US 12,444,318 B2
Personalizing food recommendations to reduce glycemic response
Guruduth S. Banavar, Pelham Manor, NY (US); Hal Tily, New York, NY (US); Stephanie Gline, New York, NY (US); Matvey Genkin, New York, NY (US); Vishakh Gopu, Wakefield, RI (US); Eric Patridge, New Rochelle, NY (US); Helen Messier, Cupertino, CA (US); Reza Basseda, Palisades Park, NJ (US); Debra Heald, Calgary (CA); Alla Perlina, San Diego, CA (US); Momchilo Vuyisich, Bothell, WA (US); and Tiep Ba Le, Santa Clara, CA (US)
Assigned to Viome Life Sciences, Inc., Bellevue, WA (US)
Filed by Viome Life Sciences, Inc., Bellevue, WA (US)
Filed on Jun. 30, 2022, as Appl. No. 17/855,666.
Application 17/855,666 is a continuation of application No. 17/429,920, granted, now 12,272,262, previously published as PCT/US2020/018013, filed on Feb. 12, 2020.
Claims priority of provisional application 62/804,737, filed on Feb. 12, 2019.
Prior Publication US 2022/0335853 A1, Oct. 20, 2022
Int. Cl. G09B 19/00 (2006.01); G06N 20/20 (2019.01); G16H 20/60 (2018.01); G16H 50/70 (2018.01)
CPC G09B 19/0092 (2013.01) [G06N 20/20 (2019.01); G16H 20/60 (2018.01); G16H 50/70 (2018.01)] 19 Claims
OG exemplary drawing
 
1. A method for predicting a glycemic response by a subject for one or more food items, comprising:
a) obtaining nucleic acid sequence data in electronic form for a plurality of RNA transcripts from a biological sample from the subject, wherein the nucleic acid sequence data comprises nucleic acid sequences for 10,000 or more RNA transcripts in the plurality of RNA transcripts;
b) determining, using a computer system, from the nucleic acid sequence data, a corresponding functional activity score for each respective functional activity condition in a plurality of functional activity conditions by determining a corresponding measure of transcriptional activity for each respective KEGG ortholog designation in a plurality of KEGG ortholog designations by matching the nucleic acid sequences for each respective RNA transcript in the 10,000 or more RNA transcripts to individual sequences of the plurality of KEGG ortholog designations,
thereby forming a plurality of functional activity scores;
c) obtaining phenotypic data about the subject in electronic form, the phenotypic data comprising a plurality of responses, wherein each respective response in the plurality of responses is provided by the subject in response to a query and relates to a respective phenotypic trait in a plurality of phenotypic traits for the subject;
d) determining, using a computer system, from the phenotypic data, a corresponding phenotype score for each respective biological condition in a plurality of biological conditions by assigning a corresponding numerical value to each respective response in the plurality of responses and combining respective numerical values for one or more respective responses, thereby forming a plurality of phenotype scores;
e) obtaining, in electronic form, food data comprising a respective macronutrient profile for each food item in the one or more food items, wherein each respective macronutrient profile includes a protein content, a fat content, a carbohydrate content, and a fiber content for the corresponding food item;
f) applying, using a computer system, a model to at least (i) the plurality of functional activity scores, (ii) the plurality of phenotype scores, and (iii) the food data for the one or more food items, thereby predicting the glycemic response by the subject for the one or more food items as an output of the model; and
g) administering, to the subject, a dietary modification to alter a macronutrient, micronutrient, or supplement profile of a diet of the subject, wherein the dietary modification comprises increasing an amount of a food item predicted to have low glycemic response or decreasing an amount of a food item predicted to have high glycemic response.