Although adolescent depression has been linked to individual chronic conditions, its broader role in shaping multimorbidity risk remains understudied.

A total of 87,562 UK Biobank participants were included, of whom 18,851 had documented adolescent depression. Cox proportional hazards models were applied to evaluate associations between adolescent depression and 24 chronic diseases, followed by stratified analyses by sex and age. Two-sample Mendelian randomization (MR) was then conducted to infer causality for diseases showing significant associations. Genomic colocalization analyses were performed using relevant GWAS data to identify shared causal variants. Mediation analyses were performed to detect possible mediating factors, including the frailty index, KDM biological age acceleration, allostatic load and 30 circulating biomarkers.

Adolescent depression was associated with elevated risk for 12 chronic diseases, with strongest associations for hypothyroidism (HR = 1.29 [1.18–1.42]), diabetes (HR = 1.25 [1.13–1.38]) and chronic obstructive pulmonary disease (COPD) (HR = 1.74 [1.50–2.01]). Risks were notably higher among females and younger adults. MR confirmed likely causal relationships for hypothyroidism (OR = 1.45 [1.03–2.05]), diabetes (OR = 1.01 [1.01–1.02]) and COPD (OR = 1.04 [1.02–1.06]). Genomic colocalization revealed a shared genetic signal at the CDSN/PSORS1C1 locus between adolescent depression and hypothyroidism. Mediation analyses revealed disease-specific pathways: creatinine for hypothyroidism, testosterone for diabetes, KDM biological ageing for COPD and frailty index across all three conditions.

Adolescent depression confers systemic vulnerability through genetic and metabolic mechanisms, with amplified risks in females and individuals aged ≤55 years. These findings support early, integrated interventions to mitigate long-term multimorbidity.

Psychological adaptability hinges on the dynamic balance between cognitive regulation (self-control) and emotional reactivity (impulsivity, reward/punishment sensitivity). However, traditional variable-centered approaches often fail to capture how these traits holistically co-occur, and the neural architectures predicting their longitudinal transitions in early adulthood remain under-explored.

This longitudinal study (N = 1,229 baseline; N = 432 2-year follow-up) integrated a person-centered behavioral approach with resting-state fMRI. We employed Latent Profile Analysis (LPA) to identify trait configurations based on self-control, impulsivity, and sensitivity to reward/punishment. Network-Based Statistic (NBS) analysis with family-wise error (FWE) correction was utilized to evaluate functional connectivity differences among groups. Finally, hierarchical regression and formal interaction models were conducted to test the prospective predictive validity of the identified neural circuits.

LPA delineated three distinct baseline profiles: Adaptive, Moderate, and Maladaptive. NBS analysis revealed that the ‘Adaptive’ profile is underpinned by robust cortico-striatal functional connectivity, integrating the medial prefrontal cortex and striatum. Longitudinally, initial regression models demonstrated that the baseline integrity of this circuit prospectively predicted behavioral adaptation at the 2-year follow-up. Furthermore, hierarchical regression and formal interaction analyses confirmed that this cortico-striatal circuit provided significant incremental predictive validity – above and beyond massive baseline behavioral stability – specifically in males.

These findings highlight cortico-striatal integration as a significant, gender-specific prospective predictor of longitudinal adaptation. By bridging person-centered profiling with network neuroscience, this study elucidates the neural correlates supporting future adaptive functioning and psychological resilience during the early adult transition.

Current bipolar disorder (BD) therapies suffer from limited efficacy and adverse effects, necessitating mechanistically grounded targets.

We integrated BD genome-wide association study data (158,036 cases; 2,796,499 controls) with brain proteomics (ROSMAP and Banner dorsolateral prefrontal cortex, n = 376 and 152) to perform proteome-wide association studies (PWAS). Bayesian colocalization and summary-data-based Mendelian randomization (SMR) prioritized causal genes. Cell-type-specific transcriptomics validated dysregulation in iPSC-derived neurons, astrocytes, and postmortem hippocampus/prefrontal cortex. Weighted gene co-expression networks (WGCNAs), functional enrichment, and molecular docking assessed functional pathways and druggability.

PWAS identified eight BD-associated genes (false discovery rate < 0.05), with DOC2A emerging as the top candidate. Colocalization (H4 > 0.8) and SMR supported a causal association of DOC2A with BD, with no pleiotropy (heterogeneity in dependent instruments P > 0.01); DOC2A expression decreased in BD across neurons (P = 4.26 × 10−2), astrocytes (P = 2.09 × 10−2), hippocampus (P = 9.80 × 10−3, t = −2.738), and prefrontal cortex (P = 1.44 × 10−2, t = −2.580); WGCNA positioned DOC2A as a key regulator (module membership/gene significance P < 0.05) of co-expression networks enriched for BD-associated processes including neurotransmitter secretion and postsynaptic actin cytoskeleton organization (P < 0.05); molecular docking revealed favorable-affinity binding (ΔG < −4 kcal/mol) between DOC2A and BD-related drugs and neuroprotective compounds.

Our convergent multi-omics framework highlights DOC2A dysregulation as a key contributor to synaptic dysfunction in BD and nominates it as a promising therapeutic target. The demonstrated interaction with existing neuroactive compounds provides immediate translational avenues.

Depression is often accompanied by multisystem comorbidities, but the time trajectories of these comorbidities remain unclear.

We aimed to define the temporal sequence of comorbidity accrual relative to depression diagnosis, and examine how this trajectory differs in recurrent depression.

A total of 32 953 individuals with depression were identified in the UK Biobank cohort, including 2402 with recurrent depression. The time between diagnosis of depression or recurrent depression and ten common comorbidities was established to determine the temporal order and rate of comorbidity diagnosis in relation to depression, based on the sequence of recorded diagnostic events. We further stratified the cohort by polygenic risk score, gender, age and history of antidepressant or antihypertensive medication use.

The study included 32 953 participants (mean age at diagnosis 52.6 years; 63.1% female). Hypertension and dorsopathies preceded depression diagnosis by a median of 2.6 years (interquartile range (IQR) −7.0 to 0.0) and 1.0 year (IQR −5.0 to 2.0), respectively. Alzheimer’s disease and obesity emerged after diagnosis at medians of 2.5 years (IQR 0.0–5.0) and 0.8 years (IQR −2.0 to 3.0). High genetic risk was associated with an earlier onset of pre-depression cardiometabolic conditions, with hypertension occurring 2.8 years before diagnosis in individuals with a high polygenic risk score compared with 2.3 years in individuals with a low polygenic risk score. Crucially, individuals with recurrent depression exhibited a profoundly different trajectory, with most comorbidities manifesting many years after the index diagnosis. Stratification by medication history indicated that antihypertensive drug use was associated with an earlier recorded diagnosis of cardiometabolic conditions, whereas antidepressant use was linked to a later diagnosis of neurodegenerative diseases.

These findings identify three critical windows for intervention and reveal a distinct, delayed comorbidity trajectory in recurrent depression. This underscores the need for long-term, integrated surveillance strategies tailored to depression subtype and treatment history.

Social anxiety is a common and impairing condition that often emerges in adolescence.

This study aimed to examine the prevalence and severity of social anxiety among Chinese youths in the post-COVID-19 era, and to develop a predictive model identifying key factors associated with social anxiety severity.

A total of 555 youths aged 15–25 years completed an online survey via WeChat on social anxiety (Social Phobia Inventory), depressive symptoms (Patient Health Questionnaire), sleep problems (Pittsburgh Sleep Quality Index), social support (Multidimensional Scale of Perceived Social Support) and internalised stigma (Internalized Stigma of Mental Illness Scale). Social anxiety severity and rates were described, and comparisons were made across sociodemographic groups. Hierarchical multiple regression was used to predict social anxiety severity from depression, sleep, social support and stigma. An additional regression examined which components of social anxiety (fear, avoidance, physical symptoms) predict internalised stigma.

In total, 69.55% of participants reported at least mild social anxiety, with 20% reaching severe or very severe levels. Female, younger participants and those with fewer close friends reported significantly higher anxiety. Depressive symptoms (β = 0.31, P < 0.05) and internalised stigma (β = 0.40, P < 0.05) were strong predictors of anxiety severity, while sleep problems and social support were not significant after controlling for these factors. Among social anxiety dimensions, only avoidance significantly predicted higher stigma (β = 0.17, P < 0.01).

The high post-pandemic prevalence of social anxiety among youths highlights the need for early identification, stigma reduction and interventions targeting depression and avoidance to prevent long-term impairments.

Schizophrenia progresses through high-risk, first-episode, and chronic stages, each associated with altered spontaneous brain activity. Resting state functional MRI studies highlight these changes, but inconsistencies persist, and the genetic basis remains unclear.

A neuroimaging meta-analysis was conducted to assess spontaneous brain activity alterations in each schizophrenia stage. The largest available genome-wide association study (GWAS) summary statistics for schizophrenia (N = 53,386 cases, 77,258 controls) were used, followed by Hi-C-coupled multimarker analysis of genomic annotation (H-MAGMA) to identify schizophrenia-associated genes. Transcriptome-neuroimaging association and gene prioritization analyses were performed to identify genes consistently linked to brain activity alterations. Biological relevance was explored by functional enrichment.

Fifty-two studies met the inclusion criteria, covering the high-risk (Nhigh-risk = 409, Ncontrol = 475), first-episode (Ncase = 1842, Ncontrol = 1735), and chronic (Ncase = 1242, Ncontrol = 1300) stages. High-risk stage showed reduced brain activity in the right median cingulate and paracingulate gyri. First-episode stage revealed increased activity in the right putamen and decreased activity in the left gyrus rectus and right postcentral gyrus. Chronic stage showed heightened activity in the right inferior frontal gyrus and reduced activity in the superior occipital gyrus and right postcentral gyrus. Across all stages, 199 genes were consistently linked to brain activity changes, involved in biological processes such as nervous system development, synaptic transmission, and synaptic plasticity.

Brain activity alterations across schizophrenia stages and genes consistently associated with these changes highlight their potential as universal biomarkers and therapeutic targets for schizophrenia.

Remote injury assessment during natural disasters poses major challenges for healthcare providers due to the inaccessibility of disaster sites. This study aimed to explore the feasibility of using artificial intelligence (AI) techniques for rapid assessment of traumatic injuries based on gait analysis.

We conducted an AI-based investigation using a dataset of 4500 gait images across 3 species: humans, dogs, and rabbits. Each image was categorized as either normal or limping. A deep learning model, YOLOv5—a state-of-the-art object detection algorithm—was trained to identify and classify limping gait patterns from normal ones. Model performance was evaluated through repeated experiments and statistical validation.

The YOLOv5 model demonstrated high accuracy in distinguishing between normal and limp gaits across species. Quantitative performance metrics confirmed the model’s reliability, and qualitative case studies highlighted its potential application in remote, fast traumatic assessment scenarios.

The use of AI, particularly deep convolutional neural networks like YOLOv5, shows promise in enabling fast, remote traumatic injury assessment during disaster response. This approach could assist healthcare professionals in identifying injury risks when physical access to patients is restricted, thereby improving triage efficiency and early intervention.