Abstract: Provided herein are methods of generating models for predicting the glycemic response of an individual to consumption of a food or meal. The method involves collecting from a plurality of subjects, phenotypic data, ohmic data, and glycemic response data to foods or meals having a defined macronutrient profile. This data is then used to train a machine learning algorithm to create a model that predicts glycemic response of an individual to a food or meal based on the macro nutrient profile of the food or meal.

Abstract: Provided are methods and compositions for providing one or more food item recommendations for an individual. Methods can include determining an individual set of conditions from an overall set of conditions for the individual. Methods also include generating recommendations involving consumption of food, supplement and/or ingredients to affect the one or more biological conditions.

Abstract: Provided are methods balancing a gut microbiome. Methods involve sequencing nucleic acids from a sample to determine quantifies or activities of microorganisms Sequence information is used to generate functional activity scores for functional categories. These scores can be actionable.

Abstract: Provided herein are systems and method for producing models that infer health scores for health metrics for a subject. A first model, using raw feature data derived from the subject, infers feature cluster scores for each of a plurality of feature clusters in a feature group. A second model, using the inferred feature cluster scores, infers a heath score for a health metric.