Revealing heterogeneity in dementia using data-driven unsupervised clustering of cognitive profiles
Dementia is characterized by a decline in memory and thinking that is significant enough to impair function in activities of daily living. Patients seen in dementia specialty clinics are highly heterogenous with a variety of different symptoms that progress at different rates. Recent research has focused on finding data-driven subtypes for revealing new insights into dementia's underlying heterogeneity, compared to analyzing the entire cohort as a single homogeneous group. However, existing studies on dementia subtyping suffer from the following limitations: (i) focusing on AD-related dementia only and not examining heterogeneity within dementia as a whole, (ii) using only cross-sectional baseline visit information for clustering and (iii) predominantly relying on expensive imaging biomarkers as features for clustering. In this study, we used a data-driven unsupervised clustering algorithm named SillyPutty, in combination with hierarchical clustering on neuropsychological assessment scores to estimate subtypes within a real-world clinical dementia cohort. We incorporated all longitudinal patient visits into our clustering analysis, instead of relying only on baseline visits, allowing us to explore the ongoing relationship between subtypes and disease progression over time. Results showed evidence of (i) subtypes with very mild or mild dementia being more heterogenous in their cognitive profiles and risk of disease progression.