Exploring negative symptoms heterogeneity in patients diagnosed with schizophrenia and schizoaffective disorder using cluster analysis

Abstract

Background: Dissecting the heterogeneity of schizophrenia may help foster progress in understanding its etiology and lay the groundwork for the development of new treatment options for primary or enduring negative symptoms (NS). In this regard, the present study aimed to: (1) to use cluster analysis to identify subgroups of Lebanese patients diagnosed with either schizophrenia or schizoaffective disorder based on NS clusters, and (2) to relate the statistically-derived subgroups to clinically relevant external validators (including measures if state and trait depression, stigma, insight, loneliness, social support).

Method: A total of 202 adult long-stay, chronic, and clinically remitted patients (166 diagnosed with schizophrenia and 36 with schizoaffective disorder) were enrolled. A cluster analysis approach was adopted to classify patients based on the five NS domains social withdrawal, emotional withdrawal, alogia, avolition and anhedonia.

Results: A three-cluster solution was obtained based on unique NS profiles, and divided patients into (1) low NS (LNS; 42.6%) which characterized by the lowest mean scores in all NS domains, (2) moderate NS (MNS; 25.7%), and (3) high NS (HNS; 31.7%). Post-hoc comparisons showed that depression (state and trait), loneliness and social support could accurately distinguish the schizophrenia subgroups. Additionally, individuals in the HNS cluster had longer duration of illness, longer duration of hospitalization, and were given higher dosages of antipsychotic medication compared to those in the other clusters, but these differences did not achieve the statistical significance.

Conclusion: Findings provide additional support to the categorical model of schizophrenia by confirming the existence of three alternate subtypes based on NS. The determination of distinct NS subgroups within the broad heterogeneous population of people diagnosed with schizophrenia may imply that each subgroup possibly has unique underlying mechanisms and necessitates different treatment approaches.

Keywords: Cluster analysis; Heterogeneity; Negative symptoms; Psychosis; Schizophrenia.