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Grey matter network markers identify individuals with prodromal Alzheimer’s disease who will show rapid clinical decline
Individuals with prodromal Alzheimer’s disease show considerable variability in rates of cognitive decline, which hampers the ability to detect potential treatment effects in clinical trials. Prognostic markers to select those individuals who will decline rapidly within a trial time frame are needed...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8924646/ https://www.ncbi.nlm.nih.gov/pubmed/35310828 http://dx.doi.org/10.1093/braincomms/fcac026 |
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author | Pelkmans, Wiesje Vromen, Ellen M. Dicks, Ellen Scheltens, Philip Teunissen, Charlotte E. Barkhof, Frederik van der Flier, Wiesje M. Tijms, Betty M. |
author_facet | Pelkmans, Wiesje Vromen, Ellen M. Dicks, Ellen Scheltens, Philip Teunissen, Charlotte E. Barkhof, Frederik van der Flier, Wiesje M. Tijms, Betty M. |
author_sort | Pelkmans, Wiesje |
collection | PubMed |
description | Individuals with prodromal Alzheimer’s disease show considerable variability in rates of cognitive decline, which hampers the ability to detect potential treatment effects in clinical trials. Prognostic markers to select those individuals who will decline rapidly within a trial time frame are needed. Brain network measures based on grey matter covariance patterns have been associated with future cognitive decline in Alzheimer’s disease. In this longitudinal cohort study, we investigated whether cut-offs for grey matter networks could be derived to detect fast disease progression at an individual level. We further tested whether detection was improved by adding other biomarkers known to be associated with future cognitive decline [i.e. CSF tau phosphorylated at threonine 181 (p-tau181) levels and hippocampal volume]. We selected individuals with mild cognitive impairment and abnormal CSF amyloid β(1–42) levels from the Amsterdam Dementia Cohort and the Alzheimer’s Disease Neuroimaging Initiative, when they had available baseline structural MRI and clinical follow-up. The outcome was progression to dementia within 2 years. We determined prognostic cut-offs for grey matter network properties (gamma, lambda and small-world coefficient) using time-dependent receiver operating characteristic analysis in the Amsterdam Dementia Cohort. We tested the generalization of cut-offs in the Alzheimer’s Disease Neuroimaging Initiative, using logistic regression analysis and classification statistics. We further tested whether combining these with CSF p-tau181 and hippocampal volume improved the detection of fast decliners. We observed that within 2 years, 24.6% (Amsterdam Dementia Cohort, n = 244) and 34.0% (Alzheimer’s Disease Neuroimaging Initiative, n = 247) of prodromal Alzheimer’s disease patients progressed to dementia. Using the grey matter network cut-offs for progression, we could detect fast progressors with 65% accuracy in the Alzheimer’s Disease Neuroimaging Initiative. Combining grey matter network measures with CSF p-tau and hippocampal volume resulted in the best model fit for classification of rapid decliners, increasing detecting accuracy to 72%. These data suggest that single-subject grey matter connectivity networks indicative of a more random network organization can contribute to identifying prodromal Alzheimer’s disease individuals who will show rapid disease progression. Moreover, we found that combined with p-tau and hippocampal volume this resulted in the highest accuracy. This could facilitate clinical trials by increasing chances to detect effects on clinical outcome measures. |
format | Online Article Text |
id | pubmed-8924646 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-89246462022-03-17 Grey matter network markers identify individuals with prodromal Alzheimer’s disease who will show rapid clinical decline Pelkmans, Wiesje Vromen, Ellen M. Dicks, Ellen Scheltens, Philip Teunissen, Charlotte E. Barkhof, Frederik van der Flier, Wiesje M. Tijms, Betty M. Brain Commun Original Article Individuals with prodromal Alzheimer’s disease show considerable variability in rates of cognitive decline, which hampers the ability to detect potential treatment effects in clinical trials. Prognostic markers to select those individuals who will decline rapidly within a trial time frame are needed. Brain network measures based on grey matter covariance patterns have been associated with future cognitive decline in Alzheimer’s disease. In this longitudinal cohort study, we investigated whether cut-offs for grey matter networks could be derived to detect fast disease progression at an individual level. We further tested whether detection was improved by adding other biomarkers known to be associated with future cognitive decline [i.e. CSF tau phosphorylated at threonine 181 (p-tau181) levels and hippocampal volume]. We selected individuals with mild cognitive impairment and abnormal CSF amyloid β(1–42) levels from the Amsterdam Dementia Cohort and the Alzheimer’s Disease Neuroimaging Initiative, when they had available baseline structural MRI and clinical follow-up. The outcome was progression to dementia within 2 years. We determined prognostic cut-offs for grey matter network properties (gamma, lambda and small-world coefficient) using time-dependent receiver operating characteristic analysis in the Amsterdam Dementia Cohort. We tested the generalization of cut-offs in the Alzheimer’s Disease Neuroimaging Initiative, using logistic regression analysis and classification statistics. We further tested whether combining these with CSF p-tau181 and hippocampal volume improved the detection of fast decliners. We observed that within 2 years, 24.6% (Amsterdam Dementia Cohort, n = 244) and 34.0% (Alzheimer’s Disease Neuroimaging Initiative, n = 247) of prodromal Alzheimer’s disease patients progressed to dementia. Using the grey matter network cut-offs for progression, we could detect fast progressors with 65% accuracy in the Alzheimer’s Disease Neuroimaging Initiative. Combining grey matter network measures with CSF p-tau and hippocampal volume resulted in the best model fit for classification of rapid decliners, increasing detecting accuracy to 72%. These data suggest that single-subject grey matter connectivity networks indicative of a more random network organization can contribute to identifying prodromal Alzheimer’s disease individuals who will show rapid disease progression. Moreover, we found that combined with p-tau and hippocampal volume this resulted in the highest accuracy. This could facilitate clinical trials by increasing chances to detect effects on clinical outcome measures. Oxford University Press 2022-02-08 /pmc/articles/PMC8924646/ /pubmed/35310828 http://dx.doi.org/10.1093/braincomms/fcac026 Text en © The Author(s) 2022. Published by Oxford University Press on behalf of the Guarantors of Brain. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Original Article Pelkmans, Wiesje Vromen, Ellen M. Dicks, Ellen Scheltens, Philip Teunissen, Charlotte E. Barkhof, Frederik van der Flier, Wiesje M. Tijms, Betty M. Grey matter network markers identify individuals with prodromal Alzheimer’s disease who will show rapid clinical decline |
title | Grey matter network markers identify individuals with prodromal Alzheimer’s disease who will show rapid clinical decline |
title_full | Grey matter network markers identify individuals with prodromal Alzheimer’s disease who will show rapid clinical decline |
title_fullStr | Grey matter network markers identify individuals with prodromal Alzheimer’s disease who will show rapid clinical decline |
title_full_unstemmed | Grey matter network markers identify individuals with prodromal Alzheimer’s disease who will show rapid clinical decline |
title_short | Grey matter network markers identify individuals with prodromal Alzheimer’s disease who will show rapid clinical decline |
title_sort | grey matter network markers identify individuals with prodromal alzheimer’s disease who will show rapid clinical decline |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8924646/ https://www.ncbi.nlm.nih.gov/pubmed/35310828 http://dx.doi.org/10.1093/braincomms/fcac026 |
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