DB Dwyer, JL Kalman, M Budde, J Kambeitz, A Ruef, LA Antonucci, L Kambeitz-Ilankovic, A Hasan, I Kondofersky, H Anderson-Schmidt, K Gade, D Reich-Erkelenz, K Adorjan, F Senner, S Schaupp, TFM Andlauer, AL Comes, EC Schulte, F Klöhn-Saghatolislam, A Gryaznova, M Hake, K Bartholdi, L Flatau-Nagel, M Reitt, S Quast, S Stegmaier, M Meyers, B Emons, IS Haußleiter, G Juckel, V Nieratschker, U Dannlowski, T Yoshida, M Schmauß, J Zimmermann, J Reimer, J Wiltfang, E Reininghaus, IG Anghelescu, V Arolt, BT Baune, C Konrad, A Thiel, AJ Fallgatter, C Figge, M von Hagen, M Koller, FU Lang, ME Wigand, T Becker, M Jäger, DE Dietrich, H Scherk, C Spitzer, H Folkerts, SH Witt, F Degenhardt, AJ Forstner, M Rietschel, MM Nöthen, N Mueller, S Papiol, U Heilbronner, P Falkai, TG Schulze and N Koutsouleris,
JAMA psychiatry, Feb 2020 12
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.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.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.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.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.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.