Personalized daily nutrient recommendations based on individual genetic risk scores

Abstract

The system and method can be used to determine the specific nutritional supplements, foods, beverages, meals, menus, dietary habits and recipes recommended to an individual that more accurately match the individual's needs based on the individual's personal genetic risk score.A particularly preferred method is to calculate the individual's polygenic risk score for a nutrient based on the individual's SNP genotype profile; classify the individual into a corresponding genetic risk group among a plurality of genetic risk groups based on the individual's polygenic risk score, each of the plurality of genetic risk groups being associated with a different daily dose of the nutrient that is required to reach a sufficient blood level for the subject in the genetic risk group, and identify the daily dose of the nutrient for the corresponding genetic risk group for the individual as the personalized recommended daily intake of the nutrient for the individual.

Description

[Technical field]

[0001] The present disclosure relates to systems and methods for determining personalized recommended daily intakes, such as recommended daily allowances (RDA) for nutrients, based on an individual's genetic information. In some embodiments, the systems and methods can be used to determine specific nutritional supplements, foods, beverages, meals, diets, menus, and recipes recommended to an individual that more accurately match the individual's needs based on the individual's personal genetic risk score. [Background technology]

[0002] Adequate supply of nutrients is essential to ensure the maintenance of health in individuals. To estimate the amount of nutrients that an average person needs to maintain health, regulatory authorities in most countries have established dietary reference values ​​(DRVs) for nutrients or recommended dietary allowances (RDAs) for nutrients. RDAs are average values ​​based on dietary intake data in populations presumed to be healthy, but do not provide recommendations for nutrient intake thresholds for specific individuals. In fact, the European Food Safety Authority (EFSA) has explicitly stated that DRVs are not nutritional targets or recommendations for individuals, but rather are intended to establish guidelines for populations.

[0003] To make more accurate nutritional recommendations for an individual, other factors must be considered. Genetic influences in particular can affect the variability in how nutrients are absorbed and metabolized by the body for each specific individual. Currently, there are no accurate, non-invasive methods to determine an individual's nutritional status and link it to the individual's nutritional needs. Although certain nutrients, such as vitamins and minerals, can be measured by blood tests, these tests only provide a snapshot view of an individual's nutritional status at a particular time, and do not provide a general trend regarding nutrient deficiencies based on genetic predisposition. Summary of the Invention

[0004] Genetic variants affect how nutrients are absorbed and metabolized by the body. Hundreds of genetic variants affect nutritional status in humans. Genetic variants that are single nucleotide polymorphisms (SNPs) of specific alleles are associated with changes in nutritional status, and SNPs can be identified.

[0005] By knowing the effect size for each SNP of each allele, a polygenic risk score can be established that summarizes the cumulative effect of all alleles present in an individual for a given nutrient (e.g., vitamin or mineral level). This genetic effect can then be taken into account when calculating the individual's nutritional requirements and can provide recommendations for the individual's nutrient intake, thereby establishing a personalized RDA based on the individual's genetic profile.

[0006] The present disclosure provides solutions in some embodiments of systems and methods that include algorithmic calculations for converting polygenic risk scores into quantitative recommendations for an individual's nutritional needs.

[0007] For many nutrients, the magnitude of the effect of a single gene variant is small and may not provide a valid recommendation for an individual to consume a nutrient beyond the RDA. The levels of most nutrients, such as vitamins, are influenced by multiple gene variants. For example, vitamin B12 has 30 gene variants that affect its concentration in the blood. Therefore, the evaluation of these nutrients with multiple gene variants can be complicated.

[0008] Polygenic risk scores are a powerful way to determine the magnitude of the combined effect of all genetic variants that affect a trait. For example, in medical applications, polygenic risk scores are used to determine an individual's propensity to develop a disease. In nutrition, polygenic risk scores are rarely used.

[0009] Thus, the present disclosure provides an improvement over the general Recommended Daily Allowances (RDA) for certain nutrients that are recommendations for populations and are typically set by national regulatory agencies. In some embodiments, the present systems and methods provide personalized nutrient recommendations for individual users by taking into account their individual genetic variations as determined by their polygenic risk score profile.

[0010] In some embodiments, systems and methods for transforming genetic information associated with specific genetic variations in alleles associated with nutritional traits are combined into a polygenic risk score.

[0011] In some embodiments, the systems and methods for determining polygenic risk scores can be used to adjust an individual's RDA for multiple nutrients.

[0012] In some embodiments, systems and methods for transforming polygenic risk scores for various nutrients can be used to determine specific nutritional supplements, foods, beverages, meals, diets, menus, and recipes that can be recommended to an individual that are more precisely tailored to improving or maintaining the nutritional health of the individual.

[0013] In some embodiments, the system and method provide personalized supplement and dosage recommendations based on combining dose-response calculations with genetic effect size analysis, for example, by using polygenic risk scores in one algorithm, thereby providing actionable and better patient reporting for healthcare providers. For example, the system and method may generate feasibility scorecards for nutritional traits, polygenic risk scores for selected traits, and requirement estimation algorithms for dosages for each trait. In some embodiments, the system and method use a dose-response algorithm. In a non-limiting embodiment described in Example 3 herein, a polygenic risk score for vitamin B12 and its conversion into an administrable supplementation recommendation were successfully created. Other non-limiting examples of suitable traits for converting polygenic risk scores into administrable supplementation recommendations include zinc, magnesium, vitamin D3, folic acid, vitamin B6, choline, omega-3 fatty acids, glutathione, glycine, dietary response, and estrogen metabolism. The system and method may generate personalized intake recommendations for one or more of these traits.

[0014] Further in this regard, some embodiments address the problem of how to determine the average intake required for a particular nutrient to reach sufficient blood levels for different genetic risk groups. These embodiments preferably apply an approach in which the dose-response relationship between nutrient intake, genetic effects and blood levels is taken into account for the intake recommendation. The solution provided by these embodiments is that a dose-response algorithm is created from nutrient intake and blood level data from nutrition surveys and combined with genetic data to create an algorithm that estimates the required daily intake of a particular nutrient required for a particular genetic risk group. [Brief description of the drawings]

[0015] [Figure 1] 1 is a schematic diagram of a computer-implemented system in accordance with one or more embodiments provided by the present disclosure. [Diagram 2] FIG. 1 is a schematic diagram of a generalized workflow for generating personalized nutritional intake recommendations that include genetic data (SNPs) in one or more embodiments provided by the present disclosure. [Diagram 3] Genetic effects on vitamin D plasma concentrations are shown for quintiles of the polygenic risk score (PRS). [Figure 4] Age-based recommended vitamin D intake for different genetic risk scores. [Diagram 5] 1 is a graph of the probability of vitamin B12 intake achieving sufficient blood levels using the general dose-response model from Example 3 herein. [Figure 6] 1 is a graph of the distribution of intake versus blood concentration dose response for vitamin B12 from Example 3 herein. [Figure 7] Increased vitamin B12 requirements are shown for each additional allele in the polygenic risk score. [Figure 8] 1 is a graph of the change in requirements for different polygenic risk score groups from Example 3 herein. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0016] Dietary Reference Intake (DRI) The Dietary Reference Intakes (DRIs) are a system of nutritional recommendations from the Institute of Medicine (IOM) of the National Academies (USA) introduced in 1997 to expand on existing guidelines known as Recommended Dietary Allowances (RDA).

[0017] Recommended Daily Allowance (RDA) The Recommended Daily Allowance (RDA) is the daily dietary intake level of a nutrient that is considered sufficient to meet 97.5% of the requirements of healthy individuals in each life stage and sex group. The above definition means that only 2.5% of this intake level will result in harmful nutrient deficiencies. The RDA is calculated based on the Estimated Average Requirement (EAR) and is usually about 20% higher than the EAR.

[0018] When the standard deviation (SD) of the EAR is available and nutrient requirements are symmetrically distributed, the RDA is set as 2 SD above the EAR. RDA = EAR + 2SD (EAR)

[0019] When data on variability in requirements are insufficient to calculate the SD, a coefficient of variation (CV) of 10 percent for the EAR is assumed, unless the available data in requirements show a greater variability. If the CV is assumed to be 10%, then twice that amount, when added to the EAR, is defined to be equal to the RDA. The resulting formula for the RDA is: RDA = 1.2 (EAR)

[0020] This intake level statistically represents 97.5 percent of the population's requirement.

[0021] Estimated Average Requirements (EAR) The Estimated Average Requirement (EAR) for a nutrient is calculated to meet the needs of 50% of people in a particular age group, based on a review of the scientific literature.

[0022] Adequate Intake (AI) Adequate intakes (AIs) for nutrients are amounts when an RDA has not been established and are based on what is considered adequate for a particular demographic group.

[0023] Tolerable upper intake levels (UL) The tolerable upper intake level (UL) is a warning against excessive intake of nutrients (such as fat-soluble vitamins) that may be harmful in large amounts. It is the highest daily intake of a nutrient that is likely to be safe and not cause side effects for 97.5% of healthy individuals in each age group and gender group. The above definition means that only 2.5% of people will have a harmful excess of a nutrient at this intake level.

[0024] Different national and regional authorities have different dietary reference values. For example, the European Food Safety Authority (EFSA) uses Population Reference Intakes (PRIs) instead of RDAs and Average Requirements instead of EARs, calling the collective set of information Dietary Reference Values ​​(DRVs). AIs and ULs are defined the same as in the United States, but the values ​​can differ.

[0025] Standard RDA Reference RDAs are generally established for the age and sex of individuals in a particular population.

[0026] For example, Table 1 shows the RDA for a 44 year old male, which is considered the "baseline RDA" for a 44 year old male without taking into account any individual genetic risk scores for each nutrient. [Table 1-1] [Table 1-2] NE: EAR has not yet been established or has not yet been evaluated; ND: UL cannot be determined and it is recommended that intake of these nutrients be limited to food sources only to prevent adverse effects.

[0027] Personalized RDA "Personalized RDA" or "individualized RDA" refers to a recommended daily intake of a particular nutrient that is customized for an individual based on the individual's polygenic risk score for that nutrient.

[0028] The personalized RDA can be compared to a reference RDA to determine whether an individual's needs for a particular nutrient are below average, average, or above average compared to the reference RDA for that nutrient.

[0029] DNA samples for SNP genotyping Generally, the term "sample" as used herein refers to a bodily fluid or other tissue sample type, such as blood, plasma, serum, sputum, saliva, sweat (perspiration), or urine. Techniques for obtaining such samples from a subject are known. The term also includes, for example, fluid samples obtained by contact with other tissue samples or bodily tissues such as exhaled air or by contact with the skin.

[0030] The DNA sample of an individual can be analyzed from any of the biological samples listed above from body fluids or tissues.In a preferred embodiment, the DNA sample is from oral swab.From this DNA sample, the genetic variation of single nucleotide polymorphisms (SNPs) between individuals can be measured by the SNP genotype profile of the individual, and the polygenic risk score of each nutrient for the individual can be determined.

[0031] Single nucleotide polymorphisms Single nucleotide polymorphisms (SNPs) are differences in a single nucleotide on the alleles of a gene. Over 335 million SNPs have been found across multiple human populations. Variations in an individual's DNA sequence can have different effects on how they develop disease and how they respond to pathogens, chemicals, drugs, vaccines, and other agents. SNPs are also important for personalized nutrition, such as metabolic responses to nutrients.

[0032] In some embodiments, reference SNP databases may be queried in the present systems and methods to compare an individual's SNP profile to reference SNP databases. Some reference SNP databases include: dbSNP: National Center for Biotechnology Information (NCBI) SNP database; Kaviar: SNP overview from multiple data sources including dbSNP, SNPedia: a wiki-style database supporting personalized genome annotation, interpretation and analysis OMIM: a database that describes the association between polymorphisms and diseases; dbSAP: Single Amino Acid Polymorphism Database for Protein Mutation Detection; Human Gene Mutation Database: Provides genetic mutations that cause or are associated with human genetic diseases and functional SNPs; International HapMap Project: Researchers can identify tag SNPs to determine the collection of haplotypes present in each subject. GWAS Central: Allows users to visually query actual summary-level association data in one or more genome-wide association studies.

[0033] The GWAS catalogue provides a comprehensive database of published genome-wide association studies and downloadable summary statistics that can be used for meta-analysis (e.g., to create polygenic risk scores).

[0034] Polygenic Risk Score (PGS or PRS) A polygenic risk score, also known as a genetic risk score or genome-wide score, is a numerical value based on the variation at multiple loci and their associated weights. This score serves as the best predictor of the trait. A polygenic score (PGS) is constructed from "weights" or effect sizes derived from genome-wide association studies (GWAS). GWAS genotypes a set of genetic markers, usually SNPs, on a training sample and estimates the effect size for the association of each marker with the trait of interest. These weights are then used to assign individualized polygenic scores to independent replicate samples. For quantitative traits (e.g., BMI, blood vitamin levels, etc.), a PRS combines the quantitative genetic influences on the trait (e.g., changes in vitamin blood levels) with different genetic risk groups.

[0035] Thus, PRS can be used to measure the change in intake vs. blood concentration relationship due to genetic factors of a nutritional trait. In this disclosure, the trait of interest is the recommended daily intake of a particular nutrient, which can be obtained by considering the variation in multiple genetic variants.

[0036] There are a variety of methodologies that can be used to generate weights for SNPs and methods for determining which SNPs to include.

[0037] The simplest, so-called "naive" construction method sets weights equal to the coefficient estimates from the regression of the trait on each genetic variant. The SNPs included may be selected using an algorithm that attempts to ensure that each marker is approximately independent. Failure to account for non-random genetic variant associations typically reduces the predictive accuracy of the score. This is important because genetic variants are often correlated with other nearby variants, so that the weight of a causal variant is weakened when it is more strongly correlated to its neighbors than to null variants. This is called linkage disequilibrium, a common phenomenon resulting from the common evolutionary history of adjacent genetic variants. Further restriction is possible by multiple testing of different sets of SNPs selected at various thresholds, for example all SNPs that are statistically significant hits across the genome, or all SNPs with p<0.05 or all SNPs with p<0.50, as well as the one with the best performance used for further analysis; particularly for highly polygenic traits, the best polygenic scores tend to use most or all SNPs.

[0038] Bayesian methods account for the distribution of effect sizes to improve the accuracy of polygenic scores. One of the most common current Bayesian methods uses "linkage disequilibrium prediction" (abbreviated LDpred) to set a weight for each SNP equal to the mean of its posterior distribution after linkage disequilibrium has been accounted for. LDpred tends to outperform simpler pruning and thresholding methods, especially for large sample sizes.

[0039] Penalized regression methods such as LASSO and Ridge regression can also be used to improve the accuracy of polygenic scores. Penalized regression can be interpreted as setting informative priors for how many genetic variants are expected to affect a trait and the distribution of their effect sizes. In other words, these methods effectively "penalize" large coefficients in the regression model and shrink them conservatively. Ridge regression does this by shrinking predictions with a term that penalizes the sum of squared coefficients. LASSO does something similar by penalizing the sum of absolute coefficients.

[0040] Any of the above methods can be used to calculate a polygenic risk score for a particular nutrient.

[0041] Covariates that are independently correlated with the outcome (i.e., the dose-response of dietary intake to blood concentration) are considered. To perform an automated selection of these variables, which can be very useful when many covariates are available, methods such as LASSO regression or Ridge regression, or methods that combine the advantages of Ridge and LASSO methods, such as Elastic Net regression (Zou and Hastie (2005)), can be used. Covariates can be selected from any variable and type available in the dataset. A non-exhaustive example of a variable list is shown in Table 2. This list is only an example, and in other studies the variables may vary both in value and with respect to the covariates that ultimately model it. In the vitamin B12 example, Elastic Net regression selected only age and sex as covariates for the dose-response model, but the specific value and identity of the covariates may vary depending on the nutrient. [Table 2-1] [Table 2-2] Nutrients The term "nutrient" refers to a compound that provides a beneficial effect to the body, for example, to provide energy, growth, or health. The term includes organic and inorganic compounds.

[0042] As used herein, the term "nutrients" can include, for example, macronutrients, micronutrients, essential nutrients, conditionally essential nutrients, and phytonutrients.

[0043] These terms are not necessarily mutually exclusive. For example, certain nutrients can be defined as either macronutrients or micronutrients depending on a particular classification system or list. The phrase "at least one nutrient" or "one or more nutrients" means, for example, 1, 2, 3, 4, 5, 10, 20 or more nutrients.

[0044] The term "measuring the concentration of one or more nutrients" includes measuring metabolites and / or biomarkers of individual nutrients. Thus, in some embodiments, the concentration of one or more metabolites or other indicators of the above nutrients is measured.

[0045] Macronutrients The term "macronutrient" is known in the art and is used herein according to its standard meaning to refer to a nutrient required in large amounts for the normal growth and development of an organism.

[0046] Macronutrients include, but are not limited to, carbohydrates, fats, proteins, amino acids, and water. Some minerals, such as calcium, chloride, or sodium, are also classified as macronutrients.

[0047] Micronutrients The term "micronutrients" refers to compounds that have a beneficial effect on the body, for example, to provide energy, growth, or health, but that are required in small or minimal amounts. The term includes both organic and inorganic compounds, such as individual amino acids, nucleotides, and fatty acids; vitamins, antioxidants, minerals, trace elements (e.g., iodine), and electrolytes (e.g., sodium chloride and salts thereof).

[0048] An exemplary list of vitamins includes vitamin A, vitamin D, vitamin E, vitamin K, vitamin B1, vitamin B2, vitamin B6, vitamin B12, and vitamin C, retinol, retinyl acetate, retinyl palmitate, beta-carotene, cholecalciferol, ergocalciferol, D-α-tocopherol, DL-α-tocopherol, D-α-tocopherol acetate, D-α-tocopherol acid succinate, phyllochinone, thiamine hydrochloride, thiamine mononitrate ... Examples of effective antioxidants include niacinamide, niacin nitrate, riboflavin, riboflavin sodium-5'-phosphate, nicotinic acid, nicotinamide, D-calcium pantothenate, d-sodium pantothenate, dexapanthenol, pyridoxine hydrochloride, pyridoxine-5'-phosphate, pyridoxine dipalmitate, pteroyl-monoglutamic acid, cyanocobalamin, hydroxocobalamin, D-biotin, L-ascorbic acid, L-sodium ascorbate, L-calcium ascorbate, L-potassium ascorbate, and L-ascorbyl-6-palmitate.

[0049] An exemplary list of minerals includes calcium (Ca), chloride (Cl), chromium (Cr), cobalt (Co) (as part of vitamin B12), copper (Cu), iodine (I), iron (Fe), fluoride (F1), magnesium (Mg), manganese (Mn), molybdenum (Mo), phosphorus (P), potassium (K), selenium (Se), sodium (Na), sulfur (S), and zinc (Zn). Illustrative examples of organic oxides include acetate, citrate, lactate, malate, choline, and taurine.

[0050] An exemplary list of amino acids includes L-alanine, L-arginine, L-cysteine, L-histidine, L-glutamic acid, L-glutamine, L-isoleucine, L-leucine, L-lysine, L-methionine, L-ornithine, L-phenylalanine, L-threonine, L-tryptophan, L-tyrosine, and L-valine.

[0051] An exemplary list of fatty acids includes C4:0, C6:0, C8:0, C10:0, C11:0, C12:0, C13:0, C14:0, C15:0, C16:0, C17:0, C18:0, C20:0, C21:0, C22:0, C24:0, C14:1 n-5, C15:1 n-5, C16:1 n-7, C17:1 n-7, C18:1 n-9 trans, C18:1 n-9 cis, C20:1 n-9, C22:1 n-9, C24:1 n-9, C18:2 n-6 trans, C18:2 n-6 cis, C18:3 n-6, C18:3 n-3, C20:2 n-6, C20:3 n-6, C20:3 n-3, C20:4 n-6, C22:2 n-6, C20:5 n-3, and C22:6 n-3 fatty acids. In the expression CX:, Y, X refer to the total number of carbon atoms in the fatty acid, and Y defines the total number of double bonds in the fatty acid.

[0052] Phytonutrients The term "phytonutrients" refers to bioactive, plant-derived compounds with associated health benefits.

[0053] An illustrative, non-exhaustive list of phytonutrients includes terpenoids (isoprenoids), such as carotenoids, triterpenoids, monoterpenes, and steroids; phenolic compounds, such as natural monophenols, polyphenols (e.g., flavonoids, isoflavonoids, flavonolignans, lignans, stilbenoids, curcuminoids, stilbenoids, and hydrolyzable tannins); aromatic acids (e.g., phenolic acids and hydroxycinnamic acids); capsaicin; phenylethanoids; alkylresorcinols; glucosinolates; betalains, and chlorophylls.

[0054] Essential nutrients The term "essential nutrient" as used herein refers to a nutrient that cannot be synthesized endogenously or cannot be synthesized in the concentrations required for health. For example, an essential nutrient can be a nutrient that must be obtained through a subject's diet.

[0055] An exemplary, non-exhaustive list of essential nutrients includes essential fatty acids, essential amino acids, essential vitamins, and essential nutrient minerals.

[0056] The essential amino acids for humans include phenylalanine, valine, threonine, tryptophan, methionine, leucine, isoleucine, lysine, and histidine.

[0057] Essential fatty acids for humans include alpha-linolenic acid and linoleic acid.

[0058] In addition, a nutrient may be "conditionally essential," depending, for example, on whether a subject has a particular disease, disorder, or genotype.

[0059] Users of the System and Method In some embodiments, the user is a typical consumer of food and beverage products and nutritional supplements.

[0060] In some embodiments, the user is a retailer who may be interested in optimizing recommendations of food and beverage products and nutritional supplements, taking into account personalized product offerings.

[0061] In some embodiments, the user is a healthcare professional interested in optimizing the recommendations of food and beverage products and nutritional supplements with recommended nutritional content personalized for each client.

[0062] In some embodiments, the user is interested in human food and beverage products and nutritional supplements.

[0063] In other embodiments, the user is interested in food and beverage products and nutritional supplements for animals, particularly companion animals such as dogs and cats.

[0064] Systems and methods The systems and methods provided and disclosed herein improve upon general population-based recommendation methods for calculating recommended daily intakes of nutrients by recognizing the importance of individual genetic variations, which, according to preferred embodiments, are determined by analyzing an individual's SNPs from a DNA sample, providing a weighted polygenic risk score for each nutrient, and adjusting the individual's recommended daily intake for at least one nutrient of the plurality of nutrients. For example, each nutrient may have various genetic risk score components that collectively influence the individual's nutritional supplement, food, diet, meal, menu, and recipe recommendations.

[0065] The systems and methods provided further improve upon recommended daily intakes of nutrients by collecting individual genetic data via genetic testing, thus advantageously providing more accurate RDAs for each individual.

[0066] In various embodiments, the systems and methods disclosed herein use any knowledge and distribution characteristics of RDA or other recommended intake regimen.In some embodiments, the systems and methods disclosed herein take into account the upper and lower limits of the recommended intake amount for each nutrient.In some embodiments of the systems disclosed herein, a combination of EAR and RDA is used to determine a reference RDA in a population, and a comparison is made between the reference RDA and the RDA of an individual subject.Then, the disclosed system can determine the personalized RDA amount for each nutrient for an individual over a given period of time.

[0067] In some embodiments, by comparing the personalized RDA to an average population or reference RDA for each nutrient based on information from the individual's food intake, it is possible to determine whether the individual is "low," "average," or "high." In this manner, in some embodiments, the systems and methods disclosed herein allow for a measurement of how the amount of nutrients actually consumed deviates from the recommended personalized RDA for each nutrient over the individual user's daily intake.

[0068] In some embodiments, a nutrient score for a particular nutrient, whose adequate intake is defined in terms of an Adequate Intake (AI) value, is calculated as the lesser of the maximum score and the percentage of the AI ​​value consumed by the individual. Thus, the systems disclosed herein can calculate a score for a nutrient and provide a reference RDA for a particular nutrient even if an EAR and RDA value have not been established for that nutrient.

[0069] In embodiments of the disclosed system, various software and analytical tools are provided to evaluate, plan, and optimize nutritional supplements, foods, beverages, meals, diets, menus, and recipes on an individualized basis, taking into account individual recommended nutritional intakes and how actual nutrient intakes deviate from the personalized recommended nutritional intakes. In various embodiments, the disclosed system determines nutritional adequacy in terms of the minimum amount to be consumed to achieve the individual's recommended daily intake for each nutrient.

[0070] In some embodiments, the systems and methods disclosed herein can be used by nutritionists, health care professionals, and individual users (e.g., users of wearable devices such as smart watches or fitness trackers). In an exemplary embodiment, the systems disclosed herein comprise at least one processor configured to execute an algorithm for calculating an individualized RDA for at least one of a plurality of nutrients. In this embodiment, the algorithm takes into account the RDA for at least one of a plurality of nutrients and the individual-specific recommended intake amount and compares these to the measurements of the plurality of nutrients for each nutrient in the individual's diet.

[0071] In various embodiments, user-specific inputs into the system of the present disclosure are programmable and configurable, and include gender, age, weight, height, physical activity level, whether pregnant or breastfeeding, and the like.

[0072] In some embodiments, the systems disclosed herein are configured to assess the adequacy of nutrient intake with respect to maximum amounts, in these embodiments, the systems take into account toxicity and adverse effects of ingesting large amounts of a particular nutrient or consumable.

[0073] In one embodiment, the disclosed system comprises or is connected to a database containing food or beverage composition items and their respective nutrient contents. In this embodiment, the disclosed system comprises a fuzzy search function that allows a user to input the food or beverage consumed (or to be consumed), so that the database can be searched to find the items that are closest to the items provided by the user, and the amount of nutrients in each of the food or beverage composition items can be calculated and compared to the RDA for each nutrient.

[0074] In various embodiments, the disclosed system further comprises an interface (e.g., a graphical user interface) for displaying the amount of each nutrient available in each food or beverage composition that constitutes the meal. In some embodiments, the interface allows the user to modify the amounts of various foods or beverages consumed, and the nutrient amounts are displayed accordingly based on the revised amounts of food or beverage consumed. In other embodiments, the system is configured to determine the amount of food or beverage consumed using data other than user input, such as by scanning one or more bar codes, QR codes, or RFID tags, or by tracking items ordered from a menu or purchased at a grocery store.

[0075] In some embodiments, the disclosed system includes a recommendation feature that recommends specific foods to an individual that will maximize the individual's overall nutritional balance. In such embodiments, the algorithms implemented by the disclosed system generate a list of recommendations to improve nutritional balance to most closely match the recommended daily intake of nutrients, which can be a valuable tool for dieticians in meal planning and evaluation.

[0076] In various embodiments, the systems disclosed herein calculate one or more nutritional scores tailored to an individual based on the individual's caloric intake range and corresponding healthy ranges of nutrient intake for a given time period. The calculated scores are based on whether the nutrient intake is within a healthy range and are affected by nutrient deficiencies as well as nutrient overconsumption. These scores allow an individual to determine if they are getting enough nutrients and, if not, which nutrients they need to consume in addition. The disclosed systems also provide suggestions for adding or removing consumables that, when consumed (or removed from the diet), will provide the individual with amounts of nutrients determined to be within the individual's healthy nutrient range.

[0077] Various embodiments of the disclosed system display to the user a dashboard or other suitable user interface customized based on the user's nutritional needs in relation to the individual's RDA values ​​for each nutrient determined by the methods provided by the present disclosure.

[0078] Various embodiments of the system of the present disclosure also provide a recommendation function. In these embodiments, after calculating the nutrient intake amount of the first meal, the system of the present disclosure suggests a combination of ingestibles that can be consumed during the remaining period to allow the individual to obtain the nutrients he or she needs. For example, if an individual indicates that they have eaten certain foods for breakfast and lunch, the system of the present disclosure can suggest a dinner menu that ensures that the individual obtains all the nutrients he or she needs for the day and allows the individual to consume an amount of calories that is within the calorie intake range applicable to the individual, resulting in the individual's optimal RDA per nutrient. In this embodiment, the recommendations provided by the system of the present disclosure are optimized. The system determines the impact of multiple foods stored in its database on the overall nutritional health score and suggests foods that result in an optimal increase in multiple nutrients.

[0079] In various embodiments, the disclosed systems store some or all of the values ​​necessary to calculate the RDA in one or more databases. Additionally, the disclosed systems may store a table of calorie intake ranges for individuals based on the individual's age, sex, and weight or body mass index (BMI).

[0080] In another embodiment, the disclosed system provides further customization by allowing the user to specify additional information such as body type, physical activity level, etc. In this embodiment, the disclosed system uses these additional inputs to adjust the optimal calorie intake ranges for different individuals as well as the RDAs of nutrients tracked by the system. For example, if an individual indicates that they are relatively active and fit, the system may adjust the carbohydrate nutrient range upwards to account for the individual's need for additional carbohydrates.

[0081] Thus, various embodiments of the disclosed system can advantageously be used to calculate an individual's nutritional health score by performing the following steps: (1) Preserving reference RDAs for multiple nutrients based on recommendations from nutrition-related authorities. (2) calculating and storing the RDA for each individual user based on the genetic information for multiple nutrients; (3) storing indices of nutrient intake endpoints to allow the system to adjust for over- and under-intake beyond the endpoints, as appropriate for each nutrient. (4) Weighting the scores for each nutrient and / or individual and storing the individual's RDA. (5) calculating the nutritional content for each nutrient for the particular intake and comparing it to the individual's RDA for that particular nutrient. (6) Applying an algorithm to each nutrient to generate intake recommendations by personalizing nutrition for the individual.

[0082] Various embodiments of the disclosed system further advantageously provide nutritional advice to the user based on the calculated nutrients. For example, embodiments of the disclosed system determine the amounts of nutrients needed to place an individual within a healthy amount range for those nutrients. These embodiments then analyze a database of RDAs per consumable (e.g., food or ingredient) to determine a combination of consumables that provides the necessary amounts of nutrients to place the user within a healthy amount range, taking into account the individual's genetic information in terms of the RDAs per nutrient, while placing the user within the optimal caloric intake range for that individual.

[0083] In various embodiments, the disclosed system works in conjunction with a laboratory or other testing facility that uses the disclosed system to generate actual data about individuals. For example, in one embodiment, the disclosed system allows a user to take a DNA test to determine the individual's SNP profile and calculate the individual's RDA for multiple nutrients.

[0084] In another embodiment, the disclosed system allows the user to take further tests, such as a finger prick blood spot test, to determine whether the individual is over- or under-consuming various nutrients. In such an embodiment, this testing and laboratory work allows the system to verify that its recommendations are working, i.e., that the user is actually consuming enough nutrients if the scoring function indicates that the user's intake range is within the desired range. In various embodiments, these verifications can be performed using other body fluids (e.g., urine, saliva, etc.). In these embodiments, the system can calibrate itself to ensure that the user's actual body fluid composition data ensures that the overall nutritional score calculated for an individual means that the individual is actually consuming an adequate amount of the nutrient. For example, to determine whether to meet a score of 100 for a particular RDA of a nutrient, the system can also use body fluid measurements to determine whether the individual is actually consuming a sufficient amount of that nutrient. If an individual is under-consuming (or over-consuming) a given nutrient, the body fluid measurements can be used to modify the scoring algorithm to ensure that a score of 100 actually represents the ideal intake of a particular nutrient for a particular individual.

[0085] In various embodiments, one or more of the above-referenced inputs are drawn from a database of nutritional information. For example, in certain embodiments, a list of nutrients ingested can be generated by allowing a user to input an ingested item and consulting a database of appropriate consumables for a list of nutrients contained in that ingested item. In other embodiments, the user directly inputs the nutrients ingested. In yet other embodiments, the user inputs a food item (e.g., a hamburger) and, if the food item is not in the database, the user also inputs the amount of nutrients in the food item (e.g., the amount of sodium). Thereafter, future inputs for the defined food item (e.g., a hamburger) can reference the previously entered nutrients rather than requiring the user to re-enter nutritional information.

[0086] In some embodiments, the disclosed system includes the ability to use several units of measurement and serving sizes specific to a particular food product and automatically convert between them. Thus, a portion of a food product can be entered as a given amount of grams, kilocalories, or according to some predefined size (e.g., cups, tablespoons, etc.) and converted (or normalized) into a food intake amount compatible with the data stored in the database for that food product.

[0087] Referring now to FIG. 1, there is shown a block diagram illustrating an example of the electrical system of a host device 100 that can be used to implement at least a portion of the computerized recommendation system and recommended nutrient intake amounts disclosed herein.

[0088] In one embodiment, device 100 shown in FIG. 1 corresponds to one or more servers and / or other computing devices providing some or all of the following functions: (a) enabling access to the system of the present disclosure by remote users of the system, (b) providing web page(s) that enable remote users to interface with the system of the present disclosure, (c) storing and / or calculating underlying data necessary to run the system of the present disclosure, such as recommended caloric intake ranges, personalized recommended nutrient intake ranges, and nutrient content of foods, (d) calculating and displaying recommended daily intakes of nutritional components or an overall nutritional health score, and / or (e) making recommendations of foods or other consumables that an individual can consume to help achieve an optimal recommended daily intake or nutritional health score.

[0089] In the example architecture shown in FIG. 1, device 100 includes a main unit 104, which preferably includes one or more processors 106 electrically coupled to one or more memory devices 108, other computer circuits 110, and / or one or more interface circuits 112 by an address / data bus 113. One or more processors 106 may be any suitable processor, such as the INTEL PENTIUM® or INTEL CELERON® family of microprocessors. PENTIUM® and CELERON® are trademarks registered to Intel Corporation and refer to commercially available microprocessors. It should be understood that other commercially available or specially designed microprocessors may be used as processor 106 in other embodiments. In one embodiment, processor 106 is a system on a chip ("SOC") specifically designed for use in the systems of the present disclosure.

[0090] In one embodiment, device 100 further comprises memory 108. Memory 108 preferably includes volatile and non-volatile memory. Memory 108 preferably stores one or more software programs that interact with the hardware of host device 100 and other devices in the system, as described below. Additionally or alternatively, the programs stored in memory 108 may interact with one or more client devices, such as client device 102 (described in more detail below), to provide those devices with access to media content stored in device 100. The programs stored in memory 108 may be executed by processor 106 in any suitable manner.

[0091] The interface circuit(s) 112 can be implemented using any suitable interface standard, such as an Ethernet interface and / or a Universal Serial Bus (USB) interface. One or more input devices 114 can be connected to the interface circuit 112 for inputting data and instructions to the main unit 104. For example, the input device 114 may be a keyboard, a mouse, a touch screen, a trackpad, a trackball, an isopoint, and / or a voice recognition system. In an embodiment in which the device 100 is designed to be operated or interacted with only by remote devices, the device 100 may not include the input device 114. In other embodiments, the input device 114 includes one or more storage devices, such as one or more flash drives, hard disk drives, solid state drives, cloud storage, or other storage devices or solutions, that provide data input to the host device 100.

[0092] One or more storage devices 118 may also be connected to the main unit 104 via the interface circuitry 112. For example, a hard drive, a CD drive, a DVD drive, a flash drive, and / or other storage devices may be connected to the main unit 104. The storage device 118 may store any type of data used by the device 100, as indicated by block 150, including data regarding preferred nutrient ranges, data regarding the nutrient content of various food items, data regarding users of the system, data regarding previously generated individual recommended daily nutrient intakes, data regarding nutritional health scores, data representing weights for calculating the nutritional health scores, sensitivity values ​​for calculating the nutritional health scores, and any other suitable data required to run the disclosed system. Alternatively or additionally, the storage device 118 may be implemented as a cloud-based storage such that access to the storage 118 is via the Internet or other network connection circuitry, such as the Ethernet circuitry 112.

[0093] One or more displays 120 and / or printers, speakers, or other output devices 119 may also be connected to the main unit 104 via the interface circuitry 112. The display 120 may be a liquid crystal display (LCD), a suitable projector, or any other suitable type of display. The display 120 generates visual representations of various data and functions of the host device 100 during operation of the host device 100. For example, the display 120 may be used to display information regarding a database of preferred nutrient ranges, a database of nutrient contents of various food items, a database of users of the system, a database of previously generated individual recommended daily nutrient intakes, a database of nutritional health scores, and / or databases that allow an administrator of the device 100 to interact with the other databases mentioned above.

[0094] In the illustrated embodiment, a user of the computerized personalized nutrition recommendation system interacts with the device 100 using a suitable client device, such as the client device 102. The client device 102 in various embodiments is any device capable of accessing content provided or provided by the host device 100. For example, the client device 102 may be any device capable of running a suitable web browser to access a web-based interface to the host device 100. Alternatively or additionally, one or more applications or parts of applications providing some of the functionality described herein may run on the client device 102, in which case the client device 102 needs to connect with the host device 100 only to access data stored therein, such as data on individual recommended daily intakes for nutrient ranges or nutrient content of various food items.

[0095] In one embodiment, this connection of the devices (i.e., device 100 and client device 102) is facilitated by a network connection over the Internet and / or other networks, illustrated in Figure 1 by cloud 116. This network connection may be any suitable network connection, such as an Ethernet connection, a Digital Subscriber Line (DSL), a WiFi connection, a cellular data network connection, a telephone line-based connection, a connection via coaxial cable, or another suitable network connection.

[0096] In one embodiment, host device 100 is a device that provides cloud-based services such as cloud-based authentication and access control, storage, streaming, and providing feedback. In this embodiment, the specific hardware details of host device 100 are not important to the implementer of the disclosed system; rather, in such an embodiment, the implementer of the disclosed system utilizes one or more application programmer interfaces (APIs) to interact with host device 100 in a convenient manner, such as inputting information about the user's attributes that help determine healthy nutritional ranges, inputting information about foods consumed, and other interactions that are described in more detail below.

[0097] Access to the device 100 and / or the client device 102 can be controlled by appropriate security software or measures. Individual users' access is set by the device 100 and limited to certain data and / or actions, such as entering foods consumed or viewing calculated scores, depending on the identity of the individual. Other users of either the host device 100 or the client device 102 can modify other data, such as weights, sensitivity, or healthy range values, depending on their user identity. Users of the present system may therefore be required to register with the device 100 before accessing content provided by the system of the present disclosure.

[0098] In a preferred embodiment, each client device 102 has a similar architectural or design configuration as described above with respect to device 100. That is, in one embodiment, each client device 102 includes a display device, at least one input device, at least one memory device, at least one storage device, at least one processor, and at least one network interface device. It should be appreciated that the inclusion of such components common to well-known desktop, laptop, or mobile computing systems (including smartphones, tablet computers, etc.) allows client device 102 to facilitate interaction between multiple users of the corresponding system.

[0099] In various embodiments, device 100 and / or device 102 as illustrated in FIG. 1 may actually be implemented as multiple different devices. For example, device 100 may actually be implemented as multiple server devices that work together to implement the media content access system described herein. In various embodiments, one or more additional devices not shown in FIG. 1 interact with device 100 to enable or facilitate access to the system disclosed herein. For example, in one embodiment, host device 100 communicates with one or more public, private, or proprietary information repositories (e.g., public, private, or proprietary information repositories of nutritional information, nutrient content information, health range information, environmental impact information, etc.) via network 116.

[0100] In one embodiment, the system of the present disclosure does not include a client device 102. In this embodiment, the functionality described herein is provided on the host device 100, and a user of the system interacts directly with the host device 100 using the input device 114, the display device 120, and the output device 119. In this embodiment, the host device 100 provides some or all of the functionality described herein as user-facing functionality.

[0101] The system of Figure 1 is configured to calculate an individual user's recommended daily intake for each nutrient based on genetic information and to calculate an overall nutrient score based on foods consumed or to be consumed during the day. Those skilled in the art will appreciate that this functionality is not a function of a general purpose computer, but requires that the computer be specifically programmed with instructions to calculate the results for the recommended daily intake for each nutrient for a specific individual user based on genetic information, which is done using various algorithms described in various embodiments herein.

[0102] In various embodiments, the systems disclosed herein are configured as multiple modules, each performing a specific function or set of functions. The modules in these embodiments may be software modules executed by a general-purpose processor, software modules executed by a dedicated processor, firmware modules executed on an appropriate dedicated hardware device, or hardware modules (such as application specific integrated circuits ("ASICs")) that perform the functions enumerated herein entirely by circuitry. In embodiments in which specialized hardware is used to perform some or all of the functions described herein, the systems disclosed herein may use one or more registers or other data input pins to control settings or adjust the functions of such specialized hardware. For example, a hardware module may be used that is programmed to analyze multiple recommended daily nutritional intakes for each nutrient. In still other embodiments, when the modules performing the various functions described herein are software modules executable by hardware, these modules may take the form of applications or a subset of applications that may be designed to run on a processor that executes a particular predefined operating system environment.

[0103] In another embodiment, one or more devices carried by the user provide real-time information to the system when the user is at a food purchasing facility such as a grocery store or restaurant. Devices such as RFID readers, NFC readers, body-worn camera devices, and mobile phones can receive or determine (such as by scanning RFID tags, reading bar codes, or detecting the user's physical location) the foods available to the user at a particular grocery store or restaurant. The disclosed system then makes recommendations taking into account what foods the user can immediately purchase or consume. In one such embodiment, the disclosed system may send information to the user's mobile phone when the user sits down at a restaurant, recommending that the user select certain items from a menu to optimize the user's nutritional health score over a predetermined period of time. In yet another embodiment, a voice recognition function recognizes inputs provided by the user through speech. In one such embodiment, the voice recognition system listens to the user place an order at a restaurant. In another embodiment, the voice recognition system allows the user to directly speak the items that he or she has consumed or will consume.

[0104] In one embodiment, the system disclosed herein calculates a score between 0.0 and 100 for each nutrient. In this embodiment, the intake range (and therefore the scoring function) is divided into three different regions: (1) intakes of a nutrient between 0.0 and the lower intake limit, which is based on a combination of the EAR and the RDA; (2) intakes between the lower intake limit and the upper intake limit, which corresponds to the UL; and (3) intakes above the upper intake limit. In this embodiment, intakes in the first range indicate a possible deficiency. The closer the intake is to the RDA, the less likely the intake is actually deficient. The second range can be described as the "hemostasis region," where the score approaches and / or equals the maximum value (e.g., 100) for that nutrient. The third range represents over-intake, an intake range where chronic intake in this range is generally not recommended. In the described embodiment, the scores for certain nutrients within the third range decrease until they reach a minimum score (e.g., 0).

[0105] FIG. 2 illustrates an example system according to an embodiment provided by the present disclosure. The system 200 includes a user device 202 and a recommendation system 204. In another embodiment provided by the present disclosure, the recommendation system 204 can be an example of an embodiment of the recommendation system 150 of FIG. 2. The user device 202 may be implemented as a computing device, such as a computer, a smartphone, a tablet, a smartwatch, or other wearable device, through which an associated user can communicate with the recommendation system 204. The user device 202 may also be implemented as a voice assistant, for example, configured to receive voice requests from a user and process the requests either locally on a computing device near the user or on a remote computing device (e.g., at a remote computing server).

[0106] In another embodiment, the user device 202 may be a dispensing device that communicates with the recommendation system 204 to receive user nutrient recommendations and then dispenses personalized nutritional supplements, foods, beverages, meals, menus, or recipes to individual users of the device based on the user's recommended daily intake data from the recommendation engine 212.

[0107] The recommendation system 204 includes one or more of a display 206, an attribute acquisition unit 208, an attribute comparison unit 210, an evidence-based dietary and lifestyle recommendation engine 212, an attribute analysis unit 214, an attribute storage unit 216, a memory 218, and a CPU 220. It should be noted that in some embodiments, the display 206 may additionally or alternatively be located in the user device 202. In one example, the recommendation system 204 may be configured to obtain an individual RDA 240 for each of a plurality of nutrients. For example, a user may install an application on the user device 202 that requests the user to register with a recommendation service. By registering with the service, the user device 202 may send a request for a personalized RDA 240 for each nutrient. In another example, the user may use the user device 202 to access a web portal using user-specific credentials. Through the web portal, the user may have the user device 202 request a personalized RDA recommendation from the recommendation system 204.

[0108] In another example, the recommendation system 204 may be configured to request and obtain a plurality of user attributes 222. For example, the display 206 may be configured to present an attribute questionnaire 224 to the user. The attribute acquisition unit 208 may be configured to acquire the user attributes 222. In one example, the attribute acquisition unit 208 may acquire a plurality of inputs 226 based on the attribute questionnaire 224, and determine a plurality of user attributes 222 based on the plurality of inputs. For example, the attribute acquisition unit 208 may acquire an answer to the attribute questionnaire 224 that suggests that the user's diet is equivalent to the recommended daily allowance ("RDA"), and then determine that the user attributes 222 are equivalent to the RDA of vitamin D per day. In another example, the attribute acquisition unit 208 of the user device may acquire the user attributes 222 directly from the user device 202.

[0109] In another example, the attribute acquisition unit 208 may be configured to acquire the test results of a DNA test kit, the results of a standard health test administered by a medical professional, the results of a self-assessment tool used by the user, or the results of any external or third-party test. The attribute acquisition unit 208 may be configured to determine the user attributes 222 based on the results from any of these tests or tools. For example, the user's SNP profile may be determined by a DNA test kit before the intervention of the RDA nutrition recommendation. The individual user's polygenic risk score may be calculated for each nutrient to determine the individual user's RDA nutrient recommendation for each nutrient. This evaluation may be determined for a period after the new personalized RDA intervention for each nutrient, thereby determining whether there has been an improvement or maintenance of the user's health status.

[0110] The attribute comparison unit 210 may be configured to determine a user genetic risk score 234 based on a comparison between the reference and benchmark RDAs 228 for each nutrient and the user attributes 222. For example, the score may be expressed through a lettering grade, symbol, or any other system of ranking, e.g., "high", "average", "low", or "above average", "average", "below average", allowing a user to interpret how well their current attributes fare among the benchmark references and whether they may have a nutrient deficiency based on their genetic risk profile for a particular nutrient.

[0111] The recommendation system 204 may be further configured to determine a plurality of support opportunities 238 based on the plurality of user attributes 222. The recommendation system 204 may be further configured to identify a plurality of healthy recommendations 240 based on the plurality of support opportunities 238. For example, the evidence-based diet and lifestyle recommendation engine 212 may be cloud-based. The recommendation engine 212 may include one or more of a plurality of databases 242, a plurality of dietary filters 244, and an optimization unit 246. The recommendation engine 212 may identify the plurality of healthy recommendations 240 based on the plurality of opportunities 238 and according to one or more of the plurality of databases 242, the dietary filters 244, and the optimization unit 246.

[0112] In one example, the recommendation engine 212 may be connected to further databases 242 such as a food database, a beverage database, a nutritional supplement database, a menu database, a recipe database, a dietary database, etc., all of which are annotated with the nutrient composition for each nutrient.

[0113] In another example, the recommendation engine 212 may be connected to a dietary restriction filter 244 that may note any user's food allergies or personal preferences for foods or beverages.

[0114] In another example, the recommendation system 204 may be configured to provide continuous recommendations based on prior user attributes. For example, the recommendation system 204 may include an attribute storage unit 216 and an attribute analysis unit 214 in addition to the aforementioned elements. In response to the attribute acquisition unit 108 acquiring the plurality of user attributes 222, the attribute storage unit 216 may be configured to add the acquired user attributes 222 to the attribute history database 248 as a new entry based on when the plurality of user attributes 222 are acquired. For example, when the user attributes 222 are acquired by the attribute acquisition unit 208 at the first meal of a day, the attribute storage unit 216 adds the acquired user attributes 222 to the cumulative attribute history database 248 with the date of the entry, in this example, the first meal of the day. Subsequently, if user attributes 222 are obtained by the attribute receiving unit 208 in the second or further meal of the day, the attribute storage unit 216 also adds these new attributes to the attribute history database 248 and notifies that they were obtained in the second or further meal of the day, while also storing the previous attributes from the first meal of the day for calculating the total nutrient amount per nutrient to achieve the personalized recommended daily intake per nutrient per day.

[0115] The attribute analysis unit 214 may be configured to analyze a plurality of user attributes 222 stored in the attribute history database 248, where analyzing the stored plurality of user attributes 222 includes performing a longitudinal study 250. Continuing with the above example, the attribute analysis unit 214 may perform a longitudinal study of user attributes 222 from each of the sets of user attributes 222 from the first day, from the second day, and all other user attributes 222 found in the attribute history database 248. The evidence-based dietary and lifestyle recommendation engine 212 may be further configured to generate a plurality of healthy recommendations 240 based on at least the stored user attributes 222 found in the attribute history database 248 and the analysis performed by the attribute analysis unit 214.

[0116] In one embodiment, the attribute analysis unit 214 is further configured to repeatedly analyze the plurality of user attributes 222 stored in the attribute history database 248 in response to the attribute storage unit 216 adding a new entry to the attribute history database 248, effectively reanalyzing all of the data in the attribute history database 248 immediately after a new user attribute 222 is obtained. Similarly, the evidence-based dietary and lifestyle recommendation engine 212 may be further configured to repeatedly generate the plurality of healthy recommendations 240 in response to the attribute analysis unit 214 completing the analysis, thereby effectively generating new healthy recommendations 240 that take into account all past and current user attributes 222 each time a new set of user attributes 222 is obtained.

[0117] In various embodiments, user-specific inputs into the system of the present disclosure are programmable and configurable and include gender, age, weight, height, physical activity level, BMI, and the like.

[0118] In one embodiment, the system of the present disclosure comprises or is connected to a plurality of databases 242 that contain food and beverage items, meals, menus or recipes and their respective nutrient content per nutrient. In this embodiment, the system of the present disclosure comprises a fuzzy search function that allows a user to input the food or beverage consumed (or to be consumed) and then search the database to find the items that are closest to the user-provided item. The system disclosed in this embodiment uses the nutritional information stored for the matching food items to determine whether the item is healthy in terms of the RDA per nutrient and whether it is a good choice based on the total RDA required per nutrient per day.

[0119] In various embodiments, the system of the present disclosure further includes an interface (e.g., a graphical user interface) for displaying the amount of each nutrient available in each food that makes up the meal and displaying the possible amount of energy to be ingested. In some embodiments, the interface allows the user to modify the amounts of various foods or energy to be ingested. In other embodiments, the system is configured to determine the amount of food to be ingested or the amount of energy to be ingested using data not entered by the user, such as by scanning one or more bar codes, QR codes, or RFID tags, an image recognition system, or by tracking items ordered from a menu or purchased at a grocery store.

[0120] Various embodiments of the disclosed system display a dashboard or other suitable user interface to the user, customized based on the user's needs. In embodiments of the system disclosed herein, a graphical user interface is provided that, for the first time, advantageously allows a user to input data regarding foods consumed over a given period of time and view an indication of a score appropriately based on energy intake that reflects the overall nutritional content of the consumer's diet.

[0121] In some embodiments, the present disclosure provides methods for recommending daily intakes of nutrients based on individual genetic information, thus providing more accurate nutritional recommendations.

[0122] In some embodiments, the present disclosure provides methods for improving or maintaining nutritional health by providing food, beverage or nutritional supplement recommendations based on an individual's genetic information.

[0123] In some embodiments, the present disclosure provides methods for improving or maintaining nutritional health by providing dietary, menu or recipe recommendations based on an individual's genetic information.

[0124] In one embodiment, the method includes determining a recommended daily intake of a nutrient for an individual, the method comprising: (i) determining a SNP genotype profile of an individual subject from a DNA sample derived from said subject; (ii) comparing the SNP genotype profile of each subject with a reference SNP genotype profile; (iii) calculating a nutrient-specific genetic risk score for the individual for at least one of the plurality of nutrients; (iv) using the genetic risk score effect size to adjust a dose-response algorithm and generate new intake recommendations for the individual; (v) for at least one of the plurality of nutrients, comparing the baseline recommended daily intake of the nutrient to a new recommended daily intake of the nutrient based on the individual's genetic risk score for the nutrient; (vi) providing the individual with a new recommended daily intake for at least one of the plurality of nutrients.

[0125] In another embodiment, the method provides a personalized recommended daily intake based on the individual's genetic risk score for each nutrient classified as high, normal, or low compared to a baseline recommended daily intake for at least one nutrient of the plurality of nutrients.

[0126] In a preferred embodiment, the method provides an SNP genotype profile of an individual subject determined from a DNA sample from said subject by buccal swab.

[0127] In another embodiment, the method provides a personalized recommended daily intake based on the individual's genetic risk score for each nutrient, wherein at least one of the plurality of nutrients is selected from the group comprising vitamins and / or minerals.

[0128] In a preferred embodiment, at least one of the multiple nutrients is Vitamin D.

[0129] In another embodiment, the method provides a personalized recommended daily intake based on the individual's genetic risk score for each nutrient, where the new recommended daily intake for at least one of the plurality of nutrients is recommended nutritional supplements such that the new recommended daily intake of at least one of the plurality of nutrients is satisfied.

[0130] In another embodiment, the method provides a personalized recommended daily intake based on the individual's genetic risk score for each nutrient, where the new recommended daily intake for at least one of the plurality of nutrients recommends foods, beverages, and / or a daily meal plan that satisfies the new recommended daily intake of the at least one of the plurality of nutrients.

[0131] In a preferred embodiment, the method is computer implemented.

[0132] In one embodiment, the computer-implemented method provides a personalized recommended daily intake based on the individual's genetic risk score for each nutrient, where the new recommended daily intake for at least one of the plurality of nutrients is: (i) collecting, via interaction with a computer user interface, nutrient intake data for each individual user per day; (ii) calculating the daily available nutrient intake data for each individual user for each available event at different times during the day; (iii) providing a recommendation for the individual user's recommended daily intake for each nutrient via a computer user interface by recommending nutritional supplements, foods, beverages, and / or meals for the remainder of the day so as to satisfy the individual user's new recommended daily intake for at least one of a plurality of nutrients.

[0133] In another embodiment, a computer-implemented method provides a personalized recommended daily intake based on an individual's genetic risk score for each nutrient, where the individual user's recommended daily intake for the nutrients is connected to a dispensing device that dispenses nutritional supplements, foods, beverages, or complete meals with the recommended daily intake of at least one of a plurality of nutrients personalized for the individual user.

[0134] In some embodiments, nutritional supplement, food, beverage, meal, diet, menu or recipe recommendations take into account the overall nutritional composition and overall daily energy intake recommended for an individual user.

[0135] In a particularly preferred embodiment, the present disclosure provides a method for determining a personalized recommended daily intake of nutrients for an individual, comprising: (i) determining a SNP genotype profile of an individual from a DNA sample from the individual; (ii) calculating the individual's polygenic risk score for a nutrient based on the individual's SNP genotype profile; (iii) classifying the individual into a corresponding genetic risk group of a plurality of genetic risk groups based on the individual's polygenic risk score, wherein each of the plurality of genetic risk groups is associated with a predetermined polygenic risk score or a predetermined range of polygenic risk scores that does not overlap with the ranges of other genetic risk groups, wherein the polygenic risk score for the individual matches the predetermined polygenic risk score of the corresponding genetic risk group or falls within the predetermined range of the corresponding genetic risk group, and each of the plurality of genetic risk groups is associated with a different daily dose of the nutrient required to reach a sufficient blood level of the nutrient for the subject in the genetic risk group; (iv) identifying for the individual a daily dose of the nutrient for the corresponding genetic risk group as a personalized recommended daily intake of the nutrient for the individual.

[0136] In some embodiments of this method, the multiple genetic risk groups are classified according to a genetic-based dose-response model, preferably:

number

[0137] In some embodiments of this method, the method further comprises administering nutrients to the individual in daily dosages specified by the personalized recommended daily intake, preferably for at least one week, more preferably for at least one month, and most preferably for at least one year.

[0138] In some embodiments of this method, the nutrient is selected from the group consisting of vitamin B12, zinc, magnesium, vitamin D3, folic acid, vitamin B6, choline, omega-3 fatty acids, glutathione, and glycine.

[0139] In embodiments where the nutrient is vitamin B12, optionally the genetic-based dose-response model may be as follows:

number

[0140] In some embodiments of this method, the method further comprises: (i) collecting daily available nutrient intake data for an individual via a computer user interface for each available event at different times during the day; (ii) providing, through a computer user interface, a personalized nutrient recommended daily intake for the individual by recommending at least one of a supplement, a food, a beverage, or a meal for the remainder of the day to meet the new recommended nutrient daily intake for the individual; Includes.

[0141] In some embodiments of this method, the method further comprises dispensing at least one of a supplement, a food, a beverage, or a meal from the dispensing device to the individual, the food, beverage, or meal comprising nutrients, preferably in an amount that satisfies a personalized recommended nutrient intake for the individual.

[0142] In another embodiment, the present disclosure provides a method for providing personalized nutrient recommended daily intakes for an individual, the method comprising: (i) determining a general dose-response model for a nutrient; (ii) identifying selected single nucleotide polymorphisms (SNPs) for specific alleles associated with changes in nutrient status and determining an allele effect size for each of the selected SNPs; (iii) modifying a general dose-response model for a nutrient to add a genetic term, thereby forming a genetic-based dose-response model, where the genetic term sums the effects of the selected SNPs present in each subject; (iv) applying a genetically-based dose-response model to the allele effect size for each of the selected SNPs, thereby determining, for each of a plurality of genetic risk groups, a different daily dose of the nutrient required to reach sufficient blood levels of the nutrient for the subjects within the genetic risk group, wherein each of the plurality of genetic risk groups is associated with a polygenic risk score or range of polygenic risk scores that do not overlap with those of the other genetic risk groups; (v) determining the SNP genotype profile of the individual from a DNA sample from the individual; (vi) classifying the individual into a corresponding one of the plurality of genetic risk groups based on the individual's SNP genotype profile, wherein the polygenic risk score for the individual corresponds to a predetermined polygenic risk score for the corresponding genetic risk group or falls within a predetermined range for the corresponding genetic risk group; (vii) identifying for the individual a daily dose of the nutrient for the corresponding genetic risk group as a personalized recommended daily intake of the nutrient for the individual.

[0143] In some embodiments of this method, determining a general dose-response model for a nutrient comprises applying linear regression and ordinary least squares (OLS) model fitting to daily intake data and blood concentration data of the nutrient from a plurality of individuals, preferably where the daily intake data and blood concentration data of the nutrient from the plurality of individuals is provided by one or more databases.

[0144] In some embodiments of this method, a general dose-response model for a nutrient comprises: log(yi)=b0+11 log(xi1)+β2(1)+β3(2)+...βi(n)+σε where yi is the blood concentration of the nutrient, xi is the daily intake of the nutrient, σε is the estimation error of the model, and β2(1)~βi(n) are covariates independently correlated with yi.

[0145] In some embodiments of this method, the selected SNPs are identified from a genome-wide association study, and determining the allele effect size for each of the selected SNPs comprises applying a linear regression to each of the one or more selected SNPs.

[0146] In some embodiments of this method, the genetic-based dose-response model is:

number

[0147] In some embodiments of this method, the nutrient is selected from the group consisting of vitamin B12, zinc, magnesium, vitamin D3, folic acid, vitamin B6, choline, omega-3 fatty acids, glutathione, and glycine.

[0148] In embodiments where the nutrient is vitamin B12, optionally the genetic-based dose-response model may be as follows:

number

[0149] In yet another embodiment provided by the present disclosure, a computer-implemented system is configured to perform one or more of the methods disclosed herein, preferably by storing and / or retrieving the necessary data.

[0150] As used herein, "about," "approximately," and "substantially" are understood to refer to numbers within a certain range of numerical values, e.g., within -10% to +10% of the referenced number, preferably within -5% to +5% of the referenced number, more preferably within -1% to +1% of the referenced number, and most preferably within -0.1% to +0.1% of the referenced number.

[0151] Moreover, all numerical ranges herein should be understood to include all integers, whole or fractional, within that range. Moreover, these numerical ranges should be construed to support claims directed to any number or subset of numbers within that range. For example, a disclosure of 1-10 should be construed to support ranges such as 1-8, 3-7, 1-9, 3.6-4.6, 3.5-9.9, etc.

[0152] As used in this specification and the appended claims, singular words include the plural unless the context clearly indicates otherwise. Thus, references to "a," "an," and "the" generally include the plural of the respective terms. For example, a reference to "an ingredient" or "a method" includes a plurality of such "ingredients" or "methods." The term "and / or" used in the context of "X and / or Y" should be interpreted as "X" or "Y" or "X and Y."

[0153] Similarly, the terms "comprise", "comprises", and "comprising" should be interpreted as inclusive rather than exclusive. Similarly, the terms "include", "including", and "or" should all be interpreted as inclusive unless such interpretation is clearly prevented by the context. However, the embodiments provided by the present disclosure may not include any element not specifically disclosed herein. Thus, the disclosure of an embodiment defined using the term "comprising" is also the disclosure of an embodiment "consisting essentially of" and "consisting of" the disclosed components. As used herein, the term "example", especially when followed by a list of terms, is merely exemplary and illustrative and should not be considered as exclusive or comprehensive. All embodiments disclosed herein can be combined with any other embodiment disclosed herein unless otherwise expressly indicated. EXAMPLES

[0154] Example 1: Genetic prediction of vitamin D levels in individuals From the list of candidate SNPs associated with vitamin D levels in European populations, a gene score model for prediction was designed. SNPs associated with vitamin D levels, as described in the genome-wide association study (GWAS) literature, were extracted from the GWAS catalog (https: / / www.ebi.ac.uk / gwas / , a curated database that holds publicly available results from large-scale GWAS). To filter this database, we focused on results from GWAS conducted in European (Caucasian origin) populations that test the association with "vitamin D measurements" (not "vitamin D deficiency") as the primary endpoint. In particular, references to two GWASs were useful: the GWAS by Ahn et al. (doi:10.1093 / hmg / ddq155), based on 4501 subjects, and the GWAS by Manousaki et al. (doi:10.1016 / j.ajhg.2017.06.014), based on 42,274 subjects. A set of 19 SNPs corresponding to effects on the nmol / L scale was extracted.

[0155] To avoid overlapping association signals, a pruning was applied to filter the SNPs, starting from the SNP with the lower p-value (the strongest association signal) and excluding all subsequent SNPs with r2>0.25 (i.e., carrying the same association signal) as a proxy. The same process was repeated for each subsequent SNP that was not excluded due to weak linkage equilibrium with the previous one. To calculate LD using the PLINK tool, the European reference population of the 1000 Genomes Project was used as the reference population (https: / / www.cog-genomics.org / plink2 / ).

[0156] method The vitamin D levels associated with different gene combinations (or "scores") were theoretically calculated.

[0157] All combinations were calculated assuming 16 informative biallelic genetic markers (3 16= 43,046,721 different scores). All scores were not observed in the Danish population due to very low frequencies in Scandinavia and genetic selection in the population. Strong independence (no correlation) between genetic variants was assumed. For each score, a unit increase value was calculated as the sum of the number of risk alleles across the different genotypes multiplied by the corresponding unit increase available in the GWAS catalogue (Table 3 below).

[0158] The score criteria was defined as homozygous genotype combinations for non-risk alleles (i.e., 0 risk allele count). Vitamin D prevalence in Denmark was set to 40.17 nmol / L using a weighted (population size) calculation of vitamin D levels in non-supplement users men (n=1048) and women (n=1517; see Table 1 in Hansen et al.) as calculated from Hansen et al. (2008; doi:10.3390 / nu10111801). We noted the large (5%-95%) interval associated with the values ​​available in the table of Hansen et al.

[0159] This mean and the mean of the distribution of score-related unit gains were used to estimate vitamin D-related values ​​for the baseline scores. Vitamin D levels were then calculated for each score as the vitamin D baseline score value + score-related unit gain.

[0160] result Table 3 summarizes the information from the selected candidate SNPs after pruning. [Table 3] Score-specific Vitamin D Levels

[0161] Theoretical calculation The mean score-related unit value was estimated to be an increase of 2.71 nmol / L when compared to the reference score. Assuming a vitamin D prevalence of 40.17 nmol / L, vitamin D for the reference score was estimated to be 37.46 nmol / L. For each score ranging from 1 to 32, vitamin D levels were calculated as vitamin D reference score value + score-related unit increase. For example, in subjects homozygous carriers for each of the 16 loci, the unit increase was estimated to be 5.42 and vitamin D levels were estimated to be 37.46 + 5.42 = 42.88 nmol / L. Vitamin D conditional on score ranged from 37.46 to 42.88 nmol.

[0162] Application to the 1000 Genomes Population The score calculation process was then applied to the European population (n=379 subjects) from the 1000 Genomes Project. This population is expected to be representative at the genetic level of European populations. Genetic information was extracted for 16 SNPs and scores were calculated. The mean unit increase was estimated to be 4.297 nmol / L, twice the theoretically calculated value of 2.71 nmol / L. The number of unique scores was 360 (in 379 subjects) for 43,046,721 possible combinations. Scores ranged from 16 to 31, and score-conditional vitamin D ranged from 38.36 to 41.25 mol / L (assuming a mean unit increase of 4.297 nmol / L). Using the theoretical value of 2.71 nmol / L, vitamin D ranged from 39.95 nmol / L to 42.84 nmol / L.

[0163] This population is not representative of the general population for scores on vitamin D, likely due to the characteristics and sample size of the "founder population." Considering the cumulative score frequencies of all genotype combinations, counts of 18 to 27 risk alleles (within the range observed for the 1000 Genomes cohort) were observed as the most frequent counts.

[0164] The appropriate cut-off for determining an adequate 25(OH)D concentration is 50 nmol / L, which was the consensus in Denmark (Danish Health Authority, Recommendations for Vitamin D (in Danish. https: / / www.sst.dk / da / sundhed-og-livsstil / ernaering / d-vitamin (accessed 23 October 2018)). Thus, a 25(OH)D concentration below 25 nmol / L is considered to indicate vitamin D “deficiency”, whereas concentrations between 25 and 50 nmol / L indicate “insufficiency”. Assuming a prevalence of 40.17 nmol / L in the supplement-free Danish population during the spring and current calculation process, it was theoretically expected that all individuals tested would show vitamin D insufficiency. As observed in Hansen et al. for supplement non-users, changing the prevalence to a higher value would likely change the theoretical vitamin D level to a higher value (e.g., +50 in women).

[0165] As mentioned above, due to different effect sizes between SNPs, for a given genetic score, there is a different associated risk and therefore vitamin D level. Using the number of risk alleles as the "unit of measurement", the vitamin D mean level was calculated as shown in Table 4. Genetic scores were classified into classes in Table 5. [Table 4] [Table 5] Model Improvement A large variability in vitamin D was observed in the Danish population (Hansen et al. 2018; doi:10.3390 / nu10111801). The main factors include sex and age as well as season and vitamin D supplementation. These cofactors need to be taken into account when predicting vitamin D levels based on genetic information from a reference sample population. Here, a stable vitamin D prevalence was set at 40.17 nmol / L, but the model could also be adapted to different vitamin D values ​​based on age, sex, season and vitamin D supplementation information from the study subjects.

[0166] Vitamin D Assessment Tool Script To calculate vitamin D levels using parameters based on this example, a script was developed in the program R. This can be fitted to different models of nutrients depending on parameter differences such as different sets of numbers of SNPs, different SNP unit increment values, and then different score average unit increments for different nutrients.

[0167] Example 2: New Recommended Daily Intakes for Vitamin D Vitamin D deficiency (serum 25-hydroxyvitamin D [25(OH)D]) is associated with unfavorable skeletal outcomes, including fractures and bone loss. Severe vitamin D deficiency, with 25(OH)D concentrations <30 nmol / L (or 12 ng / mL), dramatically increases the risk of excess mortality, infections, and many other diseases. Recent large-scale observational data suggest that approximately 40% of Europeans are vitamin D deficient and 13% are severely deficient. Serum / plasma 25(OH)D concentrations in the range of <75 nmol / L (or 30 ng / mL) are considered by most authors to be vitamin D deficient. Genetic association studies have shown that gene variations are significantly associated with reduced plasma vitamin D levels (Figure 3).

[0168] Development of a polygenic risk score for vitamin D From publicly available genome-wide vitamin D association data (https: / / www.ebi.ac.uk / gwas / efotraits / EFO_0004631), we selected 165 single nucleotide polymorphisms to create a polygenic risk score. Linear regression was performed on the selected SNPs for vitamin D in the Arivale cohort. The Arivale cohort consisted of individuals over 18 years of age who self-enrolled in a now-defunct Scientific Wellness Company between 2015 and 2019. Briefly, the majority of Arivale participants (~80%) were residents of Washington or California at the time of the program (for further information on the Arivale cohort, see Wilmanski et al. 2021). The distribution of the PRS was normal, with a mean reduction in vitamin D per risk allele of 0.51 ng / mL. Figure 3 shows the genetic effects on vitamin D plasma concentrations for quintiles of the polygenic risk score (PRS).

[0169] Next, we develop a model to predict vitamin D concentrations for the different polygenic risk groups, taking into account covariates known to affect vitamin D levels (age, sex, BMI, skin color, smoking, and activity level), and estimate the vitamin D intake required to yield a prediction above 75 nmol / L for 95% of subjects in each risk group. To achieve this, a linear model is fitted to the data in the following form: Intake(mcg / day)=(77.79528-[(Age×-0.3)+(Gender×-1.196157)+(BMI×-0.392055)+(p1+SkinColor×32.097239)+(Smoking×-1.410527)+(ActivityLevel×1.667853)+(prs5×2.7126]) / 0.66

[0170] Figure 4 shows the increased vitamin D intake required based on age for different genetic risk scores. As an example, a 30 year old with the lowest genetic risk score would require approximately 60 mcg / day of vitamin D (2400 IU), while a person of the same age with the highest genetic risk score would require approximately 90 mcg / day (3600 IU) to reach a plasma level of >75 nmol / L.

[0171] Example 3: Algorithm to estimate daily vitamin intake required for an individual to reach sufficient vitamin B12 levels based on genetic risk score Vitamin B12 (cobalamin) is important for producing red blood cells and is involved in nerve function. Low levels of vitamin B12 are associated with an increased risk of neurological disorders and coronary artery disease (Langan et al. 2017 and Kumar et al. 2009). Vitamin B12 is found primarily in animal-derived food sources, especially red meat, fish, and dairy products. Serum levels below 200-250pcg / mL indicate vitamin B12 deficiency (Langan, 2017; Vidal-Alaball et al., 2005; Wong, 2015). Langan (2017) establishes that vitamin B12 levels above 400pcg / mL are sufficient and should be verified if between 150pcg / mL and 400pcg / mL.

[0172] The recommended daily intake of vitamin B12 in adults is set by most national food agencies at 2.4mcg to 3mcg per day. However, these values ​​are derived from the average intake levels of vitamin B12 in healthy populations in epidemiological studies and do not reflect adequate blood levels. In fact, as shown in Figure 5, in the large-scale National Nutrition Examination Survey (NHANES) in the United States, less than 70% of the general population achieved adequate blood levels with an intake of 2.4mcg of vitamin B12 per day. The variability in response to vitamin B12 intake is partly explained by genetic factors that affect vitamin B12 absorption and metabolism.

[0173] A general dose-response algorithm for vitamin B12. Therefore, it is necessary to model genetic influences on the dose-response of vitamin B12 intake to blood levels in order to estimate individual requirements.

[0174] One solution to describe the general dose-response of vitamin B12 can be achieved by using the concept of linear regression and an ordinary least squares (OLS) model fitting procedure used to model the relationship between habitual intake and concentration.

[0175] To establish a general dose-response model, both daily intake and blood concentration data are required. The data used in this example is from the National Health and Nutrition Survey (NHANES) database, and the following description is based on a pamphlet from the National Center for Health Statistics (National Center for Health Statistics, 2014). This database is composed of a data set that includes data over a two-year interval. Each year, there are approximately 5,000 participants from 15 counties across the United States. The data collection is intended to provide health and nutritional status data useful for research and national policy. The survey includes demographic, socioeconomic, dietary, and health-related questions, including a 24-hour food recall and clinical blood biochemistry. Vitamin B12 intake can be extracted from the 24-hour food recall data using food databases and programs such as the USDA (https: / / fdc.nal.usda.gov). Figure 6 shows the raw data of vitamin B12 intake vs. blood concentration in NHANES. From this data, a first general dose-response model algorithm can be derived in the following form: log(yi)=β0+β1log(xi1)+β2Age+β3Gender+σε

[0176] where yi is the blood concentration, xi is the daily intake, and σε is the estimation error of the model. Age and Gender are independent covariates that affect the dose-response.

[0177] Application of this model to vitamin B12 data from NHANES shows that a) there is a large variability in blood B12 concentrations at equal vitamin B12 intake levels (Figure 6), and b) only 70% of individuals achieve sufficient vitamin B12 blood levels at an intake of 2.4 mcg / day (the US RDA) (Figure 5). Part of the variability in response can be attributed to genetic factors.

[0178] Development of a polygenic risk score for vitamin B12 From publicly available genome-wide vitamin B12-related data ( https: / / www.ebi.ac.uk / gwas / efotraits / EFO_0004631 ), single nucleotide polymorphisms were selected to create a polygenic risk score (Table 6 ). [Table 6] Linear regression was performed on selected SNPs for vitamin B12 in the Arivale cohort. The Arivale cohort consisted of individuals over 18 years of age who self-enrolled in a now-defunct Scientific Wellness Company between 2015 and 2019. Briefly, the majority of Arivale participants (approximately 80%) were residents of Washington or California at the time of the program (see Wilmanski et al. 2021 for further information on the Arivale cohort). Methyl-malonic acid (MMA) was measured as a biomarker of blood B12 concentration. MMA is inversely correlated with vitamin B12 blood levels. The distribution of the PRS was normal, with a mean increase in MMA per risk allele of 3.4 units (Table 7), which corresponds to a decrease of approximately 9 pg / mL of vitamin B12 (cobalamin) per risk allele. Figure 7 shows the increase in vitamin B12 requirements for each additional allele in the polygenic risk score. [Table 7] A vitamin B12 dose-response algorithm including genetic influences to calculate personalized requirements To estimate an individual's vitamin B12 requirement, a genetic term summing the effects of all risk alleles present in the individual was added to the dose-response model in the following form:

number

[0179] To achieve vitamin B12 sufficiency for 97% of high-risk individuals, daily vitamin B12 requirements may need to be higher than 1500 mcg / day (current vitamin B12 supplements generally range from 500 to 1500 mcg per capsule) (Figure 8).

[0180] Some studies have also shown that vitamin B12 absorption can be increased by measures such as application in lower doses twice per day or the addition of matrices that increase gastric pH (Brito et al. 2018). These measures could be included in personalized intake recommendations for individuals with high polygenic risk scores.

Claims

1. 1. A method for determining a personalized recommended daily intake of nutrients for an individual, comprising: (i) determining a SNP genotype profile of the individual from a DNA sample derived from the individual; (ii) calculating the individual's polygenic risk score for the nutrient based on the individual's SNP genotype profile; (iii) classifying the individual into a corresponding genetic risk group of a plurality of genetic risk groups based on the individual's polygenic risk score, each of the plurality of genetic risk groups is associated with a predetermined polygenic risk score or a predetermined range of polygenic risk scores that does not overlap with the ranges of other genetic risk groups; the polygenic risk score for the individual matches the predetermined polygenic risk score for the corresponding genetic risk group or falls within the predetermined range for the corresponding genetic risk group; each of the plurality of genetic risk groups is associated with a different daily dose for the nutrient required to achieve a sufficient blood level for the nutrient for the subject within the genetic risk group; (iv) identifying for the individual the daily dose of the nutrient for the corresponding genetic risk group as the personalized recommended daily intake of the nutrient for the individual; A method comprising:

2. 10. The method of claim 1, wherein the plurality of genetic risk groups are classified by a genetically based dose-response model.

3. the genetic-based dose-response model is as follows: [Equation 1] where yi is the blood concentration of the nutrient, xi is the daily intake of the nutrient, σε is the estimation error of the model, and β2(1) to βi(n) are covariates independently correlated with yi. The method of claim 2.

4. 3. The method of claim 1 or 2, further comprising administering the nutrients to the individual at the daily dose specified by the personalized recommended daily intake, preferably for at least one week, more preferably for at least one month, and most preferably for at least one year.

5. the nutrients are selected from the group consisting of vitamin B12, zinc, magnesium, vitamin D3, folic acid, vitamin B6, choline, omega-3 fatty acids, glutathione, and glycine; the nutrient is vitamin B12, and the genetic-based dose-response model comprises: [Equation 2] The method according to claim 1 or 2, wherein

6. 3. The method of claim 1 or 2, wherein the personalized recommended daily intake for at least one of a plurality of nutrients comprises a recommendation for at least one of a supplement, a food, a beverage, or a meal, the supplement, food, beverage, or meal comprising the nutrient and preferably comprising the daily dose of the nutrient, thereby being formulated to meet the personalized recommended daily intake of the nutrient.

7. Furthermore, (i) collecting daily available nutrient intake data for the individual via a computer user interface for each available event at different times during the day; (ii) providing the personalized nutrient recommended daily intake for the individual through the computer user interface by recommending at least one of supplements, foods, beverages, or meals for the remainder of the day to meet the new recommended daily intake for the individual; 3. The method of claim 1 or 2, comprising:

8. 3. The method of claim 1 or 2, further comprising dispensing at least one of a supplement, food, beverage, or meal comprising the nutrient from a dispensing device to the individual, preferably in an amount that meets the personalized recommended intake of the nutrient for the individual.

9. 1. A method of providing personalized nutrient recommended daily intakes for an individual, comprising: (i) determining a general dose-response model for said nutrient; (ii) identifying selected single nucleotide polymorphisms (SNPs) for specific alleles associated with said altered nutrient status and determining an allele effect size for each of said selected SNPs; (iii) modifying the general dose-response model for the nutrient to add a genetic term, thereby forming a genetic-based dose-response model; the genetic term summing the effects of the selected SNPs present in each subject; (iv) using the genetic risk score effect size to adjust a dose-response algorithm and generate new intake recommendations for the individual; (v) applying the genetic-based dose-response model to the allele effect size for each of the selected SNPs, thereby determining, for each of a plurality of genetic risk groups, a different daily dose of the nutrient required to achieve a sufficient blood level of the nutrient for subjects within the genetic risk group, wherein each of the plurality of genetic risk groups is associated with a polygenic risk score or range of polygenic risk scores that does not overlap with those of other genetic risk groups; (vi) determining the individual's SNP genotype profile from a DNA sample derived from the individual; (vii) classifying the individual into a corresponding genetic risk group of the plurality of genetic risk groups based on the SNP genotype profile of the individual, determining whether the polygenic risk score for the individual corresponds to the predetermined polygenic risk score for the corresponding genetic risk group or falls within a predetermined range for the corresponding genetic risk group; (viii) identifying for the individual the daily dose of the nutrient for the corresponding genetic risk group as the personalized recommended daily intake of the nutrient for the individual; A method comprising:

10. 10. The method of claim 9, wherein determining the general dose-response model for the nutrient comprises applying linear regression and ordinary least squares (OLS) model fitting to daily intake data and blood concentration data for the nutrient from a plurality of individuals, preferably wherein the daily intake data and the blood concentration data for the nutrient from the plurality of individuals are provided by one or more databases.

11. the general dose-response model for the nutrient comprises: log(yi)=β0+β1log(xi1)+β2Age+β3Gender+σε 11. The method of claim 9 or 10, wherein yi is the blood concentration of the nutrient, xi is the daily intake of the nutrient, and σε is the estimation error of the model.

12. 11. The method of claim 9 or 10, wherein the selected SNPs are identified from a genome-wide association study, and wherein determining the allele effect size for each of the selected SNPs comprises applying linear regression to each of one or more selected SNPs.

13. the genetic-based dose-response model comprises: [Equation 3] The method according to claim 9 or 10, wherein

14. the nutrients are selected from the group consisting of vitamin B12, zinc, magnesium, vitamin D3, folic acid, vitamin B6, choline, omega-3 fatty acids, glutathione, and glycine; Optionally, the nutrient is vitamin B12 and the genetic-based dose-response model comprises: [Equation 4] The method according to claim 9 or 10, wherein 15. A computer-implemented system configured to perform the method of claim 1 or 9.

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