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A novel longitudinal clustering approach to psychopathology across diagnostic entities in the hospital-based PsyCourse study.

Schizophrenia research

Authors: Eva C Schulte, Ivan Kondofersky, Monika Budde, Sergi Papiol, Fanny Senner, Sabrina K Schaupp, Daniela Reich-Erkelenz, Farahnaz Klöhn-Saghatolislam, Janos L Kalman, Katrin Gade, Maria Hake, Ashley L Comes, Heike Anderson-Schmidt, Kristina Adorjan, Georg Juckel, Max Schmauß, Jörg Zimmermann, Jens Reimer, Jens Wiltfang, Eva Z Reininghaus, Ion-George Anghelescu, Carsten Konrad, Christian Figge, Martin von Hagen, Markus Jäger, Detlef E Dietrich, Carsten Spitzer, Stephanie H Witt, Andreas J Forstner, Marcella Rietschel, Markus M Nöthen, Peter Falkai, Urs Heilbronner, Nikola S Mueller, Thomas G Schulze

Biological research and clinical management in psychiatry face two major impediments: the high degree of overlap in psychopathology between diagnoses and the inherent heterogeneity with regard to severity. Here, we aim to stratify cases into homogeneous transdiagnostic subgroups using psychometric information with the ultimate aim of identifying individuals with higher risk for severe illness. 397 participants of the PsyCourse study with schizophrenia- or bipolar-spectrum diagnoses were prospectively phenotyped over 18 months. Factor analysis of mixed data of different rating scales and subsequent longitudinal clustering were used to cluster disease trajectories. Five clusters of longitudinal trajectories were identified in the psychopathologic dimensions. Clusters differed significantly with regard to Global Assessment of Functioning, disease course, and-in some cases-diagnosis while there were no significant differences regarding sex, age at baseline or onset, duration of illness, or polygenic burden for schizophrenia. Longitudinal clustering may aid in identifying transdiagnostic homogeneous subgroups of individuals with severe psychiatric disease.

Copyright © 2022. Published by Elsevier B.V.

PMID: 35567871

Participating cluster members