Cargando…

Trajectories of healthy ageing among older adults with multimorbidity: A growth mixture model using harmonised data from eight ATHLOS cohorts

OBJECTIVES: In this study we aimed to 1) describe healthy ageing trajectory patterns, 2) examine the association between multimorbidity and patterns of healthy ageing trajectories, and 3) evaluate how different groups of diseases might affect the projection of healthy ageing trajectories over time....

Descripción completa

Detalles Bibliográficos
Autores principales: Nguyen, Hai, Moreno-Agostino, Dario, Chua, Kia-Chong, Vitoratou, Silia, Prina, A. Matthew
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8023455/
https://www.ncbi.nlm.nih.gov/pubmed/33822803
http://dx.doi.org/10.1371/journal.pone.0248844
_version_ 1783675116551405568
author Nguyen, Hai
Moreno-Agostino, Dario
Chua, Kia-Chong
Vitoratou, Silia
Prina, A. Matthew
author_facet Nguyen, Hai
Moreno-Agostino, Dario
Chua, Kia-Chong
Vitoratou, Silia
Prina, A. Matthew
author_sort Nguyen, Hai
collection PubMed
description OBJECTIVES: In this study we aimed to 1) describe healthy ageing trajectory patterns, 2) examine the association between multimorbidity and patterns of healthy ageing trajectories, and 3) evaluate how different groups of diseases might affect the projection of healthy ageing trajectories over time. SETTING AND PARTICIPANTS: Our study was based on 130880 individuals from the Ageing Trajectories of Health: Longitudinal Opportunities and Synergies (ATHLOS) harmonised dataset, as well as 9171 individuals from Waves 2–7 of the English Longitudinal Study of Ageing (ELSA). METHODS: Using a healthy ageing index score, which comprised 41 items, covering various domains of health and ageing, as outcome, we employed the growth mixture model approach to identify the latent classes of individuals with different healthy ageing trajectories. A multinomial logistic regression was conducted to assess if and how multimorbidity status and multimorbidity patterns were associated with changes in healthy ageing, controlled for sociodemographic and lifestyle risk factors. RESULTS: Three similar patterns of healthy ageing trajectories were identified in the ATHLOS and ELSA datasets: 1) a ‘high stable’ group (76% in ATHLOS, 61% in ELSA), 2) a ‘low stable’ group (22% in ATHLOS, 36% in ELSA) and 3) a ‘rapid decline’ group (2% in ATHLOS, 3% in ELSA). Those with multimorbidity were 1.7 times (OR = 1.7, 95% CI: 1.4–2.1) more likely to be in the ‘rapid decline’ group and 11.7 times (OR = 11.7 95% CI: 10.9–12.6) more likely to be in the ‘low stable’ group, compared with people without multimorbidity. The cardiorespiratory/arthritis/cataracts group was associated with both the ‘rapid decline’ and the ‘low stable’ groups (OR = 2.1, 95% CI: 1.2–3.8 and OR = 9.8, 95% CI: 7.5–12.7 respectively). CONCLUSION: Healthy ageing is heterogeneous. While multimorbidity was associated with higher odds of having poorer healthy ageing trajectories, the extent to which healthy ageing trajectories were projected to decline depended on the specific patterns of multimorbidity.
format Online
Article
Text
id pubmed-8023455
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher Public Library of Science
record_format MEDLINE/PubMed
spelling pubmed-80234552021-04-15 Trajectories of healthy ageing among older adults with multimorbidity: A growth mixture model using harmonised data from eight ATHLOS cohorts Nguyen, Hai Moreno-Agostino, Dario Chua, Kia-Chong Vitoratou, Silia Prina, A. Matthew PLoS One Research Article OBJECTIVES: In this study we aimed to 1) describe healthy ageing trajectory patterns, 2) examine the association between multimorbidity and patterns of healthy ageing trajectories, and 3) evaluate how different groups of diseases might affect the projection of healthy ageing trajectories over time. SETTING AND PARTICIPANTS: Our study was based on 130880 individuals from the Ageing Trajectories of Health: Longitudinal Opportunities and Synergies (ATHLOS) harmonised dataset, as well as 9171 individuals from Waves 2–7 of the English Longitudinal Study of Ageing (ELSA). METHODS: Using a healthy ageing index score, which comprised 41 items, covering various domains of health and ageing, as outcome, we employed the growth mixture model approach to identify the latent classes of individuals with different healthy ageing trajectories. A multinomial logistic regression was conducted to assess if and how multimorbidity status and multimorbidity patterns were associated with changes in healthy ageing, controlled for sociodemographic and lifestyle risk factors. RESULTS: Three similar patterns of healthy ageing trajectories were identified in the ATHLOS and ELSA datasets: 1) a ‘high stable’ group (76% in ATHLOS, 61% in ELSA), 2) a ‘low stable’ group (22% in ATHLOS, 36% in ELSA) and 3) a ‘rapid decline’ group (2% in ATHLOS, 3% in ELSA). Those with multimorbidity were 1.7 times (OR = 1.7, 95% CI: 1.4–2.1) more likely to be in the ‘rapid decline’ group and 11.7 times (OR = 11.7 95% CI: 10.9–12.6) more likely to be in the ‘low stable’ group, compared with people without multimorbidity. The cardiorespiratory/arthritis/cataracts group was associated with both the ‘rapid decline’ and the ‘low stable’ groups (OR = 2.1, 95% CI: 1.2–3.8 and OR = 9.8, 95% CI: 7.5–12.7 respectively). CONCLUSION: Healthy ageing is heterogeneous. While multimorbidity was associated with higher odds of having poorer healthy ageing trajectories, the extent to which healthy ageing trajectories were projected to decline depended on the specific patterns of multimorbidity. Public Library of Science 2021-04-06 /pmc/articles/PMC8023455/ /pubmed/33822803 http://dx.doi.org/10.1371/journal.pone.0248844 Text en © 2021 Nguyen et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Nguyen, Hai
Moreno-Agostino, Dario
Chua, Kia-Chong
Vitoratou, Silia
Prina, A. Matthew
Trajectories of healthy ageing among older adults with multimorbidity: A growth mixture model using harmonised data from eight ATHLOS cohorts
title Trajectories of healthy ageing among older adults with multimorbidity: A growth mixture model using harmonised data from eight ATHLOS cohorts
title_full Trajectories of healthy ageing among older adults with multimorbidity: A growth mixture model using harmonised data from eight ATHLOS cohorts
title_fullStr Trajectories of healthy ageing among older adults with multimorbidity: A growth mixture model using harmonised data from eight ATHLOS cohorts
title_full_unstemmed Trajectories of healthy ageing among older adults with multimorbidity: A growth mixture model using harmonised data from eight ATHLOS cohorts
title_short Trajectories of healthy ageing among older adults with multimorbidity: A growth mixture model using harmonised data from eight ATHLOS cohorts
title_sort trajectories of healthy ageing among older adults with multimorbidity: a growth mixture model using harmonised data from eight athlos cohorts
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8023455/
https://www.ncbi.nlm.nih.gov/pubmed/33822803
http://dx.doi.org/10.1371/journal.pone.0248844
work_keys_str_mv AT nguyenhai trajectoriesofhealthyageingamongolderadultswithmultimorbidityagrowthmixturemodelusingharmoniseddatafromeightathloscohorts
AT morenoagostinodario trajectoriesofhealthyageingamongolderadultswithmultimorbidityagrowthmixturemodelusingharmoniseddatafromeightathloscohorts
AT chuakiachong trajectoriesofhealthyageingamongolderadultswithmultimorbidityagrowthmixturemodelusingharmoniseddatafromeightathloscohorts
AT vitoratousilia trajectoriesofhealthyageingamongolderadultswithmultimorbidityagrowthmixturemodelusingharmoniseddatafromeightathloscohorts
AT prinaamatthew trajectoriesofhealthyageingamongolderadultswithmultimorbidityagrowthmixturemodelusingharmoniseddatafromeightathloscohorts