ArticlePloS one2023
Predictors of cognitive functioning trajectories among older Americans: A new investigation covering 20 years of age- and non-age-related cognitive change.
Article in PloS one, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Intersectional approaches to cognitive aging: a practical guide to modeling heterogeneous trajectories with GLMM-trees.Frontiers in psychology · 2026Article
- Association of childhood meat consumption with late-life cognitive function: mediation by adult education and moderation by parental literacy.Frontiers in public health · 2026Article
- The effectiveness of respiratory training as a preventive strategy against cognitive decline: a mini review.Frontiers in rehabilitation sciences · 2026Review
- A Follow-up Study on the Trajectory and Sex Differences of Different Cognitive Dimensions in the Normal Aging Elderly in Guangzhou.Alpha psychiatry · 2025Article
- The Cognitive Functions Dementia Battery: A Novel Computerized Tool for Neurocognitive Disorders.Aging and disease · 2025Article
- Social Activities and Cognitive Functioning Across Mid- and Late Life: Evidence From China.The Gerontologist · 2025Article
- Obesity accelerates brain ageing: a multimodal imaging study.Brain communications · 2025Article
- Does Old Age Comprise Distinct Subphases? Evidence from an Analysis of the Relationship between Age and Activities of Daily Living, Comorbidities, and Geriatric Syndromes.Annals of geriatric medicine and research · 2024Observational
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Authors and funding
3 authors.
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Abstract
Despite the extensive study of predictors of cognitive decline in older age, a key uncertainty is how much these predictors explain both the intercept and age- and non-age-related change in cognitive functioning (CF). We examined the contribution of a broad range of life course determinants to CF trajectories. Data came from 7,068 participants in the 1996-2016 Health and Retirement Study. CF was measured as a summary score on a 27-point cognitive battery of items. We estimated multilevel growth curve models to examine the CF trajectories in individuals ages 54-85. We found that the variation in CF level at age 54 was three times as much as the variation in age slope. All the observed individual predictors explained 38% of the variation in CF at age 54. Personal education was the most important predictor (25%), followed by race, household wealth and income, parental education, occupation, and depression. The contributions of activity limitations, chronic diseases, health behaviors (obesity, smoking, vigorous activity), childhood conditions (childhood health, nutrition, financial situation), gender, marital status, and religion were rather small (<5%). Even though the age slope varied with many adulthood factors, they only explained 5.6% of the between-person variation in age slope. Moreover, age explained 23% of within-person variation in CF from age 54 to 85. The rest non-age-related within-person variation could not be explained by the observed time-varying factors. These findings suggest that future research is urgently needed to discover the main determinants of the slope of cognitive decline to slow down the progression of cognitive impairment and dementia.
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