Evidence map›Paper›PMID 37474615›Full record

ArticleCommunications medicine2023

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

Tiago Azevedo, Richard A I Bethlehem, David J Whiteside, Nol Swaddiwudhipong, James B Rowe, Pietro Lió, Timothy Rittman, Alzheimer’s Disease Neuroimaging Initiative

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Communications medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed, 1 pooled it
3.1field-weighted citation impact, top 8% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

10 citing papers in PubMed, 1 synthesis or guideline pooled it, 14 citations in OpenAlex.

  1. Pooled it
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  8. Multi-organ imaging-derived polygenic indexes for brain and body health.medRxiv : the preprint server for health sciences · 2024
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors at 20 institutions in 4 countries.

Tiago AzevedoDepartment of Computer Science and Technology, University of Cambridge, Cambridge, UK.ORCID http://orcid.org/0000-0002-2052-3832
Richard A I BethlehemBrain Mapping Unit, Department of Psychiatry, University of Cambridge, Cambridge, UK.ORCID http://orcid.org/0000-0002-0714-0685
David J WhitesideDepartment of Clinical Neurosciences and Cambridge University Hospitals NHS Trust, University of Cambridge, Cambridge, UK.
Nol SwaddiwudhipongDepartment of Clinical Neurosciences and Cambridge University Hospitals NHS Trust, University of Cambridge, Cambridge, UK.ORCID http://orcid.org/0000-0001-8505-7433
James B RoweDepartment of Clinical Neurosciences and Cambridge University Hospitals NHS Trust, University of Cambridge, Cambridge, UK.
Pietro LióDepartment of Computer Science and Technology, University of Cambridge, Cambridge, UK.ORCID http://orcid.org/0000-0002-0540-5053
Timothy RittmanDepartment of Clinical Neurosciences and Cambridge University Hospitals NHS Trust, University of Cambridge, Cambridge, UK. tr332@medschl.cam.ac.uk.ORCID http://orcid.org/0000-0003-1063-6937
Alzheimer’s Disease Neuroimaging Initiative
University of California, San Francisco · USNorthwestern University · USBaylor College of Medicine · USCleveland Clinic · USJohns Hopkins University · USNew York University · USUniversity of California, Los Angeles · USWashington University in St. Louis · USRush University Medical Center · USUniversity of Iowa · USUniversity of Rochester Medical Center · USUniversity of Southern California · USHoward University · USOregon Health & Science University · USStanford University · USUniversity of California, Irvine · USUniversity of Cambridge · GBBrigham and Women's Hospital · USDuke Medical Center · USSt Joseph's Health Care · CA

Funding

Alzheimer's Disease Neuroimaging Initiative - SupplementU01AG024904 · NIA · NORTHERN CALIFORNIA INSTITUTE RES &EDUC · PI WEINER, MICHAEL W · 2004 to 2015
$121.0M
THERAPEUTIC EFFECTS OF INTRA-NASAL INSULIN DETEMIRP50AG005136 · NIA · UNIVERSITY OF WASHINGTON · PI GRABOWSKI, THOMAS J. · 1985 to 2019
$57.2M
VIRUS-INDUCED BRAIN PATHOLOGYP50AG005131 · NIA · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI GALASKO, DOUGLAS R · 1985 to 2018
$55.4M
WASHINGTON UNIVERSITY ALZHEIMERS DISEASE RESEARCH CENTERP50AG005681 · NIA · WASHINGTON UNIVERSITY · PI MORRIS, JOHN · 1985 to 2019
$52.1M
SUPPLEMENT TO RUSH ALZHEIMERS DISEASE CENTER COREP30AG010161 · NIA · RUSH UNIVERSITY MEDICAL CENTER · PI BENNETT, DAVID ALAN · 1991 to 2020
$49.1M
Satellite Diagnostic and Treatment Clinic CoreP50AG008702 · NIA · COLUMBIA UNIV NEW YORK MORNINGSIDE · PI DE JAGER, PHILIP L · 1989 to 2019
$46.3M
National Alzheimer's Coordinating CenterU24AG072122 · NIA · UNIVERSITY OF WASHINGTON · PI STEPHENS, KARI A · 2021 to 2025
$45.8M
TREATMENT OF DEPRESSION IN ALZHEIMER'S DISEASEP50AG005133 · NIA · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI SWEET, ROBERT A · 1985 to 2019
$43.0M
THE ROLE OF ADVANCED CAA IN ALZHEIMER'S DEMENTIAP50AG005134 · NIA · MASSACHUSETTS GENERAL HOSPITAL · PI HYMAN, BRADLEY T. · 1985 to 2018
$42.1M
VASCULAR RISK AND COGNITIVE STATUS IN A LATINO POPULATIONP50AG005142 · NIA · UNIVERSITY OF SOUTHERN CALIFORNIA · PI CHUI, HELENA CHANG · 1985 to 2019
$41.0M
VESICULAR LOCALIZATION AND FUNCTION OF PRESENILIN 1 FRAGMENTP50AG005138 · NIA · MOUNT SINAI SCHOOL OF MEDICINE OF NYU · PI GROSSMAN, HILLEL · 1985 to 2019
$37.9M
'T V ASSESSMENT - MEMORY DISORDERED PATIENTS'P50AG005146 · NIA · JOHNS HOPKINS UNIVERSITY · PI BANDEEN-ROCHE, KAREN J. · 1985 to 2019
$37.6M
Medical Research Council G1100464Medical Research Council MC_UU_00005/12NIA NIH HHS P20 AG068024NIA NIH HHS P20 AG068053NIA NIH HHS P20 AG068077NIA NIH HHS P20 AG068082NIA NIH HHS P30 AG008017NIA NIH HHS P30 AG008051NIA NIH HHS P30 AG010124NIA NIH HHS P30 AG010129NIA NIH HHS P30 AG010133NIA NIH HHS P30 AG010161NIA NIH HHS P30 AG012300NIA NIH HHS P30 AG013846NIA NIH HHS P30 AG013854NIA NIH HHS P30 AG019610NIA NIH HHS P30 AG028383NIA NIH HHS P30 AG035982NIA NIH HHS P30 AG049638NIA NIH HHS P30 AG053760NIA NIH HHS P30 AG066512NIA NIH HHS P30 AG066514NIA NIH HHS P30 AG066546NIA NIH HHS P30 AG072947NIA NIH HHS P30 AG072958NIA NIH HHS P30 AG072959NIA NIH HHS P50 AG005131NIA NIH HHS P50 AG005133NIA NIH HHS P50 AG005134NIA NIH HHS P50 AG005136NIA NIH HHS P50 AG005138NIA NIH HHS P50 AG005142NIA NIH HHS P50 AG005146NIA NIH HHS P50 AG005681NIA NIH HHS P50 AG008702NIA NIH HHS P50 AG016573NIA NIH HHS P50 AG016574NIA NIH HHS P50 AG023501NIA NIH HHS P50 AG025688NIA NIH HHS P50 AG033514NIA NIH HHS P50 AG047266NIA NIH HHS P50 AG047270NIA NIH HHS P50 AG047366NIA NIH HHS U01 AG024904NIA NIH HHS U24 AG072122Wellcome Trust
6 · The paper itself

Abstract

backgroundIdentifying prediagnostic neurodegenerative disease is a critical issue in neurodegenerative disease research, and Alzheimer's disease (AD) in particular, to identify populations suitable for preventive and early disease-modifying trials. Evidence from genetic and other studies suggests the neurodegeneration of Alzheimer's disease measured by brain atrophy starts many years before diagnosis, but it is unclear whether these changes can be used to reliably detect prediagnostic sporadic disease.

methodsWe trained a Bayesian machine learning neural network model to generate a neuroimaging phenotype and AD score representing the probability of AD using structural MRI data in the Alzheimer's Disease Neuroimaging Initiative (ADNI) Cohort (cut-off 0.5, AUC 0.92, PPV 0.90, NPV 0.93). We go on to validate the model in an independent real-world dataset of the National Alzheimer's Coordinating Centre (AUC 0.74, PPV 0.65, NPV 0.80) and demonstrate the correlation of the AD-score with cognitive scores in those with an AD-score above 0.5. We then apply the model to a healthy population in the UK Biobank study to identify a cohort at risk for Alzheimer's disease.

resultsWe show that the cohort with a neuroimaging Alzheimer's phenotype has a cognitive profile in keeping with Alzheimer's disease, with strong evidence for poorer fluid intelligence, and some evidence of poorer numeric memory, reaction time, working memory, and prospective memory. We found some evidence in the AD-score positive cohort for modifiable risk factors of hypertension and smoking.

conclusionsThis approach demonstrates the feasibility of using AI methods to identify a potentially prediagnostic population at high risk for developing sporadic Alzheimer's disease.

Identifiers

PMID37474615
PMCPMC10359360
OpenAlexW4384926326

What Socratic holds

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.