Journal of Affective Disorders

IntroductionThere is a growing body of research showing that the menstrual cycle can affect mood, although research in those with an existing depressive disorder is still scarce. Studies estimate that 60% of women with depression experience premenstrual exacerbation (PME) of their depressive symptoms. AimsWith the TIDE study, we aim to 1) examine the feasibility of daily symptom tracking for two consecutive menstrual cycles to track PME, 2) estimate the prevalence of PME in depression in a clinical cohort and 3) examine whether PME is associated with other hormone-related mood symptoms (i.e., hormonal contraceptive side effects, peripartum depression). MethodsWe aim to recruit 60 female participants aged 18 to 45, who are in treatment for a depressive episode and who have a regular natural menstrual cycle. Participants will participate in questionnaires at baseline, inquiring about demographic characteristics and previous experiences with hormonal contraceptives and pregnancy. Participants will complete retrospective menstruation questionnaires on days 1 and 10 of each menstrual cycle, as well as daily diaries for two consecutive menstrual cycles, inquiring about menstruation and mood symptoms. After completion of the diaries, participants will receive a symptom report, as well as a study evaluation questionnaire. Results and conclusionWe expect that providing the menstrual cycle overview of symptom severity will lead to increased symptom course insights in participants with PME, and that participants with PME will find it clinically relevant and significant to gain insight into symptom trajectories across the menstrual cycle. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=76 SRC="FIGDIR/small/26352210v1_ufig1.gif" ALT="Figure 1"> View larger version (27K): org.highwire.dtl.DTLVardef@1d6545eorg.highwire.dtl.DTLVardef@3ceb57org.highwire.dtl.DTLVardef@17ea0faorg.highwire.dtl.DTLVardef@7aa9f6_HPS_FORMAT_FIGEXP M_FIG C_FIG

Background: Major depressive disorder (MDD) is the leading cause of non-fatal disability in youth and disproportionately affects adolescent females. Structural MRI studies of adolescent depression have yielded inconsistent findings, potentially reflecting symptom heterogeneity and rapid developmental changes in brain morphology. Methods: In this longitudinal study, we examined associations between specific depressive symptoms and structural brain MRI measures in 9,722 youth (53% male, age range = 10.0-17.7, 24,378 observations) from the Adolescent Brain Cognitive Development (ABCD) Study. A four-wave panel graphical vector autoregression (GVAR) model was estimated to separate within-person (contemporaneous and temporal networks) from stable between-person effects. Brain measures included cortical thickness in the insula, cingulate, medial orbitofrontal cortex (mOFC) and fusiform gyrus, as well as hippocampal volume. Depressive symptoms included parent-reported depressed mood, anhedonia, lethargy, and worthlessness. Additionally, sex-differences in network structures were tested. Results: Strong within-domain associations were observed among brain measures and among symptoms, with the largest effects in the symptom domain. Cross-domain (brain-symptom) associations emerged only at the within-person level, where elevated depressed mood was associated with contemporaneous and subsequent reductions in cingulate and fusiform gyrus thickness (partial r = [-0.02 - 0.04]). No cross-domain associations were detected in the between-person networks. Sex-differences emerged only in the within-person networks. Conclusions: Associations between brain structure and depressive symptoms were subtle, symptom-specific, and dynamic rather than reflecting stable individual differences. Longitudinal within-person approaches are therefore important for understanding neurodevelopmental contributions to adolescent depression risk.

Importance: The menopausal transition is associated with an increased risk of depression. Prior depression is a well-established risk factor, but studies do not distinguish between prior reproductive and non-reproductive depression. Objective: To compare the associations of reproductive (i.e., premenstrual mood disorder and perinatal depression) and non-reproductive (i.e., not related to hormonal transitions) histories of depression with depressive symptoms during the menopausal transition. Design: Cross-sectional analysis of questionnaire data from the Multidisciplinary Menopausal Outpatient Care Project (MOPP) collected between February 2023 and October 2025. Setting: Menopause outpatient clinics Amsterdam, the Netherlands, including a specialized multidisciplinary menopause clinic. Participants: In total 364 individuals were approached; 244 enrolled at baseline. After exclusions for age <40 (n=3), premature ovarian insufficiency (n=2), premenopausal status (n=1), age >58 with final menstruation >10 years earlier (n=12), bipolar disorder (n=5), and missing survey data (n=41), 180 participants were included. Exposures: Premenstrual mood disorder measured with Premenstrual Symptom Screening Tool, perinatal depression with Edinburgh Postnatal Depression Scale Lifetime version, and reported prior non-reproductive depression in medical records. Main outcome and measures: Depressive symptom severity measured with Inventory of Depressive Symptomatology-Self Rated. We used univariable and multivariable linear regressions; multivariable models accounted for overlap between exposures. Results: Among 180 participants (median age 51; 61% perimenopausal and 39% postmenopausal), premenstrual mood disorder showed the strongest association with depressive symptom severity (B = 9.0, 95% CI 5.1-12.9, p < 0.001), followed by perinatal depression (B = 7.8, 95% CI 3.4-12.1, p < 0.001) and prior non-reproductive depression (B = 4.7, 95% CI 0.7-8.7, p = 0.021). In multivariable analysis, only premenstrual mood disorder (B = 7.2, 95% CI 2.4-12.1, p = 0.0037) and perinatal depression (B = 5.7, 95% CI 1.2-10.1, p = 0.013) remained associated with depressive symptom severity. Conclusions and Relevance: Prior reproductive depression, but not prior non-reproductive depression, was associated with greater depressive symptom severity during the menopausal transition. A history of premenstrual mood disorder and/or perinatal depression may therefore help identify individuals at increased vulnerability to depressive symptoms during this period. Future studies should replicate these findings in population-based samples.

Background: Previous evidence suggests that higher body size is associated with bipolar disorders, however, whether this association is causal remains uncertain. Interpretation is further complicated by heterogeneity across age, variation in clinical presentation, and potentially distinct underlying aetiologies. Aims: To determine whether body size exerts heterogenous causal effects on bipolar disorder subtypes and symptom profiles. Methods: By leveraging genetic instruments that differentiate effects at different life stages, summary-level univariable and multivariable Mendelian randomisation (MR) analyses were used to estimate how age-specific body size relates to adult psychiatric and symptomatic bipolar features; major depressive disorder (MDD), depressive symptom scores, subthreshold mania symptoms, bipolar disorder, bipolar type I and bipolar type II. Genetic instruments derived from genome-wide association studies (GWASs) for adult body mass index (BMI) (n= 681,275), childhood body size (n= 453,169) and mid-to-later life body size (n= 453,169) served as proxies for prepubertal and adult BMI measures. Results: In univariable MR, higher genetically proxied adult BMI increased the odds of MDD (odds ratio (OR) = 1.13, 95% CI 1.09-1.16), subthreshold mania (OR = 1.09, 95% CI 1.0-1.19)), and depressive scores (Beta = 0.07, 95% CI 0.05-0.09). There was little evidence that childhood body size had an effect on any outcome. Robust evidence suggested bipolar disorder and MDD increased adult BMI in our reverse univariable analyses. Using multivariable MR, robust evidence indicated that increased adult body size after accounting for childhood body size increased the odds of MDD, subthreshold mania and depressive scores. Conclusions: Body size may exert different causal effects on bipolar disorder depending on age and symptoms, with detrimental effects occurring during adulthood. Weaker evidence suggested varying effects across bipolar subtypes. Triangulation of findings and higher powered GWASs to detect symptom-specific genetic variants are required to explore whether body size contributes to distinct aetiologies across bipolar patients, informing the identification of novel and personalised treatment targets.

BackgroundMajor depressive disorder (MDD), a leading cause of disability worldwide, exhibits substantial heterogeneity in treatment outcomes. Patients who do not respond to standard antidepressant therapy account for the majority of MDDs disease burden. Risk factors have been implicated in treatment response, including genes impacting on how antidepressants are metabolised. Yet, despite its clinical importance, risk factors for treatment-resistant depression (TRD) remain unexplored in low- and middle-income countries (LMIC). We used data from the DIVERGE study on MDD to investigate the risk factors of TRD in Pakistan. MethodsDIVERGE is a genetic epidemiological study that recruited adult MDD patients ([&ge;]18 years) between Sep 27,2021 to Jun 30, 2025, from psychiatric care facilities across Pakistan. Detailed phenotypic information was collected by trained interviewers and blood samples taken. Infinium Global Diversity Array with Enhanced PGx-8 from Illumina was used for genotyping followed by DRAGEN calling to infer metaboliser phenotypes for Cytochrome P450 (CYP) enzyme genes. We defined TRD as minimal to no improvement after [&ge;]12 weeks of adherent antidepressant therapy. We conducted multi-level logistic regression to test the association of demographic, clinical and pharmacogenetic variables with TRD. FindingsAmong 3,677 eligible patients, polypharmacy was rampant; 86% were prescribed another psychotropic drug along with an antidepressant. Psychological therapies were uncommon (6%) while 49% of patients had previously visited to a religious leader/faith healer in relation to their mental health problems. TRD was experienced by 34% (95%CI: 32-36%) patients. The TRD group was characterised by more psychotic symptoms and suicidal behaviour (OR=1.39, 95%CI=1.04-1.84, p=0.02; OR=1.03, 95%CI=1.01-1.05, p=0.005). Social support (OR=0.55, 95%CI=0.44-0.69, p=1.4x10-7) and parents being first cousins (OR=0.81, 95%CI=0.69-0.96, p=0.01) were associated with lower odds of TRD. In 1,085 patients with CYP enzyme data, poor (OR=1.85, 95%CI=1.11-3.07, p=0.01) and ultra-rapid (OR=3.11, 95%CI=1.59-6.12, p=0.0009) metabolizers for CYP2C19 had increased risk of TRD compared with normal metabolisers. InterpretationThere was an excessive use of polypharmacy in the treatment of depression while psychological therapies were uncommon highlighting the need for more evidence-based practice. This first large study of MDD from Pakistan uncovered the importance of culture-specific forms of social support in preventing TRD, highlighting opportunities for interventions in low-income settings. Pharmacogenetic markers can be leveraged to predict TRD.

Background Somatic and psychological symptoms like depression, anxiety, and trauma-related stress often co-occur, especially in young adults, a group facing major life transitions and increased vulnerability. These overlapping symptoms pose diagnostic challenges that traditional disorder-specific models capture poorly. Transdiagnostic and dimensional approaches may offer a more meaningful framework. However, population-based data on symptom patterns in young adults remains sparse. This study investigated the patterns of psychological and somatic symptoms among young adults from Switzerland and compares these results to findings from populations with different stress exposure histories: Ukrainians who fled to Switzerland, and Ukrainians living in different regions in Ukraine during the war. Methods We analyzed cross-sectional baseline data collected in spring 2024 as part of the Mental Health Assessment of the Population (MAP) studies, where we enrolled randomly selected young adults aged 18-24 from Switzerland, Ukrainian refugees in Switzerland, and Ukrainians residing in regions with different degrees of proximity to active war zones. We assessed somatic (PHQ-15) and psychological symptoms (PHQ-9, GAD-7, PCL-5) and explored symptom patterns using descriptive statistics, correlations, and k-means clustering. Results Psychological symptom severity showed highly consistent moderate-to-strong correlations with somatic symptoms (range: 0.53-0.69), across all young adult subgroups and disorders. Rather than identifying disorder-specific patterns, symptoms clustered by overall symptom severity, emerging in three clusters: (1) high symptom burden, (2) moderate symptom burden, and (3) low symptom burden clusters with elevated somatic, depressive, anxiety, and PTSD symptoms. The cluster structure was remarkably stable across Swiss, Ukrainian, and refugee subsamples, despite markedly different stress exposure histories. Conclusion Our results support a symptom-based, dimensional approach to understanding mental health in young adults and to better capture the complexity and co-occurrence of psychological and somatic symptoms in this age group. These findings further suggest that prevention and early detection strategies should more systematically integrate both psychological and somatic symptomatology.

BackgroundDepression is a mood disorder frequently associated with episodic memory impairment. However, it remains unclear whether functional brain activity differs between depressed and non-depressed individuals during encoding or retrieval of autobiographical or non-autobiographical memories. Clarifying these differences is important for refining theoretical models of memory impairment in depression and, potentially, for developing targeted interventions. MethodsWe conducted three coordinate-based meta-analyses examining encoding and retrieval of autobiographical and non-autobiographical memory in control participants and individuals with current, remitted, or subthreshold depression, or those at risk for depression. Studies were identified via database searches and analysed using Seed-based d Mapping. ResultsWe included coordinates from 21 fMRI studies. During encoding, depression was associated with reduced activity in the thalamus, the caudate, the salience network, the frontoparietal executive control network, and motor-related areas (ten studies, N = 506). During non-autobiographical retrieval, depression was associated with higher activity in the right inferior frontal gyrus (six studies, N = 332). During autobiographical retrieval, depression was associated with reduced activity in the right insula and fusiform gyrus, alongside increased activity in the left anterior cingulate cortex and the left middle frontal gyrus (ten studies, N = 423). Between-study heterogeneity was low and no evidence for publication bias was found. DiscussionOur results indicate that depression may be associated with impaired salience integration during encoding and autobiographical retrieval. In contrast, during non-autobiographical retrieval, increased frontal activity suggests a more vigilant or self-monitoring retrieval mode. Functional brain activity changes in depression therefore appear stage- and content-specific.

Depression is accompanied by considerable comorbidity and excess mortality. We examined multimorbidity data using the validated pharmacy-based Rx-Risk Comorbidity Index and examined healthcare costs associated with chronic illness burden in the Australian Genetics of Depression Study (AGDS). Australian Pharmaceutical Benefits Scheme (PBS) record linkage for 15,890 AGDS participants was available from 01/07/2013-31/12/2017. Forty-six health morbidities were inferred by mapping the prescription data using Anatomical Therapeutic Chemical Classification System codes and PBS Item Codes. Morbidity prevalence rates were then compared with an unselected 10% Australian representative population sample (10PCT) with PBS claims data available from 01/07/2010-31/12/2014. The average number of inferred comorbidities was higher among AGDS participants (4.6 {+/-} 2.9) than 10PCT individuals (3.0 {+/-} 3.0). Excluding depression, 89.1% of AGDS participants had one or more inferred comorbidity, most commonly pain (51.0%), inflammation/pain (40.3%), and anxiety (32.3%). In the AGDS, the number of comorbidities was higher among women compared to men and positively correlated with participant age, BMI, number of depressive episodes experienced, and annual health care costs. Compared to participants with no inferred comorbidities, the median annual health care costs were ~65% higher among those with 2-3 comorbidities. This study highlights the patterns of health morbidities experienced by individuals living with depression and shows that this chronic disease burden is significantly associated with increased health costs to the individual and the health system.

Understanding depressive symptoms dynamics and their determinants is crucial for designing effective mental health support initiatives. This study compared two methods for describing youth depressive symptoms trajectories and investigated associations of early-life factors (maternal education, maternal perinatal depression, domestic violence, physical, emotional, or sexual abuse, bullying victimisation, psychiatric disorder) with trajectory features. Prospective data from 8,264 mostly White European participants (54% female), including self-reported Short Moods and Feelings Questionnaires on ten occasions between 10-25 years, were used. Trajectories were summarised using functional principal component analysis (FPCA) and P-splines linear mixed-effect (PLME) models. Estimated derivatives were used to obtain magnitude and age of peak symptoms and peak symptoms velocity. Both methods performed comparably, but PLME models tended to over-smooth trajectories. Peak symptoms and peak velocity were higher and occurred >1 year earlier in females than males. All early-life factors were associated with higher peak symptoms, and most associated with higher and earlier peak velocity. Abuse and bullying additionally associated with earlier age of peak symptoms. FPCA is a useful alternative for characterising depressive symptoms trajectories and informing time-sensitive preventative measures to reduce impact of depression before symptoms reach their peak. Early-life stressors may accelerate timeline and intensity of symptoms escalation during adolescence. Lay summaryUnderstanding development of depressive symptoms and factors shaping them is crucial for designing effective mental health support initiatives. This study used data from over 8,000 young people regularly followed up from before birth to compare two cutting-edge methods for describing depressive symptoms trajectories and examined how known risk factors for adulthood depression relate to the severity and rate of change of depressive symptoms in adolescence. We found that both methods performed well and that the peaks in depressive symptoms and their rate of change were, on average, higher and occurred over a year earlier in females than males. Our findings additionally suggest that early-life stressors (e.g., abuse, bullying) may accelerate the development of depression, highlighting the importance of early prevention.

Background: Bipolar disorder (BD) is increasingly conceptualized as a heterogeneous condition with a neurodevelopmental phenotype (NDP) identifying a subgroup with early neurodevelopmental vulnerability and poorer clinical outcomes. Sensory processing (SP) abnormalities are a core feature of neurodevelopmental disorders but remain poorly characterized in BD and may reflect underlying neurodevelopmental liability. We examined whether NDP load is associated with specific SP alterations in euthymic BD patients and whether NDP-based stratification explains SP variability better than conventional BD subtype (BD 1/2). Methods: We assessed 102 euthymic BD patients and 45 healthy controls (HC) using the Adolescent/Adult Sensory Profile (AASP). NDP load (0-3) was computed from nine clinical variables grouped into neonatal, comorbidity, and neurodevelopmental clusters; a median split defined BD without NDP (BD) and BD with NDP (BD-ND). Associations between NDP load and AASP quadrants were analyzed using Spearman correlations with FDR correction. Group differences (BD, BD-ND, HC) were assessed using Welch ANOVA and post-hoc tests. Nested and multivariable linear regressions examined whether NDP classification explained SP variance beyond BD subtype, adjusting for age, sex, anxiety, and residual mood symptoms. Results: Higher NDP load correlated with greater low registration (rho=0.35, p<0.001, q=0.004), sensory sensitivity (rho=0.30, p=0.001, q=0.004), and sensation avoiding (rho=0.23, p=0.014, q=0.040), but not sensation seeking. BD-ND showed higher low registration, sensory sensitivity, and sensation avoiding than BD and HC (all qs<0.01). NDP classification explained more SP variance than BD subtype; with robust associations after adjustment. Conclusions: Sensory processing alterations in BD are dimensionally associated with neurodevelopmental load and more accurately captured by NDP-based stratification than diagnostic subtype. SP alterations may represent a transdiagnostic marker of neurodevelopmental liability within BD, supporting biologically informed stratification approaches.

Background: Hydrogen sulfide (H2S) is an endogenous gasotransmitter synthesised in the central nervous system (CNS) primarily by cystathionine {beta}-synthase (CBS) and cystathionine {gamma}-lyase (CSE). Pre-clinical studies consistently implicates H2S deficiency in the pathophysiology of depression through disruption of synaptic plasticity, neuroinflammation, oxidative stress, and brain-derived neurotrophic factor (BDNF) signalling. Yet, we still lack direct clinical evidence quantifying circulating H2S in patients with Major Depressive Disorder (MDD), particularly from South Asian populations. In this study, we measured serum H2S levels in drug-naive patients with MDD and compared them with healthy controls at a tertiary care center in eastern India. We examined the associations between serum H2S and depression severity as assessed by the 17-item Hamilton Depression Rating Scale (HAM-D-17). This institution-based, cross-sectional analytical study was conducted at North Bengal Medical College and Hospital (NBMCH), West Bengal, India, over 12 months. Fifty drug-naive patients fulfilling DSM-5 criteria for MDD and fifty age- and sex-matched healthy controls were enrolled by consecutive sampling. We quantified serum H2S using the spectrophotometric methylene blue method and depression severity was assessed using HAM-D-17. Statistical analyses included independent-samples t-test, chi-square test, and linear regression. Serum H2S was markedly and significantly lower in MDD patients (0.068 {+/-} 0.044 {micro}mol/L) compared with healthy controls (0.524 {+/-} 0.272 {micro}mol/L; p < 0.001), representing an approximately 7.7-fold reduction. HAM-D-17 scores were significantly higher in MDD patients (28.94 {+/-} 12.78) than in controls (3.96 {+/-} 2.31; p < 0.001). Linear regression across the combined cohort revealed a significant negative association between serum H2S and HAM-D score (R{superscript 2} = 0.287; y = 24.64 - 26.84x; p < 0.001), indicating that higher serum H2S was associated with lower depression severity. Within the MDD group alone, the regression was weak (R{superscript 2} = 0.061), consistent with a floor effect. Within the control group alone, the regression was strong (R{superscript 2} = 0.772). No significant associations were found between serum H2S and any sociodemographic variable in either group. Drug-naive MDD patients exhibited substantially reduced serum H2S levels compared with healthy controls, and lower H2S was associated with greater depression severity. These findings provide direct clinical evidence from an Indian population supporting the H2S deficiency hypothesis of depression and suggest that the CBS/CSE-H2S axis may represent a novel biomarker and therapeutic target in MDD. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=106 SRC="FIGDIR/small/26352330v1_ufig1.gif" ALT="Figure 1"> View larger version (27K): org.highwire.dtl.DTLVardef@1ce64f6org.highwire.dtl.DTLVardef@1465ca2org.highwire.dtl.DTLVardef@6bba64org.highwire.dtl.DTLVardef@9a1411_HPS_FORMAT_FIGEXP M_FIG C_FIG

IntroductionThe bidirectional relationship between depression and type 2 diabetes (T2D) is well-established. Women are disproportionately affected by their co-occurrence, particularly during midlife, yet sex- and age-specific studies on phenotypic and mechanistic factors underlying risk for their co-occurrence are limited. The purpose of this study was to identify combined risk profiles (i.e., depression, T2D) in women during midlife and to determine if microRNAs (miRs) that are associated with high-risk profiles provide mechanistic insights into multimorbidity. Materials and MethodsThis study included baseline data from women during midlife (ages 40-64 years) who participated in the Diabetes Prevention Program (DPP) (n = 603). Unsupervised k-means clustering was used to identify multimorbid risk profiles. Clinical characteristics included for risk profiling included Beck Depression Inventory (BDI-I), age, BMI, waist circumference, triglycerides, high HDL, FBG, and HbA1c. Associations between risk profiles and individual miRs and principal components of co-expressed miRs were determined via logistic regression models adjusted for participant race and ethnicity. False discovery rate (q< 0.05) was used to control for multiple comparisons. ResultsTwo distinct profiles were identified, with the high-risk profile characterized by younger age yet higher adiposity, glycemic biomarkers, and depression symptom burden compared to the low-risk profile. MiR-320a and miR-320c were associated with increased odds of high-risk profile assignment, and a co-expression cluster enriched for miRs belonging to the miR-320 family (PC3) was significantly associated with increased odds of high-risk profile assignment. Across all models, Black race demonstrated at least threefold higher odds of high-risk profile assignment. DiscussionThese findings highlight distinct multimorbid risk profiles in women during midlife, emphasizing the potential utility of integrated, multidimensional approaches for risk stratification. Findings also revealed mechanisms that may underly risk for co-occurrence of T2D and depression in women during midlife and potential therapeutic targets for prevention and treatment.

BackgroundIntermittent theta burst stimulation (iTBS) and H-coil repetitive transcranial magnetic stimulation (rTMS) are FDA-cleared treatments for major depression; yet their comparative effectiveness in treatment-resistant depression (TRD) has not been evaluated in randomized trials. This pilot randomized trial was designed to obtain preliminary comparative estimates and to explore whether baseline cognitive functioning relates to early remission. MethodsTwenty-eight adults with TRD were randomized to six weeks of iTBS delivered to the dorsolateral prefrontal cortex (DLPFC) using a figure-8 coil (n=15) or H-coil rTMS delivered to the dorsomedial prefrontal cortex (DMPFC) using a H7-coil (n=13). The primary outcome was change in 17-item Hamilton Depression Rating Scale (HRSD-17) score from baseline to week 6, analyzed with ANCOVA. Additional outcomes included response, remission, and symptom trajectories through week 18. Exploratory analyses examined the association between baseline cognitive functioning, such as executive functions and memory, and remission. ResultsTwenty-five participants completed all 30 sessions. Adjusted week-6 HRSD-17 scores did not differ between groups (mean difference -0.40, 95% CI -5.23 to 4.43; p=.865). Response rates were 40.0% for iTBS and 50.0% for H-coil (p>.60), and remission rates were identical across groups (20.0%). Remitters showed higher baseline executive functioning than non-remitters in exploratory analyses, although these associations were not confirmed in adjusted models. ConclusionIn this pilot trial, iTBS and H7-coil rTMS showed symptom improvement, with no clear between-group pattern. Exploratory findings suggest a potential signal involving executive functioning that warrants further investigation. These results inform the feasibility and design of larger comparative trials. Trial registrationClinicalTrials.gov (NCT05902312)

Background: Major depressive disorder (MDD) is clinically heterogeneous, hindering identification of reproducible biomarkers. Using a semi-supervised machine learning approach, HYDRA, we previously identified two neuroanatomical dimensions from structural MRI in medication-free MDD from COORDINATE-MDD consortium. These dimensions (D1, D2) showed differential responses to selective serotonin reuptake inhibitor (SSRI) antidepressants and placebo. External replication in UK Biobank linked D2, characterized by widespread subtle neuroanatomical reductions, to an immuno-metabolic profile. Here, we examined whether these dimensions are detectable early in the course of illness. Methods: We applied the pre-trained model to structural MRI data from the multisite PRONIA cohort, comprising individuals with recent-onset depression (ROD; n = 377; mean age 25.8 years, SD 6.0; 51.3% female) and healthy controls (n = 267; mean age 25.5 years, SD 6.4; 61.0% female). Participants were assigned to clusters (C1, C2) corresponding to the previously identified dimensions (D1, D2). Clusters were compared on clinical symptom profiles, peripheral inflammatory markers, and in a subset (n = 107), proteomic ageing indices. Results: Two neuroanatomical clusters were identified in PRONIA. C1 (n = 265) showed higher negative symptom severity and elevated interleukin-2 levels. C2 (n = 140) was associated with higher residual proteomic age. Overall depressive symptom severity did not differ significantly between clusters. Conclusions: Neuroanatomical dimensions of MDD are reproducible and detectable at illness onset. Associations with negative symptom severity, inflammatory signalling, and proteomic ageing suggest these dimensions capture biologically meaningful heterogeneity early in depression. These findings support a biologically informed framework for stratified treatment approaches in MDD.

BackgroundEarly adolescence is a common period of onset for depressive symptoms. In part, this may reflect a developmental manifestation of individuals genetic propensities as they undergo physiological and hormonal changes and interact with new environments. Many commonly proposed mechanisms assume direct effects of an individuals own genes on emerging variation in their depressive symptomatology. However, estimates of genetic influence based on analyses in unrelated individuals capture not only direct genetic effects but also genetic effects from parents and other biologically related family members. AimIn data from the Norwegian Mother, Father and Child Cohort (MoBa), we used linear mixed models to distinguish developmentally-stable and adolescence-specific direct and parental indirect genetic effects. We examined effects of polygenic scores for major depressive disorder (MDD), ADHD, anxiety disorders, and educational attainment (EA) on depressive symptoms, which were assessed by maternal reports at ages 8 and 14. ResultsChildrens own MDD polygenic scores showed adolescence-specific effects on depressive symptoms ({beta}PGS*wave=0.041, [95% CI: 0.017, 0.065]). Developmentally-stable direct effects from childrens polygenic scores for MDD ({beta}=0.016, [0.006, 0.039]), ADHD ({beta}=0.024, [0.008, 0.041]) and EA ({beta}=-0.02, [-0.038, -0.002]) were also evident. The only evidence of indirect genetic effects was a stable effect of maternal EA polygenic scores ({beta}=0.04, [0.024, 0.054]). ConclusionDirect genetic effects linked to genetic liability to MDD accounted for emerging variation in depressive symptoms in adolescence. These results imply that specific etiological mechanisms related to MDD may become particularly relevant for depressive symptoms during early adolescence compared to at earlier ages.

Objective: Major depressive episodes frequently show limited response to first-line treatments, motivating the search for objective biomarkers. EEG/ECG-based support tools aggregating electrophysiological predictors may guide treatment selection. We examined whether antidepressant treatments concordant with an EEG/ECG-biomarker report were associated with higher response rates. Methods: We retrospectively analyzed adults with ICD-10 depressive disorder or bipolar depression treated with electroconvulsive therapy (ECT), repetitive transcranial magnetic stimulation (rTMS), (es)ketamine, or selective serotonin reuptake inhibitors (SSRIs) between 2022 and 2024. Resting-state EEG with simultaneous ECG generated individualized biomarker reports with modality-specific response likelihoods. Treatment chosen by clinical teams was classified as concordant or non-concordant; response was derived from routinely collected clinical scales. Results: Among 153 patients (ECT n=53, rTMS n=48, (es)ketamine n=36, SSRIs n=16), response rates were higher for concordant vs non-concordant treatments: ECT 70% vs 50%, rTMS 30% vs 13%, (es)ketamine 31% vs 10%, and SSRIs 100% vs 11%. Overall, 46% (42/92) of concordant vs. 26% (14/54) of non-concordant patients responded (absolute difference +20 percentage points; relative increase {approx}77%; number needed to treat {approx}5). Conclusion: Concordance with EEG/ECG biomarkers correlated with higher treatment response, warranting confirmation in prospective trials. Significance: EEG/ECG-based decision support may enhance antidepressant treatment response in everyday clinical practice.

BackgroundTranscranial alternating current stimulation (tACS) is a promising non-pharmacological intervention for major depressive disorder (MDD), but its effects on endogenous alpha oscillatory dynamics and their relationship to clinical improvement remain unclear. MethodsIn this double-blind, sham-controlled randomized clinical trial, 20 adults with MDD received five consecutive days of prefrontal 10 Hz tACS or sham. Resting 128-channel EEG was acquired before stimulation on Day 1 (D1), Day 5 (D5), and two-week follow-up. Changes in alpha power spectral density were quantified at the stimulation frequency (10 Hz) and at each participants individual alpha frequency (IAF), using prefrontal regions of interest and whole-head topographical analyses. Depression severity was assessed using the Hamilton Depression Rating Scale (HDRS-17). ResultsBetween-group comparisons revealed no significant differences in prefrontal alpha power changes at either 10 Hz or IAF during the intervention week or at follow-up, although right prefrontal 10 Hz power showed a trend-level reduction with tACS. In contrast, within the tACS group, greater reductions in prefrontal IAF power were associated with greater HDRS-17 improvement from D1 to follow-up, and early IAF power suppression during the intervention week predicted later symptom improvement. Whole-head analyses identified a posterior cluster of reduced 10 Hz power at follow-up in the tACS group relative to sham, whereas clinically relevant correlations were specific to IAF power and distributed across frontal-central and parietal electrodes. Depression scores improved over time in both groups, with greater reductions in HDRS-17 scores observed in the tACS group. ConclusionsFindings suggest that five days of 10 Hz tACS engages depression-relevant alpha mechanisms, with symptom improvement linked specifically to modulation of alpha power at IAF. Results support personalization of tACS in future trials.

Pediatric bipolar disorder is challenging to diagnose accurately due to symptom heterogeneity. More standardized and data-driven approaches are needed to enhance diagnostic reliability. We evaluated a clinical decision tool (nomogram), statistical methods (logistic regression, LASSO), machine learning (support vector machine, random forest, k-nearest neighbors, extreme gradient boosting), and deep learning model (multilayer perceptron) for pediatric bipolar disorder prediction across two datasets collected in academic (N=550) and community (N=511) clinical settings. We compared three modeling strategies: cross-dataset validation, cross-dataset with interaction terms, and mixed-dataset. We assessed model performance using discrimination ability, calibration, and predictor importance ranking. In the baseline cross-dataset approach, all models showed good internal discrimination in the academic dataset; but external discrimination in the community dataset substantially declined. Interaction-enhanced models slightly improved internal discrimination but not external performance or calibration. Recalibration prominently improved cross-dataset calibration without compromising discrimination, indicating that transportability problems were largely driven by probability scaling. Models trained on mixed datasets exhibited much stronger external discrimination and calibration. Across models and training strategies, family risk and PGBI-10M were consistently ranked as the most important predictors. Predictive models for pediatric bipolar disorder showed strong internal performance but limited cross-setting generalizability due to dataset shift and miscalibration. Increasing model complexity did not improve external performance, whereas training on pooled data substantially improved both discrimination and calibration. Findings suggest that sampling diversity, rather than model complexity, is more valuable for developing clinically useful and generalizable psychiatric prediction models, underscoring the importance of open and collaborative datasets.

Background: Individuals diagnosed with depression during pregnancy are more likely to develop cardiovascular disease (CVD) later in life. However, it remains unclear whether subclinical depressive symptoms or symptom trajectories across time are associated with indicators of cardiovascular health (CVH). Therefore, the present study evaluated the relationship between longitudinal depressive symptom trajectories beginning in pregnancy and future CVH. Methods: This secondary analysis of the multisite prospective nuMoM2b-Heart Health Study and included participants with complete longitudinal data from early pregnancy to 2-7 years post-delivery. Participants self-reported depressive symptoms using the Edinburgh Postnatal Depression Scale (EPDS) at 6-13 weeks gestation (early pregnancy), 22-29 weeks gestation (mid- to late-pregnancy), and 2-7 years post-delivery. Latent class mixture modeling was conducted to identify longitudinal patterns of depressive symptoms across early pregnancy, mid-late pregnancy, and extended postpartum follow-up. Structural equation modeling was used to test whether EPDS trajectories were associated with latent CVH, adjusted for length of follow-up interval, pre-pregnancy BMI, gravidity, adverse pregnancy outcomes, smoking history, age, education, income, and use of psychiatric medications. Results: A total of 3,934 participants (mean (M) {+/-} standard deviation (SD) age=27.6{+/-}5.6 years) met inclusion criteria with a mean follow-up interval of 3.2{+/-}0.9 years. A 4-class model, which provided the best fit to the EPDS data (mean posterior probability across classes=0.81), produced the following trajectories: (1) stable low (n=2412; 61.1%), (2) increasing severity (n=848; 21.5%), (3) decreasing severity (n=476; 12.1%), and (4) stable high (n=212; 5.4%). Compared to the stable low group, all groups exhibited significantly lower CVH (stable high: {beta}=0.06, p<0.01; decreasing severity: {beta}=0.05, p=0.02; increasing severity: {beta}=0.08 p<0.01). Pairwise comparisons among the three elevated-symptom groups revealed no significant differences in latent CVH (all ps >0.24). Discussion: The longitudinal course of depressive symptoms from pregnancy to 2-7 years post-delivery varied across individuals. Compared to those with consistently low depressive symptoms, individuals with higher severity symptoms at any point all exhibited lower CVH, regardless of the specific trajectory of symptoms. These findings support a life-course perspective in which depressive symptom patterns may represent an early indicator of cardiometabolic vulnerability.

BackgroundWhether gaps in smartphone passive-sensing data carry psychological signal -- beyond what a baseline self-report already provides -- is a recurring question in digital phenotyping that has rarely been tested with multiplicity control and cross-validation on the same cohort. ObjectiveTo test whether participant-level missingness in passive sensing and ecological momentary assessment (EMA) is an incremental predictor of depression beyond a single baseline self-report, in the canonical StudentLife cohort. MethodsExploratory re-analysis of the publicly available StudentLife dataset. The original 2014 publication reports n = 48 enrolled undergraduates; the public archive contains 59 distinct sensor-instrumented UIDs, but the additional 11 are sparse-data or PHQ-9-incomplete records and contribute nothing to the between-person analyses, which use only the 38 participants with paired pre-term and post-term Patient Health Questionnaire-9 (PHQ-9). We computed 89 participant-level missingness features from nine continuous sensor streams, five phone-activity logs and 27 EMA prompts, and evaluated them under leave-one-out cross-validation with nested-CV-tuned hyperparameters, cluster-bootstrap confidence intervals, an omnibus joint F-test, and Benjamini-Hochberg multiplicity control. ResultsA pre-term PHQ-9 baseline alone explained 59% of out-of-sample variance in post-term scores (n = 32; 95% cluster-bootstrap CI [0.22, 0.81]). Tuned regularized linear models trained on missingness alone reached only the cohort-mean baseline; adding missingness to pre PHQ-9 did not improve performance. The omnibus joint F-test of all nine continuous-stream missingness rates against post-term PHQ-9, adjusted for pre-term PHQ-9, was non-significant (F (9, 27) = 0.43, P = 0.91). No individual feature survived multiplicity correction. A separate within-person day-level analysis (2,186 person-days) yielded a small valence-specific prospective effect (r = +0.082, 95% CI [+0.011, +0.162]) opposite in direction to the withdrawal hypothesis. ConclusionIn this cohort, smartphone-data missingness did not add incremental predictive value beyond a single baseline PHQ-9. The analysis is exploratory and StudentLife-specific; it should not be read as evidence that missingness is never informative. Plain-language summaryMany studies use the gaps in someones smartphone data -- missing GPS readings, missed survey prompts, fewer phone interactions -- as a possible warning sign of depression. This re-analysis tested that idea on a widely used public dataset from a class of 48 college students. After accounting for each students depression score at the start of the term, the gaps in their phone data added no useful information about their depression score at the end of the term. The result is specific to this dataset and does not mean that smartphone gaps are never informative, but it shows that such claims need careful baseline comparisons. Key PointsO_ST_ABSQuestionC_ST_ABSIs smartphone-data missingness an incremental depression biomarker beyond a single baseline self-report? FindingsIn an exploratory re-analysis of the StudentLife cohort (38 participants with paired PHQ-9 scores), missingness features did not improve prediction of post-term PHQ-9 beyond pre-term PHQ-9 under leave-one-out cross-validation, nested-CV-tuned models, an omnibus joint F-test, or multiplicity-controlled univariate screens. MeaningSmartphone-data missingness should not be interpreted as a psychological signal in absence of baseline-controlled, cross-validated, multiplicity-aware evidence. The result is specific to a high-functioning undergraduate cohort over a ten-week term and requires replication.