Breast milk delivers nutrition and protection to the developing infant. There has been considerable research on the high-molecular-weight milk components; however, low-molecular-weight metabolites have received less attention. To determine the effect of maternal phenotype and diet on the human milk metabolome, milk collected at day 90 postpartum from 52 healthy women was analyzed by using proton nuclear magnetic resonance spectroscopy. Sixty-five milk metabolites were quantified (mono-, di-, and oligosaccharides; amino acids and derivatives; energy metabolites; fatty acids and associated metabolites; vitamins, nucleotides, and derivatives; and others). The biological variation, represented as the percentage CV of each metabolite, varied widely (4-120%), with several metabolites having low variation (<20%), including lactose, urea, glutamate, myo-inositol, and creatinine. Principal components analysis identified 2 clear groups of participants who were differentiable on the basis of milk oligosaccharide concentration and who were classified as secretors or nonsecretors of fucosyltransferase 2 (FUT2) gene products according to the concentration of 2'-fucosyllactose, lactodifucotetraose, and lacto-N-fucopentaose I. Exploration of the interrelations between the milk sugars by using Spearman rank correlations revealed significant positive and negative associations, including positive correlations between fucose and products of the FUT2 gene and negative correlations between fucose and products of the fucosyltransferase 3 (FUT3) gene. The total concentration of milk oligosaccharides was conserved among participants (%CV = 18%), suggesting tight regulation of total oligosaccharide production; however, concentrations of specific oligosaccharides varied widely between participants (%CV = 30.4-84.3%). The variability in certain milk metabolites suggests possible roles in infant or infant gut microbial development. This trial was registered at clinicaltrials.gov as NCT01817127.

Scientific, technological, and economic progress over the last 100 years all but eradicated problems of widespread food shortage and nutrient deficiency in developed nations. But now society is faced with a new set of nutrition problems related to energy imbalance and metabolic disease, which require new kinds of solutions. Recent developments in the area of new analytical tools enable us to systematically study large quantities of detailed and multidimensional metabolic and health data, providing the opportunity to address current nutrition problems through an approach called Precision Nutrition. This approach integrates different kinds of "big data" to expand our understanding of the complexity and diversity of human metabolism in response to diet. With these tools, we can more fully elucidate each individual's unique phenotype, or the current state of health, as determined by the interactions among biology, environment, and behavior. The tools of precision nutrition include genomics, metabolomics, microbiomics, phenotyping, high-throughput analytical chemistry techniques, longitudinal tracking with body sensors, informatics, data science, and sophisticated educational and behavioral interventions. These tools are enabling the development of more personalized and predictive dietary guidance and interventions that have the potential to transform how the public makes food choices and greatly improve population health.