Childhood maltreatment and anxiety, depression and self-harm behaviors : A Two-Sample Mendelian Randomization Study

Childhood maltreatment and anxiety, depression and self-harm behaviors : 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 Childhood maltreatment and anxiety, depression and self-harm behaviors : A Two-Sample Mendelian Randomization Study Zheng Zhang, Xinglian Wang, Haitang Qiu, Yating Wang, Jiazheng Li, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3909957/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 Objective Observational studies have shown associations between childhood maltreatment (CM) and increased risks of Major Depressive Disorder (MDD), Anxiety (ANX), and self-harm and suicidal behaviors. We conducted a Mendelian Randomization study to evaluate the causal effects of these associations. Methods We gathered genetic data from publicly available Genome-Wide Association Studies (GWAS) on childhood maltreatment, MDD, ANX, age of onset for depression, number of depressive episodes, and self-harm and suicidal behaviors. To assess the causal impact of childhood maltreatment on the incidence and symptoms of ANX and MDD, we conducted comprehensive MR analyses and sensitivity analyses using methods such as Inverse Variance Weighted (IVW), MR Egger, Weighted Median (WM), and MR-PRESSO models. The findings were reported as Odds Ratios (ORs) with Confidence Intervals (CIs). Results There was a significant association between childhood maltreatment and the risk of developing MDD (IVW: OR = 2.28, 95% CI = 1.66–3.14, P < 0.001) and ANX (IVW: OR = 1.01, 95% CI = 1-1.02, P = 0.027). Additionally, childhood maltreatment was likely to increase the self-harming behaviors (IVW: OR = 1.06, 95% CI = 1.04–1.08, P < 0.001) and the frequency of depressive episodes (IVW: β = 0.31, 95% CI = 0.17–0.46, P < 0.001), and was associated with an earlier age of onset for depression (IVW: β=-0.17, 95% CI=-0.32 to -0.02, P = 0.025). Conclusion Childhood maltreatment is a potential risk factor for MDD, ANX, and self-harming behaviors. It is associated with the frequency of depressive episodes and an earlier age of onset for depression. Childhood Maltreatment Major Depressive Disorder Anxiety Disorders Self-Harm Mendelian Randomization Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Childhood maltreatment (CM) typically refers to actions or negligence that cause actual or potential harm to children under 18, potentially affecting their health, survival, development, or dignity. Forms of maltreatment include, but are not limited to, physical, sexual, or emotional maltreatment and physical or emotional neglect[ 1 ]. The adverse impacts of CM on an individual's physical and mental health have been recognized as a significant public health issue. According to the World Health Organization, one in four adults has experienced physical maltreatment in childhood, and one in five women and one in thirteen men have been sexually maltreatment during childhood[ 2 ]. Maltreatment leads not only to short-term physical or psychological harm to children but also to severe trauma that may last a lifetime, drawing increasing scholarly attention in recent years[ 3 ]. An increasing body of research indicates that child maltreatment leads to a spectrum of internalizing problems, such as depression and anxiety[ 4 ], post-traumatic stress disorder[ 5 ], and schizophrenia[ 6 ]. Child maltreatment may also precipitate externalizing behavioral problems, including self-harm[ 7 ], suicide[ 8 ], violence[ 9 ], alcohol and substance maltreatment[ 10 ], risky sexual behavior[ 11 ] and teen pregnancies[ 12 ]. Studies have shown that CM can increase the risk of a variety of somatic[ 13 – 17 ] and mental disorders by affecting the development of the neurological, endocrine, and immune systems[ 13 ], such as depression (primarily characterized by low mood) and anxiety (primarily characterized by panic) [ 18 , 19 ]. The connection between CM and the development of depression and anxiety has been extensively explored in various studies. For instance, Wright et al. investigated the direct impact of CM on depression and anxiety through an attachment and cognitive lens[ 20 ], while Huh et al. delved into the underlying mechanisms linking CM with depression and anxiety, focusing on emotion regulation[ 21 ]. Due to ethical and practical constraints, research on risk factors for mental disorders often relies on observational methods. However, these observational studies are frequently limited by methodological issues such as potential confounding by genetic and environmental factors and reporting biases. Incorporating genetic information into samples can enhance the causal inference capabilities of observational studies[ 22 ]. Moreover, Mendelian Randomization (MR) analysis, which uses genetic variations as instrumental variables to circumvent confounders or reverse causality, is widely employed in investigating public health risk factors [ 23 ]. Despite the existence of research exploring the causal effects of CM on various mental disorders, there is a noticeable absence of studies employing Mendelian Randomization (MR) methods to examine the causal links between CM and the risks of depression, anxiety, as well as self-harm and suicidal behaviors. This study was designed and conducted using MR analysis to address this gap. Materials and Methods Research Design Our research synthesizes multiple data sources, encompassing meta-analysis data from published Genome-Wide Association Studies (GWAS), datasets from the UK Biobank (UKB), insights from the Neale lab, and summary data furnished by the Psychiatric Genomics Consortium (PGC). Within the framework of a two-sample Mendelian Randomization (MR) study, Single Nucleotide Polymorphisms (SNPs) act as proxies for phenotypic and genetic Instrumental Variables (IVs). The chosen SNPs must meet three critical MR assumptions: first, they must exhibit a strong association with childhood maltreatment (Assumption 1); second, these SNPs should be independent of potential confounders that could influence the study outcomes (Assumption 2); and finally, there should be no direct causal pathway linking the instrumental variables and the study outcomes (Assumption 3) [ 23 ]. The specific process is illustrated in Fig. 1 . Solid arrow lines indicate MR analysis processes and can only influence the outcome by exposure. Dashed arrows indicate instrumental variables independent of any confounding variables. IVW: inverse-variance weighted; LD: linkage disequilibrium; SNP: single-nucleotide polymorphism. Data Sources In this study, genetic variations were utilized as instrumental variables (IVs) for CM in a Mendelian randomization (MR) analysis to assess their causal effects on outcomes such as depression, anxiety, self-harm, and suicidal behaviors. The IVs employed were derived from the GWAS of CM based on the UK Biobank (UKB), involving 185,414 participants[ 24 ] and anxiety disorders (4,611 cases and 332,548 controls). These participants completed the Childhood Trauma Screener (CTS), a retrospective questionnaire comprising five items that cover various subtypes of CM. Childhood abuse was defined as any item score > 0, according to the CTS[ 25 ]. Depression data was procured from the Psychiatric Genomics Consortium (PGC), encompassing 59,851 cases and 113,154 controls. Complementary data pertinent to self-harm, suicidal tendencies, onset age, and the count of depressive episodes were acquired from the Neale Lab and the MRC Integrative Epidemiology Unit (MRC-IEU). All participants satisfied the DSM-III or DSM-IV and the ICD-9 or ICD-10 diagnostic criteria for mental disorders. The study was confined to GWAS data from individuals of European descent who had undergone an ethical review and provided informed consent. Table 1 presents a detailed account of the GWAS datasets. Table 1 Summary information of the GWAS database in the two-sample MR study Phenotype Data source Sample size nSNP Population PMID/GWAS ID Childhood Maltreatment UKB 185,414 16,754,619 European 33740410 Major Depressive Disorder PGC 173,005 13,554,550 European 29700475 Anxiety Neale Lab 337,159 10,894,596 European ukb-a-82 Ever self-harmed Neale Lab 117,733 12,075,154 European ukb-d-20480 Ever attempted suicide Neale Lab 4,933 10,941,854 European ukb-d-20483 Age at first episode of depression Neale Lab 190,643 10,894,596 European ukb-d-20433_irnt Number of depression episodes MRC-IEU 58,290 9,851,867 European ukb-b-1464 UKB: UK Biobank; MRC IEU: The MRC Integrative Epidemiology Unit; PGC: Psychiatric Genomics Consortium; SNP: single nucleotide polymorphism; NA: Not applicable. Selection of instrumental variables The single nucleotide polymorphisms (SNPs) used as instrumental variables for CM met the genome-wide significance threshold ( P < 5×10 − 8 ) to satisfy Assumption 1. To obtain independent SNPs, linkage disequilibrium pruning was conducted (LD r 2 10,000)[ 26 ]. To further evaluate the strength of the instrumental variables, the F-statistic for each SNP was calculated, and those with F < 10, considered weak instrumental variables, were excluded[ 27 ]; the F-statistic was determined using the formula: F =[( N - k -1)/ k ]×[ R 2 /(1- R 2 )][ 28 ], where R 2 was computed as follows: R 2 = 2×(1–MAF)×MAF×( β / SD ) 2 [ 29 , 30 ], In these formulas, N represents the sample size of the selected dataset, k is the total number of SNPs chosen for MR analysis, β is the effect estimate of the SNP on the measured variable, SD is the standard deviation of β , and MAF is the minor allele frequency. Exclusion of Confounding and Palindromic SNPs To adhere to the second assumption of Mendelian randomization, each SNP and its associated phenotypes were assessed using the Phenoscanner V2 database ( http://www.phenoscanner.medschl.cam.ac.uk/ ), and SNPs associated with traits related to Major Depressive Disorder (MDD) and Bipolar Disorder (BD) were excluded at an r 2 threshold greater than 0.80[ 31 , 32 ]. To harmonize the data for exposure and outcome, all palindromic SNPs with intermediate allele frequencies were removed from the selected SNPs[ 33 ]. Palindromic SNPs have A/T or G/C alleles, and intermediate allele frequencies range between 0.01 and 0.30[ 34 ]. Effect Estimation and Sensitivity Analysis Based on the list of SNPs determined by the established screening criteria, we employed the inverse-variance weighted (IVW), MR-Egger regression, and weighted median (WM) methods to conduct a comprehensive Mendelian Randomization (MR) analysis to assess the causal relationship between CM and the incidence and symptoms of anxiety and depression[ 35 ]. Given the potential pleiotropy of instrumental variables that might bias the results, we validated the robustness of the findings by comparing the effect estimates from these three MR methods. The IVW method assumes all SNPs are valid instrumental variables and combines the Wald ratios of each SNP for meta-analysis[ 36 ]. Effect sizes are presented as odds ratios (ORs) or regression coefficients ( β ) with their 95% confidence intervals (CIs). To satisfy the third assumption of MR, heterogeneity assessments and sensitivity analyses were conducted to examine the potential impact of instrumental variable heterogeneity and pleiotropy on MR results. We estimated the heterogeneity among SNPs using the statistic and P-value from Cochran's Q test[ 37 ] and assessed the impact of removing different SNPs on the causal effect through leave-one-out sensitivity analysis to ensure the stability of the MR estimates[ 38 ]. Additionally, the MR-Egger intercept test and MR-PRESSO global test were applied to assess pleiotropy and outliers, with MR-PRESSO also providing revised estimates after outlier removal[ 39 ]. This study conducted all statistical analyses using the R statistical software (version 4.1.0, R Foundation for Statistical Computing, Vienna, Austria). The analysis utilized several packages, including ‘dev tools,’ ‘TwoSampleMR,’ ‘LDlinkR,’ and ‘MR-PRESSO.’ All statistical tests were two-sided. The results of the Mendelian Randomization (MR) and sensitivity analyses were deemed statistically significant if the P-value was less than 0.05. Results Causal Effect Estimates from MR Analysis Following the defined screening criteria ( P < 5×10 − 8 , r 2 10) and exclusion of potential confounders related to anxiety and depression, a total of 20 SNPs were included as instrumental variables for CM. After harmonizing the datasets for Major Depressive Disorder (MDD), anxiety, self-harm, suicidal behavior, age at onset of depression, and frequency of depressive episodes in the same direction and excluding palindromic SNPs, six sets of instrumental variables were ultimately identified. The results, as shown in Fig. 2 , indicated a potential causal link between CM and the risk of MDD (OR = 2.28, 95% CI = 1.65–3.14, P < 0.001), anxiety (IVW: OR = 1.01, 95% CI = 1-1.02, P = 0.027), frequency of depressive episodes (IVW: β = 0.31, 95% CI = 0.17–0.46, P < 0.001), and self-harming behavior (OR = 1.06, 95% CI = 1.03–1.08, P < 0.001), suggesting that CM is a significant risk factor for the onset of anxiety, depression, and self-harm. Furthermore, as illustrated in Fig. 3 , MR analysis also demonstrated a correlation between CM and the age of first onset of depression (IVW: β = -0.17, 95% CI = -0.32 to -0.02, P = 0.025), indicating that higher scores of CM are associated with an earlier onset of depression. However, our study did not find a potential association between CM and suicidal behavior (IVW: OR = 1.09, 95% CI = 0.81–1.45, P = 0.573). Figure 4 presents scatter plots drawn using five different MR methods. OR: odds ratio; CI: confidence interval; IVW: inverse variance weighting; WM: weighted median; MR Egger: MR Egger regression; nSNP: number of single-nucleotide polymorphism. The horizontal x-axes indicate the genetic instruments linked to the exposure data, while the vertical y-axes represent the genetic instruments associated with the outcome data. The IVs employed in the MR analysis are indicated by black dots. Light blue: inverse-variance weighted; green: weighted-median estimator; deep blue: MR-Egger. As the inverse-variance weighted and weighted-median estimator methods produced highly similar estimates in the analysis, these figures exhibit a visual overlap. Heterogeneity and Pleiotropy In our Mendelian Randomization (MR) analysis, the MR-Egger regression did not indicate any significant horizontal pleiotropy for any outcomes studied. The specific results were as follows: for Major Depressive Disorder (MDD), the Egger intercept was 0.04 with a P-value of 0.188; for anxiety, the Egger intercept was less than − 0.01 with a P-value of 0.74; for self-harm, the Egger intercept was less than − 0.01 with a P-value of 0.368; for suicidal behavior, the Egger intercept was 0.01 with a P-value of 0.522; for the age at onset of depression, the Egger intercept was less than 0.01 with a P-value of 0.848; and for the frequency of depressive episodes, the Egger intercept was less than 0.01 with a P-value of 0.547. The heterogeneity assessed using Cochran's Q test did not reveal any evidence of heterogeneity in our results (P > 0.05). Furthermore, the results from the MR-PRESSO test were consistent with the MR-Egger regression, finding no evidence of pleiotropy or outlier SNPs, suggesting that the IVW results are reliable and unbiased. Regarding the statistical strength and power of the selected SNPs, the calculated F-statistics ranged from 132.45 to 222.17, with all power estimates exceeding 80%, well above the conventional thresholds (F > 10, Power > 80%). The specific results are presented in Table 2 . Table 2 Summary of sensitivity analysis results. Outcome MR-Egger regression Cochran’s Q MR-PRESSO R 2 (%) F Egger intercept P value Q value P value Global test P value MDD 0.04 0.188 16.34 0.09 0.085 0.11 203.45 Anxiety <-0.01 0.74 1.4 0.986 0.99 0.06 132.45 Self-harm <-0.01 0.368 12.14 0.595 0.654 0.12 221.17 Attempted suicide -0.01 0.522 19.95 0.132 0.169 0.12 221.16 Age of depression <-0.01 0.848 23.05 0.442 0.542 0.09 166.87 Number of depression <-0.01 0.547 10.65 0.386 0.482 0.12 221.17 MDD: Major Depressive Disorder; MR-PRESSO: sum of outliers and multiplicity residuals. Discussion This study utilizes Mendelian randomization to scrutinize GWAS data, revealing potential causal links between childhood maltreatment and the emergence of anxiety and depression, self-harm tendencies, as well as the age and frequency of depression onset. Notably, a substantial association with the evolution of depression was detected. Sensitivity analyses were performed to rule out the existence of pleiotropy and heterogeneity, guaranteeing the robustness and impartiality of the results derived from inverse variance weighting (IVW). Meta-analyses further reveal that all forms of maltreatment types (emotional maltreatment and neglect, physical maltreatment and neglect, and sexual maltreatment) are highly correlated with the odds ratios (ORs) for depression and anxiety disorders[ 40 , 41 ]. Specifically, emotional maltreatment shows a significant link to depression, although its impact is relatively minor compared to other forms of maltreatment. Previous studies have found that the association between emotional maltreatment and depressive disorders is significantly more potent than that with sexual or physical maltreatment[ 42 , 43 ]. One theory suggests that emotional maltreatment is often perpetrated by individuals from whom the victim expects love and respect, and the violation of this expectation may lead to more severe emotional trauma than other forms of maltreatment[ 44 ]. Given the relative scarcity of research on emotional maltreatment[ 45 ], further studies are needed to clarify the mechanistic relationships between emotional maltreatment and the onset and symptoms of mood disorders. Another theory posits that negative cognitive and emotion regulation strategies mediate the impact of childhood trauma on the onset of depression and anxiety[ 21 ]. This implies that the utilization of maladaptive cognitive emotion regulation strategies is a significant potential mechanism by which childhood trauma adversely affects the severity of depression/anxiety symptoms in adulthood. These findings align with several previous studies suggesting that emotion regulation acts as a mediator for the adverse effects of childhood trauma[ 46 ], and childhood trauma can result in emotion dysregulation later in life[ 47 ]. Furthermore, a study conducted in China indicated that CM is a significant precursor to non-suicidal self-injury, with difficulties in emotion regulation and depression as the primary mediating factors[ 48 ]. In our Mendelian Randomization (MR) analysis concerning the age of onset and the frequency of episodes in first-episode depression related to CM, we conducted MR-PRESSO tests after bidirectional correction. We excluded outlier SNPs to ensure the stability of the Inverse Variance Weighted (IVW) method. The results suggest that CM significantly increases the frequency of depressive episodes but found no causal link between CM and suicidal behavior, which is inconsistent with previous studies[ 49 ] and warrants further investigation. However, it is noteworthy that some research has found that the current emotional state can influence the reporting of abusive behaviors, as evidenced by the median scores for depression and anxiety being higher among participants with a history of maltreatment (including emotional, physical, and sexual maltreatment) compared to those without such a history[ 50 ]. Furthermore, the biological sequelae of CM, such as elevated levels of inflammation[ 51 ], are evident not only in depression and anxiety disorders[ 52 , 53 ] but also in various medical conditions, particularly in autoimmune diseases like arthritis and type 1 diabetes[ 54 ]. Studies have indicated that individuals with a history of maltreatment and neglect who suffer from mood disorders exhibit more pronounced inflammatory responses compared to those with a history of maltreatment and neglect but without mood disorders[ 55 ], potentially increasing the risk of psychological disorders. For instance, one study found that individuals with a history of both CM and depression exhibited significantly higher levels of inflammation compared to those with only depression, only CM, or the control group (with no such experiences)[ 55 ]. Therefore, the biological consequences of CM in patients with anxiety and depression merit further investigation to elucidate their higher medical burden better. Advantages and Limitations The strength of this study lies in its use of a large sample size from GWAS summary data sets, which significantly reduces confounding factors and reverse causation biases compared to observational studies, thereby enhancing the stability of the causal effect estimates. However, there are several limitations to this study. Our research subjects were of European descent, and whether the same conclusions can be drawn for other ethnic groups remains to be further investigated. Declarations Data availability statement: The data used for analysis were obtained from published studies and public databases (ieu open gwas: https://gwas.mrcieu.ac.uk/datasets/; PGC: https://pgc.unc.edu/for-researchers/download-results/). All data generated during this study are included in this article and supplementary material. Funding Statement This work received support from the Science-Health Joint Medical Scientific Research Project of Chongqing [2020GDRC026]. The study funders/sponsors played no role in the design and execution of the study; the collection, management, analysis, and interpretation of the data; the preparation, review, or approval of the manuscript; and the decision to submit the manuscript for publication. Authors’ contributions Zheng Zhang and Yuanzhi Ju led the study's design, data collection, and manuscript development. Qinghua Luo and Haitang Qiu supervised the initial analysis and contributed to manuscript drafting and revision. Xinglian Wang, Jiazheng Li and Yating Wang provided critical intellectual revisions and granted final approval for publication. All authors have approved the final manuscript and provided their consent for the public dissemination of the study. Declaration of competing interest The authors declare that they have no competing interests. Data availability statement The data used for analysis were obtained from published studies and public databases (ieu open gwas, https://gwas.mrcieu.ac.uk/datasets/ PGC: https://pgc.unc.edu/for-researchers/download-results/). All data generated during this study are included in this article and supplementary material. And the data that support the findings of this study are available from the corresponding author upon reasonable request. Ethics approval Ethical approval had been obtained in all original studies. We used publicly available summary data from original studies so that no ethical approval is required for this study. Acknowledgments We thank the Psychiatric Genomics Consortium (PGC), Neale Lab and UK Biobank researchers for providing GWAS data. References Child maltreatment. https://www.who.int/news-room/fact-sheets/detail/child-maltreatment . Accessed 28 Jan 2024. Child maltreatment. https://www.who.int/news-room/fact-sheets/detail/child-maltreatment . 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Childhood trauma and adulthood inflammation: a meta-analysis of peripheral C-reactive protein, interleukin-6 and tumour necrosis factor-α. Mol Psychiatry. 2016;21:642–9. Guo B, Zhang M, Hao W, Wang Y, Zhang T, Liu C. Neuroinflammation mechanisms of neuromodulation therapies for anxiety and depression. Transl Psychiatry. 2023;13:5. The role of inflammation. and the gut microbiome in depression and anxiety - PubMed. https://pubmed.ncbi.nlm.nih.gov/31144383/ . Accessed 28 Jan 2024. Couzin-Frankel J. Inflammation bares a dark side. Science. 2010;330:1621. Danese A, Caspi A, Williams B, Ambler A, Sugden K, Mika J, et al. Biological embedding of stress through inflammation processes in childhood. Mol Psychiatry. 2011;16:244–6. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3909957","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":270939168,"identity":"e80f7cee-7494-4109-b1b4-4554780d299d","order_by":0,"name":"Zheng Zhang","email":"","orcid":"","institution":"First Affiliated Hospital of Chongqing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Zheng","middleName":"","lastName":"Zhang","suffix":""},{"id":270939169,"identity":"74ab36f0-b7cb-40c5-bf1c-d6b20c4435d6","order_by":1,"name":"Xinglian Wang","email":"","orcid":"","institution":"First Affiliated Hospital of Chongqing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xinglian","middleName":"","lastName":"Wang","suffix":""},{"id":270939170,"identity":"1f32fafd-94e6-4794-b207-29fa2f69497b","order_by":2,"name":"Haitang Qiu","email":"","orcid":"","institution":"First Affiliated Hospital of Chongqing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Haitang","middleName":"","lastName":"Qiu","suffix":""},{"id":270939171,"identity":"e39af68b-ce25-42d5-bd8a-c7fc78dcda58","order_by":3,"name":"Yating Wang","email":"","orcid":"","institution":"Chongqing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yating","middleName":"","lastName":"Wang","suffix":""},{"id":270939172,"identity":"225fa9ed-ebd6-4f61-ac5d-5c37f8ec0b7e","order_by":4,"name":"Jiazheng Li","email":"","orcid":"","institution":"Chongqing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jiazheng","middleName":"","lastName":"Li","suffix":""},{"id":270939173,"identity":"9aa7bc0e-6db1-490b-bcfa-4ae424ce478a","order_by":5,"name":"Yuanzhi Ju","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1ElEQVRIiWNgGAWjYBACxgYIncDGzHzwQUJFDSla2NmSDR6cOUa8bQkM/Dxmkg9bmAkrZZ6RYybxc0ddHh8zg1lFYgMbA397dwJ+hwG1SPaeYStmY2ZIu5G4Q4ZB4szZDQS1SPC28SS2MTMcu5F4ho3BQCKXsBbJv20SQC2MbQUgjURpkeZtMwAqZmZjIE5Lz7Nia9m2BKBiNmaJhDPHeAj6xbA9eePNt211ifP7z3/8+KOiRo6/vZeAlgYOAxQBHrzKQUCegf0BQUWjYBSMglEwwgEAsDVD8ue4cuwAAAAASUVORK5CYII=","orcid":"","institution":"First Affiliated Hospital of Chongqing Medical University","correspondingAuthor":true,"prefix":"","firstName":"Yuanzhi","middleName":"","lastName":"Ju","suffix":""},{"id":270939174,"identity":"fbb789bf-e3a9-401d-8a77-6bbf6bebfd24","order_by":6,"name":"Qinghua Luo","email":"","orcid":"","institution":"First Affiliated Hospital of Chongqing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Qinghua","middleName":"","lastName":"Luo","suffix":""}],"badges":[],"createdAt":"2024-01-30 08:21:44","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3909957/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3909957/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":50682827,"identity":"982ac0d4-f638-4c44-934c-db1735b56acd","added_by":"auto","created_at":"2024-02-05 17:33:49","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1514703,"visible":true,"origin":"","legend":"\u003cp\u003eSchematic representation of the three hypotheses of the MR study.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-3909957/v1/e443f9d731238fbfac5f14d7.png"},{"id":50682826,"identity":"42bae540-d676-47b9-9dc4-f4bce64704bd","added_by":"auto","created_at":"2024-02-05 17:33:49","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1237129,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot of the causal relationships between child abuse and depression, anxiety, self-harm, and suicidal behaviors based on three MR methods.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-3909957/v1/dfbbecff0f47380892529ed9.png"},{"id":50682829,"identity":"1e8439ad-cc75-4bae-9136-a6932215df12","added_by":"auto","created_at":"2024-02-05 17:33:49","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":923719,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot of the causal relationships between child abuse and the onset age of depression and the number of episodes, based on three MR methods.\u003c/p\u003e\n\u003cp\u003eOR: odds ratio; CI: confidence interval; IVW: inverse variance weighting; WM: weighted median; MR Egger: MR Egger regression; nSNP: number of single-nucleotide polymorphism.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-3909957/v1/f68c98cc0e7086936443feed.png"},{"id":50683362,"identity":"d74f11ef-0c14-46e3-91da-143385b058a7","added_by":"auto","created_at":"2024-02-05 17:41:49","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":2393313,"visible":true,"origin":"","legend":"\u003cp\u003eScatter Plot of MR on the Causal Effects of Childhood Maltreatment on Depression, Anxiety Disorder, Self-Injury, Suicidal Behavior, and the Age and Frequency of the First Depressive Episode.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-3909957/v1/8da71ab7dde4a3065baccc76.png"},{"id":58800370,"identity":"6381561a-8771-47ec-8af9-80b352f8b4bb","added_by":"auto","created_at":"2024-06-21 09:30:00","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":7906816,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3909957/v1/572f39fa-f20d-4749-a9f5-da9730178d9c.pdf"},{"id":50682830,"identity":"1927252e-7922-4d04-98de-f366e277d227","added_by":"auto","created_at":"2024-02-05 17:33:49","extension":"docx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":1088102,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-3909957/v1/1bfefaf1b4c69bb5f1578d43.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Childhood maltreatment and anxiety, depression and self-harm behaviors : A Two-Sample Mendelian Randomization Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eChildhood maltreatment (CM) typically refers to actions or negligence that cause actual or potential harm to children under 18, potentially affecting their health, survival, development, or dignity. Forms of maltreatment include, but are not limited to, physical, sexual, or emotional maltreatment and physical or emotional neglect[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The adverse impacts of CM on an individual's physical and mental health have been recognized as a significant public health issue. According to the World Health Organization, one in four adults has experienced physical maltreatment in childhood, and one in five women and one in thirteen men have been sexually maltreatment during childhood[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Maltreatment leads not only to short-term physical or psychological harm to children but also to severe trauma that may last a lifetime, drawing increasing scholarly attention in recent years[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. An increasing body of research indicates that child maltreatment leads to a spectrum of internalizing problems, such as depression and anxiety[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], post-traumatic stress disorder[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], and schizophrenia[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Child maltreatment may also precipitate externalizing behavioral problems, including self-harm[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], suicide[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], violence[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], alcohol and substance maltreatment[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], risky sexual behavior[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] and teen pregnancies[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Studies have shown that CM can increase the risk of a variety of somatic[\u003cspan additionalcitationids=\"CR14 CR15 CR16\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] and mental disorders by affecting the development of the neurological, endocrine, and immune systems[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], such as depression (primarily characterized by low mood) and anxiety (primarily characterized by panic) [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe connection between CM and the development of depression and anxiety has been extensively explored in various studies. For instance, Wright et al. investigated the direct impact of CM on depression and anxiety through an attachment and cognitive lens[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], while Huh et al. delved into the underlying mechanisms linking CM with depression and anxiety, focusing on emotion regulation[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Due to ethical and practical constraints, research on risk factors for mental disorders often relies on observational methods. However, these observational studies are frequently limited by methodological issues such as potential confounding by genetic and environmental factors and reporting biases. Incorporating genetic information into samples can enhance the causal inference capabilities of observational studies[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Moreover, Mendelian Randomization (MR) analysis, which uses genetic variations as instrumental variables to circumvent confounders or reverse causality, is widely employed in investigating public health risk factors [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Despite the existence of research exploring the causal effects of CM on various mental disorders, there is a noticeable absence of studies employing Mendelian Randomization (MR) methods to examine the causal links between CM and the risks of depression, anxiety, as well as self-harm and suicidal behaviors. This study was designed and conducted using MR analysis to address this gap.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eResearch Design\u003c/h2\u003e \u003cp\u003eOur research synthesizes multiple data sources, encompassing meta-analysis data from published Genome-Wide Association Studies (GWAS), datasets from the UK Biobank (UKB), insights from the Neale lab, and summary data furnished by the Psychiatric Genomics Consortium (PGC). Within the framework of a two-sample Mendelian Randomization (MR) study, Single Nucleotide Polymorphisms (SNPs) act as proxies for phenotypic and genetic Instrumental Variables (IVs). The chosen SNPs must meet three critical MR assumptions: first, they must exhibit a strong association with childhood maltreatment (Assumption 1); second, these SNPs should be independent of potential confounders that could influence the study outcomes (Assumption 2); and finally, there should be no direct causal pathway linking the instrumental variables and the study outcomes (Assumption 3) [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. The specific process is illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSolid arrow lines indicate MR analysis processes and can only influence the outcome by exposure. Dashed arrows indicate instrumental variables independent of any confounding variables. IVW: inverse-variance weighted; LD: linkage disequilibrium; SNP: single-nucleotide polymorphism.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eData Sources\u003c/h2\u003e \u003cp\u003eIn this study, genetic variations were utilized as instrumental variables (IVs) for CM in a Mendelian randomization (MR) analysis to assess their causal effects on outcomes such as depression, anxiety, self-harm, and suicidal behaviors. The IVs employed were derived from the GWAS of CM based on the UK Biobank (UKB), involving 185,414 participants[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] and anxiety disorders (4,611 cases and 332,548 controls). These participants completed the Childhood Trauma Screener (CTS), a retrospective questionnaire comprising five items that cover various subtypes of CM. Childhood abuse was defined as any item score\u0026thinsp;\u0026gt;\u0026thinsp;0, according to the CTS[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Depression data was procured from the Psychiatric Genomics Consortium (PGC), encompassing 59,851 cases and 113,154 controls. Complementary data pertinent to self-harm, suicidal tendencies, onset age, and the count of depressive episodes were acquired from the Neale Lab and the MRC Integrative Epidemiology Unit (MRC-IEU). All participants satisfied the DSM-III or DSM-IV and the ICD-9 or ICD-10 diagnostic criteria for mental disorders. The study was confined to GWAS data from individuals of European descent who had undergone an ethical review and provided informed consent. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents a detailed account of the GWAS datasets.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary information of the GWAS database in the two-sample MR study\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhenotype\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eData source\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSample size\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003enSNP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePopulation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePMID/GWAS ID\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChildhood Maltreatment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUKB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e185,414\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16,754,619\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e33740410\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMajor Depressive Disorder\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePGC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e173,005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13,554,550\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e29700475\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnxiety\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNeale Lab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e337,159\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10,894,596\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eukb-a-82\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEver self-harmed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNeale Lab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e117,733\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12,075,154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eukb-d-20480\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEver attempted suicide\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNeale Lab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4,933\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10,941,854\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eukb-d-20483\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge at first episode of depression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNeale Lab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e190,643\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10,894,596\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eukb-d-20433_irnt\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of depression episodes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMRC-IEU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e58,290\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9,851,867\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eukb-b-1464\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eUKB: UK Biobank; MRC IEU: The MRC Integrative Epidemiology Unit; PGC: Psychiatric Genomics Consortium; SNP: single nucleotide polymorphism; NA: Not applicable.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eSelection of instrumental variables\u003c/h2\u003e \u003cp\u003eThe single nucleotide polymorphisms (SNPs) used as instrumental variables for CM met the genome-wide significance threshold (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;5\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e) to satisfy Assumption 1. To obtain independent SNPs, linkage disequilibrium pruning was conducted (LD \u003cem\u003er\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, kb\u0026thinsp;\u0026gt;\u0026thinsp;10,000)[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. To further evaluate the strength of the instrumental variables, the F-statistic for each SNP was calculated, and those with \u003cem\u003eF\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;10, considered weak instrumental variables, were excluded[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]; the F-statistic was determined using the formula: \u003cem\u003eF\u003c/em\u003e=[(\u003cem\u003eN\u003c/em\u003e-\u003cem\u003ek\u003c/em\u003e-1)/\u003cem\u003ek\u003c/em\u003e]\u0026times;[\u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e/(1-\u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e)][\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], where \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e was computed as follows: \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;2\u0026times;(1\u0026ndash;MAF)\u0026times;MAF\u0026times;(\u003cem\u003eβ\u003c/em\u003e/\u003cem\u003eSD\u003c/em\u003e)\u003csup\u003e2\u003c/sup\u003e [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], In these formulas, \u003cem\u003eN\u003c/em\u003e represents the sample size of the selected dataset, \u003cem\u003ek\u003c/em\u003e is the total number of SNPs chosen for MR analysis, \u003cem\u003eβ\u003c/em\u003e is the effect estimate of the SNP on the measured variable, \u003cem\u003eSD\u003c/em\u003e is the standard deviation of \u003cem\u003eβ\u003c/em\u003e, and MAF is the minor allele frequency.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eExclusion of Confounding and Palindromic SNPs\u003c/h2\u003e \u003cp\u003eTo adhere to the second assumption of Mendelian randomization, each SNP and its associated phenotypes were assessed using the Phenoscanner V2 database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.phenoscanner.medschl.cam.ac.uk/\u003c/span\u003e\u003cspan address=\"http://www.phenoscanner.medschl.cam.ac.uk/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), and SNPs associated with traits related to Major Depressive Disorder (MDD) and Bipolar Disorder (BD) were excluded at an \u003cem\u003er\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e threshold greater than 0.80[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. To harmonize the data for exposure and outcome, all palindromic SNPs with intermediate allele frequencies were removed from the selected SNPs[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Palindromic SNPs have A/T or G/C alleles, and intermediate allele frequencies range between 0.01 and 0.30[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eEffect Estimation and Sensitivity Analysis\u003c/h2\u003e \u003cp\u003eBased on the list of SNPs determined by the established screening criteria, we employed the inverse-variance weighted (IVW), MR-Egger regression, and weighted median (WM) methods to conduct a comprehensive Mendelian Randomization (MR) analysis to assess the causal relationship between CM and the incidence and symptoms of anxiety and depression[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Given the potential pleiotropy of instrumental variables that might bias the results, we validated the robustness of the findings by comparing the effect estimates from these three MR methods. The IVW method assumes all SNPs are valid instrumental variables and combines the Wald ratios of each SNP for meta-analysis[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Effect sizes are presented as odds ratios (ORs) or regression coefficients (\u003cem\u003eβ\u003c/em\u003e) with their 95% confidence intervals (CIs). To satisfy the third assumption of MR, heterogeneity assessments and sensitivity analyses were conducted to examine the potential impact of instrumental variable heterogeneity and pleiotropy on MR results. We estimated the heterogeneity among SNPs using the statistic and P-value from Cochran's Q test[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e] and assessed the impact of removing different SNPs on the causal effect through leave-one-out sensitivity analysis to ensure the stability of the MR estimates[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Additionally, the MR-Egger intercept test and MR-PRESSO global test were applied to assess pleiotropy and outliers, with MR-PRESSO also providing revised estimates after outlier removal[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis study conducted all statistical analyses using the R statistical software (version 4.1.0, R Foundation for Statistical Computing, Vienna, Austria). The analysis utilized several packages, including \u0026lsquo;dev tools,\u0026rsquo; \u0026lsquo;TwoSampleMR,\u0026rsquo; \u0026lsquo;LDlinkR,\u0026rsquo; and \u0026lsquo;MR-PRESSO.\u0026rsquo; All statistical tests were two-sided. The results of the Mendelian Randomization (MR) and sensitivity analyses were deemed statistically significant if the P-value was less than 0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eCausal Effect Estimates from MR Analysis\u003c/h2\u003e \u003cp\u003eFollowing the defined screening criteria (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;5\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e, \u003cem\u003er\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, \u003cem\u003eF\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;10) and exclusion of potential confounders related to anxiety and depression, a total of 20 SNPs were included as instrumental variables for CM. After harmonizing the datasets for Major Depressive Disorder (MDD), anxiety, self-harm, suicidal behavior, age at onset of depression, and frequency of depressive episodes in the same direction and excluding palindromic SNPs, six sets of instrumental variables were ultimately identified. The results, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, indicated a potential causal link between CM and the risk of MDD (OR\u0026thinsp;=\u0026thinsp;2.28, 95% CI\u0026thinsp;=\u0026thinsp;1.65\u0026ndash;3.14, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), anxiety (IVW: OR\u0026thinsp;=\u0026thinsp;1.01, 95% CI\u0026thinsp;=\u0026thinsp;1-1.02, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.027), frequency of depressive episodes (IVW: β\u0026thinsp;=\u0026thinsp;0.31, 95% CI\u0026thinsp;=\u0026thinsp;0.17\u0026ndash;0.46, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and self-harming behavior (OR\u0026thinsp;=\u0026thinsp;1.06, 95% CI\u0026thinsp;=\u0026thinsp;1.03\u0026ndash;1.08, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), suggesting that CM is a significant risk factor for the onset of anxiety, depression, and self-harm. Furthermore, as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, MR analysis also demonstrated a correlation between CM and the age of first onset of depression (IVW: β = -0.17, 95% CI = -0.32 to -0.02, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.025), indicating that higher scores of CM are associated with an earlier onset of depression. However, our study did not find a potential association between CM and suicidal behavior (IVW: OR\u0026thinsp;=\u0026thinsp;1.09, 95% CI\u0026thinsp;=\u0026thinsp;0.81\u0026ndash;1.45, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.573). Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e presents scatter plots drawn using five different MR methods.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eOR: odds ratio; CI: confidence interval; IVW: inverse variance weighting; WM: weighted median; MR Egger: MR Egger regression; nSNP: number of single-nucleotide polymorphism.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe horizontal x-axes indicate the genetic instruments linked to the exposure data, while the vertical y-axes represent the genetic instruments associated with the outcome data. The IVs employed in the MR analysis are indicated by black dots. Light blue: inverse-variance weighted; green: weighted-median estimator; deep blue: MR-Egger. As the inverse-variance weighted and weighted-median estimator methods produced highly similar estimates in the analysis, these figures exhibit a visual overlap.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eHeterogeneity and Pleiotropy\u003c/h2\u003e \u003cp\u003eIn our Mendelian Randomization (MR) analysis, the MR-Egger regression did not indicate any significant horizontal pleiotropy for any outcomes studied. The specific results were as follows: for Major Depressive Disorder (MDD), the Egger intercept was 0.04 with a P-value of 0.188; for anxiety, the Egger intercept was less than \u0026minus;\u0026thinsp;0.01 with a P-value of 0.74; for self-harm, the Egger intercept was less than \u0026minus;\u0026thinsp;0.01 with a P-value of 0.368; for suicidal behavior, the Egger intercept was 0.01 with a P-value of 0.522; for the age at onset of depression, the Egger intercept was less than 0.01 with a P-value of 0.848; and for the frequency of depressive episodes, the Egger intercept was less than 0.01 with a P-value of 0.547. The heterogeneity assessed using Cochran's Q test did not reveal any evidence of heterogeneity in our results (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003eFurthermore, the results from the MR-PRESSO test were consistent with the MR-Egger regression, finding no evidence of pleiotropy or outlier SNPs, suggesting that the IVW results are reliable and unbiased. Regarding the statistical strength and power of the selected SNPs, the calculated F-statistics ranged from 132.45 to 222.17, with all power estimates exceeding 80%, well above the conventional thresholds (F\u0026thinsp;\u0026gt;\u0026thinsp;10, Power\u0026thinsp;\u0026gt;\u0026thinsp;80%). The specific results are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary of sensitivity analysis results.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eOutcome\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eMR-Egger regression\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eCochran\u0026rsquo;s Q\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eMR-PRESSO\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c11\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eF\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEgger intercept\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eQ\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eGlobal test \u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMDD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.188\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e16.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.085\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e203.45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnxiety\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;-0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.986\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e132.45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSelf-harm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;-0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.368\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e12.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.595\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.654\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e221.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAttempted suicide\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.522\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e19.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.169\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e221.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge of depression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;-0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.848\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e23.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.442\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.542\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e166.87\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of depression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;-0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.547\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e10.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.386\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.482\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e221.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eMDD: Major Depressive Disorder; MR-PRESSO: sum of outliers and multiplicity residuals.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study utilizes Mendelian randomization to scrutinize GWAS data, revealing potential causal links between childhood maltreatment and the emergence of anxiety and depression, self-harm tendencies, as well as the age and frequency of depression onset. Notably, a substantial association with the evolution of depression was detected. Sensitivity analyses were performed to rule out the existence of pleiotropy and heterogeneity, guaranteeing the robustness and impartiality of the results derived from inverse variance weighting (IVW).\u003c/p\u003e \u003cp\u003eMeta-analyses further reveal that all forms of maltreatment types (emotional maltreatment and neglect, physical maltreatment and neglect, and sexual maltreatment) are highly correlated with the odds ratios (ORs) for depression and anxiety disorders[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Specifically, emotional maltreatment shows a significant link to depression, although its impact is relatively minor compared to other forms of maltreatment. Previous studies have found that the association between emotional maltreatment and depressive disorders is significantly more potent than that with sexual or physical maltreatment[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. One theory suggests that emotional maltreatment is often perpetrated by individuals from whom the victim expects love and respect, and the violation of this expectation may lead to more severe emotional trauma than other forms of maltreatment[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Given the relative scarcity of research on emotional maltreatment[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e], further studies are needed to clarify the mechanistic relationships between emotional maltreatment and the onset and symptoms of mood disorders. Another theory posits that negative cognitive and emotion regulation strategies mediate the impact of childhood trauma on the onset of depression and anxiety[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. This implies that the utilization of maladaptive cognitive emotion regulation strategies is a significant potential mechanism by which childhood trauma adversely affects the severity of depression/anxiety symptoms in adulthood. These findings align with several previous studies suggesting that emotion regulation acts as a mediator for the adverse effects of childhood trauma[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e], and childhood trauma can result in emotion dysregulation later in life[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Furthermore, a study conducted in China indicated that CM is a significant precursor to non-suicidal self-injury, with difficulties in emotion regulation and depression as the primary mediating factors[\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. In our Mendelian Randomization (MR) analysis concerning the age of onset and the frequency of episodes in first-episode depression related to CM, we conducted MR-PRESSO tests after bidirectional correction. We excluded outlier SNPs to ensure the stability of the Inverse Variance Weighted (IVW) method. The results suggest that CM significantly increases the frequency of depressive episodes but found no causal link between CM and suicidal behavior, which is inconsistent with previous studies[\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e] and warrants further investigation. However, it is noteworthy that some research has found that the current emotional state can influence the reporting of abusive behaviors, as evidenced by the median scores for depression and anxiety being higher among participants with a history of maltreatment (including emotional, physical, and sexual maltreatment) compared to those without such a history[\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFurthermore, the biological sequelae of CM, such as elevated levels of inflammation[\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e], are evident not only in depression and anxiety disorders[\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e] but also in various medical conditions, particularly in autoimmune diseases like arthritis and type 1 diabetes[\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. Studies have indicated that individuals with a history of maltreatment and neglect who suffer from mood disorders exhibit more pronounced inflammatory responses compared to those with a history of maltreatment and neglect but without mood disorders[\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e], potentially increasing the risk of psychological disorders. For instance, one study found that individuals with a history of both CM and depression exhibited significantly higher levels of inflammation compared to those with only depression, only CM, or the control group (with no such experiences)[\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. Therefore, the biological consequences of CM in patients with anxiety and depression merit further investigation to elucidate their higher medical burden better.\u003c/p\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eAdvantages and Limitations\u003c/h2\u003e \u003cp\u003eThe strength of this study lies in its use of a large sample size from GWAS summary data sets, which significantly reduces confounding factors and reverse causation biases compared to observational studies, thereby enhancing the stability of the causal effect estimates. However, there are several limitations to this study. Our research subjects were of European descent, and whether the same conclusions can be drawn for other ethnic groups remains to be further investigated.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability statement:\u0026nbsp;\u003c/strong\u003eThe data used for analysis were obtained from published studies and public databases (ieu open gwas: https://gwas.mrcieu.ac.uk/datasets/; PGC: https://pgc.unc.edu/for-researchers/download-results/). All data generated during this study are included in this article and supplementary material.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work received support from the Science-Health Joint Medical Scientific Research Project of Chongqing [2020GDRC026]. The study funders/sponsors played no role in the design and execution of the study; the collection, management, analysis, and interpretation of the data; the preparation, review, or approval of the manuscript; and the decision to submit the manuscript for publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eZheng Zhang and Yuanzhi Ju led the study\u0026apos;s design, data collection, and manuscript development. Qinghua Luo and Haitang Qiu supervised the initial analysis and contributed to manuscript drafting and revision. Xinglian Wang, Jiazheng Li and Yating Wang provided critical intellectual revisions and granted final approval for publication. All authors have approved the final manuscript and provided their consent for the public dissemination of the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of competing interest\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data used for analysis were obtained from published studies and public databases (ieu open gwas, https://gwas.mrcieu.ac.uk/datasets/ PGC: https://pgc.unc.edu/for-researchers/download-results/). All data generated during this study are included in this article and supplementary material.\u0026nbsp;And the data that support the findings of this study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical approval had been obtained in all original studies. We used publicly available summary data from original studies so that no ethical approval is required for this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank the Psychiatric Genomics Consortium (PGC), Neale Lab and UK Biobank researchers for providing GWAS data.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eChild maltreatment. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.who.int/news-room/fact-sheets/detail/child-maltreatment\u003c/span\u003e\u003cspan address=\"https://www.who.int/news-room/fact-sheets/detail/child-maltreatment\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Accessed 28 Jan 2024.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChild maltreatment. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.who.int/news-room/fact-sheets/detail/child-maltreatment\u003c/span\u003e\u003cspan address=\"https://www.who.int/news-room/fact-sheets/detail/child-maltreatment\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Accessed 26 Jan 2024.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFeitosa SO, Noll M, Mendon\u0026ccedil;a CR, Silveira EA, Esposito Sorpreso IC, Noll PRES. Prevalence of sexual abuse and its association with health-risk behaviors among Brazilian adolescents: A populational study. Child Abuse Negl. 2021;122:105347.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRousson AN, Fleming CB, Herrenkohl TI. Childhood maltreatment and later stressful life events as predictors of depression: A test of the stress sensitization hypothesis. Psychol Violence. 2020;10:493\u0026ndash;500.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSistad RE, Simons RM, Mojallal M, Simons JS. The indirect effect from childhood maltreatment to PTSD symptoms via thought suppression and cognitive reappraisal. Child Abuse Negl. 2021;114:104939.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChaiyachati BH, Gur RE. Effect of child abuse and neglect on schizophrenia and other psychotic disorders. Pharmacol Biochem Behav. 2021;206:173195.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTurner HA, Colburn D. Independent and Cumulative Effects of Recent Maltreatment on Suicidal Ideation and Thoughts of Self-harm in a National Sample of Youth. J Adolesc Health. 2022;70:329\u0026ndash;35.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu J, Fang Y, Gong J, Cui X, Meng T, Xiao B, et al. Associations between suicidal behavior and childhood abuse and neglect: A meta-analysis. 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Mol Psychiatry. 2011;16:244\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Childhood Maltreatment, Major Depressive Disorder, Anxiety Disorders, Self-Harm, Mendelian Randomization","lastPublishedDoi":"10.21203/rs.3.rs-3909957/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3909957/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eObservational studies have shown associations between childhood maltreatment (CM) and increased risks of Major Depressive Disorder (MDD), Anxiety (ANX), and self-harm and suicidal behaviors. We conducted a Mendelian Randomization study to evaluate the causal effects of these associations.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe gathered genetic data from publicly available Genome-Wide Association Studies (GWAS) on childhood maltreatment, MDD, ANX, age of onset for depression, number of depressive episodes, and self-harm and suicidal behaviors. To assess the causal impact of childhood maltreatment on the incidence and symptoms of ANX and MDD, we conducted comprehensive MR analyses and sensitivity analyses using methods such as Inverse Variance Weighted (IVW), MR Egger, Weighted Median (WM), and MR-PRESSO models. The findings were reported as Odds Ratios (ORs) with Confidence Intervals (CIs).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThere was a significant association between childhood maltreatment and the risk of developing MDD (IVW: OR\u0026thinsp;=\u0026thinsp;2.28, 95% CI\u0026thinsp;=\u0026thinsp;1.66\u0026ndash;3.14, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and ANX (IVW: OR\u0026thinsp;=\u0026thinsp;1.01, 95% CI\u0026thinsp;=\u0026thinsp;1-1.02, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.027). Additionally, childhood maltreatment was likely to increase the self-harming behaviors (IVW: OR\u0026thinsp;=\u0026thinsp;1.06, 95% CI\u0026thinsp;=\u0026thinsp;1.04\u0026ndash;1.08, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and the frequency of depressive episodes (IVW: β\u0026thinsp;=\u0026thinsp;0.31, 95% CI\u0026thinsp;=\u0026thinsp;0.17\u0026ndash;0.46, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and was associated with an earlier age of onset for depression (IVW: β=-0.17, 95% CI=-0.32 to -0.02, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.025).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eChildhood maltreatment is a potential risk factor for MDD, ANX, and self-harming behaviors. It is associated with the frequency of depressive episodes and an earlier age of onset for depression.\u003c/p\u003e","manuscriptTitle":"Childhood maltreatment and anxiety, depression and self-harm behaviors : A Two-Sample Mendelian Randomization Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-05 17:33:44","doi":"10.21203/rs.3.rs-3909957/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":"1a5e93d1-03a0-4785-b6f9-d9995e903a53","owner":[],"postedDate":"February 5th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-06-21T09:21:28+00:00","versionOfRecord":[],"versionCreatedAt":"2024-02-05 17:33:44","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3909957","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3909957","identity":"rs-3909957","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}