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Schulte, E.C.* ; Kondofersky, I. ; Budde, M.* ; Papiol, S.* ; Senner, F.* ; Schaupp, S.K.* ; Reich-Erkelenz, D.* ; Klöhn-Saghatolislam, F.* ; Kalman, J.L.* ; Gade, K.* ; Hake, M.* ; Comes, A.L.* ; Anderson-Schmidt, H.* ; Adorjan, K.* ; Juckel, G.* ; Schmauß, M.* ; Zimmermann, J.* ; Reimer, J.* ; Wiltfang, J.* ; Reininghaus, E.Z.* ; Anghelescu, I.G.* ; Konrad, C.* ; Figge, C.* ; von Hagen, M.* ; Jäger, M.* ; Dietrich, D.E.* ; Spitzer, C.* ; Witt, S.H.* ; Forstner, A.J.* ; Rietschel, M.* ; Nöthen, M.M.* ; Falkai, P.* ; Heilbronner, U.* ; Müller, N.S. ; Schulze, T.G.*

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

Schizophr. Res. 244, 29-38 (2022)
DOI PMC
Open Access Green as soon as Postprint is submitted to ZB.
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.
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Publication type Article: Journal article
Document type Scientific Article
Keywords Bipolar Disorder ; Cluster Analysis ; Machine Learning ; Psychopathology ; Schizophrenia
Language english
Publication Year 2022
HGF-reported in Year 2022
ISSN (print) / ISBN 0920-9964
e-ISSN 1573-2509
Journal Schizophrenia Research
Quellenangaben Volume: 244, Issue: , Pages: 29-38 Article Number: , Supplement: ,
Publisher Elsevier
Reviewing status Peer reviewed
Institute(s) Institute of Computational Biology (ICB)
POF-Topic(s) 30205 - Bioengineering and Digital Health
Research field(s) Enabling and Novel Technologies
PSP Element(s) G-503800-001
Grants Marcella Rietschel
Dr. Lisa Oehler Foundation
Brain and Behavior Research Foundation
Universidade de Aveiro
National Alliance for Research on Schizophrenia and Depression
Deutsche Forschungsgemeinschaft
Bundesministerium für Bildung und Forschung
DOI 10.1016/j.schres.2022.05.001
Scopus ID 85129915932
PubMed ID 35567871
Erfassungsdatum 2022-09-13
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