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Multitracer model for staging cortical amyloid deposition using PET imaging

OBJECTIVE: To develop and evaluate a model for staging cortical amyloid deposition using PET with high generalizability. METHODS: Three thousand twenty-seven individuals (1,763 cognitively unimpaired [CU], 658 impaired, 467 with Alzheimer disease [AD] dementia, 111 with non-AD dementia, and 28 with...

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Autores principales: Collij, Lyduine E., Heeman, Fiona, Salvadó, Gemma, Ingala, Silvia, Altomare, Daniele, de Wilde, Arno, Konijnenberg, Elles, van Buchem, Marieke, Yaqub, Maqsood, Markiewicz, Pawel, Golla, Sandeep S.V., Wottschel, Viktor, Wink, Alle Meije, Visser, Pieter Jelle, Teunissen, Charlotte E., Lammertsma, Adriaan A., Scheltens, Philip, van der Flier, Wiesje M., Boellaard, Ronald, van Berckel, Bart N.M., Molinuevo, José Luis, Gispert, Juan Domingo, Schmidt, Mark E., Barkhof, Frederik, Lopes Alves, Isadora
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Lippincott Williams & Wilkins 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7713745/
https://www.ncbi.nlm.nih.gov/pubmed/32675080
http://dx.doi.org/10.1212/WNL.0000000000010256
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author Collij, Lyduine E.
Heeman, Fiona
Salvadó, Gemma
Ingala, Silvia
Altomare, Daniele
de Wilde, Arno
Konijnenberg, Elles
van Buchem, Marieke
Yaqub, Maqsood
Markiewicz, Pawel
Golla, Sandeep S.V.
Wottschel, Viktor
Wink, Alle Meije
Visser, Pieter Jelle
Teunissen, Charlotte E.
Lammertsma, Adriaan A.
Scheltens, Philip
van der Flier, Wiesje M.
Boellaard, Ronald
van Berckel, Bart N.M.
Molinuevo, José Luis
Gispert, Juan Domingo
Schmidt, Mark E.
Barkhof, Frederik
Lopes Alves, Isadora
author_facet Collij, Lyduine E.
Heeman, Fiona
Salvadó, Gemma
Ingala, Silvia
Altomare, Daniele
de Wilde, Arno
Konijnenberg, Elles
van Buchem, Marieke
Yaqub, Maqsood
Markiewicz, Pawel
Golla, Sandeep S.V.
Wottschel, Viktor
Wink, Alle Meije
Visser, Pieter Jelle
Teunissen, Charlotte E.
Lammertsma, Adriaan A.
Scheltens, Philip
van der Flier, Wiesje M.
Boellaard, Ronald
van Berckel, Bart N.M.
Molinuevo, José Luis
Gispert, Juan Domingo
Schmidt, Mark E.
Barkhof, Frederik
Lopes Alves, Isadora
author_sort Collij, Lyduine E.
collection PubMed
description OBJECTIVE: To develop and evaluate a model for staging cortical amyloid deposition using PET with high generalizability. METHODS: Three thousand twenty-seven individuals (1,763 cognitively unimpaired [CU], 658 impaired, 467 with Alzheimer disease [AD] dementia, 111 with non-AD dementia, and 28 with missing diagnosis) from 6 cohorts (European Medical Information Framework for AD, Alzheimer's and Family, Alzheimer's Biomarkers in Daily Practice, Amsterdam Dementia Cohort, Open Access Series of Imaging Studies [OASIS]-3, Alzheimer’s Disease Neuroimaging Initiative [ADNI]) who underwent amyloid PET were retrospectively included; 1,049 individuals had follow-up scans. With application of dataset-specific cutoffs to global standard uptake value ratio (SUVr) values from 27 regions, single-tracer and pooled multitracer regional rankings were constructed from the frequency of abnormality across 400 CU individuals (100 per tracer). The pooled multitracer ranking was used to create a staging model consisting of 4 clusters of regions because it displayed a high and consistent correlation with each single-tracer ranking. Relationships between amyloid stage, clinical variables, and longitudinal cognitive decline were investigated. RESULTS: SUVr abnormality was most frequently observed in cingulate, followed by orbitofrontal, precuneal, and insular cortices and then the associative, temporal, and occipital regions. Abnormal amyloid levels based on binary global SUVr classification were observed in 1.0%, 5.5%, 17.9%, 90.0%, and 100.0% of individuals in stage 0 to 4, respectively. Baseline stage predicted decline in Mini-Mental State Examination (MMSE) score (ADNI: n = 867, F = 67.37, p < 0.001; OASIS: n = 475, F = 9.12, p < 0.001) and faster progression toward an MMSE score ≤25 (ADNI: n = 787, hazard ratio [HR](stage1) 2.00, HR(stage2) 3.53, HR(stage3) 4.55, HR(stage4) 9.91, p < 0.001; OASIS: n = 469, HR(stage4) 4.80, p < 0.001). CONCLUSION: The pooled multitracer staging model successfully classified the level of amyloid burden in >3,000 individuals across cohorts and radiotracers and detects preglobal amyloid burden and distinct risk profiles of cognitive decline within globally amyloid-positive individuals.
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spelling pubmed-77137452020-12-04 Multitracer model for staging cortical amyloid deposition using PET imaging Collij, Lyduine E. Heeman, Fiona Salvadó, Gemma Ingala, Silvia Altomare, Daniele de Wilde, Arno Konijnenberg, Elles van Buchem, Marieke Yaqub, Maqsood Markiewicz, Pawel Golla, Sandeep S.V. Wottschel, Viktor Wink, Alle Meije Visser, Pieter Jelle Teunissen, Charlotte E. Lammertsma, Adriaan A. Scheltens, Philip van der Flier, Wiesje M. Boellaard, Ronald van Berckel, Bart N.M. Molinuevo, José Luis Gispert, Juan Domingo Schmidt, Mark E. Barkhof, Frederik Lopes Alves, Isadora Neurology Article OBJECTIVE: To develop and evaluate a model for staging cortical amyloid deposition using PET with high generalizability. METHODS: Three thousand twenty-seven individuals (1,763 cognitively unimpaired [CU], 658 impaired, 467 with Alzheimer disease [AD] dementia, 111 with non-AD dementia, and 28 with missing diagnosis) from 6 cohorts (European Medical Information Framework for AD, Alzheimer's and Family, Alzheimer's Biomarkers in Daily Practice, Amsterdam Dementia Cohort, Open Access Series of Imaging Studies [OASIS]-3, Alzheimer’s Disease Neuroimaging Initiative [ADNI]) who underwent amyloid PET were retrospectively included; 1,049 individuals had follow-up scans. With application of dataset-specific cutoffs to global standard uptake value ratio (SUVr) values from 27 regions, single-tracer and pooled multitracer regional rankings were constructed from the frequency of abnormality across 400 CU individuals (100 per tracer). The pooled multitracer ranking was used to create a staging model consisting of 4 clusters of regions because it displayed a high and consistent correlation with each single-tracer ranking. Relationships between amyloid stage, clinical variables, and longitudinal cognitive decline were investigated. RESULTS: SUVr abnormality was most frequently observed in cingulate, followed by orbitofrontal, precuneal, and insular cortices and then the associative, temporal, and occipital regions. Abnormal amyloid levels based on binary global SUVr classification were observed in 1.0%, 5.5%, 17.9%, 90.0%, and 100.0% of individuals in stage 0 to 4, respectively. Baseline stage predicted decline in Mini-Mental State Examination (MMSE) score (ADNI: n = 867, F = 67.37, p < 0.001; OASIS: n = 475, F = 9.12, p < 0.001) and faster progression toward an MMSE score ≤25 (ADNI: n = 787, hazard ratio [HR](stage1) 2.00, HR(stage2) 3.53, HR(stage3) 4.55, HR(stage4) 9.91, p < 0.001; OASIS: n = 469, HR(stage4) 4.80, p < 0.001). CONCLUSION: The pooled multitracer staging model successfully classified the level of amyloid burden in >3,000 individuals across cohorts and radiotracers and detects preglobal amyloid burden and distinct risk profiles of cognitive decline within globally amyloid-positive individuals. Lippincott Williams & Wilkins 2020-09-15 /pmc/articles/PMC7713745/ /pubmed/32675080 http://dx.doi.org/10.1212/WNL.0000000000010256 Text en Copyright © 2020 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of the American Academy of Neurology. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND) (https://creativecommons.org/licenses/by-nc-nd/4.0/) , which permits downloading and sharing the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal.
spellingShingle Article
Collij, Lyduine E.
Heeman, Fiona
Salvadó, Gemma
Ingala, Silvia
Altomare, Daniele
de Wilde, Arno
Konijnenberg, Elles
van Buchem, Marieke
Yaqub, Maqsood
Markiewicz, Pawel
Golla, Sandeep S.V.
Wottschel, Viktor
Wink, Alle Meije
Visser, Pieter Jelle
Teunissen, Charlotte E.
Lammertsma, Adriaan A.
Scheltens, Philip
van der Flier, Wiesje M.
Boellaard, Ronald
van Berckel, Bart N.M.
Molinuevo, José Luis
Gispert, Juan Domingo
Schmidt, Mark E.
Barkhof, Frederik
Lopes Alves, Isadora
Multitracer model for staging cortical amyloid deposition using PET imaging
title Multitracer model for staging cortical amyloid deposition using PET imaging
title_full Multitracer model for staging cortical amyloid deposition using PET imaging
title_fullStr Multitracer model for staging cortical amyloid deposition using PET imaging
title_full_unstemmed Multitracer model for staging cortical amyloid deposition using PET imaging
title_short Multitracer model for staging cortical amyloid deposition using PET imaging
title_sort multitracer model for staging cortical amyloid deposition using pet imaging
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7713745/
https://www.ncbi.nlm.nih.gov/pubmed/32675080
http://dx.doi.org/10.1212/WNL.0000000000010256
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