Evidence map›Paper›PMID 41256143›Full record

ArticlemedRxiv : the preprint server for health sciences2025

A new ANMerge-based blood transcriptomic resource to support Alzheimer's disease research.

Nasim Mohamed Ismail, Maggie Miller, Hannah Crossland, Jalil-Ahmad Sharif, J Paul Chapple, Claes Wahlestedt, Kirill Shkura, Claude-Henry Volmar, Gregory Slabaugh, James A Timmons

Abstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in medRxiv : the preprint server for health sciences, 2025. 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

10 authors.

Nasim Mohamed IsmailSchool of Electronic Engineering and Computer Science, Queen Mary University of London, London, E1 4NS, UK.ORCID 0009-0008-6711-5159
Maggie MillerUniversity of Miami Miller School of Medicine, Miami, Florida, FL 33136, USA.ORCID 0009-0004-6909-9894
Hannah CrosslandClinical, Metabolic and Molecular Physiology Research Group, School of Medicine, University of Nottingham, Derby, DE22 3DT, UK.
Jalil-Ahmad SharifFaculty of Medicine and Dentistry, Queen Mary University of London, London, EC1M 6BQ, UK.
J Paul ChappleFaculty of Medicine and Dentistry, Queen Mary University of London, London, EC1M 6BQ, UK.
Claes WahlestedtUniversity of Miami Miller School of Medicine, Miami, Florida, FL 33136, USA.
Kirill ShkuraMSD Research and Development Innovation Centre, London, EC2M 6UR, UK.
Claude-Henry VolmarUniversity of Miami Miller School of Medicine, Miami, Florida, FL 33136, USA.
Gregory SlabaughDigital Environment Research Institute, Queen Mary University of London, London, E1 1HH, UK.
James A TimmonsUniversity of Miami Miller School of Medicine, Miami, Florida, FL 33136, USA.ORCID 0000-0002-2255-1220

Funding

NIA NIH HHS R56 AG061911
6 · The paper itself

Abstract

introductionAlzheimer's disease (AD) has greater prevalence in women and lacks effective treatments. Integrating multimodal data using machine learning (ML) may help improve diagnostics and prognostics.

methodsWe produced a large and updatable blood transcriptomic dataset (n=1021, with n=317 replicates). Technical robustness was assessed using sampling-at-random, batch adjustment and classification metrics. Transcriptomic and MRI features were concatenated to develop models for AD classification.

resultsReprofiling of blood transcriptomics resolved previous technical artefacts (sampling-at-random AUC; Legacy=0.732 vs. New=0.567). AD-associated molecular pathways were influenced by cell counts and sex, including unchanged mitochondrial DNA-encoded RNA and altered B-cell receptor biology. Several genes linked to AD-associated neuroinflammatory pathways, including DISCUSSION: We provide a new large-scale and technically robust blood AD transcriptomic dataset, highlighting details of molecular sexual dimorphism in AD and potential literature false positives, while providing a novel resource for future multimodal ML and genomic studies.

Indexed as

Alzheimer’s diseaseBloodMachine LearningMitochondriaMRIMultimodalSexual dimorphismTranscriptome

Identifiers

PMID41256143
PMCPMC12622077

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.