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Rabinovici GD, Selkoe DJ, Schindler SE, Aisen P, Apostolova LG, Atri A, Greenberg SM, Hendrix SB, Petersen RC, Weiner M, Salloway S, Cummings J. Donanemab: Appropriate use recommendations. J Prev Alzheimers Dis 2025; 12:100150. [PMID: 40155270 DOI: 10.1016/j.tjpad.2025.100150] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2025] [Revised: 03/16/2025] [Accepted: 03/17/2025] [Indexed: 04/01/2025]
Abstract
Donanemab (Kisunla®), an IgG1 monoclonal antibody targeting N-terminal pyroglutamate-modified forms of amyloid-β, is approved in the United States for treatment of early symptomatic Alzheimer's disease (AD). Appropriate Use Recommendations (AUR) were developed to guide the implementation of donanemab in real-world practice, prioritizing safety considerations and opportunity for effectiveness. The AUR were developed by the AD and Related Disorders Therapeutic Workgroup by consensus, integrating available data and expert opinion. Appropriate candidates for donanemab treatment include persons with mild cognitive impairment or mild dementia due to AD (Clinical Stages 3-4, MMSE 20-30) who have biomarker confirmation of AD pathology by PET or CSF. Tau PET is not required for eligibility. Apolipoprotein E (APOE) genotyping should be performed prior to treatment to inform an individual's risk of developing Amyloid-Related Imaging Abnormalities (ARIA). Pre-treatment MRI should be obtained no more than 12 months prior to treatment. Patients with findings of >4 cerebral microbleeds, cortical superficial siderosis or a major vascular contribution to cognitive impairment should be excluded from treatment. The decision to initiate therapy should be grounded in a shared decision-making process that emphasizes the patient's values and goals of care. Donanemab is administered as a monthly intravenous infusion. Surveillance MRIs to evaluate for ARIA should be performed prior to the 2nd, 3rd, 4th and 7th infusions, prior to the 12th dose in higher risk individuals, and at any time ARIA is suspected clinically. Clinicians may consider discontinuing treatment if amyloid clearance is demonstrated by amyloid PET, typically obtained 12-18 months after initiating treatment.
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Affiliation(s)
- G D Rabinovici
- Memory & Aging Center, Departments of Neurology, Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA.
| | - D J Selkoe
- Ann Romney Center for Neurologic Diseases, Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - S E Schindler
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
| | - P Aisen
- Alzheimer's Treatment Research Institute, University of Southern California, San Diego, CA, USA
| | - L G Apostolova
- Departments of Neurology, Radiology, Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - A Atri
- Banner Sun Health Research Institute, Banner Health, Sun City, AZ, USA; Center for Brain/Mind Medicine, Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - S M Greenberg
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | | | - R C Petersen
- Department of Neurology, Mayo Clinic, Rochester, MN, USA
| | - M Weiner
- Departments of Radiology and Biomedical Imaging, Medicine, Psychiatry and Neurology, University of California San Francisco, San Francisco, CA, USA
| | - S Salloway
- Butler Hospital and Warren Alpert Medical School of Brown University, Providence RI, USA
| | - J Cummings
- Chambers-Grundy Center for Transformative Neuroscience, Department of Brain Health, Kirk Kerkorian School of Medicine, University of Nevada Las Vegas, Las Vegas, NV, USA
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Pierce ME, Logue M, Sherva R, Miller M, Huber BR, Milberg W, Hayes JP. Association of Alzheimer's disease genetic risk with age-dependent changes in plasma amyloid-β 42:40 in Veterans. J Alzheimers Dis 2025; 104:1006-1012. [PMID: 40084666 DOI: 10.1177/13872877251321183] [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: 03/16/2025]
Abstract
BackgroundIdentifying biomarkers of Alzheimer's disease (AD) is critical for early diagnosis and AD risk assessment.ObjectiveWe examined the hypothesis that the plasma amyloid-β 42 and 40 (Aβ42:40) ratio has a curvilinear relationship with age among individuals who are at higher genetic risk for AD.MethodsThis study investigated the relationship between plasma amyloid-β 42 and 40 (Aβ42:40) ratio and age in 315 men and women Veterans, including those at genetic risk for AD. Hierarchical regression models investigated linear and nonlinear relationships between age, genetic risk, and Aβ42:40.ResultsWe observed a curvilinear relationship between age and Aβ42:40 in individuals with higher genetic risk, characterized by an increase in the Aβ42:40 during midlife followed by a decrease in older age.ConclusionsThese findings highlight distinct patterns in Aβ metabolism among genetically predisposed individuals, suggesting that early metabolic shifts may play a role in the progression of AD. Understanding these nuanced changes is essential for refining the use of Aβ42:40 ratio as a biomarker, potentially leading to more accurate risk stratification and earlier intervention strategies in AD.
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Affiliation(s)
- Meghan E Pierce
- National Center for PTSD, Behavioral Sciences Division, VA Boston Healthcare System, Boston, MA, USA
- Department of Psychiatry, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
| | - Mark Logue
- National Center for PTSD, Behavioral Sciences Division, VA Boston Healthcare System, Boston, MA, USA
- Department of Psychiatry, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
- Biomedical Genetics, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Richard Sherva
- Biomedical Genetics, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
| | - Mark Miller
- National Center for PTSD, Behavioral Sciences Division, VA Boston Healthcare System, Boston, MA, USA
- Department of Psychiatry, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
| | - Bertrand R Huber
- National Center for PTSD, Behavioral Sciences Division, VA Boston Healthcare System, Boston, MA, USA
- Department of Neurology, Boston University School of Medicine, Boston, MA, USA
- Department of Pathology and Laboratory Medicine, VA Boston Healthcare System, Boston, MA, USA
| | - William Milberg
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
- Translational Research Center for TBI and Stress Disorders (TRACTS), VA Boston Healthcare System, Boston, MA, USA
- Geriatric Research, Education and Clinical Center (GRECC), VA Boston, Healthcare System, Boston, MA, USA
| | - Jasmeet P Hayes
- Department of Psychology, The Ohio State University, & Chronic Brain Injury Program, The Ohio State University, Columbus, OH, USA
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Wu CYC, Zhang Y, Howard P, Huang F, Lee RHC. ACSL3 is a promising therapeutic target for alleviating anxiety and depression in Alzheimer's disease. GeroScience 2025; 47:2383-2397. [PMID: 39532829 PMCID: PMC11978576 DOI: 10.1007/s11357-024-01424-5] [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: 08/15/2024] [Accepted: 10/31/2024] [Indexed: 11/16/2024] Open
Abstract
Alzheimer's disease (AD), the leading cause of dementia, affects over 55 million people worldwide and is often accompanied by depression and anxiety. Both significantly impact patients' quality of life and impose substantial societal and economic burdens on healthcare systems. Identifying the complex regulatory mechanisms that contribute to the psychological and emotional deficits in AD will provide promising therapeutic targets. Biosynthesis of omega-3 (ω3) and omega-6 fatty acids (ω6-FA) through long-chain acyl-CoA synthetases (ACSL) is crucial for cell function and survival. This is due to ω3/6-FA's imperative role in modulating the plasma membrane, energy production, and inflammation. While ACSL dysfunction is known to cause heart, liver, and kidney diseases, the role of ACSL in pathological conditions in the central nervous system (e.g., depression and anxiety) remains largely unexplored. The impact of ACSLs on AD-related depression and anxiety was investigated in a mouse model of Alzheimer's disease (3xTg-AD). ACSL3 levels were significantly reduced in the hippocampus of aged 3xTg-AD mice (via capillary-based immunoassay). This reduction in ACAL3 was closely associated with increased depression and anxiety-like behavior (via forced swim, tail suspension, elevated plus maze, and sucrose preference test). Upregulation of ACSL3 via adenovirus in aged 3xTg-AD mice led to increased protein levels of brain-derived neurotrophic factor (BDNF) and vascular endothelial growth factor C (VEGF-C) (via brain histology, capillary-based immunoassay), resulting in alleviation of depression and anxiety symptoms. The present study highlights a novel neuroprotective role of ACSL3 in the brain. Targeting ACSL3 will offer an innovative approach for treating AD-related depression and anxiety.
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Affiliation(s)
- Celeste Yin-Chieh Wu
- Department of Neurology, Louisiana State University Health, LSU Health Sciences Center Shreveport, 1501 Kings Hwy, Shreveport, LA, 71103-3932, USA.
- Institute for Cerebrovascular and Neuroregeneration Research, Louisiana State University Health, Shreveport, LA, USA.
| | - Yulan Zhang
- Department of Neurology, Louisiana State University Health, LSU Health Sciences Center Shreveport, 1501 Kings Hwy, Shreveport, LA, 71103-3932, USA
- Institute for Cerebrovascular and Neuroregeneration Research, Louisiana State University Health, Shreveport, LA, USA
| | - Peyton Howard
- Department of Neurology, Louisiana State University Health, LSU Health Sciences Center Shreveport, 1501 Kings Hwy, Shreveport, LA, 71103-3932, USA
- Institute for Cerebrovascular and Neuroregeneration Research, Louisiana State University Health, Shreveport, LA, USA
| | - Fang Huang
- Department of Neurology, Louisiana State University Health, LSU Health Sciences Center Shreveport, 1501 Kings Hwy, Shreveport, LA, 71103-3932, USA
- Institute for Cerebrovascular and Neuroregeneration Research, Louisiana State University Health, Shreveport, LA, USA
| | - Reggie Hui-Chao Lee
- Department of Neurology, Louisiana State University Health, LSU Health Sciences Center Shreveport, 1501 Kings Hwy, Shreveport, LA, 71103-3932, USA
- Institute for Cerebrovascular and Neuroregeneration Research, Louisiana State University Health, Shreveport, LA, USA
- Department of Cellular Biology and Anatomy, Louisiana State University Health, Shreveport, LA, USA
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Iaccarino L, Burnham SC, Tunali I, Wang J, Navitsky M, Arora AK, Pontecorvo MJ. A practical overview of the use of amyloid-PET Centiloid values in clinical trials and research. Neuroimage Clin 2025; 46:103765. [PMID: 40101674 PMCID: PMC11960669 DOI: 10.1016/j.nicl.2025.103765] [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: 09/26/2024] [Revised: 02/25/2025] [Accepted: 03/04/2025] [Indexed: 03/20/2025]
Abstract
The density of brain amyloid-beta neuritic plaque accumulation, a marker of Alzheimer's disease (AD), can be visualized and quantified using amyloid-positron emission tomography (PET). Amyloid-PET data can be obtained using different tracers and methodologies; therefore, comparison across studies can be difficult. The introduction of Centiloids in 2015 allowed for the transformation of amyloid-PET quantitative data to a common scale, enhancing comparability across studies and potentially enabling pooled analysis. Since then, Centiloid values have been used increasingly in research and clinical trials for multiple purposes, being tested and validated with a variety of clinical, biomarker and pathological standards of truth. In clinical trials, Centiloid values have been used for patient selection, to confirm the presence of AD pathology, as well as for treatment monitoring, especially in trials of disease-modifying treatments such as amyloid-targeting therapies. Building on their widespread adoption, Centiloid values are increasingly being integrated into commercially available software solutions for quantifying amyloid-PET, paving the way for real-world applications at the community level. This article addresses frequently asked questions about Centiloid definition, implementation, interpretation, and caveats, and also summarizes the available literature on published thresholds, ultimately supporting wider access and informed use of Centiloid values in Alzheimer's disease research.
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Affiliation(s)
- Leonardo Iaccarino
- Eli Lilly and Company, Lilly Corporate Center, Indianapolis, IN 46285, USA; Eli Lilly Italia S.p.A., Sesto Fiorentino FI 50019, Italy.
| | - Samantha C Burnham
- Eli Lilly and Company, Lilly Corporate Center, Indianapolis, IN 46285, USA.
| | - Ilke Tunali
- Eli Lilly and Company, Lilly Corporate Center, Indianapolis, IN 46285, USA.
| | - Jian Wang
- Eli Lilly and Company, Lilly Corporate Center, Indianapolis, IN 46285, USA.
| | - Michael Navitsky
- Eli Lilly and Company, Lilly Corporate Center, Indianapolis, IN 46285, USA.
| | - Anupa K Arora
- Eli Lilly and Company, Lilly Corporate Center, Indianapolis, IN 46285, USA.
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Sewell KR, Oberlin LE, Karikari TK, Olvera‐Rojas M, Wan L, Morris JK, Kueck PJ, Zeng X, Huang H, Grove G, Chen Y, Lafferty TK, Sehrawat A, Kamboh MI, Marsland AL, Kramer AF, McAuley E, Burns JM, Hillman CH, Vidoni ED, Kang C, Erickson KI. Blood biomarkers differentiate AD-related versus non-AD-related cognitive deficits. Alzheimers Dement 2025; 21:e14619. [PMID: 40110626 PMCID: PMC11923558 DOI: 10.1002/alz.14619] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2024] [Revised: 01/12/2025] [Accepted: 01/14/2025] [Indexed: 03/22/2025]
Abstract
INTRODUCTION The utility of blood-based biomarkers for discriminating Alzheimer's disease (AD)-related versus non-AD-related cognitive deficits in preclinical populations remains poorly understood. Here, we tested the capability of blood markers to detect and discriminate variation in performance across multiple cognitive domains in a cognitively unimpaired sample. METHODS Participants (n = 648, aged 69.9 ± 3.8, 71% female) underwent a comprehensive cognitive assessment and assays for plasma-based biomarkers amyloid beta (Aβ)1-42/1-40 by mass spectrometry, phosphorylated tau (p-tau) 181 and 217, p-tau217/Aβ1-42, glial fibrillary acidic protein (GFAP), and neurofilament light (NfL). RESULTS Greater p-tau217 was exclusively associated with poorer episodic memory performance (β = -0.11, SE = 0.04, p = .003), and remained so after covarying for NfL. Higher NfL was non-specifically associated with poorer performance across a range of cognitive domains and remained so after covarying for p-tau217. DISCUSSION Blood-based biomarkers may differentiate non-AD-related versus AD-related cognitive deficits. This characterization will be important for early intervention and disease monitoring for AD. HIGHLIGHTS There is heterogeneity in the causes of cognitive decline in aging. AD-related blood biomarkers may help characterize these causes. Elevated p-tau217 was exclusively associated with poorer episodic memory. Elevated NfL was associated with poorer cognition in a broad range of domains. Blood biomarkers may help differentiate AD- and non-AD-related cognitive deficits.
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Affiliation(s)
- Kelsey R. Sewell
- AdventHealth Research InstituteNeuroscienceOrlandoFloridaUSA
- Centre for Healthy AgeingHealth Futures InstituteMurdoch UniversityMurdochWestern AustraliaAustralia
| | - Lauren E. Oberlin
- AdventHealth Research InstituteNeuroscienceOrlandoFloridaUSA
- Weill Cornell Institute of Geriatric PsychiatryWeill Cornell MedicineWhite PlainsNew YorkUSA
| | - Thomas K. Karikari
- Department of PsychiatrySchool of MedicineUniversity of PittsburghPittsburghPennsylvaniaUSA
| | - Marcos Olvera‐Rojas
- Department of Physical Education and SportsFaculty of Sport SciencesSport and Health University Research Institute (iMUDS)University of GranadaGranadaSpain
| | - Lu Wan
- AdventHealth Research InstituteNeuroscienceOrlandoFloridaUSA
| | - Jill K. Morris
- Alzheimer's Disease Research CenterUniversity of KansasKansas CityKansasUSA
- Department of NeurologyUniversity of Kansas Medical CenterKansas CityKansasUSA
| | - Paul J. Kueck
- Alzheimer's Disease Research CenterUniversity of KansasKansas CityKansasUSA
- Department of NeurologyUniversity of Kansas Medical CenterKansas CityKansasUSA
| | - Xuemei Zeng
- Department of PsychiatrySchool of MedicineUniversity of PittsburghPittsburghPennsylvaniaUSA
| | - Haiqing Huang
- AdventHealth Research InstituteNeuroscienceOrlandoFloridaUSA
| | - George Grove
- Department of PsychiatrySchool of MedicineUniversity of PittsburghPittsburghPennsylvaniaUSA
| | - Yijun Chen
- Department of ChemistryUniversity of PittsburghPittsburghPennsylvaniaUSA
| | - Tara K. Lafferty
- Department of PsychiatrySchool of MedicineUniversity of PittsburghPittsburghPennsylvaniaUSA
| | - Anuradha Sehrawat
- Department of PsychiatrySchool of MedicineUniversity of PittsburghPittsburghPennsylvaniaUSA
| | - M. Ilyas Kamboh
- Department of Human GeneticsSchool of Public HealthUniversity of PittsburghPittsburghPennsylvaniaUSA
| | - Anna L. Marsland
- Department of PsychiatrySchool of MedicineUniversity of PittsburghPittsburghPennsylvaniaUSA
| | - Arthur F. Kramer
- Department of PsychologyNortheastern UniversityBostonMassachusettsUSA
- Beckman Institute for Advanced Science and TechnologyUniversity of Illinois at Urbana ChampaignChampaignIllinoisUSA
- Center for Cognitive & Brain HealthNortheastern UniversityBostonMassachusettsUSA
| | - Edward McAuley
- Beckman Institute for Advanced Science and TechnologyUniversity of Illinois at Urbana ChampaignChampaignIllinoisUSA
- Department of Health and KinesiologyUniversity of Illinois at Urbana ChampaignChampaignIllinoisUSA
| | - Jeffrey M. Burns
- Department of NeurologyUniversity of Kansas Medical CenterKansas CityKansasUSA
| | - Charles H. Hillman
- Department of PsychologyNortheastern UniversityBostonMassachusettsUSA
- Department of Physical TherapyMovement, & Rehabilitation SciencesNortheastern UniversityBostonMassachusettsUSA
| | - Eric D. Vidoni
- Department of NeurologyUniversity of Kansas Medical CenterKansas CityKansasUSA
| | - Chaeryon Kang
- Department of PsychiatrySchool of MedicineUniversity of PittsburghPittsburghPennsylvaniaUSA
- Department of BiostatisticsSchool of Public HealthUniversity of PittsburghPittsburghPennsylvaniaUSA
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Liu S, Maruff P, Saint-Jalmes M, Bourgeat P, Masters CL, Goudey B. Predicting amyloid beta accumulation in cognitively unimpaired older adults: Cognitive assessments provide no additional utility beyond demographic and genetic factors. Alzheimers Dement 2025; 21:e70036. [PMID: 40110649 PMCID: PMC11923568 DOI: 10.1002/alz.70036] [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: 09/20/2024] [Revised: 01/30/2025] [Accepted: 01/31/2025] [Indexed: 03/22/2025]
Abstract
BACKGROUND Integrating non-invasive measures to estimate abnormal amyloid beta accumulation (Aβ+) is key to developing a screening tool for preclinical Alzheimer's disease (AD). The predictive capability of standard neuropsychological tests in estimating Aβ+ has not been quantified. METHODS We constructed machine learning models using six cognitive measurements alongside demographic and genetic risk factors to predict Aβ status. Data were drawn from three cohorts: Anti-Amyloid Treatment in Asymptomatic Alzheimer's Disease (A4), Alzheimer's Disease Neuroimaging Initiative (ADNI), and Australian Imaging, Biomarker & Lifestyle (AIBL) study. Internal validation was conducted within A4 with external validations in ADNI and AIBL to assess model generalizability. RESULTS The highest area under the curve (AUC) for predicting Aβ+ was observed with demographic, genetic, and cognitive variables in A4 (median AUC = 0.745), but this was not significantly different from models without cognitive variables. External validation showed no improvement in ADNI and a slight decrease in AIBL. DISCUSSION Standard neuropsychological tests do not significantly enhance Aβ+ prediction in cognitively unimpaired adults beyond demographic and genetic information. HIGHLIGHTS Standard neuropsychological tests do not significantly improve the prediction of amyloid beta positivity (Aβ+) in cognitively unimpaired older adults beyond demographic and genetic information alone. Across three well-characterized cohorts, machine learning models incorporating cognitive measures failed to significantly improve Aβ+ prediction, indicating the limited relationship between cognitive performance on these tests and the risk of pre-clinical Alzheimer's disease (AD). These findings challenge assumptions about cognitive symptoms preceding Aβ+ screening and emphasize the need for developing more sensitive cognitive tests for early AD detection.
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Affiliation(s)
- Shu Liu
- ARC Training Centre in Cognitive Computing for Medical Technologies, University of Melbourne, Carlton, Victoria, Australia
- Florey Institute of Neuroscience and Mental Health, University of Melbourne, Parkville, Victoria, Australia
| | - Paul Maruff
- Florey Institute of Neuroscience and Mental Health, University of Melbourne, Parkville, Victoria, Australia
- CogState Ltd, Melbourne, Victoria, Australia
| | - Martin Saint-Jalmes
- ARC Training Centre in Cognitive Computing for Medical Technologies, University of Melbourne, Carlton, Victoria, Australia
- Florey Institute of Neuroscience and Mental Health, University of Melbourne, Parkville, Victoria, Australia
| | - Pierrick Bourgeat
- Australian eHealth Research Centre, Dutton Park, Queensland, Australia
| | - Colin L Masters
- Florey Institute of Neuroscience and Mental Health, University of Melbourne, Parkville, Victoria, Australia
| | - Benjamin Goudey
- ARC Training Centre in Cognitive Computing for Medical Technologies, University of Melbourne, Carlton, Victoria, Australia
- Florey Institute of Neuroscience and Mental Health, University of Melbourne, Parkville, Victoria, Australia
- Australia BioCommon, University of Melbourne, North Melbourne, Victoria, Australia
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Masters CL. Should the preclinical stage of Alzheimer's disease be disclosed? Lancet Neurol 2025; 24:94-95. [PMID: 39862890 DOI: 10.1016/s1474-4422(24)00520-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2024] [Accepted: 12/13/2024] [Indexed: 01/27/2025]
Affiliation(s)
- Colin L Masters
- The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Parkville, VIC 3010, Australia.
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Rabinovici GD, Knopman DS, Arbizu J, Benzinger TLS, Donohoe KJ, Hansson O, Herscovitch P, Kuo PH, Lingler JH, Minoshima S, Murray ME, Price JC, Salloway SP, Weber CJ, Carrillo MC, Johnson KA. Updated Appropriate Use Criteria for Amyloid and Tau PET: A Report from the Alzheimer's Association and Society for Nuclear Medicine and Molecular Imaging Workgroup. J Nucl Med 2025:jnumed.124.268756. [PMID: 39778970 DOI: 10.2967/jnumed.124.268756] [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: 09/05/2024] [Accepted: 09/05/2024] [Indexed: 01/11/2025] Open
Abstract
The Alzheimer's Association and the Society of Nuclear Medicine and Molecular Imaging convened a multidisciplinary workgroup to update appropriate use criteria (AUC) for amyloid positron emission tomography (PET) and to develop AUC for tau PET. Methods: The workgroup identified key research questions that guided a systematic literature review on clinical amyloid/tau PET. Building on this review, the workgroup developed 17 clinical scenarios in which amyloid or tau PET may be considered. A modified Delphi approach was used to rate each scenario by consensus as "rarely appropriate," "uncertain," or "appropriate." Ratings were performed separately for amyloid and tau PET as stand-alone modalities. Results: For amyloid PET, 7 scenarios were rated as appropriate, 2 as uncertain, and 8 as rarely appropriate. For tau PET, 5 scenarios were rated as appropriate, 6 as uncertain, and 6 as rarely appropriate. Conclusion: AUC for amyloid and tau PET provide expert recommendations for clinical use of these technologies in the evolving landscape of diagnostics and therapeutics for Alzheimer's disease.
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Affiliation(s)
- Gil D Rabinovici
- Department of Neurology and Department of Radiology and Biomedical Imaging, University of California at San Francisco, San Francisco, California;
| | - David S Knopman
- Mayo Clinic Neurology and Neurosurgery, Rochester, Minnesota
| | - Javier Arbizu
- Department of Nuclear Medicine, University of Navarra Clinic, Pamplona, Spain
| | - Tammie L S Benzinger
- Mallinckrodt Institute of Radiology, School of Medicine, Washington University in St. Louis, St. Louis, Missouri; Knight Alzheimer's Disease Research Center, School of Medicine, Washington University in St. Louis, St. Louis, Missouri
| | - Kevin J Donohoe
- Nuclear Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts
| | - Oskar Hansson
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Faculty of Medicine, Lund University, Lund, Sweden
- Memory Clinic, Skåne University Hospital, Malmö, Sweden
| | - Peter Herscovitch
- Positron Emission Tomography Department, National Institutes of Health Clinical Center, Bethesda, Maryland
| | - Phillip H Kuo
- Medical Imaging, Medicine, and Biomedical Engineering, University of Arizona, Tucson, Arizona
| | - Jennifer H Lingler
- Department of Health and Community Systems, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Satoshi Minoshima
- Department of Radiology and Imaging Sciences, University of Utah, Salt Lake City, Utah
| | | | - Julie C Price
- Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts
| | - Stephen P Salloway
- Department of Neurology and Psychiatry the Warren Alpert School of Medicine, Brown University, Providence, Rhode Island
- Butler Hospital Memory and Aging Program, Providence, Rhode Island
| | | | | | - Keith A Johnson
- Center for Alzheimer Research and Treatment, Department of Neurology, Brigham and Women's Hospital, Boston, Massachusetts
- Molecular Neuroimaging, Massachusetts General Hospital, Boston, Massachusetts
- Harvard Medical School, Boston, Massachusetts; and
- Departments of Neurology and Radiology, Massachusetts General Hospital, Boston, Massachusetts
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Cui L, Zhang Z, Tu Y, Wang M, Guan Y, Li Y, Xie F, Guo Q. Association of precuneus Aβ burden with default mode network function. Alzheimers Dement 2025; 21:e14380. [PMID: 39559982 PMCID: PMC11772721 DOI: 10.1002/alz.14380] [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: 08/05/2024] [Revised: 10/08/2024] [Accepted: 10/10/2024] [Indexed: 11/20/2024]
Abstract
INTRODUCTION It remains unclear whether the local amyloid-beta (Aβ) burden in key regions within the default mode network (DMN) affects network and cognitive functions. METHODS Participants included 1002 individuals from the Chinese Preclinical Alzheimer's Disease Study cohort who underwent 18F-florbetapir positron emission tomography resting-state functional magnetic resonance imaging scanning and neuropsychological tests. The correlations between precuneus (PRC) Aβ burden, DMN function, and cognitive function were investigated. RESULTS In individuals with high PRC Aβ burden, there is a bidirectional relationship between DMN local function or functional connectivity and PRC Aβ deposition across various cognitive states, which is also linked to cognitive function. Even below the PRC Aβ threshold, DMN function remains related to PRC Aβ deposition and cognitive performance. DISCUSSION The findings reveal the critical role of PRC Aβ deposition in disrupting neural networks associated with cognitive decline and the necessity of early detection and monitoring of PRC Aβ deposition. HIGHLIGHTS Precuneus (PRC) Aβ burden impacts DMN function in different cognitive stages. High PRC Aβ burden is linked to early neural compensation and subsequent dysfunction. Low PRC Aβ burden correlates with neural changes before significant Aβ accumulation. Changes in DMN function and connectivity provide insights into AD progression. Early detection of regional Aβ burden can help monitor the risk of cognitive decline.
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Affiliation(s)
- Liang Cui
- Department of GerontologyShanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of MedicineShanghaiChina
| | - Zhen Zhang
- Department of GerontologyShanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of MedicineShanghaiChina
| | - You‐Yi Tu
- Department of GerontologyShanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of MedicineShanghaiChina
| | - Min Wang
- School of Life SciencesShanghai UniversityShanghaiChina
| | - Yi‐Hui Guan
- Department of Nuclear Medicine & PET CenterHuashan HospitalFudan UniversityShanghaiChina
| | - Yue‐Hua Li
- Department of GerontologyShanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of MedicineShanghaiChina
| | - Fang Xie
- Department of Nuclear Medicine & PET CenterHuashan HospitalFudan UniversityShanghaiChina
| | - Qi‐Hao Guo
- Department of GerontologyShanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of MedicineShanghaiChina
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Farrar G, Weber CJ, Rabinovici GD. Expert opinion on Centiloid thresholds suitable for initiating anti-amyloid therapy. Summary of discussion at the 2024 spring Alzheimer's Association Research Roundtable. J Prev Alzheimers Dis 2025; 12:100008. [PMID: 39800462 DOI: 10.1016/j.tjpad.2024.100008] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2024] [Revised: 08/28/2024] [Accepted: 08/28/2024] [Indexed: 05/02/2025]
Abstract
A 24-30 Centiloid (CL) threshold was collectively considered by a group of global dementia experts as a practical and implementable cut-off for anti-amyloid therapy intervention, in Alzheimer's disease patients who have been diagnosed at the mild cognitive impairment or mild dementia stage of their disease. Though additional validation is needed, knowledge of this threshold would be valuable to those involved in diagnosing and treating patients in the new AD care pathways, as well as entry into clinical trials. Therapy monitoring to determine future treatment response and assess amyloid clearance can be accomplished with amyloid PET with some technical details still to be elucidated.
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Affiliation(s)
- Gill Farrar
- GE HealthCare, Little Chalfont, Buckinghamshire, UK
| | | | - Gil D Rabinovici
- Memory & Aging Center, Departments of Neurology, Radiology & Biomedical Imaging, University of California San Francisco, San Francisco, California, USA
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11
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Jovalekic A, Bullich S, Roé-Vellvé N, Kolinger GD, Howard LR, Elsholz F, Lagos-Quintana M, Blanco-Rodriguez B, Pérez-Martínez E, Gismondi R, Perrotin A, Chapleau M, Keegan R, Mueller A, Stephens AW, Koglin N. Experiences from Clinical Research and Routine Use of Florbetaben Amyloid PET-A Decade of Post-Authorization Insights. Pharmaceuticals (Basel) 2024; 17:1648. [PMID: 39770490 PMCID: PMC11728731 DOI: 10.3390/ph17121648] [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: 10/21/2024] [Revised: 11/29/2024] [Accepted: 12/02/2024] [Indexed: 01/16/2025] Open
Abstract
Florbetaben (FBB) is a radiopharmaceutical approved by the FDA and EMA in 2014 for the positron emission tomography (PET) imaging of brain amyloid deposition in patients with cognitive impairment who are being evaluated for Alzheimer's disease (AD) or other causes of cognitive decline. Initially, the clinical adoption of FBB PET faced significant barriers, including reimbursement challenges and uncertainties regarding its integration into diagnostic clinical practice. This review examines the progress made in overcoming these obstacles and describes the concurrent evolution of the diagnostic landscape. Advances in quantification methods have further strengthened the traditional visual assessment approach. Over the past decade, compelling evidence has emerged, demonstrating that amyloid PET has a strong impact on AD diagnosis, management, and outcomes across diverse clinical scenarios, even in the absence of amyloid-targeted therapies. Amyloid PET imaging has become essential in clinical trials and the application of new AD therapeutics, particularly for confirming eligibility criteria (i.e., the presence of amyloid plaques) and monitoring biological responses to amyloid-lowering therapies. Since its approval, FBB PET has transitioned from a purely diagnostic tool aimed primarily at excluding amyloid pathology to a critical component in AD drug development, and today, it is essential in the diagnostic workup and therapy management of approved AD treatments.
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Affiliation(s)
| | - Santiago Bullich
- Life Molecular Imaging GmbH, Tegeler Str. 7, 13353 Berlin, Germany
| | - Núria Roé-Vellvé
- Life Molecular Imaging GmbH, Tegeler Str. 7, 13353 Berlin, Germany
| | | | | | - Floriana Elsholz
- Life Molecular Imaging GmbH, Tegeler Str. 7, 13353 Berlin, Germany
| | | | | | | | | | - Audrey Perrotin
- Life Molecular Imaging GmbH, Tegeler Str. 7, 13353 Berlin, Germany
| | - Marianne Chapleau
- Life Molecular Imaging Inc., 75 State Street, Floor 1, Boston, MA 02109, USA
| | - Richard Keegan
- Life Molecular Imaging Inc., 75 State Street, Floor 1, Boston, MA 02109, USA
| | - Andre Mueller
- Life Molecular Imaging GmbH, Tegeler Str. 7, 13353 Berlin, Germany
| | | | - Norman Koglin
- Life Molecular Imaging GmbH, Tegeler Str. 7, 13353 Berlin, Germany
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12
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Trinh PTH, Kim DY, Choi KH, Kim J. Impact of shortening time on diagnosis of 18F-florbetaben PET. EJNMMI Res 2024; 14:114. [PMID: 39570447 PMCID: PMC11582261 DOI: 10.1186/s13550-024-01181-8] [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: 08/20/2024] [Accepted: 11/05/2024] [Indexed: 11/22/2024] Open
Abstract
BACKGROUND 18F-Florbetaben amyloid positron emission tomography (PET) scan is crucial for diagnosing Alzheimer's disease, typically involving a 20 min acquisition. However, maintaining such prolonged scans can be challenging in some cases. This study explores the diagnostic impact and feasibility of reducing scan durations by comparing quantitative measures between shortened and standard scans. Additionally, we identified the optimal Centiloid threshold to distinguish between positive and negative amyloid results. RESULTS We analyzed 307 PET scans from our memory clinic, each followed up for a minimum of two years. The scans, conducted 90 to 110 min after approximately 300 MBq of 18F-Florbetaben injection, were categorized into four sets of 5 min durations: 5, 10, 15, and 20 min. Nuclear medicine physicians validated and rated each scan as either amyloid-positive or negative. For quantitative assessments, we employed the standardized uptake value ratio (SUVR) and Centiloid scales, comparing total SUVR and Centiloid values across five subregions (global, frontal, posterior cingulate-precuneus, lateral temporal, and parietal) using Bland-Altman analysis. Receiver operator characteristic (ROC) curves were utilized to develop optimal Centiloid thresholds. Comparing the images at 5, 10, 15, and 20 min images, SUVR and Centiloid values gradually increased with prolonged scan times. The mean SUVR difference between 5 and 20 min was 0.03 for the amyloid-positive and 0.01 for the amyloid-negative groups; Centiloid differences were 4.60 and 2.38, respectively. Additionally, no significant variation was observed in total SUVR and Centiloid values among the durations across all subregions in positive and negative groups (all p > 0.1). ROC analysis indicated that a Centiloid threshold of 21.86 at 5 min provided optimal agreement with visual assessments (AUC = 0.985, sensitivity = 0.950, specificity = 0.972), especially using the global area. CONCLUSIONS This study demonstrated that 5 min image scans with an optimal threshold of CL = 21.86 exhibited minimal bias in SUVR and Centiloid values compared to longer scans (10, 15, and 20 min). Our findings suggest that shorter scan times are a viable and effective option for brain amyloid PET imaging in clinical settings.
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Affiliation(s)
- Phuong T H Trinh
- Department of Artificial Intelligence Convergence, Chonnam National University, 77, Yongbong-Ro, Buk-Gu, Gwangju, 61186, Republic of Korea
| | - Doo-Young Kim
- Department of Artificial Intelligence Convergence, Chonnam National University, 77, Yongbong-Ro, Buk-Gu, Gwangju, 61186, Republic of Korea
| | - Kang-Ho Choi
- Department of Neurology, Chonnam National University Medical School and Hospital, 42, Jebongro, Dong-Gu, Gwangju, 61469, Republic of Korea.
| | - Jahae Kim
- Department of Artificial Intelligence Convergence, Chonnam National University, 77, Yongbong-Ro, Buk-Gu, Gwangju, 61186, Republic of Korea.
- Department of Nuclear Medicine, Chonnam National University Medical School and Hospital, 42, Jebongro, Dong-Gu, Gwangju, 61469, Republic of Korea.
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13
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Kazemeini S, Nadeem-Tariq A, Shih R, Rafanan J, Ghani N, Vida TA. From Plaques to Pathways in Alzheimer's Disease: The Mitochondrial-Neurovascular-Metabolic Hypothesis. Int J Mol Sci 2024; 25:11720. [PMID: 39519272 PMCID: PMC11546801 DOI: 10.3390/ijms252111720] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2024] [Revised: 10/28/2024] [Accepted: 10/29/2024] [Indexed: 11/16/2024] Open
Abstract
Alzheimer's disease (AD) presents a public health challenge due to its progressive neurodegeneration, cognitive decline, and memory loss. The amyloid cascade hypothesis, which postulates that the accumulation of amyloid-beta (Aβ) peptides initiates a cascade leading to AD, has dominated research and therapeutic strategies. The failure of recent Aβ-targeted therapies to yield conclusive benefits necessitates further exploration of AD pathology. This review proposes the Mitochondrial-Neurovascular-Metabolic (MNM) hypothesis, which integrates mitochondrial dysfunction, impaired neurovascular regulation, and systemic metabolic disturbances as interrelated contributors to AD pathogenesis. Mitochondrial dysfunction, a hallmark of AD, leads to oxidative stress and bioenergetic failure. Concurrently, the breakdown of the blood-brain barrier (BBB) and impaired cerebral blood flow, which characterize neurovascular dysregulation, accelerate neurodegeneration. Metabolic disturbances such as glucose hypometabolism and insulin resistance further impair neuronal function and survival. This hypothesis highlights the interconnectedness of these pathways and suggests that therapeutic strategies targeting mitochondrial health, neurovascular integrity, and metabolic regulation may offer more effective interventions. The MNM hypothesis addresses these multifaceted aspects of AD, providing a comprehensive framework for understanding disease progression and developing novel therapeutic approaches. This approach paves the way for developing innovative therapeutic strategies that could significantly improve outcomes for millions affected worldwide.
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Affiliation(s)
| | | | | | | | | | - Thomas A. Vida
- Kirk Kerkorian School of Medicine at UNLV, 625 Shadow Lane, Las Vegas, NV 89106, USA; (S.K.); (A.N.-T.); (R.S.); (J.R.); (N.G.)
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14
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Karlawish J, Grill JD. Alzheimer's disease biomarkers and the tyranny of treatment. EBioMedicine 2024; 108:105291. [PMID: 39366841 DOI: 10.1016/j.ebiom.2024.105291] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2023] [Revised: 08/02/2024] [Accepted: 08/04/2024] [Indexed: 10/06/2024] Open
Abstract
Advances in treatment are changing not only the therapeutic options for patients with Alzheimer's disease; they're also changing their diagnostic options. Technologies to detect amyloid such as PET imaging and blood or CSF testing now have a central role in Alzheimer's disease care. Notably, this role has been made possible by regulatory approval and coverage by payers of therapies. Access to treatments and the diagnostic tests needed to prescribe them is encourageing but it reveals a problem. These tests are tailored to the needs of the therapies, not to the needs of patients. Patients and families need to understand the causes of their impairments and their prognosis. This requires access to the best available diagnostic tests and this access should not depend on the availability of treatments. These tests should be used to their fullest capacity to inform patients of the causes of their cognitive impairments and their prognosis. Unfortunately, compared to diagnostic testing, treatment options are overvalued. We call this problem the tyranny of treatment.
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Affiliation(s)
- Jason Karlawish
- Departments of Medicine, Medical Ethics and Health Policy, and Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Joshua D Grill
- Departments of Psychiatry & Human Behavior and Neurobiology & Behavior, Institute for Memory Impairments and Neurological Disorders, University of California Irvine, Irvine CA, USA.
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15
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Sun P, He Z, Li A, Yang J, Zhu Y, Cai Y, Ma T, Ma S, Guo T. Spatial and temporal patterns of cortical mean diffusivity in Alzheimer's disease and suspected non-Alzheimer's disease pathophysiology. Alzheimers Dement 2024; 20:7048-7061. [PMID: 39132849 PMCID: PMC11485315 DOI: 10.1002/alz.14176] [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: 04/12/2024] [Revised: 07/12/2024] [Accepted: 07/16/2024] [Indexed: 08/13/2024]
Abstract
INTRODUCTION The spatial and temporal patterns of cortical mean diffusivity (cMD), as well as its association with Alzheimer's disease (AD) and suspected non-Alzheimer's pathophysiology (SNAP), are not yet fully understood. METHODS We compared baseline (n = 617) and longitudinal changes (n = 421) of cMD, cortical thickness, and gray matter volume and their relations to vascular risk factors, amyloid beta (Aβ), and tau positron emission tomography (PET), and longitudinal cognitive decline in Aβ PET negative and positive older adults. RESULTS cMD increases were more sensitive to detecting brain structural alterations than cortical thinning and gray matter atrophy. Tau-related cMD increases partially mediated Aβ-related cognitive decline in AD, whereas vascular disease-related increased cMD levels substantially mediated age-related cognitive decline in SNAP. DISCUSSION These findings revealed the dynamic changes of microstructural and macrostructural indicators and their associations with AD and SNAP, providing novel insights into understanding upstream and downstream events of cMD in neurodegenerative disease. HIGHLIGHTS Cortical mean diffusivity (cMD) was more sensitive to detecting structural changes than macrostructural factors. Tau-related cMD increases partially mediated amyloid beta-related cognitive decline in Alzheimer's disease (AD). White matter hyperintensity-related higher cMD mainly explained the age-related cognitive decline in suspected non-Alzheimer's pathophysiology (SNAP). cMD may assist in tracking earlier neurodegenerative signs in AD and SNAP.
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Affiliation(s)
- Pan Sun
- Institute of Biomedical EngineeringShenzhen Bay LaboratoryShenzhenChina
- Tsinghua Shenzhen International Graduate School (SIGS)Tsinghua UniversityShenzhenChina
| | - Zhengbo He
- Institute of Biomedical EngineeringShenzhen Bay LaboratoryShenzhenChina
| | - Anqi Li
- Institute of Biomedical EngineeringShenzhen Bay LaboratoryShenzhenChina
| | - Jie Yang
- Institute of Biomedical EngineeringShenzhen Bay LaboratoryShenzhenChina
| | - Yalin Zhu
- Institute of Biomedical EngineeringShenzhen Bay LaboratoryShenzhenChina
| | - Yue Cai
- Institute of Biomedical EngineeringShenzhen Bay LaboratoryShenzhenChina
| | - Ting Ma
- School of Electronic and Information EngineeringHarbin Institute of Technology (Shenzhen)ShenzhenChina
| | - Shaohua Ma
- Tsinghua Shenzhen International Graduate School (SIGS)Tsinghua UniversityShenzhenChina
| | - Tengfei Guo
- Institute of Biomedical EngineeringShenzhen Bay LaboratoryShenzhenChina
- Institute of Biomedical EngineeringPeking University Shenzhen Graduate SchoolShenzhenChina
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16
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Azargoonjahromi A. Immunotherapy in Alzheimer's disease: focusing on the efficacy of gantenerumab on amyloid-β clearance and cognitive decline. J Pharm Pharmacol 2024; 76:1115-1131. [PMID: 38767981 DOI: 10.1093/jpp/rgae066] [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: 01/18/2024] [Accepted: 05/08/2024] [Indexed: 05/22/2024]
Abstract
Gantenerumab, a human monoclonal antibody (mAb), has been thought of as a potential agent to treat Alzheimer's disease (AD) by specifically targeting regions of the amyloid-β (Aβ) peptide sequence. Aβ protein accumulation in the brain leads to amyloid plaques, causing neuroinflammation, oxidative stress, neuronal damage, and neurotransmitter dysfunction, thereby causing cognitive decline in AD. Gantenerumab involves disrupting Aβ aggregation and promoting the breakdown of larger Aβ aggregates into smaller fragments, which facilitates the action of Aβ-degrading enzymes in the brain, thus slowing down the progression of AD. Moreover, Gantenerumab acts as an opsonin, coating Aβ plaques and enhancing their recognition by immune cells, which, combined with its ability to improve the activity of microglia, makes it an intriguing candidate for promoting Aβ plaque clearance. Indeed, the multifaceted effects of Gantenerumab, including Aβ disaggregation, enhanced immune recognition, and improved microglia activity, may position it as a promising therapeutic approach for AD. Of note, reports suggest that Gantenerumab, albeit its capacity to reduce or eliminate Aβ, has not demonstrated effectiveness in reducing cognitive decline. This review, after providing an overview of immunotherapy approaches that target Aβ in AD, explores the efficacy of Gantenerumab in reducing Aβ levels and cognitive decline.
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17
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Quenon L, Collij LE, Garcia DV, Lopes Alves I, Gérard T, Malotaux V, Huyghe L, Gispert JD, Jessen F, Visser PJ, den Braber A, Ritchie CW, Boada M, Marquié M, Vandenberghe R, Luckett ES, Schöll M, Frisoni GB, Buckley C, Stephens A, Altomare D, Ford L, Birck C, Mett A, Gismondi R, Wolz R, Grootoonk S, Manber R, Shekari M, Lhommel R, Dricot L, Ivanoiu A, Farrar G, Barkhof F, Hanseeuw BJ. Amyloid-PET imaging predicts functional decline in clinically normal individuals. Alzheimers Res Ther 2024; 16:130. [PMID: 38886831 PMCID: PMC11181677 DOI: 10.1186/s13195-024-01494-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2024] [Accepted: 06/09/2024] [Indexed: 06/20/2024]
Abstract
BACKGROUND There is good evidence that elevated amyloid-β (Aβ) positron emission tomography (PET) signal is associated with cognitive decline in clinically normal (CN) individuals. However, it is less well established whether there is an association between the Aβ burden and decline in daily living activities in this population. Moreover, Aβ-PET Centiloids (CL) thresholds that can optimally predict functional decline have not yet been established. METHODS Cross-sectional and longitudinal analyses over a mean three-year timeframe were performed on the European amyloid-PET imaging AMYPAD-PNHS dataset that phenotypes 1260 individuals, including 1032 CN individuals and 228 participants with questionable functional impairment. Amyloid-PET was assessed continuously on the Centiloid (CL) scale and using Aβ groups (CL < 12 = Aβ-, 12 ≤ CL ≤ 50 = Aβ-intermediate/Aβ± , CL > 50 = Aβ+). Functional abilities were longitudinally assessed using the Clinical Dementia Rating (Global-CDR, CDR-SOB) and the Amsterdam Instrumental Activities of Daily Living Questionnaire (A-IADL-Q). The Global-CDR was available for the 1260 participants at baseline, while baseline CDR-SOB and A-IADL-Q scores and longitudinal functional data were available for different subsamples that had similar characteristics to those of the entire sample. RESULTS Participants included 765 Aβ- (61%, Mdnage = 66.0, IQRage = 61.0-71.0; 59% women), 301 Aβ± (24%; Mdnage = 69.0, IQRage = 64.0-75.0; 53% women) and 194 Aβ+ individuals (15%, Mdnage = 73.0, IQRage = 68.0-78.0; 53% women). Cross-sectionally, CL values were associated with CDR outcomes. Longitudinally, baseline CL values predicted prospective changes in the CDR-SOB (bCL*Time = 0.001/CL/year, 95% CI [0.0005,0.0024], p = .003) and A-IADL-Q (bCL*Time = -0.010/CL/year, 95% CI [-0.016,-0.004], p = .002) scores in initially CN participants. Increased clinical progression (Global-CDR > 0) was mainly observed in Aβ+ CN individuals (HRAβ+ vs Aβ- = 2.55, 95% CI [1.16,5.60], p = .020). Optimal thresholds for predicting decline were found at 41 CL using the CDR-SOB (bAβ+ vs Aβ- = 0.137/year, 95% CI [0.069,0.206], p < .001) and 28 CL using the A-IADL-Q (bAβ+ vs Aβ- = -0.693/year, 95% CI [-1.179,-0.208], p = .005). CONCLUSIONS Amyloid-PET quantification supports the identification of CN individuals at risk of functional decline. TRIAL REGISTRATION The AMYPAD PNHS is registered at www.clinicaltrialsregister.eu with the EudraCT Number: 2018-002277-22.
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Affiliation(s)
- Lisa Quenon
- Institute of Neuroscience, UCLouvain, Brussels, Belgium.
- Department of Neurology, Cliniques Universitaires Saint-Luc, Brussels, Belgium.
| | - Lyduine E Collij
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
- Clinical Memory Research Unit, Clinical Sciences Malmö, Lund University, Malmö, Sweden
| | - David Vállez Garcia
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
| | - Isadora Lopes Alves
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
- Brain Research Center, Amsterdam, The Netherlands
| | - Thomas Gérard
- Institute of Neuroscience, UCLouvain, Brussels, Belgium
- Department of Nuclear Medicine, Cliniques Universitaires Saint-Luc, Brussels, Belgium
| | - Vincent Malotaux
- Institute of Neuroscience, UCLouvain, Brussels, Belgium
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA
| | - Lara Huyghe
- Institute of Neuroscience, UCLouvain, Brussels, Belgium
| | - Juan Domingo Gispert
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina, Madrid, Spain
- Universitat Pompeu Fabra, Barcelona, Spain
- Hospital del Mar Medical Research Institute (IMIM), Barcelona, Spain
| | - Frank Jessen
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
- Department of Psychiatry, Medical Faculty, University of Cologne, Cologne, Germany
| | - Pieter Jelle Visser
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC Location VUmc, Amsterdam, The Netherlands
- Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Alzheimer Centrum Limburg, Maastricht University, Maastricht, Netherlands
| | - Anouk den Braber
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC Location VUmc, Amsterdam, The Netherlands
- Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Craig W Ritchie
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Mercè Boada
- Ace Alzheimer Center Barcelona - Universitat Internacional de Catalunya, Barcelona, Spain
- Networking Research Center for Neurodegenerative Diseases (CIBERNED), Instituto de Salud Carlos III, Madrid, Spain
| | - Marta Marquié
- Ace Alzheimer Center Barcelona - Universitat Internacional de Catalunya, Barcelona, Spain
- Networking Research Center for Neurodegenerative Diseases (CIBERNED), Instituto de Salud Carlos III, Madrid, Spain
| | - Rik Vandenberghe
- Laboratory for Cognitive Neurology, Department of Neurosciences, Leuven Brain Institute, KU Leuven, Louvain, Belgium
- Neurology Service, University Hospital Leuven, Louvain, Belgium
| | - Emma S Luckett
- Laboratory for Cognitive Neurology, Department of Neurosciences, Leuven Brain Institute, KU Leuven, Louvain, Belgium
- Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institute, Stockholm, Sweden
| | - Michael Schöll
- Wallenberg Centre for Molecular and Translational Medicine, University of Gothenburg, Göteborg, Sweden
- Department of Psychiatry and Neurochemistry, University of Gothenburg, Mölndal, Sweden
- Dementia Research Centre, Queen Square Institute of Neurology, University College London, London, UK
| | - Giovanni B Frisoni
- Laboratory of Neuroimaging of Aging (LANVIE), University of Geneva, Geneva, Switzerland
- Memory Clinic, University Hospital of Geneva, Geneva, Switzerland
| | | | | | - Daniele Altomare
- Neurology Unit, Department of Clinical and Experimental Sciences, University of Brescia, Brescia, Italy
| | - Lisa Ford
- Johnson & Johnson Innovative Medicine, Titusville, NJ, USA
| | | | - Anja Mett
- GE HealthCare, Glattbrugg, Switzerland
| | | | | | | | | | - Mahnaz Shekari
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain
- Universitat Pompeu Fabra, Barcelona, Spain
| | - Renaud Lhommel
- Institute of Neuroscience, UCLouvain, Brussels, Belgium
- Department of Nuclear Medicine, Cliniques Universitaires Saint-Luc, Brussels, Belgium
| | | | - Adrian Ivanoiu
- Institute of Neuroscience, UCLouvain, Brussels, Belgium
- Department of Neurology, Cliniques Universitaires Saint-Luc, Brussels, Belgium
| | | | - Frederik Barkhof
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
- Queen Square Institute of Neurology and Centre for Medical Image Computing, University College London, London, UK
| | - Bernard J Hanseeuw
- Institute of Neuroscience, UCLouvain, Brussels, Belgium
- Department of Neurology, Cliniques Universitaires Saint-Luc, Brussels, Belgium
- Gordon Center for Medical Imaging, Department of Radiology, Mass General Brigham, Boston, MA, USA
- WELBIO Department, WEL Research Institute, Wavre, Belgium
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Jagust WJ, Mattay VS, Krainak DM, Wang SJ, Weidner LD, Hofling AA, Koo H, Hsieh P, Kuo PH, Farrar G, Marzella L. Quantitative Brain Amyloid PET. J Nucl Med 2024; 65:670-678. [PMID: 38514082 PMCID: PMC11064834 DOI: 10.2967/jnumed.123.265766] [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: 09/19/2023] [Revised: 02/13/2024] [Indexed: 03/23/2024] Open
Abstract
Since the development of amyloid tracers for PET imaging, there has been interest in quantifying amyloid burden in the brains of patients with Alzheimer disease. Quantitative amyloid PET imaging is poised to become a valuable approach in disease staging, theranostics, monitoring, and as an outcome measure for interventional studies. Yet, there are significant challenges and hurdles to overcome before it can be implemented into widespread clinical practice. On November 17, 2022, the U.S. Food and Drug Administration, Society of Nuclear Medicine and Molecular Imaging, and Medical Imaging and Technology Alliance cosponsored a public workshop comprising experts from academia, industry, and government agencies to discuss the role of quantitative brain amyloid PET imaging in staging, prognosis, and longitudinal assessment of Alzheimer disease. The workshop discussed a range of topics, including available radiopharmaceuticals for amyloid imaging; the methodology, metrics, and analytic validity of quantitative amyloid PET imaging; its use in disease staging, prognosis, and monitoring of progression; and challenges facing the field. This report provides a high-level summary of the presentations and the discussion.
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Affiliation(s)
| | - Venkata S Mattay
- Division of Imaging and Radiation Medicine, Office of Specialty Medicine, Office of New Drugs, Center of Drug Evaluation and Research, Food and Drug Administration, Silver Spring, Maryland;
| | - Daniel M Krainak
- Division of Radiological Imaging and Radiation Therapy Devices, Office of Radiological Health, Office of Product Evaluation and Quality, Centers for Devices and Radiological Health, Food and Drug Administration, Silver Spring, Maryland
| | - Sue-Jane Wang
- Division of Biometrics I, Office of Biostatistics, Office of Translational Sciences, Center of Drug Evaluation and Research, Food and Drug Administration, Silver Spring, Maryland
| | - Lora D Weidner
- Division of Radiological Imaging and Radiation Therapy Devices, Office of Radiological Health, Office of Product Evaluation and Quality, Centers for Devices and Radiological Health, Food and Drug Administration, Silver Spring, Maryland
| | - A Alex Hofling
- Division of Imaging and Radiation Medicine, Office of Specialty Medicine, Office of New Drugs, Center of Drug Evaluation and Research, Food and Drug Administration, Silver Spring, Maryland
| | - Hayoung Koo
- Division of Imaging and Radiation Medicine, Office of Specialty Medicine, Office of New Drugs, Center of Drug Evaluation and Research, Food and Drug Administration, Silver Spring, Maryland
| | | | | | | | - Libero Marzella
- Division of Imaging and Radiation Medicine, Office of Specialty Medicine, Office of New Drugs, Center of Drug Evaluation and Research, Food and Drug Administration, Silver Spring, Maryland
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19
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Meyer MR, Kirmess KM, Eastwood S, Wente‐Roth TL, Irvin F, Holubasch MS, Venkatesh V, Fogelman I, Monane M, Hanna L, Rabinovici GD, Siegel BA, Whitmer RA, Apgar C, Bateman RJ, Holtzman DM, Irizarry M, Verbel D, Sachdev P, Ito S, Contois J, Yarasheski KE, Braunstein JB, Verghese PB, West T. Clinical validation of the PrecivityAD2 blood test: A mass spectrometry-based test with algorithm combining %p-tau217 and Aβ42/40 ratio to identify presence of brain amyloid. Alzheimers Dement 2024; 20:3179-3192. [PMID: 38491912 PMCID: PMC11095426 DOI: 10.1002/alz.13764] [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: 10/21/2023] [Revised: 01/25/2024] [Accepted: 01/31/2024] [Indexed: 03/18/2024]
Abstract
BACKGROUND With the availability of disease-modifying therapies for Alzheimer's disease (AD), it is important for clinicians to have tests to aid in AD diagnosis, especially when the presence of amyloid pathology is a criterion for receiving treatment. METHODS High-throughput, mass spectrometry-based assays were used to measure %p-tau217 and amyloid beta (Aβ)42/40 ratio in blood samples from 583 individuals with suspected AD (53% positron emission tomography [PET] positive by Centiloid > 25). An algorithm (PrecivityAD2 test) was developed using these plasma biomarkers to identify brain amyloidosis by PET. RESULTS The area under the receiver operating characteristic curve (AUC-ROC) for %p-tau217 (0.94) was statistically significantly higher than that for p-tau217 concentration (0.91). The AUC-ROC for the PrecivityAD2 test output, the Amyloid Probability Score 2, was 0.94, yielding 88% agreement with amyloid PET. Diagnostic performance of the APS2 was similar by ethnicity, sex, age, and apoE4 status. DISCUSSION The PrecivityAD2 blood test showed strong clinical validity, with excellent agreement with brain amyloidosis by PET.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | - Lucy Hanna
- Center for Statistical SciencesBrown University School of Public HealthProvidenceRhode IslandUSA
| | | | | | | | - Charles Apgar
- American College of RadiologyPhiladelphiaPennsylvaniaUSA
| | | | | | | | | | | | | | | | | | | | | | - Tim West
- C2N DiagnosticsSt. LouisMissouriUSA
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Ishibashi K, Kurihara M, Toyohara J, Ishii K, Iwata A. Pitfalls of Amyloid-Beta PET: Comparisons With 18 F-MK-6240 and 18 F-THK5351 PET. Clin Nucl Med 2024; 49:319-321. [PMID: 38363815 DOI: 10.1097/rlu.0000000000005097] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/18/2024]
Abstract
ABSTRACT We present 3 patients as pitfalls of amyloid-beta (Aβ) PET, who underwent 11 C-PiB (Aβ), 18 F-MK-6240 (Alzheimer disease [AD]-tau), and 18 F-THK5351 (astrogliosis) PET examinations. Despite negligible or tiny Aβ pathology, patients 1 and 2 were diagnosed with AD as the cause of symptoms. Despite widespread Aβ pathology, patient 3 was not diagnosed with AD as the cause of symptoms. However, if we had only conducted Aβ PET, patients 1 and 2 might not have been diagnosed with AD, whereas patient 3 might have been diagnosed with AD. Hence, both Aβ and AD-tau assessments are necessary to relate clinical symptoms to AD pathology.
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Affiliation(s)
| | - Masanori Kurihara
- Department of Neurology, Tokyo Metropolitan Institute for Geriatrics and Gerontology, Tokyo, Japan
| | | | | | - Atsushi Iwata
- Department of Neurology, Tokyo Metropolitan Institute for Geriatrics and Gerontology, Tokyo, Japan
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21
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De Strooper B, Karran E. New precision medicine avenues to the prevention of Alzheimer's disease from insights into the structure and function of γ-secretases. EMBO J 2024; 43:887-903. [PMID: 38396302 PMCID: PMC10943082 DOI: 10.1038/s44318-024-00057-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: 11/18/2023] [Revised: 01/20/2024] [Accepted: 02/06/2024] [Indexed: 02/25/2024] Open
Abstract
Two phase-III clinical trials with anti-amyloid peptide antibodies have met their primary goal, i.e. slowing of Alzheimer's disease (AD) progression. However, antibody therapy may not be the optimal therapeutic modality for AD prevention, as we will discuss in the context of the earlier small molecules described as "γ-secretase modulators" (GSM). We review here the structure, function, and pathobiology of γ-secretases, with a focus on how mutations in presenilin genes result in early-onset AD. Significant progress has been made in generating compounds that act in a manner opposite to pathogenic presenilin mutations: they stabilize the proteinase-substrate complex, thereby increasing the processivity of substrate cleavage and altering the size spectrum of Aβ peptides produced. We propose the term "γ-secretase allosteric stabilizers" (GSAS) to distinguish these compounds from the rather heterogenous class of GSM. The GSAS represent, in theory, a precision medicine approach to the prevention of amyloid deposition, as they specifically target a discrete aspect in a complex cell biological signalling mechanism that initiates the pathological processes leading to Alzheimer's disease.
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Affiliation(s)
- Bart De Strooper
- Dementia Research Institute, Institute of Neurology, University College London, at the Francis Crick Institute, London, NW1 AT, UK.
- Laboratory for the Research of Neurodegenerative Diseases, VIB Center for Brain & Disease Research, and Leuven Brain Institute, KU Leuven, Leuven, 3000, Belgium.
| | - Eric Karran
- Cambridge Research Center, AbbVie, Inc., Cambridge, MA, USA
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22
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Hayes-Larson E, Ackley SF, Turney IC, La Joie R, Mayeda ER, Glymour MM. Considerations for Use of Blood-Based Biomarkers in Epidemiologic Dementia Research. Am J Epidemiol 2024; 193:527-535. [PMID: 37846130 PMCID: PMC10911539 DOI: 10.1093/aje/kwad197] [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] [Received: 03/03/2023] [Revised: 07/13/2023] [Accepted: 10/05/2023] [Indexed: 10/18/2023] Open
Abstract
Dementia represents a growing public health burden with large social, racial, and ethnic disparities. The etiology of dementia is poorly understood, and the lack of robust biomarkers in diverse, population-representative samples is a barrier to moving dementia research forward. Existing biomarkers and other measures of pathology-derived from neuropathology, neuroimaging, and cerebrospinal fluid samples-are commonly collected from predominantly White and highly educated samples drawn from academic medical centers in urban settings. Blood-based biomarkers are noninvasive and less expensive, offering promise to expand our understanding of the pathophysiology of dementia, including in participants from historically excluded groups. Although largely not yet approved by the Food and Drug Administration or used in clinical settings, blood-based biomarkers are increasingly included in epidemiologic studies on dementia. Blood-based biomarkers in epidemiologic research may allow the field to more accurately understand the multifactorial etiology and sequence of events that characterize dementia-related pathophysiological changes. As blood-based dementia biomarkers continue to be developed and incorporated into research and practice, we outline considerations for using them in dementia epidemiology, and illustrate key concepts with Alzheimer's Disease Neuroimaging Initiative (2003-present) data. We focus on measurement, including both validity and reliability, and on the use of dementia blood-based biomarkers to promote equity in dementia research and cognitive aging. This article is part of a Special Collection on Mental Health.
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Affiliation(s)
| | | | | | | | | | - M Maria Glymour
- Correspondence to Dr. M. Maria Glymour, Department of Epidemiology, Boston University School of Public Health, 715 Albany Street, Boston, MA 02118 (e-mail: )
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23
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Lopez OL, Villemagne VL, Chang YF, Cohen AD, Klunk WE, Mathis CA, Pascoal T, Ikonomovic MD, Rowe C, Dore V, Snitz BE, Lopresti BJ, Kamboh MI, Aizenstein HJ, Kuller LH. Association Between β-Amyloid Accumulation and Incident Dementia in Individuals 80 Years or Older Without Dementia. Neurology 2024; 102:e207920. [PMID: 38165336 PMCID: PMC10870745 DOI: 10.1212/wnl.0000000000207920] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Accepted: 10/03/2023] [Indexed: 01/03/2024] Open
Abstract
BACKGROUND AND OBJECTIVES While the highest prevalence of dementia occurs in individuals older than 80 years, most imaging studies focused on younger populations. The rates of β-amyloid (Aβ) accumulation and the effect of Alzheimer disease (AD) pathology on progression to dementia in this age group remain unexplored. In this study, we examined the relationship between changes in Aβ deposition over time and incident dementia in nondemented individuals followed during a period of 11 years. METHODS We examined 94 participants (age 85.9 + 2.8 years) who had up to 5 measurements of Pittsburgh compound-B (PiB)-PET and clinical evaluations from 2009 to 2020. All 94 participants had 2 PiB-PET scans, 76 participants had 3 PiB-PET scans, 18 participants had 4 PiB-PET scans, and 10 participants had 5 PiB-PET scans. The rates of Aβ deposition were compared with 120 nondemented individuals younger than 80 years (69.3 ± 5.4 years) from the Australian Imaging, Biomarker, and Lifestyle (AIBL) study who had 3 or more annual PiB-PET assessments. RESULTS By 2020, 49% of the participants developed dementia and 63% were deceased. There was a gradual increase in Aβ deposition in all participants whether they were considered Aβ positive or negative at baseline. In a Cox model controlled for age, sex, education level, APOE-4 allele, baseline Mini-Mental State Examination, and mortality, short-term change in Aβ deposition was not significantly associated with incident dementia (HR 2.19 (0.41-11.73). However, baseline Aβ burden, cortical thickness, and white matter lesions volume were the predictors of incident dementia. Aβ accumulation was faster (p = 0.01) in the older cohort (5.6%/year) when compared with AIBL (4.1%/year). In addition, baseline Aβ deposition was a predictor of short-term change (mean time 1.88 years). DISCUSSION There was an accelerated Aβ accumulation in cognitively normal individuals older than 80 years. Baseline Aβ deposition was a determinant of incident dementia and short-term change in Aβ deposition suggesting that an active Aβ pathologic process was present when these participants were cognitively normal. Consequently, age may not be a limiting factor for the use of the emergent anti-Aβ therapies.
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Affiliation(s)
- Oscar L Lopez
- From the Departments of Neurology (O.L.L., W.E.K., M.D.I., B.E.S.), Psychiatry (O.L.L., V.L.V., A.D.C., W.E.K., T.P., H.J.A.), Neurosurgery (Y.-F.C.), Radiology (A.D.C., C.A.M., B.J.L.), Epidemiology (L.H.K.), and Human Genetics, Graduate School of Public Health (M.I.K.), University of Pittsburgh, PA; Department of Molecular Imaging and Therapy (C.R.), Austin Health, Melbourne; The Florey Institute of Neuroscience and Mental Health (C.R., V.D.), University of Melbourne; and CSIRO Health and Biosecurity (V.D.), Melbourne, Australia
| | - Victor L Villemagne
- From the Departments of Neurology (O.L.L., W.E.K., M.D.I., B.E.S.), Psychiatry (O.L.L., V.L.V., A.D.C., W.E.K., T.P., H.J.A.), Neurosurgery (Y.-F.C.), Radiology (A.D.C., C.A.M., B.J.L.), Epidemiology (L.H.K.), and Human Genetics, Graduate School of Public Health (M.I.K.), University of Pittsburgh, PA; Department of Molecular Imaging and Therapy (C.R.), Austin Health, Melbourne; The Florey Institute of Neuroscience and Mental Health (C.R., V.D.), University of Melbourne; and CSIRO Health and Biosecurity (V.D.), Melbourne, Australia
| | - Yue-Fang Chang
- From the Departments of Neurology (O.L.L., W.E.K., M.D.I., B.E.S.), Psychiatry (O.L.L., V.L.V., A.D.C., W.E.K., T.P., H.J.A.), Neurosurgery (Y.-F.C.), Radiology (A.D.C., C.A.M., B.J.L.), Epidemiology (L.H.K.), and Human Genetics, Graduate School of Public Health (M.I.K.), University of Pittsburgh, PA; Department of Molecular Imaging and Therapy (C.R.), Austin Health, Melbourne; The Florey Institute of Neuroscience and Mental Health (C.R., V.D.), University of Melbourne; and CSIRO Health and Biosecurity (V.D.), Melbourne, Australia
| | - Ann D Cohen
- From the Departments of Neurology (O.L.L., W.E.K., M.D.I., B.E.S.), Psychiatry (O.L.L., V.L.V., A.D.C., W.E.K., T.P., H.J.A.), Neurosurgery (Y.-F.C.), Radiology (A.D.C., C.A.M., B.J.L.), Epidemiology (L.H.K.), and Human Genetics, Graduate School of Public Health (M.I.K.), University of Pittsburgh, PA; Department of Molecular Imaging and Therapy (C.R.), Austin Health, Melbourne; The Florey Institute of Neuroscience and Mental Health (C.R., V.D.), University of Melbourne; and CSIRO Health and Biosecurity (V.D.), Melbourne, Australia
| | - William E Klunk
- From the Departments of Neurology (O.L.L., W.E.K., M.D.I., B.E.S.), Psychiatry (O.L.L., V.L.V., A.D.C., W.E.K., T.P., H.J.A.), Neurosurgery (Y.-F.C.), Radiology (A.D.C., C.A.M., B.J.L.), Epidemiology (L.H.K.), and Human Genetics, Graduate School of Public Health (M.I.K.), University of Pittsburgh, PA; Department of Molecular Imaging and Therapy (C.R.), Austin Health, Melbourne; The Florey Institute of Neuroscience and Mental Health (C.R., V.D.), University of Melbourne; and CSIRO Health and Biosecurity (V.D.), Melbourne, Australia
| | - Chester A Mathis
- From the Departments of Neurology (O.L.L., W.E.K., M.D.I., B.E.S.), Psychiatry (O.L.L., V.L.V., A.D.C., W.E.K., T.P., H.J.A.), Neurosurgery (Y.-F.C.), Radiology (A.D.C., C.A.M., B.J.L.), Epidemiology (L.H.K.), and Human Genetics, Graduate School of Public Health (M.I.K.), University of Pittsburgh, PA; Department of Molecular Imaging and Therapy (C.R.), Austin Health, Melbourne; The Florey Institute of Neuroscience and Mental Health (C.R., V.D.), University of Melbourne; and CSIRO Health and Biosecurity (V.D.), Melbourne, Australia
| | - Tharick Pascoal
- From the Departments of Neurology (O.L.L., W.E.K., M.D.I., B.E.S.), Psychiatry (O.L.L., V.L.V., A.D.C., W.E.K., T.P., H.J.A.), Neurosurgery (Y.-F.C.), Radiology (A.D.C., C.A.M., B.J.L.), Epidemiology (L.H.K.), and Human Genetics, Graduate School of Public Health (M.I.K.), University of Pittsburgh, PA; Department of Molecular Imaging and Therapy (C.R.), Austin Health, Melbourne; The Florey Institute of Neuroscience and Mental Health (C.R., V.D.), University of Melbourne; and CSIRO Health and Biosecurity (V.D.), Melbourne, Australia
| | - Milos D Ikonomovic
- From the Departments of Neurology (O.L.L., W.E.K., M.D.I., B.E.S.), Psychiatry (O.L.L., V.L.V., A.D.C., W.E.K., T.P., H.J.A.), Neurosurgery (Y.-F.C.), Radiology (A.D.C., C.A.M., B.J.L.), Epidemiology (L.H.K.), and Human Genetics, Graduate School of Public Health (M.I.K.), University of Pittsburgh, PA; Department of Molecular Imaging and Therapy (C.R.), Austin Health, Melbourne; The Florey Institute of Neuroscience and Mental Health (C.R., V.D.), University of Melbourne; and CSIRO Health and Biosecurity (V.D.), Melbourne, Australia
| | - Christopher Rowe
- From the Departments of Neurology (O.L.L., W.E.K., M.D.I., B.E.S.), Psychiatry (O.L.L., V.L.V., A.D.C., W.E.K., T.P., H.J.A.), Neurosurgery (Y.-F.C.), Radiology (A.D.C., C.A.M., B.J.L.), Epidemiology (L.H.K.), and Human Genetics, Graduate School of Public Health (M.I.K.), University of Pittsburgh, PA; Department of Molecular Imaging and Therapy (C.R.), Austin Health, Melbourne; The Florey Institute of Neuroscience and Mental Health (C.R., V.D.), University of Melbourne; and CSIRO Health and Biosecurity (V.D.), Melbourne, Australia
| | - Vincent Dore
- From the Departments of Neurology (O.L.L., W.E.K., M.D.I., B.E.S.), Psychiatry (O.L.L., V.L.V., A.D.C., W.E.K., T.P., H.J.A.), Neurosurgery (Y.-F.C.), Radiology (A.D.C., C.A.M., B.J.L.), Epidemiology (L.H.K.), and Human Genetics, Graduate School of Public Health (M.I.K.), University of Pittsburgh, PA; Department of Molecular Imaging and Therapy (C.R.), Austin Health, Melbourne; The Florey Institute of Neuroscience and Mental Health (C.R., V.D.), University of Melbourne; and CSIRO Health and Biosecurity (V.D.), Melbourne, Australia
| | - Beth E Snitz
- From the Departments of Neurology (O.L.L., W.E.K., M.D.I., B.E.S.), Psychiatry (O.L.L., V.L.V., A.D.C., W.E.K., T.P., H.J.A.), Neurosurgery (Y.-F.C.), Radiology (A.D.C., C.A.M., B.J.L.), Epidemiology (L.H.K.), and Human Genetics, Graduate School of Public Health (M.I.K.), University of Pittsburgh, PA; Department of Molecular Imaging and Therapy (C.R.), Austin Health, Melbourne; The Florey Institute of Neuroscience and Mental Health (C.R., V.D.), University of Melbourne; and CSIRO Health and Biosecurity (V.D.), Melbourne, Australia
| | - Brian J Lopresti
- From the Departments of Neurology (O.L.L., W.E.K., M.D.I., B.E.S.), Psychiatry (O.L.L., V.L.V., A.D.C., W.E.K., T.P., H.J.A.), Neurosurgery (Y.-F.C.), Radiology (A.D.C., C.A.M., B.J.L.), Epidemiology (L.H.K.), and Human Genetics, Graduate School of Public Health (M.I.K.), University of Pittsburgh, PA; Department of Molecular Imaging and Therapy (C.R.), Austin Health, Melbourne; The Florey Institute of Neuroscience and Mental Health (C.R., V.D.), University of Melbourne; and CSIRO Health and Biosecurity (V.D.), Melbourne, Australia
| | - M Ilyas Kamboh
- From the Departments of Neurology (O.L.L., W.E.K., M.D.I., B.E.S.), Psychiatry (O.L.L., V.L.V., A.D.C., W.E.K., T.P., H.J.A.), Neurosurgery (Y.-F.C.), Radiology (A.D.C., C.A.M., B.J.L.), Epidemiology (L.H.K.), and Human Genetics, Graduate School of Public Health (M.I.K.), University of Pittsburgh, PA; Department of Molecular Imaging and Therapy (C.R.), Austin Health, Melbourne; The Florey Institute of Neuroscience and Mental Health (C.R., V.D.), University of Melbourne; and CSIRO Health and Biosecurity (V.D.), Melbourne, Australia
| | - Howard J Aizenstein
- From the Departments of Neurology (O.L.L., W.E.K., M.D.I., B.E.S.), Psychiatry (O.L.L., V.L.V., A.D.C., W.E.K., T.P., H.J.A.), Neurosurgery (Y.-F.C.), Radiology (A.D.C., C.A.M., B.J.L.), Epidemiology (L.H.K.), and Human Genetics, Graduate School of Public Health (M.I.K.), University of Pittsburgh, PA; Department of Molecular Imaging and Therapy (C.R.), Austin Health, Melbourne; The Florey Institute of Neuroscience and Mental Health (C.R., V.D.), University of Melbourne; and CSIRO Health and Biosecurity (V.D.), Melbourne, Australia
| | - Lewis H Kuller
- From the Departments of Neurology (O.L.L., W.E.K., M.D.I., B.E.S.), Psychiatry (O.L.L., V.L.V., A.D.C., W.E.K., T.P., H.J.A.), Neurosurgery (Y.-F.C.), Radiology (A.D.C., C.A.M., B.J.L.), Epidemiology (L.H.K.), and Human Genetics, Graduate School of Public Health (M.I.K.), University of Pittsburgh, PA; Department of Molecular Imaging and Therapy (C.R.), Austin Health, Melbourne; The Florey Institute of Neuroscience and Mental Health (C.R., V.D.), University of Melbourne; and CSIRO Health and Biosecurity (V.D.), Melbourne, Australia
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24
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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: 4.5] [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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25
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Gollan TH, Stasenko A, Li C, Smirnov DS, Galasko D, Salmon DP. Autocorrection if→of function words in reading aloud: A novel marker of Alzheimer's risk. Neuropsychology 2023; 37:813-826. [PMID: 35925735 PMCID: PMC9898462 DOI: 10.1037/neu0000829] [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: 02/06/2023] Open
Abstract
OBJECTIVE The present study investigated cognitive mechanisms underlying the ability to stop "autocorrect" errors elicited by unexpected words in a read-aloud task, and the utility of autocorrection for predicting Alzheimer's disease (AD) biomarkers. METHOD Cognitively normal participants (total n = 85; n = 64 with cerebrospinal fluid [CSF] biomarkers) read aloud six short paragraphs in which 10 critical target words were replaced with autocorrect targets, for example, The player who scored that final [paint] for the local team reported [him] experience. Autocorrect targets either replaced the most expected/dominant completion (i.e., point) or a less expected/nondominant completion (i.e., basket), and within each paragraph half of the autocorrect targets were content words (e.g., point/paint) and half were function words (e.g., his/him). Participants were instructed to avoid autocorrecting. RESULTS Participants produced more autocorrect errors in paragraphs with dominant than with nondominant targets, and with function than with content targets. Cognitively normal participants with high CSF Tau/Aβ42 (i.e., an AD-like biomarker profile) produced more autocorrect total errors than those below the Tau/Aβ42 threshold, an effect also significant with dominant-function targets alone (e.g., saying his instead of him). A logistic regression model with dominant-function errors and age showed errors as the stronger predictor of biomarker status (sensitivity 83%; specificity 85%). CONCLUSIONS Difficulty stopping autocorrect errors is associated with biomarkers indicating preclinical AD, and reveals promise as a diagnostic tool. Greater vulnerability of function over content words to autocorrection in individuals with AD-like biomarkers implicates monitoring and attention (rather than semantic processing) in the earliest of cognitive changes associated with AD risk. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
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Affiliation(s)
- Tamar H. Gollan
- Department of Neurosciences, University of California, San Diego
| | - Alena Stasenko
- Department of Neurosciences, University of California, San Diego
| | - Chuchu Li
- Department of Neurosciences, University of California, San Diego
| | - Denis S. Smirnov
- Department of Neurosciences, University of California, San Diego
| | - Douglas Galasko
- Department of Neurosciences, University of California, San Diego
| | - David P. Salmon
- Department of Neurosciences, University of California, San Diego
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Jovalekic A, Roé-Vellvé N, Koglin N, Quintana ML, Nelson A, Diemling M, Lilja J, Gómez-González JP, Doré V, Bourgeat P, Whittington A, Gunn R, Stephens AW, Bullich S. Validation of quantitative assessment of florbetaben PET scans as an adjunct to the visual assessment across 15 software methods. Eur J Nucl Med Mol Imaging 2023; 50:3276-3289. [PMID: 37300571 PMCID: PMC10542295 DOI: 10.1007/s00259-023-06279-0] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Accepted: 05/19/2023] [Indexed: 06/12/2023]
Abstract
PURPOSE Amyloid positron emission tomography (PET) with [18F]florbetaben (FBB) is an established tool for detecting Aβ deposition in the brain in vivo based on visual assessment of PET scans. Quantitative measures are commonly used in the research context and allow continuous measurement of amyloid burden. The aim of this study was to demonstrate the robustness of FBB PET quantification. METHODS This is a retrospective analysis of FBB PET images from 589 subjects. PET scans were quantified with 15 analytical methods using nine software packages (MIMneuro, Hermes BRASS, Neurocloud, Neurology Toolkit, statistical parametric mapping (SPM8), PMOD Neuro, CapAIBL, non-negative matrix factorization (NMF), AmyloidIQ) that used several metrics to estimate Aβ load (SUVR, centiloid, amyloid load, and amyloid index). Six analytical methods reported centiloid (MIMneuro, standard centiloid, Neurology Toolkit, SPM8 (PET only), CapAIBL, NMF). All results were quality controlled. RESULTS The mean sensitivity, specificity, and accuracy were 96.1 ± 1.6%, 96.9 ± 1.0%, and 96.4 ± 1.1%, respectively, for all quantitative methods tested when compared to histopathology, where available. The mean percentage of agreement between binary quantitative assessment across all 15 methods and visual majority assessment was 92.4 ± 1.5%. Assessments of reliability, correlation analyses, and comparisons across software packages showed excellent performance and consistent results between analytical methods. CONCLUSION This study demonstrated that quantitative methods using both CE marked software and other widely available processing tools provided comparable results to visual assessments of FBB PET scans. Software quantification methods, such as centiloid analysis, can complement visual assessment of FBB PET images and could be used in the future for identification of early amyloid deposition, monitoring disease progression and treatment effectiveness.
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Affiliation(s)
| | | | | | | | | | | | | | | | - Vincent Doré
- Department of Molecular Imaging & Therapy, Austin Health, Melbourne, Australia
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Jo S, Lee H, Kim HJ, Suh CH, Kim SJ, Lee Y, Roh JH, Lee JH. Do radiomics or diffusion-tensor images provide additional information to predict brain amyloid-beta positivity? Sci Rep 2023; 13:9755. [PMID: 37328578 PMCID: PMC10275931 DOI: 10.1038/s41598-023-36639-7] [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: 03/05/2023] [Accepted: 06/07/2023] [Indexed: 06/18/2023] Open
Abstract
The aim of the present study was to predict amyloid-beta positivity using a conventional T1-weighted image, radiomics, and a diffusion-tensor image obtained by magnetic resonance imaging (MRI). We included 186 patients with mild cognitive impairment (MCI) who underwent Florbetaben positron emission tomography (PET), MRI (three-dimensional T1-weighted and diffusion-tensor images), and neuropsychological tests at the Asan Medical Center. We developed a stepwise machine learning algorithm using demographics, T1 MRI features (volume, cortical thickness and radiomics), and diffusion-tensor image to distinguish amyloid-beta positivity on Florbetaben PET. We compared the performance of each algorithm based on the MRI features used. The study population included 72 patients with MCI in the amyloid-beta-negative group and 114 patients with MCI in the amyloid-beta-positive group. The machine learning algorithm using T1 volume performed better than that using only clinical information (mean area under the curve [AUC]: 0.73 vs. 0.69, p < 0.001). The machine learning algorithm using T1 volume showed better performance than that using cortical thickness (mean AUC: 0.73 vs. 0.68, p < 0.001) or texture (mean AUC: 0.73 vs. 0.71, p = 0.002). The performance of the machine learning algorithm using fractional anisotropy in addition to T1 volume was not better than that using T1 volume alone (mean AUC: 0.73 vs. 0.73, p = 0.60). Among MRI features, T1 volume was the best predictor of amyloid PET positivity. Radiomics or diffusion-tensor images did not provide additional benefits.
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Affiliation(s)
- Sungyang Jo
- Department of Neurology, Asan Medical Center, University of Ulsan College of Medicine, 88, Olympic-ro 43-gil, Songpa-gu, Seoul, 05505, Republic of Korea
| | - Hyunna Lee
- Bigdata Research Center, Asan Institute for Life Science, Asan Medical Center, Seoul, Republic of Korea
| | - Hyung-Ji Kim
- Department of Neurology, Uijeongbu Eulji Medical Center, Eulji University School of Medicine, Uijeongbu, Republic of Korea
| | - Chong Hyun Suh
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Sang Joon Kim
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Yoojin Lee
- Department of Neurology, Asan Medical Center, University of Ulsan College of Medicine, 88, Olympic-ro 43-gil, Songpa-gu, Seoul, 05505, Republic of Korea
| | - Jee Hoon Roh
- Department of Physiology, Korea University College of Medicine, Seoul, Republic of Korea
| | - Jae-Hong Lee
- Department of Neurology, Asan Medical Center, University of Ulsan College of Medicine, 88, Olympic-ro 43-gil, Songpa-gu, Seoul, 05505, Republic of Korea.
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28
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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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Hansson O, Blennow K, Zetterberg H, Dage J. Blood biomarkers for Alzheimer's disease in clinical practice and trials. NATURE AGING 2023; 3:506-519. [PMID: 37202517 PMCID: PMC10979350 DOI: 10.1038/s43587-023-00403-3] [Citation(s) in RCA: 156] [Impact Index Per Article: 78.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Accepted: 03/21/2023] [Indexed: 05/20/2023]
Abstract
Blood-based biomarkers hold great promise to revolutionize the diagnostic and prognostic work-up of Alzheimer's disease (AD) in clinical practice. This is very timely, considering the recent development of anti-amyloid-β (Aβ) immunotherapies. Several assays for measuring phosphorylated tau (p-tau) in plasma exhibit high diagnostic accuracy in distinguishing AD from all other neurodegenerative diseases in patients with cognitive impairment. Prognostic models based on plasma p-tau levels can also predict future development of AD dementia in patients with mild cognitive complaints. The use of such high-performing plasma p-tau assays in the clinical practice of specialist memory clinics would reduce the need for more costly investigations involving cerebrospinal fluid samples or positron emission tomography. Indeed, blood-based biomarkers already facilitate identification of individuals with pre-symptomatic AD in the context of clinical trials. Longitudinal measurements of such biomarkers will also improve the detection of relevant disease-modifying effects of new drugs or lifestyle interventions.
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Affiliation(s)
- Oskar Hansson
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Faculty of Medicine, Lund University, Lund, Sweden.
- Memory Clinic, Skåne University Hospital, Lund, Sweden.
| | - Kaj Blennow
- Department of Psychiatry and Neurochemistry, the Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
| | - Henrik Zetterberg
- Department of Psychiatry and Neurochemistry, the Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
- Department of Neurodegenerative Disease, UCL Institute of Neurology, London, UK
- UK Dementia Research Institute at UCL, London, UK
- Hong Kong Center for 27 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, WI, USA
| | - Jeffrey Dage
- Department of Neurology, Indiana University School of Medicine, Indianapolis, IN, USA
- Indiana Alzheimer's Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, USA
- Stark Neurosciences Research Institute, Indiana University School of Medicine, Indianapolis, IN, USA
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The association of subjective sleep characteristics and plasma biomarkers of Alzheimer's disease pathology in older cognitively unimpaired adults with higher amyloid-β burden. J Neurol 2023; 270:3008-3021. [PMID: 36806992 DOI: 10.1007/s00415-023-11626-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2023] [Revised: 02/10/2023] [Accepted: 02/12/2023] [Indexed: 02/23/2023]
Abstract
We aimed to investigate the association of subjective sleep characteristics and plasma Alzheimer's disease (AD) biomarkers in older cognitively unimpaired adults with higher amyloid-β (Aβ) burden. Unimpaired cognition was determined by education-adjusted performance for the Mini-Mental State Examination and exclusion of dementia and mild cognitive impairment via standardized neuropsychological tests. We used Pittsburgh Sleep Quality Index (PSQI) to assess subjective sleep quality. The participants also underwent examination of plasma AD biomarkers and 18F-florbetapir PET scan. Correlation and multiple linear regression analyses were used to investigate the association between subjective sleep characteristics and AD biomarkers. A total of 335 participants were included and 114 were Aβ-PET positive. Multivariable regression analysis showed sleep duration > 8 h and sleep disturbance were associated with Aβ deposition in total participants. Two multiple linear regression models were applied and the results revealed in participants with Aβ-PET (+), falling asleep at ≥ 22:00 to ≤ 23:00 was associated with higher levels of Aβ42 and Aβ42/40. Other associations with higher Aβ42/40 and standard uptake value ratio contained sleep efficiency value, sleep efficiency ≥ 75%, no/mild daytime dysfunction and PSQI score ≤ 5. Higher p-Tau-181 level was associated with sleep latency > 30 min in Aβ-PET (+) group and moderate/severe sleep disturbance in Aβ-PET (-) group. Our data suggests sleep duration ≤ 8 h and no/mild sleep disturbance may be related to less Aβ burden. In participants with Aβ deposition, falling asleep at 22:00 to 23:00, higher sleep efficiency (at least ≥ 75%), no/mild daytime dysfunction, sleep latency ≤ 30 min, and good sleep quality may help improve AD pathology.
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31
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Nakaya M, Sato N, Matsuda H, Maikusa N, Shigemoto Y, Sone D, Yamao T, Ogawa M, Kimura Y, Chiba E, Ohnishi M, Kato K, Okita K, Tsukamoto T, Yokoi Y, Sakata M, Abe O. Free water derived by multi-shell diffusion MRI reflects tau/neuroinflammatory pathology in Alzheimer's disease. ALZHEIMER'S & DEMENTIA (NEW YORK, N. Y.) 2022; 8:e12356. [PMID: 36304723 PMCID: PMC9594557 DOI: 10.1002/trc2.12356] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/18/2022] [Revised: 08/03/2022] [Accepted: 08/20/2022] [Indexed: 11/23/2022]
Abstract
Introduction Free-water (FW) imaging, a new analysis method for diffusion magnetic resonance imaging (MRI), can indicate neuroinflammation and degeneration. We evaluated FW in Alzheimer's disease (AD) using tau/inflammatory and amyloid positron emission tomography (PET). Methods Seventy-one participants underwent multi-shell diffusion MRI, 18F-THK5351 PET, 11C-Pittsburgh compound B PET, and neuropsychological assessments. They were categorized into two groups: healthy controls (HCs) (n = 40) and AD-spectrum group (AD-S) (n = 31) using the Centiloid scale with amyloid PET and cognitive function. We analyzed group comparisons in FW and PET, correlations between FW and PET, and correlation analysis with neuropsychological scores. Results In AD-S group, there was a significant positive correlation between FW and 18F-THK5351 in the temporal lobes. In addition, there were negative correlations between FW and cognitive function in the temporal lobe and cingulate gyrus, and negative correlations between 18F-THK5351 and cognitive function in the same regions. Discussion FW imaging could be a biomarker for tau in AD alongside clinical correlations.
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Affiliation(s)
- Moto Nakaya
- Departmentof RadiologyNational Center Hospital of Neurology and PsychiatryOgawa‐HigashiKodairaTokyoJapan
- Department of RadiologyGraduate School of MedicineUniversity of TokyoHongoBunkyo‐kuTokyoJapan
| | - Noriko Sato
- Departmentof RadiologyNational Center Hospital of Neurology and PsychiatryOgawa‐HigashiKodairaTokyoJapan
| | - Hiroshi Matsuda
- Departmentof RadiologyNational Center Hospital of Neurology and PsychiatryOgawa‐HigashiKodairaTokyoJapan
- Drug Discovery and Cyclotron Research CenterSouthern TOHOKU Research Institute for NeuroscienceKoriyamaJapan
| | - Norihide Maikusa
- Departmentof RadiologyNational Center Hospital of Neurology and PsychiatryOgawa‐HigashiKodairaTokyoJapan
| | - Yoko Shigemoto
- Departmentof RadiologyNational Center Hospital of Neurology and PsychiatryOgawa‐HigashiKodairaTokyoJapan
| | - Daichi Sone
- Department of PsychiatryThe Jikei University School of MedicineTokyoJapan
- Integrative Brain Imaging CenterNational Center of Neurology and PsychiatryTokyoJapan
| | - Tensho Yamao
- Department of Radiological SciencesSchool of Health SciencesFukushima Medical UniversityFukushimaJapan
| | - Masayo Ogawa
- Integrative Brain Imaging CenterNational Center of Neurology and PsychiatryTokyoJapan
| | - Yukio Kimura
- Departmentof RadiologyNational Center Hospital of Neurology and PsychiatryOgawa‐HigashiKodairaTokyoJapan
| | - Emiko Chiba
- Departmentof RadiologyNational Center Hospital of Neurology and PsychiatryOgawa‐HigashiKodairaTokyoJapan
| | - Masahiro Ohnishi
- Departmentof RadiologyNational Center Hospital of Neurology and PsychiatryOgawa‐HigashiKodairaTokyoJapan
| | - Koichi Kato
- Integrative Brain Imaging CenterNational Center of Neurology and PsychiatryTokyoJapan
| | - Kyoji Okita
- Integrative Brain Imaging CenterNational Center of Neurology and PsychiatryTokyoJapan
| | - Tadashi Tsukamoto
- Department of NeurologyNational Center of Neurology and PsychiatryKodairaTokyoJapan
| | - Yuma Yokoi
- Department of PsychiatryNational Center of Neurology and PsychiatryKodairaTokyoJapan
| | - Masuhiro Sakata
- Department of PsychiatryNational Center of Neurology and PsychiatryKodairaTokyoJapan
| | - Osamu Abe
- Department of RadiologyGraduate School of MedicineUniversity of TokyoHongoBunkyo‐kuTokyoJapan
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Morató X, Pytel V, Jofresa S, Ruiz A, Boada M. Symptomatic and Disease-Modifying Therapy Pipeline for Alzheimer's Disease: Towards a Personalized Polypharmacology Patient-Centered Approach. Int J Mol Sci 2022; 23:9305. [PMID: 36012569 PMCID: PMC9409252 DOI: 10.3390/ijms23169305] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Revised: 08/11/2022] [Accepted: 08/12/2022] [Indexed: 02/07/2023] Open
Abstract
Since 1906, when Dr. Alois Alzheimer first described in a patient "a peculiar severe disease process of the cerebral cortex", people suffering from this pathology have been waiting for a breakthrough therapy. Alzheimer's disease (AD) is an irreversible, progressive neurodegenerative brain disorder and the most common form of dementia in the elderly with a long presymptomatic phase. Worldwide, approximately 50 million people are living with dementia, with AD comprising 60-70% of cases. Pathologically, AD is characterized by the deposition of amyloid β-peptide (Aβ) in the neuropil (neuritic plaques) and blood vessels (amyloid angiopathy), and by the accumulation of hyperphosphorylated tau in neurons (neurofibrillary tangles) in the brain, with associated loss of synapses and neurons, together with glial activation, and neuroinflammation, resulting in cognitive deficits and eventually dementia. The current competitive landscape in AD consists of symptomatic treatments, of which there are currently six approved medications: three AChEIs (donepezil, rivastigmine, and galantamine), one NMDA-R antagonist (memantine), one combination therapy (memantine/donepezil), and GV-971 (sodium oligomannate, a mixture of oligosaccharides derived from algae) only approved in China. Improvements to the approved therapies, such as easier routes of administration and reduced dosing frequencies, along with the developments of new strategies and combined treatments are expected to occur within the next decade and will positively impact the way the disease is managed. Recently, Aducanumab, the first disease-modifying therapy (DMT) has been approved for AD, and several DMTs are in advanced stages of clinical development or regulatory review. Small molecules, mAbs, or multimodal strategies showing promise in animal studies have not confirmed that promise in the clinic (where small to moderate changes in clinical efficacy have been observed), and therefore, there is a significant unmet need for a better understanding of the AD pathogenesis and the exploration of alternative etiologies and therapeutic effective disease-modifying therapies strategies for AD. Therefore, a critical review of the disease-modifying therapy pipeline for Alzheimer's disease is needed.
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Affiliation(s)
- Xavier Morató
- Research Center and Memory Clinic, Fundació ACE, Institut Català de Neurociències Aplicades, Universitat Internacional de Catalunya, 08017 Barcelona, Spain
| | - Vanesa Pytel
- Research Center and Memory Clinic, Fundació ACE, Institut Català de Neurociències Aplicades, Universitat Internacional de Catalunya, 08017 Barcelona, Spain
| | - Sara Jofresa
- Research Center and Memory Clinic, Fundació ACE, Institut Català de Neurociències Aplicades, Universitat Internacional de Catalunya, 08017 Barcelona, Spain
| | - Agustín Ruiz
- Research Center and Memory Clinic, Fundació ACE, Institut Català de Neurociències Aplicades, Universitat Internacional de Catalunya, 08017 Barcelona, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Neurodegenerativas (CIBERNED), Instituto de Salud Carlos III, 28029 Madrid, Spain
| | - Mercè Boada
- Research Center and Memory Clinic, Fundació ACE, Institut Català de Neurociències Aplicades, Universitat Internacional de Catalunya, 08017 Barcelona, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Neurodegenerativas (CIBERNED), Instituto de Salud Carlos III, 28029 Madrid, Spain
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Pemberton HG, Collij LE, Heeman F, Bollack A, Shekari M, Salvadó G, Alves IL, Garcia DV, Battle M, Buckley C, Stephens AW, Bullich S, Garibotto V, Barkhof F, Gispert JD, Farrar G. Quantification of amyloid PET for future clinical use: a state-of-the-art review. Eur J Nucl Med Mol Imaging 2022; 49:3508-3528. [PMID: 35389071 PMCID: PMC9308604 DOI: 10.1007/s00259-022-05784-y] [Citation(s) in RCA: 85] [Impact Index Per Article: 28.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2021] [Accepted: 03/25/2022] [Indexed: 12/15/2022]
Abstract
Amyloid-β (Aβ) pathology is one of the earliest detectable brain changes in Alzheimer's disease (AD) pathogenesis. The overall load and spatial distribution of brain Aβ can be determined in vivo using positron emission tomography (PET), for which three fluorine-18 labelled radiotracers have been approved for clinical use. In clinical practice, trained readers will categorise scans as either Aβ positive or negative, based on visual inspection. Diagnostic decisions are often based on these reads and patient selection for clinical trials is increasingly guided by amyloid status. However, tracer deposition in the grey matter as a function of amyloid load is an inherently continuous process, which is not sufficiently appreciated through binary cut-offs alone. State-of-the-art methods for amyloid PET quantification can generate tracer-independent measures of Aβ burden. Recent research has shown the ability of these quantitative measures to highlight pathological changes at the earliest stages of the AD continuum and generate more sensitive thresholds, as well as improving diagnostic confidence around established binary cut-offs. With the recent FDA approval of aducanumab and more candidate drugs on the horizon, early identification of amyloid burden using quantitative measures is critical for enrolling appropriate subjects to help establish the optimal window for therapeutic intervention and secondary prevention. In addition, quantitative amyloid measurements are used for treatment response monitoring in clinical trials. In clinical settings, large multi-centre studies have shown that amyloid PET results change both diagnosis and patient management and that quantification can accurately predict rates of cognitive decline. Whether these changes in management reflect an improvement in clinical outcomes is yet to be determined and further validation work is required to establish the utility of quantification for supporting treatment endpoint decisions. In this state-of-the-art review, several tools and measures available for amyloid PET quantification are summarised and discussed. Use of these methods is growing both clinically and in the research domain. Concurrently, there is a duty of care to the wider dementia community to increase visibility and understanding of these methods.
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Affiliation(s)
- Hugh G Pemberton
- GE Healthcare, Amersham, 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.
| | - Lyduine E Collij
- Department of Radiology and Nuclear Medicine, Amsterdam Neurocience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Fiona Heeman
- Department of Radiology and Nuclear Medicine, Amsterdam Neurocience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Ariane Bollack
- Centre for Medical Image Computing (CMIC), Department of Medical Physics and Bioengineering, University College London, London, UK
| | - Mahnaz Shekari
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain
- Universitat Pompeu Fabra, Barcelona, Spain
- IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain
| | - Gemma Salvadó
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain
- Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Malmö, Sweden
| | - Isadora Lopes Alves
- Department of Radiology and Nuclear Medicine, Amsterdam Neurocience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Brain Research Center, Amsterdam, The Netherlands
| | - David Vallez Garcia
- Department of Radiology and Nuclear Medicine, Amsterdam Neurocience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Mark Battle
- GE Healthcare, Amersham, UK
- Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Malmö, Sweden
| | | | | | | | - Valentina Garibotto
- Division of Nuclear Medicine and Molecular Imaging, University Hospitals of Geneva, Geneva, Switzerland
- NIMTLab, Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Frederik Barkhof
- 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
- Department of Radiology and Nuclear Medicine, Amsterdam Neurocience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Juan Domingo Gispert
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain
- Universitat Pompeu Fabra, Barcelona, Spain
- IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain
- Centro de Investigación Biomédica en Red Bioingeniería, Biomateriales y Nanomedicina, Madrid, Spain
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Collij LE, Salvadó G, Wottschel V, Mastenbroek SE, Schoenmakers P, Heeman F, Aksman L, Wink AM, Berckel BNM, van de Flier WM, Scheltens P, Visser PJ, Barkhof F, Haller S, Gispert JD, Lopes Alves I. Spatial-Temporal Patterns of β-Amyloid Accumulation: A Subtype and Stage Inference Model Analysis. Neurology 2022; 98:e1692-e1703. [PMID: 35292558 PMCID: PMC9071373 DOI: 10.1212/wnl.0000000000200148] [Citation(s) in RCA: 57] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2021] [Accepted: 01/18/2022] [Indexed: 11/17/2022] Open
Abstract
BACKGROUND AND OBJECTIVES β-amyloid (Aβ) staging models assume a single spatial-temporal progression of amyloid accumulation. We assessed evidence for Aβ accumulation subtypes by applying the data-driven Subtype and Stage Inference (SuStaIn) model to amyloid-PET data. METHODS Amyloid-PET data of 3,010 participants were pooled from 6 cohorts (ALFA+, EMIF-AD, ABIDE, OASIS, and ADNI). Standardized uptake value ratios were calculated for 17 regions. We applied the SuStaIn algorithm to identify consistent subtypes in the pooled dataset based on the cross-validation information criterion and the most probable subtype/stage classification per scan. The effects of demographics and risk factors on subtype assignment were assessed using multinomial logistic regression. RESULTS Participants were mostly cognitively unimpaired (n = 1890 [62.8%]), had a mean age of 68.72 (SD 9.1) years, 42.1% were APOE ε4 carriers, and 51.8% were female. A 1-subtype model recovered the traditional amyloid accumulation trajectory, but SuStaIn identified 3 optimal subtypes, referred to as frontal, parietal, and occipital based on the first regions to show abnormality. Of the 788 (26.2%) with strong subtype assignment (>50% probability), the majority was assigned to frontal (n = 415 [52.5%]), followed by parietal (n = 199 [25.3%]) and occipital subtypes (n = 175 [22.2%]). Significant differences across subtypes included distinct proportions of APOE ε4 carriers (frontal 61.8%, parietal 57.1%, occipital 49.4%), participants with dementia (frontal 19.7%, parietal 19.1%, occipital 31.0%), and lower age for the parietal subtype (frontal/occipital 72.1 years, parietal 69.3 years). Higher amyloid (Centiloid) and CSF p-tau burden was observed for the frontal subtype; parietal and occipital subtypes did not differ. At follow-up, most participants (81.1%) maintained baseline subtype assignment and 25.6% progressed to a later stage. DISCUSSION Whereas a 1-trajectory model recovers the established pattern of amyloid accumulation, SuStaIn determined that 3 subtypes were optimal, showing distinct associations with Alzheimer disease risk factors. Further analyses to determine clinical utility are warranted.
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Affiliation(s)
- Lyduine E Collij
- From the Department of Radiology and Nuclear Medicine (L.E.C., V.W., S.E.M., P.S., F.H., A.M.W., B.N.M.B., F.B., I.L.A.), Alzheimer Center and Department of Neurology (W.M.v.d.F., P.S., P.J.V.), and Department of Epidemiology & Data Science (W.M.v.d.F.), Amsterdam UMC, Vrije Universiteit Amsterdam, the Netherlands; Barcelonaβeta Brain Research Center (BBRC) (G.S., J.D.G.), Pasqual Maragall Foundation; IMIM (Hospital del Mar Medical Research Institute) (G.S., J.D.G.), Barcelona, Spain; Stevens Neuroimaging and Informatics Institute (L.A.), Keck School of Medicine, University of Southern California, Los Angeles; Centre for Medical Image Computing and Queen Square Institute of Neurology (F.B.), UCL, UK; Faculty of Medicine of the University of Geneva (S.H.); CIMC-Centre d'Imagerie Médicale de Cornavin (S.H.), Genève, Switzerland; Department of Surgical Sciences, Radiology (S.H.), Uppsala University, Sweden; Department of Radiology (S.H.), Beijing Tiantan Hospital, Capital Medical University, Beijing, China; and Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN) (J.D.G.), Madrid, Spain
| | - Gemma Salvadó
- From the Department of Radiology and Nuclear Medicine (L.E.C., V.W., S.E.M., P.S., F.H., A.M.W., B.N.M.B., F.B., I.L.A.), Alzheimer Center and Department of Neurology (W.M.v.d.F., P.S., P.J.V.), and Department of Epidemiology & Data Science (W.M.v.d.F.), Amsterdam UMC, Vrije Universiteit Amsterdam, the Netherlands; Barcelonaβeta Brain Research Center (BBRC) (G.S., J.D.G.), Pasqual Maragall Foundation; IMIM (Hospital del Mar Medical Research Institute) (G.S., J.D.G.), Barcelona, Spain; Stevens Neuroimaging and Informatics Institute (L.A.), Keck School of Medicine, University of Southern California, Los Angeles; Centre for Medical Image Computing and Queen Square Institute of Neurology (F.B.), UCL, UK; Faculty of Medicine of the University of Geneva (S.H.); CIMC-Centre d'Imagerie Médicale de Cornavin (S.H.), Genève, Switzerland; Department of Surgical Sciences, Radiology (S.H.), Uppsala University, Sweden; Department of Radiology (S.H.), Beijing Tiantan Hospital, Capital Medical University, Beijing, China; and Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN) (J.D.G.), Madrid, Spain
| | - Viktor Wottschel
- From the Department of Radiology and Nuclear Medicine (L.E.C., V.W., S.E.M., P.S., F.H., A.M.W., B.N.M.B., F.B., I.L.A.), Alzheimer Center and Department of Neurology (W.M.v.d.F., P.S., P.J.V.), and Department of Epidemiology & Data Science (W.M.v.d.F.), Amsterdam UMC, Vrije Universiteit Amsterdam, the Netherlands; Barcelonaβeta Brain Research Center (BBRC) (G.S., J.D.G.), Pasqual Maragall Foundation; IMIM (Hospital del Mar Medical Research Institute) (G.S., J.D.G.), Barcelona, Spain; Stevens Neuroimaging and Informatics Institute (L.A.), Keck School of Medicine, University of Southern California, Los Angeles; Centre for Medical Image Computing and Queen Square Institute of Neurology (F.B.), UCL, UK; Faculty of Medicine of the University of Geneva (S.H.); CIMC-Centre d'Imagerie Médicale de Cornavin (S.H.), Genève, Switzerland; Department of Surgical Sciences, Radiology (S.H.), Uppsala University, Sweden; Department of Radiology (S.H.), Beijing Tiantan Hospital, Capital Medical University, Beijing, China; and Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN) (J.D.G.), Madrid, Spain
| | - Sophie E Mastenbroek
- From the Department of Radiology and Nuclear Medicine (L.E.C., V.W., S.E.M., P.S., F.H., A.M.W., B.N.M.B., F.B., I.L.A.), Alzheimer Center and Department of Neurology (W.M.v.d.F., P.S., P.J.V.), and Department of Epidemiology & Data Science (W.M.v.d.F.), Amsterdam UMC, Vrije Universiteit Amsterdam, the Netherlands; Barcelonaβeta Brain Research Center (BBRC) (G.S., J.D.G.), Pasqual Maragall Foundation; IMIM (Hospital del Mar Medical Research Institute) (G.S., J.D.G.), Barcelona, Spain; Stevens Neuroimaging and Informatics Institute (L.A.), Keck School of Medicine, University of Southern California, Los Angeles; Centre for Medical Image Computing and Queen Square Institute of Neurology (F.B.), UCL, UK; Faculty of Medicine of the University of Geneva (S.H.); CIMC-Centre d'Imagerie Médicale de Cornavin (S.H.), Genève, Switzerland; Department of Surgical Sciences, Radiology (S.H.), Uppsala University, Sweden; Department of Radiology (S.H.), Beijing Tiantan Hospital, Capital Medical University, Beijing, China; and Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN) (J.D.G.), Madrid, Spain
| | - Pierre Schoenmakers
- From the Department of Radiology and Nuclear Medicine (L.E.C., V.W., S.E.M., P.S., F.H., A.M.W., B.N.M.B., F.B., I.L.A.), Alzheimer Center and Department of Neurology (W.M.v.d.F., P.S., P.J.V.), and Department of Epidemiology & Data Science (W.M.v.d.F.), Amsterdam UMC, Vrije Universiteit Amsterdam, the Netherlands; Barcelonaβeta Brain Research Center (BBRC) (G.S., J.D.G.), Pasqual Maragall Foundation; IMIM (Hospital del Mar Medical Research Institute) (G.S., J.D.G.), Barcelona, Spain; Stevens Neuroimaging and Informatics Institute (L.A.), Keck School of Medicine, University of Southern California, Los Angeles; Centre for Medical Image Computing and Queen Square Institute of Neurology (F.B.), UCL, UK; Faculty of Medicine of the University of Geneva (S.H.); CIMC-Centre d'Imagerie Médicale de Cornavin (S.H.), Genève, Switzerland; Department of Surgical Sciences, Radiology (S.H.), Uppsala University, Sweden; Department of Radiology (S.H.), Beijing Tiantan Hospital, Capital Medical University, Beijing, China; and Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN) (J.D.G.), Madrid, Spain
| | - Fiona Heeman
- From the Department of Radiology and Nuclear Medicine (L.E.C., V.W., S.E.M., P.S., F.H., A.M.W., B.N.M.B., F.B., I.L.A.), Alzheimer Center and Department of Neurology (W.M.v.d.F., P.S., P.J.V.), and Department of Epidemiology & Data Science (W.M.v.d.F.), Amsterdam UMC, Vrije Universiteit Amsterdam, the Netherlands; Barcelonaβeta Brain Research Center (BBRC) (G.S., J.D.G.), Pasqual Maragall Foundation; IMIM (Hospital del Mar Medical Research Institute) (G.S., J.D.G.), Barcelona, Spain; Stevens Neuroimaging and Informatics Institute (L.A.), Keck School of Medicine, University of Southern California, Los Angeles; Centre for Medical Image Computing and Queen Square Institute of Neurology (F.B.), UCL, UK; Faculty of Medicine of the University of Geneva (S.H.); CIMC-Centre d'Imagerie Médicale de Cornavin (S.H.), Genève, Switzerland; Department of Surgical Sciences, Radiology (S.H.), Uppsala University, Sweden; Department of Radiology (S.H.), Beijing Tiantan Hospital, Capital Medical University, Beijing, China; and Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN) (J.D.G.), Madrid, Spain
| | - Leon Aksman
- From the Department of Radiology and Nuclear Medicine (L.E.C., V.W., S.E.M., P.S., F.H., A.M.W., B.N.M.B., F.B., I.L.A.), Alzheimer Center and Department of Neurology (W.M.v.d.F., P.S., P.J.V.), and Department of Epidemiology & Data Science (W.M.v.d.F.), Amsterdam UMC, Vrije Universiteit Amsterdam, the Netherlands; Barcelonaβeta Brain Research Center (BBRC) (G.S., J.D.G.), Pasqual Maragall Foundation; IMIM (Hospital del Mar Medical Research Institute) (G.S., J.D.G.), Barcelona, Spain; Stevens Neuroimaging and Informatics Institute (L.A.), Keck School of Medicine, University of Southern California, Los Angeles; Centre for Medical Image Computing and Queen Square Institute of Neurology (F.B.), UCL, UK; Faculty of Medicine of the University of Geneva (S.H.); CIMC-Centre d'Imagerie Médicale de Cornavin (S.H.), Genève, Switzerland; Department of Surgical Sciences, Radiology (S.H.), Uppsala University, Sweden; Department of Radiology (S.H.), Beijing Tiantan Hospital, Capital Medical University, Beijing, China; and Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN) (J.D.G.), Madrid, Spain
| | - Alle Meije Wink
- From the Department of Radiology and Nuclear Medicine (L.E.C., V.W., S.E.M., P.S., F.H., A.M.W., B.N.M.B., F.B., I.L.A.), Alzheimer Center and Department of Neurology (W.M.v.d.F., P.S., P.J.V.), and Department of Epidemiology & Data Science (W.M.v.d.F.), Amsterdam UMC, Vrije Universiteit Amsterdam, the Netherlands; Barcelonaβeta Brain Research Center (BBRC) (G.S., J.D.G.), Pasqual Maragall Foundation; IMIM (Hospital del Mar Medical Research Institute) (G.S., J.D.G.), Barcelona, Spain; Stevens Neuroimaging and Informatics Institute (L.A.), Keck School of Medicine, University of Southern California, Los Angeles; Centre for Medical Image Computing and Queen Square Institute of Neurology (F.B.), UCL, UK; Faculty of Medicine of the University of Geneva (S.H.); CIMC-Centre d'Imagerie Médicale de Cornavin (S.H.), Genève, Switzerland; Department of Surgical Sciences, Radiology (S.H.), Uppsala University, Sweden; Department of Radiology (S.H.), Beijing Tiantan Hospital, Capital Medical University, Beijing, China; and Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN) (J.D.G.), Madrid, Spain
| | - Bart N M Berckel
- From the Department of Radiology and Nuclear Medicine (L.E.C., V.W., S.E.M., P.S., F.H., A.M.W., B.N.M.B., F.B., I.L.A.), Alzheimer Center and Department of Neurology (W.M.v.d.F., P.S., P.J.V.), and Department of Epidemiology & Data Science (W.M.v.d.F.), Amsterdam UMC, Vrije Universiteit Amsterdam, the Netherlands; Barcelonaβeta Brain Research Center (BBRC) (G.S., J.D.G.), Pasqual Maragall Foundation; IMIM (Hospital del Mar Medical Research Institute) (G.S., J.D.G.), Barcelona, Spain; Stevens Neuroimaging and Informatics Institute (L.A.), Keck School of Medicine, University of Southern California, Los Angeles; Centre for Medical Image Computing and Queen Square Institute of Neurology (F.B.), UCL, UK; Faculty of Medicine of the University of Geneva (S.H.); CIMC-Centre d'Imagerie Médicale de Cornavin (S.H.), Genève, Switzerland; Department of Surgical Sciences, Radiology (S.H.), Uppsala University, Sweden; Department of Radiology (S.H.), Beijing Tiantan Hospital, Capital Medical University, Beijing, China; and Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN) (J.D.G.), Madrid, Spain
| | - Wiesje M van de Flier
- From the Department of Radiology and Nuclear Medicine (L.E.C., V.W., S.E.M., P.S., F.H., A.M.W., B.N.M.B., F.B., I.L.A.), Alzheimer Center and Department of Neurology (W.M.v.d.F., P.S., P.J.V.), and Department of Epidemiology & Data Science (W.M.v.d.F.), Amsterdam UMC, Vrije Universiteit Amsterdam, the Netherlands; Barcelonaβeta Brain Research Center (BBRC) (G.S., J.D.G.), Pasqual Maragall Foundation; IMIM (Hospital del Mar Medical Research Institute) (G.S., J.D.G.), Barcelona, Spain; Stevens Neuroimaging and Informatics Institute (L.A.), Keck School of Medicine, University of Southern California, Los Angeles; Centre for Medical Image Computing and Queen Square Institute of Neurology (F.B.), UCL, UK; Faculty of Medicine of the University of Geneva (S.H.); CIMC-Centre d'Imagerie Médicale de Cornavin (S.H.), Genève, Switzerland; Department of Surgical Sciences, Radiology (S.H.), Uppsala University, Sweden; Department of Radiology (S.H.), Beijing Tiantan Hospital, Capital Medical University, Beijing, China; and Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN) (J.D.G.), Madrid, Spain
| | - Philip Scheltens
- From the Department of Radiology and Nuclear Medicine (L.E.C., V.W., S.E.M., P.S., F.H., A.M.W., B.N.M.B., F.B., I.L.A.), Alzheimer Center and Department of Neurology (W.M.v.d.F., P.S., P.J.V.), and Department of Epidemiology & Data Science (W.M.v.d.F.), Amsterdam UMC, Vrije Universiteit Amsterdam, the Netherlands; Barcelonaβeta Brain Research Center (BBRC) (G.S., J.D.G.), Pasqual Maragall Foundation; IMIM (Hospital del Mar Medical Research Institute) (G.S., J.D.G.), Barcelona, Spain; Stevens Neuroimaging and Informatics Institute (L.A.), Keck School of Medicine, University of Southern California, Los Angeles; Centre for Medical Image Computing and Queen Square Institute of Neurology (F.B.), UCL, UK; Faculty of Medicine of the University of Geneva (S.H.); CIMC-Centre d'Imagerie Médicale de Cornavin (S.H.), Genève, Switzerland; Department of Surgical Sciences, Radiology (S.H.), Uppsala University, Sweden; Department of Radiology (S.H.), Beijing Tiantan Hospital, Capital Medical University, Beijing, China; and Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN) (J.D.G.), Madrid, Spain
| | - Pieter Jelle Visser
- From the Department of Radiology and Nuclear Medicine (L.E.C., V.W., S.E.M., P.S., F.H., A.M.W., B.N.M.B., F.B., I.L.A.), Alzheimer Center and Department of Neurology (W.M.v.d.F., P.S., P.J.V.), and Department of Epidemiology & Data Science (W.M.v.d.F.), Amsterdam UMC, Vrije Universiteit Amsterdam, the Netherlands; Barcelonaβeta Brain Research Center (BBRC) (G.S., J.D.G.), Pasqual Maragall Foundation; IMIM (Hospital del Mar Medical Research Institute) (G.S., J.D.G.), Barcelona, Spain; Stevens Neuroimaging and Informatics Institute (L.A.), Keck School of Medicine, University of Southern California, Los Angeles; Centre for Medical Image Computing and Queen Square Institute of Neurology (F.B.), UCL, UK; Faculty of Medicine of the University of Geneva (S.H.); CIMC-Centre d'Imagerie Médicale de Cornavin (S.H.), Genève, Switzerland; Department of Surgical Sciences, Radiology (S.H.), Uppsala University, Sweden; Department of Radiology (S.H.), Beijing Tiantan Hospital, Capital Medical University, Beijing, China; and Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN) (J.D.G.), Madrid, Spain
| | - Frederik Barkhof
- From the Department of Radiology and Nuclear Medicine (L.E.C., V.W., S.E.M., P.S., F.H., A.M.W., B.N.M.B., F.B., I.L.A.), Alzheimer Center and Department of Neurology (W.M.v.d.F., P.S., P.J.V.), and Department of Epidemiology & Data Science (W.M.v.d.F.), Amsterdam UMC, Vrije Universiteit Amsterdam, the Netherlands; Barcelonaβeta Brain Research Center (BBRC) (G.S., J.D.G.), Pasqual Maragall Foundation; IMIM (Hospital del Mar Medical Research Institute) (G.S., J.D.G.), Barcelona, Spain; Stevens Neuroimaging and Informatics Institute (L.A.), Keck School of Medicine, University of Southern California, Los Angeles; Centre for Medical Image Computing and Queen Square Institute of Neurology (F.B.), UCL, UK; Faculty of Medicine of the University of Geneva (S.H.); CIMC-Centre d'Imagerie Médicale de Cornavin (S.H.), Genève, Switzerland; Department of Surgical Sciences, Radiology (S.H.), Uppsala University, Sweden; Department of Radiology (S.H.), Beijing Tiantan Hospital, Capital Medical University, Beijing, China; and Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN) (J.D.G.), Madrid, Spain
| | - Sven Haller
- From the Department of Radiology and Nuclear Medicine (L.E.C., V.W., S.E.M., P.S., F.H., A.M.W., B.N.M.B., F.B., I.L.A.), Alzheimer Center and Department of Neurology (W.M.v.d.F., P.S., P.J.V.), and Department of Epidemiology & Data Science (W.M.v.d.F.), Amsterdam UMC, Vrije Universiteit Amsterdam, the Netherlands; Barcelonaβeta Brain Research Center (BBRC) (G.S., J.D.G.), Pasqual Maragall Foundation; IMIM (Hospital del Mar Medical Research Institute) (G.S., J.D.G.), Barcelona, Spain; Stevens Neuroimaging and Informatics Institute (L.A.), Keck School of Medicine, University of Southern California, Los Angeles; Centre for Medical Image Computing and Queen Square Institute of Neurology (F.B.), UCL, UK; Faculty of Medicine of the University of Geneva (S.H.); CIMC-Centre d'Imagerie Médicale de Cornavin (S.H.), Genève, Switzerland; Department of Surgical Sciences, Radiology (S.H.), Uppsala University, Sweden; Department of Radiology (S.H.), Beijing Tiantan Hospital, Capital Medical University, Beijing, China; and Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN) (J.D.G.), Madrid, Spain
| | - Juan Domingo Gispert
- From the Department of Radiology and Nuclear Medicine (L.E.C., V.W., S.E.M., P.S., F.H., A.M.W., B.N.M.B., F.B., I.L.A.), Alzheimer Center and Department of Neurology (W.M.v.d.F., P.S., P.J.V.), and Department of Epidemiology & Data Science (W.M.v.d.F.), Amsterdam UMC, Vrije Universiteit Amsterdam, the Netherlands; Barcelonaβeta Brain Research Center (BBRC) (G.S., J.D.G.), Pasqual Maragall Foundation; IMIM (Hospital del Mar Medical Research Institute) (G.S., J.D.G.), Barcelona, Spain; Stevens Neuroimaging and Informatics Institute (L.A.), Keck School of Medicine, University of Southern California, Los Angeles; Centre for Medical Image Computing and Queen Square Institute of Neurology (F.B.), UCL, UK; Faculty of Medicine of the University of Geneva (S.H.); CIMC-Centre d'Imagerie Médicale de Cornavin (S.H.), Genève, Switzerland; Department of Surgical Sciences, Radiology (S.H.), Uppsala University, Sweden; Department of Radiology (S.H.), Beijing Tiantan Hospital, Capital Medical University, Beijing, China; and Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN) (J.D.G.), Madrid, Spain
| | - Isadora Lopes Alves
- From the Department of Radiology and Nuclear Medicine (L.E.C., V.W., S.E.M., P.S., F.H., A.M.W., B.N.M.B., F.B., I.L.A.), Alzheimer Center and Department of Neurology (W.M.v.d.F., P.S., P.J.V.), and Department of Epidemiology & Data Science (W.M.v.d.F.), Amsterdam UMC, Vrije Universiteit Amsterdam, the Netherlands; Barcelonaβeta Brain Research Center (BBRC) (G.S., J.D.G.), Pasqual Maragall Foundation; IMIM (Hospital del Mar Medical Research Institute) (G.S., J.D.G.), Barcelona, Spain; Stevens Neuroimaging and Informatics Institute (L.A.), Keck School of Medicine, University of Southern California, Los Angeles; Centre for Medical Image Computing and Queen Square Institute of Neurology (F.B.), UCL, UK; Faculty of Medicine of the University of Geneva (S.H.); CIMC-Centre d'Imagerie Médicale de Cornavin (S.H.), Genève, Switzerland; Department of Surgical Sciences, Radiology (S.H.), Uppsala University, Sweden; Department of Radiology (S.H.), Beijing Tiantan Hospital, Capital Medical University, Beijing, China; and Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN) (J.D.G.), Madrid, Spain
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Rosenich E, Bransby L, Yassi N, Fripp J, Laws SM, Martins RN, Fowler C, Rainey-Smith SR, Rowe CC, Masters CL, Maruff P, Lim YY. Differential Effects of APOE and Modifiable Risk Factors on Hippocampal Volume Loss and Memory Decline in Aβ- and Aβ+ Older Adults. Neurology 2022; 98:e1704-e1715. [PMID: 35169009 PMCID: PMC9071368 DOI: 10.1212/wnl.0000000000200118] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2021] [Accepted: 01/11/2022] [Indexed: 11/15/2022] Open
Abstract
BACKGROUND AND OBJECTIVES This prospective study sought to determine the association of modifiable/nonmodifiable components included in the Cardiovascular Risk Factors, Aging, and Incidence of Dementia (CAIDE) risk score with hippocampal volume (HV) loss and episodic memory (EM) decline in cognitively normal (CN) older adults classified as brain β-amyloid (Aβ) negative (Aβ-) or positive (Aβ+). METHODS Australian Imaging, Biomarkers and Lifestyle study participants (age 58-91 years) who completed ≥2 neuropsychological assessments and a brain Aβ PET scan (n = 592) were included in this study. We computed the CAIDE risk score (age, sex, APOE ε4 status, education, hypertension, body mass index [BMI], hypercholesterolemia, physical inactivity) and a modifiable CAIDE risk score (CAIDE-MR; education, hypertension, BMI, hypercholesterolemia, physical inactivity) for each participant. Aβ+ was classified using Centiloid >25. Linear mixed models assessed interactions between each CAIDE score, Aβ group, and time on HV loss and EM decline. Age, sex, and APOE ε4 were included as separate predictors in CAIDE-MR models to assess differential associations. Exploratory analyses examined relationships between individual modifiable risk factors and outcomes in Aβ- cognitively normal (CN) adults. RESULTS We observed a significant Aβ group × CAIDE × time interaction on HV loss (β [SE] = -0.04 [0.01]; p < 0.000) but not EM decline (β [SE] = -2.33 [9.96]; p = 0.98). Decomposition revealed a significant CAIDE × time interaction in Aβ+ participants only. When modifiable/nonmodifiable CAIDE components were considered separately, we observed a significant Aβ group × CAIDE-MR × time interaction on EM decline only (β [SE] = 3.03 [1.18]; p = 0.01). A significant CAIDE-MR score × time interaction was observed in Aβ- participants only. Significant interactions between APOE ε4 and age × time on HV loss and EM decline were observed in both groups. Exploratory analyses in Aβ- CN participants revealed a significant interaction between BMI × time on EM decline (β [SE] = -3.30 [1.43]; p = 0.02). DISCUSSION These results are consistent with studies showing that increasing age and APOE ε4 are associated with increased rates of HV loss and EM decline. In Aβ- CN adults, lower prevalence of modifiable cardiovascular risk factors was associated with less HV loss and EM decline over ∼10 years, suggesting interventions to reduce modifiable cardiovascular risk factors could be beneficial in this group.
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Affiliation(s)
- Emily Rosenich
- From the Turner Institute for Brain and Mental Health, School of Psychological Sciences (E.R., L.B., P.M., Y.Y.L.), Monash University, Clayton; Departments of Medicine and Neurology, Melbourne Brain Centre at The Royal Melbourne Hospital (N.Y., C.C.R.), and Florey Institute of Neuroscience and Mental Health (C.F., C.L.M., P.M.), University of Melbourne; Population Health and Immunity Division (N.Y.), The Walter and Eliza Hall Institute of Medical Research, Parkville; CSIRO Health and Biosecurity (J.F.), Australian e-Health Research Centre, Brisbane; Collaborative Genomics and Translation Group, School of Medical and Health Sciences (S.M.L.), and Centre of Excellence for Alzheimer's Disease Research and Care (R.N.M.), Edith Cowan University, Joondalup; School of Pharmacy and Biomedical Sciences, Faculty of Health Sciences (S.M.L.), Curtin Health Innovation Research Institute, Curtin University, Bentley; Centre for Healthy Ageing, Health Futures Institute (S.R.R.-G.), Murdoch University; Australian Alzheimer's Research Foundation (S.R.R.-G.), Sarich Neuroscience Research Institute, Nedlands; Department of Nuclear Medicine and Centre for PET (C.C.R.), Austin Health, Heidelberg; Department of Medicine (C.C.R.), Austin Health, University of Melbourne; and Cogstate Ltd. (P.M.), Melbourne, Australia
| | - Lisa Bransby
- From the Turner Institute for Brain and Mental Health, School of Psychological Sciences (E.R., L.B., P.M., Y.Y.L.), Monash University, Clayton; Departments of Medicine and Neurology, Melbourne Brain Centre at The Royal Melbourne Hospital (N.Y., C.C.R.), and Florey Institute of Neuroscience and Mental Health (C.F., C.L.M., P.M.), University of Melbourne; Population Health and Immunity Division (N.Y.), The Walter and Eliza Hall Institute of Medical Research, Parkville; CSIRO Health and Biosecurity (J.F.), Australian e-Health Research Centre, Brisbane; Collaborative Genomics and Translation Group, School of Medical and Health Sciences (S.M.L.), and Centre of Excellence for Alzheimer's Disease Research and Care (R.N.M.), Edith Cowan University, Joondalup; School of Pharmacy and Biomedical Sciences, Faculty of Health Sciences (S.M.L.), Curtin Health Innovation Research Institute, Curtin University, Bentley; Centre for Healthy Ageing, Health Futures Institute (S.R.R.-G.), Murdoch University; Australian Alzheimer's Research Foundation (S.R.R.-G.), Sarich Neuroscience Research Institute, Nedlands; Department of Nuclear Medicine and Centre for PET (C.C.R.), Austin Health, Heidelberg; Department of Medicine (C.C.R.), Austin Health, University of Melbourne; and Cogstate Ltd. (P.M.), Melbourne, Australia
| | - Nawaf Yassi
- From the Turner Institute for Brain and Mental Health, School of Psychological Sciences (E.R., L.B., P.M., Y.Y.L.), Monash University, Clayton; Departments of Medicine and Neurology, Melbourne Brain Centre at The Royal Melbourne Hospital (N.Y., C.C.R.), and Florey Institute of Neuroscience and Mental Health (C.F., C.L.M., P.M.), University of Melbourne; Population Health and Immunity Division (N.Y.), The Walter and Eliza Hall Institute of Medical Research, Parkville; CSIRO Health and Biosecurity (J.F.), Australian e-Health Research Centre, Brisbane; Collaborative Genomics and Translation Group, School of Medical and Health Sciences (S.M.L.), and Centre of Excellence for Alzheimer's Disease Research and Care (R.N.M.), Edith Cowan University, Joondalup; School of Pharmacy and Biomedical Sciences, Faculty of Health Sciences (S.M.L.), Curtin Health Innovation Research Institute, Curtin University, Bentley; Centre for Healthy Ageing, Health Futures Institute (S.R.R.-G.), Murdoch University; Australian Alzheimer's Research Foundation (S.R.R.-G.), Sarich Neuroscience Research Institute, Nedlands; Department of Nuclear Medicine and Centre for PET (C.C.R.), Austin Health, Heidelberg; Department of Medicine (C.C.R.), Austin Health, University of Melbourne; and Cogstate Ltd. (P.M.), Melbourne, Australia
| | - Jurgen Fripp
- From the Turner Institute for Brain and Mental Health, School of Psychological Sciences (E.R., L.B., P.M., Y.Y.L.), Monash University, Clayton; Departments of Medicine and Neurology, Melbourne Brain Centre at The Royal Melbourne Hospital (N.Y., C.C.R.), and Florey Institute of Neuroscience and Mental Health (C.F., C.L.M., P.M.), University of Melbourne; Population Health and Immunity Division (N.Y.), The Walter and Eliza Hall Institute of Medical Research, Parkville; CSIRO Health and Biosecurity (J.F.), Australian e-Health Research Centre, Brisbane; Collaborative Genomics and Translation Group, School of Medical and Health Sciences (S.M.L.), and Centre of Excellence for Alzheimer's Disease Research and Care (R.N.M.), Edith Cowan University, Joondalup; School of Pharmacy and Biomedical Sciences, Faculty of Health Sciences (S.M.L.), Curtin Health Innovation Research Institute, Curtin University, Bentley; Centre for Healthy Ageing, Health Futures Institute (S.R.R.-G.), Murdoch University; Australian Alzheimer's Research Foundation (S.R.R.-G.), Sarich Neuroscience Research Institute, Nedlands; Department of Nuclear Medicine and Centre for PET (C.C.R.), Austin Health, Heidelberg; Department of Medicine (C.C.R.), Austin Health, University of Melbourne; and Cogstate Ltd. (P.M.), Melbourne, Australia
| | - Simon M Laws
- From the Turner Institute for Brain and Mental Health, School of Psychological Sciences (E.R., L.B., P.M., Y.Y.L.), Monash University, Clayton; Departments of Medicine and Neurology, Melbourne Brain Centre at The Royal Melbourne Hospital (N.Y., C.C.R.), and Florey Institute of Neuroscience and Mental Health (C.F., C.L.M., P.M.), University of Melbourne; Population Health and Immunity Division (N.Y.), The Walter and Eliza Hall Institute of Medical Research, Parkville; CSIRO Health and Biosecurity (J.F.), Australian e-Health Research Centre, Brisbane; Collaborative Genomics and Translation Group, School of Medical and Health Sciences (S.M.L.), and Centre of Excellence for Alzheimer's Disease Research and Care (R.N.M.), Edith Cowan University, Joondalup; School of Pharmacy and Biomedical Sciences, Faculty of Health Sciences (S.M.L.), Curtin Health Innovation Research Institute, Curtin University, Bentley; Centre for Healthy Ageing, Health Futures Institute (S.R.R.-G.), Murdoch University; Australian Alzheimer's Research Foundation (S.R.R.-G.), Sarich Neuroscience Research Institute, Nedlands; Department of Nuclear Medicine and Centre for PET (C.C.R.), Austin Health, Heidelberg; Department of Medicine (C.C.R.), Austin Health, University of Melbourne; and Cogstate Ltd. (P.M.), Melbourne, Australia
| | - Ralph N Martins
- From the Turner Institute for Brain and Mental Health, School of Psychological Sciences (E.R., L.B., P.M., Y.Y.L.), Monash University, Clayton; Departments of Medicine and Neurology, Melbourne Brain Centre at The Royal Melbourne Hospital (N.Y., C.C.R.), and Florey Institute of Neuroscience and Mental Health (C.F., C.L.M., P.M.), University of Melbourne; Population Health and Immunity Division (N.Y.), The Walter and Eliza Hall Institute of Medical Research, Parkville; CSIRO Health and Biosecurity (J.F.), Australian e-Health Research Centre, Brisbane; Collaborative Genomics and Translation Group, School of Medical and Health Sciences (S.M.L.), and Centre of Excellence for Alzheimer's Disease Research and Care (R.N.M.), Edith Cowan University, Joondalup; School of Pharmacy and Biomedical Sciences, Faculty of Health Sciences (S.M.L.), Curtin Health Innovation Research Institute, Curtin University, Bentley; Centre for Healthy Ageing, Health Futures Institute (S.R.R.-G.), Murdoch University; Australian Alzheimer's Research Foundation (S.R.R.-G.), Sarich Neuroscience Research Institute, Nedlands; Department of Nuclear Medicine and Centre for PET (C.C.R.), Austin Health, Heidelberg; Department of Medicine (C.C.R.), Austin Health, University of Melbourne; and Cogstate Ltd. (P.M.), Melbourne, Australia
| | - Christopher Fowler
- From the Turner Institute for Brain and Mental Health, School of Psychological Sciences (E.R., L.B., P.M., Y.Y.L.), Monash University, Clayton; Departments of Medicine and Neurology, Melbourne Brain Centre at The Royal Melbourne Hospital (N.Y., C.C.R.), and Florey Institute of Neuroscience and Mental Health (C.F., C.L.M., P.M.), University of Melbourne; Population Health and Immunity Division (N.Y.), The Walter and Eliza Hall Institute of Medical Research, Parkville; CSIRO Health and Biosecurity (J.F.), Australian e-Health Research Centre, Brisbane; Collaborative Genomics and Translation Group, School of Medical and Health Sciences (S.M.L.), and Centre of Excellence for Alzheimer's Disease Research and Care (R.N.M.), Edith Cowan University, Joondalup; School of Pharmacy and Biomedical Sciences, Faculty of Health Sciences (S.M.L.), Curtin Health Innovation Research Institute, Curtin University, Bentley; Centre for Healthy Ageing, Health Futures Institute (S.R.R.-G.), Murdoch University; Australian Alzheimer's Research Foundation (S.R.R.-G.), Sarich Neuroscience Research Institute, Nedlands; Department of Nuclear Medicine and Centre for PET (C.C.R.), Austin Health, Heidelberg; Department of Medicine (C.C.R.), Austin Health, University of Melbourne; and Cogstate Ltd. (P.M.), Melbourne, Australia
| | - Stephanie R Rainey-Smith
- From the Turner Institute for Brain and Mental Health, School of Psychological Sciences (E.R., L.B., P.M., Y.Y.L.), Monash University, Clayton; Departments of Medicine and Neurology, Melbourne Brain Centre at The Royal Melbourne Hospital (N.Y., C.C.R.), and Florey Institute of Neuroscience and Mental Health (C.F., C.L.M., P.M.), University of Melbourne; Population Health and Immunity Division (N.Y.), The Walter and Eliza Hall Institute of Medical Research, Parkville; CSIRO Health and Biosecurity (J.F.), Australian e-Health Research Centre, Brisbane; Collaborative Genomics and Translation Group, School of Medical and Health Sciences (S.M.L.), and Centre of Excellence for Alzheimer's Disease Research and Care (R.N.M.), Edith Cowan University, Joondalup; School of Pharmacy and Biomedical Sciences, Faculty of Health Sciences (S.M.L.), Curtin Health Innovation Research Institute, Curtin University, Bentley; Centre for Healthy Ageing, Health Futures Institute (S.R.R.-G.), Murdoch University; Australian Alzheimer's Research Foundation (S.R.R.-G.), Sarich Neuroscience Research Institute, Nedlands; Department of Nuclear Medicine and Centre for PET (C.C.R.), Austin Health, Heidelberg; Department of Medicine (C.C.R.), Austin Health, University of Melbourne; and Cogstate Ltd. (P.M.), Melbourne, Australia
| | - Christopher C Rowe
- From the Turner Institute for Brain and Mental Health, School of Psychological Sciences (E.R., L.B., P.M., Y.Y.L.), Monash University, Clayton; Departments of Medicine and Neurology, Melbourne Brain Centre at The Royal Melbourne Hospital (N.Y., C.C.R.), and Florey Institute of Neuroscience and Mental Health (C.F., C.L.M., P.M.), University of Melbourne; Population Health and Immunity Division (N.Y.), The Walter and Eliza Hall Institute of Medical Research, Parkville; CSIRO Health and Biosecurity (J.F.), Australian e-Health Research Centre, Brisbane; Collaborative Genomics and Translation Group, School of Medical and Health Sciences (S.M.L.), and Centre of Excellence for Alzheimer's Disease Research and Care (R.N.M.), Edith Cowan University, Joondalup; School of Pharmacy and Biomedical Sciences, Faculty of Health Sciences (S.M.L.), Curtin Health Innovation Research Institute, Curtin University, Bentley; Centre for Healthy Ageing, Health Futures Institute (S.R.R.-G.), Murdoch University; Australian Alzheimer's Research Foundation (S.R.R.-G.), Sarich Neuroscience Research Institute, Nedlands; Department of Nuclear Medicine and Centre for PET (C.C.R.), Austin Health, Heidelberg; Department of Medicine (C.C.R.), Austin Health, University of Melbourne; and Cogstate Ltd. (P.M.), Melbourne, Australia
| | - Colin L Masters
- From the Turner Institute for Brain and Mental Health, School of Psychological Sciences (E.R., L.B., P.M., Y.Y.L.), Monash University, Clayton; Departments of Medicine and Neurology, Melbourne Brain Centre at The Royal Melbourne Hospital (N.Y., C.C.R.), and Florey Institute of Neuroscience and Mental Health (C.F., C.L.M., P.M.), University of Melbourne; Population Health and Immunity Division (N.Y.), The Walter and Eliza Hall Institute of Medical Research, Parkville; CSIRO Health and Biosecurity (J.F.), Australian e-Health Research Centre, Brisbane; Collaborative Genomics and Translation Group, School of Medical and Health Sciences (S.M.L.), and Centre of Excellence for Alzheimer's Disease Research and Care (R.N.M.), Edith Cowan University, Joondalup; School of Pharmacy and Biomedical Sciences, Faculty of Health Sciences (S.M.L.), Curtin Health Innovation Research Institute, Curtin University, Bentley; Centre for Healthy Ageing, Health Futures Institute (S.R.R.-G.), Murdoch University; Australian Alzheimer's Research Foundation (S.R.R.-G.), Sarich Neuroscience Research Institute, Nedlands; Department of Nuclear Medicine and Centre for PET (C.C.R.), Austin Health, Heidelberg; Department of Medicine (C.C.R.), Austin Health, University of Melbourne; and Cogstate Ltd. (P.M.), Melbourne, Australia
| | - Paul Maruff
- From the Turner Institute for Brain and Mental Health, School of Psychological Sciences (E.R., L.B., P.M., Y.Y.L.), Monash University, Clayton; Departments of Medicine and Neurology, Melbourne Brain Centre at The Royal Melbourne Hospital (N.Y., C.C.R.), and Florey Institute of Neuroscience and Mental Health (C.F., C.L.M., P.M.), University of Melbourne; Population Health and Immunity Division (N.Y.), The Walter and Eliza Hall Institute of Medical Research, Parkville; CSIRO Health and Biosecurity (J.F.), Australian e-Health Research Centre, Brisbane; Collaborative Genomics and Translation Group, School of Medical and Health Sciences (S.M.L.), and Centre of Excellence for Alzheimer's Disease Research and Care (R.N.M.), Edith Cowan University, Joondalup; School of Pharmacy and Biomedical Sciences, Faculty of Health Sciences (S.M.L.), Curtin Health Innovation Research Institute, Curtin University, Bentley; Centre for Healthy Ageing, Health Futures Institute (S.R.R.-G.), Murdoch University; Australian Alzheimer's Research Foundation (S.R.R.-G.), Sarich Neuroscience Research Institute, Nedlands; Department of Nuclear Medicine and Centre for PET (C.C.R.), Austin Health, Heidelberg; Department of Medicine (C.C.R.), Austin Health, University of Melbourne; and Cogstate Ltd. (P.M.), Melbourne, Australia
| | - Yen Ying Lim
- From the Turner Institute for Brain and Mental Health, School of Psychological Sciences (E.R., L.B., P.M., Y.Y.L.), Monash University, Clayton; Departments of Medicine and Neurology, Melbourne Brain Centre at The Royal Melbourne Hospital (N.Y., C.C.R.), and Florey Institute of Neuroscience and Mental Health (C.F., C.L.M., P.M.), University of Melbourne; Population Health and Immunity Division (N.Y.), The Walter and Eliza Hall Institute of Medical Research, Parkville; CSIRO Health and Biosecurity (J.F.), Australian e-Health Research Centre, Brisbane; Collaborative Genomics and Translation Group, School of Medical and Health Sciences (S.M.L.), and Centre of Excellence for Alzheimer's Disease Research and Care (R.N.M.), Edith Cowan University, Joondalup; School of Pharmacy and Biomedical Sciences, Faculty of Health Sciences (S.M.L.), Curtin Health Innovation Research Institute, Curtin University, Bentley; Centre for Healthy Ageing, Health Futures Institute (S.R.R.-G.), Murdoch University; Australian Alzheimer's Research Foundation (S.R.R.-G.), Sarich Neuroscience Research Institute, Nedlands; Department of Nuclear Medicine and Centre for PET (C.C.R.), Austin Health, Heidelberg; Department of Medicine (C.C.R.), Austin Health, University of Melbourne; and Cogstate Ltd. (P.M.), Melbourne, Australia
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Hu Y, Kirmess KM, Meyer MR, Rabinovici GD, Gatsonis C, Siegel BA, Whitmer RA, Apgar C, Hanna L, Kanekiyo M, Kaplow J, Koyama A, Verbel D, Holubasch MS, Knapik SS, Connor J, Contois JH, Jackson EN, Harpstrite SE, Bateman RJ, Holtzman DM, Verghese PB, Fogelman I, Braunstein JB, Yarasheski KE, West T. Assessment of a Plasma Amyloid Probability Score to Estimate Amyloid Positron Emission Tomography Findings Among Adults With Cognitive Impairment. JAMA Netw Open 2022; 5:e228392. [PMID: 35446396 PMCID: PMC9024390 DOI: 10.1001/jamanetworkopen.2022.8392] [Citation(s) in RCA: 72] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/09/2023] Open
Abstract
IMPORTANCE The diagnostic evaluation for Alzheimer disease may be improved by a blood-based diagnostic test identifying presence of brain amyloid plaque pathology. OBJECTIVE To determine the clinical performance associated with a diagnostic algorithm incorporating plasma amyloid-β (Aβ) 42:40 ratio, patient age, and apoE proteotype to identify brain amyloid status. DESIGN, SETTING, AND PARTICIPANTS This cohort study includes analysis from 2 independent cross-sectional cohort studies: the discovery cohort of the Plasma Test for Amyloidosis Risk Screening (PARIS) study, a prospective add-on to the Imaging Dementia-Evidence for Amyloid Scanning study, including 249 patients from 2018 to 2019, and MissionAD, a dataset of 437 biobanked patient samples obtained at screenings during 2016 to 2019. Data were analyzed from May to November 2020. EXPOSURES Amyloid detected in blood and by positron emission tomography (PET) imaging. MAIN OUTCOMES AND MEASURES The main outcome was the diagnostic performance of plasma Aβ42:40 ratio, together with apoE proteotype and age, for identifying amyloid PET status, assessed by accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). RESULTS All 686 participants (mean [SD] age 73.2 [6.3] years; 368 [53.6%] men; 378 participants [55.1%] with amyloid PET findings) had symptoms of mild cognitive impairment or mild dementia. The AUC of plasma Aβ42:40 ratio for PARIS was 0.79 (95% CI, 0.73-0.85) and 0.86 (95% CI, 0.82-0.89) for MissionAD. Ratio cutoffs for Aβ42:40 based on the Youden index were similar between cohorts (PARIS: 0.089; MissionAD: 0.092). A logistic regression model (LRM) incorporating Aβ42:40 ratio, apoE proteotype, and age improved diagnostic performance within each cohort (PARIS: AUC, 0.86 [95% CI, 0.81-0.91]; MissionAD: AUC, 0.89 [95% CI, 0.86-0.92]), and overall accuracy was 78% (95% CI, 72%-83%) for PARIS and 83% (95% CI, 79%-86%) for MissionAD. The model developed on the prospectively collected samples from PARIS performed well on the MissionAD samples (AUC, 0.88 [95% CI, 0.84-0.91]; accuracy, 78% [95% CI, 74%-82%]). Training the LRM on combined cohorts yielded an AUC of 0.88 (95% CI, 0.85-0.91) and accuracy of 81% (95% CI, 78%-84%). The output of this LRM is the Amyloid Probability Score (APS). For clinical use, 2 APS cutoff values were established yielding 3 categories, with low, intermediate, and high likelihood of brain amyloid plaque pathology. CONCLUSIONS AND RELEVANCE These findings suggest that this blood biomarker test could allow for distinguishing individuals with brain amyloid-positive PET findings from individuals with amyloid-negative PET findings and serve as an aid for Alzheimer disease diagnosis.
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Affiliation(s)
- Yan Hu
- C2N Diagnostics, St Louis, Missouri
| | | | | | - Gil D. Rabinovici
- Departments of Neurology, Radiology & Biomedical Imaging, University of California, San Francisco
| | - Constantine Gatsonis
- Center for Statistical Sciences, Brown University School of Public Health, Providence, Rhode Island
| | - Barry A. Siegel
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, Missouri
| | - Rachel A. Whitmer
- Department of Public Health Sciences, University of California, Davis
| | | | - Lucy Hanna
- Center for Statistical Sciences, Brown University School of Public Health, Providence, Rhode Island
| | | | | | | | | | | | | | | | | | | | | | - Randall J. Bateman
- Department of Neurology, Washington University School of Medicine, St Louis, Missouri
| | - David M. Holtzman
- Department of Neurology, Washington University School of Medicine, St Louis, Missouri
| | | | | | | | | | - Tim West
- C2N Diagnostics, St Louis, Missouri
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Lam V, Clarnette R, Francis R, Bynevelt M, Watts G, Flicker L, Orr CF, Loh P, Lautenschlager N, Reid CM, Foster JK, Dhaliwal SS, Robinson S, Corti E, Vaccarezza M, Horgan B, Takechi R, Mamo J. Efficacy of probucol on cognitive function in Alzheimer's disease: study protocol for a double-blind, placebo-controlled, randomised phase II trial (PIA study). BMJ Open 2022; 12:e058826. [PMID: 35190446 PMCID: PMC8860076 DOI: 10.1136/bmjopen-2021-058826] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
INTRODUCTION Preclinical, clinical and epidemiological studies support the hypothesis that aberrant systemic metabolism of amyloid beta (Aβ) in the peripheral circulation is causally related to the development of Alzheimer's disease (AD). Specifically, recent studies suggest that increased plasma concentrations of lipoprotein-Aβ compromise the brain microvasculature, resulting in extravasation and retention of the lipoprotein-Aβ moiety. The latter results in an inflammatory response and neurodegeneration ensues. Probucol, a historic cholesterol-lowering drug, has been shown in murine models to suppress lipoprotein-Aβ secretion, concomitant with maintaining blood-brain-barrier function, suppressing neurovascular inflammation and supporting cognitive function. This protocol details the probucol in Alzheimer's study, a drug intervention trial investigating if probucol has potential to attenuate cognitive decline, delay brain atrophy and reduce cerebral amyloid burden in patients with mild-to-moderate AD. METHODS AND ANALYSIS The study is a phase II, randomised, placebo-controlled, double-blind single-site clinical trial held in Perth, Australia. The target sample is 314 participants with mild-to-moderate AD. Participants will be recruited and randomised (1:1) to a 104-week intervention consisting of placebo induction for 2 weeks followed by 102 weeks of probucol (Lorelco) or placebo. The primary outcome is changed in cognitive performance determined via the Alzheimer's Disease Assessment Scales-Cognitive Subscale test between baseline and 104 weeks. Secondary outcomes measures will be the change in brain structure and function, cerebral amyloid load, quality of life, and the safety and tolerability of Lorelco, after a 104week intervention. ETHICS AND DISSEMINATION The study has been approved by the Bellberry Limited Human Research Ethics Committee (approval number: HREC2019-11-1063; Version 4, 6 October 2021). Informed consent will be obtained from participants prior to any study procedures being performed. The investigator group will disseminate study findings through peer-reviewed publications, key conferences and local stakeholder events. TRIAL REGISTRATION NUMBER Australian New Zealand Clinical Trials Registry (ACTRN12621000726853).
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Affiliation(s)
- Virginie Lam
- Curtin Health Innovation Research Institute, Faculty of Health Sciences, Curtin University, Perth, Western Australia, Australia
- School of Population Health, Faculty of Health Sciences, Curtin University, Perth, Western Australia, Australia
| | - Roger Clarnette
- Australian Alzheimer's Research Foundation, University of Western Australia, Nedlands, Western Australia, Australia
- School of Medicine, University of Western Australia, Crawley, Western Australia, Australia
| | - Roslyn Francis
- School of Medicine, University of Western Australia, Crawley, Western Australia, Australia
- Department of Nuclear Medicine, Sir Charles Gairdner Hospital, Nedlands, Western Australia, Australia
| | - Michael Bynevelt
- Neurological Intervention and Imaging Service of Western Australia, Sir Charles Gairdner Hospital, Nedlands, Western Australia, Australia
| | - Gerald Watts
- School of Medicine, University of Western Australia, Crawley, Western Australia, Australia
- Cardiometabolic Service, Department of Cardiology and Internal Medicine, Royal Perth Hospital, Perth, Western Australia, Australia
| | - Leon Flicker
- WA Centre for Health & Ageing, University of Western Australia, Perth, Western Australia, Australia
| | - Carolyn F Orr
- Cognitive Clinic, Royal Perth Hospital, Perth, Western Australia, Australia
| | - Poh Loh
- WA Centre for Health & Ageing, University of Western Australia, Perth, Western Australia, Australia
| | - Nicola Lautenschlager
- Academic Unit of Psychiatry of Old Age, University of Melbourne, Victoria, Victoria, Australia
- North Western Mental Health, Royal Melbourne Hospital, Parkville, Victoria, Australia
- Division of Psychiatry and WA Centre for Health and Ageing, University of Western Australia, Perth, Western Australia, Australia
| | - Christopher M Reid
- Curtin Health Innovation Research Institute, Faculty of Health Sciences, Curtin University, Perth, Western Australia, Australia
- School of Population Health, Faculty of Health Sciences, Curtin University, Perth, Western Australia, Australia
| | - Jonathan K Foster
- Synapse Neuropsychology, Perth, Western Australia, Australia
- Faculty of Health Sciences, Curtin University, Bentley, Western Australia, Australia
- School of Paediatrics and Child Health, Faculty of Health and Medical Science, University of Western Australia, Crawley, Western Australia, Australia
| | - Satvinder S Dhaliwal
- Department of Radiation Oncology, Sir Charles Gairdner Hospital, Nedlands, Western Australia, Australia
- Duke-NUS Medical School, National University of Singapore, Singapore
- Institute for Research in Molecular Medicine, Universiti Sains Malaysia, Pulau Pinang, Malaysia
| | - Suzanne Robinson
- School of Population Health, Faculty of Health Sciences, Curtin University, Perth, Western Australia, Australia
| | - Emily Corti
- Curtin Health Innovation Research Institute, Faculty of Health Sciences, Curtin University, Perth, Western Australia, Australia
| | - Mauro Vaccarezza
- Curtin Health Innovation Research Institute, Faculty of Health Sciences, Curtin University, Perth, Western Australia, Australia
- Curtin Medical School, Faculty of Health Sciences, Curtin University, Bentley, Western Australia, Australia
| | - Ben Horgan
- Faculty of Health Sciences, Curtin University, Bentley, Western Australia, Australia
| | - Ryusuke Takechi
- Curtin Health Innovation Research Institute, Faculty of Health Sciences, Curtin University, Perth, Western Australia, Australia
- School of Population Health, Faculty of Health Sciences, Curtin University, Perth, Western Australia, Australia
| | - John Mamo
- Curtin Health Innovation Research Institute, Faculty of Health Sciences, Curtin University, Perth, Western Australia, Australia
- School of Population Health, Faculty of Health Sciences, Curtin University, Perth, Western Australia, Australia
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38
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Williams ME, Elman JA, McEvoy LK, Andreassen OA, Dale AM, Eglit GML, Eyler LT, Fennema-Notestine C, Franz CE, Gillespie NA, Hagler DJ, Hatton SN, Hauger RL, Jak AJ, Logue MW, Lyons MJ, McKenzie RE, Neale MC, Panizzon MS, Puckett OK, Reynolds CA, Sanderson-Cimino M, Toomey R, Tu XM, Whitsel N, Xian H, Kremen WS. 12-year prediction of mild cognitive impairment aided by Alzheimer's brain signatures at mean age 56. Brain Commun 2021; 3:fcab167. [PMID: 34396116 PMCID: PMC8361427 DOI: 10.1093/braincomms/fcab167] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2021] [Revised: 04/26/2021] [Accepted: 05/10/2021] [Indexed: 01/22/2023] Open
Abstract
Neuroimaging signatures based on composite scores of cortical thickness and hippocampal volume predict progression from mild cognitive impairment to Alzheimer's disease. However, little is known about the ability of these signatures among cognitively normal adults to predict progression to mild cognitive impairment. Towards that end, a signature sensitive to microstructural changes that may predate macrostructural atrophy should be useful. We hypothesized that: (i) a validated MRI-derived Alzheimer's disease signature based on cortical thickness and hippocampal volume in cognitively normal middle-aged adults would predict progression to mild cognitive impairment; and (ii) a novel grey matter mean diffusivity signature would be a better predictor than the thickness/volume signature. This cohort study was part of the Vietnam Era Twin Study of Aging. Concurrent analyses compared cognitively normal and mild cognitive impairment groups at each of three study waves (ns = 246-367). Predictive analyses included 169 cognitively normal men at baseline (age = 56.1, range = 51-60). Our previously published thickness/volume signature derived from independent data, a novel mean diffusivity signature using the same regions and weights as the thickness/volume signature, age, and an Alzheimer's disease polygenic risk score were used to predict incident mild cognitive impairment an average of 12 years after baseline (follow-up age = 67.2, range = 61-71). Additional analyses adjusted for predicted brain age difference scores (chronological age minus predicted brain age) to determine if signatures were Alzheimer-related and not simply ageing-related. In concurrent analyses, individuals with mild cognitive impairment had higher (worse) mean diffusivity signature scores than cognitively normal participants, but thickness/volume signature scores did not differ between groups. In predictive analyses, age and polygenic risk score yielded an area under the curve of 0.74 (sensitivity = 80.00%; specificity = 65.10%). Prediction was significantly improved with addition of the mean diffusivity signature (area under the curve = 0.83; sensitivity = 85.00%; specificity = 77.85%; P = 0.007), but not with addition of the thickness/volume signature. A model including both signatures did not improve prediction over a model with only the mean diffusivity signature. Results held up after adjusting for predicted brain age difference scores. The novel mean diffusivity signature was limited by being yoked to the thickness/volume signature weightings. An independently derived mean diffusivity signature may thus provide even stronger prediction. The young age of the sample at baseline is particularly notable. Given that the brain signatures were examined when participants were only in their 50 s, our results suggest a promising step towards improving very early identification of Alzheimer's disease risk and the potential value of mean diffusivity and/or multimodal brain signatures.
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Affiliation(s)
- McKenna E Williams
- Center for Behavior Genetics of Aging, University of California San Diego, La Jolla, CA 92093, USA
- Joint Doctoral Program in Clinical Psychology, San Diego State University/University of California, San Diego, CA 92093, USA
- Department of Psychiatry, University of California San Diego, La Jolla, CA 92093, USA
| | - Jeremy A Elman
- Center for Behavior Genetics of Aging, University of California San Diego, La Jolla, CA 92093, USA
- Department of Psychiatry, University of California San Diego, La Jolla, CA 92093, USA
| | - Linda K McEvoy
- Department of Radiology, University of California San Diego, La Jolla, CA 92093, USA
| | - Ole A Andreassen
- NORMENT, KG Jebsen Centre for Psychosis Research, Institute of Clinical Medicine, University of Oslo, Oslo 0316, Norway
- Division of Mental Health and Addiction, Oslo University Hospital, Oslo 0372, Norway
| | - Anders M Dale
- Department of Radiology, University of California San Diego, La Jolla, CA 92093, USA
- Department of Neuroscience, University of California San Diego, La Jolla, CA 92093, USA
| | - Graham M L Eglit
- Center for Behavior Genetics of Aging, University of California San Diego, La Jolla, CA 92093, USA
- Department of Psychiatry, University of California San Diego, La Jolla, CA 92093, USA
| | - Lisa T Eyler
- Department of Psychiatry, University of California San Diego, La Jolla, CA 92093, USA
- Desert Pacific Mental Illness Research Education and Clinical Center, VA San Diego Healthcare System, CA 92093, USA
| | - Christine Fennema-Notestine
- Department of Psychiatry, University of California San Diego, La Jolla, CA 92093, USA
- Department of Radiology, University of California San Diego, La Jolla, CA 92093, USA
| | - Carol E Franz
- Center for Behavior Genetics of Aging, University of California San Diego, La Jolla, CA 92093, USA
- Department of Psychiatry, University of California San Diego, La Jolla, CA 92093, USA
| | - Nathan A Gillespie
- Virginia Institute for Psychiatric and Behavior Genetics, Virginia Commonwealth University, Richmond, VA 23284, USA
| | - Donald J Hagler
- Department of Radiology, University of California San Diego, La Jolla, CA 92093, USA
| | - Sean N Hatton
- Center for Behavior Genetics of Aging, University of California San Diego, La Jolla, CA 92093, USA
- Department of Neuroscience, University of California San Diego, La Jolla, CA 92093, USA
| | - Richard L Hauger
- Department of Psychiatry, University of California San Diego, La Jolla, CA 92093, USA
- Center of Excellence for Stress and Mental Health (CESAMH), VA San Diego Healthcare System, San Diego, CA 92093, USA
| | - Amy J Jak
- Department of Psychiatry, University of California San Diego, La Jolla, CA 92093, USA
- VA San Diego Healthcare System, San Diego, CA 92093, USA
| | - Mark W Logue
- National Center for PTSD: Behavioral Science Division, VA Boston Healthcare System, Boston, MA 02130, USA
- Department of Psychiatry and the Biomedical Genetics Section, Boston University School of Medicine, Boston, MA 02118, USA
- Department of Biostatistics, Boston University School of Public Health, Boston, MA 02118, USA
| | - Michael J Lyons
- Department of Psychological and Brain Sciences, Boston University, Boston, MA 02212, USA
| | - Ruth E McKenzie
- Department of Biostatistics, Boston University School of Public Health, Boston, MA 02118, USA
- School of Education and Social Policy, Merrimack College, North Andover, MA 01845, USA
| | - Michael C Neale
- Virginia Institute for Psychiatric and Behavior Genetics, Virginia Commonwealth University, Richmond, VA 23284, USA
| | - Matthew S Panizzon
- Center for Behavior Genetics of Aging, University of California San Diego, La Jolla, CA 92093, USA
- Department of Psychiatry, University of California San Diego, La Jolla, CA 92093, USA
| | - Olivia K Puckett
- Center for Behavior Genetics of Aging, University of California San Diego, La Jolla, CA 92093, USA
- Department of Psychiatry, University of California San Diego, La Jolla, CA 92093, USA
| | - Chandra A Reynolds
- Department of Psychology, University of California Riverside, Riverside, CA 92521, USA
| | - Mark Sanderson-Cimino
- Center for Behavior Genetics of Aging, University of California San Diego, La Jolla, CA 92093, USA
- Joint Doctoral Program in Clinical Psychology, San Diego State University/University of California, San Diego, CA 92093, USA
- Department of Psychiatry, University of California San Diego, La Jolla, CA 92093, USA
| | - Rosemary Toomey
- Department of Psychological and Brain Sciences, Boston University, Boston, MA 02212, USA
| | - Xin M Tu
- Family Medicine and Public Health, University of California San Diego, La Jolla, CA 92093, USA
| | - Nathan Whitsel
- Center for Behavior Genetics of Aging, University of California San Diego, La Jolla, CA 92093, USA
- Department of Psychiatry, University of California San Diego, La Jolla, CA 92093, USA
| | - Hong Xian
- Department of Biostatistics, St. Louis University, St. Louis, MO 63103, USA
| | - William S Kremen
- Center for Behavior Genetics of Aging, University of California San Diego, La Jolla, CA 92093, USA
- Department of Psychiatry, University of California San Diego, La Jolla, CA 92093, USA
- Center of Excellence for Stress and Mental Health (CESAMH), VA San Diego Healthcare System, San Diego, CA 92093, USA
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Bucci M, Savitcheva I, Farrar G, Salvadó G, Collij L, Doré V, Gispert JD, Gunn R, Hanseeuw B, Hansson O, Shekari M, Lhommel R, Molinuevo JL, Rowe C, Sur C, Whittington A, Buckley C, Nordberg A. A multisite analysis of the concordance between visual image interpretation and quantitative analysis of [ 18F]flutemetamol amyloid PET images. Eur J Nucl Med Mol Imaging 2021; 48:2183-2199. [PMID: 33844055 PMCID: PMC8175298 DOI: 10.1007/s00259-021-05311-5] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2020] [Accepted: 03/09/2021] [Indexed: 12/18/2022]
Abstract
BACKGROUND [18F]flutemetamol PET scanning provides information on brain amyloid load and has been approved for routine clinical use based upon visual interpretation as either negative (equating to none or sparse amyloid plaques) or amyloid positive (equating to moderate or frequent plaques). Quantitation is however fundamental to the practice of nuclear medicine and hence can be used to supplement amyloid reading methodology especially in unclear cases. METHODS A total of 2770 [18F]flutemetamol images were collected from 3 clinical studies and 6 research cohorts with available visual reading of [18F]flutemetamol and quantitative analysis of images. These were assessed further to examine both the discordance and concordance between visual and quantitative imaging primarily using thresholds robustly established using pathology as the standard of truth. Scans covered a wide range of cases (i.e. from cognitively unimpaired subjects to patients attending the memory clinics). Methods of quantifying amyloid ranged from using CE/510K cleared marked software (e.g. CortexID, Brass), to other research-based methods (e.g. PMOD, CapAIBL). Additionally, the clinical follow-up of two types of discordance between visual and quantitation (V+Q- and V-Q+) was examined with competing risk regression analysis to assess possible differences in prediction for progression to Alzheimer's disease (AD) and other diagnoses (OD). RESULTS Weighted mean concordance between visual and quantitation using the autopsy-derived threshold was 94% using pons as the reference region. Concordance from a sensitivity analysis which assessed the maximum agreement for each cohort using a range of cut-off values was also estimated at approximately 96% (weighted mean). Agreement was generally higher in clinical cases compared to research cases. V-Q+ discordant cases were 11% more likely to progress to AD than V+Q- for the SUVr with pons as reference region. CONCLUSIONS Quantitation of amyloid PET shows a high agreement vs binary visual reading and also allows for a continuous measure that, in conjunction with possible discordant analysis, could be used in the future to identify possible earlier pathological deposition as well as monitor disease progression and treatment effectiveness.
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Affiliation(s)
- Marco Bucci
- Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden.
| | - Irina Savitcheva
- Medical Radiation Physics and Nuclear Medicine, Section for Nuclear Medicine, Karolinska University Hospital, Stockholm, Sweden
| | - Gill Farrar
- Pharmaceutical Diagnostics, GE Healthcare, Amersham, UK
| | - Gemma Salvadó
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain.,IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain
| | - Lyduine Collij
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan, 1117, Amsterdam, Netherlands
| | - Vincent Doré
- Austin Health, University of Melbourne, Melbourne, Australia.,Health and Biosecurity, CSIRO, Parkville, Australia
| | - Juan Domingo Gispert
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain.,IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain.,Universitat Pompeu Fabra, Barcelona, Spain.,Centro de Investigación Biomédica en Red Bioingenieriá, Biomateriales y Nanomedicina, (CIBER-BBN), Barcelona, Spain
| | - Roger Gunn
- Invicro, London, UK.,Division of Brain Sciences, Department of Medicine, Imperial College, London, UK
| | - Bernard Hanseeuw
- Neurology and Nuclear Medicine Departments, Saint-Luc University Hospital, Av. Hippocrate, 10, 1200, Brussels, Belgium.,Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Oskar Hansson
- Clinical Memory Research Unit, Department of Clinical Sciences Malmo, Lund University, Lund, Sweden
| | - Mahnaz Shekari
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain.,IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain.,Universitat Pompeu Fabra, Barcelona, Spain
| | - Renaud Lhommel
- Neurology and Nuclear Medicine Departments, Saint-Luc University Hospital, Av. Hippocrate, 10, 1200, Brussels, Belgium
| | - José Luis Molinuevo
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain.,IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain.,Universitat Pompeu Fabra, Barcelona, Spain.,Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (CIBERFES), Madrid, Spain
| | - Christopher Rowe
- Austin Health, University of Melbourne, Melbourne, Australia.,Department of Medicine, The University of Melbourne, Melbourne, Australia
| | | | | | | | - Agneta Nordberg
- Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden. .,Department of Aging, Karolinska University Hospital, Stockholm, Sweden.
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Doré V, Krishnadas N, Bourgeat P, Huang K, Li S, Burnham S, Masters CL, Fripp J, Villemagne VL, Rowe CC. Relationship between amyloid and tau levels and its impact on tau spreading. Eur J Nucl Med Mol Imaging 2021; 48:2225-2232. [PMID: 33495928 PMCID: PMC8175299 DOI: 10.1007/s00259-021-05191-9] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2020] [Accepted: 01/02/2021] [Indexed: 12/04/2022]
Abstract
Purpose Previous studies have shown that Aβ-amyloid (Aβ) likely promotes tau to spread beyond the medial temporal lobe. However, the Aβ levels necessary for tau to spread in the neocortex is still unclear. Methods Four hundred sixty-six participants underwent tau imaging with [18F]MK6420 and Aβ imaging with [18F]NAV4694. Aβ scans were quantified on the Centiloid (CL) scale with a cut-off of 25 CL for abnormal levels of Aβ (A+). Tau scans were quantified in three regions of interest (ROI) (mesial temporal (Me); temporoparietal neocortex (Te); and rest of neocortex (R)) and four mesial temporal region (entorhinal cortex, amygdala, hippocampus, and parahippocampus). Regional tau thresholds were established as the 95%ile of the cognitively unimpaired A- subjects. The prevalence of abnormal tau levels (T+) along the Centiloid continuum was determined. Results The plots of prevalence of T+ show earlier and greater increase along the Centiloid continuum in the medial temporal area compared to neocortex. Prevalence of T+ was low but associated with Aβ level between 10 and 40 CL reaching 23% in Me, 15% in Te, and 11% in R. Between 40 and 70 CL, the prevalence of T+ subjects per CL increased fourfold faster and at 70 CL was 64% in Me, 51% in Te, and 37% in R. In cognitively unimpaired, there were no T+ in R below 50 CL. The highest prevalence of T+ were found in the entorhinal cortex, reaching 40% at 40 CL and 80% at 60 CL. Conclusion Outside the entorhinal cortex, abnormal levels of cortical tau on PET are rarely found with Aβ below 40 CL. Above 40 CL prevalence of T+ accelerates in all areas. Moderate Aβ levels are required before abnormal neocortical tau becomes detectable. Supplementary Information The online version contains supplementary material available at 10.1007/s00259-021-05191-9.
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Affiliation(s)
- Vincent Doré
- Health and Biosecurity Flagship, The Australian eHealth Research Centre, Melbourne, Victoria, Australia.
- Department of Molecular Imaging & Therapy, Austin Health, LVL1 Harrold STOKES Block, 145 Studley Road, Heidelberg, Melbourne, Victoria, 3084, Australia.
| | - Natasha Krishnadas
- Department of Molecular Imaging & Therapy, Austin Health, LVL1 Harrold STOKES Block, 145 Studley Road, Heidelberg, Melbourne, Victoria, 3084, Australia
| | - Pierrick Bourgeat
- Health and Biosecurity Flagship, The Australian eHealth Research Centre, Brisbane, Queensland, Australia
| | - Kun Huang
- Department of Molecular Imaging & Therapy, Austin Health, LVL1 Harrold STOKES Block, 145 Studley Road, Heidelberg, Melbourne, Victoria, 3084, Australia
| | - Shenpeng Li
- Health and Biosecurity Flagship, The Australian eHealth Research Centre, Melbourne, Victoria, Australia
- Department of Molecular Imaging & Therapy, Austin Health, LVL1 Harrold STOKES Block, 145 Studley Road, Heidelberg, Melbourne, Victoria, 3084, Australia
| | - Samantha Burnham
- Health and Biosecurity Flagship, The Australian eHealth Research Centre, Melbourne, Victoria, Australia
| | - Colin L Masters
- The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Melbourne, Victoria, Australia
| | - Jurgen Fripp
- Health and Biosecurity Flagship, The Australian eHealth Research Centre, Brisbane, Queensland, Australia
| | - Victor L Villemagne
- Department of Molecular Imaging & Therapy, Austin Health, LVL1 Harrold STOKES Block, 145 Studley Road, Heidelberg, Melbourne, Victoria, 3084, Australia
- Department of Medicine, The University of Melbourne, Melbourne, Victoria, Australia
| | - Christopher C Rowe
- Department of Molecular Imaging & Therapy, Austin Health, LVL1 Harrold STOKES Block, 145 Studley Road, Heidelberg, Melbourne, Victoria, 3084, Australia
- Department of Medicine, The University of Melbourne, Melbourne, Victoria, Australia
- The Australian Dementia Network (ADNeT), Melbourne, Australia
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41
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Abstract
Amyloid-β (Aβ) PET imaging has now been available for over 15 years. The ability to detect Aβ in vivo has greatly improved the clinical and research landscape of Alzheimer's disease (AD) and other neurodegenerative conditions. Aβ imaging provides very reliable, accurate, and reproducible measurements of regional and global Aβ burden in the brain. It has proved invaluable in anti-Aβ therapy trials, and is now recognized as a powerful diagnostic tool. The appropriate use of Aβ PET, when combined with comprehensive clinical evaluation by a dementia-trained specialist, can improve the accuracy of a clinical diagnosis of AD and substantially alter management. It can assist in differentiating AD from other neurodegenerative conditions, often by its ability to rule out the presence of Aβ. When combined with tau imaging, further increase in specificity for the diagnosis of AD can be achieved. The integration of Aβ PET, in conjunction with biomarkers of tau, neurodegeneration and neuroinflammation, into large, longitudinal, observational cohort studies continues to increase our understanding of the development of AD. Its incorporation into clinical trials has been pivotal in defining the most effective anti-Aβ biological therapies and optimal dosing so that effective disease modifying therapy now appears imminent. Aβ deposition is a gradual and protracted process, permitting a wide treatment window for anti-Aβ therapies and Aβ PET has made trials in this preclinical AD period feasible. Continuing improvement in Aβ tracer target to background ratio is allowing trials in earlier AD that tailor drug dosage to Aβ level. The quest to standardize quantification and define universally applicable thresholds for all Aβ tracers has produced the Centiloid method. Centiloid values that correlate well with neuropathologic findings and prognosis have been identified. Rapid cloud-based automated individual scan analysis is now possible and does not require MRI. Challenges remain, particularly around cross camera standardized uptake value ratio variation that need to be addressed. This review will compare available Aβ radiotracers, discuss approaches to quantification, as well as the clinical and research applications of Aβ PET.
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Affiliation(s)
- Natasha Krishnadas
- Florey Department of Neurosciences and Mental Health, Faculty of Medicine, Dentistry and Health Sciences, The University of Melbourne, Victoria, Australia; Department of Molecular Imaging & Therapy, Austin Health, Victoria, Australia
| | - Victor L Villemagne
- Department of Molecular Imaging & Therapy, Austin Health, Victoria, Australia
| | - Vincent Doré
- Department of Molecular Imaging & Therapy, Austin Health, Victoria, Australia; Health and Biosecurity Flagship, The Australian eHealth Research Centre, CSIRO, Victoria, Australia
| | - Christopher C Rowe
- Department of Molecular Imaging & Therapy, Austin Health, Victoria, Australia; The Australian Dementia Network (ADNeT), Melbourne, Australia; The University of Melbourne, Victoria, Australia.
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