Abstract
To personalize nutrition, the purpose of this study was to examine five key genes in the folate metabolism pathway, and dietary parameters and related interactive parameters as predictors of colorectal cancer (CRC) by measuring the healthy eating index (HEI) in multiethnic families. The five genes included methylenetetrahydrofolate reductase (MTHFR) 677 and 1298, methionine synthase (MTR) 2756, methionine synthase reductase (MTRR 66), and dihydrofolate reductase (DHFR) 19bp, and they were used to compute a total gene mutation score. We included 53 families, 53 CRC patients and 53 paired family friend members of diverse population groups in Southern California. We measured multidimensional data using the ensemble bootstrap forest method to identify variables of importance within domains of genetic, demographic, and dietary parameters to achieve dimension reduction. We then constructed predictive generalized regression (GR) modeling with a supervised machine learning validation procedure with the target variable (cancer status) being specified to validate the results to allow enhanced prediction and reproducibility. The results showed that the CRC group had increased total gene mutation scores compared to the family members (p < 0.05). Using the Akaike’s information criterion and Leave-One-Out cross validation GR methods, the HEI was interactive with thiamine (vitamin B1), which is a new finding for the literature. The natural food sources for thiamine include whole grains, legumes, and some meats and fish which HEI scoring included as part of healthy portions (versus limiting portions on salt, saturated fat and empty calories). Additional predictors included age, as well as gender and the interaction of MTHFR 677 with overweight status (measured by body mass index) in predicting CRC, with the cancer group having more men and overweight cases. The HEI score was significant when split at the median score of 77 into greater or less scores, confirmed through the machine-learning recursive tree method and predictive modeling, although an HEI score of greater than 80 is the US national standard set value for a good diet. The HEI and healthy eating are modifiable factors for healthy living in relation to dietary parameters and cancer prevention, and they can be used for personalized nutrition in the precision-based healthcare era.
Highlights
Chronic inflammation is a major risk factor for colon and rectum health for the prevention of colorectal cancer (CRC) [1,2,3,4,5,6]
We previously reported the distribution of the polymorphisms for the control and cancer groups and the four racial-ethnic subgroups [55] using the Hardy-Weinberg equilibrium (HWE) analysis
Using supervised machine-learning analytics, we presented a ground-breaking predictive modeling study which gives improved prediction accuracy and the best fitted model, to identify modeling study which gives improved prediction accuracy and the best fitted model, to identify significant predictors including interaction terms
Summary
Chronic inflammation is a major risk factor for colon and rectum health for the prevention of colorectal cancer (CRC) [1,2,3,4,5,6]. CRC is the number one most preventable cancer for men and women in the world [7]. The most significant contributing factors in CRC development have been recognized. Recent studies have documented gene-environment interactions and the development of various diseases including CRC [12,13,14,15,16] through oxidative stress pathways [17,18,19]. Deficiencies in macro and micronutrients, such as folate and B-vitamins, as methyl-donors can contribute to the impairment of the one-carbon metabolism (OCM) pathway which may lead to CRC [20,21,22,23]. Genes, diet, and interactive parameters involved in inflammatory processes related to CRC are worthy of investigation, when a poor diet is combined with excess caloric intake, weight gain, and unhealthy practices, such as smoking and overconsumption of alcohol, which increase inflammatory responses [24,25,26,27]
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