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Open access Sep 2026

Early-childhood temperament trajectories map onto transdiagnostic psychiatric risk.

Early-childhood temperament is associated with later mental health. Temperament continues to develop throughout the first years of life, and a single assessment cannot capture its trajectory. Whether departures from an individual's developmental trajectory carry psychiatric risk remains unknown. Using data from more than 50,000 children in the Norwegian Mother, Father and Child Cohort Study, we modeled longitudinal temperament at 1.5, 3, and 5 years of age with the FEMA-Long mixed-effects framework. We then quantified each child's departures from their predicted trajectories. Multivariate analysis revealed two transdiagnostic dimensions linking trajectory departures to psychiatric diagnoses across childhood and adolescence. Higher scores on the first dimension were associated with an increased hazard of subsequent ADHD diagnosis (hazard ratio = 1.54), and higher scores on the second with an increased hazard of Asperger syndrome (hazard ratio = 1.64). To examine the genetic basis of these associations, we performed longitudinal GWAS of temperament and conjunctional FDR analysis to detect loci shared with the associated diagnoses. The effects of these loci changed across early childhood, with some strengthening and others attenuating with age. These findings show that departures from predicted temperament trajectories reflect transdiagnostic psychiatric risk with a shared genetic basis. Longitudinal, trajectory-based monitoring could help identify children at elevated psychiatric risk.

J. Kopal, N. Bakken, P. Parekh et al. · 0 citations
Open access Aug 2026

An Integrative Multimodal Model for Early Diagnosis of Dementia and Differential Diagnosis of Alzheimer's Disease Using Neuroimaging, Polygenic Risk, and Cognitive Assessments

Background: Early diagnosis and etiological classification of dementia remain challenging, as clinicians typically lack tools to integrate cognitive, neuroimaging, and genetic data quantitatively. We developed and validated multimodal risk models to support early diagnosis of dementia and differential diagnosis of Alzheimer's disease (AD) versus non-AD dementias in real-world clinical settings and translated model outputs into individualized risk reports. Methods: Utilizing real-world clinical cohorts (n = 1,100 for early diagnosis of dementia, using clinical diagnoses up to three years after clinical assessment; n = 788 for AD differential diagnosis) from Norwegian Memory Clinics, we trained and validated the Multimodal Hazard Score for Real-World Data (MHS-RWD) model integrating demographics (age, sex), cognitive assessments (MMSE-NR3 or CERAD 10-word delayed recall), the MRI-derived Imaging Hazard Score, and the Polygenic Hazard Score. Discrimination performance was examined using the area under the receiver operating characteristic curve (AUC). Results: In real-world clinical data, the MHS-RWD consistently outperformed any single predictor used alone. For early diagnosis of dementia, the full model achieved an AUC of 0.89 in females and 0.84 in males. For the differential diagnosis of AD from other dementias, the multimodal model yielded an AUC of 0.91 in females and 0.83 in males. A patient-level risk report was designed to present individualized risk estimates. Conclusions: Multimodal integration of cognitive, neuroimaging, and polygenic data in the MHS-RWD tool yields strong discrimination for both early diagnosis of dementia and AD differential diagnosis. The tool relies on data obtainable in clinical care, and genetic information that is becoming increasingly available in routine practice. Delivered through intuitive patient-level risk reports, it could support etiologically informed dementia decisions in real-world settings, with potential utility in primary care.

T. T. Filiz, V. Fominykh, K. Persson et al. · 0 citations
Open access Sep 2026

Molecular Underpinnings of Retinal Traits 1 Shared with Major Psychiatric Disorders

Recent large-scale studies have enabled new knowledge about genetic underpinnings of morphological and electrophysiological alterations of the retina. Variation in retinal traits, often of neurodevelopmental origin, have been linked to major psychiatric disorders (MPDs). Here, we investigate the genetic overlap between MPDs and key retinal traits to identify underlying molecular mechanisms. We obtained genome-wide associations studies data for bipolar disorder (BD), major depression (MD), schizophrenia (SCZ), and the retinal traits retinal nerve fibre layer thickness (RNFL), ganglion cell inner plexiform layer thickness (GCIPL), and vertical cup-disc ratio (VCDR). We estimated the number of trait-influencing variants shared between traits with MiXeR and identified shared genetic loci with condFDR. Subsequently, we examined the biological pathways of the genes mapped to shared loci. This revealed that GCIPL shared the most genetic variants with MPDs (~60%), followed by RNFL (~40%), and VCDR (~20%). The genetic variants shared between retinal traits and MPDs showed disorder-specific patterns with more pronounced overlaps of SCZ and BD with RNFL, and MD negatively correlated with GCIPL. Gene-pathway analysis highlighted the importance of GABAergic neurotransmission and a two-stage neurodevelopmental process in SCZ, whereas the role of mitochondria and a weaker developmental component were observed in BD. The results also implicated synaptic functioning and gene-expression processes in MD. Furthermore, polygenic analysis suggested that the genetic architecture of retinal traits can distinguish between MPDs. Our findings indicate shared genetic underpinnings between retinal traits and SCZ, BD, and MD, implicating altered neurodevelopment and neurotransmission underlying the retinal link to major psychiatric disorders.

P. Jahołkowski, N. Parker, I. Sveen et al. · 0 citations

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