Literature DB >> 32514870

Patterns of multi-domain cognitive aging in participants of the Long Life Family Study.

Paola Sebastiani1, Stacy L Andersen2, Benjamin Sweigart3, Mengtian Du3, Stephanie Cosentino4, Bharat Thyagarajan5, Kaare Christensen6, Nicole Schupf4,7, Thomas T Perls2.   

Abstract

Maintaining good cognitive function at older age is important, but our knowledge of patterns and predictors of cognitive aging is still limited. We used Bayesian model-based clustering to group 5064 participants of the Long Life Family Study (ages 49-110 years) into clusters characterized by distinct trajectories of cognitive change in the domains of episodic memory, attention, processing speed, and verbal fluency. For each domain, we identified 4 or 5 large clusters with representative patterns of change ranging from rapid decline to exceptionally slow change. We annotated the clusters by their correlation with genetic and molecular biomarkers, non-genetic risk factors, medical history, and other markers of aging to discover correlates of cognitive changes and neuroprotection. The annotation analysis discovered both predictors of multi-domain cognitive change such as gait speed and predictors of domain-specific cognitive change such as IL6 and NTproBNP that correlate only with change of processing speed or APOE genotypes that correlate only with change of processing speed and logical memory. These patterns also suggest that cognitive decline starts at young age and that maintaining good physical function correlates with slower cognitive decline. To better understand the agreement of cognitive changes across multiple domains, we summarized the results of the cluster analysis into a score of cognitive function change. This score showed that extreme patterns of change affecting multiple cognitive domains simultaneously are rare in this study and that specific signatures of biomarkers of inflammation and metabolic disease predict severity of cognitive changes. The substantial heterogeneity of change patterns within and between cognitive domains and the net of correlations between patterns of cognitive aging and other aging traits emphasizes the importance of measuring a wide range of cognitive functions and the need for studying cognitive aging in concert with other aging traits.

Entities:  

Keywords:  Aging; Biomarker; Cognition; Neuropsychology

Mesh:

Year:  2020        PMID: 32514870      PMCID: PMC7525612          DOI: 10.1007/s11357-020-00202-3

Source DB:  PubMed          Journal:  Geroscience        ISSN: 2509-2723            Impact factor:   7.713


  42 in total

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4.  Trajectories of normal cognitive aging.

Authors:  Timothy A Salthouse
Journal:  Psychol Aging       Date:  2018-09-13

5.  Heterogeneity of healthy aging: comparing long-lived families across five healthy aging phenotypes of blood pressure, memory, pulmonary function, grip strength, and metabolism.

Authors:  Megan M Marron; Mary K Wojczynski; Ryan L Minster; Robert M Boudreau; Paola Sebastiani; Stephanie Cosentino; Bharat Thyagarajan; Svetlana V Ukraintseva; Nicole Schupf; Kaare Christensen; Mary Feitosa; Thomas Perls; Joseph M Zmuda; Anne B Newman
Journal:  Geroscience       Date:  2019-07-22       Impact factor: 7.713

6.  Age and Sex Distributions of Age-Related Biomarker Values in Healthy Older Adults from the Long Life Family Study.

Authors:  Paola Sebastiani; Bharat Thyagarajan; Fangui Sun; Lawrence S Honig; Nicole Schupf; Stephanie Cosentino; Mary F Feitosa; Mary Wojczynski; Anne B Newman; Monty Montano; Thomas T Perls
Journal:  J Am Geriatr Soc       Date:  2016-10-26       Impact factor: 5.562

Review 7.  Normal cognitive aging.

Authors:  Caroline N Harada; Marissa C Natelson Love; Kristen L Triebel
Journal:  Clin Geriatr Med       Date:  2013-11       Impact factor: 3.076

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Journal:  Psychol Aging       Date:  2002-06

10.  Biomarker signatures of aging.

Authors:  Paola Sebastiani; Bharat Thyagarajan; Fangui Sun; Nicole Schupf; Anne B Newman; Monty Montano; Thomas T Perls
Journal:  Aging Cell       Date:  2017-01-06       Impact factor: 9.304

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  7 in total

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Journal:  Front Aging Neurosci       Date:  2021-08-03       Impact factor: 5.750

2.  Digital Technology Differentiates Graphomotor and Information Processing Speed Patterns of Behavior.

Authors:  Stacy L Andersen; Benjamin Sweigart; Nancy W Glynn; Mary K Wojczynski; Bharat Thyagarajan; Jonas Mengel-From; Stephen Thielke; Thomas T Perls; David J Libon; Rhoda Au; Stephanie Cosentino; Paola Sebastianion
Journal:  J Alzheimers Dis       Date:  2021       Impact factor: 4.472

3.  Slower Decline in Processing Speed Is Associated with Familial Longevity.

Authors:  Stacy L Andersen; Mengtian Du; Stephanie Cosentino; Nicole Schupf; Andrea L Rosso; Thomas T Perls; Paola Sebastiani
Journal:  Gerontology       Date:  2021-05-04       Impact factor: 5.597

4.  Studying the Interplay Between Apolipoprotein E and Education on Cognitive Decline in Centenarians Using Bayesian Beta Regression.

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5.  Association between physical-activity trajectories and cognitive decline in adults 50 years of age or older.

Authors:  Boris Cheval; Zsófia Csajbók; Tomáš Formánek; Stefan Sieber; Matthieu P Boisgontier; Stéphane Cullati; Pavla Cermakova
Journal:  Epidemiol Psychiatr Sci       Date:  2021-12-27       Impact factor: 6.892

6.  Use of electroencephalogram, gait, and their combined signals for classifying cognitive impairment and normal cognition.

Authors:  Jin-Young Min; Sang-Won Ha; Kiwon Lee; Kyoung-Bok Min
Journal:  Front Aging Neurosci       Date:  2022-09-07       Impact factor: 5.702

7.  Cognitive Test Scores and Progressive Cognitive Decline in the Aberdeen 1921 and 1936 Birth Cohorts.

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  7 in total

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