Evidence mapPaperPMID 42342913Full record

ArticleNature aging2026

The blood metabolome of brain health in midlife and influences of genes, microbiome and exposome.

Shahzad Ahmad, Tong Wu, Matthias Arnold, Thomas Hankemeier, Mohsen Ghanbari, Gennady Roshchupkin, André G Uitterlinden, Kamil Borkowski, Julia Neitzel, Robert Kraaij and 5 more

Abstract read
In one paragraph

Article in Nature aging, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

15 authors.

Shahzad AhmadDepartment of Epidemiology, Erasmus MC, University Medical Center, Rotterdam, The Netherlands.ORCID http://orcid.org/0000-0002-8658-3790
Tong WuInstitute of Computational Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany.ORCID http://orcid.org/0000-0002-2296-3380
Matthias ArnoldInstitute of Computational Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany.ORCID http://orcid.org/0000-0002-4666-0923
Thomas HankemeierDivision of Systems Biomedicine and Pharmacology, Leiden Academic Center for Drug Research, Leiden University, Leiden, The Netherlands.ORCID http://orcid.org/0000-0001-7871-2073
Mohsen GhanbariDepartment of Epidemiology, Erasmus MC, University Medical Center, Rotterdam, The Netherlands.ORCID http://orcid.org/0000-0002-9476-7143
Gennady RoshchupkinDepartment of Epidemiology, Erasmus MC, University Medical Center, Rotterdam, The Netherlands.
André G UitterlindenDepartment of Internal Medicine, Erasmus MC, University Medical Center, Rotterdam, The Netherlands.
Kamil BorkowskiWest Coast Metabolomics Center, Genome Center, University of California, Davis, Davis, CA, USA.
Julia NeitzelDepartment of Epidemiology, Erasmus MC, University Medical Center, Rotterdam, The Netherlands.ORCID http://orcid.org/0000-0001-5739-466X
Robert KraaijDepartment of Internal Medicine, Erasmus MC, University Medical Center, Rotterdam, The Netherlands.
Alzheimer’s Disease Metabolomics Consortium
Cornelia M van DuijnDepartment of Epidemiology, Erasmus MC, University Medical Center, Rotterdam, The Netherlands.ORCID http://orcid.org/0000-0002-2374-9204
M Arfan IkramDepartment of Epidemiology, Erasmus MC, University Medical Center, Rotterdam, The Netherlands.ORCID http://orcid.org/0000-0003-0372-8585
Rima Kaddurah-DaoukDepartment of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA. rima.kaddurahdaouk@duke.edu.ORCID http://orcid.org/0000-0003-1858-5732
Gabi KastenmüllerInstitute of Computational Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany. g.kastenmueller@helmholtz-muenchen.de.ORCID http://orcid.org/0000-0002-2368-7322

Funding

Alzheimer's Gut Microbiome ProjectU19AG063744 · NIA · DUKE UNIVERSITY · PI ROB KNIGHT, Rima F Kaddurah-Daouk · 2023 to 2023
$15.6M
Deutsche Forschungsgemeinschaft (German Research Foundation) 536691227U.S. Department of Health & Human Services | NIH | National Institute on Aging (U.S. National Institute on Aging) R01AG046171U.S. Department of Health & Human Services | NIH | National Institute on Aging (U.S. National Institute on Aging) R01AG059093U.S. Department of Health & Human Services | NIH | National Institute on Aging (U.S. National Institute on Aging) RF1AG051550U.S. Department of Health & Human Services | NIH | National Institute on Aging (U.S. National Institute on Aging) RF1AG057452U.S. Department of Health & Human Services | NIH | National Institute on Aging (U.S. National Institute on Aging) RF1AG058942U.S. Department of Health & Human Services | NIH | National Institute on Aging (U.S. National Institute on Aging) RF1AG059093U.S. Department of Health & Human Services | NIH | National Institute on Aging (U.S. National Institute on Aging) U01AG061359U.S. Department of Health & Human Services | NIH | National Institute on Aging (U.S. National Institute on Aging) U19AG063744ZonMw (Netherlands Organisation for Health Research and Development) #733050814
6 · The paper itself

Abstract

Metabolic alterations are increasingly implicated in neurological disorders, including Alzheimer's disease (AD), highlighting the relevance of the peripheral metabolome, shaped by genetic and environmental exposures, for brain health. We examined the relation of 991 blood metabolites with cognition and magnetic resonance imaging (MRI) measures cross-sectionally in 1,082 dementia-free middle-aged participants of the population-based Rotterdam Study and quantified contributions of genetic variation, lifestyle, comorbidities, medication and gut microbiota to metabolite variance. Cognition-associated metabolites were replicated in two independent cohorts of older adults and tested for associations with incident AD longitudinally in one cohort. Twenty-two metabolites were associated with MRI measures. Fourteen metabolites showed replicated associations with cognition, with ergothioneine exhibiting the largest effect. The metabolite signature of cognition mirrored that of incident AD. Lifestyle, clinical variables and medication were the strongest determinants of cognition-associated and MRI-associated metabolites, explaining up to 28.6% of their variance. Antacid use was associated with worse cognition and lower ergothioneine levels, which mediated 31.5% of the negative medication effect, suggesting implications for AD prevention.

Indexed as

Alzheimer DiseaseBrainCognitionExposomeGastrointestinal MicrobiomeMetabolomeAgedCross-Sectional StudiesErgothioneineFemaleHumansLife StyleMagnetic Resonance ImagingMaleMiddle AgedErgothioneine

Identifiers

PMID42342913
PMCPMC13375541

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.