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Portals to frailty? Data-driven analyses detect early frailty profiles

BACKGROUND: Frailty is an aging condition that reflects multisystem decline and an increased risk for adverse outcomes, including differential cognitive decline and impairment. Two prominent approaches for measuring frailty are the frailty phenotype and the frailty index. We explored a complementary...

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Autores principales: Bohn, Linzy, Zheng, Yao, McFall, G. Peggy, Dixon, Roger A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7780374/
https://www.ncbi.nlm.nih.gov/pubmed/33397495
http://dx.doi.org/10.1186/s13195-020-00736-w
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author Bohn, Linzy
Zheng, Yao
McFall, G. Peggy
Dixon, Roger A.
author_facet Bohn, Linzy
Zheng, Yao
McFall, G. Peggy
Dixon, Roger A.
author_sort Bohn, Linzy
collection PubMed
description BACKGROUND: Frailty is an aging condition that reflects multisystem decline and an increased risk for adverse outcomes, including differential cognitive decline and impairment. Two prominent approaches for measuring frailty are the frailty phenotype and the frailty index. We explored a complementary data-driven approach for frailty assessment that could detect early frailty profiles (or subtypes) in relatively healthy older adults. Specifically, we tested whether (1) modalities of early frailty profiles could be empirically determined, (2) the extracted profiles were differentially related to longitudinal cognitive decline, and (3) the profile and prediction patterns were robust for males and females. METHODS: Participants (n = 649; M age = 70.61, range 53–95) were community-dwelling older adults from the Victoria Longitudinal Study who contributed data for baseline multi-morbidity assessment and longitudinal cognitive trajectory analyses. An exploratory factor analysis on 50 multi-morbidity items produced 7 separable health domains. The proportion of deficits in each domain was calculated and used as continuous indicators in a data-driven latent profile analysis (LPA). We subsequently examined how frailty profiles related to the level and rate of change in a latent neurocognitive speed variable. RESULTS: LPA results distinguished three profiles: not-clinically-frail (NCF; characterized by limited impairment across indicators; 84%), mobility-type frailty (MTF; characterized by impaired mobility function; 9%), and respiratory-type frailty (RTF; characterized by impaired respiratory function; 7%). These profiles showed differential neurocognitive slowing, such that MTF was associated with the steepest decline, followed by RTF, and then NCF. The baseline frailty index scores were the highest for MTF and RTF and increased over time. All observations were robust across sex. CONCLUSIONS: A data-driven approach to early frailty assessment detected differentiable profiles that may be characterized as morbidity-intensive portals into broader and chronic frailty. Early inventions targeting mobility or respiratory deficits may have positive downstream effects on frailty progression and cognitive decline. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13195-020-00736-w.
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spelling pubmed-77803742021-01-05 Portals to frailty? Data-driven analyses detect early frailty profiles Bohn, Linzy Zheng, Yao McFall, G. Peggy Dixon, Roger A. Alzheimers Res Ther Research BACKGROUND: Frailty is an aging condition that reflects multisystem decline and an increased risk for adverse outcomes, including differential cognitive decline and impairment. Two prominent approaches for measuring frailty are the frailty phenotype and the frailty index. We explored a complementary data-driven approach for frailty assessment that could detect early frailty profiles (or subtypes) in relatively healthy older adults. Specifically, we tested whether (1) modalities of early frailty profiles could be empirically determined, (2) the extracted profiles were differentially related to longitudinal cognitive decline, and (3) the profile and prediction patterns were robust for males and females. METHODS: Participants (n = 649; M age = 70.61, range 53–95) were community-dwelling older adults from the Victoria Longitudinal Study who contributed data for baseline multi-morbidity assessment and longitudinal cognitive trajectory analyses. An exploratory factor analysis on 50 multi-morbidity items produced 7 separable health domains. The proportion of deficits in each domain was calculated and used as continuous indicators in a data-driven latent profile analysis (LPA). We subsequently examined how frailty profiles related to the level and rate of change in a latent neurocognitive speed variable. RESULTS: LPA results distinguished three profiles: not-clinically-frail (NCF; characterized by limited impairment across indicators; 84%), mobility-type frailty (MTF; characterized by impaired mobility function; 9%), and respiratory-type frailty (RTF; characterized by impaired respiratory function; 7%). These profiles showed differential neurocognitive slowing, such that MTF was associated with the steepest decline, followed by RTF, and then NCF. The baseline frailty index scores were the highest for MTF and RTF and increased over time. All observations were robust across sex. CONCLUSIONS: A data-driven approach to early frailty assessment detected differentiable profiles that may be characterized as morbidity-intensive portals into broader and chronic frailty. Early inventions targeting mobility or respiratory deficits may have positive downstream effects on frailty progression and cognitive decline. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13195-020-00736-w. BioMed Central 2021-01-04 /pmc/articles/PMC7780374/ /pubmed/33397495 http://dx.doi.org/10.1186/s13195-020-00736-w Text en © The Author(s) 2020 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Bohn, Linzy
Zheng, Yao
McFall, G. Peggy
Dixon, Roger A.
Portals to frailty? Data-driven analyses detect early frailty profiles
title Portals to frailty? Data-driven analyses detect early frailty profiles
title_full Portals to frailty? Data-driven analyses detect early frailty profiles
title_fullStr Portals to frailty? Data-driven analyses detect early frailty profiles
title_full_unstemmed Portals to frailty? Data-driven analyses detect early frailty profiles
title_short Portals to frailty? Data-driven analyses detect early frailty profiles
title_sort portals to frailty? data-driven analyses detect early frailty profiles
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7780374/
https://www.ncbi.nlm.nih.gov/pubmed/33397495
http://dx.doi.org/10.1186/s13195-020-00736-w
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