Bidirectional causal links between psychiatric disorders and orthopaedic diseases: a two-sample Mendelian randomization study

Bidirectional causal links between psychiatric disorders and orthopaedic diseases: a 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 Bidirectional causal links between psychiatric disorders and orthopaedic diseases: a two-sample Mendelian randomization study Xiaohai Luo, Ning Wu, Zhaofu Wang, Jingzu Ma, Xiaoyu Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7687082/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 Psychiatric disorders and orthopaedic diseases frequently co-occur, but whether these associations are causal remains uncertain. We evaluated the bidirectional causal relationships between seven psychiatric disorders-major depressive disorder (MDD), bipolar disorder, schizophrenia, attention-deficit/hyperactivity disorder (ADHD), autism spectrum disorder (ASD), obsessive–compulsive disorder (OCD), and anorexia nervosa (AN)-and four orthopaedic outcomes: osteoporosis, fractures, osteoarthritis, and osteomyelitis. Methods We performed a two-sample Mendelian randomization (MR) using summary-level genome-wide association study (GWAS) data; psychiatric traits were sourced from the Psychiatric Genomics Consortium and orthopaedic outcomes from the IEU Open GWAS resource. Causal effects were estimated primarily with inverse-variance weighted (IVW) models, complemented by MR-Egger and weighted median analyses. Standard sensitivity analyses were conducted to probe heterogeneity, pleiotropy, and robustness. Results Genetically proxied MDD was associated with higher risks of osteoarthritis (IVW OR = 1.010, 95% CI 1.006-1.014, P < 0.001) and osteomyelitis (OR = 1.457, 95% CI 1.258–1.687, P = 0.001). ASD showed a positive association with fractures (OR = 1.042, 95% CI 1.002-1.082, P = 0.039), whereas AN was inversely associated with fractures (OR = 0.960, 95% CI 0.928-0.993, P = 0.018). Reverse MR analyses did not identify significant causal effects of orthopaedic diseases on psychiatric disorders. Sensitivity analyses did not materially change these estimates. Conclusions These findings support a causal link between genetic liability to MDD and increased risks of osteoarthritis and osteomyelitis, and suggest potential associations between ASD/AN and fracture risk. Given multiple comparisons and possible residual biases, signals should be interpreted cautiously and validated in larger, multi-ancestry cohorts. Mendelian randomization psychiatric disorders orthopaedic diseases fractures osteoarthritis osteomyelitis GWAS Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Psychiatric disorders and orthopaedic diseases are major public health challenges and together account for a substantial share of the global disease burden. Observational studies indicate that conditions such as major depressive disorder (MDD), bipolar disorder, and schizophrenia are linked to an increased risk of fractures and osteoporosis, yet the direction and causality of these associations remain uncertain due to residual confounding and potential reverse causality [ 1 – 3 ]. Clarifying the bidirectional causal relationships is essential for informing integrated strategies that simultaneously address mental and musculoskeletal health. Mendelian randomization (MR)—which employs germline genetic variants as instrumental variables—can mitigate confounding and reverse causation in observational epidemiology, thereby strengthening causal inference [ 4 , 5 ]. Emerging MR evidence has examined two-way relationships between specific psychiatric traits and orthopaedic outcomes (e.g., osteoporotic fracture) [ 6 , 7 ], but prior work often focused on single exposures or outcomes, limiting generalizability across the broader clinical spectrum. To address this gap, we systematically investigated the causality between seven psychiatric disorders and four orthopaedic diseases using a bidirectional two-sample MR approach. The aim of this study was to examine causal links between seven psychiatric disorders-MDD, bipolar disorder, attention-deficit/hyperactivity disorder (ADHD), schizophrenia, autism spectrum disorder (ASD), obsessive–compulsive disorder (OCD), and anorexia nervosa (AN)-and four orthopaedic diseases, including osteoporosis, fractures, osteoarthritis, and osteomyelitis. Leveraging large-scale GWAS summary statistics from established consortia and complementary MR estimators, we sought to generate robust evidence to guide integrated clinical care and future mechanistic research. Methods Study design We implemented a bidirectional two-sample MR framework to assess causal effects between seven psychiatric disorders (MDD, BD, SCZ, ADHD, ASD, OCD, AN) and four orthopaedic outcomes (OP, fractures, OA, OM), adhering to the core MR assumptions: (i) IVs are strongly associated with the exposure; (ii) IVs are independent of confounders; and (iii) IVs influence the outcome only through the exposure [8, 9]. Reporting followed STROBE-MR recommendations [10]. Data sources Summary-level GWAS data for psychiatric traits were obtained from the PGC, and orthopaedic outcomes from the IEU Open GWAS resource. All datasets were of European ancestry to limit population stratification. Dataset accessions, phenotype definitions, sample sizes (cases/controls/total), and release versions are listed in Table 1 [11]. Where available, we used the most recent meta-analyses with standardized QC. Instrument selection For each exposure, genome-wide significant SNPs were selected as IVs at P < 5×10⁻⁸. When instruments were sparse, we used a prespecified sensitivity threshold of P < 5×10⁻⁶, clearly flagged in the results. To ensure independence, IVs were LD-clumped at r² 10 was considered adequate to mitigate weak-instrument bias [12-16]. Loci reaching genome-wide significance for both the exposure and outcome were excluded from primary analyses to reduce potential violation of the exclusion restriction. Harmonization and quality control Exposure and outcome datasets were harmonized to align effect alleles. Palindromic variants with intermediate allele frequencies (A/T or C/G) were removed to avoid strand ambiguity. Where necessary, proxy variants in high LD were used according to pre-specified r² thresholds. Steiger filtering was applied to verify that the direction of effect was consistent with the hypothesized pathway from exposure to outcome; variants failing this criterion were excluded. Potential sample overlap was evaluated qualitatively; any remaining overlap and its implications for variance inflation or type-I error are addressed in the Discussion. Statistical analysis The primary causal estimator was inverse-variance weighted (IVW) under a random-effects model. To improve robustness under potential IV violations, we complemented IVW with MR-Egger and weighted median (WM) estimators [17, 18]. Between-SNP heterogeneity was assessed using Cochran’s Q (P < 0.05 indicates heterogeneity), in which case we report IVW random-effects by default. Directional horizontal pleiotropy was tested via the MR-Egger intercept (P < 0.05 suggests directional pleiotropy), and results were interpreted alongside WM and other robust estimates when present [19]. We performed outlier diagnostics (e.g., MR-PRESSO or residual-based checks) and leave-one-out analyses to identify influential variants and evaluate result stability [20]. Effect sizes are presented as ORs with 95% CIs, scaled per SD increase in genetically predicted exposure liability, with the direction defined consistently across analyses. Multiple testing and robustness Given multiple exposures, outcomes, and bidirectional tests, we controlled for multiplicity using FDR. Both unadjusted P and FDR-adjusted P (P_adj) are reported. We describe associations as providing causal evidence only when they remain significant after FDR correction; nominal findings (P < 0.05 but P_adj ≥ 0.05) are treated as exploratory and interpreted cautiously. Software and reproducibility Analyses were conducted in R (RStudio) using standard MR packages (e.g., TwoSampleMR, MR-PRESSO), with data wrangling and visualization performed using established libraries. Reproducible materials-including R scripts, package versions, session information, IV lists, and harmonization logs-are provided in the Supplementary Materials and hosted at the project repository, in line with database use policies [11]. Ethics This study used publicly available summary statistics. The original contributing studies obtained ethical approvals and participant consent; no new individual-level interventions or identifiable data were used. Results Instrument selection and strength Genome-wide significant SNPs (P < 5×10⁻⁸) were used as IVs for each exposure; when instruments were sparse, a prespecified sensitivity threshold (P < 5×10⁻⁶) was applied and flagged in Supplementary Tables. LD clumping (r² < 0.001; 10,000 kb) ensured independence. Mean and minimum F-statistics for each exposure exceeded 10 (Table S1). Primary MR estimates from psychiatric disorders to orthopaedic outcomes Genetically proxied MDD was positively associated with OA (IVW OR = 1.010, 95% CI 1.006–1.014, P < 0.001) and OM (OR = 1.457, 95% CI 1.258-1.687, P = 0.001). Both associations remained significant after FDR correction and were directionally consistent across complementary estimators (WM, MR-Egger) (Table 2; Fig. 2). For ASD, we observed a nominal positive association with fractures (OR = 1.042, 95% CI 1.002-1.082, P = 0.039) that did not survive FDR. AN showed a nominal inverse association with fractures (OR = 0.960, 95% CI 0.928–0.993, P = 0.018), which likewise did not remain significant after FDR. No other psychiatric–orthopaedic pairs met FDR-adjusted significance. Reverse-direction MR estimates from orthopaedic outcomes to psychiatric disorders Bidirectional analyses provided no FDR-significant evidence that OP, fractures, OA, or OM causally affect MDD, BD, SCZ, ADHD, ASD, OCD, or AN. Any nominal signals (P < 0.05 with P_adj ≥ 0.05) are reported as exploratory in Table 3 and were not emphasized. Heterogeneity and pleiotropy Cochran’s Q indicated heterogeneity in several models (Table S2); therefore, IVW random-effects estimates are reported as primary. MR-Egger intercepts were not significant for the key MDD-OA and MDD-OM associations (intercept P > 0.05), providing no evidence of directional horizontal pleiotropy for these outcomes. Where heterogeneity was present, WM estimates were directionally concordant with IVW. Outlier and influence analyses Outlier diagnostics (e.g., MR-PRESSO/residual checks) did not identify leverage points that materially altered the MDD-OA or MDD-OM estimates. Leave-one-out analyses showed that results were not driven by any single instrument (Fig. S1–S2). Multiple testing All exposure–outcome analyses were subjected to FDR correction. Only MDD–OA and MDD–OM remained significant after adjustment; the associations of ASD-fractures and AN-fractures are exploratory pending replication. Discussion This bidirectional two-sample MR suggests that genetic liability to MDD is associated with higher risks of osteoarthritis and osteomyelitis. These associations remained after FDR correction and were directionally consistent across complementary estimators. By contrast, the associations of ASD with fractures (positive) and AN with fractures (inverse) were nominal and did not survive FDR correction; they should therefore be interpreted cautiously as exploratory signals. Reverse-direction analyses found no FDR-significant evidence that orthopaedic diseases causally influence psychiatric disorders. Taken together, the findings argue for attention to mental–musculoskeletal comorbidity while avoiding overinterpretation of results that do not pass multiplicity control. Our results are broadly consistent with, and extend, prior work on links between mental disorders and orthopaedic outcomes. Observational studies have reported higher risks of fractures and osteoporosis among individuals with severe depression, although such designs are prone to confounding and reverse causation [ 2 ], which aligns with the direction we observe for MDD. In addition, ADHD/ASD have been associated with greater fracture risk in previous reports [ 4 ]. The present analysis differs from earlier studies with respect to AN and fractures: while prior literature often suggests increased skeletal fragility in AN [ 6 ], our inverse association did not withstand FDR correction and may reflect instrument limitations, phenotype heterogeneity, or residual bias rather than protection. Moreover, in contrast to Le [ 5 ], which did not identify a clear causal relationship with osteoarthritis, our analysis indicates a liability-level effect from MDD to osteoarthritis after multiplicity control. Such discrepancies may arise from differences in GWAS sources, instrument strength, outcome definitions, or analytic choices, and they highlight the need for replication using harmonized phenotypes and larger instruments. Methodologically, two-sample MR leverages germline variants as instrumental variables to mitigate confounding and reverse causation, thereby strengthening causal inference in this context [ 21 ]. To enhance reliability, instruments were selected at genome-wide significance with LD clumping and minor-allele-frequency thresholds, and causal effects were estimated primarily with IVW, complemented by MR-Egger and the weighted median, which showed broadly consistent directions for the main findings [ 22 ]. Sensitivity analyses included Cochran’s Q to assess heterogeneity, the MR-Egger intercept for directional horizontal pleiotropy, and MR-PRESSO/residual diagnostics for outliers [ 23 ]. While these tests did not indicate directional pleiotropy for the key endpoints and leave-one-out checks supported stability, their limited power means that residual pleiotropy cannot be entirely excluded. A strength of this study is its breadth-a bidirectional screen across seven psychiatric disorders and four orthopaedic outcomes-addressing generalizability gaps left by one-way or narrowly focused analyses [ 24 , 25 ]. Within this framework, signals for ASD-fractures and AN-fractures remained below FDR significance, underscoring the need for larger instruments and harmonized fracture definitions in future work [ 26 ]. By contrast, the MDD-osteoarthritis/osteomyelitis findings-areas comparatively under-explored in psychiatry-suggest potential pathways through which depressive liability may influence joint degeneration or susceptibility to bone/medullary infection, warranting mechanistic and clinical follow-up [ 5 ]. Clinically, if replicated, these results support integrated care that considers musculoskeletal risk in patients with high depressive liability, alongside preventive counseling (activity, nutrition, fall and infection prevention) and tailored surveillance in orthopaedic settings. Importantly, MR estimates reflect per-SD changes in genetic liability rather than treatment effects; translation to practice should therefore be cautious and context-specific. Limitations This study has several limitations. First, MR inference relies on core assumptions-relevance, independence, and exclusion restriction-i.e., that genetic instruments are strongly associated with the exposure, are not confounded, and influence the outcome only through the exposure; these assumptions may not always be fully satisfied [ 21 ]. Second, all GWAS data were from European ancestry cohorts, which limits generalizability to other populations and underscores the need for ancestry-diverse replication [ 22 ]. Third, horizontal pleiotropy can bias causal estimates. Although the MR-Egger intercept provides a test for directional pleiotropy, its statistical power is limited, so absence of evidence is not evidence of absence and residual pleiotropy cannot be excluded [ 27 ]. Future work should validate these findings in diverse ancestries, apply complementary pleiotropy-robust estimators, and continue methodological development to better diagnose and account for pleiotropic pathways. Conclusions In this bidirectional two-sample MR, genetic liability to MDD was associated with increased risks of OA and OM after multiplicity control, with directionally consistent estimates across complementary methods. Associations of ASD with fractures and AN with fractures were nominal and did not survive FDR correction, and should be considered exploratory. Reverse-direction analyses provided no FDR-significant evidence that orthopaedic outcomes causally influence psychiatric disorders. These findings support the need for integrated mental–musculoskeletal care and motivate replication in multi-ancestry cohorts, higher-resolution phenotyping, and multivariable MR to clarify potential mediators and inform prevention and co-management strategies. Declarations Ethics approval and consent to participate This study analyzed publicly available, de-identified summary statistics from previously approved genome-wide association studies (GWAS). No new human participants were recruited and no individual-level data were analyzed; therefore, institutional ethics approval and informed consent were not required. Not applicable to new data collection. Consent for publication Not applicable. Data availability No datasets were generated or analysed during the current study. Competing interests The authors declare that they have no competing interests. Funding This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Author contributions Xiaohai Luo conceived and designed the study and drafted the manuscript. Ning Wu collected datasets and assisted in writing. Zhaofu Wang provided methodological guidance and revised the manuscript. Jingzu Ma performed statistical analyses and prepared tables and figures. Xiaoyu Zhang contributed to data cleaning and supplementary materials. All authors reviewed and approved the final manuscript. 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Tables Table 1 GWAS data sources and sample characteristics Exposure or outcome ID Year Sample size Consortium Ethnicity MDD ieu-b-102 2019 500199 PGC European Bipolar disorder ieu-b-5110 2021 413466 PGC European Schizophrenia ieu-b-5102 2022 127906 PGC European ADHD ieu-a-1183 2017 55374 PGC European ASD ieu-a-1185 2017 46351 PGC European OCD ieu-a-1189 2017 33925 PGC European AN ieu-a-1186 2017 14477 PGC European Osteoporosis ebi-a-GCST90038656 2021 484598 NA European Fracture ebi-a-GCST90013913 2021 407746 NA European Osteoarthritis ebi-a-GCST90038686 2021 484598 NA European Osteomyelitis ieu-b-4975 2021 486484 UK Biobank European Psychiatric exposures (PGC) and orthopaedic outcomes (IEU Open GWAS), with accession IDs, phenotype definitions, ancestry, cases/controls/total, and release/version. All datasets are European ancestry. Table 2 Primary MR estimates from psychiatric disorders to orthopaedic outcomes. Exposure-Outcome Method β SE OR 95%CI P -value MDD-Fracture MR Egger 0.045 0.134 1.046 0.804-1.360 0.74 Weighted median 0.052 0.04 1.054 0.976-1.138 0.195 IVW 0.088 0.033 1.092 1.024-1.165 0.008 MDD-Osteoporosis MR Egger 0.001 0.004 1.001 0.993-1.009 0.886 Weighted median 0.003 0.001 1.003 1.000-1.005 0.039 IVW 0.002 0.001 1.002 1.000-1.004 0.014 MDD-Osteoarthritis MR Egger 0.006 0.008 1.006 0.991-1.021 0.440 Weighted median 0.009 0.003 1.009 1.004-1.015 0.001 IVW 0.010 0.002 1.010 1.006-1.014 0.000 MDD-steomyelitis MR Egger 0.097 0.304 1.006 0.991-1.021 0.750 Weighted median 0.337 0.105 1.009 1.004-1.015 0.001 IVW 0.376 0.075 1.457 1.258-1.687 0.001 ADHD-Fracture MR Egger 0.119 0.064 0.888 0.783-1.007 0.070 Weighted median 0.031 0.023 1.032 0.988-1.077 0.161 IVW 0.036 0.016 1.036 1.004-1.070 0.028 ASD-Fracture MR Egger 0.067 0.055 1.070 0.959-1.192 0.234 Weighted median 0.011 0.027 1.011 0.961-1.065 0.665 IVW 0.041 0.020 1.042 1.002-1.082 0.039 AN-Fracture MR Egger 0.017 0.046 0.983 0.983-1.076 0.717 Weighted median 0.038 0.020 0.963 0.925-1.002 0.062 IVW 0.041 0.017 0.960 0.928-0.993 0.018 Fracture-MDD MR Egger 0.011 0.065 0.989 0.870-1.124 0.865 Weighted median 0.048 0.026 1.050 0.997-1.104 0.063 IVW 0.064 0.02 1.066 1.024-1.110 0.002 Effect sizes are reported as OR (95% CI), P, and FDR-adjusted P (P_adj) for IVW, with WM and MR‐Egger as complementary estimators. Results remaining significant after FDR are in bold. Table 3 Reverse-direction MR estimates from orthopaedic outcomes to psychiatric disorders. Exposure Outcome Heterogeneity test Horizontal pleiotropy test Method Q Q_df P Method Intercept SE P MDD Fracture MR Egger 252 154 0.000 MR Egger 0.001 0.004 0.738 IVW 252 155 0.000 MDD Osteoporosis MR Egger 244 174 0.000 MR Egger 0.000 0.000 0.635 IVW 245 175 0.000 MDD Osteoarthritis MR Egger 193 174 0.148 MR Egger 0.000 0.000 0.61 IVW 194 175 0.157 MDD Osteomyelitis MR Egger 200 174 0.083 MR Egger 0.008 0.008 0.344 IVW 202 175 0.083 ADHD Fracture MR Egger 49.2 47 0.384 MR Egger 0.013 0.005 0.016 IVW 55.7 48 0.207 ASD Fracture MR Egger 38 32 0.215 MR Egger -0.003 0.005 0.611 IVW 38.3 33 0.241 AN Fracture MR Egger 20 12 0.067 MR Egger -0.006 0.01 0.583 IVW 20.5 13 0.083 Fracture MDD MR Egger 71 43 0.005 MR Egger 0.004 0.003 0.232 IVW 73.5 44 0.003 Effect sizes are OR (95% CI), P, and P_adj for IVW; WM and MR‐Egger provided for robustness. No associations survived FDR. Additional Declarations No competing interests reported. Supplementary Files Supplementarytable.doc Table S1. Instrument counts and F-statistics Number of SNP instruments per exposure (primary threshold; sensitivity threshold where applicable), mean/minimum F-statistics. Table S2. Heterogeneity and pleiotropy diagnostics Cochran’s Q (Q, df, P) and MR‐Egger intercept (intercept, SE, P) for each analysis; notes on outlier removal where applied. 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. 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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-7687082","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":524883627,"identity":"38a8a01b-7d1f-4dc8-b490-43bffe09c42b","order_by":0,"name":"Xiaohai Luo","email":"","orcid":"","institution":"Ningxia Hui Autonomous Region Peoples Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xiaohai","middleName":"","lastName":"Luo","suffix":""},{"id":524883630,"identity":"9378ac89-79d5-4d9b-b226-7eb35861c7bd","order_by":1,"name":"Ning Wu","email":"","orcid":"","institution":"Ningxia integrated Chinese and western medicine 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Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7ElEQVRIiWNgGAWjYPACCQZ+ZsbGBx8MbOyI1yLZ3nzYcEZBWjLx9hicOZYmzPPhEGMDQZXHzx5+8eOPBQPDjRwzZhuDA8wM7IePbsBveF6aZW+bBAPjjByzxzkGd/gYeNLSbuDTYnYgx8yAt0GCgVkix9w4x+AZM4MEjxl+LeffmBn++SPBwCaRYyZtYXCYsYGglhs5xo952CQYeHiOpUkzEKPF/sYbM2ZZoF8k2IGB3GOQlsxGyC+S/TnGH9/8qWOwPwyMyh9/bOz42Q8fw6sFCICuYmCob4BzCSgHAeYPRCgaBaNgFIyCkQwAltdJaRBbZmQAAAAASUVORK5CYII=","orcid":"","institution":"Ningxia Hui Autonomous Region Peoples Hospital","correspondingAuthor":true,"prefix":"","firstName":"Xiaoyu","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2025-09-23 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17:22:58","extension":"html","order_by":18,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":126309,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7687082/v1/e8237c28e3f8223427e893ea.html"},{"id":93258966,"identity":"ea28c6dc-04db-45e0-8920-211d9a1a960f","added_by":"auto","created_at":"2025-10-10 17:30:57","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":77146,"visible":true,"origin":"","legend":"\u003cp\u003eStudy workflow for the bidirectional two-sample MR\u003c/p\u003e\n\u003cp\u003eOverview of dataset selection, IV selection (P \u0026lt; 5×10⁻⁸; LD clumping r² \u0026lt; 0.001, 10,000 kb), harmonization (palindromic removal, Steiger filtering), primary and reverse MR analyses, sensitivity tests (Cochran’s Q, MR‐Egger intercept, outlier and leave-one-out diagnostics), and FDR control. Abbreviations: MR, Mendelian randomization; IV, instrumental variable; LD, linkage disequilibrium; OA, osteoarthritis; OM, osteomyelitis.\u003c/p\u003e","description":"","filename":"Fig.1.png","url":"https://assets-eu.researchsquare.com/files/rs-7687082/v1/1191fa377a5665a3cf3b5580.png"},{"id":93258355,"identity":"629a8f9b-5d6d-474b-9294-ad774e0fd6a8","added_by":"auto","created_at":"2025-10-10 17:22:57","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":5713,"visible":true,"origin":"","legend":"\u003cp\u003ePrimary MR estimates from psychiatric disorders to orthopaedic outcomes (forest plot)\u003c/p\u003e\n\u003cp\u003eIVW, WM, and MR‐Egger estimates are shown as ORs with 95% CIs for each exposure–outcome pair. Associations surviving FDR are highlighted. Abbreviations: IVW, inverse-variance weighted; WM, weighted median; OA, osteoarthritis; OM, osteomyelitis.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7687082/v1/1cc6d75be9b8ea3cf1230126.png"},{"id":93258360,"identity":"d539943f-41ed-41cd-b286-308c0c125740","added_by":"auto","created_at":"2025-10-10 17:22:57","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":225784,"visible":true,"origin":"","legend":"\u003cp\u003eScatter plots of SNP-specific effects for key associations\u003c/p\u003e\n\u003cp\u003eScatter plots display SNP-level Wald ratios with fitted IVW, WM, and MR‐Egger slopes for (A) MDD–OA and (B) MDD–OM. Slopes indicate causal estimates; dashed lines denote 95% CIs of the fitted methods.\u003c/p\u003e","description":"","filename":"Fig.3.png","url":"https://assets-eu.researchsquare.com/files/rs-7687082/v1/c88f65a0e9b016a2915e8859.png"},{"id":93258969,"identity":"bb66d73a-0ee7-4d80-884a-1427bf13836f","added_by":"auto","created_at":"2025-10-10 17:30:57","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":218561,"visible":true,"origin":"","legend":"\u003cp\u003eDiagnostic plots for heterogeneity and pleiotropy\u003c/p\u003e\n\u003cp\u003e(A–B) Funnel plots for MDD–OA and MDD–OM showing symmetry; (C–D) leave-one-out analyses indicating that no single SNP drives the estimates; (E–F) residual/outlier diagnostics. IVW random-effects were reported where Cochran’s Q indicated heterogeneity.\u003c/p\u003e","description":"","filename":"Fig.4.png","url":"https://assets-eu.researchsquare.com/files/rs-7687082/v1/70ed3f6a6caa59345245b292.png"},{"id":94256640,"identity":"2ea94f93-fbd0-486a-bdfe-c1b06a353c07","added_by":"auto","created_at":"2025-10-24 07:54:23","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1205712,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7687082/v1/6e0ce1d8-d5bf-4505-947d-003a5a3e697a.pdf"},{"id":93258968,"identity":"0958bd5f-9c71-49a6-9635-a6e247729041","added_by":"auto","created_at":"2025-10-10 17:30:57","extension":"doc","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":98816,"visible":true,"origin":"","legend":"\u003cp\u003eTable S1. Instrument counts and F-statistics\u003c/p\u003e\n\u003cp\u003eNumber of SNP instruments per exposure (primary threshold; sensitivity threshold where applicable), mean/minimum F-statistics.\u003c/p\u003e\n\u003cp\u003eTable S2. Heterogeneity and pleiotropy diagnostics\u003c/p\u003e\n\u003cp\u003eCochran’s Q (Q, df, P) and MR‐Egger intercept (intercept, SE, P) for each analysis; notes on outlier removal where applied.\u003c/p\u003e","description":"","filename":"Supplementarytable.doc","url":"https://assets-eu.researchsquare.com/files/rs-7687082/v1/42f84dd708461a882fed46ee.doc"}],"financialInterests":"No competing interests reported.","formattedTitle":"Bidirectional causal links between psychiatric disorders and orthopaedic diseases: a two-sample Mendelian randomization study","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePsychiatric disorders and orthopaedic diseases are major public health challenges and together account for a substantial share of the global disease burden. Observational studies indicate that conditions such as major depressive disorder (MDD), bipolar disorder, and schizophrenia are linked to an increased risk of fractures and osteoporosis, yet the direction and causality of these associations remain uncertain due to residual confounding and potential reverse causality [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Clarifying the bidirectional causal relationships is essential for informing integrated strategies that simultaneously address mental and musculoskeletal health.\u003c/p\u003e\u003cp\u003eMendelian randomization (MR)\u0026mdash;which employs germline genetic variants as instrumental variables\u0026mdash;can mitigate confounding and reverse causation in observational epidemiology, thereby strengthening causal inference [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Emerging MR evidence has examined two-way relationships between specific psychiatric traits and orthopaedic outcomes (e.g., osteoporotic fracture) [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], but prior work often focused on single exposures or outcomes, limiting generalizability across the broader clinical spectrum. To address this gap, we systematically investigated the causality between seven psychiatric disorders and four orthopaedic diseases using a bidirectional two-sample MR approach.\u003c/p\u003e\u003cp\u003eThe aim of this study was to examine causal links between seven psychiatric disorders-MDD, bipolar disorder, attention-deficit/hyperactivity disorder (ADHD), schizophrenia, autism spectrum disorder (ASD), obsessive\u0026ndash;compulsive disorder (OCD), and anorexia nervosa (AN)-and four orthopaedic diseases, including osteoporosis, fractures, osteoarthritis, and osteomyelitis. Leveraging large-scale GWAS summary statistics from established consortia and complementary MR estimators, we sought to generate robust evidence to guide integrated clinical care and future mechanistic research.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eStudy design\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe implemented a bidirectional two-sample MR framework to assess causal effects between seven psychiatric disorders (MDD, BD, SCZ, ADHD, ASD, OCD, AN) and four orthopaedic outcomes (OP, fractures, OA, OM), adhering to the core MR assumptions: (i) IVs are strongly associated with the exposure; (ii) IVs are independent of confounders; and (iii) IVs influence the outcome only through the exposure [8, 9]. Reporting followed STROBE-MR recommendations [10].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData sources\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSummary-level GWAS data for psychiatric traits were obtained from the PGC, and orthopaedic outcomes from the IEU Open GWAS resource. All datasets were of European ancestry to limit population stratification. Dataset accessions, phenotype definitions, sample sizes (cases/controls/total), and release versions are listed in Table 1 [11]. Where available, we used the most recent meta-analyses with standardized QC.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInstrument selection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor each exposure, genome-wide significant SNPs were selected as IVs at P \u0026lt; 5×10⁻⁸. When instruments were sparse, we used a prespecified sensitivity threshold of P \u0026lt; 5×10⁻⁶, clearly flagged in the results. To ensure independence, IVs were LD-clumped at r² \u0026lt; 0.001 within a 10,000 kb window. Variants with very low minor allele frequency were excluded. Instrument strength was evaluated using F-statistics (reporting mean and minimum per exposure); F \u0026gt; 10 was considered adequate to mitigate weak-instrument bias [12-16]. Loci reaching genome-wide significance for both the exposure and outcome were excluded from primary analyses to reduce potential violation of the exclusion restriction.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHarmonization and quality control\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eExposure and outcome datasets were harmonized to align effect alleles. Palindromic variants with intermediate allele frequencies (A/T or C/G) were removed to avoid strand ambiguity. Where necessary, proxy variants in high LD were used according to pre-specified r² thresholds. Steiger filtering was applied to verify that the direction of effect was consistent with the hypothesized pathway from exposure to outcome; variants failing this criterion were excluded. Potential sample overlap was evaluated qualitatively; any remaining overlap and its implications for variance inflation or type-I error are addressed in the Discussion.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe primary causal estimator was inverse-variance weighted (IVW) under a random-effects model. To improve robustness under potential IV violations, we complemented IVW with MR-Egger and weighted median (WM) estimators [17, 18]. Between-SNP heterogeneity was assessed using Cochran’s Q (P \u0026lt; 0.05 indicates heterogeneity), in which case we report IVW random-effects by default. Directional horizontal pleiotropy was tested via the MR-Egger intercept (P \u0026lt; 0.05 suggests directional pleiotropy), and results were interpreted alongside WM and other robust estimates when present [19]. We performed outlier diagnostics (e.g., MR-PRESSO or residual-based checks) and leave-one-out analyses to identify influential variants and evaluate result stability [20]. Effect sizes are presented as ORs with 95% CIs, scaled per SD increase in genetically predicted exposure liability, with the direction defined consistently across analyses.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMultiple testing and robustness\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGiven multiple exposures, outcomes, and bidirectional tests, we controlled for multiplicity using FDR. Both unadjusted P and FDR-adjusted P (P_adj) are reported. We describe associations as providing causal evidence only when they remain significant after FDR correction; nominal findings (P \u0026lt; 0.05 but P_adj ≥ 0.05) are treated as exploratory and interpreted cautiously.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSoftware and reproducibility\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAnalyses were conducted in R (RStudio) using standard MR packages (e.g., TwoSampleMR, MR-PRESSO), with data wrangling and visualization performed using established libraries. Reproducible materials-including R scripts, package versions, session information, IV lists, and harmonization logs-are provided in the Supplementary Materials and hosted at the project repository, in line with database use policies [11].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study used publicly available summary statistics. The original contributing studies obtained ethical approvals and participant consent; no new individual-level interventions or identifiable data were used.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eInstrument selection and strength\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGenome-wide significant SNPs (P \u0026lt; 5×10⁻⁸) were used as IVs for each exposure; when instruments were sparse, a prespecified sensitivity threshold (P \u0026lt; 5×10⁻⁶) was applied and flagged in Supplementary Tables. LD clumping (r² \u0026lt; 0.001; 10,000 kb) ensured independence. Mean and minimum F-statistics for each exposure exceeded 10 (Table S1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePrimary MR estimates from psychiatric disorders to orthopaedic outcomes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGenetically proxied MDD was positively associated with OA (IVW OR = 1.010, 95% CI 1.006–1.014, P \u0026lt; 0.001) and OM (OR = 1.457, 95% CI 1.258-1.687, P = 0.001). Both associations remained significant after FDR correction and were directionally consistent across complementary estimators (WM, MR-Egger) (Table 2; Fig. 2).\u003c/p\u003e\n\u003cp\u003eFor ASD, we observed a nominal positive association with fractures (OR = 1.042, 95% CI 1.002-1.082, P = 0.039) that did not survive FDR. AN showed a nominal inverse association with fractures (OR = 0.960, 95% CI 0.928–0.993, P = 0.018), which likewise did not remain significant after FDR. No other psychiatric–orthopaedic pairs met FDR-adjusted significance.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eReverse-direction MR estimates from orthopaedic outcomes to psychiatric disorders\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBidirectional analyses provided no FDR-significant evidence that OP, fractures, OA, or OM causally affect MDD, BD, SCZ, ADHD, ASD, OCD, or AN. Any nominal signals (P \u0026lt; 0.05 with P_adj ≥ 0.05) are reported as exploratory in Table 3 and were not emphasized.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHeterogeneity and pleiotropy\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCochran’s Q indicated heterogeneity in several models (Table S2); therefore, IVW random-effects estimates are reported as primary. MR-Egger intercepts were not significant for the key MDD-OA and MDD-OM associations (intercept P \u0026gt; 0.05), providing no evidence of directional horizontal pleiotropy for these outcomes. Where heterogeneity was present, WM estimates were directionally concordant with IVW.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOutlier and influence analyses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOutlier diagnostics (e.g., MR-PRESSO/residual checks) did not identify leverage points that materially altered the MDD-OA or MDD-OM estimates. Leave-one-out analyses showed that results were not driven by any single instrument (Fig. S1–S2).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMultiple testing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll exposure–outcome analyses were subjected to FDR correction. Only MDD–OA and MDD–OM remained significant after adjustment; the associations of ASD-fractures and AN-fractures are exploratory pending replication.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis bidirectional two-sample MR suggests that genetic liability to MDD is associated with higher risks of osteoarthritis and osteomyelitis. These associations remained after FDR correction and were directionally consistent across complementary estimators. By contrast, the associations of ASD with fractures (positive) and AN with fractures (inverse) were nominal and did not survive FDR correction; they should therefore be interpreted cautiously as exploratory signals. Reverse-direction analyses found no FDR-significant evidence that orthopaedic diseases causally influence psychiatric disorders. Taken together, the findings argue for attention to mental\u0026ndash;musculoskeletal comorbidity while avoiding overinterpretation of results that do not pass multiplicity control.\u003c/p\u003e\u003cp\u003eOur results are broadly consistent with, and extend, prior work on links between mental disorders and orthopaedic outcomes. Observational studies have reported higher risks of fractures and osteoporosis among individuals with severe depression, although such designs are prone to confounding and reverse causation [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], which aligns with the direction we observe for MDD. In addition, ADHD/ASD have been associated with greater fracture risk in previous reports [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The present analysis differs from earlier studies with respect to AN and fractures: while prior literature often suggests increased skeletal fragility in AN [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], our inverse association did not withstand FDR correction and may reflect instrument limitations, phenotype heterogeneity, or residual bias rather than protection. Moreover, in contrast to Le [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], which did not identify a clear causal relationship with osteoarthritis, our analysis indicates a liability-level effect from MDD to osteoarthritis after multiplicity control. Such discrepancies may arise from differences in GWAS sources, instrument strength, outcome definitions, or analytic choices, and they highlight the need for replication using harmonized phenotypes and larger instruments.\u003c/p\u003e\u003cp\u003eMethodologically, two-sample MR leverages germline variants as instrumental variables to mitigate confounding and reverse causation, thereby strengthening causal inference in this context [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. To enhance reliability, instruments were selected at genome-wide significance with LD clumping and minor-allele-frequency thresholds, and causal effects were estimated primarily with IVW, complemented by MR-Egger and the weighted median, which showed broadly consistent directions for the main findings [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Sensitivity analyses included Cochran\u0026rsquo;s Q to assess heterogeneity, the MR-Egger intercept for directional horizontal pleiotropy, and MR-PRESSO/residual diagnostics for outliers [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. While these tests did not indicate directional pleiotropy for the key endpoints and leave-one-out checks supported stability, their limited power means that residual pleiotropy cannot be entirely excluded.\u003c/p\u003e\u003cp\u003eA strength of this study is its breadth-a bidirectional screen across seven psychiatric disorders and four orthopaedic outcomes-addressing generalizability gaps left by one-way or narrowly focused analyses [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Within this framework, signals for ASD-fractures and AN-fractures remained below FDR significance, underscoring the need for larger instruments and harmonized fracture definitions in future work [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. By contrast, the MDD-osteoarthritis/osteomyelitis findings-areas comparatively under-explored in psychiatry-suggest potential pathways through which depressive liability may influence joint degeneration or susceptibility to bone/medullary infection, warranting mechanistic and clinical follow-up [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eClinically, if replicated, these results support integrated care that considers musculoskeletal risk in patients with high depressive liability, alongside preventive counseling (activity, nutrition, fall and infection prevention) and tailored surveillance in orthopaedic settings. Importantly, MR estimates reflect per-SD changes in genetic liability rather than treatment effects; translation to practice should therefore be cautious and context-specific.\u003c/p\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003eLimitations\u003c/h2\u003e\u003cp\u003eThis study has several limitations. First, MR inference relies on core assumptions-relevance, independence, and exclusion restriction-i.e., that genetic instruments are strongly associated with the exposure, are not confounded, and influence the outcome only through the exposure; these assumptions may not always be fully satisfied [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Second, all GWAS data were from European ancestry cohorts, which limits generalizability to other populations and underscores the need for ancestry-diverse replication [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Third, horizontal pleiotropy can bias causal estimates. Although the MR-Egger intercept provides a test for directional pleiotropy, its statistical power is limited, so absence of evidence is not evidence of absence and residual pleiotropy cannot be excluded [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Future work should validate these findings in diverse ancestries, apply complementary pleiotropy-robust estimators, and continue methodological development to better diagnose and account for pleiotropic pathways.\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn this bidirectional two-sample MR, genetic liability to MDD was associated with increased risks of OA and OM after multiplicity control, with directionally consistent estimates across complementary methods. Associations of ASD with fractures and AN with fractures were nominal and did not survive FDR correction, and should be considered exploratory. Reverse-direction analyses provided no FDR-significant evidence that orthopaedic outcomes causally influence psychiatric disorders. These findings support the need for integrated mental\u0026ndash;musculoskeletal care and motivate replication in multi-ancestry cohorts, higher-resolution phenotyping, and multivariable MR to clarify potential mediators and inform prevention and co-management strategies.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study analyzed publicly available, de-identified summary statistics from previously approved genome-wide association studies (GWAS). No new human participants were recruited and no individual-level data were analyzed; therefore, institutional ethics approval and informed consent were not required. Not applicable to new data collection.\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\u003eData availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo datasets were generated or analysed during the current study.\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 did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eXiaohai Luo conceived and designed the study and drafted the manuscript. Ning Wu collected datasets and assisted in writing. Zhaofu Wang provided methodological guidance and revised the manuscript. Jingzu Ma performed statistical analyses and prepared tables and figures. Xiaoyu Zhang contributed to data cleaning and supplementary materials. All authors reviewed and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank the investigators and participants of the contributing GWAS consortia for making their summary statistics publicly available.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eLi Z, Wu X, Li H, Bi C, Zhang C, Sun Y, Yan Z. Complex interplay of neurodevelopmental \u0026nbsp; disorders (NDDs), fractures, and osteoporosis: a Mendelian randomization study. BMC Psychiatry. 2024;24:232.\u003c/li\u003e\n \u003cli\u003eMeng J, Cai Y, Yao J, Yan H. 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Mendelian randomization analysis does not reveal a causal influence of mental diseases on osteoporosis. Front Endocrinol (Lausanne). 2023;14:1125427.\u003c/li\u003e\n \u003cli\u003eBarowsky S, Jung JY, Nesbit N, Silberstein M, Fava M, Loggia ML, Smoller JW, Lee PH. Cross-disorder genomics data analysis elucidates a shared genetic basis between major depression and osteoarthritis pain. Front Genet. 2021;12:687687.\u003c/li\u003e\n \u003cli\u003eYin X, Wang M, Li F, Wang Z, Gao Z. Sj\u0026ouml;gren\u0026rsquo;s syndrome and Parkinson\u0026rsquo;s disease: a bidirectional Mendelian randomization study. Front Genet. 2024;15:1370245.\u003c/li\u003e\n \u003cli\u003eHu Y, Zou Y, Zhang M, Yan J, Zheng Y, Chen Y. The relationship between major depressive disorder and dementia: a bidirectional two-sample Mendelian randomization study. 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Genet Epidemiol. 2016;40(4):304\u0026ndash;314.\u003c/li\u003e\n \u003cli\u003eAbuduxukuer R, Niu PP, Guo ZN, Xu YM, Yang Y. Circulating insulin-like growth factor 1 levels and migraine risk: a Mendelian randomization study. Neurol Ther. 2022;11:1677\u0026ndash;1689.\u003c/li\u003e\n \u003cli\u003eEllervik C, Mora S, Kuś A, \u0026Aring;svold B, Marouli E, Deloukas P, Sterenborg R, Teumer A, Burgess S, Sabater-Lleal M, Huffman J, Johnson AD, Tr\u0026eacute;gouet DA, Smith NL, Medici M, DeVries PS, Chasman DI, Kjaergaard AD. Effects of thyroid function on hemostasis, coagulation, and fibrinolysis: a Mendelian randomization study. Thyroid. 2021;31:1305\u0026ndash;1315.\u003c/li\u003e\n \u003cli\u003eJin Q, Ren F, Dai D, Sun N, Qian Y, Song P. The causality between intestinal flora and allergic diseases: insights from a bi-directional two-sample Mendelian randomization analysis. Front Immunol. 2023;14:1121273.\u003c/li\u003e\n \u003cli\u003eBurgess S, Thompson SG, CRP CHD Genetics Collaboration. Avoiding bias from weak instruments in Mendelian randomization studies. Int J Epidemiol. 2011;40(3):755\u0026ndash;764.\u003c/li\u003e\n \u003cli\u003eLawlor DA, Harbord RM, Sterne JA, Timpson N, Davey Smith G. Mendelian randomization: using genes as instruments for making causal inferences in epidemiology. Stat Med. 2008;27:1133\u0026ndash;1163.\u003c/li\u003e\n \u003cli\u003eBowden J, Davey Smith G, Burgess S. Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. Int J Epidemiol. 2015;44:512\u0026ndash;525.\u003c/li\u003e\n \u003cli\u003eCarter AR, Sanderson E, Hammerton G, Richmond RC, Davey Smith G, Heron J, Taylor AE, Davies NM, Howe LD. Mendelian randomisation for mediation analysis: current methods and challenges for implementation. Eur J Epidemiol. 2021;36(5):465\u0026ndash;478.\u003c/li\u003e\n \u003cli\u003eWang Z, Chen M, Wei YZ, Zhuo CG, Xu HF, Li WD, Ma L. The causal relationship between sleep traits and the risk of schizophrenia: a two-sample bidirectional Mendelian randomization study. BMC Psychiatry. 2022;22(1):399.\u003c/li\u003e\n \u003cli\u003eDavies NM, Holmes MV, Davey Smith G. Reading Mendelian randomisation studies: a guide, glossary, and checklist for clinicians. BMJ. 2018;362:k601.\u003c/li\u003e\n \u003cli\u003eHartwig FP, Davies NM, Hemani G, Davey Smith G. Two-sample Mendelian randomization: avoiding the downsides of a powerful, widely applicable but potentially fallible technique. Int J Epidemiol. 2016;45:1717\u0026ndash;1726.\u003c/li\u003e\n \u003cli\u003eHemani G, Zheng J, Elsworth B, Wade KH, Haberland V, Baird D, Laurin C, Burgess S, Bowden J, Langdon R, Tan VY, Yarmolinsky J, Shihab HA, Timpson NJ, Evans DM, Relton C, Martin RM, Davey Smith G, Gaunt TR, Haycock PC. The MR-Base platform supports systematic causal inference across the human phenome. eLife. 2018;7:e34408.\u003c/li\u003e\n \u003cli\u003ePrins BP, Abbasi A, Wong A, Vaez A, Nolte I, Franceschini N, Stuart PE, Guterriez Achury J, Mistry V, Bradfield JP, Valdes AM, Bras J, Shatunov A, Lu C, Han B, Raychaudhuri S, Bevan S, Mayes MD, Tsoi LC, Evangelou E, Nair RP, Grant SF, Polychronakos C, Radstake TR, van Heel DA, Dunstan ML, Wood NW, Al-Chalabi A, Dehghan A, Hakonarson H, Markus HS, Elder JT, Knight J, Arking DE, Spector TD, Koeleman BP, van Duijn CM, Martin J, Morris AP, Weersma RK, Wijmenga C, Munroe PB, Perry JR, Pouget JG, Jamshidi Y, Snieder H, Alizadeh BZ. Investigating the causal relationship of C-reactive protein with 32 complex somatic and psychiatric outcomes: a large-scale cross-consortium Mendelian randomization study. PLoS Med. 2016;13:e1001976.\u003c/li\u003e\n \u003cli\u003eZheng J, Baird D, Borges MC, Bowden J, Hemani G, Haycock P, Evans DM, Smith GD. Recent developments in Mendelian randomization studies. Curr Epidemiol Rep. 2017;4:330\u0026ndash;345.\u003c/li\u003e\n \u003cli\u003eCheng WW, Zhu Q, Zhang HY. Mineral nutrition and the risk of chronic diseases: a Mendelian randomization study. Nutrients. 2019;11:378.\u003c/li\u003e\n \u003cli\u003eYarmolinsky J, Wade KH, Richmond RC, Langdon RJ, Bull CJ, Tilling KM, Relton CL, Lewis SJ, Davey Smith G, Martin RM. Causal inference in cancer epidemiology: what is the role of Mendelian randomization? Cancer Epidemiol Biomarkers Prev. 2018;27:995\u0026ndash;1010.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1 \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGWAS data sources and sample characteristics\u003c/p\u003e\n\u003cdiv align=\"Left\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"585\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.4188%;\"\u003e\n \u003cp\u003eExposure or outcome\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.4188%;\"\u003e\n \u003cp\u003eID\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.453%;\"\u003e\n \u003cp\u003eYear\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.188%;\"\u003e\n \u003cp\u003eSample size\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.359%;\"\u003e\n \u003cp\u003eConsortium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1624%;\"\u003e\n \u003cp\u003eEthnicity\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.4188%;\"\u003e\n \u003cp\u003eMDD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.4188%;\"\u003e\n \u003cp\u003eieu-b-102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.453%;\"\u003e\n \u003cp\u003e2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.188%;\"\u003e\n \u003cp\u003e500199\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.359%;\"\u003e\n \u003cp\u003ePGC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1624%;\"\u003e\n \u003cp\u003eEuropean\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.4188%;\"\u003e\n \u003cp\u003eBipolar disorder\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.4188%;\"\u003e\n \u003cp\u003eieu-b-5110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.453%;\"\u003e\n \u003cp\u003e2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.188%;\"\u003e\n \u003cp\u003e413466\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.359%;\"\u003e\n \u003cp\u003ePGC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1624%;\"\u003e\n \u003cp\u003eEuropean\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.4188%;\"\u003e\n \u003cp\u003eSchizophrenia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.4188%;\"\u003e\n \u003cp\u003eieu-b-5102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.453%;\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.188%;\"\u003e\n \u003cp\u003e127906\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.359%;\"\u003e\n \u003cp\u003ePGC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1624%;\"\u003e\n \u003cp\u003eEuropean\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.4188%;\"\u003e\n \u003cp\u003eADHD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.4188%;\"\u003e\n \u003cp\u003eieu-a-1183\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.453%;\"\u003e\n \u003cp\u003e2017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.188%;\"\u003e\n \u003cp\u003e55374\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.359%;\"\u003e\n \u003cp\u003ePGC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1624%;\"\u003e\n \u003cp\u003eEuropean\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.4188%;\"\u003e\n \u003cp\u003eASD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.4188%;\"\u003e\n \u003cp\u003eieu-a-1185\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.453%;\"\u003e\n \u003cp\u003e2017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.188%;\"\u003e\n \u003cp\u003e46351\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.359%;\"\u003e\n \u003cp\u003ePGC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1624%;\"\u003e\n \u003cp\u003eEuropean\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.4188%;\"\u003e\n \u003cp\u003eOCD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.4188%;\"\u003e\n \u003cp\u003eieu-a-1189\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.453%;\"\u003e\n \u003cp\u003e2017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.188%;\"\u003e\n \u003cp\u003e33925\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.359%;\"\u003e\n \u003cp\u003ePGC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1624%;\"\u003e\n \u003cp\u003eEuropean\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.4188%;\"\u003e\n \u003cp\u003eAN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.4188%;\"\u003e\n \u003cp\u003eieu-a-1186\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.453%;\"\u003e\n \u003cp\u003e2017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.188%;\"\u003e\n \u003cp\u003e14477\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.359%;\"\u003e\n \u003cp\u003ePGC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1624%;\"\u003e\n \u003cp\u003eEuropean\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.4188%;\"\u003e\n \u003cp\u003eOsteoporosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.4188%;\"\u003e\n \u003cp\u003eebi-a-GCST90038656\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.453%;\"\u003e\n \u003cp\u003e2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.188%;\"\u003e\n \u003cp\u003e484598\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.359%;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1624%;\"\u003e\n \u003cp\u003eEuropean\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.4188%;\"\u003e\n \u003cp\u003eFracture\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.4188%;\"\u003e\n \u003cp\u003eebi-a-GCST90013913\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.453%;\"\u003e\n \u003cp\u003e2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.188%;\"\u003e\n \u003cp\u003e407746\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.359%;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1624%;\"\u003e\n \u003cp\u003eEuropean\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.4188%;\"\u003e\n \u003cp\u003eOsteoarthritis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.4188%;\"\u003e\n \u003cp\u003eebi-a-GCST90038686\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.453%;\"\u003e\n \u003cp\u003e2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.188%;\"\u003e\n \u003cp\u003e484598\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.359%;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1624%;\"\u003e\n \u003cp\u003eEuropean\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.4188%;\"\u003e\n \u003cp\u003eOsteomyelitis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.4188%;\"\u003e\n \u003cp\u003eieu-b-4975\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.453%;\"\u003e\n \u003cp\u003e2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.188%;\"\u003e\n \u003cp\u003e486484\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.359%;\"\u003e\n \u003cp\u003eUK Biobank\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1624%;\"\u003e\n \u003cp\u003eEuropean\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003ePsychiatric exposures (PGC) and orthopaedic outcomes (IEU Open GWAS), with accession IDs, phenotype definitions, ancestry, cases/controls/total, and release/version. All datasets are European ancestry.\u003c/p\u003e\n\u003cp\u003eTable 2 Primary MR estimates from psychiatric disorders to orthopaedic outcomes.\u0026nbsp;\u003c/p\u003e\n\u003cdiv align=\"Left\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"548\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22.4453%;\"\u003e\n \u003cp\u003eExposure-Outcome\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6204%;\"\u003e\n \u003cp\u003eMethod\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.4015%;\"\u003e\n \u003cp\u003e\u0026beta;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.4015%;\"\u003e\n \u003cp\u003eSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.94161%;\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.0584%;\"\u003e\n \u003cp\u003e95%CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.1314%;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22.4453%;\"\u003e\n \u003cp\u003eMDD-Fracture\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6204%;\"\u003e\n \u003cp\u003eMR Egger\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.4015%;\"\u003e\n \u003cp\u003e0.045\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.4015%;\"\u003e\n \u003cp\u003e0.134\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.94161%;\"\u003e\n \u003cp\u003e1.046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.0584%;\"\u003e\n \u003cp\u003e0.804-1.360\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.1314%;\"\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22.4453%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6204%;\"\u003e\n \u003cp\u003eWeighted median\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.4015%;\"\u003e\n \u003cp\u003e0.052\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.4015%;\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.94161%;\"\u003e\n \u003cp\u003e1.054\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.0584%;\"\u003e\n \u003cp\u003e0.976-1.138\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.1314%;\"\u003e\n \u003cp\u003e0.195\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22.4453%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6204%;\"\u003e\n \u003cp\u003eIVW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.4015%;\"\u003e\n \u003cp\u003e0.088\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.4015%;\"\u003e\n \u003cp\u003e0.033\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.94161%;\"\u003e\n \u003cp\u003e1.092\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.0584%;\"\u003e\n \u003cp\u003e1.024-1.165\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.1314%;\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22.4453%;\"\u003e\n \u003cp\u003eMDD-Osteoporosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6204%;\"\u003e\n \u003cp\u003eMR Egger\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.4015%;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.4015%;\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.94161%;\"\u003e\n \u003cp\u003e1.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.0584%;\"\u003e\n \u003cp\u003e0.993-1.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.1314%;\"\u003e\n \u003cp\u003e0.886\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22.4453%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6204%;\"\u003e\n \u003cp\u003eWeighted median\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.4015%;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.4015%;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.94161%;\"\u003e\n \u003cp\u003e1.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.0584%;\"\u003e\n \u003cp\u003e1.000-1.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.1314%;\"\u003e\n \u003cp\u003e0.039\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22.4453%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6204%;\"\u003e\n \u003cp\u003eIVW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.4015%;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.4015%;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.94161%;\"\u003e\n \u003cp\u003e1.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.0584%;\"\u003e\n \u003cp\u003e1.000-1.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.1314%;\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22.4453%;\"\u003e\n \u003cp\u003eMDD-Osteoarthritis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6204%;\"\u003e\n \u003cp\u003eMR Egger\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.4015%;\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.4015%;\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.94161%;\"\u003e\n \u003cp\u003e1.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.0584%;\"\u003e\n \u003cp\u003e0.991-1.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.1314%;\"\u003e\n \u003cp\u003e0.440\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22.4453%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6204%;\"\u003e\n \u003cp\u003eWeighted median\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.4015%;\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.4015%;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.94161%;\"\u003e\n \u003cp\u003e1.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.0584%;\"\u003e\n \u003cp\u003e1.004-1.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.1314%;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22.4453%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6204%;\"\u003e\n \u003cp\u003eIVW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.4015%;\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.4015%;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.94161%;\"\u003e\n \u003cp\u003e1.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.0584%;\"\u003e\n \u003cp\u003e1.006-1.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.1314%;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22.4453%;\"\u003e\n \u003cp\u003eMDD-steomyelitis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6204%;\"\u003e\n \u003cp\u003eMR Egger\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.4015%;\"\u003e\n \u003cp\u003e0.097\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.4015%;\"\u003e\n \u003cp\u003e0.304\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.94161%;\"\u003e\n \u003cp\u003e1.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.0584%;\"\u003e\n \u003cp\u003e0.991-1.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.1314%;\"\u003e\n \u003cp\u003e0.750\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22.4453%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6204%;\"\u003e\n \u003cp\u003eWeighted median\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.4015%;\"\u003e\n \u003cp\u003e0.337\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.4015%;\"\u003e\n \u003cp\u003e0.105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.94161%;\"\u003e\n \u003cp\u003e1.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.0584%;\"\u003e\n \u003cp\u003e1.004-1.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.1314%;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22.4453%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6204%;\"\u003e\n \u003cp\u003eIVW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.4015%;\"\u003e\n \u003cp\u003e0.376\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.4015%;\"\u003e\n \u003cp\u003e0.075\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.94161%;\"\u003e\n \u003cp\u003e1.457\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.0584%;\"\u003e\n \u003cp\u003e1.258-1.687\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.1314%;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22.4453%;\"\u003e\n \u003cp\u003eADHD-Fracture\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6204%;\"\u003e\n \u003cp\u003eMR Egger\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.4015%;\"\u003e\n \u003cp\u003e0.119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.4015%;\"\u003e\n \u003cp\u003e0.064\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.94161%;\"\u003e\n \u003cp\u003e0.888\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.0584%;\"\u003e\n \u003cp\u003e0.783-1.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.1314%;\"\u003e\n \u003cp\u003e0.070\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22.4453%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6204%;\"\u003e\n \u003cp\u003eWeighted median\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.4015%;\"\u003e\n \u003cp\u003e0.031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.4015%;\"\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.94161%;\"\u003e\n \u003cp\u003e1.032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.0584%;\"\u003e\n \u003cp\u003e0.988-1.077\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.1314%;\"\u003e\n \u003cp\u003e0.161\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22.4453%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6204%;\"\u003e\n \u003cp\u003eIVW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.4015%;\"\u003e\n \u003cp\u003e0.036\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.4015%;\"\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.94161%;\"\u003e\n \u003cp\u003e1.036\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.0584%;\"\u003e\n \u003cp\u003e1.004-1.070\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.1314%;\"\u003e\n \u003cp\u003e0.028\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22.4453%;\"\u003e\n \u003cp\u003eASD-Fracture\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6204%;\"\u003e\n \u003cp\u003eMR Egger\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.4015%;\"\u003e\n \u003cp\u003e0.067\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.4015%;\"\u003e\n \u003cp\u003e0.055\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.94161%;\"\u003e\n \u003cp\u003e1.070\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.0584%;\"\u003e\n \u003cp\u003e0.959-1.192\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.1314%;\"\u003e\n \u003cp\u003e0.234\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22.4453%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6204%;\"\u003e\n \u003cp\u003eWeighted median\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.4015%;\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.4015%;\"\u003e\n \u003cp\u003e0.027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.94161%;\"\u003e\n \u003cp\u003e1.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.0584%;\"\u003e\n \u003cp\u003e0.961-1.065\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.1314%;\"\u003e\n \u003cp\u003e0.665\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22.4453%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6204%;\"\u003e\n \u003cp\u003eIVW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.4015%;\"\u003e\n \u003cp\u003e0.041\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.4015%;\"\u003e\n \u003cp\u003e0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.94161%;\"\u003e\n \u003cp\u003e1.042\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.0584%;\"\u003e\n \u003cp\u003e1.002-1.082\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.1314%;\"\u003e\n \u003cp\u003e0.039\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22.4453%;\"\u003e\n \u003cp\u003eAN-Fracture\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6204%;\"\u003e\n \u003cp\u003eMR Egger\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.4015%;\"\u003e\n \u003cp\u003e0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.4015%;\"\u003e\n \u003cp\u003e0.046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.94161%;\"\u003e\n \u003cp\u003e0.983\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.0584%;\"\u003e\n \u003cp\u003e0.983-1.076\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.1314%;\"\u003e\n \u003cp\u003e0.717\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22.4453%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6204%;\"\u003e\n \u003cp\u003eWeighted median\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.4015%;\"\u003e\n \u003cp\u003e0.038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.4015%;\"\u003e\n \u003cp\u003e0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.94161%;\"\u003e\n \u003cp\u003e0.963\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.0584%;\"\u003e\n \u003cp\u003e0.925-1.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.1314%;\"\u003e\n \u003cp\u003e0.062\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22.4453%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6204%;\"\u003e\n \u003cp\u003eIVW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.4015%;\"\u003e\n \u003cp\u003e0.041\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.4015%;\"\u003e\n \u003cp\u003e0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.94161%;\"\u003e\n \u003cp\u003e0.960\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.0584%;\"\u003e\n \u003cp\u003e0.928-0.993\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.1314%;\"\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22.4453%;\"\u003e\n \u003cp\u003eFracture-MDD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6204%;\"\u003e\n \u003cp\u003eMR Egger\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.4015%;\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.4015%;\"\u003e\n \u003cp\u003e0.065\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.94161%;\"\u003e\n \u003cp\u003e0.989\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.0584%;\"\u003e\n \u003cp\u003e0.870-1.124\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.1314%;\"\u003e\n \u003cp\u003e0.865\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22.4453%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6204%;\"\u003e\n \u003cp\u003eWeighted median\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.4015%;\"\u003e\n \u003cp\u003e0.048\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.4015%;\"\u003e\n \u003cp\u003e0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.94161%;\"\u003e\n \u003cp\u003e1.050\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.0584%;\"\u003e\n \u003cp\u003e0.997-1.104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.1314%;\"\u003e\n \u003cp\u003e0.063\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22.4453%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6204%;\"\u003e\n \u003cp\u003eIVW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.4015%;\"\u003e\n \u003cp\u003e0.064\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.4015%;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.94161%;\"\u003e\n \u003cp\u003e1.066\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.0584%;\"\u003e\n \u003cp\u003e1.024-1.110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.1314%;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eEffect sizes are reported as OR (95% CI), P, and FDR-adjusted P (P_adj) for IVW, with WM and MR‐Egger as complementary estimators. Results remaining significant after FDR are in bold.\u003c/p\u003e\n\u003cp\u003eTable 3\u0026nbsp; Reverse-direction MR estimates from orthopaedic outcomes to psychiatric disorders.\u003c/p\u003e\n\u003cdiv align=\"Left\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"627\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 65px;\"\u003e\n \u003cp\u003eExposure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 89px;\"\u003e\n \u003cp\u003eOutcome\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 226px;\"\u003e\n \u003cp\u003eHeterogeneity test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 248px;\"\u003e\n \u003cp\u003eHorizontal pleiotropy test\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003eMethod\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003eQ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003eQ_df\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003eMethod\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003eIntercept\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003eSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 65px;\"\u003e\n \u003cp\u003eMDD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 89px;\"\u003e\n \u003cp\u003eFracture\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003eMR Egger\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e252\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e154\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003eMR Egger\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e0.738\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003eIVW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e252\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e155\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 65px;\"\u003e\n \u003cp\u003eMDD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 89px;\"\u003e\n \u003cp\u003eOsteoporosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003eMR Egger\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e244\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e174\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003eMR Egger\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e0.635\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003eIVW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e245\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e175\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 65px;\"\u003e\n \u003cp\u003eMDD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 89px;\"\u003e\n \u003cp\u003eOsteoarthritis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003eMR Egger\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e193\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e174\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.148\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003eMR Egger\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e0.61\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003eIVW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e194\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e175\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.157\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 65px;\"\u003e\n \u003cp\u003eMDD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 89px;\"\u003e\n \u003cp\u003eOsteomyelitis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003eMR Egger\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e174\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.083\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003eMR Egger\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e0.344\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003eIVW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e202\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e175\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.083\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 65px;\"\u003e\n \u003cp\u003eADHD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 89px;\"\u003e\n \u003cp\u003eFracture\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003eMR Egger\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e49.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.384\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003eMR Egger\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003eIVW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e55.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.207\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 65px;\"\u003e\n \u003cp\u003eASD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 89px;\"\u003e\n \u003cp\u003eFracture\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003eMR Egger\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.215\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003eMR Egger\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e-0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e0.611\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003eIVW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e38.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.241\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 65px;\"\u003e\n \u003cp\u003eAN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 89px;\"\u003e\n \u003cp\u003eFracture\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003eMR Egger\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.067\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003eMR Egger\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e-0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e0.583\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003eIVW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e20.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.083\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 65px;\"\u003e\n \u003cp\u003eFracture\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 89px;\"\u003e\n \u003cp\u003eMDD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003eMR Egger\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003eMR Egger\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e0.232\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003eIVW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e73.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eEffect sizes are OR (95% CI), P, and P_adj for IVW; WM and MR‐Egger provided for robustness. No associations survived FDR.\u003c/p\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, psychiatric disorders, orthopaedic diseases, fractures, osteoarthritis, osteomyelitis, GWAS","lastPublishedDoi":"10.21203/rs.3.rs-7687082/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7687082/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground \u003c/strong\u003ePsychiatric disorders and orthopaedic diseases frequently co-occur, but whether these associations are causal remains uncertain. We evaluated the bidirectional causal relationships between seven psychiatric disorders-major depressive disorder (MDD), bipolar disorder, schizophrenia, attention-deficit/hyperactivity disorder (ADHD), autism spectrum disorder (ASD), obsessive–compulsive disorder (OCD), and anorexia nervosa (AN)-and four orthopaedic outcomes: osteoporosis, fractures, osteoarthritis, and osteomyelitis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods \u003c/strong\u003eWe performed a two-sample Mendelian randomization (MR) using summary-level genome-wide association study (GWAS) data; psychiatric traits were sourced from the Psychiatric Genomics Consortium and orthopaedic outcomes from the IEU Open GWAS resource. Causal effects were estimated primarily with inverse-variance weighted (IVW) models, complemented by MR-Egger and weighted median analyses. Standard sensitivity analyses were conducted to probe heterogeneity, pleiotropy, and robustness.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults \u003c/strong\u003eGenetically proxied MDD was associated with higher risks of osteoarthritis (IVW OR = 1.010, 95% CI 1.006-1.014, P \u0026lt; 0.001) and osteomyelitis (OR = 1.457, 95% CI 1.258–1.687, P = 0.001). ASD showed a positive association with fractures (OR = 1.042, 95% CI 1.002-1.082, P = 0.039), whereas AN was inversely associated with fractures (OR = 0.960, 95% CI 0.928-0.993, P = 0.018). Reverse MR analyses did not identify significant causal effects of orthopaedic diseases on psychiatric disorders. Sensitivity analyses did not materially change these estimates.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions \u003c/strong\u003eThese findings support a causal link between genetic liability to MDD and increased risks of osteoarthritis and osteomyelitis, and suggest potential associations between ASD/AN and fracture risk. Given multiple comparisons and possible residual biases, signals should be interpreted cautiously and validated in larger, multi-ancestry cohorts.\u003c/p\u003e","manuscriptTitle":"Bidirectional causal links between psychiatric disorders and orthopaedic diseases: a two-sample Mendelian randomization study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-10 17:22:53","doi":"10.21203/rs.3.rs-7687082/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":"bc8438a9-45b0-4eee-9f34-b4c72948ad3a","owner":[],"postedDate":"October 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-24T07:53:49+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-10 17:22:53","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7687082","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7687082","identity":"rs-7687082","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}