Literature DB >> 34060702

The "cognitive clock": A novel indicator of brain health.

Patricia A Boyle1,2, Tianhao Wang1,3, Lei Yu1,3, Robert S Wilson1,2,3, Robert Dawe1,4, Konstantinos Arfanakis1,4,5, Julie A Schneider1,3,6, Todd Beck7, Kumar B Rajan7, Denis Evans7, David A Bennett1,3.   

Abstract

INTRODUCTION: We identified a "cognitive clock," a novel indicator of brain health that provides person-specific estimates of cognitive age, and tested the hypothesis that cognitive age is a better predictor of brain health than chronological age in two independent datasets.
METHODS: The initial analyses were based on 1057 participants from the Rush Memory and Aging Project and the Religious Orders Study who began without impairment and underwent cognitive assessments up to 24 years. A shape invariant model characterized the latent pattern of cognitive decline, conceptualized here as the "cognitive clock," and yielded person-specific estimates of cognitive age. Survival analyses examined cognitive versus chronological age for predicting Alzheimer's disease dementia, mild cognitive impairment and mortality, and regression analyses examined associations of cognitive versus chronological age with neuropathology and brain atrophy. Finally, we applied the cognitive clock to an independent validation sample of 2592 participants from the Chicago Health and Aging Project, a biracial population-based study, to confirm the predictive utility of cognitive age.
RESULTS: The "cognitive clock" showed that cognition remained stable until a cognitive age of about 80, then declined moderately until 90, then declined precipitously. In the initial dataset, cognitive age was a better predictor of dementia, mild cognitive impairment and mortality than chronological age, and was more strongly associated with neuropathology and brain atrophy. Application of the cognitive clock to the independent validation sample provided further support for the utility of cognitive age as a strong prognostic indicator of adverse outcomes. DISCUSSION: Cognitive age is a robust prognostic indicator of adverse health outcomes and may serve as a useful biomarker in aging research.
© 2021 the Alzheimer's Association.

Entities:  

Keywords:  Alzheimer's disease dementia; brain health; brain volume; cognitive aging; neuropathology

Mesh:

Year:  2021        PMID: 34060702      PMCID: PMC9014826          DOI: 10.1002/alz.12351

Source DB:  PubMed          Journal:  Alzheimers Dement        ISSN: 1552-5260            Impact factor:   16.655


  27 in total

1.  Nonparametric mixed effects models for unequally sampled noisy curves.

Authors:  J A Rice; C O Wu
Journal:  Biometrics       Date:  2001-03       Impact factor: 2.571

2.  Healthcare and Financial Decision Making and Incident Adverse Cognitive Outcomes among Older Adults.

Authors:  Christopher C Stewart; Lei Yu; Robert S Wilson; David A Bennett; Patricia A Boyle
Journal:  J Am Geriatr Soc       Date:  2019-03-18       Impact factor: 5.562

3.  Much of late life cognitive decline is not due to common neurodegenerative pathologies.

Authors:  Patricia A Boyle; Robert S Wilson; Lei Yu; Alasdair M Barr; William G Honer; Julie A Schneider; David A Bennett
Journal:  Ann Neurol       Date:  2013-07-10       Impact factor: 10.422

4.  Scam Awareness Related to Incident Alzheimer Dementia and Mild Cognitive Impairment: A Prospective Cohort Study.

Authors:  Patricia A Boyle; Lei Yu; Julie A Schneider; Robert S Wilson; David A Bennett
Journal:  Ann Intern Med       Date:  2019-04-16       Impact factor: 25.391

5.  Prediction of individual brain maturity using fMRI.

Authors:  Nico U F Dosenbach; Binyam Nardos; Alexander L Cohen; Damien A Fair; Jonathan D Power; Jessica A Church; Steven M Nelson; Gagan S Wig; Alecia C Vogel; Christina N Lessov-Schlaggar; Kelly Anne Barnes; Joseph W Dubis; Eric Feczko; Rebecca S Coalson; John R Pruett; Deanna M Barch; Steven E Petersen; Bradley L Schlaggar
Journal:  Science       Date:  2010-09-10       Impact factor: 47.728

6.  Normative Cognitive Decline in Old Age.

Authors:  Robert S Wilson; Tianhao Wang; Lei Yu; David A Bennett; Patricia A Boyle
Journal:  Ann Neurol       Date:  2020-03-14       Impact factor: 10.422

7.  Genome-wide methylation profiles reveal quantitative views of human aging rates.

Authors:  Gregory Hannum; Justin Guinney; Ling Zhao; Li Zhang; Guy Hughes; SriniVas Sadda; Brandy Klotzle; Marina Bibikova; Jian-Bing Fan; Yuan Gao; Rob Deconde; Menzies Chen; Indika Rajapakse; Stephen Friend; Trey Ideker; Kang Zhang
Journal:  Mol Cell       Date:  2012-11-21       Impact factor: 17.970

Review 8.  Religious Orders Study and Rush Memory and Aging Project.

Authors:  David A Bennett; Aron S Buchman; Patricia A Boyle; Lisa L Barnes; Robert S Wilson; Julie A Schneider
Journal:  J Alzheimers Dis       Date:  2018       Impact factor: 4.472

9.  DNA methylation age is associated with mortality in a longitudinal Danish twin study.

Authors:  Lene Christiansen; Adam Lenart; Qihua Tan; James W Vaupel; Abraham Aviv; Matt McGue; Kaare Christensen
Journal:  Aging Cell       Date:  2015-11-17       Impact factor: 9.304

10.  Contribution of TDP and hippocampal sclerosis to hippocampal volume loss in older-old persons.

Authors:  Lei Yu; Patricia A Boyle; Robert J Dawe; David A Bennett; Konstantinos Arfanakis; Julie A Schneider
Journal:  Neurology       Date:  2019-11-22       Impact factor: 9.910

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  1 in total

1.  Slower maximal walking speed is associated with poorer global cognitive function among older adults residing in China.

Authors:  Guiping Jiang; Xueping Wu
Journal:  PeerJ       Date:  2022-07-26       Impact factor: 3.061

  1 in total

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