¹IRCCS Ospedale San Raffaele, Division of Neuroscience, Milano, Italy, ²Erasmus University Medical Center, Department of Adolescent and Child Psychiatry and Psychology; The Generation R Study Group, Rotterdam, Netherlands, ³University of Helsinki, Department of Psychology, Faculty of Medicine, Helsinki, Finland, ⁴University of Milan, Department of Pharmacological and Biomolecular Sciences, Milano, Italy, ⁵IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Biological Psychiatry Unit, Brescia, Italy

Background: Pregnancy and the postpartum period constitute a high-risk window for the emergence of depressive symptoms. Peripartum depression (PPD) affects around 17% of women worldwide [1] and remains substantially underdetected, with clinically meaningful consequences for maternal functioning, mother-infant bonding, and child developmental outcomes. Genetic liability appears pronounced in PPD, potentially exceeding that of major depressive disorder (MDD) [2], suggesting that peripartum affective dysregulation may reflect both shared and specific biological vulnerability. Beyond psychiatric genetic risk, immune-inflammatory activation and hormone sensitivity have been implicated in its pathophysiology [3], yet integrated polygenic models spanning these domains are poorly investigated.

Aims & Objectives: The current study aimed at determining whether polygenic risk scores (PRSs) for psychiatric disorders, immune-inflammatory biomarkers, and hormonal traits predict depressive symptoms during pregnancy and postpartum, and to evaluate pleiotropic effects on maternal anxiety across perinatal time points.

Method: PRSs were computed using LDpred2-auto for 24 phenotypes, including psychiatric traits (e.g., MDD, bipolar disorder, schizophrenia [SCZ], ADHD, anorexia nervosa [AN], anxiety, and PPD), cardiometabolic/psychosocial correlates (e.g., BMI, education, wellbeing), and biological markers (e.g., C-reactive protein, IL-1β, IL-6, IL-10, TNFα, IFN-γ, TSH, estradiol, female testosterone). Two large-scale cohorts were included in this study, namely the Generation R study (GenR, n=5477) and the Prediction and prevention of preeclampsia and intrauterine growth restriction cohort (PREDO, n=431). Clinical outcomes including depression and anxiety were evaluated through validated self-report questionnaires in both cohorts at the 20^thgestational week and 6 months after delivery. After genotyping, quality control, imputation, and PRS computation, cohort-specific analyses included general linear mixed models adjusted for ancestry principal components, maternal age, maternal education, and genotyping platform, and 5,000 non-parametric bootstrap iterations were performed to assess predictors’ stability and 95% confidence intervals. Cohort estimates were synthesized using random-effects pooled meta-analysis, and significance corrected through False Discovery Rate.

Results: At the 20^th gestational week, meta-analytic models showed significant associations between prenatal depressive symptoms and MDD PRS 0.11 [95% CI 0.07-0.14], SCZ PRS 0.07 [0.01-0.12], and ADHD PRS 0.07 [0.01-0.13]; and between prenatal anxiety and MDD PRS 0.11 [0.08-0.15], ADHD PRS 0.09 [0.04-0.13], SCZ PRS 0.11 [0.06-0.16]. At 6 months postpartum, depressive symptoms were associated with MDD PRS 0.09 [0.05-0.13], SCZ PRS 0.08 [0.02-0.14], and AN PRS 0.09 [0.02-0.16]. Postpartum anxiety was associated with AN PRS 0.09 (0.03-0.15), neuroticism PRS 0.06 [0.02-0.09], and SCZ PRS 0.10 [0.04-0.16].

Discussion & Conclusions: The findings indicate that peripartum depression shares significant genetic liability with major psychiatric disorders, particularly MDD, as well as SCZ, ADHD, and AN, supporting a transdiagnostic vulnerability framework. No significant associations emerged for immune-inflammatory or hormonal PRSs, suggesting that stable genetic liability for baseline biomarker levels may not adequately capture dynamic, state-dependent biological sensitivity during the perinatal period. Although effect sizes were modest, PRSs may contribute to future multilayered predictive models integrating clinical, environmental, and dynamic biological factors.