Identifying healthy individuals with Alzheimer’s disease neuroimaging phenotypes in the UK Biobank

doi:10.1038/s43856-023-00313-w

Identifying healthy individuals with Alzheimer’s disease neuroimaging phenotypes in the UK Biobank

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Abstract

Spotting people with dementia early is challenging, but important to identify people for trials of treatment and prevention. We used brain scans of people with Alzheimer’s disease, the commonest type of dementia, and applied an artificial intelligence method to spot people with Alzheimer’s disease. We used this to find people in the Healthy UK Biobank study who might have early Alzheimer’s disease. The people we found had subtle changes in their memory and thinking to suggest they may have early disease, and we also found they had high blood pressure and smoked for longer. We have demonstrated an approach that could be used to select people at high risk of future dementia for clinical trials.

Original language	English
Article number	100
Journal	Communications Medicine
Volume	3
Issue number	1
DOIs	https://doi.org/10.1038/s43856-023-00313-w
State	Published - Dec 2023

Bibliographical note

Publisher Copyright:
© The Author(s) 2023.

Funding

T.A. was funded by the W.D. Armstrong Trust Fund, University of Cambridge, UK. T.R. and J.B.R. are supported by the Cambridge Centre for Parkinson-plus and NIHR Cambridge Biomedical Research Centre (BRC-1215-20014). The views expressed are those of the authors and not necessarily those of the NIHR or the Department of Health and Social Care. J.B.R. is supported by the Medical Research Council (SUAG/051 R101400) and Wellcome Trust (103838). P.L. is supported by funding from the EU GOD-DS21 scheme (Grant agreement No. 848077). Data collection and sharing for this project was 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. The NACC database is funded by NIA/NIH Grant U24 AG072122. NACC data are contributed by the NIA-funded ADCs: P50 AG005131 (PI James Brewer, MD, PhD), P50 AG005133 (PI Oscar Lopez, MD), P50 AG005134 (PI Bradley Hyman, MD, PhD), P50 AG005136 (PI Thomas Grabowski, MD), P50 AG005138 (PI Mary Sano, PhD), P50 AG005142 (PI Helena Chui, MD), P50 AG005146 (PI Marilyn Albert, PhD), P50 AG005681 (PI John Morris, MD), P30 AG008017 (PI Jeffrey Kaye, MD), P30 AG008051 (PI Thomas Wisniewski, MD), P50 AG008702 (PI Scott Small, MD), P30 AG010124 (PI John Trojanowski, MD, PhD), P30 AG010129 (PI Charles DeCarli, MD), P30 AG010133 (PI Andrew Saykin, PsyD), P30 AG010161 (PI David Bennett, MD), P30 AG012300 (PI Roger Rosenberg, MD), P30 AG013846 (PI Neil Kowall, MD), P30 AG013854 (PI Robert Vassar, PhD), P50 AG016573 (PI Frank LaFerla, PhD), P50 AG016574 (PI Ronald Petersen, MD, PhD), P30 AG019610 (PI Eric Reiman, MD), P50 AG023501 (PI Bruce Miller, MD), P50 AG025688 (PI Allan Levey, MD, PhD), P30 AG028383 (PI Linda Van Eldik, PhD), P50 AG033514 (PI Sanjay Asthana, MD, FRCP), P30 AG035982 (PI Russell Swerdlow, MD), P50 AG047266 (PI Todd Golde, MD, PhD), P50 AG047270 (PI Stephen Strittmatter, MD, PhD), P50 AG047366 (PI Victor Henderson, MD, MS), P30 AG049638 (PI Suzanne Craft, PhD), P30 AG053760 (PI Henry Paulson, MD, PhD), P30 AG066546 (PI Sudha Seshadri, MD), P20 AG068024 (PI Erik Roberson, MD, PhD), P20 AG068053 (PI Marwan Sabbagh, MD), P20 AG068077 (PI Gary Rosenberg, MD), P20 AG068082 (PI Angela Jefferson, PhD), P30 AG072958 (PI Heather Whitson, MD), P30 AG072959 (PI James Leverenz, MD). This research has been conducted using data from UK Biobank, a major biomedical database ( http://www.ukbiobank.ac.uk/ ).

Funders	Funder number
Northern California Institute for Research and Education
University of Southern California
National Institute of Biomedical Imaging and Bioengineering
National Institute for Health and Care Research
DoD Alzheimer's Disease Neuroimaging Initiative
DOD ADNI
National Institute on Aging
NHS Innovation Accelerator
NIHR Cambridge Biomedical Research Centre	BRC-1215-20014
U.S. Department of Defense	W81XWH-12-2-0012
Wellcome Trust	103838
European Commission	848077
UK Medical Research Council, Engineering and Physical Sciences Research Council	SUAG/051 R101400
National Institutes of Health (NIH)	P30 AG028383, P30 AG013846, P30 AG008017, P30 AG053760, P30 AG010133, P50 AG005146, P50 AG033514, P50 AG005681, P50 AG005142, P50 AG047366, P50 AG047266, P20 AG068053, P20 AG068077, P30 AG019610, P50 AG023501, P30 AG008051, P30 AG010129, P30 AG013854, P30 AG072958, P30 AG072959, P50 AG005138, P50 AG008702, P30 AG010124, P30 AG012300, P50 AG005134, P50 AG047270, P50 AG025688, P50 AG005136, P30 AG035982, P30 AG010161, P30 AG066546, P50 AG005131, P50 AG005133, P30 AG049638, P50 AG016574, P20 AG068082, U24 AG072122, U01 AG024904, P50 AG016573, P20 AG068024

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

ASJC Scopus subject areas

Internal Medicine
Epidemiology
Medicine (miscellaneous)
Public Health, Environmental and Occupational Health
Assessment and Diagnosis

Access to Document

10.1038/s43856-023-00313-w

Cite this

@article{d280469b31dd48699faab37e7a681d5e,

title = "Identifying healthy individuals with Alzheimer{\textquoteright}s disease neuroimaging phenotypes in the UK Biobank",

abstract = "Spotting people with dementia early is challenging, but important to identify people for trials of treatment and prevention. We used brain scans of people with Alzheimer{\textquoteright}s disease, the commonest type of dementia, and applied an artificial intelligence method to spot people with Alzheimer{\textquoteright}s disease. We used this to find people in the Healthy UK Biobank study who might have early Alzheimer{\textquoteright}s disease. The people we found had subtle changes in their memory and thinking to suggest they may have early disease, and we also found they had high blood pressure and smoked for longer. We have demonstrated an approach that could be used to select people at high risk of future dementia for clinical trials.",

author = "Tiago Azevedo and Bethlehem, \{Richard A.I.\} and Whiteside, \{David J.\} and Nol Swaddiwudhipong and Rowe, \{James B.\} and Pietro Li{\'o} and Timothy Rittman and Gail Li and Petrie, \{Eric C.\} and Peskind, \{Elaine R.\} and Teresa Villena and Walter Martinez and Carl Sadowsky and Athena Lee and Lisa Ravdin and Michael Lin and Gloria Chiang and Norman Relkin and Santulli, \{Robert B.\} and Hynes, \{Mary L.\} and Marc Seltzer and Flashman, \{Laura A.\} and Karen Anderson and Karen Blank and Pearlson, \{Godfrey D.\} and Smita Kittur and Stephen Correia and Paul Malloy and Stephen Salloway and Steven Potkin and Paul Newhouse and Angelica Garcia and Jiong Shi and Joseph Masdeu and Arthur Wiliams and Jacobo Mintzer and Abigail O{\textquoteright}Connelll and Akiva Mintz and Donna Munic and Charles Bernick and Aaron Ritter and Drake, \{Jonathan D.\} and Daiello, \{Lori A.\} and Geoffrey Tremont and Jonathan Drake and Ott, \{Brian R.\} and Sink, \{Kaycee M.\} and Hyungsub Shim and Hristina Koleva and Jicha, \{Gregory A.\}",

note = "Publisher Copyright: {\textcopyright} The Author(s) 2023.",

year = "2023",

month = dec,

doi = "10.1038/s43856-023-00313-w",

language = "English",

volume = "3",

number = "1",

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AU - Azevedo, Tiago

AU - Bethlehem, Richard A.I.

AU - Whiteside, David J.

AU - Swaddiwudhipong, Nol

AU - Rowe, James B.

AU - Lió, Pietro

AU - Rittman, Timothy

AU - Li, Gail

AU - Petrie, Eric C.

AU - Peskind, Elaine R.

AU - Villena, Teresa

AU - Martinez, Walter

AU - Sadowsky, Carl

AU - Lee, Athena

AU - Ravdin, Lisa

AU - Lin, Michael

AU - Chiang, Gloria

AU - Relkin, Norman

AU - Santulli, Robert B.

AU - Hynes, Mary L.

AU - Seltzer, Marc

AU - Flashman, Laura A.

AU - Anderson, Karen

AU - Blank, Karen

AU - Pearlson, Godfrey D.

AU - Kittur, Smita

AU - Correia, Stephen

AU - Malloy, Paul

AU - Salloway, Stephen

AU - Potkin, Steven

AU - Newhouse, Paul

AU - Garcia, Angelica

AU - Shi, Jiong

AU - Masdeu, Joseph

AU - Wiliams, Arthur

AU - Mintzer, Jacobo

AU - O’Connelll, Abigail

AU - Mintz, Akiva

AU - Munic, Donna

AU - Bernick, Charles

AU - Ritter, Aaron

AU - Drake, Jonathan D.

AU - Daiello, Lori A.

AU - Tremont, Geoffrey

AU - Drake, Jonathan

AU - Ott, Brian R.

AU - Sink, Kaycee M.

AU - Shim, Hyungsub

AU - Koleva, Hristina

AU - Jicha, Gregory A.

PY - 2023/12

Y1 - 2023/12

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AB - Spotting people with dementia early is challenging, but important to identify people for trials of treatment and prevention. We used brain scans of people with Alzheimer’s disease, the commonest type of dementia, and applied an artificial intelligence method to spot people with Alzheimer’s disease. We used this to find people in the Healthy UK Biobank study who might have early Alzheimer’s disease. The people we found had subtle changes in their memory and thinking to suggest they may have early disease, and we also found they had high blood pressure and smoked for longer. We have demonstrated an approach that could be used to select people at high risk of future dementia for clinical trials.

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ER -

Identifying healthy individuals with Alzheimer’s disease neuroimaging phenotypes in the UK Biobank

Abstract

Bibliographical note

Funding

UN SDGs

ASJC Scopus subject areas

Access to Document

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