Preprint: Deep learning-based stratification of Schizophrenia Spectrum Disorder from real-world data reveals distinct profiles of common and rare variant genetic signal
WHY IT MATTERS
If schizophrenia spectrum disorder can be divided into genetically distinct subtypes, future treatments could be personalized to match each person's specific genetic profile rather than using one-size-fits-all approaches.
Researchers studied over 22,000 people from Denmark to understand why schizophrenia affects people differently. They used artificial intelligence to find ten distinct groups within schizophrenia spectrum disorder based on clinical symptoms and genetic information. This discovery suggests that schizophrenia isn't one single disease but rather multiple related conditions with different genetic causes.
Deep learning-based stratification of Schizophrenia Spectrum Disorder from real-world data reveals distinct profiles of common and rare variant genetic signal Authors: Cobuccio, L. et al. Server: medRxiv Category: psychiatry and clinical psychology Abstract: Schizophrenia spectrum disorder (SSD) is a clinically and genetically heterogeneous condition, yet few studies have integrated real-world clinical data with both common and rare genetic variation to explore this complexity. In this study, we analyzed real-world data from 22,092 individuals in the Danish iPSYCH cohort (11,046 SSD cases and 11,046 matched population controls) leveraging nationwide registry data on diagnoses, hospitalizations, and parental history. Using a variational autoencoder (VAE), we compressed these features into a latent space and identified ten clinically distinct SSD su