Claims:1. A method for providing dietary recommendations, said method comprising:
- identifying one or more factor selected from a group consisting of microorganisms, biomolecules and a combination thereof,
- identifying one or more metabolites that promote or inhibit, at least one of, growth of the one or more microorganisms and production of the one or more biomolecules;
- preparing a ranked list of food on the basis of the metabolite content thereof;
- based on the identified one or more metabolites and the ranked list of food containing said metabolite, generating a combined list of food for one or more metabolites;
- determining the frequency of recommended food;
- based on the frequency of food, providing dietary recommending food.
2. The method as claimed in claim 1, wherein the method comprises:
- determining the microbiome composition of one or more individuals;
- identifying the factor selected from a group consisting of one or more microorganisms, biomolecules and a combination thereof,
- identifying one or more metabolites that promote, at least one of, the growth of the one or more microorganisms and the production of the one or more biomolecules in said microbiome composition; and
- preparing the ranked list of food on the basis of the metabolite content thereof;
- based on the identified one or more metabolites and the ranked list of food containing said metabolite, generating the combined list of food for one or more metabolites;
- determining the frequency of recommended food;
- based on the frequency of food, providing a personalized recommended food for the one or more individuals.

3. The method as claimed in claim 1 or 2, wherein the selected factor is one or more postbiotic and the process further comprises identifying the microorganisms which produce said one or more postbiotic.
4. The method as claimed in claim 1 or 2, wherein the identifying of the one or more metabolites comprises:
- determining a reference response of one or more microorganisms to one or more metabolites;
- varying an input flux of the one or more metabolites within a predefined range and comparing the obtained response of one or more microorganisms to one or more metabolites with the reference response to determine a percentage of, at least one of, the growth of the one or more microorganisms and the production of the biomolecules; and
- generating a response model of the one or more metabolites that promote, at least one of, the growth of the one or more microorganisms and the production of the biomolecules;
- based on the response model, identifying the one or more metabolites that promote, at least one of, the growth of the one or more microorganisms and the production of the biomolecules.
5. The method as claimed in claim 4, wherein there is at least 5 percent increase in, at least one of, the growth of the one or more microorganisms and the production of the biomolecules as compared to the reference response.
6. The method as claimed in claim 1, wherein based on frequency, food is recommended by
determining the frequency of the one or more food items identified for each metabolite of the one or more metabolites;
assigning a score to the one or more food items identified for each metabolite based on the frequency; and
generating a list of the one or more food items identified for the one or more metabolites based on the score.
7. The method as claimed in claim 2, wherein the microbiome composition of the one or more individual is obtained by:
- collecting one or more biological samples from the one or more individual, wherein the biological samples comprise microorganism nucleic acids associated with the health condition;
- generating a microorganism sequence dataset based on the microorganism nucleic acids;
- identifying the microorganisms and generating the microbiome composition.
8. The method as claimed in claim 1, wherein the factor for improving the health condition is selected using experimentally determined data or information available in public domain.
9. A system for providing dietary recommendations to an individual, the system comprising:
a. a communication bus;
b. a memory to store one or more predefined computer instructions; and
c. a processor coupled with the communication bus that is capable of executing the one or more predefined computer instructions in order to perform one or more functions, the one or more functions including:
- identifying one or more factor selected from a group consisting of microorganisms, biomolecules and a combination thereof,
- identifying one or more metabolites that promote or inhibit, at least one of, growth of the one or more microorganisms and production of the one or more biomolecules;
- preparing a ranked list of food on the basis of the metabolite content thereof;
- based on the identified one or more metabolites and the ranked list of food containing said metabolite, generating a combined list of food for one or more metabolites;
- determining the frequency of recommended food;
- based on the frequency of food, providing dietary recommending food.
, Description:FIELD OF INVENTION
[0001] The present disclosure relates in general to dietary recommendation methods. More particularly, the present disclosure relates to a method and system for providing a personalized dietary recommendation for improving a health condition or wellness.
BACKGROUND OF THE INVENTION
[0002] Recently, in food and health industry, food recommendation systems are receiving increased attention due to their relevance to improving health and wellness. Most of the existing systems are based on recommending food on the basis of nutritional deficiency, user preference, portion-size management, desired gut microbiome.
[0003] US20200066181A1 discloses techniques for generating personalized nutritional recommendations. The disclosed method comprises accessing various types of data from an individual (such as microbiome data, triglycerides data, glucose data, nutritional data, questionnaire data, and the like), which is analyzed by a prediction service to predict the value of different target biomarkers after eating one or more foods.
[0004] US10361003B2 discloses a method of predicting a response of a subject to food. The method comprises electing a food and a context of food intake for which a response of the subject is unknown; accessing a first database having data having data describing the subject but not a response of the subject to the selected food within the selected context of food intake; accessing a second database having data pertaining to responses of other subjects to foods, the responses of the other subjects including responses of at least one other subject to the selected food; and analyzing the databases based on the selected food to estimate the response of the subject to the selected food within the selected context of food intake.
[0005] However, food and diet are complex domains bringing many challenges for recommendation technologies. For making recommendations, thousands of food items/ingredients have to be considered. Besides, because foods/ingredients are usually combined with each other in a recipe instead of being consumed separately, this exponentially increases the complexity of a recommender system. There remains a need for a system which takes into account the complexity of food and the direct effect of its constituents on the factors influencing health and wellness of an individual.
SUMMARY OF THE INVENTION
[0006] The embodiments of the present disclosure described herein provide a method and system for providing a personalized dietary recommendation for improving a health condition or wellness.
[0007] Said method for providing dietary recommendations comprises identifying one or more factor selected from a group consisting of microorganisms, biomolecules and a combination thereof. The method also includes identifying one or more metabolites that promote or inhibit, at least one of, growth of the one or more microorganisms and production of the one or more biomolecules. Further, a ranked list of food is prepared on the basis of the metabolite content of the food. Further, based on the identified one or more metabolites and the ranked list of food containing said metabolite, a combined list of food for one or more metabolites is generated. The method further comprises determining the frequency of recommended food, and based on the frequency of food, providing dietary recommending food.
[0008] Said system for providing dietary recommendations comprises includes a processor coupled with a communication bus. The system also includes a memory for storing one or more predefined computer instructions. The processor is capable of executing the one or more predefined computer instructions stored in the memory in order to perform one or more functions. The processor may include one or more modules to perform the one or more functions. Said function includes identifying one or more factor selected from a group consisting of microorganisms, biomolecules and a combination thereof. Further, the processor identifies one or more metabolites that promote or inhibit, at least one of, growth of the one or more microorganisms and production of the one or more biomolecules. Additionally, a ranked list of food is prepared on the basis of the metabolite content thereof. The processor then, based on the identified one or more metabolites and the ranked list of food containing said metabolite, generates a combined list of food for one or more metabolites. The processor then determines the frequency of recommended food based on the frequency of food and generates the dietary recommendation.
BRIEF DESCRIPTION OF DRAWINGS
[0009] The accompanying drawings, similar reference numerals, may refer to identical or functionally similar elements. These reference numerals are used in the detailed description to illustrate various embodiments and to explain various aspects and advantages of the present disclosure.
[0010] FIG. 1 shows a flowchart illustrating an exemplary method for providing a personalized dietary recommendation for improving a health condition or wellness, in accordance with the embodiment of the present disclosure;
[0011] FIG. 2 shows a flowchart illustrating yet another exemplary method for providing a personalized dietary recommendation for improving a health condition or wellness, in accordance with the embodiment of the present disclosure;
[0012] FIG. 3 is an exemplary illustration of a dietary recommendation system, in accordance with the embodiment of the present disclosure.

DETAILED DESCRIPTION

[0013] For the purpose of promoting an understanding of the principles of the disclosure, reference will now be made to embodiments and specific language will be used to describe the same. It will nevertheless be understood that no limitation of the scope of the disclosure is thereby intended, such alterations and further modifications in the disclosed composition and method, and such further applications of the principles of the disclosure therein being contemplated as would normally occur to one skilled in the art to which the disclosure relates.
[0014] It will be understood by those skilled in the art that the foregoing general description and the following detailed description are exemplary and explanatory of the disclosure and are not intended to be restrictive thereof.
[0015] Reference throughout this specification to “one embodiment” “an embodiment” or similar language means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present disclosure. Thus, appearances of the phrase “in one embodiment”, “in an embodiment” and similar language throughout this specification may, but do not necessarily, all refer to the same embodiment.
[0016] As used herein “food” refers to any nutritious substance that humans or animals eat or drink or that plants absorb in order to maintain life and growth.
[0017] In its broadest scope, the present disclosure relates to a method for providing a personalized dietary recommendation for improving a health condition or wellness. FIG. 1 shows a flowchart illustrating an exemplary method for providing a personalized dietary recommendation, in accordance with the embodiment of the present disclosure. Said method 100 for providing dietary recommendations comprises a step 102 for identifying one or more factor selected from a group consisting of microorganisms, biomolecules and a combination thereof. The method 100 also includes a step 104 for identifying one or more metabolites that promote or inhibit, at least one of, growth of the one or more microorganisms and production of the one or more biomolecule. Further, in step 112 a ranked list of food is prepared on the basis of the metabolite content of the food. Further, in step 106, based on the identified one or more metabolites and the ranked list of food containing said metabolite, a combined list of food for one or more metabolites is generated. The method 100 further comprises in step 108 determination of the frequency of recommended food, and based on the frequency of food, providing dietary recommending food in step 110.
[0018] FIG. 2 shows a flowchart illustrating an exemplary method for providing a personalized dietary recommendation, in accordance with the embodiment of the present disclosure. Referring to FIG. 2, the method 100 comprises a step 112 for determining the microbiome composition of one or more individuals. The method 100 further comprises a step 102 for identifying the factor selected from a group consisting of one or more microorganisms, biomolecule and a combination thereof, followed by a step 104 for identification of one or more metabolites that promote or inhibit, at least one of, the growth of the one or more microorganisms and the production of the one or more biomolecule in said microbiome composition. Additionally, in step 112 a ranked list of food is prepared on the basis of the metabolite content thereof. In step 106, based on the identified one or more metabolites and the ranked list of food containing said metabolite, the combined list of food for one or more metabolites is generated. The method 100 further comprises in step 108 determination of the frequency of recommended food and based on the frequency of food, providing a personalized dietary recommendation in step 110.
[0019] In an embodiment, the microbiome composition of the one or more individual is obtained by collecting one or more biological samples from the one or more individual, wherein the biological samples comprise microorganism nucleic acids associated with the health condition. The sample may include but is not limited to stool sample, oral sample and urine sample. Any known method of obtaining the microbiome composition from the biological sample may be used. In an embodiment, microbial nucleic acid is extracted and a microorganism sequence dataset is generated based on the microbial nucleic acids. Based on the microbial sequence dataset, the microorganisms are identified and the microbiome composition is generated. In an exemplary embodiment, the biological sample is processed to extract the 16S rRNA genes of microorganisms from the consortium of microorganisms, followed by sequencing thereof using any known method to identify the distinct microbial species.
[0020] The factor for improving the health condition is selected using experimentally determined data or information available in public domain. In an embodiment, said factor is selected from a group consisting of microorganisms, biomolecules and a combination thereof. In an embodiment, said microorganisms include those present in gut microbiome. In an embodiment, biomolecules include postbiotics, protein molecules, other known native metabolites. Postbiotics refers to soluble factors (products or metabolic byproducts), secreted by live bacteria, or released after bacterial lysis, such as enzymes, peptides, teichoic acids, peptidoglycan-derived muropeptides, polysaccharides, cell surface proteins, and organic acids. Protein molecules include peptides which are native to the host or microbiome. Other native metabolites refer to the metabolites naturally occurring within the host.
[0021] In an embodiment, when the selected factor is one or more postbiotic, the process further comprises identifying the microorganisms which produce said one or more postbiotic.
[0022] In an embodiment, after the factor is selected, one or more metabolites are identified that promote or inhibit the production of selected factor. In an embodiment, to identify the one or more metabolites, firstly, a reference response is generated. Said reference response comprises effect of a basal diet comprising one or more metabolites on selected factor. In the next step, an input flux of the one or more metabolites is varied within a predefined range. In an embodiment, the response is generated using flux balance analysis model. In an embodiment, flux balance analysis simulations based on constraints of metabolites and gene expressions were performed to obtain reference response. Constraint based analysis allows large-scale or genome-scale metabolic reconstructions for modeling and simulation of microbial metabolism. In an embodiment, these models have been integrated with experimental flux data, to predict the metabolic response of the microorganisms.
[0023] Further, the response obtained by varying the input is compared with the reference response to determine a percentage of, at least one of, the growth of the one or more microorganisms and the production of the biomolecule. In an embodiment, a predetermined percentage increase in, at least one of, the growth of the one or more microorganisms and the production of the biomolecule as compared to reference response, is considered as positive response. Other responses are deemed as negative or unaffected. In an embodiment, positive response is one where there is at least 5 percent increase in, at least one of, the growth of the one or more microorganisms and the production of the biomolecule as compared to reference response. In an alternate embodiment, when it is desirable to reduce the growth or production of selected factor, predetermined percentage decrease in, at least one of, the growth of the one or more microorganisms and the production of the biomolecule as compared to reference response, is considered as positive response. Other responses are deemed as negative or unaffected. In an embodiment, positive response is one where there is at least 5 percent decrease in, at least one of, the growth of the one or more microorganisms and the production of the biomolecule as compared to reference response.
[0024] Based on the response, a response model is generated. The response model comprises of the one or more metabolites that promote, at least one of, the growth of the one or more microorganisms and the production of the biomolecules. In particular, the response model associates each microorganism and/or biomolecules with each metabolite and indicates positive and negative/unaffected response of the microorganisms to the one or more metabolites on the basis of the reference response. In an embodiment, response model is generated in the form of a response matrix using binary logic variables. Herein, binary variable “1” represents positive response and “0” represents negative response.
[0025] Further, the process comprises preparing a ranked list of food on the basis of the metabolite content thereof. In an embodiment, the higher the metabolite content of food, the higher is the rank of food. In an embodiment, preparing a ranked list of food comprises utilizing a machine learning mechanism, wherein the machine learning mechanism is trained using one or more data. For example, a machine learning model for ranking of foods may be created by training a learning algorithm with a sample data including metabolite abundance, its frequency of occurrence in food, its degradation rate within the food matrix etc.
[0026] In the next step, based on the identified one or more metabolites and the ranked list of food containing said metabolite, a combined list of food is generated for one or more metabolites. Next, frequency of each of the identified food for each metabolite of the one or more metabolites, is determined. A score is assigned to the one or more food items identified for each metabolite. Any known scoring system may be used. The scoring system takes into account the fact that one food item may comprise of one or more metabolites. In an embodiment, the score is a function of frequency of food appearing on the combined list. In a further embodiment, the score is a function of weighted individual metabolite content and frequency of food or a function of linear combination of metabolite content.
[0027] Further, based on the score, a list of the one or more food items identified for the one or more metabolites is generated.
[0028] The method may be used for recommending diet for one or more individual or a population demographic.
[0029] A system for providing dietary recommendations to one or more individual is also disclosed. FIG. 3 is a schematic block diagram of an electronic device 500 for providing a personalized dietary recommendation for improving a health condition or wellness, in accordance with the embodiment of the present disclosure. The electronic device 500 includes a bus 505 or other communication mechanism for communicating information, and a processor 510 coupled with the bus 505 for processing information. The electronic device 500 also includes a memory 515, such as a random-access memory (RAM) or other dynamic storage device, coupled to the bus 505 for storing information and instructions to be executed by the processor 510. The memory 515 can be used for storing temporary variables or other intermediate information during execution of instructions to be executed by the processor 510. The electronic device 500 further includes a read only memory (ROM) 520 or other static storage device coupled to bus 505 for storing static information and instructions for processor 510. A storage unit 525, such as a magnetic disk or optical disk, is provided and coupled to the bus 505 for storing data.
[0030] The electronic device 500 can be coupled via the bus 505 to a display 530, such as a cathode ray tube (CRT), and liquid crystal display (LCD) for displaying information to a user. An input device 535, including alphanumeric and other keys, is coupled to bus 505 for communicating information and command selections to the processor 510. Another type of user input device is a cursor control 540, such as a mouse, a trackball, or cursor direction keys for communicating direction information and command selections to the processor 510 and for controlling cursor movement on the display 530. The input device 535 can also be included in the display 530, for example a touch screen.
[0031] In some embodiments, the processor 510 may be capable of executing the one or more predefined computer instructions to perform one or more functions. The processor 510 may also execute one or more computer modules to perform the one or more functions. In one embodiment, the processor 510 may include a factor selection module, microbiome data module, a metabolite identification module, a food ranking module and an analysis module.
[0032] The factor selection module is configured to identify one or more factor selected from a group consisting of microorganisms, biomolecules and a combination thereof. In accordance with an embodiment, the factor selection module obtains data from various literature sources. In accordance with an embodiment, the one or more factors are selected manually and inputted in the module.
[0033] The microbiome data module may collate the pre-existing microbiome data inputted by the user and create the microbiome data in desired format. The microbiome data of the one or more individual may then be stored in the storage unit 525 or temporarily stored in the memory 515. Alternatively, a user may input the microbiome data of the individual via the input device 535 and the data may then be stored in the storage unit 525.
[0034] The processor 510 includes the metabolite identification module to identify one or more metabolites that promote or inhibit, at least one of, growth of the one or more microorganisms and production of the one or more biomolecules. The metabolite identification module receives the factor selected from a group consisting of microorganisms, biomolecules and a combination thereof, from the factor selection module.
[0035] The processor 510 includes the food ranking module to prepare a ranked list of food based on the metabolite content thereof. In accordance with an embodiment, the food ranking module comprises a module to access metabolite content data. In accordance with an aspect, metabolite content data of the one or more food may be stored in the storage unit 525 or temporarily stored in the memory 515. Alternatively, a user may input the metabolite content data of the food via the input device 535 and the data may then be stored in the storage unit 525.
[0036] The processor 510 includes the analysis module configured to analyse the list of identified metabolites and the ranked list of food and generate the dietary recommendation. The analysis module can directly access the metabolite identification module and the food ranking module to generate a combined list of food for one or more metabolites. The analysis module then determines the frequency of recommended food and based on the frequency of food, provides dietary recommendation.
[0037] The electronic device 500 may present the personalized dietary recommendations to user directly via the display 530 of the electronic device 105.
[0038] Various embodiments are related to the use of electronic device 500 for implementing the techniques described herein. In one embodiment, the techniques are performed by the electronic device 500 in response to the processor 510 executing instructions included in the memory 515. Such instructions can be read into the memory 515 from another machine-readable medium, such as the storage unit 525. Execution of the instructions included in the memory 515 causes the processor 510 to perform the process steps described herein.
[0039] The term “machine-readable medium” as used herein refers to any medium that participates in providing data that causes a machine to operate in a specific fashion. In an embodiment implemented using the electronic device 500, various machine-readable medium is involved, for example, in providing instructions to the processor 510 for execution. The machine-readable medium can be a storage media. Storage media includes both non-volatile media and volatile media. Non-volatile media includes, for example, optical or magnetic disks, such as storage unit 525. Volatile media includes dynamic memory, such as the memory 515. All such media must be tangible to enable the instructions carried by the media to be detected by a physical mechanism that reads the instructions into a machine.
[0040] Common forms of machine-readable medium include, for example, a floppy disk, a flexible disk, hard disk, magnetic tape, or any other magnetic medium, a CD-ROM, any other optical medium, punch cards, paper-tape, any other physical medium with patterns of holes, a RAM, a PROM, and EPROM, a FLASH-EPROM, any other memory chip or cartridge.
[0041] In another embodiment, the machine-readable medium can be a transmission media including coaxial cables, copper wire and fibre optics, including the wires that comprise the bus 505. Transmission media can also take the form of acoustic or light waves, such as those generated during radio-wave and infra-red data communications. Examples of machine-readable medium may include but are not limited to a carrier wave as describer hereinafter or any other medium from which the electronic device 500 can read, for example online software, download links, installation links, and online links. For example, the instructions can initially be carried on a magnetic disk of a remote computer. The remote computer can load the instructions into its dynamic memory and send the instructions over a telephone line using a modem. A modem local to the electronic device 500 can receive the data on the telephone line and use an infra-red transmitter to convert the data to an infra-red signal. An infra-red detector can receive the data carried in the infra-red signal and appropriate circuitry can place the data on the bus 505. The bus 505 carries the data to the memory 515, from which the processor 510 retrieves and executes the instructions. The instructions received by the memory 515 can optionally be stored on storage unit 525 either before or after execution by the processor 510. All such media must be tangible to enable the instructions carried by the media to be detected by a physical mechanism that reads the instructions into a machine.
[0042] The electronic device 500 also includes a communication interface 545 coupled to the bus 505. The communication interface 545 provides a two-way data communication coupling to the network 510. For example, the communication interface 545 can be an integrated service digital network (ISDN) card or a modem to provide a data communication connection to a corresponding type of telephone line. As another example, the communication interface 545 can be a local area network (LAN) card to provide a data communication connection to a compatible LAN. Wireless links can also be implemented. In any such implementation, the communication interface 545 sends and receives electrical, electromagnetic or optical signals that carry digital data streams representing various types of information.
[0043] In some embodiments, the electronic device 500 may also correspond to a server 502 that is in communication with an electronic device 555 via the network 550. The server 502 may be similar to the electronic device 500 and may also include, for example, the bus 505, the processor 510, the memory 515, the ROM 520, the storage unit 525, and the communication interface 545. Further, with respect to the server 502, functions and interdependencies between the bus 505, the processor 510, the memory 515, the ROM 520, the storage unit 525, and the communication interface 545 may also be similar to the electronic device 500. Detailed descriptions of the functions and the interdependencies have been omitted for brevity.
Examples
[0044] In order that this invention may be better understood, the following examples are set forth. These examples are for the purpose of illustration only and are not limiting of the invention, and any obvious modifications will be apparent to one skilled in the art.
[0045] Example 1: Dietary Recommendation for constipation reduction
Process: The following steps were followed
1. Selected factor: Tryptamine.
2. It was identified that higher production of tryptamine in the gut aids in reducing constipation.
3. 204 microorganisms with ability to produce tryptamine were identified.
4. A reference response was generated using reference flux of 38 dietary metabolites.
5. The input flux of 38 dietary metabolites was varied for each of the 204 microorganisms to ascertain increase/ decrease in production of tryptamine.
6. Based on this response, a response matrix was generated representing whether or not tryptamine flux value more than 5% of the reference response.
7. Based on the response matrix, a list of top 17 metabolites was generated, using scored metabolite list. (Metabolite score in this case was a function of number of organisms with positive response for that metabolite. A ranked list of metabolites was generated based on the number of organisms with positive response.)
8. For each of the 17 metabolites, a ranked list of food was generated based on the metabolite content of the food.
9. From each of ranked lists in step #8, top 10 foods were selected to generate a combined list of food.
10. Frequency of each unique food from the list generated in step #8 was calculated and each food was scored based on frequency.
11. The scored and ranked list was used for recommendation.

[0046] The obtained food predictions were validated using literature evidence. 7 out of 10 foods show literature support for usefulness of food for constipation reduction. Table 1 shows the result of dietary recommendation for reducing constipation. Table 1 also shows the literature support for usefulness of recommended food for reducing constipation.
Table 1: Dietary recommendation for reducing constipation
Predicted food Literature Support
Amaranth seed, black (Amaranthus cruentus) Prokopowicz, D.. (2001), Kamble S. (2017)
Varagu (Setaria italica) – foxtail millet Sharma,Niranjan (2017)
Wheat flour (Triticum aestivum) -
Dates, dry, dark brown (Phoenix dactylifera) Nasir MU, Sci. Lett, (2015)
Jack fruit, ripe (Artocarpus heterophyllus) Marapana R.A.U.J(2019)
Cane, jaggery (Saccharum officinarum) Samipillai S. (2010)
Sugarcane, juice (Saccharum officinarum) Samipillai S. (2010)
Coconut water Easa, A. M. (2015) and ref. therein
Egg -
Squid, red (Loligo duvaucelii) -

Thus, the dietary recommendation generated by the disclosed method is in accordance with the literature recommendation.
[0047] Example 2: Dietary Recommendation for enhanced bio-production of tryptamine in two subjects
Selected factor: microorganisms producing tryptamine
Gut microbiome from two different subjects was used to elucidate application of the method. Fecal sample from the two subjects were collected and processed to obtain the gut microbiome. The processing involved collection of the fecal sample and extraction of the 16S genes from the consortium of microorganisms. The 16S genes from the consortium of microorganisms were extracted using standardized DNA/gene extraction method. Once the DNA is extracted the bio-samples were sequenced and processed using an in-house customized NGS processing pipeline. The pipeline reported all the known microorganisms in the two subjects in two separate tables (operational taxonomic unit (OTU) table). The OTU tables are processed using the steps stated in Example 1 and metabolites critical for optimizing enhanced bio-production of tryptamine were selected. The list of metabolites was used to further optimize and personalize the recommendation of food for the individual subjects.
[0048] Observation: It was observed that subject 1 reported 121 unique microorganisms. 10 of these microorganisms were reported for influencing tryptamine bio-production and formed the factor list for further processing and optimizing tryptamine bio-production. In contrast, subject 2 reported 70 unique microorganisms and only 3 microorganism influences tryptamine bio-production. Using the method explained in Example 1, the dietary recommendation for the subjects was reported. Table 2 below shows the dietary recommendation for enhanced bio-production of tryptamine.
Table 2: Dietary recommendation for enhanced bio-production of tryptamine in two subjects
Subject No. of Microorganisms forming microbiome Selected Factor Dietary Recommendation
1 121 Haemophilus parainfluenzae;
Citrobacter freundii;
Bifidobacterium ruminantium;
Enterobacter aerogenes;
Enterobacter cloacae;
Streptococcus salivarius;
Aggregatibacter aphrophilus;
Acinetobacter baumanni;
Kluyvera ascorbata;
Streptococcus sanguinis
1. Amaranth Seed, black
2. Dates, dry, dark brown
3. Jack fruit, ripe
4. Sugarcane
5. Bengal gram, dal
2 70 Haemophilus parainfluenzae;
Shigella flexneri;
Solobacterium moorei 1. Dates, dry, dark brown
2. Pangas
3. Amaranth Seed, black
4. Varagu
5. Bengal gram, dal
[0049] Example 3: Dietary Recommendation for reducing obesity in two subjects

The example illustrates application of the method, in accordance with an embodiment of present invention, for increasing the abundance of microorganisms associated with normal BMI range. Stool samples were obtained from two subject and their microbiome composition was obtained in accordance with Example 2.

Selected factor: microorganisms for normal Body Mass Index (BMI)

Observation: It was observed that subject 1 reported 62 unique microorganisms. 2 of these microorganisms were observed to have low relative abundance in obese as compared to normal BMI individuals. In contrast, subject 2 reported 73 unique microorganisms of which 2 organisms were observed to have low relative abundance in obese. These organisms comprised the factor list. Using the method illustrated in Example 1, the dietary recommendation for the subjects was reported. Table 3 below shows the dietary recommendation for increasing the abundance of microorganisms associated with normal BMI range.

Table 3: Dietary recommendation for increasing the abundance of microorganisms associated with normal BMI range in two subjects.

Subject No. of Microorganisms forming microbiome Selected Factor Dietary Recommendation
1 62 Blautia obeum;
Coprococcus comes 1. Amaranth Seed
2. Egg
3. Bengal gram, dal
4. French Beans
2 73 Haemophilus parainfluenzae;
Lactobacillus ruminis 1. Jack fruit, ripe
2. Sugarcane
3. Egg
4. Amaranth Seed

Thus, the disclosed method provides personalized dietary recommendation which differs in different individuals.
[0050] Example 4: Scoring method
The following example illustrates the scoring method, in accordance with an embodiment of the present invention.
The example provides dietary recommendations for reducing constipation using the proposed method and alternate scoring systems. One random sample food list for each the alternate scoring systems is depicted.
The disclosed scoring and ranking method was compared with random selection of foods from the food lists of the selected metabolites. 3 different methods of scoring/ranking food were employed. Method1 contained random sorted food lists per metabolite and selection of frequency based scored food from the obtained combined food list. Method2 contained content based sorted food lists for each metabolite and random selection of food from the combined food list. This method has inherent bias for frequency. Method3 contained content based sorted food list for each metabolite and random selection of combined unique food list where there is no inherent frequency bias. The dietary recommendation obtained in accordance with disclosed process and Method1, Method 2 and Method 3 are stated in Table 4 below.
Table 4: Dietary Recommendation obtained using claimed method and other available scoring methods
Claimed Method Method 1 Method 2 Method 3
Amaranth Seed Pa Choi leaves Jaggery, cane Mango, ripe
Varagu Bitter gourd Crab Wheat, vermicelli
Wheat flour, refined Brinjal Pumpkin leaves Amaranth seed
Dates, dry, dark brown Cucumber Amaranth seed, black Egg, raw
Jack fruit, ripe Apple, green Lotus root Rice puffed
Sugarcane juice Cardamom, green Egg, yolk, raw Dates, processed
Coconut water Turkey Sheep, spleen Turkey
Egg, white, boiled Goat, liver Turkey Arecanut, dried, red
Egg
Squid, red Litchi Onion, stalk
Agathi leaves

The average % overlap with food list obtained using disclosed 2-level ranking was compared with these three methods. Table 4 shows the result of comparison of different methods.
Table 5: Comparison of different scoring methods
% food coverage w.r.t. disclosed method
Method1 < 5%
Method2 < 20%
Method3 < 20%
Observation: It was observed that that other scoring/ranking methods could not capture the foods obtained using 2-level scoring method.
Industrial Applicability:
[0051] This disclosed method provides personalized dietary recommendation based on the direct effect of one or more metabolites on factors responsible for certain health condition of wellness. The disclosed method of dietary recommendation of food can modulate growth of one or more bacteria or biomolecule production by one or more bacteria, by considering microbial metabolism. The method considers gut microbiome and/or biomolecules which are determinant of host physiology.
[0052] Using the disclosed method, an individual is provided with accurate, personalized dietary choices. Instead of providing a diet with food choices aimed at the general population, the disclosed method provides personalized food choices for the individual.
[0053] Using the disclosed method, an individual may be able to reduce weight, improve their metabolism and microbiome, avoid obesity and improve health outcomes including diseases such as cardiovascular disease, type 2 Diabetes, metabolic syndrome and the like more effectively as compared to known nutritional recommendations aimed at the general population.
[0054] In an embodiment, the method is based on identifying postbiotics which are beneficial to an individual and recommending a diet for the same. This is in line with gradually shifting focus of the health and food industry, which is recognizing postbiotics as favorable and promising alternative supplements for human health and wellness thereof.
[0055] The disclosed method is carried out by metabolite shortlisting and and metabolite content based two level scoring system. It does not require previous data for model training.
[0056] The present disclosure described herein above has several technical advantages including that the method:

• is user-friendly;
• eliminates the requirement of extensive pathology tests to determine the appropriate diet of an individual;
• reduces dependence on literature;
• recommends diet that best fits the physiology of the individual;
• can help recommend diet for various health conditions as well as overall wellness; and
• is flexible and may be used to provide a high level recommendation based on the microbiome content of the individual.

LIST OF ELEMENTS

500 electronic device
502 server
505 bus
510 processor
515 memory
520 ROM
525 storage unit
530 display
535 input device
540 cursor control
545 communication interface
550 network
555 electronic device