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Farrell ME, Thibault EG, Becker JA, Price JC, Healy BC, Hanseeuw BJ, Buckley RF, Jacobs HIL, Schultz AP, Chen CD, Sperling RA, Johnson KA. Spatial extent as a sensitive amyloid-PET metric in preclinical Alzheimer's disease. Alzheimers Dement 2024; 20:5434-5449. [PMID: 38988055 PMCID: PMC11350060 DOI: 10.1002/alz.14036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2024] [Revised: 05/03/2024] [Accepted: 05/06/2024] [Indexed: 07/12/2024]
Abstract
INTRODUCTION Spatial extent-based measures of how far amyloid beta (Aβ) has spread throughout the neocortex may be more sensitive than traditional Aβ-positron emission tomography (PET) measures of Aβ level for detecting early Aβ deposits in preclinical Alzheimer's disease (AD) and improve understanding of Aβ's association with tau proliferation and cognitive decline. METHODS Pittsburgh Compound-B (PIB)-PET scans from 261 cognitively unimpaired older adults from the Harvard Aging Brain Study were used to measure Aβ level (LVL; neocortical PIB DVR) and spatial extent (EXT), calculated as the proportion of the neocortex that is PIB+. RESULTS EXT enabled earlier detection of Aβ deposits longitudinally confirmed to reach a traditional LVL-based threshold for Aβ+ within 5 years. EXT improved prediction of cognitive decline (Preclinical Alzheimer Cognitive Composite) and tau proliferation (flortaucipir-PET) over LVL. DISCUSSION These findings indicate EXT may be more sensitive to Aβ's role in preclinical AD than level and improve targeting of individuals for AD prevention trials. HIGHLIGHTS Aβ spatial extent (EXT) was measured as the percentage of the neocortex with elevated Pittsburgh Compound-B. Aβ EXT improved detection of Aβ below traditional PET thresholds. Early regional Aβ deposits were spatially heterogeneous. Cognition and tau were more closely tied to Aβ EXT than Aβ level. Neocortical tau onset aligned with reaching widespread neocortical Aβ.
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Affiliation(s)
- Michelle E. Farrell
- Department of NeurologyMassachusetts General HospitalHarvard Medical SchoolBostonMassachusettsUSA
| | - Emma G. Thibault
- Department of RadiologyMassachusetts General HospitalHarvard Medical SchoolBostonMassachusettsUSA
| | - J. Alex Becker
- Department of RadiologyMassachusetts General HospitalHarvard Medical SchoolBostonMassachusettsUSA
| | - Julie C. Price
- Department of RadiologyMassachusetts General HospitalHarvard Medical SchoolBostonMassachusettsUSA
| | - Brian C. Healy
- Department of NeurologyMassachusetts General HospitalHarvard Medical SchoolBostonMassachusettsUSA
- Biostatistics CenterMassachusetts General HospitalHarvard Medical SchoolBostonMassachusettsUSA
| | - Bernard J. Hanseeuw
- Department of RadiologyMassachusetts General HospitalHarvard Medical SchoolBostonMassachusettsUSA
- Department of NeurologyCliniques Universitaires Saint‐LucUniversité Catholique de LouvainBruxellesBelgium
| | - Rachel F. Buckley
- Department of NeurologyMassachusetts General HospitalHarvard Medical SchoolBostonMassachusettsUSA
- Melbourne School of Psychological SciencesUniversity of MelbourneMelbourneVictoriaAustralia
- Center for Alzheimer Research and TreatmentDepartment of NeurologyBrigham and Women's HospitalHarvard Medical SchoolBostonMassachusettsUSA
| | - Heidi I. L. Jacobs
- Department of RadiologyMassachusetts General HospitalHarvard Medical SchoolBostonMassachusettsUSA
| | - Aaron P. Schultz
- Department of NeurologyMassachusetts General HospitalHarvard Medical SchoolBostonMassachusettsUSA
| | - Charles D. Chen
- Department of RadiologyMassachusetts General HospitalHarvard Medical SchoolBostonMassachusettsUSA
| | - Reisa A. Sperling
- Department of NeurologyMassachusetts General HospitalHarvard Medical SchoolBostonMassachusettsUSA
- Center for Alzheimer Research and TreatmentDepartment of NeurologyBrigham and Women's HospitalHarvard Medical SchoolBostonMassachusettsUSA
| | - Keith A. Johnson
- Department of NeurologyMassachusetts General HospitalHarvard Medical SchoolBostonMassachusettsUSA
- Department of RadiologyMassachusetts General HospitalHarvard Medical SchoolBostonMassachusettsUSA
- Center for Alzheimer Research and TreatmentDepartment of NeurologyBrigham and Women's HospitalHarvard Medical SchoolBostonMassachusettsUSA
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Murai SA, Mano T, Sanes JN, Watanabe T. Atypical intrinsic neural timescale in the left angular gyrus in Alzheimer's disease. Brain Commun 2024; 6:fcae199. [PMID: 38993284 PMCID: PMC11227993 DOI: 10.1093/braincomms/fcae199] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2023] [Revised: 04/18/2024] [Accepted: 06/07/2024] [Indexed: 07/13/2024] Open
Abstract
Alzheimer's disease is characterized by cognitive impairment and progressive brain atrophy. Recent human neuroimaging studies reported atypical anatomical and functional changes in some regions in the default mode network in patients with Alzheimer's disease, but which brain area of the default mode network is the key region whose atrophy disturbs the entire network activity and consequently contributes to the symptoms of the disease remains unidentified. Here, in this case-control study, we aimed to identify crucial neural regions that mediated the phenotype of Alzheimer's disease, and as such, we examined the intrinsic neural timescales-a functional metric to evaluate the capacity to integrate diverse neural information-and grey matter volume of the regions in the default mode network using resting-state functional MRI images and structural MRI data obtained from individuals with Alzheimer's disease and cognitively typical people. After confirming the atypically short neural timescale of the entire default mode network in Alzheimer's disease and its link with the symptoms of the disease, we found that the shortened neural timescale of the default mode network was associated with the aberrantly short neural timescale of the left angular gyrus. Moreover, we revealed that the shortened neural timescale of the angular gyrus was correlated with the atypically reduced grey matter volume of this parietal region. Furthermore, we identified an association between the neural structure, brain function and symptoms and proposed a model in which the reduced grey matter volume of the left angular gyrus shortened the intrinsic neural time of the region, which then destabilized the entire neural timescale of the default mode network and resultantly contributed to cognitive decline in Alzheimer's disease. These findings highlight the key role of the left angular gyrus in the anatomical and functional aetiology of Alzheimer's disease.
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Affiliation(s)
- Shota A Murai
- International Research Center for Neurointelligence (WPI-IRCN), The University of Tokyo Institutes for Advanced Study, Bunkyo City, Tokyo 113-0033, Japan
| | - Tatsuo Mano
- Department of Degenerative Neurological Diseases, National Center of Neurology and Psychiatry, Tokyo 187-8551, Japan
| | - Jerome N Sanes
- Department of Neuroscience, Brown University, Providence, RI 02912, USA
- Carney Institute for Brain Science, Brown University, Providence, RI 02912, USA
- Center for Neurorestoration and Neurotechnology, Veterans Affairs Providence Healthcare System, Providence, RI 02908, USA
| | - Takamitsu Watanabe
- International Research Center for Neurointelligence (WPI-IRCN), The University of Tokyo Institutes for Advanced Study, Bunkyo City, Tokyo 113-0033, Japan
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Angelopoulou E, Bougea A, Hatzimanolis A, Scarmeas N, Papageorgiou SG. Unraveling the Potential Underlying Mechanisms of Mild Behavioral Impairment: Focusing on Amyloid and Tau Pathology. Cells 2024; 13:1164. [PMID: 38995015 PMCID: PMC11240615 DOI: 10.3390/cells13131164] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2024] [Revised: 06/21/2024] [Accepted: 06/27/2024] [Indexed: 07/13/2024] Open
Abstract
The emergence of sustained neuropsychiatric symptoms (NPS) among non-demented individuals in later life, defined as mild behavioral impairment (MBI), is linked to a higher risk of cognitive decline. However, the underlying pathophysiological mechanisms remain largely unexplored. A growing body of evidence has shown that MBI is associated with alterations in structural and functional neuroimaging studies, higher genetic predisposition to clinical diagnosis of Alzheimer's disease (AD), as well as amyloid and tau pathology assessed in the blood, cerebrospinal fluid, positron-emission tomography (PET) imaging and neuropathological examination. These findings shed more light on the MBI-related potential neurobiological mechanisms, paving the way for the development of targeted pharmacological approaches. In this review, we aim to discuss the available clinical evidence on the role of amyloid and tau pathology in MBI and the potential underlying pathophysiological mechanisms. Dysregulation of the hypothalamic-pituitary-adrenal (HPA) axis, disruption of neurotrophic factors, such as the brain-derived neurotrophic factor (BDNF), abnormal neuroinflammatory responses including the kynurenine pathway, dysregulation of transforming growth factor beta (TGF-β1), epigenetic alterations including micro-RNA (miR)-451a and miR-455-3p, synaptic dysfunction, imbalance in neurotransmitters including acetylcholine, dopamine, serotonin, gamma-aminobutyric acid (GABA) and norepinephrine, as well as altered locus coeruleus (LC) integrity are some of the potential mechanisms connecting MBI with amyloid and tau pathology. The elucidation of the underlying neurobiology of MBI would facilitate the design and efficacy of relative clinical trials, especially towards amyloid- or tau-related pathways. In addition, we provide insights for future research into our deeper understanding of its underlying pathophysiology of MBI, and discuss relative therapeutic implications.
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Affiliation(s)
- Efthalia Angelopoulou
- 1st Department of Neurology, Aiginition University Hospital, National and Kapodistrian University of Athens, Vasilissis Sofias Street 72-74, 11528 Athens, Greece
| | - Anastasia Bougea
- 1st Department of Neurology, Aiginition University Hospital, National and Kapodistrian University of Athens, Vasilissis Sofias Street 72-74, 11528 Athens, Greece
| | - Alexandros Hatzimanolis
- 1st Department of Psychiatry, Aiginition University Hospital, National and Kapodistrian University of Athens, Vasilissis Sofias Street 72-74, 11528 Athens, Greece
| | - Nikolaos Scarmeas
- 1st Department of Neurology, Aiginition University Hospital, National and Kapodistrian University of Athens, Vasilissis Sofias Street 72-74, 11528 Athens, Greece
| | - Sokratis G Papageorgiou
- 1st Department of Neurology, Aiginition University Hospital, National and Kapodistrian University of Athens, Vasilissis Sofias Street 72-74, 11528 Athens, Greece
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Zhang Y, Zhang M, Wang L, Zheng Y, Li H, Xie Y, Lv X, Yu X, Wang H. Attenuated neural activity in processing decision-making feedback in uncertain conditions in patients with mild cognitive impairment. Eur Arch Psychiatry Clin Neurosci 2024:10.1007/s00406-024-01793-0. [PMID: 38916765 DOI: 10.1007/s00406-024-01793-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/19/2023] [Accepted: 03/09/2024] [Indexed: 06/26/2024]
Abstract
The present study aimed to explore the potential neural correlates during feedback evaluation during decision-making under risk and ambiguity in MCI. Nineteen individuals with MCI and twenty age-matched HCs were enrolled. Decision-making performance under risk and ambiguity was examined with the modified game of dice task (GDT) and an Iowa gambling task (IGT). Using task-related EEG data, reward positivity (RewP) and feedback P3 (fb-P3) were used to characterize participants' motivation and allocation of cognitive resources. Also, response time and event-related oscillation (ERO) were used to evaluate information processing speed, and the potent of post-feedback information integration and behavioral modulation. MCI patients had lower RewP (p = 0.022) and fb-P3 (p = 0.045) amplitudes in the GDT than HCs. Moreover, the amount and valence of feedback modulated the RewP (p = 0.008; p = 0.017) and fb-P3 (p < 0.001; p < 0.001). In the IGT, in addition to the significantly reduced fb-P3 observed in MCI patients (p = 0.010), the amount and valence of feedback modulated the RewP (p = 0.002; p = 0.020). Furthermore, MCI patients took longer to make decisions (t = 2.15, p = 0.041). The ERO analysis revealed that delta power was reduced in MCI (GDT: p = 0.045; p = 0.011). The findings suggest that, during feedback evaluation when making risky and ambiguous decisions, motivation, allocation of cognitive resources, information processing and neuronal excitability were attenuated in MCI. It implies that neural activity related to decision making was compromised in MCI.
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Affiliation(s)
- Ying Zhang
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
- Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
- Dementia Care and Research Center, Peking University Institute of Mental Health (Sixth Hospital), Beijing Dementia Key Lab, No. 51 Huayuanbei Road, Beijing, 100191, China
- National Clinical Research Center for Mental Disorders, Key Laboratory for Mental Health, National Health Commission, Beijing, China
| | - Mang Zhang
- Dementia Care and Research Center, Peking University Institute of Mental Health (Sixth Hospital), Beijing Dementia Key Lab, No. 51 Huayuanbei Road, Beijing, 100191, China
- National Clinical Research Center for Mental Disorders, Key Laboratory for Mental Health, National Health Commission, Beijing, China
| | - Luchun Wang
- Dementia Care and Research Center, Peking University Institute of Mental Health (Sixth Hospital), Beijing Dementia Key Lab, No. 51 Huayuanbei Road, Beijing, 100191, China
- National Clinical Research Center for Mental Disorders, Key Laboratory for Mental Health, National Health Commission, Beijing, China
| | - Yaonan Zheng
- Dementia Care and Research Center, Peking University Institute of Mental Health (Sixth Hospital), Beijing Dementia Key Lab, No. 51 Huayuanbei Road, Beijing, 100191, China
- National Clinical Research Center for Mental Disorders, Key Laboratory for Mental Health, National Health Commission, Beijing, China
| | - Huizi Li
- Dementia Care and Research Center, Peking University Institute of Mental Health (Sixth Hospital), Beijing Dementia Key Lab, No. 51 Huayuanbei Road, Beijing, 100191, China
- National Clinical Research Center for Mental Disorders, Key Laboratory for Mental Health, National Health Commission, Beijing, China
| | - Yuhan Xie
- Dementia Care and Research Center, Peking University Institute of Mental Health (Sixth Hospital), Beijing Dementia Key Lab, No. 51 Huayuanbei Road, Beijing, 100191, China
- National Clinical Research Center for Mental Disorders, Key Laboratory for Mental Health, National Health Commission, Beijing, China
| | - Xiaozhen Lv
- Dementia Care and Research Center, Peking University Institute of Mental Health (Sixth Hospital), Beijing Dementia Key Lab, No. 51 Huayuanbei Road, Beijing, 100191, China
- National Clinical Research Center for Mental Disorders, Key Laboratory for Mental Health, National Health Commission, Beijing, China
| | - Xin Yu
- Dementia Care and Research Center, Peking University Institute of Mental Health (Sixth Hospital), Beijing Dementia Key Lab, No. 51 Huayuanbei Road, Beijing, 100191, China
- National Clinical Research Center for Mental Disorders, Key Laboratory for Mental Health, National Health Commission, Beijing, China
| | - Huali Wang
- Dementia Care and Research Center, Peking University Institute of Mental Health (Sixth Hospital), Beijing Dementia Key Lab, No. 51 Huayuanbei Road, Beijing, 100191, China.
- National Clinical Research Center for Mental Disorders, Key Laboratory for Mental Health, National Health Commission, Beijing, China.
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5
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Iaccarino L, Llibre-Guerra JJ, McDade E, Edwards L, Gordon B, Benzinger T, Hassenstab J, Kramer JH, Li Y, Miller BL, Miller Z, Morris JC, Mundada N, Perrin RJ, Rosen HJ, Soleimani-Meigooni D, Strom A, Tsoy E, Wang G, Xiong C, Allegri R, Chrem P, Vazquez S, Berman SB, Chhatwal J, Masters CL, Farlow MR, Jucker M, Levin J, Salloway S, Fox NC, Day GS, Gorno-Tempini ML, Boxer AL, La Joie R, Bateman R, Rabinovici GD. Molecular neuroimaging in dominantly inherited versus sporadic early-onset Alzheimer's disease. Brain Commun 2024; 6:fcae159. [PMID: 38784820 PMCID: PMC11114609 DOI: 10.1093/braincomms/fcae159] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2023] [Revised: 03/14/2024] [Accepted: 05/01/2024] [Indexed: 05/25/2024] Open
Abstract
Approximately 5% of Alzheimer's disease patients develop symptoms before age 65 (early-onset Alzheimer's disease), with either sporadic (sporadic early-onset Alzheimer's disease) or dominantly inherited (dominantly inherited Alzheimer's disease) presentations. Both sporadic early-onset Alzheimer's disease and dominantly inherited Alzheimer's disease are characterized by brain amyloid-β accumulation, tau tangles, hypometabolism and neurodegeneration, but differences in topography and magnitude of these pathological changes are not fully elucidated. In this study, we directly compared patterns of amyloid-β plaque deposition and glucose hypometabolism in sporadic early-onset Alzheimer's disease and dominantly inherited Alzheimer's disease individuals. Our analysis included 134 symptomatic sporadic early-onset Alzheimer's disease amyloid-Positron Emission Tomography (PET)-positive cases from the University of California, San Francisco, Alzheimer's Disease Research Center (mean ± SD age 59.7 ± 5.6 years), 89 symptomatic dominantly inherited Alzheimer's disease cases (age 45.8 ± 9.3 years) and 102 cognitively unimpaired non-mutation carriers from the Dominantly Inherited Alzheimer Network study (age 44.9 ± 9.2). Each group underwent clinical and cognitive examinations, 11C-labelled Pittsburgh Compound B-PET and structural MRI. 18F-Fluorodeoxyglucose-PET was also available for most participants. Positron Emission Tomography scans from both studies were uniformly processed to obtain a standardized uptake value ratio (PIB50-70 cerebellar grey reference and FDG30-60 pons reference) images. Statistical analyses included pairwise global and voxelwise group comparisons and group-independent component analyses. Analyses were performed also adjusting for covariates including age, sex, Mini-Mental State Examination, apolipoprotein ε4 status and average composite cortical of standardized uptake value ratio. Compared with dominantly inherited Alzheimer's disease, sporadic early-onset Alzheimer's disease participants were older at age of onset (mean ± SD, 54.8 ± 8.2 versus 41.9 ± 8.2, Cohen's d = 1.91), with more years of education (16.4 ± 2.8 versus 13.5 ± 3.2, d = 1) and more likely to be apolipoprotein ε4 carriers (54.6% ε4 versus 28.1%, Cramer's V = 0.26), but similar Mini-Mental State Examination (20.6 ± 6.1 versus 21.2 ± 7.4, d = 0.08). Sporadic early-onset Alzheimer's disease had higher global cortical Pittsburgh Compound B-PET binding (mean ± SD standardized uptake value ratio, 1.92 ± 0.29 versus 1.58 ± 0.44, d = 0.96) and greater global cortical 18F-fluorodeoxyglucose-PET hypometabolism (mean ± SD standardized uptake value ratio, 1.32 ± 0.1 versus 1.39 ± 0.19, d = 0.48) compared with dominantly inherited Alzheimer's disease. Fully adjusted comparisons demonstrated relatively higher Pittsburgh Compound B-PET standardized uptake value ratio in the medial occipital, thalami, basal ganglia and medial/dorsal frontal regions in dominantly inherited Alzheimer's disease versus sporadic early-onset Alzheimer's disease. Sporadic early-onset Alzheimer's disease showed relatively greater 18F-fluorodeoxyglucose-PET hypometabolism in Alzheimer's disease signature temporoparietal regions and caudate nuclei, whereas dominantly inherited Alzheimer's disease showed relatively greater hypometabolism in frontal white matter and pericentral regions. Independent component analyses largely replicated these findings by highlighting common and unique Pittsburgh Compound B-PET and 18F-fluorodeoxyglucose-PET binding patterns. In summary, our findings suggest both common and distinct patterns of amyloid and glucose hypometabolism in sporadic and dominantly inherited early-onset Alzheimer's disease.
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Affiliation(s)
- Leonardo Iaccarino
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA 94158, USA
| | - Jorge J Llibre-Guerra
- The Dominantly Inherited Alzheimer Network (DIAN), St Louis, MO 63108, USA
- Department of Neurology, Washington University in St Louis, St Louis, MO 63108, USA
| | - Eric McDade
- The Dominantly Inherited Alzheimer Network (DIAN), St Louis, MO 63108, USA
- Department of Neurology, Washington University in St Louis, St Louis, MO 63108, USA
| | - Lauren Edwards
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA 94158, USA
| | - Brian Gordon
- Department of Radiology, Washington University in St Louis, St Louis, MO 63110, USA
| | - Tammie Benzinger
- Department of Radiology, Washington University in St Louis, St Louis, MO 63110, USA
| | - Jason Hassenstab
- The Dominantly Inherited Alzheimer Network (DIAN), St Louis, MO 63108, USA
- Department of Neurology, Washington University in St Louis, St Louis, MO 63108, USA
| | - Joel H Kramer
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA 94158, USA
| | - Yan Li
- Department of Biostatistics, Washington University in St Louis, St Louis, MO 63110, USA
| | - Bruce L Miller
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA 94158, USA
| | - Zachary Miller
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA 94158, USA
| | - John C Morris
- The Dominantly Inherited Alzheimer Network (DIAN), St Louis, MO 63108, USA
- Department of Neurology, Washington University in St Louis, St Louis, MO 63108, USA
| | - Nidhi Mundada
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA 94158, USA
| | - Richard J Perrin
- Department of Pathology and Immunology, Washington University in St Louis, St Louis, MO 63110, USA
| | - Howard J Rosen
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA 94158, USA
| | - David Soleimani-Meigooni
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA 94158, USA
| | - Amelia Strom
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA 94158, USA
| | - Elena Tsoy
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA 94158, USA
| | - Guoqiao Wang
- Department of Biostatistics, Washington University in St Louis, St Louis, MO 63110, USA
| | - Chengjie Xiong
- Department of Biostatistics, Washington University in St Louis, St Louis, MO 63110, USA
| | - Ricardo Allegri
- Department of Cognitive Neurology, Institute for Neurological Research Fleni, Buenos Aires 1428, Argentina
| | - Patricio Chrem
- Department of Cognitive Neurology, Institute for Neurological Research Fleni, Buenos Aires 1428, Argentina
| | - Silvia Vazquez
- Department of Cognitive Neurology, Institute for Neurological Research Fleni, Buenos Aires 1428, Argentina
| | - Sarah B Berman
- Department of Neurology, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - Jasmeer Chhatwal
- Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
| | - Colin L Masters
- Department of Neuroscience, Florey Institute, The University of Melbourne, Melbourne 3052, Australia
| | - Martin R Farlow
- Neuroscience Center, Indiana University School of Medicine at Indianapolis, Indiana, IN 46202, USA
| | - Mathias Jucker
- DZNE-German Center for Neurodegenerative Diseases, Tübingen 72076, Germany
| | - Johannes Levin
- Department of Neurology, Ludwig-Maximilians-University, Munich 80539, Germany
- German Center for Neurodegenerative Diseases, Munich 81377, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich 81377, Germany
| | - Stephen Salloway
- Memory & Aging Program, Butler Hospital, Brown University in Providence, RI 02906, USA
| | - Nick C Fox
- Dementia Research Centre, Department of Neurodegenerative Disease, University College London Institute of Neurology, London WC1N 3BG, UK
| | - Gregory S Day
- Department of Neurology, Mayo Clinic Florida, Jacksonville, FL 33224, USA
| | - Maria Luisa Gorno-Tempini
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA 94158, USA
| | - Adam L Boxer
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA 94158, USA
| | - Renaud La Joie
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA 94158, USA
| | - Randall Bateman
- The Dominantly Inherited Alzheimer Network (DIAN), St Louis, MO 63108, USA
- Department of Neurology, Washington University in St Louis, St Louis, MO 63108, USA
| | - Gil D Rabinovici
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA 94158, USA
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA 94143, USA
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Kim HH, Kwon MJ, Jo S, Park JE, Kim JW, Kim JH, Kim SE, Kim KW, Han JW. Exploration of neuroanatomical characteristics to differentiate prodromal Alzheimer's disease from cognitively unimpaired amyloid-positive individuals. Sci Rep 2024; 14:10083. [PMID: 38698190 PMCID: PMC11066072 DOI: 10.1038/s41598-024-60843-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Accepted: 04/28/2024] [Indexed: 05/05/2024] Open
Abstract
Differentiating clinical stages based solely on positive findings from amyloid PET is challenging. We aimed to investigate the neuroanatomical characteristics at the whole-brain level that differentiate prodromal Alzheimer's disease (AD) from cognitively unimpaired amyloid-positive individuals (CU A+) in relation to amyloid deposition and regional atrophy. We included 45 CU A+ participants and 135 participants with amyloid-positive prodromal AD matched 1:3 by age, sex, and education. All participants underwent 18F-florbetaben positron emission tomography and 3D structural T1-weighted magnetic resonance imaging. We compared the standardized uptake value ratios (SUVRs) and volumes in 80 regions of interest (ROIs) between CU A+ and prodromal AD groups using independent t-tests, and employed the least absolute selection and shrinkage operator (LASSO) logistic regression model to identify ROIs associated with prodromal AD in relation to amyloid deposition, regional atrophy, and their interaction. After applying False Discovery Rate correction at < 0.1, there were no differences in global and regional SUVR between CU A+ and prodromal AD groups. Regional volume differences between the two groups were observed in the amygdala, hippocampus, entorhinal cortex, insula, parahippocampal gyrus, and inferior temporal and parietal cortices. LASSO logistic regression model showed significant associations between prodromal AD and atrophy in the entorhinal cortex, inferior parietal cortex, both amygdalae, and left hippocampus. The mean SUVR in the right superior parietal cortex (beta coefficient = 0.0172) and its interaction with the regional volume (0.0672) were also selected in the LASSO model. The mean SUVR in the right superior parietal cortex was associated with an increased likelihood of prodromal AD (Odds ratio [OR] 1.602, p = 0.014), particularly in participants with lower regional volume (OR 3.389, p < 0.001). Only regional volume differences, not amyloid deposition, were observed between CU A+ and prodromal AD. The reduced volume in the superior parietal cortex may play a significant role in the progression to prodromal AD through its interaction with amyloid deposition in that region.
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Affiliation(s)
- Hak Hyeon Kim
- Department of Neuropsychiatry, Seoul National University Bundang Hospital, 82 Gumi-ro 173 Beon-gil, Bundang-gu, Seongnam-si, Gyeonggi-do, 13620, South Korea
- Department of Psychiatry, College of Medicine, Seoul National University, Seoul, South Korea
| | - Min Jeong Kwon
- Department of Brain and Cognitive Science, College of Natural Sciences, Seoul National University, Seoul, South Korea
| | - Sungman Jo
- Department of Health Science and Technology, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, South Korea
| | - Ji Eun Park
- Department of Brain and Cognitive Science, College of Natural Sciences, Seoul National University, Seoul, South Korea
| | - Ji Won Kim
- Department of Brain and Cognitive Science, College of Natural Sciences, Seoul National University, Seoul, South Korea
| | - Jae Hyoung Kim
- Department of Radiology, Seoul National University Bundang Hospital, College of Medicine, Seoul National University, Seongnam-si, Gyeonggi-do, South Korea
| | - Sang Eun Kim
- Department of Nuclear Medicine, Seoul National University Bundang Hospital, College of Medicine, Seoul National University, Seongnam-si, Gyeonggi-do, Korea
- Center for Nanomolecular Imaging and Innovative Drug Development, Advanced Institutes of Convergence Technology, Suwon, Republic of Korea
| | - Ki Woong Kim
- Department of Neuropsychiatry, Seoul National University Bundang Hospital, 82 Gumi-ro 173 Beon-gil, Bundang-gu, Seongnam-si, Gyeonggi-do, 13620, South Korea
- Department of Psychiatry, College of Medicine, Seoul National University, Seoul, South Korea
- Department of Brain and Cognitive Science, College of Natural Sciences, Seoul National University, Seoul, South Korea
- Department of Health Science and Technology, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, South Korea
| | - Ji Won Han
- Department of Neuropsychiatry, Seoul National University Bundang Hospital, 82 Gumi-ro 173 Beon-gil, Bundang-gu, Seongnam-si, Gyeonggi-do, 13620, South Korea.
- Department of Psychiatry, College of Medicine, Seoul National University, Seoul, South Korea.
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Gérard T, Colmant L, Malotaux V, Salman Y, Huyghe L, Quenon L, Dricot L, Ivanoiu A, Lhommel R, Hanseeuw B. The spatial extent of tauopathy on [ 18F]MK-6240 tau PET shows stronger association with cognitive performances than the standard uptake value ratio in Alzheimer's disease. Eur J Nucl Med Mol Imaging 2024; 51:1662-1674. [PMID: 38228971 PMCID: PMC11043108 DOI: 10.1007/s00259-024-06603-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Accepted: 01/04/2024] [Indexed: 01/18/2024]
Abstract
PURPOSE [18F]MK-6240, a second-generation tau PET tracer, is increasingly used for the detection and the quantification of in vivo cerebral tauopathy in Alzheimer's disease (AD). Given that neurological symptoms are better explained by the topography rather than by the nature of brain lesions, our study aimed to evaluate whether cognitive impairment would be more closely associated with the spatial extent than with the intensity of tau-PET signal, as measured by the standard uptake value ratio (SUVr). METHODS [18F]MK6240 tau-PET data from 82 participants in the AD spectrum were quantified in three different brain regions (Braak ≤ 2, Braak ≤ 4, and Braak ≤ 6) using SUVr and the extent of tauopathy (EOT, percentage of voxels with SUVr ≥ 1.3). PET data were first compared between diagnostic categories, and ROC curves were computed to evaluate sensitivity and specificity. PET data were then correlated to cognitive performances and cerebrospinal fluid (CSF) tau values. RESULTS The EOT in the Braak ≤ 2 region provided the highest diagnostic accuracies, distinguishing between amyloid-negative and positive clinically unimpaired individuals (threshold = 9%, sensitivity = 79%, specificity = 82%) as well as between prodromal AD and preclinical AD (threshold = 38%, sensitivity = 81%, specificity = 93%). The EOT better correlated with cognition than SUVr (∆R2 + 0.08-0.09) with the best correlation observed for EOT in the Braak ≤ 4 region (R2 = 0.64). Cognitive performances were more closely associated with PET metrics than with CSF values. CONCLUSIONS Quantifying [18F]MK-6240 tau PET in terms of EOT rather than SUVr significantly increases the correlation with cognitive performances. Quantification in the mesiotemporal lobe is the most useful to diagnose preclinical AD or prodromal AD.
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Affiliation(s)
- Thomas Gérard
- Nuclear Medicine Department, Cliniques Universitaires Saint Luc, Brussels, Belgium.
- Institute of Neurosciences, Université Catholique de Louvain, Brussels, Belgium.
| | - Lise Colmant
- Institute of Neurosciences, Université Catholique de Louvain, Brussels, Belgium
- Neurology Department, Cliniques Universitaires Saint Luc, Brussels, Belgium
| | - Vincent Malotaux
- Institute of Neurosciences, Université Catholique de Louvain, Brussels, Belgium
| | - Yasmine Salman
- Institute of Neurosciences, Université Catholique de Louvain, Brussels, Belgium
| | - Lara Huyghe
- Institute of Neurosciences, Université Catholique de Louvain, Brussels, Belgium
| | - Lisa Quenon
- Institute of Neurosciences, Université Catholique de Louvain, Brussels, Belgium
- Neurology Department, Cliniques Universitaires Saint Luc, Brussels, Belgium
| | - Laurence Dricot
- Institute of Neurosciences, Université Catholique de Louvain, Brussels, Belgium
| | - Adrian Ivanoiu
- Institute of Neurosciences, Université Catholique de Louvain, Brussels, Belgium
- Neurology Department, Cliniques Universitaires Saint Luc, Brussels, Belgium
| | - Renaud Lhommel
- Nuclear Medicine Department, Cliniques Universitaires Saint Luc, Brussels, Belgium
- Institute of Neurosciences, Université Catholique de Louvain, Brussels, Belgium
| | - Bernard Hanseeuw
- Institute of Neurosciences, Université Catholique de Louvain, Brussels, Belgium
- Neurology Department, Cliniques Universitaires Saint Luc, Brussels, Belgium
- WELBIO Department, WEL Research Institute, Avenue Pasteur, 6, 1300, Wavre, Belgium
- Department of Radiology, Gordon Center for Medical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
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Yang Z, Kinney JW, Cordes D. Uptake of 18F-AV45 in the Putamen Provides Additional Insights into Alzheimer's Disease beyond the Cortex. Biomolecules 2024; 14:157. [PMID: 38397394 PMCID: PMC10886857 DOI: 10.3390/biom14020157] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2023] [Revised: 01/09/2024] [Accepted: 01/17/2024] [Indexed: 02/25/2024] Open
Abstract
Cortical uptake in brain amyloid positron emission tomography (PET) is increasingly used for the biological diagnosis of Alzheimer's disease (AD); however, the clinical and biological relevance of the striatum beyond the cortex in amyloid PET scans remains unclear. A total of 513 amyloid-positive participants having 18F-AV45 amyloid PET scans available were included in the analysis. The associations between cognitive scores and striatal uptake were analyzed. The participants were categorized into three groups based on the residual from the linear fitting between 18F-AV45 uptake in the putamen and the cortex in the order of HighP > MidP > LowP group. We then examined the differences between these three groups in terms of clinical diagnosis, APOE genotype, CSF phosphorylated tau (ptau) concentration, hippocampal volume, entorhinal thickness, and cognitive decline rate to evaluate the additional insights provided by the putamen beyond the cortex. The 18F-AV45 uptake in the putamen was more strongly associated with ADAS-cog13 and MoCA scores (p < 0.001) compared to the uptake in the caudate nucleus. Despite comparable cortical uptakes, the HighP group had a two-fold higher risk of being ε4-homozygous or diagnosed with AD dementia compared to the LowP group. These three groups had significantly different CSF ptau concentration, hippocampal volume, entorhinal thickness, and cognitive decline rate. These findings suggest that the assessment of 18F-AV45 uptake in the putamen is of unique value for evaluating disease severity and predicting disease progression.
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Affiliation(s)
- Zhengshi Yang
- Cleveland Clinic Lou Ruvo Center for Brain Health, Las Vegas, NV 89106, USA;
- Department of Brain Health, University of Nevada Las Vegas (UNLV), Las Vegas, NV 89154, USA;
| | - Jefferson W. Kinney
- Department of Brain Health, University of Nevada Las Vegas (UNLV), Las Vegas, NV 89154, USA;
- Chambers-Grundy Center for Transformative Neuroscience, Pam Quirk Brain Health and Biomarker Laboratory, Department of Brain Health, School of Integrated Health Sciences, University of Nevada Las Vegas (UNLV), Las Vegas, NV 89154, USA
| | - Dietmar Cordes
- Cleveland Clinic Lou Ruvo Center for Brain Health, Las Vegas, NV 89106, USA;
- Department of Brain Health, University of Nevada Las Vegas (UNLV), Las Vegas, NV 89154, USA;
- Department of Psychology and Neuroscience, University of Colorado, Boulder, CO 80309, USA
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9
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Baril AA, Kojis DJ, Himali JJ, Decarli CS, Sanchez E, Johnson KA, El Fakhri G, Thibault E, Yiallourou SR, Himali D, Cavuoto MG, Pase MP, Beiser AS, Seshadri S. Association of Sleep Duration and Change Over Time With Imaging Biomarkers of Cerebrovascular, Amyloid, Tau, and Neurodegenerative Pathology. Neurology 2024; 102:e207807. [PMID: 38165370 PMCID: PMC10834132 DOI: 10.1212/wnl.0000000000207807] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Accepted: 10/13/2023] [Indexed: 01/03/2024] Open
Abstract
BACKGROUND AND OBJECTIVES Both short and long sleep duration were previously associated with incident dementia, but underlying mechanisms remain unclear. We evaluated how self-reported sleep duration and its change over time associate with (A)myloid, (T)au, (N)eurodegeneration, and (V)ascular neuroimaging markers of Alzheimer disease. METHODS Two Framingham Heart Study overlapping samples were studied: participants who underwent 11C-Pittsburg Compound B amyloid and 18F-flortaucipir tau PET imaging and participants who underwent an MRI. MRI metrics estimated neurodegeneration (total brain volume) and cerebrovascular injuries (white matter hyperintensities [WMHs] volume, covert brain infarcts, free-water [FW] fraction). Self-reported sleep duration was assessed and split into categories both at the time of neuroimaging testing and approximately 13 years before: short ≤6 hours. average 7-8 hours, and long ≥9 hours. Logistic and linear regression models were used to examine sleep duration and neuroimaging metrics. RESULTS The tested cohort was composed of 271 participants (age 53.6 ± 8.0 years; 51% male) in the PET imaging sample and 2,165 participants (age 61.3 ± 11.1 years; 45% male) in the MRI sample. No fully adjusted association was observed between cross-sectional sleep duration and neuroimaging metrics. In fully adjusted models compared with consistently sleeping 7-8 hours, groups transitioning to a longer sleep duration category over time had higher FW fraction (short to average β [SE] 0.0062 [0.0024], p = 0.009; short to long β [SE] 0.0164 [0.0076], p = 0.031; average to long β [SE] 0.0083 [0.0022], p = 0.002), and those specifically going from average to long sleep duration also had higher WMH burden (β [SE] 0.29 [0.11], p = 0.007). The opposite associations (lower WMH and FW) were observed in participants consistently sleeping ≥9 hours as compared with people consistently sleeping 7-8 hours in fully adjusted models (β [SE] -0.43 [0.20], p = 0.028; β [SE] -0.019 [0.004], p = 0.020). Each hour of increasing sleep (continuous, β [SE] 0.12 [0.04], p = 0.003; β [SE] 0.002 [0.001], p = 0.021) and extensive increase in sleep duration (≥2 hours vs 0 ± 1 hour change; β [SE] 0.24 [0.10], p = 0.019; β [SE] 0.0081 [0.0025], p = 0.001) over time was associated with higher WMH burden and FW fraction in fully adjusted models. Sleep duration change was not associated with PET amyloid or tau outcomes. DISCUSSION Longer self-reported sleep duration over time was associated with neuroimaging biomarkers of cerebrovascular pathology as evidenced by higher WMH burden and FW fraction. A longer sleep duration extending over time may be an early change in the neurodegenerative trajectory.
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Affiliation(s)
- Andrée-Ann Baril
- From the Douglas Mental Health University Institute (A.-A.B.), McGill University, Montreal, Quebec, Canada; The Framingham Heart Study (A.-A.B., D.J.K., J.J.H., D.H., M.P.P., A.S.B., S.S.); Boston University School of Public Health (D.J.K., J.J.H.), MA; Boston University School of Medicine (J.J.H., S.S.), MA; Glenn Biggs Institute for Alzheimer's & Neurodegenerative Diseases (J.J.H., S.S.), UT Health San Antonio, TX; UC Davis Center for Neuroscience (C.S.D.), CA; Sunnybrook Research Institute (E.S.), University of Toronto, Ontario, Canada; Harvard Aging Brain Institute (K.A.J.), Harvard Medical School, Boston, MA; Gordon Center for Medical Imaging (G.E.F., E.T.), Radiology Department, Massachusetts General Hospital, Harvard Medical School, Boston; Turner Institute for Brain and Mental Health (S.R.Y., M.G.C., M.P.P.), Monash University, Clayton, Australia; and Harvard T.H. Chan School of Public Health (M.P.P.), Harvard University, Boston, MA
| | - Daniel J Kojis
- From the Douglas Mental Health University Institute (A.-A.B.), McGill University, Montreal, Quebec, Canada; The Framingham Heart Study (A.-A.B., D.J.K., J.J.H., D.H., M.P.P., A.S.B., S.S.); Boston University School of Public Health (D.J.K., J.J.H.), MA; Boston University School of Medicine (J.J.H., S.S.), MA; Glenn Biggs Institute for Alzheimer's & Neurodegenerative Diseases (J.J.H., S.S.), UT Health San Antonio, TX; UC Davis Center for Neuroscience (C.S.D.), CA; Sunnybrook Research Institute (E.S.), University of Toronto, Ontario, Canada; Harvard Aging Brain Institute (K.A.J.), Harvard Medical School, Boston, MA; Gordon Center for Medical Imaging (G.E.F., E.T.), Radiology Department, Massachusetts General Hospital, Harvard Medical School, Boston; Turner Institute for Brain and Mental Health (S.R.Y., M.G.C., M.P.P.), Monash University, Clayton, Australia; and Harvard T.H. Chan School of Public Health (M.P.P.), Harvard University, Boston, MA
| | - Jayandra J Himali
- From the Douglas Mental Health University Institute (A.-A.B.), McGill University, Montreal, Quebec, Canada; The Framingham Heart Study (A.-A.B., D.J.K., J.J.H., D.H., M.P.P., A.S.B., S.S.); Boston University School of Public Health (D.J.K., J.J.H.), MA; Boston University School of Medicine (J.J.H., S.S.), MA; Glenn Biggs Institute for Alzheimer's & Neurodegenerative Diseases (J.J.H., S.S.), UT Health San Antonio, TX; UC Davis Center for Neuroscience (C.S.D.), CA; Sunnybrook Research Institute (E.S.), University of Toronto, Ontario, Canada; Harvard Aging Brain Institute (K.A.J.), Harvard Medical School, Boston, MA; Gordon Center for Medical Imaging (G.E.F., E.T.), Radiology Department, Massachusetts General Hospital, Harvard Medical School, Boston; Turner Institute for Brain and Mental Health (S.R.Y., M.G.C., M.P.P.), Monash University, Clayton, Australia; and Harvard T.H. Chan School of Public Health (M.P.P.), Harvard University, Boston, MA
| | - Charles S Decarli
- From the Douglas Mental Health University Institute (A.-A.B.), McGill University, Montreal, Quebec, Canada; The Framingham Heart Study (A.-A.B., D.J.K., J.J.H., D.H., M.P.P., A.S.B., S.S.); Boston University School of Public Health (D.J.K., J.J.H.), MA; Boston University School of Medicine (J.J.H., S.S.), MA; Glenn Biggs Institute for Alzheimer's & Neurodegenerative Diseases (J.J.H., S.S.), UT Health San Antonio, TX; UC Davis Center for Neuroscience (C.S.D.), CA; Sunnybrook Research Institute (E.S.), University of Toronto, Ontario, Canada; Harvard Aging Brain Institute (K.A.J.), Harvard Medical School, Boston, MA; Gordon Center for Medical Imaging (G.E.F., E.T.), Radiology Department, Massachusetts General Hospital, Harvard Medical School, Boston; Turner Institute for Brain and Mental Health (S.R.Y., M.G.C., M.P.P.), Monash University, Clayton, Australia; and Harvard T.H. Chan School of Public Health (M.P.P.), Harvard University, Boston, MA
| | - Erlan Sanchez
- From the Douglas Mental Health University Institute (A.-A.B.), McGill University, Montreal, Quebec, Canada; The Framingham Heart Study (A.-A.B., D.J.K., J.J.H., D.H., M.P.P., A.S.B., S.S.); Boston University School of Public Health (D.J.K., J.J.H.), MA; Boston University School of Medicine (J.J.H., S.S.), MA; Glenn Biggs Institute for Alzheimer's & Neurodegenerative Diseases (J.J.H., S.S.), UT Health San Antonio, TX; UC Davis Center for Neuroscience (C.S.D.), CA; Sunnybrook Research Institute (E.S.), University of Toronto, Ontario, Canada; Harvard Aging Brain Institute (K.A.J.), Harvard Medical School, Boston, MA; Gordon Center for Medical Imaging (G.E.F., E.T.), Radiology Department, Massachusetts General Hospital, Harvard Medical School, Boston; Turner Institute for Brain and Mental Health (S.R.Y., M.G.C., M.P.P.), Monash University, Clayton, Australia; and Harvard T.H. Chan School of Public Health (M.P.P.), Harvard University, Boston, MA
| | - Keith A Johnson
- From the Douglas Mental Health University Institute (A.-A.B.), McGill University, Montreal, Quebec, Canada; The Framingham Heart Study (A.-A.B., D.J.K., J.J.H., D.H., M.P.P., A.S.B., S.S.); Boston University School of Public Health (D.J.K., J.J.H.), MA; Boston University School of Medicine (J.J.H., S.S.), MA; Glenn Biggs Institute for Alzheimer's & Neurodegenerative Diseases (J.J.H., S.S.), UT Health San Antonio, TX; UC Davis Center for Neuroscience (C.S.D.), CA; Sunnybrook Research Institute (E.S.), University of Toronto, Ontario, Canada; Harvard Aging Brain Institute (K.A.J.), Harvard Medical School, Boston, MA; Gordon Center for Medical Imaging (G.E.F., E.T.), Radiology Department, Massachusetts General Hospital, Harvard Medical School, Boston; Turner Institute for Brain and Mental Health (S.R.Y., M.G.C., M.P.P.), Monash University, Clayton, Australia; and Harvard T.H. Chan School of Public Health (M.P.P.), Harvard University, Boston, MA
| | - Georges El Fakhri
- From the Douglas Mental Health University Institute (A.-A.B.), McGill University, Montreal, Quebec, Canada; The Framingham Heart Study (A.-A.B., D.J.K., J.J.H., D.H., M.P.P., A.S.B., S.S.); Boston University School of Public Health (D.J.K., J.J.H.), MA; Boston University School of Medicine (J.J.H., S.S.), MA; Glenn Biggs Institute for Alzheimer's & Neurodegenerative Diseases (J.J.H., S.S.), UT Health San Antonio, TX; UC Davis Center for Neuroscience (C.S.D.), CA; Sunnybrook Research Institute (E.S.), University of Toronto, Ontario, Canada; Harvard Aging Brain Institute (K.A.J.), Harvard Medical School, Boston, MA; Gordon Center for Medical Imaging (G.E.F., E.T.), Radiology Department, Massachusetts General Hospital, Harvard Medical School, Boston; Turner Institute for Brain and Mental Health (S.R.Y., M.G.C., M.P.P.), Monash University, Clayton, Australia; and Harvard T.H. Chan School of Public Health (M.P.P.), Harvard University, Boston, MA
| | - Emma Thibault
- From the Douglas Mental Health University Institute (A.-A.B.), McGill University, Montreal, Quebec, Canada; The Framingham Heart Study (A.-A.B., D.J.K., J.J.H., D.H., M.P.P., A.S.B., S.S.); Boston University School of Public Health (D.J.K., J.J.H.), MA; Boston University School of Medicine (J.J.H., S.S.), MA; Glenn Biggs Institute for Alzheimer's & Neurodegenerative Diseases (J.J.H., S.S.), UT Health San Antonio, TX; UC Davis Center for Neuroscience (C.S.D.), CA; Sunnybrook Research Institute (E.S.), University of Toronto, Ontario, Canada; Harvard Aging Brain Institute (K.A.J.), Harvard Medical School, Boston, MA; Gordon Center for Medical Imaging (G.E.F., E.T.), Radiology Department, Massachusetts General Hospital, Harvard Medical School, Boston; Turner Institute for Brain and Mental Health (S.R.Y., M.G.C., M.P.P.), Monash University, Clayton, Australia; and Harvard T.H. Chan School of Public Health (M.P.P.), Harvard University, Boston, MA
| | - Stephanie R Yiallourou
- From the Douglas Mental Health University Institute (A.-A.B.), McGill University, Montreal, Quebec, Canada; The Framingham Heart Study (A.-A.B., D.J.K., J.J.H., D.H., M.P.P., A.S.B., S.S.); Boston University School of Public Health (D.J.K., J.J.H.), MA; Boston University School of Medicine (J.J.H., S.S.), MA; Glenn Biggs Institute for Alzheimer's & Neurodegenerative Diseases (J.J.H., S.S.), UT Health San Antonio, TX; UC Davis Center for Neuroscience (C.S.D.), CA; Sunnybrook Research Institute (E.S.), University of Toronto, Ontario, Canada; Harvard Aging Brain Institute (K.A.J.), Harvard Medical School, Boston, MA; Gordon Center for Medical Imaging (G.E.F., E.T.), Radiology Department, Massachusetts General Hospital, Harvard Medical School, Boston; Turner Institute for Brain and Mental Health (S.R.Y., M.G.C., M.P.P.), Monash University, Clayton, Australia; and Harvard T.H. Chan School of Public Health (M.P.P.), Harvard University, Boston, MA
| | - Dibya Himali
- From the Douglas Mental Health University Institute (A.-A.B.), McGill University, Montreal, Quebec, Canada; The Framingham Heart Study (A.-A.B., D.J.K., J.J.H., D.H., M.P.P., A.S.B., S.S.); Boston University School of Public Health (D.J.K., J.J.H.), MA; Boston University School of Medicine (J.J.H., S.S.), MA; Glenn Biggs Institute for Alzheimer's & Neurodegenerative Diseases (J.J.H., S.S.), UT Health San Antonio, TX; UC Davis Center for Neuroscience (C.S.D.), CA; Sunnybrook Research Institute (E.S.), University of Toronto, Ontario, Canada; Harvard Aging Brain Institute (K.A.J.), Harvard Medical School, Boston, MA; Gordon Center for Medical Imaging (G.E.F., E.T.), Radiology Department, Massachusetts General Hospital, Harvard Medical School, Boston; Turner Institute for Brain and Mental Health (S.R.Y., M.G.C., M.P.P.), Monash University, Clayton, Australia; and Harvard T.H. Chan School of Public Health (M.P.P.), Harvard University, Boston, MA
| | - Marina G Cavuoto
- From the Douglas Mental Health University Institute (A.-A.B.), McGill University, Montreal, Quebec, Canada; The Framingham Heart Study (A.-A.B., D.J.K., J.J.H., D.H., M.P.P., A.S.B., S.S.); Boston University School of Public Health (D.J.K., J.J.H.), MA; Boston University School of Medicine (J.J.H., S.S.), MA; Glenn Biggs Institute for Alzheimer's & Neurodegenerative Diseases (J.J.H., S.S.), UT Health San Antonio, TX; UC Davis Center for Neuroscience (C.S.D.), CA; Sunnybrook Research Institute (E.S.), University of Toronto, Ontario, Canada; Harvard Aging Brain Institute (K.A.J.), Harvard Medical School, Boston, MA; Gordon Center for Medical Imaging (G.E.F., E.T.), Radiology Department, Massachusetts General Hospital, Harvard Medical School, Boston; Turner Institute for Brain and Mental Health (S.R.Y., M.G.C., M.P.P.), Monash University, Clayton, Australia; and Harvard T.H. Chan School of Public Health (M.P.P.), Harvard University, Boston, MA
| | - Matthew P Pase
- From the Douglas Mental Health University Institute (A.-A.B.), McGill University, Montreal, Quebec, Canada; The Framingham Heart Study (A.-A.B., D.J.K., J.J.H., D.H., M.P.P., A.S.B., S.S.); Boston University School of Public Health (D.J.K., J.J.H.), MA; Boston University School of Medicine (J.J.H., S.S.), MA; Glenn Biggs Institute for Alzheimer's & Neurodegenerative Diseases (J.J.H., S.S.), UT Health San Antonio, TX; UC Davis Center for Neuroscience (C.S.D.), CA; Sunnybrook Research Institute (E.S.), University of Toronto, Ontario, Canada; Harvard Aging Brain Institute (K.A.J.), Harvard Medical School, Boston, MA; Gordon Center for Medical Imaging (G.E.F., E.T.), Radiology Department, Massachusetts General Hospital, Harvard Medical School, Boston; Turner Institute for Brain and Mental Health (S.R.Y., M.G.C., M.P.P.), Monash University, Clayton, Australia; and Harvard T.H. Chan School of Public Health (M.P.P.), Harvard University, Boston, MA
| | - Alexa S Beiser
- From the Douglas Mental Health University Institute (A.-A.B.), McGill University, Montreal, Quebec, Canada; The Framingham Heart Study (A.-A.B., D.J.K., J.J.H., D.H., M.P.P., A.S.B., S.S.); Boston University School of Public Health (D.J.K., J.J.H.), MA; Boston University School of Medicine (J.J.H., S.S.), MA; Glenn Biggs Institute for Alzheimer's & Neurodegenerative Diseases (J.J.H., S.S.), UT Health San Antonio, TX; UC Davis Center for Neuroscience (C.S.D.), CA; Sunnybrook Research Institute (E.S.), University of Toronto, Ontario, Canada; Harvard Aging Brain Institute (K.A.J.), Harvard Medical School, Boston, MA; Gordon Center for Medical Imaging (G.E.F., E.T.), Radiology Department, Massachusetts General Hospital, Harvard Medical School, Boston; Turner Institute for Brain and Mental Health (S.R.Y., M.G.C., M.P.P.), Monash University, Clayton, Australia; and Harvard T.H. Chan School of Public Health (M.P.P.), Harvard University, Boston, MA
| | - Sudha Seshadri
- From the Douglas Mental Health University Institute (A.-A.B.), McGill University, Montreal, Quebec, Canada; The Framingham Heart Study (A.-A.B., D.J.K., J.J.H., D.H., M.P.P., A.S.B., S.S.); Boston University School of Public Health (D.J.K., J.J.H.), MA; Boston University School of Medicine (J.J.H., S.S.), MA; Glenn Biggs Institute for Alzheimer's & Neurodegenerative Diseases (J.J.H., S.S.), UT Health San Antonio, TX; UC Davis Center for Neuroscience (C.S.D.), CA; Sunnybrook Research Institute (E.S.), University of Toronto, Ontario, Canada; Harvard Aging Brain Institute (K.A.J.), Harvard Medical School, Boston, MA; Gordon Center for Medical Imaging (G.E.F., E.T.), Radiology Department, Massachusetts General Hospital, Harvard Medical School, Boston; Turner Institute for Brain and Mental Health (S.R.Y., M.G.C., M.P.P.), Monash University, Clayton, Australia; and Harvard T.H. Chan School of Public Health (M.P.P.), Harvard University, Boston, MA
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10
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Ramos-Cejudo J, Scott MR, Tanner JA, Pase MP, McGrath ER, Ghosh S, Osorio RS, Thibault E, El Fakhri G, Johnson KA, Beiser A, Seshadri S. Associations of Plasma Tau with Amyloid and Tau PET: Results from the Community-Based Framingham Heart Study. J Alzheimers Dis 2024; 100:487-494. [PMID: 38875034 DOI: 10.3233/jad-231320] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/16/2024]
Abstract
Background Associations of plasma total tau levels with future risk of AD have been described. Objective To examine the extent to which plasma tau reflects underlying AD brain pathology in cognitively healthy individuals. Methods We examined cross-sectional associations of plasma total tau with 11C-Pittsburgh Compound-B (PiB)-PET and 18F-Flortaucipir (FTP)-PET in middle-aged participants at the community-based Framingham Heart Study. Results Our final sample included 425 participants (mean age 57.6± 9.9, 50% F). Plasma total tau levels were positively associated with amyloid-β deposition in the precuneus region (β±SE, 0.11±0.05; p = 0.025). A positive association between plasma total tau and tau PET in the rhinal cortex was suggested in participants with higher amyloid-PET burden and in APOEɛ4 carriers. Conclusions Our study highlights that plasma total tau is a marker of amyloid deposition as early as in middle-age.
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Affiliation(s)
- Jaime Ramos-Cejudo
- Department of Psychiatry, New York University (NYU) Grossman School of Medicine, New York, NY, USA
| | - Matthew R Scott
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Jeremy A Tanner
- Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, University of Texas Health Sciences Center, San Antonio, TX, USA
| | - Matthew P Pase
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Clayton, VIC, Australia
- Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Emer R McGrath
- HRB Clinical Research Facility, University of Galway, Galway, Ireland
- The Framingham Study, Boston, MA, USA
- School of Medicine, University of Galway, Galway, Ireland
| | | | - Ricardo S Osorio
- Department of Psychiatry, New York University (NYU) Grossman School of Medicine, New York, NY, USA
| | - Emma Thibault
- Department of Radiology, Harvard Medical School, Boston, MA, USA
| | | | - Keith A Johnson
- Department of Radiology, Harvard Medical School, Boston, MA, USA
- Department of Neurology, Harvard Medical School, Boston, MA, USA
| | - Alexa Beiser
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
- The Framingham Study, Boston, MA, USA
- Department of Neurology, Boston University Chobanian and Avedisian School of Medicine, Boston, MA, USA
| | - Sudha Seshadri
- Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, University of Texas Health Sciences Center, San Antonio, TX, USA
- The Framingham Study, Boston, MA, USA
- Department of Neurology, Boston University Chobanian and Avedisian School of Medicine, Boston, MA, USA
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11
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Mattsson P, Cselényi Z, Forsberg Morén A, Freund-Levi Y, Wahlund LO, Halldin C, Farde L. High Contrast PET Imaging of Subcortical and Allocortical Amyloid-β in Early Alzheimer's Disease Using [11C]AZD2184. J Alzheimers Dis 2024; 98:1391-1401. [PMID: 38552111 DOI: 10.3233/jad-231013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/20/2024]
Abstract
Background Deposits of amyloid-β (Aβ) appear early in Alzheimer's disease (AD). Objective The aim of the present study was to compare the presence of cortical and subcortical Aβ in early AD using positron emission tomography (PET). Methods Eight cognitively unimpaired (CU) subjects, 8 with mild cognitive impairment (MCI) and 8 with mild AD were examined with PET and [11C]AZD2184. A data driven cut-point for Aβ positivity was defined by Gaussian mixture model of isocortex binding potential (BPND) values. Results Sixteen subjects (3 CU, 5 MCI and 8 AD) were Aβ-positive. BPND was lower in subcortical and allocortical regions compared to isocortex. Fifteen of the 16 Aβ-positive subjects displayed Aβ binding in striatum, 14 in thalamus and 10 in allocortical regions. Conclusions Aβ deposits appear to be widespread in early AD. It cannot be excluded that deposits appear simultaneously throughout the whole brain which has implications for improved diagnostics and disease monitoring.
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Affiliation(s)
- Patrik Mattsson
- Department of Clinical Neuroscience, Centre for Psychiatry Research, Karolinska Institutet and Stockholm Health Care Services, Stockholm, Sweden
| | - Zsolt Cselényi
- Department of Clinical Neuroscience, Centre for Psychiatry Research, Karolinska Institutet and Stockholm Health Care Services, Stockholm, Sweden
- PET Science Centre, Personalized Medicine and Biosamples, R&D, AstraZeneca, Stockholm, Sweden
| | - Anton Forsberg Morén
- Department of Clinical Neuroscience, Centre for Psychiatry Research, Karolinska Institutet and Stockholm Health Care Services, Stockholm, Sweden
| | - Yvonne Freund-Levi
- Department of Clinical Science and Education, Södersjukhuset, Karolinska Institutet, Stockholm, Sweden
- School of Medicine, Örebro University, Örebro, Sweden
- Department of Geriatrics, Örebro University Hospital, Örebro and Södertälje Hospital, Södertälje, Sweden
| | - Lars-Olof Wahlund
- Department of Neurobiology, Care Sciences and Society, Division of Clinical Geriatrics, Center for Alzheimer Research, Karolinska Institutet, Stockholm, Sweden
| | - Christer Halldin
- Department of Clinical Neuroscience, Centre for Psychiatry Research, Karolinska Institutet and Stockholm Health Care Services, Stockholm, Sweden
| | - Lars Farde
- Department of Clinical Neuroscience, Centre for Psychiatry Research, Karolinska Institutet and Stockholm Health Care Services, Stockholm, Sweden
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12
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Kim SJ, Jang H, Yoo H, Na DL, Ham H, Kim HJ, Kim JP, Farrar G, Moon SH, Seo SW. Clinical and Pathological Validation of CT-Based Regional Harmonization Methods of Amyloid PET. Clin Nucl Med 2024; 49:1-8. [PMID: 38048354 DOI: 10.1097/rlu.0000000000004937] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/06/2023]
Abstract
PURPOSE The CT-based regional direct comparison Centiloid (dcCL) method was developed to harmonize and quantify regional β-amyloid (Aβ) burden. In the present study, we aimed to investigate correlations between the CT-based regional dcCL scales and Aβ pathological burdens and to validate the clinical utility using thresholds derived from pathological assessment. PATIENTS AND METHODS We included a pathological cohort of 63 cases and a clinical cohort of 4062 participants, and obtained modified Consortium to Establish a Registry for Alzheimer's Disease criteria (mCERAD) scores by assessment of neuritic plaque burdens in multiple areas of each cortical region. PET and CT images were processed using the CT-based regional dcCL method to calculate scales in 6 distinct regions. RESULTS The CT-based regional dcCL scales were correlated with neuritic plaque burdens represented by mCERAD scores, globally and regionally ( r = 0.56~0.76). In addition, striatum dcCL scales reflected Aβ involvement in the striatum ( P < 0.001). The regional dcCL scales could predict significant Aβ deposition in specific brain regions with high accuracy: area under the receiver operating characteristic curve of 0.81-0.97 with an mCERAD cutoff of 1.5 and area under the receiver operating characteristic curve of 0.88-0.93 with an mCERAD cutoff of 0.5. When applying the dcCL thresholds of 1.5 mCERAD scores, the G(-)R(+) group showed lower performances in memory and global cognitive functions and had less hippocampal volume compared with the G(-)R(-) group ( P < 0.001). However, when applying the dcCL thresholds of 0.5 mCERAD scores, there were no differences in the global cognitive functions between the 2 groups. CONCLUSIONS The thresholds of regional dcCL scales derived from pathological assessments might provide clinicians with a better understanding of biomarker-guided diagnosis and distinguishable clinical phenotypes, which are particularly useful when harmonizing different PET ligands with only PET/CT.
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Affiliation(s)
| | | | - Heejin Yoo
- Alzheimer's Disease Convergence Research Center, Samsung Medical Center
| | | | | | | | | | - Gill Farrar
- Pharmaceutical Diagnostics, GE Healthcare, Chalfont St Giles, United Kingdom
| | - Seung Hwan Moon
- Department of Nuclear Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
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13
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Bollack A, Pemberton HG, Collij LE, Markiewicz P, Cash DM, Farrar G, Barkhof F. Longitudinal amyloid and tau PET imaging in Alzheimer's disease: A systematic review of methodologies and factors affecting quantification. Alzheimers Dement 2023; 19:5232-5252. [PMID: 37303269 DOI: 10.1002/alz.13158] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Revised: 04/21/2023] [Accepted: 04/25/2023] [Indexed: 06/13/2023]
Abstract
Deposition of amyloid and tau pathology can be quantified in vivo using positron emission tomography (PET). Accurate longitudinal measurements of accumulation from these images are critical for characterizing the start and spread of the disease. However, these measurements are challenging; precision and accuracy can be affected substantially by various sources of errors and variability. This review, supported by a systematic search of the literature, summarizes the current design and methodologies of longitudinal PET studies. Intrinsic, biological causes of variability of the Alzheimer's disease (AD) protein load over time are then detailed. Technical factors contributing to longitudinal PET measurement uncertainty are highlighted, followed by suggestions for mitigating these factors, including possible techniques that leverage shared information between serial scans. Controlling for intrinsic variability and reducing measurement uncertainty in longitudinal PET pipelines will provide more accurate and precise markers of disease evolution, improve clinical trial design, and aid therapy response monitoring.
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Affiliation(s)
- Ariane Bollack
- Department of Medical Physics and Biomedical Engineering, Centre for Medical Image Computing (CMIC), University College London, London, UK
| | - Hugh G Pemberton
- Department of Medical Physics and Biomedical Engineering, Centre for Medical Image Computing (CMIC), University College London, London, UK
- GE Healthcare, Amersham, UK
- UCL Queen Square Institute of Neurology, London, UK
| | - Lyduine E Collij
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, location VUmc, Amsterdam, The Netherlands
- Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Malmö, Sweden
| | - Pawel Markiewicz
- Department of Medical Physics and Biomedical Engineering, Centre for Medical Image Computing (CMIC), University College London, London, UK
| | - David M Cash
- UCL Queen Square Institute of Neurology, London, UK
- UK Dementia Research Institute at University College London, London, UK
| | | | - Frederik Barkhof
- Department of Medical Physics and Biomedical Engineering, Centre for Medical Image Computing (CMIC), University College London, London, UK
- UCL Queen Square Institute of Neurology, London, UK
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, location VUmc, Amsterdam, The Netherlands
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14
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Llibre-Guerra JJ, Iaccarino L, Coble D, Edwards L, Li Y, McDade E, Strom A, Gordon B, Mundada N, Schindler SE, Tsoy E, Ma Y, Lu R, Fagan AM, Benzinger TLS, Soleimani-Meigooni D, Aschenbrenner AJ, Miller Z, Wang G, Kramer JH, Hassenstab J, Rosen HJ, Morris JC, Miller BL, Xiong C, Perrin RJ, Allegri R, Chrem P, Surace E, Berman SB, Chhatwal J, Masters CL, Farlow MR, Jucker M, Levin J, Fox NC, Day G, Gorno-Tempini ML, Boxer AL, La Joie R, Rabinovici GD, Bateman R. Longitudinal clinical, cognitive and biomarker profiles in dominantly inherited versus sporadic early-onset Alzheimer's disease. Brain Commun 2023; 5:fcad280. [PMID: 37942088 PMCID: PMC10629466 DOI: 10.1093/braincomms/fcad280] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Revised: 10/02/2023] [Accepted: 10/17/2023] [Indexed: 11/10/2023] Open
Abstract
Approximately 5% of Alzheimer's disease cases have an early age at onset (<65 years), with 5-10% of these cases attributed to dominantly inherited mutations and the remainder considered as sporadic. The extent to which dominantly inherited and sporadic early-onset Alzheimer's disease overlap is unknown. In this study, we explored the clinical, cognitive and biomarker profiles of early-onset Alzheimer's disease, focusing on commonalities and distinctions between dominantly inherited and sporadic cases. Our analysis included 117 participants with dominantly inherited Alzheimer's disease enrolled in the Dominantly Inherited Alzheimer Network and 118 individuals with sporadic early-onset Alzheimer's disease enrolled at the University of California San Francisco Alzheimer's Disease Research Center. Baseline differences in clinical and biomarker profiles between both groups were compared using t-tests. Differences in the rates of decline were compared using linear mixed-effects models. Individuals with dominantly inherited Alzheimer's disease exhibited an earlier age-at-symptom onset compared with the sporadic group [43.4 (SD ± 8.5) years versus 54.8 (SD ± 5.0) years, respectively, P < 0.001]. Sporadic cases showed a higher frequency of atypical clinical presentations relative to dominantly inherited (56.8% versus 8.5%, respectively) and a higher frequency of APOE-ε4 (50.0% versus 28.2%, P = 0.001). Compared with sporadic early onset, motor manifestations were higher in the dominantly inherited cohort [32.5% versus 16.9% at baseline (P = 0.006) and 46.1% versus 25.4% at last visit (P = 0.001)]. At baseline, the sporadic early-onset group performed worse on category fluency (P < 0.001), Trail Making Test Part B (P < 0.001) and digit span (P < 0.001). Longitudinally, both groups demonstrated similar rates of cognitive and functional decline in the early stages. After 10 years from symptom onset, dominantly inherited participants experienced a greater decline as measured by Clinical Dementia Rating Sum of Boxes [3.63 versus 1.82 points (P = 0.035)]. CSF amyloid beta-42 levels were comparable [244 (SD ± 39.3) pg/ml dominantly inherited versus 296 (SD ± 24.8) pg/ml sporadic early onset, P = 0.06]. CSF phosphorylated tau at threonine 181 levels were higher in the dominantly inherited Alzheimer's disease cohort (87.3 versus 59.7 pg/ml, P = 0.005), but no significant differences were found for t-tau levels (P = 0.35). In summary, sporadic and inherited Alzheimer's disease differed in baseline profiles; sporadic early onset is best distinguished from dominantly inherited by later age at onset, high frequency of atypical clinical presentations and worse executive performance at baseline. Despite these differences, shared pathways in longitudinal clinical decline and CSF biomarkers suggest potential common therapeutic targets for both populations, offering valuable insights for future research and clinical trial design.
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Affiliation(s)
| | - Leonardo Iaccarino
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Dean Coble
- Division of Biostatistics, Washington University in St Louis, St Louis, MO 63108, USA
| | - Lauren Edwards
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Yan Li
- Division of Biostatistics, Washington University in St Louis, St Louis, MO 63108, USA
| | - Eric McDade
- Department of Neurology, Washington University in St Louis, St Louis, MO 63108, USA
| | - Amelia Strom
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Brian Gordon
- Malinckrodt Institute of Radiology, Washington University in St Louis, St Louis, MO 63108, USA
| | - Nidhi Mundada
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Suzanne E Schindler
- Department of Neurology, Washington University in St Louis, St Louis, MO 63108, USA
| | - Elena Tsoy
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Yinjiao Ma
- Division of Biostatistics, Washington University in St Louis, St Louis, MO 63108, USA
| | - Ruijin Lu
- Division of Biostatistics, Washington University in St Louis, St Louis, MO 63108, USA
| | - Anne M Fagan
- Department of Neurology, Washington University in St Louis, St Louis, MO 63108, USA
| | - Tammie L S Benzinger
- Malinckrodt Institute of Radiology, Washington University in St Louis, St Louis, MO 63108, USA
| | - David Soleimani-Meigooni
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94158, USA
| | | | - Zachary Miller
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Guoqiao Wang
- Division of Biostatistics, Washington University in St Louis, St Louis, MO 63108, USA
| | - Joel H Kramer
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Jason Hassenstab
- Department of Neurology, Washington University in St Louis, St Louis, MO 63108, USA
| | - Howard J Rosen
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94158, USA
| | - John C Morris
- Department of Neurology, Washington University in St Louis, St Louis, MO 63108, USA
| | - Bruce L Miller
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Chengjie Xiong
- Division of Biostatistics, Washington University in St Louis, St Louis, MO 63108, USA
| | - Richard J Perrin
- Department of Neurology, Washington University in St Louis, St Louis, MO 63108, USA
- Department of Pathology and Immunology, Washington University in St Louis, St. Louis, MO 63108, USA
| | - Ricardo Allegri
- Department of Cognitive Neurology, Institute for Neurological Research Fleni, Buenos Aires, Argentina
| | - Patricio Chrem
- Department of Cognitive Neurology, Institute for Neurological Research Fleni, Buenos Aires, Argentina
| | - Ezequiel Surace
- Department of Cognitive Neurology, Institute for Neurological Research Fleni, Buenos Aires, Argentina
| | - Sarah B Berman
- Department of Neurology, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - Jasmeer Chhatwal
- Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
| | - Colin L Masters
- Florey Institute, The University of Melbourne, Melbourne 3052, Australia
| | - Martin R Farlow
- Neuroscience Center, Indiana University School of Medicine at Indianapolis, IN 46202, USA
| | - Mathias Jucker
- DZNE-German Center for Neurodegenerative Diseases, Tübingen 72076, Germany
- Hertie-Institute for Clinical Brain Research, University of Tübingen, Tübingen 72076, Germany
| | - Johannes Levin
- Department of Neurology, Ludwig-Maximilians-University, Munich 80539, Germany
- German Center for Neurodegenerative Diseases, Munich 81377, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich 81377, Germany
| | - Nick C Fox
- Dementia Research Centre, Department of Neurodegenerative Disease, University College London Institute of Neurology, London WC1N 3BG, UK
| | - Gregory Day
- Department of Neurology, Mayo Clinic Florida, Jacksonville, FL 33224, USA
| | - Maria Luisa Gorno-Tempini
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Adam L Boxer
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Renaud La Joie
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Gil D Rabinovici
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94158, USA
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Randall Bateman
- Department of Neurology, Washington University in St Louis, St Louis, MO 63108, USA
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15
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Tian M, Zuo C, Civelek AC, Carrio I, Watanabe Y, Kang KW, Murakami K, Garibotto V, Prior JO, Barthel H, Guan Y, Lu J, Zhou R, Jin C, Wu S, Zhang X, Zhong Y, Zhang H. International Nuclear Medicine Consensus on the Clinical Use of Amyloid Positron Emission Tomography in Alzheimer's Disease. PHENOMICS (CHAM, SWITZERLAND) 2023; 3:375-389. [PMID: 37589025 PMCID: PMC10425321 DOI: 10.1007/s43657-022-00068-9] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/21/2022] [Revised: 07/19/2022] [Accepted: 07/22/2022] [Indexed: 08/18/2023]
Abstract
Alzheimer's disease (AD) is the main cause of dementia, with its diagnosis and management remaining challenging. Amyloid positron emission tomography (PET) has become increasingly important in medical practice for patients with AD. To integrate and update previous guidelines in the field, a task group of experts of several disciplines from multiple countries was assembled, and they revised and approved the content related to the application of amyloid PET in the medical settings of cognitively impaired individuals, focusing on clinical scenarios, patient preparation, administered activities, as well as image acquisition, processing, interpretation and reporting. In addition, expert opinions, practices, and protocols of prominent research institutions performing research on amyloid PET of dementia are integrated. With the increasing availability of amyloid PET imaging, a complete and standard pipeline for the entire examination process is essential for clinical practice. This international consensus and practice guideline will help to promote proper clinical use of amyloid PET imaging in patients with AD.
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Affiliation(s)
- Mei Tian
- PET Center, Huashan Hospital, Fudan University, Shanghai, 200235 China
- Human Phenome Institute, Fudan University, Shanghai, 201203 China
- Department of Nuclear Medicine and PET Center, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, 310009 China
| | - Chuantao Zuo
- PET Center, Huashan Hospital, Fudan University, Shanghai, 200235 China
- National Center for Neurological Disorders and National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, 200040 China
| | - Ali Cahid Civelek
- Department of Radiology and Radiological Science, Division of Nuclear Medicine and Molecular Imaging, Johns Hopkins Medicine, Baltimore, 21287 USA
| | - Ignasi Carrio
- Department of Nuclear Medicine, Hospital Sant Pau, Autonomous University of Barcelona, Barcelona, 08025 Spain
| | - Yasuyoshi Watanabe
- Laboratory for Pathophysiological and Health Science, RIKEN Center for Biosystems Dynamics Research, Kobe, Hyogo 650-0047 Japan
| | - Keon Wook Kang
- Department of Nuclear Medicine, Seoul National University College of Medicine, Seoul, 03080 Korea
| | - Koji Murakami
- Department of Radiology, Juntendo University Hospital, Tokyo, 113-8431 Japan
| | - Valentina Garibotto
- Diagnostic Department, University Hospitals of Geneva and NIMTlab, University of Geneva, Geneva, 1205 Switzerland
| | - John O. Prior
- Department of Nuclear Medicine and Molecular Imaging, Lausanne University Hospital, Lausanne, 1011 Switzerland
| | - Henryk Barthel
- Department of Nuclear Medicine, Leipzig University Medical Center, Leipzig, 04103 Germany
| | - Yihui Guan
- PET Center, Huashan Hospital, Fudan University, Shanghai, 200235 China
| | - Jiaying Lu
- PET Center, Huashan Hospital, Fudan University, Shanghai, 200235 China
| | - Rui Zhou
- Department of Nuclear Medicine and PET Center, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, 310009 China
| | - Chentao Jin
- Department of Nuclear Medicine and PET Center, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, 310009 China
| | - Shuang Wu
- Department of Nuclear Medicine and PET Center, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, 310009 China
| | - Xiaohui Zhang
- Department of Nuclear Medicine and PET Center, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, 310009 China
| | - Yan Zhong
- Department of Nuclear Medicine and PET Center, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, 310009 China
| | - Hong Zhang
- Department of Nuclear Medicine and PET Center, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, 310009 China
- Key Laboratory of Medical Molecular Imaging of Zhejiang Province, Hangzhou, 310009 China
- The College of Biomedical Engineering and Instrument Science of Zhejiang University, Hangzhou, 310007 China
- Key Laboratory for Biomedical Engineering of Ministry of Education, Zhejiang University, Hangzhou, 310007 China
| | - Molecular Imaging-Based Precision Medicine Task Group of A3 (China-Japan-Korea) Foresight Program
- PET Center, Huashan Hospital, Fudan University, Shanghai, 200235 China
- Human Phenome Institute, Fudan University, Shanghai, 201203 China
- Department of Nuclear Medicine and PET Center, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, 310009 China
- National Center for Neurological Disorders and National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, 200040 China
- Department of Radiology and Radiological Science, Division of Nuclear Medicine and Molecular Imaging, Johns Hopkins Medicine, Baltimore, 21287 USA
- Department of Nuclear Medicine, Hospital Sant Pau, Autonomous University of Barcelona, Barcelona, 08025 Spain
- Laboratory for Pathophysiological and Health Science, RIKEN Center for Biosystems Dynamics Research, Kobe, Hyogo 650-0047 Japan
- Department of Nuclear Medicine, Seoul National University College of Medicine, Seoul, 03080 Korea
- Department of Radiology, Juntendo University Hospital, Tokyo, 113-8431 Japan
- Diagnostic Department, University Hospitals of Geneva and NIMTlab, University of Geneva, Geneva, 1205 Switzerland
- Department of Nuclear Medicine and Molecular Imaging, Lausanne University Hospital, Lausanne, 1011 Switzerland
- Department of Nuclear Medicine, Leipzig University Medical Center, Leipzig, 04103 Germany
- Key Laboratory of Medical Molecular Imaging of Zhejiang Province, Hangzhou, 310009 China
- The College of Biomedical Engineering and Instrument Science of Zhejiang University, Hangzhou, 310007 China
- Key Laboratory for Biomedical Engineering of Ministry of Education, Zhejiang University, Hangzhou, 310007 China
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16
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Jack CR, Wiste HJ, Algeciras-Schimnich A, Figdore DJ, Schwarz CG, Lowe VJ, Ramanan VK, Vemuri P, Mielke MM, Knopman DS, Graff-Radford J, Boeve BF, Kantarci K, Cogswell PM, Senjem ML, Gunter JL, Therneau TM, Petersen RC. Predicting amyloid PET and tau PET stages with plasma biomarkers. Brain 2023; 146:2029-2044. [PMID: 36789483 PMCID: PMC10151195 DOI: 10.1093/brain/awad042] [Citation(s) in RCA: 32] [Impact Index Per Article: 32.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Revised: 12/20/2022] [Accepted: 01/21/2023] [Indexed: 02/16/2023] Open
Abstract
Staging the severity of Alzheimer's disease pathology using biomarkers is useful for therapeutic trials and clinical prognosis. Disease staging with amyloid and tau PET has face validity; however, this would be more practical with plasma biomarkers. Our objectives were, first, to examine approaches for staging amyloid and tau PET and, second, to examine prediction of amyloid and tau PET stages using plasma biomarkers. Participants (n = 1136) were enrolled in either the Mayo Clinic Study of Aging or the Alzheimer's Disease Research Center; had a concurrent amyloid PET, tau PET and blood draw; and met clinical criteria for cognitively unimpaired (n = 864), mild cognitive impairment (n = 148) or Alzheimer's clinical syndrome with dementia (n = 124). The latter two groups were combined into a cognitively impaired group (n = 272). We used multinomial regression models to estimate discrimination [concordance (C) statistics] among three amyloid PET stages (low, intermediate, high), four tau PET stages (Braak 0, 1-2, 3-4, 5-6) and a combined amyloid and tau PET stage (none/low versus intermediate/high severity) using plasma biomarkers as predictors separately within unimpaired and impaired individuals. Plasma analytes, p-tau181, Aβ1-42 and Aβ1-40 (analysed as the Aβ42/Aβ40 ratio), glial fibrillary acidic protein and neurofilament light chain were measured on the HD-X Simoa Quanterix platform. Plasma p-tau217 was also measured in a subset (n = 355) of cognitively unimpaired participants using the Lilly Meso Scale Discovery assay. Models with all Quanterix plasma analytes along with risk factors (age, sex and APOE) most often provided the best discrimination among amyloid PET stages (C = 0.78-0.82). Models with p-tau181 provided similar discrimination of tau PET stages to models with all four plasma analytes (C = 0.72-0.85 versus C = 0.73-0.86). Discriminating a PET proxy of intermediate/high from none/low Alzheimer's disease neuropathological change with all four Quanterix plasma analytes was excellent but not better than p-tau181 only (C = 0.88 versus 0.87 for unimpaired and C = 0.91 versus 0.90 for impaired). Lilly p-tau217 outperformed the Quanterix p-tau181 assay for discriminating high versus intermediate amyloid (C = 0.85 versus 0.74) but did not improve over a model with all Quanterix plasma analytes and risk factors (C = 0.85 versus 0.83). Plasma analytes along with risk factors can discriminate between amyloid and tau PET stages and between a PET surrogate for intermediate/high versus none/low neuropathological change with accuracy in the acceptable to excellent range. Combinations of plasma analytes are better than single analytes for many staging predictions with the exception that Quanterix p-tau181 alone usually performed equivalently to combinations of Quanterix analytes for tau PET discrimination.
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Affiliation(s)
- Clifford R Jack
- Department of Radiology, Mayo Clinic, Rochester, MN 55905, USA
| | - Heather J Wiste
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN 55905, USA
| | | | - Dan J Figdore
- Department of Laboratory Medicine, Mayo Clinic, Rochester, MN 55905, USA
| | | | - Val J Lowe
- Department of Nuclear Medicine, Mayo Clinic, Rochester, MN 55905, USA
| | - Vijay K Ramanan
- Department of Neurology, Mayo Clinic, Rochester, MN 55905, USA
| | | | - Michelle M Mielke
- Department of Epidemiology and Prevention, Wake Forest University School of Medicine, Winston-Salem, NC 27101, USA
| | - David S Knopman
- Department of Neurology, Mayo Clinic, Rochester, MN 55905, USA
| | | | - Bradley F Boeve
- Department of Neurology, Mayo Clinic, Rochester, MN 55905, USA
| | - Kejal Kantarci
- Department of Radiology, Mayo Clinic, Rochester, MN 55905, USA
| | | | | | | | - Terry M Therneau
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN 55905, USA
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17
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Jin P, Xu J, Liao Z, Zhang Y, Wang Y, Sun W, Yu E. A review of current evidence for mild behavioral impairment as an early potential novel marker of Alzheimer's disease. Front Psychiatry 2023; 14:1099333. [PMID: 37293396 PMCID: PMC10246741 DOI: 10.3389/fpsyt.2023.1099333] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Accepted: 03/31/2023] [Indexed: 06/10/2023] Open
Abstract
Mild behavioral impairment (MBI) is a neurobehavioral syndrome that occurs in the absence of cognitive impairment later in life (≥50 years of age). MBI is widespread in the pre-dementia stage and is closely associated with the progression of cognitive impairment, reflecting the neurobehavioral axis of pre-dementia risk states and complementing the traditional neurocognitive axis. Despite being the most common type of dementia, Alzheimer's disease (AD) does not yet have an effective treatment; therefore, early recognition and intervention are crucial. The Mild Behavioral Impairment Checklist is an effective tool for identifying MBI cases and helps identify people at risk of developing dementia. However, because the concept of MBI is still quite new, the overall understanding of it is relatively insufficient, especially in AD. Therefore, this review examines the current evidence from cognitive function, neuroimaging, and neuropathology that suggests the potential use of MBI as a risk indicator in preclinical AD.
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Affiliation(s)
- Piaopiao Jin
- The Second School of Clinical Medicine, Zhejiang Chinese Medical University, Hangzhou, China
| | - Jiaxi Xu
- The Second School of Clinical Medicine, Zhejiang Chinese Medical University, Hangzhou, China
| | - Zhengluan Liao
- Department of Geriatric VIP No. 3 (Department of Clinical Psychology), Rehabilitation Medicine Center, Zhejiang Provincial People’s Hospital, Affiliated People’s Hospital, Hangzhou Medical College, Hangzhou, China
| | - Yuhan Zhang
- The Second School of Clinical Medicine, Zhejiang Chinese Medical University, Hangzhou, China
| | - Ye Wang
- The Second School of Clinical Medicine, Zhejiang Chinese Medical University, Hangzhou, China
| | - Wangdi Sun
- Department of Psychiatry, Zhejiang Hospital, Hangzhou, China
| | - Enyan Yu
- Department of Psychiatry, The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, China
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Basal ganglia calcifications: No association with cognitive function. J Neuroradiol 2023; 50:266-270. [PMID: 35134441 DOI: 10.1016/j.neurad.2022.02.001] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Revised: 01/31/2022] [Accepted: 02/01/2022] [Indexed: 11/21/2022]
Abstract
BACKGROUND AND PURPOSE Basal ganglia calcifications (BGC), a form of vascular calcification, are a common brain computed tomography (CT) finding. We investigated whether BGC are associated with cognitive function and examined the association between vascular risk factors and BGC. MATERIAL AND METHODS Patients who visited a memory clinic of a Dutch general hospital between April 2009 and April 2015 were included. The patients underwent a standard diagnostic work up including cognitive tests (Cambridge Cognitive Examination, including the Mini Mental State Examination) and brain CT. Vascular risk factors such as hypertension, diabetes mellitus, hyperlipidemia and smoking were assessed. CTs were analyzed for presence and severity (absent, mild, moderate or severe) of BGC. Multivariable logistic regression was used to identify risk factors for BGC and linear regression for the association between BGC and cognitive function. RESULTS Of the 1992 patients, 40.3% was male. The median age was 80 years and 866 patients (43.5%) had BGC. BGC was associated with female gender (odds ratio (OR) 1.27, 95% confidence interval (CI) 1.06-1.53, p 0.011), and inversely associated with hypertension (OR 0.74, 95% CI 0.60-0.89, p 0.002) and use of antihypertensive drugs (OR 0.79, 95% CI 0.64-0.98, p 0.031). No association was found between presence and severity of BGC and cognitive function or other vascular risk factors. CONCLUSIONS No association with cognitive function was found. Risk factors for BGC were female gender, while hypertension and antihypertensive drug use were associated with a lower risk of BGC.
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Brugulat-Serrat A, Sánchez-Benavides G, Cacciaglia R, Salvadó G, Shekari M, Collij LE, Buckley C, van Berckel BNM, Perissinotti A, Niñerola-Baizán A, Milà-Alomà M, Vilor-Tejedor N, Operto G, Falcon C, Grau-Rivera O, Arenaza-Urquijo EM, Minguillón C, Fauria K, Molinuevo JL, Suárez-Calvet M, Gispert JD. APOE-ε4 modulates the association between regional amyloid deposition and cognitive performance in cognitively unimpaired middle-aged individuals. EJNMMI Res 2023; 13:18. [PMID: 36856866 PMCID: PMC9978048 DOI: 10.1186/s13550-023-00967-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Accepted: 02/10/2023] [Indexed: 03/02/2023] Open
Abstract
PURPOSE To determine whether the APOE-ε4 allele modulates the relationship between regional β-amyloid (Aβ) accumulation and cognitive change in middle-aged cognitively unimpaired (CU) participants. METHODS The 352 CU participants (mean aged 61.1 [4.7] years) included completed two cognitive assessments (average interval 3.34 years), underwent [18F]flutemetamol Aβ positron emission tomography (PET), T1w magnetic resonance imaging (MRI), as well as APOE genotyping. Global and regional Aβ PET positivity was assessed across five regions-of-interest by visual reading (VR) and regional Centiloids. Linear regression models were developed to examine the interaction between regional and global Aβ PET positivity and APOE-ε4 status on longitudinal cognitive change assessed with the Preclinical Alzheimer's Cognitive Composite (PACC), episodic memory, and executive function, after controlling for age, sex, education, cognitive baseline scores, and hippocampal volume. RESULTS In total, 57 participants (16.2%) were VR+ of whom 41 (71.9%) were APOE-ε4 carriers. No significant APOE-ε4*global Aβ PET interactions were associated with cognitive change for any cognitive test. However, APOE-ε4 carriers who were VR+ in temporal areas (n = 19 [9.81%], p = 0.04) and in the striatum (n = 8 [4.14%], p = 0.01) exhibited a higher decline in the PACC. The temporal areas findings were replicated when regional PET positivity was determined with Centiloid values. Regionally, VR+ in the striatum was associated with higher memory decline. As for executive function, interactions between APOE-ε4 and regional VR+ were found in temporal and parietal regions, and in the striatum. CONCLUSION CU APOE-ε4 carriers with a positive Aβ PET VR in regions known to accumulate amyloid at later stages of the Alzheimer's disease (AD) continuum exhibited a steeper cognitive decline. This work supports the contention that regional VR of Aβ PET might convey prognostic information about future cognitive decline in individuals at higher risk of developing AD. CLINICALTRIALS gov Identifier: NCT02485730. Registered 20 June 2015 https://clinicaltrials.gov/ct2/show/NCT02485730 and ClinicalTrials.gov Identifier:NCT02685969. Registered 19 February 2016 https://clinicaltrials.gov/ct2/show/NCT02685969 .
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Affiliation(s)
- Anna Brugulat-Serrat
- grid.430077.7Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Wellington 30, 08005 Barcelona, Spain ,grid.411142.30000 0004 1767 8811IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain ,grid.413448.e0000 0000 9314 1427Centro de Investigación Biomédica en Red de Fragilidad Y Envejecimiento Saludable (CIBERFES), Instituto de Salud Carlos III, Madrid, Spain ,grid.512357.7Global Brain Health Institute, San Francisco, CA USA
| | - Gonzalo Sánchez-Benavides
- grid.430077.7Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Wellington 30, 08005 Barcelona, Spain ,grid.411142.30000 0004 1767 8811IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain ,grid.413448.e0000 0000 9314 1427Centro de Investigación Biomédica en Red de Fragilidad Y Envejecimiento Saludable (CIBERFES), Instituto de Salud Carlos III, Madrid, Spain
| | - Raffaele Cacciaglia
- grid.430077.7Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Wellington 30, 08005 Barcelona, Spain ,grid.411142.30000 0004 1767 8811IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain ,grid.413448.e0000 0000 9314 1427Centro de Investigación Biomédica en Red de Fragilidad Y Envejecimiento Saludable (CIBERFES), Instituto de Salud Carlos III, Madrid, Spain
| | - Gemma Salvadó
- grid.430077.7Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Wellington 30, 08005 Barcelona, Spain ,grid.4514.40000 0001 0930 2361Department of Clinical Sciences, Clinical Memory Research Unit, Lund University, Lund, Sweden
| | - Mahnaz Shekari
- grid.430077.7Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Wellington 30, 08005 Barcelona, Spain ,grid.411142.30000 0004 1767 8811IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain ,grid.5612.00000 0001 2172 2676Universitat Pompeu Fabra, Barcelona, Spain
| | - Lyduine E. Collij
- grid.12380.380000 0004 1754 9227Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan, Amsterdam, The Netherlands
| | - Christopher Buckley
- grid.83440.3b0000000121901201Center for Medical Image Computing, and Queen Square Institute of Neurology, UCL, London, UK
| | - Bart N. M. van Berckel
- grid.12380.380000 0004 1754 9227Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan, Amsterdam, The Netherlands
| | - Andrés Perissinotti
- grid.410458.c0000 0000 9635 9413Nuclear Medicine Department, Hospital Clínic, Barcelona, Spain ,grid.413448.e0000 0000 9314 1427Biomedical Research Networking Center of Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Instituto de Salud Carlos III, Madrid, Spain
| | - Aida Niñerola-Baizán
- grid.410458.c0000 0000 9635 9413Nuclear Medicine Department, Hospital Clínic, Barcelona, Spain ,grid.413448.e0000 0000 9314 1427Biomedical Research Networking Center of Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Instituto de Salud Carlos III, Madrid, Spain
| | - Marta Milà-Alomà
- grid.430077.7Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Wellington 30, 08005 Barcelona, Spain ,grid.411142.30000 0004 1767 8811IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain ,grid.413448.e0000 0000 9314 1427Centro de Investigación Biomédica en Red de Fragilidad Y Envejecimiento Saludable (CIBERFES), Instituto de Salud Carlos III, Madrid, Spain ,grid.5612.00000 0001 2172 2676Universitat Pompeu Fabra, Barcelona, Spain
| | - Natàlia Vilor-Tejedor
- grid.430077.7Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Wellington 30, 08005 Barcelona, Spain ,grid.5612.00000 0001 2172 2676Universitat Pompeu Fabra, Barcelona, Spain ,grid.473715.30000 0004 6475 7299Centre for Genomic Regulation (CRG), The Barcelona Institute for Science and Technology, Barcelona, Spain
| | - Grégory Operto
- grid.430077.7Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Wellington 30, 08005 Barcelona, Spain ,grid.411142.30000 0004 1767 8811IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain ,grid.413448.e0000 0000 9314 1427Centro de Investigación Biomédica en Red de Fragilidad Y Envejecimiento Saludable (CIBERFES), Instituto de Salud Carlos III, Madrid, Spain
| | - Carles Falcon
- grid.430077.7Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Wellington 30, 08005 Barcelona, Spain ,grid.411142.30000 0004 1767 8811IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain ,grid.411142.30000 0004 1767 8811Neurologia Department, Hospital del Mar, Barcelona, Spain
| | - Oriol Grau-Rivera
- grid.430077.7Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Wellington 30, 08005 Barcelona, Spain ,grid.411142.30000 0004 1767 8811IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain ,grid.413448.e0000 0000 9314 1427Centro de Investigación Biomédica en Red de Fragilidad Y Envejecimiento Saludable (CIBERFES), Instituto de Salud Carlos III, Madrid, Spain ,grid.411142.30000 0004 1767 8811Neurologia Department, Hospital del Mar, Barcelona, Spain
| | - Eider M. Arenaza-Urquijo
- grid.430077.7Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Wellington 30, 08005 Barcelona, Spain ,grid.411142.30000 0004 1767 8811IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain ,grid.413448.e0000 0000 9314 1427Centro de Investigación Biomédica en Red de Fragilidad Y Envejecimiento Saludable (CIBERFES), Instituto de Salud Carlos III, Madrid, Spain
| | - Carolina Minguillón
- grid.430077.7Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Wellington 30, 08005 Barcelona, Spain ,grid.411142.30000 0004 1767 8811IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain ,grid.413448.e0000 0000 9314 1427Centro de Investigación Biomédica en Red de Fragilidad Y Envejecimiento Saludable (CIBERFES), Instituto de Salud Carlos III, Madrid, Spain
| | - Karine Fauria
- grid.430077.7Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Wellington 30, 08005 Barcelona, Spain ,grid.411142.30000 0004 1767 8811IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain ,grid.413448.e0000 0000 9314 1427Centro de Investigación Biomédica en Red de Fragilidad Y Envejecimiento Saludable (CIBERFES), Instituto de Salud Carlos III, Madrid, Spain
| | - José Luis Molinuevo
- grid.430077.7Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Wellington 30, 08005 Barcelona, Spain ,grid.424580.f0000 0004 0476 7612H. Lundbeck A/S, Copenhagen, Denmark
| | - Marc Suárez-Calvet
- grid.430077.7Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Wellington 30, 08005 Barcelona, Spain ,grid.411142.30000 0004 1767 8811IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain ,grid.413448.e0000 0000 9314 1427Centro de Investigación Biomédica en Red de Fragilidad Y Envejecimiento Saludable (CIBERFES), Instituto de Salud Carlos III, Madrid, Spain ,grid.411142.30000 0004 1767 8811Neurologia Department, Hospital del Mar, Barcelona, Spain
| | - Juan Domingo Gispert
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Wellington 30, 08005, Barcelona, Spain. .,IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain. .,Biomedical Research Networking Center of Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Instituto de Salud Carlos III, Madrid, Spain.
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Kim BS, Jun S, Kim H. Cognitive Trajectories and Associated Biomarkers in Patients with Mild Cognitive Impairment. J Alzheimers Dis 2023; 92:803-814. [PMID: 36806501 DOI: 10.3233/jad-220326] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/19/2023]
Abstract
BACKGROUND To diagnose mild cognitive impairment (MCI) patients at risk of progression to dementia is clinically important but challenging. OBJECTIVE We classified MCI patients based on cognitive trajectories and compared biomarkers among groups. METHODS This study analyzed amnestic MCI patients with at least three Clinical Dementia Rating (CDR) scores available over a minimum of 36 months from the Alzheimer's Disease Neuroimaging Initiative database. Patients were classified based on their progression using trajectory modeling with the CDR-sum of box scores. We compared clinical and neuroimaging biomarkers across groups. RESULTS Of 569 eligible MCI patients (age 72.7±7.4 years, women n = 223), three trajectory groups were identified: stable (58.2%), slow decliners (24.6%), and fast decliners (17.2%). In the fifth year after diagnosis, the CDR-sum of box scores increased by 1.2, 5.4, and 11.8 points for the stable, slow, and fast decliners, respectively. Biomarkers associated with cognitive decline were amyloid-β 42, total tau, and phosphorylated tau protein in cerebrospinal fluid, hippocampal volume, cortical metabolism, and amount of cortical and subcortical amyloid deposits. Cortical metabolism and the amount of amyloid deposits were associated with the rate of cognitive decline. CONCLUSION Data-driven trajectory analysis provides new insights into the various cognitive trajectories of MCI. Baseline brain metabolism, and the amount of cortical and subcortical amyloid burden can provide additional information on the rate of cognitive decline.
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Affiliation(s)
- Bum Soo Kim
- Department of Nuclear Medicine, Kosin University Gospel Hospital, University of Kosin College of Medicine, Busan, Republic of Korea
| | - Sungmin Jun
- Department of Nuclear Medicine, Kosin University Gospel Hospital, University of Kosin College of Medicine, Busan, Republic of Korea
| | - Heeyoung Kim
- Department of Nuclear Medicine, Kosin University Gospel Hospital, University of Kosin College of Medicine, Busan, Republic of Korea
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Quantitative comparative analysis of amyloid PET images using three radiopharmaceuticals. Ann Nucl Med 2023; 37:271-279. [PMID: 36749463 PMCID: PMC10129914 DOI: 10.1007/s12149-023-01824-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Accepted: 01/31/2023] [Indexed: 02/08/2023]
Abstract
OBJECTIVE Amyloid positron emission tomography (PET) with F-18 florbetaben (FBB), F-18 flutemetamol (FMM), and F-18 florapronol (FPN) is being used clinically for the evaluation of dementia. These radiopharmaceuticals are commonly used to evaluate the accumulation of beta-amyloid plaques in the brain, but there are structural differences between them. We investigated whether there are any differences in the imaging characteristics. METHODS A total of 605 subjects were enrolled retrospectively in this study, including healthy subjects (HS) and patients with mild cognitive impairment or Alzheimer's disease. Participants underwent amyloid PET imaging using one of the three radiopharmaceuticals. The PET images were analyzed visually and semi-quantitatively using a standardized uptake value ratio (SUVR). In addition, we calculated and compared the cut-off SUVR of the representative regions for each radiopharmaceutical that can distinguish between positive and negative scans. RESULTS In the negative images of the HS group, the contrast between the white matter and the gray matter was high in the FMM PET images, while striatal uptake was relatively higher in the FPN PET images. The SUVR showed significant differences across the radiopharmaceuticals in all areas except the temporal lobe, but the range of differences was relatively small. Accuracy levels for the global cut-off SUVR to discriminate between positive and negative images were highest in FMM PET, with a value of 0.989. FBB PET also showed a high value of 0.978, while FPN PET showed a relatively low value of 0.901. CONCLUSIONS Negative amyloid PET images using the three radiopharmaceuticals showed visually and quantitatively similar imaging characteristics except in the striatum. Binary classification using the cut-off of the global cortex showed high accuracy overall, although there were some differences between the three PET images.
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22
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Sun Y, Zhao Y, Hu K, Wang M, Liu Y, Liu B. Distinct spatiotemporal subtypes of amyloid deposition are associated with diverging disease profiles in cognitively normal and mild cognitive impairment individuals. Transl Psychiatry 2023; 13:35. [PMID: 36732496 PMCID: PMC9895066 DOI: 10.1038/s41398-023-02328-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Revised: 01/04/2023] [Accepted: 01/19/2023] [Indexed: 02/04/2023] Open
Abstract
We aimed to investigate the relationship between spatiotemporal changes of amyloid deposition and Alzheimer's disease (AD) profiles in cognitively normal (CN) and those with mild cognitive impairment (MCI). Using a data-driven method and amyloid-PET data, we identified and validated two subtypes in two independent datasets (discovery dataset: N = 548, age = 72.4 ± 6.78, 49% female; validation dataset: N = 348, age = 74.9 ± 8.16, 47% female) from the Alzheimer's Disease Neuroimaging Initiative across a range of individuals who were CN or had MCI. The two subtypes showed distinct regional progression patterns and presented distinct genetic, clinical and biomarker characteristics. The cortex-priority subtype was more likely to show typical clinical syndromes of symptomatic AD and vice versa. Furthermore, the regional progression patterns were associated with clinical and biomarker profiles. In sum, our findings suggest that the spatiotemporal variants of amyloid depositions are in close association with disease trajectories; these findings may provide insight into the disease monitoring and enrollment of therapeutic trials in AD.
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Affiliation(s)
- Yuqing Sun
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, China
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
| | - Yuxin Zhao
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, China
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
| | - Ke Hu
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, China
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
| | - Meng Wang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, China
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
| | - Yong Liu
- School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, 100876, China.
| | - Bing Liu
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China.
- Chinese Institute for Brain Research, Beijing, 102206, China.
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23
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Risacher SL, Apostolova LG. Neuroimaging in Dementia. Continuum (Minneap Minn) 2023; 29:219-254. [PMID: 36795879 DOI: 10.1212/con.0000000000001248] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/18/2023]
Abstract
OBJECTIVE Neurodegenerative diseases are significant health concerns with regard to morbidity and social and economic hardship around the world. This review describes the state of the field of neuroimaging measures as biomarkers for detection and diagnosis of both slowly progressing and rapidly progressing neurodegenerative diseases, specifically Alzheimer disease, vascular cognitive impairment, dementia with Lewy bodies or Parkinson disease dementia, frontotemporal lobar degeneration spectrum disorders, and prion-related diseases. It briefly discusses findings in these diseases in studies using MRI and metabolic and molecular-based imaging (eg, positron emission tomography [PET] and single-photon emission computerized tomography [SPECT]). LATEST DEVELOPMENTS Neuroimaging studies with MRI and PET have demonstrated differential patterns of brain atrophy and hypometabolism in different neurodegenerative disorders, which can be useful in differential diagnoses. Advanced MRI sequences, such as diffusion-based imaging, and functional MRI (fMRI) provide important information about underlying biological changes in dementia and new directions for development of novel measures for future clinical use. Finally, advancements in molecular imaging allow clinicians and researchers to visualize dementia-related proteinopathies and neurotransmitter levels. ESSENTIAL POINTS Diagnosis of neurodegenerative diseases is primarily based on symptomatology, although the development of in vivo neuroimaging and fluid biomarkers is changing the scope of clinical diagnosis, as well as the research into these devastating diseases. This article will help inform the reader about the current state of neuroimaging in neurodegenerative diseases, as well as how these tools might be used for differential diagnoses.
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Affiliation(s)
- Shannon L Risacher
- Address correspondence to Dr Shannon L. Risacher, 355 W 16th St, Indianapolis, IN 46202,
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Kim J, Choe YS, Park Y, Kim Y, Kim JP, Jang H, Kim HJ, Na DL, Cho SJ, Moon SH, Seo SW. Clinical outcomes of increased focal amyloid uptake in individuals with subthreshold global amyloid levels. Front Aging Neurosci 2023; 15:1124445. [PMID: 36936497 PMCID: PMC10017468 DOI: 10.3389/fnagi.2023.1124445] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Accepted: 02/14/2023] [Indexed: 03/06/2023] Open
Abstract
Background Although the standardized uptake value ratio (SUVR) method is objective and simple, cut-off optimization using global SUVR values may not reflect focal increased uptake in the cerebrum. The present study investigated clinical and neuroimaging characteristics according to focally increased β-amyloid (Aβ) uptake and global Aβ status. Methods We recruited 968 participants with cognitive continuum. All participants underwent neuropsychological tests and 498 18F-florbetaben (FBB) amyloid positron emission tomography (PET) and 470 18F-flutemetamol (FMM) PET. Each PET scan was assessed in 10 regions (left and right frontal, lateral temporal, parietal, cingulate, and striatum) with focal-quantitative SUVR-based cutoff values for each region by using an iterative outlier approach. Results A total of 62 (6.4%) subjects showed increased focal Aβ uptake with subthreshold global Aβ status [global (-) and focal (+) Aβ group, G(-)F(+) group]. The G(-)F(+) group showed worse performance in memory impairment (p < 0.001), global cognition (p = 0.009), greater hippocampal atrophy (p = 0.045), compared to those in the G(-)F(-). Participants with widespread Aβ involvement in the whole region [G(+)] showed worse neuropsychological (p < 0.001) and neuroimaging features (p < 0.001) than those with focal Aβ involvement G(-)F(+). Conclusion Our findings suggest that individuals show distinctive clinical outcomes according to focally increased Aβ uptake and global Aβ status. Thus, researchers and clinicians should pay more attention to focal increased Aβ uptake in addition to global Aβ status.
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Affiliation(s)
- Jaeho Kim
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
- Neuroscience Center, Samsung Medical Center, Seoul, Republic of Korea
- Samsung Alzheimer Research Center, Samsung Medical Center, Seoul, Republic of Korea
- Department of Neurology, Dongtan Sacred Heart Hospital, Hallym University College of Medicine, Hwaseong-si, Gyeonggi-do, Republic of Korea
| | - Yeong Sim Choe
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
- Department of Health Sciences and Technology, SAIHST, Samsung Medical Center, Sungkyunkwan University, Seoul, Republic of Korea
| | - Yuhyun Park
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
- Department of Health Sciences and Technology, SAIHST, Samsung Medical Center, Sungkyunkwan University, Seoul, Republic of Korea
| | - Yeshin Kim
- Department of Neurology, Kangwon National University College of Medicine, Chuncheon-si, Gangwon-do, Republic of Korea
| | - Jun Pyo Kim
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
- Neuroscience Center, Samsung Medical Center, Seoul, Republic of Korea
- Samsung Alzheimer Research Center, Samsung Medical Center, Seoul, Republic of Korea
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, United States
| | - Hyemin Jang
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
- Neuroscience Center, Samsung Medical Center, Seoul, Republic of Korea
- Samsung Alzheimer Research Center, Samsung Medical Center, Seoul, Republic of Korea
| | - Hee Jin Kim
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
- Neuroscience Center, Samsung Medical Center, Seoul, Republic of Korea
- Samsung Alzheimer Research Center, Samsung Medical Center, Seoul, Republic of Korea
| | - Duk L. Na
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
- Neuroscience Center, Samsung Medical Center, Seoul, Republic of Korea
- Samsung Alzheimer Research Center, Samsung Medical Center, Seoul, Republic of Korea
- Department of Health Sciences and Technology, SAIHST, Samsung Medical Center, Sungkyunkwan University, Seoul, Republic of Korea
- Samsung Medical Center, Stem Cell and Regenerative Medicine Institute, Seoul, Republic of Korea
| | - Soo-Jin Cho
- Department of Neurology, Dongtan Sacred Heart Hospital, Hallym University College of Medicine, Hwaseong-si, Gyeonggi-do, Republic of Korea
| | - Seung Hwan Moon
- Department of Nuclear Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
- *Correspondence: Seung Hwan Moon,
| | - Sang Won Seo
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
- Neuroscience Center, Samsung Medical Center, Seoul, Republic of Korea
- Samsung Alzheimer Research Center, Samsung Medical Center, Seoul, Republic of Korea
- Department of Health Sciences and Technology, SAIHST, Samsung Medical Center, Sungkyunkwan University, Seoul, Republic of Korea
- Department of Clinical Research Design and Evaluation, SAIHST, Samsung Medical Center, Sungkyunkwan University, Seoul, Republic of Korea
- Sang Won Seo,
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Li X, Ospitalieri S, Robberechts T, Hofmann L, Schmid C, Rijal Upadhaya A, Koper MJ, von Arnim CAF, Kumar S, Willem M, Gnoth K, Ramakers M, Schymkowitz J, Rousseau F, Walter J, Ronisz A, Balakrishnan K, Thal DR. Seeding, maturation and propagation of amyloid β-peptide aggregates in Alzheimer’s disease. Brain 2022; 145:3558-3570. [PMID: 36270003 DOI: 10.1093/brain/awac202] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2021] [Revised: 05/18/2022] [Accepted: 05/20/2022] [Indexed: 11/13/2022] Open
Abstract
Abstract
Alzheimer’s disease is neuropathologically characterized by the deposition of the amyloid β-peptide (Aβ) as amyloid plaques. Aβ plaque pathology starts in the neocortex before it propagates into further brain regions. Moreover, Aβ aggregates undergo maturation indicated by the occurrence of post-translational modifications. Here, we show that propagation of Aβ plaques is led by presumably non-modified Aβ followed by Aβ aggregate maturation. This sequence was seen neuropathologically in human brains and in amyloid precursor protein transgenic mice receiving intracerebral injections of human brain homogenates from cases varying in Aβ phase, Aβ load and Aβ maturation stage. The speed of propagation after seeding in mice was best related to the Aβ phase of the donor, the progression speed of maturation to the stage of Aβ aggregate maturation. Thus, different forms of Aβ can trigger propagation/maturation of Aβ aggregates, which may explain the lack of success when therapeutically targeting only specific forms of Aβ.
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Affiliation(s)
- Xiaohang Li
- Department of Imaging and Pathology, Laboratory of Neuropathology, Leuven Brain Institute, KU-Leuven , Leuven , Belgium
| | - Simona Ospitalieri
- Department of Imaging and Pathology, Laboratory of Neuropathology, Leuven Brain Institute, KU-Leuven , Leuven , Belgium
| | - Tessa Robberechts
- Department of Imaging and Pathology, Laboratory of Neuropathology, Leuven Brain Institute, KU-Leuven , Leuven , Belgium
| | - Linda Hofmann
- Institute of Pathology, Laboratory of Neuropathology, Ulm University , Ulm , Germany
| | - Christina Schmid
- Institute of Pathology, Laboratory of Neuropathology, Ulm University , Ulm , Germany
| | - Ajeet Rijal Upadhaya
- Institute of Pathology, Laboratory of Neuropathology, Ulm University , Ulm , Germany
| | - Marta J Koper
- Department of Imaging and Pathology, Laboratory of Neuropathology, Leuven Brain Institute, KU-Leuven , Leuven , Belgium
- Laboratory for the Research of Neurodegenerative Diseases, Department of Neurosciences, KU-Leuven (University of Leuven), Leuven Brain Institute , Leuven , Belgium
- Center for Brain and Disease Research, VIB , Leuven , Belgium
| | - Christine A F von Arnim
- Department of Neurology, Ulm University , Ulm , Germany
- Division of Geriatrics, University Medical Center Göttingen , Göttingen , Germany
| | - Sathish Kumar
- Department of Neurology, University of Bonn , Bonn , Germany
| | - Michael Willem
- Chair of Metabolic Biochemistry, Biomedical Center, Faculty of Medicine, Ludwig-Maximilians-University Munich , Munich , Germany
| | - Kathrin Gnoth
- Department of Drug Design and Target Validation, Fraunhofer Institute for Cell Therapy and Immunology , Halle , Germany
| | - Meine Ramakers
- Center for Brain and Disease Research, VIB , Leuven , Belgium
- Switch Laboratory, Department of Cellular and Molecular Medicine, KU-Leuven , Leuven , Belgium
| | - Joost Schymkowitz
- Center for Brain and Disease Research, VIB , Leuven , Belgium
- Switch Laboratory, Department of Cellular and Molecular Medicine, KU-Leuven , Leuven , Belgium
| | - Frederic Rousseau
- Center for Brain and Disease Research, VIB , Leuven , Belgium
- Switch Laboratory, Department of Cellular and Molecular Medicine, KU-Leuven , Leuven , Belgium
| | - Jochen Walter
- Department of Neurology, University of Bonn , Bonn , Germany
| | - Alicja Ronisz
- Department of Imaging and Pathology, Laboratory of Neuropathology, Leuven Brain Institute, KU-Leuven , Leuven , Belgium
| | - Karthikeyan Balakrishnan
- Institute of Pathology, Laboratory of Neuropathology, Ulm University , Ulm , Germany
- Department of Gene Therapy, Ulm University , Ulm , Germany
| | - Dietmar Rudolf Thal
- Department of Imaging and Pathology, Laboratory of Neuropathology, Leuven Brain Institute, KU-Leuven , Leuven , Belgium
- Institute of Pathology, Laboratory of Neuropathology, Ulm University , Ulm , Germany
- Department of Pathology, UZ-Leuven , Leuven , Belgium
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Spatio-temporal metabolic rewiring in the brain of TgF344-AD rat model of Alzheimer's disease. Sci Rep 2022; 12:16958. [PMID: 36216838 PMCID: PMC9550832 DOI: 10.1038/s41598-022-20962-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Accepted: 09/21/2022] [Indexed: 12/29/2022] Open
Abstract
Brain damage associated with Alzheimer's disease (AD) occurs even decades before the symptomatic onset, raising the need to investigate its progression from prodromal stages. In this context, animal models that progressively display AD pathological hallmarks (e.g. TgF344-AD) become crucial. Translational technologies, such as magnetic resonance spectroscopy (MRS), enable the longitudinal metabolic characterization of this disease. However, an integrative approach is required to unravel the complex metabolic changes underlying AD progression, from early to advanced stages. TgF344-AD and wild-type (WT) rats were studied in vivo on a 7 Tesla MRI scanner, for longitudinal quantitative assessment of brain metabolic profile changes using MRS. Disease progression was investigated at 4 time points, from 9 to 18 months of age, and in 4 regions: cortex, hippocampus, striatum, and thalamus. Compared to WT, TgF344-AD rats replicated common findings in AD patients, including decreased N-acetylaspartate in the cortex, hippocampus and thalamus, and decreased glutamate in the thalamus and striatum. Different longitudinal evolution of metabolic concentration was observed between TgF344-AD and WT groups. Namely, age-dependent trajectories differed between groups for creatine in the cortex and thalamus and for taurine in cortex, with significant decreases in Tg344-AD animals; whereas myo-inositol in the thalamus and striatum showed greater increase along time in the WT group. Additional analysis revealed divergent intra- and inter-regional metabolic coupling in each group. Thus, in cortex, strong couplings of N-acetylaspartate and creatine with myo-inositol in WT, but with taurine in TgF344-AD rats were observed; whereas in the hippocampus, myo-inositol, taurine and choline compounds levels were highly correlated in WT but not in TgF344-AD animals. Furthermore, specific cortex-hippocampus-striatum metabolic crosstalks were found for taurine levels in the WT group but for myo-inositol levels in the TgF344-AD rats. With a systems biology perspective of metabolic changes in AD pathology, our results shed light into the complex spatio-temporal metabolic rewiring in this disease, reported here for the first time. Age- and tissue-dependent imbalances between myo-inositol, taurine and other metabolites, such as creatine, unveil their role in disease progression, while pointing to the inadequacy of the latter as an internal reference for quantification.
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Parra MA, Calia C, Pattan V, Della Sala S. Memory markers in the continuum of the Alzheimer's clinical syndrome. Alzheimers Res Ther 2022; 14:142. [PMID: 36180965 PMCID: PMC9526252 DOI: 10.1186/s13195-022-01082-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Accepted: 09/14/2022] [Indexed: 11/16/2022]
Abstract
BACKGROUND The individual and complementary value of the Visual Short-Term Memory Binding Test (VSTMBT) and the Free and Cued Selective Reminding Test (FCSRT) as markers to trace the AD continuum was investigated. It was hypothesised that the VSTMBT would be an early indicator while the FCSRT would inform on imminent progression. METHODS Healthy older adults (n=70) and patients with mild cognitive impairment (MCI) (n=80) were recruited and followed up between 2012 and 2017. Participants with at least two assessment points entered the study. Using baseline and follow-up assessments four groups were defined: Older adults who were healthy (HOA), with very mild cognitive but not functional impairment (eMCI), and with MCI who did and did not convert to dementia (MCI converters and non-converters). RESULTS Only the VSTMBT predicted group membership in the very early stages (HOA vs eMCI). As the disease progressed, the FCSRT became a strong predictor excluding the VSTMB from the models. Their complementary value was high during the mid-prodromal stages and decreased in stages closer to dementia. DISCUSSION The study supports the notion that neuropsychological assessment for AD needs to abandon the notion of one-size-fits-all. A memory toolkit for AD needs to consider tools that are early indicators and tools that suggest imminent progression. The VSTMBT and the FSCRT are such tools.
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Affiliation(s)
- Mario A Parra
- School of Psychological Sciences and Health, University of Strathclyde, Graham Hills Building, 40 George Street, Glasgow, G1 1QE, UK.
| | - Clara Calia
- School of Health in Social Science, University of Edinburgh, Edinburgh, UK
| | - Vivek Pattan
- NHS Forth Valley, Stirling Community Hospital, Stirling, UK
| | - Sergio Della Sala
- Human Cognitive Neuroscience, Psychology Department, University of Edinburgh, Edinburgh, UK
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Sepulveda-Falla D, Sanchez JS, Almeida MC, Boassa D, Acosta-Uribe J, Vila-Castelar C, Ramirez-Gomez L, Baena A, Aguillon D, Villalba-Moreno ND, Littau JL, Villegas A, Beach TG, White CL, Ellisman M, Krasemann S, Glatzel M, Johnson KA, Sperling RA, Reiman EM, Arboleda-Velasquez JF, Kosik KS, Lopera F, Quiroz YT. Distinct tau neuropathology and cellular profiles of an APOE3 Christchurch homozygote protected against autosomal dominant Alzheimer's dementia. Acta Neuropathol 2022; 144:589-601. [PMID: 35838824 PMCID: PMC9381462 DOI: 10.1007/s00401-022-02467-8] [Citation(s) in RCA: 34] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Revised: 06/21/2022] [Accepted: 07/01/2022] [Indexed: 01/22/2023]
Abstract
We describe in vivo follow-up PET imaging and postmortem findings from an autosomal dominant Alzheimer's disease (ADAD) PSEN1 E280A carrier who was also homozygous for the APOE3 Christchurch (APOE3ch) variant and was protected against Alzheimer's symptoms for almost three decades beyond the expected age of onset. We identified a distinct anatomical pattern of tau pathology with atypical accumulation in vivo and unusual postmortem regional distribution characterized by sparing in the frontal cortex and severe pathology in the occipital cortex. The frontal cortex and the hippocampus, less affected than the occipital cortex by tau pathology, contained Related Orphan Receptor B (RORB) positive neurons, homeostatic astrocytes and higher APOE expression. The occipital cortex, the only cortical region showing cerebral amyloid angiopathy (CAA), exhibited a distinctive chronic inflammatory microglial profile and lower APOE expression. Thus, the Christchurch variant may impact the distribution of tau pathology, modulate age at onset, severity, progression, and clinical presentation of ADAD, suggesting possible therapeutic strategies.
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Affiliation(s)
- Diego Sepulveda-Falla
- Molecular Neuropathology of Alzheimer's Disease, Institute of Neuropathology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
| | - Justin S Sanchez
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Maria Camila Almeida
- Department of Molecular, Cellular and Developmental Biology, Neuroscience Research Institute, University of California, Santa Barbara, CA, 93106, USA
- Center for Natural and Human Sciences, Federal University of ABC, São Bernardo do Campo, SP, Brazil
| | - Daniela Boassa
- National Center for Microscopy and Imaging Research (NCMIR), San Diego School of Medicine (UCSD), University of California, La Jolla, San Diego, CA, 92093, USA
- Department of Neurosciences, San Diego School of Medicine (UCSD), University of California, La Jolla, San Diego, CA, 92093, USA
| | - Juliana Acosta-Uribe
- Department of Molecular, Cellular and Developmental Biology, Neuroscience Research Institute, University of California, Santa Barbara, CA, 93106, USA
| | - Clara Vila-Castelar
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Liliana Ramirez-Gomez
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Ana Baena
- Grupo de Neurociencias de Antioquia, Facultad de Medicina, Universidad de Antioquia, Medellín, Antioquia, Colombia
| | - David Aguillon
- Grupo de Neurociencias de Antioquia, Facultad de Medicina, Universidad de Antioquia, Medellín, Antioquia, Colombia
| | - Nelson David Villalba-Moreno
- Molecular Neuropathology of Alzheimer's Disease, Institute of Neuropathology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Jessica Lisa Littau
- Molecular Neuropathology of Alzheimer's Disease, Institute of Neuropathology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Andres Villegas
- Grupo de Neurociencias de Antioquia, Facultad de Medicina, Universidad de Antioquia, Medellín, Antioquia, Colombia
| | - Thomas G Beach
- Department of Neuropathology, Banner Sun Health Research Institute, Sun City, AZ, USA
| | - Charles L White
- Department of Pathology, Neuropathology Laboratory, University of Texas Southwestern Medical Center, Dallas, USA
| | - Mark Ellisman
- National Center for Microscopy and Imaging Research (NCMIR), San Diego School of Medicine (UCSD), University of California, La Jolla, San Diego, CA, 92093, USA
- Department of Neurosciences, San Diego School of Medicine (UCSD), University of California, La Jolla, San Diego, CA, 92093, USA
| | - Susanne Krasemann
- Institute of Neuropathology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Experimental Pathology Core Facility, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Markus Glatzel
- Institute of Neuropathology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Keith A Johnson
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Reisa A Sperling
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | | | - Joseph F Arboleda-Velasquez
- Schepens Eye Research Institute of Mass Eye and Ear and Department of Ophthalmology, Harvard Medical School, Boston, MA, USA
| | - Kenneth S Kosik
- Department of Molecular, Cellular and Developmental Biology, Neuroscience Research Institute, University of California, Santa Barbara, CA, 93106, USA
| | - Francisco Lopera
- Grupo de Neurociencias de Antioquia, Facultad de Medicina, Universidad de Antioquia, Medellín, Antioquia, Colombia
| | - Yakeel T Quiroz
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
- Grupo de Neurociencias de Antioquia, Facultad de Medicina, Universidad de Antioquia, Medellín, Antioquia, Colombia.
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Cerebrospinal fluid levels of proenkephalin and prodynorphin are differentially altered in Huntington's and Parkinson's disease. J Neurol 2022; 269:5136-5143. [PMID: 35737109 PMCID: PMC9363351 DOI: 10.1007/s00415-022-11187-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2022] [Revised: 05/07/2022] [Accepted: 05/11/2022] [Indexed: 11/13/2022]
Abstract
Background Proenkephalin (PENK) and prodynorphin (PDYN) are peptides mainly produced by the striatal medium spiny projection neurons (MSNs) under dopaminergic signaling. Therefore, they may represent candidate biomarkers in Huntington’s disease (HD) and Parkinson’s disease (PD), two neurodegenerative diseases characterized by striatal atrophy and/or dysfunction. Methods Using an in-house established liquid chromatography−tandem mass spectrometry (LC–MS/MS) method in multiple reaction monitoring mode (MRM) we measured cerebrospinal fluid (CSF) levels of PENK- and PDYN- derived peptides in patients with HD (n = 47), PD (n = 61), Alzheimer’s disease (n = 11), amyotrophic lateral sclerosis (n = 14) and in 92 control subjects. Moreover, we investigated the possible associations between biomarkers and disease severity scales in HD and PD and the effect of dopaminergic therapy on biomarker levels in PD. Results In HD, CSF PENK- and PDYN-derived peptide levels were significantly decreased compared to all other groups and were associated with disease severity scores. In PD, both biomarkers were within the normal range, but higher PDYN levels were found in dopamine-treated compared to untreated patients. In PD, both CSF PENK and PDYN did not correlate with clinical severity scales. Conclusions CSF PENK- and PDYN-derived peptides appeared to be promising pathogenetic and disease severity markers in HD, reflecting the ongoing striatal neurodegeneration along with the loss of MSNs. In PD patients, CSF PDYN showed a limitative role as a possible pharmacodynamic marker during dopaminergic therapy, but further investigations are needed. Supplementary Information The online version contains supplementary material available at 10.1007/s00415-022-11187-8.
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Farrell C, Mumford P, Wiseman FK. Rodent Modeling of Alzheimer's Disease in Down Syndrome: In vivo and ex vivo Approaches. Front Neurosci 2022; 16:909669. [PMID: 35747206 PMCID: PMC9209729 DOI: 10.3389/fnins.2022.909669] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Accepted: 05/16/2022] [Indexed: 12/30/2022] Open
Abstract
There are an estimated 6 million people with Down syndrome (DS) worldwide. In developed countries, the vast majority of these individuals will develop Alzheimer's disease neuropathology characterized by the accumulation of amyloid-β (Aβ) plaques and tau neurofibrillary tangles within the brain, which leads to the early onset of dementia (AD-DS) and reduced life-expectancy. The mean age of onset of clinical dementia is ~55 years and by the age of 80, approaching 100% of individuals with DS will have a dementia diagnosis. DS is caused by trisomy of chromosome 21 (Hsa21) thus an additional copy of a gene(s) on the chromosome must cause the development of AD neuropathology and dementia. Indeed, triplication of the gene APP which encodes the amyloid precursor protein is sufficient and necessary for early onset AD (EOAD), both in people who have and do not have DS. However, triplication of other genes on Hsa21 leads to profound differences in neurodevelopment resulting in intellectual disability, elevated incidence of epilepsy and perturbations to the immune system. This different biology may impact on how AD neuropathology and dementia develops in people who have DS. Indeed, genes on Hsa21 other than APP when in three-copies can modulate AD-pathogenesis in mouse preclinical models. Understanding this biology better is critical to inform drug selection for AD prevention and therapy trials for people who have DS. Here we will review rodent preclinical models of AD-DS and how these can be used for both in vivo and ex vivo (cultured cells and organotypic slice cultures) studies to understand the mechanisms that contribute to the early development of AD in people who have DS and test the utility of treatments to prevent or delay the development of disease.
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Cooper LL, O'Donnell A, Beiser AS, Thibault EG, Sanchez JS, Benjamin EJ, Hamburg NM, Vasan RS, Larson MG, Johnson KA, Mitchell GF, Seshadri S. Association of Aortic Stiffness and Pressure Pulsatility With Global Amyloid-β and Regional Tau Burden Among Framingham Heart Study Participants Without Dementia. JAMA Neurol 2022; 79:710-719. [PMID: 35666520 DOI: 10.1001/jamaneurol.2022.1261] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
Importance Aortic stiffness is associated with clinical hallmarks of Alzheimer disease and related dementias and could be a modifiable target for disease prevention. Objective To assess associations of aortic stiffness and pressure pulsatility with global amyloid-β plaques and regional tau burden in the brain of middle-aged and older adults without dementia. Design, Setting, and Participants The sample for this cross-sectional study was drawn from the Framingham Heart Study Third Generation Cohort at examination 3 (N = 3171; 2016-2019), of whom 3092 successfully underwent comprehensive hemodynamic evaluations. In a supplemental visit (2015-2021), a subset of 270 participants without dementia who represented the spectrum of vascular risk also underwent positron emission tomography. Thirteen participants were excluded for missing covariate data. The final sample size was 257 participants. Exposures Three measures of aortic stiffness and pressure pulsatility (carotid-femoral pulse wave velocity, central pulse pressure [CPP], and forward wave amplitude [FWA]) were evaluated using arterial tonometry. Main Outcomes and Measures Global amyloid-β plaques and regional tau were assessed using 11C-Pittsburgh compound B and 18F-flortaucipir positron emission tomography tracers, respectively. Results The mean (SD) age of the 257 participants was 54 (8) years, and 126 were women (49%). All participants were White Western European race. In multivariable models, higher CPP (β per SD = 0.17; 95% CI, 0.00-0.35; P = .045) and FWA (β per SD = 0.16; 95% CI, 0.00-0.31; P = .04) were associated with greater entorhinal tau burden. In similar models, higher CPP (β per SD = 0.19; 95% CI, 0.02-0.36; P = .03) and FWA (β per SD = 0.17; 95% CI, 0.01-0.32; P = .03) were associated with greater rhinal tau burden. Aortic stiffness and pressure pulsatility measures were not associated with amygdala, inferior temporal, precuneus tau burden, or global amyloid-β plaques. Associations for entorhinal and rhinal tau outcomes were more prominent in older participants (≥60 years). For example, higher levels of all aortic stiffness and pressure pulsatility measures (β per SD = 0.40-0.92; P = .001-.02) were associated with higher entorhinal tau burden among older but not younger participants in stratified analyses. Conclusions and Relevance In this cross-sectional study, abnormal central vascular hemodynamics were associated with higher tau burden in specific brain regions. Findings suggest that aortic stiffness, which is potentially modifiable, may be a probable independent target for prevention of tau-related pathologies.
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Affiliation(s)
- Leroy L Cooper
- Biology Department, Vassar College, Poughkeepsie, New York
| | - Adrienne O'Donnell
- Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts.,Boston University and the National Heart, Lung, and Blood Institute (NHLBI) Framingham Heart Study, Framingham, Massachusetts
| | - Alexa S Beiser
- Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts.,Boston University and the National Heart, Lung, and Blood Institute (NHLBI) Framingham Heart Study, Framingham, Massachusetts.,Department of Neurology, Boston University School of Medicine, Boston, Massachusetts
| | - Emma G Thibault
- Gordon Center for Medical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston
| | - Justin S Sanchez
- Gordon Center for Medical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston
| | - Emelia J Benjamin
- Boston University and the National Heart, Lung, and Blood Institute (NHLBI) Framingham Heart Study, Framingham, Massachusetts.,Cardiology and Preventive Medicine Sections, Department of Medicine, Boston University School of Medicine, Boston, Massachusetts.,Evans Department of Medicine, Boston Medical Center, Boston, Massachusetts.,Whitaker Cardiovascular Institute, Boston University School of Medicine, Boston, Massachusetts.,Department of Epidemiology, Boston University School of Public Health, Boston, Massachusetts
| | - Naomi M Hamburg
- Evans Department of Medicine, Boston Medical Center, Boston, Massachusetts.,Whitaker Cardiovascular Institute, Boston University School of Medicine, Boston, Massachusetts
| | - Ramachandran S Vasan
- Boston University and the National Heart, Lung, and Blood Institute (NHLBI) Framingham Heart Study, Framingham, Massachusetts.,Cardiology and Preventive Medicine Sections, Department of Medicine, Boston University School of Medicine, Boston, Massachusetts.,Evans Department of Medicine, Boston Medical Center, Boston, Massachusetts.,Whitaker Cardiovascular Institute, Boston University School of Medicine, Boston, Massachusetts.,Department of Epidemiology, Boston University School of Public Health, Boston, Massachusetts
| | - Martin G Larson
- Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts.,Boston University and the National Heart, Lung, and Blood Institute (NHLBI) Framingham Heart Study, Framingham, Massachusetts
| | - Keith A Johnson
- Gordon Center for Medical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston.,Departments of Radiology and Neurology, Harvard Medical School, Boston, Massachusetts
| | | | - Sudha Seshadri
- Boston University and the National Heart, Lung, and Blood Institute (NHLBI) Framingham Heart Study, Framingham, Massachusetts.,Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, San Antonio, Texas
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Abstract
Senile plaques have been studied in postmortem brains for more than 120 years and the resultant knowledge has not only helped us understand the etiology and pathogenesis of Alzheimer disease (AD), but has also pointed to possible modes of prevention and treatment. Within the last 15 years, it has become possible to image plaques in living subjects. This is arguably the single greatest advance in AD research since the identification of the Aβ peptide as the major plaque constituent. The limitations and potentialities of amyloid imaging are still not completely clear but are perhaps best glimpsed through the perspective gained from the accumulated postmortem histological studies. The basic morphological classification of plaques into neuritic, cored and diffuse has been supplemented by sophisticated immunohistochemical and biochemical analyses and increasingly detailed mapping of plaque brain distribution. Changes in plaque classification and staging have in turn contributed to changes in the definition and diagnostic criteria for AD. All of this information continues to be tested by clinicopathological correlations and it is through the insights thereby gained that we will best be able to employ the powerful tool of amyloid imaging.
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Affiliation(s)
- Thomas G Beach
- From the Civin Laboratory for Neuropathology, Banner Sun Health Research Institute, Sun City, Arizona, USA
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33
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Reinartz M, Luckett ES, Schaeverbeke J, De Meyer S, Adamczuk K, Thal DR, Van Laere K, Dupont P, Vandenberghe R. Classification of 18F-Flutemetamol scans in cognitively normal older adults using machine learning trained with neuropathology as ground truth. Eur J Nucl Med Mol Imaging 2022; 49:3772-3786. [PMID: 35522322 PMCID: PMC9399207 DOI: 10.1007/s00259-022-05808-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Accepted: 04/19/2022] [Indexed: 11/29/2022]
Abstract
Purpose End-of-life studies have validated the binary visual reads of 18F-labeled amyloid PET tracers as an accurate tool for the presence or absence of increased neuritic amyloid plaque density. In this study, the performance of a support vector machine (SVM)-based classifier will be tested against pathological ground truths and its performance determined in cognitively healthy older adults. Methods We applied SVM with a linear kernel to an 18F-Flutemetamol end-of-life dataset to determine the regions with the highest feature weights in a data-driven manner and to compare between two different pathological ground truths: based on neuritic amyloid plaque density or on amyloid phases, respectively. We also trained and tested classifiers based on the 10% voxels with the highest amplitudes of feature weights for each of the two neuropathological ground truths. Next, we tested the classifiers’ diagnostic performance in the asymptomatic Alzheimer’s disease (AD) phase, a phase of interest for future drug development, in an independent dataset of cognitively intact older adults, the Flemish Prevent AD Cohort-KU Leuven (F-PACK). A regression analysis was conducted between the Centiloid (CL) value in a composite volume of interest (VOI), as index for amyloid load, and the distance to the hyperplane for each of the two classifiers, based on the two pathological ground truths. A receiver operating characteristic analysis was also performed to determine the CL threshold that optimally discriminates between neuritic amyloid plaque positivity versus negativity, or amyloid phase positivity versus negativity, within F-PACK. Results The classifiers yielded adequate specificity and sensitivity within the end-of-life dataset (neuritic amyloid plaque density classifier: specificity of 90.2% and sensitivity of 83.7%; amyloid phase classifier: specificity of 98.4% and sensitivity of 84.0%). The regions with the highest feature weights corresponded to precuneus, caudate, anteromedial prefrontal, and also posterior inferior temporal and inferior parietal cortex. In the cognitively normal cohort, the correlation coefficient between CL and distance to the hyperplane was −0.66 for the classifier trained with neuritic amyloid plaque density, and −0.88 for the classifier trained with amyloid phases. This difference was significant. The optimal CL cut-off for discriminating positive versus negative scans was CL = 48–51 for the different classifiers (area under the curve (AUC) = 99.9%), except for the classifier trained with amyloid phases and based on the 10% voxels with highest feature weights. There the cut-off was CL = 26 (AUC = 99.5%), which closely matched the CL threshold for discriminating phases 0–2 from 3–5 based on the end-of-life dataset and the neuropathological ground truth. Discussion Among a set of neuropathologically validated classifiers trained with end-of-life cases, transfer to a cognitively normal population works best for a classifier trained with amyloid phases and using only voxels with the highest amplitudes of feature weights. Supplementary Information The online version contains supplementary material available at 10.1007/s00259-022-05808-7.
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Affiliation(s)
- Mariska Reinartz
- Laboratory for Cognitive Neurology, KU Leuven, Leuven, Belgium.,Alzheimer Research Centre KU Leuven, Leuven Brain Institute, Leuven, Belgium
| | - Emma Susanne Luckett
- Laboratory for Cognitive Neurology, KU Leuven, Leuven, Belgium.,Alzheimer Research Centre KU Leuven, Leuven Brain Institute, Leuven, Belgium
| | - Jolien Schaeverbeke
- Laboratory for Cognitive Neurology, KU Leuven, Leuven, Belgium.,Alzheimer Research Centre KU Leuven, Leuven Brain Institute, Leuven, Belgium
| | - Steffi De Meyer
- Laboratory for Cognitive Neurology, KU Leuven, Leuven, Belgium.,Alzheimer Research Centre KU Leuven, Leuven Brain Institute, Leuven, Belgium
| | | | - Dietmar Rudolf Thal
- Department of Pathology, UZ Leuven, Leuven, Belgium.,Laboratory of Neuropathology, KU Leuven, Leuven, Belgium
| | - Koen Van Laere
- Division of Nuclear Medicine, UZ Leuven, Leuven, Belgium.,Nuclear Medicine and Molecular Imaging, Department of Imaging and Pathology, KU Leuven, Leuven, Belgium
| | - Patrick Dupont
- Laboratory for Cognitive Neurology, KU Leuven, Leuven, Belgium.,Alzheimer Research Centre KU Leuven, Leuven Brain Institute, Leuven, Belgium
| | - Rik Vandenberghe
- Laboratory for Cognitive Neurology, KU Leuven, Leuven, Belgium. .,Alzheimer Research Centre KU Leuven, Leuven Brain Institute, Leuven, Belgium. .,Neurology Department, University Hospitals Leuven, Herestraat 49, 3000, Leuven, Belgium.
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Collij LE, Salvadó G, Wottschel V, Mastenbroek SE, Schoenmakers P, Heeman F, Aksman L, Wink AM, Berckel BNM, van de Flier WM, Scheltens P, Visser PJ, Barkhof F, Haller S, Gispert JD, Lopes Alves I. Spatial-Temporal Patterns of β-Amyloid Accumulation: A Subtype and Stage Inference Model Analysis. Neurology 2022; 98:e1692-e1703. [PMID: 35292558 PMCID: PMC9071373 DOI: 10.1212/wnl.0000000000200148] [Citation(s) in RCA: 41] [Impact Index Per Article: 20.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2021] [Accepted: 01/18/2022] [Indexed: 11/17/2022] Open
Abstract
BACKGROUND AND OBJECTIVES β-amyloid (Aβ) staging models assume a single spatial-temporal progression of amyloid accumulation. We assessed evidence for Aβ accumulation subtypes by applying the data-driven Subtype and Stage Inference (SuStaIn) model to amyloid-PET data. METHODS Amyloid-PET data of 3,010 participants were pooled from 6 cohorts (ALFA+, EMIF-AD, ABIDE, OASIS, and ADNI). Standardized uptake value ratios were calculated for 17 regions. We applied the SuStaIn algorithm to identify consistent subtypes in the pooled dataset based on the cross-validation information criterion and the most probable subtype/stage classification per scan. The effects of demographics and risk factors on subtype assignment were assessed using multinomial logistic regression. RESULTS Participants were mostly cognitively unimpaired (n = 1890 [62.8%]), had a mean age of 68.72 (SD 9.1) years, 42.1% were APOE ε4 carriers, and 51.8% were female. A 1-subtype model recovered the traditional amyloid accumulation trajectory, but SuStaIn identified 3 optimal subtypes, referred to as frontal, parietal, and occipital based on the first regions to show abnormality. Of the 788 (26.2%) with strong subtype assignment (>50% probability), the majority was assigned to frontal (n = 415 [52.5%]), followed by parietal (n = 199 [25.3%]) and occipital subtypes (n = 175 [22.2%]). Significant differences across subtypes included distinct proportions of APOE ε4 carriers (frontal 61.8%, parietal 57.1%, occipital 49.4%), participants with dementia (frontal 19.7%, parietal 19.1%, occipital 31.0%), and lower age for the parietal subtype (frontal/occipital 72.1 years, parietal 69.3 years). Higher amyloid (Centiloid) and CSF p-tau burden was observed for the frontal subtype; parietal and occipital subtypes did not differ. At follow-up, most participants (81.1%) maintained baseline subtype assignment and 25.6% progressed to a later stage. DISCUSSION Whereas a 1-trajectory model recovers the established pattern of amyloid accumulation, SuStaIn determined that 3 subtypes were optimal, showing distinct associations with Alzheimer disease risk factors. Further analyses to determine clinical utility are warranted.
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Affiliation(s)
- Lyduine E Collij
- From the Department of Radiology and Nuclear Medicine (L.E.C., V.W., S.E.M., P.S., F.H., A.M.W., B.N.M.B., F.B., I.L.A.), Alzheimer Center and Department of Neurology (W.M.v.d.F., P.S., P.J.V.), and Department of Epidemiology & Data Science (W.M.v.d.F.), Amsterdam UMC, Vrije Universiteit Amsterdam, the Netherlands; Barcelonaβeta Brain Research Center (BBRC) (G.S., J.D.G.), Pasqual Maragall Foundation; IMIM (Hospital del Mar Medical Research Institute) (G.S., J.D.G.), Barcelona, Spain; Stevens Neuroimaging and Informatics Institute (L.A.), Keck School of Medicine, University of Southern California, Los Angeles; Centre for Medical Image Computing and Queen Square Institute of Neurology (F.B.), UCL, UK; Faculty of Medicine of the University of Geneva (S.H.); CIMC-Centre d'Imagerie Médicale de Cornavin (S.H.), Genève, Switzerland; Department of Surgical Sciences, Radiology (S.H.), Uppsala University, Sweden; Department of Radiology (S.H.), Beijing Tiantan Hospital, Capital Medical University, Beijing, China; and Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN) (J.D.G.), Madrid, Spain
| | - Gemma Salvadó
- From the Department of Radiology and Nuclear Medicine (L.E.C., V.W., S.E.M., P.S., F.H., A.M.W., B.N.M.B., F.B., I.L.A.), Alzheimer Center and Department of Neurology (W.M.v.d.F., P.S., P.J.V.), and Department of Epidemiology & Data Science (W.M.v.d.F.), Amsterdam UMC, Vrije Universiteit Amsterdam, the Netherlands; Barcelonaβeta Brain Research Center (BBRC) (G.S., J.D.G.), Pasqual Maragall Foundation; IMIM (Hospital del Mar Medical Research Institute) (G.S., J.D.G.), Barcelona, Spain; Stevens Neuroimaging and Informatics Institute (L.A.), Keck School of Medicine, University of Southern California, Los Angeles; Centre for Medical Image Computing and Queen Square Institute of Neurology (F.B.), UCL, UK; Faculty of Medicine of the University of Geneva (S.H.); CIMC-Centre d'Imagerie Médicale de Cornavin (S.H.), Genève, Switzerland; Department of Surgical Sciences, Radiology (S.H.), Uppsala University, Sweden; Department of Radiology (S.H.), Beijing Tiantan Hospital, Capital Medical University, Beijing, China; and Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN) (J.D.G.), Madrid, Spain
| | - Viktor Wottschel
- From the Department of Radiology and Nuclear Medicine (L.E.C., V.W., S.E.M., P.S., F.H., A.M.W., B.N.M.B., F.B., I.L.A.), Alzheimer Center and Department of Neurology (W.M.v.d.F., P.S., P.J.V.), and Department of Epidemiology & Data Science (W.M.v.d.F.), Amsterdam UMC, Vrije Universiteit Amsterdam, the Netherlands; Barcelonaβeta Brain Research Center (BBRC) (G.S., J.D.G.), Pasqual Maragall Foundation; IMIM (Hospital del Mar Medical Research Institute) (G.S., J.D.G.), Barcelona, Spain; Stevens Neuroimaging and Informatics Institute (L.A.), Keck School of Medicine, University of Southern California, Los Angeles; Centre for Medical Image Computing and Queen Square Institute of Neurology (F.B.), UCL, UK; Faculty of Medicine of the University of Geneva (S.H.); CIMC-Centre d'Imagerie Médicale de Cornavin (S.H.), Genève, Switzerland; Department of Surgical Sciences, Radiology (S.H.), Uppsala University, Sweden; Department of Radiology (S.H.), Beijing Tiantan Hospital, Capital Medical University, Beijing, China; and Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN) (J.D.G.), Madrid, Spain
| | - Sophie E Mastenbroek
- From the Department of Radiology and Nuclear Medicine (L.E.C., V.W., S.E.M., P.S., F.H., A.M.W., B.N.M.B., F.B., I.L.A.), Alzheimer Center and Department of Neurology (W.M.v.d.F., P.S., P.J.V.), and Department of Epidemiology & Data Science (W.M.v.d.F.), Amsterdam UMC, Vrije Universiteit Amsterdam, the Netherlands; Barcelonaβeta Brain Research Center (BBRC) (G.S., J.D.G.), Pasqual Maragall Foundation; IMIM (Hospital del Mar Medical Research Institute) (G.S., J.D.G.), Barcelona, Spain; Stevens Neuroimaging and Informatics Institute (L.A.), Keck School of Medicine, University of Southern California, Los Angeles; Centre for Medical Image Computing and Queen Square Institute of Neurology (F.B.), UCL, UK; Faculty of Medicine of the University of Geneva (S.H.); CIMC-Centre d'Imagerie Médicale de Cornavin (S.H.), Genève, Switzerland; Department of Surgical Sciences, Radiology (S.H.), Uppsala University, Sweden; Department of Radiology (S.H.), Beijing Tiantan Hospital, Capital Medical University, Beijing, China; and Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN) (J.D.G.), Madrid, Spain
| | - Pierre Schoenmakers
- From the Department of Radiology and Nuclear Medicine (L.E.C., V.W., S.E.M., P.S., F.H., A.M.W., B.N.M.B., F.B., I.L.A.), Alzheimer Center and Department of Neurology (W.M.v.d.F., P.S., P.J.V.), and Department of Epidemiology & Data Science (W.M.v.d.F.), Amsterdam UMC, Vrije Universiteit Amsterdam, the Netherlands; Barcelonaβeta Brain Research Center (BBRC) (G.S., J.D.G.), Pasqual Maragall Foundation; IMIM (Hospital del Mar Medical Research Institute) (G.S., J.D.G.), Barcelona, Spain; Stevens Neuroimaging and Informatics Institute (L.A.), Keck School of Medicine, University of Southern California, Los Angeles; Centre for Medical Image Computing and Queen Square Institute of Neurology (F.B.), UCL, UK; Faculty of Medicine of the University of Geneva (S.H.); CIMC-Centre d'Imagerie Médicale de Cornavin (S.H.), Genève, Switzerland; Department of Surgical Sciences, Radiology (S.H.), Uppsala University, Sweden; Department of Radiology (S.H.), Beijing Tiantan Hospital, Capital Medical University, Beijing, China; and Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN) (J.D.G.), Madrid, Spain
| | - Fiona Heeman
- From the Department of Radiology and Nuclear Medicine (L.E.C., V.W., S.E.M., P.S., F.H., A.M.W., B.N.M.B., F.B., I.L.A.), Alzheimer Center and Department of Neurology (W.M.v.d.F., P.S., P.J.V.), and Department of Epidemiology & Data Science (W.M.v.d.F.), Amsterdam UMC, Vrije Universiteit Amsterdam, the Netherlands; Barcelonaβeta Brain Research Center (BBRC) (G.S., J.D.G.), Pasqual Maragall Foundation; IMIM (Hospital del Mar Medical Research Institute) (G.S., J.D.G.), Barcelona, Spain; Stevens Neuroimaging and Informatics Institute (L.A.), Keck School of Medicine, University of Southern California, Los Angeles; Centre for Medical Image Computing and Queen Square Institute of Neurology (F.B.), UCL, UK; Faculty of Medicine of the University of Geneva (S.H.); CIMC-Centre d'Imagerie Médicale de Cornavin (S.H.), Genève, Switzerland; Department of Surgical Sciences, Radiology (S.H.), Uppsala University, Sweden; Department of Radiology (S.H.), Beijing Tiantan Hospital, Capital Medical University, Beijing, China; and Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN) (J.D.G.), Madrid, Spain
| | - Leon Aksman
- From the Department of Radiology and Nuclear Medicine (L.E.C., V.W., S.E.M., P.S., F.H., A.M.W., B.N.M.B., F.B., I.L.A.), Alzheimer Center and Department of Neurology (W.M.v.d.F., P.S., P.J.V.), and Department of Epidemiology & Data Science (W.M.v.d.F.), Amsterdam UMC, Vrije Universiteit Amsterdam, the Netherlands; Barcelonaβeta Brain Research Center (BBRC) (G.S., J.D.G.), Pasqual Maragall Foundation; IMIM (Hospital del Mar Medical Research Institute) (G.S., J.D.G.), Barcelona, Spain; Stevens Neuroimaging and Informatics Institute (L.A.), Keck School of Medicine, University of Southern California, Los Angeles; Centre for Medical Image Computing and Queen Square Institute of Neurology (F.B.), UCL, UK; Faculty of Medicine of the University of Geneva (S.H.); CIMC-Centre d'Imagerie Médicale de Cornavin (S.H.), Genève, Switzerland; Department of Surgical Sciences, Radiology (S.H.), Uppsala University, Sweden; Department of Radiology (S.H.), Beijing Tiantan Hospital, Capital Medical University, Beijing, China; and Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN) (J.D.G.), Madrid, Spain
| | - Alle Meije Wink
- From the Department of Radiology and Nuclear Medicine (L.E.C., V.W., S.E.M., P.S., F.H., A.M.W., B.N.M.B., F.B., I.L.A.), Alzheimer Center and Department of Neurology (W.M.v.d.F., P.S., P.J.V.), and Department of Epidemiology & Data Science (W.M.v.d.F.), Amsterdam UMC, Vrije Universiteit Amsterdam, the Netherlands; Barcelonaβeta Brain Research Center (BBRC) (G.S., J.D.G.), Pasqual Maragall Foundation; IMIM (Hospital del Mar Medical Research Institute) (G.S., J.D.G.), Barcelona, Spain; Stevens Neuroimaging and Informatics Institute (L.A.), Keck School of Medicine, University of Southern California, Los Angeles; Centre for Medical Image Computing and Queen Square Institute of Neurology (F.B.), UCL, UK; Faculty of Medicine of the University of Geneva (S.H.); CIMC-Centre d'Imagerie Médicale de Cornavin (S.H.), Genève, Switzerland; Department of Surgical Sciences, Radiology (S.H.), Uppsala University, Sweden; Department of Radiology (S.H.), Beijing Tiantan Hospital, Capital Medical University, Beijing, China; and Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN) (J.D.G.), Madrid, Spain
| | - Bart N M Berckel
- From the Department of Radiology and Nuclear Medicine (L.E.C., V.W., S.E.M., P.S., F.H., A.M.W., B.N.M.B., F.B., I.L.A.), Alzheimer Center and Department of Neurology (W.M.v.d.F., P.S., P.J.V.), and Department of Epidemiology & Data Science (W.M.v.d.F.), Amsterdam UMC, Vrije Universiteit Amsterdam, the Netherlands; Barcelonaβeta Brain Research Center (BBRC) (G.S., J.D.G.), Pasqual Maragall Foundation; IMIM (Hospital del Mar Medical Research Institute) (G.S., J.D.G.), Barcelona, Spain; Stevens Neuroimaging and Informatics Institute (L.A.), Keck School of Medicine, University of Southern California, Los Angeles; Centre for Medical Image Computing and Queen Square Institute of Neurology (F.B.), UCL, UK; Faculty of Medicine of the University of Geneva (S.H.); CIMC-Centre d'Imagerie Médicale de Cornavin (S.H.), Genève, Switzerland; Department of Surgical Sciences, Radiology (S.H.), Uppsala University, Sweden; Department of Radiology (S.H.), Beijing Tiantan Hospital, Capital Medical University, Beijing, China; and Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN) (J.D.G.), Madrid, Spain
| | - Wiesje M van de Flier
- From the Department of Radiology and Nuclear Medicine (L.E.C., V.W., S.E.M., P.S., F.H., A.M.W., B.N.M.B., F.B., I.L.A.), Alzheimer Center and Department of Neurology (W.M.v.d.F., P.S., P.J.V.), and Department of Epidemiology & Data Science (W.M.v.d.F.), Amsterdam UMC, Vrije Universiteit Amsterdam, the Netherlands; Barcelonaβeta Brain Research Center (BBRC) (G.S., J.D.G.), Pasqual Maragall Foundation; IMIM (Hospital del Mar Medical Research Institute) (G.S., J.D.G.), Barcelona, Spain; Stevens Neuroimaging and Informatics Institute (L.A.), Keck School of Medicine, University of Southern California, Los Angeles; Centre for Medical Image Computing and Queen Square Institute of Neurology (F.B.), UCL, UK; Faculty of Medicine of the University of Geneva (S.H.); CIMC-Centre d'Imagerie Médicale de Cornavin (S.H.), Genève, Switzerland; Department of Surgical Sciences, Radiology (S.H.), Uppsala University, Sweden; Department of Radiology (S.H.), Beijing Tiantan Hospital, Capital Medical University, Beijing, China; and Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN) (J.D.G.), Madrid, Spain
| | - Philip Scheltens
- From the Department of Radiology and Nuclear Medicine (L.E.C., V.W., S.E.M., P.S., F.H., A.M.W., B.N.M.B., F.B., I.L.A.), Alzheimer Center and Department of Neurology (W.M.v.d.F., P.S., P.J.V.), and Department of Epidemiology & Data Science (W.M.v.d.F.), Amsterdam UMC, Vrije Universiteit Amsterdam, the Netherlands; Barcelonaβeta Brain Research Center (BBRC) (G.S., J.D.G.), Pasqual Maragall Foundation; IMIM (Hospital del Mar Medical Research Institute) (G.S., J.D.G.), Barcelona, Spain; Stevens Neuroimaging and Informatics Institute (L.A.), Keck School of Medicine, University of Southern California, Los Angeles; Centre for Medical Image Computing and Queen Square Institute of Neurology (F.B.), UCL, UK; Faculty of Medicine of the University of Geneva (S.H.); CIMC-Centre d'Imagerie Médicale de Cornavin (S.H.), Genève, Switzerland; Department of Surgical Sciences, Radiology (S.H.), Uppsala University, Sweden; Department of Radiology (S.H.), Beijing Tiantan Hospital, Capital Medical University, Beijing, China; and Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN) (J.D.G.), Madrid, Spain
| | - Pieter Jelle Visser
- From the Department of Radiology and Nuclear Medicine (L.E.C., V.W., S.E.M., P.S., F.H., A.M.W., B.N.M.B., F.B., I.L.A.), Alzheimer Center and Department of Neurology (W.M.v.d.F., P.S., P.J.V.), and Department of Epidemiology & Data Science (W.M.v.d.F.), Amsterdam UMC, Vrije Universiteit Amsterdam, the Netherlands; Barcelonaβeta Brain Research Center (BBRC) (G.S., J.D.G.), Pasqual Maragall Foundation; IMIM (Hospital del Mar Medical Research Institute) (G.S., J.D.G.), Barcelona, Spain; Stevens Neuroimaging and Informatics Institute (L.A.), Keck School of Medicine, University of Southern California, Los Angeles; Centre for Medical Image Computing and Queen Square Institute of Neurology (F.B.), UCL, UK; Faculty of Medicine of the University of Geneva (S.H.); CIMC-Centre d'Imagerie Médicale de Cornavin (S.H.), Genève, Switzerland; Department of Surgical Sciences, Radiology (S.H.), Uppsala University, Sweden; Department of Radiology (S.H.), Beijing Tiantan Hospital, Capital Medical University, Beijing, China; and Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN) (J.D.G.), Madrid, Spain
| | - Frederik Barkhof
- From the Department of Radiology and Nuclear Medicine (L.E.C., V.W., S.E.M., P.S., F.H., A.M.W., B.N.M.B., F.B., I.L.A.), Alzheimer Center and Department of Neurology (W.M.v.d.F., P.S., P.J.V.), and Department of Epidemiology & Data Science (W.M.v.d.F.), Amsterdam UMC, Vrije Universiteit Amsterdam, the Netherlands; Barcelonaβeta Brain Research Center (BBRC) (G.S., J.D.G.), Pasqual Maragall Foundation; IMIM (Hospital del Mar Medical Research Institute) (G.S., J.D.G.), Barcelona, Spain; Stevens Neuroimaging and Informatics Institute (L.A.), Keck School of Medicine, University of Southern California, Los Angeles; Centre for Medical Image Computing and Queen Square Institute of Neurology (F.B.), UCL, UK; Faculty of Medicine of the University of Geneva (S.H.); CIMC-Centre d'Imagerie Médicale de Cornavin (S.H.), Genève, Switzerland; Department of Surgical Sciences, Radiology (S.H.), Uppsala University, Sweden; Department of Radiology (S.H.), Beijing Tiantan Hospital, Capital Medical University, Beijing, China; and Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN) (J.D.G.), Madrid, Spain
| | - Sven Haller
- From the Department of Radiology and Nuclear Medicine (L.E.C., V.W., S.E.M., P.S., F.H., A.M.W., B.N.M.B., F.B., I.L.A.), Alzheimer Center and Department of Neurology (W.M.v.d.F., P.S., P.J.V.), and Department of Epidemiology & Data Science (W.M.v.d.F.), Amsterdam UMC, Vrije Universiteit Amsterdam, the Netherlands; Barcelonaβeta Brain Research Center (BBRC) (G.S., J.D.G.), Pasqual Maragall Foundation; IMIM (Hospital del Mar Medical Research Institute) (G.S., J.D.G.), Barcelona, Spain; Stevens Neuroimaging and Informatics Institute (L.A.), Keck School of Medicine, University of Southern California, Los Angeles; Centre for Medical Image Computing and Queen Square Institute of Neurology (F.B.), UCL, UK; Faculty of Medicine of the University of Geneva (S.H.); CIMC-Centre d'Imagerie Médicale de Cornavin (S.H.), Genève, Switzerland; Department of Surgical Sciences, Radiology (S.H.), Uppsala University, Sweden; Department of Radiology (S.H.), Beijing Tiantan Hospital, Capital Medical University, Beijing, China; and Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN) (J.D.G.), Madrid, Spain
| | - Juan Domingo Gispert
- From the Department of Radiology and Nuclear Medicine (L.E.C., V.W., S.E.M., P.S., F.H., A.M.W., B.N.M.B., F.B., I.L.A.), Alzheimer Center and Department of Neurology (W.M.v.d.F., P.S., P.J.V.), and Department of Epidemiology & Data Science (W.M.v.d.F.), Amsterdam UMC, Vrije Universiteit Amsterdam, the Netherlands; Barcelonaβeta Brain Research Center (BBRC) (G.S., J.D.G.), Pasqual Maragall Foundation; IMIM (Hospital del Mar Medical Research Institute) (G.S., J.D.G.), Barcelona, Spain; Stevens Neuroimaging and Informatics Institute (L.A.), Keck School of Medicine, University of Southern California, Los Angeles; Centre for Medical Image Computing and Queen Square Institute of Neurology (F.B.), UCL, UK; Faculty of Medicine of the University of Geneva (S.H.); CIMC-Centre d'Imagerie Médicale de Cornavin (S.H.), Genève, Switzerland; Department of Surgical Sciences, Radiology (S.H.), Uppsala University, Sweden; Department of Radiology (S.H.), Beijing Tiantan Hospital, Capital Medical University, Beijing, China; and Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN) (J.D.G.), Madrid, Spain
| | - Isadora Lopes Alves
- From the Department of Radiology and Nuclear Medicine (L.E.C., V.W., S.E.M., P.S., F.H., A.M.W., B.N.M.B., F.B., I.L.A.), Alzheimer Center and Department of Neurology (W.M.v.d.F., P.S., P.J.V.), and Department of Epidemiology & Data Science (W.M.v.d.F.), Amsterdam UMC, Vrije Universiteit Amsterdam, the Netherlands; Barcelonaβeta Brain Research Center (BBRC) (G.S., J.D.G.), Pasqual Maragall Foundation; IMIM (Hospital del Mar Medical Research Institute) (G.S., J.D.G.), Barcelona, Spain; Stevens Neuroimaging and Informatics Institute (L.A.), Keck School of Medicine, University of Southern California, Los Angeles; Centre for Medical Image Computing and Queen Square Institute of Neurology (F.B.), UCL, UK; Faculty of Medicine of the University of Geneva (S.H.); CIMC-Centre d'Imagerie Médicale de Cornavin (S.H.), Genève, Switzerland; Department of Surgical Sciences, Radiology (S.H.), Uppsala University, Sweden; Department of Radiology (S.H.), Beijing Tiantan Hospital, Capital Medical University, Beijing, China; and Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN) (J.D.G.), Madrid, Spain
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Matsuda H, Yamao T. Software development for quantitative analysis of brain amyloid PET. Brain Behav 2022; 12:e2499. [PMID: 35134278 PMCID: PMC8933769 DOI: 10.1002/brb3.2499] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/17/2021] [Revised: 12/01/2021] [Accepted: 01/02/2022] [Indexed: 11/07/2022] Open
Abstract
INTRODUCTION Centiloid (CL) scaling has become a standard quantitative measure in amyloid PET because it allows the direct comparison of results across sites, even when different analytical methods or PET tracers are used. METHODS In the present study, we developed new standalone software to easily handle a pipeline for accurate calculation of the CL scale for the five currently available amyloid PET tracers-11 C-PiB, 18 F-florbetapir, 18 F-flutemetamol, 18 F-florbetaben, and 18 F-NAV4694. This pipeline requires reorientation and coregistration of PET and MRI, anatomic standardization of coregistered PET to a standardized space using a warping parameter for coregistered MRI, application of standard volumes of interest (VOIs) to the warped PET, calculation of the standardized uptake value ratio (SUVR) for the target VOIs, and finally conversion of the SUVR to the CL scale. The PET data for these tracers were collected from the publicly available Global Alzheimer's Association Interactive Network (GAAIN) repository. We also developed software to map Z-scores for the statistical comparison of a patient's PET data with a negative control database obtained from young healthy controls in the GAAIN repository. RESULTS When whole cerebellum or whole cerebellum plus brainstem was chosen as the reference area, an excellent correlation was found between the CL scale calculated by this software and the CL scale published by GAAIN. There were no significant differences in the detection performance of significant amyloid accumulation using Z-score mapping between each 18 F-labeled tracer and 11 C-PiB. The cutoff CL values providing the most accurate detection of regional amyloid positivity in Z-score mapping were 11.8, 14.4, 14.7, 15.6, and 17.7 in the posterior cingulate gyrus and precuneus, frontal cortex, temporal cortex, parietal cortex, and striatum, respectively. CONCLUSION This software is able to not only provide reliable calculation of the global CL scale but also detect significant local amyloid accumulation in an individual patient.
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Affiliation(s)
- Hiroshi Matsuda
- Department of Biofunctional Imaging, Fukushima Medical University, Fukushima City, Fukushima, Japan.,Drug Discovery and Cyclotron Research Center, Southern Tohoku Research Institute for Neuroscience, Koriyama, Fukushima, Japan.,Department of Radiology, National Center of Neurology and Psychiatry, Kodaira, Tokyo, Japan
| | - Tensho Yamao
- Drug Discovery and Cyclotron Research Center, Southern Tohoku Research Institute for Neuroscience, Koriyama, Fukushima, Japan.,Department of Radiological Sciences, School of Health Sciences, Fukushima Medical University, Sakae, Fukushima, Japan
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Kim JP, Chun MY, Kim SJ, Jang H, Kim HJ, Jeong JH, Na DL, Seo SW. Distinctive Temporal Trajectories of Alzheimer’s Disease Biomarkers According to Sex and APOE Genotype: Importance of Striatal Amyloid. Front Aging Neurosci 2022; 14:829202. [PMID: 35197846 PMCID: PMC8859452 DOI: 10.3389/fnagi.2022.829202] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2021] [Accepted: 01/14/2022] [Indexed: 01/09/2023] Open
Abstract
PurposePreviously, sex and apolipoprotein E (APOE) genotype had distinct effects on the cognitive trajectory across the Alzheimer’s disease (AD) continuum. We therefore aimed to investigate whether these trajectory curves including β-amyloid (Aβ) accumulation in the cortex and striatum, and tau accumulation would differ according to sex and APOE genotype.MethodsWe obtained 534 subjects for 18F-florbetapir (AV45) PET analysis and 163 subjects for 18F-flortaucipir (AV1451) PET analysis from the Alzheimer’s Disease Neuroimaging Initiative database. For cortical Aβ, striatal Aβ, and tau SUVR, we fitted penalized splines to model the slopes of SUVR value as a non-linear function of baseline SUVR value. By integrating the fitted splines, we obtained the predicted SUVR curves as a function of time.ResultsThe time from initial SUVR to the cutoff values were 14.9 years for cortical Aβ, 18.2 years for striatal Aβ, and 22.7 years for tau. Although there was no difference in cortical Aβ accumulation rate between women and men, striatal Aβ accumulation was found to be faster in women than in men, and this temporal difference according to sex was more pronounced in tau accumulation. However, APOE ε4 carriers showed faster progression than non-carriers regardless of kinds of AD biomarkers’ trajectories.ConclusionOur temporal trajectory models illustrate that there is a distinct progression pattern of AD biomarkers depending on sex and APOE genotype. In this regard, our models will be able to contribute to designing personalized treatment and prevention strategies for AD in clinical practice.
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Affiliation(s)
- Jun Pyo Kim
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
- Center for Neuroimaging, Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, United States
| | - Min Young Chun
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
| | - Soo-Jong Kim
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
- Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, South Korea
- Department of Intelligent Precision Healthcare Convergence, Sungkyunkwan University, Suwon, South Korea
| | - Hyemin Jang
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
- Alzheimer’s Disease Convergence Research Center, Samsung Medical Center, Seoul, South Korea
| | - Hee Jin Kim
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
- Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, South Korea
- Alzheimer’s Disease Convergence Research Center, Samsung Medical Center, Seoul, South Korea
- Department of Digital Health, SAIHST, Sungkyunkwan University, Seoul, South Korea
| | - Jee Hyang Jeong
- Departments of Neurology, Ewha Womans University Seoul Hospital, Ewha Womans University College of Medicine, Seoul, South Korea
| | - Duk L. Na
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
- Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, South Korea
- Alzheimer’s Disease Convergence Research Center, Samsung Medical Center, Seoul, South Korea
- Stem Cell & Regenerative Medicine Institute, Samsung Medical Center, Seoul, South Korea
| | - Sang Won Seo
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
- Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, South Korea
- Department of Intelligent Precision Healthcare Convergence, Sungkyunkwan University, Suwon, South Korea
- Alzheimer’s Disease Convergence Research Center, Samsung Medical Center, Seoul, South Korea
- Department of Digital Health, SAIHST, Sungkyunkwan University, Seoul, South Korea
- *Correspondence: Sang Won Seo,
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Lee S, Kim TK, Choi JE, Choi Y, You M, Ryu J, Chun YL, Ham S, Hyeon SJ, Ryu H, Kim HS, Im HI. Dysfunction of striatal MeCP2 is associated with cognitive decline in a mouse model of Alzheimer's disease. Theranostics 2022; 12:1404-1418. [PMID: 35154497 PMCID: PMC8771549 DOI: 10.7150/thno.68439] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2021] [Accepted: 12/18/2021] [Indexed: 11/05/2022] Open
Abstract
Rationale: Cerebral Methyl-CpG binding Protein 2 (MeCP2) is involved in several psychiatric disorders that are concomitant with cognitive dysfunction. However, the regulatory function of striatal MeCP2 and its association with Alzheimer's disease (AD) has been largely neglected due to the absence of amyloid plaque accumulation in the striatal region until the later stages of AD progression. Considerable evidence indicates that neuropsychiatric symptoms related to cognitive decline are involved with striatal dysfunction. To this respect, we investigated the epigenetic function of striatal MeCP2 paralleling the pathogenesis of AD. Methods: We investigated the brain from amyloid precursor protein (APP)/presenilin1 (PS1) transgenic mice and postmortem brain samples from normal subjects and AD patients. The molecular changes in the brain, particularly in the striatal regions, were analyzed with thioflavin S staining, immunohistochemistry, immunoblotting, and MeCP2 chromatin immunoprecipitation sequencing (ChIP-seq). The cognitive function of APP/PS1 mice was assessed via three behavioral tests: 3-chamber test (3CT), Y-maze test (YMT), and passive avoidance test (PA). A multi-electrode array (MEA) was performed to analyze the neuronal activity of the striatum in APP/PS1 mice. Results: Striatal MeCP2 expression was increased in the younger (6 months) and older (10 months) ages of APP/PS1 mice, and the genome-wide occupancy of MeCP2 in the younger APP/PS1 showed dysregulated binding patterns in the striatum. Additionally, we confirmed that APP/PS1 mice showed behavioral deficits in multiple cognitive behaviors. Notably, defective cognitive phenotypes and abnormal neuronal activity in old APP/PS1 mice were rescued through the knock-down of striatal MeCP2. Conclusion: We found that the MeCP2-mediated dysregulation of the epigenome in the striatum is linked to the defects in cognitive behavior and neuronal activity in the AD animal model, and that this alteration is initiated even in the very early stages of AD pathogenesis. Together, our data indicates that MeCP2 may be a potential target for the diagnosis and treatment of AD at asymptomatic and symptomatic stages.
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Affiliation(s)
- Sangjoon Lee
- Convergence Research Center for Diagnosis, Treatment and Care System of Dementia, Korea Institute of Science and Technology (KIST), Seoul 02792, Korea
- Department of Pharmacology and Biomedical Sciences, Seoul National University College of Medicine, Seoul 03080, Korea
- Brain Science Institute, Korea Institute of Science and Technology (KIST), Seoul 02792, Korea
| | - Tae Kyoo Kim
- Convergence Research Center for Diagnosis, Treatment and Care System of Dementia, Korea Institute of Science and Technology (KIST), Seoul 02792, Korea
| | - Ji Eun Choi
- Convergence Research Center for Diagnosis, Treatment and Care System of Dementia, Korea Institute of Science and Technology (KIST), Seoul 02792, Korea
- Department of Biochemistry, Hanyang University College of Medicine, Seoul 04763, Korea
| | - Yunjung Choi
- Convergence Research Center for Diagnosis, Treatment and Care System of Dementia, Korea Institute of Science and Technology (KIST), Seoul 02792, Korea
- Department of Pharmacology and Biomedical Sciences, Seoul National University College of Medicine, Seoul 03080, Korea
| | - Minsu You
- Convergence Research Center for Diagnosis, Treatment and Care System of Dementia, Korea Institute of Science and Technology (KIST), Seoul 02792, Korea
| | - Jeewon Ryu
- Convergence Research Center for Diagnosis, Treatment and Care System of Dementia, Korea Institute of Science and Technology (KIST), Seoul 02792, Korea
| | - Yoo Lim Chun
- Convergence Research Center for Diagnosis, Treatment and Care System of Dementia, Korea Institute of Science and Technology (KIST), Seoul 02792, Korea
| | - Suji Ham
- Convergence Research Center for Diagnosis, Treatment and Care System of Dementia, Korea Institute of Science and Technology (KIST), Seoul 02792, Korea
| | - Seung Jae Hyeon
- Brain Science Institute, Korea Institute of Science and Technology (KIST), Seoul 02792, Korea
| | - Hoon Ryu
- Convergence Research Center for Diagnosis, Treatment and Care System of Dementia, Korea Institute of Science and Technology (KIST), Seoul 02792, Korea
- Brain Science Institute, Korea Institute of Science and Technology (KIST), Seoul 02792, Korea
| | - Hye-Sun Kim
- Department of Pharmacology and Biomedical Sciences, Seoul National University College of Medicine, Seoul 03080, Korea
- Seoul National University College of Medicine, Bundang Hospital, Sungnam 13620, Korea
| | - Heh-In Im
- Convergence Research Center for Diagnosis, Treatment and Care System of Dementia, Korea Institute of Science and Technology (KIST), Seoul 02792, Korea
- Brain Science Institute, Korea Institute of Science and Technology (KIST), Seoul 02792, Korea
- Division of Bio-Medical Science & Technology, KIST School, Korea University of Science and Technology, Seoul 02792, Korea
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Kim YK. Recent Updates on PET Imaging in Neurodegenerative Diseases. JOURNAL OF THE KOREAN SOCIETY OF RADIOLOGY 2022; 83:453-472. [PMID: 36238518 PMCID: PMC9514517 DOI: 10.3348/jksr.2022.0052] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/16/2022] [Revised: 05/08/2022] [Accepted: 05/16/2022] [Indexed: 11/23/2022]
Abstract
양전자방출단층촬영(PET)을 이용한 단백질병리의 생체영상기술은 퇴행성 치매의 질병 기전을 이해하는데 필요한 정보를 제공할 뿐 아니라, 질병의 조기 발견과 치료법 개발에서 중요한 역할을 수행하고 있다. 베타아밀로이드와 타우 PET 영상은 인체 뇌병리에 기반한 알츠하이머병 연속체에 대한 진단 바이오마커로 확립되어 조기진단과 감별진단을 용이하게 하고, 질병 예후를 예측하고 있다. 또한, 치매치료제 개발에서 예후 및 대리 바이오마커로의 역할이 커지고 있다. 이 종설에서는 치매를 유발하는 알츠하이머병 및 기타 퇴행성 뇌질환에서 베타아밀로이드와 타우 단백질의 뇌축적을 영상화하는 PET의 최근 임상적 적용과 최근 동향을 살펴보고, 잠재적 유용성을 소개하고자 한다.
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Affiliation(s)
- Yu Kyeong Kim
- Department of Nuclear Medicine, Seoul National University Boramae Medical Center, Seoul, Korea
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39
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McGrath ER, Beiser AS, O'Donnell A, Yang Q, Ghosh S, Gonzales MM, Himali JJ, Satizabal CL, Johnson KA, Tracy RP, Seshadri S. Blood Phosphorylated Tau 181 as a Biomarker for Amyloid Burden on Brain PET in Cognitively Healthy Adults. J Alzheimers Dis 2022; 87:1517-1526. [PMID: 35491781 DOI: 10.3233/jad-215639] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Abstract
BACKGROUND Plasma phosphorylated-tau181 (p-tau181) is a promising biomarker for Alzheimer's disease (AD) and may offer utility for predicting preclinical disease. OBJECTIVE To evaluate the prospective association between plasma p-tau181 and amyloid-β (Aβ) and tau-PET deposition in cognitively unimpaired individuals. METHODS Plasma p-tau181 levels were measured at baseline in 52 [48% women, mean 64.4 (SD 5.5) years] cognitively unimpaired Framingham Offspring cohort participants using samples stored between 2011-2014 who subsequently underwent 11C-Pittsburgh Compound-B (PiB)-PET and/or 18F-Flortaucipir (FTP)-PET scans (n = 18 with tau-PET) a mean of 6.8 (SD 0.6) years later. Our primary outcomes included Aβ-precuneus, Aβ-FLR (frontal, lateral, and retrosplenial cortices) and tau-global composite region PET deposition. Secondary outcomes included individual regional Aβ and tau PET-deposition. P-tau181 was compared with plasma neurofilament light chain (NFL) and glial fibrillary acidic protein (GFAP) in predicting PET outcomes. RESULTS P-tau181 was associated with increased Aβ deposition in the FLR (β±SE, 1.25±0.30, p < 0.0001), precuneus (1.35±0.29, p < 0.001), and other cortical regions. Plasma NFL (1.30±0.49, p = 0.01) and GFAP (1.46±0.39, p < 0.001) were also associated with FLR Aβ deposition. In models including all three biomarkers adjusted for age, sex, APOE E4 allele, AD polygenic risk score and cortical atrophy score, p-tau181 (0.93±0.31, p < 0.01, R2 = 0.18) and GFAP (0.93±0.41, p = 0.03, R2 = 0.11), but not NFL (0.25±0.51, p = 0.62, R2 = 0.01), were associated with FLR-Aβ deposition. Plasma p-tau181 was not associated with tau-PET burden. CONCLUSION In cognitively unimpaired adults, elevated plasma p-tau181 is associated with future increased Aβ deposition across multiple brain regions. Our results highlight the potential utility of p-tau181 as a blood-biomarker to screen for brain-amyloid deposition in cognitively healthy individuals in a community-setting.
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Affiliation(s)
- Emer R McGrath
- HRB Clinical Research Facility, National University of Ireland Galway, Galway, Ireland
- The Framingham Heart Study, Framingham, MA, USA
| | - Alexa S Beiser
- The Framingham Heart Study, Framingham, MA, USA
- Boston University School of Public Health, Boston, MA, USA
- Boston University School of Medicine, Boston, MA, USA
| | - Adrienne O'Donnell
- The Framingham Heart Study, Framingham, MA, USA
- Boston University School of Public Health, Boston, MA, USA
| | - Qiong Yang
- The Framingham Heart Study, Framingham, MA, USA
- Boston University School of Public Health, Boston, MA, USA
| | - Saptaparni Ghosh
- The Framingham Heart Study, Framingham, MA, USA
- Boston University School of Medicine, Boston, MA, USA
| | - Mitzi M Gonzales
- The Framingham Heart Study, Framingham, MA, USA
- Glenn Biggs Institute for Alzheimer's & Neurodegenerative Diseases, University of Texas Health Sciences Center, San Antonio, TX, USA
| | - Jayandra J Himali
- The Framingham Heart Study, Framingham, MA, USA
- Boston University School of Public Health, Boston, MA, USA
- Boston University School of Medicine, Boston, MA, USA
- Glenn Biggs Institute for Alzheimer's & Neurodegenerative Diseases, University of Texas Health Sciences Center, San Antonio, TX, USA
| | - Claudia L Satizabal
- The Framingham Heart Study, Framingham, MA, USA
- Glenn Biggs Institute for Alzheimer's & Neurodegenerative Diseases, University of Texas Health Sciences Center, San Antonio, TX, USA
| | - Keith A Johnson
- Department of Radiology, Massachusetts General Hospital, the Gordon Center for Medical Imaging and the Athinoula A. Martinos Center for Biomedical Imaging, Boston, MA, USA
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Center for Alzheimer Research and Treatment, Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Russell P Tracy
- Department of Pathology and Laboratory Medicine, Larner College of Medicine, University of Vermont, Burlington, VT, USA
- Department of Biochemistry, Larner College of Medicine, University of Vermont, Burlington, VT, USA
| | - Sudha Seshadri
- The Framingham Heart Study, Framingham, MA, USA
- Boston University School of Medicine, Boston, MA, USA
- Glenn Biggs Institute for Alzheimer's & Neurodegenerative Diseases, University of Texas Health Sciences Center, San Antonio, TX, USA
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Baradaran H, Peloso GM, Polak JF, Killiany RJ, Ghosh S, DeCarli CS, Thibault EG, Sperling RA, Johnson KA, Beiser A, Romero JR, Seshadri S. Association of Carotid Intima Media Thickening with Future Brain Region Specific Amyloid-β Burden. J Alzheimers Dis 2022; 89:223-232. [PMID: 35871328 DOI: 10.3233/jad-215679] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND Carotid atherosclerosis is associated with cognitive impairment and dementia, though there is limited evidence of a direct link between carotid disease and amyloid-β (Aβ) burden. OBJECTIVE We studied the association of baseline and progressive carotid intima media thickness (CIMT) with Aβ on 11C-Pittsburgh Compound B (PiB) to determine if those with carotid atherosclerosis would have higher Aβ burden. METHODS We studied 47 participants from the Framingham Offspring cohort with carotid ultrasounds measuring CIMT at their 6th clinic examination (aged 49.5±5.7 years) and an average of 9.6 years later, and PiB imaging measuring Aβ on average 22.1 years post baseline. We used multivariate linear regression analyses to relate baseline, follow-up, mean, and progression of internal carotid artery (ICA) and common carotid artery (CCA) CIMT to Aβ in brain regions associated with Alzheimer's disease (AD) and related dementias (ADRD), adjusting for age, sex, and other vascular risk factors. RESULTS Participants with higher mean ICA IMT had more Aβ in the precuneus (beta±standard error [β±SE]: 0.466±0.171 mm, p = 0.01) and the frontal, lateral, and retrosplenial regions (β±SE: 0.392±0.164 mm, p = 0.022) after adjusting for age, sex, vascular risk factors, and medication use. We did not find an association between any CCA IMT measures and Aβ or progression of ICA or CCA IMT and Aβ. CONCLUSION Carotid atherosclerosis, as measured by ICA IMT, is associated with increased Aβ burden later in life. These findings support a link between vascular disease and AD/ADRD pathophysiology.
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Affiliation(s)
- Hediyeh Baradaran
- Department of Radiology, University of Utah, Salt Lake City, UT, USA
| | - Gina M Peloso
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Joseph F Polak
- Department of Radiology, Tufts University School of Medicine, Boston, MA, USA
| | - Ronald J Killiany
- Department of Neurology, Boston University School of Medicine, Boston, MA, USA
- Department of Anatomy and Neurobiology, Boston University School of Medicine, Boston, MA, USA
| | - Saptaparni Ghosh
- Department of Neurology, Boston University School of Medicine, Boston, MA, USA
- NHLBI's Framingham Heart Study, Framingham, MA, USA
| | - Charles S DeCarli
- Department of Neurology, School of Medicine & Imaging of Dementia and Aging Laboratory, Center for Neuroscience, University of California Davis, Davis, CA, USA
| | - Emma G Thibault
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Reisa A Sperling
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Keith A Johnson
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Alexa Beiser
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
- Department of Neurology, Boston University School of Medicine, Boston, MA, USA
- NHLBI's Framingham Heart Study, Framingham, MA, USA
| | - Jose R Romero
- Department of Neurology, Boston University School of Medicine, Boston, MA, USA
- NHLBI's Framingham Heart Study, Framingham, MA, USA
| | - Sudha Seshadri
- Department of Neurology, Boston University School of Medicine, Boston, MA, USA
- NHLBI's Framingham Heart Study, Framingham, MA, USA
- Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, University of Texas Health Sciences Center, San Antonio, TX, USA
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Weinstein G, O’Donnell A, Davis-Plourde K, Zelber-Sagi S, Ghosh S, DeCarli CS, Thibault EG, Sperling RA, Johnson KA, Beiser AS, Seshadri S. Non-Alcoholic Fatty Liver Disease, Liver Fibrosis, and Regional Amyloid-β and Tau Pathology in Middle-Aged Adults: The Framingham Study. J Alzheimers Dis 2022; 86:1371-1383. [PMID: 35213373 PMCID: PMC11323287 DOI: 10.3233/jad-215409] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
BACKGROUND Liver steatosis and fibrosis are emerging as risk factors for multiple extrahepatic health conditions; however, their relationship with Alzheimer's disease pathology is unclear. OBJECTIVE To examine whether non-alcoholic fatty liver disease (NAFLD) and FIB-4, a non-invasive index of advanced fibrosis, are associated with brain amyloid-β (Aβ) and tau pathology. METHODS The study sample included Framingham Study participants from the Offspring and Third generation cohorts who attended exams 9 (2011-2014) and 2 (2008-2011), respectively. Participants underwent 11C-Pittsburgh Compound-B amyloid and 18F-Flortaucipir tau positron emission tomography (PET) imaging and abdomen computed tomography, or had information on all components of the FIB-4 index. Linear regression models were used to assess the relationship of NAFLD and FIB-4 with regional tau and Aβ, adjusting for potential confounders and multiple comparisons. RESULTS Of the subsample with NAFLD information (N = 169; mean age 52±9 y; 57% males), 57 (34%) had NAFLD. Of the subsample with information on liver fibrosis (N = 177; mean age 50±10 y; 51% males), 34 (19%) had advanced fibrosis (FIB-4 > 1.3). Prevalent NAFLD was not associated with Aβ or tau PET. However, FIB-4 index was significantly associated with increased rhinal tau (β= 1.03±0.33, p = 0.002). Among individuals with prevalent NAFLD, FIB-4 was related to inferior temporal, parahippocampal gyrus, entorhinal and rhinal tau (β= 2.01±0.47, p < 0.001; β= 1.60±0.53, p = 0.007, and β= 1.59±0.47, p = 0.003 and β= 1.60±0.42, p = 0.001, respectively) and to Aβ deposition overall and in the inferior temporal and parahippocampal regions (β= 1.93±0.47, p < 0.001; β= 1.59±0.38, p < 0.001, and β= 1.52±0.54, p = 0.008, respectively). CONCLUSION This study suggests a possible association between liver fibrosis and early Alzheimer's disease pathology, independently of cardio-metabolic risk factors.
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Affiliation(s)
| | - Adrienne O’Donnell
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
- The Framingham Study, Framingham, MA, USA
| | - Kendra Davis-Plourde
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
- The Framingham Study, Framingham, MA, USA
| | - Shira Zelber-Sagi
- School of Public Health, University of Haifa, Haifa, Israel
- Liver Unit, Department of Gastroenterology, Tel-Aviv Medical Center, Tel-Aviv, Israel
| | - Saptaparni Ghosh
- The Framingham Study, Framingham, MA, USA
- Department of Neurology, Boston University School of Medicine, Boston, MA, USA
| | - Charles S. DeCarli
- Department of Neurology, School of Medicine & Imaging of Dementia and Aging Laboratory, Center for Neuroscience, University of California Davis, Davis, CA, USA
| | - Emma G. Thibault
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Reisa A. Sperling
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Keith A. Johnson
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Alexa S. Beiser
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
- The Framingham Study, Framingham, MA, USA
- Department of Neurology, Boston University School of Medicine, Boston, MA, USA
| | - Sudha Seshadri
- The Framingham Study, Framingham, MA, USA
- Department of Neurology, Boston University School of Medicine, Boston, MA, USA
- Glenn Biggs Institute for Alzheimer’s and Neurodegenerative Diseases, University of Texas Health Sciences Center, San Antonio, TX, USA
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Frisoni GB, Altomare D, Thal DR, Ribaldi F, van der Kant R, Ossenkoppele R, Blennow K, Cummings J, van Duijn C, Nilsson PM, Dietrich PY, Scheltens P, Dubois B. The probabilistic model of Alzheimer disease: the amyloid hypothesis revised. Nat Rev Neurosci 2022; 23:53-66. [PMID: 34815562 PMCID: PMC8840505 DOI: 10.1038/s41583-021-00533-w] [Citation(s) in RCA: 185] [Impact Index Per Article: 92.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/04/2021] [Indexed: 01/03/2023]
Abstract
The current conceptualization of Alzheimer disease (AD) is driven by the amyloid hypothesis, in which a deterministic chain of events leads from amyloid deposition and then tau deposition to neurodegeneration and progressive cognitive impairment. This model fits autosomal dominant AD but is less applicable to sporadic AD. Owing to emerging information regarding the complex biology of AD and the challenges of developing amyloid-targeting drugs, the amyloid hypothesis needs to be reconsidered. Here we propose a probabilistic model of AD in which three variants of AD (autosomal dominant AD, APOE ε4-related sporadic AD and APOE ε4-unrelated sporadic AD) feature decreasing penetrance and decreasing weight of the amyloid pathophysiological cascade, and increasing weight of stochastic factors (environmental exposures and lower-risk genes). Together, these variants account for a large share of the neuropathological and clinical variability observed in people with AD. The implementation of this model in research might lead to a better understanding of disease pathophysiology, a revision of the current clinical taxonomy and accelerated development of strategies to prevent and treat AD.
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Affiliation(s)
- Giovanni B Frisoni
- Laboratory of Neuroimaging of Aging (LANVIE), University of Geneva, Geneva, Switzerland.
- Memory Clinic, Geneva University Hospitals, Geneva, Switzerland.
| | - Daniele Altomare
- Laboratory of Neuroimaging of Aging (LANVIE), University of Geneva, Geneva, Switzerland
- Memory Clinic, Geneva University Hospitals, Geneva, Switzerland
| | - Dietmar Rudolf Thal
- Laboratory for Neuropathology, Department of Imaging and Pathology, and Leuven Brain Institute, University of Leuven, Leuven, Belgium
- Department of Pathology, University Hospital Leuven, Leuven, Belgium
| | - Federica Ribaldi
- Laboratory of Neuroimaging of Aging (LANVIE), University of Geneva, Geneva, Switzerland
- Memory Clinic, Geneva University Hospitals, Geneva, Switzerland
- Laboratory of Alzheimer's Neuroimaging and Epidemiology (LANE), IRCCS Centro S. Giovanni di Dio Fatebenefratelli, Brescia, Italy
- Department of Molecular and Translational Medicine, University of Brescia, Brescia, Italy
| | - Rik van der Kant
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Amsterdam UMC, Amsterdam, Netherlands
- Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, Netherlands
| | - Rik Ossenkoppele
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Amsterdam UMC, Amsterdam, Netherlands
- Clinical Memory Research Unit, Lund University, Lund, Sweden
| | - Kaj Blennow
- Cinical Neurochemistry Laboratory, Institute of Neuroscience and Physiology, University of Gothenburg, Sahlgrenska University Hospital, Mölndal, Sweden
| | - Jeffrey Cummings
- Chambers-Grundy Center for Transformative Neuroscience, Department of Brain Health, School of Integrated Health Sciences; University of Nevada, Las Vegas, Las Vegas, NV, USA
| | - Cornelia van Duijn
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, Netherlands
- Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Peter M Nilsson
- Department of Clinical Sciences, Lund University, Skåne University Hospital, Malmö, Sweden
| | | | - Philip Scheltens
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Amsterdam UMC, Amsterdam, Netherlands
- Life Science Partners, Amsterdam, Netherlands
| | - Bruno Dubois
- Institut de la Mémoire et de la Maladie d'Alzheimer, IM2A, Groupe Hospitalier Pitié-Salpêtrière, Sorbonne Université, Paris, France
- Institut du Cerveau et de la Moelle Épinière, UMR-S975, INSERM, Paris, France
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Müller EG, Stokke C, Stokmo HL, Edwin TH, Knapskog AB, Revheim ME. Evaluation of semi-quantitative measures of 18F-flutemetamol PET for the clinical diagnosis of Alzheimer's disease. Quant Imaging Med Surg 2022; 12:493-509. [PMID: 34993096 DOI: 10.21037/qims-21-188] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2021] [Accepted: 07/06/2021] [Indexed: 11/06/2022]
Abstract
BACKGROUND 18F-flutemetamol positron emission tomography (PET) is used to assess cortical amyloid-β burden in patients with cognitive impairment to support a clinical diagnosis. Visual classification is the most widely used method in clinical practice although semi-quantification is beneficial to obtain an objective and continuous measure of the Aβ burden. The aims were: first to evaluate the correspondence between standardized uptake value ratios (SUVRs) from three different software, Centiloids and visual classification, second to estimate thresholds for supporting visual classification and last to assess differences in semi-quantitative measures between clinical diagnoses. METHODS This observational study included 195 patients with cognitive impairment who underwent 18F-flutemetamol PET. PET images were semi-quantified with SyngoVia, CortexID suite, and PMOD. Receiver operating characteristics curves were used to compare visual classification with composite SUVR normalized to pons (SUVRpons) and cerebellar cortex (SUVRcer), and Centiloids. We explored correlations and differences between semi-quantitative measures as well as differences in SUVR between two clinical diagnosis groups: Alzheimer's disease-group and non-Alzheimer's disease-group. RESULTS PET images from 191 patients were semi-quantified with SyngoVia and CortexID and 86 PET-magnetic resonance imaging pairs with PMOD. All receiver operating characteristics curves showed a high area under the curve (>0.98). Thresholds for a visually positive PET was for SUVRcer: 1.87 (SyngoVia) and 1.64 (CortexID) and for SUVRpons: 0.54 (SyngoVia) and 0.55 (CortexID). The threshold on the Centiloid scale was 39.6 Centiloids. All semi-quantitative measures showed a very high correlation between different software and normalization methods. Composite SUVRcer was significantly different between SyngoVia and PMOD, SyngoVia and CortexID but not between PMOD and CortexID. Composite SUVRpons were significantly different between all three software. There were significant differences in the mean rank of SUVRpons, SUVRcer, and Centiloid between Alzheimer's disease-group and non-Alzheimer's disease-group. CONCLUSIONS SUVR from different software performed equally well in discriminating visually positive and negative 18F-Flutemetamol PET images. Thresholds should be considered software-specific and cautiously be applied across software without preceding validation to categorize scans as positive or negative. SUVR and Centiloid may be used alongside a thorough clinical evaluation to support a clinical diagnosis.
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Affiliation(s)
- Ebba Gløersen Müller
- Division of Radiology and Nuclear Medicine, Department of Nuclear Medicine, Oslo University Hospital, Oslo, Norway.,Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway
| | - Caroline Stokke
- Division of Radiology and Nuclear Medicine, Department of Diagnostic Physics, Oslo University Hospital, Oslo, Norway.,Department of Physics, University of Oslo, Oslo, Norway
| | - Henning Langen Stokmo
- Division of Radiology and Nuclear Medicine, Department of Nuclear Medicine, Oslo University Hospital, Oslo, Norway.,Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway
| | - Trine Holt Edwin
- Institute of Health and Society, Faculty of Medicine, University of Oslo, Oslo, Norway.,Department of Geriatric Medicine, The memory clinic, Oslo University Hospital, Oslo, Norway
| | - Anne-Brita Knapskog
- Department of Geriatric Medicine, The memory clinic, Oslo University Hospital, Oslo, Norway
| | - Mona-Elisabeth Revheim
- Division of Radiology and Nuclear Medicine, Department of Nuclear Medicine, Oslo University Hospital, Oslo, Norway.,Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway
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44
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Kang SH, Kim J, Kim JP, Cho SH, Choe YS, Jang H, Kim HJ, Koh SB, Na DL, Seong JK, Seo SW. Harmonisation of PET imaging features with different amyloid ligands using machine learning-based classifier. Eur J Nucl Med Mol Imaging 2021; 49:321-330. [PMID: 34328533 DOI: 10.1007/s00259-021-05499-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2021] [Accepted: 07/14/2021] [Indexed: 10/20/2022]
Abstract
PURPOSE In this study, we used machine learning to develop a new method derived from a ligand-independent amyloid (Aβ) positron emission tomography (PET) classifier to harmonise different Aβ ligands. METHODS We obtained 107 paired 18F-florbetaben (FBB) and 18F-flutemetamol (FMM) PET images at the Samsung Medical Centre. To apply the method to FMM ligand, we transferred the previously developed FBB PET classifier to test similar features from the FMM PET images for application to FMM, which in turn developed a ligand-independent Aβ PET classifier. We explored the concordance rates of our classifier in detecting cortical and striatal Aβ positivity. We investigated the correlation of machine learning-based cortical tracer uptake (ML-CTU) values quantified by the classifier between FBB and FMM. RESULTS This classifier achieved high classification accuracy (area under the curve = 0.958) even with different Aβ PET ligands. In addition, the concordance rate of FBB and FMM using the classifier (87.5%) was good to excellent, which seemed to be higher than that in visual assessment (82.7%) and lower than that in standardised uptake value ratio cut-off categorisation (93.3%). FBB and FMM ML-CTU values were highly correlated with each other (R = 0.903). CONCLUSION Our findings suggested that our novel classifier may harmonise FBB and FMM ligands in the clinical setting which in turn facilitate the biomarker-guided diagnosis and trials of anti-Aβ treatment in the research field.
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Affiliation(s)
- Sung Hoon Kang
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, 06351, Seoul, South Korea
- Neuroscience Center, Samsung Medical Center, Seoul, 06351, South Korea
- Department of Neurology, Korea University Guro Hospital, Korea University College of Medicine, Seoul, South Korea
| | - Jeonghun Kim
- Medical & Health Device Division, Korea Testing Laboratory, Seoul, South Korea
| | - Jun Pyo Kim
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, 06351, Seoul, South Korea
- Neuroscience Center, Samsung Medical Center, Seoul, 06351, South Korea
| | - Soo Hyun Cho
- Department of Neurology, Chonnam National University Hospital, Chonnam National University Medical School, Gwangju, South Korea
| | - Yeong Sim Choe
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, 06351, Seoul, South Korea
- Neuroscience Center, Samsung Medical Center, Seoul, 06351, South Korea
- Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, South Korea
| | - Hyemin Jang
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, 06351, Seoul, South Korea
- Neuroscience Center, Samsung Medical Center, Seoul, 06351, South Korea
| | - Hee Jin Kim
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, 06351, Seoul, South Korea
- Neuroscience Center, Samsung Medical Center, Seoul, 06351, South Korea
| | - Seong-Beom Koh
- Department of Neurology, Korea University Guro Hospital, Korea University College of Medicine, Seoul, South Korea
| | - Duk L Na
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, 06351, Seoul, South Korea
- Neuroscience Center, Samsung Medical Center, Seoul, 06351, South Korea
| | - Joon-Kyung Seong
- Department of Artificial Intelligence, Korea University, Seoul, South Korea.
- School of Biomedical Engineering, Korea University, Seoul, South Korea.
- Interdisciplinary Program in Precision Public Health, Korea University, Seoul, South Korea.
| | - Sang Won Seo
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, 06351, Seoul, South Korea.
- Neuroscience Center, Samsung Medical Center, Seoul, 06351, South Korea.
- Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, South Korea.
- Department of Intelligent Precision Healthcare Convergence, SAIHST, Sungkyunkwan University, Seoul, South Korea.
- Samsung Alzheimer Research Center, Center for Clinical Epidemiology Medical Center, Seoul, South Korea.
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Levin F, Jelistratova I, Betthauser TJ, Okonkwo O, Johnson SC, Teipel SJ, Grothe MJ. In vivo staging of regional amyloid progression in healthy middle-aged to older people at risk of Alzheimer's disease. Alzheimers Res Ther 2021; 13:178. [PMID: 34674764 PMCID: PMC8532333 DOI: 10.1186/s13195-021-00918-0] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2021] [Accepted: 10/11/2021] [Indexed: 12/28/2022]
Abstract
BACKGROUND We investigated regional amyloid staging characteristics in 11C-PiB-PET data from middle-aged to older participants at elevated risk for AD enrolled in the Wisconsin Registry for Alzheimer's Prevention. METHODS We analyzed partial volume effect-corrected 11C-PiB-PET distribution volume ratio maps from 220 participants (mean age = 61.4 years, range 46.9-76.8 years). Regional amyloid positivity was established using region-specific thresholds. We used four stages from the frequency-based staging of amyloid positivity to characterize individual amyloid deposition. Longitudinal PET data was used to assess the temporal progression of stages and to evaluate the emergence of regional amyloid positivity in participants who were amyloid-negative at baseline. We also assessed the effect of amyloid stage on longitudinal cognitive trajectories. RESULTS The staging model suggested progressive accumulation of amyloid from associative to primary neocortex and gradually involving subcortical regions. Longitudinal PET measurements supported the cross-sectionally estimated amyloid progression. In mixed-effects longitudinal analysis of cognitive follow-up data obtained over an average period of 6.5 years following the baseline PET measurement, amyloid stage II showed a faster decline in executive function, and advanced amyloid stages (III and IV) showed a faster decline across multiple cognitive domains compared to stage 0. CONCLUSIONS Overall, the 11C-PiB-PET-based staging model was generally consistent with previously derived models from 18F-labeled amyloid PET scans and a longitudinal course of amyloid accumulation. Differences in longitudinal cognitive decline support the potential clinical utility of in vivo amyloid staging for risk stratification of the preclinical phase of AD even in middle-aged to older individuals at risk for AD.
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Affiliation(s)
- Fedor Levin
- German Center for Neurodegenerative Diseases (DZNE), Rostock/Greifswald, Rostock, Germany
| | - Irina Jelistratova
- German Center for Neurodegenerative Diseases (DZNE), Rostock/Greifswald, Rostock, Germany
| | - Tobey J Betthauser
- Division of Geriatrics and Gerontology, Department of Medicine, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI, USA
- Wisconsin Alzheimer's Disease Research Center, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI, USA
| | - Ozioma Okonkwo
- Division of Geriatrics and Gerontology, Department of Medicine, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI, USA
- Wisconsin Alzheimer's Disease Research Center, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI, USA
| | - Sterling C Johnson
- Division of Geriatrics and Gerontology, Department of Medicine, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI, USA
- Wisconsin Alzheimer's Disease Research Center, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI, USA
- Wisconsin Alzheimer's Institute, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI, USA
- Geriatric Research Education and Clinical Center, William S. Middleton Memorial Veterans Hospital, Madison, WI, USA
| | - Stefan J Teipel
- German Center for Neurodegenerative Diseases (DZNE), Rostock/Greifswald, Rostock, Germany
- Department of Psychosomatic Medicine, University of Rostock, Rostock, Germany
| | - Michel J Grothe
- German Center for Neurodegenerative Diseases (DZNE), Rostock/Greifswald, Rostock, Germany.
- 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, s/n, 41013, Seville, Spain.
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46
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Qin Q, Fu L, Wang R, Lyu J, Ma H, Zhan M, Zhou A, Wang F, Zuo X, Wei C. Prominent Striatum Amyloid Retention in Early-Onset Familial Alzheimer's Disease With PSEN1 Mutations: A Pilot PET/MR Study. Front Aging Neurosci 2021; 13:732159. [PMID: 34603009 PMCID: PMC8480470 DOI: 10.3389/fnagi.2021.732159] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2021] [Accepted: 08/13/2021] [Indexed: 11/17/2022] Open
Abstract
Background: With the advancements of amyloid imaging in recent years, this new imaging diagnostic method has aroused great interest from researchers. Till now, little is known regarding amyloid deposition specialty in patients with early-onset familial Alzheimer's disease (EOFAD), and even less is known about its role in cognitive impairments. Objectives: Our study aimed to evaluate the amyloid deposition in five patients with EOFAD, 15 patients with late-onset sporadic AD, and 12 healthy subjects utilizing 11C-labeled Pittsburgh compound-B (11C-PiB) amyloid PET imaging. Moreover, we figured out the correlation between striatal and cortical standardized uptake value ratios (SUVRs). We also investigated the correlation between 11C-PiB retention and cognitive presentation. Results: All patients with EOFAD showed high amyloid deposition in the striatum, a pattern that is not usually seen in patients with late-onset sporadic AD. The SUVR in the striatum, especially in the amygdala, showed significant correlations with cortex SUVR in EOFAD. However, neither striatal nor cortical 11C-PiB retention was related to cognitive decline. Conclusions: The amyloid distribution in patients with EOFAD differs from late-onset sporadic AD, with higher amyloid deposits in the striatum. Our study also demonstrated positive correlations in 11C-PiB retention between the striatum and other cortical areas. We revealed that the distribution of amyloid in the brain is not random but diffuses following the functional and anatomical connections. However, the degree and pattern of amyloid deposition were not correlated with cognitive deficits.
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Affiliation(s)
- Qi Qin
- Innovation Center for Neurological Disorders and Department of Neurology, Xuanwu Hospital, National Clinical Research Center for Geriatric Diseases, Capital Medical University, Beijing, China.,Center of Alzheimer's Disease, Beijing Institute for Brain Disorders, Beijing, China.,Beijing Key Laboratory of Geriatric Cognitive Disorders, Beijing, China.,Neurodegenerative Laboratory of Ministry of Education of the People's Republic of China, Beijing, China
| | - Liping Fu
- Department of Nuclear Medicine, China-Japan Friendship Hospital, Beijing, China.,Department of Nuclear Medicine, The First Medical Center, Chinese People's Liberation Army General Hospital, Beijing, China
| | - Ruimin Wang
- Department of Nuclear Medicine, The First Medical Center, Chinese People's Liberation Army General Hospital, Beijing, China
| | - Jihui Lyu
- Center for Cognitive Disorders, Beijing Geriatric Hospital, Beijing, China
| | - Huixuan Ma
- Innovation Center for Neurological Disorders and Department of Neurology, Xuanwu Hospital, National Clinical Research Center for Geriatric Diseases, Capital Medical University, Beijing, China.,Center of Alzheimer's Disease, Beijing Institute for Brain Disorders, Beijing, China.,Beijing Key Laboratory of Geriatric Cognitive Disorders, Beijing, China.,Neurodegenerative Laboratory of Ministry of Education of the People's Republic of China, Beijing, China
| | - Minmin Zhan
- Innovation Center for Neurological Disorders and Department of Neurology, Xuanwu Hospital, National Clinical Research Center for Geriatric Diseases, Capital Medical University, Beijing, China.,Center of Alzheimer's Disease, Beijing Institute for Brain Disorders, Beijing, China.,Beijing Key Laboratory of Geriatric Cognitive Disorders, Beijing, China.,Neurodegenerative Laboratory of Ministry of Education of the People's Republic of China, Beijing, China
| | - Aihong Zhou
- Innovation Center for Neurological Disorders and Department of Neurology, Xuanwu Hospital, National Clinical Research Center for Geriatric Diseases, Capital Medical University, Beijing, China.,Center of Alzheimer's Disease, Beijing Institute for Brain Disorders, Beijing, China.,Beijing Key Laboratory of Geriatric Cognitive Disorders, Beijing, China.,Neurodegenerative Laboratory of Ministry of Education of the People's Republic of China, Beijing, China
| | - Fen Wang
- Innovation Center for Neurological Disorders and Department of Neurology, Xuanwu Hospital, National Clinical Research Center for Geriatric Diseases, Capital Medical University, Beijing, China.,Center of Alzheimer's Disease, Beijing Institute for Brain Disorders, Beijing, China.,Beijing Key Laboratory of Geriatric Cognitive Disorders, Beijing, China.,Neurodegenerative Laboratory of Ministry of Education of the People's Republic of China, Beijing, China
| | - Xiumei Zuo
- Innovation Center for Neurological Disorders and Department of Neurology, Xuanwu Hospital, National Clinical Research Center for Geriatric Diseases, Capital Medical University, Beijing, China.,Center of Alzheimer's Disease, Beijing Institute for Brain Disorders, Beijing, China.,Beijing Key Laboratory of Geriatric Cognitive Disorders, Beijing, China.,Neurodegenerative Laboratory of Ministry of Education of the People's Republic of China, Beijing, China
| | - Cuibai Wei
- Innovation Center for Neurological Disorders and Department of Neurology, Xuanwu Hospital, National Clinical Research Center for Geriatric Diseases, Capital Medical University, Beijing, China.,Center of Alzheimer's Disease, Beijing Institute for Brain Disorders, Beijing, China.,Beijing Key Laboratory of Geriatric Cognitive Disorders, Beijing, China.,Neurodegenerative Laboratory of Ministry of Education of the People's Republic of China, Beijing, China
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47
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Chen CD, Joseph-Mathurin N, Sinha N, Zhou A, Li Y, Friedrichsen K, McCullough A, Franklin EE, Hornbeck R, Gordon B, Sharma V, Cruchaga C, Goate A, Karch C, McDade E, Xiong C, Bateman RJ, Ghetti B, Ringman JM, Chhatwal J, Masters CL, McLean C, Lashley T, Su Y, Koeppe R, Jack C, Klunk WE, Morris JC, Perrin RJ, Cairns NJ, Benzinger TLS. Comparing amyloid-β plaque burden with antemortem PiB PET in autosomal dominant and late-onset Alzheimer disease. Acta Neuropathol 2021; 142:689-706. [PMID: 34319442 PMCID: PMC8815340 DOI: 10.1007/s00401-021-02342-y] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2021] [Revised: 06/29/2021] [Accepted: 07/01/2021] [Indexed: 12/31/2022]
Abstract
Pittsburgh compound B (PiB) radiotracer for positron emission tomography (PET) imaging can bind to different types of amyloid-β plaques and blood vessels (cerebral amyloid angiopathy). However, the relative contributions of different plaque subtypes (diffuse versus cored/compact) to in vivo PiB PET signal on a region-by-region basis are incompletely understood. Of particular interest is whether the same staging schemes for summarizing amyloid-β burden are appropriate for both late-onset and autosomal dominant forms of Alzheimer disease (LOAD and ADAD). Here, we compared antemortem PiB PET with follow-up postmortem estimation of amyloid-β burden using stereologic methods to estimate the relative area fraction of diffuse and cored/compact amyloid-β plaques across 16 brain regions in 15 individuals with ADAD and 14 individuals with LOAD. In ADAD, we found that PiB PET correlated with diffuse plaques in the frontal, parietal, temporal, and striatal regions commonly used to summarize amyloid-β burden in PiB PET, and correlated with both diffuse and cored/compact plaques in the occipital lobe and parahippocampal gyrus. In LOAD, we found that PiB PET correlated with both diffuse and cored/compact plaques in the anterior cingulate, frontal lobe (middle frontal gyrus), and parietal lobe, and showed additional correlations with diffuse plaque in the amygdala and occipital lobe, and with cored/compact plaque in the temporal lobe. Thus, commonly used PiB PET summary regions predominantly reflect diffuse plaque burden in ADAD and a mixture of diffuse and cored/compact plaque burden in LOAD. In direct comparisons of ADAD and LOAD, postmortem stereology identified much greater mean amyloid-β plaque burdens in ADAD versus LOAD across almost all brain regions studied. However, standard PiB PET did not recapitulate these stereologic findings, likely due to non-trivial amyloid-β plaque burdens in ADAD within the cerebellum and brainstem-commonly used reference regions in PiB PET. Our findings suggest that PiB PET summary regions correlate with amyloid-β plaque burden in both ADAD and LOAD; however, they might not be reliable in direct comparisons of regional amyloid-β plaque burden between the two forms of AD.
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Affiliation(s)
- Charles D Chen
- Mallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, MO, USA
| | - Nelly Joseph-Mathurin
- Mallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, MO, USA
| | - Namita Sinha
- Department of Pathology and Immunology, Washington University in St. Louis, St. Louis, MO, USA
- Department of Pathology, University of Manitoba, Shared Health, Winnipeg, MB, Canada
| | - Aihong Zhou
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Yan Li
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA
| | - Karl Friedrichsen
- Department of Ophthalmology and Visual Sciences, Washington University in St. Louis, St. Louis, MO, USA
| | - Austin McCullough
- Mallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, MO, USA
| | - Erin E Franklin
- Department of Pathology and Immunology, Washington University in St. Louis, St. Louis, MO, USA
| | - Russ Hornbeck
- Mallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, MO, USA
| | - Brian Gordon
- Mallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, MO, USA
| | - Vijay Sharma
- Mallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, MO, USA
| | - Carlos Cruchaga
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO, USA
| | - Alison Goate
- Department of Genetics and Genomic Sciences, Ichan School of Medicine at Mount Sinai, New York, NY, USA
| | - Celeste Karch
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO, USA
| | - Eric McDade
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA
| | - Chengjie Xiong
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA
| | - Randall J Bateman
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA
| | - Bernardino Ghetti
- Department of Pathology and Laboratory Medicine, Indiana University School of Medicine, Indianapolis, IN, USA
| | - John M Ringman
- Department of Neurology, Keck School of Medicine of USC, Los Angeles, CA, USA
| | - Jasmeer Chhatwal
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Colin L Masters
- The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Melbourne, VIC, Australia
| | - Catriona McLean
- Department of Anatomic Pathology, Alfred Hospital, Melbourne, VIC, Australia
| | - Tammaryn Lashley
- UCL Queen Square Institute of Neurology, University College London, London, UK
- Queen Square Brain Bank for Neurological Disorders, University College London, London, UK
| | - Yi Su
- Banner Alzheimer's Institute, Banner Health, Phoenix, AZ, USA
- Arizona Alzheimer's Consortium, Banner Health, Phoenix, AZ, USA
| | - Robert Koeppe
- Department of Radiology, University of Michigan, Ann Arbor, MI, USA
| | - Clifford Jack
- Department of Radiology, Mayo Clinic, Rochester, MN, USA
| | - William E Klunk
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
| | - John C Morris
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA
| | - Richard J Perrin
- Department of Pathology and Immunology, Washington University in St. Louis, St. Louis, MO, USA
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA
| | - Nigel J Cairns
- Department of Pathology and Immunology, Washington University in St. Louis, St. Louis, MO, USA
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA
- College of Medicine and Health, University of Exeter, Exeter, UK
| | - Tammie L S Benzinger
- Mallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, MO, USA.
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Pfeil J, Hoenig MC, Doering E, van Eimeren T, Drzezga A, Bischof GN. Unique regional patterns of amyloid burden predict progression to prodromal and clinical stages of Alzheimer's disease. Neurobiol Aging 2021; 106:119-129. [PMID: 34284259 PMCID: PMC8461082 DOI: 10.1016/j.neurobiolaging.2021.06.014] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2021] [Revised: 05/22/2021] [Accepted: 06/15/2021] [Indexed: 01/21/2023]
Abstract
Although beta-amyloid (Aβ) positivity has shown to be associated with higher risk of progression to Alzheimer's disease (AD) in mild cognitive impairment (MCI), information on the time to conversion to manifest dementia cannot be readily deduced from this binary classification. Here, we assessed if regional patterns of Aβ deposition measured with 18F-florbetapir may serve as biomarker for progression risk in Aβ-positive cognitively normal (CN) and MCI patients, including clinical follow-up data and cerebrospinal fluid (CSF) biomarkers. Voxel-wise group comparisons between age and sex-matched Aβ-positive groups (i.e., CN-stables [n = 38] vs. CN-to-MCI/AD progressors [n = 38], MCI-stables [n = 104] versus MCI-to-AD progressors [n = 104]) revealed higher Aβ burden in precuneus, subcortical, and parietal regions in CN-to-MCI/AD progressors and cingulate, temporal, and frontal regions in MCI-to-AD progressors. Importantly, these regional patterns predicted progression to advanced stages on the AD spectrum in the short and the long-term beyond global Aβ burden and CSF biomarkers. These results suggest that distinct regional patterns of Aβ burden are a valuable biomarker for risk of disease progression in CN and MCI.
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Affiliation(s)
- Julia Pfeil
- Department of Nuclear Medicine, Multimodal Neuroimaging Group, University of Cologne, University Hospital of Cologne, Cologne, Germany.
| | - Merle C Hoenig
- Department of Nuclear Medicine, Multimodal Neuroimaging Group, University of Cologne, University Hospital of Cologne, Cologne, Germany; Research Center Juelich, Institute for Neuroscience and Medicine II, Molecular Organization of the Brain, Juelich, Germany
| | - Elena Doering
- Department of Nuclear Medicine, Multimodal Neuroimaging Group, University of Cologne, University Hospital of Cologne, Cologne, Germany; German Center for Neurodegenerative Diseases, Bonn/Cologne, Germany
| | - Thilo van Eimeren
- Department of Nuclear Medicine, Multimodal Neuroimaging Group, University of Cologne, University Hospital of Cologne, Cologne, Germany; German Center for Neurodegenerative Diseases, Bonn/Cologne, Germany; University of Cologne, University Hospital of Cologne, Department of Neurology, Cologne, Germany
| | - Alexander Drzezga
- Department of Nuclear Medicine, Multimodal Neuroimaging Group, University of Cologne, University Hospital of Cologne, Cologne, Germany; Research Center Juelich, Institute for Neuroscience and Medicine II, Molecular Organization of the Brain, Juelich, Germany; German Center for Neurodegenerative Diseases, Bonn/Cologne, Germany
| | - Gérard N Bischof
- Department of Nuclear Medicine, Multimodal Neuroimaging Group, University of Cologne, University Hospital of Cologne, Cologne, Germany
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49
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18F-florbetapir PET as a marker of myelin integrity across the Alzheimer's disease spectrum. Eur J Nucl Med Mol Imaging 2021; 49:1242-1253. [PMID: 34581847 PMCID: PMC8921113 DOI: 10.1007/s00259-021-05493-y] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2020] [Accepted: 07/08/2021] [Indexed: 01/23/2023]
Abstract
Purpose Recent evidence suggests that PET imaging with amyloid-β (Aβ) tracers can be used to assess myelin integrity in cerebral white matter (WM). Alzheimer’s disease (AD) is characterized by myelin changes that are believed to occur early in the disease course. Nevertheless, the extent to which demyelination, as measured with Aβ PET, contributes to AD progression remains unexplored. Methods Participants with concurrent 18F-florbetapir (FBP) PET, MRI, and cerebrospinal fluid (CSF) examinations were included (241 cognitively normal, 347 Aβ-positive cognitively impaired, and 207 Aβ-negative cognitively impaired subjects). A subset of these participants had also available diffusion tensor imaging (DTI) images (n = 195). We investigated cross-sectional associations of FBP retention in the white matter (WM) with MRI-based markers of WM degeneration, AD clinical progression, and fluid biomarkers. In longitudinal analyses, we used linear mixed models to assess whether FBP retention in normal-appearing WM (NAWM) predicted progression of WM hyperintensity (WMH) burden and clinical decline. Results In AD-continuum individuals, FBP retention in NAWM was (1) higher compared with WMH regions, (2) associated with DTI-based measures of WM integrity, and (3) associated with longitudinal progression of WMH burden. FBP uptake in WM decreased across the AD continuum and with increasingly abnormal CSF biomarkers of AD. Furthermore, FBP retention in the WM was associated with large-calibre axon degeneration as reflected by abnormal plasma neurofilament light chain levels. Low FBP uptake in NAWM predicted clinical decline in preclinical and prodromal AD, independent of demographics, global cortical Aβ, and WMH burden. Most of these associations were also observed in Aβ-negative cognitively impaired individuals. Conclusion These results support the hypothesis that FBP retention in the WM is myelin-related. Demyelination levels progressed across the AD continuum and were associated with clinical progression at early stages, suggesting that this pathologic process might be a relevant degenerative feature in the disease course. Supplementary Information The online version contains supplementary material available at 10.1007/s00259-021-05493-y.
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Peira E, Grazzini M, Bauckneht M, Sensi F, Bosco P, Arnaldi D, Morbelli S, Chincarini A, Pardini M, Nobili F. Probing the Role of a Regional Quantitative Assessment of Amyloid PET. J Alzheimers Dis 2021; 80:383-396. [PMID: 33554908 DOI: 10.3233/jad-201156] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
BACKGROUND In clinical practice, the amy-PET is globally inspected to provide a binary outcome, but the role of a regional assessment has not been fully investigated yet. OBJECTIVE To deepen the role of regional amyloid burden and its implication on clinical-neuropsychological features. MATERIALS Amy-PET and a complete neuropsychological assessment (Trail Making Test, Rey Auditory Verbal Learning Test, semantic verbal fluency, Symbol Digit, Stroop, visuoconstruction) were available in 109 patients with clinical suspicion of Alzheimer's disease. By averaging the standardized uptake value ratio and ELBA, a regional quantification was calculated for each scan. Patients were grouped according to their overall amyloid load: correlation maps, based on regional quantification, were calculated and compared. A regression analysis between neuropsychological assessment and the regional amyloid-β (Aβ) load was carried out. RESULTS Significant differences were observed between the correlation maps of patients at increasing levels of Aβ and the overall dataset. The Aβ uptake of the subcortical gray matter resulted not related to other brain regions independently of the global Aβ level. A significant association of semantic verbal fluency was observed with ratios of cortical and subcortical distribution of Aβ which represent a coarse measure of differences in regional distribution of Aβ. CONCLUSION Our observations confirmed the different susceptibility to Aβ accumulation among brain regions. The association between cognition and Aβ distribution deserves further investigations: it is possibly due to a direct local effect or it represents a proxy marker of a more aggressive disease subtype. Regional Aβ assessment represents an available resource on amy-PET scan with possibly clinical and prognostic implications.
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Affiliation(s)
- Enrico Peira
- INFN, Genoa, Italy.,Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Child and Maternal Health (DINOGMI), University of Genoa, Genoa, Italy
| | - Matteo Grazzini
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Child and Maternal Health (DINOGMI), University of Genoa, Genoa, Italy
| | - Matteo Bauckneht
- IRCCS Ospedale Policlinico San Martino, Genoa, Italy.,Nuclear Medicine Unit, Dept. of Health Sciences (DISSAL), University of Genoa, Genoa, Italy
| | | | | | - Dario Arnaldi
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Child and Maternal Health (DINOGMI), University of Genoa, Genoa, Italy.,Neurology Clinic, IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Silvia Morbelli
- IRCCS Ospedale Policlinico San Martino, Genoa, Italy.,Nuclear Medicine Unit, Dept. of Health Sciences (DISSAL), University of Genoa, Genoa, Italy
| | | | - Matteo Pardini
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Child and Maternal Health (DINOGMI), University of Genoa, Genoa, Italy.,Neurology Clinic, IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Flavio Nobili
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Child and Maternal Health (DINOGMI), University of Genoa, Genoa, Italy.,Neurology Clinic, IRCCS Ospedale Policlinico San Martino, Genoa, Italy
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