Fedor Levin1, Daniel Ferreira2, Catharina Lange3,4, Martin Dyrba1, Eric Westman2,5, Ralph Buchert6, Stefan J Teipel1,7, Michel J Grothe8,9. 1. German Center for Neurodegenerative Diseases (DZNE), Rostock/Greifswald, Rostock, Germany. 2. Division of Clinical Geriatrics, Department of Neurobiology, Care Sciences and Society, Center for Alzheimer Research, Karolinska Institutet, Stockholm, Sweden. 3. Department of Nuclear Medicine, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, Germany. 4. German Center for Neurodegenerative Diseases (DZNE), Dresden, Germany. 5. Department of Neuroimaging, Centre for Neuroimaging Sciences, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK. 6. Department of Diagnostic and Interventional Radiology and Nuclear Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany. 7. Department of Psychosomatic Medicine, University of Rostock, Rostock, Germany. 8. German Center for Neurodegenerative Diseases (DZNE), Rostock/Greifswald, Rostock, Germany. michel.grothe@dzne.de. 9. Unidad de Trastornos del Movimiento, Servicio de Neurología y Neurofisiología Clínica, Instituto de Biomedicina de Sevilla, Hospital Universitario Virgen del Rocío/CSIC/Universidad de Sevilla, Avda. Manuel Siurot, s/n, 41013, Sevilla, Spain. michel.grothe@dzne.de.
Abstract
BACKGROUND: Previous research has described distinct subtypes of Alzheimer's disease (AD) based on the differences in regional patterns of brain atrophy on MRI. We conducted a data-driven exploration of distinct AD neurodegeneration subtypes using FDG-PET as a sensitive molecular imaging marker of neurodegenerative processes. METHODS: Hierarchical clustering of voxel-wise FDG-PET data from 177 amyloid-positive patients with AD dementia enrolled in the Alzheimer's Disease Neuroimaging Initiative (ADNI) was used to identify distinct hypometabolic subtypes of AD, which were then further characterized with respect to clinical and biomarker characteristics. We then classified FDG-PET scans of 217 amyloid-positive patients with mild cognitive impairment ("prodromal AD") according to the identified subtypes and studied their domain-specific cognitive trajectories and progression to dementia over a follow-up interval of up to 72 months. RESULTS: Three main hypometabolic subtypes were identified: (i) "typical" (48.6%), showing a classic posterior temporo-parietal hypometabolic pattern; (ii) "limbic-predominant" (44.6%), characterized by old age and a memory-predominant cognitive profile; and (iii) a relatively rare "cortical-predominant" subtype (6.8%) characterized by younger age and more severe executive dysfunction. Subtypes classified in the prodromal AD sample demonstrated similar subtype characteristics as in the AD dementia sample and further showed differential courses of cognitive decline. CONCLUSIONS: These findings complement recent research efforts on MRI-based identification of distinct AD atrophy subtypes and may provide a potentially more sensitive molecular imaging tool for early detection and characterization of AD-related neurodegeneration variants at prodromal disease stages.
BACKGROUND: Previous research has described distinct subtypes of Alzheimer's disease (AD) based on the differences in regional patterns of brain atrophy on MRI. We conducted a data-driven exploration of distinct AD neurodegeneration subtypes using FDG-PET as a sensitive molecular imaging marker of neurodegenerative processes. METHODS: Hierarchical clustering of voxel-wise FDG-PET data from 177 amyloid-positive patients with AD dementia enrolled in the Alzheimer's Disease Neuroimaging Initiative (ADNI) was used to identify distinct hypometabolic subtypes of AD, which were then further characterized with respect to clinical and biomarker characteristics. We then classified FDG-PET scans of 217 amyloid-positive patients with mild cognitive impairment ("prodromal AD") according to the identified subtypes and studied their domain-specific cognitive trajectories and progression to dementia over a follow-up interval of up to 72 months. RESULTS: Three main hypometabolic subtypes were identified: (i) "typical" (48.6%), showing a classic posterior temporo-parietal hypometabolic pattern; (ii) "limbic-predominant" (44.6%), characterized by old age and a memory-predominant cognitive profile; and (iii) a relatively rare "cortical-predominant" subtype (6.8%) characterized by younger age and more severe executive dysfunction. Subtypes classified in the prodromal AD sample demonstrated similar subtype characteristics as in the AD dementia sample and further showed differential courses of cognitive decline. CONCLUSIONS: These findings complement recent research efforts on MRI-based identification of distinct AD atrophy subtypes and may provide a potentially more sensitive molecular imaging tool for early detection and characterization of AD-related neurodegeneration variants at prodromal disease stages.
Authors: Melissa E Murray; Neill R Graff-Radford; Owen A Ross; Ronald C Petersen; Ranjan Duara; Dennis W Dickson Journal: Lancet Neurol Date: 2011-07-27 Impact factor: 44.182
Authors: Jennifer L Shaffer; Jeffrey R Petrella; Forrest C Sheldon; Kingshuk Roy Choudhury; Vince D Calhoun; R Edward Coleman; P Murali Doraiswamy Journal: Radiology Date: 2012-12-11 Impact factor: 11.105
Authors: Xiuming Zhang; Elizabeth C Mormino; Nanbo Sun; Reisa A Sperling; Mert R Sabuncu; B T Thomas Yeo Journal: Proc Natl Acad Sci U S A Date: 2016-10-04 Impact factor: 11.205
Authors: Mara Ten Kate; Ellen Dicks; Pieter Jelle Visser; Wiesje M van der Flier; Charlotte E Teunissen; Frederik Barkhof; Philip Scheltens; Betty M Tijms Journal: Brain Date: 2018-12-01 Impact factor: 13.501
Authors: F Nobili; J Arbizu; F Bouwman; A Drzezga; F Agosta; P Nestor; Z Walker; M Boccardi Journal: Eur J Neurol Date: 2018-07-20 Impact factor: 6.089
Authors: Jennifer L Whitwell; Dennis W Dickson; Melissa E Murray; Stephen D Weigand; Nirubol Tosakulwong; Matthew L Senjem; David S Knopman; Bradley F Boeve; Joseph E Parisi; Ronald C Petersen; Clifford R Jack; Keith A Josephs Journal: Lancet Neurol Date: 2012-09-03 Impact factor: 44.182
Authors: Clifford R Jack; David A Bennett; Kaj Blennow; Maria C Carrillo; Billy Dunn; Samantha Budd Haeberlein; David M Holtzman; William Jagust; Frank Jessen; Jason Karlawish; Enchi Liu; Jose Luis Molinuevo; Thomas Montine; Creighton Phelps; Katherine P Rankin; Christopher C Rowe; Philip Scheltens; Eric Siemers; Heather M Snyder; Reisa Sperling Journal: Alzheimers Dement Date: 2018-04 Impact factor: 21.566