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Multimorbidity patterns and 18‐year transitions from normal cognition to dementia and death: A population‐based study
48
Zitationen
12
Autoren
2023
Jahr
Abstract
BACKGROUND: Several chronic diseases accelerate cognitive decline; however, it is still unknown how different patterns of multimorbidity influence individuals' trajectories across the cognitive continuum. OBJECTIVES: We aimed to investigate the impact of multimorbidity and of specific multimorbidity patterns on the transitions across cognitive stages (normal cognition, cognitive impairment, no dementia [CIND], dementia) and death. METHODS: We included 3122 dementia-free individuals from the Swedish National study on Aging and Care in Kungsholmen. Using fuzzy c-means cluster analysis, multimorbid participants were classified into mutually exclusive groups characterized by commonly coexisting chronic diseases. Participants were followed up to 18 years to detect incident CIND, dementia, or death. Transition hazard ratios (HRs), life expectancies, and time spent in different cognitive stages were estimated using multistate Markov models. RESULTS: At baseline, five multimorbidity patterns were identified: neuropsychiatric, cardiovascular, sensory impairment/cancer, respiratory/metabolic/musculoskeletal, and unspecific. Compared to the unspecific pattern, the neuropsychiatric and sensory impairment/cancer ones showed reduced hazards of reverting from CIND to normal cognition (HR 0.53, 95% CI 0.33-0.85 and HR 0.60, 95% CI 0.39-0.91). Participants in the cardiovascular pattern exhibited an increased hazard of progression from CIND to dementia (HR 1.70, 95% CI 1.15-2.52) and for all transitions to death. Subjects with the neuropsychiatric and cardiovascular patterns showed reduced life expectancy at age 75, with an anticipation of CIND (up to 1.6 and 2.2 years, respectively) and dementia onset (up to 1.8 and 3.3 years, respectively). CONCLUSIONS: Multimorbidity patterns differentially steer individual trajectories across the cognitive continuum of older adults and may be used as a risk stratification tool.
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