Abstract
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.
Original language | English |
---|---|
Article number | 38 |
Journal | Alzheimer's Research and Therapy |
Volume | 15 |
Issue number | 1 |
DOIs | |
Publication status | Published - 1 Dec 2023 |
Keywords
- Ageing
- Alzheimer’s disease
- Birth cohort
- Brain imaging
- Dementia
- Metabolites
- Polygenic scores
- Weighted-gene coexpression network analysis
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In: Alzheimer's Research and Therapy, Vol. 15, No. 1, 38, 01.12.2023.
Research output: Contribution to journal › Article › Academic › peer-review
TY - JOUR
T1 - Investigating associations between blood metabolites, later life brain imaging measures, and genetic risk for Alzheimer’s disease
AU - Green, Rebecca E.
AU - Lord, Jodie
AU - Scelsi, Marzia A.
AU - Xu, Jin
AU - Wong, Andrew
AU - Naomi-James, Sarah
AU - Handy, Alex
AU - Gilchrist, Lachlan
AU - Williams, Dylan M.
AU - Parker, Thomas D.
AU - Lane, Christopher A.
AU - Malone, Ian B.
AU - Cash, David M.
AU - Sudre, Carole H.
AU - Coath, William
AU - Thomas, David L.
AU - Keuss, Sarah
AU - Dobson, Richard
AU - The Insight 46 study team
AU - Legido-Quigley, Cristina
AU - Fox, Nick C.
AU - Schott, Jonathan M.
AU - Richards, Marcus
AU - Proitsi, Petroula
N1 - Funding Information: This study is principally funded by grants from Alzheimer’s Research UK (ARUK-PG2014-1946, ARUK-PG2017-1946), the Medical Research Council Dementias Platform UK (CSUB19166), the Wolfson Foundation (PR/ylr/18575), and the Alzheimer’s Association (SG-666374-UK). The genetic analyses are funded by the Brain Research Trust (UCC14191). Florbetapir amyloid tracer was kindly provided by Avid Radiopharmaceuticals (a wholly owned subsidiary of Eli Lilly. The NSHD is funded by the Medical Research Council (MC_UU_00019/1, MC_UU_00019/3). Funding Information: We acknowledge use of the research computing facility at King’s College London, Rosalind ( https://rosalind.kcl.ac.uk ), which is delivered in partnership with the National Institute for Health Research (NIHR) Biomedical Research Centres at South London & Maudsley and Guy’s & St. Thomas’ NHS Foundation Trusts and part-funded by capital equipment grants from the Maudsley Charity (award 980) and Guy’s & St. Thomas’ Charity (TR130505). We further acknowledge the use of BioRender.com in the creation of Fig. . Funding Information: R.G. is supported by the National Institute for Health Research (NIHR) Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King’s College London. M.A.S. was supported by the EPSRC-funded UCL Centre for Doctoral Training in Medical Imaging (EP/L016478/1). T.D.P. was supported by a Wellcome Trust Clinical Research Fellowship (200,109/Z/15/Z) and is supported by a NIHR Clinical Lectureship. D.M.C is supported by the UK Dementia Research Institute which receives its funding from DRI Ltd, funded by the UK Medical Research Council, Alzheimer’s Society and Alzheimer’s Research UK, as well as Alzheimer's Research UK (ARUK-PG2017-1946), the UCL/UCLH NIHR Biomedical Research Centre, and the UKRI Innovation Scholars: Data Science Training in Health and Bioscience (MR/V03863X/1). C.H.S. is supported by an Alzheimer’s Society Junior Fellowship (AS-JF-17–011). R.D. is supported by the following: (1) NIHR Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King’s College London, London, UK; (2) Health Data Research UK, which is funded by the UK Medical Research Council, Engineering and Physical Sciences Research Council, Economic and Social Research Council, Department of Health and Social Care (England), Chief Scientist Office of the Scottish Government Health and Social Care Directorates, Health and Social Care Research and Development Division (Welsh Government), Public Health Agency (Northern Ireland), British Heart Foundation and Wellcome Trust; (3) The BigData@Heart Consortium, funded by the Innovative Medicines Initiative-2 Joint Undertaking under grant agreement No. 116074. This Joint Undertaking receives support from the European Union’s Horizon 2020 research and innovation programme and EFPIA; it is chaired by DE Grobbee and SD Anker, partnering with 20 academic and industry partners and ESC; (4) the National Institute for Health Research University College London Hospitals Biomedical Research Centre; (5) the National Institute for Health Research (NIHR) Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King’s College London; (6) the UK Research and Innovation London Medical Imaging & Artificial Intelligence Centre for Value Based Healthcare; (7) the National Institute for Health Research (NIHR) Applied Research Collaboration South London (NIHR ARC South London) at King’s College Hospital NHS Foundation Trust. J.M.S. is supported by the Alzheimer's Association (SG-666374-UK) and acknowledges the support of the National Institute for Health Research University College London Hospitals Biomedical Research Centre. M.R. is supported by the MRC (MC_UU_00019/3). P.P. is supported by Alzheimer’s Research UK. Funding Information: We are very grateful to those study members who helped in the design of the study through focus groups and to the participants both for their contributions to Insight 46 and for their commitments to research over the last seven decades. We are grateful to the radiographers and nuclear medicine physicians at the UCL Institute of Nuclear Medicine and to the staff at the Leonard Wolfson Experimental Neurology Centre at UCL. We are particularly indebted to the support of the late Dr Chris Clark of Avid Radiopharmaceuticals who championed this study from its outset. We thank the International Genomics of Alzheimer’s Project (IGAP) for providing summary results data for these analyses. The investigators within IGAP contributed to the design and implementation of IGAP and/or provided data but did not participate in analysis or writing of this report. We acknowledge use of the research computing facility at King’s College London, Rosalind (https://rosalind.kcl.ac.uk), which is delivered in partnership with the National Institute for Health Research (NIHR) Biomedical Research Centres at South London & Maudsley and Guy’s & St. Thomas’ NHS Foundation Trusts and part-funded by capital equipment grants from the Maudsley Charity (award 980) and Guy’s & St. Thomas’ Charity (TR130505). We further acknowledge the use of BioRender.com in the creation of Fig. 1. Publisher Copyright: © 2023, The Author(s).
PY - 2023/12/1
Y1 - 2023/12/1
N2 - 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.
AB - 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.
KW - Ageing
KW - Alzheimer’s disease
KW - Birth cohort
KW - Brain imaging
KW - Dementia
KW - Metabolites
KW - Polygenic scores
KW - Weighted-gene coexpression network analysis
UR - http://www.scopus.com/inward/record.url?scp=85148703321&partnerID=8YFLogxK
U2 - https://doi.org/10.1186/s13195-023-01184-y
DO - https://doi.org/10.1186/s13195-023-01184-y
M3 - Article
C2 - 36814324
SN - 1758-9193
VL - 15
JO - Alzheimer's Research and Therapy
JF - Alzheimer's Research and Therapy
IS - 1
M1 - 38
ER -