Applying Mendelian randomization to appraise causality in relationships between nutrition and cancer
Authors: Wade Kaitlin H. , Yarmolinsky James , Giovannucci Edward , Lewis Sarah J. , Millwood Iona Y. , Munafò Marcus R. , Meddens Fleur , Burrows Kimberley , Bell Joshua A. , Davies Neil M. , Mariosa Daniela , Kanerva Noora , Vincent Emma E. , Smith-Byrne Karl , Guida Florence , Gunter Marc J. , Sanderson Eleanor , Dudbridge Frank , Burgess Stephen , Cornelis Marilyn C. , Richardson Tom G. , Borges Maria Carolina , Bowden Jack , Hemani Gibran , Cho Yoonsu , Spiller Wes , Richmond Rebecca C. , Carter Alice R. , Langdon Ryan , Lawlor Deborah A. , Walters Robin G. , Vimaleswaran Karani Santhanakrishnan , Anderson Annie , Sandu Meda R. , Tilling Kate , Davey Smith George , Martin Richard M. , Relton Caroline L. ,   with the M. R. in Nutrition, Cancer working group
Published: March 2022

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.