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Identifying healthy individuals with Alzheimer’s disease neuroimaging phenotypes in the UK Biobank

BACKGROUND: Identifying prediagnostic neurodegenerative disease is a critical issue in neurodegenerative disease research, and Alzheimer’s disease (AD) in particular, to identify populations suitable for preventive and early disease-modifying trials. Evidence from genetic and other studies suggests...

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Autores principales: Azevedo, Tiago, Bethlehem, Richard A. I., Whiteside, David J., Swaddiwudhipong, Nol, Rowe, James B., Lió, Pietro, Rittman, Timothy
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10359360/
https://www.ncbi.nlm.nih.gov/pubmed/37474615
http://dx.doi.org/10.1038/s43856-023-00313-w
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author Azevedo, Tiago
Bethlehem, Richard A. I.
Whiteside, David J.
Swaddiwudhipong, Nol
Rowe, James B.
Lió, Pietro
Rittman, Timothy
author_facet Azevedo, Tiago
Bethlehem, Richard A. I.
Whiteside, David J.
Swaddiwudhipong, Nol
Rowe, James B.
Lió, Pietro
Rittman, Timothy
author_sort Azevedo, Tiago
collection PubMed
description BACKGROUND: Identifying prediagnostic neurodegenerative disease is a critical issue in neurodegenerative disease research, and Alzheimer’s disease (AD) in particular, to identify populations suitable for preventive and early disease-modifying trials. Evidence from genetic and other studies suggests the neurodegeneration of Alzheimer’s disease measured by brain atrophy starts many years before diagnosis, but it is unclear whether these changes can be used to reliably detect prediagnostic sporadic disease. METHODS: We trained a Bayesian machine learning neural network model to generate a neuroimaging phenotype and AD score representing the probability of AD using structural MRI data in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) Cohort (cut-off 0.5, AUC 0.92, PPV 0.90, NPV 0.93). We go on to validate the model in an independent real-world dataset of the National Alzheimer’s Coordinating Centre (AUC 0.74, PPV 0.65, NPV 0.80) and demonstrate the correlation of the AD-score with cognitive scores in those with an AD-score above 0.5. We then apply the model to a healthy population in the UK Biobank study to identify a cohort at risk for Alzheimer’s disease. RESULTS: We show that the cohort with a neuroimaging Alzheimer’s phenotype has a cognitive profile in keeping with Alzheimer’s disease, with strong evidence for poorer fluid intelligence, and some evidence of poorer numeric memory, reaction time, working memory, and prospective memory. We found some evidence in the AD-score positive cohort for modifiable risk factors of hypertension and smoking. CONCLUSIONS: This approach demonstrates the feasibility of using AI methods to identify a potentially prediagnostic population at high risk for developing sporadic Alzheimer’s disease.
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spelling pubmed-103593602023-07-22 Identifying healthy individuals with Alzheimer’s disease neuroimaging phenotypes in the UK Biobank Azevedo, Tiago Bethlehem, Richard A. I. Whiteside, David J. Swaddiwudhipong, Nol Rowe, James B. Lió, Pietro Rittman, Timothy Commun Med (Lond) Article BACKGROUND: Identifying prediagnostic neurodegenerative disease is a critical issue in neurodegenerative disease research, and Alzheimer’s disease (AD) in particular, to identify populations suitable for preventive and early disease-modifying trials. Evidence from genetic and other studies suggests the neurodegeneration of Alzheimer’s disease measured by brain atrophy starts many years before diagnosis, but it is unclear whether these changes can be used to reliably detect prediagnostic sporadic disease. METHODS: We trained a Bayesian machine learning neural network model to generate a neuroimaging phenotype and AD score representing the probability of AD using structural MRI data in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) Cohort (cut-off 0.5, AUC 0.92, PPV 0.90, NPV 0.93). We go on to validate the model in an independent real-world dataset of the National Alzheimer’s Coordinating Centre (AUC 0.74, PPV 0.65, NPV 0.80) and demonstrate the correlation of the AD-score with cognitive scores in those with an AD-score above 0.5. We then apply the model to a healthy population in the UK Biobank study to identify a cohort at risk for Alzheimer’s disease. RESULTS: We show that the cohort with a neuroimaging Alzheimer’s phenotype has a cognitive profile in keeping with Alzheimer’s disease, with strong evidence for poorer fluid intelligence, and some evidence of poorer numeric memory, reaction time, working memory, and prospective memory. We found some evidence in the AD-score positive cohort for modifiable risk factors of hypertension and smoking. CONCLUSIONS: This approach demonstrates the feasibility of using AI methods to identify a potentially prediagnostic population at high risk for developing sporadic Alzheimer’s disease. Nature Publishing Group UK 2023-07-20 /pmc/articles/PMC10359360/ /pubmed/37474615 http://dx.doi.org/10.1038/s43856-023-00313-w Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This 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/) .
spellingShingle Article
Azevedo, Tiago
Bethlehem, Richard A. I.
Whiteside, David J.
Swaddiwudhipong, Nol
Rowe, James B.
Lió, Pietro
Rittman, Timothy
Identifying healthy individuals with Alzheimer’s disease neuroimaging phenotypes in the UK Biobank
title Identifying healthy individuals with Alzheimer’s disease neuroimaging phenotypes in the UK Biobank
title_full Identifying healthy individuals with Alzheimer’s disease neuroimaging phenotypes in the UK Biobank
title_fullStr Identifying healthy individuals with Alzheimer’s disease neuroimaging phenotypes in the UK Biobank
title_full_unstemmed Identifying healthy individuals with Alzheimer’s disease neuroimaging phenotypes in the UK Biobank
title_short Identifying healthy individuals with Alzheimer’s disease neuroimaging phenotypes in the UK Biobank
title_sort identifying healthy individuals with alzheimer’s disease neuroimaging phenotypes in the uk biobank
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10359360/
https://www.ncbi.nlm.nih.gov/pubmed/37474615
http://dx.doi.org/10.1038/s43856-023-00313-w
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