Attacking Heterogeneity in Schizophrenia by Deriving Clinical Subgroups From Widely Available Symptom Data.

Abstract

Previous research has identified (1) a "deficit" subtype of schizophrenia characterized by enduring negative symptoms and diminished emotionality and (2) a "distress" subtype associated with high emotionality-including anxiety, depression, and stress sensitivity. Individuals in deficit and distress categories differ sharply in development, clinical course and behavior, and show distinct biological markers, perhaps signaling different etiologies. We tested whether deficit and distress subtypes would emerge from a simple but novel data-driven subgrouping analysis, based on Positive and Negative Syndrome Scale (PANSS) negative and distress symptom dimensions, and whether subgrouping was informative regarding other facets of behavior and brain function. PANSS data, and other assessments, were available for 549 people with schizophrenia diagnoses. Negative and distress symptom composite scores were used as indicators in 2-step cluster analyses, which divided the sample into low symptom (n = 301), distress (n = 121), and deficit (n = 127) subgroups. Relative to the low-symptom group, the deficit and distress subgroups had comparably higher total PANSS symptoms (Ps < .001) and were similarly functionally impaired (eg, global functioning [GAF] Ps < .001), but showed markedly different patterns on symptom, cognitive and personality variables, among others. Initial analyses of functional magnetic resonance imaging (fMRI) data from a 182-participant subset of the full sample also suggested distinct patterns of neural recruitment during working memory. The field seeks more neuroscience-based systems for classifying psychiatric conditions, but these are inescapably behavioral disorders. More effective parsing of clinical and behavioral traits could identify homogeneous target groups for further neural system and molecular studies, helping to integrate clinical and neuroscience approaches.