Authors
Solomon PAJ, Budde M, Kohshour MO, Adorjan K, Heilbronner M, Navarro-Flores A, Papiol S, Reich-Erkelenz D, Schulte EC, Senner F, Vogl T, Kaurani L, Krugger DM, Sananbenesi F, Pena T, Burkhardt S, Schutz AL, Anghelescu IG, Arolt V, Baune BT, Dannlowski U, Dietrich DE, Fallgatter AJ, Figge C, Juckel G, Konrad C, Lang FU, Reimer J, Reininghaus EZ, SchmauB M, Spitzer C, Wiltfang J, Zimmermann J, Fischer A, Falkai P, Schulze TG, Heilbronner U, Poschmann J
Journal
BioRxiv
Citation
bioRxiv 2025.03.28.645923.
Abstract
Importance: Numerous studies indicate that the traditional categorical classification of severe mental disorders (SMD), such as schizophrenia, bipolar disorders, and major depressive disorders, does not align with the underlying biology of those disorders as they frequently overlap in terms of symptoms and risk factors. Objective: This study aimed to identify transdiagnostic patient clusters based on disease severity and explore the underlying biological mechanisms independently of the traditional categorical classification. Design: We utilized data from 443 participants of the PsyCourse Study diagnosed with SMD, a longitudinal study with deep phenotyping across four visits. We performed longitudinal clustering to group patients based on symptom trajectories and cognitive performance. The resulting clusters were compared on cross-sectional variables, including independent measures of severity as well as polygenic risk scores, serum protein quantification, miRNA expression, and DNA methylation. Results: We identified two distinct clusters that exhibited marked differences in illness severity. However, these clusters did not differ significantly in age, sex, or diagnostic proportions. We found that inflammatory serum proteins showed significant differences between the two clusters. Conclusion: Our findings demonstrate that transdiagnostic clustering can effectively differentiate patients based on disease severity. The association of disease severity with pro-inflammatory proteins highlights the potential role of inflammation in the pathophysiology of SMD. These results underscore the importance of considering illness severity in psychiatric research and clinical practice and suggest that targeting inflammatory pathways may offer novel therapeutic strategies for patients with SMD. Keywords: severe mental disorders, transdiagnostic clustering, disease severity, inflammation, proteomics, PLAUR, cognitive dysfunction, multi-omics analysis
