Identification of transdiagnostic psychiatric disorder subtypes using unsupervised learning

Pelin, Helena, Ising, Marcus, Stein, Frederike, Meinert, Susanne, Meller, Tina, Brosch, Katharina, Winter, Nils R, Krug, Axel, Leenings, Ramona, Lemke, Hannah
et al (show 20 more authors) (2021) Identification of transdiagnostic psychiatric disorder subtypes using unsupervised learning. NEUROPSYCHOPHARMACOLOGY, 46 (11). pp. 1895-1905.

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Psychiatric disorders show heterogeneous symptoms and trajectories, with current nosology not accurately reflecting their molecular etiology and the variability and symptomatic overlap within and between diagnostic classes. This heterogeneity impedes timely and targeted treatment. Our study aimed to identify psychiatric patient clusters that share clinical and genetic features and may profit from similar therapies. We used high-dimensional data clustering on deep clinical data to identify transdiagnostic groups in a discovery sample (N = 1250) of healthy controls and patients diagnosed with depression, bipolar disorder, schizophrenia, schizoaffective disorder, and other psychiatric disorders. We observed five diagnostically mixed clusters and ordered them based on severity. The least impaired cluster 0, containing most healthy controls, showed general well-being. Clusters 1-3 differed predominantly regarding levels of maltreatment, depression, daily functioning, and parental bonding. Cluster 4 contained most patients diagnosed with psychotic disorders and exhibited the highest severity in many dimensions, including medication load. Depressed patients were present in all clusters, indicating that we captured different disease stages or subtypes. We replicated all but the smallest cluster 1 in an independent sample (N = 622). Next, we analyzed genetic differences between clusters using polygenic scores (PGS) and the psychiatric family history. These genetic variables differed mainly between clusters 0 and 4 (prediction area under the receiver operating characteristic curve (AUC) = 81%; significant PGS: cross-disorder psychiatric risk, schizophrenia, and educational attainment). Our results confirm that psychiatric disorders consist of heterogeneous subtypes sharing molecular factors and symptoms. The identification of transdiagnostic clusters advances our understanding of the heterogeneity of psychiatric disorders and may support the development of personalized treatments.

Item Type: Article
Uncontrolled Keywords: Humans, Mental Disorders, Bipolar Disorder, Psychotic Disorders, Schizophrenia, Unsupervised Machine Learning
Divisions: Faculty of Health and Life Sciences
Faculty of Health and Life Sciences > Institute of Population Health
Depositing User: Symplectic Admin
Date Deposited: 09 Dec 2021 10:16
Last Modified: 18 Jan 2023 21:23
DOI: 10.1038/s41386-021-01051-0
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