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Predictors of ageing-related decline across multiple cognitive functions

It is critical to discover why some people's cognitive abilities age better than others'. We applied multivariate growth curve models to data from a narrow-age cohort measured on a multi-domain IQ measure at age 11 years and a comprehensive battery of thirteen measures of visuospatial, mem...

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Autores principales: Ritchie, Stuart J., Tucker-Drob, Elliot M., Cox, Simon R., Corley, Janie, Dykiert, Dominika, Redmond, Paul, Pattie, Alison, Taylor, Adele M., Sibbett, Ruth, Starr, John M., Deary, Ian J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5127886/
https://www.ncbi.nlm.nih.gov/pubmed/27932854
http://dx.doi.org/10.1016/j.intell.2016.08.007
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author Ritchie, Stuart J.
Tucker-Drob, Elliot M.
Cox, Simon R.
Corley, Janie
Dykiert, Dominika
Redmond, Paul
Pattie, Alison
Taylor, Adele M.
Sibbett, Ruth
Starr, John M.
Deary, Ian J.
author_facet Ritchie, Stuart J.
Tucker-Drob, Elliot M.
Cox, Simon R.
Corley, Janie
Dykiert, Dominika
Redmond, Paul
Pattie, Alison
Taylor, Adele M.
Sibbett, Ruth
Starr, John M.
Deary, Ian J.
author_sort Ritchie, Stuart J.
collection PubMed
description It is critical to discover why some people's cognitive abilities age better than others'. We applied multivariate growth curve models to data from a narrow-age cohort measured on a multi-domain IQ measure at age 11 years and a comprehensive battery of thirteen measures of visuospatial, memory, crystallized, and processing speed abilities at ages 70, 73, and 76 years (n = 1091 at age 70). We found that 48% of the variance in change in performance on the thirteen cognitive measures was shared across all measures, an additional 26% was specific to the four ability domains, and 26% was test-specific. We tested the association of a wide variety of sociodemographic, fitness, health, and genetic variables with each of these cognitive change factors. Models that simultaneously included all covariates accounted for appreciable proportions of variance in the cognitive change factors (e.g. approximately one third of the variance in general cognitive change). However, beyond physical fitness and possession of the APOE e4 allele, very few predictors were incrementally associated with cognitive change at statistically significant levels. The results highlight a small number of factors that predict differences in cognitive ageing, and underscore that correlates of cognitive level are not necessarily predictors of decline. Even larger samples will likely be required to identify additional variables with more modest associations with normal-range heterogeneity in aging-related cognitive declines.
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spelling pubmed-51278862016-12-06 Predictors of ageing-related decline across multiple cognitive functions Ritchie, Stuart J. Tucker-Drob, Elliot M. Cox, Simon R. Corley, Janie Dykiert, Dominika Redmond, Paul Pattie, Alison Taylor, Adele M. Sibbett, Ruth Starr, John M. Deary, Ian J. Intelligence Article It is critical to discover why some people's cognitive abilities age better than others'. We applied multivariate growth curve models to data from a narrow-age cohort measured on a multi-domain IQ measure at age 11 years and a comprehensive battery of thirteen measures of visuospatial, memory, crystallized, and processing speed abilities at ages 70, 73, and 76 years (n = 1091 at age 70). We found that 48% of the variance in change in performance on the thirteen cognitive measures was shared across all measures, an additional 26% was specific to the four ability domains, and 26% was test-specific. We tested the association of a wide variety of sociodemographic, fitness, health, and genetic variables with each of these cognitive change factors. Models that simultaneously included all covariates accounted for appreciable proportions of variance in the cognitive change factors (e.g. approximately one third of the variance in general cognitive change). However, beyond physical fitness and possession of the APOE e4 allele, very few predictors were incrementally associated with cognitive change at statistically significant levels. The results highlight a small number of factors that predict differences in cognitive ageing, and underscore that correlates of cognitive level are not necessarily predictors of decline. Even larger samples will likely be required to identify additional variables with more modest associations with normal-range heterogeneity in aging-related cognitive declines. Elsevier 2016 /pmc/articles/PMC5127886/ /pubmed/27932854 http://dx.doi.org/10.1016/j.intell.2016.08.007 Text en © 2016 The Authors http://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Ritchie, Stuart J.
Tucker-Drob, Elliot M.
Cox, Simon R.
Corley, Janie
Dykiert, Dominika
Redmond, Paul
Pattie, Alison
Taylor, Adele M.
Sibbett, Ruth
Starr, John M.
Deary, Ian J.
Predictors of ageing-related decline across multiple cognitive functions
title Predictors of ageing-related decline across multiple cognitive functions
title_full Predictors of ageing-related decline across multiple cognitive functions
title_fullStr Predictors of ageing-related decline across multiple cognitive functions
title_full_unstemmed Predictors of ageing-related decline across multiple cognitive functions
title_short Predictors of ageing-related decline across multiple cognitive functions
title_sort predictors of ageing-related decline across multiple cognitive functions
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5127886/
https://www.ncbi.nlm.nih.gov/pubmed/27932854
http://dx.doi.org/10.1016/j.intell.2016.08.007
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