Method for predicting reduction rate of body fat

Abstract

To provide a technique of predicting the reduction rate of the body fat of a subject by taking in polyphenol.SOLUTION: The method for predicting the reduction rate of a body fat according to an embodiment of the present invention is for predicting the reduction rate of the body fat of a subject by taking polyphenol. The method includes the steps of: acquiring intake information of food compositions of the subject including information on the intake of vitamin B12; and predicting the reduction rate of the body fat of the subject by taking in polyphenol on the basis of the acquired intake information of the subject.SELECTED DRAWING: Figure 4

Description

[Technical field]

[0001] The present invention relates to a method for predicting a body fat reduction degree, and an information processing device and a program capable of executing the method. [Background technology]

[0002] From the viewpoint of preventing lifestyle-related diseases and maintaining health, methods for efficiently reducing body fat have been sought. One of these methods is the body fat reducing effect of polyphenols, which have a variety of physiological activities. For example, Patent Document 1 discloses a packaged drink for burning body fat, which contains non-polymer catechins. Patent Document 2 discloses a body fat reducing agent, which is characterized by containing cacao polyphenols and catechins in a mass ratio of 5:1 to 5:8. Patent Document 3 discloses a body fat regulating agent containing, as an active ingredient, polyphenols derived from hops, which are effective in reducing and / or inhibiting the accumulation of body fat. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2006-77026 A [Patent Document 2] JP 2012-171916 A [Patent Document 3] International Publication No. 2005 / 074961 Summary of the Invention [Problem to be solved by the invention]

[0004] In recent years, with the development of data analysis technology, personalized healthcare, which provides health services and medical care tailored to individuals, is becoming more widespread. Therefore, it is preferable to be able to predict the degree of body fat reduction for each subject by focusing on individual differences in the degree of body fat reduction with regard to polyphenol intake.

[0005] The present invention relates to a technique for predicting the degree of body fat reduction associated with polyphenol intake in a subject. [Means for solving the problem]

[0006] A method for predicting a degree of body fat reduction according to one embodiment of the present invention is a method for predicting a degree of body fat reduction due to ingestion of polyphenols by a subject, comprising: obtaining food ingredient intake information for said subject, including information regarding vitamin B12 intake; and predicting a degree of body fat reduction due to the subject's ingestion of the polyphenols based on the acquired intake information of the subject.

[0007] A method for predicting a degree of body fat reduction according to another embodiment of the present invention is a method for predicting a degree of body fat reduction due to intake of vitamin B12 in a subject, comprising: obtaining food ingredient intake information for said subject, including information regarding polyphenol intake; and predicting a degree of body fat reduction due to the subject's intake of vitamin B12 based on the acquired intake information of the subject. Effect of the Invention

[0008] According to the present invention, it is possible to predict the degree of body fat reduction associated with a subject's ingestion of polyphenols. [Brief description of the drawings]

[0009] [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 a degree of body fat reduction through the intake of polyphenols according to a first 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 a body fat reduction degree performed by the information processing device. [Diagram 5] FIG. 1 shows the results of a test example to obtain data showing the relationship between the intake of food ingredients and the degree of body fat reduction due to the intake of catechins, and shows the results of extracting dietary factors that are correlated with the change in visceral fat (Δ visceral fat) before and after the intake of a catechin-containing beverage. [Figure 6] This is a graph showing the average value of Δvisceral fat in each of groups 1 to 4, which were obtained by dividing the subjects in the active group who ingested the catechin-containing beverage in the above test example based on their intake of vitamin B12. In this graph, the white bars show the results for each active group, and the black bars show the results for the placebo group. [Figure 7] FIG. 13 is a diagram showing the results of extracting dietary factors correlated with Δvisceral fat in Group I, a subject population with an average daily intake of vitamin B12 of less than 3 μg, in the above test example. [Figure 8] FIG. 13 is a diagram showing the results of extracting dietary factors correlated with Δvisceral fat in Group II, a subject population with an average daily intake of vitamin B12 of 11 μg or more, in the above test example. [Figure 9] This is a graph showing the average value of Δvisceral fat in each of groups 1A, 1B, 1C, and 1D, which are obtained by dividing the subjects in group 1 in Fig. 6 based on the intake of vitamin B6. In this graph, the white bars show the results in group 1 of the active group, the gray bars show the results in groups 1A, 1B, 1C, and 1D, and the black bars show the results in the placebo group. [Figure 10] 10 is a flowchart showing a flow of a process for predicting a body fat reduction degree according to a second embodiment of the present invention. [Figure 11] 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

[0010] Hereinafter, an embodiment of the present invention will be described with reference to the drawings.

[0011] <Summary of the Invention> The present invention relates to a technology for predicting the degree of body fat reduction associated with polyphenol intake by a subject using an information processing device. Although the body fat reducing effect of polyphenol intake has been known in the past (see, for example, Patent Documents 1 to 3), the present inventors analyzed the results of intervention studies on polyphenol intake and noticed that the actual body fat reducing effect varies. Therefore, as shown in the test examples described below, the present inventors found food components that have a correlation with the degree of body fat reduction associated with polyphenol intake, and completed the present invention.

[0012] In the present invention, polyphenol refers to a compound having two or more phenolic hydroxyl groups (hydroxyl groups bonded to aromatic rings such as benzene rings and naphthalene rings) in the same molecule, and particularly refers to a compound having an effect of reducing body fat. Specific examples of polyphenols include, but are not limited to, non-polymer catechins (see Patent Document 1), cacao polyphenols (see Patent Document 2), hop-derived polyphenols (see Patent Document 3), chlorogenic acid, isoflavones, oleuropein, hydroxytyrosol, tyrosol, and flavonol glycosides, as well as salts or derivatives thereof. These polyphenols have the effect of reducing body fat, and therefore it has been suggested that they may contribute to the activation of β-oxidation.

[0013] In one embodiment of the present invention, the polyphenol preferably contains non-polymer catechins from the viewpoint of obtaining a sufficient body fat reducing effect. Non-polymer catechins are known to have a body fat burning effect and an effect of promoting the expression of β-oxidation genes related to said effect (see Patent Document 1). In the present invention, the non-polymer catechins include at least one selected from non-epi-type non-polymer catechins such as catechin, gallocatechin, catechin gallate, and gallocatechin gallate, and epi-type non-polymer catechins such as epicatechin, epigallocatechin, epicatechin gallate, and epigallocatechin gallate.

[0014] Ingestion of polyphenols for reducing body fat is preferably continuous ingestion for a certain period (e.g., 2 weeks or more). Polyphenols may be ingested from one type of food containing polyphenols, or from a combination of multiple foods containing polyphenols. In addition, when polyphenols are non-polymer catechins, the daily intake for reducing body fat 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.

[0015] In the present invention, body fat refers to fat stored in the body of a mammal, including a human. Body fat includes subcutaneous fat located under the skin and visceral fat located around the internal organs. In one embodiment of the present invention, the degree of reduction in visceral fat may be predicted as the degree of reduction in body fat. Predicting the degree of reduction in visceral fat, which is highly related to lifestyle-related diseases, can contribute to the prevention of lifestyle-related diseases.

[0016] In the present invention, the food ingredient that is correlated with the degree of body fat reduction by the intake of polyphenols includes at least vitamin B12. Vitamin B12 is also called cyanocobalamin and is a type of water-soluble vitamin. It is known that vitamin B12 is involved in β-oxidation, and that β-oxidation is inhibited by a deficiency of vitamin B12. Vitamin B12 is contained in large amounts in animal foods such as fish, shellfish, liver, eggs, and meat.

[0017] Furthermore, in one embodiment of the present invention, the food components that are correlated with the degree of body fat reduction due to polyphenol intake preferably contain food components other than vitamin B12. The food components may contain at least one component selected from, for example, vitamin B6, magnesium, phosphorus, pantothenic acid, cholesterol, and aspartic acid, but are not limited thereto. These food components are preferably extracted by statistical processing from data on a subject population in an intervention study in which polyphenols were taken, as shown in the test example described below.

[0018] First Embodiment [System configuration example] The information processing device according to the first embodiment of the present invention is configured to be able to realize the method for predicting a body fat reduction degree according to the present invention, and, as an example, configures a system via the Internet 50 as described below. As shown in Fig. 1, the system according to this embodiment includes a server 100 on the Internet 50 and a plurality of user terminals 200.

[0019] 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 a degree of body fat reduction related to polyphenol intake. The server 100 is connected to, for example, a plurality of user terminals 200 via the Internet 50.

[0020] The user terminal 200 (200A, 200B, 200C...) can be a terminal used by a subject who is a user of the body fat reduction degree prediction service, and is, 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.

[0021] In this embodiment, the server 100 predicts the degree of body fat reduction due to polyphenol intake for each subject based on the intake information of food components 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 degree of body fat reduction due to polyphenol intake. An example of a method for the server 100 to acquire the intake information will be described later.

[0022] [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.

[0023] 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.

[0024] 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.

[0025] 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.

[0026] 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.

[0027] 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.

[0028] In this embodiment, the storage unit 18 may have a database such as a subject information database in addition to a program required for the process of predicting the degree of body fat reduction, which will be described later. The subject information database is referred to as necessary in the process of predicting the degree of body fat reduction. The subject information database stores attribute information of the subject who provided the intake information for each subject. The subject attribute information is not particularly limited, but may include, for example, general information such as name (nickname), subject ID for identifying the subject, age (generation), occupation, address (residential area), sex, and email address, as well as physical information such as height, weight, body fat percentage, and BMI, and information on lifestyle habits such as alcohol consumption, smoking habits, and exercise habits. In addition, the subject information database may store at least one of the acquired food ingredient intake information and predicted data on the degree of body fat reduction generated using the same for each subject ID. 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.

[0029] 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.

[0030] 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.

[0031] [Server Functional Configuration] 3, the server 100 according to this embodiment has, as functional configurations realized by the above hardware configuration, an acquisition unit 101, a prediction unit 102, and an information output unit 103. The acquisition unit 101, the prediction 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 by the CPU 11. The functions of each of these units will be described in detail in an operation example of the server 100 described later.

[0032] [Server operation example] Next, an example of the operation of the server 100 configured as above will be described. The operation of the server 100 described below is executed by the cooperation of hardware such as the CPU 11 and communication unit 19 of the server 100 and software stored in the storage unit 18. In this example of operation, an example will be described in which the server 100 predicts the degree of body fat reduction due to the intake of polyphenols by a subject based on information regarding the intake of vitamin B12 and other food components.

[0033] (Acquisition step (ST01)) 4, first, the acquisition unit 101 acquires the intake information of food components of the subject (ST01). Specifically, the intake information is information on the intake amount of food components correlated with the subject's degree of body fat reduction, and in this operation example, includes information on the subject's intake amount of vitamin B12 and information on the intake amount of food components other than vitamin B12.

[0034] The intake information of food components preferably includes at least one of information on values ​​indicating the intake of food components or information that can estimate the intake of food components. Note that the "intake of food components" refers to the intake of food components taken during a specified period or a specified number of meals. Examples of the specified period or the specified number of meals include, but are not limited to, one day, one meal, one week, etc.

[0035] The information on the value indicating the intake of the food component preferably includes at least one of information directly indicating the intake (e.g., mass) of the food component, or information on the value of an index correlated with the intake of the food component. The index correlated with the intake of the food component is an index that has been confirmed to have a significant correlation with the intake of the food component, and may be, for example, the intake of other food components related to the food component, attribute information of the subject, or an index related to the lifestyle.

[0036] The information that can estimate the intake of food components includes, for example, information about the food or menu consumed by the subject, and preferably includes, for example, the name and intake amount of the food or menu, or an image that allows the food or menu to be recognized. In this case, the server 100 can estimate the amount of food components contained in the food or menu by referring to a food component database that includes information on the amount of food components in the food or menu. By using information about the food or menu as intake information, the burden on the subject of calculating and inputting the intake amount of food components can be reduced.

[0037] The intake information of food components may be directly input to the server 100 from the user terminal 200 of the subject via, for example, a web page of the body fat reduction prediction service provided by the server 100. Alternatively, the intake information of food components may be input to the server 100 from an information processing device other than the user terminal 200. In this case, the information processing device other than the user terminal 200 may be, for example, a server related to a web service other than the above-mentioned body fat reduction prediction service, and may be configured to be able to estimate the intake of food components from information on the foods and menus ingested by the user. By linking with such an information processing device, the server 100 can easily obtain information on the intake of food components using existing applications.

[0038] (Prediction step (ST02)) Next, as shown in FIG. 4, the prediction unit 102 predicts the degree of body fat reduction due to the intake of polyphenols based on the acquired intake information of the subject (ST02). As shown in a test example described later, the present inventors have found that the degree of body fat reduction due to the intake of non-polymer catechins is positively correlated with the intake amount of vitamin B12. Furthermore, the present inventors have found that when the intake amount of vitamin B12 is low, the degree of body fat reduction is also correlated with other food components. Based on these results, the prediction unit 102 in this operation example can predict the degree of body fat reduction due to the intake of polyphenols based on information on the intake amount of vitamin B12 and information on the intake amount of food components other than vitamin B12 that are correlated with the degree of body fat reduction. The prediction accuracy can be improved by referring to the intake amounts of multiple food components. However, as described later, the prediction unit 102 can also predict the degree of body fat reduction based only on information on the intake amount of vitamin B12. Note that the program in this embodiment may be a program that causes a computer to execute the steps shown in FIG. 4, and may be a program that includes steps other than the steps shown in FIG. 4.

[0039] The predicted degree of body fat reduction in this embodiment means the amount of body fat reduction or its index when a predetermined amount of polyphenol is continuously ingested for a predetermined period of time. In addition, "high degree of body fat reduction" means that the change in body fat obtained by subtracting the body fat mass before polyphenol intake from the body fat mass after polyphenol intake is lower, that is, the change in body fat is a negative value and its absolute value is larger. For example, the amount of body fat reduction is expressed by a value indicating the amount of body fat to be reduced. Examples of values ​​indicating the amount of body fat include the volume of body fat, the area of ​​body fat at a predetermined cross-sectional part, the mass of body fat, and the body fat percentage. The volume and mass of body fat may be values ​​at a predetermined part (for example, around the abdomen) or may be total values ​​for the whole body. The index of the amount of body fat reduction may be, for example, an index that indicates the ease of reducing body fat in stages, and may correspond to a range of values ​​indicating the amount of body fat divided in stages.

[0040] Here, the information on the intake of food components used in the prediction process in this step is preferably information on values ​​indicating the intake of food components. Therefore, when the acquisition unit 101 acquires information on meals, menus, etc. as intake information, the prediction unit 102 can estimate the intake of food components based on this information and use this estimated value in the prediction process. When the intake information acquired by the acquisition unit 101 is information on foods or menus ingested by the subject, or includes information on foods or menus, the prediction unit 102 can estimate the intake of food components by referring to a food component database, etc., as described above.

[0041] The prediction process by the prediction unit 102 may be performed by a determination process of comparing a value indicating the intake amount of a food component with a predetermined reference value. Alternatively, the prediction process by the prediction unit 102 may be performed based on a prediction model generated by machine learning or the like using data obtained from a subject group. Alternatively, the prediction process by the prediction unit 102 may be performed by combining a determination process and a process using a prediction model.

[0042] First, an example of prediction processing by a determination process will be described. For example, when the value indicating the intake amount of vitamin B12 in the subject is equal to or greater than a predetermined reference value, the prediction unit 102 can predict the degree of body fat reduction due to the intake of polyphenols based on the value indicating the intake amount of vitamin B12 in the subject. As an example, when the value indicating the intake amount of vitamin B12 is the intake amount of vitamin B12 per day, the above-mentioned predetermined reference value is preferably 3 μg or more, more preferably 4 μg or more, preferably 6 μg or more, more preferably 11 μg or more. The preferred range of the above-mentioned reference value is in the intake amount of vitamin B12 per day, but may be expressed as a suitable corresponding numerical range according to the change in the unit time of intake measurement (for example, change to a predetermined hour, day, week, month, etc.) and the expression method of the numerical unit, etc.

[0043] When the value indicating the intake amount of vitamin B12 in the subject is equal to or greater than a predetermined reference value, the prediction unit 102 can predict that the higher the value indicating the intake amount of vitamin B12 in the subject, the higher the degree of body fat reduction due to the intake of polyphenols in the subject. As a specific example, a range of values ​​indicating the intake amount of vitamin B12 is set for each degree of body fat reduction, and the higher the value, the higher the degree of body fat reduction is set in stages. In this case, the prediction unit 102 can predict the degree of body fat reduction by determining which degree of body fat reduction the value indicating the intake amount of vitamin B12 in the subject belongs to.

[0044] On the other hand, when the value indicating the intake amount of vitamin B12 in the subject is less than a predetermined reference value, the prediction unit 102 can predict the degree of body fat reduction due to the intake of polyphenols based on information on the intake amount of food components other than vitamin B12. For example, when a food component positively correlated with the degree of body fat reduction is used as a food component other than vitamin B12, the prediction unit 102 can predict that the higher the value indicating the intake amount of the food component, the higher the degree of body fat reduction due to the intake of polyphenols in the subject. Examples of food components positively correlated with the degree of body fat reduction include vitamin B6, magnesium, phosphorus, pantothenic acid, cholesterol, and aspartic acid. On the other hand, when a food component negatively correlated with the degree of body fat reduction is used as a food component other than vitamin B12, the prediction unit 102 can predict that the lower the value indicating the intake amount of the food component, the lower the degree of body fat reduction due to the intake of polyphenols in the subject. In these cases, for example, a range of values ​​indicating the intake of food components is set for each body fat reduction degree, and the prediction unit 102 can predict the degree of body fat reduction by determining which body fat reduction degree the value indicating the intake of food components in the subject belongs to.

[0045] The criteria used in such a determination process can be determined based on data indicating the relationship between data indicating the degree of body fat reduction due to polyphenol intake and value data indicating the intake of food components, which are obtained in advance from a group of subjects. In order to obtain these data, for example, the following test is performed. First, data on the degree of body fat reduction before and after the intake period and value data indicating the intake of food components correlated with the degree of body fat reduction are obtained from a group of subjects who have taken a predetermined amount of polyphenols for a predetermined period. The obtained data is statistically processed to generate data indicating the above relationship.

[0046] Alternatively, the prediction process by the prediction unit 102 may be performed based on a prediction model generated by machine learning using data obtained from the subject group. Specifically, the prediction unit 102 can predict the degree of body fat reduction due to polyphenol intake by applying the intake information of the subject to a prediction model generated by learning data including data on the intake of food components correlated with the degree of body fat reduction, including vitamin B12, in a plurality of subjects and data indicating the degree of body fat reduction in a plurality of subjects. Note that the data on the intake of food components used here is preferably data indicating the value of the intake of food components, but may also be data capable of estimating the intake of food components. As the machine learning method used for the prediction process, at least one method selected from regression analysis such as multiple regression analysis or logistic regression analysis, neural networks such as multilayer perceptron, CNN (Convolutional Neural Network) and RNN (Recurrent Neural Network), support vector machine using any kernel function such as Gaussian kernel, random forest modeled as a regression tree, models using hidden Markov models, etc. can be selected.

[0047] Furthermore, 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 by a test on a subject population. In other words, the prediction model may be configured to be updated by the prediction process on the subject.

[0048] Furthermore, when the prediction process by the prediction unit 102 is performed by a method combining the above-mentioned judgment process and the prediction model, the prediction process can be performed, for example, as follows. For example, the prediction unit 102 performs the above-mentioned judgment process to determine whether or not the value indicating the intake amount of vitamin B12 in the subject is equal to or greater than a predetermined reference value, and can then perform the process of predicting the degree of body fat reduction using the above-mentioned prediction model.

[0049] (Information output step (ST03)) Next, as shown in Fig. 4, the information output unit 103 outputs information on the predicted result of the body fat reduction degree of the subject (ST03). The information obtained in this step may be provided to the subject who provided the intake information. By providing information on the predicted result of the body fat reduction degree, which is the subject of interest, it is possible to increase the subject's satisfaction and confidence in the intake of polyphenols.

[0050] As a specific operation example, the information output unit 103 can transmit information on the prediction result to each user terminal 200 that provides the intake information. The information can be transmitted, for example, by email, various messenger applications, or a notification function on a website that provides the body fat reduction degree prediction service. Alternatively, the information output unit 103 can output the information on the prediction result to an external device such as a printer. This allows the subject to be provided with the information on the prediction result via paper media such as direct mail.

[0051] 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 the body fat reduction degree, but it preferably includes at least one piece of information selected from information indicating the prediction result by the prediction unit 102 or information related to the prediction result. The output information may also include access information to a web page that displays the above-mentioned information. The output information does not include information related to the treatment or diagnosis of a disease.

[0052] The information showing the prediction result may be, for example, information directly or indirectly conveying the degree of body fat reduction caused by the ingestion of polyphenols in each subject. For example, the information showing the prediction result may be information showing the predicted value of the amount of body fat reduction, or information showing the degree of body fat reduction that expresses the predicted value of the amount of body fat reduction in stages (for example, "very easy to reduce", "easy to reduce", "difficult to reduce", etc.).

[0053] The information related to the prediction result may include, for example, at least one information selected from information on food components related to the degree of body fat reduction by the intake of polyphenols, information on polyphenols, and information on services related to body fat reduction. Specific examples will be described below. Note that, when the information output by the information output unit 103 includes information on specific food components, products, and / or services related thereto, it is preferable to include multiple options selectable by the subject.

[0054] Examples of information on food components related to the degree of body fat reduction by ingestion of polyphenols include the type of the food component, the recommended intake amount of the food component for the subject, the type and product of food containing the food component, information recommending them, etc. Providing information on the food components can reassure the subject that body fat will be reduced by ingestion of polyphenols and the food component, and can increase motivation to reduce body fat. The information on polyphenols includes the type of polyphenol in question, the recommended intake amount of the polyphenol for each subject, the types of foods and products that contain the polyphenol, and information recommending them. Services relating to body fat reduction include information on programs for ingesting foods containing polyphenols and / or the above-mentioned food ingredients, and weight loss programs including the same. The information output unit 103 may provide the subject with this information together with a program for ingestion of foods unrelated to polyphenols and suggestions for improving one's lifestyle.

[0055] In this way, the information output unit 103 can provide information for stably maintaining or increasing the degree of body fat reduction by ingesting polyphenols, thereby increasing confidence and motivation in reducing body fat by ingesting polyphenols for a variety of subjects.

[0056] [Effects of this embodiment] According to this embodiment, the server 100 can objectively predict the degree of body fat reduction caused by the intake of polyphenols, which has been conventionally recognized as an individual difference. Furthermore, the server 100 can also provide information for supporting the reduction of body fat based on new findings regarding food components that are correlated with the degree of body fat reduction caused by the intake of polyphenols. Therefore, according to this embodiment, it is possible to support efficient and stable reduction of body fat for users (subjects) who wish to reduce body fat, and also contribute to the prevention of lifestyle-related diseases and the improvement of healthy life expectancy.

[0057] [Test example] Hereinafter, a test example will be described for obtaining data showing the relationship between the degree of body fat reduction by ingestion of polyphenols and the intake of food components correlated with the degree of body fat reduction, which is used in the prediction process of the present invention. In this test example, the polyphenols are non-polymer catechins (hereinafter, "catechins") contained in tea.

[0058] In this test example, the results of seven intervention studies (comparative studies) involving the continuous intake of catechin-containing beverages were statistically analyzed to extract food components that correlate with the amount of body fat reduction. All of these intervention studies were clinical trials involving humans, with a test period of three months. The subject population consisted of a total of 1,615 healthy men and women, who were divided into two groups: an active group (746 people) and a placebo group (869 people). The active group consumed 540 mg of catechin-containing beverage per bottle. The placebo group consumed a catechin-free beverage that did not contain catechin. The composition of the catechin-free beverage, other than catechin, was substantially identical to that of the catechin-containing beverage. The subjects consumed one bottle of each beverage daily for three months.

[0059] During the study period, each subject was asked to complete a questionnaire to record the dietary intake other than the test beverage. Based on the recorded dietary information, the intake of approximately 60 types of nutrients and food components contained in the diet of each subject was calculated by referring to a food composition database.

[0060] The amount of change in visceral fat for each subject was calculated before and after the test period. The amount of visceral fat was determined by calculating the volume of fat accumulated around the internal organs using abdominal cross-sectional images taken by CT scan during exhalation. The amount of visceral fat change (Δ visceral fat) for each subject was then calculated by subtracting the amount of visceral fat before the test from the amount of visceral fat after the test. When comparing the average values ​​of Δ visceral fat for the placebo group and the active group, there was a tendency for the active group to have a smaller Δ visceral fat (i.e., a greater amount of visceral fat reduction), but there was also variation in Δ visceral fat even among subjects in the active group.

[0061] Therefore, in order to extract dietary factors other than catechin that are correlated with Δvisceral fat, the correlation between the intake of each food component and nutrient and Δvisceral fat was calculated. The results are shown in Figure 5. As can be seen from Figure 5, vitamin B12 (VB12), shown in gray background, has a correlation coefficient R of -0.1088 and a p-value of 0.0157, indicating that it is a dietary factor that is significantly correlated with Δvisceral fat.

[0062] Furthermore, to analyze the relationship between vitamin B12 and Δvisceral fat, the subjects in the active group were divided into four groups (groups 1 to 4) based on the intake of vitamin B12, and the average value and 95% confidence interval of Δvisceral fat in each group were calculated. The results are shown in FIG. 6. In the graph in FIG. 6, the vertical axis indicates the value of Δvisceral fat. A positive value of Δvisceral fat indicates that the amount of visceral fat increased due to the intervention test, and a negative value of Δvisceral fat indicates that the amount of visceral fat decreased due to the intervention test. The error bars indicate the 95% confidence interval. Among the active group, the average daily intake of vitamin B12 in group 1 was 2.5 μg, the average intake in group 2 was 4.8 μg, the average intake in group 3 was 7.6 μg, and the average intake in group 4 was 14.2 μg.

[0063] As shown in Figure 6, the delta visceral fat of groups 1 to 4 in the active group was significantly smaller than the delta visceral fat of the placebo group, indicating that the intervention trial reduced visceral fat in the active group. Furthermore, in active groups 1 to 4, the absolute value of delta visceral fat (amount of visceral fat reduction) tended to be greater in groups with higher vitamin B12 intakes. A trend test was performed on the results of each active group, with P for trend being 0.014. This confirmed a tendency for delta visceral fat to monotonically decrease (amount of visceral fat reduction monotonically increase) as we progressed from group 1 to group 4.

[0064] From the results of Figures 5 and 6, it was found that the intake of vitamin B12 is significantly correlated with Δvisceral fat, and is an important index for predicting the degree of visceral fat reduction by catechin intake. In other words, vitamin B12 is considered to be the main factor that assists the activation of β-oxidation by catechin. On the other hand, Group 1, which has a low intake of vitamin B12, also has a significant difference from the placebo group. From this, it is considered that factors other than vitamin B12 are involved in the reduction of visceral fat when the intake of vitamin B12 is low.

[0065] Therefore, the subject population was divided into Group I, whose daily average intake of vitamin B12 was less than 3 μg, and Group II, whose daily average intake was 11 μg or more. Then, for each group, the correlation between the intake of each food component and nutrient and Δvisceral fat was calculated, and multiple regression analysis was performed for those that were statistically significant. In the multiple regression analysis, the objective variable was Δvisceral fat, and the explanatory variables were the intake of each nutrient, sex, age, and visceral fat mass at baseline. The results of Group I are shown in Figure 7, and the results of Group II are shown in Figure 8.

[0066] As shown in Figure 7, in Group I, which had a low intake of vitamin B12, vitamin B6, magnesium, phosphorus, pantothenic acid, cholesterol, aspartic acid, etc., which are shown in gray background, were extracted as dietary factors that were significantly correlated with Δvisceral fat. On the other hand, as shown in Figure 8, in Group II, which had a high intake of vitamin B12, no dietary factors were extracted that were significantly correlated with Δvisceral fat. This suggests that when the intake of vitamin B12 is low, food ingredients other than vitamin B12, such as vitamin B6, magnesium, phosphorus, pantothenic acid, cholesterol, and aspartic acid, contribute to the reduction of visceral fat. In addition, when the intake of vitamin B12 is high, the contribution of food ingredients other than vitamin B12 to the reduction of visceral fat is thought to be low.

[0067] Furthermore, among the dietary factors extracted in Group I, vitamin B6 was used as an example to confirm the relationship between vitamin B6 intake and Δ visceral fat. First, the subjects in Group 1 in FIG. 6 were further divided into four groups (Groups 1A, 1B, 1C, and 1D) based on the average daily intake of vitamin B6. The average daily intake of vitamin B6 in Group 1A was 0.7 mg, the average daily intake of the same in Group 1B was 0.9 mg, the average daily intake of the same in Group 1C was 1.1 mg, and the average daily intake of the same in Group 1D was 1.5 mg. Then, the average daily intake of Δ visceral fat and the 95% confidence interval were calculated for each group using the same method as in FIG. 6. The results are shown in FIG. 9.

[0068] As shown in Figure 9, the absolute value of Δvisceral fat in group 1A, which had a low average daily intake of vitamin B6, was smaller than the absolute value of Δvisceral fat in groups 1B, 1C, and 1D, which had a higher average daily intake of vitamin B6. In addition, in groups 1A, 1B, and 1C, the absolute value of Δvisceral fat tended to be larger in groups with a higher intake of vitamin B6. These results confirmed that when the average daily intake of vitamin B12 and vitamin B6 was low, the effect of catechin intake in reducing visceral fat was low.

[0069] From the above results, it was found that vitamin B12 is significantly correlated with the degree of visceral fat reduction by catechin intake, and the higher the intake of vitamin B12, the higher the degree of visceral fat reduction by catechin intake. In addition, it was found that when the intake of vitamin B12 is low, food components other than vitamin B12 (e.g. vitamin B6, magnesium, phosphorus, pantothenic acid, cholesterol, aspartic acid) are correlated with the degree of visceral fat reduction by catechin intake. In other words, it is possible to predict the degree of visceral fat reduction by catechin intake based only on the intake of vitamin B12, but it was found that the degree of visceral fat reduction can be predicted more accurately by referring to the intake of other food components.

[0070] Furthermore, based on data such as this test example, it is possible to determine criteria for predicting the degree of visceral fat reduction in a subject through catechin intake, and it is also possible to generate a prediction model using the above data as learning data.

[0071] <Second embodiment> [Outline of this embodiment] In the above-mentioned first embodiment, the intake information including information on the intake amount of vitamin B12 is obtained, and the body fat reduction amount or its index is predicted as the body fat reduction degree when a predetermined amount of polyphenol is continuously taken for a predetermined period of time. On the other hand, by utilizing the relationship between the intake amount of polyphenol and vitamin B12 and the body fat reduction degree as shown in the test example, it is also possible to obtain information on the intake amount of polyphenol, and predict the body fat reduction degree of a subject using this information and the actual value or assumed value indicating the intake amount of vitamin B12. Note that in this embodiment, the description of the same configuration as the above-mentioned first embodiment is omitted.

[0072] The system according to the second embodiment of the present invention includes a server 100 and a plurality of user terminals 200, similar to the first embodiment (see FIG. 1). The hardware configurations of the server 100 and the user terminals 200 may be similar to those of the first embodiment (see FIG. 2).

[0073] Similarly to the first embodiment, the server 100 has an acquisition unit 101, a prediction unit 102, and an information output unit 103 as functional components realized by the above hardware configuration (see FIG. 3). In this embodiment, the specific functions of these units are different from those in the first embodiment.

[0074] [Example] Next, an operation example of the server 100 of this embodiment will be described. In this operation example, an example will be described in which the server 100 predicts a degree of body fat reduction due to the intake of vitamin B12 by a subject based on intake information including information on the intake amount of polyphenols.

[0075] (Acquisition step (ST11)) As shown in FIG. 10, first, the acquisition unit 101 acquires intake information of food components of a subject (ST11). Specifically, in this operation example, the intake information includes at least information on the intake amount of polyphenols. The intake information may also include the intake amount of food components other than polyphenols, such as vitamin B12. As in the first embodiment, the intake information of food components preferably includes at least one of information on a value indicating the intake amount of a food component or information that can estimate the intake amount of a food component.

[0076] For example, the information on polyphenol intake may include information on a value indicating the intake of a food containing polyphenol (e.g., a catechin-containing beverage, etc.). For example, the information preferably includes a value indicating the intake of a specific polyphenol-containing food or beverage per specific period (e.g., the amount of food or beverage ingested per specific period, the number of containers, the number, etc.). Furthermore, the information on the intake of food components other than polyphenols may be acquired by the same means as the information on polyphenol intake, or may be acquired by a different means. As an example, the information on polyphenol intake may be input from the user terminal 200, and the information on the intake of other food components may be input from another server that provides a nutritional management application, etc.

[0077] (Prediction step (ST12)) 10, the prediction unit 102 predicts the degree of body fat reduction due to the intake of vitamin B12 based on the acquired intake information of the subject (ST12). In this embodiment, the predicted degree of body fat reduction is preferably the amount of body fat reduction or an index thereof when the subject continues to take the amount of polyphenols derived from the intake information. From this perspective, the degree of body fat reduction predicted by the prediction unit 102 can also be said to be the degree of body fat reduction due to the intake of polyphenols and vitamin B12.

[0078] On the other hand, the value indicating the intake of vitamin B12 used in the prediction process of this embodiment may be calculated or estimated from intake information, as described below, or an assumed value of the intake may be used.

[0079] Specifically, when the intake information includes information on the polyphenol intake amount and information on the vitamin B12 intake amount, the prediction unit 102 can predict the subject's body fat reduction degree based on the information on the polyphenol intake amount and the information on the vitamin B12 intake amount. In this case, the values ​​indicating the polyphenol and vitamin B12 intake amounts can be calculated or estimated from the intake information. This makes it possible to predict the subject's body fat reduction degree according to the subject's actual food ingredient intake status.

[0080] In this embodiment, the prediction unit 102 can predict the degree of body fat reduction of the subject based on information on the polyphenol intake and an assumed value for assuming the intake of vitamin B12. The assumed value of the intake of vitamin B12 is a value set as a provisional value indicating the intake, and may be, for example, a value stored in advance. The prediction unit 102 may use a plurality of assumed values ​​to predict a plurality of corresponding body fat reduction degrees. This makes it possible to predict a change in the degree of body fat reduction due to a change in the intake of vitamin B12.

[0081] When using the assumed value, the intake information may or may not include information on the intake amount of vitamin B12. In the latter case, the prediction unit 102 can predict the change in the degree of body fat reduction using a value indicating the intake amount of vitamin B12 derived from the intake information and an assumed value different from the value.

[0082] As in the first embodiment, the prediction process by the prediction unit 102 can be performed by a judgment process in which a value indicating the intake of food components is compared with a predetermined reference value, a prediction model generated by machine learning or the like, or a combination of these.

[0083] For example, as explained in the above test example using FIG. 6, when a predetermined amount or more of polyphenols (e.g., non-polymerized catechins) is ingested, the intake of vitamin B12 and the degree of body fat reduction are positively correlated. Therefore, based on the intake information, when the value indicating the intake of polyphenols is equal to or greater than a predetermined reference value, the prediction unit 102 can predict that the higher the intake of vitamin B12, the higher the degree of body fat reduction of the subject. For example, when the polyphenol is non-polymerized catechins, the reference value is preferably 250 mg or more, more preferably 300 mg or more, even more preferably 400 mg or more, even more preferably 540 mg or more, and preferably 1500 mg or less, more preferably 1000 mg or less, and even more preferably 750 mg or less. On the other hand, when the value indicating the intake of polyphenols is less than a predetermined reference value, the prediction unit 102 can predict, for example, that the degree of body fat reduction of the subject is relatively low.

[0084] In an example using a prediction model, the prediction unit 102 can apply intake information of a subject to a prediction model generated from learning data including data on polyphenol and vitamin B12 intakes in a plurality of subjects and data showing the degree of body fat reduction in the plurality of subjects, and predict the degree of body fat reduction due to intake of vitamin B12. As the machine learning method used for the prediction process, various methods such as those exemplified in the first embodiment can be used.

[0085] (Information output step (ST13)) 10, the information output unit 103 outputs information on the predicted result of the body fat reduction degree of the subject (ST13). This step can be performed by the same operation as the information output step (ST03) of the first embodiment.

[0086] As in the first embodiment, 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 the body fat reduction degree, and preferably includes at least one piece of information selected from, for example, information showing the prediction result by the prediction unit 102, information related to the prediction result, and information on access to a web page that displays this information. Note that the output information does not include information related to the treatment or diagnosis of a disease.

[0087] The information showing the prediction result is preferably, for example, information directly or indirectly conveying the degree of body fat reduction caused by the intake of vitamin B12 in each subject, and may include various information exemplified in the first embodiment. Furthermore, in this embodiment, when the prediction process is performed using an assumed value that assumes the intake of vitamin B12, the information showing the prediction result may include information showing a prediction of the change in the degree of body fat reduction according to the change in the intake of vitamin B12. This makes it possible to show that the higher the intake of vitamin B12, the higher the degree of body fat reduction, which is thought to be able to encourage the intake of vitamin B12. In particular, for subjects who are thought to be continuously taking polyphenols based on the intake information, information for maximizing the effect of polyphenols in reducing body fat can be provided.

[0088] The information related to the prediction result may include various information exemplified in the first embodiment. Furthermore, in this embodiment, the information related to the prediction result preferably includes information related to vitamin B12, and may include, for example, types of food or products containing vitamin B12, information recommending these, etc. This can also promote the intake of vitamin B12.

[0089] [Effects of this embodiment] As described above, this embodiment can also support efficient and stable reduction of body fat for users (subjects) who wish to reduce body fat, and contribute to the prevention of lifestyle-related diseases and the improvement of healthy life expectancy. In particular, this embodiment can more effectively promote the intake of vitamin B12 contained in familiar foods, and support the reduction of body fat.

[0090] <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.

[0091] In the above-described operation example of the first embodiment, an example was described in which the prediction unit 102 predicts the degree of body fat reduction due to the intake of polyphenols based on information on the intake amount of vitamin B12 and information on the intake amount of food components other than vitamin B12 that are correlated with the degree of body fat reduction. However, the prediction unit 102 may predict the degree of body fat reduction based only on the information on the intake amount of vitamin B12. In this case, the prediction unit 102 can predict, based on the intake information, that the higher the intake amount of vitamin B12 in the subject, the higher the degree of body fat reduction due to the intake of polyphenols in the subject.

[0092] Alternatively, in the second embodiment, the prediction unit 102 may also predict the degree of body fat reduction due to the intake of vitamin B12 based on information on the intake of food components other than vitamin B12 that are correlated with the degree of body fat reduction. In this case, similar to the first embodiment, the prediction unit 102 can predict the degree of body fat reduction based on the value or assumed value of the intake of vitamin B12 when the value or assumed value of the intake of vitamin B12 is equal to or greater than a predetermined reference value, and can predict the degree of body fat reduction based on the information on the intake of the food components when the value or assumed value of the intake of vitamin B12 is less than the predetermined reference value.

[0093] 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 thereto. 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 administrator 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 body fat reduction prediction service to the administrator. In this case, the administrator's server acquires data on the intake amount of food components from the subject and transmits the data to the server 100. As a result, the server 100 can predict the subject's body fat reduction degree due to the intake of a specific food component and output the prediction result data to the administrator's server.

[0094] In the above embodiment, the server 100 includes the information output unit 103, but is not limited thereto. For example, the server 100 may store data of the prediction result in the storage unit 18 and use the data for analyzing the degree of body fat reduction in a group of subjects, without outputting the prediction result for each subject. In this example, as shown in FIG. 11, 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.

[0095] Furthermore, in the above-described embodiment, the subject of the prediction process is the user of the user terminal 200, but is not limited to this. For example, the subject may be an infant, an elderly person, or a non-human animal (pet animals such as dogs and cats, livestock such as cows, horses, pigs, etc.) who does not use the user terminal 200. In this case, for example, the user of the user terminal 200 transmits data on the subject's intake of food components to the server 100. In addition, the user can use the prediction results by the server 100 to help manage the subject's health.

[0096] 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 process of predicting the body fat reduction degree and the process of outputting information may be executed by separate servers.

[0097] Furthermore, the present invention can provide not only a method for predicting the degree of body fat reduction caused by the intake of polyphenols, etc., but also a method for predicting the degree of body fat reduction caused by a specific dieting behavior accompanied by a reduction in body fat. That is, the method for predicting the degree of body fat reduction caused by a specific dieting behavior of a subject according to one embodiment of the present invention may include a step of acquiring food component intake information of the subject, including information on the intake amount of vitamin B12 and / or polyphenols, and a step of predicting the degree of body fat reduction caused by the specific dieting behavior of the subject based on the acquired intake information of the subject. The specific dieting behavior may be any behavior that involves a reduction in body fat caused by the activation of β-oxidation, and includes, for example, the intake of specific food components other than polyphenols or vitamin B12, dietary restriction, and / or exercise behavior. Examples of the specific food components include L-carnitine and other components that are known to have a body fat burning effect (activation effect of β-oxidation). Examples of dietary restrictions include, for example, calorie intake restriction, carbohydrate restriction, lipid restriction, etc. Examples of exercise behavior include, for example, aerobic exercise such as walking, running, swimming, and yoga.

[0098] Among the inventions described in the claims of this application, the invention described as a "method for predicting the degree of body fat reduction" 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 the degree of body fat reduction" is a method for predicting the degree of body fat reduction using computer software, and is not a method in which a human operates a calculation tool such as a computer. [Explanation of symbols]

[0099] 100 Information processing device (server) 101 Acquisition Department 102 Prediction Department 103 Information output section 200 User terminals

Claims

1. A method for predicting a degree of body fat reduction due to polyphenol intake in a subject, comprising: obtaining food ingredient intake information for the subject, including information regarding vitamin B12 intake; Based on the acquired intake information of the subject, a degree of body fat reduction due to the intake of the polyphenols of the subject is predicted. A method for predicting the degree of body fat reduction.

2. Based on the intake information, it is predicted that the higher the intake of vitamin B12 in the subject, the higher the degree of body fat reduction due to the intake of the polyphenol in the subject. The method for predicting a degree of body fat reduction according to claim 1.

3. The intake information further includes information on the intake amount of food components other than vitamin B12 in the subject that correlate with the degree of body fat reduction due to the intake of the polyphenols, The degree of body fat reduction due to the intake of the polyphenols is predicted based on the information on the intake of the vitamin B12 and the information on the intake of the food components. The method for predicting a degree of body fat reduction according to claim 1 or 2.

4. If a value indicating the intake amount of vitamin B12 in the subject based on the intake information is equal to or greater than a predetermined reference value, a degree of body fat reduction due to the intake of the polyphenols is predicted based on the value indicating the intake amount of vitamin B12 in the subject; If the value indicating the intake amount of vitamin B12 based on the intake information is less than the predetermined reference value, a degree of body fat reduction due to the intake of the polyphenol is predicted based on information on the intake amount of the food component. The method for predicting a degree of body fat reduction according to claim 3.

5. The food ingredient contains at least one ingredient selected from vitamin B6, magnesium, phosphorus, pantothenic acid, cholesterol, and aspartic acid. The method for predicting a degree of body fat reduction according to claim 3.

6. The intake information of the subject is applied to a prediction model generated from training data including data on the intake amounts of the food components containing vitamin B12 for a plurality of subjects and data showing the degree of body fat reduction due to the polyphenols for the plurality of subjects, and the degree of body fat reduction due to the intake of the polyphenols is predicted. The method for predicting a degree of body fat reduction according to claim 1 or 2.

7. The polyphenols include non-polymer catechins. The method for predicting a degree of body fat reduction according to any one of claims 1 to 6.

8. Predict the reduction in visceral fat as the reduction in body fat The method for predicting a degree of body fat reduction according to claim 1 or 2.

9. An information processing device for predicting a degree of body fat reduction due to polyphenol intake of a subject, an acquisition unit that acquires food ingredient intake information of the subject, including information on the intake amount of vitamin B12; a prediction unit that predicts a degree of body fat reduction due to the subject's ingestion of the polyphenols based on the acquired intake information of the subject; An information processing device comprising:

10. A program for predicting a degree of body fat reduction due to polyphenol intake in a subject, the program comprising: obtaining food ingredient intake information for the subject, including information regarding vitamin B12 intake; predicting a degree of body fat reduction due to the polyphenol intake of the subject based on the acquired intake information of the subject; A program that executes the following.

11. 1. A method for predicting a degree of body fat reduction due to vitamin B12 intake in a subject, comprising: obtaining food ingredient intake information for the subject, including information on polyphenol intake; Based on the acquired intake information of the subject, a degree of body fat reduction due to the intake of the vitamin B12 of the subject is predicted. A method for predicting the degree of body fat reduction.

12. When the value indicating the polyphenol intake based on the intake information is equal to or greater than a predetermined reference value, it is predicted that the higher the vitamin B12 intake of the subject, the greater the degree of body fat reduction of the subject. The method for predicting a degree of body fat reduction according to claim 11.

13. the intake information includes information on the intake amount of the polyphenols and information on the intake amount of the vitamin B12; A degree of body fat reduction of the subject is predicted based on the information on the polyphenol intake amount and the information on the vitamin B12 intake amount. The method for predicting a degree of body fat reduction according to claim 11 or 12.

14. A degree of body fat reduction of the subject is predicted based on the information on the polyphenol intake amount and an assumed value for the vitamin B12 intake amount. The method for predicting a degree of body fat reduction according to claim 11 or 12.

15. The intake information of the subject is applied to a prediction model generated from training data including data on the intake amounts of the polyphenols and vitamin B12 in a plurality of subjects and data showing the degree of body fat reduction in the plurality of subjects, and the degree of body fat reduction resulting from the intake of the vitamin B12 is predicted. The method for predicting a degree of body fat reduction according to claim 11 or 12.

16. An information processing device for predicting a degree of body fat reduction due to intake of vitamin B12 by a subject, an acquisition unit that acquires food component intake information of the subject, including information on polyphenol intake; a prediction unit that predicts a degree of body fat reduction due to the intake of the subject by the vitamin B12 based on the acquired intake information of the subject; An information processing device comprising:

17. A program for predicting a degree of body fat reduction due to vitamin B12 intake of a subject, the program comprising: obtaining food ingredient intake information for the subject, including information on polyphenol intake; predicting a degree of body fat reduction due to the intake of vitamin B12 by the subject based on the acquired intake information of the subject; A program that executes the following.

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