Cluster analysis of negative symptoms identifies distinct negative symptom subgroups.

The heterogeneity of schizophrenia has been acknowledged for decades because of the diverse presentation of symptoms, illness course, and treatment response noted between individuals diagnosed with the disorder. Cluster analysis has been used as a statistical method to determine whether schizophrenia subgroups might be identified based on symptom heterogeneity. However, there is very limited research examining whether heterogeneity in negative symptoms might be useful in establishing schizophrenia subtypes, particularly research examining newer models of negative symptoms based on five latent constructs including anhedonia, asociality, avolition, blunted affect, and alogia. The Brief Negative Symptom Scale was used to assess the five negative symptoms domains in a sample of 220 outpatients diagnosed with schizophrenia or schizoaffective disorder. Cluster analysis supported a four-cluster solution, comprising clusters of subjects with low negative symptoms (LNS), severe negative symptoms (SNS), and two clusters with moderate negative symptoms, one with predominantly elevated blunted affect (BA) and one with elevated avolition (AV). The LNS, SNS, BA, and AV clusters significantly differed on external validators including clinical characteristics, neurocognition, and functional outcome. Findings suggest that schizophrenia heterogeneity can be parsed according to negative symptom subtypes that have distinct clinical and neuropsychological profiles. Implications for diagnosis and treatment are discussed.