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Ruwanpathirana GP, Williams RC, Masters CL, Rowe CC, Johnston LA, Davey CE. Impact of PET Reconstruction on Amyloid-β Quantitation in Cross-Sectional and Longitudinal Analyses. J Nucl Med 2024:jnumed.123.266188. [PMID: 38575189 DOI: 10.2967/jnumed.123.266188] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Revised: 02/13/2024] [Indexed: 04/06/2024] Open
Abstract
Amyloid-β (Aβ) accumulation in Alzheimer disease (AD) is typically measured using SUV ratio and the centiloid (CL) scale. The low spatial resolution of PET images is known to degrade quantitative metrics because of the partial-volume effect. This article examines the impact of spatial resolution, as determined by the reconstruction configuration, on the Aβ PET quantitation in both cross-sectional and longitudinal data. Methods: The cross-sectional study involved 89 subjects with 20-min [18F]florbetapir scans generated on an mCT (44 Aβ-negative [Aβ-], 45 Aβ-positive [Aβ+]) using 69 reconstruction configurations, which varied in number of iteration updates, point-spread function, time-of-flight, and postreconstruction smoothing. The subjects were classified as Aβ- or Aβ+ visually. For each reconstruction, Aβ CL was calculated using CapAIBL, and the spatial resolution was calculated as full width at half maximum (FWHM) using the barrel phantom method. The change in CLs and the effect size of the difference in CLs between Aβ- and Aβ+ groups with FWHM were examined. The longitudinal study involved 79 subjects (46 Aβ-, 33 Aβ+) with three 20-min [18F]flutemetamol scans generated on an mCT. The subjects were classified as Aβ- or Aβ+ using a cutoff CL of 20. All scans were reconstructed using low-, medium-, and high-resolution configurations, and Aβ CLs were calculated using CapAIBL. Since linear Aβ accumulation was assumed over a 10-y interval, for each reconstruction configuration, Aβ accumulation rate differences (ARDs) between the second and first periods were calculated for all subjects. Zero ARD was used as a consistency metric. The number of Aβ accumulators was also used to compare the sensitivity of CL across reconstruction configurations. Results: In the cross-sectional study, CLs in both the Aβ- and the Aβ+ groups were impacted by the FWHM of the reconstruction method. Without postreconstruction smoothing, Aβ- CLs increased for a FWHM of 4.5 mm or more, whereas Aβ+ CLs decreased across the FWHM range. High-resolution reconstructions provided the best statistical separation between groups. In the longitudinal study, the median ARD of low-resolution reconstructed data for the Aβ- group was greater than zero whereas the ARDs of higher-resolution reconstructions were not significantly different from zero, indicating more consistent rate estimates in the higher-resolution reconstructions. Higher-resolution reconstructions identified 10 additional Aβ accumulators in the Aβ- group, resulting in a 22% increased group size compared with the low-resolution reconstructions. Higher-resolution reconstructions reduced the average CLs of the negative group by 12 points. Conclusion: High-resolution PET reconstructions, inherently less impacted by partial-volume effect, may improve Aβ PET quantitation in both cross-sectional and longitudinal data. In the cross-sectional analysis, separation of CLs between Aβ- and Aβ+ cohorts increased with spatial resolution. Higher-resolution reconstructions also exhibited both improved consistency and improved sensitivity in measures of Aβ accumulation. These features suggest that higher-resolution reconstructions may be advantageous in early-stage AD therapies.
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Affiliation(s)
- Gihan P Ruwanpathirana
- Department of Biomedical Engineering, University of Melbourne, Melbourne, Victoria, Australia
- Melbourne Brain Centre Imaging Unit, University of Melbourne, Melbourne, Victoria, Australia
| | - Robert C Williams
- Melbourne Brain Centre Imaging Unit, University of Melbourne, Melbourne, Victoria, Australia
| | - Colin L Masters
- Florey Institute of Neurosciences and Mental Health, University of Melbourne, Melbourne, Victoria, Australia
- Australian Dementia Network, Melbourne, Victoria, Australia; and
| | - Christopher C Rowe
- Florey Institute of Neurosciences and Mental Health, University of Melbourne, Melbourne, Victoria, Australia
- Australian Dementia Network, Melbourne, Victoria, Australia; and
- Department of Molecular Imaging and Therapy, Austin Health, Melbourne, Victoria, Australia
| | - Leigh A Johnston
- Department of Biomedical Engineering, University of Melbourne, Melbourne, Victoria, Australia
- Melbourne Brain Centre Imaging Unit, University of Melbourne, Melbourne, Victoria, Australia
| | - Catherine E Davey
- Department of Biomedical Engineering, University of Melbourne, Melbourne, Victoria, Australia;
- Melbourne Brain Centre Imaging Unit, University of Melbourne, Melbourne, Victoria, Australia
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Pivac LN, Brown BM, Sewell KR, Doecke JD, Villemagne VL, Doré V, Weinborn M, Sohrabi HR, Gardener SL, Bucks RS, Laws SM, Taddei K, Maruff P, Masters CL, Rowe C, Martins RN, Rainey‐Smith SR. Suboptimal self-reported sleep efficiency and duration are associated with faster accumulation of brain amyloid beta in cognitively unimpaired older adults. Alzheimers Dement (Amst) 2024; 16:e12579. [PMID: 38651160 PMCID: PMC11033837 DOI: 10.1002/dad2.12579] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Revised: 02/28/2024] [Accepted: 03/10/2024] [Indexed: 04/25/2024]
Abstract
INTRODUCTION This study investigated whether self-reported sleep quality is associated with brain amyloid beta (Aβ) accumulation. METHODS Linear mixed effect model analyses were conducted for 189 cognitively unimpaired (CU) older adults (mean ± standard deviation 74.0 ± 6.2; 53.2% female), with baseline self-reported sleep data, and positron emission tomography-determined brain Aβ measured over a minimum of three time points (range 33.3-72.7 months). Analyses included random slopes and intercepts, interaction for apolipoprotein E (APOE) ε4 allele status, and time, adjusting for sex and baseline age. RESULTS Sleep duration <6 hours, in APOE ε4 carriers, and sleep efficiency <65%, in the whole sample and APOE ε4 non-carriers, is associated with faster accumulation of brain Aβ. DISCUSSION These findings suggest a role for self-reported suboptimal sleep efficiency and duration in the accumulation of Alzheimer's disease (AD) neuropathology in CU individuals. Additionally, poor sleep efficiency represents a potential route via which individuals at lower genetic risk may progress to preclinical AD. Highlights In cognitively unimpaired older adults self-report sleep is associated with brain amyloid beta (Aβ) accumulation.Across sleep characteristics, this relationship differs by apolipoprotein E (APOE) genotype.Sleep duration <6 hours is associated with faster brain Aβ accumulation in APOE ε4 carriers.Sleep efficiency < 65% is associated with faster brain Aβ accumulation in APOE ε4 non-carriers.Personalized sleep interventions should be studied for potential to slow Aβ accumulation.
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Affiliation(s)
- Louise N. Pivac
- Centre for Healthy Ageing, Health Futures InstituteMurdoch UniversityMurdochWestern AustraliaAustralia
- Alzheimer's Research Australia, Sarich Neuroscience Research InstituteNedlandsWestern AustraliaAustralia
| | - Belinda M. Brown
- Centre for Healthy Ageing, Health Futures InstituteMurdoch UniversityMurdochWestern AustraliaAustralia
| | - Kelsey R. Sewell
- Centre for Healthy Ageing, Health Futures InstituteMurdoch UniversityMurdochWestern AustraliaAustralia
| | - James D. Doecke
- Australian E‐Health Research Centre, CSIROHerstonQueenslandAustralia
| | | | - Vincent Doré
- Australian E‐Health Research Centre, CSIROHerstonQueenslandAustralia
- Department of Molecular ImagingAustin HealthHeidelbergVictoriaAustralia
| | - Michael Weinborn
- School of Psychological ScienceUniversity of Western AustraliaPerthWestern AustraliaAustralia
| | - Hamid R. Sohrabi
- Centre for Healthy Ageing, Health Futures InstituteMurdoch UniversityMurdochWestern AustraliaAustralia
| | - Samantha L. Gardener
- School of Medical and Health SciencesEdith Cowan UniversityJoondalupWestern AustraliaAustralia
| | - Romola S. Bucks
- School of Psychological ScienceUniversity of Western AustraliaPerthWestern AustraliaAustralia
- School of Population and Global HealthUniversity of Western AustraliaPerthWestern AustraliaAustralia
| | - Simon M. Laws
- School of Medical and Health SciencesEdith Cowan UniversityJoondalupWestern AustraliaAustralia
- Centre for Precision HealthEdith Cowan UniversityJoondalupWestern AustraliaAustralia
- Collaborative Genomics and Translation GroupEdith Cowan UniversityJoondalupWestern AustraliaAustralia
- Curtin Medical SchoolCurtin UniversityBentleyWestern AustraliaAustralia
| | - Kevin Taddei
- School of Medical and Health SciencesEdith Cowan UniversityJoondalupWestern AustraliaAustralia
| | - Paul Maruff
- Cogstate Ltd., MelbourneMelbourneVictoriaAustralia
- The Florey Institute of Neuroscience and Mental HealthUniversity of MelbourneMelbourneVictoriaAustralia
| | - Colin L. Masters
- The Florey Institute of Neuroscience and Mental HealthUniversity of MelbourneMelbourneVictoriaAustralia
| | - Christopher Rowe
- Department of Molecular ImagingAustin HealthHeidelbergVictoriaAustralia
- The Florey Institute of Neuroscience and Mental HealthUniversity of MelbourneMelbourneVictoriaAustralia
| | - Ralph N. Martins
- School of Medical and Health SciencesEdith Cowan UniversityJoondalupWestern AustraliaAustralia
- Department of Biomedical SciencesMacquarie UniversityMacquarie UniversitySydneyNew South WalesAustralia
| | - Stephanie R. Rainey‐Smith
- Centre for Healthy Ageing, Health Futures InstituteMurdoch UniversityMurdochWestern AustraliaAustralia
- Alzheimer's Research Australia, Sarich Neuroscience Research InstituteNedlandsWestern AustraliaAustralia
- School of Psychological ScienceUniversity of Western AustraliaPerthWestern AustraliaAustralia
- School of Medical and Health SciencesEdith Cowan UniversityJoondalupWestern AustraliaAustralia
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Ma L, Low YLC, Zhuo Y, Chu C, Wang Y, Fowler CJ, Tan ECK, Masters CL, Jin L, Pan Y. Exploring the association between cancer and cognitive impairment in the Australian Imaging Biomarkers and Lifestyle (AIBL) study. Sci Rep 2024; 14:4364. [PMID: 38388558 PMCID: PMC10884016 DOI: 10.1038/s41598-024-54875-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2023] [Accepted: 02/17/2024] [Indexed: 02/24/2024] Open
Abstract
An inverse association between cancer and Alzheimer's disease (AD) has been demonstrated; however, the association between cancer and mild cognitive impairment (MCI), and the association between cancer and cognitive decline are yet to be clarified. The AIBL dataset was used to address these knowledge gaps. The crude and adjusted odds ratios for MCI/AD and cognitive decline were compared between participants with/without cancer (referred to as C+ and C- participants). A 37% reduction in odds for AD was observed in C+ participants compared to C- participants after adjusting for all confounders. The overall risk for MCI and AD in C+ participants was reduced by 27% and 31%, respectively. The odds of cognitive decline from MCI to AD was reduced by 59% in C+ participants after adjusting for all confounders. The risk of cognitive decline from MCI to AD was halved in C+ participants. The estimated mean change in Clinical Dementia Rating-Sum of boxes (CDR-SOB) score per year was 0.23 units/year higher in C- participants than in C+ participants. Overall, an inverse association between cancer and MCI/AD was observed in AIBL, which is in line with previous reports. Importantly, an inverse association between cancer and cognitive decline has also been identified.
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Affiliation(s)
- Liwei Ma
- Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Parkville, VIC, 3010, Australia
| | - Yi Ling Clare Low
- Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Parkville, VIC, 3010, Australia
| | - Yuanhao Zhuo
- Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Parkville, VIC, 3010, Australia
| | - Chenyin Chu
- Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Parkville, VIC, 3010, Australia
| | - Yihan Wang
- Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Parkville, VIC, 3010, Australia
| | - Christopher J Fowler
- Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Parkville, VIC, 3010, Australia
| | - Edwin C K Tan
- The University of Sydney School of Pharmacy, Faculty of Medicine and Health, The University of Sydney, Sydney, NSW, 2006, Australia
| | - Colin L Masters
- Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Parkville, VIC, 3010, Australia
| | - Liang Jin
- Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Parkville, VIC, 3010, Australia.
- Drug Delivery, Disposition and Dynamics, Monash Institute of Pharmaceutical Sciences, Monash University, Parkville, VIC, 3052, Australia.
| | - Yijun Pan
- Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Parkville, VIC, 3010, Australia.
- Drug Delivery, Disposition and Dynamics, Monash Institute of Pharmaceutical Sciences, Monash University, Parkville, VIC, 3052, Australia.
- Department of Organ Anatomy, Graduate School of Medicine, Tohoku University, Sendai, Miyagi, 980-8575, Japan.
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Skampardoni I, Nasrallah IM, Abdulkadir A, Wen J, Melhem R, Mamourian E, Erus G, Doshi J, Singh A, Yang Z, Cui Y, Hwang G, Ren Z, Pomponio R, Srinivasan D, Govindarajan ST, Parmpi P, Wittfeld K, Grabe HJ, Bülow R, Frenzel S, Tosun D, Bilgel M, An Y, Marcus DS, LaMontagne P, Heckbert SR, Austin TR, Launer LJ, Sotiras A, Espeland MA, Masters CL, Maruff P, Fripp J, Johnson SC, Morris JC, Albert MS, Bryan RN, Yaffe K, Völzke H, Ferrucci L, Benzinger TL, Ezzati A, Shinohara RT, Fan Y, Resnick SM, Habes M, Wolk D, Shou H, Nikita K, Davatzikos C. Genetic and Clinical Correlates of AI-Based Brain Aging Patterns in Cognitively Unimpaired Individuals. JAMA Psychiatry 2024:2814597. [PMID: 38353984 PMCID: PMC10867779 DOI: 10.1001/jamapsychiatry.2023.5599] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/03/2023] [Accepted: 11/29/2023] [Indexed: 02/17/2024]
Abstract
Importance Brain aging elicits complex neuroanatomical changes influenced by multiple age-related pathologies. Understanding the heterogeneity of structural brain changes in aging may provide insights into preclinical stages of neurodegenerative diseases. Objective To derive subgroups with common patterns of variation in participants without diagnosed cognitive impairment (WODCI) in a data-driven manner and relate them to genetics, biomedical measures, and cognitive decline trajectories. Design, Setting, and Participants Data acquisition for this cohort study was performed from 1999 to 2020. Data consolidation and harmonization were conducted from July 2017 to July 2021. Age-specific subgroups of structural brain measures were modeled in 4 decade-long intervals spanning ages 45 to 85 years using a deep learning, semisupervised clustering method leveraging generative adversarial networks. Data were analyzed from July 2021 to February 2023 and were drawn from the Imaging-Based Coordinate System for Aging and Neurodegenerative Diseases (iSTAGING) international consortium. Individuals WODCI at baseline spanning ages 45 to 85 years were included, with greater than 50 000 data time points. Exposures Individuals WODCI at baseline scan. Main Outcomes and Measures Three subgroups, consistent across decades, were identified within the WODCI population. Associations with genetics, cardiovascular risk factors (CVRFs), amyloid β (Aβ), and future cognitive decline were assessed. Results In a sample of 27 402 individuals (mean [SD] age, 63.0 [8.3] years; 15 146 female [55%]) WODCI, 3 subgroups were identified in contrast with the reference group: a typical aging subgroup, A1, with a specific pattern of modest atrophy and white matter hyperintensity (WMH) load, and 2 accelerated aging subgroups, A2 and A3, with characteristics that were more distinct at age 65 years and older. A2 was associated with hypertension, WMH, and vascular disease-related genetic variants and was enriched for Aβ positivity (ages ≥65 years) and apolipoprotein E (APOE) ε4 carriers. A3 showed severe, widespread atrophy, moderate presence of CVRFs, and greater cognitive decline. Genetic variants associated with A1 were protective for WMH (rs7209235: mean [SD] B = -0.07 [0.01]; P value = 2.31 × 10-9) and Alzheimer disease (rs72932727: mean [SD] B = 0.1 [0.02]; P value = 6.49 × 10-9), whereas the converse was observed for A2 (rs7209235: mean [SD] B = 0.1 [0.01]; P value = 1.73 × 10-15 and rs72932727: mean [SD] B = -0.09 [0.02]; P value = 4.05 × 10-7, respectively); variants in A3 were associated with regional atrophy (rs167684: mean [SD] B = 0.08 [0.01]; P value = 7.22 × 10-12) and white matter integrity measures (rs1636250: mean [SD] B = 0.06 [0.01]; P value = 4.90 × 10-7). Conclusions and Relevance The 3 subgroups showed distinct associations with CVRFs, genetics, and subsequent cognitive decline. These subgroups likely reflect multiple underlying neuropathologic processes and affect susceptibility to Alzheimer disease, paving pathways toward patient stratification at early asymptomatic stages and promoting precision medicine in clinical trials and health care.
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Affiliation(s)
- Ioanna Skampardoni
- Centre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia
- School of Electrical and Computer Engineering, National Technical University of Athens, Greece
| | - Ilya M. Nasrallah
- Centre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia
- Department of Radiology, University of Pennsylvania, Philadelphia
| | - Ahmed Abdulkadir
- Centre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia
- Laboratory for Research in Neuroimaging, Department of Clinical Neurosciences, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Junhao Wen
- Centre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia
- Laboratory of AI and Biomedical Science, Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles
| | - Randa Melhem
- Centre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia
| | - Elizabeth Mamourian
- Centre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia
| | - Guray Erus
- Centre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia
| | - Jimit Doshi
- Centre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia
| | - Ashish Singh
- Centre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia
| | - Zhijian Yang
- Centre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia
| | - Yuhan Cui
- Centre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia
| | - Gyujoon Hwang
- Centre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia
| | - Zheng Ren
- Laboratory of AI and Biomedical Science, Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles
| | - Raymond Pomponio
- Centre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia
| | - Dhivya Srinivasan
- Centre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia
| | | | - Paraskevi Parmpi
- Centre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia
| | - Katharina Wittfeld
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Germany
- German Centre for Neurodegenerative Diseases, Site Greifswald, Greifswald, Germany
| | - Hans J. Grabe
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Germany
- German Centre for Neurodegenerative Diseases, Site Greifswald, Greifswald, Germany
| | - Robin Bülow
- Institute of Diagnostic Radiology and Neuroradiology, University Medicine Greifswald, Greifswald, Germany
| | - Stefan Frenzel
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Germany
| | - Duygu Tosun
- Department of Radiology and Biomedical Imaging, University of California, San Francisco
| | - Murat Bilgel
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, Maryland
| | - Yang An
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, Maryland
| | - Daniel S. Marcus
- Department of Radiology, Washington University School of Medicine, St Louis, Missouri
| | - Pamela LaMontagne
- Department of Radiology, Washington University School of Medicine, St Louis, Missouri
| | - Susan R. Heckbert
- Cardiovascular Health Research Unit, University of Washington, Seattle
- Department of Epidemiology, University of Washington, Seattle
| | - Thomas R. Austin
- Cardiovascular Health Research Unit, University of Washington, Seattle
- Department of Epidemiology, University of Washington, Seattle
| | - Lenore J. Launer
- Neuroepidemiology Section, Intramural Research Program, National Institute on Aging, Bethesda, Maryland
| | - Aristeidis Sotiras
- Department of Radiology and Institute of Informatics, Washington University in St Louis, St Louis, Missouri
| | - Mark A. Espeland
- Sticht Centre for Healthy Aging and Alzheimer’s Prevention, Wake Forest School of Medicine, Winston-Salem, North Carolina
- Department of Biostatistics and Data Science, Wake Forest School of Medicine, Winston-Salem, North Carolina
| | - Colin L. Masters
- Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Parkville, Victoria, Australia
| | - Paul Maruff
- Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Parkville, Victoria, Australia
| | - Jurgen Fripp
- CSIRO Health and Biosecurity, Australian e-Health Research Centre CSIRO, Brisbane, Queensland, Australia
| | - Sterling C. Johnson
- Wisconsin Alzheimer’s Institute, University of Wisconsin School of Medicine and Public Health, Madison
| | - John C. Morris
- Knight Alzheimer Disease Research Centre, Washington University in St Louis, St Louis, Missouri
| | - Marilyn S. Albert
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - R. Nick Bryan
- Department of Radiology, University of Pennsylvania, Philadelphia
| | - Kristine Yaffe
- Departments of Neurology, Psychiatry and Epidemiology and Biostatistics, University of California San Francisco, San Francisco
| | - Henry Völzke
- Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
| | - Luigi Ferrucci
- Translational Gerontology Branch, National Institute on Aging, National Institutes of Health, Baltimore, Maryland
| | - Tammie L.S. Benzinger
- Mallinckrodt Institute of Radiology, Washington University School of Medicine in St Louis, St Louis, Missouri
| | - Ali Ezzati
- Department of Neurology, University of California, Irvine
| | - Russell T. Shinohara
- Centre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, & Informatics, University of Pennsylvania, Philadelphia
| | - Yong Fan
- Centre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia
| | - Susan M. Resnick
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, Maryland
| | - Mohamad Habes
- Centre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia
- Neuroimage Analytics Laboratory and the Biggs Institute Neuroimaging Core, Glenn Biggs Institute for Alzheimer’s and Neurodegenerative Diseases, University of Texas Health Science Center San Antonio, San Antonio
| | - David Wolk
- Department of Neurology, University of Pennsylvania, Philadelphia
| | - Haochang Shou
- Centre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, & Informatics, University of Pennsylvania, Philadelphia
| | - Konstantina Nikita
- School of Electrical and Computer Engineering, National Technical University of Athens, Greece
| | - Christos Davatzikos
- Centre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia
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Kang MJY, Eratne D, Dobson H, Malpas CB, Keem M, Lewis C, Grewal J, Tsoukra V, Dang C, Mocellin R, Kalincik T, Santillo AF, Zetterberg H, Blennow K, Stehmann C, Varghese S, Li QX, Masters CL, Collins S, Berkovic SF, Evans A, Kelso W, Farrand S, Loi SM, Walterfang M, Velakoulis D. Cerebrospinal fluid neurofilament light predicts longitudinal diagnostic change in patients with psychiatric and neurodegenerative disorders. Acta Neuropsychiatr 2024; 36:17-28. [PMID: 37114460 DOI: 10.1017/neu.2023.25] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 04/29/2023]
Abstract
OBJECTIVE People with neuropsychiatric symptoms often experience delay in accurate diagnosis. Although cerebrospinal fluid neurofilament light (CSF NfL) shows promise in distinguishing neurodegenerative disorders (ND) from psychiatric disorders (PSY), its accuracy in a diagnostically challenging cohort longitudinally is unknown. METHODS We collected longitudinal diagnostic information (mean = 36 months) from patients assessed at a neuropsychiatry service, categorising diagnoses as ND/mild cognitive impairment/other neurological disorders (ND/MCI/other) and PSY. We pre-specified NfL > 582 pg/mL as indicative of ND/MCI/other. RESULTS Diagnostic category changed from initial to final diagnosis for 23% (49/212) of patients. NfL predicted the final diagnostic category for 92% (22/24) of these and predicted final diagnostic category overall (ND/MCI/other vs. PSY) in 88% (187/212), compared to 77% (163/212) with clinical assessment alone. CONCLUSIONS CSF NfL improved diagnostic accuracy, with potential to have led to earlier, accurate diagnosis in a real-world setting using a pre-specified cut-off, adding weight to translation of NfL into clinical practice.
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Affiliation(s)
- Matthew J Y Kang
- Neuropsychiatry, Royal Melbourne Hospital, Parkville, VIC, Australia
- Melbourne Neuropsychiatry Centre & Department of Psychiatry, University of Melbourne, Parkville, VIC, Australia
- Alfred Mental and Addiction Health, Alfred Health, Melbourne, VIC, Australia
| | - Dhamidhu Eratne
- Neuropsychiatry, Royal Melbourne Hospital, Parkville, VIC, Australia
- Melbourne Neuropsychiatry Centre & Department of Psychiatry, University of Melbourne, Parkville, VIC, Australia
| | - Hannah Dobson
- Neuropsychiatry, Royal Melbourne Hospital, Parkville, VIC, Australia
- Alfred Mental and Addiction Health, Alfred Health, Melbourne, VIC, Australia
| | - Charles B Malpas
- Department of Medicine, Royal Melbourne Hospital, Parkville, VIC, Australia
- Melbourne School of Psychological Sciences, University of Melbourne, Parkville, VIC, Australia
| | - Michael Keem
- Neuropsychiatry, Royal Melbourne Hospital, Parkville, VIC, Australia
- Melbourne Neuropsychiatry Centre & Department of Psychiatry, University of Melbourne, Parkville, VIC, Australia
| | - Courtney Lewis
- Neuropsychiatry, Royal Melbourne Hospital, Parkville, VIC, Australia
| | - Jasleen Grewal
- Alfred Mental and Addiction Health, Alfred Health, Melbourne, VIC, Australia
| | - Vivian Tsoukra
- Department of Neurology, Evangelismos Hospital, Athens, Greece
| | - Christa Dang
- National Ageing Research Institute, University of Melbourne, Parkville, VIC, Australia
| | | | - Tomas Kalincik
- Department of Medicine, Royal Melbourne Hospital, Parkville, VIC, Australia
- Department of Neurology, Royal Melbourne Hospital, Parkville, VIC, Australia
| | - Alexander F Santillo
- Department of Clinical Sciences Malmö, Clinical Memory Research Unit, Lund University, Lund, Sweden
- Memory Clinic, Skåne University Hospital, Malmo, Sweden
| | - Henrik Zetterberg
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, 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, Queen Square, London, UK
- UK Dementia Research Institute at UCL, London, UK
| | - Kaj Blennow
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
| | - Christiane Stehmann
- The Australian National CJD Registry, The Florey Institute of Neuroscience and Mental Health, Melbourne, VIC, Australia
| | - Shiji Varghese
- National Dementia Diagnostic Laboratory, The Florey Institute of Neuroscience and Mental Health, Melbourne, VIC, Australia
| | - Qiao-Xin Li
- National Dementia Diagnostic Laboratory, The Florey Institute of Neuroscience and Mental Health, Melbourne, VIC, Australia
- Florey Department of Neuroscience and Mental Health, University of Melbourne, Parkville, VIC, Australia
| | - Colin L Masters
- Florey Department of Neuroscience and Mental Health, University of Melbourne, Parkville, VIC, Australia
- The Florey Institute of Neuroscience and Mental Health, Parkville, VIC, Australia
| | - Steven Collins
- Department of Medicine, Royal Melbourne Hospital, Parkville, VIC, Australia
- National Dementia Diagnostic Laboratory, The Florey Institute of Neuroscience and Mental Health, Melbourne, VIC, Australia
| | - Samuel F Berkovic
- Department of Medicine, Austin Health, Epilepsy Research Centre, The University of Melbourne, Heidelberg, VIC, Australia
| | - Andrew Evans
- Neuropsychiatry, Royal Melbourne Hospital, Parkville, VIC, Australia
- Department of Neurology, Royal Melbourne Hospital, Parkville, VIC, Australia
| | - Wendy Kelso
- Neuropsychiatry, Royal Melbourne Hospital, Parkville, VIC, Australia
| | - Sarah Farrand
- Neuropsychiatry, Royal Melbourne Hospital, Parkville, VIC, Australia
- Melbourne Neuropsychiatry Centre & Department of Psychiatry, University of Melbourne, Parkville, VIC, Australia
| | - Samantha M Loi
- Neuropsychiatry, Royal Melbourne Hospital, Parkville, VIC, Australia
- Melbourne Neuropsychiatry Centre & Department of Psychiatry, University of Melbourne, Parkville, VIC, Australia
| | - Mark Walterfang
- Neuropsychiatry, Royal Melbourne Hospital, Parkville, VIC, Australia
- Melbourne Neuropsychiatry Centre & Department of Psychiatry, University of Melbourne, Parkville, VIC, Australia
- The Florey Institute of Neuroscience and Mental Health, Parkville, VIC, Australia
| | - Dennis Velakoulis
- Neuropsychiatry, Royal Melbourne Hospital, Parkville, VIC, Australia
- Melbourne Neuropsychiatry Centre & Department of Psychiatry, University of Melbourne, Parkville, VIC, Australia
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Slee MG, Rainey‐Smith SR, Villemagne VL, Doecke JD, Sohrabi HR, Taddei K, Ames D, Dore V, Maruff P, Laws SM, Masters CL, Rowe CC, Martins RN, Erickson KI, Brown BM. Physical activity and brain amyloid beta: A longitudinal analysis of cognitively unimpaired older adults. Alzheimers Dement 2024; 20:1350-1359. [PMID: 37984813 PMCID: PMC10917015 DOI: 10.1002/alz.13556] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Revised: 10/13/2023] [Accepted: 10/23/2023] [Indexed: 11/22/2023]
Abstract
INTRODUCTION The current study evaluated the relationship between habitual physical activity (PA) levels and brain amyloid beta (Aβ) over 15 years in a cohort of cognitively unimpaired older adults. METHODS PA and Aβ measures were collected over multiple timepoints from 731 cognitively unimpaired older adults participating in the Australian Imaging, Biomarkers and Lifestyle (AIBL) Study of Aging. Regression modeling examined cross-sectional and longitudinal relationships between PA and brain Aβ. Moderation analyses examined apolipoprotein E (APOE) ε4 carriage impact on the PA-Aβ relationship. RESULTS PA was not associated with brain Aβ at baseline (β = -0.001, p = 0.72) or over time (β = -0.26, p = 0.24). APOE ε4 status did not moderate the PA-Aβ relationship over time (β = 0.12, p = 0.73). Brain Aβ levels did not predict PA trajectory (β = -54.26, p = 0.59). DISCUSSION Our study did not identify a relationship between habitual PA and brain Aβ levels. HIGHLIGHTS Physical activity levels did not predict brain amyloid beta (Aβ) levels over time in cognitively unimpaired older adults (≥60 years of age). Apolipoprotein E (APOE) ε4 carrier status did not moderate the physical activity-brain Aβ relationship over time. Physical activity trajectories were not impacted by brain Aβ levels.
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Affiliation(s)
- Michael G. Slee
- Centre for Healthy AgeingHealthy Futures InstituteMurdoch UniversityMurdochWestern AustraliaAustralia
| | - Stephanie R. Rainey‐Smith
- Centre for Healthy AgeingHealthy Futures InstituteMurdoch UniversityMurdochWestern AustraliaAustralia
- School of Medical and Health SciencesEdith Cowan UniversityJoondalupWestern AustraliaAustralia
- Australian Alzheimer's Research FoundationNedlandsWestern AustraliaAustralia
- School of Psychological ScienceUniversity of Western AustraliaCrawleyWestern AustraliaAustralia
| | - Victor L. Villemagne
- Department of Molecular Imaging & TherapyAustin HealthHeidelbergVictoriaAustralia
- Department of PsychiatryUniversity of PittsburghPittsburghPennsylvaniaUSA
- Centre for Precision HealthEdith Cowan UniversityJoondalupWestern AustraliaAustralia
| | - James D. Doecke
- The Australian e‐Health Research CentreCSIROHerstonQueenslandAustralia
| | - Hamid R. Sohrabi
- Centre for Healthy AgeingHealthy Futures InstituteMurdoch UniversityMurdochWestern AustraliaAustralia
- School of Medical and Health SciencesEdith Cowan UniversityJoondalupWestern AustraliaAustralia
- Australian Alzheimer's Research FoundationNedlandsWestern AustraliaAustralia
- Department of Biomedical SciencesMacquarie UniversitySydneyNew South WalesAustralia
| | - Kevin Taddei
- School of Medical and Health SciencesEdith Cowan UniversityJoondalupWestern AustraliaAustralia
- Australian Alzheimer's Research FoundationNedlandsWestern AustraliaAustralia
| | - David Ames
- The Florey Institute of Neuroscience and Mental HealthUniversity of MelbourneParkvilleVictoriaAustralia
- National Ageing Research InstituteParkvilleVictoriaAustralia
- Academic Unit for Psychiatry of Old AgeUniversity of MelbourneCarltonVictoriaAustralia
| | - Vincent Dore
- Department of Molecular Imaging & TherapyAustin HealthHeidelbergVictoriaAustralia
| | - Paul Maruff
- The Florey Institute of Neuroscience and Mental HealthUniversity of MelbourneParkvilleVictoriaAustralia
- Cogstate LtdMelbourneVictoriaAustralia
| | - Simon M. Laws
- Centre for Precision HealthEdith Cowan UniversityJoondalupWestern AustraliaAustralia
- Collaborative Genomics and Translation GroupSchool of Medical and Health SciencesEdith Cowan UniversityJoondalupWestern AustraliaAustralia
- Curtin Medical SchoolCurtin UniversityBentleyWestern AustraliaAustralia
| | - Colin L. Masters
- The Florey Institute of Neuroscience and Mental HealthUniversity of MelbourneParkvilleVictoriaAustralia
| | - Christopher C. Rowe
- Department of Molecular Imaging & TherapyAustin HealthHeidelbergVictoriaAustralia
- The Florey Institute of Neuroscience and Mental HealthUniversity of MelbourneParkvilleVictoriaAustralia
| | - Ralph N. Martins
- School of Medical and Health SciencesEdith Cowan UniversityJoondalupWestern AustraliaAustralia
- Australian Alzheimer's Research FoundationNedlandsWestern AustraliaAustralia
- Department of Biomedical SciencesMacquarie UniversitySydneyNew South WalesAustralia
| | - Kirk I. Erickson
- Department of PsychologyUniversity of PittsburghPittsburghPennsylvaniaUSA
| | - Belinda M. Brown
- Centre for Healthy AgeingHealthy Futures InstituteMurdoch UniversityMurdochWestern AustraliaAustralia
- School of Medical and Health SciencesEdith Cowan UniversityJoondalupWestern AustraliaAustralia
- Australian Alzheimer's Research FoundationNedlandsWestern AustraliaAustralia
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Odenkirk MT, Zheng X, Kyle JE, Stratton KG, Nicora CD, Bloodsworth KJ, Mclean CA, Masters CL, Monroe ME, Doecke JD, Smith RD, Burnum-Johnson KE, Roberts BR, Baker ES. Deciphering ApoE Genotype-Driven Proteomic and Lipidomic Alterations in Alzheimer's Disease Across Distinct Brain Regions. J Proteome Res 2024. [PMID: 38236019 DOI: 10.1021/acs.jproteome.3c00604] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2024]
Abstract
Alzheimer's disease (AD) is a neurodegenerative disease with a complex etiology influenced by confounding factors such as genetic polymorphisms, age, sex, and race. Traditionally, AD research has not prioritized these influences, resulting in dramatically skewed cohorts such as three times the number of Apolipoprotein E (APOE) ε4-allele carriers in AD relative to healthy cohorts. Thus, the resulting molecular changes in AD have previously been complicated by the influence of apolipoprotein E disparities. To explore how apolipoprotein E polymorphism influences AD progression, 62 post-mortem patients consisting of 33 AD and 29 controls (Ctrl) were studied to balance the number of ε4-allele carriers and facilitate a molecular comparison of the apolipoprotein E genotype. Lipid and protein perturbations were assessed across AD diagnosed brains compared to Ctrl brains, ε4 allele carriers (APOE4+ for those carrying 1 or 2 ε4s and APOE4- for non-ε4 carriers), and differences in ε3ε3 and ε3ε4 Ctrl brains across two brain regions (frontal cortex (FCX) and cerebellum (CBM)). The region-specific influences of apolipoprotein E on AD mechanisms showcased mitochondrial dysfunction and cell proteostasis at the core of AD pathophysiology in the post-mortem brains, indicating these two processes may be influenced by genotypic differences and brain morphology.
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Affiliation(s)
- Melanie T Odenkirk
- Department of Chemistry, North Carolina State University, Raleigh, North Carolina 27606, United States of America
| | - Xueyun Zheng
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99354, United States of America
| | - Jennifer E Kyle
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99354, United States of America
| | - Kelly G Stratton
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99354, United States of America
| | - Carrie D Nicora
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99354, United States of America
| | - Kent J Bloodsworth
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99354, United States of America
| | - Catriona A Mclean
- Anatomical Pathology, Alfred Hospital, Prahran, Victoria 3181, Australia
- Florey Institute of Neuroscience and Mental Health, University of Melbourne, Melbourne, Victoria 3010, Australia
| | - Colin L Masters
- Florey Institute of Neuroscience and Mental Health, University of Melbourne, Melbourne, Victoria 3010, Australia
| | - Matthew E Monroe
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99354, United States of America
| | - James D Doecke
- CSIRO Health and Biosecurity, Herston, Queensland 4029, Australia
| | - Richard D Smith
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99354, United States of America
| | - Kristin E Burnum-Johnson
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, Washington 99354, United States of America
| | - Blaine R Roberts
- Department of Biochemistry, Emory University, Atlanta, Georgia 30322, United States of America
- Department of Neurology, Emory University, Atlanta, Georgia 30322, United States of America
| | - Erin S Baker
- Department of Chemistry, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27514, United States of America
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Yang Z, Wen J, Abdulkadir A, Cui Y, Erus G, Mamourian E, Melhem R, Srinivasan D, Govindarajan ST, Chen J, Habes M, Masters CL, Maruff P, Fripp J, Ferrucci L, Albert MS, Johnson SC, Morris JC, LaMontagne P, Marcus DS, Benzinger TLS, Wolk DA, Shen L, Bao J, Resnick SM, Shou H, Nasrallah IM, Davatzikos C. Gene-SGAN: discovering disease subtypes with imaging and genetic signatures via multi-view weakly-supervised deep clustering. Nat Commun 2024; 15:354. [PMID: 38191573 PMCID: PMC10774282 DOI: 10.1038/s41467-023-44271-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Accepted: 12/06/2023] [Indexed: 01/10/2024] Open
Abstract
Disease heterogeneity has been a critical challenge for precision diagnosis and treatment, especially in neurologic and neuropsychiatric diseases. Many diseases can display multiple distinct brain phenotypes across individuals, potentially reflecting disease subtypes that can be captured using MRI and machine learning methods. However, biological interpretability and treatment relevance are limited if the derived subtypes are not associated with genetic drivers or susceptibility factors. Herein, we describe Gene-SGAN - a multi-view, weakly-supervised deep clustering method - which dissects disease heterogeneity by jointly considering phenotypic and genetic data, thereby conferring genetic correlations to the disease subtypes and associated endophenotypic signatures. We first validate the generalizability, interpretability, and robustness of Gene-SGAN in semi-synthetic experiments. We then demonstrate its application to real multi-site datasets from 28,858 individuals, deriving subtypes of Alzheimer's disease and brain endophenotypes associated with hypertension, from MRI and single nucleotide polymorphism data. Derived brain phenotypes displayed significant differences in neuroanatomical patterns, genetic determinants, biological and clinical biomarkers, indicating potentially distinct underlying neuropathologic processes, genetic drivers, and susceptibility factors. Overall, Gene-SGAN is broadly applicable to disease subtyping and endophenotype discovery, and is herein tested on disease-related, genetically-associated neuroimaging phenotypes.
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Affiliation(s)
- Zhijian Yang
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Graduate Group in Applied Mathematics and Computational Science, University of Pennsylvania, Philadelphia, PA, USA
| | - Junhao Wen
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Laboratory of AI and Biomedical Science (LABS), Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
| | - Ahmed Abdulkadir
- Laboratory for Research in Neuroimaging, Department of Clinical Neurosciences, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland
| | - Yuhan Cui
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Guray Erus
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Elizabeth Mamourian
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Randa Melhem
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Dhivya Srinivasan
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Sindhuja T Govindarajan
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Jiong Chen
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Mohamad Habes
- Biggs Alzheimer's Institute, University of Texas San Antonio Health Science Center, San Antonio, TX, USA
| | - Colin L Masters
- The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Parkville, VIC, Australia
| | - Paul Maruff
- The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Parkville, VIC, Australia
| | - Jurgen Fripp
- CSIRO Health and Biosecurity, Australian e-Health Research Centre CSIRO, Brisbane, QLD, Australia
| | - Luigi Ferrucci
- Translational Gerontology Branch, Longitudinal Studies Section, National Institute on Aging, National Institutes of Health, MedStar Harbor Hospital, 3001 S. Hanover Street, Baltimore, MD, USA
| | - Marilyn S Albert
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Sterling C Johnson
- Wisconsin Alzheimer's Institute, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
| | - John C Morris
- Knight Alzheimer Disease Research Center, Washington University in St. Louis, St. Louis, MO, USA
| | - Pamela LaMontagne
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | - Daniel S Marcus
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | - Tammie L S Benzinger
- Knight Alzheimer Disease Research Center, Washington University in St. Louis, St. Louis, MO, USA
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | - David A Wolk
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA
| | - Li Shen
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA, USA
| | - Jingxuan Bao
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA, USA
| | - Susan M Resnick
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, MD, USA
| | - Haochang Shou
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA, USA
| | - Ilya M Nasrallah
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Christos Davatzikos
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
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Chu C, Low YLC, Ma L, Wang Y, Cox T, Doré V, Masters CL, Goudey B, Jin L, Pan Y. How Can We Use Mathematical Modeling of Amyloid-β in Alzheimer's Disease Research and Clinical Practices? J Alzheimers Dis 2024; 97:89-100. [PMID: 38007665 DOI: 10.3233/jad-230938] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2023]
Abstract
The accumulation of amyloid-β (Aβ) plaques in the brain is considered a hallmark of Alzheimer's disease (AD). Mathematical modeling, capable of predicting the motion and accumulation of Aβ, has obtained increasing interest as a potential alternative to aid the diagnosis of AD and predict disease prognosis. These mathematical models have provided insights into the pathogenesis and progression of AD that are difficult to obtain through experimental studies alone. Mathematical modeling can also simulate the effects of therapeutics on brain Aβ levels, thereby holding potential for drug efficacy simulation and the optimization of personalized treatment approaches. In this review, we provide an overview of the mathematical models that have been used to simulate brain levels of Aβ (oligomers, protofibrils, and/or plaques). We classify the models into five categories: the general ordinary differential equation models, the general partial differential equation models, the network models, the linear optimal ordinary differential equation models, and the modified partial differential equation models (i.e., Smoluchowski equation models). The assumptions, advantages and limitations of these models are discussed. Given the popularity of using the Smoluchowski equation models to simulate brain levels of Aβ, our review summarizes the history and major advancements in these models (e.g., their application to predict the onset of AD and their combined use with network models). This review is intended to bring mathematical modeling to the attention of more scientists and clinical researchers working on AD to promote cross-disciplinary research.
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Affiliation(s)
- Chenyin Chu
- Florey Institute of Neuroscience and Mental Health, University of Melbourne, Parkville, Victoria, Australia
| | - Yi Ling Clare Low
- Florey Institute of Neuroscience and Mental Health, University of Melbourne, Parkville, Victoria, Australia
| | - Liwei Ma
- Florey Institute of Neuroscience and Mental Health, University of Melbourne, Parkville, Victoria, Australia
| | - Yihan Wang
- Florey Institute of Neuroscience and Mental Health, University of Melbourne, Parkville, Victoria, Australia
| | - Timothy Cox
- The Australian e-Health Research Centre, CSIRO, Parkville, Victoria, Australia
| | - Vincent Doré
- The Australian e-Health Research Centre, CSIRO, Parkville, Victoria, Australia
| | - Colin L Masters
- Florey Institute of Neuroscience and Mental Health, University of Melbourne, Parkville, Victoria, Australia
| | - Benjamin Goudey
- Florey Institute of Neuroscience and Mental Health, University of Melbourne, Parkville, Victoria, Australia
- ARC Training Centre in Cognitive Computing for Medical Technologies, University of Melbourne, Carlton, Victoria, Australia
| | - Liang Jin
- Florey Institute of Neuroscience and Mental Health, University of Melbourne, Parkville, Victoria, Australia
- Drug Delivery, Disposition and Dynamics, Monash Institute of Pharmaceutical Sciences, Monash University, Parkville, Victoria, Australia
| | - Yijun Pan
- Florey Institute of Neuroscience and Mental Health, University of Melbourne, Parkville, Victoria, Australia
- Drug Delivery, Disposition and Dynamics, Monash Institute of Pharmaceutical Sciences, Monash University, Parkville, Victoria, Australia
- Department of Organ Anatomy, Graduate School of Medicine, Tohoku University, Sendai, Miyagi, Japan
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Ruwanpathirana GP, Williams RC, Masters CL, Rowe CC, Johnston LA, Davey CE. Inter-scanner Aβ-PET harmonization using barrel phantom spatial resolution matching. Alzheimers Dement (Amst) 2024; 16:e12561. [PMID: 38476638 PMCID: PMC10927914 DOI: 10.1002/dad2.12561] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/05/2023] [Revised: 01/15/2024] [Accepted: 01/19/2024] [Indexed: 03/14/2024]
Abstract
INTRODUCTION The standardized uptake value ratio (SUVR) is used to measure amyloid beta-positron emission tomography (Aβ-PET) uptake in the brainDifferences in PET scanner technologies and image reconstruction techniques can lead to variability in PET images across scanners. This poses a challenge for Aβ-PET studies conducted in multiple centers. The aim of harmonization is to achieve consistent Aβ-PET measurements across different scanners. In this study, we propose an Aβ-PET harmonization method of matching spatial resolution, as measured via a barrel phantom, across PET scanners. Our approach was validated using paired subject data, for which patients were imaged on multiple scanners. METHODS In this study, three different PET scanners were evaluated: the Siemens Biograph Vision 600, Siemens Biograph molecular computed tomography (mCT), and Philips Gemini TF64. A total of five, eight, and five subjects were each scanned twice with [18F]-NAV4694 across Vision-mCT, mCT-Philips, and Vision-Philips scanner pairs. The Vision and mCT scans were reconstructed using various iterations, subsets, and post-reconstruction Gaussian smoothing, whereas only one reconstruction configuration was used for the Philips scans. The full-width at half-maximum (FWHM) of each reconstruction configuration was calculated using [18F]-filled barrel phantom scans with the Society of Nuclear Medicine and Molecular Imaging (SNMMI) phantom analysis toolkit. Regional SUVRs were calculated from 72 brain regions using the automated anatomical labelling atlas 3 (AAL3) atlas for each subject and reconstruction configuration. Statistical similarity between SUVRs was assessed using paired (within subject) t-tests for each pair of reconstructions across scanners; the higher the p-value, the greater the similarity between the SUVRs. RESULTS Vision-mCT harmonization: Vision reconstruction with FWHM = 4.10 mm and mCT reconstruction with FWHM = 4.30 mm gave the maximal statistical similarity (maximum p-value) between regional SUVRs. Philips-mCT harmonization: The FWHM of the Philips reconstruction was 8.2 mm and the mCT reconstruction with the FWHM of 9.35 mm, which gave the maximal statistical similarity between regional SUVRs. Philips-Vision harmonization: The Vision reconstruction with an FWHM of 9.1 mm gave the maximal statistical similarity between regional SUVRs when compared with the Philips reconstruction of 8.2 mm and were selected as the harmonized for each scanner pair. CONCLUSION Based on data obtained from three sets of participants, each scanned on a pair of PET scanners, it has been verified that using reconstruction configurations that produce matched-barrel, phantom spatial resolutions results in maximally harmonized Aβ-PET quantitation between scanner pairs. This finding is encouraging for the use of PET scanners in multi-center trials or updates during longitudinal studies. Highlights Question: Does the process of matching the barrel phantom-derived spatial resolution between scanners harmonize amyloid beta-standardized uptake value ratio (Aβ-SUVR) quantitation? Pertinent findings: It has been validated that reconstruction pairs with matched barrel phantom-derived spatial resolution maximize the similarity between subjects paired Aβ-PET (positron emission tomography) SUVR values recorded on two scanners. Implications for patient care: Harmonization between scanners in multi-center trials and PET camera updates in longitudinal studies can be achieved using a simple and efficient phantom measurement procedure, beneficial for the validity of Aβ-PET quantitation measurements.
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Affiliation(s)
- Gihan P. Ruwanpathirana
- Department of Biomedical EngineeringThe University of MelbourneMelbourneVICAustralia
- Melbourne Brain Centre Imaging UnitThe University of MelbourneMelbourneVICAustralia
| | - Robert C. Williams
- Melbourne Brain Centre Imaging UnitThe University of MelbourneMelbourneVICAustralia
| | - Colin L. Masters
- Florey Institute of Neurosciences and Mental HealthThe University of MelbourneMelbourneVICAustralia
- The Australian Dementia Network (ADNET)MelbourneAustralia
| | - Christopher C. Rowe
- Florey Institute of Neurosciences and Mental HealthThe University of MelbourneMelbourneVICAustralia
- The Australian Dementia Network (ADNET)MelbourneAustralia
- Department of Molecular Imaging & TherapyAustin HealthMelbourneVICAustralia
| | - Leigh A. Johnston
- Department of Biomedical EngineeringThe University of MelbourneMelbourneVICAustralia
- Melbourne Brain Centre Imaging UnitThe University of MelbourneMelbourneVICAustralia
| | - Catherine E. Davey
- Department of Biomedical EngineeringThe University of MelbourneMelbourneVICAustralia
- Melbourne Brain Centre Imaging UnitThe University of MelbourneMelbourneVICAustralia
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Dean B, Duce J, Li QX, Masters CL, Scarr E. Lower levels of soluble β-amyloid precursor protein, but not β-amyloid, in the frontal cortex in schizophrenia. Psychiatry Res 2024; 331:115656. [PMID: 38071879 DOI: 10.1016/j.psychres.2023.115656] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Revised: 11/28/2023] [Accepted: 12/01/2023] [Indexed: 01/02/2024]
Abstract
We identified a sub-group (25%) of people with schizophrenia (muscarinic receptor deficit schizophrenia (MRDS)) that are characterised because of markedly lower levels of cortical muscarinic M1 receptors (CHRM1) compared to most people with the disorder (non-MRDS). Notably, bioinformatic analyses of our cortical gene expression data shows a disturbance in the homeostasis of a biochemical pathway that regulates levels of CHRM1. A step in this pathway is the processing of β-amyloid precursor protein (APP) and therefore we postulated there would be altered levels of APP in the frontal cortex from people with MRDS. Here we measure levels of CHRM1 using [3H]pirenzepine binding, soluble APP (sAPP) using Western blotting and amyloid beta peptides (Aβ1-40 and Aβ1-42) using ELISA in the frontal cortex (Brodmann's area 6: BA 6; MRDS = 14, non-MRDS = 14, controls = 14). We confirmed the MRDS cohort in this study had the expected low levels of [3H]pirenzepine binding. In addition, we showed that people with schizophrenia, independent of their sub-group status, had lower levels of sAPP compared to controls but did not have altered levels of Aβ1-40 or Aβ1-42. In conclusion, whilst changes in sAPP are not restricted to MRDS our data could indicate a role of APP, which is important in axonal and synaptic pruning, in the molecular pathology of the syndrome of schizophrenia.
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Affiliation(s)
- Brian Dean
- The Florey, Parkville, Victoria, Australia; The University of Melbourne of Melbourne Florey Department of Neuroscience and Mental Health, Parkville, Victoria, Australia.
| | - James Duce
- MSD Discovery Centre, 120 Moorgate, London, UK
| | - Qiao-Xin Li
- The Florey, Parkville, Victoria, Australia; The University of Melbourne of Melbourne Florey Department of Neuroscience and Mental Health, Parkville, Victoria, Australia
| | - Colin L Masters
- The Florey, Parkville, Victoria, Australia; The University of Melbourne of Melbourne Florey Department of Neuroscience and Mental Health, Parkville, Victoria, Australia
| | - Elizabeth Scarr
- The Department of Psychiatry, The University of Melbourne, Parkville, Victoria, Australia
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Tosun D, Yardibi O, Benzinger TLS, Kukull WA, Masters CL, Perrin RJ, Weiner MW, Simen A, Schwarz AJ. Identifying individuals with non-Alzheimer's disease co-pathologies: A precision medicine approach to clinical trials in sporadic Alzheimer's disease. Alzheimers Dement 2024; 20:421-436. [PMID: 37667412 PMCID: PMC10843695 DOI: 10.1002/alz.13447] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Revised: 07/14/2023] [Accepted: 08/04/2023] [Indexed: 09/06/2023]
Abstract
INTRODUCTION Biomarkers remain mostly unavailable for non-Alzheimer's disease neuropathological changes (non-ADNC) such as transactive response DNA-binding protein 43 (TDP-43) proteinopathy, Lewy body disease (LBD), and cerebral amyloid angiopathy (CAA). METHODS A multilabel non-ADNC classifier using magnetic resonance imaging (MRI) signatures was developed for TDP-43, LBD, and CAA in an autopsy-confirmed cohort (N = 214). RESULTS A model using demographic, genetic, clinical, MRI, and ADNC variables (amyloid positive [Aβ+] and tau+) in autopsy-confirmed participants showed accuracies of 84% for TDP-43, 81% for LBD, and 81% to 93% for CAA, outperforming reference models without MRI and ADNC biomarkers. In an ADNI cohort (296 cognitively unimpaired, 401 mild cognitive impairment, 188 dementia), Aβ and tau explained 33% to 43% of variance in cognitive decline; imputed non-ADNC explained an additional 16% to 26%. Accounting for non-ADNC decreased the required sample size to detect a 30% effect on cognitive decline by up to 28%. DISCUSSION Our results lead to a better understanding of the factors that influence cognitive decline and may lead to improvements in AD clinical trial design.
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Affiliation(s)
- Duygu Tosun
- Department of Radiology and Biomedical ImagingUniversity of California San FranciscoSan FranciscoCaliforniaUSA
| | - Ozlem Yardibi
- Takeda Pharmaceutical Company LtdCambridgeMassachusettsUSA
| | | | - Walter A. Kukull
- Department of EpidemiologyNational Alzheimer's Coordinating CenterUniversity of WashingtonSeattleWashingtonUSA
| | - Colin L. Masters
- The Florey Institute of Neuroscience and Mental HealthThe University of MelbourneParkvilleVictoriaAustralia
| | - Richard J. Perrin
- Department of Pathology & ImmunologyWashington University in St. LouisSt. LouisMissouriUSA
- Department of NeurologyWashington University in St. LouisSt. LouisMissouriUSA
| | - Michael W. Weiner
- Department of Radiology and Biomedical ImagingUniversity of California San FranciscoSan FranciscoCaliforniaUSA
| | - Arthur Simen
- Takeda Pharmaceutical Company LtdCambridgeMassachusettsUSA
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Wang Z, Lewis V, Stehmann C, Varghese S, Senesi M, McGlade A, Ellett LJ, Doecke JD, Eratne D, Velakoulis D, Masters CL, Collins SJ, Li Q. Alzheimer's disease biomarker utilization at first referral enhances differential diagnostic precision with simultaneous exclusion of Creutzfeldt-Jakob disease. Alzheimers Dement (Amst) 2024; 16:e12548. [PMID: 38352040 PMCID: PMC10862167 DOI: 10.1002/dad2.12548] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Accepted: 12/19/2023] [Indexed: 02/16/2024]
Abstract
Most suspected Creutzfeldt-Jakob disease (CJD) cases are eventually diagnosed with other disorders. We assessed the utility of investigating Alzheimer's disease (AD) biomarkers and neurofilament light (NfL) in patients when CJD is suspected. The study cohort consisted of cerebrospinal fluid (CSF) samples referred for CJD biomarker screening wherein amyloid beta 1-42 (Aβ1-42), phosphorylated tau 181 (p-tau181), and total tau (t-tau) could be assessed via Elecsys immunoassays (n = 419) and NfL via enzyme-linked immunosorbent assay (ELISA; n = 161). In the non-CJD sub cohort (n = 371), 59% (219/371) had A+T- (abnormal Aβ1-42 only) and 21% (79/371) returned A+T+ (abnormal Aβ1-42 and p-tau181). In the 48 CJD subjects, a similar AD biomarker profile distribution was observed. To partially address the prevalence of likely pre-symptomatic AD, NfL was utilized to assess for neuronal damage. NfL was abnormal in 76% (25/33) of A+T- subjects 40 to 69 years of age, 80% (20/25) of whom had normal t-tau. This study reinforces AD as an important differential diagnosis of suspected CJD, highlighting that incorporating AD biomarkers and NfL at initial testing is worthwhile.
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Affiliation(s)
- Zitianyu Wang
- National Dementia Diagnostics Laboratory (NDDL), The Florey InstituteThe University of MelbourneParkvilleAustralia
- Australian National Creutzfeldt‐Jakob Disease Registry (ANCJDR), The Florey InstituteThe University of MelbourneParkvilleAustralia
| | - Victoria Lewis
- Australian National Creutzfeldt‐Jakob Disease Registry (ANCJDR), The Florey InstituteThe University of MelbourneParkvilleAustralia
- Department of Medicine, Clinical Sciences Building, Royal Melbourne Hospital (RMH)The University of MelbourneParkvilleAustralia
| | - Christiane Stehmann
- Australian National Creutzfeldt‐Jakob Disease Registry (ANCJDR), The Florey InstituteThe University of MelbourneParkvilleAustralia
| | - Shiji Varghese
- National Dementia Diagnostics Laboratory (NDDL), The Florey InstituteThe University of MelbourneParkvilleAustralia
| | - Matteo Senesi
- Australian National Creutzfeldt‐Jakob Disease Registry (ANCJDR), The Florey InstituteThe University of MelbourneParkvilleAustralia
- Department of Medicine, Clinical Sciences Building, Royal Melbourne Hospital (RMH)The University of MelbourneParkvilleAustralia
| | - Amelia McGlade
- Australian National Creutzfeldt‐Jakob Disease Registry (ANCJDR), The Florey InstituteThe University of MelbourneParkvilleAustralia
| | - Laura J. Ellett
- Australian National Creutzfeldt‐Jakob Disease Registry (ANCJDR), The Florey InstituteThe University of MelbourneParkvilleAustralia
| | | | - Dhamidhu Eratne
- National Dementia Diagnostics Laboratory (NDDL), The Florey InstituteThe University of MelbourneParkvilleAustralia
- Neuropsychiatry, John Cade BuildingRoyal Melbourne HospitalParkvilleAustralia
| | - Dennis Velakoulis
- Neuropsychiatry, John Cade BuildingRoyal Melbourne HospitalParkvilleAustralia
| | - Colin L. Masters
- National Dementia Diagnostics Laboratory (NDDL), The Florey InstituteThe University of MelbourneParkvilleAustralia
- Australian National Creutzfeldt‐Jakob Disease Registry (ANCJDR), The Florey InstituteThe University of MelbourneParkvilleAustralia
| | - Steven J. Collins
- National Dementia Diagnostics Laboratory (NDDL), The Florey InstituteThe University of MelbourneParkvilleAustralia
- Australian National Creutzfeldt‐Jakob Disease Registry (ANCJDR), The Florey InstituteThe University of MelbourneParkvilleAustralia
- Department of Medicine, Clinical Sciences Building, Royal Melbourne Hospital (RMH)The University of MelbourneParkvilleAustralia
| | - Qiao‐Xin Li
- National Dementia Diagnostics Laboratory (NDDL), The Florey InstituteThe University of MelbourneParkvilleAustralia
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14
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Yang Z, Wen J, Erus G, Govindarajan ST, Melhem R, Mamourian E, Cui Y, Srinivasan D, Abdulkadir A, Parmpi P, Wittfeld K, Grabe HJ, Bülow R, Frenzel S, Tosun D, Bilgel M, An Y, Yi D, Marcus DS, LaMontagne P, Benzinger TL, Heckbert SR, Austin TR, Waldstein SR, Evans MK, Zonderman AB, Launer LJ, Sotiras A, Espeland MA, Masters CL, Maruff P, Fripp J, Toga A, O’Bryant S, Chakravarty MM, Villeneuve S, Johnson SC, Morris JC, Albert MS, Yaffe K, Völzke H, Ferrucci L, Bryan NR, Shinohara RT, Fan Y, Habes M, Lalousis PA, Koutsouleris N, Wolk DA, Resnick SM, Shou H, Nasrallah IM, Davatzikos C. Five dominant dimensions of brain aging are identified via deep learning: associations with clinical, lifestyle, and genetic measures. medRxiv 2023:2023.12.29.23300642. [PMID: 38234857 PMCID: PMC10793523 DOI: 10.1101/2023.12.29.23300642] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/19/2024]
Abstract
Brain aging is a complex process influenced by various lifestyle, environmental, and genetic factors, as well as by age-related and often co-existing pathologies. MRI and, more recently, AI methods have been instrumental in understanding the neuroanatomical changes that occur during aging in large and diverse populations. However, the multiplicity and mutual overlap of both pathologic processes and affected brain regions make it difficult to precisely characterize the underlying neurodegenerative profile of an individual from an MRI scan. Herein, we leverage a state-of-the art deep representation learning method, Surreal-GAN, and present both methodological advances and extensive experimental results that allow us to elucidate the heterogeneity of brain aging in a large and diverse cohort of 49,482 individuals from 11 studies. Five dominant patterns of neurodegeneration were identified and quantified for each individual by their respective (herein referred to as) R-indices. Significant associations between R-indices and distinct biomedical, lifestyle, and genetic factors provide insights into the etiology of observed variances. Furthermore, baseline R-indices showed predictive value for disease progression and mortality. These five R-indices contribute to MRI-based precision diagnostics, prognostication, and may inform stratification into clinical trials.
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Affiliation(s)
- Zhijian Yang
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Graduate Group in Applied Mathematics and Computational Science, University of Pennsylvania, Philadelphia, PA, USA
| | - Junhao Wen
- Laboratory of AI and Biomedical Science (LABS), Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, California, USA
| | - Guray Erus
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Sindhuja T. Govindarajan
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Randa Melhem
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Elizabeth Mamourian
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Yuhan Cui
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Dhivya Srinivasan
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Ahmed Abdulkadir
- Laboratory for Research in Neuroimaging, Department of Clinical Neurosciences, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland
| | - Paraskevi Parmpi
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Katharina Wittfeld
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Germany
| | - Hans J. Grabe
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Germany
- German Center for Neurodegenerative Diseases (DZNE), Site Rostock/Greifswald, Germany
| | - Robin Bülow
- Institute of Diagnostic Radiology and Neuroradiology, University of Greifswald, Germany
| | - Stefan Frenzel
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Germany
| | - Duygu Tosun
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, USA
| | - Murat Bilgel
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Yang An
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Dahyun Yi
- Institute of Human Behavioral Medicine, Medical Research Center Seoul National University, Seoul, Republic of Korea
| | - Daniel S. Marcus
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | - Pamela LaMontagne
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | - Tammie L.S. Benzinger
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | - Susan R. Heckbert
- Cardiovascular Health Research Unit and Department of Epidemiology, University of Washington, Seattle, WA, USA
| | - Thomas R. Austin
- Cardiovascular Health Research Unit and Department of Epidemiology, University of Washington, Seattle, WA, USA
| | - Shari R. Waldstein
- Department of Psychology, University of Maryland, Baltimore County, Catonsville, MD, USA
| | - Michele K. Evans
- Health Disparities Research Section, Laboratory of Epidemiology and Population Sciences, NIA/NIH/IRP, Baltimore, MD, USA
| | - Alan B. Zonderman
- Health Disparities Research Section, Laboratory of Epidemiology and Population Sciences, NIA/NIH/IRP, Baltimore, MD, USA
| | - Lenore J. Launer
- Neuroepidemiology Section, Intramural Research Program, National Institute on Aging, Bethesda, Maryland, USA
| | - Aristeidis Sotiras
- Department of Radiology and Institute of Informatics, Washington University in St. Luis, St. Luis, MO63110, USA
| | - Mark A. Espeland
- Department of Internal Medicine, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | - Colin L. Masters
- Florey Institute, The University of Melbourne, Parkville, VIC, 3052, Australia
| | - Paul Maruff
- Florey Institute, The University of Melbourne, Parkville, VIC, 3052, Australia
| | - Jurgen Fripp
- CSIRO Health and Biosecurity, Australian e-Health Research Centre CSIRO, Brisbane, Queensland, Australia
| | - Arthur Toga
- Laboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, California, USA
| | - Sid O’Bryant
- Institute for Translational Research University of North Texas Health Science Center Fort Worth Texas USA
| | - Mallar M. Chakravarty
- Computational Brain Anatomy (CoBrA) Laboratory, Cerebral Imaging Center, Douglas Mental Health University Institute, McGill University, Verdun, Quebec, Canada
| | - Sylvia Villeneuve
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Canada
| | - Sterling C. Johnson
- Wisconsin Alzheimer’s Institute, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA
| | - John C. Morris
- Knight Alzheimer Disease Research Center, Washington University in St. Louis, St. Louis, MO, USA
| | - Marilyn S. Albert
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Kristine Yaffe
- Departments of Neurology, Psychiatry and Epidemiology and Biostatistics, University of California San Francisco, San Francisco, CA, USA
| | - Henry Völzke
- Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
| | - Luigi Ferrucci
- Translational Gerontology Branch, Longitudinal Studies Section, National Institute on Aging, National Institutes of Health, MedStar Harbor Hospital, 3001 S. Hanover Street, Baltimore, MD, USA
| | - Nick R. Bryan
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Russell T. Shinohara
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, & Informatics, University of Pennsylvania, Philadelphia, PA, USA
| | - Yong Fan
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Mohamad Habes
- Biggs Alzheimer’s Institute, University of Texas San Antonio Health Science Center, USA
| | - Paris Alexandros Lalousis
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, United Kingdom
| | - Nikolaos Koutsouleris
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, United Kingdom
- Section for Precision Psychiatry, Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University Munich, Munich, Germany
| | - David A. Wolk
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA
| | - Susan M. Resnick
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Haochang Shou
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, & Informatics, University of Pennsylvania, Philadelphia, PA, USA
| | - Ilya M. Nasrallah
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Christos Davatzikos
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
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15
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Wen J, Nasrallah IM, Abdulkadir A, Satterthwaite TD, Yang Z, Erus G, Robert-Fitzgerald T, Singh A, Sotiras A, Boquet-Pujadas A, Mamourian E, Doshi J, Cui Y, Srinivasan D, Skampardoni I, Chen J, Hwang G, Bergman M, Bao J, Veturi Y, Zhou Z, Yang S, Dazzan P, Kahn RS, Schnack HG, Zanetti MV, Meisenzahl E, Busatto GF, Crespo-Facorro B, Pantelis C, Wood SJ, Zhuo C, Shinohara RT, Gur RC, Gur RE, Koutsouleris N, Wolf DH, Saykin AJ, Ritchie MD, Shen L, Thompson PM, Colliot O, Wittfeld K, Grabe HJ, Tosun D, Bilgel M, An Y, Marcus DS, LaMontagne P, Heckbert SR, Austin TR, Launer LJ, Espeland M, Masters CL, Maruff P, Fripp J, Johnson SC, Morris JC, Albert MS, Bryan RN, Resnick SM, Fan Y, Habes M, Wolk D, Shou H, Davatzikos C. Genomic loci influence patterns of structural covariance in the human brain. Proc Natl Acad Sci U S A 2023; 120:e2300842120. [PMID: 38127979 PMCID: PMC10756284 DOI: 10.1073/pnas.2300842120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Accepted: 10/31/2023] [Indexed: 12/23/2023] Open
Abstract
Normal and pathologic neurobiological processes influence brain morphology in coordinated ways that give rise to patterns of structural covariance (PSC) across brain regions and individuals during brain aging and diseases. The genetic underpinnings of these patterns remain largely unknown. We apply a stochastic multivariate factorization method to a diverse population of 50,699 individuals (12 studies and 130 sites) and derive data-driven, multi-scale PSCs of regional brain size. PSCs were significantly correlated with 915 genomic loci in the discovery set, 617 of which are newly identified, and 72% were independently replicated. Key pathways influencing PSCs involve reelin signaling, apoptosis, neurogenesis, and appendage development, while pathways of breast cancer indicate potential interplays between brain metastasis and PSCs associated with neurodegeneration and dementia. Using support vector machines, multi-scale PSCs effectively derive imaging signatures of several brain diseases. Our results elucidate genetic and biological underpinnings that influence structural covariance patterns in the human brain.
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Affiliation(s)
- Junhao Wen
- Laboratory of AI and Biomedical Science, Department of Neurology, Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA90033
- AI in Biomedical Imaging Laboratory, Department of Radiology, Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Ilya M. Nasrallah
- AI in Biomedical Imaging Laboratory, Department of Radiology, Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
- Department of Radiology, University of Pennsylvania, Philadelphia, PA19104
| | - Ahmed Abdulkadir
- AI in Biomedical Imaging Laboratory, Department of Radiology, Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Theodore D. Satterthwaite
- AI in Biomedical Imaging Laboratory, Department of Radiology, Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Zhijian Yang
- AI in Biomedical Imaging Laboratory, Department of Radiology, Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Guray Erus
- AI in Biomedical Imaging Laboratory, Department of Radiology, Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Timothy Robert-Fitzgerald
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Ashish Singh
- AI in Biomedical Imaging Laboratory, Department of Radiology, Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Aristeidis Sotiras
- Department of Radiology, Washington University School of Medicine, St. Louis, MO63110
| | - Aleix Boquet-Pujadas
- Biomedical Imaging Group, Department of Biomedical Engineering, École Polytechnique Fédérale de Lausanne, Lausanne1015, Switzerland
| | - Elizabeth Mamourian
- AI in Biomedical Imaging Laboratory, Department of Radiology, Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Jimit Doshi
- AI in Biomedical Imaging Laboratory, Department of Radiology, Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Yuhan Cui
- AI in Biomedical Imaging Laboratory, Department of Radiology, Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Dhivya Srinivasan
- AI in Biomedical Imaging Laboratory, Department of Radiology, Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Ioanna Skampardoni
- AI in Biomedical Imaging Laboratory, Department of Radiology, Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Jiong Chen
- AI in Biomedical Imaging Laboratory, Department of Radiology, Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Gyujoon Hwang
- AI in Biomedical Imaging Laboratory, Department of Radiology, Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Mark Bergman
- AI in Biomedical Imaging Laboratory, Department of Radiology, Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Jingxuan Bao
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA19104
| | - Yogasudha Veturi
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Zhen Zhou
- AI in Biomedical Imaging Laboratory, Department of Radiology, Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Shu Yang
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA19104
| | - Paola Dazzan
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, LondonWC2R 2LS, United Kingdom
| | - Rene S. Kahn
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029
| | - Hugo G. Schnack
- Department of Psychiatry, University Medical Center Utrecht, Utrecht 3584 CX Ut, Netherlands
| | - Marcus V. Zanetti
- Institute of Psychiatry, Department of Psychiatry, Faculty of Medicine, University of São Paulo, São Paulo05508-070, Brazil
| | - Eva Meisenzahl
- Department of Psychiatry and Psychotherapy, Heinrich Heine University, Düsseldorf40204, Germany
| | - Geraldo F. Busatto
- Institute of Psychiatry, Department of Psychiatry, Faculty of Medicine, University of São Paulo, São Paulo05508-070, Brazil
| | - Benedicto Crespo-Facorro
- Hospital Universitario Virgen del Rocio, School of Medicine, University of Sevilla,Sevilla41004, Spain
| | - Christos Pantelis
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne, Melbourne, VIC 3052, Australia
| | - Stephen J. Wood
- Orygen and the Centre for Youth Mental Health, Medicine, Dentistry and Health Sciences, University of Melbourne, Melbourne, VIC 3052, Australia
| | - Chuanjun Zhuo
- Key Laboratory of Real Tine Tracing of Brain Circuits in Psychiatry and Neurology, Department of Psychiatry, Tianjin Medical University, Tianjin300070, China
| | - Russell T. Shinohara
- AI in Biomedical Imaging Laboratory, Department of Radiology, Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Ruben C. Gur
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Raquel E. Gur
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Nikolaos Koutsouleris
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich 80539, Germany
| | - Daniel H. Wolf
- AI in Biomedical Imaging Laboratory, Department of Radiology, Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Andrew J. Saykin
- Indiana Alzheimer’s Disease Research Center, Department of Radiology, Indiana University School of Medicine, Indianapolis, IN46202-3082
| | - Marylyn D. Ritchie
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Li Shen
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA19104
| | - Paul M. Thompson
- Imaging Genetics Center, Department of Neurology, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA90033
| | - Olivier Colliot
- Institut du Cerveau, Sorbonne Université, Paris75013, France
| | - Katharina Wittfeld
- Department of Psychiatry and Psychotherapy, German Center for Neurodegenerative Diseases, University Medicine Greifswald, Greifswald17475, Germany
| | - Hans J. Grabe
- Department of Psychiatry and Psychotherapy, German Center for Neurodegenerative Diseases, University Medicine Greifswald, Greifswald17475, Germany
| | - Duygu Tosun
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA 94143
| | - Murat Bilgel
- Laboratory of Behavioral Neuroscience, National Institute on Aging, NIH, Baltimore21224, MD
| | - Yang An
- Laboratory of Behavioral Neuroscience, National Institute on Aging, NIH, Baltimore21224, MD
| | - Daniel S. Marcus
- Department of Radiology, Washington University School of Medicine, St. Louis, MO63110
| | - Pamela LaMontagne
- Department of Radiology, Washington University School of Medicine, St. Louis, MO63110
| | - Susan R. Heckbert
- Department of Epidemiology, University of Washington, Seattle, WA98195
| | - Thomas R. Austin
- Department of Epidemiology, University of Washington, Seattle, WA98195
| | - Lenore J. Launer
- Neuroepidemiology Section, Intramural Research Program, National Institute on Aging, Washington, MD20817
| | - Mark Espeland
- Sticht Center for Healthy Aging and Alzheimer’s Prevention, Divisions of Gerontology and Geriatric Medicine, Wake Forest School of Medicine, Winston-Salem, NC27101
| | - Colin L. Masters
- Florey Institute of Neuroscience and Mental Health, Medicine, Dentistry and Health Sciences, The University of Melbourne, Parkville, VIC3010, Australia
| | - Paul Maruff
- Florey Institute of Neuroscience and Mental Health, Medicine, Dentistry and Health Sciences, The University of Melbourne, Parkville, VIC3010, Australia
| | - Jurgen Fripp
- Health and Biosecurity, Australian e-Health Research Centre, Commonwealth Scientific and Industrial Research Organisation, Brisbane, QLD4029, Australia
| | - Sterling C. Johnson
- Wisconsin Alzheimer's Institute, Department of Medicine, University of Wisconsin School of Medicine and Public Health, Madison, WI53792
| | - John C. Morris
- Knight Alzheimer Disease Research Center, Department of Neurology, Washington University in St. Louis, St. Louis, MO63110
| | - Marilyn S. Albert
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD21205
| | - R. Nick Bryan
- Department of Radiology, University of Pennsylvania, Philadelphia, PA19104
| | - Susan M. Resnick
- Laboratory of Behavioral Neuroscience, National Institute on Aging, NIH, Baltimore21224, MD
| | - Yong Fan
- AI in Biomedical Imaging Laboratory, Department of Radiology, Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Mohamad Habes
- Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, Department of Radiology, University of Texas Health Science Center at San Antonio, San Antonio, TX78229
| | - David Wolk
- AI in Biomedical Imaging Laboratory, Department of Radiology, Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
- Department of Neurology, University of Pennsylvania, Philadelphia, PA19104
| | - Haochang Shou
- AI in Biomedical Imaging Laboratory, Department of Radiology, Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Christos Davatzikos
- AI in Biomedical Imaging Laboratory, Department of Radiology, Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
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16
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Liu J, van Beusekom H, Bu X, Chen G, Henrique Rosado de Castro P, Chen X, Chen X, Clarkson AN, Farr TD, Fu Y, Jia J, Jolkkonen J, Kim WS, Korhonen P, Li S, Liang Y, Liu G, Liu G, Liu Y, Malm T, Mao X, Oliveira JM, Modo MM, Ramos‐Cabrer P, Ruscher K, Song W, Wang J, Wang X, Wang Y, Wu H, Xiong L, Yang Y, Ye K, Yu J, Zhou X, Zille M, Masters CL, Walczak P, Boltze J, Ji X, Wang Y. Preserving cognitive function in patients with Alzheimer's disease: The Alzheimer's disease neuroprotection research initiative (ADNRI). Neuroprotection 2023; 1:84-98. [PMID: 38223913 PMCID: PMC10783281 DOI: 10.1002/nep3.23] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Revised: 08/07/2023] [Accepted: 08/08/2023] [Indexed: 01/16/2024]
Abstract
The global trend toward aging populations has resulted in an increase in the occurrence of Alzheimer's disease (AD) and associated socioeconomic burdens. Abnormal metabolism of amyloid-β (Aβ) has been proposed as a significant pathomechanism in AD, supported by results of recent clinical trials using anti-Aβ antibodies. Nonetheless, the cognitive benefits of the current treatments are limited. The etiology of AD is multifactorial, encompassing Aβ and tau accumulation, neuroinflammation, demyelination, vascular dysfunction, and comorbidities, which collectively lead to widespread neurodegeneration in the brain and cognitive impairment. Hence, solely removing Aβ from the brain may be insufficient to combat neurodegeneration and preserve cognition. To attain effective treatment for AD, it is necessary to (1) conduct extensive research on various mechanisms that cause neurodegeneration, including advances in neuroimaging techniques for earlier detection and a more precise characterization of molecular events at scales ranging from cellular to the full system level; (2) identify neuroprotective intervention targets against different neurodegeneration mechanisms; and (3) discover novel and optimal combinations of neuroprotective intervention strategies to maintain cognitive function in AD patients. The Alzheimer's Disease Neuroprotection Research Initiative's objective is to facilitate coordinated, multidisciplinary efforts to develop systemic neuroprotective strategies to combat AD. The aim is to achieve mitigation of the full spectrum of pathological processes underlying AD, with the goal of halting or even reversing cognitive decline.
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Affiliation(s)
- Jie Liu
- Department of Neurology, Daping HospitalThird Military Medical UniversityChongqingChina
- Chongqing Key Laboratory of Ageing and Brain DiseasesChongqingChina
| | - Heleen van Beusekom
- Division of Experimental Cardiology, Department of Cardiology, Erasmus MCUniversity Medical CenterRotterdamThe Netherlands
| | - Xian‐Le Bu
- Department of Neurology, Daping HospitalThird Military Medical UniversityChongqingChina
- Chongqing Key Laboratory of Ageing and Brain DiseasesChongqingChina
- Institute of Brain and IntelligenceThird Military Medical UniversityChongqingChina
| | - Gong Chen
- Guangdong‐HongKong‐Macau Institute of CNS Regeneration (GHMICR)Jinan UniversityGuangzhouGuangdongChina
| | | | - Xiaochun Chen
- Fujian Key Laboratory of Molecular Neurology, Department of Neurology and Geriatrics, Fujian Institute of Geriatrics, Fujian Medical University Union Hospital, Institute of NeuroscienceFujian Medical UniversityFuzhouFujianChina
| | - Xiaowei Chen
- Institute of Brain and IntelligenceThird Military Medical UniversityChongqingChina
- Guangyang Bay LaboratoryChongqing Institute for Brain and IntelligenceChongqingChina
- Center for Excellence in Brain Science and Intelligence TechnologyChinese Academy of SciencesShanghaiChina
| | - Andrew N. Clarkson
- Department of Anatomy, Brain Health Research Centre and Brain Research New ZealandUniversity of OtagoDunedinNew Zealand
| | - Tracy D. Farr
- School of Life SciencesUniversity of NottinghamNottinghamUK
| | - Yuhong Fu
- Brain and Mind Centre & School of Medical SciencesThe University of SydneySydneyNew South WalesAustralia
| | - Jianping Jia
- Innovation Center for Neurological Disorders and Department of Neurology, Xuanwu Hospital, National Clinical Research Center for Geriatric DiseasesCapital Medical UniversityBeijingChina
| | - Jukka Jolkkonen
- A.I. Virtanen Institute for Molecular SciencesUniversity of Eastern FinlandKuopioFinland
| | - Woojin Scott Kim
- Brain and Mind Centre & School of Medical SciencesThe University of SydneySydneyNew South WalesAustralia
| | - Paula Korhonen
- A.I. Virtanen Institute for Molecular SciencesUniversity of Eastern FinlandKuopioFinland
| | - Shen Li
- Department of Neurology and Psychiatry, Beijing Shijitan HospitalCapital Medical UniversityBeijingChina
| | - Yajie Liang
- Department of Diagnostic Radiology and Nuclear MedicineUniversity of Maryland School of MedicineBaltimoreMarylandUSA
| | - Guang‐Hui Liu
- University of Chinese Academy of SciencesBeijingChina
- State Key Laboratory of Membrane Biology, Institute of ZoologyChinese Academy of SciencesBeijingChina
| | - Guiyou Liu
- Beijing Institute of Brain Disorders, Laboratory of Brain Disorders, Ministry of Science and Technology, Collaborative Innovation Center for Brain DisordersCapital Medical UniversityBeijingChina
| | - Yu‐Hui Liu
- Department of Neurology, Daping HospitalThird Military Medical UniversityChongqingChina
- Chongqing Key Laboratory of Ageing and Brain DiseasesChongqingChina
- Institute of Brain and IntelligenceThird Military Medical UniversityChongqingChina
| | - Tarja Malm
- A.I. Virtanen Institute for Molecular SciencesUniversity of Eastern FinlandKuopioFinland
| | - Xiaobo Mao
- Institute for Cell Engineering, Department of NeurologyThe Johns Hopkins University School of MedicineBaltimoreMarylandUSA
| | - Joaquim Miguel Oliveira
- 3B's Research Group, I3Bs—Research Institute on Biomaterials, Biodegradables and Biomimetics, Headquarters of the European Institute of Excellence on Tissue Engineering and Regenerative MedicineUniversity of MinhoGuimarãesPortugal
- ICVS/3B's—PT Government Associate LaboratoryBraga/GuimarãesPortugal
| | - Mike M. Modo
- Department of Bioengineering, McGowan Institute for Regenerative MedicineUniversity of PittsburghPittsburghPennsylvaniaUSA
- Department of Radiology, McGowan Institute for Regenerative MedicineUniversity of PittsburghPittsburghPennsylvaniaUSA
| | - Pedro Ramos‐Cabrer
- Magnetic Resonance Imaging LaboratoryCIC BiomaGUNE Research Center, Basque Research and Technology Alliance (BRTA)Donostia‐San SebastianSpain
| | - Karsten Ruscher
- Laboratory for Experimental Brain Research, Division of Neurosurgery, Department of Clinical SciencesLund UniversityLundSweden
| | - Weihong Song
- Institute of Aging, Key Laboratory of Alzheimer's Disease of Zhejiang Province. Zhejiang Clinical Research Center for Mental Disorders, School of Mental Health and The Affiliated Kangning Hospital, Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health)Wenzhou Medical UniversityZhejiangChina
| | - Jun Wang
- Department of Neurology, Daping HospitalThird Military Medical UniversityChongqingChina
- Chongqing Key Laboratory of Ageing and Brain DiseasesChongqingChina
| | - Xuanyue Wang
- School of Optometry and Vision ScienceUniversity of New South WalesSydneyNew South WalesAustralia
| | - Yun Wang
- Neuroscience Research Institute, Department of Neurobiology, School of Basic, Medical Sciences, Key Laboratory for Neuroscience, Ministry of Education/National, Health Commission and State Key Laboratory of Natural and Biomimetic DrugsPeking UniversityBeijingChina
- PKU‐IDG/McGovern Institute for Brain ResearchPeking UniversityBeijingChina
| | - Haitao Wu
- Department of NeurobiologyBeijing Institute of Basic Medical SciencesBeijingChina
| | - Lize Xiong
- Shanghai Key Laboratory of Anesthesiology and Brain Functional Modulation, Clinical Research Center for Anesthesiology and Perioperative Medicine, Translational Research Institute of Brain and Brain‐Like Intelligence, Shanghai Fourth People's Hospital, School of MedicineTongji UniversityShanghaiChina
| | - Yi Yang
- Department of NeurologyThe First Hospital of Jilin University, Chang ChunJilinChina
| | - Keqiang Ye
- Faculty of Life and Health SciencesBrain Cognition and Brain Disease Institute (BCBDI), Shenzhen Institute of Advanced TechnologyShenzhenChina
| | - Jin‐Tai Yu
- State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Department of Neurology and Institute of Neurology, Huashan Hospital, Shanghai Medical CollegeFudan UniversityShanghaiChina
| | - Xin‐Fu Zhou
- Division of Health Sciences, School of Pharmacy and Medical Sciences and Sansom InstituteUniversity of South AustraliaAdelaideSouth AustraliaAustralia
- Suzhou Auzone BiotechSuzhouJiangsuChina
| | - Marietta Zille
- Department of Pharmaceutical Sciences, Division of Pharmacology and ToxicologyUniversity of ViennaViennaAustria
| | - Colin L. Masters
- The Florey InstituteThe University of Melbourne, ParkvilleVictoriaAustralia
| | - Piotr Walczak
- Department of Diagnostic Radiology and Nuclear MedicineUniversity of Maryland School of MedicineBaltimoreMarylandUSA
| | | | - Xunming Ji
- Department of NeurosurgeryXuanwu Hospital, Capital Medical UniversityBeijingChina
| | - Yan‐Jiang Wang
- Department of Neurology, Daping HospitalThird Military Medical UniversityChongqingChina
- Chongqing Key Laboratory of Ageing and Brain DiseasesChongqingChina
- Institute of Brain and IntelligenceThird Military Medical UniversityChongqingChina
- Guangyang Bay LaboratoryChongqing Institute for Brain and IntelligenceChongqingChina
- Center for Excellence in Brain Science and Intelligence TechnologyChinese Academy of SciencesShanghaiChina
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17
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Xia Y, Maruff P, Doré V, Bourgeat P, Laws SM, Fowler C, Rainey-Smith SR, Martins RN, Villemagne VL, Rowe CC, Masters CL, Coulson EJ, Fripp J. Longitudinal trajectories of basal forebrain volume in normal aging and Alzheimer's disease. Neurobiol Aging 2023; 132:120-130. [PMID: 37801885 DOI: 10.1016/j.neurobiolaging.2023.09.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Revised: 08/03/2023] [Accepted: 09/07/2023] [Indexed: 10/08/2023]
Abstract
Dysfunction of the cholinergic basal forebrain (BF) system and amyloid-β (Aβ) deposition are early pathological features in Alzheimer's disease (AD). However, their association in early AD is not well-established. This study investigated the nature and magnitude of volume loss in the BF, over an extended period, in 516 older adults who completed Aβ-PET and serial magnetic resonance imaging scans. Individuals were grouped at baseline according to the presence of cognitive impairment (CU, CI) and Aβ status (Aβ-, Aβ+). Longitudinal volumetric changes in the BF and hippocampus were assessed across groups. The results indicated that high Aβ levels correlated with faster volume loss in the BF and hippocampus, and the effect of Aβ varied within BF subregions. Compared to CU Aβ+ individuals, Aβ-related loss among CI Aβ+ adults was much greater in the predominantly cholinergic subregion of Ch4p, whereas no difference was observed for the Ch1/Ch2 region. The findings support early and substantial vulnerability of the BF and further reveal distinctive degeneration of BF subregions during early AD.
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Affiliation(s)
- Ying Xia
- The Australian e-Health Research Centre, CSIRO Health and Biosecurity, Brisbane, Queensland, Australia.
| | - Paul Maruff
- Cogstate Ltd, Melbourne, Victoria, Australia; The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Parkville, Victoria, Australia
| | - Vincent Doré
- Department of Nuclear Medicine and Centre for PET, Austin Health, Melbourne, Victoria, Australia; The Australian e-Health Research Centre, CSIRO Health and Biosecurity, Melbourne, Victoria, Australia
| | - Pierrick Bourgeat
- The Australian e-Health Research Centre, CSIRO Health and Biosecurity, Brisbane, Queensland, Australia
| | - Simon M Laws
- Collaborative Genomics and Translation Group, School of Medical and Health Sciences, Edith Cowan University, Joondalup, Western Australia, Australia; Centre for Precision Health, Edith Cowan University, Joondalup, Western Australia, Australia; Curtin Medical School, Curtin University, Bentley, Western Australia, Australia
| | - Christopher Fowler
- The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Parkville, Victoria, Australia
| | - Stephanie R Rainey-Smith
- Centre for Healthy Ageing, Health Futures Institute, Murdoch University, Murdoch, Western Australia, Australia; Australian Alzheimer's Research Foundation, Sarich Neuroscience Research Institute, Nedlands, Western Australia, Australia; School of Psychological Science, University of Western Australia, Crawley, Western Australia, Australia; School of Medical and Health Sciences, Edith Cowan University, Joondalup, Western Australia, Australia
| | - Ralph N Martins
- Australian Alzheimer's Research Foundation, Sarich Neuroscience Research Institute, Nedlands, Western Australia, Australia; School of Medical and Health Sciences, Edith Cowan University, Joondalup, Western Australia, Australia; Department of Biomedical Sciences, Macquarie University, Sydney, New South Wales, Australia
| | - Victor L Villemagne
- Department of Nuclear Medicine and Centre for PET, Austin Health, Melbourne, Victoria, Australia; Centre for Precision Health, Edith Cowan University, Joondalup, Western Australia, Australia; Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
| | - Christopher C Rowe
- Department of Nuclear Medicine and Centre for PET, Austin Health, Melbourne, Victoria, Australia; Florey Department of Neuroscience and Mental Health, The University of Melbourne, Parkville, Victoria, Australia
| | - Colin L Masters
- The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Parkville, Victoria, Australia
| | - Elizabeth J Coulson
- Queensland Brain Institute, The University of Queensland, Brisbane, Queensland, Australia; School of Biomedical Sciences, The University of Queensland, Brisbane, Queensland, Australia
| | - Jurgen Fripp
- The Australian e-Health Research Centre, CSIRO Health and Biosecurity, Brisbane, Queensland, Australia
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18
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Lian Y, Jia YJ, Wong J, Zhou XF, Song W, Guo J, Masters CL, Wang YJ. Clarity on the blazing trail: clearing the way for amyloid-removing therapies for Alzheimer's disease. Mol Psychiatry 2023:10.1038/s41380-023-02324-4. [PMID: 38001337 DOI: 10.1038/s41380-023-02324-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/05/2023] [Revised: 11/03/2023] [Accepted: 11/13/2023] [Indexed: 11/26/2023]
Abstract
Alzheimer's disease (AD) is a progressive neurodegenerative disorder with a complex pathogenesis. Senile plaques composed of the amyloid-β (Aβ) peptide in the brain are the core hallmarks of AD and a promising target for the development of disease-modifying therapies. However, over the past 20 years, the failures of clinical trials directed at Aβ clearance have fueled a debate as to whether Aβ is the principal pathogenic factor in AD and a valid therapeutic target. The success of the recent phase 3 trials of lecanemab (Clarity AD) and donanemab (Trailblazer Alz2), and lessons from previous Aβ clearance trials provide critical evidence to support the role of Aβ in AD pathogenesis and suggest that targeting Aβ clearance is heading in the right direction for AD treatment. Here, we analyze key questions relating to the efficacy of Aβ targeting therapies, and provide perspectives on early intervention, adequate Aβ removal, sufficient treatment period, and combinatory therapeutics, which may be required to achieve the best cognitive benefits in future trials in the real world.
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Affiliation(s)
- Yan Lian
- Department of Prevention and Health Care, Daping Hospital, Third Military Medical University, Chongqing, China
- Department of Neurology and Centre for Clinical Neuroscience, Daping Hospital, Third Military Medical University, Chongqing, China
- Key Laboratory of Ageing and Brain Disease, Chongqing, China
| | - Yu-Juan Jia
- Department of Neurology, First Hospital of Shanxi Medical University, Taiyuan, China
| | - Joelyn Wong
- The Florey Institute, The University of Melbourne, Parkville, VIC, Australia
| | - Xin-Fu Zhou
- School of Pharmacy and Medical Sciences and Sansom Institute, Division of Health Sciences, University of South Australia, Adelaide, SA, Australia
| | - Weihong Song
- Institute of Aging, Key Laboratory of Alzheimer's Disease of Zhejiang Province. Zhejiang Clinical Research Center for Mental Disorders, School of Mental Health and The Affiliated Kangning Hospital, Wenzhou Medical University, Oujiang Laboratory (Zhejiang Laboratory for Regenerative Medicine, Vision and Brain Health), Wenzhou, Zhejiang, China
| | - Junhong Guo
- Department of Neurology, First Hospital of Shanxi Medical University, Taiyuan, China.
| | - Colin L Masters
- The Florey Institute, The University of Melbourne, Parkville, VIC, Australia.
| | - Yan-Jiang Wang
- Department of Neurology and Centre for Clinical Neuroscience, Daping Hospital, Third Military Medical University, Chongqing, China.
- Key Laboratory of Ageing and Brain Disease, Chongqing, China.
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19
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Llibre-Guerra JJ, Iaccarino L, Coble D, Edwards L, Li Y, McDade E, Strom A, Gordon B, Mundada N, Schindler SE, Tsoy E, Ma Y, Lu R, Fagan AM, Benzinger TLS, Soleimani-Meigooni D, Aschenbrenner AJ, Miller Z, Wang G, Kramer JH, Hassenstab J, Rosen HJ, Morris JC, Miller BL, Xiong C, Perrin RJ, Allegri R, Chrem P, Surace E, Berman SB, Chhatwal J, Masters CL, Farlow MR, Jucker M, Levin J, Fox NC, Day G, Gorno-Tempini ML, Boxer AL, La Joie R, Rabinovici GD, Bateman R. Longitudinal clinical, cognitive and biomarker profiles in dominantly inherited versus sporadic early-onset Alzheimer's disease. Brain Commun 2023; 5:fcad280. [PMID: 37942088 PMCID: PMC10629466 DOI: 10.1093/braincomms/fcad280] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Revised: 10/02/2023] [Accepted: 10/17/2023] [Indexed: 11/10/2023] Open
Abstract
Approximately 5% of Alzheimer's disease cases have an early age at onset (<65 years), with 5-10% of these cases attributed to dominantly inherited mutations and the remainder considered as sporadic. The extent to which dominantly inherited and sporadic early-onset Alzheimer's disease overlap is unknown. In this study, we explored the clinical, cognitive and biomarker profiles of early-onset Alzheimer's disease, focusing on commonalities and distinctions between dominantly inherited and sporadic cases. Our analysis included 117 participants with dominantly inherited Alzheimer's disease enrolled in the Dominantly Inherited Alzheimer Network and 118 individuals with sporadic early-onset Alzheimer's disease enrolled at the University of California San Francisco Alzheimer's Disease Research Center. Baseline differences in clinical and biomarker profiles between both groups were compared using t-tests. Differences in the rates of decline were compared using linear mixed-effects models. Individuals with dominantly inherited Alzheimer's disease exhibited an earlier age-at-symptom onset compared with the sporadic group [43.4 (SD ± 8.5) years versus 54.8 (SD ± 5.0) years, respectively, P < 0.001]. Sporadic cases showed a higher frequency of atypical clinical presentations relative to dominantly inherited (56.8% versus 8.5%, respectively) and a higher frequency of APOE-ε4 (50.0% versus 28.2%, P = 0.001). Compared with sporadic early onset, motor manifestations were higher in the dominantly inherited cohort [32.5% versus 16.9% at baseline (P = 0.006) and 46.1% versus 25.4% at last visit (P = 0.001)]. At baseline, the sporadic early-onset group performed worse on category fluency (P < 0.001), Trail Making Test Part B (P < 0.001) and digit span (P < 0.001). Longitudinally, both groups demonstrated similar rates of cognitive and functional decline in the early stages. After 10 years from symptom onset, dominantly inherited participants experienced a greater decline as measured by Clinical Dementia Rating Sum of Boxes [3.63 versus 1.82 points (P = 0.035)]. CSF amyloid beta-42 levels were comparable [244 (SD ± 39.3) pg/ml dominantly inherited versus 296 (SD ± 24.8) pg/ml sporadic early onset, P = 0.06]. CSF phosphorylated tau at threonine 181 levels were higher in the dominantly inherited Alzheimer's disease cohort (87.3 versus 59.7 pg/ml, P = 0.005), but no significant differences were found for t-tau levels (P = 0.35). In summary, sporadic and inherited Alzheimer's disease differed in baseline profiles; sporadic early onset is best distinguished from dominantly inherited by later age at onset, high frequency of atypical clinical presentations and worse executive performance at baseline. Despite these differences, shared pathways in longitudinal clinical decline and CSF biomarkers suggest potential common therapeutic targets for both populations, offering valuable insights for future research and clinical trial design.
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Affiliation(s)
| | - Leonardo Iaccarino
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Dean Coble
- Division of Biostatistics, Washington University in St Louis, St Louis, MO 63108, USA
| | - Lauren Edwards
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Yan Li
- Division of Biostatistics, Washington University in St Louis, St Louis, MO 63108, USA
| | - Eric McDade
- Department of Neurology, Washington University in St Louis, St Louis, MO 63108, USA
| | - Amelia Strom
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Brian Gordon
- Malinckrodt Institute of Radiology, Washington University in St Louis, St Louis, MO 63108, USA
| | - Nidhi Mundada
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Suzanne E Schindler
- Department of Neurology, Washington University in St Louis, St Louis, MO 63108, USA
| | - Elena Tsoy
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Yinjiao Ma
- Division of Biostatistics, Washington University in St Louis, St Louis, MO 63108, USA
| | - Ruijin Lu
- Division of Biostatistics, Washington University in St Louis, St Louis, MO 63108, USA
| | - Anne M Fagan
- Department of Neurology, Washington University in St Louis, St Louis, MO 63108, USA
| | - Tammie L S Benzinger
- Malinckrodt Institute of Radiology, Washington University in St Louis, St Louis, MO 63108, USA
| | - David Soleimani-Meigooni
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94158, USA
| | | | - Zachary Miller
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Guoqiao Wang
- Division of Biostatistics, Washington University in St Louis, St Louis, MO 63108, USA
| | - Joel H Kramer
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Jason Hassenstab
- Department of Neurology, Washington University in St Louis, St Louis, MO 63108, USA
| | - Howard J Rosen
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94158, USA
| | - John C Morris
- Department of Neurology, Washington University in St Louis, St Louis, MO 63108, USA
| | - Bruce L Miller
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Chengjie Xiong
- Division of Biostatistics, Washington University in St Louis, St Louis, MO 63108, USA
| | - Richard J Perrin
- Department of Neurology, Washington University in St Louis, St Louis, MO 63108, USA
- Department of Pathology and Immunology, Washington University in St Louis, St. Louis, MO 63108, USA
| | - Ricardo Allegri
- Department of Cognitive Neurology, Institute for Neurological Research Fleni, Buenos Aires, Argentina
| | - Patricio Chrem
- Department of Cognitive Neurology, Institute for Neurological Research Fleni, Buenos Aires, Argentina
| | - Ezequiel Surace
- Department of Cognitive Neurology, Institute for Neurological Research Fleni, Buenos Aires, Argentina
| | - Sarah B Berman
- Department of Neurology, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - Jasmeer Chhatwal
- Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
| | - Colin L Masters
- Florey Institute, The University of Melbourne, Melbourne 3052, Australia
| | - Martin R Farlow
- Neuroscience Center, Indiana University School of Medicine at Indianapolis, IN 46202, USA
| | - Mathias Jucker
- DZNE-German Center for Neurodegenerative Diseases, Tübingen 72076, Germany
- Hertie-Institute for Clinical Brain Research, University of Tübingen, Tübingen 72076, Germany
| | - Johannes Levin
- Department of Neurology, Ludwig-Maximilians-University, Munich 80539, Germany
- German Center for Neurodegenerative Diseases, Munich 81377, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich 81377, Germany
| | - Nick C Fox
- Dementia Research Centre, Department of Neurodegenerative Disease, University College London Institute of Neurology, London WC1N 3BG, UK
| | - Gregory Day
- Department of Neurology, Mayo Clinic Florida, Jacksonville, FL 33224, USA
| | - Maria Luisa Gorno-Tempini
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Adam L Boxer
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Renaud La Joie
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Gil D Rabinovici
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94158, USA
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Randall Bateman
- Department of Neurology, Washington University in St Louis, St Louis, MO 63108, USA
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20
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Sperling RA, Donohue MC, Raman R, Rafii MS, Johnson K, Masters CL, van Dyck CH, Iwatsubo T, Marshall GA, Yaari R, Mancini M, Holdridge KC, Case M, Sims JR, Aisen PS. Trial of Solanezumab in Preclinical Alzheimer's Disease. N Engl J Med 2023; 389:1096-1107. [PMID: 37458272 PMCID: PMC10559996 DOI: 10.1056/nejmoa2305032] [Citation(s) in RCA: 19] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 09/22/2023]
Abstract
BACKGROUND Trials of monoclonal antibodies that target various forms of amyloid at different stages of Alzheimer's disease have had mixed results. METHODS We tested solanezumab, which targets monomeric amyloid, in a phase 3 trial involving persons with preclinical Alzheimer's disease. Persons 65 to 85 years of age with a global Clinical Dementia Rating score of 0 (range, 0 to 3, with 0 indicating no cognitive impairment and 3 severe dementia), a score on the Mini-Mental State Examination of 25 or more (range, 0 to 30, with lower scores indicating poorer cognition), and elevated brain amyloid levels on 18F-florbetapir positron-emission tomography (PET) were enrolled. Participants were randomly assigned in a 1:1 ratio to receive solanezumab at a dose of up to 1600 mg intravenously every 4 weeks or placebo. The primary end point was the change in the Preclinical Alzheimer Cognitive Composite (PACC) score (calculated as the sum of four z scores, with higher scores indicating better cognitive performance) over a period of 240 weeks. RESULTS A total of 1169 persons underwent randomization: 578 were assigned to the solanezumab group and 591 to the placebo group. The mean age of the participants was 72 years, approximately 60% were women, and 75% had a family history of dementia. At 240 weeks, the mean change in PACC score was -1.43 in the solanezumab group and -1.13 in the placebo group (difference, -0.30; 95% confidence interval, -0.82 to 0.22; P = 0.26). Amyloid levels on brain PET increased by a mean of 11.6 centiloids in the solanezumab group and 19.3 centiloids in the placebo group. Amyloid-related imaging abnormalities (ARIA) with edema occurred in less than 1% of the participants in each group. ARIA with microhemorrhage or hemosiderosis occurred in 29.2% of the participants in the solanezumab group and 32.8% of those in the placebo group. CONCLUSIONS Solanezumab, which targets monomeric amyloid in persons with elevated brain amyloid levels, did not slow cognitive decline as compared with placebo over a period of 240 weeks in persons with preclinical Alzheimer's disease. (Funded by the National Institute on Aging and others; A4 ClinicalTrials.gov number, NCT02008357.).
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Affiliation(s)
- Reisa A Sperling
- From the Center for Alzheimer Research and Treatment, Brigham and Women's Hospital, Massachusetts General Hospital, Harvard Medical School (R.A.S., G.A.M.), and the Departments of Neurology and Radiology, Massachusetts General Hospital, Harvard Medical School (K.J.) - both in Boston; Alzheimer's Therapeutic Research Institute, Keck School of Medicine, University of Southern California, San Diego (M.C.D., R.R., M.S.R., P.S.A.); the Florey Institute, University of Melbourne, Melbourne, VIC, Australia (C.L.M.); the Departments of Psychiatry, Neurology, and Neuroscience, Yale School of Medicine, New Haven, CT (C.H.D.); the Department of Neuropathology, Graduate School of Medicine, University of Tokyo, Tokyo (T.I.); and Eli Lilly, Indianapolis (R.Y., M.M., K.C.H., M.C., J.R.S.)
| | - Michael C Donohue
- From the Center for Alzheimer Research and Treatment, Brigham and Women's Hospital, Massachusetts General Hospital, Harvard Medical School (R.A.S., G.A.M.), and the Departments of Neurology and Radiology, Massachusetts General Hospital, Harvard Medical School (K.J.) - both in Boston; Alzheimer's Therapeutic Research Institute, Keck School of Medicine, University of Southern California, San Diego (M.C.D., R.R., M.S.R., P.S.A.); the Florey Institute, University of Melbourne, Melbourne, VIC, Australia (C.L.M.); the Departments of Psychiatry, Neurology, and Neuroscience, Yale School of Medicine, New Haven, CT (C.H.D.); the Department of Neuropathology, Graduate School of Medicine, University of Tokyo, Tokyo (T.I.); and Eli Lilly, Indianapolis (R.Y., M.M., K.C.H., M.C., J.R.S.)
| | - Rema Raman
- From the Center for Alzheimer Research and Treatment, Brigham and Women's Hospital, Massachusetts General Hospital, Harvard Medical School (R.A.S., G.A.M.), and the Departments of Neurology and Radiology, Massachusetts General Hospital, Harvard Medical School (K.J.) - both in Boston; Alzheimer's Therapeutic Research Institute, Keck School of Medicine, University of Southern California, San Diego (M.C.D., R.R., M.S.R., P.S.A.); the Florey Institute, University of Melbourne, Melbourne, VIC, Australia (C.L.M.); the Departments of Psychiatry, Neurology, and Neuroscience, Yale School of Medicine, New Haven, CT (C.H.D.); the Department of Neuropathology, Graduate School of Medicine, University of Tokyo, Tokyo (T.I.); and Eli Lilly, Indianapolis (R.Y., M.M., K.C.H., M.C., J.R.S.)
| | - Michael S Rafii
- From the Center for Alzheimer Research and Treatment, Brigham and Women's Hospital, Massachusetts General Hospital, Harvard Medical School (R.A.S., G.A.M.), and the Departments of Neurology and Radiology, Massachusetts General Hospital, Harvard Medical School (K.J.) - both in Boston; Alzheimer's Therapeutic Research Institute, Keck School of Medicine, University of Southern California, San Diego (M.C.D., R.R., M.S.R., P.S.A.); the Florey Institute, University of Melbourne, Melbourne, VIC, Australia (C.L.M.); the Departments of Psychiatry, Neurology, and Neuroscience, Yale School of Medicine, New Haven, CT (C.H.D.); the Department of Neuropathology, Graduate School of Medicine, University of Tokyo, Tokyo (T.I.); and Eli Lilly, Indianapolis (R.Y., M.M., K.C.H., M.C., J.R.S.)
| | - Keith Johnson
- From the Center for Alzheimer Research and Treatment, Brigham and Women's Hospital, Massachusetts General Hospital, Harvard Medical School (R.A.S., G.A.M.), and the Departments of Neurology and Radiology, Massachusetts General Hospital, Harvard Medical School (K.J.) - both in Boston; Alzheimer's Therapeutic Research Institute, Keck School of Medicine, University of Southern California, San Diego (M.C.D., R.R., M.S.R., P.S.A.); the Florey Institute, University of Melbourne, Melbourne, VIC, Australia (C.L.M.); the Departments of Psychiatry, Neurology, and Neuroscience, Yale School of Medicine, New Haven, CT (C.H.D.); the Department of Neuropathology, Graduate School of Medicine, University of Tokyo, Tokyo (T.I.); and Eli Lilly, Indianapolis (R.Y., M.M., K.C.H., M.C., J.R.S.)
| | - Colin L Masters
- From the Center for Alzheimer Research and Treatment, Brigham and Women's Hospital, Massachusetts General Hospital, Harvard Medical School (R.A.S., G.A.M.), and the Departments of Neurology and Radiology, Massachusetts General Hospital, Harvard Medical School (K.J.) - both in Boston; Alzheimer's Therapeutic Research Institute, Keck School of Medicine, University of Southern California, San Diego (M.C.D., R.R., M.S.R., P.S.A.); the Florey Institute, University of Melbourne, Melbourne, VIC, Australia (C.L.M.); the Departments of Psychiatry, Neurology, and Neuroscience, Yale School of Medicine, New Haven, CT (C.H.D.); the Department of Neuropathology, Graduate School of Medicine, University of Tokyo, Tokyo (T.I.); and Eli Lilly, Indianapolis (R.Y., M.M., K.C.H., M.C., J.R.S.)
| | - Christopher H van Dyck
- From the Center for Alzheimer Research and Treatment, Brigham and Women's Hospital, Massachusetts General Hospital, Harvard Medical School (R.A.S., G.A.M.), and the Departments of Neurology and Radiology, Massachusetts General Hospital, Harvard Medical School (K.J.) - both in Boston; Alzheimer's Therapeutic Research Institute, Keck School of Medicine, University of Southern California, San Diego (M.C.D., R.R., M.S.R., P.S.A.); the Florey Institute, University of Melbourne, Melbourne, VIC, Australia (C.L.M.); the Departments of Psychiatry, Neurology, and Neuroscience, Yale School of Medicine, New Haven, CT (C.H.D.); the Department of Neuropathology, Graduate School of Medicine, University of Tokyo, Tokyo (T.I.); and Eli Lilly, Indianapolis (R.Y., M.M., K.C.H., M.C., J.R.S.)
| | - Takeshi Iwatsubo
- From the Center for Alzheimer Research and Treatment, Brigham and Women's Hospital, Massachusetts General Hospital, Harvard Medical School (R.A.S., G.A.M.), and the Departments of Neurology and Radiology, Massachusetts General Hospital, Harvard Medical School (K.J.) - both in Boston; Alzheimer's Therapeutic Research Institute, Keck School of Medicine, University of Southern California, San Diego (M.C.D., R.R., M.S.R., P.S.A.); the Florey Institute, University of Melbourne, Melbourne, VIC, Australia (C.L.M.); the Departments of Psychiatry, Neurology, and Neuroscience, Yale School of Medicine, New Haven, CT (C.H.D.); the Department of Neuropathology, Graduate School of Medicine, University of Tokyo, Tokyo (T.I.); and Eli Lilly, Indianapolis (R.Y., M.M., K.C.H., M.C., J.R.S.)
| | - Gad A Marshall
- From the Center for Alzheimer Research and Treatment, Brigham and Women's Hospital, Massachusetts General Hospital, Harvard Medical School (R.A.S., G.A.M.), and the Departments of Neurology and Radiology, Massachusetts General Hospital, Harvard Medical School (K.J.) - both in Boston; Alzheimer's Therapeutic Research Institute, Keck School of Medicine, University of Southern California, San Diego (M.C.D., R.R., M.S.R., P.S.A.); the Florey Institute, University of Melbourne, Melbourne, VIC, Australia (C.L.M.); the Departments of Psychiatry, Neurology, and Neuroscience, Yale School of Medicine, New Haven, CT (C.H.D.); the Department of Neuropathology, Graduate School of Medicine, University of Tokyo, Tokyo (T.I.); and Eli Lilly, Indianapolis (R.Y., M.M., K.C.H., M.C., J.R.S.)
| | - Roy Yaari
- From the Center for Alzheimer Research and Treatment, Brigham and Women's Hospital, Massachusetts General Hospital, Harvard Medical School (R.A.S., G.A.M.), and the Departments of Neurology and Radiology, Massachusetts General Hospital, Harvard Medical School (K.J.) - both in Boston; Alzheimer's Therapeutic Research Institute, Keck School of Medicine, University of Southern California, San Diego (M.C.D., R.R., M.S.R., P.S.A.); the Florey Institute, University of Melbourne, Melbourne, VIC, Australia (C.L.M.); the Departments of Psychiatry, Neurology, and Neuroscience, Yale School of Medicine, New Haven, CT (C.H.D.); the Department of Neuropathology, Graduate School of Medicine, University of Tokyo, Tokyo (T.I.); and Eli Lilly, Indianapolis (R.Y., M.M., K.C.H., M.C., J.R.S.)
| | - Michele Mancini
- From the Center for Alzheimer Research and Treatment, Brigham and Women's Hospital, Massachusetts General Hospital, Harvard Medical School (R.A.S., G.A.M.), and the Departments of Neurology and Radiology, Massachusetts General Hospital, Harvard Medical School (K.J.) - both in Boston; Alzheimer's Therapeutic Research Institute, Keck School of Medicine, University of Southern California, San Diego (M.C.D., R.R., M.S.R., P.S.A.); the Florey Institute, University of Melbourne, Melbourne, VIC, Australia (C.L.M.); the Departments of Psychiatry, Neurology, and Neuroscience, Yale School of Medicine, New Haven, CT (C.H.D.); the Department of Neuropathology, Graduate School of Medicine, University of Tokyo, Tokyo (T.I.); and Eli Lilly, Indianapolis (R.Y., M.M., K.C.H., M.C., J.R.S.)
| | - Karen C Holdridge
- From the Center for Alzheimer Research and Treatment, Brigham and Women's Hospital, Massachusetts General Hospital, Harvard Medical School (R.A.S., G.A.M.), and the Departments of Neurology and Radiology, Massachusetts General Hospital, Harvard Medical School (K.J.) - both in Boston; Alzheimer's Therapeutic Research Institute, Keck School of Medicine, University of Southern California, San Diego (M.C.D., R.R., M.S.R., P.S.A.); the Florey Institute, University of Melbourne, Melbourne, VIC, Australia (C.L.M.); the Departments of Psychiatry, Neurology, and Neuroscience, Yale School of Medicine, New Haven, CT (C.H.D.); the Department of Neuropathology, Graduate School of Medicine, University of Tokyo, Tokyo (T.I.); and Eli Lilly, Indianapolis (R.Y., M.M., K.C.H., M.C., J.R.S.)
| | - Michael Case
- From the Center for Alzheimer Research and Treatment, Brigham and Women's Hospital, Massachusetts General Hospital, Harvard Medical School (R.A.S., G.A.M.), and the Departments of Neurology and Radiology, Massachusetts General Hospital, Harvard Medical School (K.J.) - both in Boston; Alzheimer's Therapeutic Research Institute, Keck School of Medicine, University of Southern California, San Diego (M.C.D., R.R., M.S.R., P.S.A.); the Florey Institute, University of Melbourne, Melbourne, VIC, Australia (C.L.M.); the Departments of Psychiatry, Neurology, and Neuroscience, Yale School of Medicine, New Haven, CT (C.H.D.); the Department of Neuropathology, Graduate School of Medicine, University of Tokyo, Tokyo (T.I.); and Eli Lilly, Indianapolis (R.Y., M.M., K.C.H., M.C., J.R.S.)
| | - John R Sims
- From the Center for Alzheimer Research and Treatment, Brigham and Women's Hospital, Massachusetts General Hospital, Harvard Medical School (R.A.S., G.A.M.), and the Departments of Neurology and Radiology, Massachusetts General Hospital, Harvard Medical School (K.J.) - both in Boston; Alzheimer's Therapeutic Research Institute, Keck School of Medicine, University of Southern California, San Diego (M.C.D., R.R., M.S.R., P.S.A.); the Florey Institute, University of Melbourne, Melbourne, VIC, Australia (C.L.M.); the Departments of Psychiatry, Neurology, and Neuroscience, Yale School of Medicine, New Haven, CT (C.H.D.); the Department of Neuropathology, Graduate School of Medicine, University of Tokyo, Tokyo (T.I.); and Eli Lilly, Indianapolis (R.Y., M.M., K.C.H., M.C., J.R.S.)
| | - Paul S Aisen
- From the Center for Alzheimer Research and Treatment, Brigham and Women's Hospital, Massachusetts General Hospital, Harvard Medical School (R.A.S., G.A.M.), and the Departments of Neurology and Radiology, Massachusetts General Hospital, Harvard Medical School (K.J.) - both in Boston; Alzheimer's Therapeutic Research Institute, Keck School of Medicine, University of Southern California, San Diego (M.C.D., R.R., M.S.R., P.S.A.); the Florey Institute, University of Melbourne, Melbourne, VIC, Australia (C.L.M.); the Departments of Psychiatry, Neurology, and Neuroscience, Yale School of Medicine, New Haven, CT (C.H.D.); the Department of Neuropathology, Graduate School of Medicine, University of Tokyo, Tokyo (T.I.); and Eli Lilly, Indianapolis (R.Y., M.M., K.C.H., M.C., J.R.S.)
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21
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Saint-Jalmes M, Fedyashov V, Beck D, Baldwin T, Faux NG, Bourgeat P, Fripp J, Masters CL, Goudey B. Disease progression modelling of Alzheimer's disease using probabilistic principal components analysis. Neuroimage 2023; 278:120279. [PMID: 37454702 DOI: 10.1016/j.neuroimage.2023.120279] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Revised: 06/27/2023] [Accepted: 07/12/2023] [Indexed: 07/18/2023] Open
Abstract
The recent biological redefinition of Alzheimer's Disease (AD) has spurred the development of statistical models that relate changes in biomarkers with neurodegeneration and worsening condition linked to AD. The ability to measure such changes may facilitate earlier diagnoses for affected individuals and help in monitoring the evolution of their condition. Amongst such statistical tools, disease progression models (DPMs) are quantitative, data-driven methods that specifically attempt to describe the temporal dynamics of biomarkers relevant to AD. Due to the heterogeneous nature of this disease, with patients of similar age experiencing different AD-related changes, a challenge facing longitudinal mixed-effects-based DPMs is the estimation of patient-realigning time-shifts. These time-shifts are indispensable for meaningful biomarker modelling, but may impact fitting time or vary with missing data in jointly estimated models. In this work, we estimate an individual's progression through Alzheimer's disease by combining multiple biomarkers into a single value using a probabilistic formulation of principal components analysis. Our results show that this variable, which summarises AD through observable biomarkers, is remarkably similar to jointly estimated time-shifts when we compute our scores for the baseline visit, on cross-sectional data from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Reproducing the expected properties of clinical datasets, we confirm that estimated scores are robust to missing data or unavailable biomarkers. In addition to cross-sectional insights, we can model the latent variable as an individual progression score by repeating estimations at follow-up examinations and refining long-term estimates as more data is gathered, which would be ideal in a clinical setting. Finally, we verify that our score can be used as a pseudo-temporal scale instead of age to ignore some patient heterogeneity in cohort data and highlight the general trend in expected biomarker evolution in affected individuals.
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Affiliation(s)
- Martin Saint-Jalmes
- ARC Training Centre in Cognitive Computing for Medical Technologies, University of Melbourne, Carlton, VIC, Australia; The Florey Department of Neuroscience and Mental Health, The University of Melbourne, Australia; The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Australia.
| | - Victor Fedyashov
- ARC Training Centre in Cognitive Computing for Medical Technologies, University of Melbourne, Carlton, VIC, Australia; The Florey Department of Neuroscience and Mental Health, The University of Melbourne, Australia
| | - Daniel Beck
- ARC Training Centre in Cognitive Computing for Medical Technologies, University of Melbourne, Carlton, VIC, Australia; School of Computing and Information Systems, The University of Melbourne, Australia
| | - Timothy Baldwin
- ARC Training Centre in Cognitive Computing for Medical Technologies, University of Melbourne, Carlton, VIC, Australia; School of Computing and Information Systems, The University of Melbourne, Australia; Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, United Arab Emirates
| | - Noel G Faux
- ARC Training Centre in Cognitive Computing for Medical Technologies, University of Melbourne, Carlton, VIC, Australia; The Florey Department of Neuroscience and Mental Health, The University of Melbourne, Australia; Melbourne Data Analytics Platform, The University of Melbourne, Australia
| | | | - Jurgen Fripp
- CSIRO Health and Biosecurity, Brisbane, Australia
| | - Colin L Masters
- The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Australia
| | - Benjamin Goudey
- ARC Training Centre in Cognitive Computing for Medical Technologies, University of Melbourne, Carlton, VIC, Australia; The Florey Department of Neuroscience and Mental Health, The University of Melbourne, Australia
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22
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Walia N, Eratne D, Loi SM, Farrand S, Li QX, Malpas CB, Varghese S, Walterfang M, Evans AH, Parker S, Collins SJ, Masters CL, Velakoulis D. Cerebrospinal fluid neurofilament light and cerebral atrophy in younger-onset dementia and primary psychiatric disorders. Intern Med J 2023; 53:1564-1569. [PMID: 36314730 DOI: 10.1111/imj.15956] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2022] [Accepted: 10/03/2022] [Indexed: 09/26/2023]
Abstract
BACKGROUND AND AIMS Neurodegeneration underpins the pathological processes of younger-onset dementia (YOD) and has been implicated in primary psychiatric disorders (PSYs). Cerebrospinal fluid (CSF) neurofilament light (NfL) has been used to investigate neurodegeneration severity through correlation with structural brain changes in various conditions, but has seldom been evaluated in YOD and PSYs. METHODS This retrospective study included patients with YOD or PSYs with magnetic resonance imaging (MRI) of the brain and CSF NfL analysis. Findings from brain MRI were analysed using automated volumetry (volBrain) to measure white matter (WM), grey matter (GM) and whole brain (WB) volumes expressed as percentages of total intracranial volume. Correlations between NfL and brain volume measurements were computed whilst adjusting for age. RESULTS Seventy patients (47 with YOD and 23 with PSY) were identified. YOD types included Alzheimer disease and behavioural variant frontotemporal dementia. PSY included schizophrenia and major depressive disorder. MRI brain sequences were either fast spoiler gradient-echo (FSPGR) or magnetization-prepared rapid acquisition gradient-echo (MPRAGE). In the total cohort, higher NfL was associated with reduced WB in the FSPGR and MPRAGE sequences (r = -0.402 [95% confidence interval (CI), -0.593 to -0.147], P = 0.008 and r = -0.625 [95% CI, -0.828 to -0.395], P < 0.001, respectively). Higher NfL was related to reduced GM in FSPGR (r = 0.385 [95% CI, -0.649 to -0.014], P = 0.017) and reduced WM in MPRAGE (r = -0.650 [95% CI, -0.777 to -0.307], P < 0.001). Similar relationships were seen in YOD, but not in PSY. CONCLUSION Higher CSF NfL is related to brain atrophy in YOD, further supporting its use as a nonspecific marker of neurodegeneration severity.
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Affiliation(s)
- Nirbaanjot Walia
- Neuropsychiatry, The Royal Melbourne Hospital, Melbourne, Victoria, Australia
| | - Dhamidhu Eratne
- Neuropsychiatry, The Royal Melbourne Hospital, Melbourne, Victoria, Australia
- Melbourne Neuropsychiatry Centre & Department of Psychiatry, The University of Melbourne, Melbourne, Victoria, Australia
| | - Samantha M Loi
- Neuropsychiatry, The Royal Melbourne Hospital, Melbourne, Victoria, Australia
- Melbourne Neuropsychiatry Centre & Department of Psychiatry, The University of Melbourne, Melbourne, Victoria, Australia
| | - Sarah Farrand
- Neuropsychiatry, The Royal Melbourne Hospital, Melbourne, Victoria, Australia
- Melbourne Neuropsychiatry Centre & Department of Psychiatry, The University of Melbourne, Melbourne, Victoria, Australia
| | - Qiao-Xin Li
- National Dementia and Diagnostics Laboratory, Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Melbourne, Victoria, Australia
| | - Charles B Malpas
- Clinical Outcomes Research Unit (CORe), Department of Medicine, The Royal Melbourne Hospital, The University of Melbourne, Melbourne, Victoria, Australia
- Department of Neurology, The Royal Melbourne Hospital, Melbourne, Victoria, Australia
| | - Shiji Varghese
- National Dementia and Diagnostics Laboratory, Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Melbourne, Victoria, Australia
| | - Mark Walterfang
- Neuropsychiatry, The Royal Melbourne Hospital, Melbourne, Victoria, Australia
- Melbourne Neuropsychiatry Centre & Department of Psychiatry, The University of Melbourne, Melbourne, Victoria, Australia
| | - Andrew H Evans
- Department of Neurology, The Royal Melbourne Hospital, Melbourne, Victoria, Australia
| | - Shaun Parker
- Neuropsychiatry, The Royal Melbourne Hospital, Melbourne, Victoria, Australia
| | - Steven J Collins
- National Dementia and Diagnostics Laboratory, Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Melbourne, Victoria, Australia
- Department of Medicine, The Royal Melbourne Hospital, The University of Melbourne, Melbourne, Victoria, Australia
| | - Colin L Masters
- National Dementia and Diagnostics Laboratory, Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Melbourne, Victoria, Australia
| | - Dennis Velakoulis
- Neuropsychiatry, The Royal Melbourne Hospital, Melbourne, Victoria, Australia
- Melbourne Neuropsychiatry Centre & Department of Psychiatry, The University of Melbourne, Melbourne, Victoria, Australia
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23
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Wang J, Chen M, Masters CL, Wang YJ. Translating blood biomarkers into clinical practice for Alzheimer's disease: Challenges and perspectives. Alzheimers Dement 2023; 19:4226-4236. [PMID: 37218404 DOI: 10.1002/alz.13116] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Revised: 03/23/2023] [Accepted: 04/04/2023] [Indexed: 05/24/2023]
Abstract
Early and accurate diagnosis of Alzheimer's disease (AD) in clinical practice is urgent with advances in AD treatment. Blood biomarker assays are preferential diagnostic tools for widespread clinical use with the advantages of being less invasive, cost effective, and easily accessible, and they have shown good performance in research cohorts. However, in community-based populations with maximum heterogeneity, great challenges are still faced in diagnosing AD based on blood biomarkers in terms of accuracy and robustness. Here, we analyze these challenges, including the confounding impact of systemic and biological factors, small changes in blood biomarkers, and difficulty in detecting early changes. Furthermore, we provide perspectives on several potential strategies to overcome these challenges for blood biomarkers to bridge the gap from research to clinical practice.
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Affiliation(s)
- Jun Wang
- Department of Neurology and Centre for Clinical Neuroscience, Daping Hospital, Third Military Medical University, Chongqing, China
- Chongqing Key Laboratory of Ageing and Brain Diseases, Chongqing, China
| | - Ming Chen
- Department of Clinical Laboratory Medicine, Southwest Hospital, Third Military Medical University, Chongqing, China
| | - Colin L Masters
- The Florey Institute, The University of Melbourne, Parkville, Victoria, Australia
| | - Yan-Jiang Wang
- Department of Neurology and Centre for Clinical Neuroscience, Daping Hospital, Third Military Medical University, Chongqing, China
- Chongqing Key Laboratory of Ageing and Brain Diseases, Chongqing, China
- State Key Laboratory of Trauma, Burns, and Combined Injury, Third Military Medical University, Chongqing, China
- Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China
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24
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Su H, Masters CL, Bush AI, Barnham KJ, Reid GE, Vella LJ. Exploring the significance of lipids in Alzheimer's disease and the potential of extracellular vesicles. Proteomics 2023:e2300063. [PMID: 37654087 DOI: 10.1002/pmic.202300063] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Revised: 08/07/2023] [Accepted: 08/14/2023] [Indexed: 09/02/2023]
Abstract
Lipids play a significant role in maintaining central nervous system (CNS) structure and function, and the dysregulation of lipid metabolism is known to occur in many neurological disorders, including Alzheimer's disease. Here we review what is currently known about lipid dyshomeostasis in Alzheimer's disease. We propose that small extracellular vesicle (sEV) lipids may provide insight into the pathophysiology and progression of Alzheimer's disease. This stems from the recognition that sEV likely contributes to disease pathogenesis, but also an understanding that sEV can serve as a source of potential biomarkers. While the protein and RNA content of sEV in the CNS diseases have been studied extensively, our understanding of the lipidome of sEV in the CNS is still in its infancy.
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Affiliation(s)
- Huaqi Su
- The Florey, The University of Melbourne, Parkville, Victoria, Australia
- School of Chemistry, Bio21 Molecular Science and Biotechnology Institute, The University of Melbourne, Parkville, Victoria, Australia
| | - Colin L Masters
- The Florey, The University of Melbourne, Parkville, Victoria, Australia
| | - Ashley I Bush
- The Florey, The University of Melbourne, Parkville, Victoria, Australia
| | - Kevin J Barnham
- The Florey, The University of Melbourne, Parkville, Victoria, Australia
| | - Gavin E Reid
- School of Chemistry, Bio21 Molecular Science and Biotechnology Institute, The University of Melbourne, Parkville, Victoria, Australia
- Department of Biochemistry and Pharmacology, The University of Melbourne, Parkville, Victoria, Australia
| | - Laura J Vella
- The Florey, The University of Melbourne, Parkville, Victoria, Australia
- Department of Surgery, The Royal Melbourne Hospital, The University of Melbourne, Parkville, Victoria, Australia
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25
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McKay NS, Gordon BA, Hornbeck RC, Dincer A, Flores S, Keefe SJ, Joseph-Mathurin N, Jack CR, Koeppe R, Millar PR, Ances BM, Chen CD, Daniels A, Hobbs DA, Jackson K, Koudelis D, Massoumzadeh P, McCullough A, Nickels ML, Rahmani F, Swisher L, Wang Q, Allegri RF, Berman SB, Brickman AM, Brooks WS, Cash DM, Chhatwal JP, Day GS, Farlow MR, la Fougère C, Fox NC, Fulham M, Ghetti B, Graff-Radford N, Ikeuchi T, Klunk W, Lee JH, Levin J, Martins R, Masters CL, McConathy J, Mori H, Noble JM, Reischl G, Rowe C, Salloway S, Sanchez-Valle R, Schofield PR, Shimada H, Shoji M, Su Y, Suzuki K, Vöglein J, Yakushev I, Cruchaga C, Hassenstab J, Karch C, McDade E, Perrin RJ, Xiong C, Morris JC, Bateman RJ, Benzinger TLS. Positron emission tomography and magnetic resonance imaging methods and datasets within the Dominantly Inherited Alzheimer Network (DIAN). Nat Neurosci 2023; 26:1449-1460. [PMID: 37429916 PMCID: PMC10400428 DOI: 10.1038/s41593-023-01359-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Accepted: 05/15/2023] [Indexed: 07/12/2023]
Abstract
The Dominantly Inherited Alzheimer Network (DIAN) is an international collaboration studying autosomal dominant Alzheimer disease (ADAD). ADAD arises from mutations occurring in three genes. Offspring from ADAD families have a 50% chance of inheriting their familial mutation, so non-carrier siblings can be recruited for comparisons in case-control studies. The age of onset in ADAD is highly predictable within families, allowing researchers to estimate an individual's point in the disease trajectory. These characteristics allow candidate AD biomarker measurements to be reliably mapped during the preclinical phase. Although ADAD represents a small proportion of AD cases, understanding neuroimaging-based changes that occur during the preclinical period may provide insight into early disease stages of 'sporadic' AD also. Additionally, this study provides rich data for research in healthy aging through inclusion of the non-carrier controls. Here we introduce the neuroimaging dataset collected and describe how this resource can be used by a range of researchers.
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Affiliation(s)
| | | | | | - Aylin Dincer
- Washington University in St. Louis, St. Louis, MO, USA
| | - Shaney Flores
- Washington University in St. Louis, St. Louis, MO, USA
| | - Sarah J Keefe
- Washington University in St. Louis, St. Louis, MO, USA
| | | | | | | | | | - Beau M Ances
- Washington University in St. Louis, St. Louis, MO, USA
| | | | | | - Diana A Hobbs
- Washington University in St. Louis, St. Louis, MO, USA
| | | | | | | | | | | | | | - Laura Swisher
- Washington University in St. Louis, St. Louis, MO, USA
| | - Qing Wang
- Washington University in St. Louis, St. Louis, MO, USA
| | | | | | - Adam M Brickman
- Columbia University Irving Medical Center, New York, NY, USA
| | - William S Brooks
- Neuroscience Research Australia, Sydney, New South Wales, Australia
| | - David M Cash
- UK Dementia Research Institute at University College London, London, UK
- University College London, London, UK
| | - Jasmeer P Chhatwal
- Massachusetts General and Brigham & Women's Hospitals, Harvard Medical School, Boston, MA, USA
| | | | | | - Christian la Fougère
- Department of Radiology, University of Tübingen, Tübingen, Germany
- German Center for Neurodegenerative Diseases (DZNE), Tübingen, Germany
| | - Nick C Fox
- UK Dementia Research Institute at University College London, London, UK
- University College London, London, UK
| | - Michael Fulham
- Royal Prince Alfred Hospital, Sydney, New South Wales, Australia
| | | | | | | | | | | | - Johannes Levin
- German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
| | - Ralph Martins
- Edith Cowan University, Joondalup, Western Australia, Australia
| | | | | | | | - James M Noble
- Columbia University Irving Medical Center, New York, NY, USA
| | - Gerald Reischl
- Department of Radiology, University of Tübingen, Tübingen, Germany
| | | | | | - Raquel Sanchez-Valle
- Alzheimer's Disease and Other Cognitive Disorders Unit, Neurology Service, Hospital Clínic de Barcelona, IDIBAPS, University of Barcelona, Barcelona, Spain
| | - Peter R Schofield
- Neuroscience Research Australia, Sydney, New South Wales, Australia
- School of Biomedical Sciences, University of New South Wales, Sydney, New South Wales, Australia
| | | | | | - Yi Su
- Banner Alzheimer's Institute, Phoenix, AZ, USA
| | | | - Jonathan Vöglein
- German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
- Department of Neurology, Ludwig-Maximilians-Universität München, München, Germany
| | - Igor Yakushev
- School of Medicine, Technical University of Munich, Munich, Germany
| | | | | | - Celeste Karch
- Washington University in St. Louis, St. Louis, MO, USA
| | - Eric McDade
- Washington University in St. Louis, St. Louis, MO, USA
| | | | | | - John C Morris
- Washington University in St. Louis, St. Louis, MO, USA
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Chen CD, McCullough A, Gordon B, Joseph-Mathurin N, Flores S, McKay NS, Hobbs DA, Hornbeck R, Fagan AM, Cruchaga C, Goate AM, Perrin RJ, Wang G, Li Y, Shi X, Xiong C, Pontecorvo MJ, Klein G, Su Y, Klunk WE, Jack C, Koeppe R, Snider BJ, Berman SB, Roberson ED, Brosch J, Surti G, Jiménez-Velázquez IZ, Galasko D, Honig LS, Brooks WS, Clarnette R, Wallon D, Dubois B, Pariente J, Pasquier F, Sanchez-Valle R, Shcherbinin S, Higgins I, Tunali I, Masters CL, van Dyck CH, Masellis M, Hsiung R, Gauthier S, Salloway S, Clifford DB, Mills S, Supnet-Bell C, McDade E, Bateman RJ, Benzinger TLS. Longitudinal head-to-head comparison of 11C-PiB and 18F-florbetapir PET in a Phase 2/3 clinical trial of anti-amyloid-β monoclonal antibodies in dominantly inherited Alzheimer's disease. Eur J Nucl Med Mol Imaging 2023; 50:2669-2682. [PMID: 37017737 PMCID: PMC10330155 DOI: 10.1007/s00259-023-06209-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Accepted: 03/18/2023] [Indexed: 04/06/2023]
Abstract
PURPOSE Pittsburgh Compound-B (11C-PiB) and 18F-florbetapir are amyloid-β (Aβ) positron emission tomography (PET) radiotracers that have been used as endpoints in Alzheimer's disease (AD) clinical trials to evaluate the efficacy of anti-Aβ monoclonal antibodies. However, comparing drug effects between and within trials may become complicated if different Aβ radiotracers were used. To study the consequences of using different Aβ radiotracers to measure Aβ clearance, we performed a head-to-head comparison of 11C-PiB and 18F-florbetapir in a Phase 2/3 clinical trial of anti-Aβ monoclonal antibodies. METHODS Sixty-six mutation-positive participants enrolled in the gantenerumab and placebo arms of the first Dominantly Inherited Alzheimer Network Trials Unit clinical trial (DIAN-TU-001) underwent both 11C-PiB and 18F-florbetapir PET imaging at baseline and during at least one follow-up visit. For each PET scan, regional standardized uptake value ratios (SUVRs), regional Centiloids, a global cortical SUVR, and a global cortical Centiloid value were calculated. Longitudinal changes in SUVRs and Centiloids were estimated using linear mixed models. Differences in longitudinal change between PET radiotracers and between drug arms were estimated using paired and Welch two sample t-tests, respectively. Simulated clinical trials were conducted to evaluate the consequences of some research sites using 11C-PiB while other sites use 18F-florbetapir for Aβ PET imaging. RESULTS In the placebo arm, the absolute rate of longitudinal change measured by global cortical 11C-PiB SUVRs did not differ from that of global cortical 18F-florbetapir SUVRs. In the gantenerumab arm, global cortical 11C-PiB SUVRs decreased more rapidly than global cortical 18F-florbetapir SUVRs. Drug effects were statistically significant across both Aβ radiotracers. In contrast, the rates of longitudinal change measured in global cortical Centiloids did not differ between Aβ radiotracers in either the placebo or gantenerumab arms, and drug effects remained statistically significant. Regional analyses largely recapitulated these global cortical analyses. Across simulated clinical trials, type I error was higher in trials where both Aβ radiotracers were used versus trials where only one Aβ radiotracer was used. Power was lower in trials where 18F-florbetapir was primarily used versus trials where 11C-PiB was primarily used. CONCLUSION Gantenerumab treatment induces longitudinal changes in Aβ PET, and the absolute rates of these longitudinal changes differ significantly between Aβ radiotracers. These differences were not seen in the placebo arm, suggesting that Aβ-clearing treatments may pose unique challenges when attempting to compare longitudinal results across different Aβ radiotracers. Our results suggest converting Aβ PET SUVR measurements to Centiloids (both globally and regionally) can harmonize these differences without losing sensitivity to drug effects. Nonetheless, until consensus is achieved on how to harmonize drug effects across radiotracers, and since using multiple radiotracers in the same trial may increase type I error, multisite studies should consider potential variability due to different radiotracers when interpreting Aβ PET biomarker data and, if feasible, use a single radiotracer for the best results. TRIAL REGISTRATION ClinicalTrials.gov NCT01760005. Registered 31 December 2012. Retrospectively registered.
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Affiliation(s)
- Charles D Chen
- Mallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, MO, USA
- Washington University School of Medicine, 660 South Euclid, Campus Box 8225, St. Louis, MO, 63110, USA
| | - Austin McCullough
- Mallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, MO, USA
| | - Brian Gordon
- Mallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, MO, USA
| | - Nelly Joseph-Mathurin
- Mallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, MO, USA
| | - Shaney Flores
- Mallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, MO, USA
| | - Nicole S McKay
- Mallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, MO, USA
| | - Diana A Hobbs
- Mallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, MO, USA
| | - Russ Hornbeck
- Mallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, MO, USA
| | - Anne M Fagan
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA
| | - Carlos Cruchaga
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO, USA
| | - Alison M Goate
- Department of Genetics and Genomic Sciences, Ichan School of Medicine at Mount Sinai, New York, NY, USA
| | - Richard J Perrin
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA
- Department of Pathology and Immunology, Washington University in St. Louis, St. Louis, MO, USA
| | - Guoqiao Wang
- Department of Biostatistics, Washington University in St. Louis, St. Louis, MO, USA
| | - Yan Li
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA
| | - Xinyu Shi
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA
| | - Chengjie Xiong
- Department of Biostatistics, Washington University in St. Louis, St. Louis, MO, USA
| | - Michael J Pontecorvo
- Avid Radiopharmaceuticals, Philadelphia, PA, USA
- Eli Lilly and Company, Indianapolis, IN, USA
| | | | - Yi Su
- Banner Alzheimer's Institute, Banner Health, Phoenix, AZ, USA
- Arizona Alzheimer's Consortium, Phoenix, AZ, USA
| | - William E Klunk
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
| | - Clifford Jack
- Department of Radiology, Mayo Clinic, Rochester, MN, USA
| | - Robert Koeppe
- Department of Radiology, University of Michigan, Ann Arbor, MI, USA
| | - B Joy Snider
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA
| | - Sarah B Berman
- Department of Neurology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Erik D Roberson
- Department of Neurology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Jared Brosch
- Department of Neurology, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Ghulam Surti
- Department of Psychiatry and Human Behavior, Warren Alpert Medical School of Brown University, Providence, RI, USA
| | | | - Douglas Galasko
- Department of Neurology, University of California San Diego, San Diego, CA, USA
| | | | - William S Brooks
- Prince of Wales Medical Research Institute, University of New South Wales, Sydney, NSW, Australia
| | - Roger Clarnette
- Department of Internal Medicine, University of Western Australia, Crawley, WA, Australia
| | - David Wallon
- Department of Neurology and CNR-MAJ, Normandie Univ, UNIROUEN, INSERM U1245, CHU Rouen, F-76000, Rouen, France
| | - Bruno Dubois
- Sorbonne Université, AP-HP, GRC No. 21, APM, Hôpital de La Pitié-Salpêtrière, Paris, France
- Institut du Cerveau Et de La Moelle Épinière, INSERM U1127, CNRS UMR 7225, Paris, France
- Institut de La Mémoire Et de La Maladie d'Alzheimer, Département de Neurologie, Hôpital de La Pitié-Salpêtrière, Paris, France
| | - Jérémie Pariente
- Department of Neurology, Hôpital Pierre-Paul Riquet, Centre Hospitalier Universitaire de Toulouse, Toulouse, France
- Toulouse NeuroImaging Centre, Université de Toulouse, INSERM, UPS, Toulouse, France
| | - Florence Pasquier
- Univ. Lille, INSERM, CHU Lille, 59000, Lille, France
- CNR-MAJ, Labex DISTALZ, LiCEND, 59000, Lille, France
| | - Raquel Sanchez-Valle
- Alzheimer's Disease and Other Cognitive Disorders Unit, Hospital ClínicInstitut d'Investigacions Biomèdiques August Pi I Sunyer (IDIBAPS), Fundació Clínic Per a La Recerca Biomèdica, University of Barcelona, Barcelona, Spain
| | | | | | - Ilke Tunali
- Eli Lilly and Company, Indianapolis, IN, USA
| | - Colin L Masters
- The Florey Institute of Neuroscience and Mental Health, Melbourne, VIC, Australia
| | | | | | - Robin Hsiung
- Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, BC, Canada
| | - Serge Gauthier
- Douglas Mental Health University Institute, Montreal, QC, Canada
| | - Steve Salloway
- Alpert Medical School of Brown University, Providence, RI, USA
| | - David B Clifford
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA
| | - Susan Mills
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA
| | | | - Eric McDade
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA
| | - Randall J Bateman
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA
| | - Tammie L S Benzinger
- Mallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, MO, USA.
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Chatterjee P, Vermunt L, Gordon BA, Pedrini S, Boonkamp L, Armstrong NJ, Xiong C, Singh AK, Li Y, Sohrabi HR, Taddei K, Molloy MP, Benzinger TL, Morris JC, Karch CM, Berman SB, Chhatwal J, Cruchaga C, Graff-Radford NR, Day GS, Farlow M, Fox NC, Goate AM, Hassenstab J, Lee JH, Levin J, McDade E, Mori H, Perrin RJ, Sanchez-Valle R, Schofield PR, Levey A, Jucker M, Masters CL, Fagan AM, Bateman RJ, Martins RN, Teunissen CE. Plasma glial fibrillary acidic protein in autosomal dominant Alzheimer's disease: Associations with Aβ-PET, neurodegeneration, and cognition. Alzheimers Dement 2023; 19:2790-2804. [PMID: 36576155 PMCID: PMC10300233 DOI: 10.1002/alz.12879] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Revised: 09/22/2022] [Accepted: 10/21/2022] [Indexed: 12/29/2022]
Abstract
BACKGROUND Glial fibrillary acidic protein (GFAP) is a promising candidate blood-based biomarker for Alzheimer's disease (AD) diagnosis and prognostication. The timing of its disease-associated changes, its clinical correlates, and biofluid-type dependency will influence its clinical utility. METHODS We evaluated plasma, serum, and cerebrospinal fluid (CSF) GFAP in families with autosomal dominant AD (ADAD), leveraging the predictable age at symptom onset to determine changes by stage of disease. RESULTS Plasma GFAP elevations appear a decade before expected symptom onset, after amyloid beta (Aβ) accumulation and prior to neurodegeneration and cognitive decline. Plasma GFAP distinguished Aβ-positive from Aβ-negative ADAD participants and showed a stronger relationship with Aβ load in asymptomatic than symptomatic ADAD. Higher plasma GFAP was associated with the degree and rate of neurodegeneration and cognitive impairment. Serum GFAP showed similar relationships, but these were less pronounced for CSF GFAP. CONCLUSION Our findings support a role for plasma GFAP as a clinical biomarker of Aβ-related astrocyte reactivity that is associated with cognitive decline and neurodegeneration. HIGHLIGHTS Plasma glial fibrillary acidic protein (GFAP) elevations appear a decade before expected symptom onset in autosomal dominant Alzheimer's disease (ADAD). Plasma GFAP was associated to amyloid positivity in asymptomatic ADAD. Plasma GFAP increased with clinical severity and predicted disease progression. Plasma and serum GFAP carried similar information in ADAD, while cerebrospinal fluid GFAP did not.
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Affiliation(s)
- Pratishtha Chatterjee
- Macquarie Medical School, Macquarie University, North Ryde, NSW 2019, Australia; School of Medical Sciences, Edith Cowan University, Sarich Neuroscience Research Institute, Nedlands, WA 6009, Australia
| | - Lisa Vermunt
- Neurochemistry Laboratory, Department of Clinical Chemistry, Amsterdam Neuroscience, programme Neurodegeneration, Amsterdam University Medical Centers, Vrije Universiteit, Amsterdam, The Netherlands
| | - Brian A. Gordon
- Department of Radiology, Washington University School of Medicine, Saint Louis, MO, USA
| | - Steve Pedrini
- School of Medical Sciences, Edith Cowan University, Sarich Neuroscience Research Institute, Nedlands, WA 6009, Australia
| | - Lynn Boonkamp
- Neurochemistry Laboratory, Department of Clinical Chemistry, Amsterdam Neuroscience, programme Neurodegeneration, Amsterdam University Medical Centers, Vrije Universiteit, Amsterdam, The Netherlands
| | - Nicola J. Armstrong
- Department of Mathematics & Statistics, Curtin University, Bentley, WA, Australia
| | - Chengjie Xiong
- Knight Alzheimer’s Disease Research Center, Washington University School of Medicine, St. Louis, MO, USA; Hope Center for Neurological Disorders, Washington University School of Medicine, St. Louis, MO, USA; Division of Biostatistics, Washington University School of Medicine, Saint Louis, MO, USA
| | - Abhay K. Singh
- Macquarie Business School, Macquarie University, North Ryde, NSW, Australia
| | - Yan Li
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA; Division of Biostatistics, Washington University School of Medicine, Saint Louis, MO, USA
| | - Hamid R. Sohrabi
- Department of Biomedical Sciences, Macquarie University, North Ryde, NSW 2019, Australia; School of Medical Sciences, Edith Cowan University, Sarich Neuroscience Research Institute, Nedlands, WA 6009, Australia; School of Psychiatry and Clinical Neurosciences, University of Western Australia, Crawley, WA, Australia; Australian Alzheimer’s Research Foundation, Nedlands, WA, Australia; Centre for Healthy Ageing, Health Future Institute, Murdoch University, Murdoch, WA, Australia
| | - Kevin Taddei
- School of Medical Sciences, Edith Cowan University, Sarich Neuroscience Research Institute, Nedlands, WA 6009, Australia; Australian Alzheimer’s Research Foundation, Nedlands, WA, Australia
| | - Mark P. Molloy
- Bowel Cancer and Biomarker Laboratory, Kolling Institute, The University of Sydney, St Leonards, NSW, Australia; Australian Proteome Analysis Facility, Macquarie University, North Ryde, NSW, Australia
| | - Tammie L.S. Benzinger
- Department of Radiology, Washington University School of Medicine, Saint Louis, MO, USA
| | - John C. Morris
- Knight Alzheimer’s Disease Research Center, Washington University School of Medicine, St. Louis, MO, USA; Hope Center for Neurological Disorders, Washington University School of Medicine, St. Louis, MO, USA; Department of Neurology, Washington University School of Medicine, Saint Louis, MO, USA
| | - Celeste M. Karch
- Department of Psychiatry, Washington University School of Medicine, Saint Louis, MO, USA
| | - Sarah B. Berman
- University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Jasmeer Chhatwal
- Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Carlos Cruchaga
- Knight Alzheimer’s Disease Research Center, Washington University School of Medicine, St. Louis, MO, USA; Hope Center for Neurological Disorders, Washington University School of Medicine, St. Louis, MO, USA; Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
| | | | - Gregory S Day
- Department of Neurology, Mayo Clinic Jacksonville, Jacksonville, FL, USA
| | - Martin Farlow
- Department of Neurology, Indiana University, Indianapolis, IN, USA
| | - Nick C. Fox
- Dementia Research Centre, Queen Square Institute of Neurology, University College London, London, UK
| | - Alison M. Goate
- Department of Genetics & Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Jason Hassenstab
- Department of Neurology, Washington University School of Medicine, Saint Louis, MO, USA
| | - Jae-Hong Lee
- Department of Neurology, University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-ro 43-gil, Songpa-gu, Seoul05505, Republic of Korea
| | - Johannes Levin
- German Center for Neurodegenerative Diseases (DZNE), Munich, Germany; Department of Neurology, Ludwig-Maximilians-Universität München, Munich, Germany; Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
| | - Eric McDade
- Department of Neurology, Washington University School of Medicine, Saint Louis, MO, USA
| | - Hiroshi Mori
- Osaka Metropolitan University, Nagaoka Sutoku University, Osaka, Japan
| | - Richard J. Perrin
- Department of Pathology and Immunology, Washington University School of Medicine, Saint Louis, MO, USA; Dominantly Inherited Alzheimer Network, Washington University School of Medicine, St. Louis, MO, USA; Knight Alzheimer’s Disease Research Center, Washington University School of Medicine, St. Louis, MO, USA; Hope Center for Neurological Disorders, Washington University School of Medicine, St. Louis, MO, USA; Department of Neurology, Washington University School of Medicine, Saint Louis, MO, USA
| | - Raquel Sanchez-Valle
- Alzheimer’s Disease and other Cognitive Disorders Unit, Neurology Service, Hospital Clinic, Barcelona, Spain
| | - Peter R. Schofield
- Neuroscience Research Australia, Sydney, New South Wales, Australia; School of Medical Sciences, University of New South Wales, Sydney, New South Wales, Australia
| | - Allan Levey
- Department of Neurology, Emory University, Atlanta, GA, USA
| | - Mathias Jucker
- German Center for Neurodegenerative Diseases (DZNE), Tübingen, Germany. Department of Cellular Neurology, Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany
| | - Colin L. Masters
- The Florey Institute of Neuroscience and Mental Health, Melbourne, Victoria, Australia; University of Melbourne, Melbourne, Victoria, Australia
| | - Anne M. Fagan
- Knight Alzheimer’s Disease Research Center, Washington University School of Medicine, St. Louis, MO, USA; Hope Center for Neurological Disorders, Washington University School of Medicine, St. Louis, MO, USA; Department of Neurology, Washington University School of Medicine, Saint Louis, MO, USA
| | - Randall J. Bateman
- Dominantly Inherited Alzheimer Network, Washington University School of Medicine, St. Louis, MO, USA; Department of Neurology, Washington University School of Medicine, Saint Louis, MO, USA
| | - Ralph N. Martins
- Macquarie Medical School, Macquarie University, North Ryde, NSW 2019, Australia; School of Medical Sciences, Edith Cowan University, Sarich Neuroscience Research Institute, Nedlands, WA 6009, Australia; The Cooperative Research Centre for Mental Health, Carlton South, Australia; KaRa Institute of Neurological Disease, Sydney, Macquarie Park, Australia; Australian Alzheimer’s Research Foundation, Nedlands, WA, Australia
| | - Charlotte E. Teunissen
- Neurochemistry Laboratory, Department of Clinical Chemistry, Amsterdam Neuroscience, programme Neurodegeneration, Amsterdam University Medical Centers, Vrije Universiteit, Amsterdam, The Netherlands
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Bourgeat P, Doré V, Rowe CC, Benzinger T, Tosun D, Goyal MS, LaMontagne P, Jin L, Weiner MW, Masters CL, Fripp J, Villemagne VL. A universal neocortical mask for Centiloid quantification. Alzheimers Dement (Amst) 2023; 15:e12457. [PMID: 37492802 PMCID: PMC10363815 DOI: 10.1002/dad2.12457] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/12/2023] [Revised: 05/22/2023] [Accepted: 06/21/2023] [Indexed: 07/27/2023]
Abstract
INTRODUCTION The Centiloid (CL) project was developed to harmonize the quantification of amyloid beta (Aβ) positron emission tomography (PET) scans to a unified scale. The CL neocortical mask was defined using 11C Pittsburgh compound B (PiB), overlooking potential differences in regional distribution among Aβ tracers. We created a universal mask using an independent dataset of five Aβ tracers, and investigated its impact on inter-tracer agreement, tracer variability, and group separation. METHODS Using data from the Alzheimer's Dementia Onset and Progression in International Cohorts (ADOPIC) study (Australian Imaging Biomarkers and Lifestyle + Alzheimer's Disease Neuroimaging Initiative + Open Access Series of Imaging Studies), age-matched pairs of mild Alzheimer's disease (AD) and healthy controls (HC) were selected: 18F-florbetapir (N = 147 pairs), 18F-florbetaben (N = 22), 18F-flutemetamol (N = 10), 18F-NAV (N = 42), 11C-PiB (N = 63). The images were spatially and standardized uptake value ratio normalized. For each tracer, the mean AD-HC difference image was thresholded to maximize the overlap with the standard neocortical mask. The universal mask was defined as the intersection of all five masks. It was evaluated on the Global Alzheimer's Association Interactive Network (GAAIN) head-to-head datasets in terms of inter-tracer agreement and variance in the young controls (YC) and on the ADOPIC dataset comparing separation between HC/AD and HC/mild cognitive impairment (MCI). RESULTS In the GAAIN dataset, the universal mask led to a small reduction in the variance of the YC, and a small increase in the inter-tracer agreement. In the ADOPIC dataset, it led to a better separation between HC/AD and HC/MCI at baseline. DISCUSSION The universal CL mask led to an increase in inter-tracer agreement and group separation. Those increases were, however, very small, and do not provide sufficient benefits to support departing from the existing standard CL mask, which is suitable for the quantification of all Aβ tracers. HIGHLIGHTS This study built an amyloid universal mask using a matched cohort for the five most commonly used amyloid positron emission tomography tracers.There was a high overlap between each tracer-specific mask.Differences in quantification and group separation between the standard and universal mask were small.The existing standard Centiloid mask is suitable for the quantification of all amyloid beta tracers.
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Affiliation(s)
- Pierrick Bourgeat
- Australian eHealth Research CentreCSIRO Health and BiosecurityBrisbaneQueenslandAustralia
| | - Vincent Doré
- Australian eHealth Research CentreCSIRO Health and BiosecurityBrisbaneQueenslandAustralia
- Department of Molecular Imaging & TherapyAustin HealthMelbourneVictoriaAustralia
| | - Christopher C. Rowe
- Department of Molecular Imaging & TherapyAustin HealthMelbourneVictoriaAustralia
- The Florey Institute of Neuroscience and Mental HealthUniversity of Melbourne, ParkvilleMelbourneVictoriaAustralia
| | - Tammie Benzinger
- Mallinckrodt Institute of RadiologyWashington University School of MedicineSt. LouisMissouriUSA
| | - Duygu Tosun
- San Francisco Veterans Affairs Medical CenterSan FranciscoCaliforniaUSA
- Department of Radiology and Biomedical ImagingUniversity of California, San FranciscoSan FranciscoCaliforniaUSA
| | - Manu S. Goyal
- Mallinckrodt Institute of RadiologyWashington University School of MedicineSt. LouisMissouriUSA
- Department of NeurologyWashington University School of MedicineSt. LouisMissouriUSA
| | - Pamela LaMontagne
- Mallinckrodt Institute of RadiologyWashington University School of MedicineSt. LouisMissouriUSA
| | - Liang Jin
- The Florey Institute of Neuroscience and Mental HealthUniversity of Melbourne, ParkvilleMelbourneVictoriaAustralia
| | - Michael W. Weiner
- San Francisco Veterans Affairs Medical CenterSan FranciscoCaliforniaUSA
- Department of Radiology and Biomedical ImagingUniversity of California, San FranciscoSan FranciscoCaliforniaUSA
| | - Colin L. Masters
- The Florey Institute of Neuroscience and Mental HealthUniversity of Melbourne, ParkvilleMelbourneVictoriaAustralia
| | - Jurgen Fripp
- Australian eHealth Research CentreCSIRO Health and BiosecurityBrisbaneQueenslandAustralia
| | - Victor L. Villemagne
- Department of Molecular Imaging & TherapyAustin HealthMelbourneVictoriaAustralia
- Department of PsychiatryThe University of PittsburghPittsburghPennsylvaniaUSA
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Stehmann C, Senesi M, Sarros S, McGlade A, Lewis V, Ellett L, Barber D, Simpson M, Klug G, McLean CA, Masters CL, Collins SJ. Creutzfeldt-Jakob disease surveillance in Australia: update to 31 December 2022. Commun Dis Intell (2018) 2023; 47. [PMID: 37357180 DOI: 10.33321/cdi.2023.47.37] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 06/27/2023]
Abstract
Creutzfeldt-Jakob disease surveillance in Australia: update to 31 December 2022 Nationwide surveillance of Creutzfeldt-Jakob disease (CJD) and other human prion diseases is performed by the Australian National Creutzfeldt-Jakob Disease Registry (ANCJDR). National surveillance encompasses the period since 1 January 1970, with prospective surveillance occurring from 1 October 1993. Over this prospective surveillance period, considerable developments have occurred in pre-mortem diagnostics; in the delineation of new disease subtypes; and in a heightened awareness of prion diseases in healthcare settings. Surveillance practices of the ANCJDR have evolved and adapted accordingly. This report summarises the activities of the ANCJDR during 2022. Since the ANCJDR began offering diagnostic cerebrospinal fluid (CSF) 14-3-3 protein testing in Australia in September 1997, the annual number of referrals has steadily increased. In 2022, a total of 599 domestic CSF specimens were referred for diagnostic testing and 79 persons with suspected human prion disease were formally added to the national register. As of 31 December 2022, just under half of the 79 suspect case notifications (36/79) remain classified as 'incomplete'; 15 cases were classified as 'definite' and 23 as 'probable' prion disease; five cases were excluded through neuropathological examination. For 2022, fifty-five percent of all suspected human-prion-disease-related deaths in Australia underwent neuropathological examination. No cases of variant or iatrogenic CJD were identified. The SARS-CoV-2 pandemic did not affect prion disease surveillance outcomes in Australia during 2022.
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Affiliation(s)
| | - Matteo Senesi
- Department of Medicine, The University of Melbourne, Victoria, 3010, Australia
| | - Shannon Sarros
- The Florey, The University of Melbourne, Victoria, 3010, Australia
| | - Amelia McGlade
- The Florey, The University of Melbourne, Victoria, 3010, Australia
| | - Victoria Lewis
- Department of Medicine, The University of Melbourne, Victoria, 3010, Australia
| | - Laura Ellett
- The Florey, The University of Melbourne, Victoria, 3010, Australia
| | - Daniel Barber
- The Florey, The University of Melbourne, Victoria, 3010, Australia
| | - Marion Simpson
- The Florey, The University of Melbourne, Victoria, 3010, Australia
| | - Genevieve Klug
- The Florey, The University of Melbourne, Victoria, 3010, Australia
| | - Catriona A McLean
- The Florey, The University of Melbourne, Victoria, 3010, Australia
- The Alfred Hospital, Department of Anatomical Pathology, 55 Commercial Rd, Melbourne Vic 3004 Australia
| | - Colin L Masters
- The Florey, The University of Melbourne, Victoria, 3010, Australia
| | - Steven J Collins
- The Florey, The University of Melbourne, Victoria, 3010, Australia
- Department of Medicine, The University of Melbourne, Victoria, 3010, Australia
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Horie K, Li Y, Barthélemy NR, Gordon BA, Hassenstab J, Benzinger TL, Fagan AM, Morris JC, Karch CM, Xiong C, Allegri R, Mendez PC, Ikeuchi T, Kasuga K, Noble J, Farlow M, Chhatwal J, Day GS, Schofield PR, Masters CL, Levin J, Jucker M, Lee JH, Hoon Roh J, Sato C, Sachdev P, Koyama A, Reyderman L, Bateman RJ, McDade E. Change in Cerebrospinal Fluid Tau Microtubule Binding Region Detects Symptom Onset, Cognitive Decline, Tangles, and Atrophy in Dominantly Inherited Alzheimer's Disease. Ann Neurol 2023; 93:1158-1172. [PMID: 36843330 PMCID: PMC10238659 DOI: 10.1002/ana.26620] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Revised: 02/14/2023] [Accepted: 02/16/2023] [Indexed: 02/28/2023]
Abstract
OBJECTIVE Identifying cerebrospinal fluid measures of the microtubule binding region of tau (MTBR-tau) species that reflect tau aggregation could provide fluid biomarkers that track Alzheimer's disease related neurofibrillary tau pathological changes. We examined the cerebrospinal fluid (CSF) MTBR-tau species in dominantly inherited Alzheimer's disease (DIAD) mutation carriers to assess the association with Alzheimer's disease (AD) biomarkers and clinical symptoms. METHODS Cross-sectional and longitudinal CSF from 229 DIAD mutation carriers and 130 mutation non-carriers had sequential characterization of N-terminal/mid-domain phosphorylated tau (p-tau) followed by MTBR-tau species and tau positron emission tomography (tau PET), other soluble tau and amyloid biomarkers, comprehensive clinical and cognitive assessments, and brain magnetic resonance imaging of atrophy. RESULTS CSF MTBR-tau species located within the putative "border" region and one species corresponding to the "core" region of aggregates in neurofibrillary tangles (NFTs) increased during the presymptomatic stage and decreased during the symptomatic stage. The "border" MTBR-tau species were associated with amyloid pathology and CSF p-tau; whereas the "core" MTBR-tau species were associated stronger with tau PET and CSF measures of neurodegeneration. The ratio of the border to the core species provided a continuous measure of increasing amounts that tracked clinical progression and NFTs. INTERPRETATION Changes in CSF soluble MTBR-tau species preceded the onset of dementia, tau tangle increase, and atrophy in DIAD. The ratio of 4R-specific MTBR-tau (border) to the NFT (core) MTBR-tau species corresponds to the pathology of NFTs in DIAD and change with disease progression. The dynamics between different MTBR-tau species in the CSF may serve as a marker of tau-related disease progression and target engagement of anti-tau therapeutics. ANN NEUROL 2023;93:1158-1172.
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Affiliation(s)
- Kanta Horie
- Department of Neurology, Washington University School of Medicine, Saint Louis, MO, 63110, USA
- Eisai Inc., Nutley, NJ, 07110, USA
- The Tracy Family SILQ Center, Washington University School of Medicine, Saint Louis, MO, 63110, USA
| | - Yan Li
- Department of Neurology, Washington University School of Medicine, Saint Louis, MO, 63110, USA
| | - Nicolas R. Barthélemy
- Department of Neurology, Washington University School of Medicine, Saint Louis, MO, 63110, USA
- The Tracy Family SILQ Center, Washington University School of Medicine, Saint Louis, MO, 63110, USA
| | - Brian A. Gordon
- Department of Radiology, Washington University School of Medicine, Saint Louis, MO, 63110, USA
| | - Jason Hassenstab
- Department of Neurology, Washington University School of Medicine, Saint Louis, MO, 63110, USA
| | - Tammie. L.S. Benzinger
- Department of Radiology, Washington University School of Medicine, Saint Louis, MO, 63110, USA
| | - Anne M. Fagan
- Department of Neurology, Washington University School of Medicine, Saint Louis, MO, 63110, USA
| | - John C. Morris
- Department of Neurology, Washington University School of Medicine, Saint Louis, MO, 63110, USA
| | - Celeste M. Karch
- Department of Psychiatry, Washington University School of Medicine, Saint Louis, MO, 63110, USA
| | - Chengjie Xiong
- Division of Biostatistics, Washington University School of Medicine, Saint Louis, MO, 63110, USA
| | - Ricardo Allegri
- Fundación para la Lucha contra las Enfermedades Neurológicas de la Infancia (FLENI) Instituto de Investigaciones Neurológicas Raúl Correa, Buenos Aires, Argentina
| | - Patricio Chrem Mendez
- Fundación para la Lucha contra las Enfermedades Neurológicas de la Infancia (FLENI) Instituto de Investigaciones Neurológicas Raúl Correa, Buenos Aires, Argentina
| | | | | | - James Noble
- Taub Institute for Research on Alzheimer’s Disease and the Aging Brain, G.H. Sergievsky Center, Department of Neurology, Columbia University Irving Medical Center, New York, NY 10032 USA
| | - Martin Farlow
- Department of Neurology, Indiana University, Indianapolis, IN 46202, USA
| | - Jasmeer Chhatwal
- Massachusetts General Hospital, Harvard Medical School Boston, MA 02114, USA
| | - Gregory S. Day
- Department of Neurology, Mayo Clinic in Florida, Jacksonville, FL 32224, USA
| | - Peter R. Schofield
- Neuroscience Research Australia, Sydney, 2031 NSW, Australia
- School of Biomedical Sciences, University of New South Wales, Sydney, 2052 NSW, Australia
| | - Colin L. Masters
- The Florey Institute and the University of Melbourne, Parkville, Victoria 3010, Australia
| | - Johannes Levin
- German Center for Neurodegenerative Diseases (DZNE) Munich, Marchioninistr 15, D-83177 Munchen, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
- Department of Neurology, Ludwig-Maximilians Universität München, Marchioninistr 15, 83177 Munich, Germany
| | - Mathias Jucker
- German Center for Neurodegenerative Diseases (DZNE) Tübingen; and Hertie-Institute for Clinical Brain Research, University of Tübingen, D-72076 Tübingen, Germany
| | - Jae-Hong Lee
- Department of Neurology, Asan Medical Center, Seoul 05505, Korea
| | - Jee Hoon Roh
- Departments of Biomedical Sciences, Physiology, and Neurology, Korea University College of Medicine, Seoul 02841, Korea
| | - Chihiro Sato
- Department of Neurology, Washington University School of Medicine, Saint Louis, MO, 63110, USA
- The Tracy Family SILQ Center, Washington University School of Medicine, Saint Louis, MO, 63110, USA
| | | | | | | | - Randall J. Bateman
- Department of Neurology, Washington University School of Medicine, Saint Louis, MO, 63110, USA
- The Tracy Family SILQ Center, Washington University School of Medicine, Saint Louis, MO, 63110, USA
| | - Eric McDade
- Department of Neurology, Washington University School of Medicine, Saint Louis, MO, 63110, USA
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Lim YY, Yassi N, Bransby L, Ayton S, Buckley RF, Eratne D, Velakoulis D, Li QX, Fowler C, Masters CL, Maruff P. CSF Aβ 42 and tau biomarkers in cognitively unimpaired Aβ- middle-aged and older APOE ε4 carriers. Neurobiol Aging 2023; 129:209-218. [PMID: 37399739 DOI: 10.1016/j.neurobiolaging.2023.05.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2023] [Revised: 05/09/2023] [Accepted: 05/10/2023] [Indexed: 07/05/2023]
Abstract
This study aimed to determine the relationship between the apolipoprotein E (APOE) ε4 allele and cerebrospinal fluid (CSF) and neuroimaging biomarkers of Alzheimer's disease, and cognition in cognitively unimpaired (CU) middle-aged adults (n = 82; Mage = 58.2), and in Aβ- CU older adults (n = 71; Mage = 71.8). Aβ- CU middle-aged ε4 carriers showed lower CSF Aβ42 levels, higher levels of CSF total tau (t-tau) and neurofilament light (NfL), and poorer cognitive performance compared to noncarriers (Cohen's d: 0.30-0.56). In Aβ- CU older adults, ε4 carriers also had lower CSF Aβ42 levels and higher levels of CSF t-tau and p-tau181, compared to noncarriers (Cohen's d: 0.65-0.74). In both Aβ- middle-aged and older adults, hippocampal and total brain volume were equivalent between ε4 carriers and noncarriers. In Aβ- CU middle-aged adults, APOE ε4 is associated with decreased levels of Aβ, increased tau and NfL, and poorer cognition. Similar relationships were observed in Aβ- CU older adults. These results have implications for understanding clinicopathological relationships between APOE ε4 and the emergence of cognitive and biomarker abnormalities in Aβ- adults.
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Affiliation(s)
- Yen Ying Lim
- Turner Institute of Brain and Mental Health, School of Psychological Sciences, Monash University, Clayton, Australia.
| | - Nawaf Yassi
- Population Health and Immunity Division, Walter and Eliza Hall Institute of Medical Research, Parkville, Australia; Department of Neurology, Melbourne Brain Centre at the Royal Melbourne Hospital, University of Melbourne, Parkville, Australia
| | - Lisa Bransby
- Turner Institute of Brain and Mental Health, School of Psychological Sciences, Monash University, Clayton, Australia
| | - Scott Ayton
- Florey Institute of Neuroscience and Mental Health, University of Melbourne, Parkville, Australia
| | - Rachel F Buckley
- Department of Neurology, Massachusetts General Hospital/Harvard Medical School, Boston, MA, USA
| | - Dhamidhu Eratne
- Melbourne Neuropsychiatry Centre, University of Melbourne, Parkville, Australia
| | - Dennis Velakoulis
- Melbourne Neuropsychiatry Centre, University of Melbourne, Parkville, Australia
| | - Qiao-Xin Li
- Florey Institute of Neuroscience and Mental Health, University of Melbourne, Parkville, Australia
| | - Christopher Fowler
- Florey Institute of Neuroscience and Mental Health, University of Melbourne, Parkville, Australia
| | - Colin L Masters
- Florey Institute of Neuroscience and Mental Health, University of Melbourne, Parkville, Australia
| | - Paul Maruff
- Turner Institute of Brain and Mental Health, School of Psychological Sciences, Monash University, Clayton, Australia; Florey Institute of Neuroscience and Mental Health, University of Melbourne, Parkville, Australia; Cogstate Ltd., Melbourne, Australia
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Huang X, Li Y, Fowler C, Doecke JD, Lim YY, Drysdale C, Zhang V, Park K, Trounson B, Pertile K, Rumble R, Pickering JW, Rissman RA, Sarsoza F, Abdel‐Latif S, Lin Y, Doré V, Villemagne V, Rowe CC, Fripp J, Martins R, Wiley JS, Maruff P, Mintzer JE, Masters CL, Gu BJ. Leukocyte surface biomarkers implicate deficits of innate immunity in sporadic Alzheimer's disease. Alzheimers Dement 2023; 19:2084-2094. [PMID: 36349985 PMCID: PMC10166765 DOI: 10.1002/alz.12813] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Revised: 08/24/2022] [Accepted: 09/02/2022] [Indexed: 11/10/2022]
Abstract
INTRODUCTION Blood-based diagnostics and prognostics in sporadic Alzheimer's disease (AD) are important for identifying at-risk individuals for therapeutic interventions. METHODS In three stages, a total of 34 leukocyte antigens were examined by flow cytometry immunophenotyping. Data were analyzed by logistic regression and receiver operating characteristic (ROC) analyses. RESULTS We identified leukocyte markers differentially expressed in the patients with AD. Pathway analysis revealed a complex network involving upregulation of complement inhibition and downregulation of cargo receptor activity and Aβ clearance. A proposed panel including four leukocyte markers - CD11c, CD59, CD91, and CD163 - predicts patients' PET Aβ status with an area under the curve (AUC) of 0.93 (0.88 to 0.97). CD163 was the top performer in preclinical models. These findings have been validated in two independent cohorts. CONCLUSION Our finding of changes on peripheral leukocyte surface antigens in AD implicates the deficit in innate immunity. Leukocyte-based biomarkers prove to be both sensitive and practical for AD screening and diagnosis.
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Affiliation(s)
- Xin Huang
- The Florey Institute of Neurosciencethe University of MelbourneParkvilleVictoriaAustralia
| | - Yihan Li
- The Florey Institute of Neurosciencethe University of MelbourneParkvilleVictoriaAustralia
| | - Christopher Fowler
- The Florey Institute of Neurosciencethe University of MelbourneParkvilleVictoriaAustralia
| | - James D. Doecke
- The Australian e‐Health Research CentreCSIROBrisbaneQueenslandAustralia
| | - Yen Ying Lim
- Turner Institute for Brain and Mental HealthSchool of Psychological SciencesMonash UniversityClaytonVictoriaAustralia
| | - Candace Drysdale
- The Florey Institute of Neurosciencethe University of MelbourneParkvilleVictoriaAustralia
| | - Vicky Zhang
- The Florey Institute of Neurosciencethe University of MelbourneParkvilleVictoriaAustralia
| | - Keunha Park
- The Florey Institute of Neurosciencethe University of MelbourneParkvilleVictoriaAustralia
| | - Brett Trounson
- The Florey Institute of Neurosciencethe University of MelbourneParkvilleVictoriaAustralia
| | - Kelly Pertile
- The Florey Institute of Neurosciencethe University of MelbourneParkvilleVictoriaAustralia
| | - Rebecca Rumble
- The Florey Institute of Neurosciencethe University of MelbourneParkvilleVictoriaAustralia
| | - John W. Pickering
- Department of MedicineUniversity of OtagoNew Zealand and Department of Emergency MedicineChristchurch HospitalChristchurchNew Zealand
| | - Robert A. Rissman
- Department of NeurosciencesUniversity of CaliforniaSan DiegoCaliforniaUSA
| | - Floyd Sarsoza
- Department of NeurosciencesUniversity of CaliforniaSan DiegoCaliforniaUSA
| | - Sara Abdel‐Latif
- Alzheimer's Therapeutic Research InstituteUniversity of Southern CaliforniaSan DiegoCaliforniaUSA
| | - Yong Lin
- National Clinical Research Center for Aging and MedicineHuashan HospitalFudan UniversityShanghaiChina
| | - Vincent Doré
- The Australian e‐Health Research CentreCSIROBrisbaneQueenslandAustralia
- Department of Molecular Imaging & Therapy, Austin Health, Melbourne, Australia, and Department of Medicinethe University of MelbourneMelbourneAustralia
| | - Victor Villemagne
- Department of Molecular Imaging & Therapy, Austin Health, Melbourne, Australia, and Department of Medicinethe University of MelbourneMelbourneAustralia
| | - Christopher C. Rowe
- Department of Molecular Imaging & Therapy, Austin Health, Melbourne, Australia, and Department of Medicinethe University of MelbourneMelbourneAustralia
| | - Jurgen Fripp
- The Australian e‐Health Research CentreCSIROBrisbaneQueenslandAustralia
| | - Ralph Martins
- Centre of Excellence for Alzheimer's Disease Research and CareSchool of Medical and Health SciencesEdith Cowan UniversityJoondalupWestern AustraliaAustralia
| | - James S. Wiley
- The Florey Institute of Neurosciencethe University of MelbourneParkvilleVictoriaAustralia
| | - Paul Maruff
- The Florey Institute of Neurosciencethe University of MelbourneParkvilleVictoriaAustralia
- CogState Ltd.MelbourneVictoriaAustralia
| | | | - Colin L. Masters
- The Florey Institute of Neurosciencethe University of MelbourneParkvilleVictoriaAustralia
| | - Ben J. Gu
- The Florey Institute of Neurosciencethe University of MelbourneParkvilleVictoriaAustralia
- National Clinical Research Center for Aging and MedicineHuashan HospitalFudan UniversityShanghaiChina
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Chatterjee P, Pedrini S, Doecke JD, Thota R, Villemagne VL, Doré V, Singh AK, Wang P, Rainey-Smith S, Fowler C, Taddei K, Sohrabi HR, Molloy MP, Ames D, Maruff P, Rowe CC, Masters CL, Martins RN. Plasma Aβ42/40 ratio, p-tau181, GFAP, and NfL across the Alzheimer's disease continuum: A cross-sectional and longitudinal study in the AIBL cohort. Alzheimers Dement 2023; 19:1117-1134. [PMID: 36574591 DOI: 10.1002/alz.12724] [Citation(s) in RCA: 40] [Impact Index Per Article: 40.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Revised: 05/22/2022] [Accepted: 05/23/2022] [Indexed: 12/29/2022]
Abstract
INTRODUCTION Plasma amyloid beta (Aβ)1-42/Aβ1-40 ratio, phosphorylated-tau181 (p-tau181), glial fibrillary acidic protein (GFAP), and neurofilament light (NfL) are putative blood biomarkers for Alzheimer's disease (AD). However, head-to-head cross-sectional and longitudinal comparisons of the aforementioned biomarkers across the AD continuum are lacking. METHODS Plasma Aβ1-42, Aβ1-40, p-tau181, GFAP, and NfL were measured utilizing the Single Molecule Array (Simoa) platform and compared cross-sectionally across the AD continuum, wherein Aβ-PET (positron emission tomography)-negative cognitively unimpaired (CU Aβ-, n = 81) and mild cognitive impairment (MCI Aβ-, n = 26) participants were compared with Aβ-PET-positive participants across the AD continuum (CU Aβ+, n = 39; MCI Aβ+, n = 33; AD Aβ+, n = 46) from the Australian Imaging, Biomarker & Lifestyle Flagship Study of Ageing (AIBL) cohort. Longitudinal plasma biomarker changes were also assessed in MCI (n = 27) and AD (n = 29) participants compared with CU (n = 120) participants. In addition, associations between baseline plasma biomarker levels and prospective cognitive decline and Aβ-PET load were assessed over a 7 to 10-year duration. RESULTS Lower plasma Aβ1-42/Aβ1-40 ratio and elevated p-tau181 and GFAP were observed in CU Aβ+, MCI Aβ+, and AD Aβ+, whereas elevated plasma NfL was observed in MCI Aβ+ and AD Aβ+, compared with CU Aβ- and MCI Aβ-. Among the aforementioned plasma biomarkers, for models with and without AD risk factors (age, sex, and apolipoprotein E (APOE) ε4 carrier status), p-tau181 performed equivalent to or better than other biomarkers in predicting a brain Aβ-/+ status across the AD continuum. However, for models with and without the AD risk factors, a biomarker panel of Aβ1-42/Aβ1-40, p-tau181, and GFAP performed equivalent to or better than any of the biomarkers alone in predicting brain Aβ-/+ status across the AD continuum. Longitudinally, plasma Aβ1-42/Aβ1-40, p-tau181, and GFAP were altered in MCI compared with CU, and plasma GFAP and NfL were altered in AD compared with CU. In addition, lower plasma Aβ1-42/Aβ1-40 and higher p-tau181, GFAP, and NfL were associated with prospective cognitive decline and lower plasma Aβ1-42/Aβ1-40, and higher p-tau181 and GFAP were associated with increased Aβ-PET load prospectively. DISCUSSION These findings suggest that plasma biomarkers are altered cross-sectionally and longitudinally, along the AD continuum, and are prospectively associated with cognitive decline and brain Aβ-PET load. In addition, although p-tau181 performed equivalent to or better than other biomarkers in predicting an Aβ-/+ status across the AD continuum, a panel of biomarkers may have superior Aβ-/+ status predictive capability across the AD continuum. HIGHLIGHTS Area under the curve (AUC) of p-tau181 ≥ AUC of Aβ42/40, GFAP, NfL in predicting PET Aβ-/+ status (Aβ-/+). AUC of Aβ42/40+p-tau181+GFAP panel ≥ AUC of Aβ42/40/p-tau181/GFAP/NfL for Aβ-/+. Longitudinally, Aβ42/40, p-tau181, and GFAP were altered in MCI versus CU. Longitudinally, GFAP and NfL were altered in AD versus CU. Aβ42/40, p-tau181, GFAP, and NfL are associated with prospective cognitive decline. Aβ42/40, p-tau181, and GFAP are associated with increased PET Aβ load prospectively.
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Affiliation(s)
- Pratishtha Chatterjee
- Macquarie Medical School, Macquarie University, North Ryde, New South Wales, Australia
- School of Medical and Health Sciences, Edith Cowan University, Perth, Western Australia, Australia
| | - Steve Pedrini
- School of Medical and Health Sciences, Edith Cowan University, Perth, Western Australia, Australia
| | - James D Doecke
- Australian eHealth Research Centre, CSIRO, Brisbane, Queensland, Australia
| | - Rohith Thota
- Macquarie Medical School, Macquarie University, North Ryde, New South Wales, Australia
- School of Biomedical Sciences and Pharmacy, College of Health, Medicine and Wellbeing, University of Newcastle, Newcastle, New South Wales, Australia
| | - Victor L Villemagne
- Department of Psychiatry, University of Pittsburgh, Pennsylvania, Pittsburgh, USA
- Department of Molecular Imaging & Therapy, Austin Health, Heidelberg, Victoria, Australia
| | - Vincent Doré
- Australian eHealth Research Centre, CSIRO, Brisbane, Queensland, Australia
- Department of Molecular Imaging & Therapy, Austin Health, Heidelberg, Victoria, Australia
| | - Abhay K Singh
- Macquarie Business School, Macquarie University, North Ryde, New South Wales, Australia
| | - Penghao Wang
- College of Science, Health, Engineering and Education, Murdoch University, Perth, Western Australia, Australia
| | - Stephanie Rainey-Smith
- School of Medical and Health Sciences, Edith Cowan University, Perth, Western Australia, Australia
- Australian Alzheimer's Research Foundation, Sarich Neuroscience Research Institute, Nedlands, Western Australia, Australia
- Centre for Healthy Ageing, Murdoch University, Perth, Western Australia, Australia
- School of Psychological Science, University of Western Australia, Crawley, Western Australia, Australia
| | - Christopher Fowler
- The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Parkville, Victoria, Australia
| | - Kevin Taddei
- School of Medical and Health Sciences, Edith Cowan University, Perth, Western Australia, Australia
- Australian Alzheimer's Research Foundation, Sarich Neuroscience Research Institute, Nedlands, Western Australia, Australia
| | - Hamid R Sohrabi
- Macquarie Medical School, Macquarie University, North Ryde, New South Wales, Australia
- School of Medical and Health Sciences, Edith Cowan University, Perth, Western Australia, Australia
- Australian Alzheimer's Research Foundation, Sarich Neuroscience Research Institute, Nedlands, Western Australia, Australia
- School of Psychiatry and Clinical Neurosciences, University of Western Australia, Crawley, Western Australia, Australia
- Centre for Healthy Ageing, Health Future Institute, Murdoch University, Murdoch, Western Australia, Australia
| | - Mark P Molloy
- School of Medical Sciences, Faculty of Medicine and Health, University of Sydney, New South Wales, Australia
- Australian Proteome Analysis Facility (APAF), Macquarie University, Sydney, New South Wales, Australia
- Bowel Cancer and Biomarker Research Laboratory, Kolling Institute of Medical Research, Royal North Shore Hospital, St Leonards, New South Wales, Australia
| | - David Ames
- National Ageing Research Institute, Parkville, Victoria, Australia
- Academic Unit for Psychiatry of Old Age, University of Melbourne, Melbourne, Victoria, Australia
| | - Paul Maruff
- The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Parkville, Victoria, Australia
- Cogstate Ltd., Melbourne, Victoria, Australia
| | - Christopher C Rowe
- Department of Molecular Imaging & Therapy, Austin Health, Heidelberg, Victoria, Australia
- The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Parkville, Victoria, Australia
| | - Colin L Masters
- The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Parkville, Victoria, Australia
| | - Ralph N Martins
- Macquarie Medical School, Macquarie University, North Ryde, New South Wales, Australia
- School of Medical and Health Sciences, Edith Cowan University, Perth, Western Australia, Australia
- Australian Alzheimer's Research Foundation, Sarich Neuroscience Research Institute, Nedlands, Western Australia, Australia
- School of Psychiatry and Clinical Neurosciences, University of Western Australia, Crawley, Western Australia, Australia
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Hampel H, Hu Y, Hardy J, Blennow K, Chen C, Perry G, Kim SH, Villemagne VL, Aisen P, Vendruscolo M, Iwatsubo T, Masters CL, Cho M, Lannfelt L, Cummings JL, Vergallo A. The amyloid-β pathway in Alzheimer's disease: a plain language summary. Neurodegener Dis Manag 2023. [PMID: 36994753 DOI: 10.2217/nmt-2022-0037] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/31/2023] Open
Abstract
WHAT IS THIS SUMMARY ABOUT? This plain language summary of an article published in Molecular Psychiatry, reviews the evidence supporting the role of the amyloid-β (Aβ) pathway and its dysregulation in Alzheimer's disease (AD), and highlights the rationale for drugs targeting the Aβ pathway in the early stages of the disease. WHY IS THIS IMPORTANT? Aβ is a protein fragment (or peptide) that exists in several forms distinguished by their size, shape/structure, degree of solubility and disease relevance. The accumulation of Aβ plaques is a hallmark of AD. However, smaller, soluble aggregates of Aβ - including Aβ protofibrils - also play a role in the disease. Because Aβ-related disease mechanisms are complex, the diagnosis, treatment and management of AD should be reflective of and guided by up-to-date scientific knowledge and research findings in this area. This article describes the Aβ protein and its role in AD, summarizing the evidence showing that altered Aβ clearance from the brain may lead to the imbalance, toxic buildup and misfolding of the protein - triggering a cascade of cellular, molecular and systematic events that ultimately lead to AD. WHAT ARE THE KEY TAKEAWAYS? The physiological balance of brain Aβ levels in the context of AD is complex. Despite many unanswered questions, mounting evidence indicates that Aβ has a central role in driving AD progression. A better understanding of the Aβ pathway biology will help identify the best therapeutic targets for AD and inform treatment approaches.
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Affiliation(s)
- Harald Hampel
- Eisai Inc., Alzheimer's Disease & Brain Health, Nutley, NJ, USA
| | - Yan Hu
- Eisai Inc., Alzheimer's Disease & Brain Health, Nutley, NJ, USA
| | - John Hardy
- UK Dementia Research Institute at UCL & Department of Neurodegenerative Disease, UCL Institute of Neurology, University College London, London, UK
| | - Kaj Blennow
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
- Institute of Neuroscience & Physiology, Department of Psychiatry & Neurochemistry, the Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden
| | - Christopher Chen
- Memory Aging & Cognition Centre, Departments of Pharmacology & Psychological Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - George Perry
- Department of Biology & Neurosciences Institute, University of Texas at San Antonio, San Antonio, TX, USA
| | - Seung Hyun Kim
- Department of Neurology, College of Medicine, Hanyang University, Seoul, Republic of Korea; Cell Therapy Center, Hanyang University Hospital, Seoul, Republic of Korea
| | | | - Paul Aisen
- University of Southern California Alzheimer's Therapeutic Research Institute, San Diego, CA, USA
| | - Michele Vendruscolo
- Centre for Misfolding Diseases, Department of Chemistry, University of Cambridge, Cambridge, UK
| | - Takeshi Iwatsubo
- Department of Neuropathology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Colin L Masters
- Florey Institute & The University of Melbourne, Parkville, VIC, Australia
| | - Min Cho
- Eisai Inc., Alzheimer's Disease & Brain Health, Nutley, NJ, USA
| | - Lars Lannfelt
- Uppsala University, Department of Public Health/Geriatrics, Uppsala, Sweden
- BioArctic AB, Stockholm, Sweden
| | - Jeffrey L Cummings
- Chambers-Grundy Center for Transformative Neuroscience, Department of Brain Health, School of Integrated Health Sciences, University of Nevada Las Vegas, Las Vegas, NV, USA
| | - Andrea Vergallo
- Eisai Inc., Alzheimer's Disease & Brain Health, Nutley, NJ, USA
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35
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Pettigrew C, Nazarovs J, Soldan A, Singh V, Wang J, Hohman T, Dumitrescu L, Libby J, Kunkle B, Gross AL, Johnson S, Lu Q, Engelman C, Masters CL, Maruff P, Laws SM, Morris JC, Hassenstab J, Cruchaga C, Resnick SM, Kitner-Triolo MH, An Y, Albert M. Alzheimer's disease genetic risk and cognitive reserve in relationship to long-term cognitive trajectories among cognitively normal individuals. Alzheimers Res Ther 2023; 15:66. [PMID: 36978190 PMCID: PMC10045505 DOI: 10.1186/s13195-023-01206-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Accepted: 03/12/2023] [Indexed: 03/30/2023]
Abstract
BACKGROUND Both Alzheimer's disease (AD) genetic risk factors and indices of cognitive reserve (CR) influence risk of cognitive decline, but it remains unclear whether they interact. This study examined whether a CR index score modifies the relationship between AD genetic risk factors and long-term cognitive trajectories in a large sample of individuals with normal cognition. METHODS Analyses used data from the Preclinical AD Consortium, including harmonized data from 5 longitudinal cohort studies. Participants were cognitively normal at baseline (M baseline age = 64 years, 59% female) and underwent 10 years of follow-up, on average. AD genetic risk was measured by (i) apolipoprotein-E (APOE) genetic status (APOE-ε2 and APOE-ε4 vs. APOE-ε3; N = 1819) and (ii) AD polygenic risk scores (AD-PRS; N = 1175). A CR index was calculated by combining years of education and literacy scores. Longitudinal cognitive performance was measured by harmonized factor scores for global cognition, episodic memory, and executive function. RESULTS In mixed-effects models, higher CR index scores were associated with better baseline cognitive performance for all cognitive outcomes. APOE-ε4 genotype and AD-PRS that included the APOE region (AD-PRSAPOE) were associated with declines in all cognitive domains, whereas AD-PRS that excluded the APOE region (AD-PRSw/oAPOE) was associated with declines in executive function and global cognition, but not memory. There were significant 3-way CR index score × APOE-ε4 × time interactions for the global (p = 0.04, effect size = 0.16) and memory scores (p = 0.01, effect size = 0.22), indicating the negative effect of APOE-ε4 genotype on global and episodic memory score change was attenuated among individuals with higher CR index scores. In contrast, levels of CR did not attenuate APOE-ε4-related declines in executive function or declines associated with higher AD-PRS. APOE-ε2 genotype was unrelated to cognition. CONCLUSIONS These results suggest that APOE-ε4 and non-APOE-ε4 AD polygenic risk are independently associated with global cognitive and executive function declines among individuals with normal cognition at baseline, but only APOE-ε4 is associated with declines in episodic memory. Importantly, higher levels of CR may mitigate APOE-ε4-related declines in some cognitive domains. Future research is needed to address study limitations, including generalizability due to cohort demographic characteristics.
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Affiliation(s)
- Corinne Pettigrew
- Johns Hopkins University School of Medicine, 1600 McElderry St, Baltimore, MD, 21205, USA.
| | - Jurijs Nazarovs
- University of Wisconsin-Madison School of Medicine and Public Health, 750 Highland Ave, Madison, WI, 53726, USA
| | - Anja Soldan
- Johns Hopkins University School of Medicine, 1600 McElderry St, Baltimore, MD, 21205, USA
| | - Vikas Singh
- University of Wisconsin-Madison School of Medicine and Public Health, 750 Highland Ave, Madison, WI, 53726, USA
| | - Jiangxia Wang
- Johns Hopkins Bloomberg School of Public Health, 615 N Wolfe St, Baltimore, MD, 21205, USA
| | - Timothy Hohman
- Vanderbilt Memory and Alzheimer's Center, Vanderbilt University Medical Center, 1207 17th Ave South, Nashville, TN, 37212, USA
| | - Logan Dumitrescu
- Vanderbilt Memory and Alzheimer's Center, Vanderbilt University Medical Center, 1207 17th Ave South, Nashville, TN, 37212, USA
| | - Julia Libby
- Vanderbilt Memory and Alzheimer's Center, Vanderbilt University Medical Center, 1207 17th Ave South, Nashville, TN, 37212, USA
| | - Brian Kunkle
- John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Alden L Gross
- Johns Hopkins Bloomberg School of Public Health, 615 N Wolfe St, Baltimore, MD, 21205, USA
| | - Sterling Johnson
- University of Wisconsin-Madison School of Medicine and Public Health, 750 Highland Ave, Madison, WI, 53726, USA
| | - Qiongshi Lu
- University of Wisconsin-Madison School of Medicine and Public Health, 750 Highland Ave, Madison, WI, 53726, USA
| | - Corinne Engelman
- University of Wisconsin-Madison School of Medicine and Public Health, 750 Highland Ave, Madison, WI, 53726, USA
| | - Colin L Masters
- The Florey Institute, University of Melbourne, 30 Royal Parade, Parkville, VIC, 3052, Australia
| | - Paul Maruff
- The Florey Institute, University of Melbourne, 30 Royal Parade, Parkville, VIC, 3052, Australia
| | - Simon M Laws
- Centre for Precision Health and Collaborative Genomics and Translation Group, Edith Cowan University, 270 Jundaloop Drive, Jundaloop, WA, 6027, Australia
- Curtin Medical School, Curtin University, Kent Street, Bentley, WA, 6102, Australia
| | - John C Morris
- Washington University School of Medicine, 660 S Euclid Ave, St. Louis, MO, 63110, USA
| | - Jason Hassenstab
- Washington University School of Medicine, 660 S Euclid Ave, St. Louis, MO, 63110, USA
| | - Carlos Cruchaga
- Washington University School of Medicine, 660 S Euclid Ave, St. Louis, MO, 63110, USA
| | - Susan M Resnick
- National Institute on Aging Intramural Research Program, 251 Bayview Blvd, Baltimore, MD, 21224, USA
| | - Melissa H Kitner-Triolo
- National Institute on Aging Intramural Research Program, 251 Bayview Blvd, Baltimore, MD, 21224, USA
| | - Yang An
- National Institute on Aging Intramural Research Program, 251 Bayview Blvd, Baltimore, MD, 21224, USA
| | - Marilyn Albert
- Johns Hopkins University School of Medicine, 1600 McElderry St, Baltimore, MD, 21205, USA
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Vermunt L, Sutphen C, Dicks E, de Leeuw DM, Allegri R, Berman SB, Cash DM, Chhatwal JP, Cruchaga C, Day G, Ewers M, Farlow M, Fox NC, Ghetti B, Graff-Radford N, Hassenstab J, Jucker M, Karch CM, Kuhle J, Laske C, Levin J, Masters CL, McDade E, Mori H, Morris JC, Perrin RJ, Preische O, Schofield PR, Suárez-Calvet M, Xiong C, Scheltens P, Teunissen CE, Visser PJ, Bateman RJ, Benzinger TLS, Fagan AM, Gordon BA, Tijms BM. Axonal damage and astrocytosis are biological correlates of grey matter network integrity loss: a cohort study in autosomal dominant Alzheimer disease. medRxiv 2023:2023.03.21.23287468. [PMID: 37016671 PMCID: PMC10071836 DOI: 10.1101/2023.03.21.23287468] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/06/2023]
Abstract
Brain development and maturation leads to grey matter networks that can be measured using magnetic resonance imaging. Network integrity is an indicator of information processing capacity which declines in neurodegenerative disorders such as Alzheimer disease (AD). The biological mechanisms causing this loss of network integrity remain unknown. Cerebrospinal fluid (CSF) protein biomarkers are available for studying diverse pathological mechanisms in humans and can provide insight into decline. We investigated the relationships between 10 CSF proteins and network integrity in mutation carriers (N=219) and noncarriers (N=136) of the Dominantly Inherited Alzheimer Network Observational study. Abnormalities in Aβ, Tau, synaptic (SNAP-25, neurogranin) and neuronal calcium-sensor protein (VILIP-1) preceded grey matter network disruptions by several years, while inflammation related (YKL-40) and axonal injury (NfL) abnormalities co-occurred and correlated with network integrity. This suggests that axonal loss and inflammation play a role in structural grey matter network changes. Key points Abnormal levels of fluid markers for neuronal damage and inflammatory processes in CSF are associated with grey matter network disruptions.The strongest association was with NfL, suggesting that axonal loss may contribute to disrupted network organization as observed in AD.Tracking biomarker trajectories over the disease course, changes in CSF biomarkers generally precede changes in brain networks by several years.
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37
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Nho K, Risacher SL, Apostolova L, Bice PJ, Brosch J, Deardorff R, Faber K, Farlow MR, Foroud T, Gao S, Rosewood T, Kim JP, Nudelman K, Yu M, Aisen P, Sperling R, Hooli B, Shcherbinin S, Svaldi D, Jack CR, Jagust WJ, Landau S, Vasanthakumar A, Waring JF, Doré V, Laws SM, Masters CL, Porter T, Rowe CC, Villemagne VL, Dumitrescu L, Hohman TJ, Libby JB, Mormino E, Buckley RF, Johnson K, Yang HS, Petersen RC, Ramanan VK, Vemuri P, Cohen AD, Fan KH, Kamboh MI, Lopez OL, Bennett DA, Ali M, Benzinger T, Cruchaga C, Hobbs D, De Jager PL, Fujita M, Jadhav V, Lamb BT, Tsai AP, Castanho I, Mill J, Weiner MW, Saykin AJ. Novel CYP1B1-RMDN2 Alzheimer's disease locus identified by genome-wide association analysis of cerebral tau deposition on PET. medRxiv 2023:2023.02.27.23286048. [PMID: 36993271 PMCID: PMC10055458 DOI: 10.1101/2023.02.27.23286048] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/31/2023]
Abstract
Determining the genetic architecture of Alzheimer's disease (AD) pathologies can enhance mechanistic understanding and inform precision medicine strategies. Here, we performed a genome-wide association study of cortical tau quantified by positron emission tomography in 3,136 participants from 12 independent studies. The CYP1B1-RMDN2 locus was associated with tau deposition. The most significant signal was at rs2113389, which explained 4.3% of the variation in cortical tau, while APOE4 rs429358 accounted for 3.6%. rs2113389 was associated with higher tau and faster cognitive decline. Additive effects, but no interactions, were observed between rs2113389 and diagnosis, APOE4 , and Aβ positivity. CYP1B1 expression was upregulated in AD. rs2113389 was associated with higher CYP1B1 expression and methylation levels. Mouse model studies provided additional functional evidence for a relationship between CYP1B1 and tau deposition but not Aβ. These results may provide insight into the genetic basis of cerebral tau and novel pathways for therapeutic development in AD.
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Morgan KA, de Veer M, Miles LA, Kelderman CAA, McLean CA, Masters CL, Barnham KJ, White JM, Paterson BM, Donnelly PS. Pre-targeting amyloid-β with antibodies for potential molecular imaging of Alzheimer's disease. Chem Commun (Camb) 2023; 59:2243-2246. [PMID: 36723107 DOI: 10.1039/d2cc06850h] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
With the aim of developing the concept of pretargeted click chemistry for the diagnosis of Alzheimer's disease two antibodies specific for amyloid-β were modified to incorporate trans-cyclooctene functional groups. Two bis(thiosemicarbazone) compounds with pendant 1,2,4,5-tetrazine functional groups were prepared and radiolabelled with positron emitting copper-64. The new copper-64 complexes rapidly react with the trans-cyclooctene functionalized antibodies in a bioorthogonal click reaction and cross the blood-brain barrier in mice.
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Affiliation(s)
- Katherine A Morgan
- School of Chemistry and Bio21 Molecular Science and Biotechnology Institute, University of Melbourne, Melbourne, Victoria, 3010, Australia.
| | - Michael de Veer
- Monash Biomedical Imaging, Monash University, Clayton, Victoria 3800, Australia
| | - Luke A Miles
- The Florey Institute of Neuroscience and Mental Health, University of Melbourne, Melbourne, Victoria, 3010, Australia
| | | | - Catriona A McLean
- The Florey Institute of Neuroscience and Mental Health, University of Melbourne, Melbourne, Victoria, 3010, Australia
| | - Colin L Masters
- The Florey Institute of Neuroscience and Mental Health, University of Melbourne, Melbourne, Victoria, 3010, Australia
| | - Kevin J Barnham
- The Florey Institute of Neuroscience and Mental Health, University of Melbourne, Melbourne, Victoria, 3010, Australia
| | - Jonathan M White
- School of Chemistry and Bio21 Molecular Science and Biotechnology Institute, University of Melbourne, Melbourne, Victoria, 3010, Australia.
| | - Brett M Paterson
- Monash Biomedical Imaging, Monash University, Clayton, Victoria 3800, Australia.,School of Chemistry, Monash University, Clayton, Victoria 3800, Australia
| | - Paul S Donnelly
- School of Chemistry and Bio21 Molecular Science and Biotechnology Institute, University of Melbourne, Melbourne, Victoria, 3010, Australia.
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Chatterjee P, Doré V, Pedrini S, Krishnadas N, Thota R, Bourgeat P, Ikonomovic MD, Rainey-Smith SR, Burnham SC, Fowler C, Taddei K, Mulligan R, Ames D, Masters CL, Fripp J, Rowe CC, Martins RN, Villemagne VL. Plasma Glial Fibrillary Acidic Protein Is Associated with 18F-SMBT-1 PET: Two Putative Astrocyte Reactivity Biomarkers for Alzheimer's Disease. J Alzheimers Dis 2023; 92:615-628. [PMID: 36776057 PMCID: PMC10041433 DOI: 10.3233/jad-220908] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/12/2023]
Abstract
BACKGROUND Astrocyte reactivity is an early event along the Alzheimer's disease (AD) continuum. Plasma glial fibrillary acidic protein (GFAP), posited to reflect astrocyte reactivity, is elevated across the AD continuum from preclinical to dementia stages. Monoamine oxidase-B (MAO-B) is also elevated in reactive astrocytes observed using 18F-SMBT-1 PET in AD. OBJECTIVE The objective of this study was to evaluate the association between the abovementioned astrocyte reactivity biomarkers. METHODS Plasma GFAP and Aβ were measured using the Simoa ® platform in participants who underwent brain 18F-SMBT-1 and Aβ-PET imaging, comprising 54 healthy control (13 Aβ-PET+ and 41 Aβ-PET-), 11 mild cognitively impaired (3 Aβ-PET+ and 8 Aβ-PET-) and 6 probable AD (5 Aβ-PET+ and 1 Aβ-PET-) individuals. Linear regressions were used to assess associations of interest. RESULTS Plasma GFAP was associated with 18F-SMBT-1 signal in brain regions prone to early Aβ deposition in AD, such as the supramarginal gyrus (SG), posterior cingulate (PC), lateral temporal (LT) and lateral occipital cortex (LO). After adjusting for age, sex, APOE ɛ4 genotype, and soluble Aβ (plasma Aβ 42/40 ratio), plasma GFAP was associated with 18F-SMBT-1 signal in the SG, PC, LT, LO, and superior parietal cortex (SP). On adjusting for age, sex, APOE ɛ4 genotype and insoluble Aβ (Aβ-PET), plasma GFAP was associated with 18F-SMBT-1 signal in the SG. CONCLUSION There is an association between plasma GFAP and regional 18F-SMBT-1 PET, and this association appears to be dependent on brain Aβ load.
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Affiliation(s)
- Pratishtha Chatterjee
- Macquarie Medical School, Macquarie University, North Ryde, New South Wales, Australia.,School of Medical and Health Sciences, Edith Cowan University, Joondalup, Western Australia, Australia
| | - Vincent Doré
- The Australian eHealth Research Centre, CSIRO, Brisbane, Queensland, Australia.,Department of Molecular Imaging & Therapy, Austin Health, Heidelberg, Victoria, Australia
| | - Steve Pedrini
- School of Medical and Health Sciences, Edith Cowan University, Joondalup, Western Australia, Australia.,Australian Alzheimer's Research Foundation, Sarich Neuroscience Research Institute, Nedlands, Western Australia, Australia
| | - Natasha Krishnadas
- Department of Molecular Imaging & Therapy, Austin Health, Heidelberg, Victoria, Australia.,The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Parkville, Victoria, Australia
| | - Rohith Thota
- Macquarie Medical School, Macquarie University, North Ryde, New South Wales, Australia.,School of Biomedical Sciences and Pharmacy, College of Health, Medicine and Wellbeing, University of Newcastle, Newcastle, New South Wales, Australia
| | - Pierrick Bourgeat
- Health and Biosecurity Flagship, The Australian eHealth Research Centre, Queensland, Australia
| | - Milos D Ikonomovic
- Department of Neurology, University of Pittsburgh, Pennsylvania, PA, USA.,Geriatric Research Education and Clinical Center, VA Pittsburgh Healthcare System, Pittsburgh, Pennsylvania, PA, USA
| | - Stephanie R Rainey-Smith
- School of Medical and Health Sciences, Edith Cowan University, Joondalup, Western Australia, Australia.,Centre for Healthy Ageing, Health Futures Institute, Murdoch University, Murdoch, Western Australia, Australia.,Australian Alzheimer's Research Foundation, Sarich Neuroscience Research Institute, Nedlands, Western Australia, Australia.,School of Psychological Science, University of Western Australia, Crawley, Western Australia, Australia
| | - Samantha C Burnham
- Health and Biosecurity Flagship, The Australian eHealth Research Centre, Queensland, Australia
| | - Christopher Fowler
- The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Parkville, Victoria, Australia
| | - Kevin Taddei
- School of Medical and Health Sciences, Edith Cowan University, Joondalup, Western Australia, Australia.,Australian Alzheimer's Research Foundation, Sarich Neuroscience Research Institute, Nedlands, Western Australia, Australia
| | - Rachel Mulligan
- Department of Molecular Imaging & Therapy, Austin Health, Heidelberg, Victoria, Australia
| | - David Ames
- National Ageing Research Institute, Parkville, Victoria, Australia.,Academic Unit for Psychiatry of Old Age, University of Melbourne, Melbourne, Victoria, Australia
| | - Colin L Masters
- The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Parkville, Victoria, Australia
| | - Jürgen Fripp
- The Australian eHealth Research Centre, CSIRO, Brisbane, Queensland, Australia
| | - Christopher C Rowe
- Department of Molecular Imaging & Therapy, Austin Health, Heidelberg, Victoria, Australia.,The Florey Department of Neuroscience and Mental Health, The University of Melbourne, Parkville, Victoria, Australia
| | - Ralph N Martins
- Macquarie Medical School, Macquarie University, North Ryde, New South Wales, Australia.,School of Medical and Health Sciences, Edith Cowan University, Joondalup, Western Australia, Australia.,Australian Alzheimer's Research Foundation, Sarich Neuroscience Research Institute, Nedlands, Western Australia, Australia.,School of Psychiatry and Clinical Neurosciences, University of Western Australia, Crawley, Western Australia, Australia
| | - Victor L Villemagne
- Department of Molecular Imaging & Therapy, Austin Health, Heidelberg, Victoria, Australia.,Department of Psychiatry, University of Pittsburgh, Pennsylvania, PA, USA
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Senesi M, Lewis V, Varghese S, Stehmann C, McGlade A, Doecke JD, Ellett L, Sarros S, Fowler CJ, Masters CL, Li QX, Collins SJ. Diagnostic performance of CSF biomarkers in a well-characterized Australian cohort of sporadic Creutzfeldt-Jakob disease. Front Neurol 2023; 14:1072952. [PMID: 36846121 PMCID: PMC9944944 DOI: 10.3389/fneur.2023.1072952] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Accepted: 01/04/2023] [Indexed: 02/11/2023] Open
Abstract
The most frequently utilized biomarkers to support a pre-mortem clinical diagnosis of sporadic Creutzfeldt-Jakob disease (sCJD) include concentrations of the 14-3-3 and total tau (T-tau) proteins, as well as the application of protein amplification techniques, such as the real time quaking-induced conversion (RT-QuIC) assay, in cerebrospinal fluid (CSF). Utilizing CSF from a cohort of neuropathologically confirmed (definite) sCJD (n = 50) and non-CJD controls (n = 48), we established the optimal cutpoints for the fully automated Roche Elecsys® immunoassay for T-tau and the CircuLexTM 14-3-3 Gamma ELISA and compared these to T-tau protein measured using a commercially available assay (INNOTEST hTAU Ag) and 14-3-3 protein detection by western immunoblot (WB). These CSF specimens were also assessed for presence of misfolded prion protein using the RT-QuIC assay. T-tau showed similar diagnostic performance irrespective of the assay utilized, with ~90% sensitivity and specificity. The 14-3-3 protein detection by western blot (WB) has 87.5% sensitivity and 66.7% specificity. The 14-3-3 ELISA demonstrated 81.3% sensitivity and 84.4% specificity. RT-QuIC was the single best performing assay, with a sensitivity of 92.7% and 100% specificity. Our study indicates that a combination of all three CSF biomarkers increases sensitivity and offers the best chance of case detection pre-mortem. Only a single sCJD case in our cohort was negative across the three biomarkers, emphasizing the value of autopsy brain examination on all suspected CJD cases to ensure maximal case ascertainment.
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Affiliation(s)
- Matteo Senesi
- Australian National Creutzfeldt-Jakob Disease Registry (ANCJDR), The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Parkville, VIC, Australia,Department of Medicine, Royal Melbourne Hospital (RMH), The University of Melbourne, Parkville, VIC, Australia
| | - Victoria Lewis
- Australian National Creutzfeldt-Jakob Disease Registry (ANCJDR), The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Parkville, VIC, Australia,Department of Medicine, Royal Melbourne Hospital (RMH), The University of Melbourne, Parkville, VIC, Australia
| | - Shiji Varghese
- National Dementia Diagnostics Laboratory (NDDL), The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Parkville, VIC, Australia
| | - Christiane Stehmann
- Australian National Creutzfeldt-Jakob Disease Registry (ANCJDR), The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Parkville, VIC, Australia
| | - Amelia McGlade
- Australian National Creutzfeldt-Jakob Disease Registry (ANCJDR), The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Parkville, VIC, Australia
| | | | - Laura Ellett
- Australian National Creutzfeldt-Jakob Disease Registry (ANCJDR), The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Parkville, VIC, Australia
| | - Shannon Sarros
- Australian National Creutzfeldt-Jakob Disease Registry (ANCJDR), The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Parkville, VIC, Australia
| | - Christopher J. Fowler
- The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Parkville, VIC, Australia
| | - Colin L. Masters
- Australian National Creutzfeldt-Jakob Disease Registry (ANCJDR), The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Parkville, VIC, Australia,National Dementia Diagnostics Laboratory (NDDL), The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Parkville, VIC, Australia,The Florey Institute of Neuroscience and Mental Health, Florey Department, The University of Melbourne, Parkville, VIC, Australia
| | - Qiao-Xin Li
- Department of Medicine, Royal Melbourne Hospital (RMH), The University of Melbourne, Parkville, VIC, Australia,The Florey Institute of Neuroscience and Mental Health, Florey Department, The University of Melbourne, Parkville, VIC, Australia,Qiao-Xin Li ✉
| | - Steven J. Collins
- Australian National Creutzfeldt-Jakob Disease Registry (ANCJDR), The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Parkville, VIC, Australia,Department of Medicine, Royal Melbourne Hospital (RMH), The University of Melbourne, Parkville, VIC, Australia,National Dementia Diagnostics Laboratory (NDDL), The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Parkville, VIC, Australia,*Correspondence: Steven J. Collins ✉
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Vöglein J, Franzmeier N, Morris JC, Dieterich M, McDade E, Simons M, Preische O, Hofmann A, Hassenstab J, Benzinger TL, Fagan A, Noble JM, Berman SB, Graff-Radford NR, Ghetti B, Farlow MR, Chhatwal JP, Salloway S, Xiong C, Karch CM, Cairns N, Perrin RJ, Day G, Martins R, Sanchez-Valle R, Mori H, Shimada H, Ikeuchi T, Suzuki K, Schofield PR, Masters CL, Goate A, Buckles V, Fox NC, Chrem P, Allegri R, Ringman JM, Yakushev I, Laske C, Jucker M, Höglinger G, Bateman RJ, Danek A, Levin J. Pattern and implications of neurological examination findings in autosomal dominant Alzheimer disease. Alzheimers Dement 2023; 19:632-645. [PMID: 35609137 PMCID: PMC9684350 DOI: 10.1002/alz.12684] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2021] [Revised: 03/21/2022] [Accepted: 03/27/2022] [Indexed: 11/10/2022]
Abstract
INTRODUCTION As knowledge about neurological examination findings in autosomal dominant Alzheimer disease (ADAD) is incomplete, we aimed to determine the frequency and significance of neurological examination findings in ADAD. METHODS Frequencies of neurological examination findings were compared between symptomatic mutation carriers and non mutation carriers from the Dominantly Inherited Alzheimer Network (DIAN) to define AD neurological examination findings. AD neurological examination findings were analyzed regarding frequency, association with and predictive value regarding cognitive decline, and association with brain atrophy in symptomatic mutation carriers. RESULTS AD neurological examination findings included abnormal deep tendon reflexes, gait disturbance, pathological cranial nerve examination findings, tremor, abnormal finger to nose and heel to shin testing, and compromised motor strength. The frequency of AD neurological examination findings was 65.1%. Cross-sectionally, mutation carriers with AD neurological examination findings showed a more than two-fold faster cognitive decline and had greater parieto-temporal atrophy, including hippocampal atrophy. Longitudinally, AD neurological examination findings predicted a significantly greater decline over time. DISCUSSION ADAD features a distinct pattern of neurological examination findings that is useful to estimate prognosis and may inform clinical care and therapeutic trial designs.
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Affiliation(s)
- Jonathan Vöglein
- Department of Neurology, Ludwig-Maximilians-Universität München, Germany
- German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
| | - Nicolai Franzmeier
- Institute for Stroke and Dementia Research, Ludwig-Maximilians-Universität München, Germany
| | - John C. Morris
- Washington University School of Medicine, 660 South Euclid, Saint Louis, MO 63110, USA
| | - Marianne Dieterich
- Department of Neurology, Ludwig-Maximilians-Universität München, Germany
- German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
- German Center for Vertigo and Balance Disorders, Ludwig-Maximilians-Universität München, Germany
| | - Eric McDade
- Washington University School of Medicine, 660 South Euclid, Saint Louis, MO 63110, USA
| | - Mikael Simons
- German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
| | - Oliver Preische
- German Center for Neurodegenerative Diseases (DZNE), Tübingen, Germany
- Hertie Institute for Clinical Brain Research, University of Tübingen, Germany
| | - Anna Hofmann
- German Center for Neurodegenerative Diseases (DZNE), Tübingen, Germany
- Hertie Institute for Clinical Brain Research, University of Tübingen, Germany
| | - Jason Hassenstab
- Washington University School of Medicine, 660 South Euclid, Saint Louis, MO 63110, USA
| | - Tammie L. Benzinger
- Washington University School of Medicine, 660 South Euclid, Saint Louis, MO 63110, USA
| | - Anne Fagan
- Washington University School of Medicine, 660 South Euclid, Saint Louis, MO 63110, USA
| | - James M. Noble
- Department of Neurology, Taub Institute for Research on Alzheimer’s Disease and the Aging Brain, and Gertrude H. Sergievsky Center, Columbia University Irving Medical Center, 710 West 168 Street Box 176, New York, NY 10032, USA
| | - Sarah B. Berman
- University of Pittsburgh, 3471 Fifth Ave #900, Pittsburgh, PA 15213, USA
| | | | | | - Martin R. Farlow
- Indiana University School of Medicine, Indianapolis, IN 46202, USA
| | - Jasmeer P. Chhatwal
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
| | - Stephen Salloway
- Butler Hospital, 345 Blackstone Boulevard, Providence, RI 02906, USA
| | - Chengjie Xiong
- Division of Biostatistics, Washington University School of Medicine, 660 South Euclid, Saint Louis, MO 63110, USA
| | - Celeste M. Karch
- Washington University School of Medicine, 660 South Euclid, Saint Louis, MO 63110, USA
| | - Nigel Cairns
- Washington University School of Medicine, 660 South Euclid, Saint Louis, MO 63110, USA
- Medical School and Living Systems Institute, University of Exeter, Stocker Road, Exeter, EX4 4QD, United Kingdom
| | - Richard J. Perrin
- Washington University School of Medicine, 660 South Euclid, Saint Louis, MO 63110, USA
| | - Gregory Day
- Department of Neurology, Mayo Clinic, Jacksonville, FL, USA
| | - Ralph Martins
- Edith Cowan University, 270 Joondalup Drive, Joondalup WA 6027, Australia
| | - Raquel Sanchez-Valle
- Alzheimer’s disease and other cognitive disorders group. Service of Neurology, Hospital Clinic de Barcelona, IDIBAPS, University of Barcelona, Barcelona, Spain
| | - Hiroshi Mori
- Osaka City University Medical School, Asahimachi, Abenoku, Osaka 545-8585, Japan
| | - Hiroyuki Shimada
- Osaka City University Medical School, Asahimachi, Abenoku, Osaka 545-8585, Japan
| | - Takeshi Ikeuchi
- Brain Research Institute, Niigata University, 1-757 Asahimachi, Niigata 951-8585, Japan
| | | | - Peter R. Schofield
- Neuroscience Research Australia, Sydney 2031 Australia
- School of Medical Sciences, University of New South Wales, Sydney 2052 Australia
| | - Colin L. Masters
- Florey Institute, University of Melbourne, Level 5, Kenneth Myer Building, 30 Royal Parade, Parkville, Victoria, 3010, Australia
| | - Alison Goate
- Department of Neuroscience, Icahn School of Medicine at Mount Sinai, 1425 Madison Ave, B1065, New York, NY 10029,USA
| | - Virginia Buckles
- Washington University School of Medicine, 660 South Euclid, Saint Louis, MO 63110, USA
| | - Nick C. Fox
- Dementia Research Centre, Institute of Neurology, University College London, Queen Square, London WC1 3BG United Kingdom
| | | | | | - John M. Ringman
- Keck School of Medicine of University of Southern California, Center for the Health Professionals, 1540 Alcazar Street, Suite 209F, Los Angeles, CA 90089, USA
| | - Igor Yakushev
- Department of Nuclear Medicine, Technical University of Munich, Munich, Germany
| | - Christoph Laske
- German Center for Neurodegenerative Diseases (DZNE), Tübingen, Germany
- Section for Dementia Research, Hertie Institute for Clinical Brain Research and Department of Psychiatry and Psychotherapy, University of Tübingen, 72076 Tübingen, Germany
| | - Mathias Jucker
- German Center for Neurodegenerative Diseases (DZNE), Tübingen, Germany
- Hertie Institute for Clinical Brain Research, University of Tübingen, Germany
| | - Günter Höglinger
- German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
- Department of Neurology, Medizinische Hochschule Hannover, Hannover, Germany
| | - Randall J. Bateman
- Washington University School of Medicine, 660 South Euclid, Saint Louis, MO 63110, USA
| | - Adrian Danek
- Department of Neurology, Ludwig-Maximilians-Universität München, Germany
- German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
| | - Johannes Levin
- Department of Neurology, Ludwig-Maximilians-Universität München, Germany
- German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
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Krishnadas N, Doré V, Robertson JS, Ward L, Fowler C, Masters CL, Bourgeat P, Fripp J, Villemagne VL, Rowe CC. Rates of regional tau accumulation in ageing and across the Alzheimer's disease continuum: an AIBL 18F-MK6240 PET study. EBioMedicine 2023; 88:104450. [PMID: 36709581 PMCID: PMC9900352 DOI: 10.1016/j.ebiom.2023.104450] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2022] [Revised: 12/15/2022] [Accepted: 01/10/2023] [Indexed: 01/28/2023] Open
Abstract
BACKGROUND Tau positron emission tomography (PET) imaging enables longitudinal observation of tau accumulation in Alzheimer's disease (AD). 18F-MK6240 is a high affinity tracer for the paired helical filaments of tau in AD, widely used in clinical trials, despite sparse longitudinal natural history data. We aimed to evaluate the natural history of tau accumulation, and the impact of disease stage and reference region on the magnitude and effect size of regional change. METHODS One hundred and eighty-four participants: 89 cognitively unimpaired (CU) beta-amyloid negative (Aβ-), 44 CU Aβ+, 51 cognitively impaired Aβ+ (26 with mild cognitive impairment [MCI] and 25 with dementia) had follow-up 18F-MK6240 PET for one to four years (median 1.48). Regional standardised uptake value ratios (SUVR) were generated. Two reference regions were examined: cerebellar cortex and eroded subcortical white matter. FINDINGS CU Aβ- participants had very low rates of tau accumulation in the mesial temporal lobe (MTL). In CU Aβ+, significantly higher rate of accumulation was seen in the MTL (particularly the amygdala), extending into the inferior temporal lobes. In MCI Aβ+, the rate of accumulation was greatest in the lateral temporal, parietal and lateral occipital cortex, and plateaued in the MTL. Accumulation was global in AD Aβ+, except for a plateau in the MTL. The eroded subcortical white matter reference region showed no significant advantage over the cerebellar cortex and appeared prone to spill-over in AD participants. Data fitting suggested approximately 15-20 years to accumulate tau to typical AD levels. INTERPRETATION Tau accumulation occurs slowly. Rates vary according to brain region, disease stage and tend to plateau at high levels. Rates of tau accumulation are best measured in the MTL and inferior temporal cortex in preclinical AD and in large neocortical areas, in MCI and AD. FUNDING NHMRC; Cerveau Technologies.
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Affiliation(s)
- Natasha Krishnadas
- Florey Department of Neurosciences & Mental Health, The University of Melbourne, Parkville, Victoria, 3052, Australia; Department of Molecular Imaging & Therapy, Austin Health, Heidelberg, Victoria, 3084, Australia.
| | - Vincent Doré
- Department of Molecular Imaging & Therapy, Austin Health, Heidelberg, Victoria, 3084, Australia; Health and Biosecurity Flagship, The Australian eHealth Research Centre, Melbourne, Victoria, Australia
| | - Joanne S Robertson
- Florey Institute of Neurosciences & Mental Health, Parkville, Victoria, 3010, Australia
| | - Larry Ward
- Florey Institute of Neurosciences & Mental Health, Parkville, Victoria, 3010, Australia
| | - Christopher Fowler
- Florey Institute of Neurosciences & Mental Health, Parkville, Victoria, 3010, Australia
| | - Colin L Masters
- Florey Institute of Neurosciences & Mental Health, Parkville, Victoria, 3010, Australia
| | - Pierrick Bourgeat
- Health and Biosecurity Flagship, The Australian eHealth Research Centre, Brisbane, Queensland, Australia
| | - Jurgen Fripp
- Health and Biosecurity Flagship, The Australian eHealth Research Centre, Brisbane, Queensland, Australia
| | | | - Christopher C Rowe
- Florey Department of Neurosciences & Mental Health, The University of Melbourne, Parkville, Victoria, 3052, Australia; Department of Molecular Imaging & Therapy, Austin Health, Heidelberg, Victoria, 3084, Australia; Florey Institute of Neurosciences & Mental Health, Parkville, Victoria, 3010, Australia
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Mukherjee S, Dubois C, Perez K, Varghese S, Birchall IE, Leckey M, Davydova N, McLean C, Nisbet RM, Roberts BR, Li QX, Masters CL, Streltsov VA. Quantitative proteomics of tau and Aβ in detergent fractions from Alzheimer's disease brains. J Neurochem 2023; 164:529-552. [PMID: 36271678 DOI: 10.1111/jnc.15713] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2022] [Revised: 09/16/2022] [Accepted: 10/17/2022] [Indexed: 11/27/2022]
Abstract
The two hallmarks of Alzheimer's disease (AD) are amyloid-β (Aβ) plaques and neurofibrillary tangles marked by phosphorylated tau. Increasing evidence suggests that aggregating Aβ drives tau accumulation, a process that involves synaptic degeneration leading to cognitive impairment. Conversely, there is a realization that non-fibrillar (oligomeric) forms of Aβ mediate toxicity in AD. Fibrillar (filamentous) aggregates of proteins across the spectrum of the primary and secondary tauopathies were the focus of recent structural studies with a filament structure-based nosologic classification, but less emphasis was given to non-filamentous co-aggregates of insoluble proteins in the fractions derived from post-mortem human brains. Here, we revisited sarkosyl-soluble and -insoluble extracts to characterize tau and Aβ species by quantitative targeted mass spectrometric proteomics, biochemical assays, and electron microscopy. AD brain sarkosyl-insoluble pellets were greatly enriched with Aβ42 at almost equimolar levels to N-terminal truncated microtubule-binding region (MTBR) isoforms of tau with multiple site-specific post-translational modifications (PTMs). MTBR R3 and R4 tau peptides were most abundant in the sarkosyl-insoluble materials with a 10-fold higher concentration than N-terminal tau peptides. This indicates that the major proportion of the enriched tau was the aggregation-prone N-terminal and proline-rich region (PRR) of truncated mixed 4R and 3R tau with more 4R than 3R isoforms. High concentration and occupancies of site-specific phosphorylation pT181 (~22%) and pT217 (~16%) (key biomarkers of AD) along with other PTMs in the PRR and MTBR indicated a regional susceptibility of PTMs in aggregated tau. Immunogold labelling revealed that tau may exist in globular non-filamentous form (N-terminal intact tau) co-localized with Aβ in the sarkosyl-insoluble pellets along with tau filaments (N-truncated MTBR tau). Our results suggest a model that Aβ and tau interact forming globular aggregates, from which filamentous tau and Aβ emerge. These characterizations contribute towards unravelling the sequence of events which lead to end-stage AD changes.
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Affiliation(s)
- Soumya Mukherjee
- The Florey Institute, The University of Melbourne, Parkville, Victoria, Australia
| | - Celine Dubois
- The Florey Institute, The University of Melbourne, Parkville, Victoria, Australia
| | - Keyla Perez
- The Florey Institute, The University of Melbourne, Parkville, Victoria, Australia
| | - Shiji Varghese
- The Florey Institute, The University of Melbourne, Parkville, Victoria, Australia
| | - Ian E Birchall
- The Florey Institute, The University of Melbourne, Parkville, Victoria, Australia
| | - Miranda Leckey
- The Florey Institute, The University of Melbourne, Parkville, Victoria, Australia
| | - Natalia Davydova
- National Deuteration Facility, Australian Nuclear Science and Technology Organization, Lucas Heights, New South Wales, Australia
| | - Catriona McLean
- The Florey Institute, The University of Melbourne, Parkville, Victoria, Australia.,Department of Anatomical Pathology, Alfred Hospital, Prahran, Victoria, Australia
| | - Rebecca M Nisbet
- The Florey Institute, The University of Melbourne, Parkville, Victoria, Australia
| | - Blaine R Roberts
- The Florey Institute, The University of Melbourne, Parkville, Victoria, Australia
| | - Qiao-Xin Li
- The Florey Institute, The University of Melbourne, Parkville, Victoria, Australia
| | - Colin L Masters
- The Florey Institute, The University of Melbourne, Parkville, Victoria, Australia
| | - Victor A Streltsov
- The Florey Institute, The University of Melbourne, Parkville, Victoria, Australia
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Yang Z, Wen J, Abdulkadir A, Cui Y, Erus G, Mamourian E, Melhem R, Srinivasan D, Govindarajan ST, Chen J, Habes M, Masters CL, Maruff P, Fripp J, Ferrucci L, Albert MS, Johnson SC, Morris JC, LaMontagne P, Marcus DS, Benzinger TLS, Wolk DA, Shen L, Bao J, Resnick SM, Shou H, Nasrallah IM, Davatzikos C. Gene-SGAN: a method for discovering disease subtypes with imaging and genetic signatures via multi-view weakly-supervised deep clustering. ArXiv 2023:arXiv:2301.10772v1. [PMID: 36748000 PMCID: PMC9900969] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
Disease heterogeneity has been a critical challenge for precision diagnosis and treatment, especially in neurologic and neuropsychiatric diseases. Many diseases can display multiple distinct brain phenotypes across individuals, potentially reflecting disease subtypes that can be captured using MRI and machine learning methods. However, biological interpretability and treatment relevance are limited if the derived subtypes are not associated with genetic drivers or susceptibility factors. Herein, we describe Gene-SGAN - a multi-view, weakly-supervised deep clustering method - which dissects disease heterogeneity by jointly considering phenotypic and genetic data, thereby conferring genetic correlations to the disease subtypes and associated endophenotypic signatures. We first validate the generalizability, interpretability, and robustness of Gene-SGAN in semi-synthetic experiments. We then demonstrate its application to real multi-site datasets from 28,858 individuals, deriving subtypes of Alzheimer's disease and brain endophenotypes associated with hypertension, from MRI and SNP data. Derived brain phenotypes displayed significant differences in neuroanatomical patterns, genetic determinants, biological and clinical biomarkers, indicating potentially distinct underlying neuropathologic processes, genetic drivers, and susceptibility factors. Overall, Gene-SGAN is broadly applicable to disease subtyping and endophenotype discovery, and is herein tested on disease-related, genetically-driven neuroimaging phenotypes.
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Sewell KR, Rainey-Smith SR, Peiffer J, Sohrabi HR, Taddei K, Ames D, Maruff P, Masters CL, Rowe CC, Martins RN, Erickson KI, Brown BM. The relationship between objective physical activity and change in cognitive function. Alzheimers Dement 2023. [PMID: 36656659 DOI: 10.1002/alz.12950] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2022] [Revised: 12/19/2022] [Accepted: 12/22/2022] [Indexed: 01/20/2023]
Abstract
INTRODUCTION The current study investigated the association between objectively measured physical activity and cognition in older adults over approximately 8 years. METHODS We utilized data from 199 cognitively unimpaired individuals from the Australian Imaging, Biomarkers and Lifestyle (AIBL) study, aged ≥60. Actigraphy was used to measure physical activity (intensity, total activity, and energy expenditure) at baseline. Cognition was assessed using a comprehensive cognitive battery every 18-months. RESULTS Higher baseline energy expenditure predicted better episodic recall memory and global cognition over the follow-up period (p = 0.031; p = 0.047, respectively). Those with higher physical activity intensity and greater total activity also had better global cognition over time (both p = 0.005). Finally, higher total physical activity predicted improved episodic recall memory over time (p = 0.022). DISCUSSION These results suggest that physical activity can preserve cognition and that activity intensity may play an important role in this association. HIGHLIGHTS Greater total physical activity predicts preserved episodic memory and global cognition. Moderate intensity physical activity (>3.7 metabolic equivalents of task [MET]) predicts preserved global cognition. Expending > 373 kilocalories per day may benefit episodic memory and global cognition.
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Affiliation(s)
- Kelsey R Sewell
- Centre for Healthy Ageing, Health Futures Institute, Murdoch University, Murdoch, Western Australia, Australia
| | - Stephanie R Rainey-Smith
- Centre for Healthy Ageing, Health Futures Institute, Murdoch University, Murdoch, Western Australia, Australia.,School of Medical and Health Sciences, Edith Cowan University, Joondalup, Western Australia, Australia.,Australian Alzheimer's Research Foundation, Sarich Neuroscience Research Institute, Nedlands, Western Australia, Australia.,School of Psychological Science, University of Western Australia, Perth, Western Australia, Australia
| | - Jeremiah Peiffer
- Centre for Healthy Ageing, Health Futures Institute, Murdoch University, Murdoch, Western Australia, Australia
| | - Hamid R Sohrabi
- Centre for Healthy Ageing, Health Futures Institute, Murdoch University, Murdoch, Western Australia, Australia.,School of Medical and Health Sciences, Edith Cowan University, Joondalup, Western Australia, Australia.,Australian Alzheimer's Research Foundation, Sarich Neuroscience Research Institute, Nedlands, Western Australia, Australia.,Department of Biomedical Sciences, Macquarie University, Sydney, New South Wales, Australia
| | - Kevin Taddei
- School of Medical and Health Sciences, Edith Cowan University, Joondalup, Western Australia, Australia.,Australian Alzheimer's Research Foundation, Sarich Neuroscience Research Institute, Nedlands, Western Australia, Australia
| | - David Ames
- Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Parkville, Victoria, Australia.,National Ageing Research Institute, Parkville, Victoria, Australia.,Academic Unit for Psychiatry of Old Age, St George's Hospital, University of Melbourne, Kew, Victoria, Australia
| | - Paul Maruff
- Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Parkville, Victoria, Australia.,Cogstate Ltd, Melbourne, Victoria, Australia
| | - Colin L Masters
- Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Parkville, Victoria, Australia
| | - Christopher C Rowe
- Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Parkville, Victoria, Australia.,Department of Molecular Imaging & Therapy, Austin Health, Heidelberg, Victoria, Australia
| | - Ralph N Martins
- School of Medical and Health Sciences, Edith Cowan University, Joondalup, Western Australia, Australia.,Australian Alzheimer's Research Foundation, Sarich Neuroscience Research Institute, Nedlands, Western Australia, Australia.,Department of Biomedical Sciences, Macquarie University, Sydney, New South Wales, Australia
| | - Kirk I Erickson
- Department of Psychology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Belinda M Brown
- Centre for Healthy Ageing, Health Futures Institute, Murdoch University, Murdoch, Western Australia, Australia.,School of Medical and Health Sciences, Edith Cowan University, Joondalup, Western Australia, Australia.,Australian Alzheimer's Research Foundation, Sarich Neuroscience Research Institute, Nedlands, Western Australia, Australia
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Angioni D, Hansson O, Bateman RJ, Rabe C, Toloue M, Braunstein JB, Agus S, Sims JR, Bittner T, Carrillo MC, Fillit H, Masters CL, Salloway S, Aisen P, Weiner M, Vellas B, Gauthier S. Can We Use Blood Biomarkers as Entry Criteria and for Monitoring Drug Treatment Effects in Clinical Trials? A Report from the EU/US CTAD Task Force. J Prev Alzheimers Dis 2023; 10:418-425. [PMID: 37357282 DOI: 10.14283/jpad.2023.68] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/27/2023]
Abstract
In randomized clinical trials (RCTs) for Alzheimer's Disease (AD), cerebrospinal fluid (CSF) and positron emission tomography (PET) biomarkers are currently used for the detection and monitoring of AD pathological features. The use of less resource-intensive plasma biomarkers could decrease the burden to study volunteers and limit costs and time for study enrollment. Blood-based markers (BBMs) could thus play an important role in improving the design and the conduct of RCTs on AD. It remains to be determined if the data available on BBMs are strong enough to replace CSF and PET biomarkers as entry criteria and monitoring tools in RCTs.
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Affiliation(s)
- D Angioni
- D. Angioni, Gerontopole of Toulouse, Alzheimer's Disease Research and Clinical Center, Toulouse University Hospital, Toulouse, France,
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47
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Naismith SL, Michaelian JC, Santos C, Mehrani I, Robertson J, Wallis K, Lin X, Ward SA, Martins R, Masters CL, Breakspear M, Ahern S, Fripp J, Schofield PR, Sachdev PS, Rowe CC. Tackling Dementia Together via The Australian Dementia Network (ADNeT): A Summary of Initiatives, Progress and Plans. J Alzheimers Dis 2023; 96:913-925. [PMID: 37927266 PMCID: PMC10741334 DOI: 10.3233/jad-230854] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/14/2023] [Indexed: 11/07/2023]
Abstract
In 2018, the Australian Dementia Network (ADNeT) was established to bring together Australia's leading dementia researchers, people with living experience and clinicians to transform research and clinical care in the field. To address dementia diagnosis, treatment, and care, ADNeT has established three core initiatives: the Clinical Quality Registry (CQR), Memory Clinics, and Screening for Trials. Collectively, the initiatives have developed an integrated clinical and research community, driving practice excellence in this field, leading to novel innovations in diagnostics, clinical care, professional development, quality and harmonization of healthcare, clinical trials, and translation of research into practice. Australia now has a national Registry for Mild Cognitive Impairment and dementia with 55 participating clinical sites, an extensive map of memory clinic services, national Memory and Cognition Clinic Guidelines and specialized screening for trials sites in five states. This paper provides an overview of ADNeT's achievements to date and future directions. With the increase in dementia cases expected over coming decades, and with recent advances in plasma biomarkers and amyloid lowering therapies, the nationally coordinated initiatives and partnerships ADNeT has established are critical for increased national prevention efforts, co-ordinated implementation of emerging treatments for Alzheimer's disease, innovation of early and accurate diagnosis, driving continuous improvements in clinical care and patient outcome and access to post-diagnostic support and clinical trials. For a heterogenous disorder such as dementia, which is now the second leading cause of death in Australia following cardiovascular disease, the case for adequate investment into research and development has grown even more compelling.
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Affiliation(s)
- Sharon L. Naismith
- Healthy Brain Ageing Program, School of Psychology, Charles Perkins Centre and the Brain and Mind Centre, University of Sydney, Sydney, New South Wales, Australia
| | - Johannes C. Michaelian
- Healthy Brain Ageing Program, School of Psychology, Charles Perkins Centre and the Brain and Mind Centre, University of Sydney, Sydney, New South Wales, Australia
| | - Cherry Santos
- The University of Melbourne, Melbourne, Victoria, Australia
| | - Inga Mehrani
- Centre for Healthy Brain Ageing (CHeBA), Discipline of Psychiatry and Mental Health, University of New South Wales, Sydney, Australia
| | - Joanne Robertson
- The Florey Institute, The University of Melbourne, Parkville, Victoria, Australia
| | - Kasey Wallis
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
| | - Xiaoping Lin
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
| | - Stephanie A. Ward
- Centre for Healthy Brain Ageing (CHeBA), Discipline of Psychiatry and Mental Health, University of New South Wales, Sydney, Australia
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
- Department of Geriatric Medicine, The Prince of Wales Hospital, Sydney, New South Wales, Australia
| | - Ralph Martins
- School of Medical Sciences, Edith Cowan University, Perth, Western Australia, Australia and Department of Biomedical Sciences, Macquarie University, Sydney, New South Wales, Australia
| | - Colin L. Masters
- The Florey Institute, The University of Melbourne, Parkville, Victoria, Australia
| | - Michael Breakspear
- School of Psychology, College of Engineering, Science and the Environment, University of Newcastle, New South Wales, Australia and School of Medicine and Public Health, College of Health and Medicine, University of Newcastle, New South Wales, Australia
| | - Susannah Ahern
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
| | - Jurgen Fripp
- Australian e-Health Research Centre, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Queensland, Australia
| | - Peter R. Schofield
- Neuroscience Research Australia, Sydney, Australia and School of Biomedical Sciences, University of New South Wales, Sydney, Australia
| | - Perminder S. Sachdev
- Centre for Healthy Brain Ageing (CHeBA), Discipline of Psychiatry and Mental Health, University of New South Wales, Sydney, Australia
- Neuropsychiatric Institute, Prince of Wales Hospital, Randwick, New South Wales, Australia
| | - Christopher C. Rowe
- The University of Melbourne, Melbourne, Victoria, Australia
- The Florey Institute, The University of Melbourne, Parkville, Victoria, Australia
- Department of Molecular Imaging and Therapy, Austin Health, The University of Melbourne, Victoria, Australia
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Winston CN, Langford O, Levin N, Raman R, Yarasheski K, West T, Abdel-Latif S, Donohue M, Nakamura A, Toba K, Masters CL, Doecke J, Sperling RA, Aisen PS, Rissman RA. Evaluation of Blood-Based Plasma Biomarkers as Potential Markers of Amyloid Burden in Preclinical Alzheimer's Disease. J Alzheimers Dis 2023; 92:95-107. [PMID: 36710683 DOI: 10.3233/jad-221118] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
BACKGROUND Participant eligibility for the A4 Study was determined by amyloid PET imaging. Given the disadvantages of amyloid PET imaging in accessibility and cost, blood-based biomarkers may serve as a sufficient biomarker and more cost-effective screening tool for patient enrollment into preclinical AD trials. OBJECTIVE To determine if a blood-based screening test can adequately identify amyloid burden in participants screened into a preclinical AD trial. METHODS In this cross-sectional study, 224 participants from the A4 Study received an amyloid PET scan (18Florbetapir) within 90 days of blood sample collection. Blood samples from all study participants were processed within 2 h after phlebotomy. Plasma amyloid measures were quantified by Shimazdu and C2 N Diagnostics using mass spectrometry-based platforms. A corresponding subset of blood samples (n = 100) was processed within 24 h after phlebotomy and analyzed by C2 N. RESULTS Plasma Aβ42/Aβ40 demonstrated the highest association for Aβ accumulation in the brain with an AUC 0.76 (95%CI = 0.69, 0.82) at C2 N and 0.80 (95%CI = 0.75, 0.86) at Shimadzu. Blood samples processed to plasma within 2 h after phlebotomy provided a better prediction of amyloid PET status than blood samples processed within 24 h (AUC 0.80 versus 0.64; p < 0.001). Age, sex, and APOE ɛ4 carrier status did not the diagnostic performance of plasma Aβ42/Aβ40 to predict amyloid PET positivity in A4 Study participants. CONCLUSION Plasma Aβ42/Aβ40 may serve as a potential biomarker for predicting elevated amyloid in the brain. Utilizing blood testing over PET imaging may improve screening efficiency into clinical trials.
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Affiliation(s)
- Charisse N Winston
- Department of Neurosciences, University of California San Diego, La Jolla, CA, USA
| | - Oliver Langford
- Alzheimer's Therapeutic Research Institute, Keck School of Medicine University of Southern California, San Diego, CA, USA
| | - Natalie Levin
- Department of Neurosciences, University of California San Diego, La Jolla, CA, USA
| | - Rema Raman
- Alzheimer's Therapeutic Research Institute, Keck School of Medicine University of Southern California, San Diego, CA, USA
| | | | - Tim West
- C2N Diagnostics, St. Louis, MO, USA
| | - Sara Abdel-Latif
- Alzheimer's Therapeutic Research Institute, Keck School of Medicine University of Southern California, San Diego, CA, USA
| | - Michael Donohue
- Alzheimer's Therapeutic Research Institute, Keck School of Medicine University of Southern California, San Diego, CA, USA
| | - Akinori Nakamura
- Department of Biomarker Research, National Center for Geriatrics and Gerontology, Obu, Aichi, Japan
| | - Kenji Toba
- National Center for Geriatrics and Gerontology, Obu, Aichi, Japan.,Tokyo Metropolitan Institute of Gerontology, Tokyo, Japan
| | - Colin L Masters
- The Florey Institute, The University of Melbourne, Parkville, VIC, Australia
| | - James Doecke
- The Commonwealth Scientific and Industrial Research Organization, Brisbane, QLD, Australia
| | | | - Paul S Aisen
- Alzheimer's Therapeutic Research Institute, Keck School of Medicine University of Southern California, San Diego, CA, USA
| | - Robert A Rissman
- Department of Neurosciences, University of California San Diego, La Jolla, CA, USA.,Department of Neurosciences, University of California San Diego and VA San Diego Healthcare System, La Jolla, CA, USA
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49
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Affiliation(s)
- Clive Harper
- Neuropathology, Dept. of Health Sciences, Charles Perkins Centre, University of Sydney, Australia
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50
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Feizpour A, Doré V, Doecke JD, Saad ZS, Triana-Baltzer G, Slemmon R, Maruff P, Krishnadas N, Bourgeat P, Huang K, Fowler C, Rainey-Smith SR, Bush AI, Ward L, Robertson J, Martins RN, Masters CL, Villemagne VL, Fripp J, Kolb HC, Rowe CC. Two-Year Prognostic Utility of Plasma p217+tau across the Alzheimer's Continuum. J Prev Alzheimers Dis 2023; 10:828-836. [PMID: 37874105 DOI: 10.14283/jpad.2023.83] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2023]
Abstract
BACKGROUND Plasma p217+tau has shown high concordance with cerebrospinal fluid (CSF) and positron emission tomography (PET) measures of amyloid-β (Aβ) and tau in Alzheimer's Disease (AD). However, its association with longitudinal cognition and comparative performance to PET Aβ and tau in predicting cognitive decline are unknown. OBJECTIVES To evaluate whether p217+tau can predict the rate of cognitive decline observed over two-year average follow-up and compare this to prediction based on Aβ (18F-NAV4694) and tau (18F-MK6240) PET. We also explored the sample size required to detect a 30% slowing in cognitive decline in a 2-year trial and selection test cost using p217+tau (pT+) as compared to PET Aβ (A+) and tau (T+) with and without p217+tau pre-screening. DESIGN A prospective observational cohort study. SETTING Participants of the Australian Imaging, Biomarker and Lifestyle Flagship Study of Ageing (AIBL) and Australian Dementia Network (ADNeT). PARTICIPANTS 153 cognitively unimpaired (CU) and 50 cognitively impaired (CI) individuals. MEASUREMENTS Baseline p217+tau Simoa® assay, 18F-MK6240 tau-PET and 18F-NAV4694 Aβ-PET with neuropsychological follow-up (MMSE, CDR-SB, AIBL-PACC) over 2.4 ± 0.8 years. RESULTS In CI, p217+tau was a significant predictor of change in MMSE (β = -0.55, p < 0.001) and CDR-SB (β =0.61, p < 0.001) with an effect size similar to Aβ Centiloid (MMSE β = -0.48, p = 0.002; CDR-SB β = 0.43, p = 0.004) and meta-temporal (MetaT) tau SUVR (MMSE: β = -0.62, p < 0.001; CDR-SB: β = 0.65, p < 0.001). In CU, only MetaT tau SUVR was significantly associated with change in AIBL-PACC (β = -0.22, p = 0.008). Screening pT+ CI participants into a trial could lead to 24% reduction in sample size compared to screening with PET for A+ and 6-13% compared to screening with PET for T+ (different regions). This would translate to an 81-83% biomarker test cost-saving assuming the p217+tau test cost one-fifth of a PET scan. In a trial requiring PET A+ or T+, p217+tau pre-screening followed by PET in those who were pT+ would cost more in the CI group, compared to 26-38% biomarker test cost-saving in the CU. CONCLUSIONS Substantial cost reduction can be achieved using p217+tau alone to select participants with MCI or mild dementia for a clinical trial designed to slow cognitive decline over two years, compared to participant selection by PET. In pre-clinical AD trials, p217+tau provides significant cost-saving if used as a pre-screening measure for PET A+ or T+ but in MCI/mild dementia trials this may add to cost both in testing and in the increased number of participants needed for testing.
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Affiliation(s)
- A Feizpour
- Professor Christopher C Rowe, Department of Molecular Imaging and Therapy, Austin Health, 145 Studley Road, Heidelberg, VIC. 3084, Australia. Telephone: +61-3-9496 3321. Fax +61-3-9458 5023.
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