title: Siri, What Should I Eat?
abstract: Zeevi et al. report that extensive monitoring of a human cohort for variations in dietary intake, lifestyle, host phenotype, and the gut microbiome has enabled the development of a machine-learning algorithm that accurately predicts the individual glycemic response to meals, providing an important first step toward personalized nutrition.
authors.affiliation: University of California, San Francisco, Department of Microbiology and Immunology, G.W. Hooper Research Foundation, 513 Parnassus Avenue, HSE 1001F, San Francisco, CA 94143-0552, USA; Charité Universitätsmedizin Berlin, Department of Endocrinology and Metabolic Diseases, Charitéplatz 1, 10117 Berlin, Germany.
authors.forename: Reiner
authors.initials: R
authors.lastname: Jumpertz von Schwartzenberg
authors.affiliation: University of California, San Francisco, Department of Microbiology and Immunology, G.W. Hooper Research Foundation, 513 Parnassus Avenue, HSE 1001F, San Francisco, CA 94143-0552, USA. Electronic address: peter.turnbaugh@ucsf.edu.
authors.forename: Peter J
authors.initials: PJ
authors.lastname: Turnbaugh
mesh_terms: D000465:Algorithms; D001786:Blood Glucose / Q000032:analysis*; D003924:Diabetes Mellitus, Type 2 / Q000097:blood*; D006801:Humans; D019518:Postprandial Period
chemical_list: D001786:Blood Glucose