ArticleJAMA psychiatry2020
An Investigation of Psychosis Subgroups With Prognostic Validation and Exploration of Genetic Underpinnings: The PsyCourse Study.
Article in JAMA psychiatry, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers, 3 of them syntheses that pooled it.
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Who cites it
36 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Comorbid Schizophrenia and Psychotic Symptoms in Patients With Bipolar Disorder: A Meta-Analysis of the Global Literature.Bipolar disorders · 2026Pooled it
- Accuracy of machine learning methods in predicting prognosis of patients with psychotic spectrum disorders: a systematic review.BMJ open · 2025Pooled it
- Integrating Clinical Neuropsychology and Psychotic Spectrum Disorders: A Systematic Analysis of Cognitive Dynamics, Interventions, and Underlying Mechanisms.Medicina (Kaunas, Lithuania) · 2024Pooled it
- Predictors of adherence to exercise interventions in people with schizophrenia.European archives of psychiatry and clinical neuroscience · 2024Trial
- Psychosocial Interventions, Recovery, and Mediating Mechanisms in Schizophrenia-Spectrum Disorders: A Systematic Review and Meta-Analysis of Longitudinal Studies.Brain sciences · 2026Review
- Ensemble clustering: A practical tutorial.Behavior research methods · 2026Article
- Polygenic associations with phenotypic classes across the psychosis-affective spectrum.medRxiv : the preprint server for health sciences · 2026Article
- The impact of collider bias on genetic prediction in psychotic disorders.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2026Article
- Where is mania in the meta-structure of psychopathology?Psychological medicine · 2025Article
- Pathway-Specific Polygenic Scores for Predicting Clinical Lithium Treatment Response in Patients With Bipolar Disorder.Biological psychiatry global open science · 2025Article
- Transdiagnostic types of formal thought disorder and their association with gray matter brain structure: a model-based cluster analytic approach.Molecular psychiatry · 2025Article
- Differentiating biomarker features and familial characteristics of B-SNIP psychosis Biotypes.Translational psychiatry · 2025Article
- Subtyping first-episode psychosis based on longitudinal symptom trajectories using machine learning.Npj mental health research · 2025Article
- Demographic, Premorbid, and Clinical Characteristics of Schizophrenia Spectrum Patients with High and Low Polygenic Liability to the Disorder.Diseases (Basel, Switzerland) · 2025Article
- Deconstructing depression by machine learning: the POKAL-PSY study.European archives of psychiatry and clinical neuroscience · 2024Article
- Association of polygenic score and the involvement of cholinergic and glutamatergic pathways with lithium treatment response in patients with bipolar disorder.Molecular psychiatry · 2023Article
- Unsupervised Machine Learning Driven Analysis of Verbatims of Treatment-Resistant Schizophrenia Patients Having Followed Avatar Therapy.Journal of personalized medicine · 2023Article
- Metabotyping: a tool for identifying subgroups for tailored nutrition advice.The Proceedings of the Nutrition Society · 2023Review
- Uncovering psychiatric phenotypes using unsupervised machine learning: A data-driven symptoms approach.European psychiatry : the journal of the Association of European Psychiatrists · 2023Article
- Association of Polygenic Score and the involvement of Cholinergic and Glutamatergic Pathways with Lithium Treatment Response in Patients with Bipolar Disorder.Research square · 2023Article
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Abstract
Importance: Identifying psychosis subgroups could improve clinical and research precision. Research has focused on symptom subgroups, but there is a need to consider a broader clinical spectrum, disentangle illness trajectories, and investigate genetic associations. Objective: To detect psychosis subgroups using data-driven methods and examine their illness courses over 1.5 years and polygenic scores for schizophrenia, bipolar disorder, major depression disorder, and educational achievement. Design, Setting, and Participants: This ongoing multisite, naturalistic, longitudinal (6-month intervals) cohort study began in January 2012 across 18 sites. Data from a referred sample of 1223 individuals (765 in the discovery sample and 458 in the validation sample) with DSM-IV diagnoses of schizophrenia, bipolar affective disorder (I/II), schizoaffective disorder, schizophreniform disorder, and brief psychotic disorder were collected from secondary and tertiary care sites. Discovery data were extracted in September 2016 and analyzed from November 2016 to January 2018, and prospective validation data were extracted in October 2018 and analyzed from January to May 2019. Main Outcomes and Measures: A clinical battery of 188 variables measuring demographic characteristics, clinical history, symptoms, functioning, and cognition was decomposed using nonnegative matrix factorization clustering. Subtype-specific illness courses were compared with mixed models and polygenic scores with analysis of covariance. Supervised learning was used to replicate results in validation data with the most reliably discriminative 45 variables. Results: Of the 765 individuals in the discovery sample, 341 (44.6%) were women, and the mean (SD) age was 42.7 (12.9) years. Five subgroups were found and labeled as affective psychosis (n = 252), suicidal psychosis (n = 44), depressive psychosis (n = 131), high-functioning psychosis (n = 252), and severe psychosis (n = 86). Illness courses with significant quadratic interaction terms were found for psychosis symptoms (R2 = 0.41; 95% CI, 0.38-0.44), depression symptoms (R2 = 0.28; 95% CI, 0.25-0.32), global functioning (R2 = 0.16; 95% CI, 0.14-0.20), and quality of life (R2 = 0.20; 95% CI, 0.17-0.23). The depressive and severe psychosis subgroups exhibited the lowest functioning and quadratic illness courses with partial recovery followed by reoccurrence of severe illness. Differences were found for educational attainment polygenic scores (mean [SD] partial η2 = 0.014 [0.003]) but not for diagnostic polygenic risk. Results were largely replicated in the validation cohort. Conclusions and Relevance: Psychosis subgroups were detected with distinctive clinical signatures and illness courses and specificity for a nondiagnostic genetic marker. New data-driven clinical approaches are important for future psychosis taxonomies. The findings suggest a need to consider short-term to medium-term service provision to restore functioning in patients stratified into the depressive and severe psychosis subgroups.
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