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Elman JA, Schork NJ, Rangan AV. Exploring the Genetic Heterogeneity of Alzheimer's Disease: Evidence for Genetic Subtypes. J Alzheimers Dis 2024:JAD231252. [PMID: 38995775 DOI: 10.3233/jad-231252] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/14/2024]
Abstract
Background Alzheimer's disease (AD) exhibits considerable phenotypic heterogeneity, suggesting the potential existence of subtypes. AD is under substantial genetic influence, thus identifying systematic variation in genetic risk may provide insights into disease origins. Objective We investigated genetic heterogeneity in AD risk through a multi-step analysis. Methods We performed principal component analysis (PCA) on AD-associated variants in the UK Biobank (AD cases = 2,739, controls = 5,478) to assess structured genetic heterogeneity. Subsequently, a biclustering algorithm searched for distinct disease-specific genetic signatures among subsets of cases. Replication tests were conducted using the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset (AD cases = 500, controls = 470). We categorized a separate set of ADNI individuals with mild cognitive impairment (MCI; n = 399) into genetic subtypes and examined cognitive, amyloid, and tau trajectories. Results PCA revealed three distinct clusters ("constellations") driven primarily by different correlation patterns in a region of strong LD surrounding the MAPT locus. Constellations contained a mixture of cases and controls, reflecting disease-relevant but not disease-specific structure. We found two disease-specific biclusters among AD cases. Pathway analysis linked bicluster-associated variants to neuron morphogenesis and outgrowth. Disease-relevant and disease-specific structure replicated in ADNI, and bicluster 2 exhibited increased cerebrospinal fluid p-tau and cognitive decline over time. Conclusions This study unveils a hierarchical structure of AD genetic risk. Disease-relevant constellations may represent haplotype structure that does not increase risk directly but may alter the relative importance of other genetic risk factors. Biclusters may represent distinct AD genetic subtypes. This structure is replicable and relates to differential pathological accumulation and cognitive decline over time.
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Affiliation(s)
- Jeremy A Elman
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
- Center for Behavior Genetics of Aging, University of California San Diego, La Jolla, CA, USA
| | - Nicholas J Schork
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
- The Translational Genomics Research Institute, Quantitative Medicine and Systems Biology, Phoenix, AZ, USA
| | - Aaditya V Rangan
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
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2
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Baumeister H, Vogel JW, Insel PS, Kleineidam L, Wolfsgruber S, Stark M, Gellersen HM, Yakupov R, Schmid MC, Lüsebrink F, Brosseron F, Ziegler G, Freiesleben SD, Preis L, Schneider LS, Spruth EJ, Altenstein S, Lohse A, Fliessbach K, Vogt IR, Bartels C, Schott BH, Rostamzadeh A, Glanz W, Incesoy EI, Butryn M, Janowitz D, Rauchmann BS, Kilimann I, Goerss D, Munk MH, Hetzer S, Dechent P, Ewers M, Scheffler K, Wuestefeld A, Strandberg O, van Westen D, Mattsson-Carlgren N, Janelidze S, Stomrud E, Palmqvist S, Spottke A, Laske C, Teipel S, Perneczky R, Buerger K, Schneider A, Priller J, Peters O, Ramirez A, Wiltfang J, Heneka MT, Wagner M, Düzel E, Jessen F, Hansson O, Berron D. A generalizable data-driven model of atrophy heterogeneity and progression in memory clinic settings. Brain 2024; 147:2400-2413. [PMID: 38654513 PMCID: PMC11224599 DOI: 10.1093/brain/awae118] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Revised: 02/02/2024] [Accepted: 03/03/2024] [Indexed: 04/26/2024] Open
Abstract
Memory clinic patients are a heterogeneous population representing various aetiologies of pathological ageing. It is not known whether divergent spatiotemporal progression patterns of brain atrophy, as previously described in Alzheimer's disease patients, are prevalent and clinically meaningful in this group of older adults. To uncover distinct atrophy subtypes, we applied the Subtype and Stage Inference (SuStaIn) algorithm to baseline structural MRI data from 813 participants enrolled in the DELCODE cohort (mean ± standard deviation, age = 70.67 ± 6.07 years, 52% females). Participants were cognitively unimpaired (n = 285) or fulfilled diagnostic criteria for subjective cognitive decline (n = 342), mild cognitive impairment (n = 118) or dementia of the Alzheimer's type (n = 68). Atrophy subtypes were compared in baseline demographics, fluid Alzheimer's disease biomarker levels, the Preclinical Alzheimer Cognitive Composite (PACC-5) as well as episodic memory and executive functioning. PACC-5 trajectories over up to 240 weeks were examined. To test whether baseline atrophy subtype and stage predicted clinical trajectories before manifest cognitive impairment, we analysed PACC-5 trajectories and mild cognitive impairment conversion rates of cognitively unimpaired participants and those with subjective cognitive decline. Limbic-predominant and hippocampal-sparing atrophy subtypes were identified. Limbic-predominant atrophy initially affected the medial temporal lobes, followed by further temporal regions and, finally, the remaining cortical regions. At baseline, this subtype was related to older age, more pathological Alzheimer's disease biomarker levels, APOE ε4 carriership and an amnestic cognitive impairment. Hippocampal-sparing atrophy initially occurred outside the temporal lobe, with the medial temporal lobe spared up to advanced atrophy stages. This atrophy pattern also affected individuals with positive Alzheimer's disease biomarkers and was associated with more generalized cognitive impairment. Limbic-predominant atrophy, in all participants and in only unimpaired participants, was linked to more negative longitudinal PACC-5 slopes than observed in participants without or with hippocampal-sparing atrophy and increased the risk of mild cognitive impairment conversion. SuStaIn modelling was repeated in a sample from the Swedish BioFINDER-2 cohort. Highly similar atrophy progression patterns and associated cognitive profiles were identified. Cross-cohort model generalizability, at both the subject and the group level, was excellent, indicating reliable performance in previously unseen data. The proposed model is a promising tool for capturing heterogeneity among older adults at early at-risk states for Alzheimer's disease in applied settings. The implementation of atrophy subtype- and stage-specific end points might increase the statistical power of pharmacological trials targeting early Alzheimer's disease.
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Affiliation(s)
- Hannah Baumeister
- German Center for Neurodegenerative Diseases (DZNE), 39120 Magdeburg, Germany
| | - Jacob W Vogel
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, 222 42 Lund, Sweden
| | - Philip S Insel
- Department of Psychiatry and Behavioral Sciences, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Luca Kleineidam
- German Center for Neurodegenerative Diseases (DZNE), 53127 Bonn, Germany
- Department of Neurodegenerative Disease and Geriatric Psychiatry, University of Bonn Medical Center, 53127 Bonn, Germany
| | - Steffen Wolfsgruber
- German Center for Neurodegenerative Diseases (DZNE), 53127 Bonn, Germany
- Department of Neurodegenerative Disease and Geriatric Psychiatry, University of Bonn Medical Center, 53127 Bonn, Germany
| | - Melina Stark
- Department of Neurodegenerative Disease and Geriatric Psychiatry, University of Bonn Medical Center, 53127 Bonn, Germany
| | - Helena M Gellersen
- German Center for Neurodegenerative Diseases (DZNE), 39120 Magdeburg, Germany
| | - Renat Yakupov
- German Center for Neurodegenerative Diseases (DZNE), 39120 Magdeburg, Germany
- Institute of Cognitive Neurology and Dementia Research (IKND), Otto-von-Guericke University, 39120 Magdeburg, Germany
| | - Matthias C Schmid
- German Center for Neurodegenerative Diseases (DZNE), 53127 Bonn, Germany
- Institute for Medical Biometry, University Hospital Bonn, 53127 Bonn, Germany
| | - Falk Lüsebrink
- German Center for Neurodegenerative Diseases (DZNE), 39120 Magdeburg, Germany
| | - Frederic Brosseron
- German Center for Neurodegenerative Diseases (DZNE), 53127 Bonn, Germany
| | - Gabriel Ziegler
- Institute of Cognitive Neurology and Dementia Research (IKND), Otto-von-Guericke University, 39120 Magdeburg, Germany
| | - Silka D Freiesleben
- German Center for Neurodegenerative Diseases (DZNE), 10117 Berlin, Germany
- Department of Psychiatry and Neurosciences, Charité—Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, 10117 Berlin, Germany
| | - Lukas Preis
- Department of Psychiatry and Neurosciences, Charité—Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, 10117 Berlin, Germany
| | - Luisa-Sophie Schneider
- Department of Psychiatry and Neurosciences, Charité—Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, 10117 Berlin, Germany
| | - Eike J Spruth
- German Center for Neurodegenerative Diseases (DZNE), 10117 Berlin, Germany
- Department of Psychiatry and Neurosciences, Charité—Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, 10117 Berlin, Germany
| | - Slawek Altenstein
- German Center for Neurodegenerative Diseases (DZNE), 10117 Berlin, Germany
- Department of Psychiatry and Neurosciences, Charité—Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, 10117 Berlin, Germany
| | - Andrea Lohse
- Department of Psychiatry and Neurosciences, Charité—Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, 10117 Berlin, Germany
| | - Klaus Fliessbach
- German Center for Neurodegenerative Diseases (DZNE), 53127 Bonn, Germany
- Department of Neurodegenerative Disease and Geriatric Psychiatry, University of Bonn Medical Center, 53127 Bonn, Germany
| | - Ina R Vogt
- German Center for Neurodegenerative Diseases (DZNE), 53127 Bonn, Germany
| | - Claudia Bartels
- Department of Psychiatry and Psychotherapy, University Medical Center Göttingen, 37075 Göttingen, Germany
| | - Björn H Schott
- Department of Psychiatry and Psychotherapy, University Medical Center Göttingen, 37075 Göttingen, Germany
- German Center for Neurodegenerative Diseases (DZNE), 37075 Göttingen, Germany
- Leibniz Institute for Neurobiology, 39118 Magdeburg, Germany
| | - Ayda Rostamzadeh
- Department of Psychiatry, Medical Faculty, University of Cologne, 50937 Cologne, Germany
| | - Wenzel Glanz
- German Center for Neurodegenerative Diseases (DZNE), 39120 Magdeburg, Germany
| | - Enise I Incesoy
- German Center for Neurodegenerative Diseases (DZNE), 39120 Magdeburg, Germany
- Institute of Cognitive Neurology and Dementia Research (IKND), Otto-von-Guericke University, 39120 Magdeburg, Germany
- Department of Psychiatry and Psychotherapy, Otto-von-Guericke University, 39120 Magdeburg, Germany
| | - Michaela Butryn
- German Center for Neurodegenerative Diseases (DZNE), 39120 Magdeburg, Germany
| | - Daniel Janowitz
- Institute for Stroke and Dementia Research (ISD), Ludwig-Maximilians-Universität, 81377 Munich, Germany
| | - Boris-Stephan Rauchmann
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilians-Universität, 80336 Munich, Germany
- Sheffield Institute for Translational Neuroscience (SITraN), University of Sheffield, Sheffield S10 2HQ, UK
- Department of Neuroradiology, Ludwig-Maximilians-Universität, 81377 Munich, Germany
| | - Ingo Kilimann
- German Center for Neurodegenerative Diseases (DZNE), 18147 Rostock, Germany
- Department of Psychosomatic Medicine, Rostock University Medical Center, 18147 Rostock, Germany
| | - Doreen Goerss
- German Center for Neurodegenerative Diseases (DZNE), 18147 Rostock, Germany
- Department of Psychosomatic Medicine, Rostock University Medical Center, 18147 Rostock, Germany
| | - Matthias H Munk
- German Center for Neurodegenerative Diseases (DZNE), 72076 Tübingen, Germany
- Department of Psychiatry and Psychotherapy, University of Tübingen, 72076 Tübingen, Germany
| | - Stefan Hetzer
- Berlin Center for Advanced Neuroimaging, Charité—Universitätsmedizin Berlin, 10117 Berlin, Germany
| | - Peter Dechent
- MR-Research in Neurosciences, Department of Cognitive Neurology, Georg-August-University Göttingen, 37075 Göttingen, Germany
| | - Michael Ewers
- Institute for Stroke and Dementia Research (ISD), Ludwig-Maximilians-Universität, 81377 Munich, Germany
- German Center for Neurodegenerative Diseases (DZNE), 81377 Munich, Germany
| | - Klaus Scheffler
- Department for Biomedical Magnetic Resonance, University of Tübingen, 72076 Tübingen, Germany
| | - Anika Wuestefeld
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, 222 42 Lund, Sweden
| | - Olof Strandberg
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, 222 42 Lund, Sweden
| | - Danielle van Westen
- Diagnostic Radiology, Institution of Clinical Sciences Lund, Lund University, 211 84 Lund, Sweden
- Image and Function, Skåne University Hospital, 211 84 Lund, Sweden
| | - Niklas Mattsson-Carlgren
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, 222 42 Lund, Sweden
- Department of Neurology, Skåne University Hospital, Lund University, 211 84 Lund, Sweden
- Wallenberg Center for Molecular Medicine, Lund University, 22184 Lund, Sweden
| | - Shorena Janelidze
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, 222 42 Lund, Sweden
| | - Erik Stomrud
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, 222 42 Lund, Sweden
- Memory Clinic, Skåne University Hospital, 205 02 Malmö, Sweden
| | - Sebastian Palmqvist
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, 222 42 Lund, Sweden
- Memory Clinic, Skåne University Hospital, 205 02 Malmö, Sweden
| | - Annika Spottke
- German Center for Neurodegenerative Diseases (DZNE), 53127 Bonn, Germany
- Department of Neurology, University of Bonn, 53127 Bonn, Germany
| | - Christoph Laske
- German Center for Neurodegenerative Diseases (DZNE), 72076 Tübingen, Germany
- Department of Psychiatry and Psychotherapy, University of Tübingen, 72076 Tübingen, Germany
- Section for Dementia Research, Hertie Institute for Clinical Brain Research, 72076 Tübingen, Germany
| | - Stefan Teipel
- German Center for Neurodegenerative Diseases (DZNE), 18147 Rostock, Germany
- Department of Psychosomatic Medicine, Rostock University Medical Center, 18147 Rostock, Germany
| | - Robert Perneczky
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilians-Universität, 80336 Munich, Germany
- German Center for Neurodegenerative Diseases (DZNE), 81377 Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), 81377 Munich, Germany
- Ageing Epidemiology Research Unit (AGE), School of Public Health, Imperial College London, London SW7 2AZ, UK
| | - Katharina Buerger
- Institute for Stroke and Dementia Research (ISD), Ludwig-Maximilians-Universität, 81377 Munich, Germany
- German Center for Neurodegenerative Diseases (DZNE), 81377 Munich, Germany
| | - Anja Schneider
- German Center for Neurodegenerative Diseases (DZNE), 53127 Bonn, Germany
- Department of Neurodegenerative Disease and Geriatric Psychiatry, University of Bonn Medical Center, 53127 Bonn, Germany
| | - Josef Priller
- German Center for Neurodegenerative Diseases (DZNE), 10117 Berlin, Germany
- Department of Psychiatry and Neurosciences, Charité—Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, 10117 Berlin, Germany
- Department of Psychiatry and Psychotherapy, Technical University of Munich, 81675 Munich, Germany
- Centre for Clinical Brain Sciences, University of Edinburgh and UK DRI, Edinburgh EH16 4SB, UK
| | - Oliver Peters
- German Center for Neurodegenerative Diseases (DZNE), 10117 Berlin, Germany
- Department of Psychiatry and Neurosciences, Charité—Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, 10117 Berlin, Germany
| | - Alfredo Ramirez
- German Center for Neurodegenerative Diseases (DZNE), 53127 Bonn, Germany
- Department of Neurodegenerative Disease and Geriatric Psychiatry, University of Bonn Medical Center, 53127 Bonn, Germany
- Excellence Cluster on Cellular Stress Responses in Aging-Associated Diseases (CECAD), University of Cologne, 50931 Cologne, Germany
- Division of Neurogenetics and Molecular Psychiatry, Department of Psychiatry and Psychotherapy, University of Cologne, 50931 Cologne, Germany
- Department of Psychiatry & Glenn Biggs Institute for Alzheimer’s and Neurodegenerative Diseases, The University of Texas at San Antonio, San Antonio, TX 78229, USA
| | - Jens Wiltfang
- Department of Psychiatry and Psychotherapy, University Medical Center Göttingen, 37075 Göttingen, Germany
- German Center for Neurodegenerative Diseases (DZNE), 37075 Göttingen, Germany
- Institute of Biomedicine (iBiMED), Department of Medical Sciences, University of Aveiro, 3810-193 Aveiro, Portugal
| | - Michael T Heneka
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, 4362, Belvaux, Luxembourg
| | - Michael Wagner
- German Center for Neurodegenerative Diseases (DZNE), 53127 Bonn, Germany
- Department of Neurodegenerative Disease and Geriatric Psychiatry, University of Bonn Medical Center, 53127 Bonn, Germany
| | - Emrah Düzel
- German Center for Neurodegenerative Diseases (DZNE), 39120 Magdeburg, Germany
- Institute of Cognitive Neurology and Dementia Research (IKND), Otto-von-Guericke University, 39120 Magdeburg, Germany
- Center for Behavioral Brain Sciences (CBBS), Otto-von-Guericke University Magdeburg, 39106 Magdeburg, Germany
| | - Frank Jessen
- German Center for Neurodegenerative Diseases (DZNE), 53127 Bonn, Germany
- Department of Psychiatry, Medical Faculty, University of Cologne, 50937 Cologne, Germany
- Excellence Cluster on Cellular Stress Responses in Aging-Associated Diseases (CECAD), University of Cologne, 50931 Cologne, Germany
| | - Oskar Hansson
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, 222 42 Lund, Sweden
- Memory Clinic, Skåne University Hospital, 205 02 Malmö, Sweden
| | - David Berron
- German Center for Neurodegenerative Diseases (DZNE), 39120 Magdeburg, Germany
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, 222 42 Lund, Sweden
- Center for Behavioral Brain Sciences (CBBS), Otto-von-Guericke University Magdeburg, 39106 Magdeburg, Germany
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Rodriguez-Rodriguez P, Arroyo-Garcia LE, Tsagkogianni C, Li L, Wang W, Végvári Á, Salas-Allende I, Plautz Z, Cedazo-Minguez A, Sinha SC, Troyanskaya O, Flajolet M, Yao V, Roussarie JP. A cell autonomous regulator of neuronal excitability modulates tau in Alzheimer's disease vulnerable neurons. Brain 2024; 147:2384-2399. [PMID: 38462574 PMCID: PMC11224620 DOI: 10.1093/brain/awae051] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Revised: 01/12/2024] [Accepted: 01/19/2024] [Indexed: 03/12/2024] Open
Abstract
Neurons from layer II of the entorhinal cortex (ECII) are the first to accumulate tau protein aggregates and degenerate during prodromal Alzheimer's disease. Gaining insight into the molecular mechanisms underlying this vulnerability will help reveal genes and pathways at play during incipient stages of the disease. Here, we use a data-driven functional genomics approach to model ECII neurons in silico and identify the proto-oncogene DEK as a regulator of tau pathology. We show that epigenetic changes caused by Dek silencing alter activity-induced transcription, with major effects on neuronal excitability. This is accompanied by the gradual accumulation of tau in the somatodendritic compartment of mouse ECII neurons in vivo, reactivity of surrounding microglia, and microglia-mediated neuron loss. These features are all characteristic of early Alzheimer's disease. The existence of a cell-autonomous mechanism linking Alzheimer's disease pathogenic mechanisms in the precise neuron type where the disease starts provides unique evidence that synaptic homeostasis dysregulation is of central importance in the onset of tau pathology in Alzheimer's disease.
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Affiliation(s)
| | | | - Christina Tsagkogianni
- Department of Neurobiology Care Sciences and Society, Karolinska Institutet, 17 164, Solna, Sweden
| | - Lechuan Li
- Department of Computer Science, Rice University, Houston, TX 77004, USA
| | - Wei Wang
- Bioinformatics Resource Center, The Rockefeller University, New York, NY 10065, USA
| | - Ákos Végvári
- Division of Chemistry I, Department of Medical Biochemistry and Biophysics, Karolinska Institutet, 17 164, Solna, Sweden
| | - Isabella Salas-Allende
- Laboratory of Molecular and Cellular Neuroscience, The Rockefeller University, New York, NY 10065, USA
| | - Zakary Plautz
- Laboratory of Molecular and Cellular Neuroscience, The Rockefeller University, New York, NY 10065, USA
| | - Angel Cedazo-Minguez
- Department of Neurobiology Care Sciences and Society, Karolinska Institutet, 17 164, Solna, Sweden
| | - Subhash C Sinha
- Helen and Robert Appel Alzheimer’s Disease Research Institute, Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY 10065, USA
| | - Olga Troyanskaya
- Department of Computer Science, Princeton University, Princeton, NJ 08540, USA
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544, USA
- Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, NY 10010, USA
| | - Marc Flajolet
- Laboratory of Molecular and Cellular Neuroscience, The Rockefeller University, New York, NY 10065, USA
| | - Vicky Yao
- Division of Chemistry I, Department of Medical Biochemistry and Biophysics, Karolinska Institutet, 17 164, Solna, Sweden
| | - Jean-Pierre Roussarie
- Department of Anatomy & Neurobiology, Boston University Chobanian and Avedisian School of Medicine, Boston, MA 02118, USA
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Langley J, Bennett IJ, Hu XP. Examining iron-related off-target binding effects of 18F-AV1451 PET in the cortex of Aβ+ individuals. Eur J Neurosci 2024; 60:3614-3628. [PMID: 38722153 DOI: 10.1111/ejn.16362] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Revised: 12/22/2023] [Accepted: 04/01/2024] [Indexed: 07/06/2024]
Abstract
The presence of neurofibrillary tangles containing hyper-phosphorylated tau is a characteristic of Alzheimer's disease (AD) pathology. The positron emission tomography (PET) radioligand sensitive to tau neurofibrillary tangles (18F-AV1451) also binds with iron. This off-target binding effect may be enhanced in older adults on the AD spectrum, particularly those with amyloid-positive biomarkers. Here, we examined group differences in 18F-AV1451 PET after controlling for iron-sensitive measures from magnetic resonance imaging (MRI) and its relationships to tissue microstructure and cognition in 40 amyloid beta positive (Aβ+) individuals, 20 amyloid beta negative (Aβ-) with MCI and 31 Aβ- control participants. After controlling for iron, increased 18F-AV1451 PET uptake was found in the temporal lobe and hippocampus of Aβ+ participants compared to Aβ- MCI and control participants. Within the Aβ+ group, significant correlations were seen between 18F-AV1451 PET uptake and tissue microstructure and these correlations remained significant after controlling for iron. These findings indicate that off-target binding of iron to the 18F-AV1451 ligand may not affect its sensitivity to Aβ status or cognition in early-stage AD.
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Affiliation(s)
- Jason Langley
- Center for Advanced Neuroimaging, University of California Riverside, Riverside, California, USA
| | - Ilana J Bennett
- Department of Psychology, University of California Riverside, Riverside, California, USA
| | - Xiaoping P Hu
- Center for Advanced Neuroimaging, University of California Riverside, Riverside, California, USA
- Department of Bioengineering, University of California Riverside, Riverside, California, USA
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Matthews DC, Kinney JW, Ritter A, Andrews RD, Toledano Strom EN, Lukic AS, Koenig LN, Revta C, Fillit HM, Zhong K, Tousi B, Leverenz JB, Feldman HH, Cummings J. Relationships between plasma biomarkers, tau PET, FDG PET, and volumetric MRI in mild to moderate Alzheimer's disease patients. ALZHEIMER'S & DEMENTIA (NEW YORK, N. Y.) 2024; 10:e12490. [PMID: 38988416 PMCID: PMC11233274 DOI: 10.1002/trc2.12490] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/05/2024] [Revised: 05/24/2024] [Accepted: 05/25/2024] [Indexed: 07/12/2024]
Abstract
INTRODUCTION The "A/T/N" (amyloid/tau/neurodegeneration) framework provides a biological basis for Alzheimer's disease (AD) diagnosis and can encompass additional changes such as inflammation ("I"). A spectrum of T/N/I imaging and plasma biomarkers was acquired in a phase 2 clinical trial of rasagiline in mild to moderate AD patients. We evaluated these to understand biomarker distributions and relationships within this population. METHODS Plasma biomarkers of pTau-181, neurofilament light chain (NfL), glial fibrillary acidic protein (GFAP), other inflammation-related proteins, imaging measures including fluorodeoxyglucose (FDG) positron emission tomography (PET), flortaucipir PET, and volumetric magnetic resonance imaging (MRI), and cognitive endpoints were analyzed to assess characteristics and relationships for the overall population (N = 47 at baseline and N = 21 for longitudinal cognitive comparisons) and within age-decade subgroups (57-69, 70-79, 80-90 years). RESULTS Data demonstrate wide clinical and biomarker heterogeneity in this population influenced by age and sex. Plasma pTau-181 and GFAP correlate with tau PET, most strongly in left inferior temporal cortex (p = 0.0002, p = 0.0006, respectively). In regions beyond temporal cortex, tau PET uptake decreased with age for the same pTau-181 or GFAP concentrations. FDG PET and brain volumes correlate with tau PET in numerous regions (such as inferior temporal: p = 0.0007, p = 0.00001, respectively). NfL, GFAP, and all imaging modalities correlate with baseline MMSE; subsequent MMSE decline is predicted by baseline parahippocampal and lateral temporal tau PET (p = 0.0007) and volume (p = 0.0006). Lateral temporal FDG PET (p = 0.006) and volume (p = 0.0001) are most strongly associated with subsequent ADAS-cog decline. NfL correlates with FDG PET and baseline MMSE but not tau PET. Inflammation biomarkers are intercorrelated but correlated with other biomarkers in only the youngest group. DISCUSSION Associations between plasma biomarkers, imaging biomarkers, and cognitive status observed in this study provide insight into relationships among biological processes in mild to moderate AD. Findings show the potential to characterize AD patients regarding likely tau pathology, neurodegeneration, prospective clinical decline, and the importance of covariates such as age. Highlights Plasma pTau-181 and GFAP correlated with regional and global tau PET in mild to moderate AD.NfL correlated with FDG PET and cognitive endpoints but not plasma pTau-181 or tau PET.Volume and FDG PET showed strong relationships to tau PET, one another, and cognitive status.Temporal volumes most strongly predicted decline in both MMSE and ADAS-cog.Volume and plasma biomarkers can enrich for elevated tau PET with age a significant covariate.
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Affiliation(s)
| | - Jefferson W Kinney
- Department of Brain Health University of Nevada Las Vegas Las Vegas Nevada USA
| | - Aaron Ritter
- Hoag Pickup Family Neurosciences Institute Newport Beach California USA
| | | | - Erin N Toledano Strom
- Chambers-Grundy Center for Transformative Neuroscience Department of Brain Health School of Integrated Health Sciences, University of Nevada Las Vegas Las Vegas Nevada USA
| | - Ana S Lukic
- ADM Diagnostics, Inc. Northbrook Illinois USA
| | | | - Carolyn Revta
- Alzheimer's Disease Cooperative Study University of California, San Diego, School of Medicine La Jolla California USA
| | | | - Kate Zhong
- CNS Innovations LLC Henderson Nevada USA
| | - Babak Tousi
- Cleveland Clinical Lous Ruvo Center for Brain Health Cleveland Ohio USA
| | | | - Howard H Feldman
- Alzheimer's Disease Cooperative Study University of California, San Diego, School of Medicine La Jolla California USA
- Department of Neurosciences University of California San Diego La Jolla California USA
| | - Jeffrey Cummings
- Chambers-Grundy Center for Transformative Neuroscience Department of Brain Health School of Integrated Health Sciences, University of Nevada Las Vegas Las Vegas Nevada USA
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6
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Singh NA, Alnobani A, Graff‐Radford J, Machulda MM, Mielke MM, Schwarz CG, Senjem ML, Jack CR, Lowe VJ, Kanekiyo T, Josephs KA, Whitwell JL. Relationships between PET and blood plasma biomarkers in corticobasal syndrome. Alzheimers Dement 2024; 20:4765-4774. [PMID: 38885334 PMCID: PMC11247700 DOI: 10.1002/alz.13914] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2024] [Revised: 04/26/2024] [Accepted: 04/28/2024] [Indexed: 06/20/2024]
Abstract
INTRODUCTION Corticobasal syndrome (CBS) can result from underlying Alzheimer's disease (AD) pathologies. Little is known about the utility of blood plasma metrics to predict positron emission tomography (PET) biomarker-confirmed AD in CBS. METHODS A cohort of eighteen CBS patients (8 amyloid beta [Aβ]+; 10 Aβ-) and 8 cognitively unimpaired (CU) individuals underwent PET imaging and plasma analysis. Plasma concentrations were compared using a Kruskal-Wallis test. Spearman correlations assessed relationships between plasma concentrations and PET uptake. RESULTS CBS Aβ+ group showed a reduced Aβ42/40 ratio, with elevated phosphorylated tau (p-tau)181, glial fibrillary acidic protein (GFAP), and neurofilament light (NfL) concentrations, while CBS Aβ- group only showed elevated NfL concentration compared to CU. Both p-tau181 and GFAP were able to differentiate CBS Aβ- from CBS Aβ+ and showed positive associations with Aβ and tau PET uptake. DISCUSSION This study supports use of plasma p-tau181 and GFAP to detect AD in CBS. NfL shows potential as a non-specific disease biomarker of CBS regardless of underlying pathology. HIGHLIGHTS Plasma phosphorylated tau (p-tau)181 and glial fibrillary acidic protein (GFAP) concentrations differentiate corticobasal syndrome (CBS) amyloid beta (Aβ)- from CBS Aβ+. Plasma neurofilament light concentrations are elevated in CBS Aβ- and Aβ+ compared to controls. Plasma p-tau181 and GFAP concentrations were associated with Aβ and tau positron emission tomography (PET) uptake. Aβ42/40 ratio showed a negative correlation with Aβ PET uptake.
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Affiliation(s)
| | - Alla Alnobani
- Department of Neuroscience, Mayo ClinicJacksonvilleFloridaUSA
| | | | - Mary M. Machulda
- Department of Psychiatry & Psychology, Mayo ClinicRochesterMinnesotaUSA
| | - Michelle M. Mielke
- Department of Epidemiology and PreventionWake Forest UniversityWinston‐SalemNorth CarolinaUSA
| | | | | | | | - Val J. Lowe
- Department of RadiologyMayo ClinicRochesterMinnesotaUSA
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7
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Sirisi S, Sánchez-Aced É, Belbin O, Lleó A. APP dyshomeostasis in the pathogenesis of Alzheimer's disease: implications for current drug targets. Alzheimers Res Ther 2024; 16:144. [PMID: 38951839 PMCID: PMC11218153 DOI: 10.1186/s13195-024-01504-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2023] [Accepted: 06/17/2024] [Indexed: 07/03/2024]
Abstract
The Amyloid precursor protein (APP) is a transmembrane glycoprotein from which amyloid-β (Aβ) peptides are generated after proteolytic cleavage. Aβ peptides are the main constituent of amyloid plaques in Alzheimer's Disease (AD). The physiological functions of APP in the human adult brain are very diverse including intracellular signaling, synaptic and neuronal plasticity, and cell adhesion, among others. There is growing evidence that APP becomes dysfunctional in AD and that this dyshomeostasis may impact several APP functions beyond Aβ generation. The vast majority of current anti-amyloid approaches in AD have focused on reducing the synthesis of Aβ or increasing the clearance of brain Aβ aggregates following a paradigm in which Aβ plays a solo in APP dyshomeostasis. A wider view places APP at the center stage in which Aβ is an important, but not the only, factor involved in APP dyshomeostasis. Under this paradigm, APP dysfunction is universal in AD, but with some differences across different subtypes. Little is known about how to approach APP dysfunction therapeutically beyond anti-Aβ strategies. In this review, we will describe the role of APP dyshomeostasis in AD beyond Aβ and the potential therapeutic strategies targeting APP.
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Affiliation(s)
- Sònia Sirisi
- Network Center for Biomedical Research in Neurodegenerative Diseases (CIBERNED), Madrid, Spain
| | - Érika Sánchez-Aced
- Network Center for Biomedical Research in Neurodegenerative Diseases (CIBERNED), Madrid, Spain
| | - Olivia Belbin
- Network Center for Biomedical Research in Neurodegenerative Diseases (CIBERNED), Madrid, Spain
| | - Alberto Lleó
- Sant Pau Memory Unit, Neurology Department and Sant Pau Biomedical Research Institute, Hospital de la Santa Creu i Sant Pau, Universitat Autònoma de Barcelona, Sant Quintí 77, Barcelona, 08041, Spain.
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8
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Jack CR, Andrews JS, Beach TG, Buracchio T, Dunn B, Graf A, Hansson O, Ho C, Jagust W, McDade E, Molinuevo JL, Okonkwo OC, Pani L, Rafii MS, Scheltens P, Siemers E, Snyder HM, Sperling R, Teunissen CE, Carrillo MC. Revised criteria for diagnosis and staging of Alzheimer's disease: Alzheimer's Association Workgroup. Alzheimers Dement 2024. [PMID: 38934362 DOI: 10.1002/alz.13859] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2024] [Revised: 03/21/2024] [Accepted: 04/04/2024] [Indexed: 06/28/2024]
Abstract
The National Institute on Aging and the Alzheimer's Association convened three separate work groups in 2011 and single work groups in 2012 and 2018 to create recommendations for the diagnosis and characterization of Alzheimer's disease (AD). The present document updates the 2018 research framework in response to several recent developments. Defining diseases biologically, rather than based on syndromic presentation, has long been standard in many areas of medicine (e.g., oncology), and is becoming a unifying concept common to all neurodegenerative diseases, not just AD. The present document is consistent with this principle. Our intent is to present objective criteria for diagnosis and staging AD, incorporating recent advances in biomarkers, to serve as a bridge between research and clinical care. These criteria are not intended to provide step-by-step clinical practice guidelines for clinical workflow or specific treatment protocols, but rather serve as general principles to inform diagnosis and staging of AD that reflect current science. HIGHLIGHTS: We define Alzheimer's disease (AD) to be a biological process that begins with the appearance of AD neuropathologic change (ADNPC) while people are asymptomatic. Progression of the neuropathologic burden leads to the later appearance and progression of clinical symptoms. Early-changing Core 1 biomarkers (amyloid positron emission tomography [PET], approved cerebrospinal fluid biomarkers, and accurate plasma biomarkers [especially phosphorylated tau 217]) map onto either the amyloid beta or AD tauopathy pathway; however, these reflect the presence of ADNPC more generally (i.e., both neuritic plaques and tangles). An abnormal Core 1 biomarker result is sufficient to establish a diagnosis of AD and to inform clinical decision making throughout the disease continuum. Later-changing Core 2 biomarkers (biofluid and tau PET) can provide prognostic information, and when abnormal, will increase confidence that AD is contributing to symptoms. An integrated biological and clinical staging scheme is described that accommodates the fact that common copathologies, cognitive reserve, and resistance may modify relationships between clinical and biological AD stages.
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Affiliation(s)
- Clifford R Jack
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA
| | - J Scott Andrews
- Global Evidence & Outcomes, Takeda Pharmaceuticals Company Limited, Cambridge, Massachusetts, USA
| | - Thomas G Beach
- Civin Laboratory for Neuropathology, Banner Sun Health Research Institute, Sun City, Arizona, USA
| | - Teresa Buracchio
- Office of Neuroscience, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
| | - Billy Dunn
- The Michael J. Fox Foundation for Parkinson's Research, New York, New York, USA
| | - Ana Graf
- Novartis, Neuroscience Global Drug Development, Basel, Switzerland
| | - Oskar Hansson
- Department of Clinical Sciences Malmö, Faculty of Medicine, Lund University, Lund, Sweden
- Memory Clinic, Skåne University Hospital, Malmö, Lund, Sweden
| | - Carole Ho
- Development, Denali Therapeutics, South San Francisco, California, USA
| | - William Jagust
- School of Public Health and Helen Wills Neuroscience Institute, University of California Berkeley, Berkeley, California, USA
| | - Eric McDade
- Department of Neurology, Washington University St. Louis School of Medicine, St. Louis, Missouri, USA
| | - Jose Luis Molinuevo
- Department of Global Clinical Development H. Lundbeck A/S, Experimental Medicine, Copenhagen, Denmark
| | - Ozioma C Okonkwo
- Department of Medicine, Division of Geriatrics and Gerontology, University of Wisconsin School of Medicine, Madison, Wisconsin, USA
| | - Luca Pani
- University of Miami, Miller School of Medicine, Miami, Florida, USA
| | - Michael S Rafii
- Alzheimer's Therapeutic Research Institute (ATRI), Keck School of Medicine at the University of Southern California, San Diego, California, USA
| | - Philip Scheltens
- Amsterdam University Medical Center (Emeritus), Neurology, Amsterdam, the Netherlands
| | - Eric Siemers
- Clinical Research, Acumen Pharmaceuticals, Zionsville, Indiana, USA
| | - Heather M Snyder
- Medical & Scientific Relations Division, Alzheimer's Association, Chicago, Illinois, USA
| | - Reisa Sperling
- Department of Neurology, Brigham and Women's Hospital, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Charlotte E Teunissen
- Department of Laboratory Medicine, Amsterdam UMC, Neurochemistry Laboratory, Amsterdam, the Netherlands
| | - Maria C Carrillo
- Medical & Scientific Relations Division, Alzheimer's Association, Chicago, Illinois, USA
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9
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Fall AB, Preti MG, Eshmawey M, Kagerer SM, Van De Ville D, Unschuld PG. Functional network centrality indicates interactions between APOE4 and age across the clinical spectrum of AD. Neuroimage Clin 2024; 43:103635. [PMID: 38941766 DOI: 10.1016/j.nicl.2024.103635] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2024] [Revised: 06/07/2024] [Accepted: 06/18/2024] [Indexed: 06/30/2024]
Abstract
Advanced age is the most important risk factor for Alzheimer's disease (AD), and carrier-status of the Apolipoprotein E4 (APOE4) allele is the strongest known genetic risk factor. Many studies have consistently shown a link between APOE4 and synaptic dysfunction, possibly reflecting pathologically accelerated biological aging in persons at risk for AD. To test the hypothesis that distinct functional connectivity patterns characterize APOE4 carriers across the clinical spectrum of AD, we investigated 128 resting state functional Magnetic Resonance Imaging (fMRI) datasets from the Alzheimer's Disease Neuroimaging Initiative database (ADNI), representing all disease stages from cognitive normal to clinical dementia. Brain region centralities within functional networks, computed as eigenvector centrality, were tested for multivariate associations with chronological age, APOE4 carrier status and clinical stage (as well as their interactions) by partial least square analysis (PLSC). By PLSC analysis two distinct brain activity patterns could be identified, which reflected interactive effects of age, APOE4 and clinical disease stage. A first component including sensorimotor regions and parietal regions correlated with age and AD clinical stage (p < 0.001). A second component focused on medial-frontal regions and was specifically related to the interaction between age and APOE4 (p = 0.032). Our findings are consistent with earlier reports on altered network connectivity in APOE4 carriers. Results of our study highlight promise of graph-theory based network centrality to identify brain connectivity linked to genetic risk, clinical stage and age. Our data suggest the existence of brain network activity patterns that characterize APOE4 carriers across clinical stages of AD.
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Affiliation(s)
- Aïda B Fall
- Faculty of Medicine, University of Geneva (UNIGE), Geneva, Switzerland; Geriatric Psychiatry Service, University Hospitals of Geneva (HUG), Thônex, Switzerland; CIBM Center for Biomedical Imaging, Switzerland.
| | - Maria Giulia Preti
- Faculty of Medicine, University of Geneva (UNIGE), Geneva, Switzerland; CIBM Center for Biomedical Imaging, Switzerland; Neuro-X Institute, École Polytechnique Fédérale de Lausanne, Geneva, Switzerland
| | - Mohamed Eshmawey
- Geriatric Psychiatry Service, University Hospitals of Geneva (HUG), Thônex, Switzerland
| | - Sonja M Kagerer
- Faculty of Medicine, University of Geneva (UNIGE), Geneva, Switzerland; Institute for Regenerative Medicine (IREM), University of Zurich, Zurich, Switzerland; Psychogeriatric Medicine, Psychiatric University Hospital Zurich (PUK), Zurich, Switzerland
| | - Dimitri Van De Ville
- Faculty of Medicine, University of Geneva (UNIGE), Geneva, Switzerland; CIBM Center for Biomedical Imaging, Switzerland; Neuro-X Institute, École Polytechnique Fédérale de Lausanne, Geneva, Switzerland
| | - Paul G Unschuld
- Faculty of Medicine, University of Geneva (UNIGE), Geneva, Switzerland; Geriatric Psychiatry Service, University Hospitals of Geneva (HUG), Thônex, Switzerland
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10
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Koychev I, Adler AI, Edison P, Tom B, Milton JE, Butchart J, Hampshire A, Marshall C, Coulthard E, Zetterberg H, Hellyer P, Cormack F, Underwood BR, Mummery CJ, Holman RR. Protocol for a double-blind placebo-controlled randomised controlled trial assessing the impact of oral semaglutide in amyloid positivity (ISAP) in community dwelling UK adults. BMJ Open 2024; 14:e081401. [PMID: 38908839 DOI: 10.1136/bmjopen-2023-081401] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 06/24/2024] Open
Abstract
INTRODUCTION Glucagon-like peptide-1 receptor agonists (GLP-1 RAs), currently marketed for type 2 diabetes and obesity, may offer novel mechanisms to delay or prevent neurotoxicity associated with Alzheimer's disease (AD). The impact of semaglutide in amyloid positivity (ISAP) trial is investigating whether the GLP-1 RA semaglutide reduces accumulation in the brain of cortical tau protein and neuroinflammation in individuals with preclinical/prodromal AD. METHODS AND ANALYSIS ISAP is an investigator-led, randomised, double-blind, superiority trial of oral semaglutide compared with placebo. Up to 88 individuals aged ≥55 years with brain amyloid positivity as assessed by positron emission tomography (PET) or cerebrospinal fluid, and no or mild cognitive impairment, will be randomised. People with the low-affinity binding variant of the rs6971 allele of the Translocator Protein 18 kDa (TSPO) gene, which can interfere with interpreting TSPO PET scans (a measure of neuroinflammation), will be excluded.At baseline, participants undergo tau, TSPO PET and MRI scanning, and provide data on physical activity and cognition. Eligible individuals are randomised in a 1:1 ratio to once-daily oral semaglutide or placebo, starting at 3 mg and up-titrating to 14 mg over 8 weeks. They will attend safety visits and provide blood samples to measure AD biomarkers at weeks 4, 8, 26 and 39. All cognitive assessments are repeated at week 26. The last study visit will be at week 52, when all baseline measurements will be repeated. The primary end point is the 1-year change in tau PET signal. ETHICS AND DISSEMINATION The study was approved by the West Midlands-Edgbaston Research Ethics Committee (22/WM/0013). The results of the study will be disseminated through scientific presentations and peer-reviewed publications. TRIAL REGISTRATION NUMBER ISRCTN71283871.
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Affiliation(s)
- Ivan Koychev
- Department of Psychiatry, University of Oxford, Oxford, UK
| | - Amanda I Adler
- Diabetes Trials Unit, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Paul Edison
- Faculty of Medicine, Department of Brain Sciences, Imperial College London, London, UK
| | - Brian Tom
- Medical Research Council Biostatistics Unit, University of Cambridge, UK
| | - Joanne E Milton
- Diabetes Trials Unit, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Joe Butchart
- Royal Devon University Healthcare Foundation Trust, Exeter, UK
- University of Exeter Medical School, Exeter, UK
| | - Adam Hampshire
- Faculty of Medicine, Department of Brain Sciences, Imperial College London, London, UK
| | - Charles Marshall
- Preventive Neurology Unit, Wolfson Institute of Population Health, Queen Mary University of London, London, UK
| | | | - Henrik Zetterberg
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, the Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
- Department of Neurodegenerative Disease, UCL Institute of Neurology, Queen Square, London, UK
- UK Dementia Research Institute at UCL, London, UK
- Hong Kong Center for Neurodegenerative Diseases, Clear Water Bay, Hong Kong, People's Republic of China
- Wisconsin Alzheimer's Disease Research Center, University of Wisconsin School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, USA18 Dementia Research Centre, Institute of Neurology, University College London, Queen Square, London, UK
| | - Peter Hellyer
- Faculty of Medicine, Department of Brain Sciences, Imperial College London, London, UK
| | | | - Benjamin R Underwood
- Department of Psychiatry, University of Cambridge, Cambridge, UK
- Cambridgeshire and Peterborough NHS Foundation trust, Cambridge, UK
| | - Catherine J Mummery
- Dementia Research Centre, Institute of Neurology, University College London, Queen Square, London, UK
| | - Rury R Holman
- Diabetes Trials Unit, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
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11
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Mastenbroek SE, Vogel JW, Collij LE, Serrano GE, Tremblay C, Young AL, Arce RA, Shill HA, Driver-Dunckley ED, Mehta SH, Belden CM, Atri A, Choudhury P, Barkhof F, Adler CH, Ossenkoppele R, Beach TG, Hansson O. Disease progression modelling reveals heterogeneity in trajectories of Lewy-type α-synuclein pathology. Nat Commun 2024; 15:5133. [PMID: 38879548 PMCID: PMC11180185 DOI: 10.1038/s41467-024-49402-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Accepted: 06/04/2024] [Indexed: 06/19/2024] Open
Abstract
Lewy body (LB) diseases, characterized by the aggregation of misfolded α-synuclein proteins, exhibit notable clinical heterogeneity. This may be due to variations in accumulation patterns of LB neuropathology. Here we apply a data-driven disease progression model to regional neuropathological LB density scores from 814 brain donors with Lewy pathology. We describe three inferred trajectories of LB pathology that are characterized by differing clinicopathological presentation and longitudinal antemortem clinical progression. Most donors (81.9%) show earliest pathology in the olfactory bulb, followed by accumulation in either limbic (60.8%) or brainstem (21.1%) regions. The remaining donors (18.1%) initially exhibit abnormalities in brainstem regions. Early limbic pathology is associated with Alzheimer's disease-associated characteristics while early brainstem pathology is associated with progressive motor impairment and substantial LB pathology outside of the brain. Our data provides evidence for heterogeneity in the temporal spread of LB pathology, possibly explaining some of the clinical disparities observed in Lewy body disease.
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Affiliation(s)
- Sophie E Mastenbroek
- Department of Radiology and Nuclear Medicine, Vrije Universiteit Amsterdam, Amsterdam University Medical Center, location VUmc, Amsterdam, The Netherlands.
- Amsterdam Neuroscience, Brain imaging, Amsterdam, The Netherlands.
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Faculty of Medicine, Lund University, Lund, Sweden.
| | - Jacob W Vogel
- Department of Clinical Sciences Malmö, Faculty of Medicine, SciLifeLab, Lund University, Lund, Sweden
| | - Lyduine E Collij
- Department of Radiology and Nuclear Medicine, Vrije Universiteit Amsterdam, Amsterdam University Medical Center, location VUmc, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Brain imaging, Amsterdam, The Netherlands
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Faculty of Medicine, Lund University, Lund, Sweden
| | | | | | - Alexandra L Young
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
| | | | - Holly A Shill
- Department of Neurology, Barrow Neurological Institute, Phoenix, AZ, USA
| | - Erika D Driver-Dunckley
- Department of Neurology, Parkinson's Disease and Movement Disorders Center, Mayo Clinic, Scottsdale, AZ, USA
| | - Shyamal H Mehta
- Department of Neurology, Parkinson's Disease and Movement Disorders Center, Mayo Clinic, Scottsdale, AZ, USA
| | | | - Alireza Atri
- Banner Sun Health Research Institute, Sun City, AZ, USA
- Department of Neurology, Center for Mind/Brain Medicine, Brigham & Women's Hospital & Harvard Medical School, Boston, MA, USA
| | | | - Frederik Barkhof
- Department of Radiology and Nuclear Medicine, Vrije Universiteit Amsterdam, Amsterdam University Medical Center, location VUmc, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Brain imaging, Amsterdam, The Netherlands
- Institutes of Neurology & Healthcare Engineering, University College London, London, UK
| | - Charles H Adler
- Department of Neurology, Parkinson's Disease and Movement Disorders Center, Mayo Clinic, Scottsdale, AZ, USA
| | - Rik Ossenkoppele
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Faculty of Medicine, Lund University, Lund, Sweden
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam University Medical Center location VUmc, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
| | | | - Oskar Hansson
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Faculty of Medicine, Lund University, Lund, Sweden.
- Memory Clinic, Skåne University Hospital, Malmö, Sweden.
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12
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Rudroff T, Rainio O, Klén R. AI for the prediction of early stages of Alzheimer's disease from neuroimaging biomarkers - A narrative review of a growing field. Neurol Sci 2024:10.1007/s10072-024-07649-8. [PMID: 38866971 DOI: 10.1007/s10072-024-07649-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2024] [Accepted: 06/10/2024] [Indexed: 06/14/2024]
Abstract
OBJECTIVES The objectives of this narrative review are to summarize the current state of AI applications in neuroimaging for early Alzheimer's disease (AD) prediction and to highlight the potential of AI techniques in improving early AD diagnosis, prognosis, and management. METHODS We conducted a narrative review of studies using AI techniques applied to neuroimaging data for early AD prediction. We examined single-modality studies using structural MRI and PET imaging, as well as multi-modality studies integrating multiple neuroimaging techniques and biomarkers. Furthermore, they reviewed longitudinal studies that model AD progression and identify individuals at risk of rapid decline. RESULTS Single-modality studies using structural MRI and PET imaging have demonstrated high accuracy in classifying AD and predicting progression from mild cognitive impairment (MCI) to AD. Multi-modality studies, integrating multiple neuroimaging techniques and biomarkers, have shown improved performance and robustness compared to single-modality approaches. Longitudinal studies have highlighted the value of AI in modeling AD progression and identifying individuals at risk of rapid decline. However, challenges remain in data standardization, model interpretability, generalizability, clinical integration, and ethical considerations. CONCLUSION AI techniques applied to neuroimaging data have the potential to improve early AD diagnosis, prognosis, and management. Addressing challenges related to data standardization, model interpretability, generalizability, clinical integration, and ethical considerations is crucial for realizing the full potential of AI in AD research and clinical practice. Collaborative efforts among researchers, clinicians, and regulatory agencies are needed to develop reliable, robust, and ethical AI tools that can benefit AD patients and society.
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Affiliation(s)
- Thorsten Rudroff
- Department of Health and Human Physiology, University of Iowa, Iowa City, IA, 52242, USA.
- Department of Neurology, University of Iowa Hospitals and Clinics, Iowa City, IA, 52242, USA.
| | - Oona Rainio
- Turku PET Centre, University of Turku and Turku University Hospital, Turku, Finland
| | - Riku Klén
- Turku PET Centre, University of Turku and Turku University Hospital, Turku, Finland
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13
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Nabizadeh F. Disruption in functional networks mediated tau spreading in Alzheimer's disease. Brain Commun 2024; 6:fcae198. [PMID: 38978728 PMCID: PMC11227975 DOI: 10.1093/braincomms/fcae198] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2024] [Revised: 04/27/2024] [Accepted: 06/07/2024] [Indexed: 07/10/2024] Open
Abstract
Alzheimer's disease may be conceptualized as a 'disconnection syndrome', characterized by the breakdown of neural connectivity within the brain as a result of amyloid-beta plaques, tau neurofibrillary tangles and other factors leading to progressive degeneration and shrinkage of neurons, along with synaptic dysfunction. It has been suggested that misfolded tau proteins spread through functional connections (known as 'prion-like' properties of tau). However, the local effect of tau spreading on the synaptic function and communication between regions is not well understood. I aimed to investigate how the spreading of tau aggregates through connections can locally influence functional connectivity. In total, the imaging data of 211 participants including 117 amyloid-beta-negative non-demented and 94 amyloid-beta-positive non-demented participants were recruited from the Alzheimer's Disease Neuroimaging Initiative. Furthermore, normative resting-state functional MRI connectomes were used to model tau spreading through functional connections, and functional MRI of the included participants was used to determine the effect of tau spreading on functional connectivity. I found that lower functional connectivity to tau epicentres is associated with tau spreading through functional connections in both amyloid-beta-negative and amyloid-beta-positive participants. Also, amyloid-beta-PET in tau epicentres mediated the association of tau spreading and functional connectivity to epicentres suggesting a partial mediating effect of amyloid-beta deposition in tau epicentres on the local effect of tau spreading on functional connectivity. My findings provide strong support for the notion that tau spreading through connection is locally associated with disrupted functional connectivity between tau epicentre and non-epicentre regions independent of amyloid-beta pathology. Also, I defined several groups based on the relationship between tau spreading and functional disconnection, which provides quantitative assessment to investigate susceptibility or resilience to functional disconnection related to tau spreading. I showed that amyloid-beta, other copathologies and the apolipoprotein E epsilon 4 allele can be a leading factor towards vulnerability to tau relative functional disconnection.
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Affiliation(s)
- Fardin Nabizadeh
- School of Medicine, Iran University of Medical Sciences, Tehran 441265421414, Iran
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14
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Huo Y, Jing R, Li P, Chen P, Si J, Liu G, Liu Y. Delineating the Heterogeneity of Alzheimer's Disease and Mild Cognitive Impairment Using Normative Models of the Dynamic Brain Functional Networks. Biol Psychiatry 2024:S0006-3223(24)01365-9. [PMID: 38857821 DOI: 10.1016/j.biopsych.2024.05.025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/05/2024] [Revised: 05/15/2024] [Accepted: 05/30/2024] [Indexed: 06/12/2024]
Abstract
BACKGROUND Alzheimer's Disease (AD), identified as the most common type of dementia, presents considerable heterogeneity in clinical manifestations. Early intervention at the stage of mild cognitive impairment (MCI) holds potential in AD prevention. However, characterizing the heterogeneity of neurobiological abnormalities and identifying MCI subtypes pose significant challenges. METHODS We constructed sex-specific normative age models of dynamic brain functional networks and mapped the deviations of the brain characteristics for individuals from multiple datasets, including 295 AD patients, 441 MCI patients, and 1160 normal controls (NC). Then, based on these individual deviation patterns, subtypes for both AD and MCI were identified using the clustering method and comprehensively assessed their similarity and differences. RESULTS Individuals with AD and MCI were clustered into 2 subtypes, and these subtypes exhibited significant differences in both their intrinsic brain functional phenotypes and spatial atrophy patterns, as well as in disease progression and cognitive decline trajectories. The subtypes with positive deviations in AD and MCI shared similar deviation patterns, as well as those with negative deviations. There was a potential transformation of MCI with negative deviation patterns into AD, and these MCI have a more severe cognitive decline rate. CONCLUSIONS This study quantifies neurophysiological heterogeneity by analyzing deviation patterns from the dynamic functional connectome normative model and identifies disease subtypes in AD and MCI using a comprehensive resting-state fMRI multicenter dataset. It provides new insights for developing early prevention and personalized treatment strategies for AD.
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Affiliation(s)
- Yanxi Huo
- School of Instrument Science and Opto-Electronics Engineering, Beijing Information Science and Technology University, Beijing 100192, China
| | - Rixing Jing
- School of Instrument Science and Opto-Electronics Engineering, Beijing Information Science and Technology University, Beijing 100192, China.
| | - Peng Li
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Chinese Academy of Medical Sciences Research Unit, Peking University, Beijing 100191, China
| | - Pindong Chen
- Brainnetome Center & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Juanning Si
- School of Instrument Science and Opto-Electronics Engineering, Beijing Information Science and Technology University, Beijing 100192, China
| | - Guozhong Liu
- School of Instrument Science and Opto-Electronics Engineering, Beijing Information Science and Technology University, Beijing 100192, China
| | - Yong Liu
- School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing 100876, China.
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15
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Earnest T, Bani A, Ha SM, Hobbs DA, Kothapalli D, Yang B, Lee JJ, Benzinger TLS, Gordon BA, Sotiras A. Data-driven decomposition and staging of flortaucipir uptake in Alzheimer's disease. Alzheimers Dement 2024; 20:4002-4019. [PMID: 38683905 PMCID: PMC11180875 DOI: 10.1002/alz.13769] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Revised: 02/06/2024] [Accepted: 02/06/2024] [Indexed: 05/02/2024]
Abstract
INTRODUCTION Previous approaches pursuing in vivo staging of tau pathology in Alzheimer's disease (AD) have typically relied on neuropathologically defined criteria. In using predefined systems, these studies may miss spatial deposition patterns which are informative of disease progression. METHODS We selected discovery (n = 418) and replication (n = 132) cohorts with flortaucipir imaging. Non-negative matrix factorization (NMF) was applied to learn tau covariance patterns and develop a tau staging system. Flortaucipir components were also validated by comparison with amyloid burden, gray matter loss, and the expression of AD-related genes. RESULTS We found eight flortaucipir covariance patterns which were reproducible and overlapped with relevant gene expression maps. Tau stages were associated with AD severity as indexed by dementia status and neuropsychological performance. Comparisons of flortaucipir uptake with amyloid and atrophy also supported our model of tau progression. DISCUSSION Data-driven decomposition of flortaucipir uptake provides a novel framework for tau staging which complements existing systems. HIGHLIGHTS NMF reveals patterns of tau deposition in AD. Data-driven staging of flortaucipir tracks AD severity. Learned flortaucipir patterns overlap with AD-related gene expression.
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Affiliation(s)
- Tom Earnest
- Mallinckrodt Institute of RadiologyWashington University School of Medicine in St LouisSaint LouisMissouriUSA
| | - Abdalla Bani
- Mallinckrodt Institute of RadiologyWashington University School of Medicine in St LouisSaint LouisMissouriUSA
| | - Sung Min Ha
- Mallinckrodt Institute of RadiologyWashington University School of Medicine in St LouisSaint LouisMissouriUSA
| | - Diana A. Hobbs
- Mallinckrodt Institute of RadiologyWashington University School of Medicine in St LouisSaint LouisMissouriUSA
| | - Deydeep Kothapalli
- Mallinckrodt Institute of RadiologyWashington University School of Medicine in St LouisSaint LouisMissouriUSA
| | - Braden Yang
- Mallinckrodt Institute of RadiologyWashington University School of Medicine in St LouisSaint LouisMissouriUSA
| | - John J. Lee
- Mallinckrodt Institute of RadiologyWashington University School of Medicine in St LouisSaint LouisMissouriUSA
| | - Tammie L. S. Benzinger
- Mallinckrodt Institute of RadiologyWashington University School of Medicine in St LouisSaint LouisMissouriUSA
| | - Brian A. Gordon
- Mallinckrodt Institute of RadiologyWashington University School of Medicine in St LouisSaint LouisMissouriUSA
| | - Aristeidis Sotiras
- Mallinckrodt Institute of RadiologyWashington University School of Medicine in St LouisSaint LouisMissouriUSA
- Institute for Informatics, Data Science & BiostatisticsWashington University School of Medicine in St LouisSaint LouisMissouriUSA
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16
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Ourry V, Binette AP, St-Onge F, Strikwerda-Brown C, Chagnot A, Poirier J, Breitner J, Arenaza-Urquijo EM, Rabin JS, Buckley R, Gonneaud J, Marchant NL, Villeneuve S. How Do Modifiable Risk Factors Affect Alzheimer's Disease Pathology or Mitigate Its Effect on Clinical Symptom Expression? Biol Psychiatry 2024; 95:1006-1019. [PMID: 37689129 DOI: 10.1016/j.biopsych.2023.09.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Revised: 08/11/2023] [Accepted: 09/03/2023] [Indexed: 09/11/2023]
Abstract
Epidemiological studies show that modifiable risk factors account for approximately 40% of the population variability in risk of developing dementia, including sporadic Alzheimer's disease (AD). Recent findings suggest that these factors may also modify disease trajectories of people with autosomal-dominant AD. With positron emission tomography imaging, it is now possible to study the disease many years before its clinical onset. Such studies can provide key knowledge regarding pathways for either the prevention of pathology or the postponement of its clinical expression. The former "resistance pathway" suggests that modifiable risk factors could affect amyloid and tau burden decades before the appearance of cognitive impairment. Alternatively, the resilience pathway suggests that modifiable risk factors may mitigate the symptomatic expression of AD pathology on cognition. These pathways are not mutually exclusive and may appear at different disease stages. Here, in a narrative review, we present neuroimaging evidence that supports both pathways in sporadic AD and autosomal-dominant AD. We then propose mechanisms for their protective effect. Among possible mechanisms, we examine neural and vascular mechanisms for the resistance pathway. We also describe brain maintenance and functional compensation as bases for the resilience pathway. Improved mechanistic understanding of both pathways may suggest new interventions.
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Affiliation(s)
- Valentin Ourry
- Department of Psychiatry, Faculty of Medicine, McGill University, Montreal, Quebec, Canada; Douglas Mental Health University Institute, Montreal, Quebec, Canada.
| | - Alexa Pichet Binette
- Department of Psychiatry, Faculty of Medicine, McGill University, Montreal, Quebec, Canada; Douglas Mental Health University Institute, Montreal, Quebec, Canada; Clinical Memory Research Unit, Department of Clinical Sciences, Lunds Universitet, Malmö, Sweden
| | - Frédéric St-Onge
- Department of Psychiatry, Faculty of Medicine, McGill University, Montreal, Quebec, Canada; Douglas Mental Health University Institute, Montreal, Quebec, Canada; Integrated Program in Neuroscience, Faculty of Medicine, McGill University, Montreal, Quebec, Canada
| | - Cherie Strikwerda-Brown
- Department of Psychiatry, Faculty of Medicine, McGill University, Montreal, Quebec, Canada; Douglas Mental Health University Institute, Montreal, Quebec, Canada; School of Psychological Science, The University of Western Australia, Perth, Western Australia, Australia
| | - Audrey Chagnot
- UK Dementia Research Institute, Edinburgh Medical School, University of Edinburgh, Edinburgh, United Kingdom; Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom
| | - Judes Poirier
- Department of Psychiatry, Faculty of Medicine, McGill University, Montreal, Quebec, Canada; Douglas Mental Health University Institute, Montreal, Quebec, Canada
| | - John Breitner
- Department of Psychiatry, Faculty of Medicine, McGill University, Montreal, Quebec, Canada; Douglas Mental Health University Institute, Montreal, Quebec, Canada
| | - Eider M Arenaza-Urquijo
- Environment and Health over the Lifecourse Programme, Barcelona Institute for Global Health (ISGlobal), Barcelona, Spain; Department of Radiology, Mayo Clinic, Rochester, Minnesota
| | - Jennifer S Rabin
- Division of Neurology, Department of Medicine, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Ontario, Canada; Harquail Centre for Neuromodulation, Hurvitz Brain Sciences Program, Sunnybrook Research Institute, University of Toronto, Toronto, Ontario, Canada; Rehabilitation Sciences Institute, University of Toronto, Toronto, Ontario, Canada
| | - Rachel Buckley
- Melbourne School of Psychological Sciences University of Melbourne, Parkville, Victoria, Australia; Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts; Harvard Medical School, Boston, Massachusetts; Center for Alzheimer Research and Treatment, Department of Neurology, Brigham and Women's Hospital, Boston, Massachusetts
| | - Julie Gonneaud
- Normandie University, UNICAEN, INSERM, U1237, PhIND "Physiopathology and Imaging of Neurological Disorders," Institut Blood and Brain @ Caen-Normandie, GIP Cyceron, Caen, France
| | - Natalie L Marchant
- Division of Psychiatry, University College London, London, United Kingdom
| | - Sylvia Villeneuve
- Department of Psychiatry, Faculty of Medicine, McGill University, Montreal, Quebec, Canada; Douglas Mental Health University Institute, Montreal, Quebec, Canada; McConnell Brain Imaging Center, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada.
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17
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Kouri N, Frankenhauser I, Peng Z, Labuzan SA, Boon BDC, Moloney CM, Pottier C, Wickland DP, Caetano-Anolles K, Corriveau-Lecavalier N, Tranovich JF, Wood AC, Hinkle KM, Lincoln SJ, Spychalla AJ, Senjem ML, Przybelski SA, Engelberg-Cook E, Schwarz CG, Kwan RS, Lesser ER, Crook JE, Carter RE, Ross OA, Lachner C, Ertekin-Taner N, Ferman TJ, Fields JA, Machulda MM, Ramanan VK, Nguyen AT, Reichard RR, Jones DT, Graff-Radford J, Boeve BF, Knopman DS, Petersen RC, Jack CR, Kantarci K, Day GS, Duara R, Graff-Radford NR, Dickson DW, Lowe VJ, Vemuri P, Murray ME. Clinicopathologic Heterogeneity and Glial Activation Patterns in Alzheimer Disease. JAMA Neurol 2024; 81:619-629. [PMID: 38619853 PMCID: PMC11019448 DOI: 10.1001/jamaneurol.2024.0784] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Accepted: 01/05/2024] [Indexed: 04/16/2024]
Abstract
Importance Factors associated with clinical heterogeneity in Alzheimer disease (AD) lay along a continuum hypothesized to associate with tangle distribution and are relevant for understanding glial activation considerations in therapeutic advancement. Objectives To examine clinicopathologic and neuroimaging characteristics of disease heterogeneity in AD along a quantitative continuum using the corticolimbic index (CLix) to account for individuality of spatially distributed tangles found at autopsy. Design, Setting, and Participants This cross-sectional study was a retrospective medical record review performed on the Florida Autopsied Multiethnic (FLAME) cohort accessioned from 1991 to 2020. Data were analyzed from December 2022 to December 2023. Structural magnetic resonance imaging (MRI) and tau positron emission tomography (PET) were evaluated in an independent neuroimaging group. The FLAME cohort includes 2809 autopsied individuals; included in this study were neuropathologically diagnosed AD cases (FLAME-AD). A digital pathology subgroup of FLAME-AD cases was derived for glial activation analyses. Main Outcomes and Measures Clinicopathologic factors of heterogeneity that inform patient history and neuropathologic evaluation of AD; CLix score (lower, relative cortical predominance/hippocampal sparing vs higher, relative cortical sparing/limbic predominant cases); neuroimaging measures (ie, structural MRI and tau-PET). Results Of the 2809 autopsied individuals in the FLAME cohort, 1361 neuropathologically diagnosed AD cases were evaluated. A digital pathology subgroup included 60 FLAME-AD cases. The independent neuroimaging group included 93 cases. Among the 1361 FLAME-AD cases, 633 were male (47%; median [range] age at death, 81 [54-96] years) and 728 were female (53%; median [range] age at death, 81 [53-102] years). A younger symptomatic onset (Spearman ρ = 0.39, P < .001) and faster decline on the Mini-Mental State Examination (Spearman ρ = 0.27; P < .001) correlated with a lower CLix score in FLAME-AD series. Cases with a nonamnestic syndrome had lower CLix scores (median [IQR], 13 [9-18]) vs not (median [IQR], 21 [15-27]; P < .001). Hippocampal MRI volume (Spearman ρ = -0.45; P < .001) and flortaucipir tau-PET uptake in posterior cingulate and precuneus cortex (Spearman ρ = -0.74; P < .001) inversely correlated with CLix score. Although AD cases with a CLix score less than 10 had higher cortical tangle count, we found lower percentage of CD68-activated microglia/macrophage burden (median [IQR], 0.46% [0.32%-0.75%]) compared with cases with a CLix score of 10 to 30 (median [IQR], 0.75% [0.51%-0.98%]) and on par with a CLix score of 30 or greater (median [IQR], 0.40% [0.32%-0.57%]; P = .02). Conclusions and Relevance Findings show that AD heterogeneity exists along a continuum of corticolimbic tangle distribution. Reduced CD68 burden may signify an underappreciated association between tau accumulation and microglia/macrophages activation that should be considered in personalized therapy for immune dysregulation.
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Affiliation(s)
- Naomi Kouri
- Department of Neuroscience, Mayo Clinic, Jacksonville, Florida
| | - Isabelle Frankenhauser
- Department of Neuroscience, Mayo Clinic, Jacksonville, Florida
- Paracelsus Medical Private University, Salzburg, Austria
| | - Zhongwei Peng
- Department of Quantitative Health Sciences, Mayo Clinic, Jacksonville, Florida
| | | | | | | | - Cyril Pottier
- Department of Neuroscience, Mayo Clinic, Jacksonville, Florida
| | - Daniel P. Wickland
- Department of Quantitative Health Sciences, Mayo Clinic, Jacksonville, Florida
| | | | - Nick Corriveau-Lecavalier
- Department of Radiology, Mayo Clinic, Rochester, Minnesota
- Department of Neurology, Mayo Clinic, Rochester, Minnesota
| | | | - Ashley C. Wood
- Department of Neuroscience, Mayo Clinic, Jacksonville, Florida
| | - Kelly M. Hinkle
- Department of Neuroscience, Mayo Clinic, Jacksonville, Florida
| | | | | | | | - Scott A. Przybelski
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota
| | | | | | - Rain S. Kwan
- Department of Quantitative Health Sciences, Mayo Clinic, Jacksonville, Florida
| | - Elizabeth R. Lesser
- Department of Quantitative Health Sciences, Mayo Clinic, Jacksonville, Florida
| | - Julia E. Crook
- Department of Quantitative Health Sciences, Mayo Clinic, Jacksonville, Florida
| | - Rickey E. Carter
- Department of Quantitative Health Sciences, Mayo Clinic, Jacksonville, Florida
| | - Owen A. Ross
- Department of Neuroscience, Mayo Clinic, Jacksonville, Florida
| | - Christian Lachner
- Department of Psychiatry and Psychology, Mayo Clinic, Jacksonville, Florida
| | - Nilüfer Ertekin-Taner
- Department of Neuroscience, Mayo Clinic, Jacksonville, Florida
- Department of Neurology, Mayo Clinic, Jacksonville, Florida
| | - Tanis J. Ferman
- Department of Psychiatry and Psychology, Mayo Clinic, Jacksonville, Florida
| | - Julie A. Fields
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, Minnesota
| | - Mary M. Machulda
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, Minnesota
| | | | - Aivi T. Nguyen
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota
| | - R. Ross Reichard
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota
| | - David T. Jones
- Department of Radiology, Mayo Clinic, Rochester, Minnesota
- Department of Neurology, Mayo Clinic, Rochester, Minnesota
| | | | | | | | | | | | - Kejal Kantarci
- Department of Radiology, Mayo Clinic, Rochester, Minnesota
| | - Gregory S. Day
- Department of Neurology, Mayo Clinic, Jacksonville, Florida
| | - Ranjan Duara
- Wien Center for Alzheimer’s Disease and Memory Disorders, Mount Sinai Medical Center, Miami Beach, Florida
| | | | | | - Val J. Lowe
- Department of Radiology, Mayo Clinic, Rochester, Minnesota
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18
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Karlsson L, Vogel J, Arvidsson I, Åström K, Strandberg O, Seidlitz J, Bethlehem RAI, Stomrud E, Ossenkoppele R, Ashton NJ, Zetterberg H, Blennow K, Palmqvist S, Smith R, Janelidze S, La Joie R, Rabinovici GD, Binette AP, Mattsson-Carlgren N, Hansson O. A machine learning-based prediction of tau load and distribution in Alzheimer's disease using plasma, MRI and clinical variables. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.05.31.24308264. [PMID: 38853877 PMCID: PMC11160861 DOI: 10.1101/2024.05.31.24308264] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2024]
Abstract
Tau positron emission tomography (PET) is a reliable neuroimaging technique for assessing regional load of tau pathology in the brain, commonly used in Alzheimer's disease (AD) research and clinical trials. However, its routine clinical use is limited by cost and accessibility barriers. Here we explore using machine learning (ML) models to predict clinically useful tau-PET outcomes from low-cost and non-invasive features, e.g., basic clinical variables, plasma biomarkers, and structural magnetic resonance imaging (MRI). Results demonstrated that models including plasma biomarkers yielded highly accurate predictions of tau-PET burden (best model: R-squared=0.66-0.68), with especially high contribution from plasma P-tau217. In contrast, MRI variables stood out as best predictors (best model: R-squared=0.28-0.42) of asymmetric tau load between the two hemispheres (an example of clinically relevant spatial information). The models showed high generalizability to external test cohorts with data collected at multiple sites. Based on these results, we also propose a proof-of-concept two-step classification workflow, demonstrating how the ML models can be translated to a clinical setting. This study reveals current potential in predicting tau-PET information from scalable cost-effective variables, which could improve diagnosis and prognosis of AD.
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Affiliation(s)
- Linda Karlsson
- Clinical Memory Research Unit, Department of Clinical Sciences in Malmö, Lund University, Lund, Sweden
| | - Jacob Vogel
- Clinical Memory Research Unit, Department of Clinical Sciences in Malmö, Lund University, Lund, Sweden
- Department of Clinical Sciences, SciLifeLab, Lund University, Lund, Sweden
| | - Ida Arvidsson
- Centre for Mathematical Sciences, Lund University, Lund, Sweden
| | - Kalle Åström
- Centre for Mathematical Sciences, Lund University, Lund, Sweden
| | - Olof Strandberg
- Clinical Memory Research Unit, Department of Clinical Sciences in Malmö, Lund University, Lund, Sweden
| | - Jakob Seidlitz
- Penn/CHOP Lifespan Brain Institute, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, 19104 USA
- Department of Child and Adolescent Psychiatry and Behavioral Science, The Children’s Hospital of Philadelphia, Philadelphia, PA, 19104 USA
- Institute for Translational Medicine and Therapeutics, University of Pennsylvania, Philadelphia, PA, 19104 USA
| | - Richard A. I. Bethlehem
- University of Cambridge, Department of Psychology, Cambridge Biomedical Campus, Cambridge, CB2 3EB, UK
| | - Erik Stomrud
- Clinical Memory Research Unit, Department of Clinical Sciences in Malmö, Lund University, Lund, Sweden
- Memory Clinic, Skåne University Hospital, Malmö, Sweden
| | - Rik Ossenkoppele
- Clinical Memory Research Unit, Department of Clinical Sciences in Malmö, Lund University, Lund, Sweden
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Amsterdam UMC, Amsterdam, Netherlands
| | - Nicholas J. Ashton
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, the Sahlgrenska Academy, University of Gothenburg, Mölndal, Sweden
- Institute of Psychiatry, Psychology and Neuroscience, Maurice Wohl Institute Clinical Neuroscience, King’s College London, London, UK
| | - Henrik Zetterberg
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, the Sahlgrenska Academy, 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
- Hong Kong Center for Neurodegenerative Diseases, Clear Water Bay, Hong Kong, China
- Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, USA
| | - Kaj Blennow
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, the Sahlgrenska Academy, University of Gothenburg, Mölndal, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
- Paris Brain Institute, ICM, Pitié-Salpêtrière Hospital, Sorbonne University, Paris, France
- Neurodegenerative Disorder Research Center, Division of Life Sciences and Medicine, and Department of Neurology, Institute on Aging and Brain Disorders, University of Science and Technology of China and First Affiliated Hospital of USTC, Hefei, P.R. China
| | - Sebastian Palmqvist
- Clinical Memory Research Unit, Department of Clinical Sciences in Malmö, Lund University, Lund, Sweden
- Memory Clinic, Skåne University Hospital, Malmö, Sweden
| | - Ruben Smith
- Clinical Memory Research Unit, Department of Clinical Sciences in Malmö, Lund University, Lund, Sweden
- Memory Clinic, Skåne University Hospital, Malmö, Sweden
| | - Shorena Janelidze
- Clinical Memory Research Unit, Department of Clinical Sciences in Malmö, Lund University, Lund, Sweden
| | - Renaud La Joie
- Department of Neurology, Memory and Aging Center, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
| | - Gil D. Rabinovici
- Department of Neurology, Memory and Aging Center, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, USA
| | - Alexa Pichet Binette
- Clinical Memory Research Unit, Department of Clinical Sciences in Malmö, Lund University, Lund, Sweden
| | - Niklas Mattsson-Carlgren
- Clinical Memory Research Unit, Department of Clinical Sciences in Malmö, Lund University, Lund, Sweden
- Memory Clinic, Skåne University Hospital, Malmö, Sweden
| | - Oskar Hansson
- Clinical Memory Research Unit, Department of Clinical Sciences in Malmö, Lund University, Lund, Sweden
- Memory Clinic, Skåne University Hospital, Malmö, Sweden
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19
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Ahmadi N, Dratva MA, Heyworth N, Wang X, Blennow K, Banks SJ, Sudermann EE. Moving Beyond Depression: Mood Symptoms Across the Spectrum Relate to Tau Pathology in Older Women at Risk for Alzheimer's Disease. Int J Aging Hum Dev 2024:914150241253257. [PMID: 38751054 DOI: 10.1177/00914150241253257] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/26/2024]
Abstract
We examined how symptoms across the mood spectrum relate to Alzheimer's disease (AD) biomarkers in older women at high risk for AD. Participants included 25 women aged 65+ with mild cognitive deficits and elevated AD genetic risk. The Profile of Mood States Questionnaire measured mood symptoms and a total mood disturbance (TMD) score. Tau burden in the meta-temporal region of interest was measured using MK-6240 Tau positron emission tomography (PET) imaging. A subset (n = 12) also had p-Tau181, and Aß40/42 levels measured in plasma. Higher TMD scores related to higher tau PET standardized uptake value ratio (SUVR). Greater negative mood symptoms correlated with higher tau PET SUVR, while greater vigor correlated with lower SUVR. Similar results were seen with plasma p-Tau181 levels, but not with Aβ40/42 levels. In conclusion, positive and negative mood symptoms related to tau pathology in older women at high risk for AD, highlighting a role of mental well-being in AD risk.
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Affiliation(s)
| | - Melanie A Dratva
- Department of Neurosciences, University of California, San Diego, USA
| | - Nadine Heyworth
- Department of Neurosciences, University of California, San Diego, USA
| | - Xin Wang
- Department of Neurosciences, University of California, San Diego, USA
| | - Kaj Blennow
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, University of Gothenburg, Mölndal, Sweden
- Clinical Neurochemistry Lab, Sahlgrenska University Hospital, Mölndal, Sweden
- Paris Brain Institute, ICM, Pitié-Salpêtrière Hospital, Sorbonne University, Paris, France
- Neurodegenerative Disorder Research Center, Division of Life Sciences and Medicine, and Department of Neurology, Institute on Aging and Brain Disorders, University of Science and Technology of China and First Affiliated Hospital of USTC, Hefei, P.R. China
| | - Sarah J Banks
- Department of Neurosciences, University of California, San Diego, USA
- Department of Psychiatry, University of California, San Diego, USA
| | - Erin E Sudermann
- Department of Psychiatry, University of California, San Diego, USA
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20
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Baciu M, Roger E. Finding the Words: How Does the Aging Brain Process Language? A Focused Review of Brain Connectivity and Compensatory Pathways. Top Cogn Sci 2024. [PMID: 38734967 DOI: 10.1111/tops.12736] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Revised: 04/30/2024] [Accepted: 05/02/2024] [Indexed: 05/13/2024]
Abstract
As people age, there is a natural decline in cognitive functioning and brain structure. However, the relationship between brain function and cognition in older adults is neither straightforward nor uniform. Instead, it is complex, influenced by multiple factors, and can vary considerably from one person to another. Reserve, compensation, and maintenance mechanisms may help explain why some older adults can maintain high levels of performance while others struggle. These mechanisms are often studied concerning memory and executive functions that are particularly sensitive to the effects of aging. However, language abilities can also be affected by age, with changes in production fluency. The impact of brain changes on language abilities needs to be further investigated to understand the dynamics and patterns of aging, especially successful aging. We previously modeled several compensatory profiles of language production and lexical access/retrieval in aging within the Lexical Access and Retrieval in Aging (LARA) model. In the present paper, we propose an extended version of the LARA model, called LARA-Connectivity (LARA-C), incorporating recent evidence on brain connectivity. Finally, we discuss factors that may influence the strategies implemented with aging. The LARA-C model can serve as a framework to understand individual performance and open avenues for possible personalized interventions.
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Affiliation(s)
- Monica Baciu
- LPNC, Psychology Department, Grenoble Alps University
- Neurology Department, Grenoble Alps University Hospital
| | - Elise Roger
- LPNC, Psychology Department, Grenoble Alps University
- Communication and Aging Laboratory, Research Center of the University Institute of Geriatrics of Montreal
- Faculty of Medicine, University of Montreal
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21
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Iaccarino L, Llibre-Guerra JJ, McDade E, Edwards L, Gordon B, Benzinger T, Hassenstab J, Kramer JH, Li Y, Miller BL, Miller Z, Morris JC, Mundada N, Perrin RJ, Rosen HJ, Soleimani-Meigooni D, Strom A, Tsoy E, Wang G, Xiong C, Allegri R, Chrem P, Vazquez S, Berman SB, Chhatwal J, Masters CL, Farlow MR, Jucker M, Levin J, Salloway S, Fox NC, Day GS, Gorno-Tempini ML, Boxer AL, La Joie R, Bateman R, Rabinovici GD. Molecular neuroimaging in dominantly inherited versus sporadic early-onset Alzheimer's disease. Brain Commun 2024; 6:fcae159. [PMID: 38784820 PMCID: PMC11114609 DOI: 10.1093/braincomms/fcae159] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2023] [Revised: 03/14/2024] [Accepted: 05/01/2024] [Indexed: 05/25/2024] Open
Abstract
Approximately 5% of Alzheimer's disease patients develop symptoms before age 65 (early-onset Alzheimer's disease), with either sporadic (sporadic early-onset Alzheimer's disease) or dominantly inherited (dominantly inherited Alzheimer's disease) presentations. Both sporadic early-onset Alzheimer's disease and dominantly inherited Alzheimer's disease are characterized by brain amyloid-β accumulation, tau tangles, hypometabolism and neurodegeneration, but differences in topography and magnitude of these pathological changes are not fully elucidated. In this study, we directly compared patterns of amyloid-β plaque deposition and glucose hypometabolism in sporadic early-onset Alzheimer's disease and dominantly inherited Alzheimer's disease individuals. Our analysis included 134 symptomatic sporadic early-onset Alzheimer's disease amyloid-Positron Emission Tomography (PET)-positive cases from the University of California, San Francisco, Alzheimer's Disease Research Center (mean ± SD age 59.7 ± 5.6 years), 89 symptomatic dominantly inherited Alzheimer's disease cases (age 45.8 ± 9.3 years) and 102 cognitively unimpaired non-mutation carriers from the Dominantly Inherited Alzheimer Network study (age 44.9 ± 9.2). Each group underwent clinical and cognitive examinations, 11C-labelled Pittsburgh Compound B-PET and structural MRI. 18F-Fluorodeoxyglucose-PET was also available for most participants. Positron Emission Tomography scans from both studies were uniformly processed to obtain a standardized uptake value ratio (PIB50-70 cerebellar grey reference and FDG30-60 pons reference) images. Statistical analyses included pairwise global and voxelwise group comparisons and group-independent component analyses. Analyses were performed also adjusting for covariates including age, sex, Mini-Mental State Examination, apolipoprotein ε4 status and average composite cortical of standardized uptake value ratio. Compared with dominantly inherited Alzheimer's disease, sporadic early-onset Alzheimer's disease participants were older at age of onset (mean ± SD, 54.8 ± 8.2 versus 41.9 ± 8.2, Cohen's d = 1.91), with more years of education (16.4 ± 2.8 versus 13.5 ± 3.2, d = 1) and more likely to be apolipoprotein ε4 carriers (54.6% ε4 versus 28.1%, Cramer's V = 0.26), but similar Mini-Mental State Examination (20.6 ± 6.1 versus 21.2 ± 7.4, d = 0.08). Sporadic early-onset Alzheimer's disease had higher global cortical Pittsburgh Compound B-PET binding (mean ± SD standardized uptake value ratio, 1.92 ± 0.29 versus 1.58 ± 0.44, d = 0.96) and greater global cortical 18F-fluorodeoxyglucose-PET hypometabolism (mean ± SD standardized uptake value ratio, 1.32 ± 0.1 versus 1.39 ± 0.19, d = 0.48) compared with dominantly inherited Alzheimer's disease. Fully adjusted comparisons demonstrated relatively higher Pittsburgh Compound B-PET standardized uptake value ratio in the medial occipital, thalami, basal ganglia and medial/dorsal frontal regions in dominantly inherited Alzheimer's disease versus sporadic early-onset Alzheimer's disease. Sporadic early-onset Alzheimer's disease showed relatively greater 18F-fluorodeoxyglucose-PET hypometabolism in Alzheimer's disease signature temporoparietal regions and caudate nuclei, whereas dominantly inherited Alzheimer's disease showed relatively greater hypometabolism in frontal white matter and pericentral regions. Independent component analyses largely replicated these findings by highlighting common and unique Pittsburgh Compound B-PET and 18F-fluorodeoxyglucose-PET binding patterns. In summary, our findings suggest both common and distinct patterns of amyloid and glucose hypometabolism in sporadic and dominantly inherited early-onset Alzheimer's disease.
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Affiliation(s)
- Leonardo Iaccarino
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA 94158, USA
| | - Jorge J Llibre-Guerra
- The Dominantly Inherited Alzheimer Network (DIAN), St Louis, MO 63108, USA
- Department of Neurology, Washington University in St Louis, St Louis, MO 63108, USA
| | - Eric McDade
- The Dominantly Inherited Alzheimer Network (DIAN), St Louis, MO 63108, USA
- Department of Neurology, Washington University in St Louis, St Louis, MO 63108, USA
| | - Lauren Edwards
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA 94158, USA
| | - Brian Gordon
- Department of Radiology, Washington University in St Louis, St Louis, MO 63110, USA
| | - Tammie Benzinger
- Department of Radiology, Washington University in St Louis, St Louis, MO 63110, USA
| | - Jason Hassenstab
- The Dominantly Inherited Alzheimer Network (DIAN), St Louis, MO 63108, USA
- Department of Neurology, Washington University in St Louis, St Louis, MO 63108, USA
| | - Joel H Kramer
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA 94158, USA
| | - Yan Li
- Department of Biostatistics, Washington University in St Louis, St Louis, MO 63110, USA
| | - Bruce L Miller
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA 94158, USA
| | - Zachary Miller
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA 94158, USA
| | - John C Morris
- The Dominantly Inherited Alzheimer Network (DIAN), St Louis, MO 63108, USA
- Department of Neurology, Washington University in St Louis, St Louis, MO 63108, USA
| | - Nidhi Mundada
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA 94158, USA
| | - Richard J Perrin
- Department of Pathology and Immunology, Washington University in St Louis, St Louis, MO 63110, USA
| | - Howard J Rosen
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA 94158, USA
| | - David Soleimani-Meigooni
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA 94158, USA
| | - Amelia Strom
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA 94158, USA
| | - Elena Tsoy
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA 94158, USA
| | - Guoqiao Wang
- Department of Biostatistics, Washington University in St Louis, St Louis, MO 63110, USA
| | - Chengjie Xiong
- Department of Biostatistics, Washington University in St Louis, St Louis, MO 63110, USA
| | - Ricardo Allegri
- Department of Cognitive Neurology, Institute for Neurological Research Fleni, Buenos Aires 1428, Argentina
| | - Patricio Chrem
- Department of Cognitive Neurology, Institute for Neurological Research Fleni, Buenos Aires 1428, Argentina
| | - Silvia Vazquez
- Department of Cognitive Neurology, Institute for Neurological Research Fleni, Buenos Aires 1428, Argentina
| | - Sarah B Berman
- Department of Neurology, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - Jasmeer Chhatwal
- Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
| | - Colin L Masters
- Department of Neuroscience, Florey Institute, The University of Melbourne, Melbourne 3052, Australia
| | - Martin R Farlow
- Neuroscience Center, Indiana University School of Medicine at Indianapolis, Indiana, IN 46202, USA
| | - Mathias Jucker
- DZNE-German Center for Neurodegenerative Diseases, Tübingen 72076, Germany
| | - Johannes Levin
- Department of Neurology, Ludwig-Maximilians-University, Munich 80539, Germany
- German Center for Neurodegenerative Diseases, Munich 81377, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich 81377, Germany
| | - Stephen Salloway
- Memory & Aging Program, Butler Hospital, Brown University in Providence, RI 02906, USA
| | - Nick C Fox
- Dementia Research Centre, Department of Neurodegenerative Disease, University College London Institute of Neurology, London WC1N 3BG, UK
| | - Gregory S Day
- Department of Neurology, Mayo Clinic Florida, Jacksonville, FL 33224, USA
| | - Maria Luisa Gorno-Tempini
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA 94158, USA
| | - Adam L Boxer
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA 94158, USA
| | - Renaud La Joie
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA 94158, USA
| | - Randall Bateman
- The Dominantly Inherited Alzheimer Network (DIAN), St Louis, MO 63108, USA
- Department of Neurology, Washington University in St Louis, St Louis, MO 63108, USA
| | - Gil D Rabinovici
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA 94158, USA
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA 94143, USA
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22
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Elman JA, Schork NJ, Rangan AV. Exploring the genetic heterogeneity of Alzheimer's disease: Evidence for genetic subtypes. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2023.05.02.23289347. [PMID: 37205553 PMCID: PMC10187457 DOI: 10.1101/2023.05.02.23289347] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/21/2023]
Abstract
Background Alzheimer's disease (AD) exhibits considerable phenotypic heterogeneity, suggesting the potential existence of subtypes. AD is under substantial genetic influence, thus identifying systematic variation in genetic risk may provide insights into disease origins. Objective We investigated genetic heterogeneity in AD risk through a multi-step analysis. Methods We performed principal component analysis (PCA) on AD-associated variants in the UK Biobank (AD cases=2,739, controls=5,478) to assess structured genetic heterogeneity. Subsequently, a biclustering algorithm searched for distinct disease-specific genetic signatures among subsets of cases. Replication tests were conducted using the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset (AD cases=500, controls=470). We categorized a separate set of ADNI individuals with mild cognitive impairment (MCI; n=399) into genetic subtypes and examined cognitive, amyloid, and tau trajectories. Results PCA revealed three distinct clusters ("constellations") driven primarily by different correlation patterns in a region of strong LD surrounding the MAPT locus. Constellations contained a mixture of cases and controls, reflecting disease-relevant but not disease-specific structure. We found two disease-specific biclusters among AD cases. Pathway analysis linked bicluster-associated variants to neuron morphogenesis and outgrowth. Disease-relevant and disease-specific structure replicated in ADNI, and bicluster 2 exhibited increased CSF p-tau and cognitive decline over time. Conclusions This study unveils a hierarchical structure of AD genetic risk. Disease-relevant constellations may represent haplotype structure that does not increase risk directly but may alter the relative importance of other genetic risk factors. Biclusters may represent distinct AD genetic subtypes. This structure is replicable and relates to differential pathological accumulation and cognitive decline over time.
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Affiliation(s)
- Jeremy A. Elman
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
- Center for Behavior Genetics of Aging, University of California San Diego, La Jolla, CA, USA
| | - Nicholas J. Schork
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
- The Translational Genomics Research Institute, Quantitative Medicine and Systems Biology, Phoenix, AZ, USA
| | - Aaditya V. Rangan
- Department of Mathematics, New York University, New York, New York, USA
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23
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Jiang J, Shi R, Lu J, Wang M, Zhang Q, Zhang S, Wang L, Alberts I, Rominger A, Zuo C, Shi K. Detection of individual brain tau deposition in Alzheimer's disease based on latent feature-enhanced generative adversarial network. Neuroimage 2024; 291:120593. [PMID: 38554780 DOI: 10.1016/j.neuroimage.2024.120593] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Revised: 03/17/2024] [Accepted: 03/25/2024] [Indexed: 04/02/2024] Open
Abstract
OBJECTIVE The conventional methods for interpreting tau PET imaging in Alzheimer's disease (AD), including visual assessment and semi-quantitative analysis of fixed hallmark regions, are insensitive to detect individual small lesions because of the spatiotemporal neuropathology's heterogeneity. In this study, we proposed a latent feature-enhanced generative adversarial network model for the automatic extraction of individual brain tau deposition regions. METHODS The latent feature-enhanced generative adversarial network we propose can learn the distribution characteristics of tau PET images of cognitively normal individuals and output the abnormal distribution regions of patients. This model was trained and validated using 1131 tau PET images from multiple centres (with distinct races, i.e., Caucasian and Mongoloid) with different tau PET ligands. The overall quality of synthetic imaging was evaluated using structural similarity (SSIM), peak signal to noise ratio (PSNR), and mean square error (MSE). The model was compared to the fixed templates method for diagnosing and predicting AD. RESULTS The reconstructed images archived good quality, with SSIM = 0.967 ± 0.008, PSNR = 31.377 ± 3.633, and MSE = 0.0011 ± 0.0007 in the independent test set. The model showed higher classification accuracy (AUC = 0.843, 95 % CI = 0.796-0.890) and stronger correlation with clinical scales (r = 0.508, P < 0.0001). The model also achieved superior predictive performance in the survival analysis of cognitive decline, with a higher hazard ratio: 3.662, P < 0.001. INTERPRETATION The LFGAN4Tau model presents a promising new approach for more accurate detection of individualized tau deposition. Its robustness across tracers and races makes it a potentially reliable diagnostic tool for AD in practice.
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Affiliation(s)
- Jiehui Jiang
- Institute of Biomedical Engineering, School of Life Sciences, Shanghai University, Shanghai, China.
| | - Rong Shi
- School of Information and Communication Engineering, Shanghai University, Shanghai, China
| | - Jiaying Lu
- Department of Nuclear Medicine & PET Center, Huashan Hospital, Fudan University, Shanghai, China; National Research Center for Aging and Medicine and National Center for Neurological Disorders, Huashan Hospital, Fudan University, Shanghai, China
| | - Min Wang
- Institute of Biomedical Engineering, School of Life Sciences, Shanghai University, Shanghai, China.
| | - Qi Zhang
- School of Information and Communication Engineering, Shanghai University, Shanghai, China
| | - Shuoyan Zhang
- School of Information and Communication Engineering, Shanghai University, Shanghai, China
| | - Luyao Wang
- Institute of Biomedical Engineering, School of Life Sciences, Shanghai University, Shanghai, China
| | - Ian Alberts
- Department of Nuclear Medicine, Inselspital, University of Bern, Bern, Switzerland
| | - Axel Rominger
- Department of Nuclear Medicine, Inselspital, University of Bern, Bern, Switzerland
| | - Chuantao Zuo
- Department of Nuclear Medicine & PET Center, Huashan Hospital, Fudan University, Shanghai, China; National Research Center for Aging and Medicine and National Center for Neurological Disorders, Huashan Hospital, Fudan University, Shanghai, China; Human Phenome Institute, Fudan University, Shanghai, China.
| | - Kuangyu Shi
- Department of Nuclear Medicine, Inselspital, University of Bern, Bern, Switzerland; Department of Informatics, Technical University of Munich, Munich, Germany
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24
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Akram AS, Grezenko H, Singh P, Ahmed M, Hassan BD, Hagenahalli Anand V, Elashry AA, Nazir F, Khan R. Advancing the Frontier: Neuroimaging Techniques in the Early Detection and Management of Neurodegenerative Diseases. Cureus 2024; 16:e61335. [PMID: 38947709 PMCID: PMC11213966 DOI: 10.7759/cureus.61335] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/29/2024] [Indexed: 07/02/2024] Open
Abstract
Alzheimer's and Parkinson's diseases are among the most prevalent neurodegenerative conditions affecting aging populations globally, presenting significant challenges in early diagnosis and management. This narrative review explores the pivotal role of advanced neuroimaging techniques in detecting and managing these diseases at early stages, potentially slowing their progression through timely interventions. Recent advancements in MRI, such as ultra-high-field systems and functional MRI, have enhanced the sensitivity for detecting subtle structural and functional changes. Additionally, the development of novel amyloid-beta tracers and other emerging modalities like optical imaging and transcranial ultrasonography have improved the diagnostic accuracy and capability of existing methods. This review highlights the clinical applications of these technologies in Alzheimer's and Parkinson's diseases, where they have shown improved diagnostic performance, enabling earlier intervention and better prognostic outcomes. Moreover, the integration of artificial intelligence (AI) and longitudinal research is emerging as a promising enhancement to refine early detection strategies further. However, this review also addresses the technical, ethical, and accessibility challenges in the field, advocating for the more extensive use of advanced imaging technologies to overcome these barriers. Finally, we emphasize the need for a holistic approach that incorporates both neurological and psychiatric perspectives, which is crucial for optimizing patient care and outcomes in the management of neurodegenerative diseases.
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Affiliation(s)
- Ahmed S Akram
- Psychiatry, Faisalabad Medical University, Faisalabad, PAK
| | - Han Grezenko
- Medicine and Surgery, Guangxi Medical University, Nanning, CHN
- Translational Neuroscience, Barrow Neurological Institute, Phoenix, USA
| | - Prem Singh
- Neurology, Dow University of Health Sciences, Karachi, PAK
| | - Muhammad Ahmed
- Psychiatry and Behavioral Sciences, Dow University of Health Sciences, Karachi, PAK
| | | | | | | | - Faran Nazir
- Internal Medicine, Faisalabad Medical University, Faisalabad, PAK
| | - Rehman Khan
- Internal Medicine, Mayo Hospital, Lahore, PAK
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25
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Wang M, Lu J, Zhang Y, Zhang Q, Wang L, Wu P, Brendel M, Rominger A, Shi K, Zhao Q, Jiang J, Zuo C. Characterization of tau propagation pattern and cascading hypometabolism from functional connectivity in Alzheimer's disease. Hum Brain Mapp 2024; 45:e26689. [PMID: 38703095 PMCID: PMC11069321 DOI: 10.1002/hbm.26689] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2024] [Revised: 03/16/2024] [Accepted: 04/08/2024] [Indexed: 05/06/2024] Open
Abstract
Tau pathology and its spatial propagation in Alzheimer's disease (AD) play crucial roles in the neurodegenerative cascade leading to dementia. However, the underlying mechanisms linking tau spreading to glucose metabolism remain elusive. To address this, we aimed to examine the association between pathologic tau aggregation, functional connectivity, and cascading glucose metabolism and further explore the underlying interplay mechanisms. In this prospective cohort study, we enrolled 79 participants with 18F-Florzolotau positron emission tomography (PET), 18F-fluorodeoxyglucose PET, resting-state functional, and anatomical magnetic resonance imaging (MRI) images in the hospital-based Shanghai Memory Study. We employed generalized linear regression and correlation analyses to assess the associations between Florzolotau accumulation, functional connectivity, and glucose metabolism in whole-brain and network-specific manners. Causal mediation analysis was used to evaluate whether functional connectivity mediates the association between pathologic tau and cascading glucose metabolism. We examined 22 normal controls and 57 patients with AD. In the AD group, functional connectivity was associated with Florzolotau covariance (β = .837, r = 0.472, p < .001) and glucose covariance (β = 1.01, r = 0.499, p < .001). Brain regions with higher tau accumulation tend to be connected to other regions with high tau accumulation through functional connectivity or metabolic connectivity. Mediation analyses further suggest that functional connectivity partially modulates the influence of tau accumulation on downstream glucose metabolism (mediation proportion: 49.9%). Pathologic tau may affect functionally connected neurons directly, triggering downstream glucose metabolism changes. This study sheds light on the intricate relationship between tau pathology, functional connectivity, and downstream glucose metabolism, providing critical insights into AD pathophysiology and potential therapeutic targets.
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Affiliation(s)
- Min Wang
- School of Life SciencesShanghai UniversityShanghaiChina
| | - Jiaying Lu
- Department of Nuclear Medicine & PET Center, Huashan HospitalFudan UniversityShanghaiChina
- National Clinical Research Center for Aging and Medicine, Huashan HospitalFudan UniversityShanghaiChina
- National Center for Neurological Disorders, Huashan HospitalFudan UniversityShanghaiChina
| | - Ying Zhang
- School of Life SciencesShanghai UniversityShanghaiChina
| | - Qi Zhang
- School of Life SciencesShanghai UniversityShanghaiChina
| | - Luyao Wang
- School of Life SciencesShanghai UniversityShanghaiChina
| | - Ping Wu
- Department of Nuclear Medicine & PET Center, Huashan HospitalFudan UniversityShanghaiChina
- National Clinical Research Center for Aging and Medicine, Huashan HospitalFudan UniversityShanghaiChina
- National Center for Neurological Disorders, Huashan HospitalFudan UniversityShanghaiChina
| | | | - Axel Rominger
- Department of Nuclear Medicine, InselspitalBern University Hospital, University of BernBernSwitzerland
| | - Kuangyu Shi
- Department of Nuclear Medicine, InselspitalBern University Hospital, University of BernBernSwitzerland
- Computer Aided Medical Procedures, School of Computation, Information and TechnologyTechnical University of MunichMunichGermany
| | - Qianhua Zhao
- National Clinical Research Center for Aging and Medicine, Huashan HospitalFudan UniversityShanghaiChina
- National Center for Neurological Disorders, Huashan HospitalFudan UniversityShanghaiChina
- Department of Neurology, Huashan HospitalFudan UniversityShanghaiChina
| | - Jiehui Jiang
- School of Life SciencesShanghai UniversityShanghaiChina
| | - Chuantao Zuo
- Department of Nuclear Medicine & PET Center, Huashan HospitalFudan UniversityShanghaiChina
- National Clinical Research Center for Aging and Medicine, Huashan HospitalFudan UniversityShanghaiChina
- National Center for Neurological Disorders, Huashan HospitalFudan UniversityShanghaiChina
- Human Phenome InstituteFudan UniversityShanghaiChina
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26
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Kampmann M. Molecular and cellular mechanisms of selective vulnerability in neurodegenerative diseases. Nat Rev Neurosci 2024; 25:351-371. [PMID: 38575768 DOI: 10.1038/s41583-024-00806-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/01/2024] [Indexed: 04/06/2024]
Abstract
The selective vulnerability of specific neuronal subtypes is a hallmark of neurodegenerative diseases. In this Review, I summarize our current understanding of the brain regions and cell types that are selectively vulnerable in different neurodegenerative diseases and describe the proposed underlying cell-autonomous and non-cell-autonomous mechanisms. I highlight how recent methodological innovations - including single-cell transcriptomics, CRISPR-based screens and human cell-based models of disease - are enabling new breakthroughs in our understanding of selective vulnerability. An understanding of the molecular mechanisms that determine selective vulnerability and resilience would shed light on the key processes that drive neurodegeneration and point to potential therapeutic strategies to protect vulnerable cell populations.
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Affiliation(s)
- Martin Kampmann
- Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, CA, USA.
- Institute for Neurodegenerative Diseases, University of California, San Francisco, San Francisco, CA, USA.
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27
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Katsumi Y, Howe IA, Eckbo R, Wong B, Quimby M, Hochberg D, McGinnis SM, Putcha D, Wolk DA, Touroutoglou A, Dickerson BC. Default mode network tau predicts future clinical decline in atypical early Alzheimer's disease. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.04.17.24305620. [PMID: 38699357 PMCID: PMC11065041 DOI: 10.1101/2024.04.17.24305620] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/05/2024]
Abstract
Identifying individuals with early stage Alzheimer's disease (AD) at greater risk of steeper clinical decline would allow professionals and loved ones to make better-informed medical, support, and life planning decisions. Despite accumulating evidence on the clinical prognostic value of tau PET in typical late-onset amnestic AD, its utility in predicting clinical decline in individuals with atypical forms of AD remains unclear. In this study, we examined the relationship between baseline tau PET signal and the rate of subsequent clinical decline in a sample of 48 A+/T+/N+ patients with mild cognitive impairment or mild dementia due to AD with atypical clinical phenotypes (Posterior Cortical Atrophy, logopenic variant Primary Progressive Aphasia, and amnestic syndrome with multi-domain impairment and age of onset < 65 years). All patients underwent structural magnetic resonance imaging (MRI), tau (18F-Flortaucipir) PET, and amyloid (either 18F-Florbetaben or 11C-Pittsburgh Compound B) PET scans at baseline. Each patient's longitudinal clinical decline was assessed by calculating the annualized change in the Clinical Dementia Rating Sum-of-Boxes (CDR-SB) scores from baseline to follow-up (mean time interval = 14.55 ± 3.97 months). Our sample of early atypical AD patients showed an increase in CDR-SB by 1.18 ± 1.25 points per year: t(47) = 6.56, p < .001, d = 0.95. These AD patients showed prominent baseline tau burden in posterior cortical regions including the major nodes of the default mode network, including the angular gyrus, posterior cingulate cortex/precuneus, and lateral temporal cortex. Greater baseline tau in the broader default mode network predicted faster clinical decline. Tau in the default mode network was the strongest predictor of clinical decline, outperforming baseline clinical impairment, tau in other functional networks, and the magnitude of cortical atrophy and amyloid burden in the default mode network. Overall, these findings point to the contribution of baseline tau burden within the default mode network of the cerebral cortex to predicting the magnitude of clinical decline in a sample of atypical early AD patients one year later. This simple measure based on a tau PET scan could aid the development of a personalized prognostic, monitoring, and treatment plan tailored to each individual patient, which would help clinicians not only predict the natural evolution of the disease but also estimate the effect of disease-modifying therapies on slowing subsequent clinical decline given the patient's tau burden while still early in the disease course.
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Affiliation(s)
- Yuta Katsumi
- Frontotemporal Disorders Unit, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
| | - Inola A Howe
- Frontotemporal Disorders Unit, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
| | - Ryan Eckbo
- Frontotemporal Disorders Unit, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
| | - Bonnie Wong
- Frontotemporal Disorders Unit, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
| | - Megan Quimby
- Frontotemporal Disorders Unit, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
| | - Daisy Hochberg
- Frontotemporal Disorders Unit, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
| | - Scott M McGinnis
- Frontotemporal Disorders Unit, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
- Center for Brain Mind Medicine, Department of Neurology, Brigham & Women's Hospital, Boston, MA 02115, USA
| | - Deepti Putcha
- Frontotemporal Disorders Unit, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
- Center for Brain Mind Medicine, Department of Neurology, Brigham & Women's Hospital, Boston, MA 02115, USA
| | - David A Wolk
- Department of Neurology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Alexandra Touroutoglou
- Frontotemporal Disorders Unit, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA 02129, USA
- Alzheimer's Disease Research Center, Massachusetts General Hospital, Charlestown, MA 02129, USA
| | - Bradford C Dickerson
- Frontotemporal Disorders Unit, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA 02129, USA
- Alzheimer's Disease Research Center, Massachusetts General Hospital, Charlestown, MA 02129, USA
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28
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Bischof GN, Jaeger E, Giehl K, Jessen F, Onur OA, O'Bryant S, Kara E, Weiss PH, Drzezga A. Cortical Tau Aggregation Patterns associated with Apraxia in Alzheimer's Disease. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.04.09.24305535. [PMID: 38645131 PMCID: PMC11030486 DOI: 10.1101/2024.04.09.24305535] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/23/2024]
Abstract
Objectives Apraxia is a core feature of Alzheimer's disease, but the pathomechanism of this characteristic symptom is not well understood. Here, we systematically investigated apraxia profiles in a well-defined group of patients with Alzheimer's disease (AD; N=32) who additionally underwent PET imaging with the second-generation tau PET tracer [18F]PI-2620. We hypothesized that specific patterns of tau pathology might be related to apraxic deficits. Methods Patients (N=32) with a biomarker-confirmed diagnosis of Alzheimer's disease were recruited in addition to a sample cognitively unimpaired controls (CU 1 ; N=41). Both groups underwent in-depth neuropsychological assessment of apraxia (Dementia Apraxia Screening Test; DATE and the Cologne Apraxia Screening; KAS). In addition, static PET imaging with [18F]PI-2620 was performed to assess tau pathology in the AD patients. To specifically investigate the association of apraxia with regional tau-pathology, we compared the PET-data from this group with an independent sample of amyloid-negative cognitively intact participants (CU 2; N=54) by generation of z-score-deviation maps as well as voxel- based multiple regression analyses. Results We identified significant clusters of tau-aggregation in praxis-related regions (e.g., supramarginal gyrus, angular gyrus, temporal, parietal and occipital regions) that were associated with apraxia. These regions were similar between the two apraxia assessments. No correlations between tau-tracer uptake in primary motor cortical or subcortical brain regions and apraxia were observed. Conclusions These results suggest that tau deposition in specific cortical brain regions may induce local neuronal dysfunction leading to a dose-dependent functional decline in praxis performance.
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29
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Mohanty R, Ferreira D, Westman E. Multi-pathological contributions toward atrophy patterns in the Alzheimer's disease continuum. Front Neurosci 2024; 18:1355695. [PMID: 38655107 PMCID: PMC11036869 DOI: 10.3389/fnins.2024.1355695] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2023] [Accepted: 03/07/2024] [Indexed: 04/26/2024] Open
Abstract
Introduction Heterogeneity in downstream atrophy in Alzheimer's disease (AD) is predominantly investigated in relation to pathological hallmarks (Aβ, tau) and co-pathologies (cerebrovascular burden) independently. However, the proportional contribution of each pathology in determining atrophy pattern remains unclear. We assessed heterogeneity in atrophy using two recently conceptualized dimensions: typicality (typical AD atrophy at the center and deviant atypical atrophy on either extreme including limbic predominant to hippocampal sparing patterns) and severity (overall neurodegeneration spanning minimal atrophy to diffuse typical AD atrophy) in relation to Aβ, tau, and cerebrovascular burden. Methods We included 149 Aβ + individuals on the AD continuum (cognitively normal, prodromal AD, AD dementia) and 163 Aβ- cognitively normal individuals from the ADNI. We modeled heterogeneity in MRI-based atrophy with continuous-scales of typicality (ratio of hippocampus to cortical volume) and severity (total gray matter volume). Partial correlation models investigated the association of typicality/severity with (a) Aβ (global Aβ PET centiloid), tau (global tau PET SUVR), cerebrovascular (total white matter hypointensity volume) burden (b) four cognitive domains (memory, executive function, language, visuospatial composites). Using multiple regression, we assessed the association of each pathological burden and typicality/severity with cognition. Results (a) In the AD continuum, typicality (r = -0.31, p < 0.001) and severity (r = -0.37, p < 0.001) were associated with tau burden after controlling for Aβ, cerebrovascular burden and age. Findings imply greater tau pathology in limbic predominant atrophy and diffuse atrophy. (b) Typicality was associated with memory (r = 0.49, p < 0.001) and language scores (r = 0.19, p = 0.02). Severity was associated with memory (r = 0.26, p < 0.001), executive function (r = 0.24, p = 0.003) and language scores (r = 0.29, p < 0.001). Findings imply better cognitive performance in hippocampal sparing and minimal atrophy patterns. Beyond typicality/severity, tau burden but not Aβ and cerebrovascular burden explained cognition. Conclusion In the AD continuum, atrophy-based severity was more strongly associated with tau burden than typicality after accounting for Aβ and cerebrovascular burden. Cognitive performance in memory, executive function and language domains was explained by typicality and/or severity and additionally tau pathology. Typicality and severity may differentially reflect burden arising from tau pathology but not Aβ or cerebrovascular pathologies which need to be accounted for when investigating AD heterogeneity.
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Affiliation(s)
- Rosaleena Mohanty
- Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Karolinska Institutet, Huddinge, Sweden
| | - Daniel Ferreira
- Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Karolinska Institutet, Huddinge, Sweden
- Facultad de Ciencias de la Salud, Universidad Fernando Pessoa Canarias, Las Palmas, Spain
| | - Eric Westman
- Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Karolinska Institutet, Huddinge, Sweden
- Department of Neuroimaging, Center for Neuroimaging Sciences, Institute of Psychiatry, Psychology and Neuroscience, Kings College London, London, United Kingdom
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30
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Weinstein G, Kojis DJ, Ghosh S, Beiser AS, Seshadri S. Association of Neurotrophic Factors at Midlife With In Vivo Measures of β-Amyloid and Tau Burden 15 Years Later in Dementia-Free Adults. Neurology 2024; 102:e209198. [PMID: 38471064 PMCID: PMC11033983 DOI: 10.1212/wnl.0000000000209198] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Accepted: 12/13/2023] [Indexed: 03/14/2024] Open
Abstract
BACKGROUND AND OBJECTIVES Neurotrophic factors (NTFs) play an important role in Alzheimer disease (AD) pathophysiology. Brain-derived neurotrophic factor (BDNF) and vascular endothelial growth factor (VEGF) are important NTFs. However, a direct link of BDNF and VEGF circulating levels with in vivo measures of amyloid-β (Aβ) and tau burden remains to be elucidated. We explored the relationship of BDNF and VEGF serum levels with future brain Aβ and tau pathology in a cohort of cognitively healthy, predominantly middle-aged adults and tested for possible effect modifications by sex and menopausal status. METHODS This cross-sectional analysis was conducted using data from the Framingham Heart Study (FHS), a community-based cohort study. The study sample included cognitively healthy participants from the FHS Offspring and Third-generation cohorts. BDNF and VEGF were measured in the third-generation cohort during examination cycles 2 (2005-2008) and 1 (2002-2005), respectively, and in the offspring cohort during examination cycle 7 (1998-2001). Participants underwent 11C-Pittsburgh compound B amyloid and 18F-Flortaucipir tau-PET imaging (2015-2021). Linear regression models were used to assess the relationship of serum BDNF and VEGF levels with regional tau and global Aβ, adjusting for potential confounders. Interactions with sex and menopausal status were additionally tested. RESULTS The sample included 414 individuals (mean age = 41 ± 9 years; 51% female). Continuous measures of BDNF and VEGF were associated with tau signal in the rhinal region after adjustment for potential confounders (β = -0.15 ± 0.06, p = 0.018 and β = -0.19 ± 0.09, p = 0.043, respectively). High BDNF (≥32,450 pg/mL) and VEGF (≥488 pg/mL) levels were significantly related to lower rhinal tau (β = -0.27 ± 0.11, p = 0.016 and β = -0.40 ± 0.14, p = 0.004, respectively) and inferior temporal tau (β = -0.24 ± 0.11, p = 0.028 and β = -0.26 ± 0.13, p = 0.049, respectively). The BDNF-rhinal tau association was observed only among male individuals. Overall, BDNF and VEGF were not associated with global amyloid; however, high VEGF levels were associated with lower amyloid burden in postmenopausal women (β = -1.96 ± 0.70, p = 0.013, per 1 pg/mL). DISCUSSION This study demonstrates a robust association between BDNF and VEGF serum levels with in vivo measures of tau almost 2 decades later. These findings add to mounting evidence from preclinical studies suggesting a role of NTFs as valuable blood biomarkers for AD risk prediction.
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Affiliation(s)
- Galit Weinstein
- From the School of Public Health (G.W.), University of Haifa, Israel; Department of Biostatistics (D.J.K., A.S.B.), Boston University School of Public Health, Boston; The Framingham Study (D.J.K., S.G., A.S.B., S.S.); Department of Neurology (S.G., A.S.B., S.S.), Boston University Chobanian & Avedisian School of Medicine, MA; and Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases (S.S.), University of Texas Health Sciences Center, San Antonio
| | - Daniel J Kojis
- From the School of Public Health (G.W.), University of Haifa, Israel; Department of Biostatistics (D.J.K., A.S.B.), Boston University School of Public Health, Boston; The Framingham Study (D.J.K., S.G., A.S.B., S.S.); Department of Neurology (S.G., A.S.B., S.S.), Boston University Chobanian & Avedisian School of Medicine, MA; and Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases (S.S.), University of Texas Health Sciences Center, San Antonio
| | - Saptaparni Ghosh
- From the School of Public Health (G.W.), University of Haifa, Israel; Department of Biostatistics (D.J.K., A.S.B.), Boston University School of Public Health, Boston; The Framingham Study (D.J.K., S.G., A.S.B., S.S.); Department of Neurology (S.G., A.S.B., S.S.), Boston University Chobanian & Avedisian School of Medicine, MA; and Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases (S.S.), University of Texas Health Sciences Center, San Antonio
| | - Alexa S Beiser
- From the School of Public Health (G.W.), University of Haifa, Israel; Department of Biostatistics (D.J.K., A.S.B.), Boston University School of Public Health, Boston; The Framingham Study (D.J.K., S.G., A.S.B., S.S.); Department of Neurology (S.G., A.S.B., S.S.), Boston University Chobanian & Avedisian School of Medicine, MA; and Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases (S.S.), University of Texas Health Sciences Center, San Antonio
| | - Sudha Seshadri
- From the School of Public Health (G.W.), University of Haifa, Israel; Department of Biostatistics (D.J.K., A.S.B.), Boston University School of Public Health, Boston; The Framingham Study (D.J.K., S.G., A.S.B., S.S.); Department of Neurology (S.G., A.S.B., S.S.), Boston University Chobanian & Avedisian School of Medicine, MA; and Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases (S.S.), University of Texas Health Sciences Center, San Antonio
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31
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Prabhu P, Morise H, Kudo K, Beagle A, Mizuiri D, Syed F, Kotegar KA, Findlay A, Miller BL, Kramer JH, Rankin KP, Garcia PA, Kirsch HE, Vossel K, Nagarajan SS, Ranasinghe KG. Abnormal gamma phase-amplitude coupling in the parahippocampal cortex is associated with network hyperexcitability in Alzheimer's disease. Brain Commun 2024; 6:fcae121. [PMID: 38665964 PMCID: PMC11043655 DOI: 10.1093/braincomms/fcae121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Revised: 03/08/2024] [Accepted: 04/07/2024] [Indexed: 04/28/2024] Open
Abstract
While animal models of Alzheimer's disease (AD) have shown altered gamma oscillations (∼40 Hz) in local neural circuits, the low signal-to-noise ratio of gamma in the resting human brain precludes its quantification via conventional spectral estimates. Phase-amplitude coupling (PAC) indicating the dynamic integration between the gamma amplitude and the phase of low-frequency (4-12 Hz) oscillations is a useful alternative to capture local gamma activity. In addition, PAC is also an index of neuronal excitability as the phase of low-frequency oscillations that modulate gamma amplitude, effectively regulates the excitability of local neuronal firing. In this study, we sought to examine the local neuronal activity and excitability using gamma PAC, within brain regions vulnerable to early AD pathophysiology-entorhinal cortex and parahippocampus, in a clinical population of patients with AD and age-matched controls. Our clinical cohorts consisted of a well-characterized cohort of AD patients (n = 50; age, 60 ± 8 years) with positive AD biomarkers, and age-matched, cognitively unimpaired controls (n = 35; age, 63 ± 5.8 years). We identified the presence or the absence of epileptiform activity in AD patients (AD patients with epileptiform activity, AD-EPI+, n = 20; AD patients without epileptiform activity, AD-EPI-, n = 30) using long-term electroencephalography (LTM-EEG) and 1-hour long magnetoencephalography (MEG) with simultaneous EEG. Using the source reconstructed MEG data, we computed gamma PAC as the coupling between amplitude of the gamma frequency (30-40 Hz) with phase of the theta (4-8 Hz) and alpha (8-12 Hz) frequency oscillations, within entorhinal and parahippocampal cortices. We found that patients with AD have reduced gamma PAC in the left parahippocampal cortex, compared to age-matched controls. Furthermore, AD-EPI+ patients showed greater reductions in gamma PAC than AD-EPI- in bilateral parahippocampal cortices. In contrast, entorhinal cortices did not show gamma PAC abnormalities in patients with AD. Our findings demonstrate the spatial patterns of altered gamma oscillations indicating possible region-specific manifestations of network hyperexcitability within medial temporal lobe regions vulnerable to AD pathophysiology. Greater deficits in AD-EPI+ suggests that reduced gamma PAC is a sensitive index of network hyperexcitability in AD patients. Collectively, the current results emphasize the importance of investigating the role of neural circuit hyperexcitability in early AD pathophysiology and explore its potential as a modifiable contributor to AD pathobiology.
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Affiliation(s)
- Pooja Prabhu
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA 94158, USA
- Department of Data science and Computer Applications, Manipal Institute of Technology, Manipal 576104, India
| | - Hirofumi Morise
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA 94158, USA
- Medical Imaging Business Center, Ricoh Company Ltd., Kanazawa 920-0177, Japan
| | - Kiwamu Kudo
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA 94158, USA
- Medical Imaging Business Center, Ricoh Company Ltd., Kanazawa 920-0177, Japan
| | - Alexander Beagle
- Memory and Aging Center, Department of Neurology, University of California San Francisco, San Francisco, CA 94158, USA
| | - Danielle Mizuiri
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA 94158, USA
| | - Faatimah Syed
- Memory and Aging Center, Department of Neurology, University of California San Francisco, San Francisco, CA 94158, USA
| | - Karunakar A Kotegar
- Department of Data science and Computer Applications, Manipal Institute of Technology, Manipal 576104, India
| | - Anne Findlay
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA 94158, USA
| | - Bruce L Miller
- Memory and Aging Center, Department of Neurology, University of California San Francisco, San Francisco, CA 94158, USA
| | - Joel H Kramer
- Memory and Aging Center, Department of Neurology, University of California San Francisco, San Francisco, CA 94158, USA
| | - Katherine P Rankin
- Memory and Aging Center, Department of Neurology, University of California San Francisco, San Francisco, CA 94158, USA
| | - Paul A Garcia
- Epilepsy Center, Department of Neurology, University of California San Francisco, San Francisco, CA 94158, USA
| | - Heidi E Kirsch
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA 94158, USA
- Epilepsy Center, Department of Neurology, University of California San Francisco, San Francisco, CA 94158, USA
| | - Keith Vossel
- Memory and Aging Center, Department of Neurology, University of California San Francisco, San Francisco, CA 94158, USA
- Mary S. Easton Center for Alzheimer’s Research and Care, Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA 90095, USA
| | - Srikantan S Nagarajan
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA 94158, USA
| | - Kamalini G Ranasinghe
- Memory and Aging Center, Department of Neurology, University of California San Francisco, San Francisco, CA 94158, USA
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32
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Santi MD, Carvalho D, Dapueto R, Bentura M, Zeni M, Martínez-González L, Martínez A, Peralta MA, Rey A, Giglio J, Ortega MG, Savio E, Abin-Carriquiry JA, Arredondo F. Prenylated Flavanone Isolated from Dalea Species as a Potential Multitarget-Neuroprotector in an In Vitro Alzheimer's Disease Mice Model. Neurotox Res 2024; 42:23. [PMID: 38578482 DOI: 10.1007/s12640-024-00703-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Revised: 12/04/2023] [Accepted: 03/25/2024] [Indexed: 04/06/2024]
Abstract
Alzheimer's disease (AD) involves a neurodegenerative process that has not yet been prevented, reversed, or stopped. Continuing with the search for natural pharmacological treatments, flavonoids are a family of compounds with proven neuroprotective effects and multi-targeting behavior. The American genus Dalea L. (Fabaceae) is an important source of bioactive flavonoids. In this opportunity, we tested the neuroprotective potential of three prenylated flavanones isolated from Dalea species in a new in vitro pre-clinical AD model previously developed by us. Our approach consisted in exposing neural cells to conditioned media (3xTg-AD ACM) from neurotoxic astrocytes derived from hippocampi and cortices of old 3xTg-AD mice, mimicking a local neurodegenerative microenvironment. Flavanone 1 and 3 showed a neuroprotective effect against 3xTg-AD ACM, being 1 more active than 3. The structural requirements to afford neuroprotective activity in this model are a 5'-dimethylallyl and 4'-hydroxy at the B ring. In order to search the mechanistic performance of the most active flavanone, we focus on the flavonoid-mediated regulation of GSK-3β-mediated tau phosphorylation previously reported. Flavanone 1 treatment decreased the rise of hyperphosphorylated tau protein neuronal levels induced after 3xTg-AD ACM exposure and inhibited the activity of GSK-3β. Finally, direct exposure of these neurotoxic 3xTg-AD astrocytes to flavanone 1 resulted in toxicity to these cells and reduced the neurotoxicity of 3xTg-AD ACM as well. Our results allow us to present compound 1 as a natural prenylated flavanone that could be used as a precursor to development and design of future drug therapies for AD.
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Affiliation(s)
- Maria D Santi
- Instituto Multidisciplinario de Biología Vegetal (IMBIV-CONICET), Ciudad Universitaria. X5000HUA, Córdoba, Argentina
- I+D Biomédico y Química Farmacéutica, Centro Uruguayo de Imagenología Molecular (CUDIM), Montevideo, Uruguay
| | - Diego Carvalho
- Departamento de Neuroquímica, Instituto de Investigaciones Biológicas Clemente Estable, Montevideo, 11600, Uruguay
- Área de Matemática - DETEMA, Facultad de Química, Universidad de la República, Montevideo, Uruguay
| | - Rosina Dapueto
- I+D Biomédico y Química Farmacéutica, Centro Uruguayo de Imagenología Molecular (CUDIM), Montevideo, Uruguay
| | - Manuela Bentura
- I+D Biomédico y Química Farmacéutica, Centro Uruguayo de Imagenología Molecular (CUDIM), Montevideo, Uruguay
| | - Maia Zeni
- I+D Biomédico y Química Farmacéutica, Centro Uruguayo de Imagenología Molecular (CUDIM), Montevideo, Uruguay
- Área de Radioquímica, Facultad de Química, Universidad de la República, Montevideo, Uruguay
| | - Loreto Martínez-González
- Centro de Investigaciones Biológicas Margarita Salas, CSIC, Calle Ramiro Maétzu 9, Madrid, 28040, Spain
- Centro de Investigación Biomédica en Red Sobre Enfermedades Neurodegenerativas (CIBERNED), Instituto de Salud Carlos III, Avda Monforte de Lemos 3-5, Madrid, 28029, Spain
| | - Ana Martínez
- Centro de Investigaciones Biológicas Margarita Salas, CSIC, Calle Ramiro Maétzu 9, Madrid, 28040, Spain
- Centro de Investigación Biomédica en Red Sobre Enfermedades Neurodegenerativas (CIBERNED), Instituto de Salud Carlos III, Avda Monforte de Lemos 3-5, Madrid, 28029, Spain
| | - Mariana A Peralta
- Instituto Multidisciplinario de Biología Vegetal (IMBIV-CONICET), Ciudad Universitaria. X5000HUA, Córdoba, Argentina
- Farmacognosia, Departamento de Ciencias Farmacéuticas, Facultad de Ciencias Químicas, Universidad Nacional de Córdoba, Ciudad Universitaria, Haya de la torre y Medina Allende, Edificio Ciencias II, X5000HUA Córdoba, Córdoba, Argentina
| | - Ana Rey
- Área de Radioquímica, Facultad de Química, Universidad de la República, Montevideo, Uruguay
| | - Javier Giglio
- I+D Biomédico y Química Farmacéutica, Centro Uruguayo de Imagenología Molecular (CUDIM), Montevideo, Uruguay
- Área de Radioquímica, Facultad de Química, Universidad de la República, Montevideo, Uruguay
| | - Maria G Ortega
- Instituto Multidisciplinario de Biología Vegetal (IMBIV-CONICET), Ciudad Universitaria. X5000HUA, Córdoba, Argentina
- Farmacognosia, Departamento de Ciencias Farmacéuticas, Facultad de Ciencias Químicas, Universidad Nacional de Córdoba, Ciudad Universitaria, Haya de la torre y Medina Allende, Edificio Ciencias II, X5000HUA Córdoba, Córdoba, Argentina
| | - Eduardo Savio
- I+D Biomédico y Química Farmacéutica, Centro Uruguayo de Imagenología Molecular (CUDIM), Montevideo, Uruguay
| | | | - Florencia Arredondo
- I+D Biomédico y Química Farmacéutica, Centro Uruguayo de Imagenología Molecular (CUDIM), Montevideo, Uruguay.
- Departamento de Neuroquímica, Instituto de Investigaciones Biológicas Clemente Estable, Montevideo, 11600, Uruguay.
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Barthélemy NR, Salvadó G, Schindler SE, He Y, Janelidze S, Collij LE, Saef B, Henson RL, Chen CD, Gordon BA, Li Y, La Joie R, Benzinger TLS, Morris JC, Mattsson-Carlgren N, Palmqvist S, Ossenkoppele R, Rabinovici GD, Stomrud E, Bateman RJ, Hansson O. Highly accurate blood test for Alzheimer's disease is similar or superior to clinical cerebrospinal fluid tests. Nat Med 2024; 30:1085-1095. [PMID: 38382645 PMCID: PMC11031399 DOI: 10.1038/s41591-024-02869-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2023] [Accepted: 02/12/2024] [Indexed: 02/23/2024]
Abstract
With the emergence of Alzheimer's disease (AD) disease-modifying therapies, identifying patients who could benefit from these treatments becomes critical. In this study, we evaluated whether a precise blood test could perform as well as established cerebrospinal fluid (CSF) tests in detecting amyloid-β (Aβ) plaques and tau tangles. Plasma %p-tau217 (ratio of phosporylated-tau217 to non-phosphorylated tau) was analyzed by mass spectrometry in the Swedish BioFINDER-2 cohort (n = 1,422) and the US Charles F. and Joanne Knight Alzheimer Disease Research Center (Knight ADRC) cohort (n = 337). Matched CSF samples were analyzed with clinically used and FDA-approved automated immunoassays for Aβ42/40 and p-tau181/Aβ42. The primary and secondary outcomes were detection of brain Aβ or tau pathology, respectively, using positron emission tomography (PET) imaging as the reference standard. Main analyses were focused on individuals with cognitive impairment (mild cognitive impairment and mild dementia), which is the target population for available disease-modifying treatments. Plasma %p-tau217 was clinically equivalent to FDA-approved CSF tests in classifying Aβ PET status, with an area under the curve (AUC) for both between 0.95 and 0.97. Plasma %p-tau217 was generally superior to CSF tests in classification of tau-PET with AUCs of 0.95-0.98. In cognitively impaired subcohorts (BioFINDER-2: n = 720; Knight ADRC: n = 50), plasma %p-tau217 had an accuracy, a positive predictive value and a negative predictive value of 89-90% for Aβ PET and 87-88% for tau PET status, which was clinically equivalent to CSF tests, further improving to 95% using a two-cutoffs approach. Blood plasma %p-tau217 demonstrated performance that was clinically equivalent or superior to clinically used FDA-approved CSF tests in the detection of AD pathology. Use of high-performance blood tests in clinical practice can improve access to accurate AD diagnosis and AD-specific treatments.
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Affiliation(s)
- Nicolas R Barthélemy
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
- Tracy Family Stable Isotope Labeling Quantitation (SILQ) Center, Washington University School of Medicine, St. Louis, MO, USA
| | - Gemma Salvadó
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Faculty of Medicine, Lund University, Lund, Sweden
| | - Suzanne E Schindler
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
- Charles F. and Joanne Knight Alzheimer Disease Research Center (Knight ADRC), Washington University School of Medicine, St. Louis, MO, USA
| | - Yingxin He
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
- Tracy Family Stable Isotope Labeling Quantitation (SILQ) Center, Washington University School of Medicine, St. Louis, MO, USA
| | - Shorena Janelidze
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Faculty of Medicine, Lund University, Lund, Sweden
| | - Lyduine E Collij
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Faculty of Medicine, Lund University, Lund, Sweden
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, location VUmc, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Brain Imaging, Amsterdam, The Netherlands
| | - Benjamin Saef
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
| | - Rachel L Henson
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
| | - Charles D Chen
- Department of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | - Brian A Gordon
- Department of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | - Yan Li
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
- Division of Biostatistics, Washington University in St. Louis, St. Louis, MO, USA
| | - Renaud La Joie
- Department of Neurology, Memory and Aging Center, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
| | - Tammie L S Benzinger
- Department of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | - John C Morris
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
- Charles F. and Joanne Knight Alzheimer Disease Research Center (Knight ADRC), Washington University School of Medicine, St. Louis, MO, USA
| | - Niklas Mattsson-Carlgren
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Faculty of Medicine, Lund University, Lund, Sweden
- Wallenberg Center for Molecular Medicine, Lund University, Lund, Sweden
- Department of Neurology, Skåne University Hospital, Lund University, Lund, Sweden
| | - Sebastian Palmqvist
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Faculty of Medicine, Lund University, Lund, Sweden
- Memory Clinic, Skåne University Hospital, Malmö, Sweden
| | - Rik Ossenkoppele
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Faculty of Medicine, Lund University, Lund, Sweden
- Amsterdam Neuroscience, Brain Imaging, Amsterdam, The Netherlands
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC, location VUmc, Amsterdam, The Netherlands
| | - Gil D Rabinovici
- Department of Neurology, Memory and Aging Center, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, USA
| | - Erik Stomrud
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Faculty of Medicine, Lund University, Lund, Sweden
- Memory Clinic, Skåne University Hospital, Malmö, Sweden
| | - Randall J Bateman
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA.
- Tracy Family Stable Isotope Labeling Quantitation (SILQ) Center, Washington University School of Medicine, St. Louis, MO, USA.
- Charles F. and Joanne Knight Alzheimer Disease Research Center (Knight ADRC), Washington University School of Medicine, St. Louis, MO, USA.
| | - Oskar Hansson
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Faculty of Medicine, Lund University, Lund, Sweden.
- Memory Clinic, Skåne University Hospital, Malmö, Sweden.
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Therriault J, Ashton NJ, Pola I, Triana-Baltzer G, Brum WS, Di Molfetta G, Arslan B, Rahmouni N, Tissot C, Servaes S, Stevenson J, Macedo AC, Pascoal TA, Kolb HC, Jeromin A, Blennow K, Zetterberg H, Rosa-Neto P, Benedet AL. Comparison of two plasma p-tau217 assays to detect and monitor Alzheimer's pathology. EBioMedicine 2024; 102:105046. [PMID: 38471397 PMCID: PMC10943661 DOI: 10.1016/j.ebiom.2024.105046] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Revised: 02/19/2024] [Accepted: 02/21/2024] [Indexed: 03/14/2024] Open
Abstract
BACKGROUND Blood-based biomarkers of Alzheimer's disease (AD) have become increasingly important as scalable tools for diagnosis and determining clinical trial eligibility. P-tau217 is the most promising due to its excellent sensitivity and specificity for AD-related pathological changes. METHODS We compared the performance of two commercially available plasma p-tau217 assays (ALZpath p-tau217 and Janssen p-tau217+) in 294 individuals cross-sectionally. Correlations with amyloid PET and tau PET were assessed, and Receiver Operating Characteristic (ROC) analyses evaluated both p-tau217 assays for identifying AD pathology. FINDINGS Both plasma p-tau217 assays were strongly associated with amyloid and tau PET. Furthermore, both plasma p-tau217 assays identified individuals with AD vs other neurodegenerative diseases (ALZpath AUC = 0.95; Janssen AUC = 0.96). Additionally, plasma p-tau217 concentrations rose with AD severity and their annual changes correlated with tau PET annual change. INTERPRETATION Both p-tau217 assays had excellent diagnostic performance for AD. Our study supports the future clinical use of commercially-available assays for p-tau217. FUNDING This research is supported by the Weston Brain Institute, Canadian Institutes of Health Research (CIHR), Canadian Consortium on Neurodegeneration in Aging, the Alzheimer's Association, Brain Canada Foundation, the Fonds de Recherche du Québec - Santé and the Colin J. Adair Charitable Foundation.
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Affiliation(s)
- Joseph Therriault
- Translational Neuroimaging Laboratory, McGill University Research Centre for Studies in Aging, Alzheimer's Disease Research Unit, Douglas Research Institute, Le Centre Intégré Universitaire de Santé et de Services Sociaux (CIUSSS) de l'Ouest-de-l'Île-de-Montréal, Montréal, Québec H4H 1R3, Canada; Department of Neurology and Neurosurgery, Faculty of Medicine, McGill University, Montreal, Quebec H3A 2B4, Canada
| | - Nicholas James Ashton
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy, University of Gothenburg, Mölndal 6 431 41, Sweden; Wallenberg Centre for Molecular Medicine, University of Gothenburg, Gothenburg 6 431 41, Sweden; King's College London, Institute of Psychiatry, Psychology and Neuroscience, Maurice Wohl Institute Clinical Neuroscience Institute, London SE5 9RT, UK; NIHR Biomedical Research Centre for Mental Health and Biomedical Research Unit for Dementia at South London and Maudsley NHS Foundation, London SE5 8AF, UK
| | - Ilaria Pola
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy, University of Gothenburg, Mölndal 6 431 41, Sweden
| | | | - Wagner Scheeren Brum
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy, University of Gothenburg, Mölndal 6 431 41, Sweden
| | - Guglielmo Di Molfetta
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy, University of Gothenburg, Mölndal 6 431 41, Sweden
| | - Burak Arslan
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy, University of Gothenburg, Mölndal 6 431 41, Sweden
| | - Nesrine Rahmouni
- Translational Neuroimaging Laboratory, McGill University Research Centre for Studies in Aging, Alzheimer's Disease Research Unit, Douglas Research Institute, Le Centre Intégré Universitaire de Santé et de Services Sociaux (CIUSSS) de l'Ouest-de-l'Île-de-Montréal, Montréal, Québec H4H 1R3, Canada; Department of Neurology and Neurosurgery, Faculty of Medicine, McGill University, Montreal, Quebec H3A 2B4, Canada
| | - Cecile Tissot
- Translational Neuroimaging Laboratory, McGill University Research Centre for Studies in Aging, Alzheimer's Disease Research Unit, Douglas Research Institute, Le Centre Intégré Universitaire de Santé et de Services Sociaux (CIUSSS) de l'Ouest-de-l'Île-de-Montréal, Montréal, Québec H4H 1R3, Canada; Department of Neurology and Neurosurgery, Faculty of Medicine, McGill University, Montreal, Quebec H3A 2B4, Canada
| | - Stijn Servaes
- Translational Neuroimaging Laboratory, McGill University Research Centre for Studies in Aging, Alzheimer's Disease Research Unit, Douglas Research Institute, Le Centre Intégré Universitaire de Santé et de Services Sociaux (CIUSSS) de l'Ouest-de-l'Île-de-Montréal, Montréal, Québec H4H 1R3, Canada; Department of Neurology and Neurosurgery, Faculty of Medicine, McGill University, Montreal, Quebec H3A 2B4, Canada
| | - Jenna Stevenson
- Translational Neuroimaging Laboratory, McGill University Research Centre for Studies in Aging, Alzheimer's Disease Research Unit, Douglas Research Institute, Le Centre Intégré Universitaire de Santé et de Services Sociaux (CIUSSS) de l'Ouest-de-l'Île-de-Montréal, Montréal, Québec H4H 1R3, Canada; Department of Neurology and Neurosurgery, Faculty of Medicine, McGill University, Montreal, Quebec H3A 2B4, Canada
| | - Arthur Cassa Macedo
- Translational Neuroimaging Laboratory, McGill University Research Centre for Studies in Aging, Alzheimer's Disease Research Unit, Douglas Research Institute, Le Centre Intégré Universitaire de Santé et de Services Sociaux (CIUSSS) de l'Ouest-de-l'Île-de-Montréal, Montréal, Québec H4H 1R3, Canada; Department of Neurology and Neurosurgery, Faculty of Medicine, McGill University, Montreal, Quebec H3A 2B4, Canada
| | - Tharick Ali Pascoal
- Department of Neurology and Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh 15213, USA
| | | | | | - Kaj Blennow
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy, University of Gothenburg, Mölndal 6 431 41, Sweden; Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal 6 431 41, Sweden
| | - Henrik Zetterberg
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy, University of Gothenburg, Mölndal 6 431 41, Sweden; Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal 6 431 41, Sweden; Department of Neurodegenerative Disease, UCL Institute of Neurology, Queen Square, London, UK; UK Dementia Research Institute at UCL, London SE5 9RT, UK; Hong Kong Center for Neurodegenerative Diseases, Clear Water Bay, Hong Kong 1512, China; Wisconsin Alzheimer's Disease Research Center, University of Wisconsin School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI 53792, USA
| | - Pedro Rosa-Neto
- Translational Neuroimaging Laboratory, McGill University Research Centre for Studies in Aging, Alzheimer's Disease Research Unit, Douglas Research Institute, Le Centre Intégré Universitaire de Santé et de Services Sociaux (CIUSSS) de l'Ouest-de-l'Île-de-Montréal, Montréal, Québec H4H 1R3, Canada; Department of Neurology and Neurosurgery, Faculty of Medicine, McGill University, Montreal, Quebec H3A 2B4, Canada
| | - Andrea Lessa Benedet
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy, University of Gothenburg, Mölndal 6 431 41, Sweden.
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Therriault J, Schindler SE, Salvadó G, Pascoal TA, Benedet AL, Ashton NJ, Karikari TK, Apostolova L, Murray ME, Verberk I, Vogel JW, La Joie R, Gauthier S, Teunissen C, Rabinovici GD, Zetterberg H, Bateman RJ, Scheltens P, Blennow K, Sperling R, Hansson O, Jack CR, Rosa-Neto P. Biomarker-based staging of Alzheimer disease: rationale and clinical applications. Nat Rev Neurol 2024; 20:232-244. [PMID: 38429551 DOI: 10.1038/s41582-024-00942-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/05/2024] [Indexed: 03/03/2024]
Abstract
Disease staging, whereby the spatial extent and load of brain pathology are used to estimate the severity of Alzheimer disease (AD), is pivotal to the gold-standard neuropathological diagnosis of AD. Current in vivo diagnostic frameworks for AD are based on abnormal concentrations of amyloid-β and tau in the cerebrospinal fluid or on PET scans, and breakthroughs in molecular imaging have opened up the possibility of in vivo staging of AD. Focusing on the key principles of disease staging shared across several areas of medicine, this Review highlights the potential for in vivo staging of AD to transform our understanding of preclinical AD, refine enrolment criteria for trials of disease-modifying therapies and aid clinical decision-making in the era of anti-amyloid therapeutics. We provide a state-of-the-art review of recent biomarker-based AD staging systems and highlight their contributions to the understanding of the natural history of AD. Furthermore, we outline hypothetical frameworks to stage AD severity using more accessible fluid biomarkers. In addition, by applying amyloid PET-based staging to recently published anti-amyloid therapeutic trials, we highlight how biomarker-based disease staging frameworks could illustrate the numerous pathological changes that have already taken place in individuals with mildly symptomatic AD. Finally, we discuss challenges related to the validation and standardization of disease staging and provide a forward-looking perspective on potential clinical applications.
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Affiliation(s)
- Joseph Therriault
- Translational Neuroimaging Laboratory, McGill Research Centre for Studies in Aging, Alzheimer's Disease Research Unit, Douglas Research Institute, Le Centre intégré universitaire de santé et de services sociaux (CIUSSS) de l'Ouest-de-l'Île-de-Montréal, Montreal, Quebec, Canada.
- Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec, Canada.
| | - Suzanne E Schindler
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO, USA
| | - Gemma Salvadó
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
| | - Tharick A Pascoal
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
| | - Andréa Lessa Benedet
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy, University of Gothenburg, Mölndal, Sweden
| | - Nicholas J Ashton
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy, University of Gothenburg, Mölndal, Sweden
- NIHR Biomedical Research Centre, South London and Maudsley NHS Foundation, London, UK
| | - Thomas K Karikari
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy, University of Gothenburg, Mölndal, Sweden
| | - Liana Apostolova
- Department of Neurology, University of Indiana School of Medicine, Indianapolis, IN, USA
| | | | - Inge Verberk
- Neurochemistry Laboratory, Department of Clinical Chemistry, Amsterdam Neuroscience, Amsterdam, Netherlands
| | - Jacob W Vogel
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
- Department of Clinical Sciences, Malmö, SciLifeLab, Lund University, Lund, Sweden
| | - Renaud La Joie
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA, USA
| | - Serge Gauthier
- Translational Neuroimaging Laboratory, McGill Research Centre for Studies in Aging, Alzheimer's Disease Research Unit, Douglas Research Institute, Le Centre intégré universitaire de santé et de services sociaux (CIUSSS) de l'Ouest-de-l'Île-de-Montréal, Montreal, Quebec, Canada
- Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec, Canada
| | - Charlotte Teunissen
- Neurochemistry Laboratory, Department of Clinical Chemistry, Amsterdam Neuroscience, Amsterdam, Netherlands
| | - Gil D Rabinovici
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA, USA
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Henrik Zetterberg
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy, University of Gothenburg, Mölndal, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Gothenburg, Sweden
- Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, UK
- UK Dementia Research Institute at UCL, London, UK
- Hong Kong Center for Neurodegenerative Diseases, Hong Kong, China
| | - Randall J Bateman
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO, USA
- Tracy Family SILQ Center, Washington University School of Medicine, St. Louis, MO, USA
| | - Philip Scheltens
- Alzheimer Centre Amsterdam, Amsterdam Neuroscience, Amsterdam, Netherlands
| | - Kaj Blennow
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy, University of Gothenburg, Mölndal, Sweden
| | - Reisa Sperling
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Center for Alzheimer Research and Treatment, Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Oskar Hansson
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
- Memory Clinic, Skåne University Hospital, Malmö, Sweden
| | | | - Pedro Rosa-Neto
- Translational Neuroimaging Laboratory, McGill Research Centre for Studies in Aging, Alzheimer's Disease Research Unit, Douglas Research Institute, Le Centre intégré universitaire de santé et de services sociaux (CIUSSS) de l'Ouest-de-l'Île-de-Montréal, Montreal, Quebec, Canada
- Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec, Canada
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Ziontz J, Harrison TM, Chen X, Giorgio J, Adams JN, Wang Z, Jagust W. Behaviorally meaningful functional networks mediate the effect of Alzheimer's pathology on cognition. Cereb Cortex 2024; 34:bhae134. [PMID: 38602736 PMCID: PMC11008686 DOI: 10.1093/cercor/bhae134] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Revised: 01/25/2024] [Accepted: 03/12/2024] [Indexed: 04/12/2024] Open
Abstract
Tau pathology is associated with cognitive impairment in both aging and Alzheimer's disease, but the functional and structural bases of this relationship remain unclear. We hypothesized that the integrity of behaviorally meaningful functional networks would help explain the relationship between tau and cognitive performance. Using resting state fMRI, we identified unique networks related to episodic memory and executive function cognitive domains. The episodic memory network was particularly related to tau pathology measured with positron emission tomography in the entorhinal and temporal cortices. Further, episodic memory network strength mediated the relationship between tau pathology and cognitive performance above and beyond neurodegeneration. We replicated the association between these networks and tau pathology in a separate cohort of older adults, including both cognitively unimpaired and mildly impaired individuals. Together, these results suggest that behaviorally meaningful functional brain networks represent a functional mechanism linking tau pathology and cognition.
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Affiliation(s)
- Jacob Ziontz
- Helen Wills Neuroscience Institute, UC Berkeley, 250 Warren Hall, 2195 Hearst Ave, Berkeley, CA 94720, United States
| | - Theresa M Harrison
- Helen Wills Neuroscience Institute, UC Berkeley, 250 Warren Hall, 2195 Hearst Ave, Berkeley, CA 94720, United States
| | - Xi Chen
- Helen Wills Neuroscience Institute, UC Berkeley, 250 Warren Hall, 2195 Hearst Ave, Berkeley, CA 94720, United States
| | - Joseph Giorgio
- Helen Wills Neuroscience Institute, UC Berkeley, 250 Warren Hall, 2195 Hearst Ave, Berkeley, CA 94720, United States
- School of Psychological Sciences, College of Engineering, Science and the Environment, University of Newcastle, University Dr, Callaghan, Newcastle, NSW 2305, Australia
| | - Jenna N Adams
- Department of Neurobiology and Behavior and Center for the Neurobiology of Learning and Memory, 1400 Biological Sciences III, University of California, Irvine, Irvine, CA 92697, United States
| | - Zehao Wang
- Helen Wills Neuroscience Institute, UC Berkeley, 250 Warren Hall, 2195 Hearst Ave, Berkeley, CA 94720, United States
| | - William Jagust
- Helen Wills Neuroscience Institute, UC Berkeley, 250 Warren Hall, 2195 Hearst Ave, Berkeley, CA 94720, United States
- Molecular Biophysics and Integrated Bioimaging, Lawrence Berkeley National Laboratory, 1 Cyclotron Rd, Berkeley, CA 94720, United States
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Doering S, McCullough A, Gordon BA, Chen CD, McKay N, Hobbs D, Keefe S, Flores S, Scott J, Smith H, Jarman S, Jackson K, Hornbeck RC, Ances BM, Xiong C, Aschenbrenner AJ, Hassenstab J, Cruchaga C, Daniels A, Bateman RJ, Morris JC, Benzinger TLS. Deconstructing pathological tau by biological process in early stages of Alzheimer disease: a method for quantifying tau spatial spread in neuroimaging. EBioMedicine 2024; 103:105080. [PMID: 38552342 PMCID: PMC10995809 DOI: 10.1016/j.ebiom.2024.105080] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Revised: 03/05/2024] [Accepted: 03/08/2024] [Indexed: 04/08/2024] Open
Abstract
BACKGROUND Neuroimaging studies often quantify tau burden in standardized brain regions to assess Alzheimer disease (AD) progression. However, this method ignores another key biological process in which tau spreads to additional brain regions. We have developed a metric for calculating the extent tau pathology has spread throughout the brain and evaluate the relationship between this metric and tau burden across early stages of AD. METHODS 445 cross-sectional participants (aged ≥ 50) who had MRI, amyloid PET, tau PET, and clinical testing were separated into disease-stage groups based on amyloid positivity and cognitive status (older cognitively normal control, preclinical AD, and symptomatic AD). Tau burden and tau spatial spread were calculated for all participants. FINDINGS We found both tau metrics significantly elevated across increasing disease stages (p < 0.0001) and as a function of increasing amyloid burden for participants with preclinical (p < 0.0001, p = 0.0056) and symptomatic (p = 0.010, p = 0.0021) AD. An interaction was found between tau burden and tau spatial spread when predicting amyloid burden (p = 0.00013). Analyses of slope between tau metrics demonstrated more spread than burden in preclinical AD (β = 0.59), but then tau burden elevated relative to spread (β = 0.42) once participants had symptomatic AD, when the tau metrics became highly correlated (R = 0.83). INTERPRETATION Tau burden and tau spatial spread are both strong biomarkers for early AD but provide unique information, particularly at the preclinical stage. Tau spatial spread may demonstrate earlier changes than tau burden which could have broad impact in clinical trial design. FUNDING This research was supported by the Knight Alzheimer Disease Research Center (Knight ADRC, NIH grants P30AG066444, P01AG026276, P01AG003991), Dominantly Inherited Alzheimer Network (DIAN, NIH grants U01AG042791, U19AG03243808, R01AG052550-01A1, R01AG05255003), and the Barnes-Jewish Hospital Foundation Willman Scholar Fund.
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Affiliation(s)
- Stephanie Doering
- Washington University in Saint Louis School of Medicine, Saint Louis, MO, USA
| | - Austin McCullough
- Washington University in Saint Louis School of Medicine, Saint Louis, MO, USA
| | - Brian A Gordon
- Washington University in Saint Louis School of Medicine, Saint Louis, MO, USA
| | - Charles D Chen
- Washington University in Saint Louis School of Medicine, Saint Louis, MO, USA
| | - Nicole McKay
- Washington University in Saint Louis School of Medicine, Saint Louis, MO, USA
| | - Diana Hobbs
- Washington University in Saint Louis School of Medicine, Saint Louis, MO, USA
| | - Sarah Keefe
- Washington University in Saint Louis School of Medicine, Saint Louis, MO, USA
| | - Shaney Flores
- Washington University in Saint Louis School of Medicine, Saint Louis, MO, USA
| | - Jalen Scott
- Washington University in Saint Louis School of Medicine, Saint Louis, MO, USA
| | - Hunter Smith
- Washington University in Saint Louis School of Medicine, Saint Louis, MO, USA
| | - Stephen Jarman
- Washington University in Saint Louis School of Medicine, Saint Louis, MO, USA
| | - Kelley Jackson
- Washington University in Saint Louis School of Medicine, Saint Louis, MO, USA
| | - Russ C Hornbeck
- Washington University in Saint Louis School of Medicine, Saint Louis, MO, USA
| | - Beau M Ances
- Washington University in Saint Louis School of Medicine, Saint Louis, MO, USA
| | - Chengjie Xiong
- Washington University in Saint Louis School of Medicine, Saint Louis, MO, USA
| | | | - Jason Hassenstab
- Washington University in Saint Louis School of Medicine, Saint Louis, MO, USA
| | - Carlos Cruchaga
- Washington University in Saint Louis School of Medicine, Saint Louis, MO, USA
| | - Alisha Daniels
- Washington University in Saint Louis School of Medicine, Saint Louis, MO, USA
| | - Randall J Bateman
- Washington University in Saint Louis School of Medicine, Saint Louis, MO, USA
| | - John C Morris
- Washington University in Saint Louis School of Medicine, Saint Louis, MO, USA
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Yu PC, Kuo CM, Chen IC. Tau and delirium superimposed on dementia: A case report. SAGE Open Med Case Rep 2024; 12:2050313X241243148. [PMID: 38559407 PMCID: PMC10981243 DOI: 10.1177/2050313x241243148] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Accepted: 03/13/2024] [Indexed: 04/04/2024] Open
Abstract
We present a case involving a 60-year-old man with subacute delirium characterized by challenging attention shifts and obstinate behavior, contrasting with his usual mild-mannered personality. The patient developed pneumonia and a urinary tract infection following the onset of subacute delirium. Despite exhaustive investigations, the cause remained elusive until cerebrospinal fluid analysis revealed Tau positivity. Our overview suggests neurodegenerative diseases as the primary cause, rather than infectious or autoimmune factors. The case underscores a significant association between Tau and delirium superimposed on dementia, offering guidance to clinicians managing such patients.
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Affiliation(s)
- Po-Chung Yu
- Department of Psychiatry, Taichung Veterans General Hospital, Taichung City, Taiwan
| | - Ching-Min Kuo
- Department of Psychiatry, Taichung Veterans General Hospital, Taichung City, Taiwan
| | - I-Chun Chen
- Department of Psychiatry, Taichung Veterans General Hospital, Taichung City, Taiwan
- School of Medicine, College of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
- Department of Post-Baccalaureate Medicine, College of Medicine, National Chung Hsing University, Taichung City, Taiwan
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Lee J, Burkett BJ, Min HK, Senjem ML, Dicks E, Corriveau-Lecavalier N, Mester CT, Wiste HJ, Lundt ES, Murray ME, Nguyen AT, Reichard RR, Botha H, Graff-Radford J, Barnard LR, Gunter JL, Schwarz CG, Kantarci K, Knopman DS, Boeve BF, Lowe VJ, Petersen RC, Jack CR, Jones DT. Synthesizing images of tau pathology from cross-modal neuroimaging using deep learning. Brain 2024; 147:980-995. [PMID: 37804318 PMCID: PMC10907092 DOI: 10.1093/brain/awad346] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Revised: 08/30/2023] [Accepted: 09/24/2023] [Indexed: 10/09/2023] Open
Abstract
Given the prevalence of dementia and the development of pathology-specific disease-modifying therapies, high-value biomarker strategies to inform medical decision-making are critical. In vivo tau-PET is an ideal target as a biomarker for Alzheimer's disease diagnosis and treatment outcome measure. However, tau-PET is not currently widely accessible to patients compared to other neuroimaging methods. In this study, we present a convolutional neural network (CNN) model that imputes tau-PET images from more widely available cross-modality imaging inputs. Participants (n = 1192) with brain T1-weighted MRI (T1w), fluorodeoxyglucose (FDG)-PET, amyloid-PET and tau-PET were included. We found that a CNN model can impute tau-PET images with high accuracy, the highest being for the FDG-based model followed by amyloid-PET and T1w. In testing implications of artificial intelligence-imputed tau-PET, only the FDG-based model showed a significant improvement of performance in classifying tau positivity and diagnostic groups compared to the original input data, suggesting that application of the model could enhance the utility of the metabolic images. The interpretability experiment revealed that the FDG- and T1w-based models utilized the non-local input from physically remote regions of interest to estimate the tau-PET, but this was not the case for the Pittsburgh compound B-based model. This implies that the model can learn the distinct biological relationship between FDG-PET, T1w and tau-PET from the relationship between amyloid-PET and tau-PET. Our study suggests that extending neuroimaging's use with artificial intelligence to predict protein specific pathologies has great potential to inform emerging care models.
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Affiliation(s)
- Jeyeon Lee
- Department of Radiology, Mayo Clinic, Rochester, MN 55905, USA
- Department of Biomedical Engineering, Hanyang University, Seoul 04763, Korea
| | - Brian J Burkett
- Department of Radiology, Mayo Clinic, Rochester, MN 55905, USA
| | - Hoon-Ki Min
- Department of Radiology, Mayo Clinic, Rochester, MN 55905, USA
| | - Matthew L Senjem
- Department of Information Technology, Mayo Clinic, Rochester, MN 55905, USA
| | - Ellen Dicks
- Department of Neurology, Mayo Clinic, Rochester, MN 55905, USA
| | | | - Carly T Mester
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN 55905, USA
| | - Heather J Wiste
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN 55905, USA
| | - Emily S Lundt
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN 55905, USA
| | - Melissa E Murray
- Department of Neuroscience, Mayo Clinic, Jacksonville, FL 32224, USA
| | - Aivi T Nguyen
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN 55905, USA
| | - Ross R Reichard
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN 55905, USA
| | - Hugo Botha
- Department of Neurology, Mayo Clinic, Rochester, MN 55905, USA
| | | | | | | | | | - Kejal Kantarci
- Department of Radiology, Mayo Clinic, Rochester, MN 55905, USA
| | - David S Knopman
- Department of Neurology, Mayo Clinic, Rochester, MN 55905, USA
| | - Bradley F Boeve
- Department of Neurology, Mayo Clinic, Rochester, MN 55905, USA
| | - Val J Lowe
- Department of Radiology, Mayo Clinic, Rochester, MN 55905, USA
| | | | - Clifford R Jack
- Department of Radiology, Mayo Clinic, Rochester, MN 55905, USA
| | - David T Jones
- Department of Radiology, Mayo Clinic, Rochester, MN 55905, USA
- Department of Neurology, Mayo Clinic, Rochester, MN 55905, USA
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Sexton CE, Bitan G, Bowles KR, Brys M, Buée L, Maina MB, Clelland CD, Cohen AD, Crary JF, Dage JL, Diaz K, Frost B, Gan L, Goate AM, Golbe LI, Hansson O, Karch CM, Kolb HC, La Joie R, Lee SE, Matallana D, Miller BL, Onyike CU, Quiroz YT, Rexach JE, Rohrer JD, Rommel A, Sadri‐Vakili G, Schindler SE, Schneider JA, Sperling RA, Teunissen CE, Weninger SC, Worley SL, Zheng H, Carrillo MC. Novel avenues of tau research. Alzheimers Dement 2024; 20:2240-2261. [PMID: 38170841 PMCID: PMC10984447 DOI: 10.1002/alz.13533] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Revised: 10/03/2023] [Accepted: 10/05/2023] [Indexed: 01/05/2024]
Abstract
INTRODUCTION The pace of innovation has accelerated in virtually every area of tau research in just the past few years. METHODS In February 2022, leading international tau experts convened to share selected highlights of this work during Tau 2022, the second international tau conference co-organized and co-sponsored by the Alzheimer's Association, CurePSP, and the Rainwater Charitable Foundation. RESULTS Representing academia, industry, and the philanthropic sector, presenters joined more than 1700 registered attendees from 59 countries, spanning six continents, to share recent advances and exciting new directions in tau research. DISCUSSION The virtual meeting provided an opportunity to foster cross-sector collaboration and partnerships as well as a forum for updating colleagues on research-advancing tools and programs that are steadily moving the field forward.
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Affiliation(s)
| | - Gal Bitan
- Department of NeurologyDavid Geffen School of MedicineBrain Research InstituteMolecular Biology InstituteUniversity of California Los Angeles (UCLA)Los AngelesCaliforniaUSA
| | - Kathryn R. Bowles
- UK Dementia Research Institute at the University of EdinburghCentre for Discovery Brain SciencesUniversity of EdinburghEdinburghUK
| | | | - Luc Buée
- Univ LilleInsermCHU‐LilleLille Neuroscience and CognitionLabEx DISTALZPlace de VerdunLilleFrance
| | - Mahmoud Bukar Maina
- Sussex NeuroscienceSchool of Life SciencesUniversity of SussexFalmerUK
- Biomedical Science Research and Training CentreYobe State UniversityDamaturuNigeria
| | - Claire D. Clelland
- Memory and Aging CenterDepartment of NeurologyWeill Institute for NeurosciencesUniversity of CaliforniaSan FranciscoCaliforniaUSA
| | - Ann D. Cohen
- University of PittsburghSchool of MedicineDepartment of Psychiatry and Alzheimer's disease Research CenterPittsburghPennsylvaniaUSA
| | - John F. Crary
- Departments of PathologyNeuroscience, and Artificial Intelligence & Human HealthIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Jeffrey L. Dage
- Department of NeurologyIndiana University School of MedicineIndianapolisIndianaUSA
| | | | - Bess Frost
- Sam & Ann Barshop Institute for Longevity & Aging Studies Glenn Biggs Institute for Alzheimer's & Neurodegenerative Disorders Department of Cell Systems and Anatomy University of Texas Health San AntonioSan AntonioTexasUSA
| | - Li Gan
- Helen and Robert Appel Alzheimer Disease Research InstituteFeil Family Brain and Mind Research InstituteWeill Cornell MedicineNew YorkNew YorkUSA
| | - Alison M Goate
- Department of Genetics & Genomic SciencesRonald M. Loeb Center for Alzheimer's diseaseIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Lawrence I. Golbe
- CurePSPIncNew YorkNew YorkUSA
- Rutgers Robert Wood Johnson Medical SchoolNew BrunswickNew JerseyUSA
| | - Oskar Hansson
- Clinical Memory Research UnitDepartment of Clinical Sciences MalmöLund UniversityLundSweden
| | - Celeste M. Karch
- Department of PsychiatryWashington University in St. LouisSt. LouisMissouriUSA
| | | | - Renaud La Joie
- Memory and Aging CenterDepartment of NeurologyWeill Institute for NeurosciencesUniversity of CaliforniaSan FranciscoCaliforniaUSA
| | - Suzee E. Lee
- Memory and Aging CenterDepartment of NeurologyWeill Institute for NeurosciencesUniversity of CaliforniaSan FranciscoCaliforniaUSA
| | - Diana Matallana
- Aging InstituteNeuroscience ProgramPsychiatry DepartmentSchool of MedicinePontificia Universidad JaverianaBogotáColombia
- Mental Health DepartmentHospital Universitario Fundaciòn Santa FeBogotaColombia
| | - Bruce L. Miller
- Memory and Aging CenterDepartment of NeurologyWeill Institute for NeurosciencesUniversity of CaliforniaSan FranciscoCaliforniaUSA
- Department of Psychiatry and Behavioral SciencesUniversity of CaliforniaSan FranciscoCaliforniaUSA
| | - Chiadi U. Onyike
- Division of Geriatric Psychiatry and NeuropsychiatryJohns Hopkins University School of MedicineBaltimoreBaltimoreMarylandUSA
| | - Yakeel T. Quiroz
- Departments of Psychiatry and NeurologyMassachusetts General HospitalHarvard Medical SchoolBostonMassachusettsUSA
| | - Jessica E. Rexach
- Program in NeurogeneticsDepartment of NeurologyDavid Geffen School of MedicineUniversity of CaliforniaLos AngelesCaliforniaUSA
| | - Jonathan D. Rohrer
- Department of Neurodegenerative DiseaseDementia Research CentreUniversity College London Institute of Neurology, Queen SquareLondonUK
| | - Amy Rommel
- Rainwater Charitable FoundationFort WorthTexasUSA
| | - Ghazaleh Sadri‐Vakili
- Sean M. Healey &AMG Center for ALS at Mass GeneralMassachusetts General HospitalBostonMassachusettsUSA
| | - Suzanne E. Schindler
- Department of NeurologyWashington University School of MedicineSt. LouisMissouriUSA
| | | | - Reisa A. Sperling
- Center for Alzheimer Research and TreatmentBrigham and Women's HospitalMassachusetts General HospitalHarvard Medical SchoolBostonMassachusettsUSA
| | - Charlotte E. Teunissen
- Neurochemistry LaboratoryClinical Chemistry departmentAmsterdam NeuroscienceProgram NeurodegenerationAmsterdam University Medical CentersVrije Universiteit AmsterdamAmsterdamThe Netherlands
| | | | | | - Hui Zheng
- Huffington Center on AgingBaylor College of MedicineHoustonTexasUSA
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Parekh P, Badachhape AA, Tanifum EA, Annapragada AV, Ghaghada KB. Advances in nanoprobes for molecular MRI of Alzheimer's disease. WILEY INTERDISCIPLINARY REVIEWS. NANOMEDICINE AND NANOBIOTECHNOLOGY 2024; 16:e1946. [PMID: 38426638 PMCID: PMC10983770 DOI: 10.1002/wnan.1946] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Revised: 01/11/2024] [Accepted: 01/30/2024] [Indexed: 03/02/2024]
Abstract
Alzheimer's disease is the most common cause of dementia and a leading cause of mortality in the elderly population. Diagnosis of Alzheimer's disease has traditionally relied on evaluation of clinical symptoms for cognitive impairment with a definitive diagnosis requiring post-mortem demonstration of neuropathology. However, advances in disease pathogenesis have revealed that patients exhibit Alzheimer's disease pathology several decades before the manifestation of clinical symptoms. Magnetic resonance imaging (MRI) plays an important role in the management of patients with Alzheimer's disease. The clinical availability of molecular MRI (mMRI) contrast agents can revolutionize the diagnosis of Alzheimer's disease. In this article, we review advances in nanoparticle contrast agents, also referred to as nanoprobes, for mMRI of Alzheimer's disease. This article is categorized under: Diagnostic Tools > In Vivo Nanodiagnostics and Imaging Therapeutic Approaches and Drug Discovery > Nanomedicine for Neurological Disease.
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Affiliation(s)
- Parag Parekh
- Department of Radiology, Baylor College of Medicine, Houston, Texas 77030
- Department of Radiology, Texas Children's Hospital, Houston, Texas 77030
| | - Andrew A. Badachhape
- Department of Radiology, Baylor College of Medicine, Houston, Texas 77030
- Department of Radiology, Texas Children's Hospital, Houston, Texas 77030
| | - Eric A. Tanifum
- Department of Radiology, Baylor College of Medicine, Houston, Texas 77030
- Department of Radiology, Texas Children's Hospital, Houston, Texas 77030
| | - Ananth V. Annapragada
- Department of Radiology, Baylor College of Medicine, Houston, Texas 77030
- Department of Radiology, Texas Children's Hospital, Houston, Texas 77030
| | - Ketan B. Ghaghada
- Department of Radiology, Baylor College of Medicine, Houston, Texas 77030
- Department of Radiology, Texas Children's Hospital, Houston, Texas 77030
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Thompson E, Schroder A, He T, Shand C, Soskic S, Oxtoby NP, Barkhof F, Alexander DC. Combining multimodal connectivity information improves modelling of pathology spread in Alzheimer's disease. IMAGING NEUROSCIENCE (CAMBRIDGE, MASS.) 2024; 2:1-19. [PMID: 38947941 PMCID: PMC11211996 DOI: 10.1162/imag_a_00089] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/04/2023] [Revised: 12/07/2023] [Accepted: 01/02/2024] [Indexed: 07/02/2024]
Abstract
Cortical atrophy and aggregates of misfolded tau proteins are key hallmarks of Alzheimer's disease. Computational models that simulate the propagation of pathogens between connected brain regions have been used to elucidate mechanistic information about the spread of these disease biomarkers, such as disease epicentres and spreading rates. However, the connectomes that are used as substrates for these models are known to contain modality-specific false positive and false negative connections, influenced by the biases inherent to the different methods for estimating connections in the brain. In this work, we compare five types of connectomes for modelling both tau and atrophy patterns with the network diffusion model, which are validated against tau PET and structural MRI data from individuals with either mild cognitive impairment or dementia. We then test the hypothesis that a joint connectome, with combined information from different modalities, provides an improved substrate for the model. We find that a combination of multimodal information helps the model to capture observed patterns of tau deposition and atrophy better than any single modality. This is validated with data from independent datasets. Overall, our findings suggest that combining connectivity measures into a single connectome can mitigate some of the biases inherent to each modality and facilitate more accurate models of pathology spread, thus aiding our ability to understand disease mechanisms, and providing insight into the complementary information contained in different measures of brain connectivity.
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Affiliation(s)
- Elinor Thompson
- UCL Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom
| | - Anna Schroder
- UCL Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom
| | - Tiantian He
- UCL Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom
| | - Cameron Shand
- UCL Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom
| | - Sonja Soskic
- UCL Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom
| | - Neil P. Oxtoby
- UCL Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom
| | - Frederik Barkhof
- UCL Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom
- Department of Radiology & Nuclear Medicine, Amsterdam UMC, Vrije Universiteit, the Netherlands
- UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Daniel C. Alexander
- UCL Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom
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Kumar R, Le Marchand T, Adam L, Bobrovs R, Chen G, Fridmanis J, Kronqvist N, Biverstål H, Jaudzems K, Johansson J, Pintacuda G, Abelein A. Identification of potential aggregation hotspots on Aβ42 fibrils blocked by the anti-amyloid chaperone-like BRICHOS domain. Nat Commun 2024; 15:965. [PMID: 38302480 PMCID: PMC10834949 DOI: 10.1038/s41467-024-45192-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Accepted: 01/17/2024] [Indexed: 02/03/2024] Open
Abstract
Protein misfolding can generate toxic intermediates, which underlies several devastating diseases, such as Alzheimer's disease (AD). The surface of AD-associated amyloid-β peptide (Aβ) fibrils has been suggested to act as a catalyzer for self-replication and generation of potentially toxic species. Specifically tailored molecular chaperones, such as the BRICHOS protein domain, were shown to bind to amyloid fibrils and break this autocatalytic cycle. Here, we identify a site on the Aβ42 fibril surface, consisting of three C-terminal β-strands and particularly the solvent-exposed β-strand stretching from residues 26-28, which is efficiently sensed by a designed variant of Bri2 BRICHOS. Remarkably, while only a low amount of BRICHOS binds to Aβ42 fibrils, fibril-catalyzed nucleation processes are effectively prevented, suggesting that the identified site acts as a catalytic aggregation hotspot, which can specifically be blocked by BRICHOS. Hence, these findings provide an understanding how toxic nucleation events can be targeted by molecular chaperones.
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Affiliation(s)
- Rakesh Kumar
- Department of Biosciences and Nutrition, Karolinska Institutet, 141 83, Huddinge, Sweden
| | - Tanguy Le Marchand
- Université de Lyon, Centre de Resonance Magnétique Nucléaire (CRMN) à Très Hauts Champs de Lyon (UMR 5082 - CNRS, ENS Lyon, UCB Lyon 1), 69100, Villeurbanne, France
| | - Laurène Adam
- Department of Biosciences and Nutrition, Karolinska Institutet, 141 83, Huddinge, Sweden
| | - Raitis Bobrovs
- Department of Physical Organic Chemistry, Latvian Institute of Organic Synthesis, Riga, Latvia
| | - Gefei Chen
- Department of Biosciences and Nutrition, Karolinska Institutet, 141 83, Huddinge, Sweden
| | - Jēkabs Fridmanis
- Department of Physical Organic Chemistry, Latvian Institute of Organic Synthesis, Riga, Latvia
| | - Nina Kronqvist
- Department of Biosciences and Nutrition, Karolinska Institutet, 141 83, Huddinge, Sweden
| | - Henrik Biverstål
- Department of Biosciences and Nutrition, Karolinska Institutet, 141 83, Huddinge, Sweden
| | - Kristaps Jaudzems
- Department of Physical Organic Chemistry, Latvian Institute of Organic Synthesis, Riga, Latvia
| | - Jan Johansson
- Department of Biosciences and Nutrition, Karolinska Institutet, 141 83, Huddinge, Sweden
| | - Guido Pintacuda
- Université de Lyon, Centre de Resonance Magnétique Nucléaire (CRMN) à Très Hauts Champs de Lyon (UMR 5082 - CNRS, ENS Lyon, UCB Lyon 1), 69100, Villeurbanne, France
| | - Axel Abelein
- Department of Biosciences and Nutrition, Karolinska Institutet, 141 83, Huddinge, Sweden.
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Chin KS, Churilov L, Doré V, Villemagne VL, Rowe CC, Yassi N, Watson R. Tau in dementia with Lewy bodies. Aust N Z J Psychiatry 2024; 58:175-182. [PMID: 37264610 DOI: 10.1177/00048674231177219] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 06/03/2023]
Abstract
OBJECTIVE Neurofibrillary tangles are present in a proportion of people with dementia with Lewy bodies and may be associated with worse cognition. Recent advances in biomarkers for Alzheimer's disease include second-generation tau positron emission tomography as well as the detection of phosphorylated tau at threonine 181 (p-tau181) in plasma. This study aimed to investigate tau in people with dementia with Lewy bodies using a second-generation tau positron emission tomography tracer as well as plasma p-tau181. METHODS Twenty-seven participants (mean age 74.7 ± 5.5) with clinically diagnosed probable dementia with Lewy bodies underwent comprehensive clinical assessment and positron emission tomography imaging (18F-MK6240 and 18F-NAV4694). Plasma p-tau181 levels were measured using Simoa technology. RESULTS Five dementia with Lewy bodies participants (18.5%) had an abnormal tau positron emission tomography (increased tau uptake in the temporal meta-region-of-interest). Higher plasma p-tau181 concentrations correlated with higher tau deposition in the temporal region (ρ = 0.46, 95% confidence interval = [0.10, 0.72]) and classified abnormal tau positron emission tomography in dementia with Lewy bodies with an area under the curve of 0.95 (95% confidence interval = [0.86, 0.99]). Plasma p-tau181 also correlated positively with cortical amyloid-beta binding (ρ = 0.68, 95% confidence interval = [0.40, 0.84]) and classified abnormal amyloid-beta positron emission tomography in dementia with Lewy bodies with an area under the curve of 0.91 (95% confidence interval = [0.79, 0.99]). There was no association found between tau deposition and any of the clinical variables. CONCLUSIONS Tau is a common co-pathology in dementia with Lewy bodies. Plasma p-tau181 correlated with abnormal tau and amyloid-beta positron emission tomography and may potentially be used as a marker to identify co-morbid Alzheimer's disease-related pathology in dementia with Lewy bodies. The clinical implications of tau in dementia with Lewy bodies need to be further evaluated in larger longitudinal studies.
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Affiliation(s)
- Kai Sin Chin
- Department of Medicine, The Royal Melbourne Hospital, The University of Melbourne, Parkville, VIC, Australia
- Department of Aged Care, The Royal Melbourne Hospital, Parkville, VIC, Australia
- Population Health and Immunity Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, VIC, Australia
| | - Leonid Churilov
- Department of Medicine and Neurology, Melbourne Brain Centre at the Royal Melbourne Hospital, The University of Melbourne, Parkville, VIC, Australia
| | - Vincent Doré
- Department of Molecular Imaging & Therapy, Austin Health, Heidelberg, VIC, Australia
- Health and Biosecurity Flagship, The Australian eHealth Research Centre, CSIRO, Clayton South, VIC, Australia
| | - Victor L Villemagne
- Department of Molecular Imaging & Therapy, Austin Health, Heidelberg, VIC, Australia
- Department of Medicine, Austin Health, The University of Melbourne, Heidelberg, VIC, Australia
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
| | - Christopher C Rowe
- Department of Molecular Imaging & Therapy, Austin Health, Heidelberg, VIC, Australia
| | - Nawaf Yassi
- Population Health and Immunity Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, VIC, Australia
- Department of Medicine and Neurology, Melbourne Brain Centre at the Royal Melbourne Hospital, The University of Melbourne, Parkville, VIC, Australia
| | - Rosie Watson
- Department of Medicine, The Royal Melbourne Hospital, The University of Melbourne, Parkville, VIC, Australia
- Department of Aged Care, The Royal Melbourne Hospital, Parkville, VIC, Australia
- Population Health and Immunity Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, VIC, Australia
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Zhang L, Qu J, Ma H, Chen T, Liu T, Zhu D. Exploring Alzheimer's disease: a comprehensive brain connectome-based survey. PSYCHORADIOLOGY 2024; 4:kkad033. [PMID: 38333558 PMCID: PMC10848159 DOI: 10.1093/psyrad/kkad033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/28/2023] [Revised: 12/21/2023] [Accepted: 01/03/2024] [Indexed: 02/10/2024]
Abstract
Dementia is an escalating global health challenge, with Alzheimer's disease (AD) at its forefront. Substantial evidence highlights the accumulation of AD-related pathological proteins in specific brain regions and their subsequent dissemination throughout the broader area along the brain network, leading to disruptions in both individual brain regions and their interconnections. Although a comprehensive understanding of the neurodegeneration-brain network link is lacking, it is undeniable that brain networks play a pivotal role in the development and progression of AD. To thoroughly elucidate the intricate network of elements and connections constituting the human brain, the concept of the brain connectome was introduced. Research based on the connectome holds immense potential for revealing the mechanisms underlying disease development, and it has become a prominent topic that has attracted the attention of numerous researchers. In this review, we aim to systematically summarize studies on brain networks within the context of AD, critically analyze the strengths and weaknesses of existing methodologies, and offer novel perspectives and insights, intending to serve as inspiration for future research.
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Affiliation(s)
- Lu Zhang
- Department of Computer Science and Engineering, The University of Texas at Arlington, Arlington, TX 76019, USA
| | - Junqi Qu
- Department of Computer Science and Engineering, The University of Texas at Arlington, Arlington, TX 76019, USA
| | - Haotian Ma
- Department of Computer Science and Engineering, The University of Texas at Arlington, Arlington, TX 76019, USA
| | - Tong Chen
- Department of Computer Science and Engineering, The University of Texas at Arlington, Arlington, TX 76019, USA
| | - Tianming Liu
- Department of Computer Science, The University of Georgia, Athens, GA 30602, USA
| | - Dajiang Zhu
- Department of Computer Science and Engineering, The University of Texas at Arlington, Arlington, TX 76019, USA
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Li Q, Zhan J, Feng Y, Liao Z, Li X. The Association of Body Mass Index with Cognition and Alzheimer's Disease Biomarkers in the Elderly with Different Cognitive Status: A Study from the Alzheimer's Disease Neuroimaging Initiative Database. J Alzheimers Dis Rep 2024; 8:9-24. [PMID: 38229832 PMCID: PMC10789287 DOI: 10.3233/adr-230163] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Accepted: 12/04/2023] [Indexed: 01/18/2024] Open
Abstract
Background The association of body mass index (BMI) with cognition and Alzheimer's disease (AD) biomarkers of the elderly remains inconclusive. Objective To investigate the relationship between BMI and cognition as well as AD biomarkers in the elderly with different cognitive status. Methods Participants with cognitively normal (CN) were included as the CN group. Participants with mild cognitive impairment and mild dementia were included as the cognitive impairment (CI) group. The relationship between BMI and AD biomarkers (cerebrospinal fluid Aβ42 and p-tau181, hippocampal volume [HV]), global cognition (Mini-Mental State Examination [MMSE]), memory, and executive function were explored. Results In the CI group, BMI was associated with MMSE (β= 0.03, p = 0.009), Aβ42 (β= 0.006, p = 0.029), p-tau181/Aβ42 ratio (β= -0.001, p = 0.011), and HV (β= 0.05, p < 0.001). However in the CN group, BMI exhibited associations with p-tau181 (β= 0.012, p = 0.014) and memory composite score (β= -0.04, p = 0.038), but not with p-tau181/Aβ42 ratio and HV. Moreover, mediation analysis showed that in the CI group, the positive effect of BMI on HV and MMSE score was partially mediated by diastolic blood pressure. Conclusion The association of BMI with cognition and AD biomarkers varies across different cognitive status. In particular, a lower BMI was associated with worse cognition, higher Aβ burden, and lower HV in individuals with CI. Clinical practice should strengthen the monitoring and management of BMI in patients with AD.
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Affiliation(s)
- Qin Li
- Department of Neurology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Jiehong Zhan
- Department of Neurology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Yuxue Feng
- Department of Neurology, The Fifth People’s Hospital of Chongqing, Chongqing, China
| | - Zixuan Liao
- Department of Neurology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Xiaofeng Li
- Department of Neurology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
- Department of Neurology, The Fifth People’s Hospital of Chongqing, Chongqing, China
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Zhang T, Kim BM, Lee TH. Death-associated protein kinase 1 as a therapeutic target for Alzheimer's disease. Transl Neurodegener 2024; 13:4. [PMID: 38195518 PMCID: PMC10775678 DOI: 10.1186/s40035-023-00395-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2023] [Accepted: 12/18/2023] [Indexed: 01/11/2024] Open
Abstract
Alzheimer's disease (AD) is the most prevalent form of dementia in the elderly and represents a major clinical challenge in the ageing society. Neuropathological hallmarks of AD include neurofibrillary tangles composed of hyperphosphorylated tau, senile plaques derived from the deposition of amyloid-β (Aβ) peptides, brain atrophy induced by neuronal loss, and synaptic dysfunctions. Death-associated protein kinase 1 (DAPK1) is ubiquitously expressed in the central nervous system. Dysregulation of DAPK1 has been shown to contribute to various neurological diseases including AD, ischemic stroke and Parkinson's disease (PD). We have established an upstream effect of DAPK1 on Aβ and tau pathologies and neuronal apoptosis through kinase-mediated protein phosphorylation, supporting a causal role of DAPK1 in the pathophysiology of AD. In this review, we summarize current knowledge about how DAPK1 is involved in various AD pathological changes including tau hyperphosphorylation, Aβ deposition, neuronal cell death and synaptic degeneration. The underlying molecular mechanisms of DAPK1 dysregulation in AD are discussed. We also review the recent progress regarding the development of novel DAPK1 modulators and their potential applications in AD intervention. These findings substantiate DAPK1 as a novel therapeutic target for the development of multifunctional disease-modifying treatments for AD and other neurological disorders.
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Affiliation(s)
- Tao Zhang
- Fujian Key Laboratory of Translational Research in Cancer and Neurodegenerative Diseases, Institute of Basic Medicine, School of Basic Medical Sciences, Fujian Medical University, 1 Xuefu North Road, Fuzhou, 350122, Fujian, China
| | - Byeong Mo Kim
- Research Center for New Drug Development, AgingTarget Inc., 10F Ace Cheonggye Tower, 53, Seonggogae-Ro, Uiwang-Si, 16006, Gyeonggi-Do, Korea.
| | - Tae Ho Lee
- Fujian Key Laboratory of Translational Research in Cancer and Neurodegenerative Diseases, Institute of Basic Medicine, School of Basic Medical Sciences, Fujian Medical University, 1 Xuefu North Road, Fuzhou, 350122, Fujian, China.
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Sintini I, Corriveau-Lecavalier N, Jones DT, Machulda MM, Gunter JL, Schwarz CG, Botha H, Carlos AF, Kamykowski MG, Singh NA, Petersen RC, Jack CR, Lowe VJ, Graff-Radford J, Josephs KA, Whitwell JL. Longitudinal default mode sub-networks in the language and visual variants of Alzheimer's disease. Brain Commun 2024; 6:fcae005. [PMID: 38444909 PMCID: PMC10914456 DOI: 10.1093/braincomms/fcae005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Revised: 11/13/2023] [Accepted: 01/05/2024] [Indexed: 03/07/2024] Open
Abstract
Disruption of the default mode network is a hallmark of Alzheimer's disease, which has not been extensively examined in atypical phenotypes. We investigated cross-sectional and 1-year longitudinal changes in default mode network sub-systems in the visual and language variants of Alzheimer's disease, in relation to age and tau. Sixty-one amyloid-positive Alzheimer's disease participants diagnosed with posterior cortical atrophy (n = 33) or logopenic progressive aphasia (n = 28) underwent structural MRI, resting-state functional MRI and [18F]flortaucipir PET. One-hundred and twenty-two amyloid-negative cognitively unimpaired individuals and 60 amyloid-positive individuals diagnosed with amnestic Alzheimer's disease were included as controls and as a comparison group, respectively, and had structural and resting-state functional MRI. Forty-one atypical Alzheimer's disease participants, 26 amnestic Alzheimer's disease participants and 40 cognitively unimpaired individuals had one follow-up functional MRI ∼1-2 years after the baseline scan. Default mode network connectivity was calculated using the dual regression method for posterior, ventral, anterior ventral and anterior dorsal sub-systems derived from independent component analysis. A global measure of default mode network connectivity, the network failure quotient, was also calculated. Linear mixed-effects models and voxel-based analyses were computed for each connectivity measure. Both atypical and amnestic Alzheimer's disease participants had lower cross-sectional posterior and ventral and higher anterior dorsal connectivity and network failure quotient relative to cognitively unimpaired individuals. Age had opposite effects on connectivity in Alzheimer's disease participants and cognitively unimpaired individuals. While connectivity declined with age in cognitively unimpaired individuals, younger Alzheimer's disease participants had lower connectivity than the older ones, particularly in the ventral default mode network. Greater baseline tau-PET uptake was associated with lower ventral and anterior ventral default mode network connectivity in atypical Alzheimer's disease. Connectivity in the ventral default mode network declined over time in atypical Alzheimer's disease, particularly in older participants, with lower tau burden. Voxel-based analyses validated the findings of higher anterior dorsal default mode network connectivity, lower posterior and ventral default mode network connectivity and decline in ventral default mode network connectivity over time in atypical Alzheimer's disease. Visuospatial symptoms were associated with default mode network connectivity disruption. In summary, default mode connectivity disruption was similar between atypical and amnestic Alzheimer's disease variants, and discriminated Alzheimer's disease from cognitively unimpaired individuals, with decreased posterior and increased anterior connectivity and with disruption more pronounced in younger participants. The ventral default mode network declined over time in atypical Alzheimer's disease, suggesting a shift in default mode network connectivity likely related to tau pathology.
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Affiliation(s)
- Irene Sintini
- Department of Radiology, Mayo Clinic, Rochester, MN 55905, USA
| | | | - David T Jones
- Department of Radiology, Mayo Clinic, Rochester, MN 55905, USA
- Department of Neurology, Mayo Clinic, Rochester, MN 55905, USA
| | - Mary M Machulda
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN 55905, USA
| | | | | | - Hugo Botha
- Department of Neurology, Mayo Clinic, Rochester, MN 55905, USA
| | - Arenn F Carlos
- Department of Neurology, Mayo Clinic, Rochester, MN 55905, USA
| | | | | | | | - Clifford R Jack
- Department of Radiology, Mayo Clinic, Rochester, MN 55905, USA
| | - Val J Lowe
- Department of Radiology, Mayo Clinic, Rochester, MN 55905, USA
| | | | - Keith A Josephs
- Department of Neurology, Mayo Clinic, Rochester, MN 55905, USA
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Simon SS, Varangis E, Lee S, Gu Y, Gazes Y, Razlighi QR, Habeck C, Stern Y. In vivo tau is associated with change in memory and processing speed, but not reasoning, in cognitively unimpaired older adults. Neurobiol Aging 2024; 133:28-38. [PMID: 38376885 PMCID: PMC10879688 DOI: 10.1016/j.neurobiolaging.2023.10.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Revised: 08/30/2023] [Accepted: 10/01/2023] [Indexed: 02/21/2024]
Abstract
The relationship between tau deposition and cognitive decline in cognitively healthy older adults is still unclear. The tau PET tracer 18F-MK-6240 has shown favorable imaging characteristics to identify early tau deposition in aging. We evaluated the relationship between in vivo tau levels (18F-MK-6240) and retrospective cognitive change over 5 years in episodic memory, processing speed, and reasoning. For tau quantification, a set of regions of interest (ROIs) was selected a priori based on previous literature: (1) total-ROI comprising selected areas, (2) medial temporal lobe-ROI, and (3) lateral temporal lobe-ROI and cingulate/parietal lobe-ROI. Higher tau burden in most ROIs was associated with a steeper decline in memory and speed. There were no associations between tau and reasoning change. The novelty of this finding is that tau burden may affect not only episodic memory, a well-established finding but also processing speed. Our finding reinforces the notion that early tau deposition in areas related to Alzheimer's disease is associated with cognitive decline in cognitively unimpaired individuals, even in a sample with low amyloid-β pathology.
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Affiliation(s)
- Sharon Sanz Simon
- Cognitive Neuroscience Division, Department of Neurology, Columbia University, Vagelos College of Physicians and Surgeons, New York, NY, USA
| | - Eleanna Varangis
- School of Kinesiology, University of Michigan, Ann Arbor, MI, USA; Concussion Center, University of Michigan, Ann Arbor, MI, USA
| | - Seonjoo Lee
- Department of Biostatistics, Mailman School of Public Health, Columbia University, New York, NY, USA
| | - Yian Gu
- Cognitive Neuroscience Division, Department of Neurology, Columbia University, Vagelos College of Physicians and Surgeons, New York, NY, USA; Department of Epidemiology, Joseph P. Mailman School of Public Health, Columbia University, New York, NY, USA
| | - Yunglin Gazes
- Cognitive Neuroscience Division, Department of Neurology, Columbia University, Vagelos College of Physicians and Surgeons, New York, NY, USA
| | | | - Christian Habeck
- Cognitive Neuroscience Division, Department of Neurology, Columbia University, Vagelos College of Physicians and Surgeons, New York, NY, USA
| | - Yaakov Stern
- Cognitive Neuroscience Division, Department of Neurology, Columbia University, Vagelos College of Physicians and Surgeons, New York, NY, USA.
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50
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Bischof GN, Jaeger E, Giehl K, Hönig MC, Weiss PH, Drzezga A. Affection of Motor Network Regions by Tau Pathology Across the Alzheimer's Disease Spectrum. eNeuro 2024; 11:ENEURO.0242-23.2023. [PMID: 38164539 PMCID: PMC10849022 DOI: 10.1523/eneuro.0242-23.2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Revised: 09/08/2023] [Accepted: 09/19/2023] [Indexed: 01/03/2024] Open
Abstract
Stereotypical isocortical tau protein pathology along the Braak stages has been described as an instigator of neurodegeneration in Alzheimer's disease (AD). Less is known about tau pathology in motor regions, although higher-order motor deficits such as praxis dysfunction are part of the clinical description. Here, we examined how tau pathology in cytoarchitectonically mapped regions of the primary and higher-order motor network in comparison to primary visual and sensory regions varies across the clinical spectrum of AD. We analyzed tau PET scans from the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort in patients with mild cognitive impairment (MCI; N = 84) and dementia of the Alzheimer's disease type (DAD; N = 25). Additionally, an amyloid-negative sample of healthy older individuals (HC; N = 26) were included. Standard uptake ratio values (SUVRs) were extracted in native space from the left and the right hemispheres. A repeated measurement analysis of variance was conducted to assess the effect of diagnostic disease category on tau pathology in the individual motor regions, controlling for age. We observed that tau pathology varies as a function of diagnostic category in predominantly higher motor regions (i.e., supplementary motor area, superior parietal lobe, angular gyrus, and dorsal premotor cortex) compared to primary visual, sensory and motor regions. Indeed, tau in higher-order motor regions was significantly associated with decline in cognitive function. Together, these results expand our knowledge on the in vivo pattern of tau pathology in AD and suggest that higher motor regions are not spared from tau aggregation in the course of disease, potentially contributing to the symptomatic appearance of the disease.
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Affiliation(s)
- Gérard N Bischof
- Department of Nuclear Medicine, Faculty of Medicine and University Hospital of Cologne, University of Cologne, 50937 Cologne, Germany
- Molecular Organization of the Brain, Institute for Neuroscience and Medicine II, Research Center Juelich, 52428 Juelich, Germany
| | - Elena Jaeger
- Department of Nuclear Medicine, Faculty of Medicine and University Hospital of Cologne, University of Cologne, 50937 Cologne, Germany
| | - Kathrin Giehl
- Department of Nuclear Medicine, Faculty of Medicine and University Hospital of Cologne, University of Cologne, 50937 Cologne, Germany
- Molecular Organization of the Brain, Institute for Neuroscience and Medicine II, Research Center Juelich, 52428 Juelich, Germany
| | - Merle C Hönig
- Department of Nuclear Medicine, Faculty of Medicine and University Hospital of Cologne, University of Cologne, 50937 Cologne, Germany
- Molecular Organization of the Brain, Institute for Neuroscience and Medicine II, Research Center Juelich, 52428 Juelich, Germany
| | - Peter H Weiss
- Cognitive Neuroscience, Institute for Neuroscience and Medicine III, Research Center Juelich, 52428 Juelich,Germany
- Department of Neurology, Faculty of Medicine and University Hospital of Cologne, Universityof Cologne, 50937 Cologne, Germany
| | - Alexander Drzezga
- Department of Nuclear Medicine, Faculty of Medicine and University Hospital of Cologne, University of Cologne, 50937 Cologne, Germany
- Molecular Organization of the Brain, Institute for Neuroscience and Medicine II, Research Center Juelich, 52428 Juelich, Germany
- German Center for Neurodegenerative Diseases Bonn/Cologne, 53127 Bonn, Germany
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