Abstract
OBJECTIVE: Because of the complexity of Alzheimer's Disease (AD) clinical presentations across bio-psycho-social domains of functioning, data-reduction approaches, such as latent profile analysis (LPA), can be useful for studying profiles rather than individual symptoms. Previous LPA research has resulted in more precise characterization and understanding of patients, better clarity regarding the probability and rate of disease progression, and an empirical approach to identifying those who might benefit most from early intervention. Whereas previous LPA research has revealed useful cognitive, neuropsychiatric, or functional subtypes of patients with AD, no study has identified patient profiles that span the domains of health and functioning and that also include motor and sensory functioning. METHODS: LPA was conducted with data from the Advancing Reliable Measurement in Alzheimer's Disease and cognitive Aging study. Participants were 209 older adults with amnestic mild cognitive impairment (aMCI) or mild dementia of the Alzheimer's type (DAT). LPA indicator variables were from the NIH Toolbox® and included cognitive, emotional, social, motor, and sensory domains of functioning. RESULTS: The data were best modeled with a 4-profile solution. The latent profiles were most differentiated by indices of social and emotional functioning and least differentiated by motor and sensory function. CONCLUSIONS: These multi-domain patient profiles support and extend previous findings on single-domain profiles and highlight the importance of social and emotional factors for understanding patient experiences of aMCI/DAT. Future research should investigate these profiles further to better understand risk and resilience factors, the stability of these profiles over time, and responses to intervention.