Patterns of multi-domain cognitive aging in participants of the Long Life Family Study.
Aging
Biomarker
Cognition
Neuropsychology
Journal
GeroScience
ISSN: 2509-2723
Titre abrégé: Geroscience
Pays: Switzerland
ID NLM: 101686284
Informations de publication
Date de publication:
10 2020
10 2020
Historique:
received:
16
04
2020
accepted:
08
05
2020
pubmed:
10
6
2020
medline:
28
4
2021
entrez:
10
6
2020
Statut:
ppublish
Résumé
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.
Identifiants
pubmed: 32514870
doi: 10.1007/s11357-020-00202-3
pii: 10.1007/s11357-020-00202-3
pmc: PMC7525612
doi:
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
1335-1350Subventions
Organisme : NIA NIH HHS
ID : U01-AG023712, U01-AG23744, U01-AG023746, U01-AG023749, U01-AG023755, P30AG031679, R21AG056630, and K01-AG057798
Pays : United States
Organisme : NIA NIH HHS
ID : U19 AG023122
Pays : United States
Organisme : NIA NIH HHS
ID : P50 AG008702
Pays : United States
Organisme : NIA NIH HHS
ID : P30 AG066462
Pays : United States
Organisme : NIA NIH HHS
ID : U01 AG023749
Pays : United States
Organisme : NIA NIH HHS
ID : R21 AG056630
Pays : United States
Organisme : NIA NIH HHS
ID : U19 AG063893
Pays : United States
Organisme : NIA NIH HHS
ID : R01 AG061844
Pays : United States
Références
Aging Cell. 2017 Apr;16(2):329-338
pubmed: 28058805
Psychol Aging. 2009 Mar;24(1):1-16
pubmed: 19290733
Soc Personal Psychol Compass. 2009 Dec 1;3(6):979-991
pubmed: 20577582
Geroscience. 2019 Aug;41(4):383-393
pubmed: 31332674
Psychol Aging. 2019 Feb;34(1):17-24
pubmed: 30211596
Psychol Aging. 2006 Jun;21(2):318-32
pubmed: 16768578
Psychol Aging. 2005 Jun;20(2):303-16
pubmed: 16029094
Clin Geriatr Med. 2013 Nov;29(4):737-52
pubmed: 24094294
Psychol Aging. 2002 Jun;17(2):179-93
pubmed: 12061405
J Am Geriatr Soc. 2016 Nov;64(11):e189-e194
pubmed: 27783390
J Alzheimers Dis. 2015;47(4):901-13
pubmed: 26401770
Transl Neurodegener. 2018 Feb 27;7:5
pubmed: 29507718
Neuropsychology. 2013 Jul;27(4):391-401
pubmed: 23876113
J Psychiatr Res. 1975 Nov;12(3):189-98
pubmed: 1202204
Sci Rep. 2017 May 15;7(1):1910
pubmed: 28507298
JAMA Neurol. 2014 Dec;71(12):1514-9
pubmed: 25317765
Arch Neurol. 2010 Aug;67(8):980-6
pubmed: 20697049
Neurobiol Aging. 2013 Nov;34(11):2445-8
pubmed: 23759147
J Am Geriatr Soc. 2008 Sep;56(9):1618-25
pubmed: 18691275
JAMA Netw Open. 2020 Mar 2;3(3):e200413
pubmed: 32142126
J Gerontol A Biol Sci Med Sci. 2019 Jan 1;74(1):44-51
pubmed: 30060062
Alzheimer Dis Assoc Disord. 2015 Jan-Mar;29(1):32-44
pubmed: 24759546
J Gerontol B Psychol Sci Soc Sci. 2013 Jul;68(4):580-5
pubmed: 23704206
Front Genet. 2013 May 08;4:65
pubmed: 23658558
Epidemiol Rev. 2013;35:33-50
pubmed: 23349427
Front Public Health. 2013 Sep 30;1:38
pubmed: 24350207
Neurology. 2014 Aug 5;83(6):486-93
pubmed: 24991031
Sci Rep. 2018 Jul 11;8(1):10468
pubmed: 29993022
Bioinformatics. 2014 Oct;30(19):2811-2
pubmed: 24930139
J Gerontol B Psychol Sci Soc Sci. 2011 Jul;66 Suppl 1:i153-61
pubmed: 21196437
J Gerontol A Biol Sci Med Sci. 2019 Jan 1;74(1):108-113
pubmed: 29931286
Am J Epidemiol. 2009 Dec 15;170(12):1555-62
pubmed: 19910380
JAMA Neurol. 2013 Jul;70(7):867-74
pubmed: 23649824
Alzheimer Dis Assoc Disord. 2009 Apr-Jun;23(2):91-101
pubmed: 19474567
J Gerontol A Biol Sci Med Sci. 2010 Dec;65(12):1375-9
pubmed: 20813793
Aging (Albany NY). 2011 Jan;3(1):63-76
pubmed: 21258136
Psychol Aging. 2003 Dec;18(4):714-26
pubmed: 14692859
Menopause. 2019 Dec;26(12):1366-1374
pubmed: 31613825
Psychol Aging. 2012 Sep;27(3):707-19
pubmed: 22201331
Semin Hear. 2015 Aug;36(3):111-21
pubmed: 27516712
Front Genet. 2016 Aug 08;7:144
pubmed: 27551289
J Gerontol A Biol Sci Med Sci. 2012 Apr;67(4):395-405
pubmed: 22219514