The blood metabolome of brain health in midlife and influences of genes, microbiome and exposome
14 affiliations
- 1Department of Epidemiology, Erasmus MC, University Medical Center, Rotterdam, The Netherlands
- 2Division of Systems Biomedicine and Pharmacology, Leiden Academic Center for Drug Research, Leiden University, Leiden, The Netherlands
- 3Oxford-GSK Institute of Molecular and Computational Medicine (IMCM), Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- 4Sidra Medicine, Doha, Qatar
- 5Institute of Computational Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- 6Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA
- 7Department of Internal Medicine, Erasmus MC, University Medical Center, Rotterdam, The Netherlands
- 8West Coast Metabolomics Center, Genome Center, University of California, Davis, Davis, CA, USA
- 9Department of Radiology and Nuclear Medicine, Erasmus MC, University Medical Center, Rotterdam, The Netherlands
- 10Nuffield Department of Population Health, University of Oxford, Oxford, UK
- 11Department of Medicine, Duke University, Durham, NC, USA. [email protected]
- 12Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA. [email protected]
- 13Duke Institute of Brain Sciences, Duke University, Durham, NC, USA. [email protected]
- 14Institute of Computational Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany. [email protected]
Abstract
Increasing evidence suggests the involvement of metabolic alterations in neurological disorders, including Alzheimer’s disease (AD), and highlights the significance of the peripheral metabolome, influenced by genetic factors and modifiable environmental exposures, for brain health. In this study, we examined 1,387 metabolites in plasma samples from 1,082 dementia-free middle-aged participants of the population-based Rotterdam Study. We assessed the relation of metabolites with general cognition (G-factor) and magnetic resonance imaging (MRI) markers using linear regression and estimated the variance of these metabolites explained by genes, gut microbiome, lifestyle factors, common clinical comorbidities, and medication using gradient boosting decision tree analysis. Twenty-one metabolites and one metabolite were significantly associated with total brain volume and total white matter lesions, respectively. Fourteen metabolites showed significant associations with G-factor, with ergothioneine exhibiting the largest effect (adjusted mean difference = 0.122, P = 4.65x10 -7 ). Associations for nine of the 14 metabolites were replicated in an independent, older cohort. The metabolite signature of incident AD in the replication cohort resembled that of cognition in the discovery cohort, emphasizing the potential relevance of the identified metabolites to disease pathogenesis. Lifestyle, clinical variables, and medication were most important in determining these metabolites’ blood levels, with lifestyle, explaining up to 28.6% of the variance. Smoking was associated with ten metabolites linked to G-factor, while diabetes and antidiabetic medication were associated with 13 metabolites linked to MRI markers, including N-lactoyltyrosine. Antacid medication strongly affected ergothioneine levels. Mediation analysis revealed that lower ergothioneine levels may partially mediate negative effects of antacids on cognition (31.5%). Gut microbial factors were more important for the blood levels of metabolites that were more strongly associated with cognition and incident AD in the older replication cohort (beta-cryptoxanthin, imidazole propionate), suggesting they may be involved later in the disease process. The detailed results on how multiple modifiable factors affect blood levels of cognition- and brain imaging-related metabolites in dementia-free participants may help identify new AD prevention strategies.
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BibTeX
@article{ahmad2026blood,
title = {{The blood metabolome of brain health in midlife and influences of genes, microbiome and exposome}},
author = {Shahzad Ahmad and Tong Wu and Matthias Arnold and Thomas Hankemeier and Mohsen Ghanbari and Gennady V. Roshchupkin and André Gerardus Uitterlinden and Kamil Borkowski and Julia Neitzel and Robert Kraaij and Cornelia M. van Duijn and Cornelia M. van Duijn and M. Arfan Ikram and Rima F Kaddurah-Daouk and Gabi Kastenmüller},
journal = {Nature Aging},
year = {2026},
month = jun,
doi = {10.1038/s43587-026-01149-4},
url = {https://doi.org/10.1038/s43587-026-01149-4},
note = {CC BY},
}
RIS
TY - JOUR
TI - The blood metabolome of brain health in midlife and influences of genes, microbiome and exposome
AU - Shahzad Ahmad
AU - Tong Wu
AU - Matthias Arnold
AU - Thomas Hankemeier
AU - Mohsen Ghanbari
AU - Gennady V. Roshchupkin
AU - André Gerardus Uitterlinden
AU - Kamil Borkowski
AU - Julia Neitzel
AU - Robert Kraaij
AU - Cornelia M. van Duijn
AU - Cornelia M. van Duijn
AU - M. Arfan Ikram
AU - Rima F Kaddurah-Daouk
AU - Gabi Kastenmüller
PY - 2026
DA - 2026/06/24
JO - Nature Aging
DO - 10.1038/s43587-026-01149-4
UR - https://doi.org/10.1038/s43587-026-01149-4
AB - Increasing evidence suggests the involvement of metabolic alterations in neurological disorders, including Alzheimer’s disease (AD), and highlights the significance of the peripheral metabolome, influenced by genetic factors and modifiable environmental exposures, for brain health. In this study, we examined 1,387 metabolites in plasma samples from 1,082 dementia-free middle-aged participants of the population-based Rotterdam Study. We assessed the relation of metabolites with general cognition (G-factor) and magnetic resonance imaging (MRI) markers using linear regression and estimated the variance of these metabolites explained by genes, gut microbiome, lifestyle factors, common clinical comorbidities, and medication using gradient boosting decision tree analysis. Twenty-one metabolites and one metabolite were significantly associated with total brain volume and total white matter lesions, respectively. Fourteen metabolites showed significant associations with G-factor, with ergothioneine exhibiting the largest effect (adjusted mean difference = 0.122, P = 4.65x10 -7 ). Associations for nine of the 14 metabolites were replicated in an independent, older cohort. The metabolite signature of incident AD in the replication cohort resembled that of cognition in the discovery cohort, emphasizing the potential relevance of the identified metabolites to disease pathogenesis. Lifestyle, clinical variables, and medication were most important in determining these metabolites’ blood levels, with lifestyle, explaining up to 28.6% of the variance. Smoking was associated with ten metabolites linked to G-factor, while diabetes and antidiabetic medication were associated with 13 metabolites linked to MRI markers, including N-lactoyltyrosine. Antacid medication strongly affected ergothioneine levels. Mediation analysis revealed that lower ergothioneine levels may partially mediate negative effects of antacids on cognition (31.5%). Gut microbial factors were more important for the blood levels of metabolites that were more strongly associated with cognition and incident AD in the older replication cohort (beta-cryptoxanthin, imidazole propionate), suggesting they may be involved later in the disease process. The detailed results on how multiple modifiable factors affect blood levels of cognition- and brain imaging-related metabolites in dementia-free participants may help identify new AD prevention strategies.
SN - 2662-8465
ER -