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Moradi E, Prakash M, Hall A, Solomon A, Strange B, Tohka J. Machine learning prediction of future amyloid beta positivity in amyloid-negative individuals. Alzheimers Res Ther 2024; 16:46. [PMID: 38414035 PMCID: PMC10900722 DOI: 10.1186/s13195-024-01415-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: 08/25/2023] [Accepted: 02/11/2024] [Indexed: 02/29/2024]
Abstract
BACKGROUND The pathophysiology of Alzheimer's disease (AD) involves β -amyloid (A β ) accumulation. Early identification of individuals with abnormal β -amyloid levels is crucial, but A β quantification with positron emission tomography (PET) and cerebrospinal fluid (CSF) is invasive and expensive. METHODS We propose a machine learning framework using standard non-invasive (MRI, demographics, APOE, neuropsychology) measures to predict future A β -positivity in A β -negative individuals. We separately study A β -positivity defined by PET and CSF. RESULTS Cross-validated AUC for 4-year A β conversion prediction was 0.78 for the CSF-based and 0.68 for the PET-based A β definitions. Although not trained for the clinical status-change prediction, the CSF-based model excelled in predicting future mild cognitive impairment (MCI)/dementia conversion in cognitively normal/MCI individuals (AUCs, respectively, 0.76 and 0.89 with a separate dataset). CONCLUSION Standard measures have potential in detecting future A β -positivity and assessing conversion risk, even in cognitively normal individuals. The CSF-based definition led to better predictions than the PET-based definition.
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Affiliation(s)
- Elaheh Moradi
- A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, 70150, Finland.
| | - Mithilesh Prakash
- A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, 70150, Finland
| | - Anette Hall
- Institute of Clinical Medicine/Neurology, University of Eastern Finland, Kuopio, Finland
- Division of Clinical Geriatrics, Center for Alzheimer Research, Karolinska Institute, Stockholm, Sweden
| | - Alina Solomon
- Institute of Clinical Medicine/Neurology, University of Eastern Finland, Kuopio, Finland
- Division of Clinical Geriatrics, Center for Alzheimer Research, Karolinska Institute, Stockholm, Sweden
- Ageing Epidemiology Research Unit, School of Public Health, Imperial College London, London, UK
| | - Bryan Strange
- Laboratory for Clinical Neuroscience, Center for Biomedical Technology, Universidad Politécnica de Madrid, IdISSC, Madrid, Spain
- Reina Sofia Centre for Alzheimer's Research, Madrid, Spain
| | - Jussi Tohka
- A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, 70150, Finland
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Coughlan G, DeSouza B, Zhukovsky P, Hornberger M, Grady C, Buckley RF. Spatial cognition is associated with levels of phosphorylated-tau and β-amyloid in clinically normal older adults. Neurobiol Aging 2023; 130:124-134. [PMID: 37506550 DOI: 10.1016/j.neurobiolaging.2023.06.016] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Revised: 06/23/2023] [Accepted: 06/24/2023] [Indexed: 07/30/2023]
Abstract
Spatial cognition is associated with Alzheimer's disease (AD) biomarkers in the symptomatic stages of the disease. We investigated whether cerebrospinal fluid (CSF) biomarkers (phosphorylated-tau [p-tau] and β-amyloid) are associated with poorer spatial cognition in clinically normal older adults. Participants were 1875 clinically normal adults (age 67.8 [8.5] years) from the European Prevention of Alzheimer's Dementia Consortium. Mixed effect models assessed the cross-sectional association between p-tau181, β-amyloid1-42 (Aβ1-42) and p-tau181/Aβ1-42 ratio and spatial cognition measured using semi-automated Supermarket Task and the 4 Mountains Task. Levels of p-tau181, Aβ1-42, and p-tau181/Aβ1-42 ratio were significantly associated with spatial cognition scores on both tasks. The p-tau181/Aβ1-42 ratio showed the largest effect sizes (β = -0.04/0.05, p < 0.001). Lower entorhinal cortical volume was associated with poorer outcomes on both tasks (β = 0.06, p < 0.002) and accounted for 18%-22% of the direct association between p-tau181 and spatial cognition scores. In conclusion, degeneration of the entorhinal cortex mediates a significant proportion of the association between p-tau181 and spatial assessments in cognitively normal adults. Future studies should focus on increasing the sensitivity of digital spatial assessments.
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Affiliation(s)
- Gillian Coughlan
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Center for Alzheimer Research and Treatment, Department of Neurology, Brigham and Women's Hospital, Boston, MA, USA
| | - Brennan DeSouza
- Rotman Research Institute, Baycrest Health Sciences, Toronto, Ontario, Canada
| | - Peter Zhukovsky
- Campbell Family Mental Health Research Institute, Centre for Mental Health and Addiction, Toronto, Ontario, Canada
| | - Michael Hornberger
- Norwich Medical School, University of East Anglia, Norwich, United Kingdom
| | - Cheryl Grady
- Rotman Research Institute, Baycrest Health Sciences, Toronto, Ontario, Canada; Departments of Psychiatry and Psychology, University of Toronto, Toronto, Ontario, Canada
| | - Rachel F Buckley
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Center for Alzheimer Research and Treatment, Department of Neurology, Brigham and Women's Hospital, Boston, MA, USA; Melbourne School of Psychological Sciences, University of Melbourne, Melbourne, Victoria, Australia.
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Langbaum JB, Zissimopoulos J, Au R, Bose N, Edgar CJ, Ehrenberg E, Fillit H, Hill CV, Hughes L, Irizarry M, Kremen S, Lakdawalla D, Lynn N, Malzbender K, Maruyama T, Massett HA, Patel D, Peneva D, Reiman EM, Romero K, Routledge C, Weiner MW, Weninger S, Aisen PS. Recommendations to address key recruitment challenges of Alzheimer's disease clinical trials. Alzheimers Dement 2023; 19:696-707. [PMID: 35946590 PMCID: PMC9911558 DOI: 10.1002/alz.12737] [Citation(s) in RCA: 27] [Impact Index Per Article: 27.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2021] [Revised: 05/26/2022] [Accepted: 06/14/2022] [Indexed: 11/10/2022]
Abstract
Clinical trials for Alzheimer's disease (AD) are slower to enroll study participants, take longer to complete, and are more expensive than trials in most other therapeutic areas. The recruitment and retention of a large number of qualified, diverse volunteers to participate in clinical research studies remain among the key barriers to the successful completion of AD clinical trials. An advisory panel of experts from academia, patient-advocacy organizations, philanthropy, non-profit, government, and industry convened in 2020 to assess the critical challenges facing recruitment in Alzheimer's clinical trials and develop a set of recommendations to overcome them. This paper briefly reviews existing challenges in AD clinical research and discusses the feasibility and implications of the panel's recommendations for actionable and inclusive solutions to accelerate the development of novel therapies for AD.
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Affiliation(s)
| | - Julie Zissimopoulos
- Sol Price School of Public Policy and Leonard D. Schaeffer Center for Health Policy and Economics, University of Southern California, Los Angeles, California, USA
| | - Rhoda Au
- Departments of Anatomy & Neurobiology, Neurology and Epidemiology, Boston University Schools of Medicine and Public Health, Boston, Massachusetts, USA
| | | | | | | | - Howard Fillit
- Alzheimer’s Drug Discovery Foundation, New York, New York, USA
| | | | | | | | - Sarah Kremen
- The Jona Goldrich Center for Alzheimer’s and Memory Disorders, Department of Neurology, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Darius Lakdawalla
- Leonard D. Schaeffer Center for Health Policy and Economics, University of Southern California, Los Angeles, California, USA
| | - Nancy Lynn
- BrightFocus Foundation, Clarksburg, Maryland, USA
| | | | | | | | | | - Desi Peneva
- Leonard D. Schaeffer Center for Health Policy and Economics, University of Southern California, Los Angeles, California, USA
| | | | | | | | - Michael W. Weiner
- Departments of Radiology and Biomedical Imaging, Medicine, Psychiatry, and Neurology, University of California San Francisco, San Francisco, California, USA
| | | | - Paul S. Aisen
- Alzheimer’s Therapeutic Research Institute, University of Southern California, San Diego, California, USA
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Fristed E, Skirrow C, Meszaros M, Lenain R, Meepegama U, Papp KV, Ropacki M, Weston J. Leveraging speech and artificial intelligence to screen for early Alzheimer's disease and amyloid beta positivity. Brain Commun 2022; 4:fcac231. [PMID: 36381988 PMCID: PMC9639797 DOI: 10.1093/braincomms/fcac231] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Revised: 06/30/2022] [Accepted: 09/13/2022] [Indexed: 08/27/2023] Open
Abstract
Early detection of Alzheimer's disease is required to identify patients suitable for disease-modifying medications and to improve access to non-pharmacological preventative interventions. Prior research shows detectable changes in speech in Alzheimer's dementia and its clinical precursors. The current study assesses whether a fully automated speech-based artificial intelligence system can detect cognitive impairment and amyloid beta positivity, which characterize early stages of Alzheimer's disease. Two hundred participants (age 54-85, mean 70.6; 114 female, 86 male) from sister studies in the UK (NCT04828122) and the USA (NCT04928976), completed the same assessments and were combined in the current analyses. Participants were recruited from prior clinical trials where amyloid beta status (97 amyloid positive, 103 amyloid negative, as established via PET or CSF test) and clinical diagnostic status was known (94 cognitively unimpaired, 106 with mild cognitive impairment or mild Alzheimer's disease). The automatic story recall task was administered during supervised in-person or telemedicine assessments, where participants were asked to recall stories immediately and after a brief delay. An artificial intelligence text-pair evaluation model produced vector-based outputs from the original story text and recorded and transcribed participant recalls, quantifying differences between them. Vector-based representations were fed into logistic regression models, trained with tournament leave-pair-out cross-validation analysis to predict amyloid beta status (primary endpoint), mild cognitive impairment and amyloid beta status in diagnostic subgroups (secondary endpoints). Predictions were assessed by the area under the receiver operating characteristic curve for the test result in comparison with reference standards (diagnostic and amyloid status). Simulation analysis evaluated two potential benefits of speech-based screening: (i) mild cognitive impairment screening in primary care compared with the Mini-Mental State Exam, and (ii) pre-screening prior to PET scanning when identifying an amyloid positive sample. Speech-based screening predicted amyloid beta positivity (area under the curve = 0.77) and mild cognitive impairment or mild Alzheimer's disease (area under the curve = 0.83) in the full sample, and predicted amyloid beta in subsamples (mild cognitive impairment or mild Alzheimer's disease: area under the curve = 0.82; cognitively unimpaired: area under the curve = 0.71). Simulation analyses indicated that in primary care, speech-based screening could modestly improve detection of mild cognitive impairment (+8.5%), while reducing false positives (-59.1%). Furthermore, speech-based amyloid pre-screening was estimated to reduce the number of PET scans required by 35.3% and 35.5% in individuals with mild cognitive impairment and cognitively unimpaired individuals, respectively. Speech-based assessment offers accessible and scalable screening for mild cognitive impairment and amyloid beta positivity.
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Affiliation(s)
| | | | | | | | | | - Kathryn V Papp
- Center for Alzheimer Research and Treatment, Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, 02115, USA
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, 02114, USA
| | - Michael Ropacki
- Strategic Global Research & Development, Temecula, California, 94019, USA
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