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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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Honhar P, Matuskey D, Carson RE, Hillmer AT. Improving SUVR quantification by correcting for radiotracer clearance in tissue. J Cereb Blood Flow Metab 2024; 44:296-309. [PMID: 37589538 PMCID: PMC10993874 DOI: 10.1177/0271678x231196804] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Revised: 06/12/2023] [Accepted: 06/22/2023] [Indexed: 08/18/2023]
Abstract
Standardized Uptake Value Ratio (SUVR) is a widely reported semi-quantitative positron emission tomography (PET) outcome measure, partly because of its ease of measurement from short scan durations. However, in brain, SUVR is often a biased estimator of the gold-standard distribution volume ratio (DVR) due to non-equilibrium conditions, i.e., clearance of the radiotracer in relevant tissues. Factors that affect radiotracer metabolism and clearance such as medication or subject groups could lead to artificial differences in SUVR. This work developed a correction that reduces the bias in SUVR (estimated from a short 15-30 min PET imaging session) by accounting for the effects of tracer clearance observed during the late SUVR time window. The proposed correction takes the form of a one-step non-linear algebraic transform of SUVR that is a function of radiotracer dependent parameters such as clearance rates from the reference and target tissues, and population averaged reference region clearance rate (k 2 , ref ). An important observation was the need for accurate estimation of radiotracer clearance rate in target tissue, which was addressed with a regression based model. Simulations and human data from two different radiotracers (healthy controls for [11C]LSN3172176, healthy controls and Parkinson's disease subjects for [18F]FE-PE2I) were used to validate the correction and evaluate its benefits and limitations. SUVR correction in human data significantly reduced mean SUVR bias across brain regions and subjects (from ∼25% for SUVR to <10% for corrected SUVR). This correction also significantly reduced the variability of this bias across brain regions for both tracers (approximately 50% for [11C]LSN3172176, 20% for [18F]FE-PE2I). Future work should investigate the benefits of using corrected SUVR in other populations and with different tracers.
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Affiliation(s)
- Praveen Honhar
- Department of Radiology and Biomedical Imaging, Yale PET Center, Yale School of Medicine, New Haven, CT, USA
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA
| | - David Matuskey
- Department of Radiology and Biomedical Imaging, Yale PET Center, Yale School of Medicine, New Haven, CT, USA
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
- Department of Neurology, Yale School of Medicine, New Haven, CT, USA
| | - Richard E Carson
- Department of Radiology and Biomedical Imaging, Yale PET Center, Yale School of Medicine, New Haven, CT, USA
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA
| | - Ansel T Hillmer
- Department of Radiology and Biomedical Imaging, Yale PET Center, Yale School of Medicine, New Haven, CT, USA
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
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3
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Young P, Heeman F, Axelsson J, Collij LE, Hitzel A, Sanaat A, Niñerola-Baizan A, Perissinotti A, Lubberink M, Frisoni GB, Zaidi H, Barkhof F, Farrar G, Baker S, Gispert JD, Garibotto V, Rieckmann A, Schöll M. Impact of simulated reduced injected dose on the assessment of amyloid PET scans. Eur J Nucl Med Mol Imaging 2024; 51:734-748. [PMID: 37897616 PMCID: PMC10796642 DOI: 10.1007/s00259-023-06481-0] [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: 07/11/2023] [Accepted: 10/15/2023] [Indexed: 10/30/2023]
Abstract
PURPOSE To investigate the impact of reduced injected doses on the quantitative and qualitative assessment of the amyloid PET tracers [18F]flutemetamol and [18F]florbetaben. METHODS Cognitively impaired and unimpaired individuals (N = 250, 36% Aβ-positive) were included and injected with [18F]flutemetamol (N = 175) or [18F]florbetaben (N = 75). PET scans were acquired in list-mode (90-110 min post-injection) and reduced-dose images were simulated to generate images of 75, 50, 25, 12.5 and 5% of the original injected dose. Images were reconstructed using vendor-provided reconstruction tools and visually assessed for Aβ-pathology. SUVRs were calculated for a global cortical and three smaller regions using a cerebellar cortex reference tissue, and Centiloid was computed. Absolute and percentage differences in SUVR and CL were calculated between dose levels, and the ability to discriminate between Aβ- and Aβ + scans was evaluated using ROC analyses. Finally, intra-reader agreement between the reduced dose and 100% images was evaluated. RESULTS At 5% injected dose, change in SUVR was 3.72% and 3.12%, with absolute change in Centiloid 3.35CL and 4.62CL, for [18F]flutemetamol and [18F]florbetaben, respectively. At 12.5% injected dose, percentage change in SUVR and absolute change in Centiloid were < 1.5%. AUCs for discriminating Aβ- from Aβ + scans were high (AUC ≥ 0.94) across dose levels, and visual assessment showed intra-reader agreement of > 80% for both tracers. CONCLUSION This proof-of-concept study showed that for both [18F]flutemetamol and [18F]florbetaben, adequate quantitative and qualitative assessments can be obtained at 12.5% of the original injected dose. However, decisions to reduce the injected dose should be made considering the specific clinical or research circumstances.
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Affiliation(s)
- Peter Young
- Wallenberg Centre for Molecular and Translational Medicine, University of Gothenburg, Gothenburg, Sweden
- Department of Psychiatry and Neurochemistry, Institute of Physiology and Neuroscience, University of Gothenburg, Gothenburg, Sweden
| | - Fiona Heeman
- Wallenberg Centre for Molecular and Translational Medicine, University of Gothenburg, Gothenburg, Sweden
- Department of Psychiatry and Neurochemistry, Institute of Physiology and Neuroscience, University of Gothenburg, Gothenburg, Sweden
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
| | - Jan Axelsson
- Department of Radiation Sciences, Radiation Physics, Umeå University, Umeå, Sweden
| | - 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, Department of Clinical Sciences, Lund University, Malmö, Sweden
| | - Anne Hitzel
- Department of Nuclear Medicine, Toulouse University Hospital, Toulouse, France
| | - Amirhossein Sanaat
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland
| | - Aida Niñerola-Baizan
- Nuclear Medicine Department, Hospital Clínic Barcelona, Barcelona, Spain
- Biomedical Research Networking Centre of Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), ISCIII, Barcelona, Spain
| | - Andrés Perissinotti
- Nuclear Medicine Department, Hospital Clínic Barcelona, Barcelona, Spain
- Biomedical Research Networking Centre of Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), ISCIII, Barcelona, Spain
| | - Mark Lubberink
- Nuclear Medicine and PET, Department of Surgical Sciences, Uppsala University, Uppsala, Sweden
| | - Giovanni B Frisoni
- Laboratory of Neuroimaging of Aging (LANVIE), University of Geneva, Geneva, Switzerland
- Geneva Memory Center, Department of Rehabilitation and Geriatrics, Geneva University Hospitals, Geneva, Switzerland
| | - Habib Zaidi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland
- Geneva University Neurocenter, Geneva University, Geneva, Switzerland
- Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Groningen, Netherlands
- Department of Nuclear Medicine, University of Southern Denmark, Odense, Denmark
| | - Frederik Barkhof
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
- UCL Institute of Neurology, London, UK
| | | | - Suzanne Baker
- Helen Wills Neuroscience Institute, University of California Berkeley, Berkeley, USA
- Molecular Biophysics and Integrated Bioimaging, Lawrence Berkeley National Laboratory, Berkeley, United States
| | - Juan Domingo Gispert
- Barcelona βeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain
- Centro de Investigación Biomédica en Red Bioingeniería, Biomateriales y Nanomedicina, Madrid, Spain
- Hospital del Mar Medical Research Institute (IMIM), Barcelona, Spain
- Universitat Pompeu Fabra, Barcelona, Spain
| | - Valentina Garibotto
- Division of Nuclear Medicine and Molecular Imaging, University Hospitals of Geneva; NIMTLab; Center for Biomedical Imaging (CIBM), University of Geneva, Geneva, Switzerland
| | - Anna Rieckmann
- Institute for Psychology, Universität Der Bundeswehr München, Neubiberg, Germany
| | - Michael Schöll
- Wallenberg Centre for Molecular and Translational Medicine, University of Gothenburg, Gothenburg, Sweden.
- Department of Psychiatry and Neurochemistry, Institute of Physiology and Neuroscience, University of Gothenburg, Gothenburg, Sweden.
- Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, University College London, London, UK.
- Department of Clinical Physiology, Sahlgrenska University Hospital, Gothenburg, Sweden.
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Plassman BL, Ford CB, Smith VA, DePasquale N, Burke JR, Korthauer L, Ott BR, Belanger E, Shepherd-Banigan ME, Couch E, Jutkowitz E, O’Brien EC, Sorenson C, Wetle TT, Van Houtven CH. Elevated Amyloid-β PET Scan and Cognitive and Functional Decline in Mild Cognitive Impairment and Dementia of Uncertain Etiology. J Alzheimers Dis 2024; 97:1161-1171. [PMID: 38306055 PMCID: PMC11034799 DOI: 10.3233/jad-230950] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2024]
Abstract
BACKGROUND Elevated amyloid-β (Aβ) on positron emission tomography (PET) scan is used to aid diagnosis of Alzheimer's disease (AD), but many prior studies have focused on patients with a typical AD phenotype such as amnestic mild cognitive impairment (MCI). Little is known about whether elevated Aβ on PET scan predicts rate of cognitive and functional decline among those with MCI or dementia that is clinically less typical of early AD, thus leading to etiologic uncertainty. OBJECTIVE We aimed to investigate whether elevated Aβ on PET scan predicts cognitive and functional decline over an 18-month period in those with MCI or dementia of uncertain etiology. METHODS In 1,028 individuals with MCI or dementia of uncertain etiology, we evaluated the association between elevated Aβ on PET scan and change on a telephone cognitive status measure administered to the participant and change in everyday function as reported by their care partner. RESULTS Individuals with either MCI or dementia and elevated Aβ (66.6% of the sample) showed greater cognitive decline compared to those without elevated Aβ on PET scan, whose cognition was relatively stable over 18 months. Those with either MCI or dementia and elevated Aβ were also reported to have greater functional decline compared to those without elevated Aβ, even though the latter group showed significant care partner-reported functional decline over time. CONCLUSIONS Elevated Aβ on PET scan can be helpful in predicting rates of both cognitive and functional decline, even among cognitively impaired individuals with atypical presentations of AD.
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Affiliation(s)
- Brenda L. Plassman
- Department of Psychiatry and Behavioral Sciences, School of Medicine, Duke University, Durham, NC, USA
- Department of Neurology, School of Medicine, Duke University, NC, USA
| | - Cassie B. Ford
- Department of Population Health Sciences, Duke University, Durham, NC, USA
| | - Valerie A. Smith
- Department of Population Health Sciences, Duke University, Durham, NC, USA
- Department of Medicine, Division of General Internal Medicine, Duke University, Durham, NC, USA
- Durham ADAPT, Durham Veterans Affairs Medical Center, Durham, NC, USA
| | - Nicole DePasquale
- Department of Medicine, Division of General Internal Medicine, Duke University, Durham, NC, USA
| | - James R. Burke
- Department of Psychiatry and Behavioral Sciences, School of Medicine, Duke University, Durham, NC, USA
- Department of Neurology, School of Medicine, Duke University, NC, USA
| | - Laura Korthauer
- Department of Psychiatry and Human Behavior, Alpert Medical School of Brown University, Providence, RI, USA
| | - Brian R. Ott
- Department of Neurology, Alpert Medical School of Brown University, Providence, RI, USA
| | - Emmanuelle Belanger
- Department of Health Services Policy and Practice, School of Public Health, Brown University, Providence, RI, USA
| | - Megan E. Shepherd-Banigan
- Department of Population Health Sciences, Duke University, Durham, NC, USA
- Durham ADAPT, Durham Veterans Affairs Medical Center, Durham, NC, USA
- Duke-Margolis Center for Health Policy, Durham, NC, USA
| | - Elyse Couch
- Department of Health Services Policy and Practice, School of Public Health, Brown University, Providence, RI, USA
| | - Eric Jutkowitz
- Department of Health Services Policy and Practice, School of Public Health, Brown University, Providence, RI, USA
| | - Emily C. O’Brien
- Department of Population Health Sciences, Duke University, Durham, NC, USA
| | - Corinna Sorenson
- Department of Population Health Sciences, Duke University, Durham, NC, USA
- Duke-Margolis Center for Health Policy, Durham, NC, USA
- Sanford School of Public Policy, Duke University, Durham, NC, USA
| | - Terrie T. Wetle
- Department of Health Services Policy and Practice, School of Public Health, Brown University, Providence, RI, USA
- Center for Gerontology and Healthcare Research, Brown University School of Public Health, Providence, RI, USA
| | - Courtney H. Van Houtven
- Department of Population Health Sciences, Duke University, Durham, NC, USA
- Durham ADAPT, Durham Veterans Affairs Medical Center, Durham, NC, USA
- Duke-Margolis Center for Health Policy, Durham, NC, USA
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5
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Bollack A, Markiewicz PJ, Wink AM, Prosser L, Lilja J, Bourgeat P, Schott JM, Coath W, Collij LE, Pemberton HG, Farrar G, Barkhof F, Cash DM. Evaluation of novel data-driven metrics of amyloid β deposition for longitudinal PET studies. Neuroimage 2023; 280:120313. [PMID: 37595816 DOI: 10.1016/j.neuroimage.2023.120313] [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: 01/05/2023] [Revised: 05/29/2023] [Accepted: 08/04/2023] [Indexed: 08/20/2023] Open
Abstract
PURPOSE Positron emission tomography (PET) provides in vivo quantification of amyloid-β (Aβ) pathology. Established methods for assessing Aβ burden can be affected by physiological and technical factors. Novel, data-driven metrics have been developed to account for these sources of variability. We aimed to evaluate the performance of four of these amyloid PET metrics against conventional techniques, using a common set of criteria. METHODS Three cohorts were used for evaluation: Insight 46 (N=464, [18F]florbetapir), AIBL (N=277, [18F]flutemetamol), and an independent test-retest data (N=10, [18F]flutemetamol). Established metrics of amyloid tracer uptake included the Centiloid (CL) and where dynamic data was available, the non-displaceable binding potential (BPND). The four data-driven metrics computed were the amyloid load (Aβ load), the Aβ-PET pathology accumulation index (Aβ index), the Centiloid derived from non-negative matrix factorisation (CLNMF), and the amyloid pattern similarity score (AMPSS). These metrics were evaluated using reliability and repeatability in test-retest data, associations with BPND and CL, variability of the rate of change and sample size estimates to detect a 25% slowing in Aβ accumulation. RESULTS All metrics showed good reliability. Aβ load, Aβ index and CLNMF were strong associated with the BPND. The associations with CL suggest that cross-sectional measures of CLNMF, Aβ index and Aβ load are robust across studies. Sample size estimates for secondary prevention trial scenarios were the lowest for CLNMF and Aβ load compared to the CL. CONCLUSION Among the novel data-driven metrics evaluated, the Aβ load, the Aβ index and the CLNMF can provide comparable performance to more established quantification methods of Aβ PET tracer uptake. The CLNMF and Aβ load could offer a more precise alternative to CL, although further studies in larger cohorts should be conducted.
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Affiliation(s)
- Ariane Bollack
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, UCL, London, UK.
| | - Pawel J Markiewicz
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, UCL, London, UK
| | - Alle Meije Wink
- Amsterdam UMC, location VUmc, Department of Radiology and Nuclear Medicine, Amsterdam, the Netherlands
| | - Lloyd Prosser
- Dementia Research Centre, UCL Queen Square Institute of Neurology, London, UK
| | | | | | - Jonathan M Schott
- Dementia Research Centre, UCL Queen Square Institute of Neurology, London, UK
| | - William Coath
- Dementia Research Centre, UCL Queen Square Institute of Neurology, London, UK
| | - Lyduine E Collij
- Amsterdam UMC, location VUmc, Department of Radiology and Nuclear Medicine, Amsterdam, the Netherlands; Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Malmö, Sweden
| | - Hugh G Pemberton
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, UCL, London, UK; GE HealthCare, Amersham, UK; Queen Square Institute of Neurology, University College London, UK
| | | | - Frederik Barkhof
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, UCL, London, UK; Amsterdam UMC, location VUmc, Department of Radiology and Nuclear Medicine, Amsterdam, the Netherlands; Queen Square Institute of Neurology, University College London, UK
| | - David M Cash
- Queen Square Institute of Neurology, University College London, UK; UK Dementia Research Institute at University College London, London, UK
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Trajanoska K, Bhérer C, Taliun D, Zhou S, Richards JB, Mooser V. From target discovery to clinical drug development with human genetics. Nature 2023; 620:737-745. [PMID: 37612393 DOI: 10.1038/s41586-023-06388-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Accepted: 06/29/2023] [Indexed: 08/25/2023]
Abstract
The substantial investments in human genetics and genomics made over the past three decades were anticipated to result in many innovative therapies. Here we investigate the extent to which these expectations have been met, excluding cancer treatments. In our search, we identified 40 germline genetic observations that led directly to new targets and subsequently to novel approved therapies for 36 rare and 4 common conditions. The median time between genetic target discovery and drug approval was 25 years. Most of the genetically driven therapies for rare diseases compensate for disease-causing loss-of-function mutations. The therapies approved for common conditions are all inhibitors designed to pharmacologically mimic the natural, disease-protective effects of rare loss-of-function variants. Large biobank-based genetic studies have the power to identify and validate a large number of new drug targets. Genetics can also assist in the clinical development phase of drugs-for example, by selecting individuals who are most likely to respond to investigational therapies. This approach to drug development requires investments into large, diverse cohorts of deeply phenotyped individuals with appropriate consent for genetically assisted trials. A robust framework that facilitates responsible, sustainable benefit sharing will be required to capture the full potential of human genetics and genomics and bring effective and safe innovative therapies to patients quickly.
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Affiliation(s)
- Katerina Trajanoska
- Canada Excellence Research Chair in Genomic Medicine, Department of Human Genetics, Faculty of Medicine and Health Sciences, Victor Phillip Dahdaleh Institute of Genomic Medicine, McGill University, Montreal, Quebec, Canada
| | - Claude Bhérer
- Canada Excellence Research Chair in Genomic Medicine, Department of Human Genetics, Faculty of Medicine and Health Sciences, Victor Phillip Dahdaleh Institute of Genomic Medicine, McGill University, Montreal, Quebec, Canada
| | - Daniel Taliun
- Canada Excellence Research Chair in Genomic Medicine, Department of Human Genetics, Faculty of Medicine and Health Sciences, Victor Phillip Dahdaleh Institute of Genomic Medicine, McGill University, Montreal, Quebec, Canada
| | - Sirui Zhou
- Canada Excellence Research Chair in Genomic Medicine, Department of Human Genetics, Faculty of Medicine and Health Sciences, Victor Phillip Dahdaleh Institute of Genomic Medicine, McGill University, Montreal, Quebec, Canada
| | - J Brent Richards
- Lady Davis Institute for Medical Research, Jewish General Hospital, McGill University, Montreal, Quebec, Canada
- Department of Epidemiology and Biostatistics and Occupational Health, McGill University, Montreal, Quebec, Canada
| | - Vincent Mooser
- Canada Excellence Research Chair in Genomic Medicine, Department of Human Genetics, Faculty of Medicine and Health Sciences, Victor Phillip Dahdaleh Institute of Genomic Medicine, McGill University, Montreal, Quebec, Canada.
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7
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Collij LE, Farrar G, Valléz García D, Bader I, Shekari M, Lorenzini L, Pemberton H, Altomare D, Pla S, Loor M, Markiewicz P, Yaqub M, Buckley C, Frisoni GB, Nordberg A, Payoux P, Stephens A, Gismondi R, Visser PJ, Ford L, Schmidt M, Birck C, Georges J, Mett A, Walker Z, Boada M, Drzezga A, Vandenberghe R, Hanseeuw B, Jessen F, Schöll M, Ritchie C, Lopes Alves I, Gispert JD, Barkhof F. The amyloid imaging for the prevention of Alzheimer's disease consortium: A European collaboration with global impact. Front Neurol 2023; 13:1063598. [PMID: 36761917 PMCID: PMC9907029 DOI: 10.3389/fneur.2022.1063598] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2022] [Accepted: 12/08/2022] [Indexed: 01/22/2023] Open
Abstract
Background Amyloid-β (Aβ) accumulation is considered the earliest pathological change in Alzheimer's disease (AD). The Amyloid Imaging to Prevent Alzheimer's Disease (AMYPAD) consortium is a collaborative European framework across European Federation of Pharmaceutical Industries Associations (EFPIA), academic, and 'Small and Medium-sized enterprises' (SME) partners aiming to provide evidence on the clinical utility and cost-effectiveness of Positron Emission Tomography (PET) imaging in diagnostic work-up of AD and to support clinical trial design by developing optimal quantitative methodology in an early AD population. The AMYPAD studies In the Diagnostic and Patient Management Study (DPMS), 844 participants from eight centres across three clinical subgroups (245 subjective cognitive decline, 342 mild cognitive impairment, and 258 dementia) were included. The Prognostic and Natural History Study (PNHS) recruited pre-dementia subjects across 11 European parent cohorts (PCs). Approximately 1600 unique subjects with historical and prospective data were collected within this study. PET acquisition with [18F]flutemetamol or [18F]florbetaben radiotracers was performed and quantified using the Centiloid (CL) method. Results AMYPAD has significantly contributed to the AD field by furthering our understanding of amyloid deposition in the brain and the optimal methodology to measure this process. Main contributions so far include the validation of the dual-time window acquisition protocol to derive the fully quantitative non-displaceable binding potential (BP ND ), assess the value of this metric in the context of clinical trials, improve PET-sensitivity to emerging Aβ burden and utilize its available regional information, establish the quantitative accuracy of the Centiloid method across tracers and support implementation of quantitative amyloid-PET measures in the clinical routine. Future steps The AMYPAD consortium has succeeded in recruiting and following a large number of prospective subjects and setting up a collaborative framework to integrate data across European PCs. Efforts are currently ongoing in collaboration with ARIDHIA and ADDI to harmonize, integrate, and curate all available clinical data from the PNHS PCs, which will become openly accessible to the wider scientific community.
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Affiliation(s)
- Lyduine E. Collij
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Center, location VUmc, Amsterdam, Netherlands,Amsterdam Neuroscience, Brain Imaging, Amsterdam, Netherlands,*Correspondence: Lyduine E. Collij ✉
| | | | - David Valléz García
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Center, location VUmc, Amsterdam, Netherlands,Amsterdam Neuroscience, Brain Imaging, Amsterdam, Netherlands
| | - Ilona Bader
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Center, location VUmc, Amsterdam, Netherlands,Amsterdam Neuroscience, Brain Imaging, Amsterdam, Netherlands
| | | | - Luigi Lorenzini
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Center, location VUmc, Amsterdam, Netherlands,Amsterdam Neuroscience, Brain Imaging, Amsterdam, Netherlands
| | - Hugh Pemberton
- Centre for Medical Image Computing, and Queen Square Institute of Neurology, UCL, London, United Kingdom
| | - Daniele Altomare
- Laboratory of Neuroimaging of Aging (LANVIE), Université de Genève, Geneva, Switzerland
| | - Sandra Pla
- Synapse Research Management Partners, Barcelona, Spain
| | - Mery Loor
- Synapse Research Management Partners, Barcelona, Spain
| | - Pawel Markiewicz
- Centre for Medical Image Computing, and Queen Square Institute of Neurology, UCL, London, United Kingdom
| | - Maqsood Yaqub
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Center, location VUmc, Amsterdam, Netherlands
| | | | - Giovanni B. Frisoni
- Laboratory of Neuroimaging of Aging (LANVIE), Université de Genève, Geneva, Switzerland
| | - Agneta Nordberg
- Department of Neurobiology, Care Sciences and Society, Center of Alzheimer Research, Karolinska Institutet, Stockholm, Sweden
| | - Pierre Payoux
- Department of Nuclear Medicine, Centre Hospitalier Universitaire de Toulouse, Toulouse, France
| | - Andrew Stephens
- Life Molecular Imaging GmbH, Berlin, Baden-Württemberg, Germany
| | | | - Pieter Jelle Visser
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Center, location VUmc, Amsterdam, Netherlands
| | - Lisa Ford
- Janssen Pharmaceutica NV, Beerse, Belgium
| | | | | | | | - Anja Mett
- GE Healthcare, Amersham, United Kingdom
| | - Zuzana Walker
- Centre for Medical Image Computing, and Queen Square Institute of Neurology, UCL, London, United Kingdom
| | - Mercé Boada
- Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya, Barcelona, Spain,Networking Research Center on Neurodegenerative Diseases (CIBERNED), Instituto de Salud Carlos III, Madrid, Spain
| | - Alexander Drzezga
- Department of Psychiatry, University Hospital of Cologne, Cologne, North Rhine-Westphalia, Germany
| | - Rik Vandenberghe
- Faculty of Medicine, University Hospitals Leuven, Leuven, Brussels, Belgium
| | - Bernard Hanseeuw
- Institute of Neuroscience (IONS), Université Catholique de Louvain, Brussels, Belgium
| | - Frank Jessen
- Department of Psychiatry, University Hospital of Cologne, Cologne, North Rhine-Westphalia, Germany
| | - Michael Schöll
- Department of Psychiatry and Neurochemistry, University of Gothenburg, Gothenburg, Sweden
| | - Craig Ritchie
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, Scotland, United Kingdom
| | | | - Juan Domingo Gispert
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Center, location VUmc, Amsterdam, Netherlands
| | - Frederik Barkhof
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Center, location VUmc, Amsterdam, Netherlands,Amsterdam Neuroscience, Brain Imaging, Amsterdam, Netherlands,Centre for Medical Image Computing, and Queen Square Institute of Neurology, UCL, London, United Kingdom
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8
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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: 54] [Impact Index Per Article: 27.0] [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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9
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Heeman F, Yaqub M, Hendriks J, van Berckel BNM, Collij LE, Gray KR, Manber R, Wolz R, Garibotto V, Wimberley C, Ritchie C, Barkhof F, Gispert JD, Vállez García D, Lopes Alves I, Lammertsma AA. Impact of cerebral blood flow and amyloid load on SUVR bias. EJNMMI Res 2022; 12:29. [PMID: 35553267 PMCID: PMC9098761 DOI: 10.1186/s13550-022-00898-8] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Accepted: 04/25/2022] [Indexed: 11/17/2022] Open
Abstract
Background Despite its widespread use, the semi-quantitative standardized uptake value ratio (SUVR) may be biased compared with the distribution volume ratio (DVR). This bias may be partially explained by changes in cerebral blood flow (CBF) and is likely to be also dependent on the extent of the underlying amyloid-β (Aβ) burden. This study aimed to compare SUVR with DVR and to evaluate the effects of underlying Aβ burden and CBF on bias in SUVR in mainly cognitively unimpaired participants. Participants were scanned according to a dual-time window protocol, with either [18F]flutemetamol (N = 90) or [18F]florbetaben (N = 31). The validated basisfunction-based implementation of the two-step simplified reference tissue model was used to derive DVR and R1 parametric images, and SUVR was calculated from 90 to 110 min post-injection, all with the cerebellar grey matter as reference tissue. First, linear regression and Bland–Altman analyses were used to compare (regional) SUVR with DVR. Then, generalized linear models were applied to evaluate whether (bias in) SUVR relative to DVR could be explained by R1 for the global cortical average (GCA), precuneus, posterior cingulate, and orbitofrontal region. Results Despite high correlations (GCA: R2 ≥ 0.85), large overestimation and proportional bias of SUVR relative to DVR was observed. Negative associations were observed between both SUVR or SUVRbias and R1, albeit non-significant. Conclusion The present findings demonstrate that bias in SUVR relative to DVR is strongly related to underlying Aβ burden. Furthermore, in a cohort consisting mainly of cognitively unimpaired individuals, the effect of relative CBF on bias in SUVR appears limited. EudraCT Number: 2018-002277-22, registered on: 25-06-2018. Supplementary Information The online version contains supplementary material available at 10.1186/s13550-022-00898-8.
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Affiliation(s)
- Fiona Heeman
- Amsterdam UMC, Vrije Universiteit Amsterdam, Radiology and Nuclear Medicine, Amsterdam Neuroscience, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands.
| | - Maqsood Yaqub
- Amsterdam UMC, Vrije Universiteit Amsterdam, Radiology and Nuclear Medicine, Amsterdam Neuroscience, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands
| | - Janine Hendriks
- Amsterdam UMC, Vrije Universiteit Amsterdam, Radiology and Nuclear Medicine, Amsterdam Neuroscience, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands
| | - Bart N M van Berckel
- Amsterdam UMC, Vrije Universiteit Amsterdam, Radiology and Nuclear Medicine, Amsterdam Neuroscience, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands
| | - Lyduine E Collij
- Amsterdam UMC, Vrije Universiteit Amsterdam, Radiology and Nuclear Medicine, Amsterdam Neuroscience, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands
| | | | | | | | - Valentina Garibotto
- NIMTLab, Faculty of Medicine, Geneva University, Geneva, Switzerland.,Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospitals, Geneva, Switzerland
| | - Catriona Wimberley
- Edinburgh Imaging, Queen's Medical Research Institute, University of Edinburgh, Edinburgh, UK
| | - Craig Ritchie
- Edinburgh Imaging, Queen's Medical Research Institute, University of Edinburgh, Edinburgh, UK
| | - Frederik Barkhof
- Amsterdam UMC, Vrije Universiteit Amsterdam, Radiology and Nuclear Medicine, Amsterdam Neuroscience, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands.,UCL, Institutes of Neurology and Healthcare Engineering, London, UK
| | - Juan Domingo Gispert
- Barcelonaβeta Brain Research Centre, Pasqual Maragall Foundation, Barcelona, Spain.,Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Madrid, Spain.,Department of Experimental and Health Sciences, Universitat Pompeu Fabra, Barcelona, Spain.,IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain
| | - David Vállez García
- Amsterdam UMC, Vrije Universiteit Amsterdam, Radiology and Nuclear Medicine, Amsterdam Neuroscience, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands
| | - Isadora Lopes Alves
- Amsterdam UMC, Vrije Universiteit Amsterdam, Radiology and Nuclear Medicine, Amsterdam Neuroscience, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands
| | - Adriaan A Lammertsma
- Amsterdam UMC, Vrije Universiteit Amsterdam, Radiology and Nuclear Medicine, Amsterdam Neuroscience, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands
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10
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Hwang G, Abdulkadir A, Erus G, Habes M, Pomponio R, Shou H, Doshi J, Mamourian E, Rashid T, Bilgel M, Fan Y, Sotiras A, Srinivasan D, Morris JC, Albert MS, Bryan NR, Resnick SM, Nasrallah IM, Davatzikos C, Wolk DA. Disentangling Alzheimer's disease neurodegeneration from typical brain ageing using machine learning. Brain Commun 2022; 4:fcac117. [PMID: 35611306 PMCID: PMC9123890 DOI: 10.1093/braincomms/fcac117] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Revised: 02/17/2022] [Accepted: 05/04/2022] [Indexed: 11/17/2022] Open
Abstract
Neuroimaging biomarkers that distinguish between changes due to typical brain ageing and Alzheimer's disease are valuable for determining how much each contributes to cognitive decline. Supervised machine learning models can derive multivariate patterns of brain change related to the two processes, including the Spatial Patterns of Atrophy for Recognition of Alzheimer's Disease (SPARE-AD) and of Brain Aging (SPARE-BA) scores investigated herein. However, the substantial overlap between brain regions affected in the two processes confounds measuring them independently. We present a methodology, and associated results, towards disentangling the two. T1-weighted MRI scans of 4054 participants (48-95 years) with Alzheimer's disease, mild cognitive impairment (MCI), or cognitively normal (CN) diagnoses from the Imaging-based coordinate SysTem for AGIng and NeurodeGenerative diseases (iSTAGING) consortium were analysed. Multiple sets of SPARE scores were investigated, in order to probe imaging signatures of certain clinically or molecularly defined sub-cohorts. First, a subset of clinical Alzheimer's disease patients (n = 718) and age- and sex-matched CN adults (n = 718) were selected based purely on clinical diagnoses to train SPARE-BA1 (regression of age using CN individuals) and SPARE-AD1 (classification of CN versus Alzheimer's disease) models. Second, analogous groups were selected based on clinical and molecular markers to train SPARE-BA2 and SPARE-AD2 models: amyloid-positive Alzheimer's disease continuum group (n = 718; consisting of amyloid-positive Alzheimer's disease, amyloid-positive MCI, amyloid- and tau-positive CN individuals) and amyloid-negative CN group (n = 718). Finally, the combined group of the Alzheimer's disease continuum and amyloid-negative CN individuals was used to train SPARE-BA3 model, with the intention to estimate brain age regardless of Alzheimer's disease-related brain changes. The disentangled SPARE models, SPARE-AD2 and SPARE-BA3, derived brain patterns that were more specific to the two types of brain changes. The correlation between the SPARE-BA Gap (SPARE-BA minus chronological age) and SPARE-AD was significantly reduced after the decoupling (r = 0.56-0.06). The correlation of disentangled SPARE-AD was non-inferior to amyloid- and tau-related measurements and to the number of APOE ε4 alleles but was lower to Alzheimer's disease-related psychometric test scores, suggesting the contribution of advanced brain ageing to the latter. The disentangled SPARE-BA was consistently less correlated with Alzheimer's disease-related clinical, molecular and genetic variables. By employing conservative molecular diagnoses and introducing Alzheimer's disease continuum cases to the SPARE-BA model training, we achieved more dissociable neuroanatomical biomarkers of typical brain ageing and Alzheimer's disease.
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Affiliation(s)
- Gyujoon Hwang
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA
| | - Ahmed Abdulkadir
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA
| | - Guray Erus
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Mohamad Habes
- Glenn Biggs Institute for Alzheimer’s & Neurodegenerative Diseases, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - Raymond Pomponio
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Haochang Shou
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Jimit Doshi
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Elizabeth Mamourian
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Tanweer Rashid
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Murat Bilgel
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, MD, USA
| | - Yong Fan
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Aristeidis Sotiras
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Washington University in St Louis, St Louis, MO, USA
| | - Dhivya Srinivasan
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - John C. Morris
- Department of Neurology, Washington University in St Louis, St Louis, MO, USA
| | - Marilyn S. Albert
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Nick R. Bryan
- Department of Diagnostic Medicine, University of Texas, Austin, TX, USA
| | - Susan M. Resnick
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, MD, USA
| | - Ilya M. Nasrallah
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Christos Davatzikos
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - David A. Wolk
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA
- Department of Neurology and Penn Memory Center, University of Pennsylvania, Philadelphia, PA, USA
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11
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NRM 2021 Abstract Booklet. J Cereb Blood Flow Metab 2021; 41:11-309. [PMID: 34905986 PMCID: PMC8851538 DOI: 10.1177/0271678x211061050] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
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12
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Levin F, Jelistratova I, Betthauser TJ, Okonkwo O, Johnson SC, Teipel SJ, Grothe MJ. In vivo staging of regional amyloid progression in healthy middle-aged to older people at risk of Alzheimer's disease. Alzheimers Res Ther 2021; 13:178. [PMID: 34674764 PMCID: PMC8532333 DOI: 10.1186/s13195-021-00918-0] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2021] [Accepted: 10/11/2021] [Indexed: 12/28/2022]
Abstract
BACKGROUND We investigated regional amyloid staging characteristics in 11C-PiB-PET data from middle-aged to older participants at elevated risk for AD enrolled in the Wisconsin Registry for Alzheimer's Prevention. METHODS We analyzed partial volume effect-corrected 11C-PiB-PET distribution volume ratio maps from 220 participants (mean age = 61.4 years, range 46.9-76.8 years). Regional amyloid positivity was established using region-specific thresholds. We used four stages from the frequency-based staging of amyloid positivity to characterize individual amyloid deposition. Longitudinal PET data was used to assess the temporal progression of stages and to evaluate the emergence of regional amyloid positivity in participants who were amyloid-negative at baseline. We also assessed the effect of amyloid stage on longitudinal cognitive trajectories. RESULTS The staging model suggested progressive accumulation of amyloid from associative to primary neocortex and gradually involving subcortical regions. Longitudinal PET measurements supported the cross-sectionally estimated amyloid progression. In mixed-effects longitudinal analysis of cognitive follow-up data obtained over an average period of 6.5 years following the baseline PET measurement, amyloid stage II showed a faster decline in executive function, and advanced amyloid stages (III and IV) showed a faster decline across multiple cognitive domains compared to stage 0. CONCLUSIONS Overall, the 11C-PiB-PET-based staging model was generally consistent with previously derived models from 18F-labeled amyloid PET scans and a longitudinal course of amyloid accumulation. Differences in longitudinal cognitive decline support the potential clinical utility of in vivo amyloid staging for risk stratification of the preclinical phase of AD even in middle-aged to older individuals at risk for AD.
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Affiliation(s)
- Fedor Levin
- German Center for Neurodegenerative Diseases (DZNE), Rostock/Greifswald, Rostock, Germany
| | - Irina Jelistratova
- German Center for Neurodegenerative Diseases (DZNE), Rostock/Greifswald, Rostock, Germany
| | - Tobey J Betthauser
- Division of Geriatrics and Gerontology, Department of Medicine, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI, USA
- Wisconsin Alzheimer's Disease Research Center, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI, USA
| | - Ozioma Okonkwo
- Division of Geriatrics and Gerontology, Department of Medicine, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI, USA
- Wisconsin Alzheimer's Disease Research Center, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI, USA
| | - Sterling C Johnson
- Division of Geriatrics and Gerontology, Department of Medicine, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI, USA
- Wisconsin Alzheimer's Disease Research Center, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI, USA
- Wisconsin Alzheimer's Institute, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI, USA
- Geriatric Research Education and Clinical Center, William S. Middleton Memorial Veterans Hospital, Madison, WI, USA
| | - Stefan J Teipel
- German Center for Neurodegenerative Diseases (DZNE), Rostock/Greifswald, Rostock, Germany
- Department of Psychosomatic Medicine, University of Rostock, Rostock, Germany
| | - Michel J Grothe
- German Center for Neurodegenerative Diseases (DZNE), Rostock/Greifswald, Rostock, Germany.
- Unidad de Trastornos del Movimiento, Servicio de Neurología y Neurofisiología Clínica, Instituto de Biomedicina de Sevilla, Hospital Universitario Virgen del Rocío/CSIC/Universidad de Sevilla, s/n, 41013, Seville, Spain.
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13
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The approval of a disease-modifying treatment for Alzheimer's disease: impact and consequences for the nuclear medicine community. Eur J Nucl Med Mol Imaging 2021; 48:3033-3036. [PMID: 34272989 DOI: 10.1007/s00259-021-05485-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
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