Abstract
BackgroundData-driven examination of multiple morbidities and deficits are informative for clinical and research applications in aging and dementia. Resulting profiles may change longitudinally according to dynamic alterations in extent, duration, and pattern of risk accumulation. Do such frailty-related changes include not only progression but also stability and reversion?ObjectiveWith cognitively impaired and dementia cohorts, we employed data-driven analytics to (a) detect the extent of heterogeneity in frailty-related multimorbidity and deficit burden subgroups and (b) identify key person characteristics predicting differential transition patterns.MethodsWe assembled baseline and 2-year follow-up data from the National Alzheimer's Coordinating Center for amnestic mild cognitive impairment (aMCI) and Alzheimer's disease (AD) cohorts. We applied factor analyses to 43 multimorbidity and deficit indicators. Latent Transition Analysis (LTA) was applied to the resulting domains in order to detect subgroups differing in transition patterns for multimorbidity and deficit burden. We characterized heterogeneity in change patterns by evaluating key person characteristics as differential predictors.ResultsFactor analyses revealed five domains at two time points. LTA showed that two latent burden subgroups at Time 1 (Low, Moderate) differentiated into an additional two subgroups at Time 2 (adding Mild, Severe). Transition analyses detected heterogeneous changes, including progression, stability, and reversion. Baseline classifications and transitions varied according to clinical cohort, global cognition, sex, age, and education.ConclusionsHeterogeneous frailty-related subgroup transitions can be (a) detected in aging adults living with aMCI and AD, (b) characterized as not only progression but also stability and reversion, and (c) predicted by precision characteristics.