Evidence map›Paper›PMID 41004623›Full record

ArticleJournal of Alzheimer's disease : JAD2025

Targeted serum metabolomic profiling and machine learning approach in Alzheimer's disease using the Alzheimer's disease diagnostics clinical study (ADDIA) cohort.

Dany Mukesha, Maité Sarter, Mélitine Dubray, Floris Durand, Stéphanie Boutillier, Lucas D Pham-Van, David Halter, Seval Kul, Frédéric Blanc, Hakan Gürvit and 14 more

Abstract read
In one paragraph

Article in Journal of Alzheimer's disease : JAD, 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

24 authors.

Dany MukeshaFiralis SA, 17 rue du fort, 68330 Huningue, France.ORCID 0009-0001-9514-751X
Maité SarterFiralis Molecular Precision SA, Huningue, France.ORCID 0009-0008-2601-6138
Mélitine DubrayLodiag SAS, Huningue, France.
Floris DurandLodiag SAS, Huningue, France.ORCID 0009-0001-3065-1803
Stéphanie BoutillierAmoneta Diagnostics SAS, Huningue, France.ORCID 0009-0007-2325-6403
Lucas D Pham-VanAmoneta Diagnostics SAS, Huningue, France.
David HalterLodiag SAS, Huningue, France.
Seval KulLodiag SAS, Huningue, France.ORCID 0000-0002-4716-9554
Frédéric BlancUniversity of Strasbourg and CNRS, ICube Laboratory UMR 7357 and FMTS (Fédération de Médecine Translationnelle de Strasbourg), IMIS Team, Strasbourg, France.
Hakan GürvitADDIA (H2020 - GA#674474) consortia, European Union.ORCID 0000-0003-2908-8475
Tamer DemiralpADDIA (H2020 - GA#674474) consortia, European Union.
Bruno DuboisADDIA (H2020 - GA#674474) consortia, European Union.
Audrey GabelleADDIA (H2020 - GA#674474) consortia, European Union.
Moira MarizzoniADDIA (H2020 - GA#674474) consortia, European Union.
Giovanni B FrisoniADDIA (H2020 - GA#674474) consortia, European Union.
Florence PasquierADDIA (H2020 - GA#674474) consortia, European Union.
François SellalADDIA (H2020 - GA#674474) consortia, European Union.
Adrian IvanoiuADDIA (H2020 - GA#674474) consortia, European Union.
Jean-Christophe BierADDIA (H2020 - GA#674474) consortia, European Union.
Renaud DavidADDIA (H2020 - GA#674474) consortia, European Union.
Jean-François DémonetADDIA (H2020 - GA#674474) consortia, European Union.
Eloi MagninADDIA (H2020 - GA#674474) consortia, European Union.
Guillaume SaccoUniversité Côte d'Azur, INSERM, CNRS, IPMC, France.ORCID 0000-0002-6881-1649
Hüseyin FiratFiralis SA, 17 rue du fort, 68330 Huningue, France.ORCID 0009-0005-1692-1060

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

BackgroundMetabolic biomarkers can potentially be used for early diagnosis, prognostic risk stratification and/or early treatment and prevention of individuals at risk to develop Alzheimer's disease (AD).ObjectiveOur goal was to evaluate changes in metabolite concentration levels associated with AD to identify biomarkers that could support early and accurate diagnosis and therapeutic interventions by using targeted mass spectrometry and machine learning approaches.MethodsSerum samples collected from a total of 107 individuals, including 55 individuals diagnosed with AD and 52 healthy controls (HC) enrolled previously to ADDIA cohort were analyzed using the biocrates AbsoluteIDQ

Indexed as

Alzheimer DiseaseMachine LearningMetabolomicsAgedAged, 80 and overApolipoproteins EBiomarkersCohort StudiesFemaleHumansMaleMiddle AgedApolipoproteins EBiomarkersAlzheimer's diseaseAPOE genotypingbiomarkersblood-based biomarkersmachine learningmass spectrometrymetabolomicsneurodegenerative disordersprecision medicineserum

Identifiers

PMID41004623
PMCPMC12614907

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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.