Studying the relationship between cognitive impairment and frailty phenotype: a cross-sectional analysis of the Bushehr Elderly Health (BEH) program.
Aged
Category fluency test
Cognitive impairment
Depression
Frailty
Journal
Journal of diabetes and metabolic disorders
ISSN: 2251-6581
Titre abrégé: J Diabetes Metab Disord
Pays: Switzerland
ID NLM: 101590741
Informations de publication
Date de publication:
Dec 2021
Dec 2021
Historique:
received:
12
11
2020
accepted:
23
06
2021
entrez:
13
12
2021
pubmed:
14
12
2021
medline:
14
12
2021
Statut:
epublish
Résumé
Some pathophysiological effects of physical frailty and cognitive impairment might be similar; therefore, finding the associations in epidemiologic studies could guide clinicians and researchers to recognize effective strategies for each type of frailty such as frailty phenotype and frailty index, which in turn will result in a preventive approach. The study aimed to reveal which components of frailty phenotype are more associated with cognitive impairment. The findings of this study may help other researchers clarify the related pathways. This is a cross-sectional analysis of the results of the second phase of Bushehr Elderly Health Program; a community-based elderly prospective cohort study conducted in 2015-2016. The participants were selected through a multistage stratified cluster random sampling method. Frailty was assessed based on the Fried frailty phenotype criteria. Cognitive impairment was assessed by the Mini-Mental State Examination (MMSE), the Mini-Cog, and the Category Fluency Test (CFT). Multiple logistic regression models were applied to determine the association between frailty and cognitive impairment. Depression trait was assessed using the Patient Health Questionnaire-9 (PHQ-9). Activities of daily living were assessed using the Barthel Index and Instrumental Activities of Daily Living (IADLs) using Lawton's IADL. The studyp conducted among people ≥ 60 years old (N = 2336) with women consisting 51.44% of the sample group. The mean age of the participants was 69.26 years old. The prevalence of pre-frailty and frailty were 42.59% and 7.66%, respectively. In the fully adjusted model, the odds ratio of the association between pre-frailty and frailty with cognitive impairment was 1.239, 95% CI: 1.011 - 1.519 and 1.765, 95% CI: 1.071 - 2.908, respectively (adjusted for age, sex, education, body mass index, smoking, diabetes mellitus, PHQ- 9, Barthel Index, and IADLs). In the fully adjusted multiple logistic regression models, all of the components of Fried frailty phenotype were significantly related to cognitive impairment except weight loss. Cognitive impairment may be associated with frailty phenotype. Moreover, low strength and function of muscles had a stronger association with cognitive impairment. It seems that a consideration of cognitive impairment assessment in older people along with frailty and vice versa in clinical settings is reasonable.
Sections du résumé
BACKGROUND
BACKGROUND
Some pathophysiological effects of physical frailty and cognitive impairment might be similar; therefore, finding the associations in epidemiologic studies could guide clinicians and researchers to recognize effective strategies for each type of frailty such as frailty phenotype and frailty index, which in turn will result in a preventive approach. The study aimed to reveal which components of frailty phenotype are more associated with cognitive impairment. The findings of this study may help other researchers clarify the related pathways.
METHODS
METHODS
This is a cross-sectional analysis of the results of the second phase of Bushehr Elderly Health Program; a community-based elderly prospective cohort study conducted in 2015-2016. The participants were selected through a multistage stratified cluster random sampling method. Frailty was assessed based on the Fried frailty phenotype criteria. Cognitive impairment was assessed by the Mini-Mental State Examination (MMSE), the Mini-Cog, and the Category Fluency Test (CFT). Multiple logistic regression models were applied to determine the association between frailty and cognitive impairment. Depression trait was assessed using the Patient Health Questionnaire-9 (PHQ-9). Activities of daily living were assessed using the Barthel Index and Instrumental Activities of Daily Living (IADLs) using Lawton's IADL.
RESULTS
RESULTS
The studyp conducted among people ≥ 60 years old (N = 2336) with women consisting 51.44% of the sample group. The mean age of the participants was 69.26 years old. The prevalence of pre-frailty and frailty were 42.59% and 7.66%, respectively. In the fully adjusted model, the odds ratio of the association between pre-frailty and frailty with cognitive impairment was 1.239, 95% CI: 1.011 - 1.519 and 1.765, 95% CI: 1.071 - 2.908, respectively (adjusted for age, sex, education, body mass index, smoking, diabetes mellitus, PHQ- 9, Barthel Index, and IADLs). In the fully adjusted multiple logistic regression models, all of the components of Fried frailty phenotype were significantly related to cognitive impairment except weight loss.
CONCLUSION
CONCLUSIONS
Cognitive impairment may be associated with frailty phenotype. Moreover, low strength and function of muscles had a stronger association with cognitive impairment. It seems that a consideration of cognitive impairment assessment in older people along with frailty and vice versa in clinical settings is reasonable.
Identifiants
pubmed: 34900774
doi: 10.1007/s40200-021-00847-7
pii: 847
pmc: PMC8630203
doi:
Types de publication
Journal Article
Langues
eng
Pagination
1229-1237Informations de copyright
© Springer Nature Switzerland AG 2021.
Déclaration de conflit d'intérêts
Conflict of interestOn behalf of all authors, the corresponding author states that there is no conflict of interest.
Références
J Am Geriatr Soc. 2008 Dec;56(12):2211-16
pubmed: 19093920
Rev Clin Esp (Barc). 2019 Nov;219(8):424-432
pubmed: 31109685
Age Ageing. 2013 Mar;42(2):191-6
pubmed: 23296141
JAMA Neurol. 2016 Apr;73(4):439-46
pubmed: 26831542
CMAJ. 2012 Feb 21;184(3):E191-6
pubmed: 22184363
Int J Prev Med. 2019 Aug 01;10:130
pubmed: 31516671
Expert Rev Neurother. 2011 May;11(5):665-76
pubmed: 21539487
Lancet. 2019 Oct 12;394(10206):1365-1375
pubmed: 31609228
Int J Aging Hum Dev. 2018 Apr;86(3):266-280
pubmed: 28859488
J Am Med Dir Assoc. 2015 Oct 1;16(10):899.e9-16
pubmed: 26321467
J Gerontol A Biol Sci Med Sci. 2001 Mar;56(3):M146-56
pubmed: 11253156
Ageing Res Rev. 2018 Nov;47:149-158
pubmed: 30102995
J Gerontol A Biol Sci Med Sci. 2017 Mar 1;72(3):369-375
pubmed: 27013397
J Gerontol A Biol Sci Med Sci. 2014 Nov;69(11):1375-88
pubmed: 24739495
BMJ Open. 2015 Dec 16;5(12):e009597
pubmed: 26674503
J Am Geriatr Soc. 2009 Mar;57(3):453-61
pubmed: 19245415
Int Psychogeriatr. 2014 Jan;26(1):155-63
pubmed: 24153029
Age Ageing. 2019 Jan 1;48(1):101-107
pubmed: 30307472
J Nutr Health Aging. 2012 Jan;16(1):55-61
pubmed: 22238002
Int Psychogeriatr. 2012 Sep;24(9):1429-36
pubmed: 22717010
BMJ Open. 2017 Aug 04;7(8):e013606
pubmed: 28780537
Braz J Med Biol Res. 2004 Dec;37(12):1771-7
pubmed: 15558183
Int J Environ Res Public Health. 2019 Jul 05;16(13):
pubmed: 31284406
PLoS One. 2016 Mar 18;11(3):e0151710
pubmed: 26990757
Front Med (Lausanne). 2019 Feb 19;6:26
pubmed: 30838210
Diabetes Care. 2021 Jan;44(Suppl 1):S15-S33
pubmed: 33298413
Arch Neurol. 2009 Nov;66(11):1339-44
pubmed: 19901164
N Engl J Med. 2013 Aug 8;369(6):540-8
pubmed: 23924004
J Gerontol A Biol Sci Med Sci. 2014 Nov;69(11):1373-4
pubmed: 25199913
Int J Mol Sci. 2019 Jun 11;20(11):
pubmed: 31212645
Age (Dordr). 2015 Aug;37(4):9812
pubmed: 26160251
Arch Phys Med Rehabil. 2011 Dec;92(12):1992-9
pubmed: 22133247
J Aging Phys Act. 2015 Apr;23(2):314-22
pubmed: 24812254
J Am Geriatr Soc. 2010 Feb;58(2):248-55
pubmed: 20070417
Med Sci Sports Exerc. 2003 Jul;35(7):1196-202
pubmed: 12840642
J Gerontol A Biol Sci Med Sci. 2015 Mar;70(3):319-24
pubmed: 25380599