Evidence map›Paper›PMID 36814324›Full record

ArticleAlzheimer's research & therapy2023

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

Rebecca E Green, Jodie Lord, Marzia A Scelsi, Jin Xu, Andrew Wong, Sarah Naomi-James, Alex Handy, Lachlan Gilchrist, Dylan M Williams, Thomas D Parker and 14 more

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Alzheimer's research & therapy, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
1.4field-weighted citation impact, top 19% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

9 citing papers in PubMed, 9 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

24 authors at 10 institutions in 3 countries.

Rebecca E GreenInstitute of Psychiatry, Psychology & Neuroscience, King's College London, 16 De Crespigny Park, London, SE5 8AB, UK.
Jodie LordInstitute of Psychiatry, Psychology & Neuroscience, King's College London, 16 De Crespigny Park, London, SE5 8AB, UK.
Marzia A ScelsiDepartment of Medical Physics and Biomedical Engineering, Centre for Medical Image Computing (CMIC), University College London (UCL), London, UK.
Jin XuInstitute of Psychiatry, Psychology & Neuroscience, King's College London, 16 De Crespigny Park, London, SE5 8AB, UK.
Andrew WongMRC Unit for Lifelong Health & Ageing at UCL, University College London, Floor 5, MRC LHA at UCL, 1 - 19 Torrington Place, London, WC1E 7HB, UK.
Sarah Naomi-JamesMRC Unit for Lifelong Health & Ageing at UCL, University College London, Floor 5, MRC LHA at UCL, 1 - 19 Torrington Place, London, WC1E 7HB, UK.
Alex HandyUniversity College London, Institute of Health Informatics, London, UK.
Lachlan GilchristInstitute of Psychiatry, Psychology & Neuroscience, King's College London, 16 De Crespigny Park, London, SE5 8AB, UK.
Dylan M WilliamsMRC Unit for Lifelong Health & Ageing at UCL, University College London, Floor 5, MRC LHA at UCL, 1 - 19 Torrington Place, London, WC1E 7HB, UK.
Thomas D ParkerDementia Research Centre, UCL Queen Square Institute of Neurology, 8-11 Queen Square, London, WC1N 3BG, UK.
Christopher A LaneDementia Research Centre, UCL Queen Square Institute of Neurology, 8-11 Queen Square, London, WC1N 3BG, UK.
Ian B MaloneDementia Research Centre, UCL Queen Square Institute of Neurology, 8-11 Queen Square, London, WC1N 3BG, UK.
David M CashDementia Research Centre, UCL Queen Square Institute of Neurology, 8-11 Queen Square, London, WC1N 3BG, UK.
Carole H SudreDepartment of Medical Physics and Biomedical Engineering, Centre for Medical Image Computing (CMIC), University College London (UCL), London, UK.
William CoathDementia Research Centre, UCL Queen Square Institute of Neurology, 8-11 Queen Square, London, WC1N 3BG, UK.
David L ThomasDementia Research Centre, UCL Queen Square Institute of Neurology, 8-11 Queen Square, London, WC1N 3BG, UK.
Sarah KeussDementia Research Centre, UCL Queen Square Institute of Neurology, 8-11 Queen Square, London, WC1N 3BG, UK.
Richard DobsonInstitute of Psychiatry, Psychology & Neuroscience, King's College London, 16 De Crespigny Park, London, SE5 8AB, UK.
Cristina Legido-QuigleyInstitute of Pharmaceutical Science, King's College London, London, UK.
Nick C FoxDementia Research Centre, UCL Queen Square Institute of Neurology, 8-11 Queen Square, London, WC1N 3BG, UK.
Jonathan M Schott *Dementia Research Centre, UCL Queen Square Institute of Neurology, 8-11 Queen Square, London, WC1N 3BG, UK. j.schott@ucl.ac.uk.
Marcus Richards *MRC Unit for Lifelong Health & Ageing at UCL, University College London, Floor 5, MRC LHA at UCL, 1 - 19 Torrington Place, London, WC1E 7HB, UK. m.richards@ucl.ac.uk.
Petroula Proitsi *Institute of Psychiatry, Psychology & Neuroscience, King's College London, 16 De Crespigny Park, London, SE5 8AB, UK. petroula.proitsi@kcl.ac.uk.
Insight 46 study team
University College London · GBKing's College London · GBNIHR Queen Square Dementia Biomedical Research Unit · GBMRC Unit for Lifelong Health and Ageing · GBImperial College London · GBNational Hospital for Neurology and Neurosurgery · GBNIHR Maudsley Biomedical Research CentreSouth London and Maudsley NHS Foundation Trust · GBSteno Diabetes Centers · DKUK Dementia Research Institute · GB

Funding

Medical Research Council MC_UU_00019/1Medical Research Council MC_UU_00019/3Medical Research Council MC_UU_00019/4
6 · The paper itself

Abstract

backgroundIdentifying 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).

methodsFollowing 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

resultsIn the fully adjusted model, three lipid modules were associated with a brain volume measure (p

conclusionsOur 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.

Indexed as

Alzheimer DiseaseAgedAmyloid beta-PeptidesApolipoproteins EBrainHumansLipidsMiddle AgedNeuroimagingRisk FactorsAmyloid beta-PeptidesApolipoproteins ELipidsAgeingAlzheimer’s diseaseBirth cohortBrain imagingDementiaMetabolitesPolygenic scoresWeighted-gene coexpression network analysis

Identifiers

PMID36814324
PMCPMC9945600
OpenAlexW4321480317

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.