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.
| Original language | English |
|---|---|
| Pages (from-to) | 5151-5158 |
| Number of pages | 8 |
| Journal | Alzheimer s & dementia |
| Volume | 19 |
| Issue number | 11 |
| Early online date | 2 May 2023 |
| DOIs | |
| Publication status | Published - Nov 2023 |
Keywords
- Alzheimer's disease
- amyloid
- genetics
- magnetic resonance imaging
- memory
- mild cognitive impairment
- multimodal prediction
- tau
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In: Alzheimer s & dementia, Vol. 19, No. 11, 11.2023, p. 5151-5158.
Research output: Contribution to journal › Article › Academic › peer-review
TY - JOUR
T1 - Improved multimodal prediction of progression from MCI to Alzheimer's disease combining genetics with quantitative brain MRI and cognitive measures
AU - Alzheimer's Disease Neuroimaging Initiative
AU - Reas, Emilie T
AU - Shadrin, Alexey
AU - Frei, Oleksandr
AU - Motazedi, Ehsan
AU - McEvoy, Linda
AU - Bahrami, Shahram
AU - van der Meer, Dennis
AU - Makowski, Carolina
AU - Loughnan, Robert
AU - Wang, Xin
AU - Broce, Iris
AU - Banks, Sarah J
AU - Fominykh, Vera
AU - Cheng, Weiqiu
AU - Holland, Dominic
AU - Smeland, Olav B
AU - Seibert, Tyler
AU - Selbaek, Geir
AU - Brewer, James B
AU - Fan, Chun C
AU - Andreassen, Ole A
AU - Dale, Anders M
N1 - Funding Information: Emilie T. Reas was supported by the National Institute on Aging (R00 AG057797, R01 AG077202) and American Federation for Aging Research/McKnight Foundation (311122‐00001). Ole A. Andreassen was supported by Research Council of Norway (# 324499, 324252, 273291, 223273), Norwegian Health Association (# 22731), and the European Union's Horizon 2020 Research and Innovation Action Grant (#847776 CoMorMent). Data collection and sharing for this project were funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI; National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH‐12‐2‐0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie; Alzheimer's Association; Alzheimer's Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol‐Myers Squibb Company; CereSpir, Inc.; Cogstate; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann‐La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC; Johnson & Johnson Pharmaceutical Research & Development LLC; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health ( www.fnih.org ). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer's Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California. Funding Information: Emilie T. Reas was supported by the National Institute on Aging (R00 AG057797, R01 AG077202) and American Federation for Aging Research/McKnight Foundation (311122-00001). Ole A. Andreassen was supported by Research Council of Norway (# 324499, 324252, 273291, 223273), Norwegian Health Association (# 22731), and the European Union's Horizon 2020 Research and Innovation Action Grant (#847776 CoMorMent). Data collection and sharing for this project were funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI; National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie; Alzheimer's Association; Alzheimer's Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Cogstate; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC; Johnson & Johnson Pharmaceutical Research & Development LLC; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer's Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California. Funding Information: Anders M. Dale reports that he was a founder of and holds equity in CorTechs Labs, Inc., and serves on its Scientific Advisory Board. He is a member of the Scientific Advisory Board of Human Longevity, Inc. He receives funding through research grants from GE Healthcare to UCSD. The terms of these arrangements have been reviewed by and approved by UCSD in accordance with its conflict of interest policies. Dr. Dale also reports that he has memberships with the following research consortia: Alzheimers Disease Genetics Consortium (ADGC), Enhancing Neuro Imaging Genetics Through Meta Analysis (ENIGMA), Prostate Cancer Association Group to Investigate Cancer Associated Alterations in the Genome (PRACTICAL), and Psychiatric Genomics Consortium (PGC). Ole A. Andreassen is a consultant to Cortechs.ai and has received speaker's honoraria from Sunovion, Janssen, and Lundbeck. He is also a local PI of clinical trials in mental disorders (not dementia) sponsored by Boehringer Ingelheim, Janssen, Compass, MAPS. All other authors have no conflicts of interest. Author disclosures are available in the Supporting Information. Publisher Copyright: © 2023 The Authors. Alzheimer's & Dementia published by Wiley Periodicals LLC on behalf of Alzheimer's Association.
PY - 2023/11
Y1 - 2023/11
N2 - 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.
AB - 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.
KW - Alzheimer's disease
KW - amyloid
KW - genetics
KW - magnetic resonance imaging
KW - memory
KW - mild cognitive impairment
KW - multimodal prediction
KW - tau
UR - http://www.scopus.com/inward/record.url?scp=85158836051&partnerID=8YFLogxK
U2 - https://doi.org/10.1002/alz.13112
DO - https://doi.org/10.1002/alz.13112
M3 - Article
C2 - 37132098
SN - 1552-5260
VL - 19
SP - 5151
EP - 5158
JO - Alzheimer s & dementia
JF - Alzheimer s & dementia
IS - 11
ER -