Genetic Biomarkers of Metabolic Detoxification for Personalized Lifestyle Medicine

Abstract

Metabolic detoxification (detox)-or biotransformation-is a physiological function that removes toxic substances from our body. Genetic variability and dietary factors may affect the function of detox enzymes, thus impacting the body's sensitivity to toxic substances of endogenous and exogenous origin. From a genetic perspective, most of the current knowledge relies on observational studies in humans or experimental models in vivo and in vitro, with very limited proof of causality and clinical value. This review provides health practitioners with a list of single nucleotide polymorphisms (SNPs) located within genes involved in Phase I and Phase II detoxification reactions, for which evidence of clinical utility does exist. We have selected these SNPs based on their association with interindividual variability of detox metabolism in response to certain nutrients in the context of human clinical trials. In order to facilitate clinical interpretation and usage of these SNPs, we provide, for each of them, a strength of evidence score based on recent guidelines for genotype-based dietary advice. We also present the association of these SNPs with functional biomarkers of detox metabolism in a pragmatic clinical trial, the LIFEHOUSE study.

Keywords: LIFEHOUSE study; biomarkers; biotransformation; detoxification; environmental health; functional medicine; nutrigenomics; personalized lifestyle medicine; pragmatic clinical trials; single nucleotide polymorphisms.

Figure 1

Distribution of homocysteine, oxLDL, and GGT values by genotype. The violin plots show the density plot and statistical summary of homocysteine, oxLDL, and GGT values for each genotype of the CYP1A1, CYP1B2, and COMT genes. The white dot of the box depicts the median, the thick gray bar in the center represents the interquartile range (IQR), which indicates the spread of the middle half of the distribution, and the thin line extending from the gray bar represents the rest of the distribution lying between the ±1.5× IQR range. The points represent the actual distribution of individual data. On each side of the gray line is a smoothed estimation of probabilities for new points, calculated using the Kernel Density Estimation, showing the distribution shape of the data. Wider sections of the violin plot represent a higher probability that members of the population will have the given value; the skinnier sections represent a lower probability.