학술논문
Improved multimodal prediction of progression from MCI to Alzheimer's disease combining genetics with quantitative brain MRI and cognitive measures.
Document Type
Academic Journal
Author
Reas ET; Department of Neurosciences, University of California, San Diego, La Jolla, California, USA.; Shadrin A; NORMENT Centre, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway.; Frei O; NORMENT Centre, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway.; Center for Bioinformatics, Department of Informatics, University of Oslo, Blindern, Oslo, Norway.; Motazedi E; NORMENT Centre, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway.; McEvoy L; Department of Radiology, University of California, San Diego, La Jolla, California, USA.; Bahrami S; NORMENT Centre, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway.; van der Meer D; NORMENT Centre, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway.; Makowski C; Department of Radiology, University of California, San Diego, La Jolla, California, USA.; Loughnan R; University of California, San Diego, La Jolla, California, USA.; Wang X; Department of Neurosciences, University of California, San Diego, La Jolla, California, USA.; Broce I; Department of Neurosciences, University of California, San Diego, La Jolla, California, USA.; Banks SJ; Department of Neurosciences, University of California, San Diego, La Jolla, California, USA.; Fominykh V; NORMENT Centre, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway.; Cheng W; NORMENT Centre, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway.; Holland D; Department of Neurosciences, University of California, San Diego, La Jolla, California, USA.; Smeland OB; NORMENT Centre, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway.; Seibert T; Department of Radiology, University of California, San Diego, La Jolla, California, USA.; Selbaek G; University of Oslo, Universitetet i Oslo, Oslo, Norway.; Brewer JB; Department of Neurosciences, University of California, San Diego, La Jolla, California, USA.; Fan CC; Department of Radiology, University of California, San Diego, La Jolla, California, USA.; Population Neuroscience and Genetics Lab, University of California, San Diego, La Jolla, California, USA.; Center for Human Development, University of California, San Diego, La Jolla, California, USA.; Andreassen OA; NORMENT Centre, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway.; Dale AM; Department of Neurosciences, University of California, San Diego, La Jolla, California, USA.; Department of Radiology, University of California, San Diego, La Jolla, California, USA.; Population Neuroscience and Genetics Lab, University of California, San Diego, La Jolla, California, USA.; Department of Psychiatry, University of California, San Diego, La Jolla, California, USA.
Source
Publisher: John Wiley & Sons, Ltd Country of Publication: United States NLM ID: 101231978 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1552-5279 (Electronic) Linking ISSN: 15525260 NLM ISO Abbreviation: Alzheimers Dement Subsets: MEDLINE
Subject
Language
English
Abstract
Introduction: There is a pressing need for non-invasive, cost-effective tools for early detection of Alzheimer's disease (AD).
Methods: Using data from the Alzheimer's Disease Neuroimaging Initiative (ADNI), Cox proportional models were conducted to develop a multimodal hazard score (MHS) combining age, a polygenic hazard score (PHS), brain atrophy, and memory to predict conversion from mild cognitive impairment (MCI) to dementia. Power calculations estimated required clinical trial sample sizes after hypothetical enrichment using the MHS. Cox regression determined predicted age of onset for AD pathology from the PHS.
Results: The MHS predicted conversion from MCI to dementia (hazard ratio for 80th versus 20th percentile: 27.03). Models suggest that application of the MHS could reduce clinical trial sample sizes by 67%. The PHS alone predicted age of onset of amyloid and tau.
Discussion: The MHS may improve early detection of AD for use in memory clinics or for clinical trial enrichment.
Highlights: A multimodal hazard score (MHS) combined age, genetics, brain atrophy, and memory. The MHS predicted time to conversion from mild cognitive impairment to dementia. MHS reduced hypothetical Alzheimer's disease (AD) clinical trial sample sizes by 67%. A polygenic hazard score predicted age of onset of AD neuropathology.
(© 2023 The Authors. Alzheimer's & Dementia published by Wiley Periodicals LLC on behalf of Alzheimer's Association.)
Methods: Using data from the Alzheimer's Disease Neuroimaging Initiative (ADNI), Cox proportional models were conducted to develop a multimodal hazard score (MHS) combining age, a polygenic hazard score (PHS), brain atrophy, and memory to predict conversion from mild cognitive impairment (MCI) to dementia. Power calculations estimated required clinical trial sample sizes after hypothetical enrichment using the MHS. Cox regression determined predicted age of onset for AD pathology from the PHS.
Results: The MHS predicted conversion from MCI to dementia (hazard ratio for 80th versus 20th percentile: 27.03). Models suggest that application of the MHS could reduce clinical trial sample sizes by 67%. The PHS alone predicted age of onset of amyloid and tau.
Discussion: The MHS may improve early detection of AD for use in memory clinics or for clinical trial enrichment.
Highlights: A multimodal hazard score (MHS) combined age, genetics, brain atrophy, and memory. The MHS predicted time to conversion from mild cognitive impairment to dementia. MHS reduced hypothetical Alzheimer's disease (AD) clinical trial sample sizes by 67%. A polygenic hazard score predicted age of onset of AD neuropathology.
(© 2023 The Authors. Alzheimer's & Dementia published by Wiley Periodicals LLC on behalf of Alzheimer's Association.)