Examining heterogeneity in dementia using data-driven unsupervised clustering of cognitive profiles

• Published in: PloS one Vol. 19; no. 11; p. e0313425
• Main Authors: Kumar, Sayantan, Oh, Inez Y., Schindler, Suzanne E., Ghoshal, Nupur, Abrams, Zachary, Payne, Philip R. O.
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 14.11.2024; Public Library of Science (PLoS)
• Subjects: Activities of daily living; Aged; Aged, 80 and over; Algorithms; Biology and Life Sciences; Biomarkers; Cluster Analysis; Clustering; Cognition; Cognition & reasoning; Cognitive ability; Cognitive Dysfunction - diagnosis; Computer and Information Sciences; Cross-Sectional Studies; Dementia; Dementia - diagnosis; Dementia - epidemiology; Dementia disorders; Development and progression; Disease; Disease Progression; Electronic health records; Evaluation; Feature selection; Female; Health aspects; Health risks; Heterogeneity; Humans; Male; Medicine and Health Sciences; Memory; Neuropathology; Neuropsychological Tests; Patients; Physical Sciences; Physiological aspects; Research and Analysis Methods; Risk assessment; United States
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0313425

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, rather than assuming that the cohort is homogenous. However, current studies on dementia subtyping have 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 seek to overcome such limitations, using a data-driven unsupervised clustering algorithm named SillyPutty, in combination with hierarchical clustering on cognitive assessment scores to estimate subtypes within a real-world clinical dementia cohort. We use a longitudinal patient data set for our clustering analysis, instead of relying only on baseline visits, allowing us to explore the ongoing temporal relationship between subtypes and disease progression over time. Results showed that subtypes with very mild or mild dementia were more heterogenous in their cognitive profiles and risk of disease progression.