Causal relationship between mood swing and gynecological disorders: a Mendelian randomization study

Causal relationship between mood swing and gynecological disorders: a 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 Causal relationship between mood swing and gynecological disorders: a Mendelian randomization study Jia Bian, Hongfeng Li, Yaping Shang, Fang Zhang, Lifei Tang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3261471/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 Increasing number of observational studies have associated mood instability to common female diseases, but the underlying causal relationship remains unclear. In this work, Mendelian randomization (MR) analysis was applied to explore the genetically predicted causal relationship of mood swings and several prevalent gynecological disorders. Methods Instrumental variables (IVs) of mood swings were selected from UK Biobank (UKB), with 204,412 cases and 247,207 controls being incorporated. The genetic variants for female disorders were obtained from genome-wide association studies (GWASs) and FinnGen consortium. To avoid biases caused by racial difference, only European population was included here. Five strong analytical methodologies were used to increase the validity of the results, the most substantial of which was the inverse variance weighting (IVW) method. Pleiotropy, sensitivity, and heterogeneity were assessed to strengthen the findings. Results We found mood swings was significantly positively associated with risk of endometrial cancer (OR = 2.60 [95%CI = 1.36, 4.95], P = 0.0037), cervical cancer (OR = 1.01[95%CI = 1.00,1.02], P = 0.0213) and endometriosis (OR = 2.58 [95%CI = 1.18, 5.60], P = 0.0170) by IVW method. However, there was no causal relationship between mood swing and ovarian cancer. No pleiotropy and heterogeneity existed and sensitivity tests were passed. Conclusion This study reveals genetically predicted causal relationships between mood swing and the risk of endometrial cancer, cervical cancer and endometriosis in European populations through MR analysis, which makes up for observational research's inherent limitations. Mendelian randomization Mood swing Genetic relationship Gynecological disorders Figures Figure 1 Figure 2 Introduction Gynecological disorders encompass a diverse group of conditions that affect the female reproductive system, posing substantial health challenges worldwide [ 1 , 2 ]. Among these disorders, endometrial cancer, cervical cancer, ovarian cancer, and endometriosis stand out due to their high prevalence, significant morbidity and mortality rates, and long-term consequences [ 3 – 5 ]. Gaining a comprehensive understanding of the distinct characteristics, risk factors, and underlying mechanisms of these gynecological disorders is imperative for effective prevention, early detection, and targeted treatment strategies. Endometrial cancer, originating in the endometrium, is the most common gynecological malignancy in developed nations, predominantly afflicting postmenopausal women [ 6 , 7 ]. Cervical cancer, primarily caused by persistent high-risk human papillomavirus (HPV) infection, remains a global health concern, particularly in low- and middle-income countries [ 8 ]. Ovarian cancer, a heterogeneous disease, represents the most lethal gynecological malignancy, often detected at advanced stages due to a lack of specific symptoms and effective screening methods [ 9 ]. Endometriosis, characterized by ectopic endometrial-like tissue growth, affects a substantial proportion of reproductive-aged women and is associated with significant pain and infertility [ 10 , 11 ]. The relationship between mood swings and the risk of developing gynecological disorders has become an area of interest within the field of women's health. Mood swings, characterized by rapid and intense fluctuations in emotional states, may have implications for the occurrence of gynecological conditions [ 12 , 13 ]. Understanding this relationship is crucial for comprehensive healthcare and disease prevention strategies. Evidence suggests that mood swings may influence the risk of certain gynecological disorders [ 14 ]. Endometrial cancer, cervical cancer, ovarian cancer, and endometriosis are among the conditions that have been investigated in this context. Psychological distress, hormonal imbalances, and altered immune function associated with mood swings could potentially contribute to the development of these disorders. Conversely, the presence of gynecological conditions may also impact mood stability due to the associated physical and emotional distress [ 15 , 16 ]. The findings from this Mendelian Randomization (MR) study have the potential to enhance our understanding of the etiological factors contributing to gynecological disorders and inform strategies for prevention and intervention. Overall, this MR study aims to elucidate the causal relationship between mood swing and the risk of endometrial cancer, cervical cancer, ovarian cancer, and endometriosis. By employing robust analytical methods and leveraging genetic data, we seek to provide evidence for the potential impact of mood swing on these gynecological disorders and contribute to the broader knowledge of their etiology. Material and method Study Design The study design is depicted in Fig. 1 , providing an overview of the research methodology. To identify suitable instrumental variables (IVs) associated with mood swings, a rigorous selection process was undertaken. Subsequently, a two-sample MR analysis was conducted to ascertain the genetic causal effects of mood swings on four prevalent gynecological disorders, namely endometrial cancer, cervical cancer, ovarian cancer, and endometriosis. Robust sensitivity analyses, including assessments of pleiotropy and heterogeneity, were performed to ensure the validity and reliability of the findings. It is important to note that the genetic data utilized in this study is openly accessible to researchers worldwide, thus obviating the need for additional ethical review or informed consent. Data sources The genetic variants associated with mood swings and the four gynecological disorders (endometrial cancer, cervical cancer, ovarian cancer, and endometriosis) were obtained from publicly available genetic databases and large-scale genome-wide association studies (GWAS). The specific data sources used in this study include but are not limited to the GWAS Catalog, UK Biobank, and the FinnGen dataset. These sources provide comprehensive genetic information, including single nucleotide polymorphisms (SNPs) and their corresponding effect sizes, which were utilized as instrumental variables in the MR analysis. The utilization of publicly available genetic data ensures transparency, reproducibility, and adherence to ethical standards in genomic research. Genetic association with the mood swings was obtained from the GWAS pipeline using Phesant derived variables from UKB, consisting of 451,619 European cases and 9,851,867 SNPs. Summary data of association with endometrial cancer were derived from GWAS, with 12,906 cases and 108,979 controls [ 17 ]. Besides, the genetic statistics of cervical cancer were concluded from MRC-IEU (563 cases / 198,523 controls). Genetic variants of ovarian cancer were extracted from the study of Phelan et al. [ 18 ]. FinnGen consortium provided the genetic data of endometriosis (8,288 cases / 68,969 controls). Selection of genetic instruments The instrumental variables (IVs) were carefully selected based on rigorous criteria. Firstly, single-nucleotide polymorphisms (SNPs) showing a strong association with mood swings (P 0.001) were excluded to avoid redundancy. Furthermore, the F-statistics, calculated using the formula F = beta 2 /se 2 , were utilized to assess the strength of the selected IVs. A threshold of F-statistics > 10 was applied to ensure an adequate statistical power. IVs with lower F-statistics were considered to have reduced statistical power [ 19 ]. The comprehensive information regarding the selected IVs employed in this MR study can be found in Supplementary Table 1 . Statistical analysis In our study, two-sample Mendelian randomization (MR) analyses were conducted using the TwoSampleMR package (version 0.5.6) in R software (version 4.3.1) [ 20 ]. The inverse-variance-weighted (IVW) method was employed as the primary approach for two-sample MR analysis. This method employs weighted regression of SNP-specific Wald ratios to estimate the causal effects of the exposure on the outcomes. Additionally, four other assessment approaches, namely Weighted median, Simple mode, MR Egger, and Weighted mode, were simultaneously performed to assess the consistency of our results [ 21 , 22 ]. The MR-PRESSO method was applied to identify and remove potential outliers and examine the sensitivity of the analysis [ 23 ]. The leave-one-out method was utilized to assess the sensitivity of the MR analyses. Moreover, pleiotropy and heterogeneity were evaluated to strengthen the robustness of our conclusions. Result Selected genetic instruments (IVs) In accordance with the aforementioned criteria, a rigorous selection process was undertaken to identify IVs for mood swings. Following stringent criteria, a total of 57 independent SNPs were selected as the IVs ( Supplementary Table 1 provides detailed information). Importantly, the selected IVs demonstrated no evidence of weak instrumental bias as indicated by the F statistics, all of which exceeded the threshold of 10. The F statistics, serving as a measure of the strength of the selected IVs, were included in the supplementary data for reference. By adhering to these criteria and ensuring robustness in the selection of IVs, the study maintains the integrity and validity of the Mendelian randomization analysis. These carefully selected IVs form the foundation for assessing the causal relationship between mood swings and the gynecological disorders under investigation. The causal relationship of mood swing on common gynecological disorders In the present MR study, the MR-PRESSO method was employed to identify and exclude outlier SNPs. Consequently, the final set of SNPs used for the two-sample MR analysis was reduced in comparison to the initial number of IVs as described earlier. The results of the two-sample MR analysis, employing five different methods, along with the corresponding SNP numbers, are presented in Table 1 . Scatter plots illustrating the relationship between mood swing and the four gynecological disorders are displayed in Fig. 2 . Table 1 Mendelian Randomization analysis of causal relationship between mood swing and four common gynecological disorders. IVW: Inverse variance weighted method; CI: confidence interval; SE: standard error; OR: odds ratio; P_h: P value of heterogeneity test; P_p: P value of pleiotropy test. Outcome Case/Control nSNP MR methods Beta SE OR(95%CI) P P_h P_p Endometrial cancer 12,906/108,979 57 MR Egger 0.0151 1.8663 1.0152 (0.0262,39.3712) 0.9936 0.2249 0.6110 Weighted median 1.1677 0.4379 3.2147(1.3628,7.5832) 0.0077 IVW 0.9547 0.3285 2.5978(1.3644,4.9461) 0.0037 Simple mode 1.3624 1.0171 3.9057(0.5320,28.6740) 0.1858 Weighted mode 1.2276 0.9455 3.4130(0.5350,21.7752) 0.1995 Cervical cancer 563/198,523 52 MR Egger 0.0012 0.0192 1.0012(0.9643,1.0395) 0.9519 0.7870 0.7170 Weighted median 0.0094 0.0050 1.0094(0.9995,1.0194) 0.0633 IVW 0.0080 0.0035 1.0081(1.0012,1.0150) 0.0213 Simple mode 0.0139 0.0124 1.0140(0.9897,1.0389) 0.2653 Weighted mode 0.0139 0.0126 1.0140(0.9892,1.0394) 0.2750 Ovarian cancer 25,509/40,941 52 MR Egger 2.5590 1.8130 12.9226(0.3699,451.4137) 0.1643 0.2478 0.2287 Weighted median 0.4577 0.4029 1.5805(0.7175,3.4816) 0.2559 IVW 0.3793 0.2987 1.4613(0.8137,2.6244) 0.2041 Simple mode 0.7623 0.9226 2.1432(0.3513,13.0748) 0.4125 Weighted mode 0.8489 0.8969 2.3372(0.4029,13.5574) 0.3484 Endometriosis 8,288/68,969 54 MR Egger 1.2606 2.4904 3.5275(0.0268,464.9008) 0.6149 0.2458 0.8987 Weighted median 0.9157 0.5368 2.4986(0.8724,7.1557) 0.0880 IVW 0.9461 0.3964 2.5756(1.1843,5.6013) 0.0170 Simple mode 0.7238 1.3565 2.0623(0.1444,29.4465) 0.5959 Weighted mode 0.6832 1.2874 1.9801(0.1588,24.6936) 0.5979 The scatter plots indicate a positive genetic impact of mood swing on all four gynecological disorders under investigation. Notably, mood swing demonstrated statistically significant associations with endometrial cancer (OR = 2.60 [95% CI = 1.36, 4.95], P = 0.0037), cervical cancer (OR = 1.01 [95% CI = 1.00, 1.02], P = 0.0213), and endometriosis (OR = 2.58 [95% CI = 1.18, 5.60], P = 0.0170). However, the genetic predicted effect of mood swing on ovarian cancer was not statistically significant (OR = 1.46 [95% CI = 0.81, 2.62], P = 0.2041). Therefore, based on these findings, it can be concluded that mood swing may serve as a genetically predicted causal risk factor for endometrial cancer, cervical cancer, and endometriosis in the European population, while no such association was observed for ovarian cancer. Sensitivity analysis To assess the potential weak IV bias in the selected IVs for mood swing and the four gynecological disorders (endometrial cancer, cervical cancer, ovarian cancer, and endometriosis), we calculated the F-statistic and ensured that it exceeded the threshold of 10. Robustness of the MR analyses was validated through multiplicity, heterogeneity, and sensitivity analyses, as outlined in Table 1 . The MR-Egger intercept and MR-PRESSO tests indicated no evidence of horizontal pleiotropy in any of the aforementioned analyses, with all corresponding p-values exceeding 0.05. Furthermore, heterogeneity was detected in the heterogeneity tests of MR-Egger and IVW methods in certain analyses, which is considered acceptable within the context of MR studies. Leave-one-out sensitivity analyses were performed to evaluate the stability of the estimated causal effects of mood swing on endometrial cancer, cervical cancer, ovarian cancer, and endometriosis, and the results indicated robustness ( Supplementary Fig. 9–12 ). Funnel plots and forest plots of MR analysis were shown in Supplementary Fig. 1–8 . Discussion In the present two-sample MR study, our aim was to investigate the potential causal relationship between mood swing and the risk of gynecological disorders. By utilizing genetic variants as instrumental variables, we sought to uncover insights into the impact of mood swing on endometrial cancer, cervical cancer, ovarian cancer, and endometriosis. The study design included the selection of instrumental variables based on strict criteria, followed by two-sample MR analysis and various sensitivity and robustness assessments. The observed causal relationship between mood swing and endometrial cancer is consistent with existing evidence highlighting the potential role of psychological factors in the development of gynecological disorders [ 14 ]. Psychological distress and mood disturbances have been associated with hormonal imbalances and alterations in immune function, which might influence cancer risk [ 24 – 26 ]. These findings contribute to our understanding of the multifactorial etiology of endometrial cancer and emphasize the importance of addressing psychological well-being in prevention and management strategies. The identification of a causal relationship between mood swing and cervical cancer underscores the potential impact of psychological factors on the development of this gynecological malignancy. Psychological distress and mood swings may contribute to immune dysfunction, compromising the body's ability to clear high-risk human papillomavirus (HPV) infections, thus increasing the risk of cervical cancer [ 27 ]. These findings highlight the need for comprehensive approaches that address both physical and mental health aspects in the prevention and control of cervical cancer. The observed causal relationship between mood swing and endometriosis suggests that psychological factors may play a role in the development or progression of this chronic condition. Chronic pain, fertility challenges, and reduced quality of life associated with endometriosis can lead to psychological distress and mood swings [ 28 ]. The bidirectional relationship between mood swings and endometriosis could potentially exacerbate symptom severity and impact disease progression [ 29 , 30 ]. These findings emphasize the importance of integrating psychological support and mental health interventions in the management of endometriosis. The absence of a significant causal relationship between mood swing and ovarian cancer suggests that other factors may play a more prominent role in the development of this gynecological malignancy. Ovarian cancer is a complex disease influenced by multiple genetic and environmental factors [ 31 , 32 ]. Future research should continue to explore the multifactorial etiology of ovarian cancer and elucidate the interplay between various risk factors, including psychological factors, to better understand its pathogenesis. This essay demonstrates several strengths in its approach to investigating the potential causal relationship between mood swing and four gynecological disorders. The utilization of MR analysis, a robust methodological framework, allows for the assessment of causal relationships, enhancing the validity of the findings. Additionally, the inclusion of large-scale genetic datasets adds to the strength of the study, ensuring a broader representation of populations and increasing the generalizability of the results. The comprehensive discussion of the potential causal links between mood swing and endometrial cancer, cervical cancer, ovarian cancer, and endometriosis contributes to the existing literature in this field, offering valuable insights into the etiology of these gynecological disorders. Despite its strengths, this essay also exhibits some limitations. One such limitation is the focus on European populations, which may restrict the generalizability of the findings to other ethnic groups or geographical regions. Additionally, the reliance on genetic data as instrumental variables assumes no pleiotropic effects, potentially introducing bias into the analysis. While sensitivity analyses were mentioned, providing further details on potential sources of bias and confounding would have strengthened the overall analysis. Taking these limitations into consideration, future research should strive to include diverse populations and employ comprehensive sensitivity analyses to enhance the robustness of the findings. The findings of this MR study contribute to our understanding of the complex interplay between mood swing and gynecological disorders. The observed causal relationships underscore the importance of addressing psychological factors and mental health in the prevention and management of endometrial cancer, cervical cancer, and endometriosis. Future research should further explore the underlying mechanisms linking mood swing to gynecological disorders and investigate the potential effectiveness of interventions targeting psychological well-being in reducing disease risks. These findings provide a basis for personalized interventions and improved patient outcomes in the field of gynecological health. Conclusion In conclusion, this MR study establishes a significant causal relationship between mood swing and increased risks of endometrial cancer, cervical cancer, and endometriosis in European populations. However, no significant causal association was observed for ovarian cancer. These findings emphasize the relevance of considering psychological factors in the prevention and management of gynecological disorders, informing the development of comprehensive care approaches. Further research is warranted to investigate the underlying mechanisms and validate these findings in diverse populations. Declarations Ethical approval Not applicable. Data availability statement All of the genetic data used in this work was publicly available. The original contributions presented in the study are included in the article and supplementary materials, further inquiries can be directed to the corresponding author (Jia Bian, [email protected] ). Author contributions Jia Bian: conceptualization and writing of the manuscript. Hongfeng Li and Yaping Shang: making the tables and figures. Fang Zhang and Lifei Tang: reviewing and providing critical suggestions. Funding This work was supported by Zhejiang Provincial Medical and Health Technology Plan Project (ID: 2023KY302). Competing interests The authors declare no conflict of interest. Acknowledgement We are grateful to the public accessible databases mentioned above. References Yi, M., T. Li, M. Niu, S. Luo, Q. Chu, and K. Wu, Epidemiological trends of women's cancers from 1990 to 2019 at the global, regional, and national levels: a population-based study. Biomark Res, 2021. 9(1): p. 55. Chen, L., Q. Wang, Y. Gao, J. Zhang, S. Cheng, H. Chen, et al. , The global burden and trends of maternal sepsis and other maternal infections in 204 countries and territories from 1990 to 2019 . BMC Infect Dis, 2021. 21(1): p. 1074. Gebretsadik, A., N. Bogale, and D. Dulla, Descriptive epidemiology of gynaecological cancers in southern Ethiopia: retrospective cross-sectional review . BMJ Open, 2022. 12(12): p. e062633. Huang, Z., Y. Zheng, W. Wen, C. Wu, P. Bao, C. Wang, et al. , Incidence and mortality of gynaecological cancers: Secular trends in urban Shanghai, China over 40 years . Eur J Cancer, 2016. 63: p. 1–10. Minelli, L., F. Stracci, S. Prandini, I.F. Moffa, and F. La Rosa, Gynaecological cancers in Umbria (Italy): trends of incidence, mortality and survival, 1978–1998 . Eur J Obstet Gynecol Reprod Biol, 2004. 115(1): p. 59–65. Hong, I.S., Endometrial stem/progenitor cells: Properties, origins, and functions . Genes Dis, 2023. 10(3): p. 931–947. Crosbie, E.J., S.J. Kitson, J.N. McAlpine, A. Mukhopadhyay, M.E. Powell, and N. Singh, Endometrial cancer. Lancet, 2022. 399(10333): p. 1412–1428. Ebrahimi, N., Z. Yousefi, G. Khosravi, F.E. Malayeri, M. Golabi, M. Askarzadeh, et al. , Human papillomavirus vaccination in low- and middle-income countries: progression, barriers, and future prospective . Front Immunol, 2023. 14: p. 1150238. Domínguez-Prieto, V., S. Qian, P. Villarejo-Campos, C. Meliga, S. González-Soares, I. Guijo Castellano, et al. , Understanding CAR T cell therapy and its role in ovarian cancer and peritoneal carcinomatosis from ovarian cancer . Front Oncol, 2023. 13: p. 1104547. Peterson, B., A. Mikocka-Walus, and S. Evans, ' It just stops me from living': A qualitative study of losses experienced by women with self-reported endometriosis . J Adv Nurs, 2023. Shi, J., X. Tan, G. Feng, Y. Zhuo, Z. Jiang, S. Banda, et al. , Research advances in drug therapy of endometriosis . Front Pharmacol, 2023. 14: p. 1199010. Fu, J.X., Y. Luo, M.Z. Chen, Y.H. Zhou, Y.T. Meng, T. Wang, et al. , Associations among menopausal status, menopausal symptoms, and depressive symptoms in midlife women in Hunan Province, China . Climacteric, 2020. 23(3): p. 259–266. Ojezele, M.O., A.T. Eduviere, E.A. Adedapo, and T.K. Wool, Mood Swing during Menstruation: Confounding Factors and Drug Use . Ethiop J Health Sci, 2022. 32(4): p. 681–688. Wu, C.T. and L.T. Chiu, The Impact of Psychological Distress on Cervical Cancer . Cancers (Basel), 2023. 15(4). Bradley, S., S. Rose, S. Lutgendorf, E. Costanzo, and B. Anderson, Quality of life and mental health in cervical and endometrial cancer survivors . Gynecol Oncol, 2006. 100(3): p. 479–86. van Stein, K., K. Schubert, B. Ditzen, and C. Weise, Understanding Psychological Symptoms of Endometriosis from a Research Domain Criteria Perspective . J Clin Med, 2023. 12(12). O'Mara, T.A., D.M. Glubb, F. Amant, D. Annibali, K. Ashton, J. Attia, et al. , Identification of nine new susceptibility loci for endometrial cancer . Nat Commun, 2018. 9(1): p. 3166. Phelan, C.M., K.B. Kuchenbaecker, J.P. Tyrer, S.P. Kar, K. Lawrenson, S.J. Winham, et al. , Identification of 12 new susceptibility loci for different histotypes of epithelial ovarian cancer. Nat Genet, 2017. 49(5): p. 680–691. Xu, D., Y. Chen, X. Gao, W. Xie, Y. Wang, J. Shen, et al. , The genetically predicted causal relationship of inflammatory bowel disease with bone mineral density and osteoporosis: evidence from two-sample Mendelian randomization . Front Immunol, 2023. 14: p. 1148107. Yavorska, O.O. and S. Burgess, MendelianRandomization: an R package for performing Mendelian randomization analyses using summarized data . Int J Epidemiol, 2017. 46(6): p. 1734–1739. Bowden, J., G. Davey Smith, P.C. Haycock, and S. Burgess, Consistent Estimation in Mendelian Randomization with Some Invalid Instruments Using a Weighted Median Estimator . Genet Epidemiol, 2016. 40(4): p. 304–14. Bowden, J., G. Davey Smith, and S. Burgess, Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression . Int J Epidemiol, 2015. 44(2): p. 512–25. Verbanck, M., C.Y. Chen, B. Neale, and R. Do, Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases . Nat Genet, 2018. 50(5): p. 693–698. Spurio, M.G., The magic of caressing the mind by touching the body. Take care of depression, face up to cancer. A new frontier of psycho-oncology . Psychiatr Danub, 2017. 29(Suppl 3): p. 383–388. Gui, H., X. Chen, L. Li, L. Zhu, Q. Jing, Y. Nie, et al. , Psychological distress influences lung cancer: Advances and perspectives on the immune system and immunotherapy . Int Immunopharmacol, 2023. 121: p. 110251. Cohen, S. and T.B. Herbert, Health psychology: psychological factors and physical disease from the perspective of human psychoneuroimmunology . Annu Rev Psychol, 1996. 47: p. 113–42. Lugović-Mihić, L., H. Cvitanović, I. Djaković, M. Kuna, and A. Šešerko, The Influence of Psychological Stress on HPV Infection Manifestations and Carcinogenesis . Cell Physiol Biochem, 2021. 55(S2): p. 71–88. Márki, G., A. Bokor, J. Rigó, and A. Rigó, Physical pain and emotion regulation as the main predictive factors of health-related quality of life in women living with endometriosis . Hum Reprod, 2017. 32(7): p. 1432–1438. Baciu, I., A. Irimie-Ana, A. Panaitescu, G. Peltecu, C. Gica, and N. Gica, The social and the psychological impact of endometriosis on the Romanian urban population . Journal of Mind and Medical Sciences, 2021. 8(1): p. 120–126. Aerts, L., L. Grangier, I. Streuli, P. Dällenbach, R. Marci, J.M. Wenger, et al. , Psychosocial impact of endometriosis: From co-morbidity to intervention . Best Pract Res Clin Obstet Gynaecol, 2018. 50: p. 2–10. Haruta, S., N. Furukawa, Y. Yoshizawa, T. Tsunemi, A. Nagai, R. Kawaguchi, et al. , Molecular genetics and epidemiology of epithelial ovarian cancer (Review) . Oncol Rep, 2011. 26(6): p. 1347–56. Hunn, J. and G.C. Rodriguez, Ovarian cancer: etiology, risk factors, and epidemiology . Clin Obstet Gynecol, 2012. 55(1): p. 3–23. Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterials.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3261471","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":226522650,"identity":"9b457d62-96ec-46e0-bf3b-dc5f8a6cec3b","order_by":0,"name":"Jia Bian","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzklEQVRIiWNgGAWjYDACCYaEA4wNEgwM7I2NDz6QpoXncLPhDCK1MDAwNoAY6W3SHMToMJdueHjg5w4LeXPJhw3SDAx2croNBLRYzjmQcLD3jIThztmJDcYFDMnGZgcIaDG4kZBwgLdNgnHD7cSG5BkMBxK3EaPl4N82CfsNNw82HOYhVsthoC2JG24wNjYTp+XOgYTDsmckkjecSWxmnGFAjF9u9yR/fLujznbD8ePPf3yosJMjqAUYhQnIJhBUDgLshE0dBaNgFIyCEQ4ABetNfaitiKgAAAAASUVORK5CYII=","orcid":"","institution":"the Affiliated People's Hospital of Ningbo University","correspondingAuthor":true,"prefix":"","firstName":"Jia","middleName":"","lastName":"Bian","suffix":""},{"id":226522651,"identity":"56b08a59-5b9d-44b5-a65b-b22d55e95c32","order_by":1,"name":"Hongfeng Li","email":"","orcid":"","institution":"the Affiliated People's Hospital of Ningbo University","correspondingAuthor":false,"prefix":"","firstName":"Hongfeng","middleName":"","lastName":"Li","suffix":""},{"id":226522652,"identity":"ebb7274f-3429-4ba2-997c-7254c2872a38","order_by":2,"name":"Yaping Shang","email":"","orcid":"","institution":"the Affiliated People's Hospital of Ningbo University","correspondingAuthor":false,"prefix":"","firstName":"Yaping","middleName":"","lastName":"Shang","suffix":""},{"id":226522653,"identity":"1c2537b1-5f7a-406b-bd69-e85604767995","order_by":3,"name":"Fang Zhang","email":"","orcid":"","institution":"the Affiliated People's Hospital of Ningbo University","correspondingAuthor":false,"prefix":"","firstName":"Fang","middleName":"","lastName":"Zhang","suffix":""},{"id":226522654,"identity":"5a691626-4041-4928-b4ab-ab86e46ebaad","order_by":4,"name":"Lifei Tang","email":"","orcid":"","institution":"the Affiliated People's Hospital of Ningbo University","correspondingAuthor":false,"prefix":"","firstName":"Lifei","middleName":"","lastName":"Tang","suffix":""}],"badges":[],"createdAt":"2023-08-14 06:14:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3261471/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3261471/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":41886970,"identity":"30c9a407-7aa1-47c9-9e10-3299a88ea31a","added_by":"auto","created_at":"2023-08-21 16:04:50","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":281993,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eOverview of study design.\u003c/strong\u003eSchematic showing how MR was applied to evaluate a causal association between mood swing and four common gynecological disorders in this study. Three assumptions should be satisfied here: (1) instrumental variables (IVs) are strongly related to exposure; (2) IVs are independent of any confounders; (3) IVs only affect the outcome through exposure.\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3261471/v1/483b1f72e64254e897b81545.jpg"},{"id":41886437,"identity":"b614c8a0-45a2-478d-9026-6b08eed19c02","added_by":"auto","created_at":"2023-08-21 15:56:50","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":340071,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eScatter plots to assess causal associations between mood swing and each of the following four gynecological disorders:\u003c/strong\u003e (A) endometrial cancer; (B) cervical cancer; (C) ovarian cancer and (D) endometriosis. All of these scatter plots showing a positive genetic causal relationship between mood swing and four gynecological disorders above.\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3261471/v1/4338e595c175c9b8076b1a86.jpg"},{"id":42414904,"identity":"7ce1b39e-b37d-47b8-9f0d-ab3810d55a62","added_by":"auto","created_at":"2023-08-31 09:44:34","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":563123,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3261471/v1/23c54c70-492e-47d1-bac7-ffe2cb5b66b6.pdf"},{"id":41886435,"identity":"1194a759-2580-4d45-b32b-c5591647fa54","added_by":"auto","created_at":"2023-08-21 15:56:50","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1888705,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-3261471/v1/14d0a3448d3d361f4afe8525.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Causal relationship between mood swing and gynecological disorders: a Mendelian randomization study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eGynecological disorders encompass a diverse group of conditions that affect the female reproductive system, posing substantial health challenges worldwide [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Among these disorders, endometrial cancer, cervical cancer, ovarian cancer, and endometriosis stand out due to their high prevalence, significant morbidity and mortality rates, and long-term consequences [\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Gaining a comprehensive understanding of the distinct characteristics, risk factors, and underlying mechanisms of these gynecological disorders is imperative for effective prevention, early detection, and targeted treatment strategies.\u003c/p\u003e \u003cp\u003eEndometrial cancer, originating in the endometrium, is the most common gynecological malignancy in developed nations, predominantly afflicting postmenopausal women [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Cervical cancer, primarily caused by persistent high-risk human papillomavirus (HPV) infection, remains a global health concern, particularly in low- and middle-income countries [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Ovarian cancer, a heterogeneous disease, represents the most lethal gynecological malignancy, often detected at advanced stages due to a lack of specific symptoms and effective screening methods [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Endometriosis, characterized by ectopic endometrial-like tissue growth, affects a substantial proportion of reproductive-aged women and is associated with significant pain and infertility [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe relationship between mood swings and the risk of developing gynecological disorders has become an area of interest within the field of women's health. Mood swings, characterized by rapid and intense fluctuations in emotional states, may have implications for the occurrence of gynecological conditions [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Understanding this relationship is crucial for comprehensive healthcare and disease prevention strategies.\u003c/p\u003e \u003cp\u003eEvidence suggests that mood swings may influence the risk of certain gynecological disorders [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Endometrial cancer, cervical cancer, ovarian cancer, and endometriosis are among the conditions that have been investigated in this context. Psychological distress, hormonal imbalances, and altered immune function associated with mood swings could potentially contribute to the development of these disorders. Conversely, the presence of gynecological conditions may also impact mood stability due to the associated physical and emotional distress [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe findings from this Mendelian Randomization (MR) study have the potential to enhance our understanding of the etiological factors contributing to gynecological disorders and inform strategies for prevention and intervention. Overall, this MR study aims to elucidate the causal relationship between mood swing and the risk of endometrial cancer, cervical cancer, ovarian cancer, and endometriosis. By employing robust analytical methods and leveraging genetic data, we seek to provide evidence for the potential impact of mood swing on these gynecological disorders and contribute to the broader knowledge of their etiology.\u003c/p\u003e"},{"header":"Material and method","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design\u003c/h2\u003e \u003cp\u003eThe study design is depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, providing an overview of the research methodology. To identify suitable instrumental variables (IVs) associated with mood swings, a rigorous selection process was undertaken. Subsequently, a two-sample MR analysis was conducted to ascertain the genetic causal effects of mood swings on four prevalent gynecological disorders, namely endometrial cancer, cervical cancer, ovarian cancer, and endometriosis. Robust sensitivity analyses, including assessments of pleiotropy and heterogeneity, were performed to ensure the validity and reliability of the findings. It is important to note that the genetic data utilized in this study is openly accessible to researchers worldwide, thus obviating the need for additional ethical review or informed consent.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eData sources\u003c/h2\u003e \u003cp\u003eThe genetic variants associated with mood swings and the four gynecological disorders (endometrial cancer, cervical cancer, ovarian cancer, and endometriosis) were obtained from publicly available genetic databases and large-scale genome-wide association studies (GWAS). The specific data sources used in this study include but are not limited to the GWAS Catalog, UK Biobank, and the FinnGen dataset. These sources provide comprehensive genetic information, including single nucleotide polymorphisms (SNPs) and their corresponding effect sizes, which were utilized as instrumental variables in the MR analysis. The utilization of publicly available genetic data ensures transparency, reproducibility, and adherence to ethical standards in genomic research.\u003c/p\u003e \u003cp\u003eGenetic association with the mood swings was obtained from the GWAS pipeline using Phesant derived variables from UKB, consisting of 451,619 European cases and 9,851,867 SNPs. Summary data of association with endometrial cancer were derived from GWAS, with 12,906 cases and 108,979 controls [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Besides, the genetic statistics of cervical cancer were concluded from MRC-IEU (563 cases / 198,523 controls). Genetic variants of ovarian cancer were extracted from the study of Phelan et al. [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. FinnGen consortium provided the genetic data of endometriosis (8,288 cases / 68,969 controls).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eSelection of genetic instruments\u003c/h2\u003e \u003cp\u003eThe instrumental variables (IVs) were carefully selected based on rigorous criteria. Firstly, single-nucleotide polymorphisms (SNPs) showing a strong association with mood swings (P\u0026thinsp;\u0026lt;\u0026thinsp;5\u0026times;10\u0026thinsp;\u0026minus;\u0026thinsp;8) were included. Secondly, genetic variants in linkage disequilibrium (LD) (r2\u0026thinsp;\u0026gt;\u0026thinsp;0.001) were excluded to avoid redundancy. Furthermore, the F-statistics, calculated using the formula F\u0026thinsp;=\u0026thinsp;beta\u003csup\u003e2\u003c/sup\u003e/se\u003csup\u003e2\u003c/sup\u003e, were utilized to assess the strength of the selected IVs. A threshold of F-statistics\u0026thinsp;\u0026gt;\u0026thinsp;10 was applied to ensure an adequate statistical power. IVs with lower F-statistics were considered to have reduced statistical power [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. The comprehensive information regarding the selected IVs employed in this MR study can be found in \u003cb\u003eSupplementary Table\u0026nbsp;1\u003c/b\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eIn our study, two-sample Mendelian randomization (MR) analyses were conducted using the TwoSampleMR package (version 0.5.6) in R software (version 4.3.1) [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. The inverse-variance-weighted (IVW) method was employed as the primary approach for two-sample MR analysis. This method employs weighted regression of SNP-specific Wald ratios to estimate the causal effects of the exposure on the outcomes. Additionally, four other assessment approaches, namely Weighted median, Simple mode, MR Egger, and Weighted mode, were simultaneously performed to assess the consistency of our results [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. The MR-PRESSO method was applied to identify and remove potential outliers and examine the sensitivity of the analysis [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. The leave-one-out method was utilized to assess the sensitivity of the MR analyses. Moreover, pleiotropy and heterogeneity were evaluated to strengthen the robustness of our conclusions.\u003c/p\u003e \u003c/div\u003e"},{"header":"Result","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eSelected genetic instruments (IVs)\u003c/h2\u003e \u003cp\u003eIn accordance with the aforementioned criteria, a rigorous selection process was undertaken to identify IVs for mood swings. Following stringent criteria, a total of 57 independent SNPs were selected as the IVs (\u003cb\u003eSupplementary Table\u0026nbsp;1\u003c/b\u003e provides detailed information). Importantly, the selected IVs demonstrated no evidence of weak instrumental bias as indicated by the F statistics, all of which exceeded the threshold of 10. The F statistics, serving as a measure of the strength of the selected IVs, were included in the supplementary data for reference. By adhering to these criteria and ensuring robustness in the selection of IVs, the study maintains the integrity and validity of the Mendelian randomization analysis. These carefully selected IVs form the foundation for assessing the causal relationship between mood swings and the gynecological disorders under investigation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eThe causal relationship of mood swing on common gynecological disorders\u003c/h2\u003e \u003cp\u003eIn the present MR study, the MR-PRESSO method was employed to identify and exclude outlier SNPs. Consequently, the final set of SNPs used for the two-sample MR analysis was reduced in comparison to the initial number of IVs as described earlier. The results of the two-sample MR analysis, employing five different methods, along with the corresponding SNP numbers, are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Scatter plots illustrating the relationship between mood swing and the four gynecological disorders are displayed in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eMendelian Randomization analysis of causal relationship between mood swing and four common gynecological disorders.\u003c/b\u003e IVW: Inverse variance weighted method; CI: confidence interval; SE: standard error; OR: odds ratio; P_h: P value of heterogeneity test; P_p: P value of pleiotropy test.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\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=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" 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=\"char\" char=\".\" 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=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOutcome\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCase/Control\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003enSNP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMR methods\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBeta\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eOR(95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eP_h\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eP_p\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eEndometrial cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e12,906/108,979\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMR Egger\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.8663\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.0152 (0.0262,39.3712)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.9936\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e0.2249\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e0.6110\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWeighted median\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.1677\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.4379\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3.2147(1.3628,7.5832)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.0077\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIVW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.9547\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.3285\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.5978(1.3644,4.9461)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.0037\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSimple mode\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.3624\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.0171\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3.9057(0.5320,28.6740)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.1858\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWeighted mode\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.2276\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.9455\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3.4130(0.5350,21.7752)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.1995\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eCervical cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e563/198,523\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMR Egger\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0192\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.0012(0.9643,1.0395)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.9519\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e0.7870\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e0.7170\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWeighted median\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0094\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.0094(0.9995,1.0194)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.0633\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIVW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0080\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.0081(1.0012,1.0150)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.0213\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSimple mode\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0139\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.0140(0.9897,1.0389)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.2653\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWeighted mode\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0139\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.0140(0.9892,1.0394)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.2750\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eOvarian cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e25,509/40,941\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMR Egger\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.5590\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.8130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e12.9226(0.3699,451.4137)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.1643\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e0.2478\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e0.2287\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWeighted median\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.4577\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.4029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.5805(0.7175,3.4816)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.2559\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIVW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.3793\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.2987\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.4613(0.8137,2.6244)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.2041\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSimple mode\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.7623\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.9226\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.1432(0.3513,13.0748)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.4125\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWeighted mode\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.8489\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.8969\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.3372(0.4029,13.5574)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.3484\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eEndometriosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e8,288/68,969\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMR Egger\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.2606\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.4904\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3.5275(0.0268,464.9008)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.6149\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e0.2458\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e0.8987\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWeighted median\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.9157\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.5368\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.4986(0.8724,7.1557)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.0880\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIVW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.9461\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.3964\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.5756(1.1843,5.6013)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.0170\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSimple mode\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.7238\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.3565\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.0623(0.1444,29.4465)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.5959\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWeighted mode\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.6832\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.2874\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.9801(0.1588,24.6936)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.5979\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\u003e \u003c/p\u003e \u003cp\u003eThe scatter plots indicate a positive genetic impact of mood swing on all four gynecological disorders under investigation. Notably, mood swing demonstrated statistically significant associations with endometrial cancer (OR\u0026thinsp;=\u0026thinsp;2.60 [95% CI\u0026thinsp;=\u0026thinsp;1.36, 4.95], P\u0026thinsp;=\u0026thinsp;0.0037), cervical cancer (OR\u0026thinsp;=\u0026thinsp;1.01 [95% CI\u0026thinsp;=\u0026thinsp;1.00, 1.02], P\u0026thinsp;=\u0026thinsp;0.0213), and endometriosis (OR\u0026thinsp;=\u0026thinsp;2.58 [95% CI\u0026thinsp;=\u0026thinsp;1.18, 5.60], P\u0026thinsp;=\u0026thinsp;0.0170). However, the genetic predicted effect of mood swing on ovarian cancer was not statistically significant (OR\u0026thinsp;=\u0026thinsp;1.46 [95% CI\u0026thinsp;=\u0026thinsp;0.81, 2.62], P\u0026thinsp;=\u0026thinsp;0.2041). Therefore, based on these findings, it can be concluded that mood swing may serve as a genetically predicted causal risk factor for endometrial cancer, cervical cancer, and endometriosis in the European population, while no such association was observed for ovarian cancer.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eSensitivity analysis\u003c/h2\u003e \u003cp\u003eTo assess the potential weak IV bias in the selected IVs for mood swing and the four gynecological disorders (endometrial cancer, cervical cancer, ovarian cancer, and endometriosis), we calculated the F-statistic and ensured that it exceeded the threshold of 10. Robustness of the MR analyses was validated through multiplicity, heterogeneity, and sensitivity analyses, as outlined in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The MR-Egger intercept and MR-PRESSO tests indicated no evidence of horizontal pleiotropy in any of the aforementioned analyses, with all corresponding p-values exceeding 0.05. Furthermore, heterogeneity was detected in the heterogeneity tests of MR-Egger and IVW methods in certain analyses, which is considered acceptable within the context of MR studies. Leave-one-out sensitivity analyses were performed to evaluate the stability of the estimated causal effects of mood swing on endometrial cancer, cervical cancer, ovarian cancer, and endometriosis, and the results indicated robustness (\u003cb\u003eSupplementary Fig.\u0026nbsp;9\u0026ndash;12\u003c/b\u003e). Funnel plots and forest plots of MR analysis were shown in \u003cb\u003eSupplementary Fig.\u0026nbsp;1\u0026ndash;8\u003c/b\u003e.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn the present two-sample MR study, our aim was to investigate the potential causal relationship between mood swing and the risk of gynecological disorders. By utilizing genetic variants as instrumental variables, we sought to uncover insights into the impact of mood swing on endometrial cancer, cervical cancer, ovarian cancer, and endometriosis. The study design included the selection of instrumental variables based on strict criteria, followed by two-sample MR analysis and various sensitivity and robustness assessments.\u003c/p\u003e \u003cp\u003eThe observed causal relationship between mood swing and endometrial cancer is consistent with existing evidence highlighting the potential role of psychological factors in the development of gynecological disorders [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Psychological distress and mood disturbances have been associated with hormonal imbalances and alterations in immune function, which might influence cancer risk [\u003cspan additionalcitationids=\"CR25\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. These findings contribute to our understanding of the multifactorial etiology of endometrial cancer and emphasize the importance of addressing psychological well-being in prevention and management strategies.\u003c/p\u003e \u003cp\u003eThe identification of a causal relationship between mood swing and cervical cancer underscores the potential impact of psychological factors on the development of this gynecological malignancy. Psychological distress and mood swings may contribute to immune dysfunction, compromising the body's ability to clear high-risk human papillomavirus (HPV) infections, thus increasing the risk of cervical cancer [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. These findings highlight the need for comprehensive approaches that address both physical and mental health aspects in the prevention and control of cervical cancer.\u003c/p\u003e \u003cp\u003eThe observed causal relationship between mood swing and endometriosis suggests that psychological factors may play a role in the development or progression of this chronic condition. Chronic pain, fertility challenges, and reduced quality of life associated with endometriosis can lead to psychological distress and mood swings [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. The bidirectional relationship between mood swings and endometriosis could potentially exacerbate symptom severity and impact disease progression [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. These findings emphasize the importance of integrating psychological support and mental health interventions in the management of endometriosis.\u003c/p\u003e \u003cp\u003eThe absence of a significant causal relationship between mood swing and ovarian cancer suggests that other factors may play a more prominent role in the development of this gynecological malignancy. Ovarian cancer is a complex disease influenced by multiple genetic and environmental factors [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Future research should continue to explore the multifactorial etiology of ovarian cancer and elucidate the interplay between various risk factors, including psychological factors, to better understand its pathogenesis.\u003c/p\u003e \u003cp\u003eThis essay demonstrates several strengths in its approach to investigating the potential causal relationship between mood swing and four gynecological disorders. The utilization of MR analysis, a robust methodological framework, allows for the assessment of causal relationships, enhancing the validity of the findings. Additionally, the inclusion of large-scale genetic datasets adds to the strength of the study, ensuring a broader representation of populations and increasing the generalizability of the results. The comprehensive discussion of the potential causal links between mood swing and endometrial cancer, cervical cancer, ovarian cancer, and endometriosis contributes to the existing literature in this field, offering valuable insights into the etiology of these gynecological disorders.\u003c/p\u003e \u003cp\u003eDespite its strengths, this essay also exhibits some limitations. One such limitation is the focus on European populations, which may restrict the generalizability of the findings to other ethnic groups or geographical regions. Additionally, the reliance on genetic data as instrumental variables assumes no pleiotropic effects, potentially introducing bias into the analysis. While sensitivity analyses were mentioned, providing further details on potential sources of bias and confounding would have strengthened the overall analysis. Taking these limitations into consideration, future research should strive to include diverse populations and employ comprehensive sensitivity analyses to enhance the robustness of the findings.\u003c/p\u003e \u003cp\u003eThe findings of this MR study contribute to our understanding of the complex interplay between mood swing and gynecological disorders. The observed causal relationships underscore the importance of addressing psychological factors and mental health in the prevention and management of endometrial cancer, cervical cancer, and endometriosis. Future research should further explore the underlying mechanisms linking mood swing to gynecological disorders and investigate the potential effectiveness of interventions targeting psychological well-being in reducing disease risks. These findings provide a basis for personalized interventions and improved patient outcomes in the field of gynecological health.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, this MR study establishes a significant causal relationship between mood swing and increased risks of endometrial cancer, cervical cancer, and endometriosis in European populations. However, no significant causal association was observed for ovarian cancer. These findings emphasize the relevance of considering psychological factors in the prevention and management of gynecological disorders, informing the development of comprehensive care approaches. Further research is warranted to investigate the underlying mechanisms and validate these findings in diverse populations.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll of the genetic data used in this work was publicly available. The original contributions presented in the study are included in the article and supplementary materials, further inquiries can be directed to the corresponding author (Jia Bian, [email protected]).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJia Bian: conceptualization and writing of the manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHongfeng Li and Yaping Shang: making the tables and figures.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFang Zhang and Lifei Tang: reviewing and providing critical suggestions.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by Zhejiang Provincial Medical and Health Technology Plan Project (ID: 2023KY302).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflict of interest.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe are grateful to the public accessible databases mentioned above.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eYi, M., T. Li, M. Niu, S. Luo, Q. Chu, and K. Wu, \u003cem\u003eEpidemiological trends of women's cancers from 1990 to\u003c/em\u003e 2019 \u003cem\u003eat the global, regional, and national levels: a population-based study.\u003c/em\u003e Biomark Res, 2021. 9(1): p.\u0026nbsp;55.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen, L., Q. Wang, Y. Gao, J. Zhang, S. Cheng, H. Chen, \u003cem\u003eet al.\u003c/em\u003e, \u003cem\u003eThe global burden and trends of maternal sepsis and other maternal infections in 204 countries and territories from 1990 to 2019\u003c/em\u003e. BMC Infect Dis, 2021. 21(1): p.\u0026nbsp;1074.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGebretsadik, A., N. Bogale, and D. Dulla, \u003cem\u003eDescriptive epidemiology of gynaecological cancers in southern Ethiopia: retrospective cross-sectional review\u003c/em\u003e. BMJ Open, 2022. 12(12): p.\u0026nbsp;e062633.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuang, Z., Y. Zheng, W. Wen, C. Wu, P. Bao, C. Wang, \u003cem\u003eet al.\u003c/em\u003e, \u003cem\u003eIncidence and mortality of gynaecological cancers: Secular trends in urban Shanghai, China over 40 years\u003c/em\u003e. Eur J Cancer, 2016. 63: p.\u0026nbsp;1\u0026ndash;10.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMinelli, L., F. Stracci, S. Prandini, I.F. Moffa, and F. La Rosa, \u003cem\u003eGynaecological cancers in Umbria (Italy): trends of incidence, mortality and survival, 1978\u0026ndash;1998\u003c/em\u003e. Eur J Obstet Gynecol Reprod Biol, 2004. 115(1): p.\u0026nbsp;59\u0026ndash;65.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHong, I.S., \u003cem\u003eEndometrial stem/progenitor cells: Properties, origins, and functions\u003c/em\u003e. Genes Dis, 2023. 10(3): p.\u0026nbsp;931\u0026ndash;947.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCrosbie, E.J., S.J. Kitson, J.N. McAlpine, A. Mukhopadhyay, M.E. Powell, and N. Singh, Endometrial cancer. Lancet, 2022. 399(10333): p.\u0026nbsp;1412\u0026ndash;1428.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEbrahimi, N., Z. Yousefi, G. Khosravi, F.E. Malayeri, M. Golabi, M. Askarzadeh, \u003cem\u003eet al.\u003c/em\u003e, \u003cem\u003eHuman papillomavirus vaccination in low- and middle-income countries: progression, barriers, and future prospective\u003c/em\u003e. Front Immunol, 2023. 14: p.\u0026nbsp;1150238.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDom\u0026iacute;nguez-Prieto, V., S. Qian, P. Villarejo-Campos, C. Meliga, S. Gonz\u0026aacute;lez-Soares, I. Guijo Castellano, \u003cem\u003eet al.\u003c/em\u003e, \u003cem\u003eUnderstanding CAR T cell therapy and its role in ovarian cancer and peritoneal carcinomatosis from ovarian cancer\u003c/em\u003e. Front Oncol, 2023. 13: p.\u0026nbsp;1104547.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePeterson, B., A. Mikocka-Walus, and S. Evans, '\u003cem\u003eIt just stops me from living': A qualitative study of losses experienced by women with self-reported endometriosis\u003c/em\u003e. J Adv Nurs, 2023.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShi, J., X. Tan, G. Feng, Y. Zhuo, Z. Jiang, S. Banda, \u003cem\u003eet al.\u003c/em\u003e, \u003cem\u003eResearch advances in drug therapy of endometriosis\u003c/em\u003e. Front Pharmacol, 2023. 14: p.\u0026nbsp;1199010.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFu, J.X., Y. Luo, M.Z. Chen, Y.H. Zhou, Y.T. Meng, T. Wang, \u003cem\u003eet al.\u003c/em\u003e, \u003cem\u003eAssociations among menopausal status, menopausal symptoms, and depressive symptoms in midlife women in Hunan Province, China\u003c/em\u003e. Climacteric, 2020. 23(3): p.\u0026nbsp;259\u0026ndash;266.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOjezele, M.O., A.T. Eduviere, E.A. Adedapo, and T.K. Wool, \u003cem\u003eMood Swing during Menstruation: Confounding Factors and Drug Use\u003c/em\u003e. Ethiop J Health Sci, 2022. 32(4): p.\u0026nbsp;681\u0026ndash;688.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu, C.T. and L.T. Chiu, \u003cem\u003eThe Impact of Psychological Distress on Cervical Cancer\u003c/em\u003e. Cancers (Basel), 2023. 15(4).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBradley, S., S. Rose, S. Lutgendorf, E. Costanzo, and B. Anderson, \u003cem\u003eQuality of life and mental health in cervical and endometrial cancer survivors\u003c/em\u003e. Gynecol Oncol, 2006. 100(3): p.\u0026nbsp;479\u0026ndash;86.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003evan Stein, K., K. Schubert, B. Ditzen, and C. Weise, \u003cem\u003eUnderstanding Psychological Symptoms of Endometriosis from a Research Domain Criteria Perspective\u003c/em\u003e. J Clin Med, 2023. 12(12).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eO'Mara, T.A., D.M. Glubb, F. Amant, D. Annibali, K. Ashton, J. Attia, \u003cem\u003eet al.\u003c/em\u003e, \u003cem\u003eIdentification of nine new susceptibility loci for endometrial cancer\u003c/em\u003e. Nat Commun, 2018. 9(1): p.\u0026nbsp;3166.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePhelan, C.M., K.B. Kuchenbaecker, J.P. Tyrer, S.P. Kar, K. Lawrenson, S.J. Winham, \u003cem\u003eet al.\u003c/em\u003e, \u003cem\u003eIdentification of 12 new susceptibility loci for different histotypes of epithelial ovarian cancer.\u003c/em\u003e Nat Genet, 2017. 49(5): p.\u0026nbsp;680\u0026ndash;691.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXu, D., Y. Chen, X. Gao, W. Xie, Y. Wang, J. Shen, \u003cem\u003eet al.\u003c/em\u003e, \u003cem\u003eThe genetically predicted causal relationship of inflammatory bowel disease with bone mineral density and osteoporosis: evidence from two-sample Mendelian randomization\u003c/em\u003e. Front Immunol, 2023. 14: p.\u0026nbsp;1148107.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYavorska, O.O. and S. Burgess, \u003cem\u003eMendelianRandomization: an R package for performing Mendelian randomization analyses using summarized data\u003c/em\u003e. Int J Epidemiol, 2017. 46(6): p.\u0026nbsp;1734\u0026ndash;1739.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBowden, J., G. Davey Smith, P.C. Haycock, and S. Burgess, \u003cem\u003eConsistent Estimation in Mendelian Randomization with Some Invalid Instruments Using a Weighted Median Estimator\u003c/em\u003e. Genet Epidemiol, 2016. 40(4): p.\u0026nbsp;304\u0026ndash;14.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBowden, J., G. Davey Smith, and S. Burgess, \u003cem\u003eMendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression\u003c/em\u003e. Int J Epidemiol, 2015. 44(2): p.\u0026nbsp;512\u0026ndash;25.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVerbanck, M., C.Y. Chen, B. Neale, and R. Do, \u003cem\u003eDetection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases\u003c/em\u003e. Nat Genet, 2018. 50(5): p.\u0026nbsp;693\u0026ndash;698.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSpurio, M.G., \u003cem\u003eThe magic of caressing the mind by touching the body. Take care of depression, face up to cancer. A new frontier of psycho-oncology\u003c/em\u003e. Psychiatr Danub, 2017. 29(Suppl 3): p.\u0026nbsp;383\u0026ndash;388.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGui, H., X. Chen, L. Li, L. Zhu, Q. Jing, Y. Nie, \u003cem\u003eet al.\u003c/em\u003e, \u003cem\u003ePsychological distress influences lung cancer: Advances and perspectives on the immune system and immunotherapy\u003c/em\u003e. Int Immunopharmacol, 2023. 121: p.\u0026nbsp;110251.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCohen, S. and T.B. Herbert, \u003cem\u003eHealth psychology: psychological factors and physical disease from the perspective of human psychoneuroimmunology\u003c/em\u003e. Annu Rev Psychol, 1996. 47: p.\u0026nbsp;113\u0026ndash;42.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLugović-Mihić, L., H. Cvitanović, I. Djaković, M. Kuna, and A. Šešerko, \u003cem\u003eThe Influence of Psychological Stress on HPV Infection Manifestations and Carcinogenesis\u003c/em\u003e. Cell Physiol Biochem, 2021. 55(S2): p.\u0026nbsp;71\u0026ndash;88.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eM\u0026aacute;rki, G., A. Bokor, J. Rig\u0026oacute;, and A. Rig\u0026oacute;, \u003cem\u003ePhysical pain and emotion regulation as the main predictive factors of health-related quality of life in women living with endometriosis\u003c/em\u003e. Hum Reprod, 2017. 32(7): p.\u0026nbsp;1432\u0026ndash;1438.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBaciu, I., A. Irimie-Ana, A. Panaitescu, G. Peltecu, C. Gica, and N. Gica, \u003cem\u003eThe social and the psychological impact of endometriosis on the Romanian urban population\u003c/em\u003e. Journal of Mind and Medical Sciences, 2021. 8(1): p.\u0026nbsp;120\u0026ndash;126.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAerts, L., L. Grangier, I. Streuli, P. D\u0026auml;llenbach, R. Marci, J.M. Wenger, \u003cem\u003eet al.\u003c/em\u003e, \u003cem\u003ePsychosocial impact of endometriosis: From co-morbidity to intervention\u003c/em\u003e. Best Pract Res Clin Obstet Gynaecol, 2018. 50: p.\u0026nbsp;2\u0026ndash;10.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHaruta, S., N. Furukawa, Y. Yoshizawa, T. Tsunemi, A. Nagai, R. Kawaguchi, \u003cem\u003eet al.\u003c/em\u003e, \u003cem\u003eMolecular genetics and epidemiology of epithelial ovarian cancer (Review)\u003c/em\u003e. Oncol Rep, 2011. 26(6): p.\u0026nbsp;1347\u0026ndash;56.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHunn, J. and G.C. Rodriguez, \u003cem\u003eOvarian cancer: etiology, risk factors, and epidemiology\u003c/em\u003e. Clin Obstet Gynecol, 2012. 55(1): p.\u0026nbsp;3\u0026ndash;23.\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":"Mendelian randomization, Mood swing, Genetic relationship, Gynecological disorders","lastPublishedDoi":"10.21203/rs.3.rs-3261471/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3261471/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eIncreasing number of observational studies have associated mood instability to common female diseases, but the underlying causal relationship remains unclear. In this work, Mendelian randomization (MR) analysis was applied to explore the genetically predicted causal relationship of mood swings and several prevalent gynecological disorders.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eInstrumental variables (IVs) of mood swings were selected from UK Biobank (UKB), with 204,412 cases and 247,207 controls being incorporated. The genetic variants for female disorders were obtained from genome-wide association studies (GWASs) and FinnGen consortium. To avoid biases caused by racial difference, only European population was included here. Five strong analytical methodologies were used to increase the validity of the results, the most substantial of which was the inverse variance weighting (IVW) method. Pleiotropy, sensitivity, and heterogeneity were assessed to strengthen the findings.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eWe found mood swings was significantly positively associated with risk of endometrial cancer (OR\u0026thinsp;=\u0026thinsp;2.60 [95%CI\u0026thinsp;=\u0026thinsp;1.36, 4.95], P\u0026thinsp;=\u0026thinsp;0.0037), cervical cancer (OR\u0026thinsp;=\u0026thinsp;1.01[95%CI\u0026thinsp;=\u0026thinsp;1.00,1.02], P\u0026thinsp;=\u0026thinsp;0.0213) and endometriosis (OR\u0026thinsp;=\u0026thinsp;2.58 [95%CI\u0026thinsp;=\u0026thinsp;1.18, 5.60], P\u0026thinsp;=\u0026thinsp;0.0170) by IVW method. However, there was no causal relationship between mood swing and ovarian cancer. No pleiotropy and heterogeneity existed and sensitivity tests were passed.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThis study reveals genetically predicted causal relationships between mood swing and the risk of endometrial cancer, cervical cancer and endometriosis in European populations through MR analysis, which makes up for observational research's inherent limitations.\u003c/p\u003e","manuscriptTitle":"Causal relationship between mood swing and gynecological disorders: a Mendelian randomization study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-08-21 15:56:45","doi":"10.21203/rs.3.rs-3261471/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":"76a80701-1195-464f-9723-8bcf45625703","owner":[],"postedDate":"August 21st, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2023-08-31T09:44:26+00:00","versionOfRecord":[],"versionCreatedAt":"2023-08-21 15:56:45","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3261471","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3261471","identity":"rs-3261471","version":["v1"]},"buildId":"WvIrzKhiLBfengagbw6Ux","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}