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Identification of blood metabolites associated with risk of Alzheimer’s disease by integrating genomics and metabolomics data

Molecular Psychiatry (2024)Cite this article

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Abstract

Specific metabolites have been reported to be potentially associated with Alzheimer’s disease (AD) risk. However, the comprehensive understanding of roles of metabolite biomarkers in AD etiology remains elusive. We performed a large AD metabolome-wide association study (MWAS) by developing blood metabolite genetic prediction models. We evaluated associations between genetically predicted levels of metabolites and AD risk in 39,106 clinically diagnosed AD cases, 46,828 proxy AD and related dementia (proxy-ADD) cases, and 401,577 controls. We further conducted analyses to determine microbiome features associated with the detected metabolites and characterize associations between predicted microbiome feature levels and AD risk. We identified fourteen metabolites showing an association with AD risk. Five microbiome features were further identified to be potentially related to associations of five of the metabolites. Our study provides new insights into the etiology of AD that involves blood metabolites and gut microbiome, which warrants further investigation.

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Fig. 1
Fig. 2: Fourteen metabolites whose genetically predicted levels in blood are associated with AD risk.
Fig. 3: Selected aspects of dehydroepiandrosterone (DHEA) metabolism.

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Data availability

Individual level genotype and metabolite were available from Twins UK scientific community. Complete summary statistics of Alzheimer’s disease was downloaded from the NHGRI-EBI GWAS Catalog (https://www.ebi.ac.uk/gwas/) under accession no. GCST90027158. Complete summary statistics of microbial taxa could be downloaded from the NHGRI-EBI GWAS Catalog (https://www.ebi.ac.uk/gwas/) from accession GCST90032172 to GCST90032644. The scripts and genetic prediction models are available at https://github.com/Arthur1021/Metabolites-prediction-models.

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Acknowledgements

This research is supported by the V Foundation V Scholar Award and University of Hawaii Cancer Center. Lang Wu is also supported by NCI R00CA218892 and NHGRI/NIMHD U54 HG013243. Jingjing Zhu was supported by NCI T32 Postdoctoral Fellowship (T32 CA229110: Multidisciplinary Training in Ethnic Diversity and Cancer Disparities). TwinsUK is funded by the Wellcome Trust, Medical Research Council, Versus Arthritis, European Union Horizon 2020, Chronic Disease Research Foundation (CDRF), Zoe Ltd, the National Institute for Health and Care Research (NIHR) Clinical Research Network (CRN) and Biomedical Research Centre based at Guy’s and St Thomas’ NHS Foundation Trust in partnership with King’s College London. We would like to thank Drs. Dalia Ghoneim and Jihwan Ha for their help for this study.

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  1. Cancer Epidemiology Division, Population Sciences in the Pacific Program, University of Hawaii Cancer Center, University of Hawaii at Manoa, Honolulu, HI, USA

    Shuai Liu, Hua Zhong, Jingjing Zhu & Lang Wu

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  1. Shuai Liu
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LW designed the study. SL and HZ performed the data analyses. SL, HZ, and JZ were involved in the interpretation of the data. LW supervised the work. SL drafted the manuscript. All authors substantively revised the manuscript and approved the submitted version of the manuscript.

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Correspondence to Lang Wu.

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LW has provided consulting service to Pupil Bio Inc. and received honorarium. No potential conflicts of interest were disclosed by the other authors.

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Liu, S., Zhong, H., Zhu, J. et al. Identification of blood metabolites associated with risk of Alzheimer’s disease by integrating genomics and metabolomics data. Mol Psychiatry (2024). https://doi.org/10.1038/s41380-023-02400-9

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