Transdiagnostic dimensions of psychosis in the Bipolar‐Schizophrenia Network on Intermediate Phenotypes (B‐SNIP)

Abstract

The validity of the classification of non-affective and affective psychoses as distinct entities has been disputed, but, despite calls for alternative approaches to defining psychosis syndromes, there is a dearth of empirical efforts to identify transdiagnostic phenotypes of psychosis. We aimed to investigate the validity and utility of general and specific symptom dimensions of psychosis cutting across schizophrenia, schizoaffective disorder and bipolar I disorder with psychosis. Multidimensional item-response modeling was conducted on symptom ratings of the Positive and Negative Syndrome Scale, Young Mania Rating Scale, and Montgomery-Åsberg Depression Rating Scale in the multicentre Bipolar-Schizophrenia Network on Intermediate Phenotypes (B-SNIP) consortium, which included 933 patients with a diagnosis of schizophrenia (N=397), schizoaffective disorder (N=224), or bipolar I disorder with psychosis (N=312). A bifactor model with one general symptom dimension, two distinct dimensions of non-affective and affective psychosis, and five specific symptom dimensions of positive, negative, disorganized, manic and depressive symptoms provided the best model fit. There was further evidence on the utility of symptom dimensions for predicting B-SNIP psychosis biotypes with greater accuracy than categorical DSM diagnoses. General, positive, negative and disorganized symptom dimension scores were higher in African American vs. Caucasian patients. Symptom dimensions accurately classified patients into categorical DSM diagnoses. This study provides evidence on the validity and utility of transdiagnostic symptom dimensions of psychosis that transcend traditional diagnostic boundaries of psychotic disorders. Findings further show promising avenues for research at the interface of dimensional psychopathological phenotypes and basic neurobiological dimensions of psychopathology.

Validity and utility of Hierarchical Taxonomy of Psychopathology (HiTOP): I. Psychosis superspectrum

“…The detachment spectrum has been reported in multiple studies of mental disorders 11,26,62,71‐73 . It emerged in research on psychosis as a distinct dimension of negative symptoms 7,8,30,55,74,75 . Furthermore, detachment has been replicated several times in studies of maladaptive traits 67‐70 , and its healthy range – introversion – is extensively documented 32,71,76‐78 .…”

“…In the general population, ethnic minority status was associated with elevated psychoticism 48,154 . In patients, minority status was correlated with more severe reality distortion, disorganization, and negative symptoms, although this last effect is weaker and less consistent 8,155‐157 . Multiple processes may explain effect of minority status, such as high social adversity, but are not yet fully understood 153 .…”

“…The psychosis superspectrum emerges in research on the structure of psychiatric diagnoses 11 and of maladaptive personality traits 54 . It is well‐documented as a non‐affective dimension of psychosis that encompasses positive and negative symptoms 6‐8,55 . This union of positive and negative symptoms or corresponding maladaptive traits has long been recognized clinically in diagnoses of schizophrenia and schizotypal personality disorder.…”

“…Several studies compared specific dimensions included in the psychosis superspectrum to diagnoses of psychotic disorders by analyzing their cross‐sectional associations with validators. Dimensions explained more variance in risk factors 304 , psychosis biotypes derived from neurophysiological markers 8 , cognitive deficits 305,306 , real‐world functioning 304,305 , and utilization of inpatient services 304 . In contrast, diagnoses outperformed dimensions only in accounting for illness course and utilization of outpatient services 304 .…”

“…Also, no patients are excluded from the system, because even individuals with subthreshold symptoms or unusual symptom profiles can be characterized on a set of dimensions. The HiTOP model reduces heterogeneity within constructs by grouping related symptoms together and assigning unrelated symptoms to different dimensions 6‐9 . Comorbidity is recognized in this system through assignment of related conditions to the same spectrum.…”