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Sarazin M, Lagarde J, El Haddad I, de Souza LC, Bellier B, Potier MC, Bottlaender M, Dorothée G. The path to next-generation disease-modifying immunomodulatory combination therapies in Alzheimer's disease. NATURE AGING 2024; 4:761-770. [PMID: 38839924 DOI: 10.1038/s43587-024-00630-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Accepted: 04/09/2024] [Indexed: 06/07/2024]
Abstract
The cautious optimism following recent anti-amyloid therapeutic trials for Alzheimer's disease (AD) provides a glimmer of hope after years of disappointment. Although these encouraging results represent discernible progress, they also highlight the need to enhance further the still modest clinical efficacy of current disease-modifying immunotherapies. Here, we highlight crucial milestones essential for advancing precision medicine in AD. These include reevaluating the choice of therapeutic targets by considering the key role of both central neuroinflammation and peripheral immunity in disease pathogenesis, refining patient stratification by further defining the inflammatory component within the forthcoming ATN(I) (amyloid, tau and neurodegeneration (and inflammation)) classification of AD biomarkers and defining more accurate clinical outcomes and prognostic biomarkers that better reflect disease heterogeneity. Next-generation immunotherapies will need to go beyond the current antibody-only approach by simultaneously targeting pathological proteins together with innate neuroinflammation and/or peripheral-central immune crosstalk. Such innovative immunomodulatory combination therapy approaches should be evaluated in appropriately redesigned clinical therapeutic trials, which must carefully integrate the neuroimmune component.
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Affiliation(s)
- Marie Sarazin
- Department of Neurology of Memory and Language, GHU Paris Psychiatrie & Neurosciences, Hôpital Sainte-Anne, Paris, France.
- Université Paris-Cité, Paris, France.
- Université Paris-Saclay, BioMaps, Service Hospitalier Frédéric Joliot, CEA, CNRS, Inserm, Orsay, France.
| | - Julien Lagarde
- Department of Neurology of Memory and Language, GHU Paris Psychiatrie & Neurosciences, Hôpital Sainte-Anne, Paris, France
- Université Paris-Cité, Paris, France
- Université Paris-Saclay, BioMaps, Service Hospitalier Frédéric Joliot, CEA, CNRS, Inserm, Orsay, France
| | - Inès El Haddad
- Sorbonne Université, Inserm, Centre de Recherche Saint-Antoine, CRSA, Immune System and Neuroinflammation Laboratory, Hôpital Saint-Antoine, Paris, France
| | - Leonardo Cruz de Souza
- Grupo de Pesquisa em Neurologia Cognitiva e do Comportamento, Departamento de Clínica Médica, Faculdade de Medicina, Universidade Federal de Minas Gerais (UFMG), Belo Horizonte, Brazil
- Programa de Pós-Graduação em Neurociências, UFMG, Belo Horizonte, Brazil
- Departamento de Clínica Médica, Faculdade de Medicina, UFMG, Belo Horizonte, Brazil
| | - Bertrand Bellier
- Sorbonne Université, Inserm, Centre de Recherche Saint-Antoine, CRSA, Immune System and Neuroinflammation Laboratory, Hôpital Saint-Antoine, Paris, France
| | - Marie-Claude Potier
- Paris Brain Institute (ICM), Centre National de la Recherche Scientifique (CNRS) UMR 7225, INSERM U1127, Hôpital de la Pitié-Salpêtrière, Sorbonne Université, Paris, France
| | - Michel Bottlaender
- Université Paris-Saclay, BioMaps, Service Hospitalier Frédéric Joliot, CEA, CNRS, Inserm, Orsay, France
- Université Paris-Saclay, UNIACT, Neurospin, Joliot Institute, CEA, Gif-sur-Yvette, France
| | - Guillaume Dorothée
- Sorbonne Université, Inserm, Centre de Recherche Saint-Antoine, CRSA, Immune System and Neuroinflammation Laboratory, Hôpital Saint-Antoine, Paris, France.
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2
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Ma X, Shyer M, Harris K, Wang D, Hsu YC, Farrell C, Goodwin N, Anjum S, Bukhbinder AS, Dean S, Khan T, Hunter D, Schulz PE, Jiang X, Kim Y. Deep learning to predict rapid progression of Alzheimer's disease from pooled clinical trials: A retrospective study. PLOS DIGITAL HEALTH 2024; 3:e0000479. [PMID: 38598464 PMCID: PMC11006164 DOI: 10.1371/journal.pdig.0000479] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Accepted: 02/26/2024] [Indexed: 04/12/2024]
Abstract
The rate of progression of Alzheimer's disease (AD) differs dramatically between patients. Identifying the most is critical because when their numbers differ between treated and control groups, it distorts the outcome, making it impossible to tell whether the treatment was beneficial. Much recent effort, then, has gone into identifying RPs. We pooled de-identified placebo-arm data of three randomized controlled trials (RCTs), EXPEDITION, EXPEDITION 2, and EXPEDITION 3, provided by Eli Lilly and Company. After processing, the data included 1603 mild-to-moderate AD patients with 80 weeks of longitudinal observations on neurocognitive health, brain volumes, and amyloid-beta (Aβ) levels. RPs were defined by changes in four neurocognitive/functional health measures. We built deep learning models using recurrent neural networks with attention mechanisms to predict RPs by week 80 based on varying observation periods from baseline (e.g., 12, 28 weeks). Feature importance scores for RP prediction were computed and temporal feature trajectories were compared between RPs and non-RPs. Our evaluation and analysis focused on models trained with 28 weeks of observation. The models achieved robust internal validation area under the receiver operating characteristic (AUROCs) ranging from 0.80 (95% CI 0.79-0.82) to 0.82 (0.81-0.83), and the area under the precision-recall curve (AUPRCs) from 0.34 (0.32-0.36) to 0.46 (0.44-0.49). External validation AUROCs ranged from 0.75 (0.70-0.81) to 0.83 (0.82-0.84) and AUPRCs from 0.27 (0.25-0.29) to 0.45 (0.43-0.48). Aβ plasma levels, regional brain volumetry, and neurocognitive health emerged as important factors for the model prediction. In addition, the trajectories were stratified between predicted RPs and non-RPs based on factors such as ventricular volumes and neurocognitive domains. Our findings will greatly aid clinical trialists in designing tests for new medications, representing a key step toward identifying effective new AD therapies.
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Affiliation(s)
- Xiaotian Ma
- Department of Health Data Science and Artificial Intelligence, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas, United States of America
| | - Madison Shyer
- Department of Neurology, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, Texas, United States of America
| | - Kristofer Harris
- Department of Neurology, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, Texas, United States of America
| | - Dulin Wang
- Department of Health Data Science and Artificial Intelligence, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas, United States of America
| | - Yu-Chun Hsu
- Department of Health Data Science and Artificial Intelligence, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas, United States of America
| | - Christine Farrell
- Department of Neurology, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, Texas, United States of America
| | - Nathan Goodwin
- Department of Neurology, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, Texas, United States of America
| | - Sahar Anjum
- Department of Neurology, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, Texas, United States of America
| | - Avram S. Bukhbinder
- Department of Neurology, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, Texas, United States of America
- Division of Pediatric Neurology, Massachusetts General Hospital, Boston, Massachusetts, United States of America
| | - Sarah Dean
- Department of Neurology, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, Texas, United States of America
| | - Tanveer Khan
- Department of Neurology, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, Texas, United States of America
| | - David Hunter
- Department of Neurology, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, Texas, United States of America
| | - Paul E. Schulz
- Department of Neurology, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, Texas, United States of America
| | - Xiaoqian Jiang
- Department of Health Data Science and Artificial Intelligence, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas, United States of America
| | - Yejin Kim
- Department of Health Data Science and Artificial Intelligence, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas, United States of America
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Devanarayan V, Ye Y, Charil A, Andreozzi E, Sachdev P, Llano DA, Tian L, Zhu L, Hampel H, Kramer L, Dhadda S, Irizarry M. Predicting clinical progression trajectories of early Alzheimer's disease patients. Alzheimers Dement 2024; 20:1725-1738. [PMID: 38087949 PMCID: PMC10984448 DOI: 10.1002/alz.13565] [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: 04/26/2023] [Revised: 09/06/2023] [Accepted: 11/07/2023] [Indexed: 03/16/2024]
Abstract
BACKGROUND Models for forecasting individual clinical progression trajectories in early Alzheimer's disease (AD) are needed for optimizing clinical studies and patient monitoring. METHODS Prediction models were constructed using a clinical trial training cohort (TC; n = 934) via a gradient boosting algorithm and then evaluated in two validation cohorts (VC 1, n = 235; VC 2, n = 421). Model inputs included baseline clinical features (cognitive function assessments, APOE ε4 status, and demographics) and brain magnetic resonance imaging (MRI) measures. RESULTS The model using clinical features achieved R2 of 0.21 and 0.31 for predicting 2-year cognitive decline in VC 1 and VC 2, respectively. Adding MRI features improved the R2 to 0.29 in VC 1, which employed the same preprocessing pipeline as the TC. Utilizing these model-based predictions for clinical trial enrichment reduced the required sample size by 20% to 49%. DISCUSSION Our validated prediction models enable baseline prediction of clinical progression trajectories in early AD, benefiting clinical trial enrichment and various applications.
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Affiliation(s)
- Viswanath Devanarayan
- Clinical Evidence GenerationEisai Inc.NutleyNew JerseyUSA
- Department of MathematicsStatistics and Computer ScienceUniversity of Illinois ChicagoChicagoIllinoisUSA
| | - Yuanqing Ye
- Clinical Evidence GenerationEisai Inc.NutleyNew JerseyUSA
| | - Arnaud Charil
- Clinical Evidence GenerationEisai Inc.NutleyNew JerseyUSA
| | | | | | - Daniel A. Llano
- Carle Illinois College of MedicineUrbanaIllinoisUSA
- Department of Molecular and Integrative PhysiologyUniversity of Illinois Urbana‐ChampaignUrbanaIllinoisUSA
| | - Lu Tian
- Department of Biomedical Data ScienceStanford University School of MedicinePalo AltoCaliforniaUSA
| | - Liang Zhu
- Clinical Evidence GenerationEisai Inc.NutleyNew JerseyUSA
| | - Harald Hampel
- Clinical Evidence GenerationEisai Inc.NutleyNew JerseyUSA
| | - Lynn Kramer
- Clinical Evidence GenerationEisai Inc.NutleyNew JerseyUSA
| | - Shobha Dhadda
- Clinical Evidence GenerationEisai Inc.NutleyNew JerseyUSA
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Wang C, Tachimori H, Yamaguchi H, Sekiguchi A, Li Y, Yamashita Y. A multimodal deep learning approach for the prediction of cognitive decline and its effectiveness in clinical trials for Alzheimer's disease. Transl Psychiatry 2024; 14:105. [PMID: 38383536 PMCID: PMC10882004 DOI: 10.1038/s41398-024-02819-w] [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: 02/24/2023] [Revised: 02/01/2024] [Accepted: 02/07/2024] [Indexed: 02/23/2024] Open
Abstract
Alzheimer's disease is one of the most important health-care challenges in the world. For decades, numerous efforts have been made to develop therapeutics for Alzheimer's disease, but most clinical trials have failed to show significant treatment effects on slowing or halting cognitive decline. Among several challenges in such trials, one recently noticed but unsolved is biased allocation of fast and slow cognitive decliners to treatment and placebo groups during randomization caused by the large individual variation in the speed of cognitive decline. This allocation bias directly results in either over- or underestimation of the treatment effect from the outcome of the trial. In this study, we propose a stratified randomization method using the degree of cognitive decline predicted by an artificial intelligence model as a stratification index to suppress the allocation bias in randomization and evaluate its effectiveness by simulation using ADNI data set.
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Affiliation(s)
- Caihua Wang
- Bio Science & Engineering Laboratories, FUJIFILM Corporation, Ashigarakami-gun, Kanagawa, Japan
| | - Hisateru Tachimori
- Department of Information Medicine, National Institute of Neuroscience, National Center of Neurology and Psychiatry, Tokyo, Japan
- Endowed Course for Health System Innovation, Keio University School of Medicine, Tokyo, Japan
| | - Hiroyuki Yamaguchi
- Department of Information Medicine, National Institute of Neuroscience, National Center of Neurology and Psychiatry, Tokyo, Japan
- Department of Psychiatry, Yokohama City University School of Medicine, Yokohama, Japan
| | - Atsushi Sekiguchi
- Department of Behavioral Medicine, National Institute of Mental Health, National Center of Neurology and Psychiatry, Tokyo, Japan
| | - Yuanzhong Li
- Bio Science & Engineering Laboratories, FUJIFILM Corporation, Ashigarakami-gun, Kanagawa, Japan.
| | - Yuichi Yamashita
- Department of Information Medicine, National Institute of Neuroscience, National Center of Neurology and Psychiatry, Tokyo, Japan
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Garcia MJ, Leadley R, Ross J, Bozeat S, Redhead G, Hansson O, Iwatsubo T, Villain N, Cummings J. Prognostic and Predictive Factors in Early Alzheimer's Disease: A Systematic Review. J Alzheimers Dis Rep 2024; 8:203-240. [PMID: 38405341 PMCID: PMC10894607 DOI: 10.3233/adr-230045] [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: 06/14/2023] [Accepted: 12/24/2023] [Indexed: 02/27/2024] Open
Abstract
Background Alzheimer's disease (AD) causes progressive decline of cognition and function. There is a lack of systematic literature reviews on prognostic and predictive factors in its early clinical stages (eAD), i.e., mild cognitive impairment due to AD and mild AD dementia. Objective To identify prognostic factors affecting eAD progression and predictive factors for treatment efficacy and safety of approved and/or under late-stage development disease-modifying treatments. Methods Databases were searched (August 2022) for studies reporting prognostic factors associated with eAD progression and predictive factors for treatment response. The Quality in Prognostic Factor Studies tool or the Cochrane risk of bias tool were used to assess risk of bias. Two reviewers independently screened the records. A single reviewer performed data extraction and quality assessment. A second performed a 20% check. Content experts reviewed and interpreted the data collected. Results Sixty-one studies were included. Self-reporting, diagnosis definition, and missing data led to high risk of bias. Population size ranged from 110 to 11,451. Analyses found data indicating that older age was and depression may be associated with progression. Greater baseline cognitive impairment was associated with progression. APOE4 may be a prognostic factor, a predictive factor for treatment efficacy and predicts an adverse response (ARIA). Elevated biomarkers (CSF/plasma p-tau, CSF t-tau, and plasma neurofilament light) were associated with disease progression. Conclusions Age was the strongest risk factor for progression. Biomarkers were associated with progression, supporting their use in trial selection and aiding diagnosis. Baseline cognitive impairment was a prognostic factor. APOE4 predicted ARIA, aligning with emerging evidence and relevant to treatment initiation/monitoring.
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Affiliation(s)
| | - Regina Leadley
- Mtech Access Ltd, IT Centre, Innovation Way, Heslington, York, UK
| | - Janine Ross
- Mtech Access Ltd, IT Centre, Innovation Way, Heslington, York, UK
| | | | | | - Oskar Hansson
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Faculty of Medicine, Lund University, Lund, Sweden
- Memory Clinic, Skåne University Hospital, Lund, Sweden
| | | | - Nicolas Villain
- AP-HP Sorbonne Université, Pitié-Salpêtrière Hospital, Department of Neurology, Institute of Memory and Alzheimer’s Disease, Paris, France
- Sorbonne Université, INSERM U1127, CNRS 7225, Institut du Cerveau –ICM, Paris, France
| | - Jeffrey Cummings
- Chambers-Grundy Center for TransformativeNeuroscience, Department of Brain Health, School of IntegratedHealth Sciences, University of Nevada Las Vegas, Las Vegas, NV, USA
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6
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Dubbelman MA, Hendriksen HMA, Harrison JE, Vijverberg EGB, Prins ND, Kroeze LA, Ottenhoff L, Van Leeuwenstijn MMSSA, Verberk IMW, Teunissen CE, van de Giessen EM, Van Harten AC, Van Der Flier WM, Sikkes SAM. Cognitive and Functional Change Over Time in Cognitively Healthy Individuals According to Alzheimer Disease Biomarker-Defined Subgroups. Neurology 2024; 102:e207978. [PMID: 38165338 PMCID: PMC10962908 DOI: 10.1212/wnl.0000000000207978] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Accepted: 10/04/2023] [Indexed: 01/03/2024] Open
Abstract
BACKGROUND AND OBJECTIVES It is unclear to what extent cognitive outcome measures are sensitive to capture decline in Alzheimer disease (AD) prevention trials. We aimed to analyze the sensitivity to changes over time of a range of neuropsychological tests in several cognitively unimpaired, biomarker-defined patient groups. METHODS Cognitively unimpaired individuals from the Amsterdam Dementia Cohort and the SCIENCe project with available AD biomarkers, obtained from CSF, PET scans, and plasma at baseline, were followed over time (4.5 ± 3.1 years, range 0.6-18.9 years). Based on common inclusion criteria for clinical trials, we defined groups (amyloid, phosphorylated tau [p-tau], APOE ε4). Linear mixed models, adjusted for age, sex, and education, were used to estimate change over time in neuropsychological tests, a functional outcome, and 2 cognitive composite measures. Standardized regression coefficients of time in years (βtime) were reported as outcome of interest. We analyzed change over time with full follow-up, as well as with follow-up limited to 1.5 and 3 years. RESULTS We included 387 individuals (aged 61.7 ± 8.6 years; 44% female) in the following (partly overlapping) biomarker groups: APOE ε4 carriers (n = 212), amyloid-positive individuals (n = 109), amyloid-positive APOE ε4 carriers (n = 66), CSF p-tau-positive individuals (n = 127), plasma p-tau-positive individuals (n = 71), and amyloid and CSF p-tau-positive individuals (n = 50), or in a control group (normal biomarkers; n = 65). An executive functioning task showed most decline in all biomarker groups (βtime range -0.30 to -0.71), followed by delayed word list recognition (βtime range -0.18 to -0.50). Functional decline (βtime range -0.17 to -0.63) was observed in all, except the CSF and plasma tau-positive groups. Both composites showed comparable amounts of change (βtime range -0.12 to -0.62) in all groups, except plasma p-tau-positive individuals. When limiting original follow-up duration, many effects disappeared or even flipped direction. DISCUSSION In conclusion, functional, composite, and neuropsychological outcome measures across all cognitive domains detect changes over time in various biomarker-defined groups, with changes being most evident among individuals with more AD pathology. AD prevention trials should use sufficiently long follow-up duration and/or more sensitive outcome measures to optimally capture subtle cognitive changes over time.
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Affiliation(s)
- Mark A Dubbelman
- From the Alzheimer Center Amsterdam, Neurology (M.A.D., H.M.A.H., J.E.H., E.G.B.V., L.A.K., L.O., M.M.S.S.A.V.L., I.M.W.V., C.E.T., A.C.V.H., W.M.V.D.F., S.A.M.S.), and Departments of Radiology & Nuclear Medicine (E.M.v.d.G.), Epidemiology & Data Science (W.M.V.D.F.), and Neurochemistry Laboratory, Department of Laboratory Medicine (I.M.W.V., C.E.T.), Amsterdam UMC, Vrije Universiteit Amsterdam; Neurodegeneration, Amsterdam Neuroscience; Brain Research Center (N.D.P., L.O.); and Department of Clinical, Neuro and Developmental Psychology (S.A.M.S.), Faculty of Behavioral and Movement Sciences, Vrije Universiteit, Amsterdam, the Netherlands
| | - Heleen M A Hendriksen
- From the Alzheimer Center Amsterdam, Neurology (M.A.D., H.M.A.H., J.E.H., E.G.B.V., L.A.K., L.O., M.M.S.S.A.V.L., I.M.W.V., C.E.T., A.C.V.H., W.M.V.D.F., S.A.M.S.), and Departments of Radiology & Nuclear Medicine (E.M.v.d.G.), Epidemiology & Data Science (W.M.V.D.F.), and Neurochemistry Laboratory, Department of Laboratory Medicine (I.M.W.V., C.E.T.), Amsterdam UMC, Vrije Universiteit Amsterdam; Neurodegeneration, Amsterdam Neuroscience; Brain Research Center (N.D.P., L.O.); and Department of Clinical, Neuro and Developmental Psychology (S.A.M.S.), Faculty of Behavioral and Movement Sciences, Vrije Universiteit, Amsterdam, the Netherlands
| | - John E Harrison
- From the Alzheimer Center Amsterdam, Neurology (M.A.D., H.M.A.H., J.E.H., E.G.B.V., L.A.K., L.O., M.M.S.S.A.V.L., I.M.W.V., C.E.T., A.C.V.H., W.M.V.D.F., S.A.M.S.), and Departments of Radiology & Nuclear Medicine (E.M.v.d.G.), Epidemiology & Data Science (W.M.V.D.F.), and Neurochemistry Laboratory, Department of Laboratory Medicine (I.M.W.V., C.E.T.), Amsterdam UMC, Vrije Universiteit Amsterdam; Neurodegeneration, Amsterdam Neuroscience; Brain Research Center (N.D.P., L.O.); and Department of Clinical, Neuro and Developmental Psychology (S.A.M.S.), Faculty of Behavioral and Movement Sciences, Vrije Universiteit, Amsterdam, the Netherlands
| | - Everard G B Vijverberg
- From the Alzheimer Center Amsterdam, Neurology (M.A.D., H.M.A.H., J.E.H., E.G.B.V., L.A.K., L.O., M.M.S.S.A.V.L., I.M.W.V., C.E.T., A.C.V.H., W.M.V.D.F., S.A.M.S.), and Departments of Radiology & Nuclear Medicine (E.M.v.d.G.), Epidemiology & Data Science (W.M.V.D.F.), and Neurochemistry Laboratory, Department of Laboratory Medicine (I.M.W.V., C.E.T.), Amsterdam UMC, Vrije Universiteit Amsterdam; Neurodegeneration, Amsterdam Neuroscience; Brain Research Center (N.D.P., L.O.); and Department of Clinical, Neuro and Developmental Psychology (S.A.M.S.), Faculty of Behavioral and Movement Sciences, Vrije Universiteit, Amsterdam, the Netherlands
| | - Niels D Prins
- From the Alzheimer Center Amsterdam, Neurology (M.A.D., H.M.A.H., J.E.H., E.G.B.V., L.A.K., L.O., M.M.S.S.A.V.L., I.M.W.V., C.E.T., A.C.V.H., W.M.V.D.F., S.A.M.S.), and Departments of Radiology & Nuclear Medicine (E.M.v.d.G.), Epidemiology & Data Science (W.M.V.D.F.), and Neurochemistry Laboratory, Department of Laboratory Medicine (I.M.W.V., C.E.T.), Amsterdam UMC, Vrije Universiteit Amsterdam; Neurodegeneration, Amsterdam Neuroscience; Brain Research Center (N.D.P., L.O.); and Department of Clinical, Neuro and Developmental Psychology (S.A.M.S.), Faculty of Behavioral and Movement Sciences, Vrije Universiteit, Amsterdam, the Netherlands
| | - Lior A Kroeze
- From the Alzheimer Center Amsterdam, Neurology (M.A.D., H.M.A.H., J.E.H., E.G.B.V., L.A.K., L.O., M.M.S.S.A.V.L., I.M.W.V., C.E.T., A.C.V.H., W.M.V.D.F., S.A.M.S.), and Departments of Radiology & Nuclear Medicine (E.M.v.d.G.), Epidemiology & Data Science (W.M.V.D.F.), and Neurochemistry Laboratory, Department of Laboratory Medicine (I.M.W.V., C.E.T.), Amsterdam UMC, Vrije Universiteit Amsterdam; Neurodegeneration, Amsterdam Neuroscience; Brain Research Center (N.D.P., L.O.); and Department of Clinical, Neuro and Developmental Psychology (S.A.M.S.), Faculty of Behavioral and Movement Sciences, Vrije Universiteit, Amsterdam, the Netherlands
| | - Lois Ottenhoff
- From the Alzheimer Center Amsterdam, Neurology (M.A.D., H.M.A.H., J.E.H., E.G.B.V., L.A.K., L.O., M.M.S.S.A.V.L., I.M.W.V., C.E.T., A.C.V.H., W.M.V.D.F., S.A.M.S.), and Departments of Radiology & Nuclear Medicine (E.M.v.d.G.), Epidemiology & Data Science (W.M.V.D.F.), and Neurochemistry Laboratory, Department of Laboratory Medicine (I.M.W.V., C.E.T.), Amsterdam UMC, Vrije Universiteit Amsterdam; Neurodegeneration, Amsterdam Neuroscience; Brain Research Center (N.D.P., L.O.); and Department of Clinical, Neuro and Developmental Psychology (S.A.M.S.), Faculty of Behavioral and Movement Sciences, Vrije Universiteit, Amsterdam, the Netherlands
| | - Mardou M S S A Van Leeuwenstijn
- From the Alzheimer Center Amsterdam, Neurology (M.A.D., H.M.A.H., J.E.H., E.G.B.V., L.A.K., L.O., M.M.S.S.A.V.L., I.M.W.V., C.E.T., A.C.V.H., W.M.V.D.F., S.A.M.S.), and Departments of Radiology & Nuclear Medicine (E.M.v.d.G.), Epidemiology & Data Science (W.M.V.D.F.), and Neurochemistry Laboratory, Department of Laboratory Medicine (I.M.W.V., C.E.T.), Amsterdam UMC, Vrije Universiteit Amsterdam; Neurodegeneration, Amsterdam Neuroscience; Brain Research Center (N.D.P., L.O.); and Department of Clinical, Neuro and Developmental Psychology (S.A.M.S.), Faculty of Behavioral and Movement Sciences, Vrije Universiteit, Amsterdam, the Netherlands
| | - Inge M W Verberk
- From the Alzheimer Center Amsterdam, Neurology (M.A.D., H.M.A.H., J.E.H., E.G.B.V., L.A.K., L.O., M.M.S.S.A.V.L., I.M.W.V., C.E.T., A.C.V.H., W.M.V.D.F., S.A.M.S.), and Departments of Radiology & Nuclear Medicine (E.M.v.d.G.), Epidemiology & Data Science (W.M.V.D.F.), and Neurochemistry Laboratory, Department of Laboratory Medicine (I.M.W.V., C.E.T.), Amsterdam UMC, Vrije Universiteit Amsterdam; Neurodegeneration, Amsterdam Neuroscience; Brain Research Center (N.D.P., L.O.); and Department of Clinical, Neuro and Developmental Psychology (S.A.M.S.), Faculty of Behavioral and Movement Sciences, Vrije Universiteit, Amsterdam, the Netherlands
| | - Charlotte E Teunissen
- From the Alzheimer Center Amsterdam, Neurology (M.A.D., H.M.A.H., J.E.H., E.G.B.V., L.A.K., L.O., M.M.S.S.A.V.L., I.M.W.V., C.E.T., A.C.V.H., W.M.V.D.F., S.A.M.S.), and Departments of Radiology & Nuclear Medicine (E.M.v.d.G.), Epidemiology & Data Science (W.M.V.D.F.), and Neurochemistry Laboratory, Department of Laboratory Medicine (I.M.W.V., C.E.T.), Amsterdam UMC, Vrije Universiteit Amsterdam; Neurodegeneration, Amsterdam Neuroscience; Brain Research Center (N.D.P., L.O.); and Department of Clinical, Neuro and Developmental Psychology (S.A.M.S.), Faculty of Behavioral and Movement Sciences, Vrije Universiteit, Amsterdam, the Netherlands
| | - Elsmarieke M van de Giessen
- From the Alzheimer Center Amsterdam, Neurology (M.A.D., H.M.A.H., J.E.H., E.G.B.V., L.A.K., L.O., M.M.S.S.A.V.L., I.M.W.V., C.E.T., A.C.V.H., W.M.V.D.F., S.A.M.S.), and Departments of Radiology & Nuclear Medicine (E.M.v.d.G.), Epidemiology & Data Science (W.M.V.D.F.), and Neurochemistry Laboratory, Department of Laboratory Medicine (I.M.W.V., C.E.T.), Amsterdam UMC, Vrije Universiteit Amsterdam; Neurodegeneration, Amsterdam Neuroscience; Brain Research Center (N.D.P., L.O.); and Department of Clinical, Neuro and Developmental Psychology (S.A.M.S.), Faculty of Behavioral and Movement Sciences, Vrije Universiteit, Amsterdam, the Netherlands
| | - Argonde C Van Harten
- From the Alzheimer Center Amsterdam, Neurology (M.A.D., H.M.A.H., J.E.H., E.G.B.V., L.A.K., L.O., M.M.S.S.A.V.L., I.M.W.V., C.E.T., A.C.V.H., W.M.V.D.F., S.A.M.S.), and Departments of Radiology & Nuclear Medicine (E.M.v.d.G.), Epidemiology & Data Science (W.M.V.D.F.), and Neurochemistry Laboratory, Department of Laboratory Medicine (I.M.W.V., C.E.T.), Amsterdam UMC, Vrije Universiteit Amsterdam; Neurodegeneration, Amsterdam Neuroscience; Brain Research Center (N.D.P., L.O.); and Department of Clinical, Neuro and Developmental Psychology (S.A.M.S.), Faculty of Behavioral and Movement Sciences, Vrije Universiteit, Amsterdam, the Netherlands
| | - Wiesje M Van Der Flier
- From the Alzheimer Center Amsterdam, Neurology (M.A.D., H.M.A.H., J.E.H., E.G.B.V., L.A.K., L.O., M.M.S.S.A.V.L., I.M.W.V., C.E.T., A.C.V.H., W.M.V.D.F., S.A.M.S.), and Departments of Radiology & Nuclear Medicine (E.M.v.d.G.), Epidemiology & Data Science (W.M.V.D.F.), and Neurochemistry Laboratory, Department of Laboratory Medicine (I.M.W.V., C.E.T.), Amsterdam UMC, Vrije Universiteit Amsterdam; Neurodegeneration, Amsterdam Neuroscience; Brain Research Center (N.D.P., L.O.); and Department of Clinical, Neuro and Developmental Psychology (S.A.M.S.), Faculty of Behavioral and Movement Sciences, Vrije Universiteit, Amsterdam, the Netherlands
| | - Sietske A M Sikkes
- From the Alzheimer Center Amsterdam, Neurology (M.A.D., H.M.A.H., J.E.H., E.G.B.V., L.A.K., L.O., M.M.S.S.A.V.L., I.M.W.V., C.E.T., A.C.V.H., W.M.V.D.F., S.A.M.S.), and Departments of Radiology & Nuclear Medicine (E.M.v.d.G.), Epidemiology & Data Science (W.M.V.D.F.), and Neurochemistry Laboratory, Department of Laboratory Medicine (I.M.W.V., C.E.T.), Amsterdam UMC, Vrije Universiteit Amsterdam; Neurodegeneration, Amsterdam Neuroscience; Brain Research Center (N.D.P., L.O.); and Department of Clinical, Neuro and Developmental Psychology (S.A.M.S.), Faculty of Behavioral and Movement Sciences, Vrije Universiteit, Amsterdam, the Netherlands
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7
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Veitch DP, Weiner MW, Miller M, Aisen PS, Ashford MA, Beckett LA, Green RC, Harvey D, Jack CR, Jagust W, Landau SM, Morris JC, Nho KT, Nosheny R, Okonkwo O, Perrin RJ, Petersen RC, Rivera Mindt M, Saykin A, Shaw LM, Toga AW, Tosun D. The Alzheimer's Disease Neuroimaging Initiative in the era of Alzheimer's disease treatment: A review of ADNI studies from 2021 to 2022. Alzheimers Dement 2024; 20:652-694. [PMID: 37698424 PMCID: PMC10841343 DOI: 10.1002/alz.13449] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Revised: 07/27/2023] [Accepted: 08/01/2023] [Indexed: 09/13/2023]
Abstract
The Alzheimer's Disease Neuroimaging Initiative (ADNI) aims to improve Alzheimer's disease (AD) clinical trials. Since 2006, ADNI has shared clinical, neuroimaging, and cognitive data, and biofluid samples. We used conventional search methods to identify 1459 publications from 2021 to 2022 using ADNI data/samples and reviewed 291 impactful studies. This review details how ADNI studies improved disease progression understanding and clinical trial efficiency. Advances in subject selection, detection of treatment effects, harmonization, and modeling improved clinical trials and plasma biomarkers like phosphorylated tau showed promise for clinical use. Biomarkers of amyloid beta, tau, neurodegeneration, inflammation, and others were prognostic with individualized prediction algorithms available online. Studies supported the amyloid cascade, emphasized the importance of neuroinflammation, and detailed widespread heterogeneity in disease, linked to genetic and vascular risk, co-pathologies, sex, and resilience. Biological subtypes were consistently observed. Generalizability of ADNI results is limited by lack of cohort diversity, an issue ADNI-4 aims to address by enrolling a diverse cohort.
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Affiliation(s)
- Dallas P. Veitch
- Department of Veterans Affairs Medical CenterNorthern California Institute for Research and Education (NCIRE)San FranciscoCaliforniaUSA
- Department of Veterans Affairs Medical CenterCenter for Imaging of Neurodegenerative DiseasesSan FranciscoCaliforniaUSA
| | - Michael W. Weiner
- Department of Veterans Affairs Medical CenterCenter for Imaging of Neurodegenerative DiseasesSan FranciscoCaliforniaUSA
- Department of Radiology and Biomedical ImagingUniversity of CaliforniaSan FranciscoCaliforniaUSA
- Department of MedicineUniversity of CaliforniaSan FranciscoCaliforniaUSA
- Department of Psychiatry and Behavioral SciencesUniversity of CaliforniaSan FranciscoCaliforniaUSA
- Department of NeurologyUniversity of CaliforniaSan FranciscoCaliforniaUSA
| | - Melanie Miller
- Department of Veterans Affairs Medical CenterNorthern California Institute for Research and Education (NCIRE)San FranciscoCaliforniaUSA
- Department of Veterans Affairs Medical CenterCenter for Imaging of Neurodegenerative DiseasesSan FranciscoCaliforniaUSA
| | - Paul S. Aisen
- Alzheimer's Therapeutic Research InstituteUniversity of Southern CaliforniaSan DiegoCaliforniaUSA
| | - Miriam A. Ashford
- Department of Veterans Affairs Medical CenterNorthern California Institute for Research and Education (NCIRE)San FranciscoCaliforniaUSA
| | - Laurel A. Beckett
- Division of BiostatisticsDepartment of Public Health SciencesUniversity of CaliforniaDavisCaliforniaUSA
| | - Robert C. Green
- Division of GeneticsDepartment of MedicineBrigham and Women's HospitalBroad Institute Ariadne Labs and Harvard Medical SchoolBostonMassachusettsUSA
| | - Danielle Harvey
- Division of BiostatisticsDepartment of Public Health SciencesUniversity of CaliforniaDavisCaliforniaUSA
| | | | - William Jagust
- Helen Wills Neuroscience InstituteUniversity of California BerkeleyBerkeleyCaliforniaUSA
| | - Susan M. Landau
- Helen Wills Neuroscience InstituteUniversity of California BerkeleyBerkeleyCaliforniaUSA
| | - John C. Morris
- Knight Alzheimer's Disease Research CenterWashington University School of MedicineSaint LouisMissouriUSA
- Department of NeurologyWashington University School of MedicineSaint LouisMissouriUSA
- Department of Pathology and ImmunologyWashington University School of MedicineSaint LouisMissouriUSA
| | - Kwangsik T. Nho
- Department of Radiology and Imaging Sciences and the Indiana Alzheimer's Disease Research CenterIndiana University School of MedicineIndianapolisIndianaUSA
- Center for Computational Biology and BioinformaticsIndiana University School of MedicineIndianapolisIndianaUSA
| | - Rachel Nosheny
- Department of Veterans Affairs Medical CenterCenter for Imaging of Neurodegenerative DiseasesSan FranciscoCaliforniaUSA
- Department of Psychiatry and Behavioral SciencesUniversity of CaliforniaSan FranciscoCaliforniaUSA
| | - Ozioma Okonkwo
- Wisconsin Alzheimer's Disease Research Center and Department of MedicineUniversity of Wisconsin School of Medicine and Public HealthMadisonWisconsinUSA
| | - Richard J. Perrin
- Knight Alzheimer's Disease Research CenterWashington University School of MedicineSaint LouisMissouriUSA
- Department of NeurologyWashington University School of MedicineSaint LouisMissouriUSA
- Department of Pathology and ImmunologyWashington University School of MedicineSaint LouisMissouriUSA
| | | | - Monica Rivera Mindt
- Department of PsychologyLatin American and Latino Studies InstituteAfrican and African American StudiesFordham UniversityNew YorkNew YorkUSA
- Department of NeurologyIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Andrew Saykin
- Department of Radiology and Imaging Sciences and the Indiana Alzheimer's Disease Research CenterIndiana University School of MedicineIndianapolisIndianaUSA
- Department of Medical and Molecular GeneticsIndiana University School of MedicineIndianapolisIndianaUSA
| | - Leslie M. Shaw
- Department of Pathology and Laboratory Medicine and the PENN Alzheimer's Disease Research CenterCenter for Neurodegenerative ResearchPerelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Arthur W. Toga
- Laboratory of Neuro ImagingInstitute of Neuroimaging and InformaticsKeck School of Medicine of University of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Duygu Tosun
- Department of Veterans Affairs Medical CenterCenter for Imaging of Neurodegenerative DiseasesSan FranciscoCaliforniaUSA
- Department of Radiology and Biomedical ImagingUniversity of CaliforniaSan FranciscoCaliforniaUSA
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8
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Chen SD, Zhang W, Feng YW, Wu BS, Yang L, Zhang YR, Wang HF, Guo Y, Deng YT, Feng JF, Cheng W, Dong Q, Yu JT. Genome-wide Survival Study Identifies PARL as a Novel Locus for Clinical Progression and Neurodegeneration in Alzheimer's Disease. Biol Psychiatry 2023; 94:732-742. [PMID: 36870520 DOI: 10.1016/j.biopsych.2023.02.992] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Revised: 01/05/2023] [Accepted: 02/22/2023] [Indexed: 03/06/2023]
Abstract
BACKGROUND Variability exists in the trajectories of Alzheimer's disease (AD). We aimed to identify genetic modulators of clinical progression in AD. METHODS We conducted the first genome-wide survival study on AD using a two-stage approach. The discovery and replication stage separately included 1158 and 211,817 individuals without dementia from the Alzheimer's Disease Neuroimaging Initiative and the UK Biobank, respectively (325 and 1103 progressed in average follow-up of 4.33 and 8.63 years, respectively). Cox proportional hazards models were applied with time to AD dementia as the phenotype of clinical progression. A series of bioinformatic analyses and functional experiments was performed to validate the novel findings. RESULTS We found that APOE and PARL, a novel locus tagged by rs6795172 (hazard ratio = 1.66, p = 1.45 × 10-9), were significantly associated with AD clinical progression and were successfully replicated. The novel locus was linked to accelerated cognitive changes, higher tau levels, and faster atrophy of AD-specific brain structures, which were also verified in UK Biobank neuroimaging follow-up. Gene analysis and summary data-based Mendelian randomization indicated PARL as the most functionally relevant gene in the locus. Expression quantitative trait locus analyses and dual-luciferase reporter assays confirmed that PARL expression could be regulated by rs6795172. Three different AD mouse models consistently showed decreased PARL expression accompanied by elevated tau levels, and in vitro experiments revealed that knockdown/overexpression of PARL inversely changed tau levels. CONCLUSIONS Collectively, genetic, bioinformatic, and functional evidence suggests that PARL modulates clinical progression and neurodegeneration in AD. Targeting PARL may potentially modify AD progression and have implications for disease-modifying therapies.
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Affiliation(s)
- Shi-Dong Chen
- Department of Neurology and Institute of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, National Center for Neurological Disorders, Shanghai, China
| | - Wei Zhang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, Shanghai, China
| | - Yi-Wei Feng
- Department of Neurology and Institute of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, National Center for Neurological Disorders, Shanghai, China
| | - Bang-Sheng Wu
- Department of Neurology and Institute of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, National Center for Neurological Disorders, Shanghai, China
| | - Liu Yang
- Department of Neurology and Institute of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, National Center for Neurological Disorders, Shanghai, China
| | - Ya-Ru Zhang
- Department of Neurology and Institute of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, National Center for Neurological Disorders, Shanghai, China
| | - Hui-Fu Wang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, Shanghai, China
| | - Yu Guo
- Department of Neurology and Institute of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, National Center for Neurological Disorders, Shanghai, China
| | - Yue-Ting Deng
- Department of Neurology and Institute of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, National Center for Neurological Disorders, Shanghai, China
| | - Jian-Feng Feng
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, Shanghai, China; Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai, China; Fudan ISTBI-ZJNU Algorithm Centre for Brain-Inspired Intelligence, Zhejiang Normal University, Jinhua, China; MOE Frontiers Center for Brain Science, Fudan University, Shanghai, Shanghai, China; Zhangjiang Fudan International Innovation Center, Shanghai, China
| | - Wei Cheng
- Department of Neurology and Institute of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, National Center for Neurological Disorders, Shanghai, China; Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, Shanghai, China; Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai, China; Fudan ISTBI-ZJNU Algorithm Centre for Brain-Inspired Intelligence, Zhejiang Normal University, Jinhua, China
| | - Qiang Dong
- Department of Neurology and Institute of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, National Center for Neurological Disorders, Shanghai, China.
| | - Jin-Tai Yu
- Department of Neurology and Institute of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, National Center for Neurological Disorders, Shanghai, China.
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9
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Reus LM, Ophoff RA. Novel Risk Locus Influences Risk to Clinical Progression to Alzheimer's Disease-type Dementia: A Step Toward the Disentanglement of Heterogeneity in Progression. Biol Psychiatry 2023; 94:689-691. [PMID: 37778864 DOI: 10.1016/j.biopsych.2023.08.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/21/2023] [Accepted: 08/22/2023] [Indexed: 10/03/2023]
Affiliation(s)
- Lianne M Reus
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, The Netherlands; Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands.
| | - Roel A Ophoff
- Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, California; Department of Human Genetics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California; Department of Psychiatry, Erasmus University Medical Center, Rotterdam, The Netherlands
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10
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Knopman DS, Hershey L. Implications of the Approval of Lecanemab for Alzheimer Disease Patient Care: Incremental Step or Paradigm Shift? Neurology 2023; 101:610-620. [PMID: 37295957 PMCID: PMC10573150 DOI: 10.1212/wnl.0000000000207438] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Accepted: 04/06/2023] [Indexed: 06/12/2023] Open
Abstract
The amyloid cascade model of the pathogenesis of Alzheimer disease (AD) is well supported in observational studies. Its therapeutic corollary asserts that removal of amyloid-β peptide ("amyloid") would provide clinical benefits. After 2 decades of pursuing the strategy of amyloid removal without success, clinical trials of the antiamyloid monoclonal antibody (AAMA) donanemab and a phase 3 clinical trial of lecanemab have reported clinical benefits linked to amyloid removal. Lecanemab (trade name, Leqembi) is the first with published phase 3 trial results. When administered through IV every 2 weeks to patients with elevated brain amyloid and mild cognitive impairment or mild dementia, lecanemab delayed cognitive and functional worsening by approximately 5 months in an 18-month double-blind, placebo-controlled trial. The trial was well conducted, and the results favoring lecanemab were internally consistent. The demonstration that lecanemab treatment delayed clinical progression in persons with mild symptoms due to AD is a major conceptual achievement, but a better appreciation of the magnitude and durability of benefits for individual patients will require extended observations from clinical practice settings. Amyloid-related imaging abnormalities (ARIA) that were largely asymptomatic occurred in approximately 20%, slightly more than half of which were attributable to treatment and the rest to underlying AD-related amyloid angiopathy. Persons who were homozygous for the APOE ε4 allele had greater ARIA risks. Hemorrhagic complications with longer-term lecanemab use need to be better understood. Administration of lecanemab will place unprecedented pressures on dementia care personnel and infrastructure, both of which need to grow exponentially to meet the challenge.
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Affiliation(s)
- David S Knopman
- From the Department of Neurology (D.S.K.), Mayo Clinic, Rochester, MN; and Department of Neurology (L.H.), University of Oklahoma Health Sciences Center.
| | - Linda Hershey
- From the Department of Neurology (D.S.K.), Mayo Clinic, Rochester, MN; and Department of Neurology (L.H.), University of Oklahoma Health Sciences Center
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11
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van der Flier WM, Tijms BM. Treatments for AD: towards the right target at the right time. Nat Rev Neurol 2023; 19:581-582. [PMID: 37658242 DOI: 10.1038/s41582-023-00869-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/03/2023]
Affiliation(s)
- Wiesje M van der Flier
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands.
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands.
- Epidemiology and Data Science, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands.
| | - Betty M Tijms
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
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12
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Sintini I, Graff-Radford J, Schwarz CG, Machulda MM, Singh NA, Carlos AF, Senjem ML, Jack CR, Lowe VJ, Josephs KA, Whitwell JL. Longitudinal rates of atrophy and tau accumulation differ between the visual and language variants of atypical Alzheimer's disease. Alzheimers Dement 2023; 19:4396-4406. [PMID: 37485642 PMCID: PMC10592409 DOI: 10.1002/alz.13396] [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: 04/03/2023] [Revised: 05/19/2023] [Accepted: 06/19/2023] [Indexed: 07/25/2023]
Abstract
INTRODUCTION Atypical variants of Alzheimer's disease (AD) include the visual variant, known as posterior cortical atrophy (PCA), and the language variant, known as logopenic progressive aphasia (LPA). Clinically, rates of disease progression differ between them. METHODS We evaluated 34 PCA and 29 LPA participants. Structural magnetic resonance imaging and 18 F-flortaucipir positron emission tomography were performed at baseline and at 1-year follow-up. Rates of change in tau uptake and grey matter volumes were compared between PCA and LPA with linear mixed-effects models and voxel-based analyses. RESULTS PCA had faster rates of occipital atrophy. LPA had faster rates of left temporal atrophy and faster rates of tau accumulation in the parietal, right temporal, and occipital lobes. Age was negatively associated with rates of atrophy and tau accumulation. DISCUSSION Longitudinal patterns of neuroimaging abnormalities differed between PCA and LPA, although with divergent results for tau accumulation and atrophy. HIGHLIGHTS The language variant of Alzheimer's disease accumulates tau faster than the visual variant. Each variant shows faster rates of atrophy than the other in its signature regions. Age negatively influences rates of atrophy and tau accumulation in both variants.
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Affiliation(s)
- Irene Sintini
- Department of Radiology, Mayo Clinic, Rochester, MN, USA, 55905
| | | | | | - Mary M. Machulda
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester MN, USA, 55905
| | | | - Arenn F. Carlos
- Department of Neurology, Mayo Clinic, Rochester, MN, USA, 55905
| | - Matthew L. Senjem
- Department of Radiology, Mayo Clinic, Rochester, MN, USA, 55905
- Department of Information Technology, Mayo Clinic, Rochester, MN, USA, 55905
| | | | - Val J. Lowe
- Department of Radiology, Mayo Clinic, Rochester, MN, USA, 55905
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13
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Schultz SA, Shirzadi Z, Schultz AP, Liu L, Fitzpatrick CD, McDade E, Barthelemy NR, Renton A, Esposito B, Joseph‐Mathurin N, Cruchaga C, Chen CD, Goate A, Allegri RF, Benzinger TLS, Berman S, Chui HC, Fagan AM, Farlow MR, Fox NC, Gordon BA, Day GS, Graff‐Radford NR, Hassenstab JJ, Hanseeuw BJ, Hofmann A, Jack CR, Jucker M, Karch CM, Koeppe RA, Lee J, Levey AI, Levin J, Martins RN, Mori H, Morris JC, Noble J, Perrin RJ, Rosa‐Neto P, Salloway SP, Sanchez‐Valle R, Schofield PR, Xiong C, Johnson KA, Bateman RJ, Sperling RA, Chhatwal JP. Location of pathogenic variants in PSEN1 impacts progression of cognitive, clinical, and neurodegenerative measures in autosomal-dominant Alzheimer's disease. Aging Cell 2023; 22:e13871. [PMID: 37291760 PMCID: PMC10410059 DOI: 10.1111/acel.13871] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Revised: 04/30/2023] [Accepted: 05/03/2023] [Indexed: 06/10/2023] Open
Abstract
Although pathogenic variants in PSEN1 leading to autosomal-dominant Alzheimer disease (ADAD) are highly penetrant, substantial interindividual variability in the rates of cognitive decline and biomarker change are observed in ADAD. We hypothesized that this interindividual variability may be associated with the location of the pathogenic variant within PSEN1. PSEN1 pathogenic variant carriers participating in the Dominantly Inherited Alzheimer Network (DIAN) observational study were grouped based on whether the underlying variant affects a transmembrane (TM) or cytoplasmic (CY) protein domain within PSEN1. CY and TM carriers and variant non-carriers (NC) who completed clinical evaluation, multimodal neuroimaging, and lumbar puncture for collection of cerebrospinal fluid (CSF) as part of their participation in DIAN were included in this study. Linear mixed effects models were used to determine differences in clinical, cognitive, and biomarker measures between the NC, TM, and CY groups. While both the CY and TM groups were found to have similarly elevated Aβ compared to NC, TM carriers had greater cognitive impairment, smaller hippocampal volume, and elevated phosphorylated tau levels across the spectrum of pre-symptomatic and symptomatic phases of disease as compared to CY, using both cross-sectional and longitudinal data. As distinct portions of PSEN1 are differentially involved in APP processing by γ-secretase and the generation of toxic β-amyloid species, these results have important implications for understanding the pathobiology of ADAD and accounting for a substantial portion of the interindividual heterogeneity in ongoing ADAD clinical trials.
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Affiliation(s)
| | - Zahra Shirzadi
- Massachusetts General Hospital, Harvard Medical SchoolBostonMassachusettsUSA
| | - Aaron P. Schultz
- Massachusetts General Hospital, Harvard Medical SchoolBostonMassachusettsUSA
| | - Lei Liu
- Brigham and Women's HospitalBostonMassachusettsUSA
- Ann Romney Center for Neurologic DiseasesBostonMassachusettsUSA
| | | | - Eric McDade
- Washington University in St. Louis School of MedicineSt. LouisMissouriUSA
| | | | - Alan Renton
- Department of Genetics and Genomic SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Bianca Esposito
- Department of Genetics and Genomic SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | | | - Carlos Cruchaga
- Washington University in St. Louis School of MedicineSt. LouisMissouriUSA
| | - Charles D. Chen
- Washington University in St. Louis School of MedicineSt. LouisMissouriUSA
| | - Alison Goate
- Department of Genetics and Genomic SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | | | | | - Sarah Berman
- University of PittsburghPittsburghPennsylvaniaUSA
| | - Helena C. Chui
- Department of Neurology, Keck School of MedicineUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Anne M. Fagan
- Washington University in St. Louis School of MedicineSt. LouisMissouriUSA
| | - Martin R. Farlow
- Indiana Alzheimer's Disease Research CenterIndianapolisIndianaUSA
| | - Nick C. Fox
- Dementia Research Centre & UK Dementia Research InstituteUCL Institute of NeurologyLondonUK
| | - Brian A. Gordon
- Washington University in St. Louis School of MedicineSt. LouisMissouriUSA
| | | | | | | | - Bernard J. Hanseeuw
- Institute of Neuroscience, UCLouvainBrusselsBelgium
- Gordon Center for Medical Imaging in the Radiology Department of MGHBostonMassachusettsUSA
| | - Anna Hofmann
- German Center for Neurodegenerative Diseases (DZNE)TuebingenGermany
| | | | - Mathias Jucker
- German Center for Neurodegenerative Diseases (DZNE)TuebingenGermany
| | - Celeste M. Karch
- Washington University in St. Louis School of MedicineSt. LouisMissouriUSA
| | | | - Jae‐Hong Lee
- Asan Medical CenterUniversity of Ulsan College of MedicineSeoulSouth Korea
| | - Allan I. Levey
- Emory Goizueta Alzheimer's Disease Research CenterAtlantaGeorgiaUSA
| | - Johannes Levin
- German Center for Neurodegenerative Diseases (DZNE)MunichGermany
- Department of NeurologyLudwig‐Maximilians‐Universität MünchenMunichGermany
- Munich Cluster for Systems Neurology (SyNergy)MunichGermany
| | | | | | - John C. Morris
- Washington University in St. Louis School of MedicineSt. LouisMissouriUSA
| | | | - Richard J. Perrin
- Washington University in St. Louis School of MedicineSt. LouisMissouriUSA
| | - Pedro Rosa‐Neto
- Translational Neuroimaging Laboratory, Le Centre intégré universitaire de santé et de services sociaux (CIUSSS) de l'Ouest‐de‐l'Île‐de‐Montréal; Department of Neurology and NeurosurgeryMcGill UniversityMontrealCanada
| | | | - Raquel Sanchez‐Valle
- Alzheimer's disease and other cognitive disorders Unit, Neurology Department, Hospital Clínic de BarcelonaInstitut d'Investigacions BiomediquesBarcelonaSpain
| | - Peter R. Schofield
- Neuroscience Research AustraliaRandwickNew South WalesAustralia
- School of Medical SciencesUniversity of New South WalesSydneyNew South WalesAustralia
| | - Chengjie Xiong
- Washington University in St. Louis School of MedicineSt. LouisMissouriUSA
| | - Keith A. Johnson
- Massachusetts General Hospital, Harvard Medical SchoolBostonMassachusettsUSA
- Brigham and Women's HospitalBostonMassachusettsUSA
| | - Randall J. Bateman
- Washington University in St. Louis School of MedicineSt. LouisMissouriUSA
| | - Reisa A. Sperling
- Massachusetts General Hospital, Harvard Medical SchoolBostonMassachusettsUSA
- Brigham and Women's HospitalBostonMassachusettsUSA
| | - Jasmeer P. Chhatwal
- Massachusetts General Hospital, Harvard Medical SchoolBostonMassachusettsUSA
- Brigham and Women's HospitalBostonMassachusettsUSA
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Lim AC, Barnes LL, Weissberger GH, Lamar M, Nguyen AL, Fenton L, Herrera J, Han SD. Quantification of race/ethnicity representation in Alzheimer's disease neuroimaging research in the USA: a systematic review. COMMUNICATIONS MEDICINE 2023; 3:101. [PMID: 37491471 PMCID: PMC10368705 DOI: 10.1038/s43856-023-00333-6] [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: 03/09/2023] [Accepted: 07/05/2023] [Indexed: 07/27/2023] Open
Abstract
BACKGROUND Racial and ethnic minoritized groups are disproportionately at risk for Alzheimer's Disease (AD), but are not sufficiently recruited in AD neuroimaging research in the United States. This is important as sample composition impacts generalizability of findings, biomarker cutoffs, and treatment effects. No studies have quantified the breadth of race/ethnicity representation in the AD literature. METHODS This review identified median race/ethnicity composition of AD neuroimaging US-based research samples available as free full-text articles on PubMed. Two types of published studies were analyzed: studies that directly report race/ethnicity data (i.e., direct studies), and studies that do not report race/ethnicity but used data from a cohort study/database that does report this information (i.e., indirect studies). RESULTS Direct studies (n = 719) have median representation of 88.9% white or 87.4% Non-Hispanic white, 7.3% Black/African American, and 3.4% Hispanic/Latino ethnicity, with 0% Asian American, Native Hawaiian/Pacific Islander, and American Indian/Alaska Native, Multiracial, and Other Race participants. Cohort studies/databases (n = 44) from which indirect studies (n = 1745) derived are more diverse, with median representation of 84.2% white, 83.7% Non-Hispanic white, 11.6% Black/African American, 4.7% Hispanic/Latino, and 1.75% Asian American participants. Notably, 94% of indirect studies derive from just 10 cohort studies/databases. Comparisons of two time periods using a median split for publication year, 1994-2017 and 2018-2022, indicate that sample diversity has improved recently, particularly for Black/African American participants (3.39% from 1994-2017 and 8.29% from 2018-2022). CONCLUSIONS There is still underrepresentation of all minoritized groups relative to Census data, especially for Hispanic/Latino and Asian American individuals. The AD neuroimaging literature will benefit from increased representative recruitment of ethnic/racial minorities. More transparent reporting of race/ethnicity data is needed.
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Affiliation(s)
- Aaron C Lim
- Department of Family Medicine, Keck School of Medicine of USC, Alhambra, CA, USA
| | - Lisa L Barnes
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, USA
| | - Gali H Weissberger
- The Interdisciplinary Department of Social Sciences, Bar-Ilan University, Raman Gat, Israel
| | - Melissa Lamar
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, USA
| | - Annie L Nguyen
- Department of Family Medicine, Keck School of Medicine of USC, Alhambra, CA, USA
| | - Laura Fenton
- Department of Psychology, USC Dornsife College of Letters, Arts, and Sciences, Los Angeles, CA, USA
| | - Jennifer Herrera
- Department of Family Medicine, Keck School of Medicine of USC, Alhambra, CA, USA
| | - S Duke Han
- Department of Family Medicine, Keck School of Medicine of USC, Alhambra, CA, USA.
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, USA.
- Department of Psychology, USC Dornsife College of Letters, Arts, and Sciences, Los Angeles, CA, USA.
- USC School of Gerontology, Los Angeles, CA, USA.
- Department of Neurology, Keck School of Medicine of USC, Los Angeles, CA, USA.
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Li W, Pang Y, Wang Y, Mei F, Guo M, Wei Y, Li X, Qin W, Wang W, Jia L, Jia J. Aberrant palmitoylation caused by a ZDHHC21 mutation contributes to pathophysiology of Alzheimer's disease. BMC Med 2023; 21:223. [PMID: 37365538 DOI: 10.1186/s12916-023-02930-7] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/18/2023] [Accepted: 06/08/2023] [Indexed: 06/28/2023] Open
Abstract
BACKGROUND The identification of pathogenic mutations in Alzheimer's disease (AD) causal genes led to a better understanding of the pathobiology of AD. Familial Alzheimer's disease (FAD) is known to be associated with mutations in the APP, PSEN1, and PSEN2 genes involved in Aβ production; however, these genetic defects occur in only about 10-20% of FAD cases, and more genes and new mechanism causing FAD remain largely obscure. METHODS We performed exome sequencing on family members with a FAD pedigree and identified gene variant ZDHHC21 p.T209S. A ZDHHC21T209S/T209S knock-in mouse model was then generated using CRISPR/Cas9. The Morris water navigation task was then used to examine spatial learning and memory. The involvement of aberrant palmitoylation of FYN tyrosine kinase and APP in AD pathology was evaluated using biochemical methods and immunostaining. Aβ and tau pathophysiology was evaluated using ELISA, biochemical methods, and immunostaining. Field recordings of synaptic long-term potentiation were obtained to examine synaptic plasticity. The density of synapses and dendritic branches was quantified using electron microscopy and Golgi staining. RESULTS We identified a variant (c.999A > T, p.T209S) of ZDHHC21 gene in a Han Chinese family. The proband presented marked cognitive impairment at 55 years of age (Mini-Mental State Examination score = 5, Clinical Dementia Rating = 3). Considerable Aβ retention was observed in the bilateral frontal, parietal, and lateral temporal cortices. The novel heterozygous missense mutation (p.T209S) was detected in all family members with AD and was not present in those unaffected, indicating cosegregation. ZDHHC21T209S/T209S mice exhibited cognitive impairment and synaptic dysfunction, suggesting the strong pathogenicity of the mutation. The ZDHHC21 p.T209S mutation significantly enhanced FYN palmitoylation, causing overactivation of NMDAR2B, inducing increased neuronal sensitivity to excitotoxicity leading to further synaptic dysfunction and neuronal loss. The palmitoylation of APP was also increased in ZDHHC21T209S/T209S mice, possibly contributing to Aβ production. Palmitoyltransferase inhibitors reversed synaptic function impairment. CONCLUSIONS ZDHHC21 p.T209S is a novel, candidate causal gene mutation in a Chinese FAD pedigree. Our discoveries strongly suggest that aberrant protein palmitoylation mediated by ZDHHC21 mutations is a new pathogenic mechanism of AD, warranting further investigations for the development of therapeutic interventions.
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Affiliation(s)
- Wenwen Li
- Innovation Center for Neurological Disorders and Department of Neurology, Xuanwu Hospital, Capital Medical University, National Clinical Research Center for Geriatric Diseases, Beijing, China
| | - Yana Pang
- Innovation Center for Neurological Disorders and Department of Neurology, Xuanwu Hospital, Capital Medical University, National Clinical Research Center for Geriatric Diseases, Beijing, China
| | - Yan Wang
- Innovation Center for Neurological Disorders and Department of Neurology, Xuanwu Hospital, Capital Medical University, National Clinical Research Center for Geriatric Diseases, Beijing, China
| | - Fan Mei
- Institute of Systems Biomedicine, Department of Pathology, School of Basic Medical Sciences, Peking University Health Science Center, Beijing, China
| | - Mengmeng Guo
- Innovation Center for Neurological Disorders and Department of Neurology, Xuanwu Hospital, Capital Medical University, National Clinical Research Center for Geriatric Diseases, Beijing, China
| | - Yiping Wei
- Innovation Center for Neurological Disorders and Department of Neurology, Xuanwu Hospital, Capital Medical University, National Clinical Research Center for Geriatric Diseases, Beijing, China
| | - Xinyue Li
- Innovation Center for Neurological Disorders and Department of Neurology, Xuanwu Hospital, Capital Medical University, National Clinical Research Center for Geriatric Diseases, Beijing, China
| | - Wei Qin
- Innovation Center for Neurological Disorders and Department of Neurology, Xuanwu Hospital, Capital Medical University, National Clinical Research Center for Geriatric Diseases, Beijing, China
| | - Wei Wang
- Innovation Center for Neurological Disorders and Department of Neurology, Xuanwu Hospital, Capital Medical University, National Clinical Research Center for Geriatric Diseases, Beijing, China
| | - Longfei Jia
- Innovation Center for Neurological Disorders and Department of Neurology, Xuanwu Hospital, Capital Medical University, National Clinical Research Center for Geriatric Diseases, Beijing, China
| | - Jianping Jia
- Innovation Center for Neurological Disorders and Department of Neurology, Xuanwu Hospital, Capital Medical University, National Clinical Research Center for Geriatric Diseases, Beijing, China.
- Beijing Key Laboratory of Geriatric Cognitive Disorders, Beijing, China.
- Clinical Center for Neurodegenerative Disease and Memory Impairment, Capital Medical University, Beijing, China.
- Center of Alzheimer's Disease, Beijing Institute of Brain Disorders, Collaborative Innovation Center for Brain Disorders, Capital Medical University, Beijing, China.
- Key Laboratory of Neurodegenerative Diseases, Ministry of Education, Beijing, China.
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16
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Verdi S, Rutherford S, Fraza C, Tosun D, Altmann A, Raket LL, Schott JM, Marquand AF, Cole JH. Personalising Alzheimer's Disease progression using brain atrophy markers. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.06.15.23291418. [PMID: 37398392 PMCID: PMC10312850 DOI: 10.1101/2023.06.15.23291418] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/04/2023]
Abstract
INTRODUCTION Neuroanatomical normative modelling can capture individual variability in Alzheimer's Disease (AD). We used neuroanatomical normative modelling to track individuals' disease progression in people with mild cognitive impairment (MCI) and patients with AD. METHODS Cortical thickness and subcortical volume neuroanatomical normative models were generated using healthy controls (n~58k). These models were used to calculate regional Z-scores in 4361 T1-weighted MRI time-series scans. Regions with Z-scores <-1.96 were classified as outliers and mapped on the brain, and also summarised by total outlier count (tOC). RESULTS Rate of change in tOC increased in AD and in people with MCI who converted to AD and correlated with multiple non-imaging markers. Moreover, a higher annual rate of change in tOC increased the risk of MCI progression to AD. Brain Z-score maps showed that the hippocampus had the highest rate of atrophy change. CONCLUSIONS Individual-level atrophy rates can be tracked by using regional outlier maps and tOC.
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Affiliation(s)
- Serena Verdi
- Centre for Medical Image Computing, University College London, London, UK
- Dementia Research Centre, UCL Queen Square Institute of Neurology, London, UK
| | - Saige Rutherford
- Donders Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, 6525EN, the Netherlands
- Department of Cognitive Neuroscience, Radboud University Medical Centre, Nijmegen, 6525EN, the Netherlands
| | - Charlotte Fraza
- Donders Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, 6525EN, the Netherlands
- Department of Cognitive Neuroscience, Radboud University Medical Centre, Nijmegen, 6525EN, the Netherlands
| | - Duygu Tosun
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Andre Altmann
- Centre for Medical Image Computing, University College London, London, UK
| | - Lars Lau Raket
- Department of Clinical Sciences, Lund University, Lund, Sweden
| | - Jonathan M Schott
- Dementia Research Centre, UCL Queen Square Institute of Neurology, London, UK
| | - Andre F Marquand
- Donders Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, 6525EN, the Netherlands
- Department of Cognitive Neuroscience, Radboud University Medical Centre, Nijmegen, 6525EN, the Netherlands
| | - James H Cole
- Centre for Medical Image Computing, University College London, London, UK
- Dementia Research Centre, UCL Queen Square Institute of Neurology, London, UK
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17
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Soldevila-Domenech N, De Toma I, Forcano L, Diaz-Pellicer P, Cuenca-Royo A, Fagundo B, Lorenzo T, Gomis-Gonzalez M, Sánchez-Benavides G, Fauria K, Sastre C, Fernandez De Piérola Í, Molinuevo JL, Verdejo-Garcia A, de la Torre R. Intensive assessment of executive functions derived from performance in cognitive training games. iScience 2023; 26:106886. [PMID: 37260752 PMCID: PMC10227423 DOI: 10.1016/j.isci.2023.106886] [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: 10/24/2022] [Revised: 02/26/2023] [Accepted: 05/11/2023] [Indexed: 06/02/2023] Open
Abstract
Traditional neuropsychological tests accurately describe the current cognitive state but fall short to characterize cognitive change over multiple short time periods. We present an innovative approach to remote monitoring of executive functions on a monthly basis, which leverages the performance indicators from self-administered computerized cognitive training games (NUP-EXE). We evaluated the measurement properties of NUP-EXE in N = 56 individuals (59% women, 60-80 years) at increased risk of Alzheimer's disease (APOE-ϵ4 carriers with subjective cognitive decline) who completed a 12-month multimodal intervention for preventing cognitive decline. NUP-EXE presented good psychometric properties and greater sensitivity to change than traditional tests. Improvements in NUP-EXE correlated with improvements in functionality and were affected by participants' age and gender. This novel data collection methodology is expected to allow a more accurate characterization of an individual's response to a cognitive decline preventive intervention and to inform development of outcome measures for a new generation of intervention trials.
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Affiliation(s)
- Natalia Soldevila-Domenech
- Neurosciences Research Programme, Hospital del Mar Medical Research Institute (IMIM), Barcelona, Spain
- Department of Medicine and Life Sciences, Pompeu Fabra University, Barcelona, Spain
| | - Ilario De Toma
- Neurosciences Research Programme, Hospital del Mar Medical Research Institute (IMIM), Barcelona, Spain
| | - Laura Forcano
- Neurosciences Research Programme, Hospital del Mar Medical Research Institute (IMIM), Barcelona, Spain
- CIBER de Fisiopatología de la Obesidad y la Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain
| | - Patrícia Diaz-Pellicer
- Neurosciences Research Programme, Hospital del Mar Medical Research Institute (IMIM), Barcelona, Spain
- Department of Medicine and Life Sciences, Pompeu Fabra University, Barcelona, Spain
| | - Aida Cuenca-Royo
- Neurosciences Research Programme, Hospital del Mar Medical Research Institute (IMIM), Barcelona, Spain
| | - Beatriz Fagundo
- Neurosciences Research Programme, Hospital del Mar Medical Research Institute (IMIM), Barcelona, Spain
| | - Thais Lorenzo
- Neurosciences Research Programme, Hospital del Mar Medical Research Institute (IMIM), Barcelona, Spain
- Department of Medicine and Life Sciences, Pompeu Fabra University, Barcelona, Spain
| | - Maria Gomis-Gonzalez
- Neurosciences Research Programme, Hospital del Mar Medical Research Institute (IMIM), Barcelona, Spain
| | - Gonzalo Sánchez-Benavides
- Neurosciences Research Programme, Hospital del Mar Medical Research Institute (IMIM), Barcelona, Spain
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (CIBERFES), Madrid, Spain
| | - Karine Fauria
- Neurosciences Research Programme, Hospital del Mar Medical Research Institute (IMIM), Barcelona, Spain
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (CIBERFES), Madrid, Spain
| | | | | | - José Luis Molinuevo
- Neurosciences Research Programme, Hospital del Mar Medical Research Institute (IMIM), Barcelona, Spain
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain
| | - Antonio Verdejo-Garcia
- School of Psychological Sciences and Turner Institute for Brain and Mental Health, Monash University, Melbourne, VIC, Australia
| | - Rafael de la Torre
- Neurosciences Research Programme, Hospital del Mar Medical Research Institute (IMIM), Barcelona, Spain
- Department of Medicine and Life Sciences, Pompeu Fabra University, Barcelona, Spain
- CIBER de Fisiopatología de la Obesidad y la Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain
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18
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Liu KY, Villain N, Ayton S, Ackley SF, Planche V, Howard R, Thambisetty M. Key questions for the evaluation of anti-amyloid immunotherapies for Alzheimer's disease. Brain Commun 2023; 5:fcad175. [PMID: 37389302 PMCID: PMC10306158 DOI: 10.1093/braincomms/fcad175] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Revised: 05/09/2023] [Accepted: 06/01/2023] [Indexed: 07/01/2023] Open
Abstract
The clinical benefit associated with anti-amyloid immunotherapies, a new class of drugs for the treatment of Alzheimer's disease, is predicated on their ability to modify disease course by lowering brain amyloid levels. At the time of writing, two amyloid-lowering antibodies, aducanumab and lecanemab, have obtained United States Food and Drug Administration accelerated approval, with further agents of this class in the Alzheimer's disease treatment pipeline. Based on limited published clinical trial data to date, regulators, payors and physicians will need to assess their efficacy, clinical effectiveness and safety, as well as cost and accessibility. We propose that attention to three important questions related to treatment efficacy, clinical effectiveness and safety should guide evidence-based consideration of this important class of drugs. These are: (1) Were trial statistical analyses appropriate and did they convincingly support claims of efficacy? (2) Do reported treatment effects outweigh safety concerns and are they generalizable to a representative clinical population of people with Alzheimer's disease? and (3) Do the data convincingly demonstrate disease course modification, suggesting that increasing clinical benefits beyond the duration of the trials are likely? We suggest specific approaches to interpreting trial results for these drugs and highlight important areas of uncertainty where additional data and a cautious interpretation of existing results is warranted. Safe, effective and accessible treatments for Alzheimer's disease are eagerly awaited by millions of patients and their caregivers worldwide. While amyloid-targeting immunotherapies may be promising disease-modifying Alzheimer's disease treatments, rigorous and unbiased assessment of clinical trial data is critical to regulatory decision-making and subsequently determining their provision and utility in routine clinical practice. Our recommendations provide a framework for evidence-based appraisal of these drugs by regulators, payors, physicians and patients.
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Affiliation(s)
- Kathy Y Liu
- Division of Psychiatry, University College London, London W1T 7NF, UK
| | - Nicolas Villain
- AP-HP.Sorbonne Université, Institut de la Mémoire et de la Maladie d’Alzheimer, Département de Neurologie, Hôpital Pitié-Salpêtrière, 75013 Paris, France
- Sorbonne Université, Institut national de la Santé et de la Recherche Medical (INSERM) U1127, Centre National de la Recherche Scientifique (CNRS) 7225, Institut du Cerveau—ICM, 75013 Paris, France
| | - Scott Ayton
- Melbourne Dementia Research Centre, Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Parkville, VIC 3052, Australia
| | - Sarah F Ackley
- Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, CA 94158, USA
| | - Vincent Planche
- Univ. Bordeaux, CNRS, UMR 5293, Institut des Maladies Neurodégénératives, F-33000 Bordeaux, France
- Centre Mémoire Ressources Recherches, Pôle de Neurosciences Cliniques, CHU de Bordeaux, F-33000 Bordeaux, France
| | - Robert Howard
- Division of Psychiatry, University College London, London W1T 7NF, UK
| | - Madhav Thambisetty
- Clinical and Translational Neuroscience Unit, Laboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, MD 21224, USA
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19
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Wang X, Ye T, Zhou W, Zhang J. Uncovering heterogeneous cognitive trajectories in mild cognitive impairment: a data-driven approach. Alzheimers Res Ther 2023; 15:57. [PMID: 36941651 PMCID: PMC10026406 DOI: 10.1186/s13195-023-01205-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2022] [Accepted: 03/12/2023] [Indexed: 03/23/2023]
Abstract
BACKGROUND Given the complex and progressive nature of mild cognitive impairment (MCI), the ability to delineate and understand the heterogeneous cognitive trajectories is crucial for developing personalized medicine and informing trial design. The primary goals of this study were to examine whether different cognitive trajectories can be identified within subjects with MCI and, if present, to characterize each trajectory in relation to changes in all major Alzheimer's disease (AD) biomarkers over time. METHODS Individuals with a diagnosis of MCI at the first visit and ≥ 1 follow-up cognitive assessment were selected from the Alzheimer's Disease Neuroimaging Initiative database (n = 936; age 73 ± 8; 40% female; 16 ± 3 years of education; 50% APOE4 carriers). Based on the Alzheimer's Disease Assessment Scale-Cognitive Subscale-13 (ADAS-Cog-13) total scores from baseline up to 5 years follow-up, a non-parametric k-means longitudinal clustering method was performed to obtain clusters of individuals with similar patterns of cognitive decline. We further conducted a series of linear mixed-effects models to study the associations of cluster membership with longitudinal changes in other cognitive measures, neurodegeneration, and in vivo AD pathologies. RESULTS Four distinct cognitive trajectories emerged. Cluster 1 consisted of 255 individuals (27%) with a nearly non-existent rate of change in the ADAS-Cog-13 over 5 years of follow-up and a healthy-looking biomarker profile. Individuals in the cluster 2 (n = 336, 35%) and 3 (n = 240, 26%) groups showed relatively mild and moderate cognitive decline trajectories, respectively. Cluster 4, comprising about 11% of our study sample (n = 105), exhibited an aggressive cognitive decline trajectory and was characterized by a pronouncedly abnormal biomarker profile. CONCLUSIONS Individuals with MCI show substantial heterogeneity in cognitive decline. Our findings may potentially contribute to improved trial design and patient stratification.
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Affiliation(s)
- Xiwu Wang
- Department of Psychiatry, Wenzhou Seventh People's Hospital, Wenzhou, China
| | - Teng Ye
- Department of Ultrasound, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Wenjun Zhou
- Research and Development, Hangzhou Shansier Medical Technologies Co., Ltd., Hangzhou, China.
| | - Jie Zhang
- Department of Data Science, Hangzhou Shansier Medical Technologies Co., Ltd., Hangzhou, China.
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20
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Shand C, Markiewicz PJ, Cash DM, Alexander DC, Donohue MC, Barkhof F, Oxtoby NP. Heterogeneity in Preclinical Alzheimer's Disease Trial Cohort Identified by Image-based Data-Driven Disease Progression Modelling. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.02.07.23285572. [PMID: 36798314 PMCID: PMC9934776 DOI: 10.1101/2023.02.07.23285572] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/12/2023]
Abstract
Importance Undetected biological heterogeneity adversely impacts trials in Alzheimer's disease because rate of cognitive decline - and perhaps response to treatment - differs in subgroups. Recent results show that data-driven approaches can unravel the heterogeneity of Alzheimer's disease progression. The resulting stratification is yet to be leveraged in clinical trials. Objective Investigate whether image-based data-driven disease progression modelling could identify baseline biological heterogeneity in a clinical trial, and whether these subgroups have prognostic or predictive value. Design Screening data from the Anti-Amyloid Treatment in Asymptomatic Alzheimer Disease (A4) Study collected between April 2014 and December 2017, and longitudinal data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) observational study downloaded in February 2022 were used. Setting The A4 Study is an interventional trial involving 67 sites in the US, Canada, Australia, and Japan. ADNI is a multi-center observational study in North America. Participants Cognitively unimpaired amyloid-positive participants with a 3-Tesla T1-weighted MRI scan. Amyloid positivity was determined using florbetapir PET imaging (in A4) and CSF Aβ(1-42) (in ADNI). Main Outcomes and Measures Regional volumes estimated from MRI scans were used as input to the Subtype and Stage Inference (SuStaIn) algorithm. Outcomes included cognitive test scores and SUVr values from florbetapir and flortaucipir PET. Results We included 1,240 Aβ+ participants (and 407 Aβ- controls) from the A4 Study, and 731 A4-eligible ADNI participants. SuStaIn identified three neurodegeneration subtypes - Typical, Cortical, Subcortical - comprising 523 (42%) individuals. The remainder are designated subtype zero (insufficient atrophy). Baseline PACC scores (A4 primary outcome) were significantly worse in the Cortical subtype (median = -1.27, IQR=[-3.34,0.83]) relative to both subtype zero (median=-0.013, IQR=[-1.85,1.67], P<.0001) and the Subcortical subtype (median=0.03, IQR=[-1.78,1.61], P=.0006). In ADNI, over a four-year period (comparable to A4), greater cognitive decline in the mPACC was observed in both the Typical (-0.23/yr; 95% CI, [-0.41,-0.05]; P=.01) and Cortical (-0.24/yr; [-0.42,-0.06]; P=.009) subtypes, as well as the CDR-SB (Typical: +0.09/yr, [0.06,0.12], P<.0001; and Cortical: +0.07/yr, [0.04,0.10], P<.0001). Conclusions and Relevance In a large secondary prevention trial, our image-based model detected neurodegenerative heterogeneity predictive of cognitive heterogeneity. We argue that such a model is a valuable tool to be considered in future trial design to control for previously undetected variance.
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Affiliation(s)
- Cameron Shand
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
| | - Pawel J Markiewicz
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London, UK
| | - David M Cash
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Daniel C Alexander
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
| | - Michael C Donohue
- Alzheimer's Therapeutic Research Institute, Keck School of Medicine, University of Southern California, San Diego, USA
| | - Frederik Barkhof
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London, UK
- Department of Radiology and Nuclear Medicine, VU University Medical Center, Amsterdam, Netherlands
| | - Neil P Oxtoby
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
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21
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Maheux E, Koval I, Ortholand J, Birkenbihl C, Archetti D, Bouteloup V, Epelbaum S, Dufouil C, Hofmann-Apitius M, Durrleman S. Forecasting individual progression trajectories in Alzheimer's disease. Nat Commun 2023; 14:761. [PMID: 36765056 PMCID: PMC9918533 DOI: 10.1038/s41467-022-35712-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Accepted: 12/19/2022] [Indexed: 02/12/2023] Open
Abstract
The anticipation of progression of Alzheimer's disease (AD) is crucial for evaluations of secondary prevention measures thought to modify the disease trajectory. However, it is difficult to forecast the natural progression of AD, notably because several functions decline at different ages and different rates in different patients. We evaluate here AD Course Map, a statistical model predicting the progression of neuropsychological assessments and imaging biomarkers for a patient from current medical and radiological data at early disease stages. We tested the method on more than 96,000 cases, with a pool of more than 4,600 patients from four continents. We measured the accuracy of the method for selecting participants displaying a progression of clinical endpoints during a hypothetical trial. We show that enriching the population with the predicted progressors decreases the required sample size by 38% to 50%, depending on trial duration, outcome, and targeted disease stage, from asymptomatic individuals at risk of AD to subjects with early and mild AD. We show that the method introduces no biases regarding sex or geographic locations and is robust to missing data. It performs best at the earliest stages of disease and is therefore highly suitable for use in prevention trials.
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Affiliation(s)
- Etienne Maheux
- Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, CNRS, Inria, Inserm, AP-HP, Hôpital Pitié-Salpêtrière, Paris, France
| | - Igor Koval
- Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, CNRS, Inria, Inserm, AP-HP, Hôpital Pitié-Salpêtrière, Paris, France
| | - Juliette Ortholand
- Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, CNRS, Inria, Inserm, AP-HP, Hôpital Pitié-Salpêtrière, Paris, France
| | - Colin Birkenbihl
- Department of bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, Germany
- Bonn-Aachen International Center for IT, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, 53115, Germany
| | - Damiano Archetti
- IRCCS Instituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Vincent Bouteloup
- Université de Bordeaux, CNRS UMR 5293, Institut des Maladies Neurodégénératives, Bordeaux, France
- Centre Hospitalier Universitaire (CHU) de Bordeaux, pôle de neurosciences cliniques, centre mémoire de ressources et de recherche, Bordeaux, France
| | - Stéphane Epelbaum
- Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, CNRS, Inria, Inserm, AP-HP, Hôpital Pitié-Salpêtrière, Institut de la mémoire et de la maladie d'Alzheimer (IM2A), center of excellence of neurodegenerative diseases (CoEN), department of Neurology, DMU Neurosciences, Paris, France
| | - Carole Dufouil
- Université de Bordeaux, CNRS UMR 5293, Institut des Maladies Neurodégénératives, Bordeaux, France
- Centre Hospitalier Universitaire (CHU) de Bordeaux, pôle de neurosciences cliniques, centre mémoire de ressources et de recherche, Bordeaux, France
| | - Martin Hofmann-Apitius
- Department of bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, Germany
- Bonn-Aachen International Center for IT, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, 53115, Germany
| | - Stanley Durrleman
- Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, CNRS, Inria, Inserm, AP-HP, Hôpital Pitié-Salpêtrière, Paris, France.
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22
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Jutten RJ, Papp KV, Hendrix S, Ellison N, Langbaum JB, Donohue MC, Hassenstab J, Maruff P, Rentz DM, Harrison J, Cummings J, Scheltens P, Sikkes SAM. Why a clinical trial is as good as its outcome measure: A framework for the selection and use of cognitive outcome measures for clinical trials of Alzheimer's disease. Alzheimers Dement 2023; 19:708-720. [PMID: 36086926 PMCID: PMC9931632 DOI: 10.1002/alz.12773] [Citation(s) in RCA: 19] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2022] [Revised: 06/29/2022] [Accepted: 07/22/2022] [Indexed: 11/11/2022]
Abstract
A crucial aspect of any clinical trial is using the right outcome measure to assess treatment efficacy. Compared to the rapidly evolved understanding and measurement of pathophysiology in preclinical and early symptomatic stages of Alzheimer's disease (AD), relatively less progress has been made in the evolution of clinical outcome assessments (COAs) for those stages. The current paper aims to provide a benchmark for the design and evaluation of COAs for use in early AD trials. We discuss lessons learned on capturing cognitive changes in predementia stages of AD, including challenges when validating novel COAs for those early stages and necessary evidence for their implementation in clinical trials. Moving forward, we propose a multi-step framework to advance the use of more effective COAs to assess clinically meaningful changes in early AD, which will hopefully contribute to the much-needed consensus around more appropriate outcome measures to assess clinical efficacy of putative treatments. HIGHLIGHTS: We discuss lessons learned on capturing cognitive changes in predementia stages of AD. We propose a framework for the design and evaluation of performance based cognitive tests for use in early AD trials. We provide recommendations to facilitate the implementation of more effective cognitive outcome measures in AD trials.
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Affiliation(s)
- Roos J. Jutten
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Kathryn V. Papp
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
- Department of Neurology, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | | | | | | | - Michael C. Donohue
- Alzheimer’s Therapeutic Research Institute, Keck School of Medicine, University of Southern California, San Diego, California, USA
| | - Jason Hassenstab
- Knight Alzheimer Disease Research Center, Department of Neurology, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Paul Maruff
- Cogstate Ltd., Melbourne, Victoria, Australia
- The Florey Institute of Neuroscience and Mental Health, Melbourne, Victoria, Australia
| | - Dorene M. Rentz
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
- Department of Neurology, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - John Harrison
- Metis Cognition Ltd., Kilmington, UK
- Department of Psychiatry, Psychology & Neuroscience, King’s College London, UK
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam UMC, location VUmc, VU University, Amsterdam, The Netherlands
| | - Jeffrey Cummings
- Chambers-Grundy Center for Transformative Neuroscience, Department of Brain Health, School of Integrated Health Sciences, University of Nevada Las Vegas (UNLV), Las Vegas, Nevada, USA
| | - Philip Scheltens
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam UMC, location VUmc, VU University, Amsterdam, The Netherlands
| | - Sietske A. M. Sikkes
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam UMC, location VUmc, VU University, Amsterdam, The Netherlands
- Department of Clinical, Neuro and Developmental Psychology, Faculty of Movement and Behavioral Sciences, VU University, Amsterdam, The Netherlands
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23
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Qian J, Zhang Y, Betensky RA, Hyman BT, Serrano-Pozo A. Neuropathology-Independent Association Between APOE Genotype and Cognitive Decline Rate in the Normal Aging-Early Alzheimer Continuum. Neurol Genet 2023; 9:e200055. [PMID: 36698453 PMCID: PMC9869750 DOI: 10.1212/nxg.0000000000200055] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2022] [Accepted: 11/16/2022] [Indexed: 01/22/2023]
Abstract
Background and Objectives We previously found that the APOE genotype affects the rate of cognitive decline in mild-to-moderate Alzheimer disease (AD) dementia independently of its effects on AD neuropathologic changes (ADNC) and copathologies. In this study, we tested the hypothesis that the APOE alleles differentially affect the rate of cognitive decline at the normal aging-early AD continuum and that this association is independent of their effects on classical ADNC and copathologies. Methods We analyzed APOE associations with the cognitive trajectories (Clinical Dementia Rating scale Sum of Boxes [CDR-SOB] and Mini-Mental State Examination [MMSE]) of more than 1,000 individuals from a national clinicopathologic sample who had either no, mild (sparse neuritic plaques and the Braak neurofibrillary tangle [NFT] stage I/II), or intermediate (moderate neuritic plaques and the Braak NFT stage III/IV) ADNC levels at autopsy via 2 latent classes reverse-time longitudinal modeling. Results Carrying the APOEε4 allele was associated with a faster rate of cognitive decline by both CDR-SOB and MMSE relative to APOEε3 homozygotes. This association remained statistically significant after adjusting for ADNC severity, comorbid pathologies, and the effects of ADNC on the slope of cognitive decline. Our modeling strategy identified 2 latent classes in which APOEε4 carriers declined faster than APOEε3 homozygotes, with latent class 1 members representing slow decliners (CDR-SOB: 76.7% of individuals, 0.195 vs 0.146 points/y in APOEε4 vs APOEε3/ε3; MMSE: 88.6% of individuals, -0.303 vs -0.153 points/y in APOEε4 vs APOEε3/ε3), whereas latent class 2 members were fast decliners (CDR-SOB: 23.3% of participants, 1.536 vs 1.487 points/y in APOEε4 vs APOEε3/ε3; MMSE: 11.4% of participants, -2.538 vs -2.387 points/y in APOEε4 vs APOEε3/ε3). Compared with slow decliners, fast decliners were more likely to carry the APOEε4 allele, younger at initial visit and death, more impaired at initial and last visits, and more likely to have intermediate (vs none or mild) ADNC levels, as well as concurrent Lewy bodies and hippocampal sclerosis at autopsy. Discussion In a large national sample selected to represent the normal aging-early AD continuum, the APOEε4 allele is associated with a modest but statistically significant acceleration of the cognitive decline rate even after controlling for its effects on ADNC and comorbid pathologies.
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Affiliation(s)
- Jing Qian
- University of Massachusetts School of Public Health & Health Sciences (J.Q., Y.Z.), Amherst; Massachusetts General Hospital Biostatistics Center (J.Q.), Boston; New York University School of Global Public Health (R.A.B.); New York University Alzheimer's Disease Research Center (R.A.B.); Massachusetts General Hospital Neurology Department (B.T.H., A.S.-P.), Boston; Massachusetts Alzheimer's Disease Research Center (B.T.H., A.S.-P.), Charlestown; and Harvard Medical School (B.T.H., A.S.-P.), Boston, MA
| | - Yiding Zhang
- University of Massachusetts School of Public Health & Health Sciences (J.Q., Y.Z.), Amherst; Massachusetts General Hospital Biostatistics Center (J.Q.), Boston; New York University School of Global Public Health (R.A.B.); New York University Alzheimer's Disease Research Center (R.A.B.); Massachusetts General Hospital Neurology Department (B.T.H., A.S.-P.), Boston; Massachusetts Alzheimer's Disease Research Center (B.T.H., A.S.-P.), Charlestown; and Harvard Medical School (B.T.H., A.S.-P.), Boston, MA
| | - Rebecca A Betensky
- University of Massachusetts School of Public Health & Health Sciences (J.Q., Y.Z.), Amherst; Massachusetts General Hospital Biostatistics Center (J.Q.), Boston; New York University School of Global Public Health (R.A.B.); New York University Alzheimer's Disease Research Center (R.A.B.); Massachusetts General Hospital Neurology Department (B.T.H., A.S.-P.), Boston; Massachusetts Alzheimer's Disease Research Center (B.T.H., A.S.-P.), Charlestown; and Harvard Medical School (B.T.H., A.S.-P.), Boston, MA
| | - Bradley T Hyman
- University of Massachusetts School of Public Health & Health Sciences (J.Q., Y.Z.), Amherst; Massachusetts General Hospital Biostatistics Center (J.Q.), Boston; New York University School of Global Public Health (R.A.B.); New York University Alzheimer's Disease Research Center (R.A.B.); Massachusetts General Hospital Neurology Department (B.T.H., A.S.-P.), Boston; Massachusetts Alzheimer's Disease Research Center (B.T.H., A.S.-P.), Charlestown; and Harvard Medical School (B.T.H., A.S.-P.), Boston, MA
| | - Alberto Serrano-Pozo
- University of Massachusetts School of Public Health & Health Sciences (J.Q., Y.Z.), Amherst; Massachusetts General Hospital Biostatistics Center (J.Q.), Boston; New York University School of Global Public Health (R.A.B.); New York University Alzheimer's Disease Research Center (R.A.B.); Massachusetts General Hospital Neurology Department (B.T.H., A.S.-P.), Boston; Massachusetts Alzheimer's Disease Research Center (B.T.H., A.S.-P.), Charlestown; and Harvard Medical School (B.T.H., A.S.-P.), Boston, MA
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24
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Villain N, Planche V, Levy R. High-clearance anti-amyloid immunotherapies in Alzheimer's disease. Part 1: Meta-analysis and review of efficacy and safety data, and medico-economical aspects. Rev Neurol (Paris) 2022; 178:1011-1030. [PMID: 36184326 DOI: 10.1016/j.neurol.2022.06.012] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Revised: 04/25/2022] [Accepted: 06/15/2022] [Indexed: 12/14/2022]
Abstract
In 2021, aducanumab, an immunotherapy targeting amyloid-β, was approved for Alzheimer's disease (AD) by the US Food and Drug Administration thanks to positive results on a putative biological surrogate marker. This approval has raised an unprecedented controversy. It was followed by a refusal of the European Medicine Agency, which does not allow the marketing of drugs solely on biological arguments and raised safety issues, and important US coverage limitations by the Centers for Medicare & Medicaid Services. Two other anti-amyloid immunotherapies showed significant results regarding a clinical outcome in phase 2 trials, and five drugs are being studied in phase 3 trials. Compared to those tested in previous trials of the 2010s, the common feature and novelty of these anti-amyloid immunotherapies is their ability to induce a high clearance of amyloid load, as measured with positron emission tomography, in the brain of early-stage biomarker-proven AD patients. Here, we review the available evidence regarding efficacy and safety data and medico-economical aspects for high-clearance anti-amyloid immunotherapies. We also perform frequentist and Bayesian meta-analyses of the clinical efficacy and safety of the highest dose groups from the two aducanumab phase 3 trials and the donanemab and lecanemab phase 2 trials. When pooled together, the data from high-clearance anti-amyloid immunotherapies trials confirm a statistically significant clinical effect of these drugs on cognitive decline after 18 months (difference in cognitive decline measured with CDR-SB after 18 months between the high dose immunotherapy groups vs. placebo = -0.24 points; P=0.04, frequentist random-effect model), with results on ADAS-Cog being the most statistically robust. However, this effect remains below the previously established minimal clinically relevant values. In parallel, the drugs significantly increased the occurrence of amyloid-related imaging abnormalities-edema (ARIA-E: risk ratio=13.39; P<0.0001), ARIA-hemorrhage (risk ratio=2.78; P=0.0002), and symptomatic and serious ARIA (7/1321=0.53% in the high dose groups versus 0/1446 in the placebo groups; risk ratio=6.44; P=0.04). The risk/benefit ratio of high-clearance immunotherapies in early AD is so far questionable after 18 months. Identifying subgroups of better responders, the perspective of combination therapies, and a longer follow-up may help improve their clinical relevance. Finally, the preliminary evidence from medico-economical analyses seems to indicate that the current cost of aducanumab in the US is not in reasonable alignment with its clinical benefits.
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Affiliation(s)
- N Villain
- Assistance Publique - Hôpitaux de Paris, Department of Neurology, Institute of Memory and Alzheimer's Disease, Pitié-Salpêtrière Hospital, Paris, France; Sorbonne Université, Inserm U1127, CNRS 7225, Institut du Cerveau - ICM, Paris, France.
| | - V Planche
- CNRS, IMN, UMR 5293, University Bordeaux, 33000 Bordeaux, France; Pôle de Neurosciences Cliniques, Centre Mémoire Ressources Recherches, CHU de Bordeaux, 33000 Bordeaux, France
| | - R Levy
- Assistance Publique - Hôpitaux de Paris, Department of Neurology, Institute of Memory and Alzheimer's Disease, Pitié-Salpêtrière Hospital, Paris, France; Sorbonne Université, Inserm U1127, CNRS 7225, Institut du Cerveau - ICM, Paris, France
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25
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Biel D, Luan Y, Brendel M, Hager P, Dewenter A, Moscoso A, Otero Svaldi D, Higgins IA, Pontecorvo M, Römer S, Steward A, Rubinski A, Zheng L, Schöll M, Shcherbinin S, Ewers M, Franzmeier N. Combining tau-PET and fMRI meta-analyses for patient-centered prediction of cognitive decline in Alzheimer’s disease. Alzheimers Res Ther 2022; 14:166. [PMID: 36345046 PMCID: PMC9639286 DOI: 10.1186/s13195-022-01105-5] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Accepted: 10/20/2022] [Indexed: 11/09/2022]
Abstract
Background Tau-PET is a prognostic marker for cognitive decline in Alzheimer’s disease, and the heterogeneity of tau-PET patterns matches cognitive symptom heterogeneity. Thus, tau-PET may allow precision-medicine prediction of individual tau-related cognitive trajectories, which can be important for determining patient-specific cognitive endpoints in clinical trials. Here, we aimed to examine whether tau-PET in cognitive-domain-specific brain regions, identified via fMRI meta-analyses, allows the prediction of domain-specific cognitive decline. Further, we aimed to determine whether tau-PET-informed personalized cognitive composites capture patient-specific cognitive trajectories more sensitively than conventional cognitive measures. Methods We included Alzheimer’s Disease Neuroimaging Initiative (ADNI) participants classified as controls (i.e., amyloid-negative, cognitively normal, n = 121) or Alzheimer’s disease-spectrum (i.e., amyloid-positive, cognitively normal to dementia, n = 140), plus 111 AVID-1451-A05 participants for independent validation (controls/Alzheimer’s disease-spectrum=46/65). All participants underwent baseline 18F-flortaucipir tau-PET, amyloid-PET, and longitudinal cognitive testing to assess annual cognitive changes (i.e., episodic memory, language, executive functioning, visuospatial). Cognitive changes were calculated using linear mixed models. Independent meta-analytical task-fMRI activation maps for each included cognitive domain were obtained from the Neurosynth database and applied to tau-PET to determine tau-PET signal in cognitive-domain-specific brain regions. In bootstrapped linear regression, we assessed the strength of the relationship (i.e., partial R2) between cognitive-domain-specific tau-PET vs. global or temporal-lobe tau-PET and cognitive changes. Further, we used tau-PET-based prediction of domain-specific decline to compose personalized cognitive composites that were tailored to capture patient-specific cognitive decline. Results In both amyloid-positive cohorts (ADNI [age = 75.99±7.69] and A05 [age = 74.03±9.03]), cognitive-domain-specific tau-PET outperformed global and temporal-lobe tau-PET for predicting future cognitive decline in episodic memory, language, executive functioning, and visuospatial abilities. Further, a tau-PET-informed personalized cognitive composite across cognitive domains enhanced the sensitivity to assess cognitive decline in amyloid-positive subjects, yielding lower sample sizes required for detecting simulated intervention effects compared to conventional cognitive endpoints (i.e., memory composite, global cognitive composite). However, the latter effect was less strong in A05 compared to the ADNI cohort. Conclusion Combining tau-PET with task-fMRI-derived maps of major cognitive domains facilitates the prediction of domain-specific cognitive decline. This approach may help to increase the sensitivity to detect Alzheimer’s disease-related cognitive decline and to determine personalized cognitive endpoints in clinical trials. Supplementary Information The online version contains supplementary material available at 10.1186/s13195-022-01105-5.
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26
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Vassilaki M, Kremers WK, Machulda MM, Knopman DS, Petersen RC, Laporta ML, Berry DJ, Lewallen DG, Maradit Kremers H. Long-term Cognitive Trajectory After Total Joint Arthroplasty. JAMA Netw Open 2022; 5:e2241807. [PMID: 36374499 PMCID: PMC9664257 DOI: 10.1001/jamanetworkopen.2022.41807] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
IMPORTANCE Individuals with total joint arthroplasty (TJA) have long-term exposure to metal-containing implants; however, whether long-term exposure to artificial implants is associated with cognitive function is unknown. OBJECTIVE To compare long-term cognitive trajectories in individuals with and without TJA. DESIGN, SETTING, AND PARTICIPANTS This population-based cohort study assessed serial cognitive evaluations of 5550 participants (≥50 years of age) from the Mayo Clinic Study of Aging between November 1, 2004, and December 31, 2020. EXPOSURES Total joint arthroplasty of the hip or the knee. MAIN OUTCOMES AND MEASURES Linear mixed-effects models were used to compare the annualized rate of change in global and domain-specific cognitive scores in participants with and without TJA, adjusting for age, sex, educational level, apolipoprotein E ε4 carrier status, and cognitive test practice effects. RESULTS A total of 5550 participants (mean [SD] age at baseline, 73.04 [10.02] years; 2830 [51.0%] male) were evaluated. A total of 952 participants had undergone at least 1 TJA of the hip (THA, n = 430) or the knee (TKA, n = 626) before or after entry into the cohort. Participants with TJA were older, more likely to be female, and had a higher body mass index than participants without TJA. No difference was observed in the rate of cognitive decline in participants with and without TJA until 80 years of age. A slightly faster cognitive decline at 80 years or older and more than 8 years from surgery was observed (b = -0.03; 95% CI, -0.04 to -0.02). In stratified analyses by surgery type, the faster decline was observed primarily among older participants with TKA (b = -0.04; 95% CI, -0.06 to -0.02). CONCLUSIONS AND RELEVANCE In this cohort study, long-term cognitive trajectories in individuals with and without TJA were largely similar except for a slightly faster decline among the oldest patients with TKA; however, the magnitude of difference was small and of unknown clinical significance.
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Affiliation(s)
- Maria Vassilaki
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota
| | - Walter K Kremers
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota
| | - Mary M Machulda
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, Minnesota
| | | | - Ronald C Petersen
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota
- Department of Neurology, Mayo Clinic, Rochester, Minnesota
| | - Mariana L Laporta
- Department of Anesthesiology and Perioperative Medicine, Mayo Clinic, Rochester, Minnesota
| | - Daniel J Berry
- Department of Orthopedic Surgery, Mayo Clinic, Rochester, Minnesota
| | - David G Lewallen
- Department of Orthopedic Surgery, Mayo Clinic, Rochester, Minnesota
| | - Hilal Maradit Kremers
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota
- Department of Orthopedic Surgery, Mayo Clinic, Rochester, Minnesota
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27
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Giorgio J, Jagust WJ, Baker S, Landau SM, Tino P, Kourtzi Z. A robust and interpretable machine learning approach using multimodal biological data to predict future pathological tau accumulation. Nat Commun 2022; 13:1887. [PMID: 35393421 PMCID: PMC8989879 DOI: 10.1038/s41467-022-28795-7] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2020] [Accepted: 02/11/2022] [Indexed: 01/21/2023] Open
Abstract
The early stages of Alzheimer’s disease (AD) involve interactions between multiple pathophysiological processes. Although these processes are well studied, we still lack robust tools to predict individualised trajectories of disease progression. Here, we employ a robust and interpretable machine learning approach to combine multimodal biological data and predict future pathological tau accumulation. In particular, we use machine learning to quantify interactions between key pathological markers (β-amyloid, medial temporal lobe atrophy, tau and APOE 4) at mildly impaired and asymptomatic stages of AD. Using baseline non-tau markers we derive a prognostic index that: (a) stratifies patients based on future pathological tau accumulation, (b) predicts individualised regional future rate of tau accumulation, and (c) translates predictions from deep phenotyping patient cohorts to cognitively normal individuals. Our results propose a robust approach for fine scale stratification and prognostication with translation impact for clinical trial design targeting the earliest stages of AD. The authors present a machine learning approach that combines baseline multimodal data to accurately predict individualised trajectories of future pathological tau accumulation at asymptomatic and mildly impaired stages of Alzheimer’s disease.
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Affiliation(s)
- Joseph Giorgio
- Department of Psychology, University of Cambridge, Cambridge, UK
| | - William J Jagust
- Helen Wills Neuroscience Institute, University of California, Berkeley, CA, USA.,Molecular Biophysics & Integrated Bioimaging, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Suzanne Baker
- Molecular Biophysics & Integrated Bioimaging, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Susan M Landau
- Helen Wills Neuroscience Institute, University of California, Berkeley, CA, USA
| | - Peter Tino
- School of Computer Science, University of Birmingham, Birmingham, UK
| | - Zoe Kourtzi
- Department of Psychology, University of Cambridge, Cambridge, UK.
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Seo Y, Jang H, Lee H. Potential Applications of Artificial Intelligence in Clinical Trials for Alzheimer’s Disease. Life (Basel) 2022; 12:life12020275. [PMID: 35207561 PMCID: PMC8879055 DOI: 10.3390/life12020275] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2021] [Revised: 02/05/2022] [Accepted: 02/09/2022] [Indexed: 01/18/2023] Open
Abstract
Clinical trials for Alzheimer’s disease (AD) face multiple challenges, such as the high screen failure rate and the even allocation of heterogeneous participants. Artificial intelligence (AI), which has become a potent tool of modern science with the expansion in the volume, variety, and velocity of biological data, offers promising potential to address these issues in AD clinical trials. In this review, we introduce the current status of AD clinical trials and the topic of machine learning. Then, a comprehensive review is focused on the potential applications of AI in the steps of AD clinical trials, including the prediction of protein and MRI AD biomarkers in the prescreening process during eligibility assessment and the likelihood stratification of AD subjects into rapid and slow progressors in randomization. Finally, this review provides challenges, developments, and the future outlook on the integration of AI into AD clinical trials.
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Affiliation(s)
| | | | - Hyejoo Lee
- Correspondence: ; Tel.: +82-2-3410-1233; Fax: +82-2-3410-0052
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29
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Pelkmans W, Vromen EM, Dicks E, Scheltens P, Teunissen CE, Barkhof F, van der Flier WM, Tijms BM. Grey matter network markers identify individuals with prodromal Alzheimer’s disease who will show rapid clinical decline. Brain Commun 2022; 4:fcac026. [PMID: 35310828 PMCID: PMC8924646 DOI: 10.1093/braincomms/fcac026] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2021] [Revised: 11/22/2021] [Accepted: 02/07/2022] [Indexed: 11/25/2022] Open
Abstract
Individuals with prodromal Alzheimer’s disease show considerable variability in rates of cognitive decline, which hampers the ability to detect potential treatment effects in clinical trials. Prognostic markers to select those individuals who will decline rapidly within a trial time frame are needed. Brain network measures based on grey matter covariance patterns have been associated with future cognitive decline in Alzheimer’s disease. In this longitudinal cohort study, we investigated whether cut-offs for grey matter networks could be derived to detect fast disease progression at an individual level. We further tested whether detection was improved by adding other biomarkers known to be associated with future cognitive decline [i.e. CSF tau phosphorylated at threonine 181 (p-tau181) levels and hippocampal volume]. We selected individuals with mild cognitive impairment and abnormal CSF amyloid β1–42 levels from the Amsterdam Dementia Cohort and the Alzheimer’s Disease Neuroimaging Initiative, when they had available baseline structural MRI and clinical follow-up. The outcome was progression to dementia within 2 years. We determined prognostic cut-offs for grey matter network properties (gamma, lambda and small-world coefficient) using time-dependent receiver operating characteristic analysis in the Amsterdam Dementia Cohort. We tested the generalization of cut-offs in the Alzheimer’s Disease Neuroimaging Initiative, using logistic regression analysis and classification statistics. We further tested whether combining these with CSF p-tau181 and hippocampal volume improved the detection of fast decliners. We observed that within 2 years, 24.6% (Amsterdam Dementia Cohort, n = 244) and 34.0% (Alzheimer’s Disease Neuroimaging Initiative, n = 247) of prodromal Alzheimer’s disease patients progressed to dementia. Using the grey matter network cut-offs for progression, we could detect fast progressors with 65% accuracy in the Alzheimer’s Disease Neuroimaging Initiative. Combining grey matter network measures with CSF p-tau and hippocampal volume resulted in the best model fit for classification of rapid decliners, increasing detecting accuracy to 72%. These data suggest that single-subject grey matter connectivity networks indicative of a more random network organization can contribute to identifying prodromal Alzheimer’s disease individuals who will show rapid disease progression. Moreover, we found that combined with p-tau and hippocampal volume this resulted in the highest accuracy. This could facilitate clinical trials by increasing chances to detect effects on clinical outcome measures.
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Affiliation(s)
- Wiesje Pelkmans
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Ellen M. Vromen
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Ellen Dicks
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Philip Scheltens
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Charlotte E. Teunissen
- Neurochemistry Laboratory, Department of Clinical Chemistry, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands
| | - Frederik Barkhof
- Department of Radiology & Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
- Queen Square Institute of Neurology and Centre for Medical Image Computing, UCL, London, UK
| | - Wiesje M. van der Flier
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
- Department of Epidemiology & Biostatistics, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Betty M. Tijms
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
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Duara R, Barker W. Heterogeneity in Alzheimer's Disease Diagnosis and Progression Rates: Implications for Therapeutic Trials. Neurotherapeutics 2022; 19:8-25. [PMID: 35084721 PMCID: PMC9130395 DOI: 10.1007/s13311-022-01185-z] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/05/2022] [Indexed: 01/03/2023] Open
Abstract
The clinical presentation and the pathological processes underlying Alzheimer's disease (AD) can be very heterogeneous in severity, location, and composition including the amount and distribution of AB deposition and spread of neurofibrillary tangles in different brain regions resulting in atypical clinical patterns and the existence of distinct AD variants. Heterogeneity in AD may be related to demographic factors (such as age, sex, educational and socioeconomic level) and genetic factors, which influence underlying pathology, the cognitive and behavioral phenotype, rate of progression, the occurrence of neuropsychiatric features, and the presence of comorbidities (e.g., vascular disease, neuroinflammation). Heterogeneity is also manifest in the individual resilience to the development of neuropathology (brain reserve) and the ability to compensate for its cognitive and functional impact (cognitive and functional reserve). The variability in specific cognitive profiles and types of functional impairment may be associated with different progression rates, and standard measures assessing progression may not be equivalent for individual cognitive and functional profiles. Other factors, which may govern the presence, rate, and type of progression of AD, include the individuals' general medical health, the presence of specific systemic conditions, and lifestyle factors, including physical exercise, cognitive and social stimulation, amount of leisure activities, environmental stressors, such as toxins and pollution, and the effects of medications used to treat medical and behavioral conditions. These factors that affect progression are important to consider while designing a clinical trial to ensure, as far as possible, well-balanced treatment and control groups.
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Affiliation(s)
- Ranjan Duara
- Wien Center for Alzheimer's Disease and Memory Disorders, Mount Sinai Medical Center, Miami Beach, FL, USA
- Departments of Neurology, University of Florida College of Medicine, Gainesville, FL, USA
- Herbert Wertheim College of Medicine, Florida International University, Miami, FL, USA
| | - Warren Barker
- Wien Center for Alzheimer's Disease and Memory Disorders, Mount Sinai Medical Center, Miami Beach, FL, USA.
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Jellinger KA. Recent update on the heterogeneity of the Alzheimer’s disease spectrum. J Neural Transm (Vienna) 2021; 129:1-24. [DOI: 10.1007/s00702-021-02449-2] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Accepted: 11/25/2021] [Indexed: 02/03/2023]
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Abstract
On 7 June 2021, aducanumab was granted accelerated approval for the treatment of Alzheimer disease (AD) by the FDA on the basis of amyloid-lowering effects considered reasonably likely to confer clinical benefit. This decision makes aducanumab the first new drug to be approved for the treatment of AD since 2003 and the first drug to ever be approved for modification of the course of AD. Many have questioned how scientific evidence, expert advice and the best interests of patients and families were considered in the approval decision. In this article, we argue that prior to approval, the FDA and Biogen's shared interpretation of clinical trial data - that high-dose aducanumab was substantially clinically effective - avoided conventional scientific scrutiny, was prominently advanced by patient representative groups who had been major recipients of Biogen funds, and raised concerns that safeguards were insufficient to mitigate regulatory capture within the FDA. Here, we reflect on events leading to the FDA's decision on 7 June 2021 and consider whether any lessons can be learned for the field.
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Knopman DS, Perlmutter JS. Prescribing Aducanumab in the Face of Meager Efficacy and Real Risks. Neurology 2021; 97:545-547. [PMID: 34233938 DOI: 10.1212/wnl.0000000000012452] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2021] [Accepted: 06/22/2021] [Indexed: 11/15/2022] Open
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Buckley RF, Knopman DS. Cognitive Heterogeneity in Alzheimer Clinical Trials: Harnessing Noise to Achieve Meaningfulness. Neurology 2021; 96:1017-1018. [PMID: 34550902 DOI: 10.1212/wnl.0000000000012027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Affiliation(s)
- Rachel F Buckley
- From the Department of Neurology (R.F.B.), Massachusetts General Hospital and Harvard Medical School; Center for Alzheimer Research and Treatment (R.F.B.), Department of Neurology, Brigham and Women's Hospital, Boston, MA; Melbourne School of Psychological Science (R.F.B.), University of Melbourne, VIC, Australia; and Department of Neurology (D.K.), Mayo Clinic, Rochester, MN.
| | - David S Knopman
- From the Department of Neurology (R.F.B.), Massachusetts General Hospital and Harvard Medical School; Center for Alzheimer Research and Treatment (R.F.B.), Department of Neurology, Brigham and Women's Hospital, Boston, MA; Melbourne School of Psychological Science (R.F.B.), University of Melbourne, VIC, Australia; and Department of Neurology (D.K.), Mayo Clinic, Rochester, MN
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