US20180144820A1 - System and method for implementing meal selection based on vitals, genotype and phenotype - Google Patents

System and method for implementing meal selection based on vitals, genotype and phenotype Download PDF

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US20180144820A1
US20180144820A1 US15/792,673 US201715792673A US2018144820A1 US 20180144820 A1 US20180144820 A1 US 20180144820A1 US 201715792673 A US201715792673 A US 201715792673A US 2018144820 A1 US2018144820 A1 US 2018144820A1
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Prior art keywords
user
data
food
foods
micronutrient
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US15/792,673
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Inventor
Neil Grimmer
Joshua Anthony
Ryan Yockey
Matt Van Horn
Jon Allen
Erin Barrett
Barbara Winters
Heather Cutter
Angie Westbrock
Matt Town
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Nederlandse Organisatie voor Toegepast Natuurwetenschappelijk Onderzoek TNO
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Habit LLC
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Priority to US15/792,673 priority Critical patent/US20180144820A1/en
Priority to US15/896,987 priority patent/US11244752B2/en
Publication of US20180144820A1 publication Critical patent/US20180144820A1/en
Assigned to NEDERLANDSE ORGANISATIE VOOR TOEGEPAST-NATUURWETENSCHAPPELIJK ONDERZOEK TNO reassignment NEDERLANDSE ORGANISATIE VOOR TOEGEPAST-NATUURWETENSCHAPPELIJK ONDERZOEK TNO ASSIGNMENT OF ASSIGNORS INTEREST (SEE DOCUMENT FOR DETAILS). Assignors: HABIT, LLC
Assigned to HABIT, LLC reassignment HABIT, LLC EMPLOYEE AGREEMENT Assignors: CUTTER, Heather, YOCKEY, Ryan
Assigned to HABIT, LLC reassignment HABIT, LLC ASSIGNMENT OF ASSIGNORS INTEREST (SEE DOCUMENT FOR DETAILS). Assignors: TOWN, Matt, ANTHONY, JOSHUA, BARRETT, Erin, WESTBROCK, Angie, WINTERS, Barbara, GRIMMER, NEIL
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    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
    • G16H20/60—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to nutrition control, e.g. diets
    • A—HUMAN NECESSITIES
    • A23—FOODS OR FOODSTUFFS; TREATMENT THEREOF, NOT COVERED BY OTHER CLASSES
    • A23L—FOODS, FOODSTUFFS, OR NON-ALCOHOLIC BEVERAGES, NOT COVERED BY SUBCLASSES A21D OR A23B-A23J; THEIR PREPARATION OR TREATMENT, e.g. COOKING, MODIFICATION OF NUTRITIVE QUALITIES, PHYSICAL TREATMENT; PRESERVATION OF FOODS OR FOODSTUFFS, IN GENERAL
    • A23L33/00—Modifying nutritive qualities of foods; Dietetic products; Preparation or treatment thereof
    • A23L33/30—Dietetic or nutritional methods, e.g. for losing weight
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61P—SPECIFIC THERAPEUTIC ACTIVITY OF CHEMICAL COMPOUNDS OR MEDICINAL PREPARATIONS
    • A61P25/00—Drugs for disorders of the nervous system
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61P—SPECIFIC THERAPEUTIC ACTIVITY OF CHEMICAL COMPOUNDS OR MEDICINAL PREPARATIONS
    • A61P25/00—Drugs for disorders of the nervous system
    • A61P25/18—Antipsychotics, i.e. neuroleptics; Drugs for mania or schizophrenia
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/90—Details of database functions independent of the retrieved data types
    • G06F16/95—Retrieval from the web
    • G06F16/953—Querying, e.g. by the use of web search engines
    • G06F16/9535—Search customisation based on user profiles and personalisation
    • G06F17/30867—

Definitions

  • the disclosed embodiments relate generally to health diagnostic systems and methods, and in particular, to recommending meals, recipes, foods and/or supplements based on a person's vitals, genotypic and phenotypic data.
  • U.S. Patent Application Publication No. 2012/0130732 describes methods and systems for providing personalized nutrition and exercise advice to a subject.
  • the methods consider subject features individually, rather than together. For example, as illustrated in FIG. 3 , identification of low serum ferritin levels in an individual result in a monotonous static recommendation to eat red meat and liver, take iron supplements, swim, and exercise less often. This advice does not consider, however, how the interplay of other features of the subject affect the recommendations provided.
  • This publication also does not comprehensively evaluate a user's genetics, phenotypical and other information about a user to produce diet types for macronutrient recommendations or combine macronutrient and micronutrient needs of a user into daily, weekly, or other frequent meal, food, or supplement recommendations that exhibit variety and that are ranked for a user and that also may output recipes, or supplement regimens.
  • U.S. Patent Application Publication No. 2012/0295256 describes methods and systems for providing weight management advice by considering features associated with weight management. However, the methods only consider recommendations related to weight management, without considering other health considerations.
  • U.S. Patent Application Publication No. 2013/0280681 describes methods and systems for providing food selection recommendations based on a user's dietary history. However, the methods do not consider the biological differences between individuals that inform healthy eating.
  • the disclosed systems and methods use data from individual users, including their vitals data, such as waist circumference, blood pressure and age; genotypical data including data on a user's DNA and genetic variations such as particular single nucleotide polymorphisms (SNPs), and phenotypical data relating to markers obtained from blood samples from the individual.
  • vitals data such as waist circumference, blood pressure and age
  • genotypical data including data on a user's DNA and genetic variations such as particular single nucleotide polymorphisms (SNPs), and phenotypical data relating to markers obtained from blood samples from the individual.
  • SNPs single nucleotide polymorphisms
  • a list of available meals, recipes, hero foods, snacks or supplements can be selected, customized, prioritized, and delivered for each user within a community of users that is tailored to the well-being of each user and that delivers a variety of healthy, different, and interesting food recommendations on a daily, weekly, monthly, or other frequent basis and that introduces healthy meal variation for each user over time.
  • a user or each user in a population of users is provided a variety of different prepared meals that may be delivered to the user, recipes that may be prepared by the user, food recommendations or supplement recommendations, all in order to help the user on a daily, weekly, monthly, or other frequent basis achieve a desired state of wellbeing or one or more health goals through healthy and personalized consumption.
  • a system for recommending foods to a user based on health data comprises a database, a memory and a processor.
  • the database stores user health data for each user within a community of users, including vitals, genotypical and phenotypical data, user food preference data and foods data that includes macronutrient and micronutrient data for foods that may be recommended to a user.
  • the memory stores program instructions, including program instructions that are capable of implementing (i) decision tree logic that classifies user health data into predetermined diet types and micronutrient recommendations, (ii) a filtering engine to filter the food data to determine available foods for a user based on the user's diet type and the user's food preference data; and (iii) a ranking engine that ranks available meals for the user based on the micronutrient recommendations and the food data.
  • the processor is coupled to the database and the memory and, when executing the program instructions, causes the decision tree logic to classify the user by diet type and nutrient recommendations, causes the filtering engine to determine available foods for the user and causes the ranking engine to rank and translate the micronutrient recommendations and the food data for the available foods for the user into specific food recommendations for the user.
  • the recommended foods are prepared meals.
  • the recommended foods may be one or more of the following: prepared meals, recipes, snacks, hero foods, which are foods high in certain nutrients of value to users, or nutritional supplements.
  • the health data in some embodiments may include activity levels for at least some users.
  • the health data may further include in some embodiments user goals such as weight loss or endurance that are used by the filtering engine or the ranking engine to select foods for the user.
  • the food data may also include calorie information used by the filtering engine or the ranking engine to select foods for the user.
  • the system may also makes lifestyle recommendations to the user to improve the user's wellbeing based on the health data.
  • the vitals used by the system in some embodiments include waist circumference and blood pressure and may further include age, gender, height, weight, activity level and other information about a user.
  • the genotypical data in some embodiments includes genetic variants including single nucleotide polymorphisms that are correlated with one or more of the following: body fat, blood pressure, heart health and inflammation among other data.
  • the phenotypical data in some embodiments includes information on some or all of the following: the user's insulin sensitivity, cholesterol, triglicerides, and nutrient and mineral levels, among other data.
  • the user's food preference data in some embodiments includes information on foods that the user will not eat or the user's food religion, such as vegan or kosher.
  • a method for recommending foods to a user based on health data includes maintaining a database of users that stores (i) for at least some users, a diet type vector for each user comprising macronutrient and micronutrient ranges determined based on decision logic from the user's health data, including vitals, genotypical and phenotypical data, (ii) user food preference data, and (iii) food data including macronutrient and micronutrient data corresponding to foods that may be recommended to a user.
  • the method includes filtering the food data based on the user's diet type vector and the user food preference data to determine a set of available foods for the user.
  • a food is excluded from the list of available foods for the requesting user if the food does not match the requesting user's preference data.
  • the method includes presenting to the requesting user the list of available foods matching the user's diet type.
  • the list of matching foods may also be ranked based on the micronutrients in the user's diet type vector and the food data corresponding to the matching foods. Many other factors may also be used to influence the ranking.
  • the disclosure provides a multi-nutrient challenge beverage for measuring the metabolic adaptability of a user, including: a) from 44 to 57 grams total fats; b) 75 ⁇ 15 grams total carbohydrates; and c) 20 ⁇ 3 grams total protein.
  • the fat content of the beverage comprises from 10% to 20% of the total weight of the beverage.
  • the fat content of the beverage is primarily from an edible vegetable oil.
  • the edible vegetable oil is palm oil.
  • the carbohydrate content of the beverage comprises from 10% to 30% of the total weight of the beverage.
  • the carbohydrate content of the beverage is primarily from monosaccharide sugar.
  • the monosaccharide sugar is dextrose.
  • the protein content of the beverage comprises from 2.5% to 10% of the total weight of the beverage.
  • the protein content of the beverage is primarily from a milk protein isolate.
  • the beverage further including one of more of a tastant, an emulsifier, a thickening agent, and a preservative.
  • the disclosure provides a method for measuring the metabolic adaptability of a user, including: (A) obtaining data on a user's blood insulin levels, blood glucose levels, and blood triglyceride levels prior to consumption of a multi-nutrient challenge beverage, after a first period of time following consumption of the multi-nutrient challenge beverage, and after a second period of time following consumption of the multi-nutrient challenge beverage; and (B) inputting the obtained data into a metabolic adaptability classifier, wherein the first period of time and second period of time following consumption of the multi-nutrient challenge beverage are each no longer than 120 minutes, and wherein the challenge beverage is a challenge beverage as described above.
  • the data obtained on the user's blood insulin levels, blood glucose levels, and blood triglyceride levels is derived from a dried blood sample collected by the user.
  • FIG. 1 is a block diagram illustrating an implementation of a personalized food and nutrition recommendation system, in accordance with some embodiments.
  • FIG. 2A is a flow chart illustrating a method of processing user vitals, genotypical and phenotypical data to determine a diet type for a user in accordance with some embodiments.
  • FIG. 2B is a flow chart illustrating a method of processing user diet type determined based on a user's vitals, genotypic and phenotypic data and information on available meals, recipes, foods and/or supplements to determine available meals, recipes, foods or supplements for a user in accordance with some embodiments.
  • FIG. 2C is a flow chart illustrating a method of ranking available meals, recipes, foods and/or supplements for a user based on a user's diet type and vitals, genotypic and phenotypic data in accordance with some embodiments.
  • FIG. 3 is a list of phenotypic data that is used in accordance with some embodiments for processing a user's diet type.
  • FIG. 4 is a list of genotypic data that is used in accordance with some embodiments for processing a user's diet type.
  • FIG. 5 depicts a mapping of combinations of macronutrient recommendations into diet types in accordance with some embodiments.
  • FIG. 6 depicts an illustrative set of ranges for seven individualized diet types into which to categorize users based on their vitals, genotype and phenotype in accordance with some embodiments.
  • FIG. 7 depicts an illustrative collection of food groups and serving sizes for seven different diet types in accordance with some embodiments.
  • FIG. 8 is a list of micronutrients and in some cases foods that are used in accordance with some embodiments for determining micronutrient recommendations and meal or food ranking in accordance with some embodiments.
  • FIG. 9 depicts a method of interacting with a user over a network connection related to delivering meal, recipe, food and supplement related information based on the user's vitals, genotype and phenotype and other information provided by the user in accordance with some embodiments.
  • FIG. 10 depicts an illustrative classifier that produces macronutrient and micronutrient recommendations based on vitals, genotypic and/or phenotypic data for a user in accordance with some embodiments.
  • FIGS. 11A and 11B depict an illustrative classifier for determining a carbohydrate recommendation based on vitals, genotypic and/or phenotypic data in accordance with some embodiments.
  • FIGS. 12A, 12B, and 12C depict an illustrative classifier for determining a fats recommendation based on vitals, genotypic and/or phenotypic data in accordance with some embodiments.
  • FIG. 13 depicts an illustrative classifier for determining a protein recommendation based on vitals, genotypic and/or phenotypic data in accordance with some embodiments.
  • FIGS. 14A, 14B, 14C, 14D, and 14E depict a list of hero foods that are recommended to users in some embodiments.
  • FIG. 15 is a block diagram illustrating an implementation of a personalized food and nutrition recommendation method, in accordance with some embodiments.
  • FIG. 16 depicts an illustrative classifier for determining monounsaturated fatty acid and fiber recommendations based on vitals, genotypic and/or phenotypic data in accordance with some embodiments.
  • FIG. 17 depicts an illustrative classifier for determining dietary protein flexibility recommendations based on vitals, genotypic and/or phenotypic data in accordance with some embodiments.
  • FIG. 18 depicts an illustrative classifier for determining dietary carbohydrate flexibility recommendations based on vitals, genotypic and/or phenotypic data in accordance with some embodiments.
  • FIG. 19 depicts an illustrative classifier for determining dietary fat flexibility recommendations based on vitals, genotypic and/or phenotypic in accordance with some embodiments.
  • FIGS. 20 depicts an illustrative classifier for determining carbohydrate micronutrient recommendations based on vitals, genotypic and/or phenotypic data in accordance with some embodiments.
  • FIG. 19 illustrates insulin levels in subjects before and after consuming a multi-nutrient challenge beverage, as measured using capillary blood samples spotted on a substrate (insulin ADX) and venous blood collected in a catheter (insulin venous).
  • FIG. 20 illustrates a linear regression comparing insulin levels measured using capillary blood samples spotted on a substrate (insulin ADX) with venous blood collected in a catheter (insulin venous) before and after consuming a first multi-nutrient challenge beverage.
  • FIG. 21 illustrates a linear regression comparing insulin levels measured using capillary blood samples spotted on a substrate (insulin ADX) with venous blood collected in a catheter (insulin venous) before and after consuming a second multi-nutrient challenge beverage.
  • FIG. 22 illustrates glucose levels in subjects before and after consuming a multi-nutrient challenge beverage, as measured using capillary blood samples spotted on a substrate (insulin ADX) and venous blood collected in a catheter (insulin venous).
  • FIG. 23 illustrates a linear regression comparing glucose levels measured using capillary blood samples spotted on a substrate (insulin ADX) with venous blood collected in a catheter (insulin venous) before and after consuming a first multi-nutrient challenge beverage.
  • FIG. 24 illustrates a linear regression comparing glucose levels measured using capillary blood samples spotted on a substrate (insulin ADX) with venous blood collected in a catheter (insulin venous) before and after consuming a second multi-nutrient challenge beverage.
  • FIG. 25 illustrates triglyceride levels in subjects before and after consuming a multi-nutrient challenge beverage, as measured using capillary blood samples spotted on a substrate (insulin ADX) and venous blood collected in a catheter (insulin venous).
  • FIG. 26 illustrates a linear regression comparing triglyceride levels measured using capillary blood samples spotted on a substrate (insulin ADX) with venous blood collected in a catheter (insulin venous) before and after consuming a first multi-nutrient challenge beverage.
  • FIG. 27 illustrates a linear regression comparing triglyceride levels measured using capillary blood samples spotted on a substrate (insulin ADX) with venous blood collected in a catheter (insulin venous) before and after consuming a second multi-nutrient challenge beverage.
  • FIGS. 28A, 28B, 28C, 28D, 28E, 28F, 28G, and 28H are a flow chart illustrating a method of providing food recommendations based on the features of a user in accordance with some embodiments.
  • FIG. 29 depicts an illustrative method of collecting data from users and about meals and available ingredients and classifying the users into diet types and the meals according to their data in order to match users with a variety of different, heathy meal options on a daily, weekly, monthly or other frequency basis that are individualized for the user and that may be delivered to the user, in accordance with some embodiments.
  • the various implementations described herein include systems, methods and/or devices used to enable individualized meal and food recommendations to a user based on that user's health vitals, such as height, weight, blood pressure, age, waist circumference; the user's genotype and in particular genetic markers, such as SNPs, and phenotype data as determined by blood tests.
  • health vitals such as height, weight, blood pressure, age, waist circumference
  • genetic markers such as SNPs, and phenotype data as determined by blood tests.
  • the disclosed systems and methods use data from individual users, including their vitals data, such as waist circumference, blood pressure and age; genotypical data including data on a user's DNA and genetic variations such as particular single nucleotide polymorphisms (SNPs), and phenotypical data relating to markers obtained from blood samples from the individual.
  • vitals data such as waist circumference, blood pressure and age
  • genotypical data including data on a user's DNA and genetic variations such as particular single nucleotide polymorphisms (SNPs), and phenotypical data relating to markers obtained from blood samples from the individual.
  • SNPs single nucleotide polymorphisms
  • phenotypical data relating to markers obtained from blood samples from the individual.
  • FIG. 1 depicts a block diagram of a system 100 according to some embodiments of the invention.
  • the system implements personalized nutrition analysis for a user and facilitates identifying meals, recipes, and foods or supplements (collectively foods) for users and may further facilitate selling and delivering meals and other foods to users.
  • the system 100 includes a plurality of users at user devices 101 that communicate with a server, such as a web server interface 104 , typically via a network.
  • the network may include the Internet, local area networks, wide area networks, wired networks, optical networks, wireless networks, telephone networks, cellular networks, email networks and any other type of network or bus connection that allows the exchange of data typically, though not limited to, through the Internet Protocol.
  • the user devices 101 may be mobile devices, such as mobile phones, tablets, or laptop computers, for example. Alternatively, the devices 101 may be desktop or other computers or devices.
  • the user devices 101 enable a plurality of users to interact with the web interface server 104 to provide information about the user to the web server 104 and to receive information back from the web server interface 104 .
  • the user devices 101 includes a processor, memory, a screen, and input devices such as a touchscreen, keyboard, keys, a mouse, or a microphone. The user interacts with the user device 101 and the web server interface 104 to exchange information between the system 100 and the user 101 .
  • the system 100 also may include devices 102 associated with health service providers and devices 103 associated with meal, recipe or supplement providers.
  • the devices 102 and 103 are similar to the user devices described above.
  • the system 100 further includes a user health database 105 , a meal and recipe database 106 and a meals processing engine 107 .
  • the user devices 101 may be used by users to provide health information about themselves to the system 100 .
  • the user may log into the web server interface 104 and upon authentication provide to the system 100 information about the user's vitals, such as the information shown in FIG. 1 .
  • the user may further provide genotype and phenotype information, for example, of the types shown in FIGS. 3 and 4 .
  • the user may in some embodiments also provide information about the user's goals, such as general wellbeing, weight loss, increase of muscle mass and/or improving endurance.
  • the user may also in some embodiments provide information about the food preferences, for example food religion (e.g., vegan, kosher, gluten free), or a list of foods that the user prefers or does not like. This information may be elicited through a browser interface with questions or lists of questions with dropdown predetermined choices according to some embodiments.
  • the devices 102 may be used by health service providers to provide vitals, genotype or phenotype information regarding the user to the system 100 .
  • the user and/or healthcare providers may enter or upload data via the web server.
  • the user and or health service providers may upload the data for particular users directly to a database associated with the system 100 , such as the database 105 .
  • the database 105 may be centralized or distributed and accessible by the system 100 .
  • the webserver 104 and devices 101 and 102 are used for inputting data about each user's vitals, genotype and phenotype.
  • the web server interface 104 may serve a browser page that authenticates users and/or health service providers and allows them to enter relevant data into particular fields.
  • the web server interface may facilitate uploading files to the database 105 or otherwise facilitating access to the database 105 to provide relevant information about users to the system.
  • the web server interface 104 may further include parsing and filtering functionality that receives data on the vitals, genotypes and phenotypes of users and converts the data into a recommendation context with data populating fields that will be used by the system 100 for nutritional analysis according to some embodiments described herein.
  • goals and food preference information may be filtered and stored in the database 105 .
  • Devices 103 may be associated with meal, recipe or health supplements providers (hereinafter meal providers).
  • the meal providers may provide meals, recipes or supplement information to the system to be stored in the meal and recipe database 106 .
  • the devices 103 may provide meal related information to the meal and recipe database 106 via the web server interface through browser entry, through uploading data via the web server interface 104 or via the meals processing engine 107 .
  • the devices 101 may further include activity trackers associated with a user that provide additional information about users to the system 100 .
  • activity trackers may provide daily information about how many calories a user has burned, how much sleep a user has gotten, how many steps a user has taken, heart rate information, distance walked or run.
  • other information about the user's activities may be provided such as the type of activity done by the user and the duration, such as swimming for one hour.
  • the user's device may automatically upload activity information or may upload it in response to synchronization operations initiated by the user.
  • the user may provide activity level, sleep and other data about the user to the system 100 via a webpage served by the web server interface 104 by uploading or linking a file with activity data.
  • the meals processing engine 107 receives data from the web server interface 104 or the devices, such as devices 103 regarding meals, recipes or other foods or supplements and converts the data into a format usable by the system 100 and then stores the data in the database 106 .
  • the information regarding meals and recipes includes in some embodiments the number of calories associated with the meal and macronutrient information, such as the calories from protein, fat and carbohydrates.
  • the meal information in some embodiments includes the number of grams of fat, protein and carbohydrates.
  • the meal and food information includes amounts associated with micronutrients, such as vitamins, or dietary fibers, or types of fats such as saturated, monounsaturated, or polyunsaturated fats.
  • the data associated with foods, meals and/or recipes in terms of macronutrients and micronutrients may be directly provided to the database 106 or may be converted by a conversion process in the web server interface 104 or meals processing engine 107 in some embodiments into actionable macronutrient and micronutrient information.
  • hero foods, snacks or supplements may be described to the system in terms of micronutrient and other macronutrient information by the same processes describe above.
  • the web server interface 104 may maintain a user profile for each user.
  • the user profile may include, for example, some or all of the following information:
  • the system 100 processes the information received from users and providers to produce recommendations for meals, recipes and supplements.
  • the web interface server 104 includes information on each user in the user profile.
  • the user profile may specify, for example that a user is to be given a meal recommendation for each meal three times a day. Alternatively, the user profile may specify only one meal a day or five meals a week.
  • the profile may also call for delivery of the meals or alternatively recipe recommendations according to some embodiments. Additional details of how the system may be configured for users is discussed below.
  • the system 100 determines foods for users, including in some embodiments prepared meals, recipes, snacks, hero foods, supplements or some or all of the foregoing. In some embodiments, the determination is made in real time on request by a user. In some embodiments, the system 100 determines meals for users at some frequency determined by a user selecting from available options. When the web server interface 104 determines that the system is ready to identify recommended meals for a user the recommendation process starts. This process uses the decision tree engine 108 to produce macronutrient 109 and micronutrient 110 classifications for each user, which result in each user being classified in one of several possible diet types. Each diet type specifies ranges for protein, fats and carbohydrates as shown in FIG. 6 . The ranges may be specified in grams or as percentages of calories.
  • the macronutrient 109 recommendations and the meal and recipe database 106 are inputs to a user specific filtering engine 115 .
  • the filtering engine 115 filters meal data based on the user's macronutrient classifications or diet type.
  • the filtering engine may also filter the meals and recipes based on the user's goals, or food religion or food preferences. For example, if the user does not like fish, meals with fish will be excluded by the filter. Similarly, users whose food religion is vegan will have meals and recipes that include meat filtered out. When goals such as weight loss are factored in, certain meals may be filtered out based on calories or macronutrient factors, including those specific to the user.
  • the result of the filtering engine 115 is a set of available meals, recipes or supplements for the user, sometimes referred to as the available meals 128 .
  • the meal ranker engine 130 receives the available meals as well the user's macronutrient 109 classifications or diet type, and micronutrient 110 classifications.
  • the meal ranker engine may also receive the following information from the databases 105 and 106 :
  • the meal ranker algorithm outputs recommendations for one or more users.
  • the meal ranker algorithm may rank meals, recipes, supplements, hero foods, snacks or other information.
  • the meal ranker algorithm may take into account other user meals in a day or supplements that the user regularly takes. It may also take into account the activity level of the user, in addition to macronutrient and micronutrients.
  • FIG. 2 depicts a method 200 of determining a diet type and a micronutrient recommendation for a user based on vitals, genotypical and phenotypical data.
  • the vitals data includes information specific to the user, including, for example, the following information: age, sex, waist circumference (size or high/medium/low), and blood pressure measurements.
  • the phenotypical data is based on blood work done on the user.
  • the phenotypical information may include the data set forth in FIG. 3 .
  • the user is given a challenge beverage and samples of the user's blood are taken at different times before and after drinking the challenge beverage.
  • the phenotypical data provides information about the user's metabolic health, insulin sensitivity, heart health, micronutrient levels, cholesterol and triglyceride levels and inflammation.
  • the genotypical markers in some embodiments are those indicated in FIG. 4 .
  • the genotypical markers are single nucleotide polymorphisms (SNPs) that have a bearing on, for example, gluten sensitivity, endurance performance, blood pressure and sodium, insulin sensitivity, heart health, and inflammation. More, fewer or different SNPs may be used as compared to the ones identified in FIG. 4 .
  • the vitals, phenotypical and genotypical data may be uploaded to the system by a user or health care provider.
  • individual data elements may be stored as part of a recommendation context for the user. Diagnostic measurements, which may be combinations of data elements from the vitals, genotypical and phenotypical data, may also be determined and stored in connection with a user as part of the recommendation context for the user.
  • the recommendation context includes actionable data related to a user's genotype, phonotype and vitals that are to be used to determine the user's diet type, macronutrient and micronutrient recommendations, which in turn form the basis of meal, recipe, food and supplement recommendations.
  • decision tree logic is used on the recommendation context, including the vitals, genotype and phenotype information.
  • the decision tree logic classifies the user according to specific rules specified herein that result in diet type, macronutrient and micronutrient recommendations.
  • the diet type, macronutrient and micronutrient classifications are based not just on one piece of information. Rather, they are based on combinations of genotypical, phenotypical and vitals information. In some embodiments, the diet type, macronutrient and micronutrient classifications may also factor in the user's goals and activity levels.
  • the decision tree logic presents a specific implementation of determining diet types, macronutrient and micronutrient recommendations.
  • the decision trees operate based on input from vitals, genotypical and phenotypical information for each user and are a particular application of rules that classify users into at least one of several diet types and recommended micronutrient levels.
  • the diet types then become the basis for meal and recipe recommendations.
  • the system may optionally transmit the personalized diet type, macronutrient and micronutrient information to the user.
  • the information may be part of a recommendation to supplement the user's diet with particular hero foods or particular vitamin supplements or part of a narrative or set of coaching instructions for the user.
  • the macronutrient and micronutrient information is stored for the user.
  • the diet type is determined for the user and may be stored in the database 105 in association with the user.
  • the diet type may be determined in 212 directly from macronutrient information.
  • diet type may be determined based on mapping one or more macronutrient recommendations or one or more macronutrient and micronutrient recommendations to a set of predetermined diet types for the system.
  • the macronutrient recommendation may be broken down into eight combinations: Fats (f and F), Carbohydrates (c and C), and Protein (p and P).
  • the upper case letter designation refers to an increased level as compared to the lower level.
  • the table below shows an example of mapping sets of macronutrient recommendations to five diet types or diet type vectors.
  • FIG. 5 shows another mapping of diet types based on macronutrient recommendations according to some embodiments.
  • the diet types each reflect different levels of macronutrients that are personalized for the user based on vitals, genotype and phenotype data.
  • FIG. 6 shows a table 600 that provides illustrative ranges for the seven diet types, or diet type vectors, shown in FIG. 5 , according to some embodiments. Referring to the table 600 , each diet type is shown with a recommended daily calorie intake of 2000 calories.
  • the number of calories may be customized for each person based on sex, age, activity level and other factors or may be considered on a meal by meal basis.
  • the table also includes recommended percentage ranges for each diet type or diet type vector that correspond in some embodiments to macronutrient recommendations.
  • the macronutrient recommendations are shown as elements 605 .
  • Table elements 610 show illustrative values for calories associated with carbohydrates, fat and protein for each diet type for an exemplary meal falling within the ranges of the diet type.
  • recommended meals falls within the macronutrient ranges 605 for each user.
  • Table elements 615 show illustrative values in grams of carbohydrates, fat and protein for each diet type for an exemplary meal falling within the ranges of the diet type.
  • the diet types may range in number, but in some embodiments there are between six and nine biological diet types. There may be more or fewer depending on the design of the system or the overall vitals, phenotypical and genotypical variation found within the entire user community or groups of users defined by geography, organizations, families or other factors if desired.
  • the diet type information may be transmitted to the user in 214 .
  • the diet types in some embodiments may contain informative labels for the user to comprehend the type of diet that is recommended for the user.
  • diet type labels may include “balanced harvester, grain seeker, protein seeker, hunter, and other terms that are associated with macronutrient attributes of the diet type.
  • the system may optionally transmit narratives describing ranges and the types of foods, snacks and meals that the user should eat.
  • the narratives may include additional information about goals, micronutrient intake, supplements and other information related to the user's nutritional needs.
  • FIG. 2B depicts a method of determining available meals, recipes or foods for a user based on a user's diet type and other information.
  • the system 100 collects and stores information from the user, such as on goals, weight loss, fitness, well-being, increasing muscle mass or improving endurance.
  • the information on goals may be collected from the user by serving a webpage with a drop down menu of choices for the user to select.
  • the goals set forth herein are illustrative only and may include any goals that have a bearing on the number of calories or types of meals, foods or supplements that a user with those goals might want to eat.
  • the goals are stored in the database 105 associated with the system 100 .
  • the system 100 collects and stores user activity data, such as one or more user's daily exercise or activity levels in the database 105 .
  • This data collection may be done by synchronizing a remote activity level tracker device or database associated with the user with the database 105 to transfer data to the database 105 on a user's activity levels.
  • a user may upload a general description of the user's regular activity, daily activity, weekly activities, monthly activities or one time activities. The user may be prompted to enter this data or may be given a web page with drop down menus to use to describe regular or one time activities.
  • the system may determine recommended meals or foods for users in some embodiments based on activity levels in a particular day. Alternatively, the activity levels may be used to determine calories burned by the user over periods of time and then used in meal recommendations to the user.
  • the system 100 collects and stores food intake information associated with the user in some embodiments.
  • the food intake information may include: (i) information the user identifies to the system, for example in some embodiments, in response to a web page that the system provides to the user asking for food intake information; or (ii) information on meals or recipes that the user has purchased and consumed through the system.
  • the user may identify for the system foods and supplements that the user has eaten or plans to eat in order to get meal or recipe recommendations for breakfast, lunch or dinner in a given day; to get snack, supplement or other food recommendations over the course of several days or a week based on what the user is expected to eat during that time period.
  • the food intake information for one or more users may be stored in the database 105 .
  • the system 100 collects and stores food preference information for each user.
  • the food preference information may include in some embodiments: (i) a list of foods that the user is allergic to; (ii) a list of foods that the user does not like to eat; or (iii) a list of foods that the user likes to eat; (iv) the user's food religion (kosher, vegan, pescatarian and similar).
  • Food preferences for one or more user are stored in the database 105 .
  • the food preferences may be provided by each user in response to a web pages soliciting this information with selectable choices. This information may also be uploaded by a user or a health or other service provider to the database 105 .
  • the system receives information on meals, recipes and/or hero foods that are available for recommendation to the user and stores the information in the meals and recipe database 106 .
  • This information may be provided in some embodiments by administrators of the system 100 to the database meals and recipe database 106 .
  • meals, recipe and other food and supplement information may be provided by health service providers 102 , meal or recipe providers 103 or users 101 .
  • the information such as recipes or available foods or meals in the database 106 may be designated to be specific to a user or specific to a group of users, for example a family, those users in a geographic area, or those users who work at a particular organization.
  • some meals, foods, recipes or supplements may be designated in the database 106 to be available to all users or many groups of users.
  • the meals and recipe information for each meal or recipe includes information on the calories of the meal or recipe and macronutrient information, such as calories from fat, carbs and protein or grams of fat, carbs and protein.
  • the information may also include information of the type shown in FIG. 7 for each meal or recipe.
  • the meal and recipe information may also include information on micronutrients, such as the volume, weight, or RDA percentage of one or more micronutrients.
  • the meal processing engine 107 may provide macronutrient and micronutrient information based on the contents of the meal, recipe, food or supplement and known averages for the types of food in the recipe or meal or the types of nutrients in the food or supplements being described.
  • the macronutrient and micronutrient information for the meal, recipe, food or supplement may be input by a meal or recipe provider or an administrator of the system.
  • Meals or foods may also be stored with a breakfast, lunch, dinner, snack, hero food, supplement or other similar designation to facilitate specific recommendations to the user.
  • Meals or recipes may be designated in more than one category in some embodiments.
  • meals, recipes, foods and/or supplements in the database 106 that are associated with the user may be filtered in order to determine available meals, recipes, foods or supplements for the user.
  • One or more filters may be selected an applied for each user.
  • the available meals and recipes are filtered based on the user's biological diet type 116 . This filtering is based on, for example, macronutrient recommendations and meals that do not fit within macronutrient ranges are filtered out.
  • a user's food preferences are used to filter the available meals, recipes, foods or supplements.
  • meals or recipes with fish will be filtered out.
  • other meals with one or more ingredients that are not allowed or desired for a user are filtered out in some embodiments.
  • a user may provide other criteria in 118 that is used to filter meals. For example, a user might have a goal of not exceeding 500 calories at dinner. This criteria may be used to filter available dinners that have fewer than 500 calories. Similarly, a user may specify a criteria that the user is searching for one or more dinner meals or recipe. This criteria may be used to filter out breakfast or lunch recipes.
  • the available meals, recipes, foods and/or supplements 120 are generated and stored in connection with the user. These are available meals, recipes, food and/or supplements for a user based on each user's preferences, biological diet type and other criteria in some embodiments.
  • FIG. 2C depicts a method of generating meal, recipe, food or supplement recommendations for a user according to some embodiments.
  • the method of 2 C may be applied to selecting meals or recipes.
  • the method of 2 C may be applied to selecting snacks, such as hero foods or other snacks with an ingredient list or supplements.
  • Available meals, recipes, foods or supplements stored in 232 may be retrieved in 240 in connection with a particular user in order to make one or more recommendations to the user.
  • the system 100 retrieves macronutrient and micronutrient recommendations for the user, diet type information associated with the user, and other meal ranking parameters.
  • One or more of the following meal ranking parameters may be used in some embodiments:
  • the meal ranking parameters in some instances are specific to users, user groups or geographies where users are located. In other instances, the meal ranking parameters may be specific to the meal preparer, or to the specific meals or recipes or ingredients.
  • a meal ranker algorithm is applied.
  • the meal ranker algorithm ranks meals based on the user's micronutrient recommendations and the ability of the meal to provide those micronutrients. This is performed in some embodiments by applying for at least some micronutrients recommend for the user, the following equation:
  • Each micronutrient subject to the calculation is then summed together for each meal.
  • the highest ranked meal has the lowest micronutrient score.
  • the meals are ranked from first to last based on the lowest to highest micronutrient score.
  • the top X meals or recipes are then transmitted or recommended to the user in 244 .
  • the value of X may be any number that is designed to give the user some choices without flooding the user with too many choices.
  • snacks supplements or hero foods are being ranked or recommended, those may be transmitted in 246 to the user.
  • the foods, such as prepared meals, recipes, hero foods or supplements, are ranked and/or recommended for the user and may also be stored for the user.
  • micronutrients or basic foods
  • the user may be given a web page to specify what recommendations the user is looking for in order to drive the method of FIG. 2C .
  • the user may be seeking a dinner recipe or to order meals for the next week.
  • the user may specify that the user wants the top 10 recommended meals and/or recipes in some embodiments.
  • the user may specify that the user wants only dinner recipes or breakfast, lunch and/or dinner meals and recipes to choose from.
  • the user may specify snacks or supplements.
  • the meal ranker algorithm will select from the available meals, recipes, foods and supplements and make recommendations according to the methods described herein after ranking.
  • Still other techniques may take into account meals (and ingredients) being made available to other users based on their respective diet types so that there are economies of scale for the food preparation process when there are a plurality of users for which meals are being prepared. Still other techniques may store selections of the user in response to past meal recommendations. This may be used to determine both what the user likes because of the user choices as well as what the user does not like because the user does not selected certain recommended meals. In some embodiments, different hueristic equations may be used to optimize selections for users. In some embodiments, the other ranking parameters may be given a score between 0 and 1 (or more than that) and then added to the micronutrient summation.
  • Meal ranking is then performed for each meal based on its overall score with the low score representing a higher rank.
  • a user's diet type and recommended meals and foods are based on an individualized determination of each user's macronutrient and micronutrient needs. Referring to FIG. 10 , these needs are determined by receiving vitals 1002 , phenotype 1004 and genotype 1006 data from each user.
  • the vitals data may include data such as shown below:
  • body mass index BMI
  • BMI body mass index
  • the system In addition to the vitals information, the system also utilizes measurements of phenotypic and genotypic biomarkers to assess a number of physiological factors such as metabolic health and endurance, insulin response, etc., as is more fully described below.
  • the phenotype and genotype data in some embodiments is as shown in FIGS. 3 and 4 respectively.
  • the phenotype data generally includes information obtained from blood testing on the user.
  • the user's blood is sampled after fasting and at future times after ingestion of a challenge beverage as described in more detail below.
  • the challenge beverage is designed to provide carbohydrates, fats and proteins to the user and then measure the user's response at intervals.
  • the blood samples provide some insight into the user's ability to process sugars, fats and proteins based on the changes in biomarkers present in the blood over time.
  • the blood samples also may include information about cholesterol, vitamin and/or mineral levels, triglicerides, hormone levels and other information.
  • the user takes a blood sample at a fasting state, drinks the challenge beverage and then takes blood samples at a number of different time points, usually from one to three time intervals, with a fasting level, a measure at 30 minutes and another at two hours finding use in many situations, although other time periods can be done, including, but not limited to, thirty minutes, one hour, two hours and three hours.
  • the blood levels of one or more of the following phenotypic biomarkers are then assayed and input into the system, with from one, 5, 10, 15, 20, 25 or all 28 being tested in some embodiments.
  • C-peptide biomarkers are used as a measure of metabolic health and insulin sensitivity as it relates to metabolic health.
  • the blood level of carotenoids in the plasma are tested for all time points as an indication of carotenoid intake.
  • a disposition index is measured as this is an indicator of beta cell function and thus can be used to assess metabolic health and insulin sensitivity.
  • a hepatic insulin index is done on each time point, which measures hepatic glucose production (HGP) and calculates indices of hepatic insulin resistance as an indicator of metabolic health, insulin sensitivity.
  • several different cholesterol levels are determined at all time points, including HDL, LDL, total cholesterol and using a ratio of total cholesterol: HDL cholesterol.
  • total cholesterol is measured at all time points.
  • HDL cholesterol levels are measured at all time points, which is an indicator of heart health.
  • LDL cholesterol levels are measured at all time points as well.
  • high sensitivity C-reactive protein is measured at all time points as a biomarker for inflammation.
  • the cut points of low risk ⁇ 1.0 mg/L
  • average risk 1.0 to 3.0 mg/L
  • high risk >3.0 mg/L
  • a magnesium category test is measured at all time points which is a marker for blood pressure, inflammation and insulin sensitivity.
  • an Omega-3 index is done at all time points, which can be used for recommendations regarding the intake of omega 3 for heart health.
  • a potassium category test is done at all time points, which is relevant to blood pressure and heart health.
  • the ratio of two essential amino acids ARA/AA and EPA is measured at all time points.
  • the AA/EPA ratio is an indication of levels of cellular inflammation, with a ratio of 1.5 to 3 indicating low inflammation, 3 to 6 indicating moderate inflammation, 7 to 15 is elevated inflammation and >15 indicating high inflammation.
  • sodium levels are measured at all time points as an indicator of blood pressure and heart health and for intake recommendations.
  • vitamin A levels are measured at all time points for intake recommendations.
  • vitamin B6 levels are measured at all time points as an indicator of blood pressure and heart health and for intake recommendations.
  • vitamin C levels are measured at all time points as an indicator of blood pressure and for intake recommendations.
  • vitamin D levels are measured at all time points for intake recommendations.
  • vitamin B6 levels are measured at all time points for intake recommendations.
  • zinc levels are measured at all time points for intake recommendations.
  • genotype data is taken from DNA analysis on the user.
  • Certain single nucleotide polymorphisms (SNPs) or genetic markers may be selected based on their correlation with health and dietary intake and are depicted in FIG. 4 .
  • SNPs single nucleotide polymorphisms
  • 34 genotypic biomarkers are tested, with from at least about 5, 10, 15 20, 25, 30 or all 34 finding use in many embodiments.
  • any number of standard SNP detection techniques can be used, including, but not limited to, hybridization methods, enzyme based methods and nucleic acid sequencing methods.
  • Hybridization methods include, but are not limited to, dynamic allele-specific hybridization (DASH) genotyping which takes advantage of the differences in the melting temperature in DNA that results from the instability of mismatched base pairs; this is frequently done as in known in the art using molecular beacon technologies or SNP microarray technologies.
  • Enzymatic methods include enzyme based amplification technologies, where the amplification only occurs and/or doesn't occur based on the presence or absence of the SNP, such as polymerase chain reaction (PCR), oligonucleotide ligation assays (OLA), primer extension methods, etc.
  • Nucleic acid sequencing methods utilize a number of different technologies, including single molecule sequencing (Pacific Biosciences), sequencing by synthesis (Illumina), pyrosequencing (454), ion semiconductor (Ion Torrent), and sequencing by ligation (SOLiD).
  • the user's blood is tested for the presence of the angiotensin I-converting enzyme insertion/deletion (ACE I/D) polymorphism ACE rs1799752, the presence of which is associated with human physical performance including endurance, see Ma et al., PLOS, The Association of Sport Performance with ACE and ACTN3 Genetic Polymorphisms: A Systematic Review and Meta-Analysis. PLoS ONE8(1): e54685, hereby incorporated by reference in its entirety.
  • ACE I/D angiotensin I-converting enzyme insertion/deletion
  • the user's blood is tested for the presence of the angiotensin I-converting enzyme insertion/deletion (ACE I/D) polymorphism ACE rs4646994, the presence of which is associated with blood pressure and sodium recommendations.
  • ACE I/D angiotensin I-converting enzyme insertion/deletion
  • the most influential dietary factor for the renin-angiotensin system (RAS) is sodium. Interactions between the ACE I/D polymorphism, sodium intake and the RAS system determine blood pressure and therefore influence risk for hypertension.
  • the user's blood is tested for the presence of the ADAMT69 risk allele rs4607103, the presence of which is associated with insulin sensitivity, insulin secretion and fiber recommendations.
  • the user's blood is tested for the presence of the ADRB3 rs4994, the presence of which is associated with human physical performance including endurance,
  • the user's blood is tested for the presence of the AGT rs5051 SNP, the presence of which is associated with blood pressure and sodium recommendations.
  • the user's blood is tested for the presence of the AGT rs699 SNP, the presence of which is associated with blood pressure and sodium recommendations.
  • the user's blood is tested for the presence of the APOA5-A4-C3-A1 rs964184, the presence of which is associated with macro fat recommendations, diet type, blood pressure, insulin sensitivity (specifically fat consumption).
  • CETP Cholesteryl ester transfer protein
  • the user's blood is tested for the CETP rs1532624 allele, the presence of which is an indicator or useful for classifying the carbohydrate diet types and insulin sensitivity low carb tree.
  • the user's blood is tested for CYP1A2 rs762551, with the rs762551(A) allele being associated as a “fast metabolizer” and the (C) allele is by comparison a slower metabolizer of certain substrates (including caffeine).
  • the FADS1 gene codes for the fatty acid delta-5 desaturase, a key enzyme in the metabolism of long-chain polyunsaturated omega-3 and omega-6 fatty acids.
  • the user's blood is tested for one or both of FADS1 rs174546 or rs174548, as variants in the fatty acid desaturase 1 (FADS1) gene are also associated with altered polyunsaturated fatty acids (PUFAs) such as omega-3, and the presence of these SNPs is used as an indicator of heart health, blood pressure for the epa dha recommendation (omega 3), for intake omega-3.
  • PUFAs polyunsaturated fatty acids
  • the FTO gene encodes the fat mass and obesity-associated protein (also known as alpha-ketoglutarate-dependent dioxygenase FTO).
  • the user's blood is tested for the FTO rs11221980 SNP, the presence of which is used for diet type classification (carbs and fats), and as a marker for insulin sensitivity for fat consumption, insulin sensitivity for low carbohydrates, and weight maintenance for energy balance.
  • the user's blood is tested for the FTO rs9939609 SNP, the presence of which is used for diet type classification (carbohydrates, proteins and fats), and as a marker for blood pressure relating to fat.
  • SNPs associated with group-specific component (vitamin D binding protein) GC gene area tested as they have been linked by several studies to vitamin D serum concentrations.
  • the allele associated with lower vitamin D, and thus the potential for vitamin D insufficiency, is rs2282679(C).
  • the user's blood is tested for the GC rs2282679 SNP, the presence of which is related to the recommendation for vitamin D levels as well as for inflammation.
  • the user's blood is tested for the presence of the GC rs4588 SNP, the presence of which is related to the recommendation for vitamin D levels as well as for inflammation.
  • the user's blood is tested for the presence of the GC rs7041 SNP, the presence of which is related to the recommendation for vitamin D levels as well as for inflammation.
  • GCKR glucosease regulatory protein (GCKR) gene
  • SNP rs780094 The T-allele of GCKR (glucokinase regulatory protein (GCKR) gene) SNP rs780094 is associated with increased triglycerides. Accordingly, in some embodiments, the user's blood is tested for the presence of the GCJR rs7800094 SNP, the presence of which is related to insulin sensitivity for fasting glucose levels.
  • GCKR glucokinase regulatory protein
  • HLA-DQ is a gene family for a ⁇ heterodimer cell surface receptor.
  • a user's blood is tested for an HLA-DQ SNP, as a number of these are related to celiac disease and gluten sensitivity.
  • the SNP is the HLA-DQ2.2 rs2395182 SNP.
  • the SNP is the HLA-DQ2.2 rs4713586 SNP.
  • the SNP is the HLA-DQ2.2 rs7775228 SNP.
  • the SNP is the HLA-DQ2.5 rs2187668.
  • the SNP is the HLA-DQ7 rs4639334 SNP.
  • the rs4402960 SNP in the insulin like growth factor 2 mRNA binding protein are associated with type-2 diabetes risk and is thus used as a biomarker for the fat diet type and insulin sensitivity for fat consumption.
  • the user's blood is tested for the presence of the IGF2BP2 rs4402960 SNP.
  • the IL6 rs1800795 SNP is a SNP in the promoter of the IL-6 gene that is associated with inflammation.
  • the user's blood is tested for the presence of the IL6 rs1800795 SNP.
  • the MCM6 gene encodes the protein DNA replication licensing factor MCM6, one of the highly conserved minichromosome maintenance complex proteins that are essential for the initiation of eukaryotic genome replication.
  • the MCM6 rs4988235 SNP is associated with lactose intolerance and lactose sensitivity.
  • the user's blood is tested for the presence of the MCM6 rs4988235 SNP.
  • the MTHFR gene encodes the vitamin-dependent enzyme, methylenetetrahydrofolate reductase, involved in folate metabolism and thus associated with blood pressure in terms of riboflavin.
  • the MTHFR rs1801133 SNP Homozygous rs1801133(T;T) individuals have ⁇ 30% of the expected MTHFR enzyme activity, and rs1801133(C;T) heterozygotes have ⁇ 65% activity, compared to the most common genotype, rs1801133(C;C).
  • the user's blood is tested for the presence of the MTHFR rs1801133 SNP.
  • the nitrous oxide synthase gene NOS3 gene variant rs1799983 is strongly associated with coronary artery disease; a large study found that homozygosity for rs1799983(T;T) increases risk of ischemic heart disease and can be used as a biomarker for blood pressure for cocoa flavanols and resveratrol recommendations.
  • the user's blood is tested for the presence of the NOS3 gene variant rs1799983.
  • the PPARG rs1801282 associates with type 2 diabetes and interact with physical activity, as diet type (fats), insulin sensitivity for fat consumption In some embodiments, the user's blood is tested for the presence of PPARG rs1801282 (Pro12Ala).
  • the user's blood is tested for the presence of the R577X rs1815739 SNP.
  • This SNP in the ACTN3 gene, encodes a premature stop codon in a muscle protein called alpha-actinin-3.
  • the polymorphism alters position 577 of the alpha-actinin-3 protein.
  • the (C;C) genotype is often called RR, whereas the (T;T) genotype is often called XX.
  • the (T;T) is under-represented in elite strength athletes, consistent with previous reports indicating that alpha-actinin-3 deficiency appears to impair muscle performance and is accordingly a marker for muscle performance.
  • the user's blood is tested for the presence of the TCF7L2 (Transcription Factor 7 Like 2) rs7903146 SNP as this is one of two SNPs within the TCF7L2 gene that have been reported to be associated with type-2 diabetes, It is used as a biomarker for diet types relating to carbohydrates and fats, blood pressure for fat, insulin sensitivity for low carbohydrates, and weight maintenance for energy balance.
  • TCF7L2 Transcription Factor 7 Like 2
  • rs7903146 SNP is one of two SNPs within the TCF7L2 gene that have been reported to be associated with type-2 diabetes, It is used as a biomarker for diet types relating to carbohydrates and fats, blood pressure for fat, insulin sensitivity for low carbohydrates, and weight maintenance for energy balance.
  • the TNF rs1800629 SNP in the tumor necrosis factor-alpha gene, rs1800629 is also known as the TNF-308 SNP. Occasionally the rs1800629(A) allele is referred to as 308.2 or TNF2, with the more common (G) allele being 308.1 or TNF1. The (A) allele is associated with higher levels of TNF expression.
  • This SNP has been linked to a wide variety of conditions including inflammation. Accordingly, in some embodiments, the user's blood is tested for this SNP.
  • the user's blood is tested for the presence of the VDR rs1544410, also known as the BsmI polymorphism, is a SNP in the Vitamin D receptor (VDR) and is used as a marker for Vitamin D.
  • VDR Vitamin D receptor
  • the decision tree Engine 108 receive the vitals, genotype and phenotype data for each user and convert this data into macronutrient and micronutrient recommendations.
  • the recommendations are essentially vectors that correlate relevant macronutrients or micronutrients with a level or range for each user.
  • the user's vector includes values as shown for Carbohydrates, Fats and Protein.
  • An illustrative decision tree for carbohydrates is shown in FIGS. 11A and B.
  • An illustrative decision tree for Fats is shown in FIG. 12 .
  • An illustrative decision tree for Protein is shown in FIG. 13 .
  • the decision trees receive the inputs of vitals, genotype and genotype data, and through the application of rules and logic, the decision trees produce the user's macronutrient recommendation vector.
  • the range of values produced and included in the user's macronutrient recommendation vector may be as shown in FIGS. 5 and 10 .
  • values, value ranges thresholds may be applied.
  • the macronutrient recommendations may be mapped into diet types.
  • the decision tree or decision logic may directly output diet types from input values.
  • the macronutrient recommendations and diet types for each user in some embodiments are based on vitals, phenotype and genotype data for each user.
  • micronutrient recommendations for each user are similarly based on the vitals, phenotype and genotype data for each user. However, certain micronutrient recommendations may be based on less than all three data types.
  • a list of micronutrients and/or foods, levels for all or some of which may be determined for each user are shown in FIG. 8 .
  • Meals, recipes, foods, snacks and supplements that are stored in the database 106 also may include information on levels of micronutrients such as those in the list of FIG. 8 .
  • the decision logic may include determining intermediate values that are used in determining multiple macronutrient or micronutrient recommendations. Some examples of intermediate values include
  • the decision tree engine may implemented in program instructions that implement decision tree logic that are stored in memory of a computer and then are executed by a processor within the computer to process the inputs and produce macronutrient, micronutrient and diet types based on the vitals, genotypical and phenotypical data for each user.
  • the decision trees may be static. Alternatively, the decision tree logic may be updated over time.
  • the relevant vitals, phenotypical or genotypical data for each user that is used in the recommendations may also change over time in some embodiments.
  • the changes in decision tree logic may be driven by new scientific information about food and the impact of genotype or phenotype on health in some embodiments.
  • the decision tree logic be updated based on feedback from results of users of the system as the vitals and phenotypical data of users change over time based on their meals. activity levels and aging.
  • each of the methods and processes shown and described herein may be implemented on a server or other computer and the web server interface, decision tree engine, filtering engine and meal ranker engine may implemented by a server or other network connected computer.
  • These computers may one computer or may be centralized or distributed and may share data with each other and other network elements shown in FIG. 1 via the Internet, local area networks, wide area networks or other networks.
  • the processes in some embodiments are implements as program instructions that may be stored as software or firmware in the memory of a device or other computer and executed by a processor.
  • the device includes a memory, a processor, input/output units, and networking units.
  • the processor executes program instructions to perform the processes shown and described herein, including database queries, web interfaces, meals processing, health decision trees, filtering, meal ranking and other user interactions to ensure user registration, meal and food recommendations and in other instances payment and arranging for delivery of meals or other food.
  • the databases include stored data regarding users, which may be stored in an encrypted and secure manner. Additional information that is collected or generated during the processes shown and described herein may be stored in the databases.
  • the databased are network connected and may store or provide information in response to queries to any of the network elements in order to facilitate the processes shown and described herein.
  • FIG. 15 illustrates methods and systems for personalized food and nutrition recommendation system 1500 , in accordance with some embodiments.
  • Information about the user 1502 is collected, e.g., one or more of genotypic information 1506 , phenotypic information 1508 which, in some embodiments includes metabolic adaptability information determined, for example, through analysis of the user's blood following consumption of a multi-nutrient challenge beverage as described herein, food preferences 1510 (e.g., food likes, dislikes, food religions, or other dietary preferences), anthropometrics 1512 (e.g., physical measurements of the individual), goals 1514 (e.g., weight loss, muscle building, or increases in energy), dietary patters 1516 (e.g., eating habits or food logs), and activity patterns 1518 (e.g., typical physical activities, exercise logs, or measured caloric outputs).
  • food preferences 1510 e.g., food likes, dislikes, food religions, or other dietary preferences
  • anthropometrics 1512 e.g.
  • information about the user is collected multiple times, e.g., before initial classification and one or more times after adapting a particular diet.
  • information collected after implementation of a food habit is used to track changes in the user and/or adjust classification of the user based on changes accompanying the adapted food habits. For example, a user initially identified as having elevated blood pressure may be initially classified as requiring a diet low in fats. However, upon re-testing after implementing a low fat diet, it may be found that the user's blood pressure has been reduced. This information can be used to reclassify the user as no longer requiring a diet low in fats, e.g., in combination with other risk factors.
  • the information about the user is applied to one or more food recommendation classifiers, e.g., one or more of diet type classifier 1520 , micronutrient recommendation classifier 1522 , caloric recommendation classifier 1524 , hero food classifier 1525 , and a supplement recommendation classifier 1552 , to provide one or more food classifications and/or recommendations for the user, e.g., one or more of a diet type 1526 , a micronutrient recommendation profile 1528 , a source recommendation profile 1530 , a caloric recommendation 1532 , a hero food recommendation 1533 , and a supplement recommendation classifier.
  • diet type classifier 1520 e.g., one or more of diet type classifier 1520 , micronutrient recommendation classifier 1522 , caloric recommendation classifier 1524 , hero food classifier 1525 , and a supplement recommendation classifier 1552 .
  • a diet type classifier 1520 e.g., one or more of diet type classifier 1520 , micronutrient
  • a diet type classification model e.g., diet type classifier 1520 in FIG. 15 , health decision tree engine 108 in FIG. 1 , and/or and illustrative macronutrient classification models in FIGS. 11-13 ).
  • a micronutrient classification model e.g., one or more illustrative micronutrient classification model in FIGS. 16-23 .
  • micronutrient profile P(z k ) includes a respective value v(z i ) for each micronutrient Z i in the plurality of micronutrients Z, by comparing the diet type D j and micronutrient recommendation profile R j assigned to the user to the nutrition profiles P N of foods N in the plurality of foods L (e.g., via one or more of user specific filtering engine 115 as described with respect to FIG. 1 , meal ranker engine 125 as described with respect to FIG. 1 , and food selection classifier 1536 described with respect to FIG. 15 ).
  • assigning a respective diet type D j includes assigning macronutrient recommendations for fat, carbohydrate, and protein intake to the user and then matching the assigned macronutrient recommendations to a diet type D (e.g., one of the seven diet types described above with reference to FIG. 5 ).
  • the method includes assigning a macronutrient fat intake recommendation F j to the user by inputting a third sub-plurality X 3 of the plurality of first features X and a third sub-plurality Y 3 of the plurality of second features Y into a fat recommendation classification model (e.g., the fat recommendation classifier described above with reference to FIG. 12 ).
  • a fat recommendation classification model e.g., the fat recommendation classifier described above with reference to FIG. 12
  • the user is assigned either a low fat dietary recommendation (f) or a regular fat dietary recommendation (F).
  • the fat macronutrient dietary recommendation is one of more than two classes of recommendations, e.g., one of three, four, five, or more classes of recommendations.
  • the method also includes assigning a macronutrient carbohydrate intake recommendation C j to the user by inputting a fourth sub-plurality X 4 of the plurality of first features X and a fourth sub-plurality Y 4 of the plurality of second features Y into a carbohydrate recommendation classification model (e.g., the carbohydrate recommendation classifier described above with reference to FIG. 11 ).
  • a carbohydrate recommendation classification model e.g., the carbohydrate recommendation classifier described above with reference to FIG. 11 .
  • the user is assigned either a low carbohydrate dietary recommendation (c) or a regular fat dietary recommendation (C).
  • the carbohydrate macronutrient dietary recommendation is one of more than two classes of recommendations, e.g., one of three, four, five, or more classes of recommendations.
  • the method also includes assigning a macronutrient protein intake recommendation P j to the user by inputting a fifth sub-plurality X 5 of the plurality of first features X and a fifth sub-plurality Y 5 of the plurality of second features Y into a carbohydrate recommendation classification model (e.g., the protein recommendation classifier described above with reference to FIG. 11 ).
  • a carbohydrate recommendation classification model e.g., the protein recommendation classifier described above with reference to FIG. 11 .
  • the user is assigned either a low protein dietary recommendation (p) or a regular protein dietary recommendation (P).
  • the user is assigned either a low protein dietary recommendation (p), a regular protein dietary recommendation (P), or a high protein dietary recommendation (P+).
  • the user is assigned either a low protein dietary recommendation (p), a regular protein dietary recommendation (P), a high protein dietary recommendation (P+), or an extra high protein dietary recommendation (P++).
  • the carbohydrate macronutrient dietary recommendation is one of more than four classes of recommendations, e.g., one of five, six, seven, or more classes of recommendations.
  • every combination of fat, carbohydrate, and protein dietary recommendations defines a different diet type.
  • certain combinations of fat, carbohydrate, and protein dietary recommendations are classified in a same diet type (for example, in the diet type classifications described above with respect to FIG. 5 , FCP+ and FCP++ combinations both correspond to Diet Type 2).
  • one or more combination of fat, carbohydrate, and protein dietary recommendations is associated with more than one diet type, for example, based on one or more additional factors (e.g., a particular genotypic marker, phenotypic marker, metabolic adaptability feature, food preference, food religion, anthropometric feature, user goal, dietary pattern, or activity pattern).
  • additional factors e.g., a particular genotypic marker, phenotypic marker, metabolic adaptability feature, food preference, food religion, anthropometric feature, user goal, dietary pattern, or activity pattern.
  • the food classifications and/or recommendations assigned to the user are used to provide ranked food recommendations 1548 using food selection classifier 1536 .
  • the user's food classifications and/or recommendations, along with list of foods 1534 are input into food selection classifier 1536 , which optionally includes one or more of diet type prioritization algorithm 1538 , preference filter 1540 , allergy and/or sensitivity filter 1542 , source filter 1544 , and micronutrient ranking algorithm 1546 .
  • any or all of these components are used in any order to rank foods for recommendation to a user.
  • food selection classifier 1536 assigns a numerical value to one or more of foods 1536 .
  • the numerical value for a particular food reflects both a diet type suitability of the food for a user and a micronutrient suitability of the food for a user.
  • the food is assigned a first number corresponding to a diet type of the food and a second number corresponding to a micronutrient profile of the food.
  • a food assigned to a first Diet Type may be assigned a value of 1 and a food assigned to a second Diet Type may be assigned a value of 5.
  • a second value is assigned to each food based on a similarity of the micronutrients in the food to a micronutrient recommendation profile of the user.
  • the two numbers are kept separate, e.g., as an ordered pair of numbers (X, Y) or X. Y.
  • the two numbers may be combined arithmetically, e.g., by generating a sum of the two numbers. In this fashion, the foods can then be ranked numerically to determine which foods are best suited for the user.
  • Diet type prioritization algorithm 1538 filters or ranks foods (e.g., meals) based on a comparison between the diet type assigned to a user and a diet type assigned to the food (e.g., meal). For example, in some embodiments, each food is classified as belonging to one of the Diet Types (e.g., Diet Types 1-7, as described herein with reference to FIG. 5 ) and foods having the same Diet Type designation as a user's Diet Type assignment are prioritized over foods having different Diet Type designations as the user's Diet Type assignment. In some embodiments, a food having a Diet Type designation that is different from the user's Diet Type assignment is filtered out (e.g., removed from a list of eligible foods for the user).
  • Diet Types e.g., Diet Types 1-7, as described herein with reference to FIG. 5
  • a food having a Diet Type designation that is different from the user's Diet Type assignment is filtered out (e.g., removed
  • the food is assigned a Diet Type designation based on the fat, carbohydrate, and protein contents of the food.
  • the fat, carbohydrate, and protein contents of the food are used to classify the food according to the same fat, carbohydrate, and protein consumption recommendations assigned to users. For example, a food with a carbohydrate content below a threshold value (e.g., according to the percent of carbohydrates by weight or calories in the food) is assigned a low carbohydrate food designation (c) that corresponds to a low carbohydrate dietary recommendation (c).
  • a food with a carbohydrate content above a threshold value is assigned a high carbohydrate food designation (C) that corresponds to a low carbohydrate dietary recommendation (C).
  • the food is assigned one of a plurality of fiber dietary recommendations (e.g., for F) and protein dietary recommendations (e.g., p or P; or p, P, or P+; or p, P, P+, or P++).
  • the combination of fat, carbohydrate, and protein classification of the food is then mapped to a Diet Type (e.g., one of Diet Types 1-7, as described herein with reference to FIG. 5 ).
  • preference filter 1540 is applied to deprioritize foods that does not comply with a user's preference (e.g., vegetarian, dairy-free, gluten free, kosher, etc.). In some embodiments, the system removes a food that does not comply with a user's preference from a list of eligible foods for the user.
  • a user's preference e.g., vegetarian, dairy-free, gluten free, kosher, etc.
  • allergy/sensitivity filter 1542 is applied to deprioritize foods the user is allergic to and or is sensitive.
  • the system removes a food the user is allergic to or sensitive to from a list of eligible foods for the user.
  • food selection classifier 1536 applies a sodium filter to deprioritize or remove meals with a sodium content above a threshold level when the user has been identified as having a salt sensitivity.
  • food sensitivities are determined based on a user feature 1504 (e.g., a genotype 1506 , phenotype 1508 , or metabolic adaptability characteristic).
  • source filter 1544 is applied to deprioritize foods that do not comply with a source recommendation for the user (e.g., a MUFA or Fiber source recommendation as described herein with reference to FIG. 16 ).
  • the system removes a food that does not comply with a source recommendation for the user from a list of eligible foods for the user.
  • micronutrient ranking algorithm 1546 is applied to prioritize foods with micronutrient profiles that most closely match a micronutrient recommendation profile assigned to the user (e.g., user micronutrient classifications 110 described herein with reference to FIG. 1 and/or micronutrient recommendation profile 1528 as described herein with reference to FIG. 15 ).
  • food selection classifier 1536 adjusts the ranking of one or more meals (e.g., deprioritizes) belonging to a same meal family (e.g., meals having similar bases that vary, for example, primarily by the identity of the protein) as a higher ranked meal. For example, where a list of available meals includes both beef over noodles and chicken over noodles, the lower ranked meal will be deprioritized with in the ranking to avoid presenting the user with highly similar meal choices.
  • meals e.g., deprioritizes
  • a same meal family e.g., meals having similar bases that vary, for example, primarily by the identity of the protein
  • the systems and methods described herein also include providing a caloric recommendation C j to the user by inputting a sixth sub-plurality X 6 of the plurality of first features X and a sixth sub-plurality Y 6 of the plurality of second features Y into a caloric recommendation classification model (e.g., caloric recommendation classifier 1524 illustrated in FIG. 15 ).
  • the caloric recommendation classifier uses features of the user, e.g., one or more of gender, age, height, weight, waist circumference, and activity levels, to assign a caloric recommendation (e.g., caloric recommendation 1532 illustrated in FIG. 15 ) to the user, for example, a recommendation on how many calories to consume at a single meal, an entire day, a week, etc.
  • food selection classifier 1536 applies caloric recommendation 1532 to prioritize foods (e.g., meals) that closely match the user's caloric requirements.
  • the system deprioritizes a food (e.g., a meal) that does not conform with a user's caloric recommendation, e.g., a food with a calorie content that exceeds a maximum calorie content determined based on the user's caloric recommendation and/or a food with a calorie content less than a minimum calorie content determined based on the user's caloric recommendation.
  • the system removes a food that does not conform to a user's caloric recommendation from a list of eligible foods for the user.
  • one or more ranked food recommendations 1548 are presented to the user, e.g., through a web-based user interface.
  • the ranked food recommendations correspond to meals that can be prepared and/or delivered to the user.
  • the user selects user food selections 1550 from ranked food recommendations 1548 , which are prepared and/or delivered to the user in some embodiments (e.g., as food delivery 1556 illustrated in FIG. 15 ).
  • ranked food recommendations 1548 represent a sub-plurality of all available foods 1534 , which most closely fit food classifications and/or recommendations for the user.
  • the user selects a number of meals to be displayed, e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, or more meals for a particular week.
  • the user specifies the number and types of meals to be displayed, e.g., a certain number of breakfasts, a certain number of lunches, and a certain number of dinners.
  • the system selects the meals that best match the user's food profile (e.g., classifications and/or recommendations) and displays suggested meals to the user.
  • the system also displays one or more alternative meals to the user that the user may select in lieu of a suggested meal.
  • the alternative meals are those ranked just below the suggested meals by the food selection classifier.
  • the system monitors and analyses user food selections 1550 over one or more user selection events and uses the information to refine food selection classifier 1536 for the user. For example, where the user consistently chooses an alternative meal containing chicken for a suggested meal containing salmon, the system may update food selection classifier 1536 for the user to more heavily weight meals containing chicken and/or less heavily weight meals containing salmon. In some embodiments, a learning classifier algorithm is implemented to refine the output of food selection classifier 1536 for the individual.
  • the system monitors and analyses user food selections 1550 over one or more user selection events for a plurality of users and uses the information to refine a master list of meals (e.g., foods 1536 ), selection of meals for a particular menu (e.g., selection of foods 1534 from a master list of foods), and/or development of new meals to be added to a master list of meals. For example, if the system identifies a pattern that users select meals containing chicken more often than meals containing beef, the system may refine an algorithm used to select potential meals to offer chicken dishes more often and/or beef dishes less often on a global scale (e.g., for all or a subset of users of the system.)
  • the methods and systems described herein apply features 1504 of the user to a supplement recommendation classifier (e.g., supplement recommendation classifier 1552 illustrated in FIG. 15 ) to provide a supplement recommendation (e.g., supplement recommendation 1554 ).
  • the supplements recommended to a user are selected from a predetermined list of supplements that address different health needs, e.g., one or more of metabolic health, cholesterol reduction, maintenance of polyunsaturated fat (e.g., omega-3 fatty acids) levels, blood pressure control, cardiac health, and general health (e.g., in a gender-specific or gender-neutral fashion).
  • the supplement recommendation classifier ranks potential supplement recommendations for a user (e.g., based on a classifier that considers, for example, one or more of the importance of the supplement to health and the user's need for the particular supplement) and selects up to a predetermined number of supplement recommendations to provide the user (e.g., the top 2, 3, 4, 5, 6, 7, 8, 9, or more supplements).
  • the supplement recommendation classifier may rank a first supplement over a second supplement because the first supplement has been shown to greatly reduce incidence of cardiac failure, while the second supplement has a largely cosmetic effect, regardless of the user's relevant needs for the two supplements.
  • the supplement recommendation classifier may rank the second supplement, with the largely cosmetic effect, higher than the first supplement, associated with greatly reduced incidence of cardiac failure, if a user has a much greater need for the second supplement than for the first supplement.
  • a metabolic supplement is recommended to a user that would benefit from assistance with maintaining blood glucose levels.
  • a metabolic supplement contains one or more of green tea catechins and chromium picolinate, known to contribute to maintenance of normal blood sugar.
  • a phytosterol supplement is recommended to a user that would benefit from assistance maintaining healthy cholesterol levels because phytosterols have been shown to reduce cholesterol levels.
  • a cardiac health supplement is recommended to a user that would benefit from assistance maintaining a healthy cardiac system.
  • a cardiac health supplement contains one or more of coenzyme Q10 and grapeseed extract, both of which promote healthy blood vessels.
  • an omega-3 fatty acid supplement is recommended to a user that would benefit from assistance maintaining healthy polyunsaturated fat levels.
  • an omega-3 fatty acid supplement contains one or more of fish oil and algal oil because EPA and DHA contribute to maintenance of healthy omega-3 fatty acid levels.
  • an omega-3 fatty acid supplement is recommended to a user that would benefit from assistance lowering their blood pressure.
  • an omega-3 fatty acid supplement contains one or more of fish oil and algal oil because EPA and DHA contribute to maintenance of normal blood pressure.
  • recommended supplements are delivered to the user (e.g., along with user food selections as part of food delivery 1556 ).
  • the system provides feedback to one or both of the food selection classifier engine (e.g. meal ranker engine 125 as described herein with reference to FIG. 1 and/or food selection classifier 1536 as described herein with reference to FIG. 15 ) and hero food recommendation engine, that the user has been provided a supplement.
  • the food selection classifier engine and/or hero food recommendation engine considers that the user is taking supplements when making a future food recommendation.
  • the food selection classifier in response to an input that the user has or will be provided a fish oil supplement, deprioritizes foods (e.g., meals) containing fish and/or foods (e.g., meals) high in omega-3 fatty acids, because the user is receiving a large amount of omega-3 fatty acids from the fish oil supplements.
  • the system will remove a food (e.g., a meal) containing fish and/or high in omega-3 fatty acids, from a list of foods available to the user while the user is receiving fish oil supplements.
  • a hero food recommendation engine e.g., meal ranker engine 125 in FIG. 1 and/or hero food recommendation classifier engine 1525 in FIG. 15 ) deprioritizes and/or removes a hero food recommendation high in omega-3 fatty acids while the user is receiving fish oil supplements.
  • the systems and methods described herein also include providing a hero food recommendation H j to the user by inputting a seventh sub-plurality X 7 of the plurality of first features X and a seventh sub-plurality Y 7 of the plurality of second features Y into a hero food recommendation classification model (e.g., a meal ranker engine 125 as described herein with respect to FIG. 1 and/or a hero food recommendation classifier engine 1525 as described herein with respect to FIG. 15 ).
  • the hero food recommendation classifier uses features and/or Diet Type assignments to recommend one or more hero foods (e.g., one or more hero foods shown in FIG. 14 ) to the user.
  • the methods described herein include assigning one or more source recommendation to an individual.
  • the source recommendations include a fiber source recommendation, suggesting that the user eat foods higher in fiber (e.g., a recommendation that the user consumes foods with a minimum amount of fiber or in which a minimum percentage of carbohydrates are fibers).
  • the source recommendations include a monounsaturated fatty acid source recommendation, suggesting that the user eat foods higher in monounsaturated fatty acids (e.g., a recommendation that the user consumes foods with a minimum amount of monounsaturated fatty acids or in which a minimum percentage of fats are monounsaturated fatty acids).
  • FIG. 16 shows an illustrative classifier for providing monounsaturated fatty acid (MUFA) and fiber source recommendations (e.g., an exemplary source recommendation profile S j , as illustrated in FIG. 15 ), in accordance with some embodiments.
  • a classifier providing source recommendations is implemented as part of a micronutrient recommendation classifier, e.g., as illustrated in FIG. 15 .
  • a classifier providing source recommendations is implemented separate from a micronutrient recommendation classifier.
  • user features e.g., genotypes, phenotypes, vitals, anthropometrics, and metabolic adaptability traits
  • the source recommendation assigned to the user trait is represented by an ‘X’ on the right side of the table.
  • identifying the user as having elevated blood pressure results in both a MUFA and a fiber recommendation, in accordance with some embodiments.
  • WC waist circumference
  • FTO risk variant Individuals with an increased waist circumference (WC) plus the FTO risk variant will also get a fiber recommendation because of their increased WC (e.g., independent of their rs9939609 allele status).
  • Individuals with a low disposition index with impaired fasting glucose (IFG), impaired glucose tolerance (IGT), or IGT & IFG will also get a fiber recommendation because of their IFG, IGT, or IGT & IFG.
  • IFG impaired fasting glucose
  • IGT impaired glucose tolerance
  • IGT & IFG Individuals with a low disposition index with impaired fasting glucose (IFG), impaired glucose tolerance (IG
  • the methods described herein include providing the user with information about their metabolic flexibility associated with consuming one or more of fats, carbohydrates, and protein.
  • FIG. 17 shows an illustrative classifier for providing the user with information about their metabolic flexibility associated with consuming protein, in accordance with some embodiments.
  • user features e.g., genotypes, phenotypes, vitals, anthropometrics, and metabolic adaptability traits
  • FIG. 17 user features (e.g., genotypes, phenotypes, vitals, anthropometrics, and metabolic adaptability traits) that result in information about a user's protein consumption flexibility are shown of the left hand side of the table.
  • the flexibility associated with the user's feature is shown on the right side of the table. For example, determining the user has elevated blood pressure identifies the user as having flexibility to consume a diet rich in protein (e.g., in which 18-30% of the user's calories come from protein).
  • FIG. 18 shows an illustrative classifier for providing micronutrient recommendations based on user features (e.g., as described above with respect to health decision tree engine 108 in FIG. 1 and/or micronutrient recommendation classifier 1522 in FIG. 15 ), in accordance with some embodiments.
  • user features e.g., genotypes, phenotypes, vitals, anthropometrics, and metabolic adaptability traits
  • micronutrient is identified at the left of the table.
  • a default micronutrient recommendation is provided (e.g., one associated with a daily recommended intake for the micronutrient) and the system modifies the micronutrient recommendation when detecting a user feature associated with an increased need for, or beneficial results of, consuming more or less of the particular micronutrient.
  • base-line recommendations (DRI) for the micronutrient are shown in the column next to the micronutrient.
  • Modified micronutrient recommendations for a user identified with a particular feature are shown below the feature identified and in-line with the micronutrient. For example, as illustrated in FIG.
  • a user identified as having elevated or high impaired glucose tolerance is assigned one or more of the following recommendations: that they consume 90 grams of whole grains, that 5 grams out of every 100 grams of carbohydrates they consume are alpha-cyclodextrin, 8 grams out of every 100 grams of carbohydrates they consume are arabinoxylan, 3.5 grams out of every 100 grams of carbohydrates they consume are beta-glucans, and 14 grams of every 100 grams of carbohydrates they consume are resistant starch.
  • the systems and methods provided herein apply classifiers providing recommendations for one or more of the micronutrients listed in FIG. 8 .
  • a micronutrient classifier is informed by studies linking improved health to the administration of a micronutrient to subjects with a specific feature (e.g., genotype, phenotype, metabolic flexibility, anthropometric characteristic, etc.).
  • the disclosure provides a method 2800 for providing personalized food recommendations.
  • the method includes obtaining ( 2802 ) feature data about a user, for example, one or more features as described herein with reference to FIG. 1 (e.g., via user health database 105 ), FIG. 2 (e.g., storing ( 202 ) user vitals, genotypic, and phenotypic data), FIG. 3 (e.g., illustrative phenotypes), FIG. 4 (e.g., illustrative genotypes), FIG. 10 (e.g., user vitals data 1002 , user phenotypic data 1004 , and user genotypic data 1006 ), and FIG. 15 (e.g., feature data 1504 ).
  • FIG. 1 e.g., via user health database 105
  • FIG. 2 e.g., storing ( 202 ) user vitals, genotypic, and phenotypic data
  • FIG. 3 e.g., illustrative pheno
  • a diet type classification model e.g., health decision tree engine 108 as described herein with reference to FIG. 1 and/or diet type classifier 1520 as described herein with reference to FIG. 15 ).
  • assigning a respective diet type includes ( 2822 ): assigning a macronutrient fat intake recommendation F j to the user by inputting a third sub-plurality X 3 of the plurality of first features X and a third sub-plurality Y 3 of the plurality of second features Y into a fat recommendation classification model (e.g., as described herein with reference to FIG. 12 ), assigning a macronutrient carbohydrate intake recommendation C j to the user by inputting a fourth sub-plurality X 4 of the plurality of first features X and a fourth sub-plurality Y 4 of the plurality of second features Y into a carbohydrate recommendation classification model (e.g., as described herein with reference to FIG.
  • a macronutrient protein intake recommendation P j assigning a macronutrient protein intake recommendation P j to the user by inputting a fifth sub-plurality X 5 of the plurality of first features X and a fifth sub-plurality Y 5 of the plurality of second features Y into a protein recommendation classification model (e.g., as described herein with reference to FIG. 13 ).
  • a micronutrient classification model e.g., health decision tree engine 108 as described herein with reference to FIG. 1 and/or micronutrient recommendation classifier 1520 as described herein with reference to FIG. 15
  • the method includes assigning ( 2826 ) one or more source recommendations S j to the user by inputting user features, including a sub-plurality of first features X and a sub-plurality of second features Y, into a source classification model (e.g., micronutrient recommendation classifier 1520 as described herein with reference to FIG. 15 or a classifier implemented separately from micronutrient recommendation classifier 1520 and/or a an illustrative source classifier as described herein with reference to FIG. 16 ).
  • a source recommendation includes a recommendation for dietary fiber (e.g., as described herein with reference to FIGS. 15 and 16 ).
  • a source recommendation includes a recommendation for dietary monounsaturated fatty acids (e.g., as described herein with reference to FIGS. 15 and 16 ).
  • the method includes assigning ( 2832 ) a caloric recommendation C j to the user by inputting user features into a caloric recommendation classification model (e.g., caloric recommendation classifier 1525 as described herein with reference to in FIG. 15 ).
  • a caloric recommendation classification model e.g., caloric recommendation classifier 1525 as described herein with reference to in FIG. 15 .
  • the caloric recommendation is based on a user daily activity level ( 2834 ). For example, in some embodiments the user is presented with a questionnaire asking about their physical activity levels during a normal day (e.g., at work, school, and/or home).
  • the caloric recommendation is based on a user exercise level ( 2836 ).
  • the user is presented with a questionnaire asking about the physical activities they routinely engage in (e.g., sports, weight-lifting, cardiovascular exercising, and outdoor activities). For example, the user is asked about one or more of what activities they routinely participate in, how often they participate in the activities, and how vigorously they participate in the activities.
  • activity information is provided by an electronic activity monitor.
  • the user's reported daily physical activity levels and/or leisure activity levels are weighted according to a model of the caloric output and/or caloric requirement for each activity and then used to arithmetically personalize a daily caloric requirement, e.g., as based off of a starting caloric requirement for a male or female, optionally considering other features of the individual (e.g., one or more phenotype, metabolic adaptability characteristic, or anthropometric measurement).
  • the method includes assigning ( 2838 ) one or more hero food recommendations H j (e.g., one or more hero foods as described herein with reference to FIG. 14 ) to the user by inputting user features, including a sub-plurality of first features X, a sub-plurality of second features Y, and/or a dietary type, into a hero food recommendation classification model (e.g., caloric recommendation classifier 1525 as described herein with reference to in FIG. 15 ).
  • a hero food recommendation classification model e.g., caloric recommendation classifier 1525 as described herein with reference to in FIG. 15 .
  • the method includes assigning ( 2838 ) one or more supplement recommendations V j to the user by inputting user features, including a sub-plurality of first features X and a sub-plurality of second features Y, into a supplement recommendation classification model (e.g., supplement recommendation classifier 1552 as described herein with reference to in FIG. 15 ).
  • a supplement recommendation classification model e.g., supplement recommendation classifier 1552 as described herein with reference to in FIG. 15 .
  • the method includes recommending one or more foods to the user by inputting (F) one or more of the user features and/or recommendations into a food recommendation classifier (e.g., meal ranker engine 125 as described herein with reference to FIG. 1 and/or food selection classifier 1536 as described herein with reference to FIG. 15 ).
  • a food recommendation classifier e.g., meal ranker engine 125 as described herein with reference to FIG. 1 and/or food selection classifier 1536 as described herein with reference to FIG. 15 .
  • a plurality of foods e.g., a plurality of meals
  • the food recommendation classifier selects one or more foods (e.g., meals) that best match the dietary needs of the user based on the one or more user features and/or recommendations.
  • ranking one or more foods includes deprioritizing ( 2844 ) a food N i that does not conform to a user preference. For example, deprioritizing a meal containing chicken for a user with a vegetarian preference.
  • deprioritizing ( 2846 ) includes assigning the food a lower rank in the ranking of the one or more foods in the plurality of foods L. For example, assigning a meal containing beef a lower ranking than a meal containing salmon for a user with a preference for fish as a protein.
  • deprioritizing ( 2848 ) includes removing the food from a list of eligible foods for the user.
  • removing a dish containing pork as an option for a user with a kosher food preference will result in different rules for food prioritization. For example, in one embodiment, a preference for a particular food religion will result in removing a food from a list of foods available to the user, while a preference for a particular protein source may just prioritize meals containing that protein as compared to meals containing other proteins.
  • ranking one or more foods includes prioritizing ( 2850 ) foods N by comparing the diet type D j assigned to the user with the diet types D k assigned to each food N i .
  • prioritizing ( 2852 ) includes assigning a food N 1 having a same diet type D k1 as the diet type D j assigned to the user a higher rank in the ranking of the one or more foods than a food N 2 having a different diet type D k2 as the diet type D j assigned to the user.
  • prioritizing includes removing a food N 3 having a different diet type D k3 as the diet type D j assigned to the user from a list of eligible foods for the user, e.g., the plurality of foods.
  • a diet type associated with a high protein requirement e.g., associated with a P+ or P++ dietary protein recommendation as described herein
  • a low carbohydrate requirement e.g., associated with a c dietary carbohydrate recommendation as described herein
  • ranking one or more foods includes deprioritizing ( 2856 ) a food N i that does not conform to a user allergy and/or sensitivity. For example, deprioritizing a meal high in caffeine for a user with a caffeine sensitivity.
  • deprioritizing ( 2858 ) includes assigning the food a lower rank in the ranking of the one or more foods in the plurality of foods L. For example, assigning a meal containing a cream sauce a lower ranking than a meal containing a tomato sauce for a user with a lactose sensitivity.
  • deprioritizing ( 2860 ) includes removing the food from a list of eligible foods for the user.
  • removing a dish containing peanut butter as an option for a user with a peanut allergy.
  • different types of food sensitivities and allergies will result in different rules for food prioritization.
  • a peanut allergy will result in removing a food from a list of foods available to the user, while sensitivity for caffeine may just result in deprioritizing meals containing caffeine.
  • ranking one or more foods includes deprioritizing ( 2862 ) foods N by comparing the source recommendation S j assigned to the user with the nutrition profile P N of each food N i , e.g., deprioritizing a food N i that does not conform to a user source recommendation. For example, deprioritizing a meal low in fiber for a user with a fiber source recommendation.
  • deprioritizing ( 2864 ) includes assigning a food N 1 that does not conform to a user source recommendation a lower rank in the ranking of the one or more foods than a food N 2 that does conform to a user source recommendation.
  • deprioritizing includes removing the food from a list of eligible foods for the user. For example, removing a dish having a low fiber content as an option for a user with fiber source recommendation.
  • different types of source recommendations will result in different rules for food prioritization. For example, in one embodiment, a fiber source recommendation with result in the removal of foods with low fiber content, while a monounsaturated fatty acid source recommendation will result in the prioritization of foods rich in monounsaturated fatty acids.
  • ranking one or more foods includes prioritizing ( 2868 ) foods N by comparing the micronutrient recommendation profile R j assigned to the user with the micronutrient profile P(z ki ) assigned to each food N i .
  • prioritizing ( 2870 ) includes assigning, within a diet type D k , a food N 1 , having a micronutrient profile P(z k1 ) that more closely matches the user's micronutrient recommendation profile R j than the micronutrient profile P(z k2 ) of a food N 2 having the same diet type as food N 1 , a higher ranking than food N 2 .
  • ranking one or more foods includes deprioritizing ( 2872 ) (e.g., further lowering a ranking of) a food N 1 having a lower ranking than a food N 2 when food N 1 and food N 2 belong to a same food family. For example, where two meals are substantially identical other than for the identity of the protein (e.g., a chicken dish and a beef dish served over rice), if the chicken dish is ranked higher than the beef dish, the beef dish is deprioritized with respect to other, previously lower ranked dishes, in order to provide the user with diverse food choices/recommendations.
  • deprioritizing 2872
  • two meals are substantially identical other than for the identity of the protein (e.g., a chicken dish and a beef dish served over rice)
  • the chicken dish is ranked higher than the beef dish
  • the beef dish is deprioritized with respect to other, previously lower ranked dishes, in order to provide the user with diverse food choices/recommendations.
  • ranking one or more foods includes deprioritizing ( 2874 ) a food N i by comparing a supplement recommended to the user to the nutrition profile P N of each food N. For example, where the method includes recommending and/or delivering a nutrient supplement in addition to one or more foods, the system will compensate for the nutrients by deprioritizing foods rich in that nutrient.
  • deprioritizing ( 2876 ) includes lowering the ranking of food N 1 that is rich in a nutrient present in the supplement recommended to the user. For example, where the user is receiving a fish oil supplement, a meal containing salmon is ranked below a meal containing chicken because salmon is rich in omega-3 fatty acids.
  • deprioritizing includes removing a food N 1 that is rich in a nutrient present in the supplement recommended to the user from a list of eligible foods for the user. For example, where the user is receiving a fish oil supplement, a meal containing salmon is removed from a list of foods eligible to the user.
  • different supplement recommendations will result in different rules for food prioritization. For example, in one embodiment, receiving a fish oil supplement will remove meals containing salmon as an available food, while receiving a multivitamin supplement will result in lowering a ranking of a food rich in one of the vitamins in the supplement.
  • ranking one or more foods includes deprioritizing ( 2880 ) foods N by comparing a caloric recommendation C j assigned to the user with the nutrition profile P N of each food N i . For example, ranking a higher calorie meal above a lower calorie meal for an extremely active user with a high caloric recommendation.
  • deprioritizing ( 2882 ) includes assigning a food N 1 that does not conform to a user caloric recommendation a lower rank in the ranking of the one or more foods than a food N 2 that does conform to a user caloric recommendation.
  • deprioritizing ( 2884 ) includes removing a food N 1 that does not conform to a user caloric recommendation from a list of eligible foods for the user.
  • the method includes presenting ( 2886 ) to the user a sub-plurality of ranked foods from the list of ranked foods for selection of one or more foods to be prepared and/or delivered to the user. For example, after ranking a group of 100 foods, the system displays the five foods ranked highest according to the ranking classifier (e.g., meal ranker engine 125 as described herein with respect to FIG. 1 and/or food selection classifier 1536 as described herein with reference to FIG. 15 ).
  • presenting ( 2888 ) includes providing ( 2888 ) at least one primary food recommendation and at least one secondary food recommendation that the user may substitute for the primary food recommendation. For example, he system displays to the user the highest ranked food according to the ranking classifier as the default food for the user, but also displays the second highest ranked food according to the ranking classifier as a substitute for the default food.
  • the method includes preparing and/or delivering ( 2890 ) a food selected (G) for the user based on a system recommendation (e.g., a food selected based on a recommendation from a diet type classifier, a micronutrient recommendation classifier, a source recommendation classifier, a hero food recommendation classifier, a supplement recommendation classifier, and/or a food selection classifier).
  • a system recommendation e.g., a food selected based on a recommendation from a diet type classifier, a micronutrient recommendation classifier, a source recommendation classifier, a hero food recommendation classifier, a supplement recommendation classifier, and/or a food selection classifier.
  • the food is selected based on a diet type D j assigned to the user ( 2892 ).
  • the food is selected based on a micronutrient recommendation profile R j assigned to the user ( 2894 ).
  • the food is selected based on a source recommendation S j assigned to the user ( 2898 ).
  • the food is a hero food selected based on a hero food recommendation H j assigned to the user ( 2898 ). In some embodiments, the food is a supplement selected based on a supplement recommendation V j assigned to the user ( 2902 ). In some embodiments, the food is selected based on a ranking of foods from a list of foods available to the user ( 2904 ). In some embodiments, the food is a prepared meal ( 2906 ). In some embodiments, the food is selected by the user based on a ranking of foods presented to the user ( 2908 ). In some embodiments, the food is a prepared meal ( 2910 ).
  • the method includes providing ( 2912 ) the user with a food recommendation based (H) on a system recommendation (e.g., a food selected based on a recommendation from a diet type classifier, a micronutrient recommendation classifier, a source recommendation classifier, a hero food recommendation classifier, a supplement recommendation classifier, and/or a food selection classifier).
  • a system recommendation e.g., a food selected based on a recommendation from a diet type classifier, a micronutrient recommendation classifier, a source recommendation classifier, a hero food recommendation classifier, a supplement recommendation classifier, and/or a food selection classifier.
  • the food recommendation is based on a diet type D j assigned to the user ( 2914 ).
  • the food recommendation is based on a micronutrient recommendation profile R j assigned to the user ( 2914 ).
  • the food recommendation is based on a source recommendation S j assigned to the user ( 2916 ).
  • the food recommendation is based on a hero food recommendation H j assigned to the user ( 2918 ). In some embodiments, the food recommendation is based on a supplement recommendation V j assigned to the user ( 2920 ). In some embodiments, the food recommendation is based on a caloric recommendation C j assigned to the user.
  • FIG. 29 depicts an illustrative method of collecting data from users and about meals and available ingredients and classifying the users into diet types and the meals according to their data in order to match users with a variety of different, heathy meal options on a daily, weekly, monthly or other frequency basis that are individualized for the user and that may be delivered to the user.
  • a user population 2902 associated with a system according to some embodiments of the invention for making meal, food, recipe and supplement recommendations to each user.
  • each user provides information a DNA sample and a blood sample as described in this application from which genotype and phenotype data may be obtained. In addition other information including but not limited to vitals, goals, and exercise is collected.
  • the collected genotype, phenotype and other data 2905 is stored or otherwise made available on the system and for each user, specific genotypical and phenotypical biomarkers are selected for use in classifying a user according to a diet type.
  • certain data from the other data is selected to be used in the classification of each user into a diet type.
  • the biomarkers selected may change over time.
  • each user is classified into a diet type that is stored on the system for that user along with data corresponding to the user's micronutrients needs and other information that is useful for selecting meals for the user such as calories, allergies and other information described elsewhere herein.
  • This information 2909 including diet types, micronutrient needs and other information may be provided to the meal ranking and recommendation algorithm.
  • a set of meals and/or ingredients are available.
  • the meals may include foods, prepared meals, supplements or recipes.
  • Data corresponding to each meal, supplement or food is collected in 2912 and stored.
  • data associated with each meal 2913 is received and processed in order select a subset of data or to create new data corresponding to the meal that will be used in meal selection for the user.
  • the system receives selected data associated with the meal such as protein, carbohydrates, fats, micronutrient data, calories and other detailed information as described elsewhere herein and optionally codes the meals in a form that facilitates correlating meals with diet types and ranking them.
  • a meal might be coded 0, 5 or 10 and if there are six diet types, all codes 0, 5 or 10 might be available for consumption by certain diet types. However, for others only meal types 5 and 10 might be available, while for still others only diet type 0 may be available.
  • the meals may be coded and the code used along with a map correlating diet type with acceptable codes in a meal ranking algorithm.
  • the selected meal data, micronutrient data and any selected codes 2017 may be provided to the meal ranking process 2918 .
  • a meal ranking and recommendation is performed in order to provide a healthy variety of food recommendations to a user on a daily, weekly, monthly or other basis.
  • the recommendations which may be a ranked subset from a large number of choices compatible with a user's diet type and micronutrient needs, may be of food, supplements, recipes, prepared meals, or hero foods as described elsewhere herein, including in connection with FIGS. 1 and 15 , the meal ranker engine 125 and the element 1536 .
  • the meals recommended for each user are presented to the corresponding user through email, messaging or the user logging in to the system and being presented with them there. The user selects a meal or multiple meals, foods, recipes or supplements in 2922 for the day, week or month.
  • the user is presented with a healthy variety of meals that are each a match for the user's genotype and phenotype and the user's selections may also be fed back into the meal ranker 2918 as shown so that the user's preferences are considered in the recommendation.
  • selected meals, foods, or supplements may be delivered to the user.
  • classifiers for determining nutritional recommendations based on user vitals, genotypic and/or phenotypic data can be developed or refined by training a decision rule using data from one or more training sets and applying the trained decision rule to data from users interested in receiving nutritional recommendations.
  • Information on pattern recognition and prediction algorithms for use in data analysis algorithms for constructing decision rules if found, for example, in National Research Council; Panel on Discriminant Analysis Classification and Clustering, Discriminant Analysis and Clustering, Washington, D.C.: National Academy Press and Dudoit et al., 2002, “Comparison of discrimination methods for the classification of tumors using gene expression data.” JASA 97; 77-87, the entire contents of which are hereby incorporated by reference herein in their entirety for all purposes.
  • a classifier for determining nutritional recommendations based on user vitals, genotypic, and/or phenotypic data may be built de novo by compiling existing clinical study results, performing and/or integrating new clinical study results, and/or observational theory.
  • one or more classifiers are further refined after implementation based on individual or population feedback.
  • the metabolic adaptability of an individual informs a diet type classifier
  • the metabolic adaptability of the individual may be determined one or more times following adaption of a particular diet type to track changes in the individual's metabolic adaptability following implementation of a particular diet. In this fashion, detrimental changes to the user's metabolic adaptability when on a particular diet can be identified and the diet type classifier can be refined such that the individual is classified into a more suitable diet type.
  • a refined classifier is implemented in a user-independent fashion, e.g., refinement of a particular classifier based on data from a plurality of users leads to a change in a diet type classifier used to assign diet types to all users.
  • a refined classifier is implemented in a user-specific fashion, e.g., refinement of a food selection classifier based on observations that a particular user chooses certain types of meals (e.g., meals containing quinoa, or does not choose certain types of foods (e.g., meals including salmon as a protein), leads to a change in the food selection classifier implemented for that specific user, but not other users.
  • decision rule includes, but are not limited to: discriminant analysis including linear, logistic, and more flexible discrimination techniques (see, e.g., Gnanadesikan, 1977, Methods for Statistical Data Analysis of Multivariate Observations, New York: Wiley 1977; tree-based algorithms such as classification and regression trees (CART) and variants (see, e.g., Breiman, 1984, Classification and Regression Trees, Belmont, Calif.: Wadsworth International Group; generalized additive models (see, e.g., Tibshirani, 1990, Generalized Additive Models, London: Chapman and Hall; neural networks (see, e.g., Neal, 1996, Bayesian Learning for Neural Networks, New York: Springer-Verlag; and Insua, 1998, Feedforward neural networks for nonparametric regression In: Practical Nonparametric and Semiparametric Bayesian Statistics, pp.
  • discriminant analysis including linear, logistic, and more flexible discrimination techniques
  • CART classification and regression trees
  • variants
  • Suitable data analysis algorithms for decision rules include, but are not limited to, logistic regression, or a nonparametric algorithm that detects differences in the distribution of feature values (e.g., a Wilcoxon Signed Rank Test (unadjusted and adjusted)).
  • the decision rule is based on multiple measured values, e.g., two, three, four, five, ten, twenty, or more measured values, corresponding to observables from multiple data sets, e.g., two, three, four, five, ten, twenty, or more data sets.
  • decision rules may also be built using a classification tree algorithm.
  • Other data analysis algorithms known in the art include, but are not limited to, Classification and Regression Tree (CART), Multiple Additive Regression Tree (MART), Prediction Analysis for Microarrays (PAM), and Random Forest analysis. Such algorithms classify complex spectra and/or other information in order to distinguish subjects as normal or as having a particular medical condition.
  • data analysis algorithms include, but are not limited to, ANOVA and nonparametric equivalents, linear discriminant analysis, logistic regression analysis, nearest neighbor classifier analysis, neural networks, principal component analysis, quadratic discriminant analysis, regression classifiers and support vector machines. Such algorithms may be used to construct a decision rule and/or increase the speed and efficiency of the application of the decision rule and to avoid investigator bias.
  • algorithm classifiers see Duda, 2001, Pattern Classification, John Wiley & Sons, Inc., New York. pp. 396-408 and pp.
  • a challenge food or beverage may be used to evaluate a user's biological response to various foods and macronutrients. Exogenous factors, including food and drink, constantly stress our body's capacity to maintain physiological homeostasis. Our body's ability to adequately react to these external challenges to maintain homeostasis is termed “phenotypic flexibility.” Phenotypic flexibility is determined by a series of interconnected physiological processes and molecular mechanisms. Challenge tests that temporarily disturb homeostasis, including challenge tests based on carbohydrates (oral glucose tolerance test, OGTT), lipids (oral lipid tolerance test, OLTT), protein (oral protein tolerance test, OPTT), and/or combinations thereof, have been used to test these processes and access phenotypic flexibility.
  • a challenge test includes consuming a food that includes relative large quantities of glucose, lipids, and protein.
  • the challenge beverage includes only glucose, lipids or protein in large quantities, or a combination of them.
  • the challenge food is a beverage or a solid food.
  • a challenge beverage includes or is made with the following ingredients:
  • the water is heated and mixed with the other ingredients.
  • the natural flavors may include vanilla in some embodiments or cassia flavors or combinations of both. In some embodiments, the natural flavors may be entirely different, or encompass other flavors in combination with natural flavors identified herein.
  • the beverage in some embodiments is sterilized, homogenized and packed. The sterilization in some embodiments is by direct steam injection.
  • the challenge beverage serving size in some embodiments is approximately 415 mg. However, the overall portion may be much smaller or larger depending on a range of factors, including the size of the individual, the expected range of the test results, the number of types of macronutrients present in the challenge beverage and taste. There may be in some embodiments multiple challenge beverage or food options for a single person to take multiple tests.
  • a blood test is done prior to the consumption of a challenge beverage. Blood tests at time intervals are done as described above after a user consumes a challenge beverage.
  • the disclosure provides a multi-nutrient challenge beverage for measuring the metabolic adaptability of a user containing fats, carbohydrates, and proteins.
  • the multi-nutrient challenge beverage contains from 44 to 66 grams total fats, 75 ⁇ 15 grams total carbohydrates, and 20 ⁇ 3 grams total protein.
  • the multi-nutrient challenge beverage contains 60 ⁇ 6 grams total fats. In other embodiments, the multi-nutrient challenge beverage contains 50 ⁇ 6, 51 ⁇ 6, 52 ⁇ 6, 53 ⁇ 6, 54 ⁇ 6, 55 ⁇ 6, 56 ⁇ 6, 57 ⁇ 6, 58 ⁇ 6, or 59 ⁇ 6 grams totals fats. In some embodiments, the fat content of the multi-nutrient challenge beverage comprises from 10% to 20% of the total weight of the beverage. In other embodiments, the fat content of the multi-nutrient challenge beverage comprises 10% ⁇ 2%, 11% ⁇ 2%, 12% ⁇ 2%, 13% ⁇ 2%, 14% ⁇ 2%, 15% ⁇ 2%, 16% ⁇ 2%, 17% ⁇ 2%, 18% ⁇ 2%, 19% ⁇ 2%, or 20% ⁇ 2% of the total weight of the beverage.
  • the fat content of the beverage is primarily (e.g., at least 85%, 90%, 95%, or 99% of the fat content is derived) from an edible vegetable oil.
  • Vegetable oils are primarily triglycerides extracted from plants. Non-limiting examples of vegetable oils include, but are not limited to, palm oil, coconut oil, corn oil, cottonseed oil, olive oil, peanut oil, rapeseed oil (e.g., canola oil), safflower oil, sesame oil, soybean oil, sunflower oil, and mixtures thereof.
  • the edible vegetable oil is palm oil.
  • the fat content of the beverage is primarily (e.g., at least 85%, 90%, 95%, or 99% of the fat content is derived) from edible nut oil.
  • Nut oils are primarily triglycerides extracted from nuts. Non-limiting examples of nut oils include, but are not limited to, almond oil, beech nut oil, brazil nut oil, cashew oil, hazelnut oil, macadamia nut oil, mongongo nut oil, pecan oil, pine nut oil, pistachio nut oil, walnut oil, pumpkin seed oil, and mixtures thereof.
  • the multi-nutrient challenge beverage contains 80 ⁇ 15 grams total carbohydrates. In other embodiments, the multi-nutrient challenge beverage contains 60 ⁇ 5, 65 ⁇ 5, 70 ⁇ 5, 75 ⁇ 5, 80 ⁇ 5, 85 ⁇ 5, or 90 ⁇ 5, grams totals carbohydrates. In some embodiments, the carbohydrate content of the multi-nutrient challenge beverage comprises from 10% to 30% of the total weight of the beverage. In other embodiments, the carbohydrate content of the multi-nutrient challenge beverage comprises 20% ⁇ 8%, 20% ⁇ 6%, 20% ⁇ 4%, 20% ⁇ 2%, about 18%, about 19%, about 20%, about 21%, or about 22% of the total weight of the beverage.
  • the carbohydrate content of the multi-nutrient challenge beverage comprises 10% ⁇ 2%, 11% ⁇ 2%, 12% ⁇ 2%, 13% ⁇ 2%, 14% ⁇ 2%, 15% ⁇ 2%, 16% ⁇ 2%, 17% ⁇ 2%, 18% ⁇ 2%, 19% ⁇ 2%, 20% ⁇ 2%, 21% ⁇ 2%, 22% ⁇ 2%, 23% ⁇ 2%, 24% ⁇ 2%, 25% ⁇ 2%, 26% ⁇ 2%, 27% ⁇ 2%, 28% ⁇ 2%, 29% ⁇ 2%, or 30% ⁇ 2% of the total weight of the beverage.
  • the carbohydrate content of the beverage is primarily (e.g., at least 85%, 90%, 95%, or 99% of the carbohydrate content is derived) from monosaccharide sugar.
  • monosaccharide sugars include, but are not limited to, pentose sugars (e.g., arabinose, lyxose, ribose, xylose, ribulose, and xylulose), hexose sugars (e.g., allose, altroses, glucose (dextrose), mannose, gulose, Idose, galactose, talose, psicose, fructose, sorbose, and tagatose), heptose sugars (e.g., sedoheptulose, mannoheptulose, and L-glycero-D-manno-heptose).
  • the carbohydrate content of the beverage e.g., sedoh
  • the multi-nutrient challenge beverage contains 20 ⁇ 10 grams total protein. In some embodiments, the multi-nutrient challenge beverage contains 10 ⁇ 5, 15 ⁇ 5, 20 ⁇ 5, 25 ⁇ 5, or 30 ⁇ 5 grams total protein. In other embodiments, the multi-nutrient challenge beverage contains 15 ⁇ 2, 16 ⁇ 2, 17 ⁇ 2, 18 ⁇ 2, 19 ⁇ 2, 20 ⁇ 2, 21 ⁇ 2, 22 ⁇ 2, 23 ⁇ 2, 24 ⁇ 2, or 25 ⁇ 2 grams total protein. In some embodiments, the protein content of the multi-nutrient challenge beverage comprises from 2.5% to 10% of the total weight of the beverage.
  • the protein content of the multi-nutrient challenge beverage comprises 2% ⁇ 2%, 3% ⁇ 2%, 4% ⁇ 2%, 5% ⁇ 2%, 6% ⁇ 2%, 7% ⁇ 2%, 8% ⁇ 2%, 9% ⁇ 2%, or 10% ⁇ 2%, of the total weight of the beverage.
  • the protein content of the beverage is primarily (e.g., at least 85%, 90%, 95%, or 99% of the protein content is derived) from protein isolated from an edible source, e.g., from soy, whey, or milk.
  • the protein content of the beverage is primarily (e.g., at least 85%, 90%, 95%, or 99% of the protein content is derived) from a milk protein isolate.
  • Protein isolates, such as milk protein isolates are used as emulsifiers and stabilizers in dairy products such as yogurt, ice cream and ice cream novelties, and liquid and powdered nutritional formulations. They are also used as a protein source in protein-enrichment applications such as powdered and ready-to-drink beverages for sports nutrition, adult nutrition, and weight management.
  • milk protein e.g., lactose-free skim milk or milk protein isolate
  • soy milk whey protein
  • caseinate soy protein
  • soy protein egg whites
  • gelatins gelatins, collagen and combinations thereof.
  • a multi-nutrient challenge beverage also contains one or more of a tastant (e.g., a flavoring agent), an emulsifier, a thickening agent, and a preservative.
  • a tastant e.g., a flavoring agent
  • an emulsifier e.g., a thickening agent
  • a preservative e.g., a preservative
  • Non-limiting examples of tastants include vanilla, cocoa, strawberry, and peanut butter.
  • Non-limiting examples of emulsifiers useful in a challenge beverage include canola lecithin, propane-1,2-diol alginate, konjac, polyoxyl 8 stearate, polyoxyethylene stearate, polysorbate 20, polysorbate 80, ammonium phosphatides, diphosphates, methyl cellulose, hydroxypropyl cellulose, hydroxypropyl methyl cellulose, ethyl methyl cellulose, carboxymethylcellulose, sodium carboxy methyl cellulose, sodium caseinate, magnesium stearate, sorbitan monostearate, sorbitan tristearate, sorbitan monolaurate, and sorbitan monopalmitate.
  • canola lecithin is used as an emulsifying agent in a challenge beverage described herein.
  • the emulsifier is present in the challenge beverage at from about 0.01% to 2.0% by weight.
  • Non-limiting examples of thickening agents include gellan gum, alginic acid, sodium alginate, potassium alginate, ammonium alginate, calcium alginate, propane-1,2-diol alginate, agar, carrageenan, processed Vietnamesea seaweed, locust bean gum (carob gum), guar gum, tragacanth, acacia gum, xanthan gum, karaya gum, tara gum, pectin, xanthan, starches and modified starches, and mixtures thereof.
  • gellan gum is used as a thickening agent in a challenge beverage described herein.
  • Non-limiting examples of preservatives include citrates, e.g., sodium citrate and potassium citrate, benzoic acid, benzoates, e.g., sodium, calcium, and potassium benzoate, sorbates, e.g., sodium, calcium, and potassium sorbate, polyphosphates, e.g., sodium hexametaphosphate (SHMP), dimethyl dicarbonate, and mixtures thereof.
  • antioxidants such as ascorbic acid, EDTA, BHA, BHT, TBHQ, EMIQ, dehydroacetic acid, ethoxyquin, heptylparaben, and combinations thereof.
  • sodium citrate is used as a preservative in a challenge beverage described herein.
  • a challenge beverage composition including, but not limited to, one or more flavanols, aeidulants, coloring agents, minerals, vitamins, herbs, soluble fibers, non-caloric sweeteners, oils, carbonation components, and the like.
  • a method for measuring the metabolic adaptability of a user includes obtaining data on a user's blood insulin levels, blood glucose levels, and blood triglyceride levels prior to consumption of a multi-nutrient challenge beverage, after a first period of time following consumption of the multi-nutrient challenge beverage, and after a second period of time following consumption of the multi-nutrient challenge beverage, and inputting the obtained data into a metabolic adaptability classifier.
  • the first period of time and second period of time following consumption of the multi-nutrient challenge beverage are each no longer than 120 minutes.
  • the challenge beverage is a challenge beverage described herein.
  • the data obtained on the user's blood insulin levels, blood glucose levels, and blood triglyceride levels is derived from a dried blood sample collected by the user.
  • a trial was established using two challenge beverages containing 75 grams of carbohydrates, 50-60 grams of fats, and 20 grams of protein. Specifically, the study was designed to assess postprandial lipid and glycemic responses and gastrointestinal tolerance for the challenge beverages, assess the feasibility of assessing postprandial responses in dried capillary blood samples, and assess the feasibility of performing the test over a shorter time frame, e.g., within two hours.
  • FIG. 19 shows plots of the average insulin levels detected in the venous catheter collected blood samples (Insulin Venous) and the dried capillary blood samples (Insulin ADX) for both challenge beverages.
  • FIGS. 20 and 21 illustrate linear regressions comparing the insulin levels detected in the venous samples and the capillary samples for Challenge Beverage A ( FIG. 20 ) and Challenge Beverage B ( FIG. 21 ).
  • FIG. 20 there was a strong correlation between the insulin levels detected in the venous catheter collected blood sample and the dried capillary blood sample for both challenge beverages, evidencing that insulin sampling could be performed using dried blood spot (DBS) technology.
  • DBS dried blood spot
  • FIG. 22 shows plots of the average glucose levels detected in the venous catheter collected blood samples (Glucose Venous) and the dried capillary blood samples (Glucose ADX) for both challenge beverages.
  • FIGS. 23 and 24 illustrate linear regressions comparing the glucose levels detected in the venous samples and the capillary samples for Challenge Beverage A ( FIG. 23 ) and Challenge Beverage B ( FIG. 24 ).
  • DBS dried blood spot
  • FIG. 25 shows plots of the average triglyceride levels detected in the venous catheter collected blood samples (Triglycerides Venous) and the dried capillary blood samples (Triglycerides ADX) for both challenge beverages.
  • FIGS. 26 and 27 illustrate linear regressions comparing the triglyceride levels detected in the venous samples and the capillary samples for Challenge Beverage A ( FIG. 26 ) and Challenge Beverage B ( FIG. 27 ).
  • FIG. 26 shows that there was a strong correlation between the triglyceride levels detected in the venous catheter collected blood sample and the dried capillary blood sample for both challenge beverages, evidencing that triglyceride sampling could be performed using dried blood spot (DBS) technology.
  • DBS dried blood spot
  • the use of dried capillary blood samples as compared to venous liquid samples, requires minimal sample volumes, facilitates non-invasive sampling, does not require special training for collection, and provides stability of the sample at room temperature. All of the benefits facilitate home sample collection and delivery to a clinical laboratory by regular mail.
  • first first
  • second second
  • first contact first contact
  • first contact second contact
  • first contact second contact
  • the term “if” may be construed to mean “when” or “upon” or “in response to determining” or “in accordance with a determination” or “in response to detecting,” that a stated condition precedent is true, depending on the context.
  • the phrase “if it is determined [that a stated condition precedent is true]” or “if [a stated condition precedent is true]” or “when [a stated condition precedent is true]” may be construed to mean “upon determining” or “in response to determining” or “in accordance with a determination” or “upon detecting” or “in response to detecting” that the stated condition precedent is true, depending on the context.

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US11244752B2 (en) 2022-02-08
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US20180240542A1 (en) 2018-08-23
EP3529379B1 (fr) 2022-05-18
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