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Quispialaya KM, Therriault J, Aliaga A, Tissot C, Servaes S, Rahmouni N, Karikari TK, Benedet AL, Ashton NJ, Macedo AC, Lussier FZ, Stevenson J, Wang YT, Arias JF, Hosseini A, Matsudaira T, Jean-Claude B, Gilfix BM, Zimmer ER, Soucy JP, Pascoal TA, Gauthier S, Zetterberg H, Blennow K, Rosa-Neto P. Plasma phosphorylated tau181 outperforms [ 18F] fluorodeoxyglucose positron emission tomography in the identification of early Alzheimer disease. Eur J Neurol 2024:e16255. [PMID: 39447157 DOI: 10.1111/ene.16255] [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: 10/20/2023] [Revised: 02/02/2024] [Accepted: 02/06/2024] [Indexed: 10/26/2024]
Abstract
BACKGROUND AND PURPOSE This study was undertaken to compare the performance of plasma p-tau181 with that of [18F]fluorodeoxyglucose (FDG) positron emission tomography (PET) in the identification of early biological Alzheimer disease (AD). METHODS We included 533 cognitively impaired participants from the Alzheimer's Disease Neuroimaging Initiative. Participants underwent PET scans, biofluid collection, and cognitive tests. Receiver operating characteristic analyses were used to determine the diagnostic accuracy of plasma p-tau181 and [18F]FDG-PET using clinical diagnosis and core AD biomarkers ([18F]florbetapir-PET and cerebrospinal fluid [CSF] p-tau181) as reference standards. Differences in the diagnostic accuracy between plasma p-tau181 and [18F]FDG-PET were determined by bootstrap-based tests. Correlations of [18F]FDG-PET and plasma p-tau181 with CSF p-tau181, amyloid β (Aβ) PET, and cognitive performance were evaluated to compare associations between measurements. RESULTS We observed that both plasma p-tau181 and [18F]FDG-PET identified individuals with positive AD biomarkers in CSF or on Aβ-PET. In the MCI group, plasma p-tau181 outperformed [18F]FDG-PET in identifying AD measured by CSF (p = 0.0007) and by Aβ-PET (p = 0.001). We also observed that both plasma p-tau181 and [18F]FDG-PET metabolism were associated with core AD biomarkers. However, [18F]FDG-PET uptake was more closely associated with cognitive outcomes (Montreal Cognitive Assessment, Mini-Mental State Examination, Clinical Dementia Rating Sum of Boxes, and logical memory delayed recall, p < 0.001) than plasma p-tau181. CONCLUSIONS Overall, although both plasma p-tau181 and [18F]FDG-PET were associated with core AD biomarkers, plasma p-tau181 outperformed [18F]FDG-PET in identifying individuals with early AD pathophysiology. Taken together, our study suggests that plasma p-tau181 may aid in detecting individuals with underlying early AD.
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Affiliation(s)
- Kely Monica Quispialaya
- Translational Neuroimaging Laboratory, McGill University Research Centre for Studies in Aging, Douglas Hospital, McGill University, Montreal, Quebec, Canada
- Montreal Neurological Institute, Montreal, Quebec, Canada
- Department of Experimental Medicine, McGill University, Montreal, Quebec, Canada
| | - Joseph Therriault
- Translational Neuroimaging Laboratory, McGill University Research Centre for Studies in Aging, Douglas Hospital, McGill University, Montreal, Quebec, Canada
- Montreal Neurological Institute, Montreal, Quebec, Canada
- Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec, Canada
| | - Antonio Aliaga
- Translational Neuroimaging Laboratory, McGill University Research Centre for Studies in Aging, Douglas Hospital, McGill University, Montreal, Quebec, Canada
- Department of Experimental Medicine, McGill University, Montreal, Quebec, Canada
- Department of Pharmacology, Graduate Program in Biological Sciences: Biochemistry (PPGBioq) and Pharmacology and Therapeutics (PPGFT), Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
| | - Cécile Tissot
- Translational Neuroimaging Laboratory, McGill University Research Centre for Studies in Aging, Douglas Hospital, McGill University, Montreal, Quebec, Canada
- Montreal Neurological Institute, Montreal, Quebec, Canada
- Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec, Canada
| | - Stijn Servaes
- Translational Neuroimaging Laboratory, McGill University Research Centre for Studies in Aging, Douglas Hospital, McGill University, Montreal, Quebec, Canada
- Montreal Neurological Institute, Montreal, Quebec, Canada
- Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec, Canada
| | - Nesrine Rahmouni
- Translational Neuroimaging Laboratory, McGill University Research Centre for Studies in Aging, Douglas Hospital, McGill University, Montreal, Quebec, Canada
- Montreal Neurological Institute, Montreal, Quebec, Canada
- Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec, Canada
| | - Thomas K Karikari
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, Mölndal, Sweden
- Department of Neurology and Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
| | - Andrea L Benedet
- Translational Neuroimaging Laboratory, McGill University Research Centre for Studies in Aging, Douglas Hospital, McGill University, Montreal, Quebec, Canada
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, Mölndal, Sweden
| | - Nicholas J Ashton
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, Mölndal, Sweden
- Wallenberg Centre for Molecular Medicine, University of Gothenburg, Gothenburg, Sweden
| | - Arthur C Macedo
- Translational Neuroimaging Laboratory, McGill University Research Centre for Studies in Aging, Douglas Hospital, McGill University, Montreal, Quebec, Canada
- Montreal Neurological Institute, Montreal, Quebec, Canada
- Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec, Canada
| | - Firoza Z Lussier
- Translational Neuroimaging Laboratory, McGill University Research Centre for Studies in Aging, Douglas Hospital, McGill University, Montreal, Quebec, Canada
- Department of Neurology and Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
| | - Jenna Stevenson
- Translational Neuroimaging Laboratory, McGill University Research Centre for Studies in Aging, Douglas Hospital, McGill University, Montreal, Quebec, Canada
| | - Yi-Ting Wang
- Translational Neuroimaging Laboratory, McGill University Research Centre for Studies in Aging, Douglas Hospital, McGill University, Montreal, Quebec, Canada
- Montreal Neurological Institute, Montreal, Quebec, Canada
- Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec, Canada
| | - Jaime Fernandez Arias
- Translational Neuroimaging Laboratory, McGill University Research Centre for Studies in Aging, Douglas Hospital, McGill University, Montreal, Quebec, Canada
- Montreal Neurological Institute, Montreal, Quebec, Canada
- Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec, Canada
| | - Ali Hosseini
- Translational Neuroimaging Laboratory, McGill University Research Centre for Studies in Aging, Douglas Hospital, McGill University, Montreal, Quebec, Canada
- Montreal Neurological Institute, Montreal, Quebec, Canada
- Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec, Canada
| | - Takashi Matsudaira
- Department of Biofunctional Imaging, Hamamatsu University School of Medicine, Hamamatsu, Japan
- Department of Neurology, National Hospital Organization Shizuoka Institute of Epilepsy and Neurological Disorders, Urushiyama, Japan
| | - Bertrand Jean-Claude
- Department of Experimental Medicine, McGill University, Montreal, Quebec, Canada
| | - Brian M Gilfix
- Department of Specialized Medicine, McGill University, Montreal, Quebec, Canada
| | - Eduardo R Zimmer
- Translational Neuroimaging Laboratory, McGill University Research Centre for Studies in Aging, Douglas Hospital, McGill University, Montreal, Quebec, Canada
- Department of Pharmacology, Graduate Program in Biological Sciences: Biochemistry (PPGBioq) and Pharmacology and Therapeutics (PPGFT), Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
- Department of Pharmacology, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
- Brain Institute of Rio Grande do Sul, Pontifícia Universidade Católica do Rio Grande do Sul, Porto Alegre, Brazil
| | | | - Tharick A Pascoal
- Department of Neurology and Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
| | - Serge Gauthier
- Translational Neuroimaging Laboratory, McGill University Research Centre for Studies in Aging, Douglas Hospital, McGill University, Montreal, Quebec, Canada
- Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec, Canada
- Department of Psychiatry, McGill University, Montreal, Quebec, Canada
| | - Henrik Zetterberg
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, Mölndal, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
- Department of Neurodegenerative Disease, University College London Institute of Neurology, London, UK
- UK Dementia Research Institute at University College London, London, UK
- Hong Kong Centre for Neurodegenerative Diseases, China
- Wisconsin Alzheimer's Disease Research Center, University of Wisconsin School of Medicine and Public Health, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Kaj Blennow
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, Mölndal, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
| | - Pedro Rosa-Neto
- Translational Neuroimaging Laboratory, McGill University Research Centre for Studies in Aging, Douglas Hospital, McGill University, Montreal, Quebec, Canada
- Montreal Neurological Institute, Montreal, Quebec, Canada
- Department of Experimental Medicine, McGill University, Montreal, Quebec, Canada
- Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec, Canada
- Department of Psychiatry, McGill University, Montreal, Quebec, Canada
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Farrell MB. Brain Imaging: Amyloid-β PET. J Nucl Med Technol 2024:jnmt.124.268854. [PMID: 39438059 DOI: 10.2967/jnmt.124.268854] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2024] Open
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Mustafa M, Musselman D, Jayaweera D, da Fonseca Ferreira A, Marzouka G, Dong C. HIV-Associated Neurocognitive Disorder (HAND) and Alzheimer's Disease Pathogenesis: Future Directions for Diagnosis and Treatment. Int J Mol Sci 2024; 25:11170. [PMID: 39456951 PMCID: PMC11508543 DOI: 10.3390/ijms252011170] [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: 09/04/2024] [Revised: 10/11/2024] [Accepted: 10/15/2024] [Indexed: 10/28/2024] Open
Abstract
HIV-associated neurocognitive disorder (HAND) and Alzheimer's disease (AD) are two neurocognitive disorders with overlapping clinical presentations and pathophysiology. The two have been thought to be two separate entities. However, the introduction and widespread use of antiretroviral therapy (ART) has altered the clinical manifestations of HAND, shifting from a pattern of subcortical dementia to one more akin to cortical dementia, resembling AD. Thus, the line between the two disease entities is not clear-cut. In this review, we discuss the concept of Alzheimer's disease-like dementia (ADLD) in HIV, which describes this phenomenon. While the mechanisms of HIV-associated ADLD remain to be elucidated, potential mechanisms include HIV-specific pathways, including epigenetic imprinting from initial viral infection, persistent and low viral load (which can only be detected by ultra-sensitive PCR), HIV-related inflammation, and putative pathways underlying traditional AD risk factors. Importantly, we have shown that HIV-specific microRNAs (miRs) encapsulated in extracellular vesicles (EV-miRs) play an important role in mediating the detrimental effects in the cardiovascular system. A useful preclinical model to study ADLD would be to expose AD mice to HIV-positive EVs to identify candidate EV-miRs that mediate the HIV-specific effects underlying ADLD. Characterization of the candidate EV-miRs may provide novel therapeutic armamentaria for ADLD.
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Affiliation(s)
- Mohammed Mustafa
- Department of Medicine, Jackson Memorial Hospital, University of Miami Miller School of Medicine, Miami, FL 33136, USA; (M.M.); (D.J.)
| | - Dominique Musselman
- Department of Psychiatry and Behavioral Sciences, University of Miami Miller School of Medicine, Miami, FL 33136, USA;
| | - Dushyantha Jayaweera
- Department of Medicine, Jackson Memorial Hospital, University of Miami Miller School of Medicine, Miami, FL 33136, USA; (M.M.); (D.J.)
- Interdisciplinary Stem Cell Institute, University of Miami Miller School of Medicine, Miami, FL 33136, USA;
| | - Andrea da Fonseca Ferreira
- Interdisciplinary Stem Cell Institute, University of Miami Miller School of Medicine, Miami, FL 33136, USA;
| | - George Marzouka
- Department of Medicine, Jackson Memorial Hospital, University of Miami Miller School of Medicine, Miami, FL 33136, USA; (M.M.); (D.J.)
- Division of Cardiovascular Disease, Department of Medicine, Miami VA Health System, University of Miami, Miami, FL 33136, USA
| | - Chunming Dong
- Department of Medicine, Jackson Memorial Hospital, University of Miami Miller School of Medicine, Miami, FL 33136, USA; (M.M.); (D.J.)
- Interdisciplinary Stem Cell Institute, University of Miami Miller School of Medicine, Miami, FL 33136, USA;
- Division of Cardiovascular Disease, Department of Medicine, Miami VA Health System, University of Miami, Miami, FL 33136, USA
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Schindler SE, Petersen KK, Saef B, Tosun D, Shaw LM, Zetterberg H, Dage JL, Ferber K, Triana-Baltzer G, Du-Cuny L, Li Y, Coomaraswamy J, Baratta M, Mordashova Y, Saad ZS, Raunig DL, Ashton NJ, Meyers EA, Rubel CE, Rosenbaugh EG, Bannon AW, Potter WZ. Head-to-head comparison of leading blood tests for Alzheimer's disease pathology. Alzheimers Dement 2024. [PMID: 39394841 DOI: 10.1002/alz.14315] [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: 06/06/2024] [Revised: 09/10/2024] [Accepted: 09/11/2024] [Indexed: 10/14/2024]
Abstract
INTRODUCTION Blood tests have the potential to improve the accuracy of Alzheimer's disease (AD) clinical diagnosis, which will enable greater access to AD-specific treatments. This study compared leading commercial blood tests for amyloid pathology and other AD-related outcomes. METHODS Plasma samples from the Alzheimer's Disease Neuroimaging Initiative were assayed with AD blood tests from C2N Diagnostics, Fujirebio Diagnostics, ALZPath, Janssen, Roche Diagnostics, and Quanterix. Outcomes measures were amyloid positron emission tomography (PET), tau PET, cortical thickness, and dementia severity. Logistic regression models assessed the classification accuracies of individual or combined plasma biomarkers for binarized outcomes, and Spearman correlations evaluated continuous relationships between individual plasma biomarkers and continuous outcomes. RESULTS Measures of plasma p-tau217, either individually or in combination with other plasma biomarkers, had the strongest relationships with all AD outcomes. DISCUSSION This study identified the plasma biomarker analytes and assays that most accurately classified amyloid pathology and other AD-related outcomes. HIGHLIGHTS Plasma p-tau217 measures most accurately classified amyloid and tau status. Plasma Aβ42/Aβ40 had relatively low accuracy in classification of amyloid status. Plasma p-tau217 measures had higher correlations with cortical thickness than NfL. Correlations of plasma biomarkers with dementia symptoms were relatively low.
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Affiliation(s)
- Suzanne E Schindler
- Department of Neurology, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Kellen K Petersen
- Department of Neurology, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Benjamin Saef
- Department of Neurology, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Duygu Tosun
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, California, USA
| | - Leslie M Shaw
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Henrik Zetterberg
- Institute of Neuroscience and Physiology, Department of Psychiatry and Neurochemistry, The Sahlgrenska Academy at University of Gothenburg, Mölndal, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
- UK Dementia Research Institute Fluid Biomarkers Laboratory, UK DRI at UCL, London, UK
- Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, Queen Square, London, UK
- Hong Kong Center for Neurodegenerative Diseases, Hong Kong, China
- Wisconsin Alzheimer's Disease Research Center, University of Wisconsin School of Medicine and Public Health, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Jeffrey L Dage
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, USA
- Stark Neurosciences Research Institute, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Kyle Ferber
- Biogen, Biomarkers Group, Cambridge, Massachusetts, USA
| | - Gallen Triana-Baltzer
- Neuroscience Biomarkers, Johnson and Johnson Innovative Medicine, San Diego, California, USA
| | - Lei Du-Cuny
- AbbVie, Ludwigshafen am Rhein, Rheinland-Pfalz, Germany
| | - Yan Li
- Department of Neurology, Washington University in St. Louis, St. Louis, Missouri, USA
| | | | - Michael Baratta
- Takeda Pharmaceutical Company Ltd., Cambridge, Massachusetts, USA
| | | | - Ziad S Saad
- Neuroscience Biomarkers, Johnson and Johnson Innovative Medicine, San Diego, California, USA
| | - David L Raunig
- Takeda Pharmaceutical Company Ltd., Cambridge, Massachusetts, USA
| | - Nicholas J Ashton
- Institute of Neuroscience and Physiology, Department of Psychiatry and Neurochemistry, The Sahlgrenska Academy at University of Gothenburg, Mölndal, Sweden
- Banner Alzheimer's Institute, Phoenix, Arizona, USA
- Banner Sun Health Research Institute, Sun City, Arizona, USA
| | | | | | - Erin G Rosenbaugh
- The Foundation for the National Institutes of Health, North Bethesda, Maryland, USA
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Wisch JK, Gordon BA, Barthélemy NR, Horie K, Henson RL, He Y, Flores S, Benzinger TLS, Morris JC, Bateman RJ, Ances BM, Schindler SE. Predicting continuous amyloid PET values with CSF tau phosphorylation occupancies. Alzheimers Dement 2024; 20:6365-6373. [PMID: 39041391 PMCID: PMC11497729 DOI: 10.1002/alz.14132] [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: 04/24/2024] [Revised: 06/18/2024] [Accepted: 06/19/2024] [Indexed: 07/24/2024]
Abstract
INTRODUCTION Cerebrospinal fluid (CSF) tau phosphorylation at multiple sites is associated with cortical amyloid and other pathologic changes in Alzheimer's disease. These relationships can be non-linear. We used an artificial neural network to assess the ability of 10 different CSF tau phosphorylation sites to predict continuous amyloid positron emission tomography (PET) values. METHODS CSF tau phosphorylation occupancies at 10 sites (including pT181/T181, pT217/T217, pT231/T231 and pT205/T205) were measured by mass spectrometry in 346 individuals (57 cognitively impaired, 289 cognitively unimpaired). We generated synthetic amyloid PET scans using biomarkers and evaluated their performance. RESULTS Concentration of CSF pT217/T217 had low predictive error (average error: 13%), but also a low predictive range (ceiling 63 Centiloids). CSF pT231/T231 has slightly higher error (average error: 19%) but predicted through a greater range (87 Centiloids). DISCUSSION Tradeoffs exist in biomarker selection. Some phosphorylation sites offer greater concordance with amyloid PET at lower levels, while others perform better over a greater range. HIGHLIGHTS Novel pTau isoforms can predict cortical amyloid burden. pT217/T217 accurately predicts cortical amyloid burden in low-amyloid individuals. Traditional CSF biomarkers correspond with higher levels of amyloid.
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Affiliation(s)
- Julie K. Wisch
- Department of NeurologyWashington University in St. LouisSt. LouisMissouriUSA
| | - Brian A. Gordon
- Department of RadiologyWashington University in St. LouisSt. LouisMissouriUSA
- Knight Alzheimer Disease Research CenterWashington University School of MedicineSt LouisMissouriUSA
| | - Nicolas R. Barthélemy
- Department of NeurologyWashington University in St. LouisSt. LouisMissouriUSA
- SILQ Center for Neurodegenerative BiologySt. LouisMissouriUSA
| | - Kanta Horie
- Department of NeurologyWashington University in St. LouisSt. LouisMissouriUSA
- SILQ Center for Neurodegenerative BiologySt. LouisMissouriUSA
| | - Rachel L. Henson
- Hope CenterWashington University in Saint LouisSt. LouisMissouriUSA
| | - Yingxin He
- Department of NeurologyWashington University in St. LouisSt. LouisMissouriUSA
- SILQ Center for Neurodegenerative BiologySt. LouisMissouriUSA
| | - Shaney Flores
- Department of RadiologyWashington University in St. LouisSt. LouisMissouriUSA
| | - Tammie L. S. Benzinger
- Department of RadiologyWashington University in St. LouisSt. LouisMissouriUSA
- Knight Alzheimer Disease Research CenterWashington University School of MedicineSt LouisMissouriUSA
| | - John C. Morris
- Department of NeurologyWashington University in St. LouisSt. LouisMissouriUSA
- Knight Alzheimer Disease Research CenterWashington University School of MedicineSt LouisMissouriUSA
| | - Randall J. Bateman
- Department of NeurologyWashington University in St. LouisSt. LouisMissouriUSA
- Knight Alzheimer Disease Research CenterWashington University School of MedicineSt LouisMissouriUSA
- SILQ Center for Neurodegenerative BiologySt. LouisMissouriUSA
- Hope CenterWashington University in Saint LouisSt. LouisMissouriUSA
| | - Beau M. Ances
- Department of NeurologyWashington University in St. LouisSt. LouisMissouriUSA
- Knight Alzheimer Disease Research CenterWashington University School of MedicineSt LouisMissouriUSA
| | - Suzanne E. Schindler
- Department of NeurologyWashington University in St. LouisSt. LouisMissouriUSA
- Knight Alzheimer Disease Research CenterWashington University School of MedicineSt LouisMissouriUSA
- Hope CenterWashington University in Saint LouisSt. LouisMissouriUSA
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Kim YE, Lim JS, Suh CH, Heo H, Roh JH, Cheong EN, Lee Y, Kim JW, Lee JH. Effects of strategic white matter hyperintensities of cholinergic pathways on basal forebrain volume in patients with amyloid-negative neurocognitive disorders. Alzheimers Res Ther 2024; 16:185. [PMID: 39148136 PMCID: PMC11325579 DOI: 10.1186/s13195-024-01536-2] [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: 03/17/2024] [Accepted: 07/16/2024] [Indexed: 08/17/2024]
Abstract
BACKGROUND The cholinergic neurotransmitter system is crucial to cognitive function, with the basal forebrain (BF) being particularly susceptible to Alzheimer's disease (AD) pathology. However, the interaction of white matter hyperintensities (WMH) in cholinergic pathways and BF atrophy without amyloid pathology remains poorly understood. METHODS We enrolled patients who underwent neuropsychological tests, magnetic resonance imaging, and 18F-florbetaben positron emission tomography due to cognitive impairment at the teaching university hospital from 2015 to 2022. Among these, we selected patients with negative amyloid scans and additionally excluded those with Parkinson's dementia that may be accompanied by BF atrophy. The WMH burden of cholinergic pathways was quantified by the Cholinergic Pathways Hyperintensities Scale (CHIPS) score, and categorized into tertile groups because the CHIPS score did not meet normal distribution. Segmentation of the BF on volumetric T1-weighted MRI was performed using FreeSurfer, then was normalized for total intracranial volume. Multivariable regression analysis was performed to investigate the association between BF volumes and CHIPS scores. RESULTS A total of 187 patients were enrolled. The median CHIPS score was 12 [IQR 5.0; 24.0]. The BF volume of the highest CHIPS tertile group (mean ± SD, 3.51 ± 0.49, CHIPSt3) was significantly decreased than those of the lower CHIPS tertile groups (3.75 ± 0.53, CHIPSt2; 3.83 ± 0.53, CHIPSt1; P = 0.02). In the univariable regression analysis, factors showing significant associations with the BF volume were the CHIPSt3 group, age, female, education, diabetes mellitus, smoking, previous stroke history, periventricular WMH, and cerebral microbleeds. In multivariable regression analysis, the CHIPSt3 group (standardized beta [βstd] = -0.25, P = 0.01), female (βstd = 0.20, P = 0.04), and diabetes mellitus (βstd = -0.22, P < 0.01) showed a significant association with the BF volume. Sensitivity analyses showed a negative correlation between CHIPS score and normalized BF volume, regardless of WMH severity. CONCLUSIONS We identified a significant correlation between strategic WMH burden in the cholinergic pathway and BF atrophy independently of amyloid positivity and WMH severity. These results suggest a mechanism of cholinergic neuronal loss through the dying-back phenomenon and provide a rationale that strategic WMH assessment may help identify target groups that may benefit from acetylcholinesterase inhibitor treatment.
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Affiliation(s)
- Ye Eun Kim
- Department of Neurology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Jae-Sung Lim
- Department of Neurology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.
| | - Chong Hyun Suh
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Hwon Heo
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Jee Hoon Roh
- Department of Biomedical Sciences, Department of Physiology, Korea University College of Medicine, Seoul, Korea
- Department of Neurology, Korea University Anam Hospital, Seoul, Korea
| | - E-Nae Cheong
- Department of Medical Science and Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Yoojin Lee
- Department of Neurology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Jae Woo Kim
- Department of Neurology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Jae-Hong Lee
- Department of Neurology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.
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Johns E, Vossler HA, Young CB, Carlson ML, Winer JR, Younes K, Park J, Rathmann‐Bloch J, Smith V, Harrison TM, Landau S, Henderson V, Wagner A, Sha SJ, Zeineh M, Zaharchuk G, Poston KL, Davidzon GA, Mormino EC. Florbetaben amyloid PET acquisition time: Influence on Centiloids and interpretation. Alzheimers Dement 2024; 20:5299-5310. [PMID: 38962867 PMCID: PMC11350032 DOI: 10.1002/alz.13893] [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: 02/22/2024] [Revised: 04/18/2024] [Accepted: 04/22/2024] [Indexed: 07/05/2024]
Abstract
INTRODUCTION Amyloid positron emission tomography (PET) acquisition timing impacts quantification. METHODS In florbetaben (FBB) PET scans of 245 adults with and without cognitive impairment, we investigated the impact of post-injection acquisition time on Centiloids (CLs) across five reference regions. CL equations for FBB were derived using standard methods, using FBB data collected between 90 and 110 min with paired Pittsburgh compound B data. Linear mixed models and t-tests evaluated the impact of acquisition time on CL increases. RESULTS CL values increased significantly over the scan using the whole cerebellum, cerebellar gray matter, and brainstem as reference regions, particularly in amyloid-positive individuals. In contrast, CLs based on white matter-containing reference regions decreased across the scan. DISCUSSION The quantification of CLs in FBB PET imaging is influenced by both the overall scan acquisition time and the choice of reference region. Standardized acquisition protocols or the application of acquisition time-specific CL equations should be implemented in clinical protocols. HIGHLIGHTS Acquisition timing affects florbetaben positron emission tomography (PET) scan quantification, especially in amyloid-positive participants. The impact of acquisition timing on quantification varies across common reference regions. Consistent acquisitions and/or appropriate post-injection adjustments are needed to ensure comparability of PET data.
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Affiliation(s)
- Emily Johns
- Department of Neurology and Neurological SciencesStanford University School of MedicineStanfordCaliforniaUSA
| | - Hillary A. Vossler
- Department of Neurology and Neurological SciencesStanford University School of MedicineStanfordCaliforniaUSA
| | - Christina B. Young
- Department of Neurology and Neurological SciencesStanford University School of MedicineStanfordCaliforniaUSA
| | - Mackenzie L. Carlson
- Department of Neurology and Neurological SciencesStanford University School of MedicineStanfordCaliforniaUSA
| | - Joseph R. Winer
- Department of Neurology and Neurological SciencesStanford University School of MedicineStanfordCaliforniaUSA
| | - Kyan Younes
- Department of Neurology and Neurological SciencesStanford University School of MedicineStanfordCaliforniaUSA
| | - Jennifer Park
- Department of Neurology and Neurological SciencesStanford University School of MedicineStanfordCaliforniaUSA
| | | | - Viktorija Smith
- Department of Neurology and Neurological SciencesStanford University School of MedicineStanfordCaliforniaUSA
| | - Theresa M. Harrison
- Helen Wills Neuroscience InstituteUniversity of California BerkeleyBerkeleyCaliforniaUSA
| | - Susan Landau
- Helen Wills Neuroscience InstituteUniversity of California BerkeleyBerkeleyCaliforniaUSA
| | - Victor Henderson
- Department of Neurology and Neurological SciencesStanford University School of MedicineStanfordCaliforniaUSA
- Department of Epidemiology and Population HealthStanford UniversityStanfordCaliforniaUSA
| | - Anthony Wagner
- Helen Wills Neuroscience InstituteUniversity of California BerkeleyBerkeleyCaliforniaUSA
- Stanford UniversityWu Tsai Neuroscience InstituteStanfordCaliforniaUSA
| | - Sharon J. Sha
- Department of Neurology and Neurological SciencesStanford University School of MedicineStanfordCaliforniaUSA
- Stanford UniversityWu Tsai Neuroscience InstituteStanfordCaliforniaUSA
| | - Michael Zeineh
- Department of RadiologyStanford University School of MedicineStanfordCaliforniaUSA
| | - Greg Zaharchuk
- Department of RadiologyStanford University School of MedicineStanfordCaliforniaUSA
| | - Kathleen L. Poston
- Department of Neurology and Neurological SciencesStanford University School of MedicineStanfordCaliforniaUSA
- Stanford UniversityWu Tsai Neuroscience InstituteStanfordCaliforniaUSA
| | - Guido A. Davidzon
- Department of RadiologyStanford University School of MedicineStanfordCaliforniaUSA
| | - Elizabeth C. Mormino
- Department of Neurology and Neurological SciencesStanford University School of MedicineStanfordCaliforniaUSA
- Stanford UniversityWu Tsai Neuroscience InstituteStanfordCaliforniaUSA
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8
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Xiong C, Luo J, Wolk DA, Shaw LM, Roberson ED, Murchison CF, Henson RL, Benzinger TLS, Bui Q, Agboola F, Grant E, Gremminger EN, Moulder KL, Geldmacher DS, Clay OJ, Babulal G, Cruchaga C, Holtzman DM, Bateman RJ, Morris JC, Schindler SE. Baseline levels and longitudinal changes in plasma Aβ42/40 among Black and white individuals. Nat Commun 2024; 15:5539. [PMID: 38956096 PMCID: PMC11219932 DOI: 10.1038/s41467-024-49859-w] [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: 12/20/2023] [Accepted: 06/21/2024] [Indexed: 07/04/2024] Open
Abstract
Blood-based biomarkers of Alzheimer disease (AD) may facilitate testing of historically under-represented groups. The Study of Race to Understand Alzheimer Biomarkers (SORTOUT-AB) is a multi-center longitudinal study to compare AD biomarkers in participants who identify their race as either Black or white. Plasma samples from 324 Black and 1,547 white participants underwent analysis with C2N Diagnostics' PrecivityAD test for Aβ42 and Aβ40. Compared to white individuals, Black individuals had higher average plasma Aβ42/40 levels at baseline, consistent with a lower average level of amyloid pathology. Interestingly, this difference resulted from lower average levels of plasma Aβ40 in Black participants. Despite the differences, Black and white individuals had similar longitudinal rates of change in Aβ42/40, consistent with a similar rate of amyloid accumulation. Our results agree with multiple recent studies demonstrating a lower prevalence of amyloid pathology in Black individuals, and additionally suggest that amyloid accumulates consistently across both groups.
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Affiliation(s)
- Chengjie Xiong
- Division of Biostatistics, Washington University, St. Louis, MO, USA
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
| | - Jingqin Luo
- Division of Public Health Sciences, Department of Surgery, Washington University School of Medicine, St. Louis, MO, USA
- Siteman Cancer Center Biostatistics and Qualitative Research Shared Resource, Washington University School of Medicine, St. Louis, MO, USA
| | - David A Wolk
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Leslie M Shaw
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Erik D Roberson
- Alzheimer's Disease Center, Department of Neurology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Charles F Murchison
- Alzheimer's Disease Center, Department of Neurology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Rachel L Henson
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
| | - Tammie L S Benzinger
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
- Mallinckrodt Institute of Radiology, Washington University, St. Louis, MO, USA
| | - Quoc Bui
- Division of Biostatistics, Washington University, St. Louis, MO, USA
| | - Folasade Agboola
- Division of Biostatistics, Washington University, St. Louis, MO, USA
| | - Elizabeth Grant
- Division of Biostatistics, Washington University, St. Louis, MO, USA
| | | | - Krista L Moulder
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
| | - David S Geldmacher
- Alzheimer's Disease Center, Department of Neurology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Olivio J Clay
- Alzheimer's Disease Center, Department of Neurology, University of Alabama at Birmingham, Birmingham, AL, USA
- Department of Psychology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Ganesh Babulal
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
| | - Carlos Cruchaga
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
| | - David M Holtzman
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
| | - Randall J Bateman
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
| | - John C Morris
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
| | - Suzanne E Schindler
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA.
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9
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Schindler SE, Galasko D, Pereira AC, Rabinovici GD, Salloway S, Suárez-Calvet M, Khachaturian AS, Mielke MM, Udeh-Momoh C, Weiss J, Batrla R, Bozeat S, Dwyer JR, Holzapfel D, Jones DR, Murray JF, Partrick KA, Scholler E, Vradenburg G, Young D, Algeciras-Schimnich A, Aubrecht J, Braunstein JB, Hendrix J, Hu YH, Mattke S, Monane M, Reilly D, Somers E, Teunissen CE, Shobin E, Vanderstichele H, Weiner MW, Wilson D, Hansson O. Acceptable performance of blood biomarker tests of amyloid pathology - recommendations from the Global CEO Initiative on Alzheimer's Disease. Nat Rev Neurol 2024; 20:426-439. [PMID: 38866966 DOI: 10.1038/s41582-024-00977-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/16/2024] [Indexed: 06/14/2024]
Abstract
Anti-amyloid treatments for early symptomatic Alzheimer disease have recently become clinically available in some countries, which has greatly increased the need for biomarker confirmation of amyloid pathology. Blood biomarker (BBM) tests for amyloid pathology are more acceptable, accessible and scalable than amyloid PET or cerebrospinal fluid (CSF) tests, but have highly variable levels of performance. The Global CEO Initiative on Alzheimer's Disease convened a BBM Workgroup to consider the minimum acceptable performance of BBM tests for clinical use. Amyloid PET status was identified as the reference standard. For use as a triaging test before subsequent confirmatory tests such as amyloid PET or CSF tests, the BBM Workgroup recommends that a BBM test has a sensitivity of ≥90% with a specificity of ≥85% in primary care and ≥75-85% in secondary care depending on the availability of follow-up testing. For use as a confirmatory test without follow-up tests, a BBM test should have performance equivalent to that of CSF tests - a sensitivity and specificity of ~90%. Importantly, the predictive values of all biomarker tests vary according to the pre-test probability of amyloid pathology and must be interpreted in the complete clinical context. Use of BBM tests that meet these performance standards could enable more people to receive an accurate and timely Alzheimer disease diagnosis and potentially benefit from new treatments.
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Affiliation(s)
- Suzanne E Schindler
- Department of Neurology, Knight Alzheimer's Disease Research Center, Washington University School of Medicine, St Louis, MO, USA.
| | - Douglas Galasko
- Department of Neurosciences, University of California, San Diego, La Jolla, CA, USA
| | - Ana C Pereira
- Department of Neurology, Icahn School of Medicine, Mount Sinai, New York, NY, USA
| | - Gil D Rabinovici
- Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, CA, USA
- Department of Radiology and Biomedical Imaging, Weill Institute for Neurosciences, University of California, San Francisco, CA, USA
| | - Stephen Salloway
- Department of Neurology, Alpert Medical School, Brown University, Providence, RI, USA
| | - Marc Suárez-Calvet
- Barcelonaβeta Brain Research Center, Pasqual Maragall Foundation, Barcelona, Spain
| | | | - Michelle M Mielke
- Department of Epidemiology and Prevention, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | - Chi Udeh-Momoh
- Department of Epidemiology and Prevention, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | - Joan Weiss
- US Department of Health and Human Services, Health Resources and Services Administration, Bureau of Health Workforce, Rockville, MD, USA
| | | | | | - John R Dwyer
- Global Alzheimer's Platform Foundation, Washington, DC, USA
| | - Drew Holzapfel
- The Global CEO Initiative on Alzheimer's Disease, Philadelphia, PA, USA
| | | | | | | | - Emily Scholler
- The Global CEO Initiative on Alzheimer's Disease, Philadelphia, PA, USA
| | - George Vradenburg
- Davos Alzheimer's Collaborative, Philadelphia, PA, USA
- UsAgainstAlzheimer's, Washington, DC, USA
| | | | | | | | | | | | | | - Soeren Mattke
- The USC Brain Health Observatory, University of Southern California, Los Angeles, CA, USA
| | | | | | | | - Charlotte E Teunissen
- Neurochemistry Laboratory, Department of Clinical Chemistry, Amsterdam Neuroscience, Amsterdam University Medical Centers, Vrije Universitiet, Amsterdam, The Netherlands
| | | | | | - Michael W Weiner
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA, USA
- Department of Medicine, University of California, San Francisco, CA, USA
- Department of Psychiatry, University of California, San Francisco, CA, USA
- Department of Neurology, University of California, San Francisco, CA, USA
| | | | - Oskar Hansson
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden.
- Memory Clinic, Skåne University Hospital, Malmö, Sweden.
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10
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Xu L, Ren C, Jing C, Wang G, Wei H, Kong M, Ba M. Predicting amyloid-PET and clinical conversion in apolipoprotein E ε3/ε3 non-demented individuals with multidimensional factors. Eur J Neurosci 2024; 60:3742-3758. [PMID: 38698692 DOI: 10.1111/ejn.16376] [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: 01/08/2024] [Revised: 04/14/2024] [Accepted: 04/16/2024] [Indexed: 05/05/2024]
Abstract
The apolipoprotein E (APOE) ε4 is a well-established risk factor of amyloid-β (Aβ) in Alzheimer's disease (AD). However, because of the high prevalence of APOE ε3, there may be a large number of people with APOE ε3/ε3 who are non-demented and have Aβ pathology. There are limited studies on assessing Aβ status and clinical conversion in the APOE ε3/ε3 non-demented population. Two hundred and ninety-three non-demented individuals with APOE ε3/ε3 from ADNI database were divided into Aβ-positron emission tomography (Aβ-PET) positivity (+) and Aβ-PET negativity (-) groups using cut-off value of >1.11. Stepwise regression searched for a single or multidimensional clinical variables for predicting Aβ-PET (+), and the receiver operating characteristic curve (ROC) assessed the accuracy of the predictive models. The Cox regression model explored the risk factors associated with clinical conversion to mild cognitive impairment (MCI) or AD. The results showed that the combination of sex, education, ventricle and white matter hyperintensity (WMH) volume can accurately predict Aβ-PET status in cognitively normal (CN), and the combination of everyday cognition study partner total (EcogSPTotal) score, age, plasma p-tau 181 and WMH can accurately predict Aβ-PET status in MCI individuals. EcogSPTotal score were independent predictors of clinical conversion to MCI or AD. The findings may provide a non-invasive and effective tool to improve the efficiency of screening Aβ-PET (+), accelerate and reduce costs of AD trial recruitment in future secondary prevention trials or help to select patients at high risk of disease progression in clinical trials.
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Affiliation(s)
- Lijuan Xu
- Department of Neurology, the Affiliated Yantai Yuhuangding Hospital of Qingdao University, Shandong, China
| | - Chao Ren
- Department of Neurology, the Affiliated Yantai Yuhuangding Hospital of Qingdao University, Shandong, China
| | - Chenxi Jing
- Department of Neurology, the Affiliated Yantai Yuhuangding Hospital of Qingdao University, Shandong, China
| | - Gang Wang
- School of Ulsan Ship and Ocean College, Ludong University, Yantai, China
| | - Hongchun Wei
- Department of Neurology, the Affiliated Yantai Yuhuangding Hospital of Qingdao University, Shandong, China
| | - Min Kong
- Department of Neurology, Yantaishan Hospital, Yantai City, Shandong, China
| | - Maowen Ba
- Department of Neurology, the Affiliated Yantai Yuhuangding Hospital of Qingdao University, Shandong, China
- Yantai Regional Sub Center of National Center for Clinical Medical Research of Neurological Diseases, Shandong, China
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11
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Mackay GA, Gall C, Jampana R, Sleith C, Lip GYH. Scottish Intercollegiate Guidelines Network Guidance on Dementia: The Investigation of Suspected Dementia (SIGN 168) with Focus on Biomarkers-Executive Summary. Thromb Haemost 2024. [PMID: 38788775 DOI: 10.1055/a-2332-6426] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/26/2024]
Abstract
This is an executive summary of the recent guidance produced by the Scottish Intercollegiate Guidelines Network (SIGN) dementia guideline group with regards to the investigation of suspected dementia. This is a sub-section of the broader SIGN 168 guideline released in November 2023. The guideline group included clinicians with expertise in Old Age Psychiatry, Neurology, Radiology, and Nuclear Medicine supported by colleagues from the SIGN and Healthcare Improvement Scotland teams. There was representation from carers and support organizations with experience of dementia, to ensure the recommendations were appropriate from the perspective of the people being assessed for possible dementia and their carers. As the 2018 National Institute for Health and Clinical Excellence (NICE) dementia review included a review of the evidenced investigation of dementia, the SIGN guideline development group decided to focus on a review on the up-to-date evidence regarding the role of imaging and fluid biomarkers in the diagnosis of dementia. To give context to the consideration of more advanced diagnostic biomarker investigations, the guideline and this summary include the NICE guidance on the use of standard investigations as well as more specialist investigations. The evidence review supports consideration of the use of structural imaging, nuclear medicine imaging, and established Alzheimer's cerebrospinal fluid biomarkers (amyloid and tau) in the diagnosis of dementia. Although routine use of amyloid positron emission tomography imaging was not recommended, its potential use, under specialist direction, in patients with atypical or young-onset presentations of suspected Alzheimer's dementia was included as a clinical good practice point.
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Affiliation(s)
- Graham Andrew Mackay
- Department of Neurology, Aberdeen Royal Infirmary, Foresterhill, Aberdeen, United Kingdom
| | - Claire Gall
- Department of Neurology, Queen Elizabeth University Hospital, Glasgow, United Kingdom
| | - Ravi Jampana
- Department of Neuroradiology, Queen Elizabeth University Hospital, Glasgow, United Kingdom
| | - Carolyn Sleith
- Healthcare Improvement Scotland, Edinburgh, United Kingdom
| | - Gregory Y H Lip
- Liverpool Centre for Cardiovascular Science at University of Liverpool, Liverpool John Moores University and Liverpool Heart and Chest Hospital, Liverpool, United Kingdom
- Department of Clinical Medicine, Danish Center for Health Services Research, Aalborg University, Aalborg, Denmark
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Gallo A, Lipari A, Di Francesco S, Ianuà E, Liperoti R, Cipriani MC, Martone AM, De Candia E, Landi F, Montalto M. Platelets and Neurodegenerative Diseases: Current Knowledge and Future Perspectives. Int J Mol Sci 2024; 25:6292. [PMID: 38927999 PMCID: PMC11203688 DOI: 10.3390/ijms25126292] [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: 05/01/2024] [Revised: 06/01/2024] [Accepted: 06/03/2024] [Indexed: 06/28/2024] Open
Abstract
Platelets have a fundamental role in mediating hemostasis and thrombosis. However, more recently, a new idea is making headway, highlighting the importance of platelets as significant actors in modulating immune and inflammatory responses. In particular, platelets have an important role in the development of vascular amyloid-b-peptide(ab) deposits, known to play a relevant role in Alzheimer's disease (AD) through accumulation and deposition within the frontal cortex and hippocampus in the brain. The involvement of platelets in the pathogenesis of AD opens up the highly attractive possibility of applying antiplatelet therapy for the treatment and/or prevention of AD, but conclusive results are scarce. Even less is known about the potential role of platelets in mild cognitive impairment (MCI). The aim to this brief review is to summarize current knowledge on this topic and to introduce the new perspectives on the possible role of platelet activation as therapeutic target both in AD and MCI.
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Affiliation(s)
- Antonella Gallo
- Department of Geriatrics, Orthopedics and Rheumatology, Fondazione Policlinico Universitario Agostino Gemelli, IRCCS, 00168 Rome, Italy; (R.L.); (M.C.C.); (A.M.M.); (F.L.); (M.M.)
| | - Alice Lipari
- Department of Geriatrics, Orthopedics and Rheumatology, Università Cattolica del Sacro Cuore, 00168 Rome, Italy (S.D.F.); (E.I.)
| | - Silvino Di Francesco
- Department of Geriatrics, Orthopedics and Rheumatology, Università Cattolica del Sacro Cuore, 00168 Rome, Italy (S.D.F.); (E.I.)
| | - Eleonora Ianuà
- Department of Geriatrics, Orthopedics and Rheumatology, Università Cattolica del Sacro Cuore, 00168 Rome, Italy (S.D.F.); (E.I.)
| | - Rosa Liperoti
- Department of Geriatrics, Orthopedics and Rheumatology, Fondazione Policlinico Universitario Agostino Gemelli, IRCCS, 00168 Rome, Italy; (R.L.); (M.C.C.); (A.M.M.); (F.L.); (M.M.)
- Department of Geriatrics, Orthopedics and Rheumatology, Università Cattolica del Sacro Cuore, 00168 Rome, Italy (S.D.F.); (E.I.)
| | - Maria Camilla Cipriani
- Department of Geriatrics, Orthopedics and Rheumatology, Fondazione Policlinico Universitario Agostino Gemelli, IRCCS, 00168 Rome, Italy; (R.L.); (M.C.C.); (A.M.M.); (F.L.); (M.M.)
| | - Anna Maria Martone
- Department of Geriatrics, Orthopedics and Rheumatology, Fondazione Policlinico Universitario Agostino Gemelli, IRCCS, 00168 Rome, Italy; (R.L.); (M.C.C.); (A.M.M.); (F.L.); (M.M.)
| | - Erica De Candia
- Haemorrhagic and Thrombotic Diseases Unit, Fondazione Policlinico Universitario Agostino Gemelli, IRCCS, 00168 Rome, Italy;
- Department of Translation Medicine and Surgery, Università Cattolica del Sacro Cuore, 00168 Rome, Italy
| | - Francesco Landi
- Department of Geriatrics, Orthopedics and Rheumatology, Fondazione Policlinico Universitario Agostino Gemelli, IRCCS, 00168 Rome, Italy; (R.L.); (M.C.C.); (A.M.M.); (F.L.); (M.M.)
- Department of Geriatrics, Orthopedics and Rheumatology, Università Cattolica del Sacro Cuore, 00168 Rome, Italy (S.D.F.); (E.I.)
| | - Massimo Montalto
- Department of Geriatrics, Orthopedics and Rheumatology, Fondazione Policlinico Universitario Agostino Gemelli, IRCCS, 00168 Rome, Italy; (R.L.); (M.C.C.); (A.M.M.); (F.L.); (M.M.)
- Department of Geriatrics, Orthopedics and Rheumatology, Università Cattolica del Sacro Cuore, 00168 Rome, Italy (S.D.F.); (E.I.)
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Tagmazian AA, Schwarz C, Lange C, Pitkänen E, Vuoksimaa E. ArcheD, a residual neural network for prediction of cerebrospinal fluid amyloid-beta from amyloid PET images. Eur J Neurosci 2024; 59:3030-3044. [PMID: 38576196 DOI: 10.1111/ejn.16332] [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: 10/27/2023] [Revised: 02/28/2024] [Accepted: 02/29/2024] [Indexed: 04/06/2024]
Abstract
Detection and measurement of amyloid-beta (Aβ) in the brain is a key factor for early identification and diagnosis of Alzheimer's disease (AD). We aimed to develop a deep learning model to predict Aβ cerebrospinal fluid (CSF) concentration directly from amyloid PET images, independent of tracers, brain reference regions or preselected regions of interest. We used 1870 Aβ PET images and CSF measurements to train and validate a convolutional neural network ("ArcheD"). We evaluated the ArcheD performance in relation to episodic memory and the standardized uptake value ratio (SUVR) of cortical Aβ. We also compared the brain region's relevance for the model's CSF prediction within clinical-based and biological-based classifications. ArcheD-predicted Aβ CSF values correlated with measured Aβ CSF values (r = 0.92; q < 0.01), SUVR (rAV45 = -0.64, rFBB = -0.69; q < 0.01) and episodic memory measures (0.33 < r < 0.44; q < 0.01). For both classifications, cerebral white matter significantly contributed to CSF prediction (q < 0.01), specifically in non-symptomatic and early stages of AD. However, in late-stage disease, the brain stem, subcortical areas, cortical lobes, limbic lobe and basal forebrain made more significant contributions (q < 0.01). Considering cortical grey matter separately, the parietal lobe was the strongest predictor of CSF amyloid levels in those with prodromal or early AD, while the temporal lobe played a more crucial role for those with AD. In summary, ArcheD reliably predicted Aβ CSF concentration from Aβ PET scans, offering potential clinical utility for Aβ level determination.
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Affiliation(s)
- Arina A Tagmazian
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
| | - Claudia Schwarz
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
- Department of Neurology, University Medicine Greifswald, Greifswald, Germany
| | - Catharina Lange
- Department of Nuclear Medicine, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Esa Pitkänen
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
| | - Eero Vuoksimaa
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
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Rhodius-Meester HFM, van Maurik IS, Collij LE, van Gils AM, Koikkalainen J, Tolonen A, Pijnenburg YAL, Berkhof J, Barkhof F, van de Giessen E, Lötjönen J, van der Flier WM. Computerized decision support is an effective approach to select memory clinic patients for amyloid-PET. PLoS One 2024; 19:e0303111. [PMID: 38768188 PMCID: PMC11104589 DOI: 10.1371/journal.pone.0303111] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2023] [Accepted: 04/18/2024] [Indexed: 05/22/2024] Open
Abstract
BACKGROUND The use of amyloid-PET in dementia workup is upcoming. At the same time, amyloid-PET is costly and limitedly available. While the appropriate use criteria (AUC) aim for optimal use of amyloid-PET, their limited sensitivity hinders the translation to clinical practice. Therefore, there is a need for tools that guide selection of patients for whom amyloid-PET has the most clinical utility. We aimed to develop a computerized decision support approach to select patients for amyloid-PET. METHODS We included 286 subjects (135 controls, 108 Alzheimer's disease dementia, 33 frontotemporal lobe dementia, and 10 vascular dementia) from the Amsterdam Dementia Cohort, with available neuropsychology, APOE, MRI and [18F]florbetaben amyloid-PET. In our computerized decision support approach, using supervised machine learning based on the DSI classifier, we first classified the subjects using only neuropsychology, APOE, and quantified MRI. Then, for subjects with uncertain classification (probability of correct class (PCC) < 0.75) we enriched classification by adding (hypothetical) amyloid positive (AD-like) and negative (normal) PET visual read results and assessed whether the diagnosis became more certain in at least one scenario (PPC≥0.75). If this was the case, the actual visual read result was used in the final classification. We compared the proportion of PET scans and patients diagnosed with sufficient certainty in the computerized approach with three scenarios: 1) without amyloid-PET, 2) amyloid-PET according to the AUC, and 3) amyloid-PET for all patients. RESULTS The computerized approach advised PET in n = 60(21%) patients, leading to a diagnosis with sufficient certainty in n = 188(66%) patients. This approach was more efficient than the other three scenarios: 1) without amyloid-PET, diagnostic classification was obtained in n = 155(54%), 2) applying the AUC resulted in amyloid-PET in n = 113(40%) and diagnostic classification in n = 156(55%), and 3) performing amyloid-PET in all resulted in diagnostic classification in n = 154(54%). CONCLUSION Our computerized data-driven approach selected 21% of memory clinic patients for amyloid-PET, without compromising diagnostic performance. Our work contributes to a cost-effective implementation and could support clinicians in making a balanced decision in ordering additional amyloid PET during the dementia workup.
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Affiliation(s)
- Hanneke F. M. Rhodius-Meester
- Alzheimer Center Amsterdam, Neurology, Amsterdam UMC Location VUmc, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
- Department of Internal Medicine, Geriatric Medicine Section, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Department of Geriatric Medicine, The Memory Clinic, Oslo University Hospital, Oslo, Norway
| | - Ingrid S. van Maurik
- Alzheimer Center Amsterdam, Neurology, Amsterdam UMC Location VUmc, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
- Epidemiology and Data Science, Amsterdam UMC Location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Public Health, Methodology, Amsterdam, The Netherlands
| | - Lyduine E. Collij
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Aniek M. van Gils
- Alzheimer Center Amsterdam, Neurology, Amsterdam UMC Location VUmc, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
| | | | | | - Yolande A. L. Pijnenburg
- Alzheimer Center Amsterdam, Neurology, Amsterdam UMC Location VUmc, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
| | - Johannes Berkhof
- Epidemiology and Data Science, Amsterdam UMC Location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Public Health, Methodology, Amsterdam, The Netherlands
| | - Frederik Barkhof
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Queen Square Institute of Neurology and Centre for Medical Image Computing, University College London, London, United Kingdom
| | - Elsmarieke van de Giessen
- Alzheimer Center Amsterdam, Neurology, Amsterdam UMC Location VUmc, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | | | - Wiesje M. van der Flier
- Alzheimer Center Amsterdam, Neurology, Amsterdam UMC Location VUmc, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
- Epidemiology and Data Science, Amsterdam UMC Location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Public Health, Methodology, Amsterdam, The Netherlands
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15
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Chen K, Wang M, Wu J, Zuo C, Huang Y, Wang W, Zhao M, Zhang Y, Zhang X, Chen S, Liu W, Li M, Ge J, Ma X, Wang J, Zheng L, Guan Y, Dong Q, Cui M, Xie F, Zhao Q, Yu J. Incremental value of amyloid PET in a tertiary memory clinic setting in China. Alzheimers Dement 2024; 20:2516-2525. [PMID: 38329281 PMCID: PMC11032579 DOI: 10.1002/alz.13728] [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: 12/08/2023] [Revised: 01/04/2024] [Accepted: 01/05/2024] [Indexed: 02/09/2024]
Abstract
INTRODUCTION The objective of this study is to investigate the incremental value of amyloid positron emission tomography (Aβ-PET) in a tertiary memory clinic setting in China. METHODS A total of 1073 patients were offered Aβ-PET using 18F-florbetapir. The neurologists determined a suspected etiology (Alzheimer's disease [AD] or non-AD) with a percentage estimate of their confidence and medication prescription both before and after receiving the Aβ-PET results. RESULTS After disclosure of the Aβ-PET results, etiological diagnoses changed in 19.3% of patients, and diagnostic confidence increased from 69.3% to 85.6%. Amyloid PET results led to a change of treatment plan in 36.5% of patients. Compared to the late-onset group, the early-onset group had a more frequent change in diagnoses and a higher increase in diagnostic confidence. DISCUSSION Aβ-PET has significant impacts on the changes of diagnoses and management in Chinese population. Early-onset cases are more likely to benefit from Aβ-PET than late-onset cases. HIGHLIGHTS Amyloid PET contributes to diagnostic changes and its confidence in Chinese patients. Amyloid PET leads to a change of treatment plans in Chinese patients. Early-onset cases are more likely to benefit from amyloid PET than late-onset cases.
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Affiliation(s)
- Ke‐Liang Chen
- Department of Neurology and National Center for Neurological DiseasesHuashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan UniversityShanghaiChina
| | - Ming‐Yu Wang
- School of MedicineQingdao UniversityQingdaoShandongChina
- Departments of NeurologyWeifang People's HospitalWeifangShandongChina
| | - Jie Wu
- Department of Neurology and National Center for Neurological DiseasesHuashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan UniversityShanghaiChina
| | - Chuan‐Tao Zuo
- Department of Nuclear Medicine & PET CenterHuashan HospitalFudan UniversityShanghaiChina
| | - Yu‐Yuan Huang
- Department of Neurology and National Center for Neurological DiseasesHuashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan UniversityShanghaiChina
| | - Wei‐Yi Wang
- Department of Nuclear Medicine & PET CenterHuashan HospitalFudan UniversityShanghaiChina
| | - Meng Zhao
- Department of Neurologythe First Hospital of Jilin UniversityChangchunJilinChina
| | - Ya‐Ru Zhang
- Department of Neurology and National Center for Neurological DiseasesHuashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan UniversityShanghaiChina
| | - Xue Zhang
- Department of NeurologyQingdao shi zhongxin yiyuanQingdaoShandongChina
| | - Shu‐Fen Chen
- Department of Neurology and National Center for Neurological DiseasesHuashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan UniversityShanghaiChina
| | - Wei‐Shi Liu
- Department of Neurology and National Center for Neurological DiseasesHuashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan UniversityShanghaiChina
| | - Meng‐Meng Li
- Department of Neurology and National Center for Neurological DiseasesHuashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan UniversityShanghaiChina
| | - Jing‐Jie Ge
- Department of Nuclear Medicine & PET CenterHuashan HospitalFudan UniversityShanghaiChina
| | - Xiao‐Xi Ma
- Department of Neurology and National Center for Neurological DiseasesHuashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan UniversityShanghaiChina
| | - Jie Wang
- Department of Neurology and National Center for Neurological DiseasesHuashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan UniversityShanghaiChina
| | - Li Zheng
- Department of Neurology and National Center for Neurological DiseasesHuashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan UniversityShanghaiChina
| | - Yi‐Hui Guan
- Department of Nuclear Medicine & PET CenterHuashan HospitalFudan UniversityShanghaiChina
| | - Qiang Dong
- Department of Neurology and National Center for Neurological DiseasesHuashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan UniversityShanghaiChina
| | - Mei Cui
- Department of Neurology and National Center for Neurological DiseasesHuashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan UniversityShanghaiChina
| | - Fang Xie
- Department of Nuclear Medicine & PET CenterHuashan HospitalFudan UniversityShanghaiChina
| | - Qian‐Hua Zhao
- Department of Neurology and National Center for Neurological DiseasesHuashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan UniversityShanghaiChina
| | - Jin‐Tai Yu
- Department of Neurology and National Center for Neurological DiseasesHuashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan UniversityShanghaiChina
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Ackley S, Calmasini C, Bouteloup V, Hill-Jarrett TG, Swinnerton KN, Chêne G, Dufouil C, Glymour MM. Contribution of Global Amyloid-PET Imaging for Predicting Future Cognition in the MEMENTO Cohort. Neurology 2024; 102:e208054. [PMID: 38412412 DOI: 10.1212/wnl.0000000000208054] [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: 03/01/2023] [Accepted: 10/16/2023] [Indexed: 02/29/2024] Open
Abstract
BACKGROUND AND OBJECTIVES Global amyloid-PET is associated with cognition and cognitive decline, but most research on this association does not account for past cognitive information. We assessed the prognostic benefit of amyloid-PET measures for future cognition when prior cognitive assessments are available, evaluating the added value of amyloid measures beyond information on multiple past cognitive assessments. METHODS The French MEMENTO cohort (a cohort of outpatients from French research memory centers to improve knowledge on Alzheimer disease and related disorders) includes older outpatients with incipient cognitive changes, but no dementia diagnosis at inclusion. Global amyloid burden was assessed using positron emission tomography (amyloid-PET) for a subset of participants; semiannual cognitive testing was subsequently performed. We predicted mini-mental state examination (MMSE) scores using demographic characteristics (age, sex, marital status, and education) alone or in combination with information on prior cognitive measures. The added value of amyloid burden as a predictor in these models was evaluated with percent reduction of the mean squared error (MSE). All models were conducted separately for evaluating the added value of dichotomous amyloid positivity status compared with a continuous amyloid-standardized uptake-value ratio. RESULTS Our analytic sample comprised 510 individuals who underwent amyloid-PET scans with at least 4 MMSE assessments. The mean age at the PET scan was 71.6 (standard deviation 7.4) years; 60.7% were female. The median follow-up was 4.6 years (interquartile range: 0.9 years). Adding amyloid burden when adjusting for only demographic characteristics reduced the MSE of predictions by 5.08% (95% CI 0.97%-10.86%) and 12.64% (95% CI 3.35%-25.28%) for binary and continuous amyloid, respectively. If the model included 1 past MMSE measure, the MSE improvement was 3.51% (95% CI 1.01%-7.28%) when adding binary amyloid and 8.83% (95% CI 2.63%-16.37%) when adding continuous amyloid. Improvements in model fit were smaller with the addition of amyloid burden when more than 1 past cognitive assessment was included. For all models incorporating past cognitive assessments, differences in predictions amounted to a fraction of 1 MMSE point on average. DISCUSSION In a clinical setting, global amyloid burden did not appreciably improve cognitive predictions when past cognitive assessments were available. TRIAL REGISTRATION INFORMATION ClinicalTrials.gov Identifier: NCT02164643.
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Affiliation(s)
- Sarah Ackley
- From the Department of Epidemiology (S.A., M.M.G.), Boston University, MA; Department of Epidemiology and Biostatistics (C.C., K.N.S.), University of California, San Francisco; University Bordeaux (V.B., G.C., C.D.), Inserm, UMR 1219; Pole de sante publique Centre Hospitalier Universitaire (CHU) de Bordeaux (V.B., G.C., C.D.), France; and Memory & Aging Center (T.G.H.-J.), University of California, San Francisco
| | - Camilla Calmasini
- From the Department of Epidemiology (S.A., M.M.G.), Boston University, MA; Department of Epidemiology and Biostatistics (C.C., K.N.S.), University of California, San Francisco; University Bordeaux (V.B., G.C., C.D.), Inserm, UMR 1219; Pole de sante publique Centre Hospitalier Universitaire (CHU) de Bordeaux (V.B., G.C., C.D.), France; and Memory & Aging Center (T.G.H.-J.), University of California, San Francisco
| | - Vincent Bouteloup
- From the Department of Epidemiology (S.A., M.M.G.), Boston University, MA; Department of Epidemiology and Biostatistics (C.C., K.N.S.), University of California, San Francisco; University Bordeaux (V.B., G.C., C.D.), Inserm, UMR 1219; Pole de sante publique Centre Hospitalier Universitaire (CHU) de Bordeaux (V.B., G.C., C.D.), France; and Memory & Aging Center (T.G.H.-J.), University of California, San Francisco
| | - Tanisha G Hill-Jarrett
- From the Department of Epidemiology (S.A., M.M.G.), Boston University, MA; Department of Epidemiology and Biostatistics (C.C., K.N.S.), University of California, San Francisco; University Bordeaux (V.B., G.C., C.D.), Inserm, UMR 1219; Pole de sante publique Centre Hospitalier Universitaire (CHU) de Bordeaux (V.B., G.C., C.D.), France; and Memory & Aging Center (T.G.H.-J.), University of California, San Francisco
| | - Kaitlin N Swinnerton
- From the Department of Epidemiology (S.A., M.M.G.), Boston University, MA; Department of Epidemiology and Biostatistics (C.C., K.N.S.), University of California, San Francisco; University Bordeaux (V.B., G.C., C.D.), Inserm, UMR 1219; Pole de sante publique Centre Hospitalier Universitaire (CHU) de Bordeaux (V.B., G.C., C.D.), France; and Memory & Aging Center (T.G.H.-J.), University of California, San Francisco
| | - Geneviève Chêne
- From the Department of Epidemiology (S.A., M.M.G.), Boston University, MA; Department of Epidemiology and Biostatistics (C.C., K.N.S.), University of California, San Francisco; University Bordeaux (V.B., G.C., C.D.), Inserm, UMR 1219; Pole de sante publique Centre Hospitalier Universitaire (CHU) de Bordeaux (V.B., G.C., C.D.), France; and Memory & Aging Center (T.G.H.-J.), University of California, San Francisco
| | - Carole Dufouil
- From the Department of Epidemiology (S.A., M.M.G.), Boston University, MA; Department of Epidemiology and Biostatistics (C.C., K.N.S.), University of California, San Francisco; University Bordeaux (V.B., G.C., C.D.), Inserm, UMR 1219; Pole de sante publique Centre Hospitalier Universitaire (CHU) de Bordeaux (V.B., G.C., C.D.), France; and Memory & Aging Center (T.G.H.-J.), University of California, San Francisco
| | - M M Glymour
- From the Department of Epidemiology (S.A., M.M.G.), Boston University, MA; Department of Epidemiology and Biostatistics (C.C., K.N.S.), University of California, San Francisco; University Bordeaux (V.B., G.C., C.D.), Inserm, UMR 1219; Pole de sante publique Centre Hospitalier Universitaire (CHU) de Bordeaux (V.B., G.C., C.D.), France; and Memory & Aging Center (T.G.H.-J.), University of California, San Francisco
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17
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Frisoni GB, Festari C, Massa F, Cotta Ramusino M, Orini S, Aarsland D, Agosta F, Babiloni C, Borroni B, Cappa SF, Frederiksen KS, Froelich L, Garibotto V, Haliassos A, Jessen F, Kamondi A, Kessels RP, Morbelli SD, O'Brien JT, Otto M, Perret-Liaudet A, Pizzini FB, Vandenbulcke M, Vanninen R, Verhey F, Vernooij MW, Yousry T, Boada Rovira M, Dubois B, Georges J, Hansson O, Ritchie CW, Scheltens P, van der Flier WM, Nobili F. European intersocietal recommendations for the biomarker-based diagnosis of neurocognitive disorders. Lancet Neurol 2024; 23:302-312. [PMID: 38365381 DOI: 10.1016/s1474-4422(23)00447-7] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2023] [Revised: 10/30/2023] [Accepted: 11/13/2023] [Indexed: 02/18/2024]
Abstract
The recent commercialisation of the first disease-modifying drugs for Alzheimer's disease emphasises the need for consensus recommendations on the rational use of biomarkers to diagnose people with suspected neurocognitive disorders in memory clinics. Most available recommendations and guidelines are either disease-centred or biomarker-centred. A European multidisciplinary taskforce consisting of 22 experts from 11 European scientific societies set out to define the first patient-centred diagnostic workflow that aims to prioritise testing for available biomarkers in individuals attending memory clinics. After an extensive literature review, we used a Delphi consensus procedure to identify 11 clinical syndromes, based on clinical history and examination, neuropsychology, blood tests, structural imaging, and, in some cases, EEG. We recommend first-line and, if needed, second-line testing for biomarkers according to the patient's clinical profile and the results of previous biomarker findings. This diagnostic workflow will promote consistency in the diagnosis of neurocognitive disorders across European countries.
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Affiliation(s)
- Giovanni B Frisoni
- Laboratory of Neuroimaging of Aging (LANVIE), University of Geneva, Geneva, Switzerland; Geneva Memory Center, Department of Rehabilitation and Geriatrics, Geneva University Hospitals, Geneva, Switzerland.
| | - Cristina Festari
- Laboratory of Alzheimer's Neuroimaging and Epidemiology, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Federico Massa
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DINOGMI), University of Genoa, Genoa, Italy; IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Matteo Cotta Ramusino
- Unit of Behavioral Neurology and Dementia Research Center (DRC), IRCCS Mondino Foundation, Pavia, Italy
| | - Stefania Orini
- Alzheimer's Unit-Memory Clinic, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy; Department of Clinical and Experimental Sciences, University of Brescia, Brescia, Italy
| | - Dag Aarsland
- Centre for Age-Related Medicine, Stavanger University Hospital, Stavanger, Norway; UK Dementia Research Institute, Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Federica Agosta
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy; Vita-Salute San Raffaele University, Milan, Italy
| | - Claudio Babiloni
- Department of Physiology and Pharmacology "V Erspamer", Sapienza University of Rome, Rome, Italy; Hospital San Raffaele of Cassino, Cassino, Italy
| | - Barbara Borroni
- Department of Clinical and Experimental Sciences, University of Brescia, Brescia, Italy; Department of Continuity of Care and Frailty, ASST Spedali Civili, Brescia, Italy
| | - Stefano F Cappa
- Centro Ricerca sulle Demenze, IRCCS Mondino Foundation, Pavia, Italy; University Institute for Advanced Studies (IUSS), Pavia, Italy
| | - Kristian S Frederiksen
- Danish Dementia Research Centre, Department of Neurology, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark; Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Lutz Froelich
- Department of Geriatric Psychiatry, Central Institute of Mental Health Mannheim, Medical Faculty Mannheim, University of Heidelberg, Heidelberg, Germany
| | - Valentina Garibotto
- Laboratory of Neuroimaging and Innovative Molecular Tracers (NIMTlab), Geneva University Neurocenter and Faculty of Medicine, University of Geneva, Geneva, Switzerland; Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospitals, Geneva, Switzerland; CIBM Center for Biomedical Imaging, Geneva, Switzerland
| | | | - Frank Jessen
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany; Department of Psychiatry, University of Cologne, Medical Faculty, Cologne, Germany; Excellence Cluster on Cellular Stress Responses in Aging-Associated Diseases (CECAD), University of Cologne, Cologne, Germany
| | - Anita Kamondi
- National Institute of Mental Health, Neurology and Neurosurgery, Budapest, Hungary; Department of Neurology, Semmelweis University, Budapest, Hungary
| | - Roy Pc Kessels
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, Netherlands; Radboud UMC Alzheimer Center and Department of Medical Psychology, Radboud University Medical Center, Nijmegen, Netherlands; Vincent van Gogh Institute for Psychiatry, Venray, Netherlands
| | - Silvia D Morbelli
- Department of Health Sciences (DISSAL), University of Genoa, Genoa, Italy; IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - John T O'Brien
- Department of Psychiatry, School of Clinical Medicine, University of Cambridge, Cambridge, UK
| | - Markus Otto
- Department of Neurology, Martin Luther University of Halle-Wittenberg, Halle (Saale), Germany
| | | | - Francesca B Pizzini
- Department of Diagnostic and Public Health, Verona University Hospital, Verona University, Verona, Italy
| | - Mathieu Vandenbulcke
- Department of Neurosciences, KU Leuven, Leuven, Belgium; Department of Geriatric Psychiatry, University Psychiatric Centre KU Leuven, Leuven-Kortenberg, Belgium
| | - Ritva Vanninen
- University of Eastern Finland and Kuopio University Hospital, Kuopio, Finland
| | - Frans Verhey
- Department of Psychiatry and Neuropsychology-Alzheimer Centre Limburg, School for Mental Health and Neuroscience, Maastricht University, Maastricht, Netherlands
| | - Meike W Vernooij
- Department of Epidemiology and Department of Radiology and Nuclear Medicine Erasmus MC University Medical Center, Rotterdam, Netherlands
| | - Tarek Yousry
- Lysholm Department of Neuroradiology and the Neuroradiological Academic Unit, Department of Brain Repair and Rehabilitation, University College London Hospitals NHS Foundation Trust National Hospital for Neurology and Neurosurgery, London, UK
| | - Mercè Boada Rovira
- Fundació ACE, Institut Català de Neurociències Aplicades, Barcelona, Spain
| | - Bruno Dubois
- Institut de La Mémoire et de La Maladie d'Alzheimer, Neurology Department, Salpêtrière Hospital, Assistance Publique-Hôpital de Paris, Paris, France; Sorbonne University, Paris, France
| | | | - Oskar Hansson
- Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Malmö, Sweden; Memory Clinic, Skåne University Hospital, Malmö, Sweden
| | - Craig W Ritchie
- Edinburgh Dementia Prevention, Centre for Clinical Brain Sciences, Western General Hospital, University of Edinburgh, Edinburgh, UK; Brain Health Scotland, Edinburgh, UK
| | - Philip Scheltens
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Amsterdam UMC, Amsterdam, Netherlands
| | - Wiesje M van der Flier
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Amsterdam UMC, Amsterdam, Netherlands; Amsterdam Neuroscience-Neurodegeneration, Amsterdam, Netherlands; Epidemiology and Data Science, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, Netherlands
| | - Flavio Nobili
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DINOGMI), University of Genoa, Genoa, Italy; IRCCS Ospedale Policlinico San Martino, Genoa, Italy
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18
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Vaishnav N, Gonzalez R, Karunungan K, Tyler A, Zheng W, Arias JJ. Creating an Unprotected Class: Addressing Legal Risks in the Era of Biologically-Defined Alzheimer's Disease. J Alzheimers Dis 2024; 98:187-195. [PMID: 38393896 DOI: 10.3233/jad-230067] [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: 02/25/2024]
Abstract
Background Documentation of preclinical biomarker tests for Alzheimer's disease (AD) in the medical record may expose patients to employment and insurance discrimination risks. There is a gap in research describing clinicians' approaches to documenting biomarker results. Objective To evaluate discrimination risks faced by patients undergoing biomarker testing for AD through a qualitative analysis of clinician documentation practices. Methods Semi-structured interviews using hypothetical patient scenarios. The qualitative analysis focused on interviewees' responses related to documentation and disclosure of results. Results We collected and analyzed 17 interviews with dementia experts; and identified three approaches to documenting biomarkers as: an association with active AD, noninformative, and an increased susceptibility for AD. Those who associated biomarkers with active disease were more likely to favor disclosure to employers and insurers, which could increase discrimination risks. Conclusions This study demonstrates the variety of documentation and disclosure practices likely to emerge for preclinical AD biomarker tests and highlights a need for guidelines in this area.
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Affiliation(s)
- Neil Vaishnav
- Department of Medicine, University of California, San Francisco, CA, USA
| | - Rosa Gonzalez
- Department of Neurology, Cleveland Clinic Foundation, Cleveland, OH, USA
| | - Krystal Karunungan
- Department of Neurology, Memory and Aging Center, Weill Institute for Neurosciences, University of California, San Francisco, CA, USA
| | - Ana Tyler
- Department of Neurology, Memory and Aging Center, Weill Institute for Neurosciences, University of California, San Francisco, CA, USA
| | - William Zheng
- Department of Neurology, Memory and Aging Center, Weill Institute for Neurosciences, University of California, San Francisco, CA, USA
| | - Jalayne J Arias
- School of Public Health, Georgia State University, Atlanta, GA, USA
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19
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Kim JE, Tamres LK, Orbell SL, Cheng RZ, Klunk WE, Aizenstein HJ, Butters MA, McDade E, Lingler JH. "And Does That Necessarily Mean Absolutely Alzheimer's?" An Analysis of Questions Raised Following Amyloid PET Results Disclosure. Am J Geriatr Psychiatry 2024; 32:45-54. [PMID: 37634955 PMCID: PMC10841154 DOI: 10.1016/j.jagp.2023.08.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Revised: 08/03/2023] [Accepted: 08/07/2023] [Indexed: 08/29/2023]
Abstract
INTRODUCTION Amyloid PET scans provide individuals with mild cognitive impairment (MCI) information about their risk of progressing to Alzheimer's dementia (AD). Given the wide-ranging implications of this information, best practice guidelines are needed to support researchers and clinicians disclosing these high-stakes test results. To inform the development of such guidelines, this analysis aims to describe questions and concerns raised during the disclosure of amyloid PET results in the context of MCI. METHODS Qualitative description was performed to analyze (n = 34) transcripts of audio-recorded amyloid PET results disclosure sessions involving MCI care dyads. The analysis focused on characterizing the frequency and nature of questions raised during an open question-and-answer (Q&A) period following the return of scan results using a standardized protocol. RESULTS Nearly all (n = 32/34) dyads posed questions during Q&A. Questions fell within six main categories with the most common being requests for clarification regarding AD/MCI, and next steps given the result. Questions were interspersed with comments reflecting the need for emotional support. Independently administered assessments of comprehension of results showed that, following the disclosure and Q&A, 31/32 participants with MCI and 31/31 care partners scored ≥4 on a 5-point scale. The number of questions asked by care partners during Q&A positively correlated with their level of comprehension (n = 31, Spearman's r = 0.370, p = 0.040). DISCUSSION This analysis highlights the value of providing opportunities for patients and their family members to ask questions upon learning patients' brain amyloid status. Disclosing clinicians should be prepared to provide clarification, resources, and support to patients and families during the return of amyloid PET results.
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Affiliation(s)
- Jeong Eun Kim
- University of Pittsburgh School of Nursing (JEK, LKT, SLO, RZC, JHL), Pittsburgh, PA.
| | - Lisa K Tamres
- University of Pittsburgh School of Nursing (JEK, LKT, SLO, RZC, JHL), Pittsburgh, PA; University of Pittsburgh Alzheimer's Disease Research Center (LKT, WEK, HJA, MAB, EM, JHL), Pittsburgh, PA
| | - Staci L Orbell
- University of Pittsburgh School of Nursing (JEK, LKT, SLO, RZC, JHL), Pittsburgh, PA
| | - Rebekah Z Cheng
- University of Pittsburgh School of Nursing (JEK, LKT, SLO, RZC, JHL), Pittsburgh, PA
| | - William E Klunk
- University of Pittsburgh Alzheimer's Disease Research Center (LKT, WEK, HJA, MAB, EM, JHL), Pittsburgh, PA; Department of Psychiatry, School of Medicine, University of Pittsburgh (WEK, HJA, MAB, EM, JHL), Pittsburgh, PA
| | - Howard J Aizenstein
- University of Pittsburgh Alzheimer's Disease Research Center (LKT, WEK, HJA, MAB, EM, JHL), Pittsburgh, PA; Department of Psychiatry, School of Medicine, University of Pittsburgh (WEK, HJA, MAB, EM, JHL), Pittsburgh, PA
| | - Meryl A Butters
- University of Pittsburgh Alzheimer's Disease Research Center (LKT, WEK, HJA, MAB, EM, JHL), Pittsburgh, PA; Department of Psychiatry, School of Medicine, University of Pittsburgh (WEK, HJA, MAB, EM, JHL), Pittsburgh, PA
| | - Eric McDade
- University of Pittsburgh Alzheimer's Disease Research Center (LKT, WEK, HJA, MAB, EM, JHL), Pittsburgh, PA; Department of Psychiatry, School of Medicine, University of Pittsburgh (WEK, HJA, MAB, EM, JHL), Pittsburgh, PA
| | - Jennifer H Lingler
- University of Pittsburgh School of Nursing (JEK, LKT, SLO, RZC, JHL), Pittsburgh, PA; University of Pittsburgh Alzheimer's Disease Research Center (LKT, WEK, HJA, MAB, EM, JHL), Pittsburgh, PA; Department of Psychiatry, School of Medicine, University of Pittsburgh (WEK, HJA, MAB, EM, JHL), Pittsburgh, PA
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20
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Plassman BL, Ford CB, Smith VA, DePasquale N, Burke JR, Korthauer L, Ott BR, Belanger E, Shepherd-Banigan ME, Couch E, Jutkowitz E, O’Brien EC, Sorenson C, Wetle TT, Van Houtven CH. Elevated Amyloid-β PET Scan and Cognitive and Functional Decline in Mild Cognitive Impairment and Dementia of Uncertain Etiology. J Alzheimers Dis 2024; 97:1161-1171. [PMID: 38306055 PMCID: PMC11034799 DOI: 10.3233/jad-230950] [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: 02/03/2024]
Abstract
BACKGROUND Elevated amyloid-β (Aβ) on positron emission tomography (PET) scan is used to aid diagnosis of Alzheimer's disease (AD), but many prior studies have focused on patients with a typical AD phenotype such as amnestic mild cognitive impairment (MCI). Little is known about whether elevated Aβ on PET scan predicts rate of cognitive and functional decline among those with MCI or dementia that is clinically less typical of early AD, thus leading to etiologic uncertainty. OBJECTIVE We aimed to investigate whether elevated Aβ on PET scan predicts cognitive and functional decline over an 18-month period in those with MCI or dementia of uncertain etiology. METHODS In 1,028 individuals with MCI or dementia of uncertain etiology, we evaluated the association between elevated Aβ on PET scan and change on a telephone cognitive status measure administered to the participant and change in everyday function as reported by their care partner. RESULTS Individuals with either MCI or dementia and elevated Aβ (66.6% of the sample) showed greater cognitive decline compared to those without elevated Aβ on PET scan, whose cognition was relatively stable over 18 months. Those with either MCI or dementia and elevated Aβ were also reported to have greater functional decline compared to those without elevated Aβ, even though the latter group showed significant care partner-reported functional decline over time. CONCLUSIONS Elevated Aβ on PET scan can be helpful in predicting rates of both cognitive and functional decline, even among cognitively impaired individuals with atypical presentations of AD.
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Affiliation(s)
- Brenda L. Plassman
- Department of Psychiatry and Behavioral Sciences, School of Medicine, Duke University, Durham, NC, USA
- Department of Neurology, School of Medicine, Duke University, NC, USA
| | - Cassie B. Ford
- Department of Population Health Sciences, Duke University, Durham, NC, USA
| | - Valerie A. Smith
- Department of Population Health Sciences, Duke University, Durham, NC, USA
- Department of Medicine, Division of General Internal Medicine, Duke University, Durham, NC, USA
- Durham ADAPT, Durham Veterans Affairs Medical Center, Durham, NC, USA
| | - Nicole DePasquale
- Department of Medicine, Division of General Internal Medicine, Duke University, Durham, NC, USA
| | - James R. Burke
- Department of Psychiatry and Behavioral Sciences, School of Medicine, Duke University, Durham, NC, USA
- Department of Neurology, School of Medicine, Duke University, NC, USA
| | - Laura Korthauer
- Department of Psychiatry and Human Behavior, Alpert Medical School of Brown University, Providence, RI, USA
| | - Brian R. Ott
- Department of Neurology, Alpert Medical School of Brown University, Providence, RI, USA
| | - Emmanuelle Belanger
- Department of Health Services Policy and Practice, School of Public Health, Brown University, Providence, RI, USA
| | - Megan E. Shepherd-Banigan
- Department of Population Health Sciences, Duke University, Durham, NC, USA
- Durham ADAPT, Durham Veterans Affairs Medical Center, Durham, NC, USA
- Duke-Margolis Center for Health Policy, Durham, NC, USA
| | - Elyse Couch
- Department of Health Services Policy and Practice, School of Public Health, Brown University, Providence, RI, USA
| | - Eric Jutkowitz
- Department of Health Services Policy and Practice, School of Public Health, Brown University, Providence, RI, USA
| | - Emily C. O’Brien
- Department of Population Health Sciences, Duke University, Durham, NC, USA
| | - Corinna Sorenson
- Department of Population Health Sciences, Duke University, Durham, NC, USA
- Duke-Margolis Center for Health Policy, Durham, NC, USA
- Sanford School of Public Policy, Duke University, Durham, NC, USA
| | - Terrie T. Wetle
- Department of Health Services Policy and Practice, School of Public Health, Brown University, Providence, RI, USA
- Center for Gerontology and Healthcare Research, Brown University School of Public Health, Providence, RI, USA
| | - Courtney H. Van Houtven
- Department of Population Health Sciences, Duke University, Durham, NC, USA
- Durham ADAPT, Durham Veterans Affairs Medical Center, Durham, NC, USA
- Duke-Margolis Center for Health Policy, Durham, NC, USA
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21
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D'Amico F, Sofia L, Bauckneht M, Morbelli S. Amyloid PET Imaging: Standard Procedures and Semiquantification. Methods Mol Biol 2024; 2785:165-175. [PMID: 38427194 DOI: 10.1007/978-1-0716-3774-6_11] [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: 03/02/2024]
Abstract
Amyloid plaques are a neuropathologic hallmark of Alzheimer's disease (AD), which can be imaged through positron emission tomography (PET) technology using radiopharmaceuticals that selectively bind to the fibrillar aggregates of amyloid-β plaques (Amy-PET). Several radiotracers for amyloid PET have been validated (11C-Pittsburgh compound B and the 18F-labeled compounds such as 18F-florbetaben, 18F-florbetapir, and 18F-flutemetamol). Images can be interpreted by means of visual/qualitative, semiquantitative, and quantitative criteria. Here, we summarize the main differences between the available radiotracers for Amy-PET, the proposed interpretation criteria, and main proposed quantification methods.
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Affiliation(s)
- Francesca D'Amico
- Nuclear Medicine Unit, Department of Health Sciences, University of Genoa, Genoa, Italy
- IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Luca Sofia
- Nuclear Medicine Unit, Department of Health Sciences, University of Genoa, Genoa, Italy
- IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Matteo Bauckneht
- Nuclear Medicine Unit, Department of Health Sciences, University of Genoa, Genoa, Italy
- IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Silvia Morbelli
- Nuclear Medicine Unit, Department of Health Sciences, University of Genoa, Genoa, Italy.
- IRCCS Ospedale Policlinico San Martino, Genoa, Italy.
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22
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Loreto F, Verdi S, Kia SM, Duvnjak A, Hakeem H, Fitzgerald A, Patel N, Lilja J, Win Z, Perry R, Marquand AF, Cole JH, Malhotra P. Alzheimer's disease heterogeneity revealed by neuroanatomical normative modeling. ALZHEIMER'S & DEMENTIA (AMSTERDAM, NETHERLANDS) 2024; 16:e12559. [PMID: 38487076 PMCID: PMC10937817 DOI: 10.1002/dad2.12559] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Revised: 10/11/2023] [Accepted: 01/30/2024] [Indexed: 03/17/2024]
Abstract
INTRODUCTION Overlooking the heterogeneity in Alzheimer's disease (AD) may lead to diagnostic delays and failures. Neuroanatomical normative modeling captures individual brain variation and may inform our understanding of individual differences in AD-related atrophy. METHODS We applied neuroanatomical normative modeling to magnetic resonance imaging from a real-world clinical cohort with confirmed AD (n = 86). Regional cortical thickness was compared to a healthy reference cohort (n = 33,072) and the number of outlying regions was summed (total outlier count) and mapped at individual- and group-levels. RESULTS The superior temporal sulcus contained the highest proportion of outliers (60%). Elsewhere, overlap between patient atrophy patterns was low. Mean total outlier count was higher in patients who were non-amnestic, at more advanced disease stages, and without depressive symptoms. Amyloid burden was negatively associated with outlier count. DISCUSSION Brain atrophy in AD is highly heterogeneous and neuroanatomical normative modeling can be used to explore anatomo-clinical correlations in individual patients.
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Affiliation(s)
- Flavia Loreto
- Department of Brain SciencesFaculty of MedicineImperial College LondonLondonUK
| | - Serena Verdi
- Centre for Medical Image ComputingMedical Physics and Biomedical EngineeringUniversity College LondonLondonUK
- Dementia Research CentreUCL Queen Square Institute of NeurologyLondonUK
| | - Seyed Mostafa Kia
- Donders Centre for Cognitive NeuroimagingDonders Institute for BrainCognition and BehaviourRadboud UniversityNijmegenThe Netherlands
- Department of Cognitive NeuroscienceRadboud University Medical CentreNijmegenThe Netherlands
- Department of PsychiatryUtrecht University Medical CenterUtrechtThe Netherlands
| | - Aleksandar Duvnjak
- Department of Brain SciencesFaculty of MedicineImperial College LondonLondonUK
| | - Haneen Hakeem
- Department of Brain SciencesFaculty of MedicineImperial College LondonLondonUK
| | - Anna Fitzgerald
- Department of Brain SciencesFaculty of MedicineImperial College LondonLondonUK
| | - Neva Patel
- Department of Nuclear MedicineImperial College Healthcare NHS TrustLondonUK
| | | | - Zarni Win
- Department of Nuclear MedicineImperial College Healthcare NHS TrustLondonUK
| | - Richard Perry
- Department of Brain SciencesFaculty of MedicineImperial College LondonLondonUK
- Department of NeurologyImperial College Healthcare NHS TrustLondonUK
| | - Andre F. Marquand
- Donders Centre for Cognitive NeuroimagingDonders Institute for BrainCognition and BehaviourRadboud UniversityNijmegenThe Netherlands
- Department of Cognitive NeuroscienceRadboud University Medical CentreNijmegenThe Netherlands
| | - James H. Cole
- Centre for Medical Image ComputingMedical Physics and Biomedical EngineeringUniversity College LondonLondonUK
- Dementia Research CentreUCL Queen Square Institute of NeurologyLondonUK
| | - Paresh Malhotra
- Department of Brain SciencesFaculty of MedicineImperial College LondonLondonUK
- Department of NeurologyImperial College Healthcare NHS TrustLondonUK
- UK Dementia Research Institute Care Research and Technology CentreImperial College London and the University of SurreyLondonUK
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23
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Figdore DJ, Wiste HJ, Bornhorst JA, Bateman RJ, Li Y, Graff‐Radford J, Knopman DS, Vemuri P, Lowe VJ, Jr CRJ, Petersen RC, Algeciras‐Schimnich A. Performance of the Lumipulse plasma Aβ42/40 and pTau181 immunoassays in the detection of amyloid pathology. ALZHEIMER'S & DEMENTIA (AMSTERDAM, NETHERLANDS) 2024; 16:e12545. [PMID: 38304322 PMCID: PMC10831129 DOI: 10.1002/dad2.12545] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Revised: 12/29/2023] [Accepted: 01/05/2024] [Indexed: 02/03/2024]
Abstract
INTRODUCTION This study evaluated the performance of the Lumipulse plasma beta-amyloid (Aβ) 42/40 and pTau181 compared to other assays to detect an abnormal amyloid-positron emission tomography (PET). METHODS Plasma samples from cognitively unimpaired (N = 179) and MCI/AD dementia (N = 36) individuals were retrospectively evaluated. Plasma Aβ42/40 and pTau181 were measured using the Lumipulse and Simoa immunoassays. An immunoprecipitation mass spectrometry (IP-MS) assay for plasma Aβ42/40 was also evaluated. Amyloid-PET status was the outcome measure. RESULTS Lumipulse and IP-MS Aβ42/40 exhibited the highest diagnostic accuracy for detecting an abnormal amyloid-PET (areas under the curve [AUCs] of 0.81 and 0.84, respectively). The Lumipulse and Simoa pTau181 assays exhibited lower performance (AUCs of 0.74 and 0.72, respectively). The Simoa Aβ42/40 assay demonstrated the lowest diagnostic accuracy (AUC 0.57). Combining Aβ42/40 and pTau181 did not significantly improve performance over Aβ42/40 alone for Lumipulse (AUC 0.83) or over pTau181 alone for Simoa (AUC 0.71). DISCUSSION The Lumipulse Aβ42/40 assay showed similar performance to the IP-MS Aβ42/40 assay for detection of an abnormal amyloid-PET; and both assays performed better than the two p-tau181 immunoassays. The Simoa Aβ42/Aβ40 assay was the least accurate at predicting an abnormal amyloid-PET status. Highlights Lumipulse plasma Aβ42/Aβ40 AUC for abnormal amyloid-PET detection was 0.81.This performance was comparable to previously reported IP-MS and higher than Simoa.Performance of Alzheimer's disease blood biomarkers varies between assays.
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Affiliation(s)
- Daniel J. Figdore
- Department of Laboratory Medicine and PathologyMayo ClinicRochesterMinnesotaUSA
| | - Heather J. Wiste
- Department of Quantitative Health SciencesMayo ClinicRochesterMinnesotaUSA
| | - Joshua A. Bornhorst
- Department of Laboratory Medicine and PathologyMayo ClinicRochesterMinnesotaUSA
| | - Randall J. Bateman
- Department of NeurologyWashington University School of MedicineSt. LouisMissouriUSA
| | - Yan Li
- Department of NeurologyWashington University School of MedicineSt. LouisMissouriUSA
| | | | | | | | - Val J. Lowe
- Department of RadiologyMayo ClinicRochesterMinnesotaUSA
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24
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Zilioli A, Misirocchi F, Pancaldi B, Mutti C, Ganazzoli C, Morelli N, Pellegrini FF, Messa G, Scarlattei M, Mohanty R, Ruffini L, Westman E, Spallazzi M. Predicting amyloid-PET status in a memory clinic: The role of the novel antero-posterior index and visual rating scales. J Neurol Sci 2023; 455:122806. [PMID: 38006829 DOI: 10.1016/j.jns.2023.122806] [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: 09/18/2023] [Revised: 10/27/2023] [Accepted: 11/16/2023] [Indexed: 11/27/2023]
Abstract
INTRODUCTION Visual rating scales are increasingly utilized in clinical practice to assess atrophy in crucial brain regions among patients with cognitive disorders. However, their capacity to predict Alzheimer's disease (AD)-related pathology remains unexplored, particularly within a heterogeneous memory clinic population. This study aims to assess the accuracy of a novel visual rating assessment, the antero-posterior index (API) scale, in predicting amyloid-PET status. Furthermore, the study seeks to determine the optimal cohort-based cutoffs for the medial temporal atrophy (MTA) and parietal atrophy (PA) scales and to integrate the main visual rating scores into a predictive model. METHODS We conducted a retrospective analysis of brain MRI and high-resolution TC scans from 153 patients with cognitive disorders who had undergone amyloid-PET assessments due to suspected AD pathology in a real-world memory clinic setting. RESULTS The API scale (cutoff ≥1) exhibited the highest accuracy (AUC = 0.721) among the visual rating scales. The combination of the cohort-based MTA and PA threshold with the API yielded favorable accuracy (AUC = 0.787). Analyzing a cohort of MCI/Mild dementia patients below 75 years of age, the API scale and the predictive model improved their accuracy (AUC = 0.741 and 0.813, respectively), achieving excellent results in the early-onset population (AUC = 0.857 and 0.949, respectively). CONCLUSION Our study emphasizes the significance of visual rating scales in predicting amyloid-PET positivity within a real-world memory clinic. Implementing the novel API scale, alongside our cohort-based MTA and PA thresholds, has the potential to substantially enhance diagnostic accuracy.
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Affiliation(s)
- Alessandro Zilioli
- Department of Medicine and Surgery, Unit of Neurology, University of Parma, Parma, Italy
| | - Francesco Misirocchi
- Department of Medicine and Surgery, Unit of Neurology, University of Parma, Parma, Italy.
| | - Beatrice Pancaldi
- Department of Medicine and Surgery, Unit of Neurology, University of Parma, Parma, Italy
| | - Carlotta Mutti
- Department of Medicine and Surgery, Unit of Neurology, University-Hospital of Parma, Parma, Italy; Sleep Disorders Center, Department of Medicine and Surgery, University of Parma, Parma, Italy
| | | | - Nicola Morelli
- Department of Neurology, G. da Saliceto Hospital, Piacenza, Italy
| | | | - Giovanni Messa
- Center for Cognitive Disorders, AUSL Parma, Parma, Italy
| | - Maura Scarlattei
- Nuclear Medicine Unit, University Hospital of Parma, Parma, Italy
| | - Rosaleena Mohanty
- Division of Clinical Geriatrics; Center for Alzheimer Research; Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Blickagången 16 (NEO building, floor 7th), 14152, Huddinge, Stockholm, Sweden
| | - Livia Ruffini
- Nuclear Medicine Unit, University Hospital of Parma, Parma, Italy
| | - Eric Westman
- Division of Clinical Geriatrics; Center for Alzheimer Research; Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Blickagången 16 (NEO building, floor 7th), 14152, Huddinge, Stockholm, Sweden; Department of Neuroimaging, Center for Neuroimaging Sciences, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Marco Spallazzi
- Department of Medicine and Surgery, Unit of Neurology, University-Hospital of Parma, Parma, Italy
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25
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Rabinovici GD, Carrillo MC, Apgar C, Gareen IF, Gutman R, Hanna L, Hillner BE, March A, Romanoff J, Siegel BA, Smith K, Song Y, Weber C, Whitmer RA, Gatsonis C. Amyloid Positron Emission Tomography and Subsequent Health Care Use Among Medicare Beneficiaries With Mild Cognitive Impairment or Dementia. JAMA Neurol 2023; 80:1166-1173. [PMID: 37812437 PMCID: PMC10562987 DOI: 10.1001/jamaneurol.2023.3490] [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: 06/15/2023] [Accepted: 08/11/2023] [Indexed: 10/10/2023]
Abstract
Importance Results of amyloid positron emission tomography (PET) have been shown to change the management of patients with mild cognitive impairment (MCI) or dementia who meet Appropriate Use Criteria (AUC). Objective To determine if amyloid PET is associated with reduced hospitalizations and emergency department (ED) visits over 12 months in patients with MCI or dementia. Design, Setting, and Participants This nonrandomized controlled trial analyzed participants in the Imaging Dementia-Evidence for Amyloid Scanning (IDEAS) study, an open-label, multisite, longitudinal study that enrolled participants between February 2016 and December 2017 and followed up through December 2018. These participants were recruited at 595 clinical sites that provide specialty memory care across the US. Eligible participants were Medicare beneficiaries 65 years or older with a diagnosis of MCI or dementia within the past 24 months who met published AUC for amyloid PET. Each IDEAS study participant was matched to a control Medicare beneficiary who had not undergone amyloid PET. Data analysis was conducted on December 13, 2022. Exposure Participants underwent amyloid PET at imaging centers. Main Outcomes and Measures The primary end points were the proportions of patients with 12-month inpatient hospital admissions and ED visits. One of 4 secondary end points was the rate of hospitalizations and rate of ED visits in participants with positive vs negative amyloid PET results. Health care use was ascertained from Medicare claims data. Results The 2 cohorts (IDEAS study participants and controls) each comprised 12 684 adults, including 6467 females (51.0%) with a median (IQR) age of 77 (73-81) years. Over 12 months, 24.0% of the IDEAS study participants were hospitalized, compared with 25.1% of the matched control cohort, for a relative reduction of -4.49% (97.5% CI, -9.09% to 0.34%). The 12-month ED visit rates were nearly identical between the 2 cohorts (44.8% in both IDEAS study and control cohorts) for a relative reduction of -0.12% (97.5% CI, -3.19% to 3.05%). Both outcomes fell short of the prespecified effect size of 10% or greater relative reduction. Overall, 1467 of 6848 participants (21.4%) with positive amyloid PET scans were hospitalized within 12 months compared with 1081 of 4209 participants (25.7%) with negative amyloid PET scans (adjusted odds ratio, 0.83; 95% CI, 0.78-0.89). Conclusions and Relevance Results of this nonrandomized controlled trial showed that use of amyloid PET was not associated with a significant reduction in 12-month hospitalizations or ED visits. Rates of hospitalization were lower in patients with positive vs negative amyloid PET results.
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Affiliation(s)
- Gil D. Rabinovici
- Department of Neurology, Memory and Aging Center, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco
- Associate Editor, JAMA Neurology
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco
| | | | - Charles Apgar
- Center for Research and Innovation, American College of Radiology, Reston, Virginia
| | - Ilana F. Gareen
- Center for Statistical Sciences, Brown University School of Public Health, Providence, Rhode Island
- Department of Epidemiology, Brown University School of Public Health, Providence, Rhode Island
| | - Roee Gutman
- Center for Statistical Sciences, Brown University School of Public Health, Providence, Rhode Island
- Department of Biostatistics, Brown University School of Public Health, Providence, Rhode Island
| | - Lucy Hanna
- Center for Statistical Sciences, Brown University School of Public Health, Providence, Rhode Island
| | - Bruce E. Hillner
- Department of Medicine, Virginia Commonwealth University, Richmond
| | - Andrew March
- Center for Research and Innovation, American College of Radiology, Reston, Virginia
| | - Justin Romanoff
- Center for Statistical Sciences, Brown University School of Public Health, Providence, Rhode Island
| | - Barry A. Siegel
- Edward Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, Missouri
| | - Karen Smith
- Department of Neurology, Memory and Aging Center, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco
| | - Yunjie Song
- Center for Statistical Sciences, Brown University School of Public Health, Providence, Rhode Island
- Department of Epidemiology, Brown University School of Public Health, Providence, Rhode Island
- Department of Biostatistics, Brown University School of Public Health, Providence, Rhode Island
| | | | - Rachel A. Whitmer
- Department of Public Health Sciences and Neurology, University of California, Davis, Davis
| | - Constantine Gatsonis
- Center for Statistical Sciences, Brown University School of Public Health, Providence, Rhode Island
- Department of Biostatistics, Brown University School of Public Health, Providence, Rhode Island
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26
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Cho H, Mundada NS, Apostolova LG, Carrillo MC, Shankar R, Amuiri AN, Zeltzer E, Windon CC, Soleimani-Meigooni DN, Tanner JA, Heath CL, Lesman-Segev OH, Aisen P, Eloyan A, Lee HS, Hammers DB, Kirby K, Dage JL, Fagan A, Foroud T, Grinberg LT, Jack CR, Kramer J, Kukull WA, Murray ME, Nudelman K, Toga A, Vemuri P, Atri A, Day GS, Duara R, Graff-Radford NR, Honig LS, Jones DT, Masdeu J, Mendez M, Musiek E, Onyike CU, Riddle M, Rogalski EJ, Salloway S, Sha S, Turner RS, Wingo TS, Wolk DA, Koeppe R, Iaccarino L, Dickerson BC, La Joie R, Rabinovici GD. Amyloid and tau-PET in early-onset AD: Baseline data from the Longitudinal Early-onset Alzheimer's Disease Study (LEADS). Alzheimers Dement 2023; 19 Suppl 9:S98-S114. [PMID: 37690109 PMCID: PMC10807231 DOI: 10.1002/alz.13453] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Revised: 08/07/2023] [Accepted: 08/08/2023] [Indexed: 09/12/2023]
Abstract
INTRODUCTION We aimed to describe baseline amyloid-beta (Aβ) and tau-positron emission tomograrphy (PET) from Longitudinal Early-onset Alzheimer's Disease Study (LEADS), a prospective multi-site observational study of sporadic early-onset Alzheimer's disease (EOAD). METHODS We analyzed baseline [18F]Florbetaben (Aβ) and [18F]Flortaucipir (tau)-PET from cognitively impaired participants with a clinical diagnosis of mild cognitive impairment (MCI) or AD dementia aged < 65 years. Florbetaben scans were used to distinguish cognitively impaired participants with EOAD (Aβ+) from EOnonAD (Aβ-) based on the combination of visual read by expert reader and image quantification. RESULTS 243/321 (75.7%) of participants were assigned to the EOAD group based on amyloid-PET; 231 (95.1%) of them were tau-PET positive (A+T+). Tau-PET signal was elevated across cortical regions with a parietal-predominant pattern, and higher burden was observed in younger and female EOAD participants. DISCUSSION LEADS data emphasizes the importance of biomarkers to enhance diagnostic accuracy in EOAD. The advanced tau-PET binding at baseline might have implications for therapeutic strategies in patients with EOAD. HIGHLIGHTS 72% of patients with clinical EOAD were positive on both amyloid- and tau-PET. Amyloid-positive patients with EOAD had high tau-PET signal across cortical regions. In EOAD, tau-PET mediated the relationship between amyloid-PET and MMSE. Among EOAD patients, younger onset and female sex were associated with higher tau-PET.
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Affiliation(s)
- Hanna Cho
- Memory and Aging Center, UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, California, USA
- Department of Neurology, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea
- Global Brain Health Institute, University of California, San Francisco, California, USA
| | - Nidhi S Mundada
- Memory and Aging Center, UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, California, USA
| | - Liana G Apostolova
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, USA
- Department of Radiology and Imaging Sciences, Center for Neuroimaging, Indiana University School of Medicine Indianapolis, Indianapolis, Indiana, USA
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Maria C Carrillo
- Medical & Scientific Relations Division, Alzheimer's Association, Chicago, Illinois, USA
| | - Ranjani Shankar
- Memory and Aging Center, UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, California, USA
| | - Alinda N Amuiri
- Memory and Aging Center, UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, California, USA
| | - Ehud Zeltzer
- Memory and Aging Center, UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, California, USA
| | - Charles C Windon
- Memory and Aging Center, UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, California, USA
| | - David N Soleimani-Meigooni
- Memory and Aging Center, UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, California, USA
| | - Jeremy A Tanner
- Memory and Aging Center, UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, California, USA
| | - Courtney Lawhn Heath
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, California, USA
| | - Orit H Lesman-Segev
- Memory and Aging Center, UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, California, USA
- Department of Diagnostic Imaging, Sheba Medical Center, Tel HaShomer, Israel
| | - Paul Aisen
- Alzheimer's Therapeutic Research Institute, University of Southern California, San Diego, California, USA
| | - Ani Eloyan
- Department of Biostatistics, Center for Statistical Sciences, Brown University, Rhode Island, USA
| | - Hye Sun Lee
- Biostatistics Collaboration Unit, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Dustin B Hammers
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Kala Kirby
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Jeffrey L Dage
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Anne Fagan
- Department of Neurology, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Tatiana Foroud
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Lea T Grinberg
- Memory and Aging Center, UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, California, USA
- Department of Pathology, University of California - San Francisco, San Francisco, California, USA
| | - Clifford R Jack
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA
| | - Joel Kramer
- Memory and Aging Center, UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, California, USA
| | - Walter A Kukull
- Department of Epidemiology, University of Washington, Seattle, Washington, USA
| | - Melissa E Murray
- Department of Neuroscience, Mayo Clinic, Jacksonville, Florida, USA
| | - Kelly Nudelman
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Arthur Toga
- Laboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, Los Angeles, California, USA
| | | | - Alireza Atri
- Banner Sun Health Research Institute, Sun City, Arizona, USA
| | - Gregory S Day
- Department of Neurology, Mayo Clinic, Jacksonville, Florida, USA
| | - Ranjan Duara
- Wien Center for Alzheimer's Disease and Memory Disorders, Mount Sinai Medical Center, Miami, Florida, USA
| | | | - Lawrence S Honig
- Taub Institute and Department of Neurology, Columbia University Irving Medical Center, New York, New York, USA
| | - David T Jones
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA
- Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
| | - Joseph Masdeu
- Nantz National Alzheimer Center, Houston Methodist and Weill Cornell Medicine, Houston, Texas, USA
| | - Mario Mendez
- Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, California, USA
| | - Erik Musiek
- Department of Neurology, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Chiadi U Onyike
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Meghan Riddle
- Department of Neurology, Alpert Medical School, Brown University, Rhode Island, USA
| | - Emily J Rogalski
- Department of Psychiatry and Behavioral Sciences, Mesulam Center for Cognitive Neurology and Alzheimer's Disease, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA
| | - Stephen Salloway
- Department of Neurology, Alpert Medical School, Brown University, Rhode Island, USA
| | - Sharon Sha
- Department of Neurology & Neurological Sciences, Stanford University, Palo Alto, California, USA
| | | | - Thomas S Wingo
- Department of Neurology and Human Genetics, Emory University School of Medicine, Atlanta, Georgia, USA
| | - David A Wolk
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Robert Koeppe
- Division of Nuclear Medicine, Department of Radiology, University of Michigan, Ann Arbor, Michigan, USA
| | - Leonardo Iaccarino
- Memory and Aging Center, UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, California, USA
| | - Bradford C Dickerson
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Renaud La Joie
- Memory and Aging Center, UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, California, USA
| | - Gil D Rabinovici
- Memory and Aging Center, UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, California, USA
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, California, USA
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27
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Chaparro CIP, Simões BT, Borges JP, Castanho MARB, Soares PIP, Neves V. A Promising Approach: Magnetic Nanosystems for Alzheimer's Disease Theranostics. Pharmaceutics 2023; 15:2316. [PMID: 37765284 PMCID: PMC10536416 DOI: 10.3390/pharmaceutics15092316] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Revised: 09/06/2023] [Accepted: 09/08/2023] [Indexed: 09/29/2023] Open
Abstract
Among central nervous system (CNS) disorders, Alzheimer's disease (AD) is the most prevalent neurodegenerative disorder and a major cause of dementia worldwide. The yet unclear etiology of AD and the high impenetrability of the blood-brain barrier (BBB) limit most therapeutic compounds from reaching the brain. Although many efforts have been made to effectively deliver drugs to the CNS, both invasive and noninvasive strategies employed often come with associated side effects. Nanotechnology-based approaches such as nanoparticles (NPs), which can act as multifunctional platforms in a single system, emerged as a potential solution for current AD theranostics. Among these, magnetic nanoparticles (MNPs) are an appealing strategy since they can act as contrast agents for magnetic resonance imaging (MRI) and as drug delivery systems. The nanocarrier functionalization with specific moieties, such as peptides, proteins, and antibodies, influences the particles' interaction with brain endothelial cell constituents, facilitating transport across the BBB and possibly increasing brain penetration. In this review, we introduce MNP-based systems, combining surface modifications with the particles' physical properties for molecular imaging, as a novel neuro-targeted strategy for AD theranostics. The main goal is to highlight the potential of multifunctional MNPs and their advances as a dual nanotechnological diagnosis and treatment platform for neurodegenerative disorders.
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Affiliation(s)
- Catarina I. P. Chaparro
- Instituto de Medicina Molecular, Faculdade de Medicina, Universidade de Lisboa, 1649-028 Lisbon, Portugal; (C.I.P.C.); (B.T.S.); (M.A.R.B.C.)
- i3N/CENIMAT, Department of Materials Science, NOVA School of Science and Technology, NOVA University of Lisbon, Campus de Caparica, 2829-516 Caparica, Portugal;
| | - Beatriz T. Simões
- Instituto de Medicina Molecular, Faculdade de Medicina, Universidade de Lisboa, 1649-028 Lisbon, Portugal; (C.I.P.C.); (B.T.S.); (M.A.R.B.C.)
| | - João P. Borges
- i3N/CENIMAT, Department of Materials Science, NOVA School of Science and Technology, NOVA University of Lisbon, Campus de Caparica, 2829-516 Caparica, Portugal;
| | - Miguel A. R. B. Castanho
- Instituto de Medicina Molecular, Faculdade de Medicina, Universidade de Lisboa, 1649-028 Lisbon, Portugal; (C.I.P.C.); (B.T.S.); (M.A.R.B.C.)
| | - Paula I. P. Soares
- i3N/CENIMAT, Department of Materials Science, NOVA School of Science and Technology, NOVA University of Lisbon, Campus de Caparica, 2829-516 Caparica, Portugal;
| | - Vera Neves
- Instituto de Medicina Molecular, Faculdade de Medicina, Universidade de Lisboa, 1649-028 Lisbon, Portugal; (C.I.P.C.); (B.T.S.); (M.A.R.B.C.)
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Nisenbaum L, Martone R, Chen T, Rajagovindan R, Dent G, Beaver J, Rubel C, Racine A, He P, Harrison K, Dean R, Vandijck M, Haeberlein SB. CSF biomarker concordance with amyloid PET in Phase 3 studies of aducanumab. Alzheimers Dement 2023; 19:3379-3388. [PMID: 36795603 DOI: 10.1002/alz.12919] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Revised: 12/09/2022] [Accepted: 12/12/2022] [Indexed: 02/17/2023]
Abstract
INTRODUCTION We assessed the use of cerebrospinal fluid (CSF) biomarkers as an alternative to positron emission tomography (PET) for brain amyloid beta (Aβ) pathology confirmation in the EMERGE and ENGAGE clinical trials. METHODS EMERGE and ENGAGE were randomized, placebo-controlled, Phase 3 trials of aducanumab in participants with early Alzheimer's disease. Concordance between CSF biomarkers (Aβ42, Aβ40, phosphorylated tau 181, and total tau) and amyloid PET status (visual read) at screening was examined. RESULTS Robust concordance between CSF biomarkers and amyloid PET visual status was observed (for Aβ42/Aβ40, AUC: 0.90; 95% CI: 0.83-0.97; p < 0.0001), confirming CSF biomarkers as a reliable alternative to amyloid PET in these studies. Compared with single CSF biomarkers, CSF biomarker ratios showed better agreement with amyloid PET visual reads, demonstrating high diagnostic accuracy. DISCUSSION These analyses add to the growing body of evidence supporting CSF biomarkers as reliable alternatives to amyloid PET imaging for brain Aβ pathology confirmation. HIGHLIGHTS CSF biomarkers and amyloid PET concordance were assessed in Ph3 aducanumab trials. Robust concordance between CSF biomarkers and amyloid PET was observed. CSF biomarker ratios increased diagnostic accuracy over single CSF biomarkers. CSF Aβ42/Aβ40 demonstrated high concordance with amyloid PET. Results support CSF biomarker testing as a reliable alternative to amyloid PET.
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Affiliation(s)
| | | | | | | | | | | | | | | | - Ping He
- Biogen, Cambridge, Massachusetts, USA
| | | | - Robert Dean
- Robert A. Dean Consulting, LLC, Indianapolis, Indiana, USA
- Department of Pathology and Laboratory Medicine, Indiana University School of Medicine, Indianapolis, Indiana, USA
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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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Ladefoged CN, Anderberg L, Madsen K, Henriksen OM, Hasselbalch SG, Andersen FL, Højgaard L, Law I. Estimation of brain amyloid accumulation using deep learning in clinical [ 11C]PiB PET imaging. EJNMMI Phys 2023; 10:44. [PMID: 37450069 PMCID: PMC10348957 DOI: 10.1186/s40658-023-00562-7] [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: 02/28/2023] [Accepted: 07/05/2023] [Indexed: 07/18/2023] Open
Abstract
INTRODUCTION Estimation of brain amyloid accumulation is valuable for evaluation of patients with cognitive impairment in both research and clinical routine. The development of high throughput and accurate strategies for the determination of amyloid status could be an important tool in patient selection for clinical trials and amyloid directed treatment. Here, we propose the use of deep learning to quantify amyloid accumulation using standardized uptake value ratio (SUVR) and classify amyloid status based on their PET images. METHODS A total of 1309 patients with cognitive impairment scanned with [11C]PIB PET/CT or PET/MRI were included. Two convolutional neural networks (CNNs) for reading-based amyloid status and SUVR prediction were trained using 75% of the PET/CT data. The remaining PET/CT (n = 300) and all PET/MRI (n = 100) data was used for evaluation. RESULTS The prevalence of amyloid positive patients was 61%. The amyloid status classification model reproduced the expert reader's classification with 99% accuracy. There was a high correlation between reference and predicted SUVR (R2 = 0.96). Both reference and predicted SUVR had an accuracy of 97% compared to expert classification when applying a predetermined SUVR threshold of 1.35 for binary classification of amyloid status. CONCLUSION The proposed CNN models reproduced both the expert classification and quantitative measure of amyloid accumulation in a large local dataset. This method has the potential to replace or simplify existing clinical routines and can facilitate fast and accurate classification well-suited for a high throughput pipeline.
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Affiliation(s)
- Claes Nøhr Ladefoged
- Department of Clinical Physiology and Nuclear Medicine, Rigshospitalet, University of Copenhagen, Blegdamsvej 9, 2100, Copenhagen, Denmark.
| | - Lasse Anderberg
- Department of Clinical Physiology and Nuclear Medicine, Rigshospitalet, University of Copenhagen, Blegdamsvej 9, 2100, Copenhagen, Denmark
| | - Karine Madsen
- Department of Clinical Physiology and Nuclear Medicine, Rigshospitalet, University of Copenhagen, Blegdamsvej 9, 2100, Copenhagen, Denmark
| | - Otto Mølby Henriksen
- Department of Clinical Physiology and Nuclear Medicine, Rigshospitalet, University of Copenhagen, Blegdamsvej 9, 2100, Copenhagen, Denmark
| | - Steen Gregers Hasselbalch
- Danish Dementia Research Centre, Rigshospitalet, University of Copenhagen, Blegdamsvej 9, 2100, Copenhagen, Denmark
| | - Flemming Littrup Andersen
- Department of Clinical Physiology and Nuclear Medicine, Rigshospitalet, University of Copenhagen, Blegdamsvej 9, 2100, Copenhagen, Denmark
| | - Liselotte Højgaard
- Department of Clinical Physiology and Nuclear Medicine, Rigshospitalet, University of Copenhagen, Blegdamsvej 9, 2100, Copenhagen, Denmark
| | - Ian Law
- Department of Clinical Physiology and Nuclear Medicine, Rigshospitalet, University of Copenhagen, Blegdamsvej 9, 2100, Copenhagen, Denmark
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Teipel SJ, Spottke A, Boecker H, Daamen M, Graf E, Sahlmann J, Buchert R, Mohnike W, Mohnike K, Kurth J, Jessen F, Krause BJ. Patient-related benefits of amyloid PET imaging in dementia: Rationale and design of the German randomized coverage with evidence development study ENABLE. ALZHEIMER'S & DEMENTIA (NEW YORK, N. Y.) 2023; 9:e12383. [PMID: 37560401 PMCID: PMC10407881 DOI: 10.1002/trc2.12383] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Revised: 03/17/2023] [Accepted: 03/24/2023] [Indexed: 08/11/2023]
Abstract
The utility of amyloid positron emission tomography (PET) for the etiological diagnosis of dementia and its impact on functional status of patients in routine care are currently unclear. Here, we describe the design of ENABLE, a randomized controlled two-armed coverage with evidence development (CED) study in Germany. Approximately 1126 patients with mild to moderate dementia of unclear etiology will be randomly assigned to either an amyloid PET or a no amyloid PET group. Patients will be followed-up for 24 months. The study has been registered at the German Clinical Trials Register (https://drks.de/search/de/trial/DRKS00030839) with the registration code DRKS00030839. The primary endpoint of ENABLE is the ability to perform functional activities of daily living at 18 months. Secondary endpoints include change in diagnosis, diagnostic confidence, and cognitive and clinical outcomes of patients. We expect that the CED study ENABLE will inform about patient relevant effects of amyloid PET in routine care. Furthermore, we anticipate that ENABLE will support physicians' and payers' decisions on provision of health care for patients with dementia. Highlights Study design focuses on the usefulness of amyloid positron emission tomography (PET) in routine care.Study design addresses the patient-relevant effect of amyloid PET.Patient representatives were involved in the creation of the study design.The study will help improve routine care for people with dementia.
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Affiliation(s)
- Stefan J. Teipel
- Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE) Rostock/GreifswaldRostockGermany
- Department of Psychosomatic MedicineRostock University Medical CenterRostockGermany
| | - Annika Spottke
- Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE) BonnBonnGermany
| | - Henning Boecker
- Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE) BonnBonnGermany
| | - Marcel Daamen
- Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE) BonnBonnGermany
| | - Erika Graf
- Institute of Medical Biometry and Statistics (IMBI)Faculty of Medicine − University Medical Center FreiburgFreiburgGermany
| | - Jörg Sahlmann
- Institute of Medical Biometry and Statistics (IMBI)Faculty of Medicine − University Medical Center FreiburgFreiburgGermany
| | - Ralph Buchert
- Department of Diagnostic and Interventional Radiology and Nuclear MedicineUniversity Medical Center Hamburg‐EppendorfHamburgGermany
| | - Wolfgang Mohnike
- PET e.V.BerlinGermany
- Diagnostic Therapeutic Center Berlin‐Frankfurter TorBerlinGermany
| | - Konrad Mohnike
- PET e.V.BerlinGermany
- Diagnostic Therapeutic Center Berlin‐Frankfurter TorBerlinGermany
| | - Jens Kurth
- Department of Nuclear MedicineRostock University Medical CenterRostockGermany
| | - Frank Jessen
- Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE) BonnBonnGermany
- Department of PsychiatryUniversity Hospital of Cologne, Medical Faculty, University of CologneCologneGermany
| | - Bernd J. Krause
- Department of Nuclear MedicineRostock University Medical CenterRostockGermany
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Ferreiro AL, Choi J, Ryou J, Newcomer EP, Thompson R, Bollinger RM, Hall-Moore C, Ndao IM, Sax L, Benzinger TLS, Stark SL, Holtzman DM, Fagan AM, Schindler SE, Cruchaga C, Butt OH, Morris JC, Tarr PI, Ances BM, Dantas G. Gut microbiome composition may be an indicator of preclinical Alzheimer's disease. Sci Transl Med 2023; 15:eabo2984. [PMID: 37315112 PMCID: PMC10680783 DOI: 10.1126/scitranslmed.abo2984] [Citation(s) in RCA: 84] [Impact Index Per Article: 84.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2022] [Accepted: 05/26/2023] [Indexed: 06/16/2023]
Abstract
Alzheimer's disease (AD) pathology is thought to progress from normal cognition through preclinical disease and ultimately to symptomatic AD with cognitive impairment. Recent work suggests that the gut microbiome of symptomatic patients with AD has an altered taxonomic composition compared with that of healthy, cognitively normal control individuals. However, knowledge about changes in the gut microbiome before the onset of symptomatic AD is limited. In this cross-sectional study that accounted for clinical covariates and dietary intake, we compared the taxonomic composition and gut microbial function in a cohort of 164 cognitively normal individuals, 49 of whom showed biomarker evidence of early preclinical AD. Gut microbial taxonomic profiles of individuals with preclinical AD were distinct from those of individuals without evidence of preclinical AD. The change in gut microbiome composition correlated with β-amyloid (Aβ) and tau pathological biomarkers but not with biomarkers of neurodegeneration, suggesting that the gut microbiome may change early in the disease process. We identified specific gut bacterial taxa associated with preclinical AD. Inclusion of these microbiome features improved the accuracy, sensitivity, and specificity of machine learning classifiers for predicting preclinical AD status when tested on a subset of the cohort (65 of the 164 participants). Gut microbiome correlates of preclinical AD neuropathology may improve our understanding of AD etiology and may help to identify gut-derived markers of AD risk.
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Affiliation(s)
- Aura L. Ferreiro
- Edison Family Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St. Louis, MO 63110, USA
- Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, MO 63130, USA
- Department of Pathology and Immunology, Division of Laboratory and Genomic Medicine, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - JooHee Choi
- Edison Family Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Jian Ryou
- Edison Family Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Erin P. Newcomer
- Edison Family Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St. Louis, MO 63110, USA
- Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, MO 63130, USA
| | - Regina Thompson
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Rebecca M. Bollinger
- Program in Occupational Therapy, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Carla Hall-Moore
- Division of Gastroenterology, Hepatology, and Nutrition, Department of Pediatrics, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - I. Malick Ndao
- Division of Gastroenterology, Hepatology, and Nutrition, Department of Pediatrics, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Laurie Sax
- Division of Gastroenterology, Hepatology, and Nutrition, Department of Pediatrics, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Tammie L. S. Benzinger
- Department of Radiology, Washington University School of Medicine, St. Louis, MO 63110, USA
- Charles F. and Joanne Knight Alzheimer’s Disease Research Center, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Susan L. Stark
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, USA
- Program in Occupational Therapy, Washington University School of Medicine, St. Louis, MO 63110, USA
- Charles F. and Joanne Knight Alzheimer’s Disease Research Center, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - David M. Holtzman
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, USA
- Charles F. and Joanne Knight Alzheimer’s Disease Research Center, Washington University School of Medicine, St. Louis, MO 63110, USA
- Hope Center for Neurological Disorders, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Anne M. Fagan
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, USA
- Charles F. and Joanne Knight Alzheimer’s Disease Research Center, Washington University School of Medicine, St. Louis, MO 63110, USA
- Hope Center for Neurological Disorders, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Suzanne E. Schindler
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, USA
- Charles F. and Joanne Knight Alzheimer’s Disease Research Center, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Carlos Cruchaga
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, USA
- Hope Center for Neurological Disorders, Washington University School of Medicine, St. Louis, MO 63110, USA
- NeuroGenomics and Informatics, Washington University School of Medicine, St. Louis, MO 63110, USA
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Omar H. Butt
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - John C. Morris
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, USA
- Charles F. and Joanne Knight Alzheimer’s Disease Research Center, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Phillip I. Tarr
- Division of Gastroenterology, Hepatology, and Nutrition, Department of Pediatrics, Washington University School of Medicine, St. Louis, MO 63110, USA
- Department of Molecular Microbiology, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Beau M. Ances
- Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, MO 63130, USA
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, USA
- Department of Radiology, Washington University School of Medicine, St. Louis, MO 63110, USA
- Charles F. and Joanne Knight Alzheimer’s Disease Research Center, Washington University School of Medicine, St. Louis, MO 63110, USA
- Hope Center for Neurological Disorders, Washington University School of Medicine, St. Louis, MO 63110, USA
- Department of Molecular Microbiology, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Gautam Dantas
- Edison Family Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St. Louis, MO 63110, USA
- Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, MO 63130, USA
- Department of Pathology and Immunology, Division of Laboratory and Genomic Medicine, Washington University School of Medicine, St. Louis, MO 63110, USA
- Department of Molecular Microbiology, Washington University School of Medicine, St. Louis, MO 63110, USA
- Department of Pediatrics, Washington University School of Medicine, St. Louis, MO 63110, USA
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Pemberton HG, Buckley C, Battle M, Bollack A, Patel V, Tomova P, Cooke D, Balhorn W, Hegedorn K, Lilja J, Brand C, Farrar G. Software compatibility analysis for quantitative measures of [ 18F]flutemetamol amyloid PET burden in mild cognitive impairment. EJNMMI Res 2023; 13:48. [PMID: 37225974 DOI: 10.1186/s13550-023-00994-3] [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: 09/26/2022] [Accepted: 05/05/2023] [Indexed: 05/26/2023] Open
Abstract
RATIONALE Amyloid-β (Aβ) pathology is one of the earliest detectable brain changes in Alzheimer's disease pathogenesis. In clinical practice, trained readers will visually categorise positron emission tomography (PET) scans as either Aβ positive or negative. However, adjunct quantitative analysis is becoming more widely available, where regulatory approved software can currently generate metrics such as standardised uptake value ratios (SUVr) and individual Z-scores. Therefore, it is of direct value to the imaging community to assess the compatibility of commercially available software packages. In this collaborative project, the compatibility of amyloid PET quantification was investigated across four regulatory approved software packages. In doing so, the intention is to increase visibility and understanding of clinically relevant quantitative methods. METHODS Composite SUVr using the pons as the reference region was generated from [18F]flutemetamol (GE Healthcare) PET in a retrospective cohort of 80 amnestic mild cognitive impairment (aMCI) patients (40 each male/female; mean age = 73 years, SD = 8.52). Based on previous autopsy validation work, an Aβ positivity threshold of ≥ 0.6 SUVrpons was applied. Quantitative results from MIM Software's MIMneuro, Syntermed's NeuroQ, Hermes Medical Solutions' BRASS and GE Healthcare's CortexID were analysed using intraclass correlation coefficient (ICC), percentage agreement around the Aβ positivity threshold and kappa scores. RESULTS Using an Aβ positivity threshold of ≥ 0.6 SUVrpons, 95% agreement was achieved across the four software packages. Two patients were narrowly classed as Aβ negative by one software package but positive by the others, and two patients vice versa. All kappa scores around the same Aβ positivity threshold, both combined (Fleiss') and individual software pairings (Cohen's), were ≥ 0.9 signifying "almost perfect" inter-rater reliability. Excellent reliability was found between composite SUVr measurements for all four software packages, with an average measure ICC of 0.97 and 95% confidence interval of 0.957-0.979. Correlation coefficient analysis between the two software packages reporting composite z-scores was strong (r2 = 0.98). CONCLUSION Using an optimised cortical mask, regulatory approved software packages provided highly correlated and reliable quantification of [18F]flutemetamol amyloid PET with a ≥ 0.6 SUVrpons positivity threshold. In particular, this work could be of interest to physicians performing routine clinical imaging rather than researchers performing more bespoke image analysis. Similar analysis is encouraged using other reference regions as well as the Centiloid scale, when it has been implemented by more software packages.
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Affiliation(s)
- Hugh G Pemberton
- GE Healthcare, Pollards Wood, Chalfont St Giles, Amersham, HP8 4SP, UK.
- Centre for Medical Image Computing (CMIC), Department of Medical Physics and Bioengineering, University College London, London, UK.
- UCL Queen Square Institute of Neurology, University College London, London, UK.
| | | | - Mark Battle
- GE Healthcare, Pollards Wood, Chalfont St Giles, Amersham, HP8 4SP, UK
- Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Malmö, Sweden
| | - Ariane Bollack
- Centre for Medical Image Computing (CMIC), Department of Medical Physics and Bioengineering, University College London, London, UK
| | - Vrajesh Patel
- GE Healthcare, Pollards Wood, Chalfont St Giles, Amersham, HP8 4SP, UK
| | - Petya Tomova
- GE Healthcare, Pollards Wood, Chalfont St Giles, Amersham, HP8 4SP, UK
| | | | | | | | | | - Christine Brand
- GE Healthcare, Pollards Wood, Chalfont St Giles, Amersham, HP8 4SP, UK
| | - Gill Farrar
- GE Healthcare, Pollards Wood, Chalfont St Giles, Amersham, HP8 4SP, UK
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Largent EA, Grill JD, O'Brien K, Wolk D, Harkins K, Karlawish J. Testing for Alzheimer Disease Biomarkers and Disclosing Results Across the Disease Continuum. Neurology 2023; 100:1010-1019. [PMID: 36720642 PMCID: PMC10238153 DOI: 10.1212/wnl.0000000000206891] [Citation(s) in RCA: 19] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Accepted: 12/20/2022] [Indexed: 02/02/2023] Open
Abstract
Three pathologic processes are characteristic of Alzheimer disease (AD): β-amyloid, hyperphosphorylated tau, and neurodegeneration. Our understanding of AD is undergoing a transformation due to our ability to measure biomarkers of these processes across different stages of cognitive impairment. There is growing interest in using AD biomarker tests in care and research and, with this, a growing need for guidance on how to return these sensitive results to patients and participants. Here, we propose a 5-step approach informed by clinical and research experience designing and implementing AD biomarker disclosure processes, extant evidence describing how individuals react to AD biomarker information, ethics, law, and the literature on breaking bad news. The clinician should (1) determine the appropriateness of AD biomarker testing and return of results for the particular patient or research participant. If testing is appropriate, the next steps are to (2) provide pretest education and seek consent for testing from the individual and their support person, (3) administer testing, (4) return the results to the individual and their support person, and (5) follow-up to promote the recipient's well-being.
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Affiliation(s)
- Emily A Largent
- From the Department of Medical Ethics and Health Policy (E.A.L., J.K.), University of Pennsylvania Perelman School of Medicine, Philadelphia; Department of Psychiatry and Human Behavior (J.D.G.), and Department of Neurobiology and Behavior (J.D.G.), University of California, Irvine; Department of Neurology (K.O.B., D.W., J.K.), and Department of Medicine (K.H., J.K.), University of Pennsylvania Perelman School of Medicine, Philadelphia.
| | - Joshua D Grill
- From the Department of Medical Ethics and Health Policy (E.A.L., J.K.), University of Pennsylvania Perelman School of Medicine, Philadelphia; Department of Psychiatry and Human Behavior (J.D.G.), and Department of Neurobiology and Behavior (J.D.G.), University of California, Irvine; Department of Neurology (K.O.B., D.W., J.K.), and Department of Medicine (K.H., J.K.), University of Pennsylvania Perelman School of Medicine, Philadelphia
| | - Kyra O'Brien
- From the Department of Medical Ethics and Health Policy (E.A.L., J.K.), University of Pennsylvania Perelman School of Medicine, Philadelphia; Department of Psychiatry and Human Behavior (J.D.G.), and Department of Neurobiology and Behavior (J.D.G.), University of California, Irvine; Department of Neurology (K.O.B., D.W., J.K.), and Department of Medicine (K.H., J.K.), University of Pennsylvania Perelman School of Medicine, Philadelphia
| | - David Wolk
- From the Department of Medical Ethics and Health Policy (E.A.L., J.K.), University of Pennsylvania Perelman School of Medicine, Philadelphia; Department of Psychiatry and Human Behavior (J.D.G.), and Department of Neurobiology and Behavior (J.D.G.), University of California, Irvine; Department of Neurology (K.O.B., D.W., J.K.), and Department of Medicine (K.H., J.K.), University of Pennsylvania Perelman School of Medicine, Philadelphia
| | - Kristin Harkins
- From the Department of Medical Ethics and Health Policy (E.A.L., J.K.), University of Pennsylvania Perelman School of Medicine, Philadelphia; Department of Psychiatry and Human Behavior (J.D.G.), and Department of Neurobiology and Behavior (J.D.G.), University of California, Irvine; Department of Neurology (K.O.B., D.W., J.K.), and Department of Medicine (K.H., J.K.), University of Pennsylvania Perelman School of Medicine, Philadelphia
| | - Jason Karlawish
- From the Department of Medical Ethics and Health Policy (E.A.L., J.K.), University of Pennsylvania Perelman School of Medicine, Philadelphia; Department of Psychiatry and Human Behavior (J.D.G.), and Department of Neurobiology and Behavior (J.D.G.), University of California, Irvine; Department of Neurology (K.O.B., D.W., J.K.), and Department of Medicine (K.H., J.K.), University of Pennsylvania Perelman School of Medicine, Philadelphia
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Yoo J, Cheon M, Kang MJ. A Case Report of Early-Onset Alzheimer's Disease Using 18F-FDG PET and 18F-FBB PET. Diagnostics (Basel) 2023; 13:diagnostics13101671. [PMID: 37238154 DOI: 10.3390/diagnostics13101671] [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: 04/06/2023] [Revised: 05/02/2023] [Accepted: 05/03/2023] [Indexed: 05/28/2023] Open
Abstract
We describe a 40-year-old female patient who presented with sleep disturbance, intermittent headache, and gradual subjective cognitive decline. 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET) showed mild FDG hypometabolism in bilateral parietal and temporal lobes. However, 18F-florbetaben (FBB) amyloid PET revealed diffuse amyloid retention in the lateral temporal cortex, frontal cortex, posterior cingulate cortex/precuneus, parietal cortex, and cerebellum. This finding supports the clinical significance of amyloid imaging in diagnostic work-up of early-onset Alzheimer's disease (EOAD).
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Affiliation(s)
- Jang Yoo
- Department of Nuclear Medicine, VHS Medical Center, Seoul 05368, Republic of Korea
| | - Miju Cheon
- Department of Nuclear Medicine, VHS Medical Center, Seoul 05368, Republic of Korea
| | - Min-Ju Kang
- Department of Neurology, VHS Medical Center, Seoul 05368, Republic of Korea
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Altomare D, Barkhof F, Caprioglio C, Collij LE, Scheltens P, Lopes Alves I, Bouwman F, Berkhof J, van Maurik IS, Garibotto V, Moro C, Delrieu J, Payoux P, Saint-Aubert L, Hitzel A, Molinuevo JL, Grau-Rivera O, Gispert JD, Drzezga A, Jessen F, Zeyen P, Nordberg A, Savitcheva I, Jelic V, Walker Z, Edison P, Demonet JF, Gismondi R, Farrar G, Stephens AW, Frisoni GB. Clinical Effect of Early vs Late Amyloid Positron Emission Tomography in Memory Clinic Patients: The AMYPAD-DPMS Randomized Clinical Trial. JAMA Neurol 2023:2804755. [PMID: 37155177 PMCID: PMC10167601 DOI: 10.1001/jamaneurol.2023.0997] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/10/2023]
Abstract
Importance Amyloid positron emission tomography (PET) allows the direct assessment of amyloid deposition, one of the main hallmarks of Alzheimer disease. However, this technique is currently not widely reimbursed because of the lack of appropriately designed studies demonstrating its clinical effect. Objective To assess the clinical effect of amyloid PET in memory clinic patients. Design, Setting, and Participants The AMYPAD-DPMS is a prospective randomized clinical trial in 8 European memory clinics. Participants were allocated (using a minimization method) to 3 study groups based on the performance of amyloid PET: arm 1, early in the diagnostic workup (within 1 month); arm 2, late in the diagnostic workup (after a mean [SD] 8 [2] months); or arm 3, if and when the managing physician chose. Participants were patients with subjective cognitive decline plus (SCD+; SCD plus clinical features increasing the likelihood of preclinical Alzheimer disease), mild cognitive impairment (MCI), or dementia; they were assessed at baseline and after 3 months. Recruitment took place between April 16, 2018, and October 30, 2020. Data analysis was performed from July 2022 to January 2023. Intervention Amyloid PET. Main Outcome and Measure The main outcome was the difference between arm 1 and arm 2 in the proportion of participants receiving an etiological diagnosis with a very high confidence (ie, ≥90% on a 50%-100% visual numeric scale) after 3 months. Results A total of 844 participants were screened, and 840 were enrolled (291 in arm 1, 271 in arm 2, 278 in arm 3). Baseline and 3-month visit data were available for 272 participants in arm 1 and 260 in arm 2 (median [IQR] age: 71 [65-77] and 71 [65-77] years; 150/272 male [55%] and 135/260 male [52%]; 122/272 female [45%] and 125/260 female [48%]; median [IQR] education: 12 [10-15] and 13 [10-16] years, respectively). After 3 months, 109 of 272 participants (40%) in arm 1 had a diagnosis with very high confidence vs 30 of 260 (11%) in arm 2 (P < .001). This was consistent across cognitive stages (SCD+: 25/84 [30%] vs 5/78 [6%]; P < .001; MCI: 45/108 [42%] vs 9/102 [9%]; P < .001; dementia: 39/80 [49%] vs 16/80 [20%]; P < .001). Conclusion and Relevance In this study, early amyloid PET allowed memory clinic patients to receive an etiological diagnosis with very high confidence after only 3 months compared with patients who had not undergone amyloid PET. These findings support the implementation of amyloid PET early in the diagnostic workup of memory clinic patients. Trial Registration EudraCT Number: 2017-002527-21.
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Affiliation(s)
- Daniele Altomare
- Laboratory of Neuroimaging of Aging (LANVIE), University of Geneva, Geneva, Switzerland
- Geneva Memory Center, Geneva University Hospitals, Geneva, Switzerland
- Neurology Unit, Department of Clinical and Experimental Sciences, University of Brescia, Brescia, Italy
| | - Frederik Barkhof
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centers (UMC)-Location VUmc, Amsterdam, the Netherlands
- Institute of Neurology, Institute of Healthcare Engineering, University College London, London, United Kingdom
| | - Camilla Caprioglio
- Laboratory of Neuroimaging of Aging (LANVIE), University of Geneva, Geneva, Switzerland
- Geneva Memory Center, Geneva University Hospitals, Geneva, Switzerland
| | - Lyduine E Collij
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centers (UMC)-Location VUmc, Amsterdam, the Netherlands
| | - Philip Scheltens
- Alzheimer Center, Department of Neurology, Amsterdam University Medical Centers-Location VUmc, Amsterdam, the Netherlands
| | - Isadora Lopes Alves
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centers (UMC)-Location VUmc, Amsterdam, the Netherlands
| | - Femke Bouwman
- Alzheimer Center, Department of Neurology, Amsterdam University Medical Centers-Location VUmc, Amsterdam, the Netherlands
| | - Johannes Berkhof
- Department of Epidemiology and Data Science, Amsterdam University Medical Centers-Location VUmc, Amsterdam, the Netherlands
| | - Ingrid S van Maurik
- Alzheimer Center, Department of Neurology, Amsterdam University Medical Centers-Location VUmc, Amsterdam, the Netherlands
- Department of Epidemiology and Data Science, Amsterdam University Medical Centers-Location VUmc, Amsterdam, the Netherlands
| | - Valentina Garibotto
- Laboratory of Neuroimaging and Innovative Molecular Tracers (NIMTlab), Geneva University Neurocenter and Faculty of Medicine, University of Geneva, Geneva, Switzerland
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospitals, Geneva, Switzerland
| | - Christian Moro
- Laboratory of Neuroimaging of Aging (LANVIE), University of Geneva, Geneva, Switzerland
- Geneva Memory Center, Geneva University Hospitals, Geneva, Switzerland
| | - Julien Delrieu
- Gérontopôle, Department of Geriatrics, Toulouse University Hospital, Toulouse, France
- Maintain Aging Research Team, CERPOP, Inserm, Université Paul Sabatier, Toulouse, France
| | - Pierre Payoux
- Department of Nuclear Medicine, Toulouse University Hospital, Toulouse, France
- Toulouse NeuroImaging Center (ToNIC), UMR1214 Inserm, Université de Toulouse III, Toulouse, France
| | - Laure Saint-Aubert
- Department of Nuclear Medicine, Toulouse University Hospital, Toulouse, France
- Toulouse NeuroImaging Center (ToNIC), UMR1214 Inserm, Université de Toulouse III, Toulouse, France
| | - Anne Hitzel
- Department of Nuclear Medicine, Toulouse University Hospital, Toulouse, France
| | - José Luis Molinuevo
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain
- H. Lundbeck, Copenhagen, Denmark
| | - Oriol Grau-Rivera
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain
- Hospital del Mar Medical Research Institute (IMIM), Barcelona, Spain
- Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (CIBERFES), Madrid, Spain
| | - Juan Domingo Gispert
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain
- Hospital del Mar Medical Research Institute (IMIM), Barcelona, Spain
- Centro de Investigación Biomédica en Red Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Barcelona, Spain
| | - Alexander Drzezga
- Department of Nuclear Medicine, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
- German Center for Neurodegenerative Diseases (DZNE), Bonn-Cologne, Germany
- Institute of Neuroscience and Medicine (INM-2), Molecular Organization of the Brain, Forschungszentrum Jülich, Germany
| | - Frank Jessen
- German Center for Neurodegenerative Diseases (DZNE), Bonn-Cologne, Germany
- Department of Psychiatry, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
- Excellence Cluster Cellular Stress Responses in Aging-Related Diseases (CECAD), Medical Faculty, University of Cologne, Cologne, Germany
| | - Philip Zeyen
- Department of Psychiatry, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Agneta Nordberg
- Department of Neurobiology, Care Sciences and Society, Center of Alzheimer Research, Karolinska Institutet, Stockholm, Sweden
- Theme Aging, Karolinska University Hospital, Stockholm, Sweden
| | - Irina Savitcheva
- Medical Radiation Physics and Nuclear Medicine, Section for Nuclear Medicine, Karolinska University Hospital, Stockholm, Sweden
| | - Vesna Jelic
- Cognitive Disorders Clinic, Theme Inflammation and Aging, Karolinska University Hospital-Huddinge, Stockholm, Sweden
| | - Zuzana Walker
- Division of Psychiatry, University College London, London, United Kingdom
- St Margaret's Hospital, Essex Partnership University NHS Foundation Trust, Essex, United Kingdom
| | - Paul Edison
- Division of Neurology, Department of Brain Sciences, Imperial College London, London, United Kingdom
| | | | | | | | | | - Giovanni B Frisoni
- Laboratory of Neuroimaging of Aging (LANVIE), University of Geneva, Geneva, Switzerland
- Geneva Memory Center, Geneva University Hospitals, Geneva, Switzerland
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Chun MY, Jang H, Kim HJ, Kim JP, Gallacher J, Allué JA, Sarasa L, Castillo S, Pascual-Lucas M, Na DL, Seo SW. Contribution of clinical information to the predictive performance of plasma β-amyloid levels for amyloid positron emission tomography positivity. Front Aging Neurosci 2023; 15:1126799. [PMID: 36998318 PMCID: PMC10044013 DOI: 10.3389/fnagi.2023.1126799] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2022] [Accepted: 02/24/2023] [Indexed: 03/15/2023] Open
Abstract
BackgroundEarly detection of β-amyloid (Aβ) accumulation, a major biomarker for Alzheimer’s disease (AD), has become important. As fluid biomarkers, the accuracy of cerebrospinal fluid (CSF) Aβ for predicting Aβ deposition on positron emission tomography (PET) has been extensively studied, and the development of plasma Aβ is beginning to receive increased attention recently. In the present study, we aimed to determine whether APOE genotypes, age, and cognitive status increase the predictive performance of plasma Aβ and CSF Aβ levels for Aβ PET positivity.MethodsWe recruited 488 participants who underwent both plasma Aβ and Aβ PET studies (Cohort 1) and 217 participants who underwent both cerebrospinal fluid (CSF) Aβ and Aβ PET studies (Cohort 2). Plasma and CSF samples were analyzed using ABtest-MS, an antibody-free liquid chromatography-differential mobility spectrometry-triple quadrupole mass spectrometry method and INNOTEST enzyme-linked immunosorbent assay kits, respectively. To evaluate the predictive performance of plasma Aβ and CSF Aβ, respectively, logistic regression and receiver operating characteristic analyses were performed.ResultsWhen predicting Aβ PET status, both plasma Aβ42/40 ratio and CSF Aβ42 showed high accuracy (plasma Aβ area under the curve (AUC) 0.814; CSF Aβ AUC 0.848). In the plasma Aβ models, the AUC values were higher than plasma Aβ alone model, when the models were combined with either cognitive stage (p < 0.001) or APOE genotype (p = 0.011). On the other hand, there was no difference between the CSF Aβ models, when these variables were added.ConclusionPlasma Aβ might be a useful predictor of Aβ deposition on PET status as much as CSF Aβ, particularly when considered with clinical information such as APOE genotype and cognitive stage.
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Affiliation(s)
- Min Young Chun
- Departments of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
- Department of Neurology, Yonsei University College of Medicine, Seoul, Republic of Korea
- Department of Neurology, Yongin Severance Hospital, Yonsei University Health System, Yongin, Republic of Korea
| | - Hyemin Jang
- Departments of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
- Neuroscience Center, Samsung Medical Center, Seoul, Republic of Korea
- Alzheimer's Disease Convergence Research Center, Samsung Medical Center, Seoul, Republic of Korea
- *Correspondence: Hyemin Jang, ; Sang Won Seo,
| | - Hee Jin Kim
- Departments of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
- Alzheimer's Disease Convergence Research Center, Samsung Medical Center, Seoul, Republic of Korea
- Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, Republic of Korea
- Department of Digital Health, SAIHST, Sungkyunkwan University, Seoul, Republic of Korea
| | - Jun Pyo Kim
- Departments of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
- Center for Neuroimaging, Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, United States
| | - John Gallacher
- Department of Psychiatry, Warneford Hospital, University of Oxford, Oxford, United Kingdom
| | | | | | | | | | - Duk L. Na
- Departments of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
- Alzheimer's Disease Convergence Research Center, Samsung Medical Center, Seoul, Republic of Korea
| | - Sang Won Seo
- Departments of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
- Alzheimer's Disease Convergence Research Center, Samsung Medical Center, Seoul, Republic of Korea
- Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, Republic of Korea
- Department of Digital Health, SAIHST, Sungkyunkwan University, Seoul, Republic of Korea
- Department of Clinical Research Design and Evaluation, SAIHST, Sungkyunkwan University, Seoul, Republic of Korea
- *Correspondence: Hyemin Jang, ; Sang Won Seo,
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Stefani A, Mozersky J, Kotagal V, Högl B, Ingravallo F, Ju YES, Avidan A, Sharp R, Videnovic A, Schenck CH, St Louis EK. Ethical Aspects of Prodromal Synucleinopathy Prognostic Counseling. Semin Neurol 2023; 43:166-177. [PMID: 36693433 DOI: 10.1055/a-2019-0245] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Abstract
Alpha-synucleinopathies can be identified in their prodromal phase, raising several ethical issues. In this review, we first provide definitions of prodromal α-synucleinopathies and discuss the importance of distinguishing between prodromes and risk factors. Next, we discuss the implications of a diagnosis of prodromal α-synucleinopathy and considerations regarding prognostic counseling in both clinical and research settings. We review available data on patient preferences regarding disclosure as well as providers' perspectives. We examine the pros and cons of disclosing a diagnosis of prodromal α-synucleinopathy, taking into consideration the differences between clinical and research settings. Asking about willingness to know in clinical and research settings and the shared decision-making process applied to prognostic counseling is discussed. Concerning research settings, ethical aspects regarding clinical trials are addressed. Availability of direct-to-consumer technologies will likely lead to novel contexts requiring prognostic counseling, and future neuroprotective or neuromodulating treatments may require further considerations on the timing, role, and importance of prognostic counseling. Recommendations on how to address ethical gaps should be a priority for patients, medical professional societies, and research workgroups. Ethical issues must be considered as an integral part of the overall clinical and research approach to prodromal synucleinopathies.
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Affiliation(s)
- Ambra Stefani
- Department of Neurology, Medical University of Innsbruck, Innsbruck, Austria.,Neurological Clinical Research Institute, Massachusetts General Hospital, Harvard University, Boston, Massachusetts
| | - Jessica Mozersky
- Division of General Medical Sciences, Washington University in Saint Louis, Saint Louis, Missouri
| | - Vikas Kotagal
- Department of Neurology, University of Michigan-Ann Arbor, Ann Arbor, Michigan
| | - Birgit Högl
- Department of Neurology, Medical University of Innsbruck, Innsbruck, Austria
| | - Francesca Ingravallo
- Department of Medical and Surgical Sciences, University of Bologna, Bologna, Emilia-Romagna, Italy
| | - Yo-El S Ju
- Division of General Medical Sciences, Washington University in Saint Louis, Saint Louis, Missouri
| | - Alon Avidan
- Department of Neurology, University of California-Los Angeles, Los Angeles, California
| | - Richard Sharp
- Department of Quantitative Health Sciences, Mayo Clinic College of Medicine and Science, Rochester, Minnesota
| | - Aleksandar Videnovic
- Neurological Clinical Research Institute, Massachusetts General Hospital, Harvard University, Boston, Massachusetts
| | - Carlos H Schenck
- Department of Psychiatry, University of Minnesota, Minneapolis, Minnesota
| | - Erik K St Louis
- Department of Quantitative Health Sciences, Mayo Clinic College of Medicine and Science, Rochester, Minnesota
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Loreto F, Gontsarova A, Scott G, Patel N, Win Z, Carswell C, Perry R, Malhotra P. Visual atrophy rating scales and amyloid PET status in an Alzheimer's disease clinical cohort. Ann Clin Transl Neurol 2023; 10:619-631. [PMID: 36872523 PMCID: PMC10109315 DOI: 10.1002/acn3.51749] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Revised: 02/08/2023] [Accepted: 02/10/2023] [Indexed: 03/07/2023] Open
Abstract
OBJECTIVES Visual rating scales (VRS) are the quantification method closest to the approach used in routine clinical practice to assess brain atrophy. Previous studies have suggested that the medial temporal atrophy (MTA) rating scale is a reliable diagnostic marker for AD, equivalent to volumetric quantification, while others propose a higher diagnostic utility for the Posterior Atrophy (PA) scale in early-onset AD. METHODS Here, we reviewed 14 studies that assessed the diagnostic accuracy of PA and MTA, we explored the issue of cut-off heterogeneity, and assessed 9 rating scales in a group of patients with biomarker-confirmed diagnosis. A neuroradiologist blinded to all clinical information rated the MR images of 39 amyloid-positive and 38 amyloid-negative patients using 9 validated VRS assessing multiple brain regions. Automated volumetric analyses were performed on a subset of patients (n = 48) and on a group of cognitively normal individuals (n = 28). RESULTS No single VRS could differentiate amyloid-positive from amyloid-negative patients with other neurodegenerative conditions. 44% of amyloid-positive patients were deemed to have age-appropriate levels of MTA. In the amyloid-positive group, 18% had no abnormal MTA or PA scores. These findings were substantially affected by cut-off selection. Amyloid-positive and amyloid-negative patients had comparable hippocampal and parietal volumes, and MTA but not PA scores correlated with the respective volumetric measures. INTERPRETATION Consensus guidelines are needed before VRS can be recommended for use in the diagnostic workup of AD. Our data are suggestive of high intragroup variability and non-superiority of volumetric quantification of atrophy over visual assessment.
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Affiliation(s)
- Flavia Loreto
- Department of Brain Sciences, Faculty of Medicine, Imperial College London, London, UK
| | | | - Gregory Scott
- Department of Brain Sciences, Faculty of Medicine, Imperial College London, London, UK.,UK Dementia Research Institute Care Research and Technology Centre, Imperial College London and the University of Surrey, London, UK
| | - Neva Patel
- Department of Nuclear Medicine, Imperial College Healthcare NHS Trust, London, UK
| | - Zarni Win
- Department of Nuclear Medicine, Imperial College Healthcare NHS Trust, London, UK
| | | | - Richard Perry
- Department of Brain Sciences, Faculty of Medicine, Imperial College London, London, UK.,Department of Neurology, Imperial College Healthcare NHS Trust, London, UK
| | - Paresh Malhotra
- Department of Brain Sciences, Faculty of Medicine, Imperial College London, London, UK.,UK Dementia Research Institute Care Research and Technology Centre, Imperial College London and the University of Surrey, London, UK.,Department of Neurology, Imperial College Healthcare NHS Trust, London, UK
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Hirose T, Takayama T, Shibata N, Murakami K, Arai H. A Pilot Study on Cerebral Blood Flow and Mini-Mental State Examination to Predict Amyloid Deposition in Preclinical Alzheimer's Disease. PSYCHIAT CLIN PSYCH 2023; 33:1-7. [PMID: 38764533 PMCID: PMC11082584 DOI: 10.5152/pcp.2023.22524] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/24/2022] [Accepted: 02/04/2023] [Indexed: 05/21/2024] Open
Abstract
Background Earlier differential diagnosis of dementia remains a major challenge. Although amyloid deposition by positron emission tomography is an emerging standard for the diagnosis of Alzheimer's disease, it is too expensive for routine use in clinical settings. We conducted a pilot study on the potential usefulness of single-photon emission computed tomography and the Mini-Mental State Examination to predict amyloid positron emission tomography positivity in preclinical Alzheimer's disease. Methods Eighteen subjects, including 11 with mild cognitive impairment and 7 with subjective cognitive decline, underwent 18F-florbetapir positron emission tomography, 99mTc-ethylcysteinate dimer cerebral perfusion single-photon emission computed tomography, and the Mini-Mental State Examination. For the assessment of amyloid deposition, visual judgment as a qualitative method and a semiautomatic software analysis as a quantitative method were used. Results Six subjects were judged as amyloid positive, including 4 mild cognitive impairment and 2 subjective cognitive decline subjects. Compared to the amyloid positron emission tomography-negative group, this group showed a statistically significant difference in the Mini-Mental State Examination recall score [2 (1 : 3) vs. 3 (2 : 3), P = .041] and single-photon emission computed tomography findings from the amyloid-negative group. In the mild cognitive impairment subgroup, correlations were found between amyloid deposition and single-photon emission computed tomography indicators, while in the subjective cognitive decline subgroup, only the Mini-Mental State Examination recall score correlated with amyloid deposition. Conclusion The Mini-Mental State Examination recall score and single-photon emission computed tomography indicators may be worthwhile for further evaluation as predictors of amyloid deposition in the preclinical stage.
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Affiliation(s)
- Takumi Hirose
- Department of Psychiatry and Behavioral Science, Graduate School of Medicine, Juntendo University, Tokyo, Japan
| | - Toshiki Takayama
- Department of Psychiatry and Behavioral Science, Graduate School of Medicine, Juntendo University, Tokyo, Japan
| | - Nobuto Shibata
- Department of Psychiatry and Behavioral Science, Graduate School of Medicine, Juntendo University, Tokyo, Japan
| | - Koji Murakami
- Department of Radiology, Juntendo University School of Medicine, Tokyo, Japan
| | - Heii Arai
- Department of Psychiatry and Behavioral Science, Graduate School of Medicine, Juntendo University, Tokyo, Japan
- Alzclinic Tokyo, Tokyo, Japan
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Franceschi AM, Petrover DR, Giliberto L, Clouston SAP, Gordon ML. Semiquantitative Approach to Amyloid Positron Emission Tomography Interpretation in Clinical Practice. World J Nucl Med 2023; 22:15-21. [PMID: 36923983 PMCID: PMC10010866 DOI: 10.1055/s-0042-1757290] [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: 11/07/2022] Open
Abstract
Objective Amyloid positron emission tomography (PET) plays a vital role in the in vivo detection of β-amyloid accumulation in Alzheimer's disease. Increasingly, trainees and infrequent readers are relying on semiquantitative analyses to support clinical diagnostic efforts. Our objective was to determine if the visual assessment of amyloid PET may be facilitated by relying on semiquantitative analysis. Methods We conducted a retrospective review of [ 18 F]-florbetaben PET/computed tomographies (CTs) from 2016 to 2018. Visual interpretation to determine Aβ+ status was conducted by two readers blinded to each other's interpretation. Scans were then post-processed utilizing the MIMneuro software, which generated regional-based semiquantitative Z-scores indicating cortical Aβ-burden. Results Of 167 [ 18 F]-florbetaben PET/CTs, 92/167 (reader-1) and 101/167 (reader-2) were positive for amyloid deposition (agreement = 92.2%, κ = 0.84). Additional nine scans were identified as possible Aβ-positive based solely on semiquantitative analyses. Largest semiquantitative differences were identified in the left frontal lobe (Z = 7.74 in Aβ + ; 0.50 in Aβ - ). All unilateral regions showed large statistically significant differences in Aβ-burden ( P ≤ 2.08E-28). Semiquantitative scores were highly sensitive to Aβ+ status and accurate in their ability to identify amyloid positivity, defined as a positive scan by both readers (AUC ≥ 0.90 [0.79-1.00]). Spread analyses suggested that amyloid deposition was most severe in the left posterior cingulate gyrus. The largest differences between Aβ +/Aβ- were in the left frontal lobe. Analyses using region-specific cutoffs indicated that the presence of amyloid in the temporal and anterior cingulate cortex, while exhibiting relatively low Z-scores, was most common. Conclusion Visual assessment and semiquantitative analysis provide highly congruent results, thereby enhancing reader confidence and improving scan interpretation. This is particularly relevant, given recent advances in amyloid-targeting disease-modifying therapeutics.
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Affiliation(s)
- Ana M Franceschi
- Neuroradiology Section, Department of Radiology, Lenox Hill Hospital, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, New York, New York, United States
| | - David R Petrover
- Neuroradiology Section, Department of Radiology, Lenox Hill Hospital, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, New York, New York, United States
| | - Luca Giliberto
- Institute for Neurology and Neurosurgery, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Manhasset, New York, United States.,Litwin-Zucker Research Center, Feinstein Institutes for Medical Research, Northwell Health, New York, United States
| | - Sean A P Clouston
- Department of Family, Population and Preventative Medicine and Program in Public Health, Renaissance School of Medicine at Stony Brook University, Stony Brook, New York, United States
| | - Marc L Gordon
- Litwin-Zucker Research Center, Feinstein Institutes for Medical Research, Northwell Health, New York, United States.,Departments of Neurology and Psychiatry, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, New York, United States
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Javeed A, Dallora AL, Berglund JS, Ali A, Ali L, Anderberg P. Machine Learning for Dementia Prediction: A Systematic Review and Future Research Directions. J Med Syst 2023; 47:17. [PMID: 36720727 PMCID: PMC9889464 DOI: 10.1007/s10916-023-01906-7] [Citation(s) in RCA: 21] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2022] [Accepted: 01/03/2023] [Indexed: 02/02/2023]
Abstract
Nowadays, Artificial Intelligence (AI) and machine learning (ML) have successfully provided automated solutions to numerous real-world problems. Healthcare is one of the most important research areas for ML researchers, with the aim of developing automated disease prediction systems. One of the disease detection problems that AI and ML researchers have focused on is dementia detection using ML methods. Numerous automated diagnostic systems based on ML techniques for early prediction of dementia have been proposed in the literature. Few systematic literature reviews (SLR) have been conducted for dementia prediction based on ML techniques in the past. However, these SLR focused on a single type of data modality for the detection of dementia. Hence, the purpose of this study is to conduct a comprehensive evaluation of ML-based automated diagnostic systems considering different types of data modalities such as images, clinical-features, and voice data. We collected the research articles from 2011 to 2022 using the keywords dementia, machine learning, feature selection, data modalities, and automated diagnostic systems. The selected articles were critically analyzed and discussed. It was observed that image data driven ML models yields promising results in terms of dementia prediction compared to other data modalities, i.e., clinical feature-based data and voice data. Furthermore, this SLR highlighted the limitations of the previously proposed automated methods for dementia and presented future directions to overcome these limitations.
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Affiliation(s)
- Ashir Javeed
- Aging Research Center, Karolinska Institutet, Tomtebodavagen, Stockholm, 17165, Solna, Sweden
- Department of Health, Blekinge Institute of Technology, Valhallavägen 1, Karlskrona, 37141, Blekinge, Sweden
| | - Ana Luiza Dallora
- Department of Health, Blekinge Institute of Technology, Valhallavägen 1, Karlskrona, 37141, Blekinge, Sweden
| | - Johan Sanmartin Berglund
- Department of Health, Blekinge Institute of Technology, Valhallavägen 1, Karlskrona, 37141, Blekinge, Sweden.
| | - Arif Ali
- Department of Computer Science, University of Science and Technology Bannu, Township, Bannu, 28100, Khyber-Pakhtunkhwa, Pakistan
| | - Liaqat Ali
- Department of Electrical Engineering, University of Science and Technology Bannu, Township, Bannu, 28100, Khyber-Pakhtunkhwa, Pakistan
| | - Peter Anderberg
- Department of Health, Blekinge Institute of Technology, Valhallavägen 1, Karlskrona, 37141, Blekinge, Sweden
- School of Health Sciences, University of Skovde, Högskolevägen 1, Skövde, SE-541 28, Skövde, Sweden
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43
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Smith AM, Obuchowski NA, Foster NL, Klein G, Mozley PD, Lammertsma AA, Wahl RL, Sunderland JJ, Vanderheyden JL, Benzinger TLS, Kinahan PE, Wong DF, Perlman ES, Minoshima S, Matthews D. The RSNA QIBA Profile for Amyloid PET as an Imaging Biomarker for Cerebral Amyloid Quantification. J Nucl Med 2023; 64:294-303. [PMID: 36137760 PMCID: PMC9902844 DOI: 10.2967/jnumed.122.264031] [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: 02/16/2022] [Revised: 08/05/2022] [Accepted: 08/05/2022] [Indexed: 02/04/2023] Open
Abstract
A standardized approach to acquiring amyloid PET images increases their value as disease and drug response biomarkers. Most 18F PET amyloid brain scans often are assessed only visually (per regulatory labels), with a binary decision indicating the presence or absence of Alzheimer disease amyloid pathology. Minimizing technical variance allows precise, quantitative SUV ratios (SUVRs) for early detection of β-amyloid plaques and allows the effectiveness of antiamyloid treatments to be assessed with serial studies. Methods: The Quantitative Imaging Biomarkers Alliance amyloid PET biomarker committee developed and validated a profile to characterize and reduce the variability of SUVRs, increasing statistical power for these assessments. Results: On achieving conformance, sites can justify a claim that brain amyloid burden reflected by the SUVR is measurable to a within-subject coefficient of variation of no more than 1.94% when the same radiopharmaceutical, scanner, acquisition, and analysis protocols are used. Conclusion: This overview explains the claim, requirements, barriers, and potential future developments of the profile to achieve precision in clinical and research amyloid PET imaging.
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Affiliation(s)
- Anne M Smith
- Siemens Medical Solutions USA, Inc., Knoxville, Tennessee;
| | | | - Norman L Foster
- Department of Neurology, University of Utah, Salt Lake City, Utah
| | | | - P David Mozley
- Weill Medical College of Cornell University, New York, New York
| | - Adriaan A Lammertsma
- Amsterdam Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centers, Location VUmc, Amsterdam, The Netherlands
- Medical Imaging Center, Department of Nuclear Medicine and Molecular Imaging, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Richard L Wahl
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, Missouri
- Department of Radiation Oncology, Washington University in Saint Louis, St. Louis, Missouri
| | - John J Sunderland
- Division of Nuclear Medicine, Department of Radiology, University of Iowa, Iowa City, Iowa
| | | | - Tammie L S Benzinger
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, Missouri
- Department of Radiation Oncology, Washington University in Saint Louis, St. Louis, Missouri
| | - Paul E Kinahan
- Department of Radiology, School of Medicine, University of Washington, Seattle, Washington
| | - Dean F Wong
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, Missouri
| | | | - Satoshi Minoshima
- Department of Radiology and Imaging Sciences, University of Utah, Salt Lake City, Utah; and
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Bonakdarpour B, Takarabe C. Brain Networks, Clinical Manifestations, and Neuroimaging of Cognitive Disorders: The Role of Computed Tomography (CT), Magnetic Resonance Imaging (MRI), Positron Emission Tomography (PET), and Other Advanced Neuroimaging Tests. Clin Geriatr Med 2023; 39:45-65. [PMID: 36404032 DOI: 10.1016/j.cger.2022.07.004] [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: 11/27/2022]
Abstract
In this article, we briefly discuss imaging modalities used in clinical settings for neuroanatomical characterization and for diagnosis of the underlying disease. We then discuss how each neuroimaging tool can be used in the context of clinical syndromes. The major underlying causes relevant to our discussion include Alzheimer disease, Lewy body disease, cerebrovascular disease, frontotemporal degeneration, autoimmune diseases, and systemic or metabolic derangements.
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Affiliation(s)
- Borna Bonakdarpour
- Mesulam Center for Cognitive Neurology and Alzheimer's Disease, Ken and Ruth Davee Department of Neurology, Northwestern University Feinberg School of Medicine.
| | - Clara Takarabe
- Mesulam Center for Cognitive Neurology and Alzheimer's Disease, Ken and Ruth Davee Department of Neurology, Northwestern University Feinberg School of Medicine
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45
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Kim J, Jang H, Park YH, Youn J, Seo SW, Kim HJ, Na DL. Motor Symptoms in Early- versus Late-Onset Alzheimer's Disease. J Alzheimers Dis 2023; 91:345-354. [PMID: 36404549 DOI: 10.3233/jad-220745] [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: 11/16/2022]
Abstract
BACKGROUND Age at onset was suggested as one possible risk factor for motor dysfunction in Alzheimer's disease (AD). OBJECTIVE We investigated the association of motor symptoms with cognition or neurodegeneration in patients with AD, and whether this association differs by the age at onset. METHODS We included 113 amyloid positive AD patients and divided them into early-onset AD (EOAD) and late-onset AD (LOAD), who underwent the Unified Parkinson's Disease Rating Scale (UPDRS)-Part III (=UPDRS) scoring, Mini-Mental State Examination (MMSE)/Clinical Deterioration Rating Sum-of-Boxes (CDR-SOB), and magnetic resonance image (MRI). Multiple linear regression was used to evaluate the association of UPDRS and MMSE/CDR-SOB or MRI neurodegeneration measures, and whether the association differs according to the group. RESULTS The prevalence of motor symptoms and their severity did not differ between the groups. Lower MMSE (β= -1.1, p < 0.001) and higher CDR-SOB (β= 2.0, p < 0.001) were significantly associated with higher UPDRS. There was no interaction effect between MMSE/CDR-SOB and AD group on UPDRS. Global or all regional cortical thickness and putaminal volume were negatively associated with UPDRS score, but the interaction effect of neurodegeneration and AD group on UPDRS score was significant only in parietal lobe (p for interaction = 0.035), which showed EOAD to have a more pronounced association between parietal thinning and motor symptoms. CONCLUSION Our study suggested that the severity of motor deterioration in AD is related to the severity of cognitive impairment itself rather than age at onset, and motor symptoms might occur through multiple mechanisms including cortical and subcortical atrophy.
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Affiliation(s)
- Jinhee Kim
- Departments of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Hyemin Jang
- Departments of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.,Samsung Alzheimers Convergence Research Center, Samsung Medical Center, Seoul, Korea.,Neuroscience Center, Samsung Medical Center, Seoul, Korea
| | - Yu-Hyun Park
- Departments of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.,Samsung Alzheimers Convergence Research Center, Samsung Medical Center, Seoul, Korea
| | - Jinyoung Youn
- Departments of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.,Neuroscience Center, Samsung Medical Center, Seoul, Korea
| | - Sang Won Seo
- Departments of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.,Samsung Alzheimers Convergence Research Center, Samsung Medical Center, Seoul, Korea.,Neuroscience Center, Samsung Medical Center, Seoul, Korea.,Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, Korea.,Department of Clinical Research Design & Evaluation, SAIHST, Sungkyunkwan University, Seoul, Korea
| | - Hee Jin Kim
- Departments of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.,Samsung Alzheimers Convergence Research Center, Samsung Medical Center, Seoul, Korea.,Neuroscience Center, Samsung Medical Center, Seoul, Korea
| | - Duk L Na
- Department of Neurology, Sungkyunkwan University, Seoul, Korea
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Caprioglio C, Ribaldi F, Visser LNC, Minguillon C, Collij LE, Grau-Rivera O, Zeyen P, Molinuevo JL, Gispert JD, Garibotto V, Moro C, Walker Z, Edison P, Demonet JF, Barkhof F, Scheltens P, Alves IL, Gismondi R, Farrar G, Stephens AW, Jessen F, Frisoni GB, Altomare D. Analysis of Psychological Symptoms Following Disclosure of Amyloid-Positron Emission Tomography Imaging Results to Adults With Subjective Cognitive Decline. JAMA Netw Open 2023; 6:e2250921. [PMID: 36637820 PMCID: PMC9857261 DOI: 10.1001/jamanetworkopen.2022.50921] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/14/2023] Open
Abstract
IMPORTANCE Individuals who are amyloid-positive with subjective cognitive decline and clinical features increasing the likelihood of preclinical Alzheimer disease (SCD+) are at higher risk of developing dementia. Some individuals with SCD+ undergo amyloid-positron emission tomography (PET) as part of research studies and frequently wish to know their amyloid status; however, the disclosure of a positive amyloid-PET result might have psychological risks. OBJECTIVE To assess the psychological outcomes of the amyloid-PET result disclosure in individuals with SCD+ and explore which variables are associated with a safer disclosure in individuals who are amyloid positive. DESIGN, SETTING, AND PARTICIPANTS This prospective, multicenter study was conducted as part of The Amyloid Imaging to Prevent Alzheimer Disease Diagnostic and Patient Management Study (AMYPAD-DPMS) (recruitment period: from April 2018 to October 2020). The setting was 5 European memory clinics, and participants included patients with SCD+ who underwent amyloid-PET. Statistical analysis was performed from July to October 2022. EXPOSURES Disclosure of amyloid-PET result. MAIN OUTCOMES AND MEASURES Psychological outcomes were defined as (1) disclosure related distress, assessed using the Impact of Event Scale-Revised (IES-R; scores of at least 33 indicate probable presence of posttraumatic stress disorder [PTSD]); and (2) anxiety and depression, assessed using the Hospital Anxiety and Depression scale (HADS; scores of at least 15 indicate probable presence of severe mood disorder symptoms). RESULTS After disclosure, 27 patients with amyloid-positive SCD+ (median [IQR] age, 70 [66-74] years; gender: 14 men [52%]; median [IQR] education: 15 [13 to 17] years, median [IQR] Mini-Mental State Examination [MMSE] score, 29 [28 to 30]) had higher median (IQR) IES-R total score (10 [2 to 14] vs 0 [0 to 2]; P < .001), IES-R avoidance (0.00 [0.00 to 0.69] vs 0.00 [0.00 to 0.00]; P < .001), IES-R intrusions (0.50 [0.13 to 0.75] vs 0.00 [0.00 to 0.25]; P < .001), and IES-R hyperarousal (0.33 [0.00 to 0.67] vs 0.00 [0.00 to 0.00]; P < .001) scores than the 78 patients who were amyloid-negative (median [IQR], age, 67 [64 to 74] years, 45 men [58%], median [IQR] education: 15 [12 to 17] years, median [IQR] MMSE score: 29 [28 to 30]). There were no observed differences between amyloid-positive and amyloid-negative patients in the median (IQR) HADS Anxiety (-1.0 [-3.0 to 1.8] vs -2.0 [-4.8 to 1.0]; P = .06) and Depression (-1.0 [-2.0 to 0.0] vs -1.0 [-3.0 to 0.0]; P = .46) deltas (score after disclosure - scores at baseline). In patients with amyloid-positive SCD+, despite the small sample size, higher education was associated with lower disclosure-related distress (ρ = -0.43; P = .02) whereas the presence of study partner was associated with higher disclosure-related distress (W = 7.5; P = .03). No participants with amyloid-positive SCD+ showed probable presence of PTSD or severe anxiety or depression symptoms at follow-up. CONCLUSIONS AND RELEVANCE The disclosure of a positive amyloid-PET result to patients with SCD+ was associated with a bigger psychological change, yet such change did not reach the threshold for clinical concern.
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Affiliation(s)
- Camilla Caprioglio
- Laboratory of Neuroimaging of Aging (LANVIE), University of Geneva, Geneva, Switzerland
- Geneva Memory Center, Geneva University Hospitals, Geneva, Switzerland
| | - Federica Ribaldi
- Laboratory of Neuroimaging of Aging (LANVIE), University of Geneva, Geneva, Switzerland
- Geneva Memory Center, Geneva University Hospitals, Geneva, Switzerland
| | - Leonie N. C. Visser
- Division of Clinical Geriatrics, Department of Neurobiology, Care Sciences and Society, Center for Alzheimer Research, Karolinska Institutet, Stockholm/Solna, Sweden
- Department of Medical Psychology, Amsterdam Public Health Research Institute, Amsterdam University Medical Centers (UMC)–Location VUmc, Amsterdam, the Netherlands
| | - Carolina Minguillon
- Barcelonaßeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain
- IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain
- Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (CIBERFES), Madrid, Spain
| | - Lyduine E. Collij
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centers (UMC)–Location VUmc, Amsterdam, the Netherlands
| | - Oriol Grau-Rivera
- Barcelonaßeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain
- IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain
- Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (CIBERFES), Madrid, Spain
| | - Philip Zeyen
- Department of Psychiatry, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - José Luis Molinuevo
- Barcelonaßeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain
- H. Lundbeck A/S, Denmark
| | - Juan Domingo Gispert
- Barcelonaßeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain
- IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain
- Centro de Investigación Biomédica en Red Bioingeniería, Biomateriales y Nanomedicina, (CIBER-BBN), Barcelona, Spain
| | - Valentina Garibotto
- Laboratory of Neuroimaging and Innovative Molecular Tracers (NIMTlab), Geneva University Neurocenter and Faculty of Medicine, University of Geneva, Geneva, Switzerland
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospitals, Geneva, Switzerland
- Center for Biomedical Imaging (CIBM), Geneva, Switzerland
| | - Christian Moro
- Laboratory of Neuroimaging of Aging (LANVIE), University of Geneva, Geneva, Switzerland
- Geneva Memory Center, Geneva University Hospitals, Geneva, Switzerland
| | - Zuzana Walker
- Division of Psychiatry, University College London, London, United Kingdom
- Margaret’s Hospital, Essex Partnership University NHS Foundation Trust, Essex, United Kingdom
| | - Paul Edison
- Division of Neurology, Department of Brain Sciences, Imperial College London, United Kingdom
| | | | - Frederik Barkhof
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centers (UMC)–Location VUmc, Amsterdam, the Netherlands
- Queen Square Institute of Neurology and Centre for Medical Image Computing, University College London, London, United Kingdom
| | - Philip Scheltens
- Alzheimer Center, Department of Neurology, Amsterdam University Medical Centers (UMC)–Location VUmc, Amsterdam, the Netherlands
| | - Isadora Lopes Alves
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centers (UMC)–Location VUmc, Amsterdam, the Netherlands
| | | | | | | | - Frank Jessen
- Laboratory of Neuroimaging and Innovative Molecular Tracers (NIMTlab), Geneva University Neurocenter and Faculty of Medicine, University of Geneva, Geneva, Switzerland
- German Center for Neurodegenerative Diseases (DZNE), Bonn-Cologne, Germany
- Excellence Cluster Cellular Stress Responses in Aging-Related Diseases (CECAD), Medical Faculty, University of Cologne, Germany
| | - Giovanni B. Frisoni
- Laboratory of Neuroimaging of Aging (LANVIE), University of Geneva, Geneva, Switzerland
- Geneva Memory Center, Geneva University Hospitals, Geneva, Switzerland
| | - Daniele Altomare
- Laboratory of Neuroimaging of Aging (LANVIE), University of Geneva, Geneva, Switzerland
- Geneva Memory Center, Geneva University Hospitals, Geneva, Switzerland
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Couch E, Belanger E, Gadbois EA, DePasquale N, Zhang W, Wetle T. "I know that my role is going to change": a mixed-methods study of the relationship between amyloid-β PET scan results and caregiver burden. Aging Clin Exp Res 2023; 35:387-397. [PMID: 36484946 PMCID: PMC9735001 DOI: 10.1007/s40520-022-02314-6] [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: 09/20/2022] [Accepted: 11/27/2022] [Indexed: 12/13/2022]
Abstract
BACKGROUND Caregiver burden consists of disease specific and perceived stressors, respectively referred to as objective and subjective indicators of burden, and is associated with negative outcomes. Previous research has found that care partners to persons living with cognitive impairment and elevated levels of amyloid-β, as measured by a positron emission tomography (PET) scan, may experience caregiver burden. AIMS To elucidate the relationship between amyloid scan results and subjective and objective indicators of burden. METHODS A parallel mixed-methods design using survey data from 1338 care partners to persons with mild cognitive impairment (MCI) and dementia who received an amyloid scan from the CARE-IDEAS study; and semi-structured interviews with a subsample of 62 care partners. Logistic regression models were used to investigate objective factors associated with caregiver burden. A thematic analysis of semi-structured interviews was used to investigate subjective indicators by exploring care partners' perceptions of their role following an amyloid scan. RESULTS Elevated amyloid was not associated with burden. However, the scan result influenced participants perceptions of their caregiving role and coping strategies. Care partners to persons with elevated amyloid expected increasing responsibility, whereas partners to persons without elevated amyloid and mild cognitive impairment did not anticipate changes to their role. Care partners to persons with elevated amyloid reported using knowledge gained from the scan to develop coping strategies. All care partners described needing practical and emotional support. CONCLUSIONS Amyloid scans can influence subjective indicators of burden and present the opportunity to identify and address care partners' support needs.
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Affiliation(s)
- Elyse Couch
- grid.40263.330000 0004 1936 9094Center for Gerontology and Healthcare Research, Brown University School of Public Health, Center for Gerontology and Healthcare Research, Providence, RI USA
| | - Emmanuelle Belanger
- grid.40263.330000 0004 1936 9094Center for Gerontology and Healthcare Research, Brown University School of Public Health, Center for Gerontology and Healthcare Research, Providence, RI USA ,grid.40263.330000 0004 1936 9094Department of Health Services, Brown University School of Public Health, Policy & Practice, Providence, RI USA
| | - Emily A. Gadbois
- grid.40263.330000 0004 1936 9094Center for Gerontology and Healthcare Research, Brown University School of Public Health, Center for Gerontology and Healthcare Research, Providence, RI USA
| | - Nicole DePasquale
- grid.26009.3d0000 0004 1936 7961Division of General Internal Medicine, Duke University School of Medicine, Durham, NC USA
| | - Wenhan Zhang
- grid.26009.3d0000 0004 1936 7961Department of Population Health Sciences, Duke University School of Medicine, Durham, NC USA
| | - Terrie Wetle
- grid.40263.330000 0004 1936 9094Center for Gerontology and Healthcare Research, Brown University School of Public Health, Center for Gerontology and Healthcare Research, Providence, RI USA
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Liu M, Xie X, Xie J, Tian S, Du X, Feng H, Zhang H. Early-onset Alzheimer's disease with depression as the first symptom: a case report with literature review. Front Psychiatry 2023; 14:1192562. [PMID: 37181906 PMCID: PMC10174310 DOI: 10.3389/fpsyt.2023.1192562] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Accepted: 04/05/2023] [Indexed: 05/16/2023] Open
Abstract
Background Alzheimer's disease is a common neurodegenerative disease, and patients with early-onset Alzheimer's disease (onset age < 65 years) often have atypical symptoms, which are easily misdiagnosed and missed. Multimodality neuroimaging has become an important diagnostic and follow-up method for AD with its non-invasive and quantitative advantages. Case presentation We report a case of a 59-year-old female with a diagnosis of depression at the age of 50 after a 46-year-old onset and a 9-year follow-up observation, who developed cognitive dysfunction manifested by memory loss and disorientation at the age of 53, and eventually developed dementia. Combined with neuropsychological scales (MMSE and MOCA scores decreased year by year and finally reached the dementia criteria) and the application of multimodal imaging. MRI showed that the hippocampus atrophied year by year and the cerebral cortex was extensively atrophied. 18F-FDG PET image showed hypometabolism in right parietal lobes, bilateral frontal lobes, bilateral joint parieto-temporal areas, and bilateral posterior cingulate glucose metabolism. The 18F-AV45 PET image showed the diagnosis of early-onset Alzheimer's disease was confirmed by the presence of Aβ deposits in the cerebral cortex. Conclusion Early-onset Alzheimer's disease, which starts with depression, often has atypical symptoms and is prone to misdiagnosis. The combination of neuropsychological scales and neuroimaging examinations are good screening tools that can better assist in the early diagnosis of Alzheimer's disease. Graphical Abstract.
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Affiliation(s)
- Meichen Liu
- Department of Neurology, The First Affiliated Hospital, Dalian Medical University, Dalian, China
| | - Xueting Xie
- Department of Neurology, The First Affiliated Hospital, Dalian Medical University, Dalian, China
| | - Jinghui Xie
- Department of Nuclear Medicine, The First Affiliated Hospital, Dalian Medical University, Dalian, China
| | - Shiyun Tian
- Department of Radiology, The First Affiliated Hospital, Dalian Medical University, Dalian, China
| | - Xuemei Du
- Department of Nuclear Medicine, The First Affiliated Hospital, Dalian Medical University, Dalian, China
| | - Hongbo Feng
- Department of Radiology, The First Affiliated Hospital, Dalian Medical University, Dalian, China
| | - Huimin Zhang
- Department of Neurology, The First Affiliated Hospital, Dalian Medical University, Dalian, China
- *Correspondence: Huimin Zhang,
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Nallapu BT, Petersen KK, Lipton RB, Grober E, Sperling RA, Ezzati A. Association of Alcohol Consumption with Cognition in Older Population: The A4 Study. J Alzheimers Dis 2023; 93:1381-1393. [PMID: 37182868 PMCID: PMC10392870 DOI: 10.3233/jad-221079] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/16/2023]
Abstract
BACKGROUND Alcohol use disorders have been categorized as a 'strongly modifiable' risk factor for dementia. OBJECTIVE To investigate the cross-sectional association between alcohol consumption and cognition in older adults and if it is different across sexes or depends on amyloid-β (Aβ) accumulation in the brain. METHODS Cognitively unimpaired older adults (N = 4387) with objective and subjective cognitive assessments and amyloid positron emission tomography (PET) imaging were classified into four categories based on their average daily alcohol use. Multivariable linear regression was then used to test the main effects and interactions with sex and Aβ levels. RESULTS Individuals who reported no alcohol consumption had lower scores on the Preclinical Alzheimer Cognitive Composite (PACC) compared to those consuming one or two drinks/day. In sex-stratified analysis, the association between alcohol consumption and cognition was more prominent in females. Female participants who consumed two drinks/day had better performance on PACC and Cognitive Function Index (CFI) than those who reported no alcohol consumption. In an Aβ-stratified sample, the association between alcohol consumption and cognition was present only in the Aβ- subgroup. The interaction between Aβ status and alcohol consumption on cognition was not significant. CONCLUSION Low or moderate consumption of alcohol was associated with better objective cognitive performance and better subjective report of daily functioning in cognitively unimpaired individuals. The association was present only in Aβ- individuals, suggesting that the pathophysiologic mechanism underlying the effect of alcohol on cognition is independent of Aβ pathology. Further investigation is required with larger samples consuming three or more drinks/day.
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Affiliation(s)
- Bhargav T. Nallapu
- Saul B. Korey Department of Neurology, Albert Einstein College of Medicine, New York City, NY, USA
| | - Kellen K. Petersen
- Saul B. Korey Department of Neurology, Albert Einstein College of Medicine, New York City, NY, USA
| | - Richard B. Lipton
- Saul B. Korey Department of Neurology, Albert Einstein College of Medicine, New York City, NY, USA
| | - Ellen Grober
- Saul B. Korey Department of Neurology, Albert Einstein College of Medicine, New York City, NY, USA
| | - Reisa A. Sperling
- Harvard Aging Brain Study, 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, Boston, MA, USA
| | - Ali Ezzati
- Saul B. Korey Department of Neurology, Albert Einstein College of Medicine, New York City, NY, USA
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50
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Pascoal TA, Leuzy A, Therriault J, Chamoun M, Lussier F, Tissot C, Strandberg O, Palmqvist S, Stomrud E, Ferreira PCL, Ferrari‐Souza JP, Smith R, Benedet AL, Gauthier S, Hansson O, Rosa‐Neto P. Discriminative accuracy of the A/T/N scheme to identify cognitive impairment due to Alzheimer's disease. ALZHEIMER'S & DEMENTIA (AMSTERDAM, NETHERLANDS) 2023; 15:e12390. [PMID: 36733847 PMCID: PMC9886860 DOI: 10.1002/dad2.12390] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Revised: 11/09/2022] [Accepted: 11/29/2022] [Indexed: 02/03/2023]
Abstract
Introduction The optimal combination of amyloid-β/tau/neurodegeneration (A/T/N) biomarker profiles for the diagnosis of Alzheimer's disease (AD) dementia is unclear. Methods We examined the discriminative accuracy of A/T/N combinations assessed with neuroimaging biomarkers for the differentiation of AD from cognitively unimpaired (CU) elderly and non-AD neurodegenerative diseases in the TRIAD, BioFINDER-1 and BioFINDER-2 cohorts (total n = 832) using area under the receiver operating characteristic curves (AUC). Results For the diagnosis of AD dementia (vs. CU elderly), T biomarkers performed as well as the complete A/T/N system (AUC range: 0.90-0.99). A and T biomarkers in isolation performed as well as the complete A/T/N system in differentiating AD dementia from non-AD neurodegenerative diseases (AUC range; A biomarker: 0.84-1; T biomarker: 0.83-1). Discussion In diagnostic settings, the use of A or T neuroimaging biomarkers alone can reduce patient burden and medical costs compared with using their combination, without significantly compromising accuracy.
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Affiliation(s)
- Tharick A. Pascoal
- Department of PsychiatrySchool of MedicineUniversity of PittsburghPittsburghPennsylvaniaUSA
- Department of NeurologySchool of MedicineUniversity of PittsburghPittsburghPennsylvaniaUSA
- Translational Neuroimaging LaboratoryThe McGill University Research Centre for Studies in AgingDepartment of Neurology and NeurosurgeryFaculty of MedicineMcGill UniversityMontrealQuébecCanada
| | - Antoine Leuzy
- Clinical Memory Research UnitDepartment of Clinical SciencesLund UniversityLundSweden
| | - Joseph Therriault
- Translational Neuroimaging LaboratoryThe McGill University Research Centre for Studies in AgingDepartment of Neurology and NeurosurgeryFaculty of MedicineMcGill UniversityMontrealQuébecCanada
- Montreal Neurological InstituteMcGill UniversityMontrealQuébecCanada
| | - Mira Chamoun
- Translational Neuroimaging LaboratoryThe McGill University Research Centre for Studies in AgingDepartment of Neurology and NeurosurgeryFaculty of MedicineMcGill UniversityMontrealQuébecCanada
- Montreal Neurological InstituteMcGill UniversityMontrealQuébecCanada
| | - Firoza Lussier
- Department of PsychiatrySchool of MedicineUniversity of PittsburghPittsburghPennsylvaniaUSA
- Translational Neuroimaging LaboratoryThe McGill University Research Centre for Studies in AgingDepartment of Neurology and NeurosurgeryFaculty of MedicineMcGill UniversityMontrealQuébecCanada
| | - Cecile Tissot
- Department of NeurologySchool of MedicineUniversity of PittsburghPittsburghPennsylvaniaUSA
- Clinical Memory Research UnitDepartment of Clinical SciencesLund UniversityLundSweden
| | - Olof Strandberg
- Clinical Memory Research UnitDepartment of Clinical SciencesLund UniversityLundSweden
- Memory ClinicSkåne University HospitalLundSweden
| | - Sebastian Palmqvist
- Clinical Memory Research UnitDepartment of Clinical SciencesLund UniversityLundSweden
- Memory ClinicSkåne University HospitalLundSweden
| | - Erik Stomrud
- Clinical Memory Research UnitDepartment of Clinical SciencesLund UniversityLundSweden
- Memory ClinicSkåne University HospitalLundSweden
| | - Pamela C. L. Ferreira
- Department of PsychiatrySchool of MedicineUniversity of PittsburghPittsburghPennsylvaniaUSA
| | - João Pedro Ferrari‐Souza
- Department of PsychiatrySchool of MedicineUniversity of PittsburghPittsburghPennsylvaniaUSA
- Graduate Program in Biological Sciences: BiochemistryUniversidade Federal do Rio Grande do SulPorto AlegreRSBrazil
| | - Ruben Smith
- Clinical Memory Research UnitDepartment of Clinical SciencesLund UniversityLundSweden
- Memory ClinicSkåne University HospitalLundSweden
| | - Andrea Lessa Benedet
- Translational Neuroimaging LaboratoryThe McGill University Research Centre for Studies in AgingDepartment of Neurology and NeurosurgeryFaculty of MedicineMcGill UniversityMontrealQuébecCanada
- Montreal Neurological InstituteMcGill UniversityMontrealQuébecCanada
| | - Serge Gauthier
- Translational Neuroimaging LaboratoryThe McGill University Research Centre for Studies in AgingDepartment of Neurology and NeurosurgeryFaculty of MedicineMcGill UniversityMontrealQuébecCanada
| | - Oskar Hansson
- Clinical Memory Research UnitDepartment of Clinical SciencesLund UniversityLundSweden
- Memory ClinicSkåne University HospitalLundSweden
| | - Pedro Rosa‐Neto
- Translational Neuroimaging LaboratoryThe McGill University Research Centre for Studies in AgingDepartment of Neurology and NeurosurgeryFaculty of MedicineMcGill UniversityMontrealQuébecCanada
- Montreal Neurological InstituteMcGill UniversityMontrealQuébecCanada
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