Evidence map›Paper›PMID 41948539›Full record

ArticleAlzheimer's & dementia (Amsterdam, Netherlands)

Application of machine learning to blood-based biomarkers of Alzheimer's disease in Down syndrome.

Patrick H Luckett, Melissa Petersen, Sid O'Bryant, Mark Mapstone, Brad T Christian, Benjamin Handen, Elizabeth Head, Beau M Ances, Alzheimer's Biomarker Consortium‐Down Syndrome

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Alzheimer's & dementia (Amsterdam, Netherlands). The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Patrick H LuckettDivision of Neurotechnology Department of Neurological Surgery Washington University School of Medicine St. Louis Missouri USA.
Melissa PetersenDepartment of Family Medicine Institute for Translational Research University of North Texas Health Science Center Fort Worth Texas USA.
Sid O'BryantDepartment of Pharmacology & Neuroscience Institute for Translational Research University of North Texas Health Science Center Fort Worth Texas USA.
Mark MapstoneDepartment of Neurology University of California Irvine Irvine California USA.
Brad T ChristianWaisman Center University of Wisconsin-Madison Madison Wisconsin USA.
Benjamin HandenDepartment of Psychiatry University of Pittsburgh Pittsburgh Pennsylvania USA.
Elizabeth HeadDepartment of Pathology & Laboratory Medicine, and Neurology School of Medicine University of California Irvine Irvine California USA.
Beau M AncesDepartment of Neurology Washington University School of Medicine St. Louis Missouri USA.
Alzheimer's Biomarker Consortium‐Down Syndrome

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionBlood-based biomarkers can improve Alzheimer's disease (AD) characterization in Down syndrome (DS). This study applied hierarchical clustering and machine learning-based feature selection to identify biomarkers associated with disease progression.

methodsCross-sectional blood-based biomarkers were analyzed from 211 DS participants (

resultsThe strongest predictors overall were neurofilament light chain (NfL), tau/amyloid beta (Aβ)40, Aβ42/Aβ40, alpha-2-macroglobulin (A2M), and interleukin (IL)-10. Within the CS group, NfL, tau/Aβ40, A2M, and IL-10 were strong predictors. In MCI, Aβ42/Aβ40, NfL, A2M, and IL-10 were strong predictors. In DS-AD, Aβ42/Aβ40, NfL, and tau/Aβ40 were the top predictors. Cluster membership varied based on disease stage. DISCUSSION: These findings reveal evolving biomarker signatures and clustering patterns across cognitive stages, underscoring their potential for disease monitoring.

Indexed as

biomarkerDown syndromemachine learning

Identifiers

PMID41948539
PMCPMC13052191

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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.