Apparatus, method and recording medium having instructions recorded thereon for determining a gut microbiome index

Abstract

The present disclosure proposes an apparatus for determining a gut microbiome index. The apparatus according to the present disclosure acquires test information on a biological sample of a subject from a testing device, and determines, based on the test information, first information on the similarity of gut microorganisms, second information on the ratio of gut harmful microorganisms, third information on the ratio of gut beneficial microorganisms, and / or fourth information on the diversity of gut microorganisms, and can determine, based on the determined information, a gut microbiome index that indicates the state of the subject's gut microbiome.

Description

[Technical field]

[0001] The present disclosure relates to techniques for determining a gut microbiome index. [Background technology]

[0002] Many microorganisms live in symbiosis in the human body, and although these microorganisms are distributed in the respiratory tract, oral cavity, skin, etc., they are mostly found in the digestive organs, including the large intestine. In this case, when the symbiotic microorganisms form a community without causing disease, it is called a normal flora. The normal flora plays a role in preventing harmful pathogenic microorganisms from taking root in the body. In particular, in the intestines, hundreds of types of microorganisms live in a complex ecosystem, so maintaining a normal flora is important for humans to stay healthy. Homeostasis is when the normal flora and the intestinal immune system work together to maintain a balanced state. However, if an external factor causes an imbalance in the normal flora or the ratio between the normal flora and harmful pathogenic microorganisms changes and deviates from the normal range, it becomes a state of dysbiosis. Recent research has revealed that an imbalance that occurs in the intestines is related to various symptoms or diseases, and it has been reported that it is linked to inflammatory bowel disease, metabolic syndrome, obesity, diabetes, cardiovascular disease, etc.

[0003] Therefore, it is important to accurately understand the state of the intestinal flora in order to determine an individual's health condition or risk of disease. With the development of next-generation sequencing (NGS) technology and bioinformatics that allows the analysis of a wide range of genome information, it is now possible to analyze the intestinal flora in a short period of time. The intestinal microbiome refers to the overall genetic information of the intestinal microbial community analyzed in this way, and the intestinal microbiome can be used to understand the state of the intestinal flora.

[0004] However, the state of the gut microbiome cannot be derived simply based on a few microbial species, but must be approached from an ecological perspective, since the gut microbiome itself is a complex ecosystem of hundreds of microorganisms. However, existing diagnostic methods are limited because they rely only on known functional properties of microorganisms detected in the gut or on the subjects' health records. Furthermore, if the state of the gut microbiome is not accurately diagnosed, attempts to regulate the gut microbiome (e.g., taking probiotics) may be less effective.

[0005] Therefore, a method for diagnosing the health status of a subject by comprehensively considering the ecological indicators of the intestinal microbiome is required. In addition, a method for classifying the intestinal type of a subject based on the intestinal microbiome and proposing a method for adjusting the intestinal microbiome suitable for the intestinal type of the subject is required. Summary of the Invention [Problem to be solved by the invention]

[0006] The present disclosure provides techniques for determining a gut microbiome index. [Means for solving the problem]

[0007] As an aspect of the present disclosure, a device for determining a gut microbiome index may be proposed. The device according to an aspect of the present disclosure may include a communication circuit; one or more processors; and one or more memories storing instructions that, when executed by the one or more processors, cause the one or more processors to perform calculations. The one or more processors use the communication circuit to acquire test information on a subject's biological sample from one or more test devices, and based on the test information, determine first information on a microbial similarity between a preset reference sample and the biological sample, second information on a ratio of the subject's gut harmful microorganisms, third information on a ratio of the subject's gut beneficial microorganisms, and fourth information on the diversity of the subject's gut microorganisms, and based on the first information, the second information, the third information, and the fourth information, determine a gut microbiome index indicating the state of the subject's gut microbiome.

[0008] In one embodiment, the one or more processors may use the communications circuitry to communicate information indicative of the gut microbiome index to a device of the subject.

[0009] In one embodiment, the one or more memories further store information regarding the reference sample, and the one or more processors can determine a similarity between the distribution of abundance ratios of each microbial species in the biological sample and the distribution of abundance ratios of each microbial species in the reference sample based on the test information and the information regarding the reference sample, and determine the first information based on the similarity.

[0010] In one embodiment, the one or more processors can determine an abundance ratio of the harmful microbial species relative to pre-set symbiotic microbial species and harmful microbial species in the biological sample based on the test information, and determine the second information based on the abundance ratio.

[0011] In one embodiment, the one or more processors can determine an abundance ratio of a predetermined beneficial microbial species in the biological sample based on the test information, and determine the third information based on the abundance ratio.

[0012] In one embodiment, the one or more processors can determine the number of all microbial species in the biological sample and the degree of evenness of the distribution of the abundance ratios of each of the all microbial species based on the test information, and determine the fourth information based on the number of all microbial species and the degree of evenness.

[0013] In one embodiment, the one or more processors may determine the gut microbiome index by applying pre-set weighting values ​​to each of the first information, the second information, the third information, and the fourth information.

[0014] As an aspect of the present disclosure, a method for determining a gut microbiome index may be proposed. The method according to an aspect of the present disclosure may be performed in an apparatus including one or more processors and one or more memories in which instructions to be executed by the one or more processors are stored. The method for determining a gut microbiome index may include a step in which the one or more processors acquire test information on a biological sample of a subject from one or more test devices; a step in which the one or more processors determine, based on the test information, first information on a microbial similarity between a preset reference sample and the biological sample, second information on a ratio of gut harmful microorganisms of the subject, third information on a ratio of gut beneficial microorganisms of the subject, and fourth information on the diversity of gut microorganisms of the subject; and a step in which the one or more processors determine, based on the first information, the second information, the third information, and the fourth information, a gut microbiome index indicating the state of the gut microbiome of the subject.

[0015] As an aspect of the present disclosure, a non-transitory computer-readable recording medium having recorded thereon instructions for determining a gut microbiome index may be proposed. The instructions recorded on the recording medium according to an aspect of the present disclosure are instructions for execution on a computer, and when executed by one or more processors, the one or more processors may determine, based on test information on a subject's biological sample obtained from one or more test devices, first information on a microbial similarity between a preset reference sample and the biological sample, second information on a ratio of the subject's gut harmful microorganisms, third information on a ratio of the subject's gut beneficial microorganisms, and fourth information on the diversity of the subject's gut microorganisms, and may determine a gut microbiome index indicating the state of the subject's gut microbiome based on the first information, the second information, the third information, and the fourth information.

[0016] As an aspect of the present disclosure, a device for determining a gut type may be proposed. The device according to an aspect of the present disclosure may include a communication circuit; one or more processors; and one or more memories storing instructions that, when executed by the one or more processors, cause the one or more processors to perform calculations. The one or more processors use the communication circuit to acquire test information on a biological sample of a subject from one or more testing devices, and determine the gut type of the subject to a first type based on a value of an inflammation-specific biomarker for the biological sample among the test information and / or a gut microbiome index indicating a state of the gut microbiome of the subject determined based on the test information, and acquire gut management information associated with the gut type from the one or more memories, and the gut management information may indicate a gut microbiome adjustment plan for the subject having the gut type.

[0017] In one embodiment, the one or more processors may communicate, using the communications circuitry, the information indicative of the bowel type and the bowel management information to a device for the subject.

[0018] In one embodiment, the inflammation-specific biomarker may be at least one selected from microorganisms of the phylum Proteobacteria and the genus Fusobacterium.

[0019] In one embodiment, the one or more processors can determine the intestinal type to be the first type when the abundance ratio of the Proteobacteria microorganisms in the biological sample exceeds 10% or the abundance ratio of the Fusobacterium microorganisms in the biological sample exceeds 1%.

[0020] In one embodiment, the one or more processors may determine the gut type to be the first type if the value of the inflammation-specific biomarker for the biological sample meets a predetermined first criterion and / or the gut microbiome index meets a predetermined second criterion.

[0021] In one embodiment, the one or more processors can determine first information regarding the microbial similarity between a predetermined reference sample and the biological sample based on the test information, second information regarding the ratio of harmful microorganisms in the subject's intestine, third information regarding the ratio of beneficial microorganisms in the subject's intestine, and fourth information regarding the diversity of microorganisms in the subject's intestine based on the first information, the second information, the third information, and the fourth information, and determine the gut microbiome index based on the first information, the second information, the third information, and the fourth information.

[0022] In one embodiment, the one or more processors may determine the gut microbiome index by applying pre-set weighting values ​​to each of the first information, the second information, the third information, and the fourth information.

[0023] In one embodiment, the one or more processors use the communication circuitry to ,before Information indicative of the gut type, the gut management information and the information indicative of the gut microbiome index may be transmitted to a device of the subject.

[0024] In one embodiment, if the intestinal type of the subject has not been determined to be the first type, the one or more processors may determine the intestinal type to be a second type if the presence ratio of Prevotella genus microorganisms in the biological sample exceeds 3%, and may determine the intestinal type to be a third type if the presence ratio is 3% or less.

[0025] In one embodiment, the gut microbiome adjustment suggestion of the gut management information may include at least one selected from the following: intake of a specific probiotic corresponding to the gut type, intake of a specific food, and application of a specific lifestyle habit.

[0026] In one embodiment, the gut microbiome modulation suggestions of the gut management information may be prioritized according to the degree to which they change the gut microbiome index of the subject having the gut type.

[0027] In one embodiment, the gut microbiome modulation plan comprises: (i) recommending a probiotic to a subject classified into the first type, the probiotic comprising one or more microorganisms including one or more Lactobacillus paracasei, one or more Lactobacillus fermentum, one or more Lactobacillus plantarum groups, and / or one or more Lactococcus lactis groups; (ii) recommending a probiotic to a subject classified into the second type, the probiotic comprising one or more microorganisms including one or more Lactobacillus paracasei, one or more Lactobacillus fermentum, one or more Lactobacillus plantarum groups, and / or one or more Lactococcus lactis groups; To the mold offering to the subject classified as such a probiotic containing one or more microorganisms including one or more Lactobacillus fermentum and / or one or more Lactococcus lactis group; or (iii) class 3 To the mold The classified subject may be offered a probiotic comprising one or more microorganisms including one or more Lactobacillus fermentum, one or more Lactobacillus plantarum groups and / or one or more Lactococcus lactis groups.

[0028] In one embodiment, Lactobacillus paracasei may include Lactobacillus paracasei subsp. paracasei, Lactobacillus paracasei subsp. tolerance, or Lactobacillus paracasei subsp. tolerance HM0866 (Accession Number: KCTC14409BP).

[0029] In one embodiment, the Lactobacillus fermentum may include Lactobacillus fermentum HM0740 (Accession Number: KCTC14406BP).

[0030] In one embodiment, the Lactobacillus plantarum group may include one or more selected from the group consisting of Lactobacillus plantarum, Lactobacillus pentosus, Lactobacillus paraplantarum, Lactobacillus fabifermentans, Lactobacillus xiangfangensis, Lactobacillus herbarum, Lactobacillus modestisalitolerans, and subsp. thereof.

[0031] In one embodiment, Lactobacillus plantarum may include Lactobacillus plantarum subsp. plantarum, Lactobacillus plantarum subsp. argentoratensis, or Lactobacillus plantarum subsp. plantarum HM0782 (Accession Number: KCTC14407BP).

[0032] In one embodiment, the Lactococcus lactis group may include one or more selected from the group consisting of Lactococcus lactis subsp. lactis, Lactococcus lactis subsp. hordniae, Lactococcus lactis subsp. cremoris, Lactococcus lactis subsp. tructae, and Lactococcus lactis subsp. lactis HM0850 (Accession Number: KCTC14408BP).

[0033] In one embodiment, Lactobacillus paracasei may include Lactobacillus paracasei subsp. tolerance HM0866 (Accession Number: KCTC14409BP), Lactobacillus fermentum may include Lactobacillus fermentum HM0740 (Accession Number: KCTC14406BP), Lactobacillus plantarum may include Lactobacillus plantarum subsp. plantarum HM0782 (Accession Number: KCTC14407BP), and the Lactococcus lactis group may include Lactococcus lactis subsp. lactis HM0850 (Accession Number: KCTC14408BP).

[0034] As an aspect of the present disclosure, a method for determining a gut type may be proposed. The method according to an aspect of the present disclosure may be a method performed by an apparatus including one or more processors and one or more memories in which instructions to be executed by the one or more processors are stored. The method for determining a gut type includes the steps of: the one or more processors acquiring test information on a biological sample of a subject from one or more testing devices; the one or more processors determining the gut type of the subject to a first type based on a value of an inflammation-specific biomarker for the biological sample from the test information; and the one or more processors acquiring gut management information associated with the gut type from the one or more memories, and the gut management information may indicate a gut microbiome regulation plan for the subject having the gut type.

[0035] According to one aspect of the present disclosure, a non-transitory computer-readable recording medium having recorded thereon instructions for determining a gut type may be proposed. The instructions recorded on the recording medium according to one aspect of the present disclosure are instructions to be executed on a computer, and when executed by one or more processors, the one or more processors determine the gut type of the subject to a first type based on a value of an inflammation-specific biomarker for the biological sample among test information on the biological sample of the subject acquired from one or more test devices, and acquire gut management information associated with the gut type from one or more memories, and the gut management information may indicate a gut microbiome regulation plan for the subject having the gut type. Effect of the Invention

[0036] According to various embodiments of the present disclosure, the health condition of a subject can be diagnosed by comprehensively taking into account various ecological indicators of the subject's gut microbiome.

[0037] According to various embodiments of the present disclosure, a subject's gut type can be classified in detail based on the subject's gut microbiome.

[0038] According to various embodiments of the present disclosure, a method for regulating the intestinal microbiome appropriate for a subject's intestinal type can be provided. [Brief description of the drawings]

[0039] [Figure 1] FIG. 2 is a diagram illustrating an operation process of an apparatus according to an embodiment of the present disclosure. [Diagram 2] FIG. 1 shows a block diagram of an apparatus according to one embodiment of the present disclosure. [Diagram 3] FIG. 1 illustrates a microbial similarity related index according to one embodiment of the present disclosure. [Figure 4] FIG. 1 illustrates a harmful microorganism-associated index according to one embodiment of the present disclosure. [Diagram 5] FIG. 1 illustrates a beneficial microorganism-associated index according to one embodiment of the present disclosure. [Figure 6]FIG. 1 illustrates a microbial diversity related index according to one embodiment of the present disclosure. [Figure 7] FIG. 1 illustrates a function for determining a gut microbiome index according to one embodiment of the present disclosure. [Figure 8] FIG. 13 shows experimental results of the accuracy of the gut microbiome index according to one embodiment of the present disclosure. [Figure 9] FIG. 13 shows experimental results of the accuracy of the gut microbiome index according to one embodiment of the present disclosure. [Figure 10] FIG. 2 is a diagram illustrating an operation process of an apparatus according to an embodiment of the present disclosure. [Figure 11] FIG. 13 illustrates bowel management information according to one embodiment of the present disclosure. [Figure 12] FIG. 1 illustrates a method for determining a gut microbiome index according to one embodiment of the present disclosure. [Figure 13] FIG. 1 is a diagram illustrating a method for determining intestinal type according to one embodiment of the present disclosure. [Figure 14] FIG. 1 shows the change in the average gut microbiome index before and after ingestion of the strains, according to one embodiment of the present disclosure. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0040] The various examples described in this document are exemplified for the purpose of clearly explaining the technical idea of ​​the present disclosure, and are not intended to limit the technical idea to a specific embodiment. The technical idea of ​​the present disclosure includes various modifications, equivalents, alternatives, and embodiments selectively combined from all or part of each embodiment described in this document. In addition, the scope of the technical idea of ​​the present disclosure is not limited to the various examples presented below and the specific description thereof.

[0041] Terms used in this document, including technical or scientific terms, may have the meaning commonly understood by one of ordinary skill in the art to which this disclosure belongs, unless otherwise defined.

[0042] As used in this document, the terms "including," "may include," "include," "may comprise," "can comprise," "have," "can have," and the like refer to the presence of a feature (e.g., a function, operation, or component) of interest, but do not exclude the presence of other additional features, i.e., such terms should be understood as open-ended terms that include the possibility of including other embodiments.

[0043] As used in this document, singular terms may include plural meanings unless the context indicates otherwise, and this also applies to singular terms recited in the claims.

[0044] As used in this document, the terms "first", "second", "primarily", "secondarily", and the like, unless otherwise indicated in the context, are used to distinguish one object from another in referring to multiple similar objects and do not limit the order or importance of those objects among themselves.

[0045] As used herein, phrases such as "A, B, and C," "A, B, or C," "A, B, and / or C," or "at least one of A, B, and C," "at least one of A, B, or C," "at least one of A, B, and / or C," "at least one selected from A, B, and C," "at least one selected from A, B, or C," and "at least one selected from A, B, and / or C" may refer to each listed item or to all possible combinations of listed items. For example, "at least one selected from A and B" may refer to any of (1) A, (2) at least one of A, (3) B, (4) at least one of B, (5) at least one of A and at least one of B, (6) at least one of A and B, (7) at least one of B and A, or (8) A and B.

[0046] As used in this document, the phrase "based on" is used to describe one or more factors that influence the decision, judgement, act or behavior described in the phrase or sentence in which it appears, and does not exclude additional factors that influence that decision, judgement, act or behavior.

[0047] As used in this document, the expression that a component (e.g., a first component) is "coupled" or "connected" to another component (e.g., a second component) may mean that the one component is not only directly coupled or connected to the other component, but also that the one component is coupled or connected through a new other component (e.g., a third component).

[0048] As used herein, the phrase "configured to" may have meanings such as "set to," "capable of," "modified to," "made to," "capable of," etc., depending on the context. The phrase is not limited to the meaning of "specially designed in hardware." For example, a processor configured to perform a particular operation may mean a generic-purpose processor that can perform the particular operation by executing software.

[0049] The term "module" as used herein may refer to software or hardware components such as field-programmable gate arrays (FPGAs) and application specific integrated circuits (ASICs). However, "module" is not limited to hardware and software. A "module" may be configured to be stored on an addressable storage medium and to execute on one or more processors. In one embodiment, a "module" may include components such as software components, object-oriented software components, class components, and task components, as well as processors, functions, attributes, procedures, subroutines, program code segments, drivers, firmware, microcode, circuits, data, databases, data structures, tables, arrays, and variables.

[0050] Descriptions of dimensions, values, and ranges used in this document are not intended to be limited to only those dimensions, values, and ranges, unless otherwise specified in the context, and may mean equivalent ranges that include those dimensions, values, and ranges.

[0051] Hereinafter, various embodiments of the present disclosure will be described with reference to the accompanying drawings. In the accompanying drawings and the description of the drawings, identical or substantially equivalent components may be given the same reference numerals. In addition, in the following description of various embodiments, duplicated descriptions of identical or corresponding components may be omitted, but this does not mean that the components are not included in the embodiments.

[0052] 1 is a diagram showing an operation process of an apparatus 100 according to an embodiment of the present disclosure. The apparatus 100 according to an embodiment of the present disclosure can determine a gut microbiome index of a subject by comprehensively considering various factors.

[0053] A subject (110) and a service provider (120) may exchange samples or information to determine the gut microbiome index of the subject (110). In the present disclosure, the subject (110) may be a person (natural person) who wishes to have his / her gut microbiome diagnosed. In the present disclosure, the service provider (120) may diagnose the gut microbiome of the subject (110) using a biological sample (112) provided by the subject (110). The service provider (120) may be one or more entities that operate one or more testing devices (130) and / or a device (100) for determining the gut microbiome index. In the present disclosure, the microbiome may refer to a microbial community, the entire microbial population, or a microbial ecosystem that coexists and survives in a given environment in the human body. The gut microbiome may refer to a microbial community that lives in the human gut and coexists with humans. In some cases, the microbiome may refer to the entire genetic information of a microbial community. In the present disclosure, the gut microbiome index may be an index indicative of the state (e.g., ecological balance, imbalance, etc.) of the gut microbiome of a subject (110).

[0054] A subject 110 may provide a biosample 112 to a service provider 120. In the present disclosure, a biosample may be a human sample obtained from the body of the subject 110. In one embodiment, the biosample may be a stool sample from the subject 110. In one embodiment, the biosample may be tissue, cells, blood, body fluids, serum, plasma, chromosomes, proteins, red blood cells, hair, urine, saliva, or sweat, etc.

[0055] One or more testing devices (hereinafter, "testing device") (130) may test the biological sample (112) using various methods. In one embodiment, the testing device (130) may perform molecular biological tests such as Next Generation Sequencing (NGS) and Polymerase Chain Reaction (PCR), biomarker tests, genetic tests, etc., on the biological sample (112). Information from various tests (hereinafter, "test information") performed by the testing device (130) on the biological sample (112) of the subject (110) may be transmitted to the device (100). In one embodiment, the test information may be various raw data on the biological sample (112) or may be data that has been subjected to a predetermined process by the testing device (130). In one embodiment, the device (100) may directly receive the test information (132) from the testing device (130). That is, the device (100) may directly communicate with the inspection device (130) through various wired / wireless communication methods to receive the inspection information (132). In one embodiment, the device (100) may indirectly receive the inspection information (132) from the inspection device (130). That is, once the inspection device (130) prepares the inspection information (132), a server or other recording medium may store the inspection information (132), and the device (100) may communicate with the server to receive the inspection information (132) or receive the inspection information (132) from the recording medium. Alternatively, the service provider (120) may input the inspection information (132) to the device (100), and the device (100) may receive the inspection information (132).

[0056] In one embodiment, the test information (132) of the subject (110) is obtained by obtaining genomic DNA of intestinal microorganisms from the subject's biological sample (112), obtaining 16S rRNA genetic information of the intestinal microorganisms from the DNA of the intestinal microorganisms, and analyzing the thus obtained 16S rRNA genetic information of the intestinal microorganisms to determine the species ( species) level and information obtained by performing a step of obtaining the occupancy ratio of these bacterial species. Based on such test information (132), the first to fourth information of the subject can be determined. In addition, the 16S rRNA genetic information can be analyzed using a Next Generation Sequencing (NGS) platform.

[0057] In one embodiment, analyzing the 16S rRNA genetic information of gut microbes can specifically amplify the variable region of 16S rRNA. Primer The method may include a step of performing PCR using a primer set, preferably a step of performing PCR using a primer set capable of specifically amplifying the V3-V4 region of 16S rRNA, and more preferably a step of performing PCR using a universal primer having the following sequence to generate an amplicon, exemplary sequences of the universal primer are as follows:

[0058] Forward universal primer (SEQ ID NO: 75): 5'-CCTACGGGNGGCWGCAG-3'

[0059] Reverse universal primer (SEQ ID NO: 76): 5'-GACTACHVGGGTATCTAATCC-3'

[0060] In one embodiment, obtaining the species-level bacterial species and the occupancy ratio of these species in the intestinal microbial community can be performed by analyzing the bacterial community information from the phylum to the species level using the 16S ribosomal RNA gene sequence database (EzTaxon) and the EasyBioCloud analysis system (http: / / www.ezbiocloud.com) of standard strains and uncultured microorganisms, using thousands of gene sequences generated from one sample by the next generation genome sequence analysis (NGS) technique. If the results of the next generation genome sequence analysis technique are the same, the method for analyzing the community information is not limited to EzTaxon and the EasyBioCloud analysis system. Here, the database used for identifying and classifying microorganisms can be appropriately selected and used by a person skilled in the art as necessary, and may be, for example, one or more databases selected from the group consisting of EzBioCloud, SILVA, RDP, and Greengene, but is not limited thereto.

[0061] In one embodiment, the size of a microbial community may be expressed as the percentage of a specific microbial community in the total gut microbial flora, and the percentage of the microbial community may be expressed as the frequency of 16S rRNA reads of a specific microorganism among the total sequencing reads.

[0062] In one embodiment, the device 100 can obtain the test information 132 from the test device 130 and determine various information related to the gut microbiome of the subject 110 based on the test information 132. The gut microbiome may include a large number of microorganisms. One or two microbial species do not cause health abnormalities or diseases, but all microorganisms in the gut microbiome contribute to the gut ecological balance with their own or collective roles. Therefore, in order to diagnose whether the gut microbiome is in an ecological imbalance, various factors need to be considered comprehensively from an ecological perspective. Thus, the device 100 can determine various information related to the gut microbiome of the subject 110. Factors that affect the gut ecological balance / imbalance may include the similarity of gut microorganisms, the ratio of gut harmful microorganisms, the ratio of gut beneficial microorganisms, and / or the diversity of gut microorganisms. Here, the similarity of gut microbes may refer to the degree to which the gut microbiome of the subject (110) is similar to that of a healthy human (hereinafter, a "healthy individual"). A healthy individual may refer to a human whose gut microbiome is classified as being normal or in a balanced state according to a predetermined standard.

[0063] In one embodiment, the device (100) can determine information regarding the similarity of gut microbes (hereinafter, "first information"). That is, the first information can be information indicating how similar the gut microbiome of the subject (110) is to the gut microbiome of a healthy individual. The device (100) can store information regarding a reference sample (134). The information regarding the reference sample (134) can be information obtained by performing the above-mentioned various tests on a predetermined reference sample (114). The reference sample (114) can be a biological sample from a healthy individual classified according to a preset criterion. The device (100) can determine the above-mentioned first information based on the microbial similarity between the biological sample (112) and the reference sample (114).

[0064] In one embodiment, the device (100) can determine information regarding the ratio of harmful microorganisms in the intestine of the subject (110) (hereinafter, "second information"). That is, the second information can include information regarding the abundance or absolute amount (abundance) of harmful microorganisms in the intestine of the subject (110), information regarding the diversity of harmful microbial species, and / or information regarding the uniformity of the abundance of each harmful microbial species. In one embodiment, the second information can include information regarding the ratio of harmful microorganisms to beneficial microorganisms in the intestine of the subject (110). In one embodiment, the device (100) can store information identifying which microorganisms are harmful and which are beneficial.

[0065] In one embodiment, the device 100 can determine information regarding the ratio of beneficial microorganisms in the intestine of the subject 110 (hereinafter, "third information"). That is, the third information can include information regarding the abundance ratio or absolute amount (abundance) of beneficial microorganisms in the intestine of the subject 110, information regarding the diversity of beneficial microbial species, and / or information regarding the uniformity of the abundance ratio of each beneficial microbial species, etc. In one embodiment, the third information can include information regarding the ratio of beneficial microorganisms to harmful microorganisms in the intestine of the subject 110.

[0066] In one embodiment, the device 100 can determine information regarding the gut microbial diversity of the subject 110 (hereinafter, the "fourth information"), i.e., the fourth information can be information regarding how diverse microbial species are distributed in the gut of the subject 110.

[0067] The device (100) may determine a gut microbiome index by comprehensively considering various ecological information regarding the gut microbiome of the subject (110). In one embodiment, the gut microbiome index may be used to distinguish between healthy people and people with a disease (hereinafter, "disease person"). A disease person may refer to a person whose gut microbiome is classified as being in an imbalanced state according to a predetermined criterion. In one embodiment, the device (100) may determine a gut microbiome index of the subject (110) based on the first information, the second information, the third information, and / or the fourth information. In one embodiment, the device (100) may input the first information, the second information, the third information, and / or the fourth information into a preset function to determine the gut microbiome index. In one embodiment, the device (100) may apply a preset weighting value to each of the first information, the second information, the third information, and / or the fourth information in determining the gut microbiome index.

[0068] The information (136) indicative of the gut microbiome index determined by the device (100) may be provided to the subject (110) by a variety of methods. In one embodiment, the device (100) may transmit the information (136) indicative of the gut microbiome index to the subject's device (140) by an electronic communication method. In the present disclosure, the subject's device (140) may take a variety of forms. For example, the subject's device (140) may be a portable communication device (e.g., a smartphone), a computing device (e.g., a tablet PC, a laptop), a portable multimedia device, a wearable device, or a combination of the above devices. In one embodiment, the service provider (120) may transmit the information (136) indicative of the gut microbiome index to the subject (110) by a non-electronic communication method (e.g., mail, face-to-face communication by a medical professional, etc.).

[0069] FIG. 2 is a block diagram of an apparatus (100) according to an embodiment of the present disclosure. In an embodiment, the apparatus (100) may include one or more processors (210) and / or one or more memories (220) as components. In an embodiment, at least one of the components of the apparatus (100) may be omitted, or other components may be added to the apparatus (100). In an embodiment, additionally or alternatively, some components may be integrated or embodied as one or more individual components. In the present disclosure, one or more processors (210) may be expressed as processor (210). The term processor (210) may mean a collection of one or more processors, unless otherwise clearly expressed in the context. In the present disclosure, one or more memories (220) may be expressed as memory (220). The term memory (220) may mean a collection of one or more memories, unless otherwise clearly expressed in the context. In one embodiment, at least some of the components inside / outside the device 100 are connected to each other via a bus, a general purpose input / output (GPIO), a serial peripheral interface (SPI), or a mobile industry processor interface (MIPI), etc., and can exchange information (data, signals, etc.).

[0070] The processor 210 may execute software (e.g., instructions, programs, etc.) to control at least one component of the device 100 coupled to the processor 210. The processor 210 may also perform various operations such as calculations, processing, data generation, processing, etc. related to the present disclosure. The processor 210 may also load data, etc. from the memory 220 and store data, etc. in the memory 220. In one embodiment, the processor 210 may obtain the test information 132 from the test device 130 using a communication circuit described below. The processor 210 may determine the first information, second information, third information, and / or fourth information described above based on the test information 132. The processor 210 may determine a gut microbiome index of the subject 110 based on the first information, second information, third information, and / or fourth information. The processor (210) can use the communications circuitry to communicate information indicative of the gut microbiome index (136) to the subject's device (140).

[0071] The memory 220 may store various data. The data stored in the memory 220 may include software (e.g., instructions, programs, etc.) that may be acquired, processed, or used by at least one component of the device 100. The memory 220 may include volatile and / or non-volatile memory. In the present disclosure, the instructions or programs may be software stored in the memory 220, and may include an operation system for controlling resources of the device 100, applications, and / or middleware that provides various functions to applications so that the applications can utilize the resources of the device 100. In one embodiment, the memory 220 may store instructions that, when executed by the processor 210, cause the processor 210 to perform operations. In one embodiment, the memory 220 may store information about the reference sample 134 described above and / or information identifying harmful microorganisms and beneficial microorganisms, etc. In one embodiment, the processor 210 may control the communication circuit 230 to acquire information from a predetermined server. The information obtained from the server may be stored in memory 220. In one embodiment, information regarding the reference sample 134 and / or information identifying harmful and beneficial microorganisms may be obtained from the server and stored in memory 220.

[0072] In one embodiment, the device 100 may further include a communication circuit (230). The communication circuit 230 may be omitted from the device 100 in some embodiments. The communication circuit 230 may provide wireless or wired communication between the device 100 and a server, or between the device 100 and other devices. For example, the communication circuit (230) may perform wireless communication using a method such as eMBB (enhanced Mobile Broadband), URLLC (Ultra Reliable Low-Latency Communications), MMTC (Massive Machine Type Communications), LTE (Long-Term Evolution), LTE-A (LTE Advance), NR (New Radio), UMTS (Universal Mobile Telecommunications System), GSM (Global System for Mobile communications), CDMA (Code Division Multiple Access), WCDMA (registered trademark) (Wideband CDMA), WiBro (Wireless Broadband), WiFi (Wireless Fidelity), Bluetooth (registered trademark), NFC (Near Field Communication), GPS (Global Positioning System), or GNSS (Global Navigation Satellite System). For example, the communication circuitry 230 may perform wired communication using a method such as Universal Serial Bus (USB), High Definition Multimedia Interface (HDMI), Recommended Standard-232 (RS-232), or Plain Old Telephone Service (POTS). In one embodiment, the communication circuitry 230 may communicate with the test device 130 and / or the subject's device 140. In one embodiment, the device 100 may be embodied in an integrated manner with another device (e.g., the test device 130).In this case, the communication circuit 230 may function as a connection circuit or interface that links the device 100 to the other device.

[0073] In an embodiment, the device 100 may further include an input / output interface 240. The input / output interface 240 may be omitted from the device 100 in some embodiments. The input / output interface 240 may receive input from a user of the device 100 and output (display) information to the user. The user may be an operator of the device 100 belonging to the service provider 120. In an embodiment, the input / output interface 240 may include an input device and / or an output device. The input device may be a device that receives input of information to be transmitted to at least one component of the device 100 from the outside. For example, the input device may include a mouse, a keyboard, a touchpad, etc. The output device may be a device that provides various information of the device 100 to the user in an audiovisual form. For example, the output device may include a display, a projector, a hologram, etc. In an embodiment, the input / output interface 240 may receive input of information about the reference sample 134 and / or information identifying harmful microorganisms and beneficial microorganisms, etc., from the user. In one embodiment, the input / output interface 240 may display information to a user, such as information indicative of the gut microbiome index 136.

[0074] In one embodiment, the device 100 may take a variety of forms. For example, the device 100 may be a computer device, a back-end server, a front-end server, a portable communication device, a portable multimedia device, or a combination of the above-mentioned devices. However, the device 100 of the present disclosure is not limited to the above-mentioned devices.

[0075] 3 is a diagram showing a microbial similarity-related index according to an embodiment of the present disclosure. As described above, various factors may affect the ecological balance / imbalance of the gut microbiome. Factors that affect the ecological balance / imbalance of the gut may include the similarity of gut microorganisms, the ratio of gut harmful microorganisms, the ratio of gut beneficial microorganisms, and / or the diversity of gut microorganisms.

[0076] [Similarity of gut microbes]

[0077] The similarity of gut microbes will now be described. The gut microbiome can be affected by various environmental stimuli (e.g., disease, diet, medicine, lifestyle, etc.). As a result, the composition of the gut microbiome can change. However, since the gut microbiome of a healthy person has homeostasis, the composition of the microbiome changed by the influence of the stimuli can be restored to the state before the influence of the stimuli. Due to homeostasis, the gut microbiomes of healthy people can be similar to each other in composition. In contrast, the gut microbiome of a sick person can be in an unbalanced state and can be different in composition from the gut microbiome of a healthy person. Therefore, by determining the microbial similarity with the gut microbiome of a healthy person, it can be judged or determined whether a particular gut microbiome is in a balanced / unbalanced state. That is, as described above, the similarity of gut microbes can be indicated by the degree to which the gut microbiome of the subject (110) is similar to the gut microbiome of a healthy person.

[0078] The device (100) may determine the similarity of gut microorganisms by various methods. The above-mentioned first information may be information indicating the similarity of gut microorganisms. In one embodiment, the processor (210) of the device (100) may determine the similarity of gut microorganisms based on the test information (132) and the information on the reference sample (134). The processor (210) may determine the distribution of the ratio (i.e., abundance ratio) of each microbial species in the biological sample (112) of the subject (110) based on the test information (132). The processor (210) may determine the distribution of the ratio of each microbial species in the reference sample (114) based on the information on the reference sample (134). In one embodiment, the distribution of the abundance ratio of each microbial species in the reference sample (114) may have already been calculated and may be included in the information on the reference sample (134). The processor (210) may determine the similarity between the distribution of the abundance ratio of each microbial species in the biological sample (112) and the distribution of the abundance ratio of each microbial species in the reference sample (114). The processor (210) can determine the above-mentioned first information based on the determined similarity.

[0079] In one embodiment, the information on the reference sample (134) may be constructed, acquired, or processed from a gut microbiome database of the service provider (120) and / or a third party, and may be information on individuals classified as healthy in each database. For example, healthy individuals may be a group of individuals excluding individuals who fall into one or more of the following categories: those who have a disease such as irritable bowel syndrome; those who have an infectious disease; those who have a BMI of a certain value (e.g., 25) or more; and those who have taken medication such as painkillers in the past two weeks.

[0080] The process of determining the first information by the processor (210) may be performed by calculating one of the microbial similarity related indices (310). That is, the processor (210) may calculate one of the microbial similarity related indices (310) and determine the first information indicating the similarity of the intestinal microorganisms based on the calculated value. For example, to determine the first information, the processor (210) may calculate the +avg_jsd_ctl index from the microbial similarity related indices (310). +avg_jsd_ctl may mean the average of the Jensen-Shannon distance between the biological sample (112) and each of the samples of healthy individuals. The Jensen-Shannon distance may be calculated as shown in the following mathematical formula 1.

[0081]

number

[0082] P and Q can be a biological sample (112) and a comparison sample, respectively. i , q i may indicate the abundance ratio of a specific microorganism i in samples P and Q, respectively. M may be an average sample of P and Q, and may have the average abundance ratio of each microbial species in P and Q as the abundance ratio of the microbial species. Based on information on the biological sample (112) and the comparison sample, the KL divergence, Jensen-Shannon divergence, and Jensen-Shannon distance may be calculated in order. In the same manner, the Jensen-Shannon distance may be calculated for multiple comparison samples, and the +avg_jsd_ctl index may be determined based on this.

[0083] The microorganism similarity related index (310) may be ±avg_jsd_case, ±avg_jsd_ctl, ±avg_braycurtis_case, ±avg_braycurtis_ctl, ±avg_aitchison_case, ±avg_aitchison_ctl, etc. As described above, the processor (210) may determine the first information by calculating one of these. Here, ±avg_jsd_case may mean the average of the Jensen-Shannon distances between the biological sample (112) and each of the samples from the sick person. +avg_jsd_ctl has been described above. -avg_jsd_ctl may be an index having a value with the opposite sign to the value of +avg_jsd_ctl described above. ±avg_braycurtis_case may mean the average of the Bray-Curtis Dissimilarity between the biological sample (112) and each of the samples from the sick person. ±avg_braycurtis_ctl may mean the average of the Bray-Curtis dissimilarity between the biological sample (112) and each of the samples from healthy individuals. ±avg_aitchison_case may mean the average of the Aitchison Distance between the biological sample (112) and each of the samples from diseased individuals. ±avg_aitchison_ctl may mean the average of the Aitchison Distance between the biological sample (112) and each of the samples from healthy individuals. In the present disclosure, when there is a positive (+) correlation between the value of an index and the degree to which the index accurately distinguishes healthy individuals from diseased individuals, the index may be expressed as having a "+" sign. On the other hand, when there is a negative (-) correlation, the index may be expressed as having a "-" sign.

[0084] The aforementioned microbial similarity related index (310) is not limited to those presented in the present disclosure, and any index that can indicate the similarity between microbial species between two samples may be used as the microbial similarity related index (310) according to the present disclosure.

[0085] [Index for determining each piece of information]

[0086] In determining the first to fourth information, the indices to be used may be determined in advance. That is, a process of selecting a predetermined index may be preemptively performed to select an index to be used to determine each piece of information, and the device (100) may determine the first to fourth information for the subject (110) based on the preselected index. In this case, the index selection process may be performed by the device (100) or by another electronic device. Meanwhile, according to an embodiment, the device (100) may perform the index selection process for each subject each time, and determine the first to fourth information based on the selected index. In one embodiment, the index selection process used to determine the first to fourth information may use a gut microbiome database of the service provider (120) and / or a third party, but is not limited thereto.

[0087] A method for selecting the indices to be used to determine the first information to the fourth information from among the various indices will be described below. By this selection method, the indices to be used to determine the first information, the second information, the third information, and the fourth information can be selected from the various microbial similarity-related indices (310), the harmful microorganism-related indices (410), the beneficial microorganism-related indices (510), and the microbial diversity-related indices (610), respectively.

[0088] First, it can be determined how accurately each index distinguishes between healthy and diseased individuals. To this end, a value of each index can be calculated for a plurality of heterogeneous sample groups. Samples in a certain sample group (e.g., a set of samples in an OO experiment conducted at an OO hospital) are biological samples from healthy or diseased individuals, and diseases of diseased individuals may include, for example, Crohn's disease, irritable bowel syndrome, obesity, etc. Depending on the value of the index, it can be determined whether each sample in a given sample group is a sample from a healthy individual or a sample from a diseased individual. The accuracy of the index can be determined depending on the degree to which the index accurately distinguishes actual samples from healthy individuals or samples from diseased individuals, respectively.

[0089] In order to quantify the accuracy of each index, the mean balanced accuracy and the number of significance for each index may be determined. First, the balanced accuracy that an index has for a given sample group may be calculated. The balanced accuracy may be calculated as in Equation 2 below.

[0090]

number

[0091] TP (True Positive) may mean the number of cases where an actual healthy person's sample is judged as a healthy person's sample when the index discriminates samples in the sample group. TN (True Negative) may mean the number of cases where an actual sick person's sample in the sample group is judged as a sick person's sample, FP (False Positive) may mean the number of cases where an actual sick person's sample in the sample group is judged as a healthy person's sample, and FN (False Negative) may mean the number of cases where an actual healthy person's sample in the sample group is judged as a sick person's sample. Through this mathematical formula 2, the balanced accuracy of one index for one sample group can be calculated. In the same manner, the balanced accuracy of the index for each of multiple sample groups can be calculated. The average value of these balanced accuracy values ​​can be calculated to derive the above-mentioned average balanced accuracy. The above-mentioned meaningful number may mean the number of sample groups among multiple sample groups in which healthy people and sick people are meaningfully classified by the index.

[0092] In one embodiment, the process of selecting an index used to determine the first to fourth information may take into consideration average balanced accuracy, meaningful number, and / or various other factors. In one embodiment, an index with a high average balanced accuracy may be selected, or an index with a high meaningful number may be selected. In one embodiment, a predetermined number (e.g., 10) of indices with high average balanced accuracy may be first selected, and the index with the highest meaningful number among the selected indices may be selected as the index used to determine each piece of information. Conversely, indices may be first selected according to the meaningful number, and then the index with the highest average balanced accuracy among the selected indices may be selected as the index used to determine each piece of information. The average balanced accuracy and meaningful number in the illustrated embodiment (310) are exemplary values.

[0093] In one embodiment, the collinearity between the indices may be additionally considered in the process of selecting the indices used to determine the first information to the fourth information. The collinearity between the indices may be calculated based on, for example, Spearman correlation. The collinearity (p) between the indices may be calculated as in the following Equation 3.

[0094]

number

[0095] For a given sample group, x i may be the value of the exponent X for the i-th sample. For example, x3 may be the value of the +shannon exponent for the 3rd sample. TIFF0007636443000004.tif7114 For each x i It can be the average of the y values. i may be the value of the index Y for the i-th sample. TIFF0007636443000005.tif7114 For each y i It may be an average of the values.

[0096] As described above, the indices used to determine the first to fourth information may be primarily selected based on the average balanced accuracy, the meaningful number, and / or various other factors. Then, the collinearity between the primarily selected indices may be calculated. If the collinearity between two given indices is high, this may mean that the correlation between the two indices is strong and meaningfully affects each other's values. Conversely, if the collinearity between two indices is low, this may mean that the correlation between the two indices is weak or nonexistent and therefore does not meaningfully affect each other's values. If the collinearity is high and the index values ​​affect each other, the index (e.g., +shannon) may not accurately indicate a particular category (e.g., gut microbial diversity). Therefore, among the primarily selected indices, indices that have high collinearity (e.g., 0.7 or more) with other indices may be eliminated.

[0097] For example, -avg_jsd_ctl, -dysbiosis_index, +ben_sp_numotu, and +shannon may be primarily selected as the indices used to determine the first to fourth information. When the similarity of intestinal microorganisms, the ratio of intestinal harmful microorganisms, the ratio of intestinal beneficial microorganisms, and the diversity of intestinal microorganisms are each viewed as one category, the index may be the index with the highest average balanced accuracy in each category. Here, if +shannon in the category of intestinal microbial diversity shows high collinearity (e.g., 0.81) with +ben_sp_numotu in the category of beneficial intestinal microorganisms, +shannon may be eliminated, and +numotu with the next highest average balanced accuracy after +shannon in the category of intestinal microbial diversity may be selected. Here, if +numotu also shows high collinearity (e.g. 0.93) with +ben_sp_numotu, +numotu is eliminated and +invsimpson, which has the next highest average balanced accuracy after +numotu in the gut microbial diversity category, may be selected. If +invsimpson does not show high collinearity with indices in other categories, -avg_jsd_ctl, -dysbiosis_index, +ben_sp_numotu, and +invsimpson may be selected as the indices to be used to finally determine the first to fourth information.

[0098] [Ratio of harmful intestinal microorganisms]

[0099] FIG. 4 is a diagram showing a harmful microorganism-related index according to an embodiment of the present disclosure. Intestinal harmful microorganisms may cause intestinal infection and inflammatory reactions, or induce various diseases such as colon cancer. Therefore, if the intestinal harmful microorganisms increase, the intestinal microbiome may become unbalanced. However, if there are sufficient microorganisms that reside in the intestine and coexist with the human body (hereinafter, "symbiotic microorganisms"), the activity or proliferation of harmful microorganisms may be suppressed through immune regulation by the symbiotic microorganisms. Therefore, the ratio of intestinal harmful microorganisms may be determined as one of the factors for diagnosing the intestinal microbiome. The ratio of intestinal harmful microorganisms may be the ratio between intestinal symbiotic microorganisms and harmful microorganisms. In an embodiment of the present disclosure, information on already known harmful microorganisms or symbiotic microorganisms [including information that can detect the microorganisms (e.g., biomarkers)] may be utilized, and may include information that a general technician can determine that a microorganism is harmful or symbiotic from published information. When selecting such harmful microorganisms or symbiotic microorganisms, a statistical analysis method such as LEfSe or a biological analysis method such as a literature survey may be used, but is not limited thereto.

[0100] The device (100) can determine the ratio of intestinal harmful microorganisms by various methods. The above-mentioned second information can be information indicating the ratio of intestinal harmful microorganisms. In one embodiment, the processor (210) of the device (100) can determine the ratio of intestinal harmful microorganisms based on the test information (132). Specifically, the processor (210) can determine the abundance ratio of symbiotic microbial species in the biological sample (112). In one embodiment, the memory (220) can store information identifying which of the microorganisms is a preset symbiotic microorganism, and the processor (210) can determine the abundance ratio of symbiotic microbial species in the biological sample (112) based on the information. Meanwhile, the processor (210) can determine the abundance ratio of harmful microbial species in the biological sample (112). In this case, the processor (210) can use the information identifying the preset harmful microorganisms stored in the memory (220). The processor (210) can determine the abundance ratio of harmful microbial species with respect to symbiotic microbial species and harmful microbial species in the biological sample (112). That is, the ratio may be the ratio of the abundance ratio of the harmful microbial species to the sum of the abundance ratio of the symbiotic microbial species and the abundance ratio of the harmful microbial species. The processor 210 may determine the second information based on the abundance ratio of the harmful microbial species to the symbiotic microbial species and the harmful microbial species.

[0101] The process of determining the second information by the processor (210) may be performed by calculating any one of the harmful microorganism ratio related indices (410). That is, the processor (210) may calculate any one of the harmful microorganism ratio related indices (410) and determine the second information indicating the ratio of intestinal harmful microorganisms based on the calculated value. For example, to determine the second information, the processor (210) may calculate +dysbiosis_index from the harmful microorganism ratio related indices (410). +dysbiosis_index may mean the dysbiosis index for the biological sample (112). The dysbiosis index may be calculated as shown in the following mathematical formula 4.

[0102]

number

[0103] p kmay be the abundance ratio of microorganism k in the biological sample (112). "Pathobiont" means harmful microorganisms, and the microorganism k belonging thereto may be, for example, one or more microorganisms belonging to the Bacteroidaceae, Desulfovibrionaceae, Oscillospiraceae, Odoribacteraceae, Lachnospiraceae, Erysipelotrichaceae, Enterococcaceae, Lactobacillaceae, Veillonellaceae, Christensenellaceae, Enterobacteriaceae, Pasteurellaceae, Fusobacteriaceae, Neisseriaceae, Veillonellaceae, Gemellaceae, or Porphyromonadaceae family, but is not limited thereto. "Commensal" refers to symbiotic microorganisms, and the microorganisms k may be, for example, one or more of microorganisms belonging to the Lachnospiraceae, Eggerthellaceae, Oscillospiraceae, Rikenellaceae, Erysipelotrichaceae, Christensenellaceae, Coriobacteriaceae, Peptostreptococcaceae, Bacteroidales, Clostridiales, Erysipelotrichaceae, Clostridiales, or Bifidobacteriaceae family; or microorganisms belonging to the Mollicutes class, but are not limited thereto. In one embodiment, a person skilled in the art can analyze harmful microorganisms or symbiotic microorganisms from the microbiome database and derive the second information using the harmful microorganism ratio related index (410). In one embodiment, the harmful microorganisms may include the microorganisms listed in Table 1, and the symbiotic microorganisms may include the microorganisms listed in Table 2. Based on the information about the biological sample (112), the dysbiosis index, i.e., +dysbiosis_index, can be calculated according to mathematical formula 4.

[0104] The 16S rRNA sequence information in Tables 1 and 2 below corresponds to the 16S rRNA sequence information of the type species representative of the described type of microorganism.

[0105] [Table 1]

[0106] [Table 2] TIFF0007636443000009.tif204145 TIFF0007636443000010.tif126145

[0107] The harmful microorganism ratio-related index (410) may be ±hrm_sum, ±hrm_sp_numotu, ±hrm_sp_shannon, ±hrm_sp_invsimpson, ±hrm_sp_berger_parker_d, ±hrm_sp_shannon_e, ±hrm_sp_simpson_e, ±hrm_sp_heip_e, ±hrm_sp_alatalo_e, ±dysbiosis_index, etc. As described above, the processor (210) can determine the second information by calculating one of these. Here, ±hrm_sum may be an index indicating the sum of the abundance ratios of each harmful microorganism species. ±hrm_sp_numotu, ±hrm_sp_shannon, ±hrm_sp_invsimpson, ±hrm_sp_berger_parker_d, ±hrm_sp_shannon_e, ±hrm_sp_simpson_e, ±hrm_sp_heip_e and ±hrm_sp_alatalo_e may be indices corresponding to ±numotu, ±shannon, ±invsimpson, ±berger_parker_d, ±shannon_e, ±simpson_e, ±heip_e and ±alatalo_e, which are microbial diversity-related indices described below. That is, the indices may be used not only as microbial diversity-related indices but also as harmful microbial ratio-related indices. ±dysbiosis_index may be a dysbiosis index.

[0108] The harmful microorganism-related index used in determining the second information may be one that has been pre-selected by the index selection method described above. The average balanced accuracy and meaningful number in the illustrated embodiment (410) are exemplary values. Meanwhile, the harmful microorganism ratio-related index (410) is not limited to those presented in the present disclosure, and any index that can indicate the ratio of intestinal harmful microorganisms may be used as the harmful microorganism ratio-related index (410) according to the present disclosure.

[0109] [Ratio of beneficial intestinal microorganisms]

[0110] 5 is a diagram showing a beneficial microorganism-related index according to an embodiment of the present disclosure. Intestinal beneficial microorganisms can perform various functions, such as synthesizing short-chain fatty acids and causing immunoregulation and anti-inflammatory responses. Therefore, if intestinal beneficial microorganisms decrease, the intestinal microbiome may become unbalanced. The ratio of intestinal beneficial microorganisms can be determined as one of the factors for diagnosing the intestinal microbiome.

[0111] In one embodiment of the present disclosure, information on already known beneficial microorganisms (including information that can detect the microorganism (e.g., biomarkers)) may be utilized, and may include information that a person skilled in the art could determine beneficial microorganisms from published information. When selecting such beneficial microorganisms, statistical analysis methods such as LEfSe or biological analysis methods such as literature review may be used, but are not limited thereto.

[0112] In one embodiment of the present disclosure, the beneficial microorganisms may be, but are not limited to, one or more of the following microorganisms: Lachnospiraceae, Eggerthellaceae, Oscillospiraceae, Rikenellaceae, Erysipelotrichaceae, Christensenellaceae, Coriobacteriaceae, Peptostreptococcaceae, Bacteroidales, Clostridiales, Erysipelotrichaceae, Clostridiales, or Bifidobacteriaceae family; or the following microorganisms: Mollicutes class. In one embodiment of the present disclosure, the beneficial microorganisms may include, but are not limited to, the microorganisms listed in Table 2 above.

[0113] The device (100) can determine the ratio of beneficial microorganisms in the intestine by various methods. The third information mentioned above can be information indicating the ratio of beneficial microorganisms in the intestine. In one embodiment, the ratio of beneficial microorganisms in the intestine can be determined by the sum of the abundance ratios of each beneficial microorganism in the intestine. In one embodiment, the processor (210) of the device (100) can determine the ratio of beneficial microorganisms in the intestine based on the test information (132). Specifically, the processor (210) can determine the abundance ratio of beneficial microorganism species in the biological sample (112). In this case, the processor (210) can use information identifying the preset beneficial microorganisms stored in the memory (220). In one embodiment, the abundance ratio of beneficial microorganism species can be the abundance ratio of beneficial microorganism species to all microorganisms in the intestinal microbiome. The processor (210) can determine the third information mentioned above based on the abundance ratio of beneficial microorganism species.

[0114] The process of determining the third information by the processor (210) may be performed by calculating one of the beneficial microorganism ratio-related indices (510). That is, the processor (210) may calculate one of the beneficial microorganism ratio-related indices (510) and determine the third information indicating the ratio of beneficial microorganisms in the intestine based on the value. For example, to determine the third information, the processor (210) may calculate the +ben_sp_numotu index from the beneficial microorganism ratio-related indices (510). +ben_sp_numotu may mean the number of OTUs (Operational Taxonomic Units) of beneficial microbial species in the biological sample (112). OTU may be a unit that classifies DNA fragments obtained as a result of gene sequencing such as NGS at the level of microbial species. That is, the number of OTUs of beneficial microbial species may correspond to the number of beneficial microbial species. +ben_sp_numotu may be calculated as in the following mathematical formula 5.

[0115]

number

[0116] N can be the number of all beneficial microbial species that have been preset.i Ip can mean the abundance ratio of the i-th beneficial microbial species in the biological sample (112). i may be an indicator function for the OTU of the i-th beneficial microbial species in the biological sample 112. The indicator function may be a function that indicates the OTU of the beneficial microbial species whose abundance ratio in the biological sample 112 is greater than 0. Based on information about the biological sample 112, the number of OTUs of the beneficial microbial species, i.e., +ben_sp_numotu, may be calculated by Equation 5.

[0117] The beneficial microorganism ratio-related index (510) may be ±ben_sum, ±ben_sp_numotu, ±ben_sp_shannon, ±ben_sp_invsimpson, ±ben_sp_berger_parker_d, ±ben_sp_shannon_e, ±ben_sp_simpson_e, ±ben_sp_heip_e, ±ben_sp_alatalo_e, etc. As described above, the processor (210) can determine the third information by calculating one of these. Here, ±ben_sum may be an index indicating the sum of the abundance ratios of each beneficial microorganism species. ±ben_sp_numotu, ±ben_sp_shannon, ±ben_sp_invsimpson, ±ben_sp_berger_parker_d, ±ben_sp_shannon_e, ±ben_sp_simpson_e, ±ben_sp_heip_e, and ±ben_sp_alatalo_e may be indices corresponding to ±numotu, ±shannon, ±invsimpson, ±berger_parker_d, ±shannon_e, ±simpson_e, ±heip_e, and ±alatalo_e, which are microbial diversity-related indices described below. In other words, the indices can be used not only as microbial diversity-related indices, but also as beneficial microbial ratio-related indices (510).

[0118] The beneficial microorganism-related index used to determine the third information may be one that has been pre-selected by the index selection method described above. The average balanced accuracy and meaningful number in the illustrated embodiment (510) are exemplary values. Meanwhile, the beneficial microorganism ratio-related index (510) is not limited to those presented in the present disclosure, and any index that can indicate the ratio of beneficial microorganisms in the intestine may be used as the beneficial microorganism ratio-related index (510) according to the present disclosure.

[0119] [Diversity of gut microbes]

[0120] FIG. 6 is a diagram showing a microbial diversity-related index according to an embodiment of the present disclosure. In general, it is difficult for one or two microorganisms to perform all the functions required in the intestine. Each microbial species in the intestinal microbiome plays its own role. The more diverse the microbial species in the intestinal microbiome, the more intestinal functions can be performed, and the better the resilience of intestinal functions to various stimuli (e.g., external disease factors). For example, diseased individuals with inflammatory bowel disease (IBD), colon cancer, etc. may have lower gut microbial diversity than healthy individuals. If gut microbial diversity is low, the gut microbiome may be in an unbalanced state, so the gut microbial diversity can be determined to diagnose the gut microbiome.

[0121] The device (100) can determine the diversity of intestinal microorganisms by various methods. The above-mentioned fourth information can be information indicative of the diversity of intestinal microorganisms. In one embodiment, the processor (210) of the device (100) can determine the diversity of intestinal microorganisms based on the test information (132). Specifically, the processor (210) can determine the number of all microbial species in the biological sample (112). The processor (210) can determine the degree to which the distribution of the abundance ratio of each of the all microbial species in the biological sample (112) is even. The processor (210) can determine the above-mentioned fourth information based on the determined number and the degree of evenness of all microbial species.

[0122] The process of determining the fourth information by the processor (210) may be performed by calculating any one of the microbial diversity related indices (610). That is, the processor (210) may calculate any one of the microbial diversity related indices (610) and determine the fourth information indicating the diversity of gut microorganisms based on the calculated value. For example, to determine the fourth information, the processor (210) may calculate the +invsimpson index from the microbial diversity related indices (610). +invsimpson may mean the inverse of the Simpson's Index for the biological sample (112). +invsimpson may be calculated as in the following mathematical formula 6.

[0123]

number

[0124] S can represent the number of all microbial species in the biological sample (112). i may be the abundance ratio of the microorganism i in the biological sample 112. Based on information about the biological sample 112, +invsimpson may be calculated using mathematical formula 6.

[0125] The microbial diversity-related index (610) may be ±numotu, ±shannon, ±invsimpson, ±berger_parker_d, ±shannon_e, ±simpson_e, ±heip_e, ±alatalo_e, and the like. As described above, the processor (210) may determine the fourth information by calculating one of these. Here, ±numotu may mean the number of OTUs. ±shannon may mean Shannon's Index. ±invsimpson may mean the inverse of Simpson's Index. ±berger_parker_d may mean Berger Parker's Index. ±shannon_e may mean Shannon's Equitability. ±simpson_e may mean Simpson's Evenness. ±heip_e may mean Heip's Index. ±alatalo_e may mean the Alatalo Index. For example, if information such as the number and abundance ratio of each microbial species in the biological sample (112) is input into any one of the above-mentioned microbial diversity-related indices (610) and calculated, the resulting value (value of the index) can reflect how diverse the microbial species are in the biological sample (112).

[0126] The microbial diversity-related index used to determine the fourth information may be one that has been pre-selected by the index selection method described above. The average balanced accuracy and meaningful number in the illustrated embodiment (610) are exemplary values. Meanwhile, the microbial diversity-related index (610) is not limited to those presented in the present disclosure, and any index that can indicate the degree of diversity of gut microorganisms may be used as the microbial diversity-related index (610) according to the present disclosure.

[0127] [Gut Microbiome Index]

[0128] 7 illustrates a function (700) for determining a gut microbiome index, according to one embodiment of the present disclosure. As previously described, the processor (210) may determine the first information, the second information, the third information, and / or the fourth information regarding the gut microbiome of the subject (110) based on the test information (132) regarding the biological sample (112) of the subject (110) of FIG. 1. In one embodiment, the processor (210) may determine the gut microbiome index of the subject (110) in a variety of ways based on at least one selected from the first information, the second information, the third information, and the fourth information.

[0129] In one embodiment, the processor 210 may apply preset weights c1, c2, c3, c4 to the first information, the second information, the third information, and / or the fourth information, respectively. In one embodiment, the weights c1, c2, c3, c4 may have different values ​​for the first information, the second information, the third information, and the fourth information, respectively. For example, the weights c1, c2, c3, c4 may have values ​​of -1.50751, -0.64560, 0.98436, and 0.07459, respectively. In one embodiment, the weights c1, c2, c3, c4 may be predetermined by a Linear Mixed Effect Model. Each of the first to fourth information may be set as an independent variable of the linear mixed effect model, and the degree to which each piece of information accurately distinguishes between healthy and diseased individuals may be set as a dependent variable. The variations in the first to fourth information values ​​may have a mixed effect on the dependent variable value. Based on a linear mixed effects model that indicates such effects, the weights (c1, c2, c3, c4) may be determined. In one embodiment, the memory (220) may store information indicating the determined weights (c1, c2, c3, c4).

[0130] In one embodiment, the processor (210) may determine the gut microbiome index based on at least one selected from the weighted information. In one embodiment, the processor (210) may determine the gut microbiome index based on a sum of the first to fourth information to which the weighted values ​​have been applied. In one embodiment, the processor (210) may determine the gut microbiome index based on the illustrated function (700). The function (700) may be a function that performs a logit operation on the sum of the first to fourth information to which the weighted values ​​have been applied. In one embodiment, the memory (220) may store information indicating the function (700) for determining the gut microbiome index.

[0131] FIG. 8 is a diagram showing an experimental result (800) of the accuracy of a gut microbiome index according to an embodiment of the present disclosure. The experimental result (800) shows an average ROC curve (Receiver Operating Characteristics Curve) for the gut microbiome index according to the present disclosure and average ROC curves for other different indices. In the experimental result (800), the horizontal axis may be FPR (False Positive Rate). FPR may mean the rate at which the index erroneously classifies actual diseased samples as healthy samples. FPR may be calculated according to the following mathematical formula 7. In the experimental result (800), the vertical axis may be TPR (True Positive Rate). TPR may mean the rate at which the index correctly classifies actual healthy samples as healthy samples. TPR may be calculated in the same manner as the above-mentioned sensitivity.

[0132]

number

[0133] The more the ROC curve is biased towards the upper left, the more accurately the index classifies healthy people from diseased people. The more the ROC curve is drawn to the left, the fewer false positives (healthy people are judged to be diseased), and the more the ROC curve is drawn to the top, the fewer false negatives (disease people are judged to be healthy). The average ROC curve can be a curve that shows the average of the ROC curves for multiple samples.

[0134] In the experimental results (800), it was confirmed that the ROC curve for the gut microbiome index (gmi_lmm) was biased to the upper left compared to the ROC curves for the indices +invsimpson, +avg_jsd_ctl, +ben_sp_numotu, and +dysbiosis_index. In other words, it was confirmed that the gut microbiome index is more accurate than the indices +invsimpson, +avg_jsd_ctl, +ben_sp_numotu, and +dysbiosis_index.

[0135] FIG. 9 is a diagram showing an experimental result (900) of the accuracy of a gut microbiome index according to an embodiment of the present disclosure. The experimental result (900) shows a precision-recall curve for a gut microbiome index according to the present disclosure and a precision-recall curve for other different indices. In the experimental result (900), the horizontal axis may be recall (True Positive Rate). Recall may mean the ratio of samples of actual healthy people that the index correctly classifies as samples of healthy people. Recall may be calculated in the same manner as the above-mentioned sensitivity. In the experimental result (900), the vertical axis may be precision (Positive Predictive Value). Precision may mean the ratio of samples of actual healthy people among samples classified as samples of healthy people by the index. Precision may be calculated as shown in the following mathematical formula 8.

[0136]

number

[0137] The more the precision-recall curve is biased toward the upper right, the more accurately the index classifies healthy people and diseased people. The more the precision-recall curve is drawn to the right, the higher the recall, and the more it is drawn upwards, the higher the precision. In general, when a curve based on the upper right contains other curves, it can be said that the included curve has better performance than the other curves.

[0138] In the results of this experiment (900), it was confirmed that the precision-recall curve (gmi_lmm) of the gut microbiome index was biased to the upper right side compared to the precision-recall curves of the invsimpson, avg_jsd_ctl, ben_sp_numotu, and dysbiosis_index indices. In other words, it was confirmed that the gut microbiome index has higher precision than the invsimpson, avg_jsd_ctl, ben_sp_numotu, and dysbiosis_index indices.

[0139] [Determination of intestinal type]

[0140] 10 is a diagram showing the operation process of the device (100) according to an embodiment of the present disclosure. The device (100) according to an embodiment of the present disclosure can determine the intestinal type of a subject by comprehensively taking into account various factors. As described above, the processor (210) of the device (100) can obtain test information (132) related to the biological sample (112) of the subject (110) from the testing device (130). The processor (210) can determine the intestinal type of the subject (110) based on the test information (132).

[0141] In one embodiment, the processor 210 may classify the gut types into a first type 1010, a second type 1020, and / or a third type 1030. The first type 1010 may be a type having a high number of pro-inflammatory microorganisms in the gut microbiome. The first type 1010 may be a gut type having a microbiome similar to that of a diseased individual. The second type 1020 may be a type having a high number of Prevotella genus microorganisms in the gut microbiome. The second type 1020 may have a gut microbiome similar to that of hunter-gatherers. The third type 1030 may be a type having a high number of Bacteroides genus microorganisms in the gut microbiome. The third type 1030 may have a gut microbiome similar to that of Westerners.

[0142] The processor 210 may determine the intestinal type of the subject 110 in various ways. Depending on the embodiment, the intestinal type of the subject 110 may be determined from the biological sample 112 through a single process, or the intestinal type of the subject 110 may be determined by sequentially performing two or more processes. In one embodiment, the processor 210 may determine the intestinal type of the subject 110 based on at least one selected from the first process 1041, the second process 1042, and the third process 1043. When two or more processes are performed, the processor 210 may perform the two or more processes sequentially / parallel in a predetermined order.

[0143] First, the first step (1041) will be described. In the first step (1041), the processor (210) may determine the biological sample (112) as a sample corresponding to one of the first type (1010), the second type (1020), and the third type (1030) based on the Jensen-Shannon distance. The memory (220) may store information on the reference sample corresponding to each of the first type (1010), the second type (1020), and / or the third type (1030). The processor (210) may determine the Jensen-Shannon distance between the biological sample (112) and the reference sample of each intestinal type. The processor (210) may determine which type of reference sample the biological sample (112) is similar to based on the Jensen-Shannon distance, and then may determine the intestinal type corresponding to the biological sample (112). For example, if the Jensen-Shannon distance between the biological sample (112) and a reference sample corresponding to the first type (1010) is close, the processor (210) may determine that the biological sample (112) corresponds to the first type (1010). In one embodiment, the degree of similarity between the biological sample (112) and a reference sample of a certain intestinal type and whether to classify the biological sample into the certain intestinal type may be determined based on any clustering algorithm (e.g., R Package).

[0144] Next, the second step (1042) will be described. In the second step (1042), the processor (210) may determine that the biological sample (112) corresponds to one of the second type (1020) and the third type (1030) based on the presence of microorganisms of the genus Prevotella and / or microorganisms of the genus Bacteroides. In one embodiment, the processor (210) may perform the second step (1042) based on the test information (132). In one embodiment, the test information (132) may include information indicating the abundance ratio of microorganisms of the genus Prevotella and / or microorganisms of the genus Bacteroides in the biological sample (112). In one embodiment, the processor (210) may determine that the intestinal type of the subject (110) is the second type (1020) or the third type (1030) based on the determination that the abundance ratio of microorganisms of the genus Prevotella in the biological sample (112) meets a predetermined criterion. In one embodiment, the processor (210) may determine the intestinal type of the subject (110) as the second type (1020) if the abundance ratio of Prevotella microorganisms among all intestinal microorganisms in the biological sample (112) exceeds approximately 3%, and may determine the intestinal type as the third type (1030) if the abundance ratio is approximately 3% or less. In one embodiment, the processor (210) may determine the intestinal type of the subject (110) as the second type (1020) or the third type (1030) by determining that the ratio of Prevotella microorganisms to Bacteroides microorganisms in the biological sample (112) meets a predetermined criterion. In one embodiment, the processor 210 may determine the enteric type as the second type (1020) if the ratio of Prevotella microorganisms to Bacteroides microorganisms in the biological sample 112 exceeds 0.5, and may determine the enteric type as the third type (1030) if the ratio is 0.5 or less.

[0145] Next, the third step (1043) will be described. In the third step (1043), the processor (210) may determine the biological sample (112) as the first type (1010) based on the values ​​of specific biomarkers and / or the gut microbiome index. In one embodiment, the processor (210) may determine the intestinal type of the subject (110) based on the values ​​(measurements) of one or more biomarkers for the biological sample (112). In one embodiment, the test information (132) may include values ​​of one or more biomarkers for the biological sample (112). In one embodiment, at least one of the biomarkers may be an inflammation-specific biomarker. In one embodiment, the processor (210) may determine the intestinal type of the subject (110) as the first type (1010) based on the values ​​of the inflammation-specific biomarkers for the biological sample (112). In one embodiment, upon determining that the value of the inflammation-specific biomarker satisfies a preset criterion (hereinafter, the "first criterion"), the processor (210) may determine the intestinal type of the subject (110) to be the first type (1010). In the present disclosure, a variable satisfying a predetermined criterion may mean that the variable is equal to or greater than, equal to or less than, or is greater than or less than the reference value of the criterion, depending on the embodiment.In one embodiment, the inflammation-specific biomarker is a microorganism belonging to the phylum Proteobacteria; a microorganism belonging to the genus Fusobacterium; a microorganism belonging to the genus Prevotella; a microorganism belonging to the genus Bacteroidaceae, Desulfovibrionaceae, Oscillospiraceae, Odoribacteraceae, Lachnospiraceae, Erysipelotrichaceae, Enterococcaceae, Lactobacillaceae The microorganisms may be at least one selected from, but are not limited to, microorganisms belonging to the family Veillonellaceae, Christensenellaceae, Enterobacteriaceae, Pasteurellaceae, Fusobacteriaceae, Neisseriaceae, Veillonellaceae, Gemellaceae, or Porphyromonadaceae; or the microorganisms listed in Table 1; and microorganisms known to those skilled in the art as inflammation-specific biomarkers may also be used to determine the first type. In one embodiment, the processor (210) can determine the intestinal type of the subject (110) to be the first type (1010) by determining that the abundance ratio of microorganisms belonging to the phylum Proteobacteria (Phylum) among all intestinal microorganisms in the biological sample (112) exceeds approximately 10%, or the abundance ratio of microorganisms belonging to the genus Fusobacterium (Genus) among all intestinal microorganisms in the biological sample (112) exceeds approximately 1%. In one embodiment, by determining that the abundance ratio of microorganisms belonging to the phylum Proteobacteria among all gut microorganisms in the biological sample (112) is greater than approximately 10% and the abundance ratio of microorganisms belonging to the genus Fusobacterium among all gut microorganisms in the biological sample (112) is greater than approximately 1%, the processor (210) may determine the gut type of the subject (110) to be the first type (1010). In one embodiment, the value of a particular biomarker may be a percentage representing the abundance ratio of microorganisms belonging to the particular biomarker among all gut microorganisms in the biological sample (112) of the subject (110).

[0146] As described above, the processor 210 may determine the gut microbiome index of the subject 110 based on the test information 132. In one embodiment, the processor 210 may determine the gut type of the subject 110 based on the gut microbiome index determined for the biological sample 112. In one embodiment, upon determining that the gut microbiome index meets a pre-defined criterion (hereinafter, the "second criterion"), the processor 210 may determine the gut type of the subject 110 to be the first type 1010. In one embodiment, upon determining that the gut microbiome index is less than approximately 40, the processor 210 may determine the gut type of the subject 110 to be the first type 1010.

[0147] In one embodiment, the processor 210 may determine the gut type of the subject 110 to be the first type 1010 based on a combination of the inflammation-specific biomarker values ​​for the biological sample 112 and the gut microbiome index of the subject 110. In one embodiment, the processor 210 may determine the gut type of the subject 110 to be the first type 1010 by determining that the inflammation-specific biomarker values ​​for the biological sample 112 meet the first criterion described above or that the gut microbiome index of the subject 110 meets the second criterion described above. In one embodiment, the processor 210 may determine the gut type of the subject 110 to be the first type 1010 by determining that the inflammation-specific biomarker values ​​for the biological sample 112 meet the first criterion described above and that the gut microbiome index of the subject 110 meets the second criterion described above.

[0148] As described above, at least two or more steps selected from the first step (1041), the second step (1042), and the third step (1043) may be performed sequentially / in parallel according to a predetermined order. In one embodiment, the processor (210) may perform the third step (1043). First, the processor (210) may determine whether the intestinal type of the subject (110) is the first type (1010) based on one of the methods of the third step (1043) described above. If the intestinal type of the subject (110) can be determined to be the first type (1010), the processor (210) may determine the intestinal type to be the first type (1010). In one embodiment, if the intestinal type of the subject (110) is not determined to be the first type (1010), the processor (210) may additionally perform classification of the subject (110) according to the first step (1041) and / or the second step (1042) described above. For example, the processor (210) may determine the intestinal type to be the second type (1020) if the abundance ratio of the Prevotella genus microorganisms in the biological sample (112) exceeds approximately 3%, and may determine the intestinal type to be the third type (1030) if the abundance ratio is approximately 3% or less.

[0149] In one embodiment, the processor 210 may perform the third step 1043 after performing the second step 1042. First, the processor 210 may determine whether the gut type of the subject 110 is the second type 1020 to the third type 1030 based on one of the methods of the second step 1042 described above. Second, the processor 210 may perform the third step 1043 again on the subject 110 classified as the second type 1020 or the third type 1030 to determine whether the biological sample 112 of the subject 110 satisfies the above-mentioned conditions for being determined as the first type 1010. If the biological sample (112) satisfies the above-mentioned conditions for being determined to be the first type (1010), the processor (210) can again determine (override) the intestinal type of the subject (110) to be the first type (1010).

[0150] In one embodiment, the processor (210) may determine the gut type in the order of the first step (1041), the second step (1042), and the third step (1043). First, the processor (210) may determine the biological sample (112) as one of the first type (1010), the second type (1020), and the third type (1030) based on the first step (1041). Next, the processor (210) may reclassify the biological sample (112) by the second step (1042). For example, if the biological sample (112) is determined to be the third type (1030) in the first step (1041), the classification of the biological sample (112) may be determined again to be the second type (1020) by the second step (1042), or may be maintained as the third type (1030). Thereafter, the processor (210) may perform a third step (1043). In the case where the biological sample (112) has been classified into the second type (1020) or the third type (1030) through the previous first step (1041) and second step (1042), if the biological sample (112) satisfies the above-mentioned conditions for being classified into the first type (1010), the processor (210) may again determine (override) the classification of the biological sample (112) into the first type (1010).

[0151] In one embodiment, the processor 210 may classify the intestinal type of the subject 110 into more detailed categories than the first type 1010 to the third type 1030. In one embodiment, the gut microbiome index may be used to classify the intestinal type into more detailed categories. In one embodiment, the position of the gut microbiome index of the subject 110 in the distribution of the gut microbiome indexes of all subjects may be used to classify the intestinal type into more detailed categories (e.g., type 2-1, type 2-2, type 2-3, etc.) based on the percentage of the gut microbiome index of the subject 110 in the top or bottom of the gut microbiome index of the corresponding gut type (e.g., type 2 (1020)). For example, among subjects who fall into the second type (1020), those with a gut microbiome index of less than approximately 40 may be further classified as type 2-1, those with an index between approximately 40 and approximately 70 as type 2-2, and those with an index over approximately 70 as type 2-3.

[0152] In one embodiment, the first step (1041) described above may be additionally performed on the already classified intestinal type in order to further classify the intestinal type. That is, if the intestinal type is primarily classified into one of the first type (1010) to third type (1030), the intestinal type may be further classified into more detailed types based on the Jensen-Shannon distance between the biological sample and the reference sample of each subintestinal type within the classified intestinal type. The first step (1041) is as described above.

[0153] In one embodiment, biomarkers characteristic of each of the detailed intestinal types (e.g., type 2-1, etc.) may be pre-selected (determined) by one of the above-mentioned methods. The sub-intestinal type of the subject (110) may be determined using the pre-selected biomarkers. The test information (132) may include values ​​that the biological sample (112) of the subject (110) has for each biomarker. The processor (210) may determine the sub-intestinal type of the subject (110) based on the test information (132). In one embodiment, biomarkers characteristic of the sub-intestinal types may be pre-selected based on various literature.

[0154] [Providing custom solutions based on intestinal type]

[0155] Once the gut type of the subject (110) has been determined by the above process, the processor (210) can obtain gut management information (1060) associated with the determined gut type from the memory (220). In one embodiment, the memory (220) can store gut management information for each gut type. In one embodiment, the gut management information (1060) can indicate a proposal for modulating the gut microbiome of the subject (110) having the gut type.

[0156] The processor 210 may use the communications circuitry 230 to communicate information indicative of the determined gut type and / or gut management information 1060 associated with the gut type to the subject's device 140. In one embodiment, the processor 210 may use the communications circuitry 230 to communicate information indicative of the determined gut type, gut management information 1060 associated with the gut type, and / or information indicative of the gut microbiome index 136 of the subject 110 to the subject's device 140.

[0157] FIG. 11 is a diagram illustrating gut management information (1060) according to an embodiment of the present disclosure. The gut management information (1060) may include one or more suggestions for adjusting the gut microbiome. The gut management information (1060) may be associated with a specific gut type. In one embodiment, the gut type may refer to the first, second, and third gut types (1010, 1020, and 1030) described above, or may refer to a gut type according to a more detailed classification, where the detailed classification may be a classification according to age. The gut management information (1060) may include a gut microbiome adjustment suggestion that can meaningfully change (improve) the gut microbiome of a subject (110) having the associated gut type. That is, the intestinal management information (1060) may include an intestinal microbiome adjustment plan that can meaningfully change (increase) the intestinal microbiome index of the subject (110) having the intestinal type, that can meaningfully change (increase) the intestinal microbiome index depending on the age of the subject (110) having the intestinal type, that can change (reduce) inflammation-inducing microorganisms present in the intestine of the subject (110) having the intestinal type, that can change (increase) inflammation-suppressing microorganisms present in the intestine of the subject (110) having the intestinal type, or that can improve constipation and / or diarrhea symptoms of the subject (110) having the intestinal type.

[0158] In one embodiment, the gut microbiome modulation plan may include at least one selected from the following: ingestion of a specific probiotic corresponding to the gut type, ingestion of a specific food, and application of a specific lifestyle. In one embodiment, the gut microbiome modulation plan may include information identifying one or more probiotics, information identifying one or more foods, and / or information identifying one or more lifestyle habits that can meaningfully change the gut microbiome in a gut having the gut type. In one embodiment, the probiotic may be a customized health functional food. In one embodiment, the information identifying the probiotic may be information suggesting a customized health functional food including one or more microorganisms to be suggested to the subject according to one embodiment.

[0159] In one embodiment, the gut microbiome modulation plan may include information proposing a probiotic to a subject classified as the first type (1010) including one or more microorganisms including one or more Lactobacillus paracasei, one or more Lactobacillus fermentum, one or more Lactobacillus plantarum groups, and / or one or more Lactococcus lactis groups. The probiotic information proposed to the subject of the first type (1010) may further include proposing one or more microbial species representing subgroups of microorganisms belonging to the same genus to increase diversity. Subjects who take the thus proposed probiotic may have an increase in gut microbiome index after taking the strain.(i) Lactobacillus paracasei, which is proposed for subjects classified into the first type, may include Lactobacillus paracasei subsp. paracasei, Lactobacillus paracasei subsp. tolerans, or Lactobacillus paracasei subsp. tolerans HM0866 (accession number: KCTC14409BP, the same below); (ii) Lactobacillus fermentum may include Lactobacillus fermentum HM0740 (accession number: KCTC14406BP, the same below); (iii) Lactobacillus plantarum group may include Lactobacillus plantarum, Lactobacillus pentosus, Lactobacillus paraplantarum, Lactobacillus fabifermentans, Lactobacillus xiangfangensis, Lactobacillus herbarum, Lactobacillus modestisalitolerans, or one or more of these subspecies (subsp.), where Lactobacillus plantarum may include Lactobacillus plantarum subsp. plantarum, Lactobacillus plantarum subsp. argentoratensis, or Lactobacillus plantarum subsp. plantarum HM0782 (accession number: KCTC14407BP, the same below); (iv) the Lactococcus lactis group may include Lactococcus lactis subsp. lactis (e.g., Lactococcus lactis subsp. lactis HM0850 (accession number: KCTC14408BP, the same below)), Lactococcus lactis subsp. hordniae, Lactococcus lactis subsp. cremoris, or Lactococcus lactis subsp. tructae.

[0160] In one embodiment, the gut microbiome modulation proposal may include information proposing a probiotic to a subject classified as the second type (1020) including one or more Lactobacillus fermentum and / or one or more microorganisms including one or more Lactococcus lactis groups. Subjects who consume the thus-proposed probiotic may have an increased gut microbiome index after consumption of the strains. (i) The Lactobacillus fermentum proposed to a subject classified as the second type may include Lactococcus fermentum HM0740; (ii) The Lactococcus lactis group may include Lactococcus lactis subsp. lactis (e.g., Lactococcus lactis subsp. lactis HM0850), Lactococcus lactis subsp. hordniae, Lactococcus lactis subsp. cremoris, or Lactococcus lactis subsp. tructae.

[0161] In one embodiment, the gut microbiome modulation suggestion may include information suggesting a probiotic to a subject classified as type 3 (1030) comprising one or more microorganisms including one or more Lactobacillus fermentum, one or more Lactobacillus plantarum groups, and / or one or more Lactococcus lactis groups. The probiotic information suggested to the subject classified as type 3 (1030) may further include suggesting a microbial strain that enhances such activity to improve dietary fiber utilization. Subjects who ingest the thus suggested probiotic may have an increased gut microbiome index after ingestion of the strain. (i) Lactobacillus fermentum suggested for subjects classified as type 3 may include Lactobacillus fermentum HM0740; (ii) the Lactobacillus plantarum group may include Lactobacillus plantarum, Lactobacillus pentosus, Lactobacillus paraplantarum, Lactobacillus fabifermentans, Lactobacillus xiangfangensis, Lactobacillus herbarum, Lactobacillus modestisalitolerans, or one or more of these subspecies (subsp.), where Lactobacillus plantarum may include Lactobacillus plantarum subsp. plantarum, Lactobacillus plantarum subsp. argentoratensis, or Lactobacillus plantarum subsp. plantarum HM0782; (iii) the Lactococcus lactis group may include Lactococcus lactis subsp. lactis (eg, Lactococcus lactis subsp. lactis HM0850), Lactococcus lactis subsp. hordniae, Lactococcus lactis subsp. cremoris, or Lactococcus lactis subsp. tructae.

[0162] In one embodiment, a priority may be assigned between one or more gut microbiome modulation proposals that are specific to the gut management information (1060). The priority may be assigned differently depending on the gut type associated with the gut management information (1060). That is, one or more gut microbiome modulation proposals in the gut management information (1060) may be prioritized according to the degree to which they change (increase) the gut microbiome index of a subject (110) having the corresponding gut type.

[0163] In the illustrated embodiment of intestinal management information for the first type (1110), the intestinal management information (1060) may indicate gut microbiome modulation suggestions such as "eat nuts", "eat vegetables", "exercise", "eat yogurt", "eat probiotics", "eat brown rice", and "eat fruit". The gut microbiome modulation suggestions may be assigned priorities from 1st to 7th. In the "eat probiotics" suggestion, the intestinal management information (1060) may further include information specifying which probiotics should be taken. The illustrated embodiment of intestinal management information for the second type (1120) and the embodiment of intestinal management information for the third type (1130) may also include information regarding gut microbiome modulation suggestions for the subjects (110) of the second type (1020) and the third type (1030) in a manner similar to that described above.

[0164] In one embodiment, the gut management information (1060) may include gut microbiome modulation proposals that differ from each other depending on at least one or more factors such as gut type, gut microbiome value, specific biomarker value, Prevotella value, Bacteroides value, etc. In one embodiment, the gut management information (1060) may specify different priorities between gut microbiome modulation proposals depending on at least one or more factors such as gut type, gut microbiome value, specific biomarker value, Prevotella value, Bacteroides value, etc.

[0165] In one embodiment, the intestinal management information (1060) may include classification information (1070) of the subject (110) based on the gut microbiome index of the subject (110). The subject (110) may be classified based on the gut microbiome index. The classification information (1070) may be information indicating which classification the subject (110) belongs to based on the gut microbiome index. In one embodiment, the gut microbiome modulation plan of the intestinal management information (1060) may be associated with a specific classification of the subject (110) indicated by the classification information (1070). That is, the intestinal management information (1060) may include a gut microbiome modulation plan that can meaningfully change (improve) the gut microbiome of the subject (110) classified into a specific classification.

[0166] In one embodiment, the subject (110) may be classified in various manners based on the gut microbiome index. In one embodiment, the higher the gut microbiome index, the closer the subject is to being healthy, and the lower the gut microbiome index, the closer the subject is to being sick. In one embodiment, one or more intervals may be set for the gut microbiome index, and the subject (110) may be classified according to the intervals. For example, if the gut microbiome index is expressed as a value between 0 and 100, the subject (110) may be classified as "dangerous" if the gut microbiome index is between 0 and 40, as "caution" if the gut microbiome index is between 40 and 70, and as "good" if the gut microbiome index is between 70 and 100. The number of intervals set for the gut microbiome index and the range of the intervals may be set differently depending on the embodiment.

[0167] In one embodiment, the interval set for the gut microbiome index may be set based on the gut microbiome index distribution of all subjects. For example, the gut microbiome index distribution of all subjects may be divided into quartiles. In this case, the subject (110) may be classified as "balanced" if the gut microbiome index is less than the top 25%, "slightly balanced" if the gut microbiome index is between the top 25% and less than 50%, "slightly unbalanced" if the gut microbiome index is between the top 50% and less than 75%, and "unbalanced" if the gut microbiome index is between the top 75% and less than 100%.

[0168] In one embodiment, the interval set for the gut microbiome index may be set based on the accuracy of the gut microbiome index. The accuracy of the gut microbiome index may be calculated by calculating the gut microbiome index for each sample of one or more sample groups and examining the results. For example, it may be assumed that a sample with a gut microbiome index greater than a predetermined value (e.g., 47) is likely to be a sample from an actual healthy person, and a sample with a gut microbiome index less than the predetermined value is likely to be a sample from an actual sick person. In this case, the interval of the gut microbiome index may be divided according to the reference value (e.g., 47). That is, if the gut microbiome index is less than the reference value (e.g., 47), the subject (110) may be classified as "imbalanced" and if the gut microbiome index is equal to or greater than the reference value (e.g., 47), the subject (110) may be classified as "balanced."

[0169] 12 is a diagram illustrating a method 1200 for determining a gut microbiome index according to one embodiment of the present disclosure. The method 1200 for determining a gut microbiome index according to various embodiments of the present disclosure may be performed by the device 100. The method 1200 may include a step of acquiring test information on a biological sample of a subject from a test device (S1210), a step of determining first information, second information, third information, and / or fourth information based on the test information (S1220), and / or a step of determining a gut microbiome index of the subject based on the first information, second information, third information, and / or fourth information (S1230).

[0170] In step S1210, the processor (210) may obtain test information (132) relating to the biological sample (112) of the subject (110) from the test device (130) using the communication circuitry (230). In step S1220, the processor (210) may determine first information, second information, third information, and / or fourth information based on the test information (132). In step S1230, the processor (210) may determine a gut microbiome index of the subject (110) based on the first information, second information, third information, and / or fourth information.

[0171] In one embodiment, the method (1200) may further include the processor (210) transmitting, using the communications circuitry (230), the information indicative of the gut microbiome index (136) to a device (140) of the subject.

[0172] In one embodiment, the memory 220 can store information about the reference sample 134. The method 1200 can further include the processor 210 determining a degree of similarity between the distribution of abundance ratios of each microbial species in the biological sample 112 and the distribution of abundance ratios of each microbial species in the reference sample 114 based on the test information 132 and / or the information about the reference sample 134, and / or determining first information based on the determined degree of similarity.

[0173] In one embodiment, the method (1200) may further include a step in which the processor (210) determines an abundance ratio of harmful microbial species relative to pre-defined commensal microbial species and harmful microbial species in the biological sample (112) based on the test information (132), and / or a step in which second information is determined based on the determined abundance ratio.

[0174] In one embodiment, the method (1200) may further include a step in which the processor (210) determines an abundance ratio of a predetermined beneficial microbial species in the biological sample (112) based on the test information (132), and / or a step in which the processor (210) determines third information based on the determined abundance ratio.

[0175] In one embodiment, the method (1200) may further include a step in which the processor (210) determines the number of all microbial species in the biological sample (112) and the degree to which the abundance ratio of each of the microbial species is evenly distributed based on the test information (132), and / or a step in which the processor (210) determines fourth information based on the determined number and / or the degree of evenness of all microbial species.

[0176] In one embodiment, the method (1200) may further include a step in which the processor (210) applies pre-set weighting values ​​(c1, c2, c3, c4) to each of the first information, the second information, the third information and / or the fourth information to determine a gut microbiome index.

[0177] 13 is a diagram showing a method for determining an intestinal type (1300) according to an embodiment of the present disclosure. The method for determining an intestinal type (1300) according to various embodiments of the present disclosure may be performed by an apparatus (100). The method (1300) may include a step (S1310) of acquiring test information on a biological sample of a subject from a testing apparatus, a step (S1320) of determining the intestinal type of the subject as a first type based on the value of an inflammation-specific biomarker for the biological sample, and / or a step (S1330) of acquiring intestinal management information associated with the intestinal type from a memory.

[0178] In step S1310, the processor (210) can obtain test information (132) related to the biological sample (112) of the subject (110) from the test device (130) using the communication circuit (230). In step S1320, the processor (210) can determine the intestinal type of the subject (110) as the first type (1010) based on the value of the inflammation-specific biomarker for the biological sample (112) in the test information (132). In step S1330, the processor (210) can obtain intestinal management information (1060) associated with the determined intestinal type from the memory (220).

[0179] In one embodiment, the method (1300) may further include the processor (210) using the communications circuitry (230) to transmit the information indicative of the bowel type and / or the bowel management information (1060) to the subject's device (140).

[0180] In one embodiment, the method (1300) may further include a step in which the processor (210) determines the Enterobacteria type to be the first type (1010) if the number of Proteobacteria for the biological sample (112) is greater than approximately 10% or the number of Fusobacterium for the biological sample (112) is greater than approximately 1%.

[0181] In one embodiment, the method (1300) may further include a step in which the processor (210) determines a gut microbiome index indicating the state of the gut microbiome of the subject (110) based on the test information (132), and / or a step in which the processor (210) determines the gut type as the first type (1010) based on the value of the inflammation-specific biomarker for the biological sample (112) and the gut microbiome index.

[0182] In one embodiment, the method (1300) may further include a step in which the processor (210) determines the gut type to be the first type (1010) if the value of an inflammation-specific biomarker for the biological sample (112) satisfies a first preset criterion or the gut microbiome index satisfies a second preset criterion.

[0183] In one embodiment, the method (1300) may further include the processor (210) determining the first information, the second information, the third information, and / or the fourth information based on the test information (132), and / or determining a gut microbiome index based on the first information, the second information, the third information, and / or the fourth information.

[0184] In one embodiment, the method (1300) may further include the processor (210) applying pre-set weights (c1, c2, c3, c4) to each of the first information, the second information, the third information and / or the fourth information to determine a gut microbiome index.

[0185] In one embodiment, the method (1300) may further include the processor (210) using the communications circuitry (230) to transmit the information indicative of the gut type, the gut management information (1060) and / or the information indicative of the gut microbiome index (136) to a device (140) of the subject.

[0186] In one embodiment, if the intestinal type of the subject (110) has not been determined to be the first type (1010), the method (1300) may further include a step in which the processor (210) determines the intestinal type to be the second type (1020) if the value of Prevotella in the biological sample (112) is greater than approximately 3%, and determines the intestinal type to be the third type (1030) if the value is less than approximately 3%.

[0187] The method according to the present disclosure may be a computer-implemented method. Although the steps of the method are illustrated and described in a certain order in the present disclosure, the steps may be performed in any combination of orders other than sequentially according to the present disclosure. In an embodiment, at least some steps may be performed in parallel, iteratively, or heuristically. The present disclosure does not exclude variations or modifications of the method. In an embodiment, at least some steps may be omitted and other steps may be added.

[0188] Various embodiments of the present disclosure may be embodied as software recorded on a machine-readable recording medium. The software may be software for implementing the various embodiments of the present disclosure described above. The software may be inferred from the various embodiments of the present disclosure by a programmer skilled in the art to which the present disclosure pertains. For example, the software may be machine-readable instructions (e.g., code or code segments) or a program. The machine may be a device that can be operated by an instruction word called from the recording medium, such as a computer. In one embodiment, the machine may be the device (100) according to an embodiment of the present disclosure. In one embodiment, a processor of the machine may execute the called instruction and cause components of the machine to perform a function corresponding to the instruction. In one embodiment, the processor may be the processor (210) according to an embodiment of the present disclosure. The recording medium may refer to any type of recording medium in which data can be stored and that can be read by the machine. The recording medium may include, for example, a ROM, a RAM, a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, and the like. In one embodiment, the recording medium may be a memory (220). In one embodiment, the recording medium may be embodied in a distributed form in computer systems connected via a network. The software may be stored and executed in a distributed manner in the computer systems. The recording medium may be a non-transitory recording medium. A non-transitory recording medium refers to a tangible medium that exists regardless of whether data is stored semi-permanently or temporarily, and does not include a signal that is transmitted transitorily.

[0189] [Customized health functional foods based on intestinal type]

[0190] As an aspect of the present disclosure, a health functional food composition for providing to a subject may be proposed. In one embodiment, the health functional food composition may be a custom-type health functional food composition, and the health functional food composition may be provided to a subject classified as having a specific intestinal type. Here, the specific intestinal type may be determined by an apparatus or method according to one embodiment of the present disclosure, for example, the first type, the second type, and / or the third type according to one embodiment of the present disclosure, or may be a more detailed type. In one embodiment, the health functional food composition may increase the gut microbiome index of a subject. In one embodiment, the health functional food composition may refer to a food manufactured and processed using raw materials or ingredients having functionality useful to the human body. Such a health functional food composition may include additional ingredients that a person skilled in the art may consider for the manufacture and distribution of the composition [e.g., dietary fibers having prebiotic activity such as fructo-oligosaccharides, inulin, and partially hydrolyzed guar gum (PHGG)], and in particular, may include prebiotics that can help the growth of probiotics ingested together.

[0191] In one embodiment, the prebiotic may be one or more of those listed in Tables 3 and 4 below.

[0192] [Table 3] TIFF0007636443000016.tif144169

[0193] [Table 4] TIFF0007636443000018.tif203169 TIFF0007636443000019.tif90169

[0194] In one embodiment, prebiotics may refer to nondigestible food ingredients that can selectively stimulate the growth and activity of one or more probiotic microorganisms in the large intestine to promote human health. Prebiotics may be nondigestible carbohydrates (oligo- or polysaccharides) or sugar alcohols that are not broken down or absorbed in the upper digestive tract. For example, inulin, fructo-oligosaccharides, xylo-oligosaccharides, or transgalacto-oligosaccharides may be used.

[0195] In one embodiment, the health functional food composition may include a probiotic that includes one or more of the microorganisms described as being provided to a subject in one embodiment of the present disclosure. In one embodiment, the health functional food composition may include a combination of one or more of the microorganisms described as being provided to a subject in one embodiment of the present disclosure.

[0196] In one embodiment, a customized health functional food composition may be provided for administration to a subject classified as type 1, comprising one or more microorganisms selected from the group consisting of Lactobacillus paracasei subsp. tolerans HM0866, Lactobacillus fermentum HM0740, Lactobacillus plantarum subsp. plantarum HM0782, and Lactococcus lactis subsp. lactis HM0850.

[0197] In one embodiment, a customized health functional food composition may be provided to a subject classified into the second type, the composition comprising one or more microorganisms selected from the group consisting of Lactobacillus fermentum HM0740 and Lactococcus lactis subsp. lactis HM0850.

[0198] In one embodiment, a customized health functional food composition may be provided for administration to a subject classified as type 3, comprising one or more microorganisms selected from the group consisting of Lactobacillus fermentum HM0740, Lactobacillus plantarum subsp. plantarum HM0782, and Lactococcus lactis subsp. lactis HM0850.

[0199] Although the technical idea of ​​the present disclosure has been described above through various embodiments, the technical idea of ​​the present disclosure includes various replacements, modifications, and alterations that can be made within the scope of those skilled in the art to which the present disclosure pertains. It should be understood that such replacements, modifications, and alterations can be included within the scope of the appended claims. The embodiments according to the present disclosure can be combined with each other. Each embodiment can be combined in various ways depending on the number of cases, and the combined embodiments also belong to the scope of the present disclosure.

[0200] [Experimental Example 1]

[0201] 1. Biological Specimen Collection

[0202] In the second citizen science project of the applicant service provider (IRB No. P01-2019-05-11-004, approved by the Ministry of Health and Welfare's Shared Institution Bioethics Committee for Human Subject Application Testing), voluntary volunteers were recruited from healthy Korean men and women aged 19 years or older, and approximately 1,081 people were selected as test subjects. Subjects were provided with a stool collection kit, which consists of a stool collection bucket, stool collection paper, and instructions for using the kit, along with a research explanation and consent form for research consent.

[0203] Of the selected subjects, 452 were given the different microbial strains shown in Table 5 below for two weeks, and biological samples were taken from the subjects before and after the intake to analyze the gut microbiome index.

[0204] [Table 5]

[0205] The subjects' feces were delivered soaked in a buffer solution to prevent microbial alteration. The composition of the buffer solution is shown in Table 6. Subjects were advised to send the stool collection kits after completing the collection process within 48 hours after collection, and to store the samples at room temperature in a cool place out of direct sunlight until the time of shipping in order to prevent alteration of the samples.

[0206] [Table 6]

[0207] 2. Analysis of gut microbial communities

[0208] (1) Analysis of 16S ribosomal RNA gene sequences

[0209] The fecal DNA was extracted from the collected fecal samples. All samples were collected in a DNA buffer solution, and immediately after collection, they were homogenized using FastPrep (MP Biomedicals) at speed 6.0 for 40 seconds to extract genomic DNA by a physical method.

[0210] Specifically, DNA extracted by the above genomic DNA extraction method was used to generate various types of amplicons targeting a wide range of taxonomic groups through PCR reaction using universal primers. The sequences of the universal primers are as follows, and the composition of the PCR premix for amplicon formation and PCR conditions are shown in Tables 7 and 8, respectively.

[0211] Forward universal primer (SEQ ID NO: 75): 5-CCTACGGGNGGCWGCAG-3'

[0212] Reverse universal primer (SEQ ID NO: 76): 5-GACTACHVGGGTATCTAATCC-3'

[0213] [Table 7]

[0214] [Table 8]

[0215] The amplicons thus generated were purified and then subjected to amplicon quality control (QC) using Bioanalyzer (Agilent), qPCR, etc. to confirm that the 16S rRNA sequence of the intestinal microorganisms extracted from the genomic DNA of the feces was obtained. The 16S ribosomal RNA gene sequence of the above sample was then analyzed using Next Generation Sequencing (NGS) technology via MiSeq (Illumina).

[0216] The sample pretreatment and QC process includes sample preparation (sample dilution and dissolution), DNA extraction (cell lysis and DNA extraction), DNA amplification process using PCR, Pooing (sample mixing), and DNA This includes sequence analysis (using an NGS machine).

[0217] Specifically, in the DNA amplification process, the Gel QC result confirmed that a DNA band was shown around 650 bp, and the DNA concentration was set to 5 ng / μl or more as a result of DNA quantitative analysis using Picogreen reagent. In the sample mixing stage, the Bioanalyzer QC result confirmed that no short peaks other than the main peak were observed in the DNA peak, and quality control was performed using the picogreen QC result of a DNA concentration of 5 ng / μl or more as the standard.

[0218] (2) Analysis of microbial communities

[0219] After generating thousands of gene sequences from each sample using the above-mentioned next-generation sequencing (NGS) technique, the bacterial community information was analyzed from the phylum to species level using the 16S ribosomal RNA gene sequence database of standard strains and uncultured microorganisms (EzTaxon) and the EasyBioCloud analysis system (http: / / www.ezbiocloud.com).

[0220] 3. Analysis of Intestinal Typology

[0221] Using the microbial community analysis data of the subjects analyzed in the above item 2, the intestinal types of the subjects were analyzed and classified into types 1, 2, and 3 using the first and second steps according to an embodiment of the present disclosure. The results of the analysis of the intestinal types of the subjects who ingested each strain are shown in Table 9 below.

[0222] [Table 9]

[0223] 4. Analysis of gut microbiome index

[0224] The gut microbiome index was analyzed by the method according to one embodiment of the present disclosure using the microbial community analysis data of the subjects analyzed in the above item 2. Samples were selected according to the gut microbiome index to confirm the increase or decrease in the gut microbiome index, and the change in the average gut microbiome index before and after the intake of the strain was calculated and shown in FIG.

[0225] For type 2, samples with a gut microbiome index of less than 70 points were selected, and for types 1 and 3, samples with a gut microbiome index of less than 60 points were selected. The results of this selection are as follows:

[0226] [Table 10]

[0227] Specifically, the gut microbiome index was calculated using the function shown in FIG. 7, where the first information was calculated by the above-mentioned mathematical formula 1, the second information was calculated by the mathematical formula 4, the third information was calculated by the mathematical formula 5, and the fourth information was calculated by the mathematical formula 6. FIG. 14 shows that the gut microbiome index of the subjects with the first type was increased by 5% or more for all of the A strain, B strain, C strain, and D strain. In particular, when the B strain and the D strain were ingested, the gut microbiome index increased by about 30%. Therefore, it is possible to recommend that subjects with the first type ingest the A strain to the D strain in order to increase the gut microbiome index, and a customized health functional food containing one or more of these strains should increase the gut microbiome index of the consumer. Furthermore, when such strains are recommended or ingested in combination, the gut microbiome index can be further increased.

[0228] In subjects with type 2, the results showed that when strains B and D were ingested, the gut microbiome index increased by 5% or more. Therefore, subjects with type 2 can be recommended to ingest strains B and D to increase their gut microbiome index, and customized health functional foods containing one or more of these strains should increase the gut microbiome index of the consumer. Furthermore, when these strains are recommended or ingested in combination, the gut microbiome index can be further increased.

[0229] In subjects with type 3, the results showed that when strains B, C, and D were ingested, the gut microbiome index increased by 5% or more. Therefore, subjects with type 3 can be recommended to ingest strains B, C, and D to increase their gut microbiome index, and customized health functional foods containing one or more of these strains should increase the gut microbiome index of the consumer. Furthermore, when these strains are recommended or ingested in combination, the gut microbiome index can be further increased. [Accession number]

[0230] Depository institution name: Korea Institute of Bioscience and Biotechnology

[0231] Accession number: KCTC14406BP

[0232] Date of acceptance: 20201210

[0233] Depository institution name: Korea Institute of Bioscience and Biotechnology

[0234] Accession number: KCTC14407BP

[0235] Date of acceptance: 20201210

[0236] Depository institution name: Korea Institute of Bioscience and Biotechnology

[0237] Accession number: KCTC14408BP

[0238] Date of acceptance: 20201210

[0239] Depository institution name: Korea Institute of Bioscience and Biotechnology

[0240] Accession number: KCTC14409BP

[0241] Date of acceptance: 20201210

[0242] [Table 11]

[0243]

Table 12

[0244]

Table 13

[0245]

Table 14

Claims

1. Communication circuits; one or more processors; and one or more memories having instructions stored therein that, when executed by the one or more processors, cause the one or more processors to perform operations; The one or more processors: Selecting an index used to determine the first information, the second information, the third information, and the fourth information from a microbial similarity-related index, a harmful microorganism-related index, a beneficial microorganism-related index, and a microbial diversity-related index based on at least one of a mean balanced accuracy or a number of significance for each index; obtaining test information relating to a biological sample from the subject from one or more testing devices using the communications circuitry; Based on the selected index and the test information, the first information on the microbial similarity between the biological sample and a predetermined reference sample, which is a biological sample from a healthy person classified according to a predetermined criterion, the second information on the ratio of intestinal harmful microorganisms of the subject, the third information on the ratio of intestinal beneficial microorganisms of the subject, and the fourth information on the diversity of intestinal microorganisms of the subject are determined; The device determines a gut microbiome index indicating the state of the subject's gut microbiome by applying predetermined weighting values ​​to each of the first information, the second information, the third information, and the fourth information.

2. The one or more processors: The device of claim 1 , wherein the communications circuitry is used to communicate information indicative of the gut microbiome index to a device of the subject.

3. the one or more memories further store information regarding the reference sample; The one or more processors: determining a degree of similarity between a distribution of abundance ratios of each microbial species in the biological sample and a distribution of abundance ratios of each microbial species in the reference sample based on the test information and information about the reference sample; The apparatus of claim 1 , further comprising: determining the first information based on the degree of similarity.

4. The one or more processors: determining an abundance ratio of the harmful microbial species relative to a predetermined symbiotic microbial species and a predetermined harmful microbial species in the biological sample based on the test information; The apparatus of claim 1 , further comprising: determining the second information based on the abundance ratio.

5. The one or more processors: determining an abundance ratio of a predetermined beneficial microbial species in the biological sample based on the test information; The apparatus of claim 1 , further comprising: determining the third information based on the presence ratio.

6. The one or more processors: determining the degree of uniformity of the number of all microbial species in the biological sample and the distribution of the abundance ratio of each of the all microbial species based on the test information; The apparatus of claim 1 , further comprising: determining the fourth information based on the number of all microbial species and the degree of uniformity.

7. 1. A method performed in an apparatus including one or more processors and one or more memories storing instructions for execution by the one or more processors, comprising: selecting, by the one or more processors, an index used to determine the first information, the second information, the third information, and the fourth information from among a microbial similarity-related index, a harmful microorganism-related index, a beneficial microorganism-related index, and a microbial diversity-related index based on at least one of an average balanced accuracy or a meaningful number for each index; obtaining, by the one or more processors, test information relating to a biological sample of a subject from one or more testing devices; determining, based on the selected index and the test information, the first information on the microbial similarity between the biological sample and a preset reference sample, which is a biological sample from a healthy individual classified according to a preset criterion, the second information on the ratio of gut harmful microorganisms of the subject, the third information on the ratio of gut beneficial microorganisms of the subject, and the fourth information on gut microbial diversity of the subject; and The method includes a step of the one or more processors determining a gut microbiome index indicating the state of the subject's gut microbiome by applying predetermined weighting values ​​to each of the first information, the second information, the third information, and the fourth information.

8. A non-transitory computer-readable medium having instructions recorded thereon that, when executed by one or more processors, cause the one or more processors to perform operations, comprising: The instructions cause the one or more processors to: Selecting an index used to determine the first information, the second information, the third information, and the fourth information from a microbial similarity-related index, a harmful microorganism-related index, a beneficial microorganism-related index, and a microbial diversity-related index based on at least one of the average balanced accuracy or the meaningful number for each index; Based on the selected index and test information on the subject's biological sample obtained from one or more test devices, determine the first information on the microbial similarity between the biological sample and a preset reference sample, which is a biological sample from a healthy person classified according to a preset criterion, the second information on the ratio of intestinal harmful microorganisms of the subject, the third information on the ratio of intestinal beneficial microorganisms of the subject, and the fourth information on the diversity of intestinal microorganisms of the subject; A computer-readable recording medium that determines a gut microbiome index indicating the state of the subject's gut microbiome by applying predetermined weighting values ​​to each of the first information, the second information, the third information, and the fourth information.

9. The one or more processors: Determine whether the intestinal type of the subject is classified into a first type in which inflammation-inducing microorganisms are abundant in the intestinal microbiome based on the numerical value of an inflammation-specific biomarker for the biological sample among the test information and / or a gut microbiome index indicating the state of the intestinal microbiome of the subject determined based on the test information; obtaining bowel management information associated with the bowel type from the one or more memories; The device of claim 1 , wherein the gut management information is configured to indicate a gut microbiome modulation suggestion for the subject having the gut typology.

10. The one or more processors: The device of claim 9 , wherein the communications circuitry is adapted to communicate the information indicative of the bowel type and the bowel management information to a device for the subject.

11. 10. The device of claim 9, wherein the inflammation-specific biomarker is at least one selected from the group consisting of microorganisms of the phylum Proteobacteria and the genus Fusobacterium.

12. The one or more processors: The device described in claim 11, wherein the intestinal type is determined to be the first type when the abundance ratio of the Proteobacteria microorganisms in the biological sample exceeds 10% or the abundance ratio of the Fusobacterium microorganisms in the biological sample exceeds 1%.

13. The one or more processors: The device of claim 9, wherein the intestinal type is determined to be the first type if the value of the inflammation-specific biomarker for the biological sample satisfies a predetermined first criterion and / or the intestinal microbiome index satisfies a predetermined second criterion.

14. The one or more processors: The device of claim 9 , wherein the communications circuitry is used to communicate information indicative of the gut type, the gut management information, and the gut microbiome index to a device for the subject.

15. The one or more processors: If the intestinal type of the subject is not determined to be the first type, The apparatus according to claim 9, wherein if the abundance ratio of Prevotella genus microorganisms in the biological sample exceeds 3%, the intestinal type is determined to be the second type, and if the abundance ratio is 3% or less, the intestinal type is determined to be the third type.

16. The device of claim 9 , wherein the gut microbiome adjustment suggestions of the gut management information include at least one selected from the following: intake of a specific probiotic corresponding to the gut type, intake of a specific food, and application of a specific lifestyle habit.

17. The device of claim 16, wherein the gut microbiome modulation suggestions of the gut management information are prioritized according to the degree to which they alter the gut microbiome index of the subject having the gut type.

18. The one or more processors are configured to determine whether the gut typology of the subject is classified into a second typology in which Prevotella genus microorganisms are prevalent in the gut microbiome, and whether the gut typology of the subject is classified into a third typology in which Bacteroides genus microorganisms are prevalent in the gut microbiome; The gut microbiome modulation scheme includes: (i) offering to the subject classified into the first type a probiotic comprising one or more microorganisms including one or more Lactobacillus paracasei, one or more Lactobacillus fermentum, one or more Lactobacillus plantarum groups, and / or one or more Lactococcus lactis groups; (ii) offering to the subject classified into the second type a probiotic comprising one or more microorganisms including one or more Lactobacillus fermentum and / or one or more Lactococcus lactis groups; or (iii) Proposing a probiotic comprising one or more microorganisms including one or more Lactobacillus fermentum, one or more Lactobacillus plantarum groups and / or one or more Lactococcus lactis groups to a subject classified into the third type.

19. 19. The device according to claim 18, wherein the Lactobacillus paracasei comprises Lactobacillus paracasei subsp. paracasei, Lactobacillus paracasei subsp. tolerance, or Lactobacillus paracasei subsp. tolerance HM0866 (Accession Number: KCTC14409BP).

20. The device of claim 18, wherein the Lactobacillus fermentum comprises Lactobacillus fermentum HM0740 (Accession Number: KCTC14406BP).

21. The Lactobacillus plantarum group includes Lactobacillus plantarum, Lactobacillus pentosus, Lactobacillus paraplantarum, Lactobacillus fabifermentans, Lactobacillus xiangfangensis, Lactobacillus herbarum, and Lactobacillus modestisalithorens.

20. The device of claim 18, comprising one or more selected from the group consisting of Lactobacillus subtilis (L. modestisalitolerans) and subsp. thereof.

22. 22. The device according to claim 21, wherein the Lactobacillus plantarum comprises Lactobacillus plantarum subsp. plantarum, Lactobacillus plantarum subsp. argentoratensis, or Lactobacillus plantarum subsp. plantarum HM0782 (Accession Number: KCTC14407BP).

23. The Lactococcus lactis group includes Lactococcus lactis subsp. lactis, Lactococcus lactis subsp. holdniae, Lactococcus lactis subsp. cremoris, Lactococcus lactis subsp. tractae, and Lactobacillus lactis subsp. The device according to claim 18, comprising one or more selected from the group consisting of Lactococcus lactis subsp. lactis HM0850 (Accession No.: KCTC14408BP).

24. Lactobacillus paracasei includes Lactobacillus paracasei subsp. tolerance HM0866 (Accession No.: KCTC14409BP); Lactobacillus fermentum includes Lactobacillus fermentum HM0740 (Accession No.: KCTC14406BP); Lactobacillus plantarum includes Lactobacillus plantarum subsp. plantarum HM0782 (Accession No.: KCTC14407BP); The device according to claim 18, wherein the Lactococcus lactis group includes Lactococcus lactis subsp. lactis HM0850 (Accession Number: KCTC14408BP).

25. 1. A method performed in an apparatus including one or more processors and one or more memories storing instructions for execution by the one or more processors, comprising: selecting, by the one or more processors, an index used to determine the first information, the second information, the third information, and the fourth information from among a microbial similarity-related index, a harmful microorganism-related index, a beneficial microorganism-related index, and a microbial diversity-related index based on at least one of an average balanced accuracy or a meaningful number for each index; obtaining, by the one or more processors, test information relating to a biological sample of a subject from one or more testing devices; determining, based on the selected index and the test information, the first information on the microbial similarity between the biological sample and a predetermined reference sample, which is a biological sample from a healthy individual classified according to a predetermined criterion, the second information on the ratio of gut harmful microorganisms of the subject, the third information on the ratio of gut beneficial microorganisms of the subject, and the fourth information on gut microbial diversity of the subject; determining, by the one or more processors, a gut microbiome index indicative of a state of the gut microbiome of the subject by applying preset weights to the first information, the second information, the third information, and the fourth information, respectively; determining, by the one or more processors, a first intestinal type of the subject based on a value of an inflammation-specific biomarker for the biological sample among the test information; and obtaining, by the one or more processors, bowel management information associated with the bowel type from the one or more memories; The method, wherein the gut management information indicates a gut microbiome modulation proposal for the subject having the gut type.

26. A non-transitory computer-readable medium having instructions recorded thereon that, when executed by one or more processors, cause the one or more processors to perform operations, comprising: The instructions cause the one or more processors to: Selecting an index used to determine the first information, the second information, the third information, and the fourth information from a microbial similarity-related index, a harmful microorganism-related index, a beneficial microorganism-related index, and a microbial diversity-related index based on at least one of the average balanced accuracy or the meaningful number for each index; Based on the selected index and test information on the subject's biological sample obtained from one or more test devices, determine the first information on the microbial similarity between the biological sample and a preset reference sample, which is a biological sample from a healthy person classified according to a preset criterion, the second information on the ratio of intestinal harmful microorganisms of the subject, the third information on the ratio of intestinal beneficial microorganisms of the subject, and the fourth information on the diversity of intestinal microorganisms of the subject; determining a gut microbiome index indicating a state of the gut microbiome of the subject by applying a preset weighting value to each of the first information, the second information, the third information, and the fourth information; determining the intestinal type of the subject as a first type based on the value of an inflammation-specific biomarker for the biological sample among the test information; obtaining bowel management information associated with the bowel type from one or more memories; The intestinal management information is a computer-readable recording medium indicating a gut microbiome modulation plan for the subject having the gut type.

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