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

BACKGROUND: 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 am...

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Autores principales: 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
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9945600/
https://www.ncbi.nlm.nih.gov/pubmed/36814324
http://dx.doi.org/10.1186/s13195-023-01184-y
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author 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
author_facet 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
author_sort Green, Rebecca E.
collection PubMed
description BACKGROUND: 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). METHODS: 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(FDR) < 0.05) modules or were identified as a hub in a previous analysis on cognitive function in the same cohort. Regression models included adjustments for age, sex, APOE genotype, lipid medication use, childhood cognitive ability, and social factors. Finally, associations were tested between AD polygenic risk scores (PRS), including and excluding the APOE region, and metabolites and modules that significantly associated (p(FDR) < 0.05) with an imaging outcome (N = 1638). RESULTS: In the fully adjusted model, three lipid modules were associated with a brain volume measure (p(FDR) < 0.05): one enriched in sphingolipids (hippocampal volume: ß = 0.14, 95% CI = [0.055,0.23]), one in several fatty acid pathways (whole-brain volume: ß =  − 0.072, 95%CI = [− 0.12, − 0.026]), and another in diacylglycerols and phosphatidylethanolamines (whole-brain volume: ß =  − 0.066, 95% CI = [− 0.11, − 0.020]). Twenty-two hub metabolites were associated (p(FDR) < 0.05) with an imaging outcome (whole-brain volume: 22; hippocampal volume: 4). Some nominal associations were reported for amyloid-β, and with an AD PRS in our genetic analysis, but none survived multiple testing correction. CONCLUSIONS: Our findings highlight key metabolites, with functions in membrane integrity and cell signalling, that associated with structural brain measures in later life. Future research should focus on replicating this work and interrogating causality. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13195-023-01184-y.
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spelling pubmed-99456002023-02-23 Investigating associations between blood metabolites, later life brain imaging measures, and genetic risk for Alzheimer’s disease 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 Alzheimers Res Ther Research BACKGROUND: 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). METHODS: 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(FDR) < 0.05) modules or were identified as a hub in a previous analysis on cognitive function in the same cohort. Regression models included adjustments for age, sex, APOE genotype, lipid medication use, childhood cognitive ability, and social factors. Finally, associations were tested between AD polygenic risk scores (PRS), including and excluding the APOE region, and metabolites and modules that significantly associated (p(FDR) < 0.05) with an imaging outcome (N = 1638). RESULTS: In the fully adjusted model, three lipid modules were associated with a brain volume measure (p(FDR) < 0.05): one enriched in sphingolipids (hippocampal volume: ß = 0.14, 95% CI = [0.055,0.23]), one in several fatty acid pathways (whole-brain volume: ß =  − 0.072, 95%CI = [− 0.12, − 0.026]), and another in diacylglycerols and phosphatidylethanolamines (whole-brain volume: ß =  − 0.066, 95% CI = [− 0.11, − 0.020]). Twenty-two hub metabolites were associated (p(FDR) < 0.05) with an imaging outcome (whole-brain volume: 22; hippocampal volume: 4). Some nominal associations were reported for amyloid-β, and with an AD PRS in our genetic analysis, but none survived multiple testing correction. CONCLUSIONS: Our findings highlight key metabolites, with functions in membrane integrity and cell signalling, that associated with structural brain measures in later life. Future research should focus on replicating this work and interrogating causality. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13195-023-01184-y. BioMed Central 2023-02-22 /pmc/articles/PMC9945600/ /pubmed/36814324 http://dx.doi.org/10.1186/s13195-023-01184-y Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
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
Investigating associations between blood metabolites, later life brain imaging measures, and genetic risk for Alzheimer’s disease
title Investigating associations between blood metabolites, later life brain imaging measures, and genetic risk for Alzheimer’s disease
title_full Investigating associations between blood metabolites, later life brain imaging measures, and genetic risk for Alzheimer’s disease
title_fullStr Investigating associations between blood metabolites, later life brain imaging measures, and genetic risk for Alzheimer’s disease
title_full_unstemmed Investigating associations between blood metabolites, later life brain imaging measures, and genetic risk for Alzheimer’s disease
title_short Investigating associations between blood metabolites, later life brain imaging measures, and genetic risk for Alzheimer’s disease
title_sort investigating associations between blood metabolites, later life brain imaging measures, and genetic risk for alzheimer’s disease
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9945600/
https://www.ncbi.nlm.nih.gov/pubmed/36814324
http://dx.doi.org/10.1186/s13195-023-01184-y
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