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Predicting Cognitive Decline in Older Adults Using Baseline Metrics of AD Pathologies, Cerebrovascular Disease, and Neurodegeneration
BACKGROUND AND OBJECTIVES: Dementia is a growing socioeconomic challenge that requires early intervention. Identifying biomarkers that reliably predict clinical progression early in the disease process would better aid selection of individuals for future trial participation. Here, we compared the ab...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Lippincott Williams & Wilkins
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9984210/ https://www.ncbi.nlm.nih.gov/pubmed/36357185 http://dx.doi.org/10.1212/WNL.0000000000201572 |
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author | Prosser, Lloyd Macdougall, Amy Sudre, Carole H. Manning, Emily N. Malone, Ian B. Walsh, Phoebe Goodkin, Olivia Pemberton, Hugh Barkhof, Frederik Biessels, Geert Jan Cash, David M. Barnes, Josephine |
author_facet | Prosser, Lloyd Macdougall, Amy Sudre, Carole H. Manning, Emily N. Malone, Ian B. Walsh, Phoebe Goodkin, Olivia Pemberton, Hugh Barkhof, Frederik Biessels, Geert Jan Cash, David M. Barnes, Josephine |
author_sort | Prosser, Lloyd |
collection | PubMed |
description | BACKGROUND AND OBJECTIVES: Dementia is a growing socioeconomic challenge that requires early intervention. Identifying biomarkers that reliably predict clinical progression early in the disease process would better aid selection of individuals for future trial participation. Here, we compared the ability of baseline, single time-point biomarkers (CSF amyloid 1–42, CSF ptau-181, white matter hyperintensities (WMH), cerebral microbleeds, whole-brain volume, and hippocampal volume) to predict decline in cognitively normal individuals who later converted to mild cognitive impairment (MCI) (CNtoMCI) and those with MCI who later converted to an Alzheimer disease (AD) diagnosis (MCItoAD). METHODS: Standardized baseline biomarker data from AD Neuroimaging Initiative 2 (ADNI2)/GO and longitudinal diagnostic data (including ADNI3) were used. Cox regression models assessed biomarkers in relation to time to change in clinical diagnosis using all follow-up time points available. Models were fit for biomarkers univariately and together in a multivariable model. Hazard ratios (HRs) were compared to evaluate biomarkers. Analyses were performed separately in CNtoMCI and MCItoAD groups. RESULTS: For CNtoMCI (n = 189), there was strong evidence that higher WMH volume (individual model: HR 1.79, p = 0.002; fully adjusted model: HR 1.98, p = 0.003) and lower hippocampal volume (individual: HR 0.54, p = 0.001; fully adjusted: HR 0.40, p < 0.001) were associated with conversion to MCI individually and independently. For MCItoAD (n = 345), lower hippocampal (individual model: HR 0.45, p < 0.001; fully adjusted model: HR 0.55, p < 0.001) and whole-brain volume (individual: HR 0.31, p < 0.001; fully adjusted: HR 0.48, p = 0.02), increased CSF ptau (individual: HR 1.88, p < 0.001; fully adjusted: HR 1.61, p < 0.001), and lower CSF amyloid (individual: HR 0.37, p < 0.001; fully adjusted: HR 0.62, p = 0.008) were most strongly associated with conversion to AD individually and independently. DISCUSSION: Lower hippocampal volume was a consistent predictor of clinical conversion to MCI and AD. CSF and brain volume biomarkers were predictive of conversion to AD from MCI, whereas WMH were predictive of conversion to MCI from cognitively normal. The predictive ability of WMH in the CNtoMCI group may be interpreted as some being on a different pathologic pathway, such as vascular cognitive impairment. |
format | Online Article Text |
id | pubmed-9984210 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Lippincott Williams & Wilkins |
record_format | MEDLINE/PubMed |
spelling | pubmed-99842102023-03-04 Predicting Cognitive Decline in Older Adults Using Baseline Metrics of AD Pathologies, Cerebrovascular Disease, and Neurodegeneration Prosser, Lloyd Macdougall, Amy Sudre, Carole H. Manning, Emily N. Malone, Ian B. Walsh, Phoebe Goodkin, Olivia Pemberton, Hugh Barkhof, Frederik Biessels, Geert Jan Cash, David M. Barnes, Josephine Neurology Research Article BACKGROUND AND OBJECTIVES: Dementia is a growing socioeconomic challenge that requires early intervention. Identifying biomarkers that reliably predict clinical progression early in the disease process would better aid selection of individuals for future trial participation. Here, we compared the ability of baseline, single time-point biomarkers (CSF amyloid 1–42, CSF ptau-181, white matter hyperintensities (WMH), cerebral microbleeds, whole-brain volume, and hippocampal volume) to predict decline in cognitively normal individuals who later converted to mild cognitive impairment (MCI) (CNtoMCI) and those with MCI who later converted to an Alzheimer disease (AD) diagnosis (MCItoAD). METHODS: Standardized baseline biomarker data from AD Neuroimaging Initiative 2 (ADNI2)/GO and longitudinal diagnostic data (including ADNI3) were used. Cox regression models assessed biomarkers in relation to time to change in clinical diagnosis using all follow-up time points available. Models were fit for biomarkers univariately and together in a multivariable model. Hazard ratios (HRs) were compared to evaluate biomarkers. Analyses were performed separately in CNtoMCI and MCItoAD groups. RESULTS: For CNtoMCI (n = 189), there was strong evidence that higher WMH volume (individual model: HR 1.79, p = 0.002; fully adjusted model: HR 1.98, p = 0.003) and lower hippocampal volume (individual: HR 0.54, p = 0.001; fully adjusted: HR 0.40, p < 0.001) were associated with conversion to MCI individually and independently. For MCItoAD (n = 345), lower hippocampal (individual model: HR 0.45, p < 0.001; fully adjusted model: HR 0.55, p < 0.001) and whole-brain volume (individual: HR 0.31, p < 0.001; fully adjusted: HR 0.48, p = 0.02), increased CSF ptau (individual: HR 1.88, p < 0.001; fully adjusted: HR 1.61, p < 0.001), and lower CSF amyloid (individual: HR 0.37, p < 0.001; fully adjusted: HR 0.62, p = 0.008) were most strongly associated with conversion to AD individually and independently. DISCUSSION: Lower hippocampal volume was a consistent predictor of clinical conversion to MCI and AD. CSF and brain volume biomarkers were predictive of conversion to AD from MCI, whereas WMH were predictive of conversion to MCI from cognitively normal. The predictive ability of WMH in the CNtoMCI group may be interpreted as some being on a different pathologic pathway, such as vascular cognitive impairment. Lippincott Williams & Wilkins 2023-02-21 /pmc/articles/PMC9984210/ /pubmed/36357185 http://dx.doi.org/10.1212/WNL.0000000000201572 Text en Copyright © 2022 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of the American Academy of Neurology. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0 (CC BY) (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Prosser, Lloyd Macdougall, Amy Sudre, Carole H. Manning, Emily N. Malone, Ian B. Walsh, Phoebe Goodkin, Olivia Pemberton, Hugh Barkhof, Frederik Biessels, Geert Jan Cash, David M. Barnes, Josephine Predicting Cognitive Decline in Older Adults Using Baseline Metrics of AD Pathologies, Cerebrovascular Disease, and Neurodegeneration |
title | Predicting Cognitive Decline in Older Adults Using Baseline Metrics of AD Pathologies, Cerebrovascular Disease, and Neurodegeneration |
title_full | Predicting Cognitive Decline in Older Adults Using Baseline Metrics of AD Pathologies, Cerebrovascular Disease, and Neurodegeneration |
title_fullStr | Predicting Cognitive Decline in Older Adults Using Baseline Metrics of AD Pathologies, Cerebrovascular Disease, and Neurodegeneration |
title_full_unstemmed | Predicting Cognitive Decline in Older Adults Using Baseline Metrics of AD Pathologies, Cerebrovascular Disease, and Neurodegeneration |
title_short | Predicting Cognitive Decline in Older Adults Using Baseline Metrics of AD Pathologies, Cerebrovascular Disease, and Neurodegeneration |
title_sort | predicting cognitive decline in older adults using baseline metrics of ad pathologies, cerebrovascular disease, and neurodegeneration |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9984210/ https://www.ncbi.nlm.nih.gov/pubmed/36357185 http://dx.doi.org/10.1212/WNL.0000000000201572 |
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