Evidence map›Paper›PMID 40684252›Full record

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

Staging of Alzheimer's disease progression in Down syndrome using mixed clinical and plasma biomarker measures with machine learning.

Mina Idris, Fedal Saini, Phoebe Ivain, R Asaad Baksh, Leda A Bianchi, Sarah E Pape, Henrik Zetterberg, Peter A Wijeratne, André Strydom

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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 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. 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

9 authors.

Mina IdrisDepartment of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, Greater London, UK.ORCID 0000-0002-8617-2908
Fedal SainiDepartment of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, Greater London, UK.
Phoebe IvainDepartment of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, Greater London, UK.
R Asaad BakshDepartment of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, Greater London, UK.
Leda A BianchiDepartment of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, Greater London, UK.
Sarah E PapeDepartment of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, Greater London, UK.
Henrik ZetterbergDepartment of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, the Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden.
Peter A WijeratneSussex Artificial Intelligence Centre, School of Engineering and Informatics, University of Sussex, Falmer, Brighton, UK.
André StrydomDepartment of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, Greater London, UK.

Funding

AD Strategic FundAlzheimer's Association ADSF-21-831376-CAlzheimer's Association ADSF-21-831377-CAlzheimer's Association ADSF-21-831381-CAlzheimer's Association ADSF-24-1284328-CAlzheimer's Drug Discovery Foundation 201809-2016862Alzheimer's Society AS-CP-18-0020Baily Thomas Charitable FundCure Alzheimer's FundErling-Persson Family Foundation, Familjen Rönströms StiftelseEU Joint Programme - Neurodegenerative Disease Research JPND2021-00694European Partnership on MetrologyEuropean Union's Horizon Europe Research and Innovation Programme 22HLT07Fondation Jérôme LejeuneH2020 Marie Skłodowska-CurieHjärnfonden FO2022-0270HORIZON EUROPE European Research Council 101053962Marie Skłodowska-Curie 860197Medical Research Council MR/R024901/1Medical Research Council MR/S005145/1Medical Research Council MR/S011277/1National Institute for Health and Care Research University College London Hospitals Biomedical Research CentreOlav Thon StiftelsenStiftelsen för Gamla TjänarinnorSwedish Research Council 101053962Swedish Research Council 2019-02397Swedish Research Council 2022-01018Swedish Research Council 2023-00356Swedish State Support for Clinical Research ALFGBG-71320The Alzheimer's SocietyThe Bluefield ProjectUCLH Biomedical Research CentreUK Dementia Research Institute UKDRI-1003UK Dementia Research Institute at UCL UKDRI-1003Vetenskapsrådet 101053962Vetenskapsrådet 2019-02397Vetenskapsrådet 2022-01018Vetenskapsrådet 2023-00356Wellcome TrustWellcome Trust Strategic Award 098330/Z/12/Z
6 · The paper itself

Abstract

introductionAdults with Down syndrome (DS) have a high risk for Alzheimer's disease (AD). Although the sequence of plasma biomarker and cognitive changes in AD in DS is well studied, their related trajectories are not fully characterized. Data-driven methods can estimate disease progression from cross-sectional data.

methodsIn 57 adults with DS and no AD, we used the event-based model to sequence plasma biomarker and cognitive changes in preclinical AD. Generalized additive models assessed the relationship between age and plasma biomarkers.

resultsThe earliest changes occurred in the amyloid beta 42/40 ratio, followed by memory changes. Later alterations in neurofilament light and tau concentrations preceded executive and visuomotor function changes, with glial fibrillary acidic protein levels changing last. The highest rate of plasma biomarker changes occurred between ages 39 and 52.

conclusionChanges in DS follow a pattern similar to that of sporadic and familial AD. Event-based modeling offers individual-level staging, potentially optimizing diagnosis and clinical trial patient selection. HIGHLIGHTS: The pre-clinical stages of Alzheimer's disease (AD) development in Down syndrome (DS) are not well defined, despite the extremely high prevalence of AD. Better understanding of early AD progression would aid in diagnostics and treatment. Data-driven methods, such as the event-based model, can aid in clarifying the sequence of cognitive and plasma biomarker changes in the early stages of AD while accounting for baseline variability. We find that plasma amyloid beta 42/40 ratio and memory changes precede changes in plasma biomarker levels of neurodegeneration, with changes in executive and visuomotor functions occurring later, before neuroinflammatory marker changes. Combining plasma biomarkers could be a useful measure of preclinical AD for trials, particularly in individuals between 39 and52 years of age.

Indexed as

Alzheimer DiseaseBiomarkersDisease ProgressionDown SyndromeMachine LearningAdultAgedAmyloid beta-PeptidesCross-Sectional StudiesFemaleHumansMaleMiddle AgedNeurofilament ProteinsNeuropsychological TestsPeptide FragmentsAmyloid beta-PeptidesBiomarkersneurofilament protein LNeurofilament ProteinsPeptide Fragmentstau ProteinsAlzheimer's diseaseblood plasma biomarkerscognitioncognitive declinedementiaDown syndromeevent‐based modelmachine learningmemorytrisomy 21

Identifiers

PMID40684252
PMCPMC12276070

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Registered trials

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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 OpenQuestion graph.