Methods for and compositions for determining food item recommendations

Abstract

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

What is claimed is: 
     
         1 . A method comprising:
 (a) generating -omic data from a subject;   (b) determining, from the -omic data, the presence of one or more biological conditions in the subject;   (c) accessing a knowledge base, wherein the knowledge base indicates, for each of a plurality of items selected from foods, supplements and ingredients, a desirability rating of consuming the item for the one or more biological conditions; and   (d) for each item, implementing computer logic to determine a final recommendation for the item, wherein the final recommendation is based on the combined desirability ratings for the biological conditions.   
     
     
         2 . The method of  claim 1 , wherein the subject is a human. 
     
     
         3 . The method of  claim 1 , wherein the -omic data is generated from a biological sample from the subject. 
     
     
         4 . The method of  claim 1 , wherein the biological sample comprises a gut microbiome sample or a blood sample. 
     
     
         5 . The method of  claim 1 , wherein generating -omic data comprises performing high-throughput sequencing on nucleic acids from a sample from the subject to produce sequence data. 
     
     
         6 . The method of  claim 1 , wherein the functional activity conditions are determined from functional activity scores determined from the -omic data. 
     
     
         7 . The method of  claim 5 , wherein the functional activity scores are integrative scores comprising more than one type of input data, e.g., KO identifiers, taxonomy identifiers or human gene identifiers. 
     
     
         8 . The method of  claim 5 , wherein the functional activity scores are non-integrative scores comprising only one type of input data. 
     
     
         9 . The method of  claim 1 , wherein the -omic data comprises gut microbiome metatranscriptomic data. 
     
     
         10 . The method of  claim 1 , wherein the biological conditions comprise functional activity conditions. 
     
     
         11 . The method of  claim 10 , wherein the functional activity conditions include or more of: butyrate production pathways, LPS biosynthesis pathways, methane gas production pathways, sulfide gas production pathways, flagellar assembly pathways, ammonia production pathways, putrescine production pathways, oxalate metabolism pathways, uric acid production pathways, salt stress pathways, biofilm chemotaxis in virulence pathways, TMA production pathways, primary bile acid pathways, secondary bile acid pathways, acetate pathways, propionate pathways, branched chain amino acid pathways, long chain fatty acid metabolism pathways, long chain carbohydrate metabolic pathways, cadaverine production pathways, tryptophan pathways, starch metabolism pathways, fucose metabolism pathways, inflammatory activity, metabolic fitness, digestive efficiency, intestinal barrier health, protein fermentation, gas production, microbial richness, detoxification potential (ability of microbiome to detoxify the body), gut neuro-balance (impact of microbiome on the brain, e.g., by production of neurotransmitters), neurological health, cardiovascular health, hormonal balance, musculoskeletal health, hepatic function, urogenital health, mitochondrial activity, immune function, gastrointestinal health, diabetes, skin conditions, infectious disease, stress response, mitochondrial health, mitochondrial biogenesis, oxidative stress, aging and senescence. 
     
     
         12 . The method of  claim 10 , wherein the -omic data comprises metatranscriptomic data, and determining functional activity conditions comprises executing computer logic to determine functional activity scores from the metatranscriptomic data. 
     
     
         13 . The method of  claim 10 , wherein determining comprises: (i) executing computer logic to determine biological pathway scores and taxa activity scores, and (ii) deriving functional activity scores from the biological pathway scores and taxa activity scores. 
     
     
         14 . The method of  claim 12 , wherein determining biological pathway scores comprises determining activity of functional orthologs (e.g., in a KEGG Orthology). 
     
     
         15 . The method of  claim 12 , wherein functional activity scores are measured as continuous variables or in categories. 
     
     
         16 . The method of  claim 15 , wherein a functional activity score outside of a reference range or outside one or more reference categories indicates a functional activity condition. 
     
     
         17 . The method of  claim 1 , wherein the -omic data comprises phenotypic data used to determine phenotypic conditions. 
     
     
         18 . The method of  claim 1 , wherein the -omic data comprises both metatranscriptomic data and phenotypic data. 
     
     
         19 . The method of  claim 1 , wherein the -omic data comprises proteomic, which data is used to determine functional activity conditions. 
     
     
         20 . The method of  claim 1 , wherein the biological conditions includes at least one condition selected from the conditions of Table 1. 
     
     
         21 . The method of  claim 1 , wherein a plurality of items in the knowledge base are foods selected from the foods of Table 2. 
     
     
         22 . The method of  claim 1 , wherein a plurality of items in the knowledge base are supplements selected from the supplements of Table 3. 
     
     
         23 . The method of  claim 1 , wherein a plurality of items in the knowledge base are ingredients selected from the compounds of Table 4. 
     
     
         24 . The method of  claim 1 , wherein the desirability ratings for foods comprise a plurality of ratings hierarchically arranged from least desirable to consume for the condition to most desirable to consume for the biological condition. 
     
     
         25 . The method of  claim 17 , wherein the plurality of desirability ratings is four ratings, wherein two ratings are undesirable ratings (e.g., “avoid” and “minimize”, one rating is highly desirable (e.g., “indulge” or “superfood”) and another rating is desirable or neutral (e.g., “enjoy”). 
     
     
         26 . The method of  claim 1 , wherein the desirability ratings for supplements or ingredients comprise a positive recommendation or no recommendation. 
     
     
         27 . The method of  claim 1 , wherein a plurality of the desirability ratings are based on literature sources and expert curation. 
     
     
         28 . The method of  claim 1 , wherein the logic determines a final recommendation by prioritizing, first, ratings indicating a negative effect of the item on a condition, and, prioritizing second, ratings indicating a most beneficial effect of the item on a condition. 
     
     
         29 . The method of  claim 28 , wherein the hierarchy of rating, from least to most beneficial, is 1-4, and the priority of ratings to produce a final recommendation for a plurality of the items is 1>2>4>3 (e.g., “avoid”>“minimize”>“superfood”>“enjoy”). 
     
     
         30 . The method of  claim 1 , further comprising, determining from the -omic data, a predicted glycemic response by the subject to each of one or more items in the knowledge base, which response indicates a glycemic response desirability rating; and incorporating the glycemic response desirability rating in determining the final recommendation for the item. 
     
     
         31 . The method of  claim 30 , wherein the glycemic response desirability rating is either positive or negative. 
     
     
         32 . The method of  claim 1 , further comprising, determining whether the subject has a sensitivity (i.e., an adverse reaction) for an item; and incorporating any adverse reaction in determining the final recommendation for the item. 
     
     
         33 . A method comprising:
 (a) generating functional activity scores by:
 (i) obtaining a gut microbiome sample from a subject; 
 (ii) sequencing nucleic acids from the sample to produce sequence data; 
 (iii) determining from the sequence data,
 (1) gene (e.g., KEGG Orthology) activity scores; and 
 (2) taxa activity scores; 
 
 (iv) determining from the gene activity scores and the taxa activity scores, a functional activity score for each of a plurality of functional categories; 
   (b) optionally, generating a glycemic response score for each of a plurality of food items selected from foods, supplements and ingredients by:
 (i) executing logic that determines a glycemic response score for a subject based on macronutrient content of the food and the subject's gene activity scores and taxa activity scores; 
   (c) optionally, determining food sensitivities in the subject;   (d) generating phenotype scores by:
 (i) obtaining phenotype data from the subject; 
 (ii) determining, from the phenotype data, a phenotype score for each of a plurality of phenotype categories; 
   (d) accessing from computer memory a food database that includes, for each food item and each sub-optimal functional activity and phenotype status, a hierarchical recommendation of the food or supplement;   (e) generating an overall hierarchical recommendation for each food or supplement based on combined recommendations of a food for each sub-optimal condition present and, optionally, the predicted glycemic response and/or food sensitivity to the food or supplement.   
     
     
         34 . A method comprising:
 a) receiving a biological sample from a subject;   b) sequencing nucleic acids from biological sample to produce nucleic acid sequence data;   c) collecting phenotypic data from the subject;   d) determining phenotypic conditions in the subject from the phenotypic data and functional activity conditions in the subject from the nucleic acid sequence data;   e) accessing a knowledge base that includes for each of a plurality of food items desirability ranking of the food for each of the phenotypic conditions and functional activity conditions present in the subject;   f) using a recommendation engine, executing logic to produce a recommendation for each food item for the subject; and   f) outputting the food recommendations to an electronic device accessible by the subject.