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@ARTICLE{Pelin:903472,
      author       = {Pelin, Helena and Ising, Marcus and Stein, Frederike and
                      Meinert, Susanne and Meller, Tina and Brosch, Katharina and
                      Winter, Nils R. and Krug, Axel and Leenings, Ramona and
                      Lemke, Hannah and Nenadić, Igor and Heilmann-Heimbach,
                      Stefanie and Forstner, Andreas J. and Nöthen, Markus M. and
                      Opel, Nils and Repple, Jonathan and Pfarr, Julia and
                      Ringwald, Kai and Schmitt, Simon and Thiel, Katharina and
                      Waltemate, Lena and Winter, Alexandra and Streit, Fabian and
                      Witt, Stephanie and Rietschel, Marcella and Dannlowski, Udo
                      and Kircher, Tilo and Hahn, Tim and Müller-Myhsok, Bertram
                      and Andlauer, Till F. M.},
      title        = {{I}dentification of transdiagnostic psychiatric disorder
                      subtypes using unsupervised learning},
      journal      = {Neuropsychopharmacology},
      volume       = {46},
      number       = {11},
      issn         = {0893-133X},
      address      = {Basingstoke},
      publisher    = {Nature Publishing Group},
      reportid     = {FZJ-2021-05144},
      pages        = {1895-1905},
      year         = {2021},
      abstract     = {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.},
      cin          = {INM-1},
      ddc          = {610},
      cid          = {I:(DE-Juel1)INM-1-20090406},
      pnm          = {5251 - Multilevel Brain Organization and Variability
                      (POF4-525)},
      pid          = {G:(DE-HGF)POF4-5251},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:34127797},
      UT           = {WOS:000661452800002},
      doi          = {10.1038/s41386-021-01051-0},
      url          = {https://juser.fz-juelich.de/record/903472},
}