Investigating the disconnection between cytokine and symptom clusters in clinical high risk populations: Towards a comprehensive cross-dimensional analysis

Abstract

Objective

Clustering individuals at the Clinical High-Risk(CHR) stage of psychosis often relies on single dimensions, and the independence or overlap of clustering results across different dimensions lacks sufficient evidence. Additionally, it remains unclear whether combining different dimensions—such as biological markers(e.g., cytokines) and symptomatic dimensions—can enhance predictive efficacy.

Methods

This study included 370 individuals with CHR and conducted a three-year follow-up, 50 CHR individuals transitioned to psychosis. The participants underwent thorough symptom assessments, encompassing both clinical symptoms and cognitive impairments. Baseline measurements of eight cytokines were obtained. Latent Class Analysis(LCA) was employed to construct clusters based on both symptom profiles and cytokine levels separately. Survival analysis was utilized to explore differences in conversion rates among different clusters.

Results

The LCA determined the selection of the four-cluster solution for symptoms, cytokines, and the integrated clusters. Symptom-Cluster-2 exhibited the most severe clinical symptoms and cognitive impairments, while Symptom-Cluster-4 displayed the mildest clinical symptoms and cognitive impairments. Cytokine-Cluster-1 was characterized by the highest levels of inflammatory cytokines, excluding vascular endothelial growth factor, whereas Symptom-Cluster-4 exhibited the lowest levels of cytokines. The clusters identified based on symptoms and cytokines showed substantial inconsistency. Survival analysis comparing conversion rates across four clusters revealed no significant difference in symptom(χ² = 6.731, p = 0.081) and cytokine(χ² = 7.139, p = 0.068) clusters but was significant in integrated clusters(χ² = 9.234, p = 0.026).

Conclusion

The study emphasizes the distinct perspectives on psychosis risk offered by symptom and cytokine dimensions, advocating for the integration of these dimensions in a cross-modal approach to enhance predictive accuracy.