Assessment of the causal relationship between depression and traits of sarcopenia: A bidirectional two-sample Mendelian randomization study

Assessment of the causal relationship between depression and traits of sarcopenia: A bidirectional two-sample Mendelian randomization study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Assessment of the causal relationship between depression and traits of sarcopenia: A bidirectional two-sample Mendelian randomization study Jianyu Tan, Yiting Kong, Ming Ai, Su Hong, Yingxiao Zhang, Li Kuang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2657221/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background : Growing evidence reveals a significant association between depression and sarcopenia. However, the causal association between them remains elusive. We aimed to investigate the bidirectional relationship between depression and traits of sarcopenia. Methods : We used genetic variants associated with depression (n=2,113,907), grip strength (n=256,523), appendicular lean mass (n=450,243), and walking pace (n=459,915) in bidirectional two-sample Mendelian randomization. The random-effects inverse-variance weighted method was adopted as the primary method. Results : Mendelian randomization results revealed a causal relationship between depression and appendicular lean mass [β (95% confidence interval (CI)) = -0.051 (-0.086−(-0.016)), P=0.004], walking pace [OR (95% CI) = 0.973 (0.955−0.992), P=0.005]. Walking pace also revealed a causal relationship with depression [OR (95% CI) = 0.663 (0.507−0.864), P=0.002] in the reverse analysis. We observed no causal relationships between depression and grip strength. The leave-one-out sensitivity analysis verified our results. Conclusions : This Mendelian randomization analysis verified the bidirectional relationship between depression and sarcopenia. Early diagnosis and prevention of either disease may enhance the management of another. Depression Sarcopenia Mendelian randomization Grip strength Walking pace Appendicular lean mass Figures Figure 1 1 Background Depression is one of the leading causes of disability, increasing the overall global burden of disease( 1 , 2 ). It increases mortality and cardiovascular disease risk and is related to poor health and suicide attempts at a later age( 3 , 4 ). Sarcopenia is a disease characterized by age-related loss of skeletal muscle mass and function, causing degraded physical performance( 5 ). Its prevalence has been reported to be 5 − 10%( 6 ) and may be underestimated in the elder population( 7 ). Observational studies have presented a significant association between depression and sarcopenia. A recent meta-analysis including 19 articles (1,476 cases of sarcopenia and 364 cases of depression) demonstrated a significant correlation between sarcopenia and depression. However, the heterogeneity of the included studies was significant owing to the complexity of sarcopenia diagnosis( 8 ). A cohort study including 162,167 participants demonstrated that lower grip strength was associated with an increased risk of depression( 9 ). A Chinese prospective study also presented that patients with sarcopenia were more likely to have new-onset depressive symptoms than those without it( 10 ). Moreover, depressive symptoms may contribute to insufficient physical activity( 11 , 12 ) and poor diet quality( 13 ) causing sarcopenia. As a randomized control study is impossible to conduct and observational studies cannot rule out all the potential confounders, the causal relationship between depression and sarcopenia remains elusive. Mendelian randomization (MR) is a method for investigating potential causal relationships, which utilizes genetic variants strongly associated with exposure( 14 , 15 ). Genetic variants were randomly assigned before birth, making it a natural randomized controlled trial. Therefore, the genetic variants are relatively independent of confounding factors after birth and established well before the onset of the disease. Thus, MR avoided the influence of confounding and reverse causation( 16 – 18 ), which was common in typical observational studies. Many Genome-Wide Association Study (GWAS) studies have identified thousands of genetic variations associated with various exposures, greatly expanding the applications of MR( 19 , 20 ). We used a bidirectional two-sample MR to explore the causality between depression and traits of sarcopenia. 2 Methods 2.1 Study design A simple description of the MR study design is depicted in Fig. 1 . MR is based on three assumptions: ( 1 ) genetic variants are associated with exposure; ( 2 ) genetic variants are independent of confounding factors; ( 3 ) genetic variants affect outcomes only through exposure( 21 ). A total of six MR analyses were performed to investigate the association between depression and traits of sarcopenia. In the first stage, depression was examined as an exposure whereas sarcopenia-related traits were examined as an outcome. In the second stage, the analysis was reversed. 2.2 Data sources and instruments We adopted publicly available summary-level data. Detailed information on the data sources and sample sizes is summarized in Supplementary Table 1. All ethical approvals were obtained in the original studies. 2.2.1 Sarcopenia-related traits We defined sarcopenia per the definition provided by the European Working Group on Sarcopenia in Older People in 2019( 5 ). A participant with both low muscle strength and low muscle mass is diagnosed with sarcopenia. Physical performance is an important parameter for evaluating the severity of sarcopenia( 5 ). Grip strength is recommended to be a good measure of muscle strength( 22 ). We used summary-level data on grip strength, which was related to a meta-analysis about muscle weakness( 23 ). The data included 256,523 participants and were adjusted for age, gender, and demographic structure. Participants were considered to have low muscle strength if their grip strength was < 30 kg for males and < 20 kg for females( 5 ). Appendicular lean mass (ALM) is a parameter recommended for muscle mass, which is mostly affected by skeletal muscle. Since sarcopenia is mainly caused by a decrease in skeletal muscle mass, ALM has a high predictive power for sarcopenia-related outcomes( 24 , 25 ). We adopted summary-level data from a recent GWAS on ALM, which was conducted among 450,243 UK Biobank participants adjusted for appendicular fat mass, age, and other covariates( 26 ). Genetic-predicted walking pace was based on summary statistics from the UK Biobank, which included 459,915 participants. “How would you describe your usual walking pace?” was used to assess the walking pace. “Slow pace is defined as 4 miles per hour,” asserts the hint for this question. 2.2.2 Depression We obtained the summary-level statistics for depression from the largest GWAS meta-analysis to date, which included a total of 2,113,907 individuals from the UK Biobank, Psychiatric Genomics Consortium (PGC), and 23andMe( 27 ). The definition of depression was based on ICD-9/ICD-10, self-reported depressive symptoms, or hospital admission records; details have been reported in the original studies( 28 – 30 ). Owing to general access constraints, the reverse-direction analysis relied on the summary-level statistics from PGC and UK Biobank without 23andMe samples. This yielded a sample size of 500,199. 2.2.3 Selection of genetic variants Single nucleotide polymorphisms (SNPs) associated with each exposure at a threshold of P < 5×10 − 8 were extracted from the statistics. Further, SNPs with potential linkage disequilibrium were removed. We retained SNPs with a low likelihood of linkage disequilibrium (r2 < 0.001) and a large physical distance (≥ 10,000 kb). We used the MR-PRESSO method to detect any potential outliers. All the outliers were removed before analysis (details of removed outliers are mentioned in supplementary table 2). F-statistics was used to assess the strength of the genetic variants. SNPs with F-statistics > 10 were considered suitable for MR analyses( 31 ). R 2 was also calculated to represent the proportion of the variability of an exposure explained by the genetic variants( 32 ). 2.3 Statistical analyses We adopted the inverse-variance weighted (IVW) method as the primary MR analysis method to investigate the potential bidirectional causal relationships between depression and traits of sarcopenia. If there was no directional pleiotropy, IVW was considered to be the most effective method of evaluating causal estimates( 33 ). Considering the significant heterogeneity among variants, we selected a random-effects IVW model. However, IVW results may be biased if the instrument SNPs exhibit any level of horizontal pleiotropy, implying that the SNPs influence outcomes via a pathway other than exposure. Therefore, the results of IVW were compared to those of other well-established MR methods. The weighted median method, which selects the median MR estimate as the causal estimate, and the MR Egger regression, which allows the intercept to be freely estimated as an indicator of average pleiotropic bias( 34 ), are two of these methods. The weighted mode-based estimation method was considered to have low bias and low Type 1 error rate inflation( 35 ). The simple mode-based estimation method clusters the SNPs into groups based on the similarity of causal effect estimates and predicts the causal effect based on the cluster with the most SNPs( 36 ). When the outcome was continuous, the effect estimate was reported in β values (i.e. ALM), and when the outcome was dichotomous, the effect estimate was reported in odds ratios (i.e. depression, grip strength, and walking pace). The heterogeneity of the IVW method was investigated using Cochran’s Q statistics. The MR-Egger intercept revealed directional horizontal pleiotropy. Furthermore, the leave-one-out sensitivity test was performed to estimate the effects of outlier and pleiotropic SNPs on causal estimates( 37 ). The Bonferroni-corrected significance level of P < 0.008 (0.05/6) was applied. All statistical analyses were performed using the TwoSampleMR package in R (Version 4.2.2). 3 Results 3.1 The causal effect of depression on sarcopenia-related traits Among the 102 depression-associated variants, one SNP was unavailable in the grip strength and ALM datasets and two SNPs were unavailable in the walking pace dataset. We further excluded six variants for each outcome owing to ambiguities in the palindrome. Additionally, we removed 16 outliers for ALM and 13 outliers for walking pace based on the MR-PRESSO results (supplementary table 2). Finally, 95, 79, and 81 variants were included as genetic instruments for grip strength, ALM, and walking pace, respectively. The F-statistics and R 2 indicated that all genetic instruments were appropriate for MR analysis (Table 1 ). Table 1 The R 2 and F-statistics for the genetic instruments Exposure Outcomes No. of SNP F statistics R 2 Depression Grip strength 95 42.43 0.50% Depression ALM 79 42.62 0.42% Depression Walking pace 81 41.91 0.75% Grip strength Depression 13 40.95 0.21% ALM Depression 570 97.99 12.70% Walking pace Depression 49 40.50 0.43% SNP, single-nucleotide polymorphisms; ALM, appendicular lean mass The results of the MR analysis are presented in Table 2 . Cochran’s Q value indicated significant heterogeneity, thus, the random-effects IVW model was used. The findings revealed a significant causal relationship between depression and ALM [β (95% confidence interval (CI)) = -0.051 (-0.086−(-0.016)), P = 0.004] and between depression and walking pace [OR (95% CI) = 0.973 (0.955 − 0.992), P = 0.005]. We observed no significant association between depression and grip strength [OR (95% CI) = 1.090 (1.000 − 1.188), P = 0.051]. The scatter plots revealed that the relationships in other models were essentially the same (supplementary Fig. 1). The forest plots revealed that each SNP had a consistent effect (supplementary Fig. 3). The intercept of MR-Egger regression indicated that there was no potential horizontal pleiotropy (all P-values > 0.05). The funnel plots also revealed no directional pleiotropy (supplementary Fig. 5). Moreover, the leave-one-out analysis suggested that these findings did not significantly alter after removing any single variant (supplementary Fig. 7). Table 2 Mendelian randomization results for the relationship between depression and traits of sarcopenia Exposure Outcomes Heterogeneity test MR Egger MR results P Cochran’s Q( P ) Intercept( P ) Method Effect estimates (95% CI) Depression Grip strength 167.35 (< 0.001) -0.001 (0.790) MR Egger 1.167 (0.701 − 1.940) 0.554 Weighted Median 1.065 (0.961 − 1.180) 0.231 IVW 1.090 (1.000 − 1.188) 0.051 Simple mode 1.116 (0.855 − 1.456) 0.421 Weighted mode 1.065 (0.823 − 1.377) 0.634 Depression ALM 296.36 (< 0.001) -0.002 (0.409) MR Egger 0.030 (-0.164 − 0.223) 0.764 Weighted Median -0.045 (-0.080−(-0.010)) 0.009 IVW -0.051 (-0.086−(-0.016)) 0.004 Simple mode -0.178 (-0.311−(-0.045)) 0.001 Weighted mode -0.033 (-0.119 − 0.052) 0.470 Depression Walking pace 167.74 (< 0.001) -0.003 (0.051) MR Egger 1.080 (0.973 − 1.200) 0.154 Weighted Median 0.975 (0.955 − 0.996) 0.017 IVW 0.973 (0.955 − 0.992) 0.005 Simple mode 0.930 (0.862 − 1.004) 0.066 Weighted mode 0.933 (0.867 − 1.004) 0.068 Effect estimates for ALM are expressed as β (95% CI) and for grip strength or walking pace are expressed as odds ratio (95% CI). P < 0.008 was considered significant. IVW, inverse-variance weighted; ALM, appendicular lean mass 3.2 The causal effect of sarcopenia-related traits on depression We removed one SNP for grip strength and walking pace and 14 SNPs for ALM from reverse direction analysis owing to ambiguities in the palindrome. For walking pace and ALM, 5 and 13 outliers detected by MR-PRESSO were removed, respectively (supplementary table 2). Finally, as genetic instruments, we included 13 variants for grip strength, 570 variants for ALM, and 49 variants for walking pace. R2 and F-statistics indicated that all variants were appropriate for MR analysis (Table 1 ). As presented in Table 3 , the results of the random-effects IVW model indicated a significant causal relationship between walking pace and depression [OR (95% CI) = 0.663 (0.507–0.864), P = 0.002]. However, neither ALM nor grip strength was observed to be associated with depression. Table 3 Mendelian randomization results for the relationship between traits of sarcopenia and depression Exposure Outcomes Heterogeneity test MR Egger MR results P Cochran’s Q(P) Intercept(P) Method OR (95% CI) Grip strength Depression 17.43 (0.0957) -0.001 (0.930) MR Egger 1.028 (0.805 − 1.311) 0.830 Weighted Median 1.002 (0.933 − 1.076) 0.959 IVW 1.017 (0.958 − 1.079) 0.586 Simple mode 1.007 (0.901 − 1.126) 0.904 Weighted mode 0.999 (0.909 − 1.098) 0.984 ALM Depression 961.48 (< 0.001) -0.00005 (0.934) MR Egger 0.985 (0.930 − 1.043) 0.608 Weighted Median 1.000 (0.967 − 1.035) 1.000 IVW 0.983 (0.959 − 1.007) 0.169 Simple mode 1.046 (0.950 − 1.151) 0.358 Weighted mode 1.052 (0.981 − 1.128) 0.154 Walking pace Depression 181.55 (< 0.001) 0.006 (0.299) MR Egger 0.363 (0.115 − 1.149) 0.091 Weighted Median 0.653 (0.510 − 0.837) 0.001 IVW 0.663 (0.507 − 0.864) 0.002 Simple mode 0.619 (0.330 − 1.161) 0.141 Weighted mode 0.566 (0.303 − 1.058) 0.080 P < 0.008 was considered significant. IVW, inverse-variance weighted; ALM, appendicular lean mass The scatter plots revealed that the results were consistent across all the methods (supplementary Fig. 2). The forest plots demonstrating the effect of each SNP are presented in supplementary Fig. 4. MR Egger test indicated no significant horizontal pleiotropy. The funnel plots revealed no directional pleiotropy (supplementary Fig. 6). The leave-one-out analysis revealed robust results after removing any variant (supplementary Fig. 8). 4 Discussion The results of this bidirectional two-sample MR study revealed that genetically predicted depression was causally associated with ALM and walking pace. The reverse MR analyses also revealed a significant association between walking pace and depression. Our finding that depression is negatively associated with ALM is consistent with the findings of many previous observational studies( 38 – 41 ). Muscle mass was observed to be reduced in middle-aged males with depression in a German study that used magnetic resonance tomography to evaluate muscle mass( 40 ). Wu et. al demonstrated an inverse relationship between muscle mass and depressive symptoms in elder Chinese people. The reason for reduced muscle mass in elder people with depressive symptoms is multifactorial. Aside from age, there are numerous reasons for muscle mass loss in older people, including decreased physical activity, various physical diseases, and poor appetite caused by depression, which leads to low protein and vitamin intake. Patients with depression may have a higher prevalence of substance abuse and higher levels of cytokines (tumor necrosis factor-alpha and interleukin-6) and endocrine factors that contribute to the loss of muscle mass( 42 – 45 ). Our study also discovered a bidirectional causal relationship between depression and walking pace. This is consistent with an English cohort study recruiting 4,581 community people aged > 60 years that revealed a bidirectional inverse relationship between walking pace and depressive symptoms( 46 ). The causal relationship between depression and walking pace has been previously verified by some studies( 47 – 49 ). Seclusion and decreased activity, both of which are common in depression, may cause gait slowing( 50 , 51 ). Slow gait speed may lead to social isolation, thus, contributing to depression in the reverse relationship( 52 ). For the elderly, gait speed may be an indicator of physical activity. The slower walking pace may indicate a lack of physical activity, which increases the risk of depression( 53 ). In contrast with previous studies, we observed no causal association between depression and grip strength. A previous cohort study observed that lower grip strength was significantly associated with an increased risk of depression( 9 ). They used a stricter definition of low grip strength (< 26 kg for males and < 16 kg for females( 9 )), and the depression was diagnosed based on ICD-10 in that study, which was more accurate than the diagnostic criteria used in our study. Another cohort study revealed a significant association between grip strength and depression (diagnosed by CED-D score > 16)( 54 ). This suggests that the relationship between depression and grip strength is noticeable in more severe conditions. To the best of our knowledge, this is the first study to use MR to investigate the causal relationship between depression and sarcopenia. The MR method minimized the potential confounding and avoided reverse causality, which is an important strength of the study. Furthermore, we used a bidirectional MR design to investigate the bidirectional relationship between depression and sarcopenia. Our study has some limitations to be considered. First, we failed to investigate the relationship based on the diagnosis of sarcopenia. We were unable to obtain public datasets for sarcopenia, making the MR study based on the diagnosis of sarcopenia impossible. Since grip strength, ALM, and walking pace are known to be good predictors of sarcopenia( 25 ), we believe that our study adequately supports the relationship between depression and sarcopenia. Second, the datasets used in this study had significant sample overlap, which might have caused inflated Type 1 errors owing to weak instrument bias( 32 ). We were unable to determine and correct sample overlap as we used summary-level statistics from publicly available data. However, since the association estimates were independent, the two-sample MR analysis was less likely to have weak instrument bias than the one-sample MR( 55 ). Last, Cochran's Q test identified significant heterogeneities caused by genetic variants. Therefore, we performed the leave-one-out sensitivity analysis, and the results were robust. 5 Conclusions Conclusively, our study revealed a causal relationship between depression and traits of sarcopenia (ALM and walking pace), and the reverse analysis indicated that walking pace is also significantly associated with depression. Our findings suggested that preventing sarcopenia may help with the prevention of depression and preventing depression may help with the prevention of sarcopenia. Abbreviations MR Mendelian randomization GWAS Genome-Wide Association Study ALM Appendicular lean mass PGC Psychiatric Genomics Consortium SNPs Single nucleotide polymorphisms IVW inverse-variance weighted OR odds ratio CI confidence interval Declarations Ethics approval and consent to participate All the data sources used in this study are summarized data from relevant GWAS studies and the IEU OpenGWAS project database, which are publicly available for free download and do not require approval from the review agency. Consent for publication NOT APPLICABLE. Availability of data and materials The datasets presented in this study can be found in online repositories. The websites for these datasets have been provided in the supplementary materials. Competing interests The authors declare that they have no competing interests. Funding This research was supported by the National Natural Science Foundation of China (Grant NO. 1971286), and the Natural Science Foundation of Chongqing, China (Grant NO. CSTC2021-jscx-gksb-N0002). Authors' contributions JYT and LK designed the research. JYT wrote the final manuscript. JYT and YXZ analyzed the data. YTK, MA, SH contributed to the explanation of the results and writing of the manuscript. All authors have read and approved the manuscript. Acknowledgements The authors acknowledge the efforts of this research team and appreciate all the participants for attending this research. References Walker ER, McGee RE, Druss BG. Mortality in mental disorders and global disease burden implications: a systematic review and meta-analysis. JAMA Psychiatry. 2015;72(4):334-41. 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The Effects of Gait Speed and Psychomotor Speed on Risk for Depression and Anxiety in Older Adults with Medical Comorbidities. J Am Geriatr Soc. 2021;69(5):1265-71. Sanders JB, Bremmer MA, Deeg DJ, Beekman AT. Do depressive symptoms and gait speed impairment predict each other's incidence? A 16-year prospective study in the community. J Am Geriatr Soc. 2012;60(9):1673-80. Singh A, Misra N. Loneliness, depression and sociability in old age. Ind Psychiatry J. 2009;18(1):51-5. Vancampfort D, Stubbs B. Physical activity and metabolic disease among people with affective disorders: Prevention, management and implementation. J Affect Disord. 2017;224:87-94. Studenski SA, Peters KW, Alley DE, Cawthon PM, McLean RR, Harris TB, et al. The FNIH sarcopenia project: rationale, study description, conference recommendations, and final estimates. J Gerontol A Biol Sci Med Sci. 2014;69(5):547-58. Carvalho AF, Maes M, Solmi M, Brunoni AR, Lange S, Husain MI, et al. Is dynapenia associated with the onset and persistence of depressive and anxiety symptoms among older adults? Findings from the Irish longitudinal study on ageing. Aging Ment Health. 2021;25(3):468-75. Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterial.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-2657221","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":185498277,"identity":"0644e5d4-6d94-43b4-b5f2-818159f3a848","order_by":0,"name":"Jianyu Tan","email":"","orcid":"","institution":"The First Affiliated Hospital of Chongqing Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jianyu","middleName":"","lastName":"Tan","suffix":""},{"id":185498279,"identity":"c6d5521d-ed29-4c69-b2b8-8ff61cead2ac","order_by":1,"name":"Yiting Kong","email":"","orcid":"","institution":"The First Affiliated Hospital of Chongqing Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yiting","middleName":"","lastName":"Kong","suffix":""},{"id":185498281,"identity":"7b70f1ef-e8d9-4af8-a206-4422a32326de","order_by":2,"name":"Ming Ai","email":"","orcid":"","institution":"The First Affiliated Hospital of Chongqing Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Ming","middleName":"","lastName":"Ai","suffix":""},{"id":185498283,"identity":"c2bad3c7-44c1-4083-9ccc-1acbcb882022","order_by":3,"name":"Su Hong","email":"","orcid":"","institution":"The First Affiliated Hospital of Chongqing Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Su","middleName":"","lastName":"Hong","suffix":""},{"id":185498285,"identity":"7eaec6aa-0cc6-4258-b6b9-96c78aebf0ec","order_by":4,"name":"Yingxiao Zhang","email":"","orcid":"","institution":"The First Affiliated Hospital of Chongqing Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yingxiao","middleName":"","lastName":"Zhang","suffix":""},{"id":185498287,"identity":"1c6e3c24-b660-49de-bedb-4d168de97cd1","order_by":5,"name":"Li Kuang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2ElEQVRIiWNgGAWjYBACCTBZIMEjz8x88EFCRQ2xWgwk5Azb25INHpw5RrQWBmOGM2fMJB+2MBPWIjkj+dnDLwYWiY0z0tIqEhvYGPjbuxPwapGWSDM3ljGQSGyXSD52I3GHDIPEmbMb8GqRk0gwk5YAagHZciPxDBvQX7mEtKR/A2tpuJFjVpDYxkxYi7REjpnkBwMJsPcZiNIi2fOmTBoWyBIJZ47xEPSLxPH0bZI/KurAUfnxR0WNHH97L34tIMDMg8ThwakMGTD+IErZKBgFo2AUjFgAABNHRb9ClqfsAAAAAElFTkSuQmCC","orcid":"","institution":"The First Affiliated Hospital of Chongqing Medical University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Li","middleName":"","lastName":"Kuang","suffix":""}],"badges":[],"createdAt":"2023-03-05 11:44:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2657221/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2657221/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":34785621,"identity":"4337c5fd-8ad4-4923-ac38-a5dee12a4d8b","added_by":"auto","created_at":"2023-03-24 16:15:59","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":142054,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDescription of the Mendelian randomization study design\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-2657221/v1/ad2944b3c5683a3f0f672106.jpeg"},{"id":39842124,"identity":"02461e0a-1774-4da3-ac7b-be96f17a32b8","added_by":"auto","created_at":"2023-07-11 09:14:55","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":423856,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2657221/v1/63d4e9f7-8c02-44ba-b0d7-ab5e56388a3c.pdf"},{"id":34785623,"identity":"1b5e670f-c977-434c-8c97-2968b8d64a8d","added_by":"auto","created_at":"2023-03-24 16:15:59","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":5812528,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-2657221/v1/4fd06c35f68f45729dc3d972.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Assessment of the causal relationship between depression and traits of sarcopenia: A bidirectional two-sample Mendelian randomization study","fulltext":[{"header":"1 Background","content":"\u003cp\u003eDepression is one of the leading causes of disability, increasing the overall global burden of disease(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). It increases mortality and cardiovascular disease risk and is related to poor health and suicide attempts at a later age(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSarcopenia is a disease characterized by age-related loss of skeletal muscle mass and function, causing degraded physical performance(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Its prevalence has been reported to be 5\u0026thinsp;\u0026minus;\u0026thinsp;10%(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e) and may be underestimated in the elder population(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eObservational studies have presented a significant association between depression and sarcopenia. A recent meta-analysis including 19 articles (1,476 cases of sarcopenia and 364 cases of depression) demonstrated a significant correlation between sarcopenia and depression. However, the heterogeneity of the included studies was significant owing to the complexity of sarcopenia diagnosis(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). A cohort study including 162,167 participants demonstrated that lower grip strength was associated with an increased risk of depression(\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). A Chinese prospective study also presented that patients with sarcopenia were more likely to have new-onset depressive symptoms than those without it(\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Moreover, depressive symptoms may contribute to insufficient physical activity(\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e) and poor diet quality(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e) causing sarcopenia. As a randomized control study is impossible to conduct and observational studies cannot rule out all the potential confounders, the causal relationship between depression and sarcopenia remains elusive.\u003c/p\u003e \u003cp\u003eMendelian randomization (MR) is a method for investigating potential causal relationships, which utilizes genetic variants strongly associated with exposure(\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Genetic variants were randomly assigned before birth, making it a natural randomized controlled trial. Therefore, the genetic variants are relatively independent of confounding factors after birth and established well before the onset of the disease. Thus, MR avoided the influence of confounding and reverse causation(\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e), which was common in typical observational studies. Many Genome-Wide Association Study (GWAS) studies have identified thousands of genetic variations associated with various exposures, greatly expanding the applications of MR(\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). We used a bidirectional two-sample MR to explore the causality between depression and traits of sarcopenia.\u003c/p\u003e"},{"header":"2 Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study design\u003c/h2\u003e \u003cp\u003eA simple description of the MR study design is depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. MR is based on three assumptions: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) genetic variants are associated with exposure; (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) genetic variants are independent of confounding factors; (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) genetic variants affect outcomes only through exposure(\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). A total of six MR analyses were performed to investigate the association between depression and traits of sarcopenia. In the first stage, depression was examined as an exposure whereas sarcopenia-related traits were examined as an outcome. In the second stage, the analysis was reversed.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Data sources and instruments\u003c/h2\u003e \u003cp\u003eWe adopted publicly available summary-level data. Detailed information on the data sources and sample sizes is summarized in Supplementary Table\u0026nbsp;1. All ethical approvals were obtained in the original studies.\u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e2.2.1 Sarcopenia-related traits\u003c/h2\u003e \u003cp\u003eWe defined sarcopenia per the definition provided by the European Working Group on Sarcopenia in Older People in 2019(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). A participant with both low muscle strength and low muscle mass is diagnosed with sarcopenia. Physical performance is an important parameter for evaluating the severity of sarcopenia(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGrip strength is recommended to be a good measure of muscle strength(\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). We used summary-level data on grip strength, which was related to a meta-analysis about muscle weakness(\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). The data included 256,523 participants and were adjusted for age, gender, and demographic structure. Participants were considered to have low muscle strength if their grip strength was \u0026lt;\u0026thinsp;30 kg for males and \u0026lt;\u0026thinsp;20 kg for females(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Appendicular lean mass (ALM) is a parameter recommended for muscle mass, which is mostly affected by skeletal muscle. Since sarcopenia is mainly caused by a decrease in skeletal muscle mass, ALM has a high predictive power for sarcopenia-related outcomes(\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). We adopted summary-level data from a recent GWAS on ALM, which was conducted among 450,243 UK Biobank participants adjusted for appendicular fat mass, age, and other covariates(\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGenetic-predicted walking pace was based on summary statistics from the UK Biobank, which included 459,915 participants. \u0026ldquo;How would you describe your usual walking pace?\u0026rdquo; was used to assess the walking pace. \u0026ldquo;Slow pace is defined as \u0026lt;\u0026thinsp;3 miles per hour; steady average pace is defined as between 3\u0026thinsp;\u0026minus;\u0026thinsp;4 miles per hour; the fast pace is defined as \u0026gt;\u0026thinsp;4 miles per hour,\u0026rdquo; asserts the hint for this question.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.2.2 Depression\u003c/h2\u003e \u003cp\u003eWe obtained the summary-level statistics for depression from the largest GWAS meta-analysis to date, which included a total of 2,113,907 individuals from the UK Biobank, Psychiatric Genomics Consortium (PGC), and 23andMe(\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). The definition of depression was based on ICD-9/ICD-10, self-reported depressive symptoms, or hospital admission records; details have been reported in the original studies(\u003cspan additionalcitationids=\"CR29\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). Owing to general access constraints, the reverse-direction analysis relied on the summary-level statistics from PGC and UK Biobank without 23andMe samples. This yielded a sample size of 500,199.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.2.3 Selection of genetic variants\u003c/h2\u003e \u003cp\u003eSingle nucleotide polymorphisms (SNPs) associated with each exposure at a threshold of P\u0026thinsp;\u0026lt;\u0026thinsp;5\u0026times;10\u0026thinsp;\u0026minus;\u0026thinsp;8 were extracted from the statistics. Further, SNPs with potential linkage disequilibrium were removed. We retained SNPs with a low likelihood of linkage disequilibrium (r2\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and a large physical distance (\u0026ge;\u0026thinsp;10,000 kb). We used the MR-PRESSO method to detect any potential outliers. All the outliers were removed before analysis (details of removed outliers are mentioned in supplementary table 2). F-statistics was used to assess the strength of the genetic variants. SNPs with F-statistics\u0026thinsp;\u0026gt;\u0026thinsp;10 were considered suitable for MR analyses(\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). R\u003csup\u003e2\u003c/sup\u003e was also calculated to represent the proportion of the variability of an exposure explained by the genetic variants(\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Statistical analyses\u003c/h2\u003e \u003cp\u003eWe adopted the inverse-variance weighted (IVW) method as the primary MR analysis method to investigate the potential bidirectional causal relationships between depression and traits of sarcopenia. If there was no directional pleiotropy, IVW was considered to be the most effective method of evaluating causal estimates(\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). Considering the significant heterogeneity among variants, we selected a random-effects IVW model. However, IVW results may be biased if the instrument SNPs exhibit any level of horizontal pleiotropy, implying that the SNPs influence outcomes via a pathway other than exposure. Therefore, the results of IVW were compared to those of other well-established MR methods. The weighted median method, which selects the median MR estimate as the causal estimate, and the MR Egger regression, which allows the intercept to be freely estimated as an indicator of average pleiotropic bias(\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e), are two of these methods. The weighted mode-based estimation method was considered to have low bias and low Type 1 error rate inflation(\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). The simple mode-based estimation method clusters the SNPs into groups based on the similarity of causal effect estimates and predicts the causal effect based on the cluster with the most SNPs(\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWhen the outcome was continuous, the effect estimate was reported in β values (i.e. ALM), and when the outcome was dichotomous, the effect estimate was reported in odds ratios (i.e. depression, grip strength, and walking pace).\u003c/p\u003e \u003cp\u003eThe heterogeneity of the IVW method was investigated using Cochran\u0026rsquo;s Q statistics. The MR-Egger intercept revealed directional horizontal pleiotropy. Furthermore, the leave-one-out sensitivity test was performed to estimate the effects of outlier and pleiotropic SNPs on causal estimates(\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e). The Bonferroni-corrected significance level of P\u0026thinsp;\u0026lt;\u0026thinsp;0.008 (0.05/6) was applied. All statistical analyses were performed using the TwoSampleMR package in R (Version 4.2.2).\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.1 The causal effect of depression on sarcopenia-related traits\u003c/h2\u003e \u003cp\u003eAmong the 102 depression-associated variants, one SNP was unavailable in the grip strength and ALM datasets and two SNPs were unavailable in the walking pace dataset. We further excluded six variants for each outcome owing to ambiguities in the palindrome. Additionally, we removed 16 outliers for ALM and 13 outliers for walking pace based on the MR-PRESSO results (supplementary table 2). Finally, 95, 79, and 81 variants were included as genetic instruments for grip strength, ALM, and walking pace, respectively. The F-statistics and R\u003csup\u003e2\u003c/sup\u003e indicated that all genetic instruments were appropriate for MR analysis (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe R\u003csup\u003e2\u003c/sup\u003e and F-statistics for the genetic instruments\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExposure\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOutcomes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo. of SNP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eF statistics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDepression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGrip strength\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e42.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.50%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDepression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eALM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e42.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.42%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDepression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWalking pace\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e41.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.75%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrip strength\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDepression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e40.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.21%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDepression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e570\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e97.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e12.70%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWalking pace\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDepression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e40.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.43%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eSNP, single-nucleotide polymorphisms; ALM, appendicular lean mass\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe results of the MR analysis are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Cochran\u0026rsquo;s Q value indicated significant heterogeneity, thus, the random-effects IVW model was used. The findings revealed a significant causal relationship between depression and ALM [β (95% confidence interval (CI)) = -0.051 (-0.086\u0026minus;(-0.016)), P\u0026thinsp;=\u0026thinsp;0.004] and between depression and walking pace [OR (95% CI)\u0026thinsp;=\u0026thinsp;0.973 (0.955\u0026thinsp;\u0026minus;\u0026thinsp;0.992), P\u0026thinsp;=\u0026thinsp;0.005]. We observed no significant association between depression and grip strength [OR (95% CI)\u0026thinsp;=\u0026thinsp;1.090 (1.000\u0026thinsp;\u0026minus;\u0026thinsp;1.188), P\u0026thinsp;=\u0026thinsp;0.051]. The scatter plots revealed that the relationships in other models were essentially the same (supplementary Fig.\u0026nbsp;1). The forest plots revealed that each SNP had a consistent effect (supplementary Fig.\u0026nbsp;3). The intercept of MR-Egger regression indicated that there was no potential horizontal pleiotropy (all P-values\u0026thinsp;\u0026gt;\u0026thinsp;0.05). The funnel plots also revealed no directional pleiotropy (supplementary Fig.\u0026nbsp;5). Moreover, the leave-one-out analysis suggested that these findings did not significantly alter after removing any single variant (supplementary Fig.\u0026nbsp;7).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMendelian randomization results for the relationship between depression and traits of sarcopenia\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eExposure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eOutcomes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHeterogeneity test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMR Egger\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eMR results\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCochran\u0026rsquo;s Q(\u003cem\u003eP\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIntercept(\u003cem\u003eP\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMethod\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEffect estimates (95% CI)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eDepression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eGrip strength\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e167.35 (\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e-0.001 (0.790)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMR Egger\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.167 (0.701\u0026thinsp;\u0026minus;\u0026thinsp;1.940)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.554\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eWeighted Median\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.065 (0.961\u0026thinsp;\u0026minus;\u0026thinsp;1.180)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.231\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIVW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.090 (1.000\u0026thinsp;\u0026minus;\u0026thinsp;1.188)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.051\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSimple mode\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.116 (0.855\u0026thinsp;\u0026minus;\u0026thinsp;1.456)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.421\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eWeighted mode\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.065 (0.823\u0026thinsp;\u0026minus;\u0026thinsp;1.377)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.634\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eDepression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eALM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e296.36 (\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e-0.002 (0.409)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMR Egger\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.030 (-0.164\u0026thinsp;\u0026minus;\u0026thinsp;0.223)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.764\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eWeighted Median\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.045 (-0.080\u0026minus;(-0.010))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIVW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.051 (-0.086\u0026minus;(-0.016))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003e0.004\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSimple mode\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.178 (-0.311\u0026minus;(-0.045))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003e0.001\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eWeighted mode\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.033 (-0.119\u0026thinsp;\u0026minus;\u0026thinsp;0.052)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.470\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eDepression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eWalking pace\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e167.74 (\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e-0.003 (0.051)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMR Egger\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.080 (0.973\u0026thinsp;\u0026minus;\u0026thinsp;1.200)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.154\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eWeighted Median\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.975 (0.955\u0026thinsp;\u0026minus;\u0026thinsp;0.996)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIVW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.973 (0.955\u0026thinsp;\u0026minus;\u0026thinsp;0.992)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003e0.005\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSimple mode\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.930 (0.862\u0026thinsp;\u0026minus;\u0026thinsp;1.004)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.066\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eWeighted mode\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.933 (0.867\u0026thinsp;\u0026minus;\u0026thinsp;1.004)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.068\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eEffect estimates for ALM are expressed as β (95% CI) and for grip strength or walking pace are expressed as odds ratio (95% CI).\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.008 was considered significant.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eIVW, inverse-variance weighted; ALM, appendicular lean mass\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.2 The causal effect of sarcopenia-related traits on depression\u003c/h2\u003e \u003cp\u003eWe removed one SNP for grip strength and walking pace and 14 SNPs for ALM from reverse direction analysis owing to ambiguities in the palindrome. For walking pace and ALM, 5 and 13 outliers detected by MR-PRESSO were removed, respectively (supplementary table 2). Finally, as genetic instruments, we included 13 variants for grip strength, 570 variants for ALM, and 49 variants for walking pace. R2 and F-statistics indicated that all variants were appropriate for MR analysis (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAs presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, the results of the random-effects IVW model indicated a significant causal relationship between walking pace and depression [OR (95% CI)\u0026thinsp;=\u0026thinsp;0.663 (0.507\u0026ndash;0.864), P\u0026thinsp;=\u0026thinsp;0.002]. However, neither ALM nor grip strength was observed to be associated with depression.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMendelian randomization results for the relationship between traits of sarcopenia and depression\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eExposure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eOutcomes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHeterogeneity test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMR Egger\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eMR results\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCochran\u0026rsquo;s Q(P)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIntercept(P)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMethod\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eGrip strength\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eDepression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e17.43 (0.0957)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e-0.001 (0.930)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMR Egger\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.028 (0.805\u0026thinsp;\u0026minus;\u0026thinsp;1.311)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.830\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eWeighted Median\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.002 (0.933\u0026thinsp;\u0026minus;\u0026thinsp;1.076)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.959\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIVW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.017 (0.958\u0026thinsp;\u0026minus;\u0026thinsp;1.079)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.586\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSimple mode\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.007 (0.901\u0026thinsp;\u0026minus;\u0026thinsp;1.126)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.904\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eWeighted mode\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.999 (0.909\u0026thinsp;\u0026minus;\u0026thinsp;1.098)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.984\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eALM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eDepression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e961.48 (\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e-0.00005 (0.934)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMR Egger\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.985 (0.930\u0026thinsp;\u0026minus;\u0026thinsp;1.043)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.608\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eWeighted Median\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.000 (0.967\u0026thinsp;\u0026minus;\u0026thinsp;1.035)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIVW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.983 (0.959\u0026thinsp;\u0026minus;\u0026thinsp;1.007)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.169\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSimple mode\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.046 (0.950\u0026thinsp;\u0026minus;\u0026thinsp;1.151)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.358\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eWeighted mode\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.052 (0.981\u0026thinsp;\u0026minus;\u0026thinsp;1.128)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.154\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eWalking pace\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eDepression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e181.55 (\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e0.006 (0.299)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMR Egger\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.363 (0.115\u0026thinsp;\u0026minus;\u0026thinsp;1.149)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.091\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eWeighted Median\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.653 (0.510\u0026thinsp;\u0026minus;\u0026thinsp;0.837)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003e0.001\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIVW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.663 (0.507\u0026thinsp;\u0026minus;\u0026thinsp;0.864)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003e0.002\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSimple mode\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.619 (0.330\u0026thinsp;\u0026minus;\u0026thinsp;1.161)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.141\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eWeighted mode\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.566 (0.303\u0026thinsp;\u0026minus;\u0026thinsp;1.058)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.080\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.008 was considered significant.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eIVW, inverse-variance weighted; ALM, appendicular lean mass\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe scatter plots revealed that the results were consistent across all the methods (supplementary Fig.\u0026nbsp;2). The forest plots demonstrating the effect of each SNP are presented in supplementary Fig.\u0026nbsp;4. MR Egger test indicated no significant horizontal pleiotropy. The funnel plots revealed no directional pleiotropy (supplementary Fig.\u0026nbsp;6). The leave-one-out analysis revealed robust results after removing any variant (supplementary Fig.\u0026nbsp;8).\u003c/p\u003e \u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eThe results of this bidirectional two-sample MR study revealed that genetically predicted depression was causally associated with ALM and walking pace. The reverse MR analyses also revealed a significant association between walking pace and depression.\u003c/p\u003e \u003cp\u003eOur finding that depression is negatively associated with ALM is consistent with the findings of many previous observational studies(\u003cspan additionalcitationids=\"CR39 CR40\" citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e). Muscle mass was observed to be reduced in middle-aged males with depression in a German study that used magnetic resonance tomography to evaluate muscle mass(\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e). Wu et. al demonstrated an inverse relationship between muscle mass and depressive symptoms in elder Chinese people. The reason for reduced muscle mass in elder people with depressive symptoms is multifactorial. Aside from age, there are numerous reasons for muscle mass loss in older people, including decreased physical activity, various physical diseases, and poor appetite caused by depression, which leads to low protein and vitamin intake. Patients with depression may have a higher prevalence of substance abuse and higher levels of cytokines (tumor necrosis factor-alpha and interleukin-6) and endocrine factors that contribute to the loss of muscle mass(\u003cspan additionalcitationids=\"CR43 CR44\" citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOur study also discovered a bidirectional causal relationship between depression and walking pace. This is consistent with an English cohort study recruiting 4,581 community people aged\u0026thinsp;\u0026gt;\u0026thinsp;60 years that revealed a bidirectional inverse relationship between walking pace and depressive symptoms(\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e). The causal relationship between depression and walking pace has been previously verified by some studies(\u003cspan additionalcitationids=\"CR48\" citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e). Seclusion and decreased activity, both of which are common in depression, may cause gait slowing(\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e). Slow gait speed may lead to social isolation, thus, contributing to depression in the reverse relationship(\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e). For the elderly, gait speed may be an indicator of physical activity. The slower walking pace may indicate a lack of physical activity, which increases the risk of depression(\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn contrast with previous studies, we observed no causal association between depression and grip strength. A previous cohort study observed that lower grip strength was significantly associated with an increased risk of depression(\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). They used a stricter definition of low grip strength (\u0026lt;\u0026thinsp;26 kg for males and \u0026lt;\u0026thinsp;16 kg for females(\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e)), and the depression was diagnosed based on ICD-10 in that study, which was more accurate than the diagnostic criteria used in our study. Another cohort study revealed a significant association between grip strength and depression (diagnosed by CED-D score\u0026thinsp;\u0026gt;\u0026thinsp;16)(\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e). This suggests that the relationship between depression and grip strength is noticeable in more severe conditions.\u003c/p\u003e \u003cp\u003eTo the best of our knowledge, this is the first study to use MR to investigate the causal relationship between depression and sarcopenia. The MR method minimized the potential confounding and avoided reverse causality, which is an important strength of the study. Furthermore, we used a bidirectional MR design to investigate the bidirectional relationship between depression and sarcopenia.\u003c/p\u003e \u003cp\u003eOur study has some limitations to be considered. First, we failed to investigate the relationship based on the diagnosis of sarcopenia. We were unable to obtain public datasets for sarcopenia, making the MR study based on the diagnosis of sarcopenia impossible. Since grip strength, ALM, and walking pace are known to be good predictors of sarcopenia(\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e), we believe that our study adequately supports the relationship between depression and sarcopenia. Second, the datasets used in this study had significant sample overlap, which might have caused inflated Type 1 errors owing to weak instrument bias(\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). We were unable to determine and correct sample overlap as we used summary-level statistics from publicly available data. However, since the association estimates were independent, the two-sample MR analysis was less likely to have weak instrument bias than the one-sample MR(\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e). Last, Cochran's Q test identified significant heterogeneities caused by genetic variants. Therefore, we performed the leave-one-out sensitivity analysis, and the results were robust.\u003c/p\u003e"},{"header":"5 Conclusions","content":"\u003cp\u003eConclusively, our study revealed a causal relationship between depression and traits of sarcopenia (ALM and walking pace), and the reverse analysis indicated that walking pace is also significantly associated with depression. Our findings suggested that preventing sarcopenia may help with the prevention of depression and preventing depression may help with the prevention of sarcopenia.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eMR Mendelian randomization\u003c/p\u003e\n\u003cp\u003eGWAS Genome-Wide Association Study\u003c/p\u003e\n\u003cp\u003eALM Appendicular lean mass\u003c/p\u003e\n\u003cp\u003ePGC Psychiatric Genomics Consortium\u003c/p\u003e\n\u003cp\u003eSNPs Single nucleotide polymorphisms\u003c/p\u003e\n\u003cp\u003eIVW inverse-variance weighted\u003c/p\u003e\n\u003cp\u003eOR odds ratio\u003c/p\u003e\n\u003cp\u003eCI confidence interval\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll the data sources used in this study are summarized data from relevant GWAS studies and the IEU OpenGWAS project database, which are publicly available for free download and do not require approval from the review agency.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNOT APPLICABLE.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets presented in this study can be found in online\u0026nbsp;repositories. The websites for these datasets have been provided\u0026nbsp;in the supplementary materials.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was supported by the National Natural Science Foundation of China (Grant NO. 1971286), and the Natural Science Foundation of Chongqing, China (Grant NO. CSTC2021-jscx-gksb-N0002).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJYT and LK designed the research. JYT wrote the final manuscript. JYT and YXZ analyzed the data. YTK, MA, SH contributed to the explanation of the results and writing of the manuscript. All authors have read and approved the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors acknowledge the efforts of this research team and appreciate all the participants for attending this research.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWalker ER, McGee RE, Druss BG. Mortality in mental disorders and global disease burden implications: a systematic review and meta-analysis. JAMA Psychiatry. 2015;72(4):334-41.\u003c/li\u003e\n\u003cli\u003eGlobal, regional, and national incidence, prevalence, and years lived with disability for 354 diseases and injuries for 195 countries and territories, 1990-2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet. 2018;392(10159):1789-858.\u003c/li\u003e\n\u003cli\u003eEurelings LS, van Dalen JW, Ter Riet G, Moll van Charante EP, Richard E, van Gool WA, et al. 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J Affect Disord. 2017;224:87-94.\u003c/li\u003e\n\u003cli\u003eStudenski SA, Peters KW, Alley DE, Cawthon PM, McLean RR, Harris TB, et al. The FNIH sarcopenia project: rationale, study description, conference recommendations, and final estimates. J Gerontol A Biol Sci Med Sci. 2014;69(5):547-58.\u003c/li\u003e\n\u003cli\u003eCarvalho AF, Maes M, Solmi M, Brunoni AR, Lange S, Husain MI, et al. Is dynapenia associated with the onset and persistence of depressive and anxiety symptoms among older adults? Findings from the Irish longitudinal study on ageing. Aging Ment Health. 2021;25(3):468-75.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Depression, Sarcopenia, Mendelian randomization, Grip strength, Walking pace, Appendicular lean mass","lastPublishedDoi":"10.21203/rs.3.rs-2657221/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2657221/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e: Growing evidence reveals a significant association between depression and sarcopenia. However, the causal association between them remains elusive. We aimed to investigate the bidirectional relationship between depression and traits of sarcopenia.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: We used genetic variants associated with depression (n=2,113,907), grip strength (n=256,523), appendicular lean mass (n=450,243), and walking pace (n=459,915) in bidirectional two-sample Mendelian randomization. The random-effects inverse-variance weighted method was adopted as the primary method.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: Mendelian randomization results revealed a causal relationship between depression and appendicular lean mass [β (95% confidence interval (CI)) = -0.051 (-0.086−(-0.016)), P=0.004], walking pace [OR (95% CI) = 0.973 (0.955−0.992), P=0.005]. Walking pace also revealed a causal relationship with depression [OR (95% CI) = 0.663 (0.507−0.864), P=0.002] in the reverse analysis. We observed no causal relationships between depression and grip strength. The leave-one-out sensitivity analysis verified our results.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e: This Mendelian randomization analysis verified the bidirectional relationship between depression and sarcopenia. Early diagnosis and prevention of either disease may enhance the management of another.\u003c/p\u003e","manuscriptTitle":"Assessment of the causal relationship between depression and traits of sarcopenia: A bidirectional two-sample Mendelian randomization study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-03-24 16:15:54","doi":"10.21203/rs.3.rs-2657221/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":"1185f937-05e1-47ba-adc5-676dd40eb219","owner":[],"postedDate":"March 24th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2023-07-11T09:14:48+00:00","versionOfRecord":[],"versionCreatedAt":"2023-03-24 16:15:54","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-2657221","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2657221","identity":"rs-2657221","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}