Long COVID has been an important health concern in children and adolescents, yet factors associated with its development remain incompletely understood. Selective serotonin reuptake inhibitors (SSRIs) and serotonin-norepinephrine reuptake inhibitors (SNRIs) are widely prescribed for pediatric neuropsychiatric conditions and may influence immune and autonomic pathways involved in postinfectious symptoms. Here we show associations between SSRI/SNRI use and long coronavirus disease (COVID)-related outcomes in a retrospective cohort of 110,955 children and adolescents with pre-existing neuropsychiatric conditions across 37 US health systems participating in the National Institutes of Health Researching COVID to Enhance Recovery consortium. SSRI/SNRI use was not associated with clinician-recorded long COVID diagnosis but showed heterogeneous associations with individual symptoms. Lower risks were observed for some symptoms, including fever, chills and hair loss, whereas higher risks were observed for neurological and systemic outcomes, including postural orthostatic tachycardia syndrome, cognitive dysfunction and fatigue. These findings suggest that antidepressant exposure may be associated with differing post-COVID symptom patterns in youth and warrant further investigation.

Research and clinical practice on childhood maltreatment largely rely on retrospective self-reports. However, self-reports are often considered unreliable due to concerns about memory biases and shifting subjective appraisals. Here, in a meta-analysis of 49 studies including n = 38,332 individuals followed for an average of 2.4 years (range = 2 months to 12 years), we found that retrospective self-reports of maltreatment are overall highly stable (r = 0.79). However, stability was lower in population-representative samples than in clinical or convenience samples, for neglect compared with abuse, and in children compared with adults. In children, but not adults, stability declined with longer follow-up. These findings challenge the view that retrospective self-reports are inherently unstable, although further research is needed to investigate long-term stability. Reshaping trauma-related appraisals may require deliberate intervention and might be most effective during childhood when memories appear more malleable.

Depression has been linked to reduced size of subcortical regions and abnormal functional connectivity in frontal and default mode networks. However, recent meta-analyses have failed to identify significant converging correlates of depression across the literature such that a conclusive mapping of the neuroimaging correlates of depression remains elusive. We leveraged 23,417 participants across 6 population datasets to comprehensively establish the neuroimaging correlates of depression. We found reductions in gray matter volume/cortical surface area associated with depression in the frontal cortex, anterior cingulate and insula, confirming previous studies showing the importance of prefrontal and default mode regions in depression. Our findings demonstrate multiple surprising results, including a lack of depression correlates in subcortical brain regions and significant depression correlates in somatomotor and visual regions. Overall, these results shed new light on key brain regions involved in the pathophysiology of depression, updating our understanding of the neuroimaging correlates of depression symptoms.

The long-term consequences of attention deficit hyperactivity disorder (ADHD) in females remain underrecognized both in research and clinical practice. Multimorbidity (two or more long-term conditions) is increasingly viewed as a key outcome yet its prevalence, complexity and sociodemographic determinants are understudied in this group. Here, in this longitudinal cohort study, we examined the combined influence of childhood socioeconomic deprivation and ADHD diagnosis on multimorbidity risk in women in early adulthood (that is, aged 18-32 years) and used latent class analysis to explore clustering patterns. Three findings emerged: females with childhood ADHD had a significantly higher risk of adult multimorbidity; childhood ADHD and socioeconomic deprivation increased this risk both in isolation and synergistically, with 39% of the multimorbidity burden attributable to their interaction; and distinct multimorbidity clusters emerged, with the most adverse marked by psychiatric complexity. These results highlight girls with ADHD from disadvantaged backgrounds as a high-risk group requiring earlier, integrated care.

The biological mechanisms underlying major neuropsychiatric disorders remain largely elusive. Given the frequent association of immune dysregulation with these conditions, we used blood-derived multi-omics data from 1,274 healthy adolescents in the IMAGEN cohort to identify transdiagnostic biomarkers and mechanisms that could inform diagnosis and treatment. We first conducted genome-wide analyses to identify single nucleotide polymorphisms associated with DNA methylation and gene expression, with findings replicated in external datasets. These quantitative trait loci were further explored through Mendelian randomization analyses across 6 neuropsychiatric disorders (attention deficit hyperactivity disorder, autism spectrum disorder, bipolar disorder, major depressive disorder, schizophrenia and insomnia), leading to the identification of 73 putatively causal CpG sites and 62 genes that were either unique to individual disorders or, as in the case of MRPL2, shared among the disorders. Notably, the identified genes were significantly enriched in pathways linked to both psychiatric and autoimmune diseases, suggesting a shared genomic architecture between autoimmune and neuropsychiatric disorders. Two-step Mendelian randomization and colocalization analyses revealed potential transdiagnostic regulatory pathways, in which the expression of three genes (MAD1L1, MRPL2 and HLA-DRB1) mediated the effects of CpG methylation on schizophrenia and insomnia. Specifically, DNA methylation at cg06770790 repressed MRPL2, which was putatively causal for insomnia (β = -0.38, P = 1.29 × 10[-4]) and schizophrenia (β = -0.38, P = 1 × 10[-4]). Conversely, increased expression of MAD1L1 and HLA-DRB1, driven by methylation at several CpG sites, was potentially causal for schizophrenia. Our findings highlight key molecular mechanisms and genes implicated in neuropsychiatric disorders, offering promising new targets for therapeutic intervention.

Although adverse childhood experiences (ACEs) increase risk for mental illness at the population level, existing ACEs screens are less helpful in forecasting individual outcomes, suggesting they may not capture significant elements of childhood adversity. We have previously identified unpredictable parental and household experiences as an ACE that portends poorer cognitive and mental health. However, the contribution of unpredictability to established ACEs in real-world settings is unknown. Here, leveraging existing ACEs screening in California, we added the five-item Questionnaire on Unpredictability in Childhood (QUIC-5) in 19 pediatric clinics spanning broad sociodemographic constituencies and compared in ~30,000 children the link of each screen with mental health diagnoses. Scores on either the ACEs or QUIC-5 associated with probabilities of depression, externalizing symptoms, sleep disorders, anxiety and somatic symptoms. Each screen provided unique contributions and combining them often doubled the strength of associations. For depression and sleep disorders, the QUIC-5 identified vulnerable individuals missed by ACEs screen, improving risk detection and facilitating future interventions.

Disease heterogeneity and commonality pose significant challenges to precision medicine, as traditional approaches frequently focus on single disease entities and overlook shared mechanisms across conditions. Inspired by pan-cancer and multi-organ research, we introduce the concept of "pan-disease" to investigate the heterogeneity and shared etiology in brain, eye, and heart diseases. Leveraging individual-level data from 129,340 participants, as well as summary-level data, curated from the MULTI consortium, we applied a weakly-supervised deep learning model (Surreal-GAN) to multi-organ imaging, genetic, proteomic, and RNA-seq data, identifying 11 AI-derived biomarkers, called Multi-organ AI Endophenotypes (MAEs), for the brain (Brain 1-6), eye (Eye 1-3), and heart (Heart 1-2), respectively. We found Brain 3 to be a risk factor for Alzheimer's disease (AD) progression and mortality, whereas Brain 5 was protective against AD progression. Crucially, in data from an anti-amyloid AD drug (solanezumab), heterogeneity in cognitive decline trajectories was observed across treatment groups. At week 240, patients with lower brain 1-3 expression had slower cognitive decline, whereas patients with higher expression had faster cognitive decline. A multi-layer causal pathway pinpointed Brain 1 as a mediational endophenotype linking the FLRT2 protein to migraine, exemplifying novel therapeutic targets and pathways. Additionally, genes associated with Eye 1 and Eye 3 were enriched in cancer drug-related gene sets with causal links to specific cancer types and proteins. Finally, Heart 1 and Heart 2 had the highest mortality risk and unique medication history profiles, with Heart 1 showing favorable responses to antihypertensive medications and Heart 2 to digoxin treatment. The 11 MAEs provide novel AI dimensional representations for precision medicine and highlight the potential of AI-driven patient stratification for disease risk monitoring, clinical trials, and drug discovery.