Evidence map›Paper›PMID 36845047›Full record

ArticleFrontiers in nutrition2023

Operationalisation of a standardised scoring system to assess adherence to the World Cancer Research Fund/American Institute for Cancer Research cancer prevention recommendations in the UK biobank.

Fiona C Malcomson, Solange Parra-Soto, Liya Lu, Frederick K Ho, Aurora Perez-Cornago, Marissa M Shams-White, Moniek van Zutphen, Ellen Kampman, Renate M Winkels, Panagiota Mitrou and 5 more

Abstract read
In one paragraph

Article in Frontiers in nutrition, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–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

5 citing papers in PubMed.

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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

15 authors.

Fiona C MalcomsonHuman Nutrition and Exercise Research Centre, Centre for Healthier Lives, Population Health Sciences Institute, Newcastle University, Newcastle upon Tyne, United Kingdom.
Solange Parra-SotoSchool of Health and Wellbeing, University of Glasgow, Glasgow, United Kingdom.
Liya LuCentre for Cancer, Population Health Sciences Institute, Newcastle University, Newcastle upon Tyne, United Kingdom.
Frederick K HoSchool of Health and Wellbeing, University of Glasgow, Glasgow, United Kingdom.
Aurora Perez-CornagoCancer Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom.
Marissa M Shams-WhiteRisk Factor Assessment Branch, Epidemiology and Genomics Research Program, Division of Cancer Control and Population Sciences, National Cancer Institute, Bethesda, MD, United States.
Moniek van ZutphenDivision of Human Nutrition and Health, Wageningen University and Research, Wageningen, Netherlands.
Ellen KampmanDivision of Human Nutrition and Health, Wageningen University and Research, Wageningen, Netherlands.
Renate M WinkelsDivision of Human Nutrition and Health, Wageningen University and Research, Wageningen, Netherlands.
Panagiota MitrouWorld Cancer Research Fund International, London, United Kingdom.
Martin WisemanWorld Cancer Research Fund International, London, United Kingdom.
Dora RomagueraHealth Research Institute of the Balearic Islands (IdISBa), Palma de Mallorca, Spain.
Carlos Celis-MoralesSchool of Cardiovascular and Metabolic Health, University of Glasgow, Glasgow, United Kingdom.
Linda SharpCentre for Cancer, Population Health Sciences Institute, Newcastle University, Newcastle upon Tyne, United Kingdom.
John C MathersHuman Nutrition and Exercise Research Centre, Centre for Healthier Lives, Population Health Sciences Institute, Newcastle University, Newcastle upon Tyne, United Kingdom.

Funding

Cancer Research UK 29017
6 · The paper itself

Abstract

Introduction: In 2018, The World Cancer Research Fund (WCRF)/American Institute for Cancer Research (AICR) published ten evidence-based Cancer Prevention Recommendations designed to reduce the risk of cancer Methods: UK Biobank recruited >500,000 individuals aged 37-73 years, between 2006 and 2010. In 2021, we held a workshop with experts which aimed to reach consensus on how to operationalise the scoring system using data available within UK Biobank. We used data on anthropometric measurements, physical activity and diet to calculate adherence scores. 24 h dietary assessment data were used to measure adherence to the following recommendations: "Eat a diet rich in wholegrains, vegetables, fruit, and beans", "Limit consumption of "fast foods" and other processed foods high in fat, starches or sugars" and "Limit consumption of sugar-sweetened drinks"; food frequency questionnaire data were used to assess adherence to "Limit consumption of red and processed meat" and "Limit alcohol consumption". Participants were allocated points for meeting, partially meeting or not meeting each recommendation, using cut-offs defined in the standardised scoring system. Results: At our workshop, discussions included the use of national guidelines to assess adherence to the recommendation on alcohol consumption, as well as challenges faced including defining the adapted ultra-processed food variables. A total score was calculated for 158,415 participants (mean 3.9 points, range 0-7 points). We also describe the methodology to derive a partial 5-point adherence score using data from the food frequency questionnaire in 314,616 participants. Conclusion: We describe the methodology used to estimate adherence to the 2018 WCRF/AICR Cancer Prevention Recommendations for participants in the UK Biobank, including some of the challenges faced operationalising the standardised scoring system.

Indexed as

cancer prevention recommendationscancer risklifestylelifestyle recommendationsscoring system

Identifiers

PMID36845047
PMCPMC9950547

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LicenceCC BY
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Registered trials

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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.