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Potential modifiable factors associated with late-life cognitive trajectories

OBJECTIVE: There is variability across individuals in cognitive aging. To investigate the associations of several modifiable factors with high and low cognitive performance. METHODS: Data came from 17,724 community-dwelling individuals aged 65–98 years. Global cognition, verbal fluency, episodic mem...

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Autores principales: Wu, Zimu, Woods, Robyn L., Chong, Trevor T. -J., Orchard, Suzanne G., McNeil, John J., Shah, Raj C., Wolfe, Rory, Murray, Anne M., Storey, Elsdon, Ryan, Joanne
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9381981/
https://www.ncbi.nlm.nih.gov/pubmed/35989918
http://dx.doi.org/10.3389/fneur.2022.950644
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author Wu, Zimu
Woods, Robyn L.
Chong, Trevor T. -J.
Orchard, Suzanne G.
McNeil, John J.
Shah, Raj C.
Wolfe, Rory
Murray, Anne M.
Storey, Elsdon
Ryan, Joanne
author_facet Wu, Zimu
Woods, Robyn L.
Chong, Trevor T. -J.
Orchard, Suzanne G.
McNeil, John J.
Shah, Raj C.
Wolfe, Rory
Murray, Anne M.
Storey, Elsdon
Ryan, Joanne
author_sort Wu, Zimu
collection PubMed
description OBJECTIVE: There is variability across individuals in cognitive aging. To investigate the associations of several modifiable factors with high and low cognitive performance. METHODS: Data came from 17,724 community-dwelling individuals aged 65–98 years. Global cognition, verbal fluency, episodic memory, and psychomotor speed were assessed over up to seven years. Group-based multi-trajectory modeling identified distinct cognitive trajectories. Structural equation modeling examined the direct/indirect associations of social/behavioral factors and several chronic conditions with cognitive trajectories. RESULTS: Seven trajectory subgroups were identified. In the structural equation modeling we compared two subgroups-participants with the highest (14.2%) and lowest (4.1%) cognitive performance with the average subgroup. Lower education, never alcohol intake, and frailty directly predicted increased risk of low performance, and decreased likelihood of high performance. Hypertension (RR: 0.69, 95%CI: 0.60–0.80), obesity (RR: 0.84, 95%CI: 0.73–0.97), diabetes (RR: 0.69, 95%CI: 0.56–0.86) and depression (RR: 0.68, 95%CI: 0.54–0.85) only predicted lower likelihood of high cognitive performance, while dyslipidemia was only associated with low performance (RR: 1.30, 95%CI: 1.07–1.57). Living alone predicted increased risk of low cognitive performance and several comorbidities. Smoking did not predict cognitive trajectories but was associated with increased risk of diabetes, obesity and frailty. Findings were similar when examining the direct associations between modifiable risk factors and all seven cognitive subgroups. CONCLUSIONS: Although several modifiable factors were associated with high performance, and reversely with low performance, this was not observed for obesity, hypertension and dyslipidemia. Further, health behaviors may affect cognitive function indirectly, via geriatric conditions. This indicates that strategies to promote healthy cognitive aging, may be distinct from those targeting dementia prevention.
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spelling pubmed-93819812022-08-18 Potential modifiable factors associated with late-life cognitive trajectories Wu, Zimu Woods, Robyn L. Chong, Trevor T. -J. Orchard, Suzanne G. McNeil, John J. Shah, Raj C. Wolfe, Rory Murray, Anne M. Storey, Elsdon Ryan, Joanne Front Neurol Neurology OBJECTIVE: There is variability across individuals in cognitive aging. To investigate the associations of several modifiable factors with high and low cognitive performance. METHODS: Data came from 17,724 community-dwelling individuals aged 65–98 years. Global cognition, verbal fluency, episodic memory, and psychomotor speed were assessed over up to seven years. Group-based multi-trajectory modeling identified distinct cognitive trajectories. Structural equation modeling examined the direct/indirect associations of social/behavioral factors and several chronic conditions with cognitive trajectories. RESULTS: Seven trajectory subgroups were identified. In the structural equation modeling we compared two subgroups-participants with the highest (14.2%) and lowest (4.1%) cognitive performance with the average subgroup. Lower education, never alcohol intake, and frailty directly predicted increased risk of low performance, and decreased likelihood of high performance. Hypertension (RR: 0.69, 95%CI: 0.60–0.80), obesity (RR: 0.84, 95%CI: 0.73–0.97), diabetes (RR: 0.69, 95%CI: 0.56–0.86) and depression (RR: 0.68, 95%CI: 0.54–0.85) only predicted lower likelihood of high cognitive performance, while dyslipidemia was only associated with low performance (RR: 1.30, 95%CI: 1.07–1.57). Living alone predicted increased risk of low cognitive performance and several comorbidities. Smoking did not predict cognitive trajectories but was associated with increased risk of diabetes, obesity and frailty. Findings were similar when examining the direct associations between modifiable risk factors and all seven cognitive subgroups. CONCLUSIONS: Although several modifiable factors were associated with high performance, and reversely with low performance, this was not observed for obesity, hypertension and dyslipidemia. Further, health behaviors may affect cognitive function indirectly, via geriatric conditions. This indicates that strategies to promote healthy cognitive aging, may be distinct from those targeting dementia prevention. Frontiers Media S.A. 2022-08-03 /pmc/articles/PMC9381981/ /pubmed/35989918 http://dx.doi.org/10.3389/fneur.2022.950644 Text en Copyright © 2022 Wu, Woods, Chong, Orchard, McNeil, Shah, Wolfe, Murray, Storey and Ryan. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neurology
Wu, Zimu
Woods, Robyn L.
Chong, Trevor T. -J.
Orchard, Suzanne G.
McNeil, John J.
Shah, Raj C.
Wolfe, Rory
Murray, Anne M.
Storey, Elsdon
Ryan, Joanne
Potential modifiable factors associated with late-life cognitive trajectories
title Potential modifiable factors associated with late-life cognitive trajectories
title_full Potential modifiable factors associated with late-life cognitive trajectories
title_fullStr Potential modifiable factors associated with late-life cognitive trajectories
title_full_unstemmed Potential modifiable factors associated with late-life cognitive trajectories
title_short Potential modifiable factors associated with late-life cognitive trajectories
title_sort potential modifiable factors associated with late-life cognitive trajectories
topic Neurology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9381981/
https://www.ncbi.nlm.nih.gov/pubmed/35989918
http://dx.doi.org/10.3389/fneur.2022.950644
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