Frailty in motion: Amnestic mild cognitive impairment and Alzheimer's disease cohorts display heterogeneity in multimorbidity classification and longitudinal transitions DOI Creative Commons
Linzy Bohn, Yao Zheng, G. Peggy McFall

et al.

Journal of Alzheimer s Disease, Journal Year: 2025, Volume and Issue: unknown

Published: March 2, 2025

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

Language: Английский

Frailty in motion: Amnestic mild cognitive impairment and Alzheimer's disease cohorts display heterogeneity in multimorbidity classification and longitudinal transitions DOI Creative Commons
Linzy Bohn, Yao Zheng, G. Peggy McFall

et al.

Journal of Alzheimer s Disease, Journal Year: 2025, Volume and Issue: unknown

Published: March 2, 2025

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

Language: Английский

Citations

0