Method for predicting immune function activity

Abstract

To provide a method for predicting immune function activity capable of predicting immune function activity of an object person due to ingestion of a specific food component, and an information processing device and program capable of executing the method.SOLUTION: According to an embodiment of the present invention, a prediction method for immune function activity is a method for predicting immune function activity due to ingestion of a specific food component of an object person, and includes the steps of: acquiring information about intestinal bacterial flora of the object person including first bacterial information about the existence of first intestinal bacteria that negatively correlate to immune function activity due to ingestion of the specific food component, and second bacterial information about the existence of second intestinal bacteria that positively relate to immune function activity by the ingestion of the specific food component; and predicting immune function activity of the object person due to the ingestion of the specific food component on the basis of the acquired first bacterial information and second bacterial information of the object person.SELECTED DRAWING: Figure 4

Description

[Technical field]

[0001] The present invention relates to a method for predicting immune function activity due to the ingestion of specific food components by a subject, and an information processing device and program capable of executing the method. [Background technology]

[0002] From the viewpoint of maintaining health, attention has been paid to the effect of ingesting certain food components on immune function. For example, Non-Patent Document 1 discloses that the ingestion of tea catechins enhances the activity of NK cells. In addition, Non-Patent Document 2 discloses that EGC-M5, a metabolite of catechins, enhances the activity of NK cells. [Prior art documents] [Non-patent literature]

[0003] [Non-Patent Document 1] Ryo Iketani et al., Japanese Journal of Clinical Pharmacology and Therapeutics, 2019, Vol. 50, p. 139-145 [Non-Patent Document 2] Yoon Hee Kim et al., Journal of Agricultural and Food Chemistry, 2016, Vol. 18, pp. 3591-3597 Summary of the Invention [Problem to be solved by the invention]

[0004] On the other hand, it is known that the type and ratio of intestinal bacteria that compose the intestinal flora vary from person to person. For this reason, even if a food ingredient is known to be effective for immune function, depending on the intestinal flora possessed, the food ingredient may not be metabolized and may not affect immune function. For this reason, when continuously ingesting a specific food ingredient, it is preferable to be able to predict for each subject whether or not the subject has a constitution in which the specific food ingredient is likely to affect immune function.

[0005] The present invention relates to a method for predicting immune function activity, which is capable of predicting immune function activity of a subject due to the ingestion of a specific food component, and an information processing device and program capable of executing the method. [Means for solving the problem]

[0006] A method for predicting immune function activity according to one embodiment of the present invention is a method for predicting immune function activity caused by intake of a specific food component by a subject, comprising the steps of: acquiring information on the intestinal flora of the subject, the information including first bacterial information on the presence of a first intestinal bacterium that is negatively correlated with immune function activity due to ingestion of the specific food component, and second bacterial information on the presence of a second intestinal bacterium that is positively correlated with immune function activity due to ingestion of the specific food component; and predicting the immune function activity of the subject due to intake of the specific food component based on the acquired first bacterial information and second bacterial information of the subject. Effect of the Invention

[0007] According to the method for predicting immune function activity of the present invention, it is possible to predict the immune function activity of a subject due to the ingestion of a specific food component. [Brief description of the drawings]

[0008] [Figure 1] FIG. 1 is a diagram showing an example of the configuration of a system including an information processing device capable of implementing a method for predicting immune function activity due to the ingestion of specific food components according to one embodiment of the present invention. [Diagram 2] FIG. 2 is a diagram showing a hardware configuration of the information processing device. [Diagram 3] FIG. 2 is a diagram showing a functional configuration of the information processing device. [Figure 4] 10 is a flowchart showing a flow of a process for predicting immune function activity by the information processing device. [Diagram 5]1 shows the results of a test example for obtaining data showing the relationship between the first and second intestinal bacteria and immune function activity, which is used in the prediction method of the present invention. (A) is a graph in which 58 subjects were classified into non-carriers (50 subjects) and carriers (8 subjects) of bacteria belonging to the genus Anaerosporobacter (hereinafter, "An bacteria"), and the average value of ΔNK cell activity in each group was calculated. (B) is a graph in which the data on ΔNK cell activity of the above-mentioned 58 subjects are plotted, with the horizontal axis representing the proportion (presence rate) of the number of An bacteria and the vertical axis representing the value of ΔNK cell activity, and the plots corresponding to the carriers of An bacteria (8 subjects) are shown in gray, and the plots corresponding to the non-carriers of An bacteria (50 subjects) are shown in black. [Figure 6] Graphs showing the results of the above test examples. (A) is a graph plotting data on ΔNK cell activity of non-carriers of An bacteria (50 individuals) with the horizontal axis representing the proportion (presence rate) of bacteria belonging to the genus Alistipes (hereinafter, "Al bacteria") and the vertical axis representing ΔNK cell activity values. Furthermore, in the same figure, non-carriers of An bacteria are divided into three groups based on the proportion of Al bacteria, and group I, which has a high proportion of Al bacteria, and group II, which has a low proportion, are shown. (B) is a graph showing the average value of ΔNK cell activity in each of groups I and II. [Figure 7] 1 shows the results of the above test example. (A) is a graph showing the change in EGC-M5 (ΔEGC-M5) in subjects in Group I and Group II. (B) is a graph showing the change in EGC (ΔEGC) in subjects in Group I and Group II. [Figure 8] This figure explains that, based on the results of the above test example, the NK cell activity predicted by catechin intake can be classified into three groups based on the presence or absence of An bacteria in the intestinal flora and the amount of Al bacteria. The horizontal axis shows the presence or absence of An bacteria, and the vertical axis shows the amount of Al bacteria. [Figure 9] FIG. 13 is a diagram showing a functional configuration of an information processing device according to another embodiment of the present invention. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0009] Hereinafter, an embodiment of the present invention will be described with reference to the drawings.

[0010] [System Overview] The information processing device according to the present invention is configured to be able to realize the method for predicting immune function activity according to the present invention, and, as one example, configures a system via the Internet 50 as described below. In the example shown in FIG. 1, a system according to an embodiment of the present invention includes a server 100 on the Internet 50 and a plurality of user terminals 200.

[0011] The server 100 may be, for example, a web server (information processing device) operated by an operator of a website that can provide a service for predicting immune function based on the intake of specific food components. The server 100 is connected to, for example, a plurality of user terminals 200 via the Internet 50.

[0012] The user terminal 200 (200A, 200B, 200C...) may be a terminal used by a subject who is a user of the immune function prediction service, and may be, for example, a smartphone, a mobile phone, a tablet PC (Personal Computer), a notebook PC, a desktop PC, etc. The user terminal 200, for example, accesses the server 100, receives a web page or the like generated by the server 100, and displays it on a screen using a browser or the like.

[0013] In this embodiment, the server 100 predicts immune function activity due to the ingestion of a specific food component in each subject based on the intestinal bacteria information provided by the subject using the user terminal 200. Furthermore, in this embodiment, the server 100 can provide the user terminal 200 with information regarding the predicted immune function activity due to the ingestion of a specific food component.

[0014] The operator of the immune function prediction service, for example, sends an intestinal flora test kit to each subject, and returns a sample collected by the subject using the kit to the operator or a person entrusted by the operator. In this embodiment, the collected sample is stool, blood, urine, saliva, etc., and from the viewpoint of more accurately detecting intestinal bacteria contained in the intestinal flora, stool is preferable.

[0015] Data measured from collected samples is input to the server 100, and the server 100 generates prediction data for immune function activity due to the intake of specific food components based on the data. The process of predicting immune function activity will be described later.

[0016] The food ingredient in the present invention is an edible ingredient, including ingredients contained in beverages. The food ingredient in the present invention is an ingredient that can act on immune function, and includes one or more kinds of food ingredients selected from, for example, polyphenols (catechin, anthocyanin, rutin, chlorogenic acid, sesamin, etc.), lactic acid bacteria, vitamin E, vitamin C, vitamin A, carotenoids (β-carotene, lycopene, lutein, astaxanthin, etc.), omega-3 fatty acids, etc. In this embodiment, catechin is taken as an example of a food ingredient. Catechin exists in foods such as tea as epigallocatechin (EGC), epigallocatechin gallate (EGCg), etc. EGC-M5, a metabolite of EGC, is known to activate NK cells (see Non-Patent Document 2). Furthermore, it is preferable that "ingestion" of a particular food item refers to continuous ingestion over a specified period of time (eg, two weeks or more).

[0017] Ingestion of catechins for NK cell activation, which will be described later, is preferably continuous ingestion for a predetermined period (e.g., 2 weeks or more). Catechins may be ingested from one type of food containing catechins, or from a combination of multiple foods containing catechins. In addition, when the catechins are non-polymer catechins, the daily intake amount for NK cell activation is preferably 100 mg or more, more preferably 200 mg or more, even more preferably 300 mg or more, and even more preferably 400 mg or more.

[0018] In the present invention, the "immune function activity due to the ingestion of a specific food component" predicted means a change in immune function activity before and after the ingestion of a specific food component. In other words, in the present invention, "ingestion of a specific food component activates immune function" means that the value indicating immune function activity is higher after ingestion of the specific food component than before ingestion, and "ingestion of a specific food component does not activate immune function" means that the value indicating immune function activity does not change before and after ingestion of the specific food component, or the value indicating immune function activity is lower after ingestion than before ingestion. The value indicating the activity of immune function may be a value measured by a known method (for example, the method disclosed in Medical Technology, 1993, Vol. 21, No. 7, pp. 574-580). In the present invention, the immune function activity means the activity of immune cells or molecules involved in immune response. The immune cells in the present invention are not particularly limited to immune cells related to natural immunity or immune cells related to adaptive immunity. For example, the immune cells in the present invention include one or more types of immune cells selected from eosinophils, neutrophils, basophils, macrophages, dendritic cells, natural killer (NK) cells, B cells, helper T cells, killer T cells, regulatory T cells, lymphocytes, etc. Molecules involved in immune responses include, for example, one or more molecules selected from cytokines and antibodies. Examples of cytokines include interleukins, interferons (IFN-α, IFN-β, IFN-γ, etc.), tumor necrosis factors (TNF-α, TNF-β, etc.), chemokines, hematopoietic colony-stimulating factors (CSFs), transforming growth factors (TGFs), etc. Examples of antibodies include IgM, IgG, IgA, IgD, IgE, etc. In this embodiment, the immune function activity due to the ingestion of a specific food component is preferably, for example, NK cell activity due to the ingestion of catechin. It should be noted that the method for predicting immune function activity according to the present invention is not related to the treatment or diagnosis of a disease.

[0019] [Hardware configuration of information processing device] As shown in FIG. 2, the server 100 includes, for example, a central processing unit (CPU) 11, a read only memory (ROM) 12, a random access memory (RAM) 13, an input / output interface 15, and a bus 14 connecting these to each other.

[0020] The CPU 11 appropriately accesses the RAM 13 etc. as necessary, and performs various arithmetic processing while controlling all the blocks of the server 100. The ROM 12 is a non-volatile memory in which the OS, programs, various parameters, and other firmware to be executed by the CPU 11 are fixedly stored. The RAM 13 is used as a working area for the CPU 11, and temporarily stores the OS, various applications being executed, and various data being processed.

[0021] The input / output interface 15 is connected to a display unit 16, an operation reception unit 17, a storage unit 18, a communication unit 19, and the like.

[0022] The display unit 16 is a display device using, for example, a Liquid Crystal Display (LCD), an Organic ElectroLuminescence Display (OLED), a Cathode Ray Tube (CRT), or the like.

[0023] The operation reception unit 17 is, for example, a pointing device such as a mouse, a keyboard, a touch panel, or other input device. When the operation reception unit 17 is a touch panel, the touch panel can be integrated with the display unit 16.

[0024] The storage unit 18 is, for example, a non-volatile memory such as a hard disk drive (HDD), a flash memory (SSD; Solid State Drive), or other solid-state memory. The storage unit 18 stores the OS, various applications, and various data.

[0025] In this embodiment, the storage unit 18 may have a database such as a subject information database in addition to programs such as applications necessary for the immune function activity prediction process described below. The subject information database is referred to and used as necessary in the immune function activity prediction process. The subject information database stores attribute information of the subject who provided the collected sample for each subject. The subject attribute information includes, but is not limited to, general information such as the name (nickname), subject ID for identifying the subject, age (generation), occupation, address (area of ​​residence), sex, and email address, as well as information on lifestyle habits such as dietary fiber intake, alcohol consumption, smoking habits, and exercise habits. In addition, the subject information database may store, for each subject ID, at least one of the acquired intestinal flora information and prediction data of immune function activity due to the intake of a specific food component generated using the information. It should be noted that databases such as the subject information database may be stored in a storage device or server externally connected to the server 100, rather than in the storage unit 18.

[0026] The communication unit 19 is, for example, a NIC (Network Interface Card) for Ethernet or various modules for wireless communication such as wireless LAN, and is responsible for communication processing with the user terminal 200.

[0027] Although not shown, the basic hardware configuration of the user terminal 200 may be substantially the same as the hardware configuration of the server 100 described above.

[0028] [Server Functional Configuration] As shown in FIG. 3, the server 100 according to this embodiment has an acquisition unit 101, a prediction unit 102, and an information output unit 103 as functional components realized by the above hardware configuration.

[0029] The acquiring unit 101, the predicting unit 102, and the information output unit 103 are each configured by loading an information processing program stored in the ROM 12 into the RAM 13 and executing it with the CPU 11. The functions of these units will be described in detail in an operation example of the server 100 described later.

[0030] [Server operation] Next, a description will be given of the operation of the server 100 configured as above. The operation is executed by the cooperation of hardware such as the CPU 11 and the communication unit 19 of the server 100 and software stored in the storage unit 18.

[0031] (Acquisition step (ST31)) As shown in Fig. 4, first, the acquisition unit 101 acquires information on the intestinal flora of the subject (ST31). Specifically, in this step, first bacterial information on the presence of a first intestinal bacterium and second bacterial information on the presence of a second intestinal bacterium are acquired as information on the intestinal flora of the subject. The first bacterial information and the second bacterial information may be information generated by the server 100 from data on the intestinal flora of the subject measured from a collected sample, or may be information generated by another information processing device.

[0032] The first enterobacteria are enterobacteria that are negatively correlated with immune function activity caused by the ingestion of a specific food component. In other words, the first enterobacteria are bacteria for which data has been obtained that show that when they are not present in the intestinal flora or when the amount of the bacteria is small, immune function activity is enhanced after the ingestion of a specific food component. When the specific food component is catechin, the first enterobacteria preferably include bacteria belonging to at least one genus selected from the genera Anaerosporobacter, Bariatricus, Klebsiella, Kocuria, Massiliprevotella, Megasphaera, Morganella, Petroclostridium, and Weissella, and more preferably include bacteria belonging to the genus Anaerosporobacter (hereinafter referred to as "An bacteria").

[0033] The second intestinal bacteria are intestinal bacteria that are positively correlated with immune function activity due to the ingestion of a specific food component. In other words, the second intestinal bacteria are bacteria for which data has been obtained that show that the presence or abundance of the bacteria in the intestinal flora increases immune function activity after the ingestion of a specific food component. When the specific food component is catechin, the second enterobacteria preferably include bacteria belonging to at least one genus selected from the genera Alistipes, Catabacter, Enterocloster, and Porphyromonas, and more preferably include bacteria belonging to the genus Alistipes (hereinafter referred to as "Al bacteria").

[0034] The first bacterial information and the second bacterial information may include, as information regarding the presence of each intestinal bacterium, information indicating the presence or absence of each intestinal bacterium, or information regarding a value indicating the bacterial amount. The information indicating the presence or absence of intestinal bacteria may be information directly indicating the presence or absence of the intestinal bacteria, or may be information regarding an indicator that has a correlation with the presence or absence of the intestinal bacteria. An indicator that is correlated with the presence or absence of intestinal bacteria is an indicator that can predict the presence of the intestinal bacteria, and may be, for example, an indicator related to the presence or bacterial quantity of other intestinal bacteria, attribute information or lifestyle of the subject, or an indicator derived from specific biological components obtained from a collected sample.

[0035] The information on the value indicating the bacterial load of the intestinal bacteria includes, for example, one or more pieces of information selected from information on the ratio of the bacterial count of the intestinal bacteria in the intestinal flora, the absolute bacterial load in a sample such as stool, and other indices that have a correlation with the bacterial load of the intestinal bacteria. By using the information on the value indicating the bacterial load of the intestinal bacteria, it is possible to improve the prediction accuracy in the prediction process. An index correlated with the bacterial load of the intestinal bacteria is an index capable of predicting the bacterial load of the intestinal bacteria, and may be, for example, an index relating to the bacterial load of other intestinal bacteria, attribute information or lifestyle of a subject, or an index derived from specific components obtained from a collected sample.

[0036] It is preferable that the information on the value indicating the amount of intestinal bacteria includes information on the proportion of intestinal bacterial count, since this makes it possible to grasp the proportion of each intestinal bacterium in the intestinal bacterial flora regardless of the amount of intestinal bacteria possessed by each subject. The ratio of the number of enterobacteria means the ratio of the number of enterobacteria to the number of all enterobacteria in the enterobacteria flora. The ratio of the number of enterobacteria can be calculated relatively easily, for example, by extracting the nucleic acid of enterobacteria from a sample such as a subject's stool and performing quantitative analysis such as real-time PCR or sequencing.

[0037] (Prediction step (ST32)) Next, as shown in FIG. 4, the prediction unit 102 predicts immune function activity caused by the subject's intake of a specific food component, based on the acquired first bacterial information and second bacterial information of the subject (ST32). The present inventors have found that there are not only intestinal bacteria (e.g., second intestinal bacteria) that are positively correlated with immune function activity caused by ingestion of a specific food component, but also intestinal bacteria (e.g., first intestinal bacteria) that are negatively correlated with the immune function activity caused by ingestion of a specific food component. Furthermore, the present inventors have found that even when a positively correlated intestinal bacteria (second intestinal bacteria) exists, the presence of a negatively correlated intestinal bacteria (first intestinal bacteria) may prevent immune function activity from being obtained by ingestion of a specific food component. Therefore, in this embodiment, by using both of these pieces of information to predict immune function activity due to the ingestion of a specific food component, it is possible to accurately predict the immune function activity of a subject due to the ingestion of a food component. Note that the program in this embodiment may be a program that causes a computer to execute the steps shown in Figure 4, and may be a program that includes steps other than the steps shown in Figure 4.

[0038] In this embodiment, the prediction unit 102 can predict that the subject's immune function will not be activated by ingestion of a specific food component, for example, when a first intestinal bacterium is present or a value indicating the bacterial amount of the first intestinal bacterium is greater than a first reference value. In addition to or instead of this, the prediction unit 102 can predict that the immune function of the subject will be activated by ingestion of a specific food component, for example, when a first intestinal bacterium is not present or a value indicating the bacterial amount of the first intestinal bacterium is equal to or less than a first reference value, and a second intestinal bacterium is present or a value indicating the bacterial amount of the second intestinal bacterium is equal to or greater than a second reference value. As an example, the prediction unit 102 can predict that the NK cells of the subject will not be activated by ingestion of catechins when An bacteria are present. Also, the prediction unit 102 can predict that the NK cells of the subject will be activated by ingestion of catechins when An bacteria are not present and the ratio of the number of Al bacteria is equal to or greater than a predetermined value greater than 0.

[0039] As a specific example, when the specific food component is catechin, the prediction unit 102 can perform the prediction process using the following reference values. For example, the first standard value as the proportion of An bacteria in the intestinal flora is preferably 0.2%, more preferably 0.05%, and even more preferably 0%, from the viewpoint of accurately predicting subjects with high NK cell activity.

[0040] For example, when the ratio of the number of An bacteria is equal to or less than the first reference value, the prediction unit 102 can predict that the immune function of the subject may be activated. For example, the second standard value as the proportion of Al bacteria in the intestinal flora is preferably 0.5%, more preferably 0.9%, and even more preferably 1.6%, from the viewpoint of accurately predicting subjects with high NK cell activity. For example, when the ratio of the number of Al bacteria is equal to or greater than the second reference value, the prediction unit 102 can predict that the immune function of the subject will be activated. Note that the numerical value of the reference value may be appropriately changed based on data obtained from a group of subjects.

[0041] Furthermore, the prediction unit 102 may predict in stages the activation of the immune function of the subject caused by the ingestion of a specific food component. Specifically, the prediction unit 102 may predict the activation level of the immune function of the subject caused by the ingestion of a specific food component. The activity level referred to here means an index that indicates the level of immune function activation caused by the intake of a particular food component. For example, the prediction unit 102 can predict the activity level based on the presence or absence of the first enterobacteria and a value indicating the bacterial amount of the second enterobacteria. As a specific example, the prediction unit 102 can predict that the activity of the immune function of the subject due to the intake of a specific food component is a first activity level when the first intestinal bacteria is present. Then, the prediction unit 102 can predict that the activity level is a second activity level higher than the first activity level when the first intestinal bacteria is not present and the second intestinal bacteria is less than a second reference value. Then, the prediction unit 102 can predict that the activity level is a third activity level higher than the second activity level when the first intestinal bacteria is not present and the value indicating the bacterial amount of the second intestinal bacteria is equal to or greater than the second reference value and less than a third reference value. Furthermore, the prediction unit 102 can predict that the activity level is a fourth activity level higher than the third activity level when the first intestinal bacteria is not present and the value indicating the bacterial amount of the second intestinal bacteria is equal to or greater than the third reference value.

[0042] The criteria used in the prediction process by the prediction unit 102 can be determined, for example, based on data obtained in advance from a subject population showing the relationship between the first bacterial information and the second bacterial information and the activation of immune function due to the intake of a specific food component. In order to obtain data showing these relationships, for example, the following test is carried out. First, data on immune function activity before and after ingestion and data on intestinal flora are obtained from a group of subjects who have ingested a specific food component. The obtained data is statistically processed to extract intestinal bacteria that are positively and negatively correlated with immune function activity. Further, data showing the relationship between the extracted intestinal bacteria and immune function activity is generated by statistical processing. Specific test examples for obtaining the above data in this embodiment will be described later.

[0043] Furthermore, the prediction process by the prediction unit 102 is not limited to a method of obtaining a qualitative prediction result using the above-mentioned criteria, and the prediction unit 102 may calculate a numerical value indicating the activity of the subject's immune function due to the intake of a specific food component. In other words, the prediction unit 102 may predict the immune function activity value of the subject due to the ingestion of a specific food component. The immune function activity value of the subject due to the ingestion of a specific food component here means the amount of change in the immune function activity value before and after the ingestion of the specific food component.

[0044] The prediction unit 102 can perform the prediction process of the immune function activity value by, for example, generating a prediction model using a machine learning technique from data showing the relationship between intestinal bacteria and immune function activity obtained from a subject population such as that described above, and the prediction unit 102 can perform the prediction process of the activity value based on this. Specifically, the prediction unit 102 can apply the first and second bacterial information of a subject to a prediction model generated from learning data including data on the bacterial load of a first enterobacterium, data on the bacterial load of a second enterobacterium, and data on the activity value of immune function due to the intake of a specific food component, and predict the activity value of immune function due to the intake of a specific food component. Note that the data on the bacterial load of each enterobacterium used here is preferably data indicating the bacterial load value of each enterobacterium, but may also be data that allows the bacterial load value to be estimated, for example, by using an index that has been confirmed to have a significant correlation with the bacterial load, or by changing the expression of the numerical unit of the bacterial load, etc. The prediction model used in the prediction process by the prediction unit 102 may be generated using data obtained from each subject in the prediction process in addition to data obtained in advance from a subject population. In other words, the prediction model may be configured to be updated by the prediction process for the subject. The machine learning method used in the prediction process may be one or more methods selected from the following: regression analysis such as multiple regression analysis or logistic regression analysis; neural networks such as multi-layer perceptrons, CNNs (Convolutional Neural Networks) and RNNs (Recurrent Neural Networks); support vector machines using any kernel function such as a Gaussian kernel; random forests modeled as regression trees; and models using hidden Markov models. The prediction process using the prediction model described above can be applied not only to prediction of immune function activity values, but also to prediction of immune function activity levels, for example.

[0045] (Information output step (ST33)) 4, the information output unit 103 outputs information on the predicted immune function activity of the subject due to the intake of a specific food component (ST33). The information output unit 103 may provide the information obtained in this step to the subject who provided the intake information. As a specific operation, the information output unit 103 may transmit the above information to each user terminal 200 that is the provider of each collected sample. The information may be provided, for example, by email, various messenger applications, or using a notification function on a website that provides the immune function prediction service. In this step, by providing the subject who provided the collected sample with information about the predicted results of the immune function activity of interest, the subject's satisfaction can be increased.

[0046] The information output by the information output unit 103 is not particularly limited as long as it is information related to the prediction result of immune function activity. For example, the information may be information indicating the prediction result by the prediction unit 102, or may be information related to the prediction result. The information output by information output unit 103 may include one or more pieces of information described above, and may also include access information to a web page that displays this information.

[0047] An example of information showing the prediction results is information indicating whether or not a particular food component is likely to affect immune function in each subject. Examples of information related to the prediction result include information on the intake of a specific food ingredient, information on foods containing a specific food ingredient (hereinafter, "specific ingredient-containing foods"), information on services related to specific ingredient-containing foods, etc. Specific examples are described below. Note that when the information output by the information output unit 103 includes information on specific food ingredients, products, and / or services related thereto, it is preferable to include multiple options selectable by the subject.

[0048] The information regarding the intake of a particular food ingredient may include, for example, information regarding the amount and / or method of intake of a particular food ingredient. The information regarding foods containing a specific ingredient may include, for example, types of foods containing a specific ingredient, information about specific products, and / or information recommending them. The services relating to foods containing specific ingredients may include information on programs for taking foods containing specific ingredients, campaigns for products that are foods containing specific ingredients, and the like. The information output unit 103 may provide the subject with this information together with a food intake program unrelated to immune function and suggestions for improving lifestyle.

[0049] Furthermore, the information output unit 103 can provide information on foods and methods for improving the intestinal flora to subjects whose immune function is predicted not to be activated or whose activity level (activity value) is low by ingesting a specific food component. This makes it possible to provide positive information to subjects whose predicted results are negative, thereby increasing the subject's satisfaction.

[0050] Thus, according to this embodiment, the server 100 can objectively predict the immune function activity caused by the intake of a specific food component for each subject based on the first bacteria information and the second intestinal bacteria information of the intestinal flora of the subject. This makes it possible to provide the subject with information for determining whether or not to continue taking a specific food component.

[0051] Furthermore, according to this embodiment, the prediction unit 102 refers to information on the second intestinal bacteria that is positively correlated with immune function activity due to the intake of a specific food component, as well as information on the first intestinal bacteria that is negatively correlated with the immune function activity. This can improve the prediction accuracy of immune function activity.

[0052] [Example of a test to obtain data showing the relationship between the first and second intestinal bacteria and immune function activity] Hereinafter, a test example for obtaining data showing the relationship between the first and second intestinal bacteria and immune function activity, which is used in the prediction process of the present invention, will be described. In this test example, the specific food component is catechin, and the immune cells are NK cells.

[0053] In this test example, a double-blind, randomized, cross-over comparative study was conducted to verify the effect of continuous intake of a catechin-containing beverage on immune function. The subject population consisted of 60 healthy men and women aged 55 to 65 years. The study divided the subjects into two groups. One group consumed a catechin-containing beverage for two weeks, followed by a non-catechin-containing beverage for two weeks. The other group consumed a non-catechin-containing beverage for two weeks, followed by a catechin-containing beverage for two weeks. The catechin-containing beverage contained 490 mg of catechin and 75 mg of caffeine, while the catechin-free beverage contained no catechin but 75 mg of caffeine. The components of these beverages other than catechin were essentially identical. Subjects consumed one bottle (350 mg) of each beverage per day for two weeks.

[0054] Before and after the study period and at specified times during the study period, feces, blood (plasma, serum, and peripheral mononuclear cell separation), and other samples were collected from each subject, and the collected samples were analyzed. Analytical items included fecal intestinal flora, NK cell activity, IFN-γ, TNF-α, etc. The NK cell activity value after the test minus the NK cell activity value before the test was defined as ΔNK cell activity. The NK cell activity value was measured from the peripheral mononuclear cell isolated sample by a known method.

[0055] Analysis of the stool samples was outsourced to Techno Suruga Lab Co., Ltd. As an example, the analysis was performed using the following procedure. Stool samples were collected using a stool collection container (e.g., manufactured by Techno Suruga Lab Co., Ltd.). DNA was then extracted from the collected stool samples, and bacterial flora analysis was performed using a next-generation sequencer MiSeq (manufactured by Illumina). Specifically, the stool samples collected by the subjects were transported frozen (-80°C) to Techno Suruga Lab Co., Ltd., and DNA extraction was outsourced. Then, PCR was performed using KAPA HiFi Hot Start Ready Mix (KapaBiosystems) to amplify the 16S rRNA gene fragment. Using the amplified 16s rRNA gene fragment as a template, a DNA library for next-generation sequencing was prepared using the NexteraXT index kit (Illumina). Furthermore, the prepared DNA library was used for next-generation sequencing using the MiSeq ( Analysis was performed using a microarray analyzer (Illumina) and the percentage of each enterobacteria was calculated.

[0056] Then, using 16S rRNA bacterial flora analysis, we comprehensively searched for intestinal bacteria (307 genera) that are commonly contained in the intestinal flora and that correlate with ΔNK cell activity. As a result, bacteria belonging to the genera Anaerosporobacter, Bariatricus, Klebsiella, Kocuria, Massiliprevotella, Megasphaera, Morganella, Petroclostridium, and Weissella were extracted as factors that were negatively correlated with ΔNK cell activity. Bacteria belonging to the genera Alistipes, Catabacter, Enterocloster, and Porphyromonas were extracted as factors that were positively correlated with ΔNK cell activity.

[0057] The subjects were then classified into four groups (Q1 to Q4) based on ΔNK cell activity. These groups were set up so that ΔNK cell activity increased in the order of Q1, Q2, Q3, and Q4. For each group, the average value of the proportion of the number of intestinal bacteria extracted as factors positively or negatively correlated with ΔNK cell activity was calculated. Then, confounding factors such as age and sex were adjusted, and a trend test (Jonckheere-Terpstra trend test) was performed on the value of ΔNK cell activity of each group and the proportion of the number of bacteria of each intestinal bacteria. Some of the results are shown in Table 1. In Table 1, Anaerosporobacter, Bariatricus, and Megasphaera are factors negatively correlated with ΔNK cell activity, and Catabacter is a factor positively correlated with ΔNK cell activity.

[0058] [Table 1]

[0059] As shown in Table 1, none of the subjects in group Q4, which had the highest ΔNK cell activity, carried An bacteria. Therefore, we next performed an analysis focusing on An bacteria.

[0060] As shown in Figure 5(A), 58 subjects belonging to Q1 to Q4 were classified into An bacteria non-carriers (50 subjects) and carriers (8 subjects), and the average ΔNK cell activity in each group was calculated. As shown in the figure, non-carriers had a higher average value of ΔNK cell activity than carriers.

[0061] As shown in Figure 5(B), the data on ΔNK cell activity of the above-mentioned 58 subjects was plotted with the ratio (presence rate) of An bacteria on the horizontal axis and the value of ΔNK cell activity on the vertical axis. The plots corresponding to An bacteria carriers (8 subjects) are shown in gray, and the plots corresponding to An bacteria non-carriers (58 subjects) are shown in black. The plots corresponding to An bacteria carriers (8 subjects) are surrounded by a dashed line, and the approximation line of these plots is shown by a dashed straight line. As a result, the proportion of An bacteria carriers (8 individuals) (prevalence rate) was negatively correlated with the value of ΔNK cell activity. On the other hand, differences were observed in ΔNK cell activity in non-carriers of An (n=50).

[0062] These results indicate that the presence or absence of An bacteria significantly affects ΔNK cell activity. However, because there are differences in ΔNK cell activity even among An bacteria carriers, it is thought that other factors are involved in ΔNK cell activity in addition to the presence or absence of An bacteria.

[0063] Next, to explore important factors related to ΔNK cell activity in non-carriers of An bacteria, the non-carriers (50 people) were classified into four groups, Q1 to Q4, using the method described above, and the average value of the proportion of the number of enterobacteria extracted as a factor with positive or negative correlation was calculated. Then, confounding factors such as age and sex were adjusted, and a trend test (Jonckheere-Terpstra trend test) was performed on the value of ΔNK cell activity in each group and the proportion of the number of enterobacteria. As shown in Table 2, Al bacteria were identified as an important factor in non-carriers of An bacteria.

[0064] [Table 2]

[0065] Furthermore, as shown in Fig. 6(A), the data on ΔNK cell activity of the above-mentioned An bacteria non-carriers (50 individuals) was plotted with the horizontal axis representing the proportion of Al bacteria count (prevalence rate) and the vertical axis representing the value of ΔNK cell activity. The An bacteria non-carriers were then classified into three groups with approximately equal numbers of individuals based on the proportion of Al bacteria count (prevalence rate), namely, a group with a high proportion of Al bacteria count, a group with a medium proportion of said bacteria count, and a group with a low proportion of said bacteria count. The group with a high proportion of Al bacteria count was designated Group I, and the group with a low proportion of Al bacteria count was designated Group II, and the following analysis was performed.

[0066] As shown in FIG. 6(B), confounding factors such as age and sex were adjusted and the average value of ΔNK cell activity was calculated for each of groups I and II. As shown in the figure, the average ΔNK cell activity was higher in the subjects in group I than in group II. This result indicates that the NK cell activation effect of catechin was higher in the subjects in group I, who carried a high amount of Al bacteria, than in the subjects in group II, who carried a low amount of Al bacteria.

[0067] Furthermore, to investigate the effect of Al bacteria on catechin and its metabolites, the changes in epigallocatechin (EGC) and its metabolite EGC-M5 detected in stool samples from subjects in Groups I and II before and after the test were analyzed. Note that the values ​​used for the analysis were the amounts of each substance detected before the test subtracted from the amounts of each substance detected after the test.

[0068] As shown in FIG. 7(A), the change in EGC-M5 (ΔEGC-M5) in the subjects of Group I was increased to about 2.0 times the change in EGC-M5 in the subjects of Group II. On the other hand, as shown in FIG. 7(B), the change in EGC (ΔEGC) in the subjects of Group I was almost equivalent to the change in EGC in the subjects of Group II. These results demonstrated that subjects who did not carry An bacteria but had high levels of Al bacteria had high metabolic activity of EGC-M5. In other words, when An bacteria are not present but the amount of Al bacteria present is high, it is thought that the metabolic activity of EGC-M5 increases, thereby increasing ΔNK cell activity.

[0069] As shown in Figure 8, the results of this test example demonstrated that, in terms of NK cell activity due to catechin intake, subjects could be classified into three groups based on the presence or absence of An bacteria in the intestinal flora and the amount of Al bacteria. Specifically, subjects with An bacteria present can be classified into the Non-Responder group, who are unable to produce EGC-M5 and are unable to activate NK cells by ingesting catechin. Subjects with no An bacteria and low levels of Al bacteria can be classified into the Semi-Non-Responder group, who produce low amounts of EGC-M5 and are able to activate NK cells slightly by ingesting catechin. Subjects with no An bacteria present and high levels of Al bacteria can be classified into the Responder group, who produce high amounts of EGC-M5 and are able to activate NK cells by ingesting catechin.

[0070] From this result, when the information processing device predicts the NK cell activity of a subject due to catechin intake, it can execute the prediction process of the NK cell activity due to catechin intake based on, for example, the presence or absence of An bacteria and the presence or absence or amount of Al bacteria as criteria. Alternatively, based on the above data obtained from the subject population, a program can be generated using a statistical method and / or a machine learning method to derive ΔNK cell activity from the amount of An bacteria and Al bacteria, and the prediction process of NK cell activity can be executed.

[0071] [Other embodiments] Although the embodiment of the present invention has been described above, the present invention is not limited to the above-described embodiment, and it goes without saying that various modifications can be made without departing from the spirit of the present invention.

[0072] In the above embodiment, an example of a system in which the server 100 is directly connected to the user terminal 200 via the Internet 50 has been shown, but the present invention is not limited to this. For example, the server 100 may be indirectly connected to the user terminal 200 via another information processing device. For example, the other information processing device may be an information processing device (server) used by an operator of a service for subjects who are end users, and the server 100 of the present invention may be a server used by a person who provides a service for predicting immune function activity to the operator. In this case, the server of the operator obtains data of a biological sample collected from the subject and transmits the data to the server 100. This enables the server 100 to predict the immune function activity of the subject due to the intake of a specific food component and provide data of the prediction result to the server of the operator. In this example, as shown in FIG. 9, the server 100 includes an acquisition unit 101 and a prediction unit 102, and may not include an information output unit 103 that provides information to the subject.

[0073] Furthermore, in the above-described embodiment, the subject of the prediction process is the user of the user terminal 200, but is not limited thereto. For example, the subject may be an infant or a non-human animal (pet animal such as a dog or cat, or livestock such as a cow, horse, pig, etc.) who does not use the user terminal 200. In this case, for example, the user of the user terminal 200 collects a biological sample from the subject and sends it to the operator of the immune function prediction service. Based on the information on the intestinal flora obtained from the collected sample, the server 100 can predict the immune function activity of the subject due to the intake of a specific food component.

[0074] In the above embodiment, only one server 100 is shown, but the processes executed by the server 100 may be distributed and executed by a plurality of servers. For example, the immune function activity prediction process and the information provision process may be executed by different servers.

[0075] Among the inventions described in the claims of this application, the invention described as a "method for predicting immune function activity" has each step performed automatically by at least one device such as a computer through information processing by software, and is not performed by a human using a device such as a computer. In other words, the "method for predicting immune function activity" is a method for predicting immune function activity using computer software, and is not a method in which a human operates a calculation tool called a computer. [Explanation of symbols]

[0076] 100 Information processing device 101 Acquisition Department 102 Prediction Department 103 Information output section 200 User terminals

Claims

1. A method for predicting the immunological function activity by the intake of a specific food component of a subject, comprising: obtaining information on the gut microbiota of the subject, including first bacterial information regarding the presence of a first gut bacterium that is negatively correlated with the immunological function activity by the intake of the specific food component, and second bacterial information regarding the presence of a second gut bacterium that is positively correlated with the immunological function activity by the intake of the specific food component; predicting the immunological function activity of the subject by the intake of the specific food component based on the obtained first bacterial information and second bacterial information of the subject. A method for predicting immunological function activity.

2. When the first gut bacterium is present or the value indicating the amount of the first gut bacterium is greater than a first reference value, predicting that the immunological function of the subject will not be activated by the intake of the specific food component. The method for predicting immunological function activity according to Claim 1.

3. When the first gut bacterium is absent or the value indicating the amount of the first gut bacterium is less than or equal to the first reference value, and the second gut bacterium is present or the value indicating the amount of the second gut bacterium is greater than or equal to a second reference value, predicting that the immunological function of the subject will be activated by the intake of the specific food component. The method for predicting immunological function activity according to Claim 1 or 2.

4. Predicting the degree of activation of the immunological function of the subject by the intake of the specific food component based on the obtained first bacterial information and second bacterial information of the subject. The method for predicting immunological function activity according to Claim 1 or 2.

5. Predicting the activity value of the immunological function of the subject by the intake of the specific food component based on the obtained first bacterial information and second bacterial information of the subject. The method for predicting immunological function activity according to Claim 1 or 2.

6. The specific food component includes a food component that is metabolized into EGC-M5 by gut bacteria. The method for predicting immunological function activity according to Claim 1 or 2.

7. The specific food component includes catechin. The method for predicting immunological function activity according to Claim 6.

8. The first gut bacterium includes at least one gut bacterium belonging to at least one selected from the genera Anaerosporobacter, Bariatricus, Klebsiella, Kocuria, Massiliprevotella, Megasphaera, Morganella, Petrocclostridium, and Weissella. The method for predicting immune function activity according to claim 6.

9. The first intestinal bacterium includes an intestinal bacterium belonging to the genus Anaerosporobacter. The method for predicting immune function activity according to claim 8.

10. The second intestinal bacterium includes a bacterium belonging to at least one selected from the genera Alistipes, Catabacter, Enterocloster, and Porphyromonas. The method for predicting immune function activity according to claim 6.

11. The second intestinal bacterium includes a bacterium belonging to the genus Alistipes. The method for predicting immune function activity according to claim 10.

12. The prediction unit predicts the activity of immune cells as the immune function activity of the subject due to the intake of the specific food component. The method for predicting immune function activity according to claim 1 or 2.

13. The immune cells include NK cells. The method for predicting immune function activity according to claim 12.

14. An information processing apparatus for predicting immune function activity due to the intake of a specific food component by a subject, an acquisition unit that acquires information on the intestinal flora of the subject, including first bacterium information regarding the presence of a first intestinal bacterium that is negatively correlated with the immune function activity due to the intake of the specific food component, and second bacterium information regarding the presence of a second intestinal bacterium that is positively correlated with the immune function activity due to the intake of the specific food component; a prediction unit that predicts the immune function activity of the subject due to the intake of the specific food component based on the acquired first bacterium information and second bacterium information of the subject; An information processing apparatus comprising the above.

15. A program for predicting immune function activity due to the intake of a specific food component by a subject, which causes an information processing apparatus to acquire information on the intestinal flora of the subject, including first bacterium information regarding the presence of a first intestinal bacterium that is negatively correlated with the immune function activity due to the intake of the specific food component, and second bacterium information regarding the presence of a second intestinal bacterium that is positively correlated with the immune function activity due to the intake of the specific food component; predict the immune function activity of the subject due to the intake of the specific food component based on the acquired first bacterium information and second bacterium information of the subject; A program for executing the above.

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