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Green R, Lord J, Xu J, Maddock J, Kim M, Dobson R, Legido-Quigley C, Wong A, Richards M, Proitsi P. Metabolic correlates of late midlife cognitive outcomes: findings from the 1946 British Birth Cohort. Brain Commun 2021; 4:fcab291. [PMID: 35187482 PMCID: PMC8853724 DOI: 10.1093/braincomms/fcab291] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2021] [Revised: 09/17/2021] [Accepted: 12/10/2021] [Indexed: 11/14/2022] Open
Abstract
Investigating associations between metabolites and late midlife cognitive function could reveal potential markers and mechanisms relevant to early dementia. Here, we systematically explored the metabolic correlates of cognitive outcomes measured across the seventh decade of life, while untangling influencing life course factors. Using levels of 1019 metabolites profiled by liquid chromatography-mass spectrometry (age 60-64), we evaluated relationships between metabolites and cognitive outcomes in the British 1946 Birth Cohort (N = 1740). We additionally conducted pathway and network analyses to allow for greater insight into potential mechanisms, and sequentially adjusted for life course factors across four models, including sex and blood collection (Model 1), Model 1 + body mass index and lipid medication (Model 2), Model 2 + social factors and childhood cognition (Model 3) and Model 3 + lifestyle influences (Model 4). After adjusting for multiple tests, 155 metabolites, 10 pathways and 5 network modules were associated with cognitive outcomes. Of the 155, 35 metabolites were highly connected in their network module (termed 'hub' metabolites), presenting as promising marker candidates. Notably, we report relationships between a module comprised of acylcarnitines and processing speed which remained robust to life course adjustment, revealing palmitoylcarnitine (C16) as a hub (Model 4: β = -0.10, 95% confidence interval = -0.15 to -0.052, P = 5.99 × 10-5). Most associations were sensitive to adjustment for social factors and childhood cognition; in the final model, four metabolites remained after multiple testing correction, and 80 at P < 0.05. Two modules demonstrated associations that were partly or largely attenuated by life course factors: one enriched in modified nucleosides and amino acids (overall attenuation = 39.2-55.5%), and another in vitamin A and C metabolites (overall attenuation = 68.6-92.6%). Our other findings, including a module enriched in sphingolipid pathways, were entirely explained by life course factors, particularly childhood cognition and education. Using a large birth cohort study with information across the life course, we highlighted potential metabolic mechanisms associated with cognitive function in late midlife, suggesting marker candidates and life course relationships for further study.
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Affiliation(s)
- Rebecca Green
- Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, UK
- UK National Institute for Health Research (NIHR) Maudsley Biomedical Research Centre, South London and Maudsley Trust, London, UK
| | - Jodie Lord
- Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, UK
| | - Jin Xu
- Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, UK
- Institute of Pharmaceutical Science, King’s College London, London, UK
| | - Jane Maddock
- MRC Unit for Lifelong Health & Ageing at UCL, University College London, London, UK
| | - Min Kim
- Steno Diabetes Center Copenhagen, Gentofte, Denmark
| | - Richard Dobson
- Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, UK
- UK National Institute for Health Research (NIHR) Maudsley Biomedical Research Centre, South London and Maudsley Trust, London, UK
- Health Data Research UK London, University College London, London, UK
- NIHR Biomedical Research Centre at University College London, Hospitals NHS Foundation Trust, London, UK
| | - Cristina Legido-Quigley
- Institute of Pharmaceutical Science, King’s College London, London, UK
- Steno Diabetes Center Copenhagen, Gentofte, Denmark
| | - Andrew Wong
- MRC Unit for Lifelong Health & Ageing at UCL, University College London, London, UK
| | - Marcus Richards
- MRC Unit for Lifelong Health & Ageing at UCL, University College London, London, UK
| | - Petroula Proitsi
- Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, UK
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