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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...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2016
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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. |
format | Online Article Text |
id | pubmed-5127886 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
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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