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000903472 1001_ $$00000-0001-8875-4285$$aPelin, Helena$$b0$$eCorresponding author
000903472 245__ $$aIdentification of transdiagnostic psychiatric disorder subtypes using unsupervised learning
000903472 260__ $$aBasingstoke$$bNature Publishing Group$$c2021
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000903472 520__ $$aPsychiatric 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.
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000903472 7001_ $$aIsing, Marcus$$b1
000903472 7001_ $$aStein, Frederike$$b2
000903472 7001_ $$aMeinert, Susanne$$b3
000903472 7001_ $$aMeller, Tina$$b4
000903472 7001_ $$aBrosch, Katharina$$b5
000903472 7001_ $$aWinter, Nils R.$$b6
000903472 7001_ $$00000-0002-0564-2497$$aKrug, Axel$$b7
000903472 7001_ $$aLeenings, Ramona$$b8
000903472 7001_ $$aLemke, Hannah$$b9
000903472 7001_ $$aNenadić, Igor$$b10
000903472 7001_ $$aHeilmann-Heimbach, Stefanie$$b11
000903472 7001_ $$0P:(DE-Juel1)186755$$aForstner, Andreas J.$$b12
000903472 7001_ $$00000-0002-8770-2464$$aNöthen, Markus M.$$b13
000903472 7001_ $$aOpel, Nils$$b14
000903472 7001_ $$aRepple, Jonathan$$b15
000903472 7001_ $$aPfarr, Julia$$b16
000903472 7001_ $$aRingwald, Kai$$b17
000903472 7001_ $$00000-0003-4004-0587$$aSchmitt, Simon$$b18
000903472 7001_ $$aThiel, Katharina$$b19
000903472 7001_ $$00000-0001-7331-0534$$aWaltemate, Lena$$b20
000903472 7001_ $$aWinter, Alexandra$$b21
000903472 7001_ $$00000-0003-1080-4339$$aStreit, Fabian$$b22
000903472 7001_ $$00000-0002-1571-1468$$aWitt, Stephanie$$b23
000903472 7001_ $$00000-0002-5236-6149$$aRietschel, Marcella$$b24
000903472 7001_ $$aDannlowski, Udo$$b25
000903472 7001_ $$aKircher, Tilo$$b26
000903472 7001_ $$00000-0001-6541-3795$$aHahn, Tim$$b27
000903472 7001_ $$aMüller-Myhsok, Bertram$$b28
000903472 7001_ $$00000-0002-2917-5889$$aAndlauer, Till F. M.$$b29$$eCorresponding author
000903472 77318 $$2Crossref$$3journal-article$$a10.1038/s41386-021-01051-0$$bSpringer Science and Business Media LLC$$d2021-06-14$$n11$$p1895-1905$$tNeuropsychopharmacology$$v46$$x0893-133X$$y2021
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