For personalized nutrition in preparation for precision healthcare, we examined the predictors of healthy eating, using the healthy eating index (HEI) and glycemic index (GI), in family-based multi-ethnic colorectal cancer (CRC) families. A total of 106 participants, 53 CRC cases and 53 family members from multi-ethnic families participated in the study. Machine learning validation procedures, including the ensemble method and generalized regression prediction, Elastic Net with Akaike's Information Criterion with correction and Leave-One-Out cross validation methods, were applied to validate the results for enhanced prediction and reproducibility. Models were compared based on HEI scales for the scores of 77 versus 80 as the status of healthy eating, predicted from individual dietary parameters and health outcomes. Gender and CRC status were interactive as additional predictors of HEI based on the HEI score of 77. Predictors of HEI 80 as the criterion score of a good diet included five significant dietary parameters (with intake amount): whole fruit (1 cup), milk or milk alternative such as soy drinks (6 oz), whole grain (1 oz), saturated fat (15 g), and oil and nuts (1 oz). Compared to the GI models, HEI models presented more accurate and fitted models. Milk or a milk alternative such as soy drink (6 oz) is the common significant parameter across HEI and GI predictive models. These results point to the importance of healthy eating, with the appropriate amount of healthy foods, as modifiable factors for cancer prevention.
Keywords: colorectal cancer; diverse ethnic groups; generalized regression elastic net; glycemic index; healthy eating.