TY - JOUR
T1 - Potential modifiable factors associated with late-life cognitive trajectories
AU - Wu, Zimu
AU - Woods, Robyn L.
AU - Chong, Trevor T.J.
AU - Orchard, Suzanne G.
AU - McNeil, John J.
AU - Shah, Raj C.
AU - Wolfe, Rory
AU - Murray, Anne M.
AU - Storey, Elsdon
AU - Ryan, Joanne
N1 - Publisher Copyright:
Copyright © 2022 Wu, Woods, Chong, Orchard, McNeil, Shah, Wolfe, Murray, Storey and Ryan.
PY - 2022/8/3
Y1 - 2022/8/3
N2 - 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.
AB - 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.
KW - aging
KW - association
KW - behavior
KW - cognitive function
KW - social support
KW - structural equation modeling
UR - http://www.scopus.com/inward/record.url?scp=85136494115&partnerID=8YFLogxK
U2 - 10.3389/fneur.2022.950644
DO - 10.3389/fneur.2022.950644
M3 - Article
AN - SCOPUS:85136494115
SN - 1664-2295
VL - 13
JO - Frontiers in Neurology
JF - Frontiers in Neurology
M1 - 950644
ER -