Genetic Liability to Antidepressant Treatment and Risk of Female Infertility: A Two-Sample Bidirectional Mendelian Randomization Study

Genetic Liability to Antidepressant Treatment and Risk of Female Infertility: A Two-Sample Bidirectional 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 Genetic Liability to Antidepressant Treatment and Risk of Female Infertility: A Two-Sample Bidirectional Mendelian Randomization Study Menghua Wang, Baixue Bai, Tong Yin, Jingya Zhong, Kangwen Ming This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9057307/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract This study applied a two‑sample bidirectional Mendelian randomization (MR) design using genetic summary statistics from the FinnGen R9 database to examine the causal link between genetic predisposition to antidepressant treatment and female infertility, including relevant subtypes. Seventy-seven independent Single Nucleotide Polymorphisms(SNPs)were selected as instrumental variables for antidepressant treatment liability. The inverse-variance weighted (IVW) analysis suggested that genetically proxied antidepressant treatment was associated with a modestly increased risk of overall female infertility (OR = 1.119, 95% CI: 1.024–1.222, p = 0.013). No significant association was observed for anovulatory infertility. Reverse MR analysis did not support a causal effect of infertility on antidepressant treatment liability. Sensitivity analyses revealed no evidence of heterogeneity or horizontal pleiotropy. These findings suggest that genetic predisposition to antidepressant treatment is associated with female infertility risk. However, the genetic instrument captures a composite liability reflecting underlying psychiatric vulnerability and treatment decisions rather than a pure pharmacological drug effect. Therefore, the results should not be interpreted as direct evidence of antidepressant-induced infertility. Our findings underscore the clinical importance of considering mental health in the context of reproductive counseling, while cautioning that the genetic instrument does not isolate a pure drug effect. Antidepressant use Female infertility Mendelian randomization Ovulatory disorder Reproductive health Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction The rising global incidence of infertility poses a substantial burden on individuals and societies alike 1,2 . Concurrently, mental disorders represent a leading cause of disability worldwide, with profound personal and economic impacts 3,4 . Among these, depression is highly prevalent, and the use of antidepressant medication among women of reproductive age is common, with estimates ranging from approximately 3.5% to 10% 5 . Cohort studies further suggest that a history of depression is associated with a subsequently elevated risk of infertility 6 . This observed association may be underpinned by complex biological pathways, as genetic studies have revealed overlapping architectures between psychiatric disorders and reproductive traits 7-9 . For instance, genetic liability to schizophrenia has been associated with age at first birth 7,8 , and female reproductive traits show genetic correlations with major depressive disorder 9,10 . These findings suggest that the relationship between mental health and fertility is not merely environmental but may involve shared genetic etiologies. However, evidence regarding the direct impact of antidepressant medications on fertility remains inconsistent and debated. For example, some studies report that maternal antidepressant use may be linked to an increased risk of early miscarriage 11 . In contrast, a systematic review concludes that there is currently insufficient evidence to support that selective serotonin reuptake inhibitors (SSRIs)—the most commonly prescribed antidepressants—reduce fertility or compromise infertility treatment outcomes, which may be due to heterogeneity in the design and quality of the existing studies 5 . The inconsistency in existing evidence largely stems from methodological constraints inherent in conventional observational study designs. On one hand, these designs are susceptible to residual confounding from factors such as depression severity, comorbid physical conditions, lifestyle behaviors (including smoking, alcohol consumption, and physical inactivity), and socioeconomic status 12-15 . Individuals with mental health conditions often exhibit higher rates of smoking and substance use, poorer nutrition, and lower educational attainment—all of which are independently associated with adverse reproductive outcomes 13,15,16 . Disentangling the effects of medication from those of the underlying illness and associated lifestyle factors is therefore exceedingly difficult in traditional epidemiological research. More critically, the issue of reverse causation poses a fundamental challenge to causal interpretation. Infertility and psychological distress are closely intertwined in a bidirectional relationship. Women experiencing significant infertility-related stress are more likely to receive antidepressant prescriptions following assisted reproductive technology treatment, regardless of whether the procedure ultimately results in conception 17 . Furthermore, reproductive events and hormonal transitions across the female lifespan—from menarche to menopause—profoundly influence mental health outcomes 18-20 . Early menarche has been associated with increased risk of psychopathological symptoms and psychiatric disorders 21-23 , while the perimenopausal period represents a time of heightened vulnerability to depression 20,24 . These complex reciprocal relationships between reproductive function and mental health further complicate causal inference regarding antidepressant use and infertility. Given these challenges, the present study employs a bidirectional Mendelian randomization (MR) framework to address these methodological limitations. MR uses genetic variants as instrumental variables for modifiable risk factors, leveraging the principle of "natural randomization"—whereby genetic inheritance mimics the random assignment of individuals to exposure groups at conception 25,26 . This approach minimizes confounding from postnatal environmental factors and reduces bias due to reverse causation, thereby offering more robust evidence for causal inference 27,28 . The two-sample MR design, which utilizes summary statistics from non-overlapping genome-wide association study (GWAS) datasets for the exposure and outcome, further increases statistical power and facilitates the analysis of multiple exposure-outcome pairs 29,30 . Importantly, sensitivity analyses including MR-Egger, weighted median, and leave-one-out methods allow for the detection and correction of horizontal pleiotropy—a key violation of MR assumptions 31,32 . To date, the causal relationship between antidepressant use and female infertility has not been formally investigated using MR methods. Given the high prevalence of antidepressant prescribing in women of reproductive age and the substantial personal and societal burden of infertility, clarifying this relationship has important clinical and public health implications. Recent large-scale GWAS have identified numerous genetic variants associated with antidepressant medication use, as well as with female infertility and its subtypes, providing the necessary instruments for such an analysis 33,34 . The FinnGen R9 release offers comprehensive, well-powered GWAS summary statistics for both traits in European-ancestry populations, enabling robust two-sample MR analyses. Therefore, this study utilizes large-scale GWAS summary data from FinnGen R9 to perform a two-sample bidirectional MR analysis (Figure 1). We aim to evaluate the putative causal relationship between genetic liability to being prescribed antidepressant medication and the risk of female infertility (overall and ovulatory disorder subtypes). It is critical to note that the genetic instruments for "Depression medications" are proxies for a complex trait that encompasses the severity of underlying mental health conditions and the consequent clinical decision to treat, rather than a pure measure of drug exposure. Our analysis thus estimates the lifetime effect of this genetic predisposition. Based on the existing observational evidence and the known genetic overlaps between psychiatric disorders and reproductive traits, we hypothesized that genetic liability to antidepressant treatment would be associated with an increased risk of female infertility, and that reverse causation would not be supported. Methods Study Design and Data Sources This study employed a two-sample bidirectional MR design to investigate the causal relationship between antidepressant use and female infertility. All analyses utilized publicly available genetic association summary statistics from the FinnGen consortium release 9 (R9), comprising data from participants of European ancestry. The exposure was defined by the genetic variants associated with the ‘Depression medications’ phenotype in FinnGen (code: finn-b-ANTIDEPRESSANTS, n = 118669), which is based on recorded prescriptions for antidepressant drugs. The primary outcome was female infertility (code: finn-b-N14_FEMALEINFERT, n = 75450), with a key subgroup outcome of female infertility associated with anovulation (code: finn-b-N14_FIANOV, n = 118152) analyzed to explore etiological specificity. As this study involved the re-analysis of published, de-identified summary data, no separate ethical approval was required. In all analyses and figures, “antidepressant use” refers to the genetic instrument for the liability to this exposure, derived from the FinnGen “Depression medications” GWAS. Selection of Genetic Instrumental Variables and Statistical Analysis We extracted SNPs associaWe identified genetic variants associated with the exposure (“Depression medications”) at the genome-wide significance threshold ( p < 5 × 10⁻⁸). To obtain independent instrumental variables, we performed LD clumping using the clump_data function from the TwoSampleMR R package (version 0.5.7), with reference to the European panel from the 1000 Genomes Project (phase 3). The clumping parameters were set as follows: r² threshold < 0.001, physical distance window = 10,000 kb. Only the SNP with the lowest p-value (lead SNP) in each LD block was retained. During subsequent harmonization, palindromic SNPs with ambiguous strand orientation (such as A/T or G/C variants) were excluded to ensure accurate allele alignment between the exposure and outcome datasets. The harmonization was performed using the harmonise_data function with default settings, which aligns effect alleles and removes incompatible or strand-ambiguous SNPs. The strength of each IV set was evaluated by calculating the F-statistic using the formula F = (beta² / se²) derived from GWAS summary statistics; a mean F-statistic > 10 across all SNPs was considered indicative of strong instruments and a low risk of weak instrument bias. The primary causal estimate was derived using the IVW method under a fixed-effects model. To test the robustness of the results and key MR assumptions, we conducted a series of sensitivity analyses: (1) the weighted median method, which provides a consistent estimate even if up to 50% of the IVs are invalid; (2) MR-Egger regression, which allows for directional pleiotropy and provides an intercept term to test for its presence (a non-zero intercept p -value < 0.05 suggests significant horizontal pleiotropy); and (3) leave-one-out analysis, iteratively removing each SNP to assess if the overall estimate was driven by any single influential variant. Heterogeneity across SNP-specific Wald ratio estimates was quantified using Cochran’s Q statistic, with a p-value < 0.05 indicating significant heterogeneity. To examine reverse causation, we performed a complementary MR analysis, selecting IVs for female infertility ( p < 5 × 10⁻⁸, clumped with the same parameters) as the exposure and antidepressant use as the outcome. All statistical analyses were conducted in R (version 4.3.1) using the Two Sample MR package. Results Characteristics of Instrumental Variables In the forward MR analysis evaluating the effect of genetic liability to antidepressant treatment (proxied by the “Depression medications” phenotype) on infertility outcomes, 77 independent SNPs associated with antidepressant use at the genome-wide significance threshold ( p < 5 × 10⁻⁸) were selected as instrumental variables. For the reverse MR analysis, 25 SNPs were selected as instruments for female infertility. The mean F-statistics for both exposure IV sets substantially exceeded the threshold of 10 (Table 1), indicating a low risk of weak instrument bias and ensuring the robustness of subsequent causal estimates. Table 1 . Summary of MR Results for Exposure → Outcome IVW weighted median MR Egger SNP F OR (95% CI) p OR (95% CI) p OR (95% CI) p Antidepressant use → Female Infertility 77 20.93 1.119 (1.024–1.222) 0.0126 1.089 (0.959–1.237) 0.1898 1.075 (0.861–1.341) 0.5240 Antidepressant use → Female infertility, associated with anovulation 77 20.93 1.125 (0.908–1.394) 0.2803 1.048 (0.768–1.431) 0.7674 1.568 (0.917–2.681) 0.1044 Female Infertility → Antidepressant use 25 21.59 1.004 (0.960–1.050) 0.8669 1.003 (0.943–1.068) 0.9133 0.981 (0.883–1.090) 0.7256 Forward MR: Effect of Genetic Liability to Antidepressant Use on Infertility The primary analysis using the inverse-variance weighted (IVW) method indicated a significant positive causal association of genetically predicted antidepressant use on the risk of female infertility (OR = 1.119, 95% CI: 1.024–1.222, p = 0.013) (Table 1; Figure 2A). This finding was visually supported by the scatter plot (Figure 2A) and the consistency of individual SNP estimates in the forest plot (Figure 2D). Leave-one-out sensitivity analysis confirmed that the overall result was not driven by any single influential SNP (Figure 2C). In the pre-specified subgroup analysis focusing on etiological subtypes, we found no significant evidence for a causal effect of antidepressant use on the risk of female infertility specifically associated with anovulation (IVW OR = 1.13, 95% CI: 0.91–1.39, p = 0.2803) (Table 1; Figure 3A-D). Sensitivity analyses using complementary MR methods yielded directionally consistent but statistically non-significant point estimates for the effect on overall female infertility (Weighted Median OR = 1.09, 95% CI: 0.96–1.24, p = 0.190; MR-Egger OR = 1.08, 95% CI: 0.86–1.34, p = 0.524) (Table 1). Reverse Mendelian Randomization: Effect of Female Infertility on Antidepressant Use Reverse MR analysis, which assessed the potential for reverse causation, found no significant causal effect of genetically predicted female infertility on antidepressant use (IVW OR = 1.00, 95% CI: 0.96–1.05, p = 0.8669) (Table 1; Figure 4A-D). Results from the Weighted Median and MR-Egger methods were concordant with this null finding (Table 1). Assessment of Heterogeneity and Pleiotropy Cochran’s Q test indicated no significant heterogeneity among the variant-specific estimates for the primary exposure-outcome analysis (IVW Q = 72.87, df = 76, p = 0.581; MR-Egger Q = 72.72, df = 75, p = 0.553) (Table 2). The funnel plot showed a symmetrical distribution of SNP effects (Figure 2B). Furthermore, the MR-Egger intercept test revealed no significant evidence of horizontal pleiotropy (intercept = 0.0029, SE = 0.0077, p = 0.703) (Table 3), supporting the validity of the instrumental variable assumptions. Table 2 . Heterogeneity was assessed using two methods, and both showed no evidence of heterogeneity Exposure Outcome Methods Q statistic Q_df p -value Antidepressant use Female infertility MR Egger 72.72 75 0.553 Antidepressant use Female infertility IVW 72.87 76 0.581 Table 3 . MR-Egger Intercept Test for Horizontal Pleiotropy Exposure Outcome Egger-intercept se p -value Antidepressant use Female infertility 0.0029409 0.0076705 0.703 Discussion In this two-sample bidirectional Mendelian randomization study using summary statistics from the FinnGen consortium R9 release, we observed that genetic liability to antidepressant treatment was associated with a modestly increased risk of overall female infertility. No statistically significant association was detected for infertility related to anovulation. Reverse-direction analyses did not provide evidence supporting a causal effect of female infertility on antidepressant treatment liability. The direction of effect was broadly consistent across sensitivity analyses; however, only the inverse-variance weighted estimate reached statistical significance. No substantial heterogeneity or horizontal pleiotropy was detected. The observed association between genetic liability to antidepressant treatment and female infertility adds to a growing body of evidence implicating psychiatric and behavioral factors in reproductive health outcomes. Previous research has demonstrated significant genetic overlap between psychiatric disorders and reproductive traits 7,8,10 . For instance, Mehta and colleagues (2016) reported genetic correlations between schizophrenia and age at first birth in women, while Ni et al. (2018) found that genetic risk for schizophrenia was associated with later age at first birth. More recently, Wang et al. (2023) 9 identified bidirectional causal relationships between female reproductive traits and major depressive disorder. These findings collectively suggest that the genetic architecture underlying mental health and fertility is intertwined, and our results are consistent with this broader framework of shared genetic etiology. However, it is important to emphasize that our exposure—genetic liability to antidepressant treatment—is not equivalent to genetic liability to depression itself, but rather captures a complex phenotype encompassing psychiatric vulnerability, healthcare-seeking behavior, and clinical prescribing practices. The discrepancy between overall infertility and the anovulatory subtype warrants cautious interpretation. Ovulatory disorders are typically linked to dysregulation of the hypothalamic–pituitary–ovarian (HPO) axis 35-37 . The absence of a statistically significant association for anovulatory infertility may suggest that central ovulatory dysfunction is unlikely to be the primary pathway underlying the observed association. Alternative mechanisms may include effects on implantation, endometrial receptivity, or early pregnancy maintenance. Experimental studies have suggested that selective serotonin reuptake inhibitors can influence sperm function and fertilization in vitro 38 , and serotonin signaling has been implicated in reproductive physiology across multiple species 39,40 . However, given the limited statistical power for this subtype analysis and the indirect nature of the genetic instrument, definitive mechanistic inferences cannot be drawn. It remains possible that the observed association reflects mechanisms beyond ovulatory regulation, although this hypothesis requires direct biological investigation. Our findings are broadly consistent with prior experimental and observational evidence, although important distinctions should be acknowledged. Prospective cohort studies have reported associations between antidepressant use during attempts to conceive and reduced fecundability, even after adjustment for depressive history 41,42 . However, observational designs remain susceptible to residual confounding and indication bias. Individuals with mental health conditions often exhibit higher rates of smoking and substance use 12,13 , poorer nutritional status 12 , lower educational attainment 15 , and reduced physical activity 14 —all of which are independently associated with adverse reproductive outcomes 16 . Furthermore, socioeconomic factors and health behaviors cluster with psychiatric morbidity, making it exceedingly difficult to isolate medication effects in traditional epidemiological studies. The MR design mitigates these concerns by leveraging the random assortment of genetic variants at conception, which is theoretically independent of confounding factors 25,26 . Moreover, previous Mendelian randomization analyses have suggested that depressive symptoms per se may not be strongly associated with female infertility 43 , raising the possibility that the genetic liability captured in our study reflects complex pathways beyond depressive symptomatology alone. This interpretation aligns with evidence that the genetic architecture of antidepressant treatment response and prescription patterns is distinct from that of depression susceptibility 33 . The phenotype "Depression medications" in the FinnGen database captures individuals who have been prescribed antidepressants, which may be influenced by factors such as symptom severity, treatment-seeking behavior, clinician prescribing practices, and healthcare system access. Taken together, these findings are compatible but not conclusive, and should be interpreted within the limitations of the genetic proxy used. The principal strength of this study lies in the application of a bidirectional Mendelian randomization design, which reduces confounding and reverse causation compared with conventional observational approaches 26,27 . The use of non-overlapping GWAS summary statistics for exposure and outcome minimizes bias from sample overlap 32 . The F-statistics for all instrumental variables substantially exceeded the conventional threshold of 10, indicating a low risk of weak instrument bias 30 . Comprehensive sensitivity analyses, including MR-Egger, weighted median, and leave-one-out methods, provided consistent results and supported the robustness of our findings 31,32 . The absence of significant heterogeneity (Cochran's Q p > 0.05) and horizontal pleiotropy (MR-Egger intercept p = 0.703) further reinforces the validity of the instrumental variable assumptions. Nevertheless, several limitations merit careful consideration. First, the "Depression medications" phenotype represents a composite liability encompassing underlying psychiatric vulnerability, health-seeking behavior, and prescribing practices, rather than a direct measure of pharmacological exposure. As such, our estimates do not isolate a pure drug effect. This distinction is critical for clinical interpretation: the genetic instrument captures a lifelong propensity toward antidepressant treatment, which may reflect the severity and chronicity of underlying mental health conditions rather than the pharmacological action of the medications themselves. Second, although no statistical evidence of horizontal pleiotropy was detected, undetected pleiotropic pathways cannot be fully excluded. Genetic variants associated with antidepressant prescribing may influence infertility through mechanisms unrelated to medication use, such as shared biological pathways linking psychiatric vulnerability to reproductive function 34,44 . Third, the exposure reflects treatment conditional on psychiatric diagnosis, which introduces the possibility of index event bias—a form of collider bias that can occur when selection is conditioned on an intermediate event 45 . Fourth, the analysis was restricted to individuals of European ancestry from FinnGen, potentially limiting generalizability to other populations. Reproductive traits and psychiatric phenotypes exhibit substantial diversity across ancestral groups 46,47 , and future studies in non-European populations are warranted. An additional consideration relates to the complex bidirectional relationships between reproductive events and mental health across the female lifespan. Early menarche has been associated with increased risk of psychopathological symptoms and psychiatric disorders 21-23 , while the perimenopausal period represents a time of heightened vulnerability to depression 20,24 . Hormonal fluctuations across the menstrual cycle and reproductive transitions can influence mood and treatment response 18,19 , and estrogen has been hypothesized to play a protective role in schizophrenia and other psychiatric conditions 48-50 . These intricate interrelationships underscore the importance of considering reproductive life stage in studies of mental health and fertility, and highlight the need for future research with more granular phenotypic data. The timing of reproductive events also merits consideration. Advanced parental age has been associated with increased risk of psychiatric disorders in offspring 45,51,52 , including schizophrenia 53,54 and autism 52 . Conversely, early age at first sexual intercourse has been linked to adverse mental health outcomes 55-57 and risky health behaviors 58,59 . These findings suggest complex intergenerational and developmental pathways that cannot be captured in the present analysis. Mendelian randomization studies using age-at-first-birth or age-at-menarche as exposures have revealed bidirectional relationships with psychiatric disorders 7-9 , highlighting the need for more comprehensive life-course approaches. From a clinical perspective, the observed association was modest in magnitude and should not be interpreted as evidence that antidepressant therapy directly causes infertility. Rather, the findings underscore the importance of integrated mental and reproductive health assessment in women of childbearing age. Women with psychiatric disorders may benefit from preconception counseling that addresses both mental health optimization and fertility considerations 60,61 . Decisions regarding antidepressant treatment should remain individualized and guided by established clinical risk–benefit considerations, weighing the substantial morbidity associated with untreated psychiatric illness against potential reproductive consequences. For women attempting to conceive or undergoing fertility treatment, multidisciplinary care involving psychiatrists, reproductive endocrinologists, and primary care providers is essential to optimize outcomes for both mother and child. Future research should aim to disentangle medication effects from underlying psychiatric liability using alternative methodologies. Triangulation of evidence across multiple study designs—including pharmacoepidemiological studies with rigorous confounding control, sibling comparison designs, and negative control exposures—would strengthen causal inference 16,25 . Mendelian randomization studies using genetic variants specific to drug target genes or metabolic pathways could help isolate pharmacological effects from behavioral and socioeconomic confounders. Additionally, studies in pregnancy cohorts with detailed medication records, depression severity assessments, and fertility outcomes are needed to complement genetic evidence. The inclusion of diverse ancestral populations and consideration of reproductive life stage will be essential for generalizability and precision. Conclusion In conclusion, this MR study provides genetic evidence consistent with a modest association between genetic liability to antidepressant treatment and overall female infertility. Given the composite nature of the genetic instrument—capturing underlying psychiatric vulnerability, healthcare-seeking behavior, and prescribing practices—these findings should not be interpreted as direct pharmacological effects of antidepressant medications. Rather, they highlight the complex interplay between mental health and reproductive function, and underscore the importance of integrated care for women of reproductive age. No evidence of reverse causation was found, supporting the directional interpretation that psychiatric liability precedes infertility risk. Further research employing complementary methodologies is required to disentangle medication effects from underlying psychiatric liability, and to elucidate the biological mechanisms linking mental health and fertility across the female lifespan. Declarations Acknowledgments We extend our sincere gratitude to the FinnGen consortium for providing the genetic summary statistics used in this study. We also thank our colleagues from the Acupuncture and Rehabilitation Department for their insightful discussions and technical support. Funding This work was supported by the following grants: Major Difficult Diseases Clinical Collaboration Project between Chinese and Western Medicine from the National Administration of Traditional Chinese Medicine (Grant No. ZDYN-2024-A-0128). Key Enterprise-University Joint Project from the Guangzhou Municipal Science and Technology Bureau (Grant No. 2024A03J0796). Guangzhou Key Laboratory of Traditional Chinese Medicine Rehabilitation (Grant No. 2024A03J0790). Conflict of Interest The authors declare no conflicts of interest, financial or otherwise, that could be construed as influencing the research presented in this manuscript. Author Contributions MW: Conceptualization, Methodology, Formal Analysis, Writing – Original Draft. BB and TY: Data Curation, Investigation, Visualization, Writing – Review & Editing. BB and JZ: Resources, Validation, Software. KM: Supervision, Project Administration, Funding Acquisition, Writing – Review & Editing. All authors read and approved the final manuscript. Ethics Approval And Consent To Participate As this study involved the re-analysis of published, de-identified summary data, no separate ethical approval was required. Trial Registration Not applicable. Data Availability Statement The datasets supporting the conclusions of this article are available in the FinnGen repository (Release 9), as described in the FinnGen study 62 . The specific genome-wide association study (GWAS) summary statistics used in this study can be accessed via the IEU OpenGWAS database (https://gwas.mrcieu.ac.uk/) using the following dataset identifiers: Antidepressant use: finn-b-ANTIDEPRESSANTS Female infertility: finn-b-N14_FEMALEINFERT Female infertility associated with anovulation: finn-b-N14_FIANOV Direct links to each dataset are: https://gwas.mrcieu.ac.uk/datasets/finn-b-ANTIDEPRESSANTS/ https://gwas.mrcieu.ac.uk/datasets/finn-b-N14_FEMALEINFERT/ https://gwas.mrcieu.ac.uk/datasets/finn-b-N14_FIANOV/ No original data were generated for this study. References Fauser, B. et al. Declining global fertility rates and the implications for family planning and family building: an IFFS consensus document based on a narrative review of the literature. Hum Reprod Update 30 , 153–173, doi:10.1093/humupd/dmad028 (2024). Harris, E. Infertility Affects 1 in 6 People Globally. JAMA 329 , 1443, doi:10.1001/jama.2023.6251 (2023). Collaborators, G. B. D. M. D. Global, regional, and national burden of 12 mental disorders in 204 countries and territories, 1990-2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet Psychiatry 9 , 137–150, doi:10.1016/S2215-0366(21)00395-3 (2022). Picco, L. et al. Economic burden of multimorbidity among older adults: impact on healthcare and societal costs. BMC Health Serv Res 16 , 173, doi:10.1186/s12913-016-1421-7 (2016). Sylvester, C., Menke, M. & Gopalan, P. Selective Serotonin Reuptake Inhibitors and Fertility: Considerations for Couples Trying to Conceive. Harv Rev Psychiatry 27 , 108–118, doi:10.1097/HRP.0000000000000204 (2019). Liang, C., Chung, H. F., Dobson, A. J. & Mishra, G. D. Association of a history of depression with infertility, miscarriage and stillbirth: a longitudinal cohort study. Epidemiol Psychiatr Sci 33 , e55, doi:10.1017/S2045796024000374 (2024). Ni, G., Gratten, J., Wray, N. R., Lee, S. H. & Schizophrenia Working Group of the Psychiatric Genomics, C. Age at first birth in women is genetically associated with increased risk of schizophrenia. Sci Rep 8 , 10168, doi:10.1038/s41598-018-28160-z (2018). Mehta, D. et al. Evidence for Genetic Overlap Between Schizophrenia and Age at First Birth in Women. JAMA Psychiatry 73 , 497–505, doi:10.1001/jamapsychiatry.2016.0129 (2016). Wang, Z., Lu, J., Weng, W., Fu, J. & Zhang, J. Women's reproductive traits and major depressive disorder: A two-sample Mendelian randomization study. J Affect Disord 326 , 139–146, doi:10.1016/j.jad.2023.01.063 (2023). Ni, G. et al. The genetic relationship between female reproductive traits and six psychiatric disorders. Sci Rep 9 , 12041, doi:10.1038/s41598-019-48403-x (2019). Evans-Hoeker, E. A. et al. Major depression, antidepressant use, and male and female fertility. Fertil Steril 109 , 879–887, doi:10.1016/j.fertnstert.2018.01.029 (2018). Apostolakopoulou, X. A. et al. Sugars, Alcohol, and Caffeine Intake From Drinks Among Outpatients With Mental Health Disorders in Greece: A Pilot Study. Cureus 14 , e21563, doi:10.7759/cureus.21563 (2022). Sagud, M., Mihaljevic Peles, A. & Pivac, N. Smoking in schizophrenia: recent findings about an old problem. Curr Opin Psychiatry 32 , 402–408, doi:10.1097/YCO.0000000000000529 (2019). Curcic, D. et al. Positive impact of prescribed physical activity on symptoms of schizophrenia: randomized clinical trial. Psychiatr Danub 29 , 459–465, doi:10.24869/psyd.2017.459 (2017). Luo, Y. et al. Gender difference in the association between education and schizophrenia in Chinese adults. BMC Psychiatry 20 , 296, doi:10.1186/s12888-020-02700-2 (2020). Wootton, R. E. et al. Associations between health behaviours, fertility and reproductive outcomes: triangulation of evidence in the Norwegian Mother, Father and Child Cohort Study (MoBa). BMC Med 21 , 125, doi:10.1186/s12916-023-02831-9 (2023). Pedro, J. et al. Infertility-related stress and the risk of antidepressants prescription in women: a 10-year register study. Hum Reprod 34 , 1505–1513, doi:10.1093/humrep/dez110 (2019). Soares, C. N., Poitras, J. R. & Prouty, J. Hormone treatment for mood disorders in women. Expert Rev Neurother 2 , 25–34, doi:10.1586/14737175.2.1.25 (2002). Bursalioglu, F. S., Aydin, N., Yazici, E. & Yazici, A. B. The Correlation Between Psychiatric Disorders and Women's Lives. J Clin Diagn Res 7 , 695–699, doi:10.7860/JCDR/2013/5635.2884 (2013). Becker, D. et al. Psychological distress around menopause. Psychosomatics 42 , 252–257, doi:10.1176/appi.psy.42.3.252 (2001). Lien, L., Haavet, O. R. & Dalgard, F. Do mental health and behavioural problems of early menarche persist into late adolescence? A three year follow-up study among adolescent girls in Oslo, Norway. Soc Sci Med 71 , 529–533, doi:10.1016/j.socscimed.2010.05.003 (2010). Deng, F. et al. Early menarche and psychopathological symptoms in young Chinese women. J Womens Health (Larchmt) 20 , 207–213, doi:10.1089/jwh.2010.2102 (2011). Platt, J. M., Colich, N. L., McLaughlin, K. A., Gary, D. & Keyes, K. M. Transdiagnostic psychiatric disorder risk associated with early age of menarche: A latent modeling approach. Compr Psychiatry 79 , 70–79, doi:10.1016/j.comppsych.2017.06.010 (2017). Tiwari, S., Prasad, R., Wanjari, M. B. & Sharma, R. Understanding the Impact of Menopause on Women With Schizophrenia-Spectrum Disorders: A Comprehensive Review. Cureus 15 , e37979, doi:10.7759/cureus.37979 (2023). Lawlor, D. A., Harbord, R. M., Sterne, J. A., Timpson, N. & Davey Smith, G. Mendelian randomization: using genes as instruments for making causal inferences in epidemiology. Stat Med 27 , 1133–1163, doi:10.1002/sim.3034 (2008). Davies, N. M., Holmes, M. V. & Davey Smith, G. Reading Mendelian randomisation studies: a guide, glossary, and checklist for clinicians. BMJ 362 , k601, doi:10.1136/bmj.k601 (2018). Burgess, S., Butterworth, A. & Thompson, S. G. Mendelian randomization analysis with multiple genetic variants using summarized data. Genet Epidemiol 37 , 658–665, doi:10.1002/gepi.21758 (2013). Burgess, S. & Thompson, S. G. Multivariable Mendelian randomization: the use of pleiotropic genetic variants to estimate causal effects. Am J Epidemiol 181 , 251–260, doi:10.1093/aje/kwu283 (2015). Bowden, J. & Holmes, M. V. Meta-analysis and Mendelian randomization: A review. Res Synth Methods 10 , 486–496, doi:10.1002/jrsm.1346 (2019). Sanderson, E., Spiller, W. & Bowden, J. Testing and correcting for weak and pleiotropic instruments in two-sample multivariable Mendelian randomization. Stat Med 40 , 5434–5452, doi:10.1002/sim.9133 (2021). Bowden, J. et al. A framework for the investigation of pleiotropy in two-sample summary data Mendelian randomization. Stat Med 36 , 1783–1802, doi:10.1002/sim.7221 (2017). Burgess, S. & Thompson, S. G. Interpreting findings from Mendelian randomization using the MR-Egger method. Eur J Epidemiol 32 , 377–389, doi:10.1007/s10654-017-0255-x (2017). Giannakopoulou, O. et al. The Genetic Architecture of Depression in Individuals of East Asian Ancestry: A Genome-Wide Association Study. JAMA Psychiatry 78 , 1258–1269, doi:10.1001/jamapsychiatry.2021.2099 (2021). Barban, N. et al. Genome-wide analysis identifies 12 loci influencing human reproductive behavior. Nat Genet 48 , 1462–1472, doi:10.1038/ng.3698 (2016). Mikhael, S., Punjala-Patel, A. & Gavrilova-Jordan, L. Hypothalamic-Pituitary-Ovarian Axis Disorders Impacting Female Fertility. Biomedicines 7 , doi:10.3390/biomedicines7010005 (2019). Practice Committee of the American Society for Reproductive, M. Diagnostic evaluation of the infertile female: a committee opinion. Fertil Steril 103 , e44–50, doi:10.1016/j.fertnstert.2015.03.019 (2015). Valera, H., Chen, A. & Grive, K. J. The Hypothalamic-Pituitary-Ovarian Axis, Ovarian Disorders, and Brain Aging. Endocrinology 166 , doi:10.1210/endocr/bqaf137 (2025). Domingues, R. R., Wiltbank, M. C. & Hernandez, L. L. The antidepressant fluoxetine (Prozac(R)) modulates estrogen signaling in the uterus and alters estrous cycles in mice. Mol Cell Endocrinol 559 , 111783, doi:10.1016/j.mce.2022.111783 (2023). Feng, Y., Qu, X. & Hao, H. Progress in the study of the effects of selective serotonin reuptake inhibitors (SSRIs) on the reproductive system. Front Pharmacol 16 , 1567863, doi:10.3389/fphar.2025.1567863 (2025). Milosavljevic, M. N. et al. Effects of selective serotonin reuptake inhibitors on motility of isolated fallopian tube. Clin Exp Pharmacol Physiol 46 , 780–787, doi:10.1111/1440-1681.13118 (2019). Casilla-Lennon, M. M., Meltzer-Brody, S. & Steiner, A. Z. The effect of antidepressants on fertility. Am J Obstet Gynecol 215 , 314 e311–315, doi:10.1016/j.ajog.2016.01.170 (2016). Sjaarda, L. A. et al. Urinary selective serotonin reuptake inhibitors across critical windows of pregnancy establishment: a prospective cohort study of fecundability and pregnancy loss. Fertil Steril 114 , 1278–1287, doi:10.1016/j.fertnstert.2020.06.037 (2020). Liao, T. Prospective cohort and Mendelian randomization study of the association between preconception depressive symptoms and fertility , Anhui Medical University, (2024). Day, F. R. et al. Physical and neurobehavioral determinants of reproductive onset and success. Nat Genet 48 , 617–623, doi:10.1038/ng.3551 (2016). D'Onofrio, B. M. et al. Paternal age at childbearing and offspring psychiatric and academic morbidity. JAMA Psychiatry 71 , 432–438, doi:10.1001/jamapsychiatry.2013.4525 (2014). Chan, S., Gomes, A. & Singh, R. S. Is menopause still evolving? Evidence from a longitudinal study of multiethnic populations and its relevance to women's health. BMC Womens Health 20 , 74, doi:10.1186/s12905-020-00932-8 (2020). Gold, E. B. The timing of the age at which natural menopause occurs. Obstet Gynecol Clin North Am 38 , 425–440, doi:10.1016/j.ogc.2011.05.002 (2011). Riecher-Rossler, A. & Hafner, H. Schizophrenia and oestrogens--is there an association? Eur Arch Psychiatry Clin Neurosci 242 , 323–328, doi:10.1007/BF02190244 (1993). Seeman, M. V. The role of estrogen in schizophrenia. J Psychiatry Neurosci 21 , 123–127 (1996). Toran-Allerand, C. D. The estrogen/neurotrophin connection during neural development: is co-localization of estrogen receptors with the neurotrophins and their receptors biologically relevant? Dev Neurosci 18 , 36–48, doi:10.1159/000111393 (1996). McGrath, J. J. et al. A comprehensive assessment of parental age and psychiatric disorders. JAMA Psychiatry 71 , 301–309, doi:10.1001/jamapsychiatry.2013.4081 (2014). Wu, S. et al. Advanced parental age and autism risk in children: a systematic review and meta-analysis. Acta Psychiatr Scand 135 , 29–41, doi:10.1111/acps.12666 (2017). Fountoulakis, K. N. et al. Paternal and maternal age as risk factors for schizophrenia: a case-control study. Int J Psychiatry Clin Pract 22 , 170–176, doi:10.1080/13651501.2017.1391292 (2018). Lan, K. C. et al. Association between paternal age and risk of schizophrenia: a nationwide population-based study. J Assist Reprod Genet 38 , 85–93, doi:10.1007/s10815-020-01936-x (2021). Lee, J. The Impact of Sexual Initiation Timing and Sexual Experience on Depressive Symptoms in South Korean Adolescents: Gender Differences in a Nationwide Cross-Sectional Study. J Pediatr Nurs 52 , e15–e20, doi:10.1016/j.pedn.2019.11.009 (2020). Kim, H. S. Sexual Debut and Mental Health Among South Korean Adolescents. J Sex Res 53 , 313–320, doi:10.1080/00224499.2015.1055855 (2016). Lu, Z. et al. Identifying causal associations between early sexual intercourse or number of sexual partners and major depressive disorders: A bidirectional two-sample Mendelian randomization analysis. J Affect Disord 333 , 121–129, doi:10.1016/j.jad.2023.04.079 (2023). Cornelius, J. R., Clark, D. B., Reynolds, M., Kirisci, L. & Tarter, R. Early age of first sexual intercourse and affiliation with deviant peers predict development of SUD: a prospective longitudinal study. Addict Behav 32 , 850–854, doi:10.1016/j.addbeh.2006.06.027 (2007). Kaestle, C. E., Halpern, C. T., Miller, W. C. & Ford, C. A. Young age at first sexual intercourse and sexually transmitted infections in adolescents and young adults. Am J Epidemiol 161 , 774–780, doi:10.1093/aje/kwi095 (2005). Hammond, J. & Lipsedge, M. Assessing Parenting Capacity in Psychiatric Mother and Baby Units: A case report and review of literature. Psychiatr Danub 27 Suppl 1 , S71–83 (2015). Ozcan, N. K., Boyacioglu, N. E., Enginkaya, S., Dinc, H. & Bilgin, H. Reproductive health in women with serious mental illnesses. J Clin Nurs 23 , 1283–1291, doi:10.1111/jocn.12332 (2014). Kurki, M. I. et al. FinnGen provides genetic insights from a well-phenotyped isolated population. Nature 613 , 508–518, doi:10.1038/s41586-022-05473-8 (2023). Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 23 Apr, 2026 Reviewers invited by journal 08 Apr, 2026 Editor assigned by journal 31 Mar, 2026 Submission checks completed at journal 30 Mar, 2026 First submitted to journal 30 Mar, 2026 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-9057307","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":623582715,"identity":"21a355c7-a45a-4a45-9676-f60f7744457f","order_by":0,"name":"Menghua Wang","email":"","orcid":"","institution":"The Affiliated Hospital of Traditional Chinese Medicine, Guangzhou Medical University Guangzhou","correspondingAuthor":false,"prefix":"","firstName":"Menghua","middleName":"","lastName":"Wang","suffix":""},{"id":623582725,"identity":"6a521c4f-38ca-4fa9-a5d9-275d27895991","order_by":1,"name":"Baixue Bai","email":"","orcid":"","institution":"The Affiliated Hospital of Traditional Chinese Medicine, Guangzhou Medical University Guangzhou","correspondingAuthor":false,"prefix":"","firstName":"Baixue","middleName":"","lastName":"Bai","suffix":""},{"id":623582726,"identity":"0e1dce2e-52e1-4ce0-a1d0-73dbd8ef3735","order_by":2,"name":"Tong Yin","email":"","orcid":"","institution":"Guangzhou University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Tong","middleName":"","lastName":"Yin","suffix":""},{"id":623582727,"identity":"cfe579af-98ef-4be2-a4cd-7b1bb32cfb10","order_by":3,"name":"Jingya Zhong","email":"","orcid":"","institution":"The Affiliated Hospital of Traditional Chinese Medicine, Guangzhou Medical University Guangzhou","correspondingAuthor":false,"prefix":"","firstName":"Jingya","middleName":"","lastName":"Zhong","suffix":""},{"id":623582728,"identity":"995ec285-df47-4368-a39d-1b729593e0a9","order_by":4,"name":"Kangwen Ming","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABG0lEQVRIie3PMWsCMRTA8SfCubzimlLafgIh5SAU9MPcUYiLFKFQOggGCrpc6XqFfghFcM41oEsO1xuEbk4O5+bQit7Z0lLOdC00/+klvF8gADbbXwwB5GrTyEYqgQJU9/dlEylFocO/yLH4nZQVOi85gQ9oJrVAU4Uooda/H8p1e37mTtWYwF3dF5VYFhEWBzR6JnNgetKOArpwmea3BHTTF3jtFZLZ0UAu6QJY0qIKqPLHCbKT957yBUF6kKCngL0uc9IdhchIaWMg8cMgQrkjCebEoyQjwkD0LI2eBN8NPPuLugg1v7mESdPtYesA4VfpSjSATdUoXb+p82pfDRPo1E8fK7qQfNYV3w77Tce0/zPj4zabzfb/2gJVzGtUQBWnTAAAAABJRU5ErkJggg==","orcid":"","institution":"The Affiliated Hospital of Traditional Chinese Medicine, Guangzhou Medical University Guangzhou","correspondingAuthor":true,"prefix":"","firstName":"Kangwen","middleName":"","lastName":"Ming","suffix":""}],"badges":[],"createdAt":"2026-03-07 09:39:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9057307/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9057307/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107066477,"identity":"50cb4b34-7798-44b7-8df9-d07ce383c49c","added_by":"auto","created_at":"2026-04-16 11:12:17","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":2910794,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDiagram for Mendelian randomization (MR). \u003c/strong\u003eBidirectional two - sample Mendelian randomization analysis was conducted using FinnGen R9 GWAS summary data. Forward MR indicated a modest causal association between the genetic liability to antidepressant treatment and overall female infertility. No evidence supported reverse causation. Sensitivity analyses confirmed the instrument validity and the absence of horizontal pleiotropy. Genetic instruments represent composite liability rather than direct drug exposure.\u003c/p\u003e","description":"","filename":"figure1workflow.png","url":"https://assets-eu.researchsquare.com/files/rs-9057307/v1/9ab9432d0ad6e4d2ec509061.png"},{"id":107066476,"identity":"7933544d-275b-466c-a51c-7bda011d0fa1","added_by":"auto","created_at":"2026-04-16 11:12:17","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":907197,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMR Analysis of the Effect of Antidepressant Use on Female Infertility\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Scatter plot of SNP effects on antidepressant use (exposure) against female infertility (outcome). The slope of the line represents the IVW estimate. (B) Funnel plot for assessing asymmetry in SNP-specific estimates. (C) Leave-one-out sensitivity analysis showing the influence of individual SNPs on the overall IVW estimate. (D) Forest plot of Wald ratio estimates for each instrumental SNP. Error bars represent 95% confidence intervals.\u003c/p\u003e","description":"","filename":"figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-9057307/v1/69bca6bb79663949fb1180ff.png"},{"id":107066483,"identity":"e8f55c1d-f633-4edc-a2de-fe4e3d9eb3d8","added_by":"auto","created_at":"2026-04-16 11:12:20","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":918991,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMR Analysis of the Effect of Antidepressant Use on The Risk of Female Infertility Associated with Anovulation.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Scatter plot of SNP effects on antidepressant use (exposure) against anovulation-related female infertility (outcome). The slope of the line represents the IVW estimate. (B) Funnel plot for assessing asymmetry in SNP-specific estimates for this outcome. (C) Leave-one-out sensitivity analysis showing the influence of individual SNPs on the overall IVW estimate for anovulatory infertility. (D) Forest plot of Wald ratio estimates for each instrumental SNP on anovulatory infertility. Error bars represent 95% confidence intervals.\u003c/p\u003e","description":"","filename":"figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-9057307/v1/5538f7ea312fcd4e4bef9560.png"},{"id":107066478,"identity":"99002426-3410-4514-be98-c1ca176da88c","added_by":"auto","created_at":"2026-04-16 11:12:18","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":528172,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eReverse\u003c/strong\u003e \u003cstrong\u003eMR Analysis of the Effect of Female Infertility on Antidepressant Use\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Scatter plot of SNP effects on female infertility (exposure) against antidepressant use (outcome). The slope of the line represents the IVW estimate for the reverse direction. (B) Funnel plot for assessing asymmetry in SNP-specific estimates in the reverse MR analysis. (C) Leave-one-out sensitivity analysis showing the influence of individual SNPs on the overall IVW estimate for the reverse causal effect. (D) Forest plot of Wald ratio estimates for each instrumental SNP for female infertility on antidepressant use. Error bars represent 95% confidence intervals.\u003c/p\u003e","description":"","filename":"figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-9057307/v1/19fc8fc9625630190059ec3e.png"},{"id":107482518,"identity":"76f38b7e-7bf8-4580-ae9d-495bc85b6779","added_by":"auto","created_at":"2026-04-22 02:23:50","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5179781,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9057307/v1/35d31385-24dd-4c0b-9a22-6578de2f85a9.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Genetic Liability to Antidepressant Treatment and Risk of Female Infertility: A Two-Sample Bidirectional Mendelian Randomization Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe rising global incidence of infertility poses a substantial burden on individuals and societies alike\u003csup\u003e1,2\u003c/sup\u003e. Concurrently, mental disorders represent a leading cause of disability worldwide, with profound personal and economic impacts\u003csup\u003e3,4\u003c/sup\u003e. Among these, depression is highly prevalent, and the use of antidepressant medication among women of reproductive age is common, with estimates ranging from approximately 3.5% to 10%\u003csup\u003e5\u003c/sup\u003e. Cohort studies further suggest that a history of depression is associated with a subsequently elevated risk of infertility\u003csup\u003e6\u003c/sup\u003e. This observed association may be underpinned by complex biological pathways, as genetic studies have revealed overlapping architectures between psychiatric disorders and reproductive traits\u003csup\u003e7-9\u003c/sup\u003e. For instance, genetic liability to schizophrenia has been associated with age at first birth\u003csup\u003e7,8\u003c/sup\u003e, and female reproductive traits show genetic correlations with major depressive disorder\u003csup\u003e9,10\u003c/sup\u003e. These findings suggest that the relationship between mental health and fertility is not merely environmental but may involve shared genetic etiologies.\u003c/p\u003e\n\u003cp\u003eHowever, evidence regarding the direct impact of antidepressant medications on fertility remains inconsistent and debated. For example, some studies report that maternal antidepressant use may be linked to an increased risk of early miscarriage\u003csup\u003e11\u003c/sup\u003e. In contrast, a systematic review concludes that there is currently insufficient evidence to support that selective serotonin reuptake inhibitors (SSRIs)\u0026mdash;the most commonly prescribed antidepressants\u0026mdash;reduce fertility or compromise infertility treatment outcomes, which may be due to heterogeneity in the design and quality of the existing studies\u003csup\u003e5\u003c/sup\u003e. The inconsistency in existing evidence largely stems from methodological constraints inherent in conventional observational study designs. On one hand, these designs are susceptible to residual confounding from factors such as depression severity, comorbid physical conditions, lifestyle behaviors (including smoking, alcohol consumption, and physical inactivity), and socioeconomic status\u003csup\u003e12-15\u003c/sup\u003e. Individuals with mental health conditions often exhibit higher rates of smoking and substance use, poorer nutrition, and lower educational attainment\u0026mdash;all of which are independently associated with adverse reproductive outcomes\u003csup\u003e13,15,16\u003c/sup\u003e. Disentangling the effects of medication from those of the underlying illness and associated lifestyle factors is therefore exceedingly difficult in traditional epidemiological research.\u003c/p\u003e\n\u003cp\u003eMore critically, the issue of reverse causation poses a fundamental challenge to causal interpretation. Infertility and psychological distress are closely intertwined in a bidirectional relationship. Women experiencing significant infertility-related stress are more likely to receive antidepressant prescriptions following assisted reproductive technology treatment, regardless of whether the procedure ultimately results in conception\u003csup\u003e17\u003c/sup\u003e. Furthermore, reproductive events and hormonal transitions across the female lifespan\u0026mdash;from menarche to menopause\u0026mdash;profoundly influence mental health outcomes\u003csup\u003e18-20\u003c/sup\u003e. Early menarche has been associated with increased risk of psychopathological symptoms and psychiatric disorders\u003csup\u003e21-23\u003c/sup\u003e, while the perimenopausal period represents a time of heightened vulnerability to depression\u003csup\u003e20,24\u003c/sup\u003e. These complex reciprocal relationships between reproductive function and mental health further complicate causal inference regarding antidepressant use and infertility.\u003c/p\u003e\n\u003cp\u003eGiven these challenges, the present study employs a bidirectional Mendelian randomization (MR) framework to address these methodological limitations. MR uses genetic variants as instrumental variables for modifiable risk factors, leveraging the principle of \u0026quot;natural randomization\u0026quot;\u0026mdash;whereby genetic inheritance mimics the random assignment of individuals to exposure groups at conception\u003csup\u003e25,26\u003c/sup\u003e. This approach minimizes confounding from postnatal environmental factors and reduces bias due to reverse causation, thereby offering more robust evidence for causal inference\u003csup\u003e27,28\u003c/sup\u003e. The two-sample MR design, which utilizes summary statistics from non-overlapping genome-wide association study (GWAS) datasets for the exposure and outcome, further increases statistical power and facilitates the analysis of multiple exposure-outcome pairs\u003csup\u003e29,30\u003c/sup\u003e. Importantly, sensitivity analyses including MR-Egger, weighted median, and leave-one-out methods allow for the detection and correction of horizontal pleiotropy\u0026mdash;a key violation of MR assumptions\u003csup\u003e31,32\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eTo date, the causal relationship between antidepressant use and female infertility has not been formally investigated using MR methods. Given the high prevalence of antidepressant prescribing in women of reproductive age and the substantial personal and societal burden of infertility, clarifying this relationship has important clinical and public health implications. Recent large-scale GWAS have identified numerous genetic variants associated with antidepressant medication use, as well as with female infertility and its subtypes, providing the necessary instruments for such an analysis\u003csup\u003e33,34\u003c/sup\u003e. The FinnGen R9 release offers comprehensive, well-powered GWAS summary statistics for both traits in European-ancestry populations, enabling robust two-sample MR analyses.\u003c/p\u003e\n\u003cp\u003eTherefore, this study utilizes large-scale GWAS summary data from FinnGen R9 to perform a two-sample bidirectional MR analysis (Figure 1). We aim to evaluate the putative causal relationship between genetic liability to being prescribed antidepressant medication and the risk of female infertility (overall and ovulatory disorder subtypes). It is critical to note that the genetic instruments for \u0026quot;Depression medications\u0026quot; are proxies for a complex trait that encompasses the severity of underlying mental health conditions and the consequent clinical decision to treat, rather than a pure measure of drug exposure. Our analysis thus estimates the lifetime effect of this genetic predisposition. Based on the existing observational evidence and the known genetic overlaps between psychiatric disorders and reproductive traits, we hypothesized that genetic liability to antidepressant treatment would be associated with an increased risk of female infertility, and that reverse causation would not be supported.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eStudy Design and Data Sources\u003c/p\u003e\n\u003cp\u003eThis study employed a two-sample bidirectional MR design to investigate the causal relationship between antidepressant use and female infertility. All analyses utilized publicly available genetic association summary statistics from the FinnGen consortium release 9 (R9), comprising data from participants of European ancestry. The exposure was defined by the genetic variants associated with the \u0026lsquo;Depression medications\u0026rsquo; phenotype in FinnGen (code: finn-b-ANTIDEPRESSANTS, n = 118669), which is based on recorded prescriptions for antidepressant drugs. The primary outcome was female infertility (code: finn-b-N14_FEMALEINFERT, n = 75450), with a key subgroup outcome of female infertility associated with anovulation (code: finn-b-N14_FIANOV, n = 118152) analyzed to explore etiological specificity. As this study involved the re-analysis of published, de-identified summary data, no separate ethical approval was required. In all analyses and figures, \u0026ldquo;antidepressant use\u0026rdquo; refers to the genetic instrument for the liability to this exposure, derived from the FinnGen \u0026ldquo;Depression medications\u0026rdquo; GWAS.\u003c/p\u003e\n\u003cp\u003eSelection of Genetic Instrumental Variables and Statistical Analysis\u003c/p\u003e\n\u003cp\u003eWe extracted SNPs associaWe identified genetic variants associated with the exposure (\u0026ldquo;Depression medications\u0026rdquo;) at the genome-wide significance threshold (\u003cem\u003ep\u003c/em\u003e \u0026lt; 5 \u0026times; 10⁻⁸). To obtain independent instrumental variables, we performed LD clumping using the clump_data function from the TwoSampleMR R package (version 0.5.7), with reference to the European panel from the 1000 Genomes Project (phase 3). The clumping parameters were set as follows: r\u0026sup2; threshold \u0026lt; 0.001, physical distance window = 10,000 kb. Only the SNP with the lowest p-value (lead SNP) in each LD block was retained. During subsequent harmonization, palindromic SNPs with ambiguous strand orientation (such as A/T or G/C variants) were excluded to ensure accurate allele alignment between the exposure and outcome datasets. The harmonization was performed using the harmonise_data function with default settings, which aligns effect alleles and removes incompatible or strand-ambiguous SNPs.\u003c/p\u003e\n\u003cp\u003eThe strength of each IV set was evaluated by calculating the F-statistic using the formula F = (beta\u0026sup2; / se\u0026sup2;) derived from GWAS summary statistics; a mean F-statistic \u0026gt; 10 across all SNPs was considered indicative of strong instruments and a low risk of weak instrument bias. The primary causal estimate was derived using the IVW method under a fixed-effects model. To test the robustness of the results and key MR assumptions, we conducted a series of sensitivity analyses: (1) the weighted median method, which provides a consistent estimate even if up to 50% of the IVs are invalid; (2) MR-Egger regression, which allows for directional pleiotropy and provides an intercept term to test for its presence (a non-zero intercept \u003cem\u003ep\u003c/em\u003e-value \u0026lt; 0.05 suggests significant horizontal pleiotropy); and (3) leave-one-out analysis, iteratively removing each SNP to assess if the overall estimate was driven by any single influential variant. Heterogeneity across SNP-specific Wald ratio estimates was quantified using Cochran\u0026rsquo;s Q statistic, with a p-value \u0026lt; 0.05 indicating significant heterogeneity. To examine reverse causation, we performed a complementary MR analysis, selecting IVs for female infertility (\u003cem\u003ep\u003c/em\u003e \u0026lt; 5 \u0026times; 10⁻⁸, clumped with the same parameters) as the exposure and antidepressant use as the outcome. All statistical analyses were conducted in R (version 4.3.1) using the Two Sample MR package.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eCharacteristics of Instrumental Variables\u003c/p\u003e\n\u003cp\u003eIn the forward MR analysis evaluating the effect of genetic liability to antidepressant treatment (proxied by the \u0026ldquo;Depression medications\u0026rdquo; phenotype) on infertility outcomes, 77 independent SNPs associated with antidepressant use at the genome-wide significance threshold (\u003cem\u003ep\u003c/em\u003e \u0026lt; 5 \u0026times; 10⁻⁸) were selected as instrumental variables. For the reverse MR analysis, 25 SNPs were selected as instruments for female infertility. The mean F-statistics for both exposure IV sets substantially exceeded the threshold of 10 (Table 1), indicating a low risk of weak instrument bias and ensuring the robustness of subsequent causal estimates.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003cstrong\u003e.\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;Summary of MR Results for Exposure \u0026rarr; Outcome\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"657\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 66px;\"\u003e\n \u003cp\u003eIVW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 66px;\"\u003e\n \u003cp\u003eweighted median\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 87px;\"\u003e\n \u003cp\u003eMR Egger\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003eSNP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003eOR\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003eOR\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003eOR\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003eAntidepressant use \u0026rarr; Female Infertility\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e20.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e1.119 (1.024\u0026ndash;1.222)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.0126\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e1.089 (0.959\u0026ndash;1.237)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.1898\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e1.075 (0.861\u0026ndash;1.341)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e0.5240\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003eAntidepressant use \u0026rarr; Female infertility, associated with anovulation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e20.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e1.125 (0.908\u0026ndash;1.394)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.2803\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e1.048 (0.768\u0026ndash;1.431)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.7674\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e1.568 (0.917\u0026ndash;2.681)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e0.1044\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003eFemale Infertility \u0026rarr; Antidepressant use\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e21.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e1.004 (0.960\u0026ndash;1.050)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.8669\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e1.003 (0.943\u0026ndash;1.068)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.9133\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e0.981 (0.883\u0026ndash;1.090)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e0.7256\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eForward MR: Effect of Genetic Liability to Antidepressant Use on Infertility\u003c/p\u003e\n\u003cp\u003eThe primary analysis using the inverse-variance weighted (IVW) method indicated a significant positive causal association of genetically predicted antidepressant use on the risk of female infertility (OR = 1.119, 95% CI: 1.024\u0026ndash;1.222, \u003cem\u003ep\u003c/em\u003e = 0.013) (Table 1; Figure 2A). This finding was visually supported by the scatter plot (Figure 2A) and the consistency of individual SNP estimates in the forest plot (Figure 2D). Leave-one-out sensitivity analysis confirmed that the overall result was not driven by any single influential SNP (Figure 2C).\u003c/p\u003e\n\u003cp\u003eIn the pre-specified subgroup analysis focusing on etiological subtypes, we found no significant evidence for a causal effect of antidepressant use on the risk of female infertility specifically associated with anovulation (IVW OR = 1.13, 95% CI: 0.91\u0026ndash;1.39, \u003cem\u003ep\u003c/em\u003e = 0.2803) (Table 1; Figure 3A-D).\u003c/p\u003e\n\u003cp\u003eSensitivity analyses using complementary MR methods yielded directionally consistent but statistically non-significant point estimates for the effect on overall female infertility (Weighted Median OR = 1.09, 95% CI: 0.96\u0026ndash;1.24, \u003cem\u003ep\u003c/em\u003e = 0.190; MR-Egger OR = 1.08, 95% CI: 0.86\u0026ndash;1.34, \u003cem\u003ep\u003c/em\u003e = 0.524) (Table 1).\u003c/p\u003e\n\u003cp\u003eReverse Mendelian Randomization: Effect of Female Infertility on Antidepressant Use\u003c/p\u003e\n\u003cp\u003eReverse MR analysis, which assessed the potential for reverse causation, found no significant causal effect of genetically predicted female infertility on antidepressant use (IVW OR = 1.00, 95% CI: 0.96\u0026ndash;1.05, \u003cem\u003ep\u003c/em\u003e = 0.8669) (Table 1; Figure 4A-D). Results from the Weighted Median and MR-Egger methods were concordant with this null finding (Table 1).\u003c/p\u003e\n\u003cp\u003eAssessment of Heterogeneity and Pleiotropy\u003c/p\u003e\n\u003cp\u003eCochran\u0026rsquo;s Q test indicated no significant heterogeneity among the variant-specific estimates for the primary exposure-outcome analysis (IVW Q = 72.87, df = 76, \u003cem\u003ep\u003c/em\u003e = 0.581; MR-Egger Q = 72.72, df = 75, \u003cem\u003ep\u003c/em\u003e = 0.553) (Table 2). The funnel plot showed a symmetrical distribution of SNP effects (Figure 2B). Furthermore, the MR-Egger intercept test revealed no significant evidence of horizontal pleiotropy (intercept = 0.0029, SE = 0.0077, \u003cem\u003ep\u003c/em\u003e = 0.703) (Table 3), supporting the validity of the instrumental variable assumptions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003cstrong\u003e. Heterogeneity was assessed using two methods, and both showed no evidence of heterogeneity\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"582\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003eExposure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003eOutcome\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003eMethods\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003eQ statistic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003eQ_df\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003eAntidepressant use\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003eFemale infertility\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003eMR Egger\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e72.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.553\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003eAntidepressant use\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003eFemale infertility\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003eIVW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e72.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.581\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003cstrong\u003e. MR-Egger Intercept Test for Horizontal Pleiotropy\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"574\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003eExposure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003eOutcome\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003eEgger-intercept\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003ese\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003eAntidepressant use\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003eFemale infertility\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e0.0029409\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e0.0076705\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e0.703\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this two-sample bidirectional Mendelian randomization study using summary statistics from the FinnGen consortium R9 release, we observed that genetic liability to antidepressant treatment was associated with a modestly increased risk of overall female infertility. No statistically significant association was detected for infertility related to anovulation. Reverse-direction analyses did not provide evidence supporting a causal effect of female infertility on antidepressant treatment liability. The direction of effect was broadly consistent across sensitivity analyses; however, only the inverse-variance weighted estimate reached statistical significance. No substantial heterogeneity or horizontal pleiotropy was detected.\u003c/p\u003e\n\u003cp\u003eThe observed association between genetic liability to antidepressant treatment and female infertility adds to a growing body of evidence implicating psychiatric and behavioral factors in reproductive health outcomes. Previous research has demonstrated significant genetic overlap between psychiatric disorders and reproductive traits\u003csup\u003e7,8,10\u003c/sup\u003e. For instance, Mehta and colleagues (2016) reported genetic correlations between schizophrenia and age at first birth in women, while Ni et al. (2018) found that genetic risk for schizophrenia was associated with later age at first birth. More recently, Wang et al. (2023) \u003csup\u003e9\u003c/sup\u003eidentified bidirectional causal relationships between female reproductive traits and major depressive disorder. These findings collectively suggest that the genetic architecture underlying mental health and fertility is intertwined, and our results are consistent with this broader framework of shared genetic etiology. However, it is important to emphasize that our exposure\u0026mdash;genetic liability to antidepressant treatment\u0026mdash;is not equivalent to genetic liability to depression itself, but rather captures a complex phenotype encompassing psychiatric vulnerability, healthcare-seeking behavior, and clinical prescribing practices.\u003c/p\u003e\n\u003cp\u003eThe discrepancy between overall infertility and the anovulatory subtype warrants cautious interpretation. Ovulatory disorders are typically linked to dysregulation of the hypothalamic\u0026ndash;pituitary\u0026ndash;ovarian (HPO) axis\u003csup\u003e35-37\u003c/sup\u003e. The absence of a statistically significant association for anovulatory infertility may suggest that central ovulatory dysfunction is unlikely to be the primary pathway underlying the observed association. Alternative mechanisms may include effects on implantation, endometrial receptivity, or early pregnancy maintenance. Experimental studies have suggested that selective serotonin reuptake inhibitors can influence sperm function and fertilization in vitro\u003csup\u003e38\u003c/sup\u003e, and serotonin signaling has been implicated in reproductive physiology across multiple species\u003csup\u003e39,40\u003c/sup\u003e. However, given the limited statistical power for this subtype analysis and the indirect nature of the genetic instrument, definitive mechanistic inferences cannot be drawn. It remains possible that the observed association reflects mechanisms beyond ovulatory regulation, although this hypothesis requires direct biological investigation.\u003c/p\u003e\n\u003cp\u003eOur findings are broadly consistent with prior experimental and observational evidence, although important distinctions should be acknowledged. Prospective cohort studies have reported associations between antidepressant use during attempts to conceive and reduced fecundability, even after adjustment for depressive history\u003csup\u003e41,42\u003c/sup\u003e. However, observational designs remain susceptible to residual confounding and indication bias. Individuals with mental health conditions often exhibit higher rates of smoking and substance use\u003csup\u003e12,13\u003c/sup\u003e, poorer nutritional status\u003csup\u003e12\u003c/sup\u003e, lower educational attainment \u003csup\u003e15\u003c/sup\u003e, and reduced physical activity\u003csup\u003e14\u003c/sup\u003e\u0026mdash;all of which are independently associated with adverse reproductive outcomes\u003csup\u003e16\u003c/sup\u003e. Furthermore, socioeconomic factors and health behaviors cluster with psychiatric morbidity, making it exceedingly difficult to isolate medication effects in traditional epidemiological studies. The MR design mitigates these concerns by leveraging the random assortment of genetic variants at conception, which is theoretically independent of confounding factors\u003csup\u003e25,26\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eMoreover, previous Mendelian randomization analyses have suggested that depressive symptoms per se may not be strongly associated with female infertility\u003csup\u003e43\u003c/sup\u003e, raising the possibility that the genetic liability captured in our study reflects complex pathways beyond depressive symptomatology alone. This interpretation aligns with evidence that the genetic architecture of antidepressant treatment response and prescription patterns is distinct from that of depression susceptibility\u003csup\u003e33\u003c/sup\u003e. The phenotype \u0026quot;Depression medications\u0026quot; in the FinnGen database captures individuals who have been prescribed antidepressants, which may be influenced by factors such as symptom severity, treatment-seeking behavior, clinician prescribing practices, and healthcare system access. Taken together, these findings are compatible but not conclusive, and should be interpreted within the limitations of the genetic proxy used.\u003c/p\u003e\n\u003cp\u003eThe principal strength of this study lies in the application of a bidirectional Mendelian randomization design, which reduces confounding and reverse causation compared with conventional observational approaches\u003csup\u003e26,27\u003c/sup\u003e. The use of non-overlapping GWAS summary statistics for exposure and outcome minimizes bias from sample overlap\u003csup\u003e32\u003c/sup\u003e. The F-statistics for all instrumental variables substantially exceeded the conventional threshold of 10, indicating a low risk of weak instrument bias\u003csup\u003e30\u003c/sup\u003e. Comprehensive sensitivity analyses, including MR-Egger, weighted median, and leave-one-out methods, provided consistent results and supported the robustness of our findings\u003csup\u003e31,32\u003c/sup\u003e. The absence of significant heterogeneity (Cochran\u0026apos;s Q p \u0026gt; 0.05) and horizontal pleiotropy (MR-Egger intercept p = 0.703) further reinforces the validity of the instrumental variable assumptions.\u003c/p\u003e\n\u003cp\u003eNevertheless, several limitations merit careful consideration. First, the \u0026quot;Depression medications\u0026quot; phenotype represents a composite liability encompassing underlying psychiatric vulnerability, health-seeking behavior, and prescribing practices, rather than a direct measure of pharmacological exposure. As such, our estimates do not isolate a pure drug effect. This distinction is critical for clinical interpretation: the genetic instrument captures a lifelong propensity toward antidepressant treatment, which may reflect the severity and chronicity of underlying mental health conditions rather than the pharmacological action of the medications themselves. Second, although no statistical evidence of horizontal pleiotropy was detected, undetected pleiotropic pathways cannot be fully excluded. Genetic variants associated with antidepressant prescribing may influence infertility through mechanisms unrelated to medication use, such as shared biological pathways linking psychiatric vulnerability to reproductive function\u003csup\u003e34,44\u003c/sup\u003e. Third, the exposure reflects treatment conditional on psychiatric diagnosis, which introduces the possibility of index event bias\u0026mdash;a form of collider bias that can occur when selection is conditioned on an intermediate event\u003csup\u003e45\u003c/sup\u003e. Fourth, the analysis was restricted to individuals of European ancestry from FinnGen, potentially limiting generalizability to other populations. Reproductive traits and psychiatric phenotypes exhibit substantial diversity across ancestral groups\u003csup\u003e46,47\u003c/sup\u003e, and future studies in non-European populations are warranted.\u003c/p\u003e\n\u003cp\u003eAn additional consideration relates to the complex bidirectional relationships between reproductive events and mental health across the female lifespan. Early menarche has been associated with increased risk of psychopathological symptoms and psychiatric disorders\u003csup\u003e21-23\u003c/sup\u003e, while the perimenopausal period represents a time of heightened vulnerability to depression\u003csup\u003e20,24\u003c/sup\u003e. Hormonal fluctuations across the menstrual cycle and reproductive transitions can influence mood and treatment response\u003csup\u003e18,19\u003c/sup\u003e, and estrogen has been hypothesized to play a protective role in schizophrenia and other psychiatric conditions\u003csup\u003e48-50\u003c/sup\u003e. These intricate interrelationships underscore the importance of considering reproductive life stage in studies of mental health and fertility, and highlight the need for future research with more granular phenotypic data.\u003c/p\u003e\n\u003cp\u003eThe timing of reproductive events also merits consideration. Advanced parental age has been associated with increased risk of psychiatric disorders in offspring\u003csup\u003e45,51,52\u003c/sup\u003e, including schizophrenia\u003csup\u003e53,54\u003c/sup\u003e and autism\u003csup\u003e52\u003c/sup\u003e. Conversely, early age at first sexual intercourse has been linked to adverse mental health outcomes\u003csup\u003e55-57\u003c/sup\u003e and risky health behaviors\u003csup\u003e58,59\u003c/sup\u003e. These findings suggest complex intergenerational and developmental pathways that cannot be captured in the present analysis. Mendelian randomization studies using age-at-first-birth or age-at-menarche as exposures have revealed bidirectional relationships with psychiatric disorders\u003csup\u003e7-9\u003c/sup\u003e, highlighting the need for more comprehensive life-course approaches.\u003c/p\u003e\n\u003cp\u003eFrom a clinical perspective, the observed association was modest in magnitude and should not be interpreted as evidence that antidepressant therapy directly causes infertility. Rather, the findings underscore the importance of integrated mental and reproductive health assessment in women of childbearing age. Women with psychiatric disorders may benefit from preconception counseling that addresses both mental health optimization and fertility considerations\u003csup\u003e60,61\u003c/sup\u003e. Decisions regarding antidepressant treatment should remain individualized and guided by established clinical risk\u0026ndash;benefit considerations, weighing the substantial morbidity associated with untreated psychiatric illness against potential reproductive consequences. For women attempting to conceive or undergoing fertility treatment, multidisciplinary care involving psychiatrists, reproductive endocrinologists, and primary care providers is essential to optimize outcomes for both mother and child.\u003c/p\u003e\n\u003cp\u003eFuture research should aim to disentangle medication effects from underlying psychiatric liability using alternative methodologies. Triangulation of evidence across multiple study designs\u0026mdash;including pharmacoepidemiological studies with rigorous confounding control, sibling comparison designs, and negative control exposures\u0026mdash;would strengthen causal inference\u003csup\u003e16,25\u003c/sup\u003e. Mendelian randomization studies using genetic variants specific to drug target genes or metabolic pathways could help isolate pharmacological effects from behavioral and socioeconomic confounders. Additionally, studies in pregnancy cohorts with detailed medication records, depression severity assessments, and fertility outcomes are needed to complement genetic evidence. The inclusion of diverse ancestral populations and consideration of reproductive life stage will be essential for generalizability and precision.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, this MR study provides genetic evidence consistent with a modest association between genetic liability to antidepressant treatment and overall female infertility. Given the composite nature of the genetic instrument\u0026mdash;capturing underlying psychiatric vulnerability, healthcare-seeking behavior, and prescribing practices\u0026mdash;these findings should not be interpreted as direct pharmacological effects of antidepressant medications. Rather, they highlight the complex interplay between mental health and reproductive function, and underscore the importance of integrated care for women of reproductive age. No evidence of reverse causation was found, supporting the directional interpretation that psychiatric liability precedes infertility risk. Further research employing complementary methodologies is required to disentangle medication effects from underlying psychiatric liability, and to elucidate the biological mechanisms linking mental health and fertility across the female lifespan.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe extend our sincere gratitude to the FinnGen consortium for providing the genetic summary statistics used in this study. We also thank our colleagues from the Acupuncture and Rehabilitation Department for their insightful discussions and technical support.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the following grants: Major Difficult Diseases Clinical Collaboration Project between Chinese and Western Medicine from the National Administration of Traditional Chinese Medicine (Grant No. ZDYN-2024-A-0128).\u003c/p\u003e\n\u003cp\u003eKey Enterprise-University Joint Project from the Guangzhou Municipal Science and Technology Bureau (Grant No. 2024A03J0796). Guangzhou Key Laboratory of Traditional Chinese Medicine Rehabilitation (Grant No. 2024A03J0790).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflicts of interest, financial or otherwise, that could be construed as influencing the research presented in this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMW: Conceptualization, Methodology, Formal Analysis, Writing \u0026ndash; Original Draft.\u003cbr\u003e\u0026nbsp;BB and TY: Data Curation, Investigation, Visualization, Writing \u0026ndash; Review \u0026amp; Editing.\u003cbr\u003e\u0026nbsp;BB and JZ: Resources, Validation, Software.\u003cbr\u003e\u0026nbsp;KM: Supervision, Project Administration, Funding Acquisition, Writing \u0026ndash; Review \u0026amp; Editing.\u003c/p\u003e\n\u003cp\u003eAll authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Approval And Consent To Participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs this study involved the re-analysis of published, de-identified summary data, no separate ethical approval was required.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTrial Registration\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\u003eThe datasets supporting the conclusions of this article are available in the FinnGen repository (Release 9), as described in the FinnGen study\u003csup\u003e62\u003c/sup\u003e. The specific genome-wide association study (GWAS) summary statistics used in this study can be accessed via the IEU OpenGWAS database (https://gwas.mrcieu.ac.uk/) using the following dataset identifiers:\u003c/p\u003e\n\u003cp\u003eAntidepressant use:\u0026nbsp;finn-b-ANTIDEPRESSANTS\u003c/p\u003e\n\u003cp\u003eFemale infertility:\u0026nbsp;finn-b-N14_FEMALEINFERT\u003c/p\u003e\n\u003cp\u003eFemale infertility associated with anovulation:\u0026nbsp;finn-b-N14_FIANOV\u003c/p\u003e\n\u003cp\u003eDirect links to each dataset are:\u003c/p\u003e\n\u003cp\u003ehttps://gwas.mrcieu.ac.uk/datasets/finn-b-ANTIDEPRESSANTS/\u003c/p\u003e\n\u003cp\u003ehttps://gwas.mrcieu.ac.uk/datasets/finn-b-N14_FEMALEINFERT/\u003c/p\u003e\n\u003cp\u003ehttps://gwas.mrcieu.ac.uk/datasets/finn-b-N14_FIANOV/\u003c/p\u003e\n\u003cp\u003eNo original data were generated for this study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eFauser, B.\u003cem\u003e et al.\u003c/em\u003e Declining global fertility rates and the implications for family planning and family building: an IFFS consensus document based on a narrative review of the literature. \u003cem\u003eHum Reprod Update\u003c/em\u003e \u003cstrong\u003e30\u003c/strong\u003e, 153\u0026ndash;173, doi:10.1093/humupd/dmad028 (2024).\u003c/li\u003e\n\u003cli\u003eHarris, E. Infertility Affects 1 in 6 People Globally. \u003cem\u003eJAMA\u003c/em\u003e \u003cstrong\u003e329\u003c/strong\u003e, 1443, doi:10.1001/jama.2023.6251 (2023).\u003c/li\u003e\n\u003cli\u003eCollaborators, G. B. D. M. D. Global, regional, and national burden of 12 mental disorders in 204 countries and territories, 1990-2019: a systematic analysis for the Global Burden of Disease Study 2019. \u003cem\u003eLancet Psychiatry\u003c/em\u003e \u003cstrong\u003e9\u003c/strong\u003e, 137\u0026ndash;150, doi:10.1016/S2215-0366(21)00395-3 (2022).\u003c/li\u003e\n\u003cli\u003ePicco, L.\u003cem\u003e et al.\u003c/em\u003e Economic burden of multimorbidity among older adults: impact on healthcare and societal costs. \u003cem\u003eBMC Health Serv Res\u003c/em\u003e \u003cstrong\u003e16\u003c/strong\u003e, 173, doi:10.1186/s12913-016-1421-7 (2016).\u003c/li\u003e\n\u003cli\u003eSylvester, C., Menke, M. \u0026amp; Gopalan, P. Selective Serotonin Reuptake Inhibitors and Fertility: Considerations for Couples Trying to Conceive. \u003cem\u003eHarv Rev Psychiatry\u003c/em\u003e \u003cstrong\u003e27\u003c/strong\u003e, 108\u0026ndash;118, doi:10.1097/HRP.0000000000000204 (2019).\u003c/li\u003e\n\u003cli\u003eLiang, C., Chung, H. F., Dobson, A. J. \u0026amp; Mishra, G. D. Association of a history of depression with infertility, miscarriage and stillbirth: a longitudinal cohort study. \u003cem\u003eEpidemiol Psychiatr Sci\u003c/em\u003e \u003cstrong\u003e33\u003c/strong\u003e, e55, doi:10.1017/S2045796024000374 (2024).\u003c/li\u003e\n\u003cli\u003eNi, G., Gratten, J., Wray, N. R., Lee, S. H. \u0026amp; Schizophrenia Working Group of the Psychiatric Genomics, C. Age at first birth in women is genetically associated with increased risk of schizophrenia. \u003cem\u003eSci Rep\u003c/em\u003e \u003cstrong\u003e8\u003c/strong\u003e, 10168, doi:10.1038/s41598-018-28160-z (2018).\u003c/li\u003e\n\u003cli\u003eMehta, D.\u003cem\u003e et al.\u003c/em\u003e Evidence for Genetic Overlap Between Schizophrenia and Age at First Birth in Women. \u003cem\u003eJAMA Psychiatry\u003c/em\u003e \u003cstrong\u003e73\u003c/strong\u003e, 497\u0026ndash;505, doi:10.1001/jamapsychiatry.2016.0129 (2016).\u003c/li\u003e\n\u003cli\u003eWang, Z., Lu, J., Weng, W., Fu, J. \u0026amp; Zhang, J. Women\u0026apos;s reproductive traits and major depressive disorder: A two-sample Mendelian randomization study. \u003cem\u003eJ Affect Disord\u003c/em\u003e \u003cstrong\u003e326\u003c/strong\u003e, 139\u0026ndash;146, doi:10.1016/j.jad.2023.01.063 (2023).\u003c/li\u003e\n\u003cli\u003eNi, G.\u003cem\u003e et al.\u003c/em\u003e The genetic relationship between female reproductive traits and six psychiatric disorders. \u003cem\u003eSci Rep\u003c/em\u003e \u003cstrong\u003e9\u003c/strong\u003e, 12041, doi:10.1038/s41598-019-48403-x (2019).\u003c/li\u003e\n\u003cli\u003eEvans-Hoeker, E. A.\u003cem\u003e et al.\u003c/em\u003e Major depression, antidepressant use, and male and female fertility. \u003cem\u003eFertil Steril\u003c/em\u003e \u003cstrong\u003e109\u003c/strong\u003e, 879\u0026ndash;887, doi:10.1016/j.fertnstert.2018.01.029 (2018).\u003c/li\u003e\n\u003cli\u003eApostolakopoulou, X. A.\u003cem\u003e et al.\u003c/em\u003e Sugars, Alcohol, and Caffeine Intake From Drinks Among Outpatients With Mental Health Disorders in Greece: A Pilot Study. \u003cem\u003eCureus\u003c/em\u003e \u003cstrong\u003e14\u003c/strong\u003e, e21563, doi:10.7759/cureus.21563 (2022).\u003c/li\u003e\n\u003cli\u003eSagud, M., Mihaljevic Peles, A. \u0026amp; Pivac, N. Smoking in schizophrenia: recent findings about an old problem. \u003cem\u003eCurr Opin Psychiatry\u003c/em\u003e \u003cstrong\u003e32\u003c/strong\u003e, 402\u0026ndash;408, doi:10.1097/YCO.0000000000000529 (2019).\u003c/li\u003e\n\u003cli\u003eCurcic, D.\u003cem\u003e et al.\u003c/em\u003e Positive impact of prescribed physical activity on symptoms of schizophrenia: randomized clinical trial. \u003cem\u003ePsychiatr Danub\u003c/em\u003e \u003cstrong\u003e29\u003c/strong\u003e, 459\u0026ndash;465, doi:10.24869/psyd.2017.459 (2017).\u003c/li\u003e\n\u003cli\u003eLuo, Y.\u003cem\u003e et al.\u003c/em\u003e Gender difference in the association between education and schizophrenia in Chinese adults. \u003cem\u003eBMC Psychiatry\u003c/em\u003e \u003cstrong\u003e20\u003c/strong\u003e, 296, doi:10.1186/s12888-020-02700-2 (2020).\u003c/li\u003e\n\u003cli\u003eWootton, R. E.\u003cem\u003e et al.\u003c/em\u003e Associations between health behaviours, fertility and reproductive outcomes: triangulation of evidence in the Norwegian Mother, Father and Child Cohort Study (MoBa). \u003cem\u003eBMC Med\u003c/em\u003e \u003cstrong\u003e21\u003c/strong\u003e, 125, doi:10.1186/s12916-023-02831-9 (2023).\u003c/li\u003e\n\u003cli\u003ePedro, J.\u003cem\u003e et al.\u003c/em\u003e Infertility-related stress and the risk of antidepressants prescription in women: a 10-year register study. \u003cem\u003eHum Reprod\u003c/em\u003e \u003cstrong\u003e34\u003c/strong\u003e, 1505\u0026ndash;1513, doi:10.1093/humrep/dez110 (2019).\u003c/li\u003e\n\u003cli\u003eSoares, C. N., Poitras, J. R. \u0026amp; Prouty, J. Hormone treatment for mood disorders in women. \u003cem\u003eExpert Rev Neurother\u003c/em\u003e \u003cstrong\u003e2\u003c/strong\u003e, 25\u0026ndash;34, doi:10.1586/14737175.2.1.25 (2002).\u003c/li\u003e\n\u003cli\u003eBursalioglu, F. S., Aydin, N., Yazici, E. \u0026amp; Yazici, A. B. The Correlation Between Psychiatric Disorders and Women\u0026apos;s Lives. \u003cem\u003eJ Clin Diagn Res\u003c/em\u003e \u003cstrong\u003e7\u003c/strong\u003e, 695\u0026ndash;699, doi:10.7860/JCDR/2013/5635.2884 (2013).\u003c/li\u003e\n\u003cli\u003eBecker, D.\u003cem\u003e et al.\u003c/em\u003e Psychological distress around menopause. \u003cem\u003ePsychosomatics\u003c/em\u003e \u003cstrong\u003e42\u003c/strong\u003e, 252\u0026ndash;257, doi:10.1176/appi.psy.42.3.252 (2001).\u003c/li\u003e\n\u003cli\u003eLien, L., Haavet, O. R. \u0026amp; Dalgard, F. Do mental health and behavioural problems of early menarche persist into late adolescence? A three year follow-up study among adolescent girls in Oslo, Norway. \u003cem\u003eSoc Sci Med\u003c/em\u003e \u003cstrong\u003e71\u003c/strong\u003e, 529\u0026ndash;533, doi:10.1016/j.socscimed.2010.05.003 (2010).\u003c/li\u003e\n\u003cli\u003eDeng, F.\u003cem\u003e et al.\u003c/em\u003e Early menarche and psychopathological symptoms in young Chinese women. \u003cem\u003eJ Womens Health (Larchmt)\u003c/em\u003e \u003cstrong\u003e20\u003c/strong\u003e, 207\u0026ndash;213, doi:10.1089/jwh.2010.2102 (2011).\u003c/li\u003e\n\u003cli\u003ePlatt, J. M., Colich, N. L., McLaughlin, K. A., Gary, D. \u0026amp; Keyes, K. M. Transdiagnostic psychiatric disorder risk associated with early age of menarche: A latent modeling approach. \u003cem\u003eCompr Psychiatry\u003c/em\u003e \u003cstrong\u003e79\u003c/strong\u003e, 70\u0026ndash;79, doi:10.1016/j.comppsych.2017.06.010 (2017).\u003c/li\u003e\n\u003cli\u003eTiwari, S., Prasad, R., Wanjari, M. B. \u0026amp; Sharma, R. Understanding the Impact of Menopause on Women With Schizophrenia-Spectrum Disorders: A Comprehensive Review. \u003cem\u003eCureus\u003c/em\u003e \u003cstrong\u003e15\u003c/strong\u003e, e37979, doi:10.7759/cureus.37979 (2023).\u003c/li\u003e\n\u003cli\u003eLawlor, D. A., Harbord, R. M., Sterne, J. A., Timpson, N. \u0026amp; Davey Smith, G. Mendelian randomization: using genes as instruments for making causal inferences in epidemiology. \u003cem\u003eStat Med\u003c/em\u003e \u003cstrong\u003e27\u003c/strong\u003e, 1133\u0026ndash;1163, doi:10.1002/sim.3034 (2008).\u003c/li\u003e\n\u003cli\u003eDavies, N. M., Holmes, M. V. \u0026amp; Davey Smith, G. Reading Mendelian randomisation studies: a guide, glossary, and checklist for clinicians. \u003cem\u003eBMJ\u003c/em\u003e \u003cstrong\u003e362\u003c/strong\u003e, k601, doi:10.1136/bmj.k601 (2018).\u003c/li\u003e\n\u003cli\u003eBurgess, S., Butterworth, A. \u0026amp; Thompson, S. G. Mendelian randomization analysis with multiple genetic variants using summarized data. \u003cem\u003eGenet Epidemiol\u003c/em\u003e \u003cstrong\u003e37\u003c/strong\u003e, 658\u0026ndash;665, doi:10.1002/gepi.21758 (2013).\u003c/li\u003e\n\u003cli\u003eBurgess, S. \u0026amp; Thompson, S. G. Multivariable Mendelian randomization: the use of pleiotropic genetic variants to estimate causal effects. \u003cem\u003eAm J Epidemiol\u003c/em\u003e \u003cstrong\u003e181\u003c/strong\u003e, 251\u0026ndash;260, doi:10.1093/aje/kwu283 (2015).\u003c/li\u003e\n\u003cli\u003eBowden, J. \u0026amp; Holmes, M. V. Meta-analysis and Mendelian randomization: A review. \u003cem\u003eRes Synth Methods\u003c/em\u003e \u003cstrong\u003e10\u003c/strong\u003e, 486\u0026ndash;496, doi:10.1002/jrsm.1346 (2019).\u003c/li\u003e\n\u003cli\u003eSanderson, E., Spiller, W. \u0026amp; Bowden, J. Testing and correcting for weak and pleiotropic instruments in two-sample multivariable Mendelian randomization. \u003cem\u003eStat Med\u003c/em\u003e \u003cstrong\u003e40\u003c/strong\u003e, 5434\u0026ndash;5452, doi:10.1002/sim.9133 (2021).\u003c/li\u003e\n\u003cli\u003eBowden, J.\u003cem\u003e et al.\u003c/em\u003e A framework for the investigation of pleiotropy in two-sample summary data Mendelian randomization. \u003cem\u003eStat Med\u003c/em\u003e \u003cstrong\u003e36\u003c/strong\u003e, 1783\u0026ndash;1802, doi:10.1002/sim.7221 (2017).\u003c/li\u003e\n\u003cli\u003eBurgess, S. \u0026amp; Thompson, S. G. Interpreting findings from Mendelian randomization using the MR-Egger method. \u003cem\u003eEur J Epidemiol\u003c/em\u003e \u003cstrong\u003e32\u003c/strong\u003e, 377\u0026ndash;389, doi:10.1007/s10654-017-0255-x (2017).\u003c/li\u003e\n\u003cli\u003eGiannakopoulou, O.\u003cem\u003e et al.\u003c/em\u003e The Genetic Architecture of Depression in Individuals of East Asian Ancestry: A Genome-Wide Association Study. \u003cem\u003eJAMA Psychiatry\u003c/em\u003e \u003cstrong\u003e78\u003c/strong\u003e, 1258\u0026ndash;1269, doi:10.1001/jamapsychiatry.2021.2099 (2021).\u003c/li\u003e\n\u003cli\u003eBarban, N.\u003cem\u003e et al.\u003c/em\u003e Genome-wide analysis identifies 12 loci influencing human reproductive behavior. \u003cem\u003eNat Genet\u003c/em\u003e \u003cstrong\u003e48\u003c/strong\u003e, 1462\u0026ndash;1472, doi:10.1038/ng.3698 (2016).\u003c/li\u003e\n\u003cli\u003eMikhael, S., Punjala-Patel, A. \u0026amp; Gavrilova-Jordan, L. Hypothalamic-Pituitary-Ovarian Axis Disorders Impacting Female Fertility. \u003cem\u003eBiomedicines\u003c/em\u003e \u003cstrong\u003e7\u003c/strong\u003e, doi:10.3390/biomedicines7010005 (2019).\u003c/li\u003e\n\u003cli\u003ePractice Committee of the American Society for Reproductive, M. Diagnostic evaluation of the infertile female: a committee opinion. \u003cem\u003eFertil Steril\u003c/em\u003e \u003cstrong\u003e103\u003c/strong\u003e, e44\u0026ndash;50, doi:10.1016/j.fertnstert.2015.03.019 (2015).\u003c/li\u003e\n\u003cli\u003eValera, H., Chen, A. \u0026amp; Grive, K. J. The Hypothalamic-Pituitary-Ovarian Axis, Ovarian Disorders, and Brain Aging. \u003cem\u003eEndocrinology\u003c/em\u003e \u003cstrong\u003e166\u003c/strong\u003e, doi:10.1210/endocr/bqaf137 (2025).\u003c/li\u003e\n\u003cli\u003eDomingues, R. R., Wiltbank, M. C. \u0026amp; Hernandez, L. L. The antidepressant fluoxetine (Prozac(R)) modulates estrogen signaling in the uterus and alters estrous cycles in mice. \u003cem\u003eMol Cell Endocrinol\u003c/em\u003e \u003cstrong\u003e559\u003c/strong\u003e, 111783, doi:10.1016/j.mce.2022.111783 (2023).\u003c/li\u003e\n\u003cli\u003eFeng, Y., Qu, X. \u0026amp; Hao, H. Progress in the study of the effects of selective serotonin reuptake inhibitors (SSRIs) on the reproductive system. \u003cem\u003eFront Pharmacol\u003c/em\u003e \u003cstrong\u003e16\u003c/strong\u003e, 1567863, doi:10.3389/fphar.2025.1567863 (2025).\u003c/li\u003e\n\u003cli\u003eMilosavljevic, M. N.\u003cem\u003e et al.\u003c/em\u003e Effects of selective serotonin reuptake inhibitors on motility of isolated fallopian tube. \u003cem\u003eClin Exp Pharmacol Physiol\u003c/em\u003e \u003cstrong\u003e46\u003c/strong\u003e, 780\u0026ndash;787, doi:10.1111/1440-1681.13118 (2019).\u003c/li\u003e\n\u003cli\u003eCasilla-Lennon, M. M., Meltzer-Brody, S. \u0026amp; Steiner, A. Z. The effect of antidepressants on fertility. \u003cem\u003eAm J Obstet Gynecol\u003c/em\u003e \u003cstrong\u003e215\u003c/strong\u003e, 314 e311\u0026ndash;315, doi:10.1016/j.ajog.2016.01.170 (2016).\u003c/li\u003e\n\u003cli\u003eSjaarda, L. A.\u003cem\u003e et al.\u003c/em\u003e Urinary selective serotonin reuptake inhibitors across critical windows of pregnancy establishment: a prospective cohort study of fecundability and pregnancy loss. \u003cem\u003eFertil Steril\u003c/em\u003e \u003cstrong\u003e114\u003c/strong\u003e, 1278\u0026ndash;1287, doi:10.1016/j.fertnstert.2020.06.037 (2020).\u003c/li\u003e\n\u003cli\u003eLiao, T. \u003cem\u003eProspective cohort and Mendelian randomization study of the association between preconception depressive symptoms and fertility\u003c/em\u003e, Anhui Medical University, (2024).\u003c/li\u003e\n\u003cli\u003eDay, F. R.\u003cem\u003e et al.\u003c/em\u003e Physical and neurobehavioral determinants of reproductive onset and success. \u003cem\u003eNat Genet\u003c/em\u003e \u003cstrong\u003e48\u003c/strong\u003e, 617\u0026ndash;623, doi:10.1038/ng.3551 (2016).\u003c/li\u003e\n\u003cli\u003eD\u0026apos;Onofrio, B. M.\u003cem\u003e et al.\u003c/em\u003e Paternal age at childbearing and offspring psychiatric and academic morbidity. \u003cem\u003eJAMA Psychiatry\u003c/em\u003e \u003cstrong\u003e71\u003c/strong\u003e, 432\u0026ndash;438, doi:10.1001/jamapsychiatry.2013.4525 (2014).\u003c/li\u003e\n\u003cli\u003eChan, S., Gomes, A. \u0026amp; Singh, R. S. Is menopause still evolving? Evidence from a longitudinal study of multiethnic populations and its relevance to women\u0026apos;s health. \u003cem\u003eBMC Womens Health\u003c/em\u003e \u003cstrong\u003e20\u003c/strong\u003e, 74, doi:10.1186/s12905-020-00932-8 (2020).\u003c/li\u003e\n\u003cli\u003eGold, E. B. The timing of the age at which natural menopause occurs. \u003cem\u003eObstet Gynecol Clin North Am\u003c/em\u003e \u003cstrong\u003e38\u003c/strong\u003e, 425\u0026ndash;440, doi:10.1016/j.ogc.2011.05.002 (2011).\u003c/li\u003e\n\u003cli\u003eRiecher-Rossler, A. \u0026amp; Hafner, H. Schizophrenia and oestrogens--is there an association? \u003cem\u003eEur Arch Psychiatry Clin Neurosci\u003c/em\u003e \u003cstrong\u003e242\u003c/strong\u003e, 323\u0026ndash;328, doi:10.1007/BF02190244 (1993).\u003c/li\u003e\n\u003cli\u003eSeeman, M. V. The role of estrogen in schizophrenia. \u003cem\u003eJ Psychiatry Neurosci\u003c/em\u003e \u003cstrong\u003e21\u003c/strong\u003e, 123\u0026ndash;127 (1996).\u003c/li\u003e\n\u003cli\u003eToran-Allerand, C. D. The estrogen/neurotrophin connection during neural development: is co-localization of estrogen receptors with the neurotrophins and their receptors biologically relevant? \u003cem\u003eDev Neurosci\u003c/em\u003e \u003cstrong\u003e18\u003c/strong\u003e, 36\u0026ndash;48, doi:10.1159/000111393 (1996).\u003c/li\u003e\n\u003cli\u003eMcGrath, J. J.\u003cem\u003e et al.\u003c/em\u003e A comprehensive assessment of parental age and psychiatric disorders. \u003cem\u003eJAMA Psychiatry\u003c/em\u003e \u003cstrong\u003e71\u003c/strong\u003e, 301\u0026ndash;309, doi:10.1001/jamapsychiatry.2013.4081 (2014).\u003c/li\u003e\n\u003cli\u003eWu, S.\u003cem\u003e et al.\u003c/em\u003e Advanced parental age and autism risk in children: a systematic review and meta-analysis. \u003cem\u003eActa Psychiatr Scand\u003c/em\u003e \u003cstrong\u003e135\u003c/strong\u003e, 29\u0026ndash;41, doi:10.1111/acps.12666 (2017).\u003c/li\u003e\n\u003cli\u003eFountoulakis, K. N.\u003cem\u003e et al.\u003c/em\u003e Paternal and maternal age as risk factors for schizophrenia: a case-control study. \u003cem\u003eInt J Psychiatry Clin Pract\u003c/em\u003e \u003cstrong\u003e22\u003c/strong\u003e, 170\u0026ndash;176, doi:10.1080/13651501.2017.1391292 (2018).\u003c/li\u003e\n\u003cli\u003eLan, K. C.\u003cem\u003e et al.\u003c/em\u003e Association between paternal age and risk of schizophrenia: a nationwide population-based study. \u003cem\u003eJ Assist Reprod Genet\u003c/em\u003e \u003cstrong\u003e38\u003c/strong\u003e, 85\u0026ndash;93, doi:10.1007/s10815-020-01936-x (2021).\u003c/li\u003e\n\u003cli\u003eLee, J. The Impact of Sexual Initiation Timing and Sexual Experience on Depressive Symptoms in South Korean Adolescents: Gender Differences in a Nationwide Cross-Sectional Study. \u003cem\u003eJ Pediatr Nurs\u003c/em\u003e \u003cstrong\u003e52\u003c/strong\u003e, e15\u0026ndash;e20, doi:10.1016/j.pedn.2019.11.009 (2020).\u003c/li\u003e\n\u003cli\u003eKim, H. S. Sexual Debut and Mental Health Among South Korean Adolescents. \u003cem\u003eJ Sex Res\u003c/em\u003e \u003cstrong\u003e53\u003c/strong\u003e, 313\u0026ndash;320, doi:10.1080/00224499.2015.1055855 (2016).\u003c/li\u003e\n\u003cli\u003eLu, Z.\u003cem\u003e et al.\u003c/em\u003e Identifying causal associations between early sexual intercourse or number of sexual partners and major depressive disorders: A bidirectional two-sample Mendelian randomization analysis. \u003cem\u003eJ Affect Disord\u003c/em\u003e \u003cstrong\u003e333\u003c/strong\u003e, 121\u0026ndash;129, doi:10.1016/j.jad.2023.04.079 (2023).\u003c/li\u003e\n\u003cli\u003eCornelius, J. R., Clark, D. B., Reynolds, M., Kirisci, L. \u0026amp; Tarter, R. Early age of first sexual intercourse and affiliation with deviant peers predict development of SUD: a prospective longitudinal study. \u003cem\u003eAddict Behav\u003c/em\u003e \u003cstrong\u003e32\u003c/strong\u003e, 850\u0026ndash;854, doi:10.1016/j.addbeh.2006.06.027 (2007).\u003c/li\u003e\n\u003cli\u003eKaestle, C. E., Halpern, C. T., Miller, W. C. \u0026amp; Ford, C. A. Young age at first sexual intercourse and sexually transmitted infections in adolescents and young adults. \u003cem\u003eAm J Epidemiol\u003c/em\u003e \u003cstrong\u003e161\u003c/strong\u003e, 774\u0026ndash;780, doi:10.1093/aje/kwi095 (2005).\u003c/li\u003e\n\u003cli\u003eHammond, J. \u0026amp; Lipsedge, M. Assessing Parenting Capacity in Psychiatric Mother and Baby Units: A case report and review of literature. \u003cem\u003ePsychiatr Danub\u003c/em\u003e \u003cstrong\u003e27 Suppl 1\u003c/strong\u003e, S71\u0026ndash;83 (2015).\u003c/li\u003e\n\u003cli\u003eOzcan, N. K., Boyacioglu, N. E., Enginkaya, S., Dinc, H. \u0026amp; Bilgin, H. Reproductive health in women with serious mental illnesses. \u003cem\u003eJ Clin Nurs\u003c/em\u003e \u003cstrong\u003e23\u003c/strong\u003e, 1283\u0026ndash;1291, doi:10.1111/jocn.12332 (2014).\u003c/li\u003e\n\u003cli\u003eKurki, M. I.\u003cem\u003e et al.\u003c/em\u003e FinnGen provides genetic insights from a well-phenotyped isolated population. \u003cem\u003eNature\u003c/em\u003e \u003cstrong\u003e613\u003c/strong\u003e, 508\u0026ndash;518, doi:10.1038/s41586-022-05473-8 (2023).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"bmc-womens-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmwh","sideBox":"Learn more about [BMC Women's Health](http://bmcwomenshealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bmwh/default.aspx","title":"BMC Women's Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Antidepressant use, Female infertility, Mendelian randomization, Ovulatory disorder, Reproductive health","lastPublishedDoi":"10.21203/rs.3.rs-9057307/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9057307/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study applied a two‑sample bidirectional Mendelian randomization (MR) design using genetic summary statistics from the FinnGen R9 database to examine the causal link between genetic predisposition to antidepressant treatment and female infertility, including relevant subtypes. Seventy-seven independent Single Nucleotide Polymorphisms(SNPs)were selected as instrumental variables for antidepressant treatment liability. The inverse-variance weighted (IVW) analysis suggested that genetically proxied antidepressant treatment was associated with a modestly increased risk of overall female infertility (OR = 1.119, 95% CI: 1.024–1.222, \u003cem\u003ep\u003c/em\u003e = 0.013). No significant association was observed for anovulatory infertility. Reverse MR analysis did not support a causal effect of infertility on antidepressant treatment liability. Sensitivity analyses revealed no evidence of heterogeneity or horizontal pleiotropy. These findings suggest that genetic predisposition to antidepressant treatment is associated with female infertility risk. However, the genetic instrument captures a composite liability reflecting underlying psychiatric vulnerability and treatment decisions rather than a pure pharmacological drug effect. Therefore, the results should not be interpreted as direct evidence of antidepressant-induced infertility. Our findings underscore the clinical importance of considering mental health in the context of reproductive counseling, while cautioning that the genetic instrument does not isolate a pure drug effect.\u003c/p\u003e","manuscriptTitle":"Genetic Liability to Antidepressant Treatment and Risk of Female Infertility: A Two-Sample Bidirectional Mendelian Randomization Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-16 11:12:12","doi":"10.21203/rs.3.rs-9057307/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"148367599040635760151075175106033975979","date":"2026-04-23T14:14:41+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-08T10:06:35+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-31T07:17:24+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-30T16:01:41+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Women's Health","date":"2026-03-30T13:20:11+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-womens-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmwh","sideBox":"Learn more about [BMC Women's Health](http://bmcwomenshealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bmwh/default.aspx","title":"BMC Women's Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"82f31546-0f1e-4584-b35e-2b6aad3efeec","owner":[],"postedDate":"April 16th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-16T11:12:12+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-16 11:12:12","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9057307","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9057307","identity":"rs-9057307","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}