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 - Funding Information:
This work was supported by the National Institute on Aging and the National Cancer Institute at the National Institutes of Health (Grant Numbers: U01AG029824 and U19AG062682); and the National Health and Medical Research Council (NHMRC) of Australia (Grant Numbers: 334047 and 1127060); and Monash University (Australia); and the Victorian Cancer Agency (Australia). JR was supported by an NHMRC Dementia Research Leader Fellowship (Grant Number: APP1135727). ZW is a recipient of the RTP scholarship awarded by Monash University and the Australian Government. The funders had no role in study design and collection, analysis, and interpretation of the results.
Funding Information:
Author AM reports receiving consulting fees from Alkahest, Inc. and grants from the National Institute on Aging. RS reports grants for clinical research regarding dementia and Alzheimer's disease from the National Institutes of Health, the Centers for Medicare and Medicaid Services, the Department of Defense, and the Illinois Department of Public Health; being as a non-compensated member of the Board of Directors of the Alzheimer's Association–Illinois Chapter; and being as a site principal investigator or sub-investigator for clinical trials and research studies for which his institution (Rush University Medical Center) is sponsored (Amylyx Pharmaceuticals, Inc., Eli Lilly and Co., Inc., Genentech, Inc., Lundbeck, Inc., Merck and Co., Inc., Navidea Biopharmaceuticals, Novartis Pharmaceuticals, Inc., Roche Holdings AG, and Takeda Development Center Americas, Inc.).
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
C2 - 35989918
AN - SCOPUS:85136494115
SN - 1664-2295
VL - 13
JO - Frontiers in Neurology
JF - Frontiers in Neurology
M1 - 950644
ER -
