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Identification of transdiagnostic psychiatric disorder subtypes using unsupervised learning.

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.

SUBMITTER: Pelin H

PROVIDER: S-EPMC8429672 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature

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Identification of transdiagnostic psychiatric disorder subtypes using unsupervised learning.

Pelin Helena H Ising Marcus M Stein Frederike F Meinert Susanne S Meller Tina T Brosch Katharina K Winter Nils R NR Krug Axel A Leenings Ramona R Lemke Hannah H Nenadić Igor I Heilmann-Heimbach Stefanie S Forstner Andreas J AJ Nöthen Markus M MM Opel Nils N Repple Jonathan J Pfarr Julia J Ringwald Kai K Schmitt Simon S Thiel Katharina K Waltemate Lena L Winter Alexandra A Streit Fabian F Witt Stephanie S Rietschel Marcella M Dannlowski Udo U Kircher Tilo T Hahn Tim T Müller-Myhsok Bertram B Andlauer Till F M TFM

Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology 20210614 11

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 transd ...[more]

PMID: 34127797

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Project description:RationaleMore targeted management of severe acute pediatric asthma could improve clinical outcomes.ObjectivesTo identify distinct clinical phenotypes of severe acute pediatric asthma using variables obtained in the first 12 h of hospitalization.MethodsWe conducted a retrospective cohort study in a quaternary care children's hospital from 2014 to 2022. Encounters for children ages 2-18 years admitted to the hospital for asthma were included. We used consensus k means clustering with patient demographics, vital signs, diagnostics, and laboratory data obtained in the first 12 h of hospitalization.Measurements and main resultsThe study population included 683 encounters divided into derivation (80%) and validation (20%) sets, and two distinct clusters were identified. Compared to Cluster 1 in the derivation set, Cluster 2 encounters (177 [32%]) were older (11 years [8; 14] vs. 5 years [3; 8]; p < .01) and more commonly males (63% vs. 53%; p = .03) of Black race (51% vs. 40%; p = .03) with non-Hispanic ethnicity (96% vs. 84%; p < .01). Cluster 2 encounters had smaller improvements in vital signs at 12-h including percent change in heart rate (-1.7 [-11.7; 12.7] vs. -7.8 [-18.5; 1.7]; p < .01), and respiratory rate (0.0 [-20.0; 22.2] vs. -11.4 [-27.3; 9.0]; p < .01). Encounters in Cluster 2 had lower percentages of neutrophils (70.0 [55.0; 83.0] vs. 85.0 [77.0; 90.0]; p < .01) and higher percentages of lymphocytes (17.0 [8.0; 32.0] vs. 9.0 [5.3; 14.0]; p < .01). Cluster 2 encounters had higher rates of invasive mechanical ventilation (23% vs. 5%; p < .01), longer hospital length of stay (4.5 [2.6; 8.8] vs. 2.9 [2.0; 4.3]; p < .01), and a higher mortality rate (7.3% vs. 0.0%; p < .01). The predicted cluster assignments in the validation set shared the same ratio (~2:1), and many of the same characteristics.ConclusionsWe identified two clinical phenotypes of severe acute pediatric asthma which exhibited distinct clinical features and outcomes.

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Identification of transdiagnostic psychiatric disorder subtypes using unsupervised learning.

Publications

Identification of transdiagnostic psychiatric disorder subtypes using unsupervised learning.

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