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Functional activity level reported by an informant is an early predictor of Alzheimer’s disease

BACKGROUND: Loss of autonomy in day-to-day functioning is one of the feared outcomes of Alzheimer’s disease (AD), and relatives may have been worried by subtle behavioral changes in ordinary life situations long before these changes are given medical attention. In the present study, we ask if such s...

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Autores principales: Vik, Alexandra, Kociński, Marek, Rye, Ingrid, Lundervold, Astri J., Lundervold, Alexander S.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10067216/
https://www.ncbi.nlm.nih.gov/pubmed/37003981
http://dx.doi.org/10.1186/s12877-023-03849-7
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author Vik, Alexandra
Kociński, Marek
Rye, Ingrid
Lundervold, Astri J.
Lundervold, Alexander S.
author_facet Vik, Alexandra
Kociński, Marek
Rye, Ingrid
Lundervold, Astri J.
Lundervold, Alexander S.
author_sort Vik, Alexandra
collection PubMed
description BACKGROUND: Loss of autonomy in day-to-day functioning is one of the feared outcomes of Alzheimer’s disease (AD), and relatives may have been worried by subtle behavioral changes in ordinary life situations long before these changes are given medical attention. In the present study, we ask if such subtle changes should be given weight as an early predictor of a future AD diagnosis. METHODS: Longitudinal data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) were used to define a group of adults with a mild cognitive impairment (MCI) diagnosis remaining stable across several visits (sMCI, n=360; 55-91 years at baseline), and a group of adults who over time converted from having an MCI diagnosis to an AD diagnosis (cAD, n=320; 55-88 years at baseline). Eleven features were used as input in a Random Forest (RF) binary classifier (sMCI vs. cAD) model. This model was tested on an unseen holdout part of the dataset, and further explored by three different permutation-driven importance estimates and a comprehensive post hoc machine learning exploration. RESULTS: The results consistently showed that measures of daily life functioning, verbal memory function, and a volume measure of hippocampus were the most important predictors of conversion from an MCI to an AD diagnosis. Results from the RF classification model showed a prediction accuracy of around 70% in the test set. Importantly, the post hoc analyses showed that even subtle changes in everyday functioning noticed by a close informant put MCI patients at increased risk for being on a path toward the major cognitive impairment of an AD diagnosis. CONCLUSION: The results showed that even subtle changes in everyday functioning should be noticed when reported by relatives in a clinical evaluation of patients with MCI. Information of these changes should also be included in future longitudinal studies to investigate different pathways from normal cognitive aging to the cognitive decline characterizing different stages of AD and other neurodegenerative disorders. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12877-023-03849-7.
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spelling pubmed-100672162023-04-03 Functional activity level reported by an informant is an early predictor of Alzheimer’s disease Vik, Alexandra Kociński, Marek Rye, Ingrid Lundervold, Astri J. Lundervold, Alexander S. BMC Geriatr Research BACKGROUND: Loss of autonomy in day-to-day functioning is one of the feared outcomes of Alzheimer’s disease (AD), and relatives may have been worried by subtle behavioral changes in ordinary life situations long before these changes are given medical attention. In the present study, we ask if such subtle changes should be given weight as an early predictor of a future AD diagnosis. METHODS: Longitudinal data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) were used to define a group of adults with a mild cognitive impairment (MCI) diagnosis remaining stable across several visits (sMCI, n=360; 55-91 years at baseline), and a group of adults who over time converted from having an MCI diagnosis to an AD diagnosis (cAD, n=320; 55-88 years at baseline). Eleven features were used as input in a Random Forest (RF) binary classifier (sMCI vs. cAD) model. This model was tested on an unseen holdout part of the dataset, and further explored by three different permutation-driven importance estimates and a comprehensive post hoc machine learning exploration. RESULTS: The results consistently showed that measures of daily life functioning, verbal memory function, and a volume measure of hippocampus were the most important predictors of conversion from an MCI to an AD diagnosis. Results from the RF classification model showed a prediction accuracy of around 70% in the test set. Importantly, the post hoc analyses showed that even subtle changes in everyday functioning noticed by a close informant put MCI patients at increased risk for being on a path toward the major cognitive impairment of an AD diagnosis. CONCLUSION: The results showed that even subtle changes in everyday functioning should be noticed when reported by relatives in a clinical evaluation of patients with MCI. Information of these changes should also be included in future longitudinal studies to investigate different pathways from normal cognitive aging to the cognitive decline characterizing different stages of AD and other neurodegenerative disorders. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12877-023-03849-7. BioMed Central 2023-03-31 /pmc/articles/PMC10067216/ /pubmed/37003981 http://dx.doi.org/10.1186/s12877-023-03849-7 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/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/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://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
Vik, Alexandra
Kociński, Marek
Rye, Ingrid
Lundervold, Astri J.
Lundervold, Alexander S.
Functional activity level reported by an informant is an early predictor of Alzheimer’s disease
title Functional activity level reported by an informant is an early predictor of Alzheimer’s disease
title_full Functional activity level reported by an informant is an early predictor of Alzheimer’s disease
title_fullStr Functional activity level reported by an informant is an early predictor of Alzheimer’s disease
title_full_unstemmed Functional activity level reported by an informant is an early predictor of Alzheimer’s disease
title_short Functional activity level reported by an informant is an early predictor of Alzheimer’s disease
title_sort functional activity level reported by an informant is an early predictor of alzheimer’s disease
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10067216/
https://www.ncbi.nlm.nih.gov/pubmed/37003981
http://dx.doi.org/10.1186/s12877-023-03849-7
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