Applying Mendelian randomization to appraise causality in relationships between nutrition and cancer

Kaitlin Wade*, James Yarmolinsky, Edward Giovannucci, Sarah J. Lewis, Iona Y Millwood, Marcus Robert Munafo, Fleur Meddens, Kimberley Burrows, Joshua A Bell, Neil Martin Davies, Daniela Mariosa, Noora Kanerva, Emma E Vincent, Karl Smith-Byrne, Florence Guida, Marc J. Gunter, Eleanor Sanderson, Frank Dudbridge, Stephen Burgess, Marilyn C CornelisTom Richardson, Maria Carolina Borges, Jack Bowden, Gibran Hemani, Yoonsu Cho, Wes Spiller, Rebecca Richmond, Alice Carter, Ryan Langdon, Deborah A Lawlor, Robin G Walters, Karani Santhanakrishnan Vimaleswaran, Annie Anderson, Meda Sandu, Kate Tilling, George Davey Smith, Richard Martin, Caroline Relton, in Nutrition and Cancer working group

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

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Abstract

Dietary factors are assumed to play an important role in cancer risk, apparent in consensus recommendations for cancer prevention that promote nutritional changes. However, the evidence in this field has been generated predominantly through observational studies, which may result in biased effect estimates because of confounding, exposure misclassification, and reverse causality. With major geographical differences and rapid changes in cancer incidence over time, it is crucial to establish which of the observational associations reflect causality and to identify novel risk factors as these may be modified to prevent the onset of cancer and reduce its progression. Mendelian randomization (MR) uses the special properties of germline genetic variation to strengthen causal inference regarding potentially modifiable exposures and disease risk. MR can be implemented through instrumental variable (IV) analysis and, when robustly performed, is generally less prone to confounding, reverse causation and measurement error than conventional observational methods and has different sources of bias (discussed in detail below). It is increasingly used to facilitate causal inference in epidemiology and provides an opportunity to explore the effects of nutritional exposures on cancer incidence and progression in a cost-effective and timely manner. Here, we introduce the concept of MR and discuss its current application in understanding the impact of nutritional factors (e.g., any measure of diet and nutritional intake, circulating biomarkers, patterns, preference or behaviour) on cancer aetiology and, thus, opportunities for MR to contribute to the development of nutritional recommendations and policies for cancer prevention. We provide applied examples of MR studies examining the role of nutritional factors in cancer to illustrate how this method can be used to help prioritise or deprioritise the evaluation of specific nutritional factors as intervention targets in randomised controlled trials. We describe possible biases when using MR, and methodological developments aimed at investigating and potentially overcoming these biases when present. Lastly, we consider the use of MR in identifying causally relevant nutritional risk factors for various cancers in different regions across the world, given notable geographical differences in some cancers. We also discuss how MR results could be translated into further research and policy. We conclude that findings from MR studies, which corroborate those from other well-conducted studies with different and orthogonal biases, are poised to substantially improve our understanding of nutritional influences on cancer. For such corroboration, there is a requirement for an interdisciplinary and collaborative approach to investigate risk factors for cancer incidence and progression.
Original languageEnglish
Article number5906
Pages (from-to)631–652
Number of pages22
JournalCancer Causes and Control
Volume33
Issue number5
Early online date11 Mar 2022
DOIs
Publication statusPublished - 1 May 2022

Bibliographical note

Funding Information:
The MR in Nutrition and Cancer workshop was supported by a Cancer Research UK (CRUK) Conference and Meeting Award (C18281/A28453), a CRUK program grant (C18281/A19169; the Integrative Cancer Epidemiology Programme [ICEP]) and the World Cancer Research Fund (WCRF). KHW was supported by the Elizabeth Blackwell Institute for Health Research, University of Bristol and the Wellcome Trust Institutional Strategic Support Fund [204813/Z/16/Z] and is now funded by the University of Bristol. JY is supported by a CRUK Population Research Postdoctoral Fellowship (C68933/A28534). JAB is supported by the Elizabeth Blackwell Institute for Health Research, University of Bristol and the Wellcome Trust Institutional Strategic Support Fund [204813/Z/16/Z]. NMD is supported by a Norwegian Research Council (295989). EEV is supported by Diabetes UK (17/0005587) and the WCRF UK, as part of the WCRF international grant programme (IIG_2019_2009). FD is supported by the Medical Research Council (MRC; MR/S037055/1). SB is supported by Sir Henry Dale Fellowship jointly funded by the Wellcome Trust and the Royal Society (204623/Z/16/Z). MCC is supported by the National Institute on Aging (K01AG053477). MCB's contribution to this work was funded by an MRC Skills Development Fellowship [MR/P014054/1]. GH is supported by the Wellcome Trust and Royal Society [208806/Z/17/Z]. ARC is funded by the UK MRC Integrative Epidemiology Unit (IEU), University of Bristol (MC_UU_00011/1 and MC_UU_00011/6) and the University of Bristol British Heart Foundation (BHF) Accelerator Award (R100643-101). DAL is a Health Research Senior Investigator (NF-0616-10102). IYM and RGW are part of the MRC Population Health Research Unit and the Clinical Trial Service Unit & Epidemiological Studies Unit, at the University of Oxford, which receive core funding from the Medical Research Council (MC_UU_00017/1, MC_UU_12026/2, MC_U137686851), CRUK (C16077/A29186, C500/A16896) and the BHF (CH/1996001/9454). KSV is supported in part by the MRC grant (MR/S024778/1). MRS is funded by National Institute for Health Research (NIHR) Biomedical Research Centre (BRC) 3-year studentship. RMM is supported in part by the NIHR Bristol Biomedical Research Centre. The views and opinions expressed by authors in this publication are those of the authors and do not necessarily reflect those of the UK NIHR or the Department of Health and Social Care. KHW, JY, SJL, MRM, KB, JAB, NMD, EEV, ES, TGR, MCB, GH, YC, WS, RCR, ARC, RL, DAL, MRS, KT, GDS, RMM and CLR are part of the MRC IEU at the University of Bristol, which is supported by the MRC (MCUU00011/1 and MCUU00011/5) and the University of Bristol.

Funding Information:
We thank the MR in Nutrition and Cancer working group, who, in addition to those in the author list (i.e., those who presented their research during the workshop), included Professor Andy Ness, Dr Aurora Pérez-Cornago, Professor Bob Steele, Dr Fränzel van Duijnhoven, Dr Giota Mitrou, Jessica Brand, Dr LJS Schweren, Dr Kostas Tsilidis, Professor Nita Forouhi, Dr Philip Haycock, Rhona Beynon, Susannah Brown, Dr Susanna Larsson, and Professor Tim Key, for their input and discussions. We also thank Cancer Research UK and the World Cancer Research Fund International for funding the workshop.

Publisher Copyright:
© 2022, The Author(s).

Structured keywords

  • ICEP

Keywords

  • cancer
  • causality
  • nutrition
  • mendelian randomization

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