Multitracer model for staging cortical amyloid deposition using PET imaging

Lyduine E. Collij(University of Geneva), Fiona Heeman(University of Geneva), Gemma Salvadó(University of Geneva), Silvia Ingala(University of Geneva), Daniele Altomare(University of Geneva), Arno de Wilde(University of Geneva), Elles Konijnenberg(University of Geneva), Marieke van Buchem(University of Geneva), Maqsood Yaqub(University of Geneva), Paweł Markiewicz(University of Geneva), Sandeep S.V. Golla(University of Geneva), Viktor Wottschel(University of Geneva), Alle Meije Wink(University of Geneva), Pieter Jelle Visser(University of Geneva), Charlotte E. Teunissen(University of Geneva), Adriaan A. Lammertsma(University of Geneva), Philip Scheltens(University of Geneva), Wiesje M. van der Flier(University of Geneva), Ronald Boellaard(University of Geneva), Bart N.M. van Berckel(University of Geneva), José Luís Molinuevo(University of Geneva), Juan Domingo Gispert(University of Geneva), Mark E. Schmidt(University of Geneva), Frederik Barkhof(University of Geneva), Isadora Lopes Alves(University of Geneva), for the ALFA Study, for the Alzheimer's Disease Neuroimaging Initiative(MACOM (United States)), Eider M. Arenaza‐Urquijo, Annabella Beteta, Anna Brugulat‐Serrat, Raffaele Cacciaglia, Alba Cañas Boccagni, Yelena G. Bodien, Marta Crous‐Bou, Carme Deulofeu, Ruth Dominguez, Karine Fauria, Carles Falcón, Marta Félez‐Sánchez, José María González de Echavarri, Oriol Grau‐Rivera, Laura L. Hernandez, Gema Huesa, Jordi Huguet, María León, Paula Marne, Tania Menchón, Marta Milà‐Alomà, Grégory Operto, Carolina Minguillón, María Pascual, Albina Polo, Sandra Pradas, Aleix Sala‐Vila, Gonzalo Sánchez‐Benavides, Mahnaz Shekari, Anna Soteras, Marc Suárez‐Calvet, Laia Tenas, Marc Vilanova, Natàlia Vilor‐Tejedor
Neurology
July 17, 2020
Cited by 100Open Access
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Abstract

<h3>Objective</h3> To develop and evaluate a model for staging cortical amyloid deposition using PET with high generalizability. <h3>Methods</h3> 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, Alzheimer9s and Family, Alzheimer9s 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. <h3>Results</h3> 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, <i>F</i> = 67.37, <i>p</i> &lt; 0.001; OASIS: n = 475, <i>F</i> = 9.12, <i>p</i> &lt; 0.001) and faster progression toward an MMSE score ≤25 (ADNI: n = 787, hazard ratio [HR]<sub>stage1</sub> 2.00, HR<sub>stage2</sub> 3.53, HR<sub>stage3</sub> 4.55, HR<sub>stage4</sub> 9.91, <i>p</i> &lt; 0.001; OASIS: n = 469, HR<sub>stage4</sub> 4.80, <i>p</i> &lt; 0.001). <h3>Conclusion</h3> The pooled multitracer staging model successfully classified the level of amyloid burden in &gt;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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