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

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.

Post‐diagnosis dietary factors, supplement use and colorectal cancer prognosis: A Global Cancer Update Programme (CUP Global) systematic literature review and meta‐analysis

“…138 Evidence from Mendelian randomisation (MR) studies could assist in prioritising certain nutritional interventions that are more likely to reduce cancer progression. 153 Higher intakes of individual food items, such as whole grains and coffee, were associated with a lower risk of all-cause mortality in this review. Whole grain intake was found to lower the risk of colorectal cancer incidence and all-cause mortality in general population studies.…”

“…RCTs,180 that could possibly, evaluate the effects of specific dietary patterns, or coffee183 that have shown survival benefits in this SLR would be informative. MR studies using instrumental exposures to account for confounding and reverse causation153 could be used to clarify the results for circulating 25(OH)D or other biomarkers, and examine the role of diet and gut microbiome on colorectal cancer prognosis 184. Additional studies are also needed in socio-demographically and ethnically diverse survivors, of different cancer stages, and at different phases of the cancer continuum.AUTHOR CONTRIBUTIONS Konstantinos K. Tsilidis and Doris S. M. Chan are co-principal investigators of CUP Global at Imperial College London (ICL).…”

Phenome-wide Mendelian randomisation analysis of 378,142 cases reveals risk factors for eight common cancers

“…Our MR analysis does, however, have limitations. Firstly, we were limited to studying phenotypes with genetic instruments available, moreover traits such as food intake or television watching can be highly correlated with other exposures making deconvolution of the causal risk factor problematic 37 – 39 . While MVMR can be used to account for the correlation between traits, calculation of conditional F-statistics for dietary traits yielded weak instruments (F < 3), which precludes their inclusion in an MVMR model due to weak instrument bias.…”

Benchmarking Mendelian Randomization methods for causal inference using genome-wide association study summary statistics

“…In recent years, MR has gained popularity due to the availability of summary statistics from thousands of genome-wide association studies (GWAS) covering a wide range of phenotypes. Leveraging the rich genetic data resources available, researchers worldwide can investigate the potential causal relationships between exposures and outcomes of interest, encompassing diverse applications such as identifying disease risk causation [4], providing evidence for epidemiological associations [5], and prioritizing targets in drug development [6, 7].…”