Towards Clinical Subtypes in Schizophrenia: Integrating Cognitive, Functional, and Digital Phenotyping Assessments

Abstract

Heterogeneity in the clinical presentation of schizophrenia impairs both proper and preventative care. The digital phenotyping data gathered from an international multi-site cohort study in people with schizophrenia (SZ) offers a novel opportunity to explore clinically meaningful subtypes in the context of clinical, functional, and cognitive data. Using a set of behavioral features derived from smartphone digital phenotyping, clinical assessment of symptoms including PANSS, clinical assessment of cognition with BACS, and clinical assessment of functioning with the social functioning assessments over the target period of twelve months, we found that the international cohort of 74 patients were categorized into three well-defined clusters that suggest clinically actionable targets from differential correlations in each. Namely, the identified clusters seemed to share phenotypic traits with the affective psychosis with more severe symptomatic presentation, a non-affective SZ with functional impairment, and a higher functioning non-affective SZ cluster. Partial correlation analysis further highlighted the emergence of different features per cluster, where anxiety symptoms were most notable for one group, whereas psychotic symptoms were most notable for the other two. Importantly, we showcase an analysis pipeline that transparently addresses challenges of missing data and potential skew so that this research methodology can be applied to future prospective validation studies. This study hopes to build a foundation for future digital phenotyping clustering work by scaling up to new sites, and populations to uncover the nature and extent of heterogeneity in schizophrenia.