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Hensen, T.* ; Ahmad, S.* ; Kastenmüller, G. ; Kraaij, R.* ; Ghanbari, M.* ; Ikram, A.* ; Kaddurah-Daouk, R.* ; Thiele, I.*

In silico metabolic modelling links microbiome-derived metabolites to risk factors of Alzheimer’s disease.

Gut Microbes Rep. 2:2443171 (2025)
Verlagsversion DOI
Creative Commons Lizenzvertrag
The gut microbiome has become increasingly recognized for its role in the pathogenesis of Alzheimer’s disease (AD) and is thought to influence AD pathogenesis via metabolic crosstalk with the host. However, mechanistic pathways connecting the gut microbiome to AD pathogenesis remain unknown. To explore potential mechanistic pathways in AD pathogenesis, we created host–microbiome whole-body metabolic models personalized with 16S rRNA microbiome data and predicted emergent metabolic contributions of gut microbiomes. We analyzed 63 metabolites in blood with previously known links with AD. These in silico predictions were then associated with major risk factors for AD in a cohort of 1,065 aging non-AD individuals and subsequently used to inform targeted analyses on serum metabolomics. Our analysis identified increased host-microbial production of L-arginine in older individuals. Lower production of deoxycholate correlated with the neuroprotective APOE E2 allele and it decreased with higher global cognition. Serum metabolomics from the same individuals of cholesterol products and bile acid metabolism corroborated the modeling predictions, suggesting a potential link between the APOE genotype and cognitive health. In conclusion, this study associated metabolic gut microbiome influences on human metabolism with risk factors for AD and identified cholesterol and bile acid metabolism to potentially link with AD pathogenesis.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Alzheimer’s Disease ; Apoe ; Bile Acid Metabolism ; Gut Microbiome ; Metabolic Modeling
ISSN (print) / ISBN 2993-3935
e-ISSN 2993-3935
Zeitschrift Gut Microbes Reports
Quellenangaben Band: 2, Heft: 1, Seiten: , Artikelnummer: 2443171 Supplement: ,
Verlag Taylor & Francis
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Computational Biology (ICB)