TY  - JOUR
AU  - Pelin, Helena
AU  - Ising, Marcus
AU  - Stein, Frederike
AU  - Meinert, Susanne
AU  - Meller, Tina
AU  - Brosch, Katharina
AU  - Winter, Nils R.
AU  - Krug, Axel
AU  - Leenings, Ramona
AU  - Lemke, Hannah
AU  - Nenadić, Igor
AU  - Heilmann-Heimbach, Stefanie
AU  - Forstner, Andreas J.
AU  - Nöthen, Markus M.
AU  - Opel, Nils
AU  - Repple, Jonathan
AU  - Pfarr, Julia
AU  - Ringwald, Kai
AU  - Schmitt, Simon
AU  - Thiel, Katharina
AU  - Waltemate, Lena
AU  - Winter, Alexandra
AU  - Streit, Fabian
AU  - Witt, Stephanie
AU  - Rietschel, Marcella
AU  - Dannlowski, Udo
AU  - Kircher, Tilo
AU  - Hahn, Tim
AU  - Müller-Myhsok, Bertram
AU  - Andlauer, Till F. M.
TI  - Identification of transdiagnostic psychiatric disorder subtypes using unsupervised learning
JO  - Neuropsychopharmacology
VL  - 46
IS  - 11
SN  - 0893-133X
CY  - Basingstoke
PB  - Nature Publishing Group
M1  - FZJ-2021-05144
SP  - 1895-1905
PY  - 2021
AB  - 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.
LB  - PUB:(DE-HGF)16
C6  - pmid:34127797
UR  - <Go to ISI:>//WOS:000661452800002
DO  - DOI:10.1038/s41386-021-01051-0
UR  - https://juser.fz-juelich.de/record/903472
ER  -