Abstract: Using deep metagenomic sequencing of individual gut microbiomes, coupled with techniques such as detailed long-term diet, fasting, and same-meal postprandial cardiometabolic blood markers analyses, intestinal microbes (defined by clusters of genomes referred to as species-level genome bins (SGBs)) have been identified as associated with different conditions and/or habits. Techniques are disclosed for using food guidance to intentionally alter the composition of the microbiome of an individual. Such alteration may include increasing the presence, abundance, or relative abundance of pro-health indicator microbe(s) and/or decreasing the presence, abundance, or relative abundance of poor health indicator microbe(s).

Abstract: Techniques are disclosed for using food guidance, such as personalized food guidance, to alter the composition of the microbiome of an individual. Such alteration may include increasing the presence, abundance, or relative abundance of a pro-health indicator microbe and/or decreasing the presence, abundance, or relative abundance of a poor health indicator microbe.

Abstract: Techniques are disclosed herein for improving the accuracy of test data obtained outside of a clinical setting. Using the technologies described herein, different techniques can be utilized to analyze, score and adjust test data associated with one or more “at home” tests. In some examples, computing systems are utilized to generate quality scores indicating the accuracy of the test data associated with a particular biomarker. In other examples, an authorized user, such as a data manager can analyze the test data utilizing a user interface to generate scores and/or adjust the test data.

Abstract: Techniques are disclosed herein for generating personalized food guidance using predicted food responses. Using the technologies described herein, instead of providing food guidance that is generalized for a group of individuals, personalized food guidance is provided that takes into account an individual's personalized responses to foods, such as the individual's glucose and fat responses, the individual's micro biome, the individual's overall health, potential health risks for the individual, and/or other data associated with the individual. Providing an individual with personalized food guidance can make choosing food easier and healthier.

Abstract: Techniques are disclosed herein for improving the accuracy of nutritional responses measured in a non-clinical setting. Using the technologies described herein, different techniques can be utilized to improve the accuracy of test data associated with one or more “at home” tests. In some examples, more than one test is utilized to improve the accuracy of test data associated with a particular biomarker. In other examples, a data accuracy service can programmatically analyze data received from an individual and determine whether the data is accurate. In some examples, a computing device is utilized to assist in determining what food item(s) are consumed, as well as determine whether a test protocol was followed.

Abstract: Techniques are disclosed herein for using standardized meals and digital devices can be utilized outside a clinical setting to assist in determining/predicting one or more clinical states associated with one or more individuals. Using the technologies described herein, different techniques can be utilized to for using nutritional response measurements. For example, response measurements can be obtained for two fat meals instead of a single fat meal. Data associated with a circadian rhythm (e.g., sleep times, awake times) can also be utilized to improve the accuracy of the measured biomarkers. In other examples, biome data and other data can be utilized.

Abstract: Techniques are disclosed herein for generating personalized nutritional recommendations using predicted values of target biomarkers. Using the technologies described herein, a programmatic analysis is performed on different data to predict values of target biomarkers that are associated with an individual. Personalized nutritional recommendations are then generated, using the predicted values, and provided to the individual. The predictions are based on data that is associated with the individual, such as microbiome data, ketone data, glucose data, nutritional data, questionnaire data, and the like. A prediction service can utilize a machine learning mechanism to generate the predicted value of the target biomarkers. A nutrition service utilizes the predicted value of the target biomarkers when generating the personalized nutritional recommendations.

Abstract: Techniques are disclosed herein for generating personalized nutritional recommendations using predicted values of target biomarkers. Using the technologies described herein, a programmatic analysis is performed on different data to predict values of target biomarkers that are associated with an individual. Personalized nutritional recommendations are then generated, using the predicted values, and provided to the individual. The predictions are based on data that is associated with the individual, such as microbiome data, ketone data, glucose data, nutritional data, questionnaire data, and the like. A prediction service can utilize a machine learning mechanism to generate the predicted value of the target biomarkers. A nutrition service utilizes the predicted value of the target biomarkers when generating the personalized nutritional recommendations.