Investigating associations between blood metabolites, later life brain imaging measures, and genetic risk for Alzheimer's disease

Identifying blood-based signatures of brain health and preclinical pathology may offer insights into early disease mechanisms and highlight avenues for intervention. Here, we systematically profiled associations between blood metabolites and whole-brain volume, hippocampal volume, and amyloid-β stat...

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Veröffentlicht in:Alzheimer's research & therapy 2023-02, Vol.15 (1), p.38-38, Article 38
Hauptverfasser: Green, Rebecca E, Lord, Jodie, Scelsi, Marzia A, Xu, Jin, Wong, Andrew, Naomi-James, Sarah, Handy, Alex, Gilchrist, Lachlan, Williams, Dylan M, Parker, Thomas D, Lane, Christopher A, Malone, Ian B, Cash, David M, Sudre, Carole H, Coath, William, Thomas, David L, Keuss, Sarah, Dobson, Richard, Legido-Quigley, Cristina, Fox, Nick C, Schott, Jonathan M, Richards, Marcus, Proitsi, Petroula
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Sprache:eng
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Zusammenfassung:Identifying blood-based signatures of brain health and preclinical pathology may offer insights into early disease mechanisms and highlight avenues for intervention. Here, we systematically profiled associations between blood metabolites and whole-brain volume, hippocampal volume, and amyloid-β status among participants of Insight 46-the neuroscience sub-study of the National Survey of Health and Development (NSHD). We additionally explored whether key metabolites were associated with polygenic risk for Alzheimer's disease (AD). Following quality control, levels of 1019 metabolites-detected with liquid chromatography-mass spectrometry-were available for 1740 participants at age 60-64. Metabolite data were subsequently clustered into modules of co-expressed metabolites using weighted coexpression network analysis. Accompanying MRI and amyloid-PET imaging data were present for 437 participants (age 69-71). Regression analyses tested relationships between metabolite measures-modules and hub metabolites-and imaging outcomes. Hub metabolites were defined as metabolites that were highly connected within significant (p  
ISSN:1758-9193
1758-9193
DOI:10.1186/s13195-023-01184-y