Abstract
The role of one-carbon metabolism (1CM), particularly folate, in colorectal cancer (CRC) development has been extensively studied, but with inconclusive results. Given the complexity of 1CM, the conventional approach, investigating components individually, may be insufficient. We used a machine learning-based Bayesian network approach to study, simultaneously, 14 circulating one-carbon metabolites, 17 related single nucleotide polymorphisms (SNPs), and several environmental factors in relation to CRC risk in 613 cases and 1190 controls from the prospective Northern Sweden Health and Disease Study. The estimated networks corresponded largely to known biochemical relationships. Plasma concentrations of folate (direct), vitamin B6 (pyridoxal 5-phosphate) (inverse), and vitamin B2 (riboflavin) (inverse) had the strongest independent associations with CRC risk. Our study demonstrates the importance of incorporating B-vitamins in future studies of 1CM and CRC development, and the usefulness of Bayesian network learning for investigating complex biological systems in relation to disease.
Highlights
Intervention trials on the effects of folic acid, vitamin B6, or vitamin B12 supplementation on colorectal adenoma or cancer occurrence have been inconclusive[19,20,21]
Body mass index (BMI), alcohol intake, physical activity, and B-vitamin intakes were similar for cases and controls
The associations represented in the estimated networks largely corresponded to plausible biochemical relationships
Summary
Intervention trials on the effects of folic acid, vitamin B6, or vitamin B12 supplementation on colorectal adenoma or cancer occurrence have been inconclusive[19,20,21]. The commonly used method of univariate modeling of single variables, typically in multivariable models adjusting for potential confounders, may miss higher-order interactions and mediating effects This is important for 1CM, given the complexity of input (diet, supplementation, and fortification), interrelationships, gene-environment interactions, and output (nucleotide synthesis, methylation, inflammation, oxidation, and energy metabolism[22,23,24]). No empirical study using observational data has so far addressed the complex interplay of plasma markers of 1CM, related SNPs, and environmental factors in relation to cancer risk. In this study of 613 colorectal cancer cases with prediagnostic blood samples and 1190 matched controls from the population-based Northern Sweden Health and Disease Study (NSHDS), we used Bayesian network learning to investigate, simultaneously, the relative contributions of and interplay among a comprehensive panel of 14 prediagnostic plasma one-carbon metabolites, 17 SNPs involved in 1CM, and a set of other environmental factors, in relation to CRC risk
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