No abstract

No abstract

No abstract

Genome-wide single nucleotide polymorphism (SNP) data are now quickly and inexpensively acquired, raising the prospect of creating personalized dietary recommendations based on an individual’s genetic variability at multiple SNPs. However, relatively little is known about most specific gene–diet interactions, and many molecular and clinical phenotypes of interest (e.g., body mass index [BMI]) involve multiple genes. In this review, we discuss direct to consumer genetic testing (DTC-GT) and the current potential for precision nutrition based on an individual’s genetic data. We review important issues such as dietary exposure and genetic architecture addressing the concepts of penetrance, pleiotropy, epistasis, polygenicity, and epigenetics. More specifically, we discuss how they complicate using genotypic data to predict phenotypes as well as response to dietary interventions. Then, several examples (including caffeine sensitivity, alcohol dependence, non-alcoholic fatty liver disease, obesity/appetite, cardiovascular, Alzheimer’s disease, folate metabolism, long-chain fatty acid biosynthesis, and vitamin D metabolism) are provided illustrating how genotypic information could be used to inform nutritional recommendations. We conclude by examining ethical considerations and practical applications for using genetic information to inform dietary choices and the future role genetics may play in adopting changes beyond population-wide healthy eating guidelines.

Human urine, which is rich in metabolites, provides valuable approaches for biomarker measurement. Maintaining the stability of metabolites in urine is critical for accurate and reliable research results and subsequent interpretation. In this study, the effect of storage temperature (4, 22, and 40 °C), storage time (24 and 48 h), and use of preservatives (boric acid (BA), thymol) and para-aminobenzoic acid (PABA) on urinary metabolites in the pooled urine samples from 20 participants was systematically investigated using large-scale targeted liquid chromatography tandem mass spectrometry (LC-MS/MS)-based metabolomics. Statistical analysis of 158 reliably detected metabolites showed that metabolites in urine with no preservative remained stable at 4 °C for 24 and 48 h as well as at 22 °C for 24 h, but significant metabolite differences were observed in urine stored at 22 °C for 48 h and at 40 °C. The mere addition of BA caused metabolite changes. Thymol was observed to be effective in maintaining metabolite stability in urine in all the conditions designed, most likely due to the inhibitory effect of thymol on urine microbiota. Our results provide valuable urine preservation guidance during sample storage, which is essential for obtaining reliable, accurate, and reproducible analytical results from urine samples.