Translational Psychiatry

Individuals with autism spectrum disorder (ASD) and co-occurring mood or psychotic disorders face substantially elevated suicide-attempt risk, but prior research is largely limited to population-level incidence and cross-sectional risk-factor studies, leaving unclear whether individual-level, prospective prediction is possible in this population. We conducted a retrospective prediction model study using Epic Cosmos electronic health record (EHR) data from 322,879 individuals with ASD and co-occurring mood or psychotic disorders to predict suicide attempts at the next clinical encounter using only information available at or before the preceding encounter. Five machine-learning algorithms were compared, with performance evaluated across discrimination, calibration, and clinical utility. All algorithms achieved strong discrimination using the full predictor set (AUROC 0.87-0.91). A parsimonious feature set (22 predictors) matched or exceeded full-model performance, with extreme gradient boosting (XGBoost) achieving the highest discrimination and standardized net benefit post-ablation (AUROC = 0.90; AUPRC = 0.34), though net benefit was comparable for elastic net and logistic regression. A prior suicide attempt was the strongest individual predictor. Decision-curve analysis indicated that model-guided decisions provided greater net benefit than treating all or no individuals across a broad range of clinically plausible thresholds. These findings demonstrate that near-term suicide-attempt risk can be predicted with high decision utility in this high-risk population using data already captured in routine care, suggesting a feasible basis for future EHR-based clinical decision-support tools.

Background: Antidepressant selection in major depressive disorder remains trial-and-error, and no biomarker identifies which drug will work for an individual patient. Because neuroplasticity is a convergent mechanism of antidepressant response, we tested whether it could serve as a drug-specific biomarker in patient-derived neurons. Methods: We developed the Neuroplasticity Score (NPS), integrating structural and synaptic features from high-content imaging of patient-derived cortical neurons, and validated it against graded brain-derived neurotrophic factor (BDNF) exposure in neurons from 16 healthy donors. NPS was then applied unmodified to 75 antidepressant treatment episodes from 44 patients across two independent retrospective cohorts: 40 episodes from 20 STAR*D participants (Cohort 1; USA multi-site) and 35 episodes from 24 participants at Sheba Medical Center and Geha Mental Health Center (Cohort 2; Israel). Results: NPS increased with escalating BDNF dose (Spearman rho=0.83; P<.001). Response rate rose monotonically across NPS tertiles in both cohorts (trend P=.001 and P<.001). Across all 75 episodes, 84.6% of episodes in the highest NPS tertile responded, compared to 48.0% overall. NPS discriminated responding from non-responding episodes with an area under the receiver operating characteristic curve of 0.81 in both cohorts. The pooled odds ratio per 1-SD increase was 6.05 (95% CI, 2.80-13.05; I-squared=26%). Within patients contributing both a responding and a non-responding episode, the highest-scoring drug was the one that worked in 86% of patients (P<.001). Conclusions: Higher antidepressant-induced neuroplasticity in a patient's own neurons corresponded to a higher likelihood of response to that specific drug across two independent cohorts.

Background: Pregnancy involves profound immune adaptations that may contribute to the development of postpartum depression (PPD); however, the relevant biomarkers and critical time window remain unclear. Therefore, we investigated whether third-trimester and postpartum cytokines and kynurenine pathway (KP) metabolites predict PPD and whether these biological signatures are related to postpartum brain morphology. Methods: Circulating cytokines and KP metabolites were assessed in 140 pregnant women during the third trimester and postpartum. Partial least squares (PLS) regression models were used to identify multivariate biological profiles predictive of PPD, followed by classification and regression tree (CART) analyses to evaluate predictive performance. In a neuroimaging subsample (n=61), voxel-based morphometry examined associations among PPD status, significant PLS-derived components, selected individual biomarkers, and postpartum grey matter volume. Results: A third-trimester cytokine signature including 21 cytokines significantly predicted PPD with high specificity. A third-trimester KP profile, driven primarily by the kynurenic acid/quinolinic acid ratio, also predicted PPD, and the combination of cytokine and KP components improved overall predictive performance to 93.3%. Postpartum biomarkers showed substantially weaker associations with PPD. The cytokine component was differentially associated with postpartum grey matter volume according to PPD status, with opposite associations observed in occipital regions between women who did and did not develop PPD. KP measures also showed distinct associations within sensorimotor regions based on PPD status Conclusions: Late pregnancy immune-inflammatory alterations are associated with subsequent PPD and postpartum brain morphology, supporting the potential utility of pregnancy-derived biological signatures for identifying women at increased risk for PPD.

Background: Major depressive disorder (MDD) is clinically heterogeneous, but relationships among cortical hemodynamic abnormalities, peripheral neuroimmune, metabolic, and oxidative stress (NIMETOX) processes, and clinical phenome features remain unclear. We applied a systems-biology approach integrating functional near-infrared spectroscopy (fNIRS), peripheral NIMETOX biomarkers, and multidimensional clinical phenomes. Methods: Patients with MDD (n = 86) and demographically matched healthy controls (HC, n = 40) underwent 89-channel high-density fNIRS during resting-state and verbal fluency task (VFT) conditions. Temporal hemodynamic variability (Difference) and area under the concentration-time curve (AUC) were derived from oxygenated hemoglobin (HbO), deoxygenated hemoglobin (HbR), and total hemoglobin (HbT) signals. NIMETOX assessments included inflammatory biomarkers, generalized and lipophilic antioxidant defenses, oxidized lipids, lipidomics, and 16S rRNA amplicon sequencing of the stool microbiome. Results: MDD showed reduced VFT Difference and AUC across HbO, HbR, and HbT. Difference reductions were widespread across prefrontal, frontal eye field, language-related, and sensorimotor cortices, whereas AUC abnormalities were more restricted and resting-state alterations were limited. VFT differences significantly differentiated MDD from HC, with further improvement after integrating NIMETOX biomarkers. Lower antioxidants, carnitines/acetylcarnitines, polyunsaturated and even-chain saturated fatty acids, inflammation, indices of gut dysbiosis and leaky gut, and adverse childhood experiences explained substantial variance in HbO, HbR, and HbT Difference. Integrated fNIRS-NIMETOX profiles explained substantial variance in overall illness severity (cross-validated R2= 0.62) and physiosomatic symptoms (cross-validated R2= 0.48). Conclusions: Systems-biology integration identified a multilevel MDD phenotype linking peripheral NIMETOX dysregulation with task-evoked cortical hemodynamic abnormalities and clinical phenome heterogeneity, supporting an interconnected gut-NIMETOX-brain pathophysiology. Key words: major depressive disorder, biomarkers, redox mechanisms, lipophilic antioxidants, oxidized lipids

Background: Major depressive disorder (MDD) is associated with brain dysfunction and age-related diseases. Epigenetic clocks based on DNA methylation patterns estimate biological aging and aim to predict morbidity and mortality. Although MDD has been linked to epigenetic age acceleration (EAA) cross-sectionally, it is unknown whether EAA is associated with MDD after trauma. Methods: We included 706 participants (66% non-white, 64% female) from the multi-ancestry AURORA study, enrolled at emergency departments (ED) within 72 hours after trauma, from whom three age-adjusted epigenetic clocks were measured at enrollment: PhenoAge, GrimAge, and DunedinPACE. Functional MRI and assessment for probable MDD were conducted two weeks and six months later, respectively. Logistic and linear regression models tested the association of EAA with MDD and functional neuroimaging endpoints. Results: A total of 660 participants had available assessments of probable MDD six months after trauma, and from those 160 screened positively (24.2%) after trauma. EAA calculated by GrimAge (OR = 1.44, 95% CI = 1.12 - 1.85, p-adj= 0.012) and DunedinPACE (OR = 1.46, 95% CI = 1.15 - 1.85, p-adj = 0.005), but not PhenoAge (p-adj= 0.239), was significantly associated with MDD 6-months after trauma. Associations remained significant after adjusting for additional confounders, including prior trauma and disability (p= 0.008 for GrimAge, p= 0.010 for DunedinPACE). Subgroup analysis based on a median age split revealed these effects were only observed in participants 35 years of age or older. DunedinPACE was significantly associated with altered connectivity of fourteen brain network pairs after FDR correction (n = 217). Conclusions: EAA is associated with a higher risk of MDD after trauma and with concomitant brain connectivity changes. Calculation of GrimAge and DunedinPACE at the time of ED presentation may help recognize patients at risk for MDD, potentially improving personalized care after trauma.

Background: Genetic and observational studies have identified metabolic factors potentially contributing to the risk of Major Depression (MD). However, studies to date have concentrated on common genetic variants, many of which are in linkage disequilibrium. This makes it difficult to localise variants to a single gene. To overcome this, we used short read whole-genome sequenced datasets (srWGS) to conduct a targeted rare variant association analysis of the FADS region, which has previously been implicated in metabolic effects on MD. Methods: Using srWGS data from 740,373 individuals from All of Us (AoU) and the UK Biobank (UKB) we tested for associations between rare variants in the FADS region and MD. Additionally we conducted association tests between rare FADS variants and five blood-based metabolites implicated by previous studies of MD. Finally, we applied Mendelian randomisation to identify causal effects of metabolites on MD using rare-variants. Results: We found FADS rare variants were significantly associated with all tested metabolites, but not with MD. Using Wald-Ratio MR we found a putative causal effect of Docosahexaenoic acid (DHA) on MD using a rare-variant instrument in the FADS2 gene. Conclusion: Our study represents the largest rare-variant analysis of MD to date. However, the lack of associations between FADS rare-variants and MD suggests our analysis is underpowered, or that genetic effects on MD are conferred substantially by higher frequency variants. Rare-variant MR suggested a causal effect of DHA on MD, and implicates the FADS2 gene.

Biologically informed polygenic scores (PGSs) may help interpret genetic associations with treatment outcomes. Here, we computed neurotransmitter-based PGSs (NeuroPGSs) for patients with major depressive disorder (MDD) and assessed their association with SSRI treatment outcomes. By targeting genetic variants involved in key neurotransmission pathways, we developed PGSs for serotonergic (5228), gamma-aminobutyric acid (GABA, 4103), dopaminergic (7061), and glutamatergic (5690) variants. We leveraged data from two cohorts of patients who received SSRIs for at least 4 weeks: the International SSRI Pharmacogenomics Consortium (ISPC) and the Pharmacogenomics Research Network Antidepressants Medication Pharmacogenomics Study (PGRN-AMPS). Treatment response was defined as [&ge;]50% reduction from baseline depression symptoms, measured using 17-item Hamilton Rating Scale for Depression (HRSD-17) in ISPC and 16-item Quick Inventory of Depressive Symptomatology (QIDS-C16) in PGRN-AMPS. Remission was defined as HRSD-17 [&le;]7 (ISPC) or QIDS-C16[&le;]5 (PGRN-AMPS). The association between each NeuroPGS and antidepressant response or remission was evaluated using logistic regression models, adjusting for age, sex and five genetic principal components, with statistical significance determined at Benjamini Hochberg-adjusted p<0.05. The GABAergic-based PGS was associated with SSRI response (aOR per SD = 1.45, 95%CI: 1.14-1.85). Patients with the highest GABAergic PGS (10th decile) had 2.59-fold higher odds of response (95%CI: 1.46-4.60) compared to those with the lowest decile (1st decile). These exploratory findings support further evaluation of NeuroPGSs; independent replication and assessment of incremental predictive value are needed before clinical application. Keywords: Polygenic score; Neurotransmitters; GABAergic; depression; SSRIs; pharmacogenomics

Background Autism spectrum disorder (ASD) shows wide phenotypic variation, from adults without intellectual disability and with low support needs to minimally verbal children with intellectual disability and severe behavioral symptoms. Genetic studies of this heterogeneity have focused mainly on high-impact de novo variants and polygenic scores; here we investigate, at the molecular level, the role of common variants. Materials and methods We analyzed genotypic and phenotypic data from 9,332 individuals with ASD in the SPARK cohort. We considered 31 traits: six latent factors derived from the Social Communication Questionnaire and the Repetitive Behavior Scale-Revised, 22 co-occurring diagnoses, parent-reported IQ range, age at diagnosis, and age at first walking. We performed within-case genome-wide association studies (GWAS) and, for the 10 traits with evidence of SNP heritability, association analyses with genetically predicted gene and protein expression, splicing, and enhancer activity in brain and blood (xWAS). Nominated genes were tested for enrichment in biological processes, brain cell types, and developmental stages. xWAS-derived transcriptional signatures were compared with chemical perturbation signatures from CMap/LINCS L1000 to prioritize candidate drugs. Results GWAS identified seven genome-wide significant loci for five traits, including loci near GABRG3 and TENM2 associated with co-occurring sleep, feeding or toileting problems. xWAS identified seven gene-trait associations involving three phenotypes (FDR < 0.1), including CACNA1G with communication skills and NEGR1 with learning disability. GWAS and xWAS nominated different genes which however converged on synaptic transmission and gated channel activity, with preferential expression in neurons during later stages of brain development. PARP and p38 MAPK inhibitors were predicted to reverse the signatures of both social-communication and repetitive-behavior phenotypes, whereas several compound classes, including histone deacetylase inhibitors and mTOR inhibitors, were predicted to reverse the signatures of some phenotypes while mimicking those of others. Conclusion Combining within-case GWAS with xWAS nominates candidate genes mediating common-variant effects on ASD heterogeneity, provides biological insight, and suggests candidates for phenotypically stratified drug repositioning.

Background: Major depressive disorder (MDD) is conceptualised within a neuroimmune metabolic oxidative (NIMETOX) framework. From a systems-biology perspective, related metabolic oxidative disturbances may extend across diagnostic boundaries, including schizophrenia (SCZ), but their relationships with childhood adversity and dimensional psychopathology remain unclear. Objectives: To characterize shared and divergent serum metabolic signatures across MDD and SCZ and their associations with childhood adversity, resilience, and dimensional clinical phenotypes. Methods: We studied 89 participants (26 HC, 34 MDD, 29 SCZ). Untargeted serum metabolomics yielded 1,419 metabolites, of which 38 selected markers were integrated into 11 metabolic modules. Group comparisons, nested cross-validated OPLS-DA, and multiple regression analyses were performed. Results: Compared with HC, MDD and SCZ exhibited reduced mitochondrial lipid bioenergetics and metabolic resilience and increased sphingolipid stress signalling, oxidative lipid damage, and renin angiotensin aldosterone system stress. PUFA membrane remodelling increased progressively from HC to SCZ to MDD, whereas antioxidants were selectively reduced in MDD. An integrated OPLS-DA discriminated HC from MDD+SCZ (cross-validated AUC=0.946). Across dimensional models, mitochondrial lipid bioenergetics, PUFA remodelling, sphingolipid stress, oxidative lipid damage, and metabolic resilience explained substantial variance in mood, positive and negative psychotic symptoms, resilience, and suicidal behaviors. A terpenoid exposure biotransformation module was strongly associated with psychosis and suicidal behaviors. Conclusion: MDD and SCZ share an interconnected NIMETOX metabolic architecture while clinical dimensions show partly distinct psychosocial metabolic configurations. These findings support a transdiagnostic NIMETOX systems biology model linking NIMETOX dysfunction with dimensional psychopathology and provide a framework for future stratification and biomarker validation.

Background: Psychiatric disorders pose a significant public health burden, and their complex genetic architecture underlies multiple biological pathways, contributing to a lack of diagnostic biomarkers. Laboratory-Wide Association Studies (LabWAS) leverage electronic health record laboratory data as intermediary phenotypes to link genetic risk to physiology. Methods: Polygenic Scores (PGS) for 25 health-related, psychiatric, and substance use phenotypes were generated for individuals of African (AFR; N=10,473) and European (EUR, N=35,131) ancestry from the Penn Medicine Biobank. Laboratory data was prepared using the LabWAS pipeline, encompassing 78 laboratory tests and 6 vital signs. Linear regression models tested the association of each lab value with PGS adjusting for sex, age, and ten within-ancestry genetic principal components. Results: Four PGS were significantly associated with at least one laboratory value in AFR individuals and 22 PGS in EUR individuals. In EUR individuals, psychiatric PGS partitioned into three physiological profiles: anxiety, attention-deficit/hyperactivity disorder, major depressive disorder, and post-traumatic stress disorder shared a metabolic/inflammatory signature (higher BMI, glucose, and monocytes; lower high density lipoprotein cholesterol and oxygen saturation); anorexia nervosa, obsessive compulsive disorder, and schizophrenia were characterized by lower BMI; and bipolar disorder demonstrated a more circumscribed profile with lower electrolytes and renal makers. Substance use disorder PGS similarly clustered: tobacco use disorder and externalizing showed metabolic/inflammatory profiles; alcohol-related phenotypes showed lower red blood cell indices; and cannabis use disorder had a more circumscribed profile. Conclusions: LabWAS reveals physiologic signatures associated with genetic risk for psychiatric disorders, offering new biological insights into their pathology.

Abstract Background: Insomnia is highly prevalent in major depressive disorder (MDD), yet the clinical and biological significance of dysfunctional beliefs and attitudes about sleep (DBAS) in MDD remains poorly understood. We examined whether distinct DBAS dimensions are associated with insomnia severity, depressive phenotypes, and neuroimmune-metabolic-oxidative (NIMETOX) biomarkers. Methods: DBAS-30 dimensions, clinical phenotypes, and NIMETOX biomarkers were assessed in 87 hospitalized patients with MDD and 31 healthy controls. Principal component analysis (PCA) was used to ascertain DBAS cognitive dimensions, and associations were examined using multivariable analyses. Results: Compared with controls, MDD patients showed lower scores on "perceived consequences of insomnia," "sleep-related worry and helplessness," and "medication-related beliefs," but higher "causal attributions to aging or biochemical imbalance" scores. A composite DBAS score reflecting these 4 MDD-associated cognitive dimensions was constructed, labeled DBAS dimensional index (DBAS-DI). This score was lower in MDD and inversely associated with Insomnia Severity Index (ISI) scores, overall MDD severity, physiosomatic symptoms, current suicidal ideation, and recurrence of illness. DBAS-DI and the expectations domain jointly explained 35.4% of the variance in ISI scores, with inverse and positive associations, respectively. The DBAS-DI score was inversely associated with indices of the acute-phase inflammatory response, oxidative stress, fecal branched-chain short-chain fatty acids (SCFAs) and positively associated with serum antioxidant capacity and protective SCFAs, such as acetate, propionate, and butyrate. Conclusions: DBAS abnormalities in MDD extend beyond insomnia-related cognitions and are associated with illness severity, suicidality, recurrence, and NIMETOX alterations. These findings suggest that dysfunctional sleep-related cognitions are embedded within broader immune-oxidative and gut microbial-metabolic processes that may contribute to the insomnia-depression interface.

Genetic studies of post-traumatic stress disorder (PTSD) implicate immune regulation, yet linking peripheral immune-related proteins to brain molecular regulation and experimentally tractable targets remains challenging. Cellular and spatial genetic analyses were used to characterize the cellular and spatial genetic context of these traits. We then conducted bidirectional Mendelian randomization (MR) across 30 predefined Ig-like plasma proteins and 16 behavioral and neuropsychiatric traits. The forward MR screen identified four associations meeting the prespecified screening criteria, all of which underwent the same post-screening robustness assessment. CD33-PTSD showed the most consistent support across instrument definitions and applicable robust estimators and was therefore advanced as the prioritized candidate to regional genetic, brain molecular QTL, and functional analyses. Standard CD33 pQTL-PTSD colocalization did not support a simple shared single-variant model, while multi-signal analyses remained scientifically unresolved under the available LD reference. Meanwhile, plasma CD33 pQTL and cortical CD33 eQTL showed strong regional sharing (PP4=0.96468). Regional localization, cross-analysis candidate selection, and functional annotation further suggested a complex regulatory context across the CD33-SIGLEC region. Among 284 compounds with prior CD33 binding evidence that entered structure-assisted screening, 11 were retained as candidates for experimental follow-up. Overall, CD33-PTSD was the prioritized association identified by our screening; subsequent regional genetic, brain molecular QTL, and functional annotation analyses further delineated the complex regulatory context of the CD33- SIGLEC locus, while the CD33-centered structure-assisted exploration yielded 11 compound candidates for subsequent experimental validation.

This study examined how prenatal CBD and {Delta}9-tetrahydrocannabinol (THC) exposure affects maternal caregiving in C57BL/6J mice. We developed the Machine-Automated Scoring of the Pup Retrieval Test (MAS-PRT) to overcome limitations of manual behavioral scoring. MAS-PRT integrates Multi-Animal DeepLabCut for dam and pup tracking with Detectron2 for dynamic nest reconstruction. Validated against 170 manually annotated retrieval trials, the pipeline showed high concordance with manual measurements and enabled reproducible extraction of encounter latency, retrieval latency, and locomotor trajectories. Prenatal exposure did not impair general nest-building or overall home-cage maternal care. However, pups from both CBD and THC groups showed reduced body weight at postnatal day 5. Cox proportional hazards modeling revealed divergent effects by compound: CBD-exposed dams exhibited a weight-dependent increase in probability of encountering and retrieving lighter pups, independent of pup sex. Spatial tracking further showed that dams traversed significantly shorter total trajectories when retrieving female progeny exposed to either compound. Longitudinal trial-by-trial analysis indicated intact task acquisition in controls and CBD dams, whereas THC dams displayed a flattened learning curve driven by lower retrieval latencies on initial trials. Together, these findings indicate that prenatal cannabinoid exposure does not produce generalized disruption of maternal care but instead induces compound-specific alterations in maternal reactivity, retrieval kinematics, and learning dynamics.

Anhedonia emerges in adolescence, has putative substrates in neural reward and dopamine systems, and is a hallmark of poor course in depression. Psychoneuroimmunology models suggest altered immune and reward systems may jointly underlie its development. When dopamine availability (rather than function) is considered, immune activation may be understood as a moderator amplifying how lower dopamine levels translate into motivational deficits. This cross-sectional study analyzed data from 55 youth with current depression (Mage=21.4 years; Female=75%). Plasma was assayed for relative quantification of 92 immune proteins, followed by dimensionality reduction via principal component (PC) analysis, forming five PC scores. Dopamine availability was indexed by basal ganglia tissue iron, quantified by MRI R2', a measure detectable in youth. Anhedonia was assessed using the Snaith-Hamilton Pleasure Scale (SHAPS), thought to capture overall anhedonia, and Positive Valence Systems Scale (PVSS), which provides overall, anticipatory, and consummatory anhedonia scores. PC1 scores--whose loadings indicated broad inflammatory activity, including cytokines, chemokines, and tissue remodeling/angiogenic markers--moderated the relationship between basal ganglia dopamine availability and anhedonia: At higher PC1 levels, lower dopamine availability was linked to greater overall anhedonia (as measured by SHAPS) and anticipatory anhedonia, but not to consummatory anhedonia. Exploratory analyses showed comparable effect patterns for PC4, reflecting T cell-related effector/apoptotic signaling. Analyses of individual basal ganglia regions suggest potential differences in dopaminergic-immune interactions across these regions, with effects most consistently observed in the putamen. Findings highlight nuanced neuroimmune pathways underlying anhedonia components in youth and support inflammation-based models of depression emphasizing dopaminergic-immune interplay.

Treatment resistance in obsessive-compulsive disorder (OCD) is common, with 40-60% of patients failing to respond to pharmacotherapy. Identifying functional differences between responders and non-responders may guide alternative treatment strategies. We investigated resting-state brain differences between selective serotonin reuptake inhibitor (SSRI) responders and non-responders and compared them with healthy controls (HC). 3T MRI scans were acquired from 80 participants with OCD and 50 HCs. Structural and 5-minute resting-state data were processed using the CONN toolbox. Regions showing group differences in amplitude of low-frequency fluctuation (ALFF) were identified for seed-based connectivity analysis. ANCOVA revealed significant ALFF differences among groups in the anterior middle temporal gyrus (R-aMTG) and temporal pole (R-TP). SSRI responders showed reductions in seed-to-voxel connectivity than non-responders between these regions and the left superior and inferior occipital cortices, left superior parietal lobule, and left temporo-occipital middle temporal gyrus. Responders also showed reductions in connectivity with the left superior parietal lobule compared with HCs. Across the OCD group, Y-BOCS scores positively correlated with ALFF in the R-aMTG (p=0.008) and R-TP (p=0.044). Altered temporal cortical activity and connectivity with posterior regions were associated with SSRI treatment response, supporting a role for temporal-posterior cortical networks in OCD treatment response.

Objective: To determine whether plasma neurofilament light chain (NfL) and glial fibrillary acidic protein (GFAP) are elevated in hospitalised patients with delirium relative to patients with dementia and primary psychiatric disorders (PPD). Methods and analysis: This was a cross-sectional observational study conducted at the Royal Melbourne Hospital, Melbourne, Australia, between April and October 2024, with comparator samples from a research biobank. In Phase 1 (Delirium Discovery Cohort), we measured plasma NfL and GFAP using the Single molecule array (Simoa) platform in hospitalised patients with delirium and compared them with dementia and PPD groups. Biomarker concentrations were the primary outcome and were compared using general linear models adjusted for age, sex, and estimated glomerular filtration rate, with bootstrapped confidence intervals. Discriminative performance was assessed as a secondary outcome using receiver operating characteristic (ROC) curve analysis. In Phase 2, we applied this threshold to independent dementia and PPD groups. Results: In Phase 1 (n =176), inpatients with delirium showed higher concentrations of both NfL and GFAP than patients with dementia ({beta} = 0.78; 95% CI [0.37, 1.20] and {beta} = 0.39 [0.07, 0.73] respectively) or PPD groups ({beta} = 1.17; [0.69, 1.66] and {beta} = 0.83 [0.51, 1.24] respectively). ROC curve analysis for differentiating delirium from dementia and PPD groups yielded an optimal NfL threshold of 24.1 pg/mL (AUC = 0.89, 96% sensitivity, 70% specificity). In comparison, GFAP showed lower accuracy (AUC = 0.85), and combining NfL with GFAP (AUC = 0.90) did not significantly improve performance. In Phase 2 (n = 154), the NfL threshold of > 24 pg/ml maintained strong performance (AUC = 0.95, 96% sensitivity, and 77% specificity). Conclusions: Plasma NfL shows promise as a biomarker for delirium in hospitalised patients, distinguishing it with high sensitivity from patients with dementia and PPD. Larger multicentre studies are needed to refine cut-offs, evaluate temporal dynamics, and determine integration into acute hospital delirium pathways.

Abstract Human induced pluripotent stem cells (hiPSCs) provide a translational platform to interrogate the impact of genetic risk factors for various disorders on human biology. It has been a common practice to engineer premature stop codons to model predicted loss-of-function mutations using isogenic hiPSCs, yet effects of mutation position for the same gene and genetic background have been rarely tested systematically and at multiple levels for comparison to those of patient-specific mutations. Ultra-rare, heterozygous, and predicted loss-of-function mutations in the SETD1A gene, which encodes a H3K4 methyltransferase, are highly penetrant genetic risk factors for schizophrenia. Here, we generated a large panel of isogenic heterozygous SETD1A mutant hiPSC lines, including premature stop-codon mutations in one early exon and one late exon in five genetic backgrounds and two patient-specific mutations, to gain insight into molecular, cellular, and functional changes in a disease-relevant and highly pure population of human hippocampal dentate granule cell-like neurons. Our multi-level analyses showed that different SETD1A mutations lead to divergent molecular and cellular phenotypes related to synapses, but convergent neuronal network deficits, while the genetic background influences gene expression with no substantial effects at cellular and network levels. Mechanistic analysis confirmed the early exon patient mutation as loss-of-function, but revealed that the predicted loss-of-function patient mutation in the late exon is likely gain-of-function. Consequently, while all mutations decreased global H3K4me3 levels, deficits were only partially rescued by inhibiting H3K4 demethylase LSD1 for early exon mutations, but not for late exon mutations. Together, these results suggest that highly penetrant mutations can lead to similar cellular and functional phenotypes across different genetic backgrounds, whereas different premature stop-codon mutations could have contrasting impacts due to distinct mechanisms. Our findings have practical implications for applying genome-editing in hiPSCs to model disorders, highlight the value of modeling patient-specific mutations and testing multiple mutations, and suggest the need for tailored mutation-dependent therapeutic strategies.

Postpartum depression (PPD) impacts nearly 20% of mothers and a much larger percentage (up to 75%) suffer from postpartum blues. Despite this high incidence, we still have an incomplete understanding of the underlying neurobiology of PPD. Deficits in neurosteroid signaling has been implicated in the vulnerability to postpartum behavioral deficits and mice with reduced expression of {delta} subunit-containing GABAA receptors (GABAARs) (Gabrd -/- or Gabrd +/-), the predominant site of action for 5-reduced neurosteroids, exhibit behavioral deficits restricted to the postpartum period. Allopregnanolone and allopregnanolone analogs (SGE-516) can improve postpartum behavioral outcomes and their ability to modulate oscillations in the basolateral amygdala (BLA) involved in emotional processing has been proposed to contribute to their antidepressant effects. Here we demonstrate that the behavioral differences in postpartum wild type, Gabrd+/-, and SGE-516-treated Gabrd+/- dams is largely driven by theta oscillations in the BLA. Previous studies have linked theta oscillations in the BLA to altered behavioral responding in response to stress or threat. Interestingly, here we demonstrate that Gabrd+/- dams exhibit a failure to properly respond to their own pup calls, with reduced theta oscillations that correlates with deficits in maternal approach behavior both of which are restored with SGE-516 treatment. Further, unbiased MoSeq analysis identified altered behavioral syllable expression in Gabrd+/- dams in response to their pup calls compared to wild type dams that are restored with SGE-516 treatment. These findings demonstrate network and behavioral signatures of postpartum behavioral deficits and characterize the effects associated with neurosteroid-based treatment response.

Background: Major depressive disorder (MDD) is a heterogeneous condition characterized by neuro-immune-metabolic-oxidative stress (NIMETOX) pathways. A systems-biology approach may help elucidate how these interconnected NIMETOX disturbances relate to early-life adversity and multidimensional clinical phenomes in adolescent MDD. Objectives: This study aimed to define the peripheral metabolic, vascular, and acute-phase, oxidative, and lipid biomarker profiles of adolescent MDD and to investigate associations with ACEs and clinical phenotypes. Methods: This cross-sectional study recruited 80 adolescents with MDD and 53 matched healthy controls and assessed C-peptide and insulin, vascular tone markers, the acute-phase inflammatory axis, oxidative stress, and lipid profiles. Results: MDD adolescents exhibited significant reductions in C-peptide, NO, ApoE, ApoA1, HDL-C, albumin, and transferrin, together with increased ApoB/ApoA1 ratio. These biomarkers correlated significantly with family dysfunction, childhood trauma, bullying exposure, and maladaptive parenting. These biomarkers and stressors accounted for 46.2% to 61.2% of the variance in psychological resilience, and major affective, physiosomatic, self-injurious, suicidal, and self-regulation phenomes. ACEs and bullying exposure, together with relevant socio-demographic data, explained 10.5% to 28.4% of the variance in selected metabolic, vascular, and acute-phase biomarkers. Multivariable classification models showed adequate discrimination between MDD and healthy controls, achieving up to 88.5% classification accuracy and an AUC of 0.948. Conclusion: Adolescent MDD is characterized by coordinated reductions in negative acute-phase proteins, metabolic and lipoprotein protective factors, and endothelial NO, without overt CRP-defined inflammation or conventional dyslipidemia. These findings support a systems-biology model of impaired peripheral homeostatic protection associated with developmental adversity and multidimensional depressive phenomes.

Circadian disruption is a transdiagnostic feature of psychiatric illness, particularly in bipolar disorder (BD), where evening chronotype is linked to greater symptom severity. The suprachiasmatic nucleus (SCN) times pineal gland (PG) melatonin synthesis through a well-described feedforward pathway. Moreover, melatonin can also phase-shift the SCN-pacemaker, consistent with a functional feedback limb. Directed effective connectivity between the SCN-containing hypothalamus and the PG has not been estimated in human neuroimaging, and it is unknown whether that directed coupling varies by psychiatric diagnosis or chronotype. We applied cross-spectral dynamic causal modeling (DCM) to resting-state fMRI data in 431 UK Biobank (healthy controls [HC, N=136], BD [N=78], major depressive disorder [MDD, N=154] and psychotic disorders [PY, N=63]). Random-effects Bayesian model selection compared a fully connected model with reduced archiature and, separately, feedforward-only (SCN[-&gt;]PG) vs feedback-only (PG[-&gt;]SCN) models. A per-subject log Bayes factor difference (dF) indexed directionality preference and was tested against diagnosis, chronotype and their interaction using ANCOVA. The fully connected model was strongly preferred (posterior probability=97.8%, protected exceedance probability [PXP]=1.00). Feedforward architecture dominated overall (70% vs 30%, PXP=1.00). Diagnosis did not predict dF (p=.403), but chronotype did (p=.036), and there was a significant diagnosis-by-chronotype interaction (p<.001). Feedforward dominance was strongest in evening chronotypes (p<.001). Model evidence favored a fully connected architecture with overall feedforward dominance. That preference varied by chronotype and, qualified by the interaction, was strongest in BD evening chronotypes. The pattern supports BD-evening chronotype as a high-risk subgroup and argues for routine chronotype assessment in BD care.