Frailty trajectories in three longitudinal studies of aging: Is the level or the rate of change more predictive of mortality?


Journal

Age and ageing
ISSN: 1468-2834
Titre abrégé: Age Ageing
Pays: England
ID NLM: 0375655

Informations de publication

Date de publication:
10 11 2021
Historique:
received: 26 11 2020
pubmed: 14 6 2021
medline: 18 11 2021
entrez: 13 6 2021
Statut: ppublish

Résumé

frailty shows an upward trajectory with age, and higher levels increase the risk of mortality. However, it is less known whether the shape of frailty trajectories differs by age at death or whether the rate of change in frailty is associated with mortality. to assess population frailty trajectories by age at death and to analyse whether the current level of the frailty index (FI) i.e. the most recent measurement or the person-specific rate of change is more predictive of mortality. 3,689 individuals from three population-based cohorts with up to 15 repeated measurements of the Rockwood frailty index were analysed. The FI trajectories were assessed by stratifying the sample into four age-at-death groups: <70, 70-80, 80-90 and >90 years. Generalised survival models were used in the survival analysis. the FI trajectories by age at death showed that those who died at <70 years had a steadily increasing trajectory throughout the 40 years before death, whereas those who died at the oldest ages only accrued deficits from age ~75 onwards. Higher level of FI was independently associated with increased risk of mortality (hazard ratio 1.68, 95% confidence interval 1.47-1.91), whereas the rate of change was no longer significant after accounting for the current FI level. The effect of the FI level did not weaken with time elapsed since the last measurement. Frailty trajectories differ as a function of age-at-death category. The current level of FI is a stronger marker for risk stratification than the rate of change.

Sections du résumé

BACKGROUND
frailty shows an upward trajectory with age, and higher levels increase the risk of mortality. However, it is less known whether the shape of frailty trajectories differs by age at death or whether the rate of change in frailty is associated with mortality.
OBJECTIVES
to assess population frailty trajectories by age at death and to analyse whether the current level of the frailty index (FI) i.e. the most recent measurement or the person-specific rate of change is more predictive of mortality.
METHODS
3,689 individuals from three population-based cohorts with up to 15 repeated measurements of the Rockwood frailty index were analysed. The FI trajectories were assessed by stratifying the sample into four age-at-death groups: <70, 70-80, 80-90 and >90 years. Generalised survival models were used in the survival analysis.
RESULTS
the FI trajectories by age at death showed that those who died at <70 years had a steadily increasing trajectory throughout the 40 years before death, whereas those who died at the oldest ages only accrued deficits from age ~75 onwards. Higher level of FI was independently associated with increased risk of mortality (hazard ratio 1.68, 95% confidence interval 1.47-1.91), whereas the rate of change was no longer significant after accounting for the current FI level. The effect of the FI level did not weaken with time elapsed since the last measurement.
CONCLUSIONS
Frailty trajectories differ as a function of age-at-death category. The current level of FI is a stronger marker for risk stratification than the rate of change.

Identifiants

pubmed: 34120182
pii: 6296905
doi: 10.1093/ageing/afab106
pmc: PMC8581383
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

2174-2182

Subventions

Organisme : NIH HHS
ID : AG08861-09
Pays : United States

Informations de copyright

© The Author(s) 2021. Published by Oxford University Press on behalf of the British Geriatrics Society. All rights reserved. For permissions, please email: journals.permissions@oup.com.

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Auteurs

Ge Bai (G)

Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.

Agnieszka Szwajda (A)

Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.

Yunzhang Wang (Y)

Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.

Xia Li (X)

Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.

Hannah Bower (H)

Clinical Epidemiology Division, Department of Medicine Solna, Karolinska Institutet, Stockholm, Sweden.

Ida K Karlsson (IK)

Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
School of Health and Welfare, Institute of Gerontology and Aging Research Network-Jönköping (ARN-J), Jönköping University, Jönköping, Sweden.

Boo Johansson (B)

Department of Psychology, Centre for Ageing and Health (AgeCap), University of Gothenburg, Gothenburg, Sweden.

Anna K Dahl Aslan (AK)

Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
School of Health Sciences, University of Skövde, Skövde, Sweden.

Nancy L Pedersen (NL)

Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.

Sara Hägg (S)

Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.

Juulia Jylhävä (J)

Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.

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Classifications MeSH