimate of the potential causal effect [19]. IVW analysis is considered reliable when there is no horizontal pleiotropy in the instrumental variables [20]. The weighted median method can produce consistent causal effect estimates even when up to 50% of information is derived from null genetic instruments [21]. MR‒Egger regression assesses the presence of horizontal pleiotropy and provides an estimate of the effect of horizontal pleiotropy as an intercept [22]. It can generate unbiased causal effect estimates when horizontal pleiotropy exists in the instrumental variables and can detect and correct for horizontal pleiotropy by removing outliers using the MR-PRESSO test [23]. Heterogeneity was quantified using Cochran's Q-test, with heterogeneity considered significant when P<0.05. If heterogeneity was present among the instrumental variables, the IVW random effects model was utilized to estimate the causal effects. The significance level (α) for hypothesis testing was set at 0.05, and a P value <0.05 was considered to indicate statistical significance. Results instrumental variable The study aimed to investigate the relationship between depression as an exposure factor and low back pain as an outcome variable. Through a screening process, 25 single nucleotide polymorphisms (SNPs) were identified as instrumental variables. All of these SNPs had F values greater than 10. These instrumental variables were identified from two genome datasets and showed potential causal associations with both depression and low back pain. The F value is a statistical indicator used to measure the strength of the relationship between the instrumental variables and the exposure factor. A higher F value suggests a stronger independent association between the instrumental variables and the exposure factor. By using these SNPs as instrumental variables, researchers can apply a two-sample Mendelian randomization (MR) analysis to assess the causal relationship between depression and low back pain. The MR method utilizes genetic variation as instrumental variables, which are randomly assigned, to estimate the causal effect of the exposure factor on the outcome variable. It is important to note that the results obtained through MR analysis require multiple statistical analyses and tests, such as inverse variance weighting (IVW), weighted median, and MR‒Egger regression, to estimate causal effects, assess heterogeneity, and detect potential horizontal pleiotropy. Additionally, if there is evidence of horizontal pleiotropy among the instrumental variables, further tests using MR‒Egger regression and MR-PRESSO can be conducted to detect and correct for potential bias. Overall, by using 25 instrumental variables with F values greater than 10, the study aims to provide a more accurate assessment of the causal relationship between depression and low back pain through two-sample MR analysis . Heterogeneity test IVW and MR‒Egger regression were conducted to test for heterogeneity among the instrumental variables. The results of the MR‒Egger regression indicated that Cochran's Q value was 16.057 with 20 degrees of freedom (df), resulting in a p value of 0.713. Similarly, the IVW analysis revealed a Cochran's Q value of 16.348 with 21 df and a p value of 0.749. These findings suggest that there is no significant heterogeneity observed among the instrumental variables. Heterogeneity, in the context of Mendelian randomization analysis, reflects potential differences in the genetic associations between the instrumental variables and the exposure factor across different genetic variants. The MR‒Egger regression and IVW methods are commonly used to assess and address heterogeneity in instrumental variable analysis. The results obtained from both MR‒Egger regression and IVW analysis indicate a lack of statistically significant heterogeneity, suggesting that the instrumental variables used in the study provide consistent and reliable estimates of the causal effect of the exposure factor on the outcome variable. It is important to note that the absence of heterogeneity does not guarantee the absence of bias or other limitations in the study design. Researchers should also consider other potential sources of bias or confounding factors that may influence the results and interpretations of the study. （Figure 2） Multiple -effects test The multiplicity test results from the R row analysis revealed that the MR‒Egger regression intercept term was estimated to be 0.0136, with a corresponding p value of 0.595. This p value is greater than the conventional significance level of 0.05, indicating that there is no significant evidence of pleiotropy between the obtained single nucleotide polymorphism (SNP) data and low back pain. Pleiotropy refers to a situation where a genetic variant affects multiple traits or outcomes. In Mendelian randomization analysis, pleiotropy can lead to biased estimates of the causal effect if the instrumental variables used in the analysis are not valid. The nonsignificant intercept term in the MR‒Egger regression suggests that the instrumental variables used in the study are not affected by strong pleiotropy. This strengthens the validity of the instrumental variable analysis and increases confidence in the estimated causal effect of the exposure factor on low back pain. However, it is important to note that the absence of pleiotropy cannot be definitively concluded based solely on this single test. MR analysis The instrumental variables used in the Mendelian randomization (MR) analysis did not provide evidence of horizontal pleiotropy. This means that the genetic variants employed as instruments for depression were not found to have direct effects on low back pain through pathways other than depression. The analysis results indicated a significant causal relationship between depression and an increased risk of low back pain, with an odds ratio of 1.713. The 95% confidence interval for this odds ratio ranged from 1.258 to 2.332, and the corresponding p value was 0.0006. ( Figure 3, Figure 4 ). These findings suggest that depression may play a role as a contributing factor in the development of low back pain. It is important to note that the MR analysis assumes that the genetic instruments used are valid and satisfy certain assumptions. While the results are statistically significant, further research and replication of findings are necessary to confirm the causal relationship between depression and low back pain. Additionally, considering a comprehensive approach to managing low back pain, including both physical and psychological factors, may be beneficial in clinical practice . Sensitivity analysis Sensitivity analyses were performed using the leave-one-out method to assess the robustness of the causal effects observed. These analyses involved systematically removing single nucleotide polymorphisms (SNPs) one at a time and re-evaluating the results. In all cases, the results remained significant, with p values less than 0.05 after the stepwise removal of SNPs (Figure 5). Furthermore, the results obtained through the inverse variance-weighted (IVW) method, which combines the estimates from individual SNPs, were consistent with the findings from the analyses that included all SNPs. This suggests that the observed causal relationship between depression and low back pain was not driven by specific SNP loci with strong effects in the instrumental variables. These findings indicate that the conclusions drawn from the MR analysis are robust and not heavily influenced by the presence of any particular SNP. Discussion Observational studies play a crucial role in understanding the relationship between phenotypes and diseases. In these studies, human chromosomes are identified before birth, and genome-wide association studies (GWAS) are commonly used to identify sequence variants across the human genome. GWAS involves the identification of specific SNPs associated with diseases. These SNPs can then be used as instrumental variables in an analysis. The reason for using SNPs as instrumental variables is that the alleles in SNPs follow the principle of random assignment, meaning they are not influenced by environmental factors. This random assignment helps to reduce bias compared to observational studies such as cohort studies. By utilizing instrumental variables, GWAS can provide a more robust assessment of the causal effect between exposure and outcome. This approach helps researchers determine whether a specific exposure, such as a phenotype, has a causal effect on the development of a disease. Please note that these methods have been widely used and are valuable in studying causal relationships. However, it is important to consider the limitations and assumptions that come with these analyses and to interpret the results cautiously. In this study, researchers analyzed SNP data related to depression and low back pain. Their MR analysis, IVW analysis (OR=1.713), WME (OR=2.134), and MR‒Egger regression (OR=2.293) all indicated a positive correlation between depression and the onset of low back pain. This suggests that as the prevalence of depression increases, the incidence of low back pain also increases, implying a causal relationship between the two. Depression and pain are both common and debilitating conditions that impose significant economic and social burdens. The relationship between these two disorders has been extensively studied for many years. Previous research suggests that higher rates of depression may influence the occurrence of low back pain. Researchers have identified common neural mechanisms between depression and low back pain, highlighting their potential interconnection. Studies have also revealed that individuals with depression often exhibit abnormal immune responses, and the immune system plays a significant role in the development of depression. Specific cytokine levels, such as interleukin-1 (IL-1), interleukin-6 (IL-6), and tumor necrosis factor (TNF-α), are elevated in the blood plasma or serum of patients with depression compared to the healthy population. These markers have long been recognized as contributing factors to low back pain. The presence of these inflammatory biomarkers provides supporting evidence for the clinical observation that chronic pain frequently coexists with depressive disorders. In a study conducted in the United States, it was found that over 40% of individuals with depression also experience disabling chronic pain. The present study has the following advantages. First, unlike traditional epidemiological survey studies, Mendelian randomization utilizes germline genetic variation as an instrumental variable for exposure to study the relationship between the exposure phenotype and the outcome phenotype, avoiding the interference of confounding factors such as social environment and lifestyle. and outcome phenotypes, avoiding the interference of confounding factors such as social environment and lifestyle, and realizing the true sense of It avoids the interference of confounding factors such as social environment and lifestyle, and realizes the true sense of random assignment without violating moral ethics. Second, the data were pooled using the public GWAS, saving time and research costs. Second, the use of public GWAS pooled data saves time and research costs, and the data consist of people of European ancestry, reducing potential bias. bias; third, compared to a single SNP, 25 SNPs were used as instrumental variables in this study, increasing the proportion of genetic variation that can be explained. This study also has some limitations. First, exposure and outcome studies used in two-sample MR analyses should not involve overlapping participants. Instead, the study was unable to estimate the extent of overlap in this study, and bias due to sample overlap was minimized by using tools (e.g., F-statistics much larger than 10) [35]. Second, the pooled data from the GWAS only relate to the European population, which has limited extrapolation and needs to be validated in other populations. Third, this study used the pooled database of 2 GWAS, and due to the lack of individual data, further subgroup analyses such as age or gender could not be performed to further refine the outcome variables for analysis. Fourth. MR can only explore the linear relationship between exposure and outcome variables and cannot perform nonlinear analysis. In conclusion, this study applied a two-sample Mendelian randomization method to infer a causal relationship between depression and low back pain, and the results suggest that there is a causal relationship between depression and low back pain, that depression may be a risk factor for low back pain, and that interventions related to depression can be included in the prevention, treatment, and prognostic assessment strategies for low back pain. Declaration Conception and design were performed by Min Liu and Meinian Liu; development of methodology was analyzed by Wenlong Yang; acquisition of data (acquired and managed patients, provided facilities, etc.) was collected by Zhijun Yang and Guanrong Peng; analysis and interpretation of data (e.g., statistical analysis, biostatistics, computational analysis) were provided by Fengyun Yang; writing of the manuscript was performed by Min Liu; supervision was conducted by Yirong Zeng. All authors reviewed the manuscript. References Dionne CE, Dunn KM, Croft PR, Nachemson AL, Buchbinder R, Walker BF, et al. A consensus approach toward the standardization of back pain definitions for use in prevalence studies. Spine (Phila Pa 1976). 2008;33:95–103. GBD 2016 Neurology Collaborators. Global, regional, and national burden of neurological disorders, 1990-2016: a systematic analysis for the Global Burden of Disease Study 2016. Lancet Neurol. 2019;18:459–80. 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Comorbidity of chronic pain and antidepressive disorders: Deciphering underlying brain circuits. Neurosci Biobehav Rev. 2020;115:131–3. Arnow BA, Hunkeler EM, Blasey CM, Lee J, Constantino MJ, Fireman B, et al. Comorbid depression, chronic pain, and disability in primary care. Psychosom Med. 2006;68:262–8. Pierce BL, Burgess S. Efficient design for Mendelian randomization studies: subsample and 2-sample instrumental variable estimators. Am J Epidemiol. 2013;178:1177–84. Table Table 1 Sample Data Set Basic Information Exposure/outcomes Sample size Number of SNPs Build Population Year of publication Depression 180866 6019632 HG19/GRCh37 European 2016 Low back pain 177860 16380287 HG19/GRCh37 European 2021 Additional Declarations No competing interests reported. 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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-3457406","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":240941133,"identity":"7c8a0ee1-2037-4fce-845d-433052946999","order_by":0,"name":"Min Liu","email":"","orcid":"","institution":"Guangzhou University of Chinese Medicine","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Min","middleName":"","lastName":"Liu","suffix":""},{"id":240941136,"identity":"f4e8da04-1733-41ae-a94a-a5aceaacda81","order_by":1,"name":"Meinian Liu","email":"","orcid":"","institution":"The Affiliated Hospital of Jiangxi University of Chinese Medicine","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Meinian","middleName":"","lastName":"Liu","suffix":""},{"id":240941139,"identity":"93d55691-275c-4663-bca2-46f3bdd8fe3d","order_by":2,"name":"Guanrong Peng","email":"","orcid":"","institution":"Guangzhou University of Chinese Medicine","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Guanrong","middleName":"","lastName":"Peng","suffix":""},{"id":240941141,"identity":"196959f2-0956-4f69-8f59-a51baa625046","order_by":3,"name":"Wenlong Yang","email":"","orcid":"","institution":"The Affiliated Hospital of Jiangxi University of Chinese Medicine","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Wenlong","middleName":"","lastName":"Yang","suffix":""},{"id":240941143,"identity":"3f0441b5-4387-48dd-8811-e70c314052db","order_by":4,"name":"Fengyun Yang","email":"","orcid":"","institution":"The Affiliated Hospital of Jiangxi University of Chinese Medicine","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Fengyun","middleName":"","lastName":"Yang","suffix":""},{"id":240941145,"identity":"153cdf3e-9aa5-4fda-bfd5-8401dbeefbcf","order_by":5,"name":"Zhijun Yang","email":"","orcid":"","institution":"The Affiliated Hospital of Jiangxi University of Chinese Medicine","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Zhijun","middleName":"","lastName":"Yang","suffix":""},{"id":240941147,"identity":"ac9498b5-2002-43d1-8528-980620951e9c","order_by":6,"name":"Yirong Zeng","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3klEQVRIie2RMQrCQBBFRwKjxaCdLAjuFSKBkMLDbBqrIIIg9kLSaK9YeAWPEF2wUi+gRUQQC4u1S+naWblbCu6r/+PzZwAcjl9FjLttrE7yXJW2SrHvBXXaxZvF1NKoXNJt3GZJIGtoEefZTN7jNBcIiZJAwBvN3NAwPfYicTz3EQ5rOYigs1iK74rHktAXo9sQK7O1nBMI/2RQkD+0gjJOPSokoYVCjIJCpFpBAjuFURKC0EdGQl8fmZm38GwfPEv9Sr66XpUqu7zRMijvOeyz1Rh/4ymrmMPhcPwvL09LR5OjE8uyAAAAAElFTkSuQmCC","orcid":"","institution":"The First Affiliated Hospital of Guangzhou University of Chinese Medicine","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Yirong","middleName":"","lastName":"Zeng","suffix":""}],"badges":[],"createdAt":"2023-10-17 11:59:29","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3457406/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3457406/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":44941013,"identity":"531939ab-2471-458c-be2e-cc30ddd60f66","added_by":"auto","created_at":"2023-10-19 17:52:43","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":36901,"visible":true,"origin":"","legend":"\u003cp\u003eTool Variable Filtering Flowchart\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-3457406/v1/0b14c8aa8aa14f74d7fe2aef.png"},{"id":44941984,"identity":"1dd25173-2673-4a79-94f2-f13d26de479b","added_by":"auto","created_at":"2023-10-19 18:00:43","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":57108,"visible":true,"origin":"","legend":"\u003cp\u003eThe funnel plot shows that the inclusion of SNPs without heterogeneity\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-3457406/v1/d739f504966dc1789f7792f8.png"},{"id":44941014,"identity":"687ea67a-a909-451f-be0e-89a529c3e66f","added_by":"auto","created_at":"2023-10-19 17:52:43","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":128108,"visible":true,"origin":"","legend":"\u003cp\u003eScatterplot showing a causal relationship between depression and rheumatic diseases. The gradient of the straight line indicates the magnitude of the causal relationship. MR, Mendelian randomization; SNP, single nucleotide polymorphism\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-3457406/v1/2f420df3ba4641e960c3bab9.png"},{"id":44941018,"identity":"3d1e2067-938a-4335-8d2d-84579dbc9d12","added_by":"auto","created_at":"2023-10-19 17:52:44","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":68456,"visible":true,"origin":"","legend":"\u003cp\u003eForest plots depicting the results of 5 different MR estimation methods on the relationship between depression and low back pain risk. MR, Mendelian randomization\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-3457406/v1/646b55d4b8150dd64937f3aa.png"},{"id":44941015,"identity":"5863b9b2-d6a2-430f-a502-4fb697f167d4","added_by":"auto","created_at":"2023-10-19 17:52:43","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":88819,"visible":true,"origin":"","legend":"\u003cp\u003eThe sensitivity analysis using the leave-one-out method showed that the results of the MR analysis were stable.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-3457406/v1/d98dcb22d9e097bc85eca675.png"},{"id":46608129,"identity":"61d2db01-d2ba-4331-a1f9-f9f67fade32c","added_by":"auto","created_at":"2023-11-17 06:29:36","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":782319,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3457406/v1/b3dbc5a8-449e-4340-9c05-c527e365f2a3.pdf"},{"id":44941019,"identity":"cf4a73cf-2788-43e8-b69c-72d50f32a2b8","added_by":"auto","created_at":"2023-10-19 17:52:44","extension":"xls","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":38912,"visible":true,"origin":"","legend":"","description":"","filename":"SNP.xls","url":"https://assets-eu.researchsquare.com/files/rs-3457406/v1/695fbcc293f1d20bf0341f4b.xls"}],"financialInterests":"No competing interests reported.","formattedTitle":"The causal relationship between depression and low back pain: a two-sample Mendelian randomized study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eLower back pain is characterized by pain located between the lower ribs and the transverse gluteal line. It is often accompanied by pain in one or both lower extremities and may present with neurological symptoms in some patients[1]. According to an epidemiological survey, lower back pain was one of the top five causes of disability worldwide from 1990 to 2016[2]. This condition not only causes physical and psychological suffering for patients but also places a significant economic burden on families and society. In the United States, a majority of people experience low back pain, with approximately 25% of adults reporting at least one day of lower back pain in the past three months[3]. In 2018, the Lancet published a series of articles focusing on lower back pain, emphasizing the need for global attention to the physical, psychological, and social consequences of this condition[4]. The articles highlighted the existing gaps between evidence and medical practice across different income populations and the lack of appropriate evidence-based guidelines for low- and middle-income individuals[5]. With the increasing burden of lower back pain due to population growth and aging [6], finding ways to address effectiveness and cost conflict has become an urgent task.\u003c/p\u003e\n\u003cp\u003eDepression is a common psychological disorder characterized by persistent feelings of sadness, loss of interest or pleasure in activities, and cognitive and behavioral impairments. As of 2019, approximately 280 million people worldwide were affected by depression[7], making it a significant global health issue[8]. Multiple factors, including psychological, genetic, biochemical, and social-environmental factors, contribute to the development of depression[9-11]. When depression becomes recurrent and reaches a moderate or severe level, it can cause significant distress and interfere with daily functioning, and in severe cases, it can even lead to suicide[12].\u003c/p\u003e\n\u003cp\u003eResearch suggests a potential association between depression and low back pain[13]. It has been observed that the incidence of depression may impact the occurrence of low back pain[13]. Additionally, early detection and comprehensive treatment of depressive symptoms may benefit individuals with low back pain[14]. However, it is important to note that the causal relationship between depression and low back pain is still not fully understood and requires further investigation. Managing depression and low back pain typically involves a multidisciplinary approach, including psychological therapy, medication, physical therapy, and lifestyle modifications. It is essential for individuals experiencing symptoms of depression or low back pain to seek professional medical help for an accurate diagnosis and appropriate treatment.\u003c/p\u003e\n\u003cp\u003eMendelian randomization (MR) analyses are a useful tool in determining whether there is a causal relationship between observed modifiable risk factors or exposures and clinical outcomes[15]. Traditional randomized controlled trials might not always be feasible or ethical to investigate causality, and observational studies may suffer from confounding or reverse causality biases[16]. In this context, MR analysis can provide valuable insights. It utilizes genetic variants as instrumental variables to mimic a randomized controlled trial-like design. These genetic variants should be associated with the exposure of interest (in this case, depression) but not associated with confounders or competing risk factors. By examining the genetic variants\u0026apos; association with the outcome of interest (low back pain), it is possible to establish a causal relationship. This particular study collected published data and conducted a two-sample MR analysis to investigate whether there is a causal relationship between depression and low back pain. The findings of the study can help clarify whether depression has a direct impact on the occurrence of low back pain. However, it is important to note that MR analysis has limitations, including the assumptions that need to be met for valid causal inference and potential pleiotropy (genetic variants affecting multiple outcomes). Therefore, additional research is needed to confirm and validate these findings.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eStudy design\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Mendelian randomization study design for the three hypotheses is as follows (Figure 1): ① Hypothesis of association: The genetic instrumental variable used in the study is expected to have a strong association with the exposure factor, which in this case is depression. This means that the selected genetic variants are chosen based on their known association with depression.② Hypothesis of independence: The genetic instrumental variable needs to be independent of potential confounders. This means that the selected genetic variants should not be associated with any other factors that could affect the outcome (low back pain) or introduce bias into the analysis. ③ Hypothesis of exclusion: The genetic instrumental variable should influence the outcome factor (low back pain) exclusively through the exposure factor (depression). This means that the selected genetic variants should only affect low back pain through their effect on depression and not through any other pathways.\u003c/p\u003e\n\u003cp\u003eTo conduct Mendelian randomization analysis, relevant genome-wide association study (GWAS) datasets were collected from the website https://gwas.mrcieu.ac.uk. The GWAS dataset for depression (ID: ebi-a-GCST003769) included 122,210 subjects of European ancestry and 6,019,632 single nucleotide polymorphisms (SNPs). The GWAS dataset for low back pain (ID: finn-b-M13_LOWBACKPAIN) included 13,178 patients and 164,682 control subjects of European ancestry (Table 1). These datasets provide the necessary genetic information to analyze the causal relationship between depression and low back pain using the Mendelian randomization approach.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInstrumental variable selection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInstrumental variables should meet the following requirements:\u0026nbsp;①\u0026nbsp;All instrumental variables should have genome-wide significance (P\u0026lt;5\u0026times;10\u003csup\u003e-6\u003c/sup\u003e);\u0026nbsp;② Chain\u0026nbsp;disequilibrium parameter (R\u003csup\u003e2\u003c/sup\u003e\u0026lt;0.001, and the range of the region is within 10,000 kb;\u0026nbsp;③\u0026nbsp;F value represents the strength of MR, and a value of \u0026gt;10 is an indicator of statistical stability[18], which suggests that there is no weak instrumental variable bias;\u0026nbsp;④ SNPs\u0026nbsp;related to confounding factors need to be excluded. SNPs associated with confounding factors need to be excluded, and SNPs associated with\u0026nbsp;sex\u0026nbsp;or age were excluded by examining the PhenoScanner database.\u0026nbsp;The\u0026nbsp;SNPs that passed the screening were used as instrumental variables in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMR analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eR4.3.1 version and R packages, such as TwoSampleMR and MR-PRESSO, were utilized for conducting statistical analysis. To ensure that the effects of SNPs on exposure and clinical outcomes corresponded to the same alleles, the summary statistics of the exposure and clinical outcome datasets were adjusted. Two-sample MR analysis employs various methods, including inverse variance weighting (IVW), weighted median, MR‒Egger regression, simple mode, and weighted mode, to estimate causality. IVW, as the primary MR analysis method, combines the MR effect estimates of each SNP with weighted averaging to obtain an overall estimate of the potential causal effect [19]. IVW analysis is considered reliable when there is no horizontal pleiotropy in the instrumental variables [20]. The weighted median method can produce consistent causal effect estimates even when up to 50% of information is derived from null genetic instruments [21]. MR‒Egger regression assesses the presence of horizontal pleiotropy and provides an estimate of the effect of horizontal pleiotropy as an intercept [22]. It can generate unbiased causal effect estimates when horizontal pleiotropy exists in the instrumental variables and can detect and correct for horizontal pleiotropy by removing outliers using the MR-PRESSO test [23]. Heterogeneity was quantified using Cochran\u0026apos;s Q-test, with heterogeneity considered significant when P\u0026lt;0.05. If heterogeneity was present among the instrumental variables, the IVW random effects model was utilized to estimate the causal effects. The significance level (\u0026alpha;) for hypothesis testing was set at 0.05, and a P value \u0026lt;0.05 was considered to indicate statistical significance.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003einstrumental variable\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study aimed to investigate the relationship between depression as an exposure factor and low back pain as an outcome variable. Through a screening process, 25 single nucleotide polymorphisms (SNPs) were identified as instrumental variables. All of these SNPs had F values greater than 10. These instrumental variables were identified from two genome datasets and showed potential causal associations with both depression and low back pain. The F value is a statistical indicator used to measure the strength of the relationship between the instrumental variables and the exposure factor. A higher F value suggests a stronger independent association between the instrumental variables and the exposure factor. By using these SNPs as instrumental variables, researchers can apply a two-sample Mendelian randomization (MR) analysis to assess the causal relationship between depression and low back pain. The MR method utilizes genetic variation as instrumental variables, which are randomly assigned, to estimate the causal effect of the exposure factor on the outcome variable. It is important to note that the results obtained through MR analysis require multiple statistical analyses and tests, such as inverse variance weighting (IVW), weighted median, and MR‒Egger regression, to estimate causal effects, assess heterogeneity, and detect potential horizontal pleiotropy. Additionally, if there is evidence of horizontal pleiotropy among the instrumental variables, further tests using MR‒Egger regression and MR-PRESSO can be conducted to detect and correct for potential bias. Overall, by using 25 instrumental variables with F values greater than 10, the study aims to provide a more accurate assessment of the causal relationship between depression and low back pain through two-sample MR analysis\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHeterogeneity\u003c/strong\u003e\u003cstrong\u003e test\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIVW and MR‒Egger regression were conducted to test for heterogeneity among the instrumental variables. The results of the MR‒Egger regression indicated that Cochran\u0026apos;s Q value was 16.057 with 20 degrees of freedom (df), resulting in a p value of 0.713. Similarly, the IVW analysis revealed a Cochran\u0026apos;s Q value of 16.348 with 21 df and a p value of 0.749. These findings suggest that there is no significant heterogeneity observed among the instrumental variables. Heterogeneity, in the context of Mendelian randomization analysis, reflects potential differences in the genetic associations between the instrumental variables and the exposure factor across different genetic variants. The MR‒Egger regression and IVW methods are commonly used to assess and address heterogeneity in instrumental variable analysis. The results obtained from both MR‒Egger regression and IVW analysis indicate a lack of statistically significant heterogeneity, suggesting that the instrumental variables used in the study provide consistent and reliable estimates of the causal effect of the exposure factor on the outcome variable. It is important to note that the absence of heterogeneity does not guarantee the absence of bias or other limitations in the study design. Researchers should also consider other potential sources of bias or confounding factors that may influence the results and interpretations of the study. （Figure 2）\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMultiple\u003c/strong\u003e\u003cstrong\u003e-effects test\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe multiplicity test results from the R row analysis revealed that the MR‒Egger regression intercept term was estimated to be 0.0136, with a corresponding p value of 0.595. This p value is greater than the conventional significance level of 0.05, indicating that there is no significant evidence of pleiotropy between the obtained single nucleotide polymorphism (SNP) data and low back pain. Pleiotropy refers to a situation where a genetic variant affects multiple traits or outcomes. In Mendelian randomization analysis, pleiotropy can lead to biased estimates of the causal effect if the instrumental variables used in the analysis are not valid. The nonsignificant intercept term in the MR‒Egger regression suggests that the instrumental variables used in the study are not affected by strong pleiotropy. This strengthens the validity of the instrumental variable analysis and increases confidence in the estimated causal effect of the exposure factor on low back pain. However, it is important to note that the absence of pleiotropy cannot be definitively concluded based solely on this single test.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMR analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe instrumental variables used in the Mendelian randomization (MR) analysis did not provide evidence of horizontal pleiotropy. This means that the genetic variants employed as instruments for depression were not found to have direct effects on low back pain through pathways other than depression. The analysis results indicated a significant causal relationship between depression and an increased risk of low back pain, with an odds ratio of 1.713. The 95% confidence interval for this odds ratio ranged from 1.258 to 2.332, and the corresponding p value was 0.0006. \u003cstrong\u003e(\u003c/strong\u003eFigure 3, Figure 4\u003cstrong\u003e). \u003c/strong\u003eThese findings suggest that depression may play a role as a contributing factor in the development of low back pain. It is important to note that the MR analysis assumes that the genetic instruments used are valid and satisfy certain assumptions. While the results are statistically significant, further research and replication of findings are necessary to confirm the causal relationship between depression and low back pain. Additionally, considering a comprehensive approach to managing low back pain, including both physical and psychological factors, may be beneficial in clinical practice\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSensitivity\u003c/strong\u003e\u003cstrong\u003e analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSensitivity analyses were performed using the leave-one-out method to assess the robustness of the causal effects observed. These analyses involved systematically removing single nucleotide polymorphisms (SNPs) one at a time and re-evaluating the results. In all cases, the results remained significant, with p values less than 0.05 after the stepwise removal of SNPs (Figure 5). Furthermore, the results obtained through the inverse variance-weighted (IVW) method, which combines the estimates from individual SNPs, were consistent with the findings from the analyses that included all SNPs. This suggests that the observed causal relationship between depression and low back pain was not driven by specific SNP loci with strong effects in the instrumental variables. These findings indicate that the conclusions drawn from the MR analysis are robust and not heavily influenced by the presence of any particular SNP.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eObservational studies play a crucial role in understanding the relationship between phenotypes and diseases. In these studies, human chromosomes are identified before birth, and genome-wide association studies (GWAS) are commonly used to identify sequence variants across the human genome. GWAS involves the identification of specific SNPs associated with diseases. These SNPs can then be used as instrumental variables in an analysis. The reason for using SNPs as instrumental variables is that the alleles in SNPs follow the principle of random assignment, meaning they are not influenced by environmental factors. This random assignment helps to reduce bias compared to observational studies such as cohort studies. By utilizing instrumental variables, GWAS can provide a more robust assessment of the causal effect between exposure and outcome. This approach helps researchers determine whether a specific exposure, such as a phenotype, has a causal effect on the development of a disease. Please note that these methods have been widely used and are valuable in studying causal relationships. However, it is important to consider the limitations and assumptions that come with these analyses and to interpret the results cautiously.\u003c/p\u003e\n\u003cp\u003eIn this study, researchers analyzed SNP data related to depression and low back pain. Their MR analysis, IVW analysis (OR=1.713), WME (OR=2.134), and MR‒Egger regression (OR=2.293) all indicated a positive correlation between depression and the onset of low back pain. This suggests that as the prevalence of depression increases, the incidence of low back pain also increases, implying a causal relationship between the two.\u0026nbsp;Depression and pain are both common and debilitating conditions that impose significant economic and social burdens. The relationship between these two disorders has been extensively studied for many years. Previous research suggests that higher rates of depression may influence the occurrence of low back pain.\u0026nbsp;Researchers have identified common neural mechanisms between depression and low back pain, highlighting their potential interconnection. Studies have also revealed that individuals with depression often exhibit abnormal immune responses, and the immune system plays a significant role in the development of depression. Specific cytokine levels, such as interleukin-1 (IL-1), interleukin-6 (IL-6), and tumor necrosis factor (TNF-\u0026alpha;), are elevated in the blood plasma or serum of patients with depression compared to the healthy population. These markers have long been recognized as contributing factors to low back pain.\u0026nbsp;The presence of these inflammatory biomarkers provides supporting evidence for the clinical observation that chronic pain frequently coexists with depressive disorders. In a study conducted in the United States, it was found that over 40% of individuals with depression also experience disabling chronic pain.\u003c/p\u003e\n\u003cp\u003eThe present study has the following advantages. First, unlike traditional epidemiological survey studies, Mendelian randomization utilizes germline genetic variation as an instrumental variable for exposure to study the relationship between the exposure phenotype and the outcome phenotype, avoiding the interference of confounding factors such as social environment and lifestyle. and outcome phenotypes, avoiding the interference of confounding factors such as social environment and lifestyle, and realizing the true sense of It avoids the interference of confounding factors such as social environment and lifestyle, and realizes the true sense of random assignment without violating moral ethics. Second, the data were pooled using the public GWAS, saving time and research costs. Second, the use of public GWAS pooled data saves time and research costs, and the data consist of people of European ancestry, reducing potential bias. bias; third, compared to a single SNP, 25 SNPs were used as instrumental variables in this study, increasing the proportion of genetic variation that can be explained.\u003c/p\u003e\n\u003cp\u003eThis study also has some limitations. First, exposure and outcome studies used in two-sample MR analyses should not involve overlapping participants. Instead, the study was unable to estimate the extent of overlap in this study, and bias due to sample overlap was minimized by using tools (e.g., F-statistics much larger than 10) [35]. Second, the pooled data from the GWAS only relate to the European population, which has limited extrapolation and needs to be validated in other populations. Third, this study used the pooled database of 2 GWAS, and due to the lack of individual data, further subgroup analyses such as age or gender could not be performed to further refine the outcome variables for analysis. Fourth. MR can only explore the linear relationship between exposure and outcome variables and cannot perform nonlinear analysis.\u003c/p\u003e\n\u003cp\u003eIn conclusion, this study applied a two-sample Mendelian randomization method to infer a causal relationship between depression and low back pain, and the results suggest that there is a causal relationship between depression and low back pain, that depression may be a risk factor for low back pain, and that interventions related to depression can be included in the prevention, treatment, and prognostic assessment strategies for low back pain.\u003c/p\u003e"},{"header":"Declaration","content":"\u003cp\u003eConception and design were performed by Min Liu and Meinian Liu; development of methodology was analyzed by Wenlong Yang; acquisition of data (acquired and managed patients, provided facilities, etc.) was collected by Zhijun Yang and Guanrong Peng; analysis and interpretation of data (e.g., statistical analysis, biostatistics, computational analysis) were provided by Fengyun Yang; writing of the manuscript was performed by Min Liu; supervision was conducted by Yirong Zeng. All authors reviewed the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eDionne CE, Dunn KM, Croft PR, Nachemson AL, Buchbinder R, Walker BF, et al. A consensus approach toward the standardization of back pain definitions for use in prevalence studies. Spine (Phila Pa 1976). 2008;33:95\u0026ndash;103.\u003c/li\u003e\n\u003cli\u003eGBD 2016 Neurology Collaborators. Global, regional, and national burden of neurological disorders, 1990-2016: a systematic analysis for the Global Burden of Disease Study 2016. Lancet Neurol. 2019;18:459\u0026ndash;80.\u003c/li\u003e\n\u003cli\u003eQaseem A, Wilt TJ, McLean RM, Forciea MA, Clinical Guidelines Committee of the American College of Physicians, Denberg TD, et al. Noninvasive Treatments for Acute, Subacute, and Chronic Low Back Pain: A Clinical Practice Guideline From the American College of Physicians. Ann Intern Med. 2017;166:514\u0026ndash;30.\u003c/li\u003e\n\u003cli\u003eHartvigsen J, Hancock MJ, Kongsted A, Louw Q, Ferreira ML, Genevay S, et al. What low back pain is and why we need to pay attention. Lancet. 2018;391:2356\u0026ndash;67.\u003c/li\u003e\n\u003cli\u003eFoster NE, Anema JR, Cherkin D, Chou R, Cohen SP, Gross DP, et al. Prevention and treatment of low back pain: evidence, challenges, and promising directions. The Lancet. 2018;391:2368\u0026ndash;83.\u003c/li\u003e\n\u003cli\u003eBuchbinder R, van Tulder M, \u0026Ouml;berg B, Costa LM, Woolf A, Schoene M, et al. Low back pain: a call for action. Lancet. 2018;391:2384\u0026ndash;8.\u003c/li\u003e\n\u003cli\u003eDenche-Zamorano \u0026Aacute;, Mendoza-Mu\u0026ntilde;oz DM, Pastor-Cisneros R, Adsuar JC, Carlos-Vivas J, Franco-Garc\u0026iacute;a JM, et al. A Cross-Sectional Study on the Associations between Physical Activity Level, Depression, and Anxiety in Smokers and Ex-Smokers. Healthcare (Basel). 2022;10:1403.\u003c/li\u003e\n\u003cli\u003eHerrman H, Kieling C, McGorry P, Horton R, Sargent J, Patel V. Reducing the global burden of depression: a Lancet-World Psychiatric Association Commission. Lancet. 2019;393:e42\u0026ndash;3.\u003c/li\u003e\n\u003cli\u003eMalhi GS, Mann JJ. Depression. Lancet. 2018;392:2299\u0026ndash;312.\u003c/li\u003e\n\u003cli\u003eLee G, Bae H. Therapeutic Effects of Phytochemicals and Medicinal Herbs on Depression. Biomed Res Int. 2017;2017:6596241.\u003c/li\u003e\n\u003cli\u003eFischer AS, Camacho MC, Ho TC, Whitfield-Gabrieli S, Gotlib IH. Neural Markers of Resilience in Adolescent Females at Familial Risk for Major Depressive Disorder. JAMA Psychiatry. 2018;75:493.\u003c/li\u003e\n\u003cli\u003eMm X, P G, Qy M, X Z, Yl W, L W, et al. Can acupuncture enhance the therapeutic effectiveness of antidepressants and reduce adverse drug reactions in patients with depression? A systematic review and meta-analysis. Journal of integrative medicine [Internet]. 2022 [cited 2023 Oct 10];20. Available from: https://pubmed.ncbi.nlm.nih.gov/35595611/\u003c/li\u003e\n\u003cli\u003eMiddleton P, Pollard H. Are chronic low back pain outcomes improved with the comanagement of concurrent depression? Chiropr Man Therap [Internet]. 2005;13. Available from: https://chiromt.biomedcentral.com/articles/10.1186/1746-1340-13-8\u003c/li\u003e\n\u003cli\u003eKao Y-C, Chen J-Y, Chen H-H, Liao K-W, Huang S‒S. The association between depression and chronic lower back pain from disc degeneration and herniation of the lumbar spine. Int J Psychiatry Med. 2022;57:165\u0026ndash;77.\u003c/li\u003e\n\u003cli\u003eSekula P, Del Greco M F, Pattaro C, K\u0026ouml;ttgen A. Mendelian Randomization as an Approach to Assess Causality Using Observational Data. JASN. 2016;27:3253\u0026ndash;65.\u003c/li\u003e\n\u003cli\u003eDavey Smith G, Hemani G. Mendelian randomization: genetic anchors for causal inference in epidemiological studies. Hum Mol Genet. 2014;23:R89-98.\u003c/li\u003e\n\u003cli\u003eLifeLines Cohort Study, Okbay A, Baselmans BML, De Neve J-E, Turley P, Nivard MG, et al. Genetic variants associated with subjective well-being, depressive symptoms, and neuroticism were identified through genome-wide analyses. Nat Genet. 2016;48:624\u0026ndash;33.\u003c/li\u003e\n\u003cli\u003eHemani G, Zheng J, Elsworth B, Wade KH, Haberland V, Baird D, et al. The MR-Base platform supports systematic causal inference across the human phenomenon.\u003c/li\u003e\n\u003cli\u003eEllingjord-Dale M, Papadimitriou N, Katsoulis M, Yee C, Dimou N, Gill D, et al. Coffee consumption and risk of breast cancer: A Mendelian randomization study. PLoS One. 2021;16:e0236904.\u003c/li\u003e\n\u003cli\u003eJin P, Xing Y, Xiao B, Wei Y, Yan K, Zhao J, et al. Diabetes and intervertebral disc degeneration: A Mendelian randomization study. Front Endocrinol (Lausanne). 2023;14:1100874.\u003c/li\u003e\n\u003cli\u003eHart DJ, Doyle DV, Spector TD. Association between metabolic factors and knee osteoarthritis in women: the Chingford Study. J Rheumatol. 1995;22:1118\u0026ndash;23.\u003c/li\u003e\n\u003cli\u003eBurgess S, Thompson SG. Interpreting findings from Mendelian randomization using the MR‒Egger method. Eur J Epidemiol. 2017;32:377\u0026ndash;89.\u003c/li\u003e\n\u003cli\u003eLi P, Wang H, Guo L, Gou X, Chen G, Lin D, et al. Association between gut microbiota and preeclampsia-eclampsia: a two-sample Mendelian randomization study. BMC Med. 2022;20:443.\u003c/li\u003e\n\u003cli\u003eBonilla-Jaime H, S\u0026aacute;nchez-Salcedo JA, Estevez-Cabrera MM, Molina-Jim\u0026eacute;nez T, Cortes-Altamirano JL, Alfaro-Rodr\u0026iacute;guez A. Depression and Pain: Use of Antidepressants. Curr Neuropharmacol. 2022;20:384\u0026ndash;402.\u003c/li\u003e\n\u003cli\u003eHooten WM. Chronic Pain and Mental Health Disorders: Shared Neural Mechanisms, Epidemiology, and Treatment. Mayo Clin Proc. 2016;91:955\u0026ndash;70.\u003c/li\u003e\n\u003cli\u003eFinan PH, Smith MT. The comorbidity of insomnia, chronic pain, and depression: dopamine as a putative mechanism. Sleep Med Rev. 2013;17:173\u0026ndash;83.\u003c/li\u003e\n\u003cli\u003eE B, M T, Cb N. The Bidirectional Relationship of Depression and Inflammation: Double Trouble. Neuron [Internet]. 2020 [cited 2023 Oct 15];107. Available from: https://pubmed.ncbi.nlm.nih.gov/32553197/\u003c/li\u003e\n\u003cli\u003eUpthegrove R, Khandaker GM. Cytokines, Oxidative Stress and Cellular Markers of Inflammation in Schizophrenia. Curr Top Behav Neurosci. 2020;44:49\u0026ndash;66.\u003c/li\u003e\n\u003cli\u003eJiang M, Qin P, Yang X. Comorbidity between depression and asthma via immune-inflammatory pathways: a meta-analysis. J Affect Disord. 2014;166:22\u0026ndash;9.\u003c/li\u003e\n\u003cli\u003eHori H, Kim Y. Inflammation and posttraumatic stress disorder. Psychiatry Clin Neurosci. 2019;73:143\u0026ndash;53.\u003c/li\u003e\n\u003cli\u003eChen M-H, Cheng C-M, Gueorguieva R, Lin W-C, Li C-T, Hong C-J, et al. Maintenance of antidepressant and antisuicidal effects by D-cycloserine among patients with treatment-resistant depression who responded to low-dose ketamine infusion: a double-blind randomized placebo-control study. Neuropsychopharmacology. 2019;44:2112\u0026ndash;8.\u003c/li\u003e\n\u003cli\u003eKang JD, Georgescu HI, McIntyre-Larkin L, Stefanovic-Racic M, Donaldson WF, Evans CH. Herniated lumbar intervertebral discs spontaneously produce matrix metalloproteinases, nitric oxide, interleukin-6, and prostaglandin E2. Spine (Phila Pa 1976). 1996;21:271\u0026ndash;7.\u003c/li\u003e\n\u003cli\u003eBecker LJ, Journ\u0026eacute;e SH, Lutz P-E, Yalcin I. Comorbidity of chronic pain and antidepressive disorders: Deciphering underlying brain circuits. Neurosci Biobehav Rev. 2020;115:131\u0026ndash;3.\u003c/li\u003e\n\u003cli\u003eArnow BA, Hunkeler EM, Blasey CM, Lee J, Constantino MJ, Fireman B, et al. Comorbid depression, chronic pain, and disability in primary care. Psychosom Med. 2006;68:262\u0026ndash;8.\u003c/li\u003e\n\u003cli\u003ePierce BL, Burgess S. Efficient design for Mendelian randomization studies: subsample and 2-sample instrumental variable estimators. Am J Epidemiol. 2013;178:1177\u0026ndash;84.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table","content":"\u003cp\u003eTable 1 Sample Data Set Basic Information\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"603\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.92691029900332%\" valign=\"top\"\u003e\n \u003cp\u003eExposure/outcomes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.956810631229235%\" valign=\"top\"\u003e\n \u003cp\u003eSample size\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.109634551495017%\" valign=\"top\"\u003e\n \u003cp\u003eNumber of SNPs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.441860465116278%\" valign=\"top\"\u003e\n \u003cp\u003eBuild\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.790697674418604%\" valign=\"top\"\u003e\n \u003cp\u003ePopulation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.774086378737543%\" valign=\"top\"\u003e\n \u003cp\u003eYear of publication\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.92691029900332%\" valign=\"top\"\u003e\n \u003cp\u003eDepression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.956810631229235%\" valign=\"top\"\u003e\n \u003cp\u003e180866\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.109634551495017%\" valign=\"top\"\u003e\n \u003cp\u003e6019632\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.441860465116278%\" valign=\"top\"\u003e\n \u003cp\u003eHG19/GRCh37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.790697674418604%\" valign=\"top\"\u003e\n \u003cp\u003eEuropean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.774086378737543%\" valign=\"top\"\u003e\n \u003cp\u003e2016\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.92691029900332%\" valign=\"top\"\u003e\u003cp\u003eLow back pain\u003c/p\u003e\u003c/td\u003e\n \u003ctd width=\"12.956810631229235%\" valign=\"top\"\u003e\n \u003cp\u003e177860\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.109634551495017%\" valign=\"top\"\u003e\n \u003cp\u003e16380287\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.441860465116278%\" valign=\"top\"\u003e\n \u003cp\u003eHG19/GRCh37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.790697674418604%\" valign=\"top\"\u003e\n \u003cp\u003eEuropean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.774086378737543%\" valign=\"top\"\u003e\n \u003cp\u003e2021\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\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, depression, low back pain","lastPublishedDoi":"10.21203/rs.3.rs-3457406/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3457406/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMendelian randomization analysis was employed to examine the potential causal link between depression and low back pain. This analysis utilizes genetic variants as instrumental variables to help establish a causal relationship. By leveraging the genetic variants associated with depression as the exposure factor, the study aimed to investigate whether there is a causal effect on the development or severity of low back pain. This approach provides valuable insights into the potential causal association between these two conditions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe entire Gene Association Study database (GWAS) was utilized for data mining in a study aiming to investigate the relationship between depression as the exposure factor and low back pain as the outcome factor. Mendelian randomization analysis was performed using regression models such as inverse variance weighting (IVW), the MR‒Egger method, the simple mode method, the weighted median method, and the weighted mode method. The objective was to uncover any potential causal relationship between the exposure factors and the outcome.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this study, a total of 25 single nucleotide polymorphism (SNP) loci were utilized as instrumental variables to assess the causal association between depression and low back pain. The inverse variance weighting method estimated that individuals with depression had a 1.71 times higher risk (OR) of experiencing low back pain than the healthy population (95% CI: 1.258 to 2.332, p=0.0006). Similarly, the weighted median method also supported a causal effect between depression and low back pain (95% CI: 1.180 to 2.789, p = 0.0007). Tests for heterogeneity using the inverse variance weighting and MR‒Egger regression methods indicated no significant heterogeneity. Furthermore, the MR‒Egger regression intercept terms and MRPRESSO method tests suggested that the results were less likely to be influenced by genetic pleiotropy. Leave-one-out analyses did not identify any nonspecific SNPs that unduly influenced the results. Overall, these findings provide stronger evidence for a causal relationship between depression and low back pain.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere may be a positive causal association between depression and low back pain.\u003c/p\u003e","manuscriptTitle":"The causal relationship between depression and low back pain: a two-sample Mendelian randomized study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-10-19 17:52:39","doi":"10.21203/rs.3.rs-3457406/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":"53d4ec6e-e527-41f9-b341-6232f64ba4f4","owner":[],"postedDate":"October 19th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2023-11-20T08:44:16+00:00","versionOfRecord":[],"versionCreatedAt":"2023-10-19 17:52:39","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3457406","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3457406","identity":"rs-3457406","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}