Evidence map›Paper›PMID 39911793›Full record

ReviewNeurology. Genetics2025

UK Biobank-A Unique Resource for Discovery and Translation Research on Genetics and Neurologic Disease.

Hannah Taylor, Melissa Lewins, M George B Foody, Oliver Gray, Jelena Bešević, Megan C Conroy, Rory Collins, Ben Lacey, Naomi Allen, Lucy Burkitt-Gray

Abstract readReview
In one paragraph

Review in Neurology. Genetics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed, 1 pooled it
–field-weighted citation impact
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

7 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Review
  4. Article
  5. Review
  6. Article
  7. Ten Years ofNeurology. Genetics · 2025
    Article
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

10 authors.

Hannah TaylorOxford Population Health (Nuffield Department of Population Health), University of Oxford, United Kingdom; and.
Melissa LewinsUK Biobank, Stockport, Greater Manchester, United Kingdom.
M George B FoodyUK Biobank, Stockport, Greater Manchester, United Kingdom.
Oliver GrayUK Biobank, Stockport, Greater Manchester, United Kingdom.
Jelena BeševićOxford Population Health (Nuffield Department of Population Health), University of Oxford, United Kingdom; and.
Megan C ConroyOxford Population Health (Nuffield Department of Population Health), University of Oxford, United Kingdom; and.ORCID https://orcid.org/0000-0002-3847-6202
Rory CollinsOxford Population Health (Nuffield Department of Population Health), University of Oxford, United Kingdom; and.
Ben LaceyOxford Population Health (Nuffield Department of Population Health), University of Oxford, United Kingdom; and.
Naomi AllenOxford Population Health (Nuffield Department of Population Health), University of Oxford, United Kingdom; and.
Lucy Burkitt-GrayUK Biobank, Stockport, Greater Manchester, United Kingdom.ORCID https://orcid.org/0000-0001-5170-0638

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

UK Biobank is a large-scale prospective study with extensive genetic and phenotypic data from half a million adults. Participants, aged 40 to 69, were recruited from the general UK population between 2006 and 2010. During recruitment, participants completed questionnaires covering lifestyle and medical history, underwent physical measurements, and provided biological samples for long-term storage. Whole-cohort assays have been conducted, including biochemical markers, genotyping, whole-exome and whole-genome sequencing, as well as proteomics and metabolomics in large subsets of the cohort, with potential for additional assays in the future. Participants consented to link their data to electronic health records, enabling the identification of health outcomes over time. Research studies using UK Biobank data have already enhanced our understanding of the role of genetic variation in neurologic disease, offering insights into potential therapeutic approaches. The integration of genetic and imaging data has led to significant discoveries regarding the relationship between genetic variants and brain structure and function, particularly in Alzheimer disease and Parkinson disease. Genetic data have also allowed Mendelian randomization analyses to be performed, enabling further investigation into the causality of associations between behavioral and physiologic factors-such as diet and blood pressure-and neurologic outcomes. Furthermore, genetic and proteomic data have been particularly useful in identifying new drug targets for neurologic disease and in enhancing risk prediction algorithms that are increasingly applied in clinical practice to identify those at higher risk. As UK Biobank continues to be enhanced, and the cases of neurologic disease accrue over time, the study will become increasingly valuable for both discovery and translational research on genetics and neurologic disease.

Identifiers

PMID39911793
PMCPMC11796045

What OpenQuestion holds

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Read underepoch 390

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.