Evidence map›Paper›PMID 42342913›Full 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 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. Review
  2. Article
  3. 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

National Centralized Repository for Alzheimer's Disease and Related Dementias (NCRAD)U24AG021886 · NIA · INDIANA UNIV-PURDUE UNIV AT INDIANAPOLIS · PI TATIANA M. FOROUD · 2002 to 2026
$119.8M
Project 4 - Mechanistic studies on the role of the gut microbiome in models for Alzheimer's diseaseU19AG063744 · NIA · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI Rima F Kaddurah-Daouk · 2019 to 2026
$54.1M
National Alzheimer's Coordinating CenterU24AG072122 · NIA · UNIVERSITY OF WASHINGTON · PI STEPHENS, KARI A · 2021 to 2025
$45.8M
UCSD Shiley-Marcos Alzheimer's Disease Research Center P30P30AG062429 · NIA · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI DOUGLAS R GALASKO · 2019 to 2026
$34.9M
Wisconsin Alzheimer's Disease Research CenterP30AG062715 · NIA · UNIVERSITY OF WISCONSIN-MADISON · PI Sanjay Asthana · 2019 to 2026
$34.5M
Research Education ComponentP30AG066512 · NIA · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · PI Mary Sherman Mittelman · 2020 to 2026
$28.4M
University of Kansas Alzheimer's Disease Research Center (KU ADRC)P30AG072973 · NIA · UNIVERSITY OF KANSAS MEDICAL CENTER · PI Mohammad Haeri · 2021 to 2026
$25.3M
Research Education ComponentP30AG072976 · NIA · INDIANA UNIVERSITY INDIANAPOLIS · PI ANDREW J SAYKIN · 2021 to 2026
$24.1M
UAB Alzheimer's Disease Research CenterP30AG086401 · NIA · UNIVERSITY OF ALABAMA AT BIRMINGHAM · PI Erik D Roberson · 2024 to 2026
$17.1M
Wake Forest Alzheimer's Disease Core CenterP30AG049638 · NIA · WAKE FOREST UNIVERSITY HEALTH SCIENCES · PI WILLIAMSON, JEFF DOUGLAS · 2016 to 2020
$12.2M
Metabolomic Signatures for Disease Sub-classification and Target Prioritization in AMP-ADU01AG061359 · NIA · DUKE UNIVERSITY · PI KADDURAH-DAOUK, RIMA F, KASTENMULLER, GABI · 2018 to 2022
$10.0M
Metabolic Signatures Underlying Vascular Risk Factors for Alzheimer-type DementiasRF1AG051550 · NIA · DUKE UNIVERSITY · PI KADDURAH-DAOUK, RIMA F, KLING, MITCHEL ALLAN · 2015 to 2016
$6.3M
Deutsche Forschungsgemeinschaft (German Research Foundation) 536691227NIA NIH HHS P30 AG049638NIA NIH HHS P30 AG062429NIA NIH HHS P30 AG062715NIA NIH HHS P30 AG066512NIA NIH HHS P30 AG072973NIA NIH HHS P30 AG072976NIA NIH HHS P30 AG086401NIA NIH HHS R01 AG046171NIA NIH HHS RF1 AG051550NIA NIH HHS RF1 AG057452NIA NIH HHS RF1 AG058942NIA NIH HHS RF1 AG059093NIA NIH HHS U01 AG061359NIA NIH HHS U19 AG063744NIA NIH HHS U24 AG021886NIA NIH HHS U24 AG072122U.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

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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.