RESUMO
OBJECTIVE: Understanding the intricate relationship between symptom dimensions, clusters, and cognitive impairments is crucial for early detection and intervention in individuals at clinical high-risk(CHR) for psychosis. This study delves into this complex interplay within a CHR sample and aims to predict the conversion to psychosis. METHODS: A comprehensive cognitive assessment was performed among 744 CHR individuals. The study included a three-year follow-up period to assess conversion to psychosis. Symptom profiles were determined using the Structured Interview for Prodromal Syndromes. By applying factor analysis, symptom dimensions were categorized as dominant negative symptoms(NS), positive symptoms-stressful(PS-S), and positive symptoms-odd(PS-O). The factor scores were used to define three dominant symptom groups. Latent class analysis(LCA) and factor mixture model(FMM) were employed to identify discrete clusters based on symptom patterns. The three-class solution was chosen for the LCA and FMM analysis. RESULTS: Individuals in the dominant NS group exhibited significantly higher conversion rates to psychosis than those in the other groups. Specific cognitive variables, including performance in the Brief Visuospatial Memory Test-Revised(Odd ratio, OR=0.702, p=0.001) and Neuropsychological Assessment Battery mazes(OR=0.776, p=0.024), significantly predicted conversion to psychosis. Notably, cognitive impairments associated with NS and PS-S affected different cognitive domains. LCA- and FMM-Cluster 1, characterized by severe NS and PS-O, exhibited more impairments in cognitive domains than other clusters. No significant difference in the conversion rate was observed among LCA and FMM clusters. CONCLUSIONS: These findings highlight the importance of NS in the development of psychosis and suggest specific cognitive domains that are affected by symptom dimensions.
RESUMO
OBJECTIVES: Patients with psychiatric disorders have an increased risk of cardiovascular pathologies. A bidirectional feedback model between the brain and heart exists widely in both psychotic and nonpsychotic disorders. The aim of this study was to compare heart rate variability (HRV) and pulse wave velocity (PWV) functions between patients with psychotic and nonpsychotic disorders and to investigate whether subgroups defined by HRV and PWV features improve the transdiagnostic psychopathology of psychiatric classification. METHODS: In total, 3448 consecutive patients who visited psychiatric or psychological health services with psychotic (N = 1839) and nonpsychotic disorders (N = 1609) and were drug-free for at least 2 weeks were selected. HRV and PWV indicators were measured via finger photoplethysmography during a 5-minute period of rest. Canonical variates were generated through HRV and PWV indicators by canonical correlation analysis (CCA). RESULTS: All HRV indicators but none of the PWV indicators were significantly reduced in the psychotic group relative to those in the nonpsychotic group. After adjusting for age, gender, and body mass index, many indices of HRV were significantly reduced in the psychotic group compared with those in the nonpsychotic group. CCA analysis revealed 2 subgroups defined by distinct and relatively homogeneous patterns along HRV and PWV dimensions and comprising 19.0% (subgroup 1, n = 655) and 80.9% (subgroup 2, n = 2781) of the sample, each with distinctive features of HRV and PWV functions. CONCLUSIONS: HRV functions are significantly impaired among psychiatric patients, especially in those with psychosis. Our results highlight important subgroups of psychiatric patients that have distinct features of HRV and PWV which transcend current diagnostic boundaries.