Evidence map›Paper›PMID 40110649›Full record

ArticleAlzheimer's & dementia : the journal of the Alzheimer's Association2025

Predicting amyloid beta accumulation in cognitively unimpaired older adults: Cognitive assessments provide no additional utility beyond demographic and genetic factors.

Shu Liu, Paul Maruff, Martin Saint-Jalmes, Pierrick Bourgeat, Colin L Masters, Benjamin Goudey, Alzheimer's Disease Neuroimaging Initiative, and the Australian Imaging Biomarkers and Lifestyle Flagship Study of Ageing

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In one paragraph

Article in Alzheimer's & dementia : the journal of the Alzheimer's Association, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
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

4 citing papers in PubMed.

  1. Review
  2. Article
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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

7 authors.

Shu LiuARC Training Centre in Cognitive Computing for Medical Technologies, University of Melbourne, Carlton, Victoria, Australia.
Paul MaruffFlorey Institute of Neuroscience and Mental Health, University of Melbourne, Parkville, Victoria, Australia.
Martin Saint-JalmesARC Training Centre in Cognitive Computing for Medical Technologies, University of Melbourne, Carlton, Victoria, Australia.
Pierrick BourgeatAustralian eHealth Research Centre, Dutton Park, Queensland, Australia.
Colin L MastersFlorey Institute of Neuroscience and Mental Health, University of Melbourne, Parkville, Victoria, Australia.
Benjamin GoudeyARC Training Centre in Cognitive Computing for Medical Technologies, University of Melbourne, Carlton, Victoria, Australia.ORCID 0000-0002-2318-985X
Alzheimer's Disease Neuroimaging Initiative, and the Australian Imaging Biomarkers and Lifestyle Flagship Study of Ageing

Funding

Project 1U19AG024904 · NIA · NORTHERN CALIFORNIA INSTITUTE/RES/EDU · PI MONICA G. RIVERA-MINDT · 2016 to 2026
$226.7M
Australian Research Council Training Centre in Cognitive Computing for Medical Technologies IC170100030NIA NIH HHS U19 AG024904
6 · The paper itself

Abstract

backgroundIntegrating non-invasive measures to estimate abnormal amyloid beta accumulation (Aβ+) is key to developing a screening tool for preclinical Alzheimer's disease (AD). The predictive capability of standard neuropsychological tests in estimating Aβ+ has not been quantified.

methodsWe constructed machine learning models using six cognitive measurements alongside demographic and genetic risk factors to predict Aβ status. Data were drawn from three cohorts: Anti-Amyloid Treatment in Asymptomatic Alzheimer's Disease (A4), Alzheimer's Disease Neuroimaging Initiative (ADNI), and Australian Imaging, Biomarker & Lifestyle (AIBL) study. Internal validation was conducted within A4 with external validations in ADNI and AIBL to assess model generalizability.

resultsThe highest area under the curve (AUC) for predicting Aβ+ was observed with demographic, genetic, and cognitive variables in A4 (median AUC = 0.745), but this was not significantly different from models without cognitive variables. External validation showed no improvement in ADNI and a slight decrease in AIBL. DISCUSSION: Standard neuropsychological tests do not significantly enhance Aβ+ prediction in cognitively unimpaired adults beyond demographic and genetic information. HIGHLIGHTS: Standard neuropsychological tests do not significantly improve the prediction of amyloid beta positivity (Aβ+) in cognitively unimpaired older adults beyond demographic and genetic information alone. Across three well-characterized cohorts, machine learning models incorporating cognitive measures failed to significantly improve Aβ+ prediction, indicating the limited relationship between cognitive performance on these tests and the risk of pre-clinical Alzheimer's disease (AD). These findings challenge assumptions about cognitive symptoms preceding Aβ+ screening and emphasize the need for developing more sensitive cognitive tests for early AD detection.

Indexed as

Alzheimer DiseaseAmyloid beta-PeptidesNeuropsychological TestsAgedAged, 80 and overAustraliaBiomarkersCognitionCohort StudiesFemaleHumansMachine LearningMaleRisk FactorsAmyloid beta-PeptidesBiomarkersmachine learningneuropsychological testspreclinical Alzheimer's disease

Identifiers

PMID40110649
PMCPMC11923568

What Socratic holds

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LicenceCC BY-NC
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Registered trials

None linked

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.