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Shared mechanisms for cognitive impairment and physical frailty: A model for complex systems
INTRODUCTION: We describe findings from a large study that provide empirical support for the emerging construct of cognitive frailty and put forth a theoretical framework that may advance the future study of complex aging conditions. While cognitive impairment and physical frailty have long been stu...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7362211/ https://www.ncbi.nlm.nih.gov/pubmed/32685657 http://dx.doi.org/10.1002/trc2.12027 |
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author | Sargent, Lana Nalls, Mike Amella, Elaine J. Slattum, Patricia W. Mueller, Martina Bandinelli, Stefania Tian, Qu Swift‐Scanlan, Theresa Lageman, Sarah K. Singleton, Andrew |
author_facet | Sargent, Lana Nalls, Mike Amella, Elaine J. Slattum, Patricia W. Mueller, Martina Bandinelli, Stefania Tian, Qu Swift‐Scanlan, Theresa Lageman, Sarah K. Singleton, Andrew |
author_sort | Sargent, Lana |
collection | PubMed |
description | INTRODUCTION: We describe findings from a large study that provide empirical support for the emerging construct of cognitive frailty and put forth a theoretical framework that may advance the future study of complex aging conditions. While cognitive impairment and physical frailty have long been studied as separate constructs, recent studies suggest they share common etiologies. We aimed to create a population predictive model to gain an understanding of the underlying biological mechanisms for the relationship between physical frailty and cognitive impairment. METHODS: Data were obtained from the longitudinal “Invecchaiare in Chianti” (Aging in Chianti, InCHIANTI Study) with a representative sample (n = 1453) of older adults from two small towns in Tuscany, Italy. Our previous work informed the candidate 132 single nucleotide polymorphisms (SNPs) and 155 protein biomarkers we tested in association with clinical outcomes using a tree boosting, machine learning (ML) technique for supervised learning analysis. RESULTS: We developed two highly accurate predictive models, with a Model I area under the curve (AUC) of 0.88 (95% confidence interval [CI] 0.83‐0.90) and a Model II AUC of 0.86 (95% CI 0.80‐0.90). These models indicate cognitive frailty is driven by dysregulation across multiple cellular processes including genetic alterations, nutrient and lipid metabolism, and elevated levels of circulating pro‐inflammatory proteins. DISCUSSION: While our results establish a foundation for understanding the underlying biological mechanisms for the relationship between cognitive decline and physical frailty, further examination of the molecular pathways associated with our predictive biomarkers is warranted. Our framework is in alignment with other proposed biological underpinnings of Alzheimer's disease such as genetic alterations, immune system dysfunction, and neuroinflammation. |
format | Online Article Text |
id | pubmed-7362211 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-73622112020-07-17 Shared mechanisms for cognitive impairment and physical frailty: A model for complex systems Sargent, Lana Nalls, Mike Amella, Elaine J. Slattum, Patricia W. Mueller, Martina Bandinelli, Stefania Tian, Qu Swift‐Scanlan, Theresa Lageman, Sarah K. Singleton, Andrew Alzheimers Dement (N Y) Theoretical Articles INTRODUCTION: We describe findings from a large study that provide empirical support for the emerging construct of cognitive frailty and put forth a theoretical framework that may advance the future study of complex aging conditions. While cognitive impairment and physical frailty have long been studied as separate constructs, recent studies suggest they share common etiologies. We aimed to create a population predictive model to gain an understanding of the underlying biological mechanisms for the relationship between physical frailty and cognitive impairment. METHODS: Data were obtained from the longitudinal “Invecchaiare in Chianti” (Aging in Chianti, InCHIANTI Study) with a representative sample (n = 1453) of older adults from two small towns in Tuscany, Italy. Our previous work informed the candidate 132 single nucleotide polymorphisms (SNPs) and 155 protein biomarkers we tested in association with clinical outcomes using a tree boosting, machine learning (ML) technique for supervised learning analysis. RESULTS: We developed two highly accurate predictive models, with a Model I area under the curve (AUC) of 0.88 (95% confidence interval [CI] 0.83‐0.90) and a Model II AUC of 0.86 (95% CI 0.80‐0.90). These models indicate cognitive frailty is driven by dysregulation across multiple cellular processes including genetic alterations, nutrient and lipid metabolism, and elevated levels of circulating pro‐inflammatory proteins. DISCUSSION: While our results establish a foundation for understanding the underlying biological mechanisms for the relationship between cognitive decline and physical frailty, further examination of the molecular pathways associated with our predictive biomarkers is warranted. Our framework is in alignment with other proposed biological underpinnings of Alzheimer's disease such as genetic alterations, immune system dysfunction, and neuroinflammation. John Wiley and Sons Inc. 2020-07-15 /pmc/articles/PMC7362211/ /pubmed/32685657 http://dx.doi.org/10.1002/trc2.12027 Text en © 2020 The Authors. Alzheimer's & Dementia: Translational Research & Clinical Interventions published by Wiley Periodicals, Inc. on behalf of Alzheimer's Association. This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made. |
spellingShingle | Theoretical Articles Sargent, Lana Nalls, Mike Amella, Elaine J. Slattum, Patricia W. Mueller, Martina Bandinelli, Stefania Tian, Qu Swift‐Scanlan, Theresa Lageman, Sarah K. Singleton, Andrew Shared mechanisms for cognitive impairment and physical frailty: A model for complex systems |
title | Shared mechanisms for cognitive impairment and physical frailty: A model for complex systems |
title_full | Shared mechanisms for cognitive impairment and physical frailty: A model for complex systems |
title_fullStr | Shared mechanisms for cognitive impairment and physical frailty: A model for complex systems |
title_full_unstemmed | Shared mechanisms for cognitive impairment and physical frailty: A model for complex systems |
title_short | Shared mechanisms for cognitive impairment and physical frailty: A model for complex systems |
title_sort | shared mechanisms for cognitive impairment and physical frailty: a model for complex systems |
topic | Theoretical Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7362211/ https://www.ncbi.nlm.nih.gov/pubmed/32685657 http://dx.doi.org/10.1002/trc2.12027 |
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