Method for predicting body fat change

Abstract

To provide a technique for predicting a body fat change of each subject due to ingestion of lipids containing α-linolenic acid as constituent fatty acids.SOLUTION: A body fat change prediction method according to the present invention is a body fat change prediction method for predicting a change in body fat of a subject due to ingestion of lipids containing α-linolenic acid as constituent fatty acids, the method including: a step of acquiring biological information of the subject including gene-expression information, which is information on expression levels of at least one associated genes or expression products thereof associated with a change in body fat due to ingestion of the lipids, in association with subject identification information; and a step of predicting a change in body fat due to ingestion of the lipids of the subject from the biological information on the basis of correlative information indicating correlations between changes in body fat in a plurality of sample subjects who ingested the lipids and at least one type of variables obtained from the biological information of the plurality of sample subjects.SELECTED DRAWING: Figure 4

Description

[Technical Field]

[0001] The present invention relates to a technique for predicting changes in body fat in a subject. [Background technology]

[0002] From the viewpoint of preventing lifestyle-related diseases and maintaining health, methods for efficiently reducing body fat such as visceral fat have been sought. For example, Patent Document 1 discloses a body fat-reducing agent and an energy metabolism promoter, which contain, as an active ingredient, an oil or fat containing α-linolenic acid as a constituent fatty acid, and which are to be taken with meals in an amount of 0.2 to 3.8 g per day as α-linolenic acid. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Publication No. 2019-19069 Summary of the Invention [Problem to be solved by the invention]

[0004] However, no technique is known for predicting changes in body fat in each subject due to the intake of lipids containing α-linolenic acid as a constituent fatty acid.

[0005] The present invention relates to a technique for predicting changes in body fat in each subject due to the intake of lipids containing α-linolenic acid as a constituent fatty acid. [Means for solving the problem]

[0006] A method for predicting a change in body fat according to one embodiment of the present invention is a computer-implemented method for predicting a change in body fat in a subject due to intake of lipids containing α-linolenic acid as a constituent fatty acid, the method comprising: acquiring biological information of the subject, including gene expression information that is information on the expression level of at least one related gene or its expression product associated with a change in body fat due to intake of the lipid, in association with subject identification information that identifies the subject; The change in body fat in a plurality of sample subjects who ingested the lipids; At least one variable obtained from the biological information of the plurality of sample subjects, the variable including the expression level of the relevant gene or its expression product associated with changes in body fat in the plurality of sample subjects; and predicting a change in body fat due to intake of the lipids of the subject from the biological information based on correlation information indicating the correlation between the above.

[0007] Another embodiment of the body fat change prediction system of the present invention is a body fat change prediction system that predicts changes in a subject's body fat due to the intake of lipids containing alpha-linolenic acid as a constituent fatty acid, and includes an acquisition unit, a memory unit, and a prediction unit. The acquisition unit Biometric information of the subject, including gene expression information that is information on the expression level of at least one relevant gene or its expression product related to changes in body fat due to the intake of the lipid, is obtained in association with subject identification information that identifies the subject. The storage unit The change in body fat in a plurality of sample subjects who ingested the lipids; At least one variable obtained from the biological information of the plurality of sample subjects, the variable including the expression level of the relevant gene or its expression product associated with changes in body fat in the plurality of sample subjects; Correlation information indicating the correlation between the two is stored. The prediction unit Based on the correlation information and the biological information, a change in body fat of the subject due to intake of the lipids is predicted.

[0008] A program according to another aspect of the present invention is a program for predicting a change in body fat of a subject due to intake of lipids containing α-linolenic acid as a constituent fatty acid, the program comprising: acquiring biological information of the subject, including gene expression information that is information on the expression level of at least one related gene or its expression product associated with a change in body fat due to intake of the lipid, in association with subject identification information that identifies the subject; The change in body fat in a plurality of sample subjects who ingested the lipids; At least one variable obtained from the biological information of the plurality of sample subjects, the variable including the expression level of the relevant gene or its expression product associated with changes in body fat in the plurality of sample subjects; predicting a change in body fat due to the intake of the lipids of the subject from the biological information based on correlation information indicating the correlation between the above-mentioned Execute the following. [Effects of the Invention]

[0009] According to the present invention, it is possible to provide a technique for predicting changes in body fat for each subject due to the intake of lipids containing α-linolenic acid as a constituent fatty acid. [Brief explanation of the drawings]

[0010] [Figure 1] 1 is a diagram illustrating an example of the configuration of a body fat change prediction system according to a first embodiment of the present invention. [Figure 2] FIG. 2 is a diagram illustrating a hardware configuration of a server included in the system. [Figure 3] FIG. 2 is a schematic diagram showing a database configuration included in a storage unit of the server. [Figure 4] FIG. 2 is a sequence diagram illustrating a processing flow of the system. [Figure 5] FIG. 10 is a diagram illustrating an example of the configuration of a body fat change prediction system according to a modified example of the above embodiment. [Figure 6]10 is a flowchart illustrating a processing flow of a server according to another modification of the embodiment. [Figure 7] 10 is a flowchart illustrating a processing flow of a server according to another modification of the embodiment. [Figure 8] 1 is a graph showing the accuracy of a prediction model for the amount of change in visceral fat area of ​​a subject according to Test Example 1 of the present invention. [Figure 9] 10 is a graph showing the accuracy of a prediction model for the amount of change in visceral fat area of ​​a subject according to Test Example 2 of the present invention. [Figure 10] 10 is a graph showing the accuracy of a prediction model for the amount of change in visceral fat area of ​​a subject according to Test Example 3 of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0011] [Summary of the Invention] The present invention relates to a technique for predicting changes in body fat in a subject due to the ingestion of lipids containing α-linolenic acid as a constituent fatty acid. Conventionally, lipids containing α-linolenic acid as a constituent fatty acid have been known to reduce body fat and promote energy metabolism (see Patent Document 1). In contrast, the present invention provides a technique for predicting changes in body fat in each subject due to the ingestion of lipids containing α-linolenic acid as a constituent fatty acid, using the expression levels of genes or their expression products in the subject.

[0012] In one embodiment of the present invention, the term "subject" refers to a person whose body fat change is to be predicted. The gender of the subject is not particularly limited. The age group of the subject is also not particularly limited, but from the viewpoint of improving the accuracy of prediction, the subject is preferably an adult, preferably 18 to 80 years old, more preferably 20 to 60 years old.

[0013] In one embodiment of the present invention, α-linolenic acid (ALA) is a type of polyunsaturated fatty acid, a linear 18-carbon ω-3 fatty acid with three double bonds. In this embodiment, lipids containing α-linolenic acid as a constituent fatty acid (hereinafter also referred to as "ALA lipids") are preferably derived from oils and fats rich in α-linolenic acid, such as one or more selected from perilla oil, linseed oil, perilla oil, chia seed oil, and sacha inchi oil. From the viewpoint of physiological effects, the α-linolenic acid content of the fatty acids constituting these oils is preferably 40% or more, more preferably 45% or more, even more preferably 50% or more, and even more preferably 52% or more. From the viewpoint of oxidation stability, it is preferably 80% or less, more preferably 70% or less, even more preferably 65% ​​or less, and even more preferably 60% or less.

[0014] The fats and oils from which the ALA lipid is derived may contain, for example, one or more of monoacylglycerol, diacylglycerol, and triacylglycerol. Accordingly, in one embodiment of the present invention, the ALA lipid may contain, for example, one or more of monoacylglycerol, diacylglycerol, and triacylglycerol. Among these, diacylglycerol is known to suppress the increase in blood triacylglycerol after eating and to have less accumulation in body fat than triacylglycerol with the same fatty acid composition. For this reason, the ALA lipid in one embodiment of the present invention is preferably one containing diacylglycerol containing α-linolenic acid as a constituent fatty acid.

[0015] The content of α-linolenic acid in the fatty acids constituting the oils and fats from which the above-mentioned ALA lipids are derived is preferably 40% by mass or more, more preferably 45% by mass or more, even more preferably 50% by mass or more, and even more preferably 52% by mass or more, from the standpoint of physiological effects; and is preferably 80% by mass or less, more preferably 70% by mass or less, even more preferably 65% ​​by mass or less, and even more preferably 60% by mass or less, from the standpoint of oxidative stability.

[0016] When the oil or fat is mainly composed of diacylglycerol, the diacylglycerol content in the oil or fat is preferably 50% by mass or more, more preferably 60% by mass or more, even more preferably 70% by mass or more, even more preferably 75% by mass or more, and even more preferably 80% by mass or more from the viewpoint of physiological effects, and is preferably 95% by mass or less, more preferably 92% by mass or less from the viewpoint of industrial productivity of the oil or fat.Furthermore, the monoacylglycerol content in the oil or fat is preferably 5% by mass or less, more preferably 0 to 3% by mass, and even more preferably 0 to 2% by mass from the viewpoints of flavor and industrial productivity of the oil or fat.

[0017] Furthermore, when the above-mentioned oils and fats are mainly composed of triacylglycerols, the content of triacylglycerols in the oils and fats is preferably 90% by mass or more, more preferably 92% by mass or more, even more preferably 93% by mass or more, from the standpoint of physiological effects and industrial productivity of the oils and fats, and is preferably 99.5% by mass or less, more preferably 99% by mass or less, and the content of monoacylglycerols in the oils and fats is preferably 5% by mass or less, more preferably 0 to 3% by mass, even more preferably 0 to 2% by mass, from the standpoint of flavor and industrial productivity of the oils and fats.

[0018] In one embodiment of the present invention, the content of α-linolenic acid in the fatty acids constituting the ALA lipid is preferably 15% by mass or more, more preferably 17% by mass or more, even more preferably 20% by mass or more, even more preferably 30% by mass or more, even more preferably 40% by mass or more, and even more preferably 50% by mass or more, from the viewpoint of obtaining a body fat reducing effect, and is preferably 75% by mass or less, more preferably 65% ​​by mass or less, and even more preferably 60% by mass or less, from the viewpoint of oxidation stability, etc. The amount of the fatty acid is expressed as the amount of free fatty acid.

[0019] The intake form of the ALA lipid is not particularly limited, and may be, for example, at least one selected from pharmaceuticals, quasi-drugs, foods, and materials or preparations used in combination with these that contain the ALA lipid.

[0020] The dosage form of the above-mentioned pharmaceuticals (including quasi-drugs) may be solid, semi-solid, or liquid, and examples thereof include oral administration in the form of tablets, capsules, granules, powders, lozenges, liquids, syrups, etc. Preparations in these various dosage forms can be prepared, as needed, by appropriately combining pharmaceutically acceptable carriers such as excipients, binders, bulking agents, disintegrants, surfactants, lubricants, dispersants, buffers, osmotic pressure adjusters, pH adjusters, emulsifiers, preservatives, stabilizers, thickeners, flow improvers, flavoring agents, effervescent agents, flavoring agents, coating agents, diluents, etc., with medicinal ingredients other than lipids containing α-linolenic acid as a constituent fatty acid.

[0021] The food may be in the form of a solid, semi-solid, or liquid, and may include various foods and beverages, as well as nutritional supplement compositions in the same form as the oral preparations described above (solid preparations such as tablets, capsules, and lozenges). Examples of the food and beverage include edible oils and fats (such as the above-mentioned oils and fats), beverages, water-in-oil oil-containing foods, oil-in-water oil-containing foods, bakery foods, confectioneries, frozen foods, retort foods, and other processed foods. The nutritional supplement composition may be, for example, a food for specified health use (FOSHU) or a food with functional claims claiming to have the physiological effects of α-linolenic acid.

[0022] In one embodiment of the present invention, the intake of ALA lipid for body fat change is preferably continuous intake for a predetermined period, and the change in body fat is preferably measured before and after the predetermined period. The predetermined period is preferably one week or more, more preferably ten days or more. Furthermore, when the subject is an adult, the lower limit of the daily intake of ALA lipid during the intake period is preferably 0.5 mg or more, more preferably 1 g or more, and even more preferably 2 g or more, from the viewpoint of obtaining a body fat reducing effect. Furthermore, the upper limit of the intake amount is not particularly limited, but is preferably 7 g or less, more preferably 6 g or less, and even more preferably 5 g or less, taking into consideration the balance with other nutrients, etc. The intake source of ALA lipid may be one type or multiple types.

[0023] In one embodiment of the present invention, body fat refers to fat stored in the body of mammals, including humans. 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 prediction of changes in body fat preferably includes prediction of changes in visceral fat, which is highly associated with lifestyle-related diseases.

[0024] In one embodiment of the present invention, "change in body fat due to intake of ALA lipid" refers to the quantitative change in body fat before and after intake of ALA lipid, and includes at least one of the level of change in body fat or the amount of change in body fat.

[0025] In one embodiment of the present invention, the "level of change in body fat" represents the degree of quantitative change in body fat in stages. The level of change in body fat may be represented, for example, as the level of change in a specific body fat evaluation index that quantitatively evaluates body fat, or as the level of quantitative change in body fat without reference to a specific body fat evaluation index. Examples of levels of change that do not refer to a specific body fat evaluation index include expressions such as "easy / difficult to reduce body fat (visceral fat)" and "large / small amount of reduction in body fat (visceral fat)." The level of change in body fat is not limited to the example expressed in two stages, but may also be expressed in three or more stages. The level of change in body fat is not limited to the example expressed in words as described above, but may also be expressed by, for example, numbers (1, 2, 3, etc.), letters such as alphabets (A, B, C, etc.), symbols, etc.

[0026] In one embodiment of the present invention, examples of the body fat evaluation index include body fat mass (body fat mass), body fat volume, body fat area in a cross section of the body, the ratio of body fat mass to body weight (body fat percentage), etc. In one embodiment of the present invention, the body fat evaluation index is preferably visceral fat area, for example, abdominal visceral fat area.

[0027] In one embodiment of the present invention, the change level of the body fat evaluation index represents the degree of change in the body fat evaluation index in stages, and is expressed, for example, in the same way as the change level of body fat, by displaying letters, words, symbols, etc. For example, if the body fat evaluation index is visceral fat area, the change level of the visceral fat area is expressed by expressions such as "visceral fat area is easy / hard to reduce" or "the amount of reduction in visceral fat area is large / small."

[0028] In one embodiment of the present invention, the "change in body fat" is expressed as the change in a body fat evaluation index, and examples thereof include the difference in the body fat evaluation index before and after intake of ALA lipid, and the rate of change, which is the ratio of this difference to the value before intake of ALA lipid. For example, if the body fat evaluation index is visceral fat area, the change in visceral fat area can be the difference in visceral fat area before and after intake of ALA lipid, or the rate of change in visceral fat area, which is the ratio of this difference to the value before intake of ALA lipid. Furthermore, in one embodiment of the present invention, "prediction of change in body fat" includes not only the aspect of predicting the difference or rate of change in the body fat evaluation index, but also the aspect of predicting the range of these possible values.

[0029] In one embodiment of the present invention, the term "gene" refers to double-stranded DNA including human genomic DNA, as well as single-stranded DNA (positive strand) including cDNA, single-stranded DNA (complementary strand) having a sequence complementary to the positive strand, and fragments thereof, and refers to DNA containing some biological information in the sequence information of the bases that make up the DNA. Furthermore, the term "gene" does not only refer to "genes" represented by a specific base sequence, but also includes nucleic acids encoding their homologs (i.e., homologs or orthologs), mutants such as genetic polymorphisms, and derivatives.

[0030] In the following description, gene names are based on the official symbols listed in NCBI ([www.ncbi.nlm.nih.gov / ]). The listed genes also include genes having substantially the same base sequence as the DNA base sequence constituting the gene, so long as they have the function of predicting changes in body fat in a subject due to the intake of 5-ALA lipid. Here, "substantially the same base sequence" means, for example, that when searched using the homology calculation algorithm NCBI BLAST under the conditions of expectation value = 10; gaps allowed; filtering = ON; match score = 1; mismatch score = -3, the gene has 90% or more, preferably 95% or more, and even more preferably 98% or more identity with the DNA base sequence constituting the gene.

[0031] In one embodiment of the present invention, the term "expression product" of a gene encompasses both transcription products and translation products of the gene. A "transcription product" refers to RNA produced by transcription from a gene (DNA), and a "translation product" refers to a protein encoded by the gene that is translated and synthesized based on the RNA. "RNA" includes total RNA, mRNA, rRNA, tRNA, non-coding RNA, and synthetic RNA. In one embodiment of the present invention, the expression level of a target molecule may be measured using RNA, DNA encoding the RNA, a protein encoded by the RNA, a molecule that interacts with the protein, a molecule that interacts with the RNA, or a molecule that interacts with the DNA. RNA is preferred, and mRNA is more preferred. Examples of molecules that interact with RNA, DNA, or proteins include DNA, RNA, proteins, polysaccharides, oligosaccharides, monosaccharides, lipids, fatty acids, and their phosphorylations, alkylations, and sugar adducts, as well as complexes of any of the above.

[0032] In one embodiment of the present invention, the expression level of a gene or its expression product comprehensively means the expression amount or activity level of the gene or expression product, and is indicated, for example, by an index showing the expression amount, an index showing the expression activity, or a level representing these indexes in a graded manner. The index showing the expression level can be appropriately selected depending on the target for measuring the expression level, the measurement method, etc.

[0033] In one embodiment of the present invention, the expression level of a gene or its expression product in a subject is measured from a biological sample collected from the subject. The biological sample can be, for example, cells, body fluids (e.g., blood), urine, secretions (e.g., saliva, skin surface lipids), etc., but non-invasively collectable samples (e.g., urine, secretions), particularly skin surface lipids, are preferred. Here, "skin surface lipids (SSL)" refers to the fat-soluble fraction present on the surface of the skin, also known as sebum. Generally, SSL primarily contains secretions from exocrine glands such as sebaceous glands in the skin, and is present on the skin surface in the form of a thin layer covering the skin surface. SSL contains RNA expressed in skin cells. The skin from which SSL can be collected may be from any part of the body, such as the head, face, neck, trunk, hands, or feet. Parts of the body where sebum is abundant, such as the face, are preferred.

[0034] The method for collecting a biological sample can be selected appropriately depending on the type of biological sample. For example, any means used for recovering or removing SSL from the skin can be used to collect SSL from the subject's skin. Preferably, an SSL-absorbent material, an SSL-adhesive material, or an instrument for scraping SSL from the skin can be used. The SSL-absorbent material or SSL-adhesive material can be any material that has affinity for SSL, including polypropylene, pulp, etc. More specific examples of means for collecting SSL from the skin include absorbing SSL into a sheet-like material such as oil blotting paper or oil blotting film, adhering SSL to a glass plate or tape, or scraping SSL off with a spatula, scraper, or the like. To improve SSL adsorption, an SSL-absorbent material pre-soaked with a highly lipid-soluble solvent may be used.

[0035] The method for extracting genes from a biological sample can be selected appropriately depending on the biological sample and the gene to be extracted. For example, RNA can be extracted from SSL using methods commonly used for extracting or purifying RNA from biological samples, such as the acid guanidinium thiocyanate-phenol-chloroform extraction (AGPC) method, the spin column method, or a method using magnetic particles.

[0036] Specific embodiments of the present invention will be described below. In a first embodiment, a method for predicting changes in body fat executed by a computer constituting a system via the Internet will be described. In a second embodiment, a method for predicting changes in body fat will be described, which includes a step of obtaining the expression level of a predetermined gene or its expression product from a biological sample collected from a subject. Note that each embodiment of the present invention is not performed for medical purposes on humans, and does not include any medical treatment or diagnostic procedure.

[0037] First Embodiment [System configuration example] The system according to the first embodiment of the present invention includes a server 100 on the Internet 50, a plurality of subject terminals 200, and a measurement terminal 300. In one embodiment of the present invention, the term "system" includes one or more computers. For example, the system may be configured with a single computer or multiple computers, and the latter example is shown in this embodiment.

[0038] The server 100 can be, for example, a web server operated by an operator of a website (body fat change prediction site) that can provide a service for predicting body fat change due to intake of ALA lipid. The server 100 may be configured by one computer or by multiple computers. The server 100 is connected to, for example, multiple subject terminals 200 and measurement terminals 300 via the Internet 50.

[0039] Subject terminal 200 (200A, 200B, 200C...) can be a terminal used by a subject (subject A, subject B, subject C) who is a user of the body fat change prediction service, and is, for example, a smartphone, a mobile phone, a tablet PC (Personal Computer), a notebook PC, a desktop PC, etc. Subject terminal 200, for example, accesses server 100, receives a web page or the like generated by server 100, and displays it on a screen using a browser or the like.

[0040] The measurement terminal 300 analyzes the expression level of a gene or its expression product extracted from a biological sample of a subject. The measurement terminal 300 may be connected to a measurement device (not shown) that measures the expression level of a gene or its expression product. The measurement terminal 300 may be used by a person who analyzes the biological sample of a subject. In the example of FIG. 1, the measurement terminal 300 is used by a service operator, but it may also be used by a person commissioned by the service operator or a person affiliated with the service operator. For convenience, in the following description, the user of the measurement terminal 300 will be referred to as the "service operator." Like the subject terminal 200, the measurement terminal 300 may be, for example, a smartphone, mobile phone, tablet PC, notebook PC, or desktop PC.

[0041] In the example of FIG. 1, to obtain a biological sample from a subject, the service operator sends each subject a biological sample collection kit. The subject then returns the biological sample collected using the kit to the service operator. As described above, the biological sample is preferably collected non-invasively, and it is more preferable to use, for example, skin surface lipids (SSL). The biological sample collection kit includes a tool for collecting the biological sample. When the biological sample is SSL, the collection tool included in the biological sample collection kit includes the above-mentioned SSL absorbent material, SSL adhesive material, or an instrument for scraping SSL from the skin, and includes, for example, a sheet-like material such as oil blotting paper or oil blotting film.

[0042] In this embodiment, the measurement terminal 300 analyzes the expression levels of genes or their expression products extracted from a biological sample to generate gene expression information, and transmits the generated gene expression information to the server 100. The server 100 predicts a change in body fat (e.g., visceral fat) from the subject's biological information including the gene expression information through predetermined processing, and transmits the prediction result to the subject terminal 200. A specific example of the operation of the system will be described later.

[0043] [Hardware configuration of information processing device] As shown in FIG. 2, the server 100 includes, for example, a CPU (Central Processing Unit) 11, a ROM (Read Only Memory) 12, a RAM (Random Access Memory) 13, an input / output interface 15, and a bus 14 connecting these to one another.

[0044] The CPU 11 accesses the RAM 13 and other memory as needed, and performs various arithmetic processing while controlling all of the blocks of the server 100. In other words, the CPU 11 functions as a control unit of the server 100. The ROM 12 is a non-volatile memory that permanently stores firmware such as the OS, programs, and various parameters to be executed by the CPU 11. The RAM 13 is used as a working area for the CPU 11, and temporarily stores the OS, various applications currently being executed, and various data currently being processed.

[0045] 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.

[0046] The display unit 16 is a display device that uses, for example, an LCD (Liquid Crystal Display), an OLED (Organic ElectroLuminescence Display), a CRT (Cathode Ray Tube), or the like.

[0047] 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.

[0048] The storage unit 18 is a non-volatile memory such as a hard disk drive (HDD), a flash memory (solid state drive (SSD)), or other solid-state memory. The storage unit 18 stores the OS, various applications, and various data.

[0049] In this embodiment, storage unit 18 may have databases such as a subject information database, a correlation information database, etc., in addition to programs and the like necessary for the body fat change prediction process described below. These databases are referenced as necessary in the body fat change prediction process.

[0050] 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 subject terminal 200 and the like.

[0051] Although not shown, the basic hardware configuration of the subject terminal 200 and the measurement terminal 300 may be substantially the same as the hardware configuration of the server 100 described above.

[0052] [Server database configuration] 3, the server 100 has a subject information database 31 and a correlation information database 32. These databases may be stored in a storage device or a server externally connected to the server 100, rather than in the storage unit 18.

[0053] The subject information database 31 stores subject information including subject identification information and attribute information for each subject registered in the body fat change prediction service. The subject identification information may be any information that can identify the subject, such as a subject ID, name (nickname), and email address. The attribute information is information indicating the subject's attributes other than the subject identification information, and may include, for example, general information such as gender, age (generation), date of birth, occupation, and address (area of ​​residence), as well as information regarding the subject's physical information and lifestyle habits. Examples of the subject's physical information include physical examination measurements such as height, weight, visceral fat area, BMI, and body fat percentage, and blood test measurements such as blood glucose level and triglyceride level. Examples of information regarding lifestyle habits include alcohol consumption, smoking habits, exercise habits, and eating habits.

[0054] Furthermore, the subject information database 31 stores gene expression information for each subject. The gene expression information includes information on the expression levels of relevant genes or their expression products measured from biological samples collected from each subject.

[0055] In this embodiment, the relevant genes are genes related to changes in body fat due to the intake of ALA lipids, specifically, genes related to changes in body fat in multiple sample subjects who have taken ALA lipids. The relevant genes can be extracted, for example, by statistical methods from data on a group of multiple sample subjects who have taken ALA lipids. The relevant genes may be one type of gene or a gene group consisting of multiple types of genes. Specific examples of relevant genes will be described later.

[0056] In this embodiment, the gene expression information includes, for example, information about the expression level of mRNA, which is a transcription product of a related gene (hereinafter referred to as "RNA information"). The RNA information includes, for example, at least one expression level information selected from the expression level of mRNA extracted from a biological sample, the expression level of cDNA obtained by reverse transcribing the mRNA, and the expression level of an amplified product of the cDNA. In this case, known devices, means, algorithms, etc. can be used to extract RNA from a biological sample, reverse transcribe it, and measure and analyze the expression level.

[0057] In this embodiment, information on the biological functions of the subject is referred to as biological information among the subject information stored in the subject information database 31. The biological information includes the gene expression information, the physical information described above, information on lifestyle habits described above, and other attribute information on the biological functions of the subject (such as gender and age (generation)).

[0058] The correlation information database 32 stores correlation information showing the correlation between changes in body fat in a plurality of sample subjects who ingested ALA lipid and at least one variable including the expression levels of related genes or their expression products related to changes in body fat in a plurality of sample subjects. The correlation information can be generated by analyzing data showing changes in body fat due to the intake of ALA lipid, obtained from a group of sample subjects who ingested ALA lipid for a predetermined period, and data including the expression levels of related genes or their expression products.

[0059] The at least one variable in the correlation information is at least one variable obtained from the biological information of multiple sample subjects, and may include at least the expression level of the associated gene or its expression product. The "variable obtained from biological information" includes at least one of variables contained in the biological information and variables calculated from the biological information. Specifically, variables obtained from biological information include, in addition to the expression level, physical examination measurements, blood test measurements, alcohol consumption, smoking amount, exercise amount, dietary intake, age, or generation. For example, if the biological information includes a date of birth, the age may be calculated from the date of birth and used as a variable.

[0060] From the viewpoint of performing highly accurate prediction processing, the correlation information preferably includes a learning model in which at least one variable including the expression level of a related gene or its expression product in multiple sample subjects is used as an explanatory variable, and the change in body fat of multiple sample subjects due to the intake of 5-ALA lipid is used as a response variable. The algorithm used in the learning model is not particularly limited, and examples thereof include a linear regression model, lasso regression, random forest, neural network, support vector machine with a linear kernel (SVM(linear)), support vector machine with an rbf kernel (SVM(rbf)), decision tree, k-nearest neighbor method, etc.

[0061] In this embodiment, the expression level of the associated gene or its expression product may be correlated with the change in body fat in multiple sample subjects, and the correlation information may be information indicating the correlation between the change in body fat in multiple sample subjects and the expression level of the associated gene or its expression product. This makes it possible to predict the change in body fat from the gene expression information. A prediction model that realizes such correlation information is, for example, a regression model, and specific examples include a random forest and a linear kernel support vector machine.

[0062] [System operation example] Next, an example of the operation of the body fat change prediction system configured as described above will be described using the sequence diagram of Figure 4. The operation of the server 100 described below is performed 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. Similarly, the operation of the subject terminal 200 and the measurement terminal 300 is performed by the cooperation of hardware such as the CPU and communication unit and software stored in the storage unit. In the following, the CPU is the main driver of the operation of each device. In this example of operation, an example will be described in which the amount of change in body fat (e.g., visceral fat area) in a subject due to continuous intake of 5-ALA lipid is predicted using information on the expression levels of the subject's relevant genes or their expression products.

[0063] In this example, a subject who wishes to receive a body fat change prediction service first browses a body fat change prediction site using subject terminal 200. The CPU of subject terminal 200 accepts input of subject information including subject identification information such as ID, password, name, and address, and attribute information such as gender and age, through input operations by the subject on the body fat change prediction site (S201). As mentioned above, among the attribute information, information related to biological functions is included in the biological information.

[0064] The CPU of the subject terminal 200 transmits the acquired subject information (S202), and the CPU 11 of the server 100 receives (acquires) the subject information including the subject identification information and attribute information (S101).

[0065] In this embodiment, "acquisition of information" refers to acquisition of information by the CPU (control unit), and is a concept that includes reception of information via the Internet 50, reception of information from other electronic devices using short-range wireless communication or wired communication, reception of input of information by input operation, etc., and reception of input of information stored in the memory unit 18 or an external storage device.

[0066] The CPU 11 stores the received subject information in the subject information database 31 (S102). Thereafter, although not shown, the CPU 11 can perform processing to send a biological sample collection kit to the subject. Such processing includes, for example, sending a notification to a terminal used by a person in charge of sending the biological sample collection kit. As a result, the biological sample collection kit is sent to the subject.

[0067] After receiving the biological sample collection kit, the subject uses the kit to collect a biological sample such as an SSL. The subject then sends the collected biological sample to the service operator along with the subject's identification information or information that allows access to the subject's identification information. The subject's identification information corresponding to the biological sample is stored, for example, in a memory unit of the measurement terminal 300 (not shown).

[0068] The service operator extracts, for example, mRNA from the biological sample and measures the expression level of the mRNA transcription product of the related gene using the measurement device and the measurement terminal 300 connected thereto. Note that the expression level to be measured only needs to include the expression level of the mRNA transcription product of the related gene, and may also include the expression levels of the mRNA transcription products of other genes.

[0069] The CPU of the measurement terminal 300 analyzes the measurement data obtained by the measurement device, calculates the expression levels of the mRNA transcription products of the relevant genes, and generates gene expression information, which is RNA information (S301).The CPU of the measurement terminal 300 associates the subject identification information corresponding to the biological sample with the generated gene expression information and transmits them to the server 100 (S302).

[0070] The CPU 11 of the server 100 receives (acquires) the gene expression information in association with the subject identification information (S103). In this embodiment, the gene expression information is included in the "biometric information" together with part of the attribute information. Therefore, in this step, the CPU 11 functions as an "acquisition unit that acquires the biometric information in association with the subject identification information." The CPU 11 stores the gene expression information in association with the subject identification information in the subject information database 31 (S104). This associates the subject identification information with the biometric information including the subject's gene expression information.

[0071] Next, based on the correlation information, CPU 11 predicts a change in body fat due to the intake of ALA lipids from biological information including the subject's gene expression information (S105). In this step, CPU 11 functions as a "prediction unit." In this operation example, the correlation information is composed of a learning model that uses the expression levels of mRNA, which are transcription products of related genes in multiple sample subjects, as explanatory variables and the change in body fat (e.g., the change in visceral fat area) due to the intake of ALA lipids in multiple sample subjects as a response variable. For example, CPU 11 can predict a change in body fat of the subject by selecting a learning model related to the correlation information from correlation information database 32 and applying the subject's RNA information to the model.

[0072] In this embodiment, the related genes preferably include at least one gene selected from the group consisting of KDELR1, KLC1, PRCC, SEC24C, and TRAPPC5, and more preferably include all five of these genes from the viewpoint of improving prediction accuracy. Furthermore, the related genes may include, in addition to or instead of the above five genes, for example, ACBD5, EYA3, KRT34, PFKFB4, SPRR1B, CARD16, AFF1, FABP9, KRT72, STXBP3, SLED1, ANKFY1, FAM13B, KRTAP1-5, PRR9, TARDBP, SUZ12P1, ARF3, FAM160B1, KRTAP17-1, PSMB3, TCHH, AR PC5, FAM83A, KRTAP19-1, PSME1, TCHHL1, B4GALT1, FBXO32, KRTAP19-3, PSORS1C2, TMED4, B4GALT5, FOXN3, KRTAP2- 2, PYGL, TMEM66, BDH1, FOXQ1, KRTAP3-1, RAC1, TNIP2, APMAP, GSR, KRTAP3-3, RHCG, TRAPPC12, C6orf132, HSPA4, KR TAP7-1, RPS6KA3, INIP, IDO1, LOC100288432, RSF1, TUSC3, CCAR1, JAK2, LOC100505839, SCAF11, UBXN4, CHD4, JMY, MARCHF8, USP53, CNN3, MAT2B, SF3A1, VPS26A, CRYAB, KIAA1033, MED1, SLC25A44, WFDC5, CYB5R4, KIAA1429, NBN, SL It is preferable that the gene contains at least one gene selected from the group consisting of C38A10, ZC3HAV1, DIS3, KIAA1432, NFYA, SLC5A1, ZDHHC7, DNAJB12, NOSIP, SMG5, ZNF124, DYNLL1, KLC3, NUBP1, SNX20, ZNF622, ELMO2, KRT25, ORC3, SORT1, and ARPC4, and it may contain all 98 of these genes.

[0073] These related genes were extracted from data on multiple sample subjects who ingested 5-ALA lipids as genes whose mRNA expression levels have a significant correlation with changes in body fat. Because these genes have a significant correlation between their respective mRNA expression levels and changes in body fat (e.g., changes in visceral fat area), it is possible to construct a prediction model for body fat changes by using at least one gene as a feature. However, by combining multiple genes among these as features, a more accurate prediction model can be constructed. Furthermore, when a group of sample subjects is divided into multiple groups, the five related genes KDELR1, KLC1, PRCC, SEC24C, and TRAPPC5 are genes that were extracted as genes whose mRNA expression levels have a significant correlation with changes in body fat, common to multiple groups. Thus, by using one or more, preferably all, of the above five genes as features, it is possible to construct a more accurate prediction model.

[0074] The related genes listed above are genes extracted from data of a male sample subject. Therefore, by applying correlation information containing at least one gene selected from the listed genes as a related gene to a male subject, changes in body fat can be predicted with higher accuracy.

[0075] Next, CPU 11 transmits (outputs) prediction result information including information on the predicted change in body fat and subject identification information (S106). In this step, CPU 11 functions as an "output unit." In this embodiment, "output of information" refers to the output of information by CPU 11, and is a concept that includes transmission of information via the Internet 50, transmission of information to other electronic devices using short-range wireless communication or wired communication, as well as output of information via display unit 16 or a speaker.

[0076] For example, based on the subject identification information, the CPU 11 can transmit prediction result information including predicted changes in body fat to the subject terminal 200. Methods for transmitting the information include, for example, email, various messenger applications, SNS (Social Networking Service), and a notification function on a website that provides the body fat change prediction service.

[0077] In the prediction result information, the information regarding the predicted change in body fat preferably includes at least one piece of information selected from information indicating the predicted change in body fat or information related to the predicted change in body fat. The prediction result information may also include access information for a web page that displays this information. Note that the prediction result information does not include information regarding the treatment or diagnosis of a disease.

[0078] Examples of information indicating predicted changes in body fat include information directly or indirectly indicating changes in body fat due to intake of 5-ALA lipid in each subject. For example, the information indicating predicted changes in body fat may include the predicted amount of change in a body fat evaluation index (e.g., the amount of reduction in visceral fat area) or its range, or may include the predicted level of change in body fat.

[0079] Information related to predicted body fat changes includes information generated according to the predicted body fat changes, such as information recommending ALA lipid-containing products and body fat reduction support services using the same. For example, the CPU 11 can generate information such that the greater the predicted amount or level of change in body fat due to intake of ALA lipid, the higher the recommendation level of the ALA lipid-containing products and / or services using the same.

[0080] In this embodiment, the CPU of the subject terminal 200 receives the prediction result information (S203) and displays the prediction result information on the display unit (S204), allowing the subject to view the prediction result of the change in his or her own body fat or information related thereto.

[0081] As described above, according to this embodiment, by using the correlation information, it is possible to predict changes in body fat of each subject due to the intake of ALA lipid. As a result, as shown in the test examples described below, it is possible to accurately predict changes in body fat of each subject due to the intake of ALA lipid. Furthermore, according to this embodiment, it is possible to provide information for determining whether the intake of ALA lipid is an effective means for reducing body fat for each subject. This makes it possible to support each subject in efficiently reducing body fat.

[0082] [Variations] In a modified example of this embodiment, the correlation information may include, as biological information showing a correlation with changes in body fat in a sample subject who ingested ALA lipid, at least one of information on the subject's body fat mass and information on the subject's age, in addition to the expression level of the relevant gene or its expression product. The information on the subject's body fat mass is selected from information including the subject's body fat evaluation index before the ingestion of ALA lipid and information from which the body fat evaluation index can be calculated, for example, the body fat evaluation indexes such as visceral fat area, visceral fat mass, body fat percentage, and body fat mass. The information on the subject's age is selected from information including the subject's age or generation and information from which the age or generation can be calculated, for example, age, generation, date of birth, etc.

[0083] A specific example of the operation of the system of this modification will be described below. As in the above example of the operation, the CPU of the subject terminal 200 accepts input of subject identification information such as a subject ID, password, name, and address, and subject information including at least one of information on body fat mass and information on the subject's age (S201), and transmits this information to the server 100 (S202).

[0084] Next, the CPU 11 of the server 100 receives (acquires) subject information including the subject identification information and at least one of information on the subject's body fat mass and information on the subject's age (S101), and stores this information (S102). In this modification, the CPU 11 in step S201 functions as "an acquisition unit that acquires the subject identification information and at least one of information on the subject's body fat mass and information on the subject's age as biometric information in association with each other."

[0085] The CPU 11 receives (acquires) the detected gene expression information in association with the subject identification information (S103) as in the above-described embodiment, and stores this information (S104). In step S103, the CPU 11 functions, for example, as an "acquisition unit that acquires gene expression information as biological information in association with the subject identification information."

[0086] Next, the CPU 11 predicts the change in body fat due to the intake of ALA lipid from the subject's biological information based on the correlation information (S105). In this modification, the correlation information is information indicating the correlation between the change in body fat in multiple sample subjects who ingested lipids and multiple variables obtained from the biological information of the multiple sample subjects, and the multiple variables include the expression levels of related genes or their expression products in the multiple sample subjects and at least one of variables obtained from information on the subject's body fat mass and variables obtained from information on the subject's age. Examples of variables obtained from information on the subject's body fat mass include body fat evaluation indices, specifically visceral fat area, visceral fat mass, body fat percentage, body fat mass, etc. Examples of variables obtained from information on the subject's age include age and generation.

[0087] In this modified example, the correlation information preferably includes a learning model in which a plurality of variables, including the expression levels of the relevant genes or their expression products in a plurality of sample subjects and at least one of a variable obtained from information on the subjects' body fat mass or a variable obtained from information on the subjects' age, are used as explanatory variables, and the change in body fat in a plurality of sample subjects is used as a target variable.

[0088] In the above-mentioned modified example, by using at least one of information on the subject's body fat mass and information on the subject's age in addition to the expression level of the relevant gene or its expression product, the accuracy of predicting changes in body fat due to the subject's intake of 5-ALA lipid can be improved. Furthermore, by using both information on the subject's body fat mass and information on the subject's age, the accuracy of predicting changes in body fat can be further improved.

[0089] Furthermore, in S101 of this modified example, CPU 11 may receive at least one of information regarding the subject's body fat mass and information regarding the subject's age from a computer other than subject terminal 200. For example, CPU 11 may receive a measurement value of the subject's body fat evaluation index from, for example, a measuring device capable of measuring the body fat evaluation index via the Internet 50. CPU 11 may also receive information regarding the subject's body fat mass and / or information regarding the subject's age via the Internet 50 from another computer (for example, a server managed by a business providing another web service) that stores this information. Note that acquisition of the subject's personal information shall be carried out appropriately in accordance with the laws of the region in which this embodiment is implemented.

[0090] Furthermore, as another variation of this embodiment, the correlation information may include other biological information related to changes in body fat in sample subjects who have ingested ALA lipids, in addition to the information described above. Examples of such biological information include the subject's gender, physical information (e.g., weight, height, BMI, etc.), and information about lifestyle habits (e.g., exercise habits, dietary habits, etc.). In this case, the learning model included in the correlation information may use, for example, the expression levels of related genes or their expression products in multiple sample subjects, and multiple variables obtained from the above biological information of multiple sample subjects, as explanatory variables, and the changes in body fat due to the intake of ALA lipids in multiple sample subjects as the response variable. In this way, by using multiple pieces of biological information showing a correlation with changes in body fat in addition to the expression levels of related genes or their expression products, the accuracy of predicting changes in body fat can be improved.

[0091] Furthermore, various examples of the method for acquiring the subject's biological information are possible. For example, the subject information is not limited to being acquired from the subject terminal 200, but may be acquired from another computer (for example, a server managed by another business that provides another web service, etc.).

[0092] Furthermore, the gene expression information is not limited to being acquired from the measurement terminal 300, but may be acquired from another computer that has the subject's gene expression information (for example, a server managed by a business that provides gene analysis services, etc.). In this case, as shown in Fig. 5, the system may not include the measurement terminal 300, and the server 100 may acquire gene expression information generated from a user's biological sample by a computer used by another business that provides gene analysis services, etc. As another example, the subject may provide his or her own gene expression information, provided by a gene analysis service, etc., to the server 100 using the subject terminal 200.

[0093] In relation to these, in the above-described embodiment, the server 100 acquires the subject information (part of the biological information) and the gene expression information from different terminals (computers) at different times, but they may also acquire them simultaneously from the same terminal. For example, as shown in the flowchart of Fig. 6, the server 100 receives the subject information including the subject identification information and the gene expression information in association with each other (S107), and stores them in the subject information database 31 (S108). The subject information and the gene expression information may be transmitted from, for example, the subject terminal 200 or the measurement terminal 300, or from another computer.

[0094] Furthermore, the method of outputting the prediction result information is not limited to the above example. For example, the server 100 can output the prediction result information to an external device such as a printer in order to provide paper media such as direct mail.

[0095] In the above-described embodiment, an example in which the prediction step is followed by the transmission (output) step of the prediction result information is described. However, the timing of the output step is not particularly limited. For example, the server 100 may transmit the prediction result information after receiving an inquiry about the prediction result from the subject terminal 200. As a specific example, as shown in the flowchart of FIG. 7, after the prediction step (S105), the CPU 11 of the server 100 stores the predicted body fat change in the subject information database 31 in association with the subject identification information (S109). The CPU 11 determines whether or not an inquiry about the prediction result has been received from the subject terminal 200 (S110), and if it is determined that the inquiry has been received (Yes in S110), it transmits the prediction result information (S106). In this example, if the CPU 11 has not received an inquiry from the subject terminal 200 (No in S110), it again determines whether or not the inquiry has been received (S110). Furthermore, if the CPU 11 has not received an inquiry for a certain period of time, it may terminate the process without transmitting the prediction result information.

[0096] In this embodiment, an example in which the correlation information is composed of a learning model has been described, but the present invention is not limited to this. For example, the correlation information may include a table that stores the correspondence between the expression levels of related genes or their expression products and changes in body fat, or a formula that derives changes in body fat from the expression levels. In particular, when there are only a small number of related genes, changes in body fat can be predicted from the expression levels of the related genes using such a table or formula.

[0097] In the above-described embodiment, RNA information is exemplified as gene expression information, but the present invention is not limited thereto. The gene expression information may include, as information on the expression level of a related gene or its expression product, information on the expression level of a molecule selected from, for example, a protein encoded by DNA or RNA, a molecule that interacts with the protein, a molecule that interacts with the RNA, or a molecule that interacts with the DNA.

[0098] In the above embodiment, the server 100 performs the steps of acquiring subject information and gene expression information, predicting body fat change, etc., but each step may be performed by a separate computer. In other words, the functional blocks of the system according to the present invention, such as the acquisition unit, prediction unit, and output unit, may each be realized by a separate computer.

[0099] In the above-described embodiment, an example of predicting the amount of change in body fat in a subject has been described, but the present invention is not limited to this. For example, when a plurality of sample subjects are classified into a plurality of groups according to the level of change in body fat, the relevant gene may be a gene (expression-varying gene) whose expression level is detected to be different in each of the plurality of groups. In this case, the correlation information may be information indicating the correlation between the level of change in body fat corresponding to each of the plurality of groups and the expression level of the relevant gene or its expression product.

[0100] In the above-mentioned modified example, the correlation information may be a learning model, specifically, a classification model for classifying the change level of body fat corresponding to each of a plurality of groups using the expression levels of related genes or their expression products. The algorithm used to generate such a classification model is not particularly limited, but examples thereof include logistic regression, random forest, support vector machine, decision tree, k-nearest neighbor method, and neural network.

[0101] A specific example of a method for generating correlation information in the above modified example will be described. First, as in the above embodiment, data on changes in body fat of a group of multiple sample subjects who have ingested ALA lipid for a predetermined period of time is obtained. This data is classified, for example, into a body fat reduction (effective) group and a body fat non-reduction (ineffective) group, and genes whose expression levels or their expression products differ significantly between these groups are extracted, and the extracted genes are designated as related genes. A learning model is then generated in which the expression levels of the related genes or their expression products in the multiple sample subjects are used as explanatory variables, and the level of change in body fat of the multiple sample subjects due to the intake of ALA lipid (body fat reduction (effective) or body fat non-reduction (ineffective)) is used as the response variable.

[0102] In the above modification, the acquisition step is performed in the same manner as in the above embodiment, and the classification model is used in the prediction step to predict the level of change in body fat of a subject due to the intake of 5-ALA lipid. The data of a sample subject group may be classified into three or more groups according to the level of change in body fat. The prediction model is not limited to a supervised learning model, but may also be a classification model that performs cluster analysis based on the expression levels of related genes or their expression products.

[0103] Second Embodiment As a second embodiment of the present invention, a method for predicting changes in body fat due to the intake of the above-mentioned 5-ALA lipid will be described, in which five predetermined genes or their expression levels are obtained from a biological sample collected from a subject. Note that in the following embodiment, explanations of terms and the like that overlap with the above explanation will be omitted.

[0104] The method for predicting body fat change of this embodiment is a method for predicting body fat change in a subject due to the ingestion of lipids containing alpha-linolenic acid as a constituent fatty acid (ALA lipid), and includes a step (obtaining step) of obtaining the expression level of at least one gene selected from the group consisting of KDELR1, KLC1, PRCC, SEC24C, and TRAPPC5 or its expression product from a biological sample collected from the subject. The above five genes correspond to the related genes of the first embodiment described above, and are therefore hereinafter referred to as "related genes." The expression levels of all five related genes are positively correlated with body fat change.

[0105] In this embodiment, the above-mentioned five related genes or their expression products function as predictive markers for changes in body fat of a subject due to the intake of ALA lipid. By using the expression levels of these related genes or their expression products, it is possible to accurately predict changes in body fat of each subject due to the intake of ALA lipid. As described above, the above-mentioned five related genes are genes extracted from data of a male sample subject, and therefore, it is preferable that the method using the above-mentioned five related genes or their expression products is a method for predicting changes in body fat of a male subject.

[0106] In the acquisition step of this embodiment, "acquiring the expression level of a gene (related gene) or its expression product" includes at least one of measuring the expression level of a related gene or its expression product, or acquiring information on the expression level of a related gene or its expression product.

[0107] The method for measuring the expression level of a related gene or its expression product can be selected appropriately depending on the target of measurement. For example, when RNA, cDNA, or DNA is used as the target, the method for measuring the expression level can be selected from PCR using DNA that hybridizes to these as a primer, nucleic acid amplification methods such as real-time RT-PCR, multiplex PCR, SmartAmp, and LAMP, hybridization methods using nucleic acids that hybridize to these as probes (DNA chips, DNA microarrays, dot blot hybridization, slot blot hybridization, Northern blot hybridization, etc.), methods for determining base sequences (sequencing), or combinations of these. For example, when RNA is used as the target of measurement of the expression level of a gene or its expression product, the expression level of RNA may be analyzed, but preferably, the RNA is converted into cDNA by reverse transcription, and the expression level of the cDNA or its amplification product is then measured.

[0108] Furthermore, for example, when the target is a gene translation product (protein), a molecule that interacts with the protein, a molecule that interacts with RNA, or a molecule that interacts with DNA, methods for measuring the expression level include protein chip analysis, immunoassays (e.g., ELISA, etc.), mass spectrometry (e.g., LC-MS / MS, MALDI-TOF / MS), the one-hybrid method (PNAS 100, 12271-12276 (2003)), and the two-hybrid method (Biol. Reprod. 58, 302-311 (1998)), and these can be selected appropriately depending on the target.

[0109] The method for obtaining information on the expression levels of related genes or their expression products can be performed in the same manner as in step S101 of the above-described first embodiment. When obtaining information on expression levels, the person who obtains the information on expression levels may be different from the person who measures the expression levels.

[0110] In the acquisition step of this embodiment, ACBD5, EYA3, KRT34, PFKFB4, SPRR1B, CARD16, AFF1, FABP9, KRT72, STXBP3, SLED1, ANKFY1, FAM13B, KRTAP1-5, PRR9, TARDBP, SUZ12P1, ARF3, FAM160B1, KRTAP17-1, PSMB3, TCHH, ARPC5, FAM83 A, KRTAP19-1, PSME1, TCHHL1, B4GALT1, FBXO32, KRTAP19-3, PSORS1C2, TMED4, B4GALT5, FOXN3, KRTAP2-2, PYGL , TMEM66, BDH1, FOXQ1, KRTAP3-1, RAC1, TNIP2, APMAP, GSR, KRTAP3-3, RHCG, TRAPPC12, C6orf132, HSPA4, KRTAP 7-1, RPS6KA3, INIP, IDO1, LOC100288432, RSF1, TUSC3, CCAR1, JAK2, LOC100505839, SCAF11, UBXN4, CHD4, JMY, MARCHF8, USP53, CNN3, MAT2B, SF3A1, VPS26A, CRYAB, KIAA1033, MED1, SLC25A44, WFDC5, CYB5R4, KIAA1429, NBN The expression level of at least one gene or its expression product selected from the group consisting of SLC38A10, ZC3HAV1, DIS3, KIAA1432, NFYA, SLC5A1, ZDHHC7, DNAJB12, NOSIP, SMG5, ZNF124, DYNLL1, KLC3, NUBP1, SNX20, ZNF622, ELMO2, KRT25, ORC3, SORT1, and ARPC4 may also be obtained.

[0111] Among these 98 genes, FOXN3, HSPA4, VPS26A, TNIP2, NUBP1, KIAA1429, ZC3HAV1, UBXN4, DNAJB12, NOSIP, MAT2B, ACBD5, SCAF11, FAM13B, MARCHF8, ZDHHC7, ANKFY1, APMAP, TMED4, IDO1, SLC25A44, EYA3, SUZ12P1, PFKFB4, TRAPPC12, ARPC4, NBN, KIAA1033, SNX20, B4GALT5, RAC1, and SLED1 The expression levels of ZNF622, SLC38A10, KIAA1432, RPS6KA3, JAK2, NFYA, RSF1, GSR, STXBP3, PSME1, ORC3, SF3A1, DIS3, ARPC5, LOC100288432, TARDBP, AFF1, PYGL, ARF3, ZNF124, ELMO2, CARD16, SMG5, INIP, TMEM66, SORT1, CHD4, B4GALT1, PSMB3, CYB5R4, CCAR1, and FAM160B1 are positively correlated with changes in body fat. In addition, the expression levels of KRTAP3-3, FAM83A, KRTAP3-1, KRTAP1-5, BDH1, MED1, KRT34, KRTAP2-2, RHCG, PRR9, SLC5A1, KRTAP17-1, CNN3, KRTAP19-3, USP53, SPRR1B, TCHH, FOXQ1, TCHHL1, WFDC5, KRT25, LOC100505839, CRYAB, DYNLL1, PSORS1C2, JMY, FBXO32, KRTAP7-1, KRTAP19-1, C6orf132, KLC3, TUSC3, KRT72, and FABP9 were negatively correlated with changes in body fat.

[0112] The acquisition process of this embodiment makes it possible to acquire the expression levels of relevant genes or their expression products for predicting changes in body fat in a subject due to the intake of ALA lipid, which can contribute to the prediction of such changes in body fat.

[0113] In this embodiment, it is preferable to further include a step of predicting a change in body fat due to the intake of ALA lipid in a subject based on the acquired expression level (prediction step).

[0114] In the prediction step of this embodiment, similar to S105 of the first embodiment, a learning model is used in which at least one variable including the expression level of a related gene or its expression product is used as an explanatory variable and the change in body fat due to the intake of ALA lipid is used as a response variable to predict the change in body fat of a subject due to the intake of ALA lipid. In this case, the prediction process of this step is performed using, for example, a computer.

[0115] Alternatively, in the prediction step of this embodiment, for example, it may be determined whether the expression level of the acquired related gene or its expression product satisfies a condition corresponding to a change level in body fat set for the expression level. In this case, the determination processing in this step is performed using, for example, a computer.

[0116] Examples of "conditions corresponding to the level of change in body fat" include the range of values ​​that the expression levels of each associated gene or its expression product can take, which are set corresponding to the level of change in body fat, and the numerical value or range of a value calculated by substituting a numerical value indicating the expression level into a predetermined mathematical formula. Note that the predetermined mathematical formula may be a mathematical formula that can calculate a numerical value indicating the level of change in body fat from numerical values ​​indicating the expression levels of one or more associated genes or their expression products.

[0117] When the condition is a range of possible values ​​for the expression level of each associated gene or its expression product, this step may include comparing the value indicating the expression level of the associated gene or its expression product with a reference value for the expression level set corresponding to the level of body fat change. The reference value in this step is a threshold value set for each associated gene or its expression product to determine the level of body fat change. The reference value can be set, for example, by classifying a group of subjects who have ingested 5-ALA lipid according to the level of body fat change, and using statistical values ​​(such as mean, median, quartiles, 95% confidence interval) of the values ​​indicating the expression level of the associated gene or its expression product calculated for each group.

[0118] Specifically, for genes or their expression products whose expression levels are positively correlated with body fat change, if the value indicating the expression level is greater than or equal to a reference value, the body fat change level can be determined to be relatively high. On the other hand, for genes or their expression products whose expression levels are negatively correlated with body fat change, if the value indicating the expression level is less than or equal to the reference value, the body fat change level can be determined to be relatively high.

[0119] For example, when body fat change is expressed in two levels, the level of body fat change can be determined by comparing the value indicating the expression level with one reference value. When body fat change is expressed in three or more levels, the level of body fat change can be determined by comparing the value indicating the expression level with multiple reference values.

[0120] In the above example, when determining the expression levels of multiple associated genes or their expression products, the body fat change level is determined for each expression level, and the overall body fat change level can be determined based on these multiple prediction results.As a method for determining the overall body fat change level, for example, the body fat change level predicted for the most associated genes or their expression products may be used as the predicted body fat change result.As another example, the multiple body fat change levels determined for the expression levels of multiple genes or their expression products may each be converted into numerical values ​​to calculate a representative value (such as the mean, median, or mode), and the body fat change level corresponding to this representative value may be used as the predicted overall body fat change result.

[0121] Furthermore, in the prediction process of this embodiment, as in the modified example of the first embodiment described above, the change in body fat due to the intake of ALA lipid by a subject may be predicted based on the expression level of the relevant gene or its expression product and at least one of information regarding the subject's body fat mass or information regarding the subject's age.

[0122] The prediction step of this embodiment may be performed by a prediction device comprising a computer. The prediction device is a body fat change prediction device that predicts a change in body fat of a subject due to the ingestion of lipids containing alpha-linolenic acid as a constituent fatty acid, and includes a control unit that predicts a change in body fat of a subject due to the ingestion of 5-ALA lipid based on the expression level of at least one gene selected from the group consisting of KDELR1, KLC1, PRCC, SEC24C, and TRAPPC5, or its expression product, obtained from a biological sample collected from the subject. This prediction device corresponds to, for example, the server 100 of the first embodiment, and the control unit corresponds to the CPU 11.

[0123] <Other embodiments> Although the embodiments of the present invention have been described above, the present invention is not limited to the above-described embodiments, and it goes without saying that various modifications can be made within the scope of the gist of the present invention.

[0124] Another embodiment of the present invention provides a method for predicting changes in body fat in a subject due to the ingestion of lipids containing alpha-linolenic acid as a constituent fatty acid (ALA lipids). The prediction method includes a step of predicting changes in body fat in a subject due to the ingestion of ALA lipids (prediction step) based on the expression level of at least one gene or its expression product selected from the group consisting of KDELR1, KLC1, PRCC, SEC24C, and TRAPPC5 from a biological sample collected from the subject. This prediction step is performed in the same manner as the prediction step of the second embodiment described above.

[0125] In another embodiment of the present invention, a predictive marker for predicting changes in body fat in a subject due to the ingestion of lipids containing alpha-linolenic acid as a constituent fatty acid (ALA lipid) can be provided. The predictive marker consists of at least one gene or expression product thereof selected from the group consisting of KDELR1, KLC1, PRCC, SEC24C, and TRAPPC5. Furthermore, a predictive marker set containing at least one of the above-mentioned predictive markers can further include ACBD5, EYA3, KRT34, PFKFB4, SPRR1B, CARD16, AFF1, FABP9, KRT72, STXBP3, SLED1, ANKFY1, FAM13B, KRTAP1-5, PRR9, TARDBP, SUZ12P1, ARF3, FAM160B1, KRTAP17-1, PSMB3, TCHH, ARP C5, FAM83A, KRTAP19-1, PSME1, TCHHL1, B4GALT1, FBXO32, KRTAP19-3, PSORS1C2, TMED4, B4GALT5, FOXN3, KRTAP2- 2, PYGL, TMEM66, BDH1, FOXQ1, KRTAP3-1, RAC1, TNIP2, APMAP, GSR, KRTAP3-3, RHCG, TRAPPC12, C6orf132, HSPA4, KR TAP7-1, RPS6KA3, INIP, IDO1, LOC100288432, RSF1, TUSC3, CCAR1, JAK2, LOC100505839, SCAF11, UBXN4, CHD4, JMY , MARCHF8, USP53, CNN3, MAT2B, SF3A1, VPS26A, CRYAB, KIAA1033, MED1, SLC25A44, WFDC5, CYB5R4, KIAA1429, NBN, It is preferable that the predictive marker comprises at least one gene or its expression product selected from the group consisting of SLC38A10, ZC3HAV1, DIS3, KIAA1432, NFYA, SLC5A1, ZDHHC7, DNAJB12, NOSIP, SMG5, ZNF124, DYNLL1, KLC3, NUBP1, SNX20, ZNF622, ELMO2, KRT25, ORC3, SORT1, and ARPC4.

[0126] Another embodiment of the present invention provides a prediction kit for predicting changes in body fat in a subject due to the ingestion of lipids containing α-linolenic acid as a constituent fatty acid. The prediction kit contains reagents for detecting the expression level of at least one gene or its expression product selected from the group consisting of, for example, KDELR1, KLC1, PRCC, SEC24C, and TRAPPC5, from a biological sample collected from the subject. Furthermore, the prediction kit may contain a reagent for detecting the expression level of at least one gene or its expression product selected from the group consisting of ACBD5, EYA3, KRT34, PFKFB4, SPRR1B, CARD16, AFF1, FABP9, KRT72, STXBP3, SLED1, ANKFY1, FAM13B, KRTAP1-5, PRR9, TARDBP, SUZ12P1, ARF3, FAM160B1, KRTAP17-1, PSMB3, TCHH, ARPC5, FAM83A, KRTAP19-1, PS ME1, TCHHL1, B4GALT1, FBXO32, KRTAP19-3, PSORS1C2, TMED4, B4GALT5, FOXN3, KRTAP2-2, PYGL, TMEM66, BDH1, F OXQ1, KRTAP3-1, RAC1, TNIP2, APMAP, GSR, KRTAP3-3, RHCG, TRAPPC12, C6orf132, HSPA4, KRTAP7-1, RPS6KA3, IN IP, IDO1, LOC100288432, RSF1, TUSC3, CCAR1, JAK2, LOC100505839, SCAF11, UBXN4, CHD4, JMY, MARCHF8, USP53, CNN3, MAT2B, SF3A1, VPS26A, CRYAB, KIAA1033, MED1, SLC25A44, WFDC5, CYB5R4, KIAA1429, NBN, SLC38A10, ZC3H It preferably contains a reagent for detecting the expression level of at least one gene or its expression product selected from the group consisting of AV1, DIS3, KIAA1432, NFYA, SLC5A1, ZDHHC7, DNAJB12, NOSIP, SMG5, ZNF124, DYNLL1, KLC3, NUBP1, SNX20, ZNF622, ELMO2, KRT25, ORC3, SORT1, and ARPC4.

[0127] Reagents for detecting the expression levels of the above-mentioned genes or their expression products include, for example, reagents for extracting and purifying RNA from the collected SSL, reagents for nucleic acid amplification or hybridization including oligonucleotides that specifically bind (hybridize) with nucleic acids derived from the above-mentioned genes (e.g., primers for PCR, adapter sequences for sequencing, etc.), reagents for immunoassays including antibodies that recognize gene expression products (proteins), as well as labeling reagents, buffer solutions, chromogenic substrates, secondary antibodies, blocking agents, control reagents used as positive and negative controls, equipment necessary for testing, and indicators or guidance for detecting the expression levels of the above-mentioned genes or their expression products.

[0128] In addition to the above-mentioned reagents, the kit for predicting a change in body fat may also contain tools and reagents necessary for collecting and storing biological samples such as SSLs. For example, tools and reagents necessary for collecting and storing SSLs include oil-removing films for collecting SSLs, reagents for storing the collected SSLs, and storage containers.

[0129] The present invention can also take the following forms. <1> A method for predicting a change in body fat in a subject due to intake of lipids containing α-linolenic acid as a constituent fatty acid, comprising: Obtaining the expression level of at least one gene selected from the group consisting of KDELR1, KLC1, PRCC, SEC24C, and TRAPPC5 or its expression product from a biological sample collected from the subject; A method for predicting body fat change. <2> In the obtaining step, the following genes may be further isolated from the biological sample: ACBD5, EYA3, KRT34, PFKFB4, SPRR1B, CARD16, AFF1, FABP9, KRT72, STXBP3, SLED1, ANKFY1, FAM13B, KRTAP1-5, PRR9, TARDBP, SUZ12P1, ARF3, FAM160B1, KRTAP17-1, PSMB3, TCHH, ARPC5, FAM83A, K RTAP19-1, PSME1, TCHHL1, B4GALT1, FBXO32, KRTAP19-3, PSORS1C2, TMED4, B4GALT5, FOXN3, KRTAP2-2, PYGL, T MEM66, BDH1, FOXQ1, KRTAP3-1, RAC1, TNIP2, APMAP, GSR, KRTAP3-3, RHCG, TRAPPC12, C6orf132, HSPA4, KRTAP7 -1, RPS6KA3, INIP, IDO1, LOC100288432, RSF1, TUSC3, CCAR1, JAK2, LOC100505839, SCAF11, UBXN4, CHD4, JMY, MARCHF8, USP53, CNN3, MAT2B, SF3A1, VPS26A, CRYAB, KIAA1033, MED1, SLC25A44, WFDC5, CYB5R4, KIAA1429, NB obtaining the expression level of at least one gene or its expression product selected from the group consisting of N, SLC38A10, ZC3HAV1, DIS3, KIAA1432, NFYA, SLC5A1, ZDHHC7, DNAJB12, NOSIP, SMG5, ZNF124, DYNLL1, KLC3, NUBP1, SNX20, ZNF622, ELMO2, KRT25, ORC3, SORT1, and ARPC4; <1> 2. A method for predicting body fat change according to claim 1. <3> The expression level of the gene or its expression product is the expression level of mRNA. <1> or <2> 2. A method for predicting body fat change according to claim 1. <4> The prediction of a change in body fat includes a prediction of a change in visceral fat. <1> or <2> 2. A method for predicting body fat change according to claim 1. <5> The lipid contains diacylglycerol containing α-linolenic acid as a constituent fatty acid. <1> or <2> 2. A method for predicting body fat change according to claim 1. <6> The biological sample is non-invasively collected from the subject. <1> or <2> 2. A method for predicting body fat change according to claim 1. <7> The biological sample is lipids on the skin surface of the subject. <1> or <2> 2. A method for predicting body fat change according to claim 1. <8> and predicting a change in body fat due to intake of the lipid of the subject based on the acquired expression level. <1> or <2> 2. A method for predicting body fat change according to claim 1. <9> A method for predicting a change in body fat of a subject due to intake of lipids containing α-linolenic acid as a constituent fatty acid, comprising: and predicting a change in body fat due to intake of the lipid of the subject based on the expression level of at least one gene selected from the group consisting of KDELR1, KLC1, PRCC, SEC24C, and TRAPPC5 or an expression product thereof, obtained from a biological sample collected from the subject. How to predict body fat changes. <10> In the prediction step, furthermore, ACBD5, EYA3, KRT34, PFKFB4, SPRR1B, CARD16, AFF1, FABP9, KRT72, STXBP3, SLED1, ANKFY1, FAM13B, KRTAP1-5, PRR9, TARDBP, SUZ12P1, ARF3, FAM160B1, KRTAP17-1, PSMB3, TCHH, ARPC5, FAM83A, KRTAP19-1, PSME 1, TCHHL1, B4GALT1, FBXO32, KRTAP19-3, PSORS1C2, TMED4, B4GALT5, FOXN3, KRTAP2-2, PYGL, TMEM66, BDH1, FOXQ 1, KRTAP3-1, RAC1, TNIP2, APMAP, GSR, KRTAP3-3, RHCG, TRAPPC12, C6orf132, HSPA4, KRTAP7-1, RPS6KA3, INIP, ID O1, LOC100288432, RSF1, TUSC3, CCAR1, JAK2, LOC100505839, SCAF11, UBXN4, CHD4, JMY, MARCHF8, USP53, CNN3, M AT2B, SF3A1, VPS26A, CRYAB, KIAA1033, MED1, SLC25A44, WFDC5, CYB5R4, KIAA1429, NBN, SLC38A10, ZC3HAV1, DIS 3. Predicting a change in body fat due to the intake of the lipid in the subject based on the expression level of at least one gene selected from the group consisting of KIAA1432, NFYA, SLC5A1, ZDHHC7, DNAJB12, NOSIP, SMG5, ZNF124, DYNLL1, KLC3, NUBP1, SNX20, ZNF622, ELMO2, KRT25, ORC3, SORT1, and ARPC4 or its expression product. <9> 2. A method for predicting a change in body fat according to claim 1. <11> In the prediction step, a learning model is used in which at least one variable including the expression level is used as an explanatory variable, and the change in body fat due to the intake of the lipid is used as a response variable to predict the change in body fat of the subject due to the intake of the lipid. <9> or <10> 2. A method for predicting a change in body fat according to claim 1. <12> In the prediction step, a change in body fat due to intake of the lipid of the subject is predicted based on the expression level and at least one of information on the body fat mass of the subject and information on the age of the subject. <9> or <10> 2. A method for predicting a change in body fat according to claim 1. <13> A predictive marker for predicting a change in body fat of a subject due to intake of lipids containing alpha-linolenic acid as a constituent fatty acid, The predictive marker is Consisting of at least one gene or its expression product selected from the group consisting of KDELR1, KLC1, PRCC, SEC24C, and TRAPPC5; Predictive markers for predicting body fat change. <14> A body fat change prediction device for predicting a change in body fat of a subject due to intake of lipids containing alpha-linolenic acid as a constituent fatty acid, comprising: a control unit that predicts a change in body fat due to the intake of the lipid of the subject based on the expression level of at least one gene selected from the group consisting of KDELR1, KLC1, PRCC, SEC24C, and TRAPPC5 or its expression product, obtained from a biological sample collected from the subject; A body fat change prediction device comprising: [Example]

[0130] Test Example 1: Construction of a regression model to predict changes in visceral fat in subjects due to ALA-DAG intake 1) Study participants Sixty-two healthy men in their 20s to 50s with a BMI of 23 to 32 were selected as study participants.

[0131] 2) Collection of lipids on the skin surface Before the ingestion test of a food containing diacylglycerol (DAG), which contains α-linolenic acid (ALA) as a constituent fatty acid (hereafter referred to as "ALA-DAG-containing food"), RNA-containing skin surface lipids (SSL) were collected from the entire face of the test participants using oil blotting film (5cm x 8cm, polypropylene, 3M). The oil blotting film was transferred to a glass vial and stored at -80°C until use for RNA extraction.

[0132] 3) Intake test of foods containing ALA-DAG The ALA-DAG-containing composition used was an ALA-DAG-containing food (containing 0.9 mg of ALA-DAG (as α-linolenic acid) per day). The ALA-DAG-containing food was prepared as follows, with reference to Test Oil 1 (paragraph

[0040] ) described in JP 2020-23482 A. First, the saturated fatty acid content of the fatty acids obtained by hydrolysis of flaxseed oil (manufactured by Archer Daniels Midland) was reduced by wintering. Next, this fatty acid and glycerin were esterified under reduced pressure using a commercially available immobilized 1,3-position-selective lipase as a catalyst. After filtering out the lipase preparation, the reaction product was subjected to molecular distillation and refinement to obtain an ALA-DAG-containing food, which is an edible oil. The resulting ALA-DAG-containing food contained 53% ALA in its constituent fatty acids, and its glyceride composition was 21% TAG, 78% DAG, and 1% MAG.

[0133] The study participants were instructed to ingest one packet of food containing ALA-DAG once a day over a meal for 10 consecutive weeks. The study participants measured their abdominal visceral fat area on the day intake began and again after 10 weeks of intake while fasting. The visceral fat meter used was the Panasonic EW-FA90 visceral fat meter (medical device approval number 22500BZX00522000). Changes in the study participants' visceral fat area (visceral fat area after 10 weeks of intake - visceral fat area on the day intake began) were confirmed, and it was found that the amount of change in visceral fat area varied greatly between study participants.

[0134] 4) Selection of subjects The oil blotting films containing SSL collected in 2) from all 62 study participants were subjected to the following analysis.

[0135] 5) RNA preparation and sequencing The oil blotting film was cut to an appropriate size, and RNA was extracted using QIAzol® Lysis Reagent (Qiagen) according to the attached protocol. The extracted RNA was reverse-transcribed at 42°C for 90 minutes using the SuperScript VILO cDNA Synthesis kit (Life Technologies Japan, Inc.) to synthesize cDNA. The random primers included with the kit were used as primers for the reverse transcription reaction. A library containing DNA derived from the 20802 gene was prepared from the resulting cDNA by multiplex PCR. Multiplex PCR was performed using the Ion AmpliSeq Transcriptome Human Gene Expression Kit (Life Technologies Japan, Inc.) under the following conditions: 99°C, 2 minutes → (99°C, 15 seconds → 62°C, 16 minutes) × 20 cycles → 4°C hold. The resulting PCR products were purified using Ampure XP (Beckman Coulter, Inc.), followed by buffer reconstitution, primer digestion, adapter ligation, purification, and amplification to prepare a library. The prepared library was loaded onto an Ion 540 chip and sequenced using an Ion S5 / XL system (Life Technologies Japan, Inc.). The gene from which each read sequence originated was determined by gene mapping using the hg19 AmpliSeq Transcriptome ERCC v1, the reference sequence for the human genome.

[0136] 6) Data analysis and selection of relevant genes The read counts of each read obtained by sequencing the subjects' SSL-derived RNA obtained in step 5 above were used as expression data for each RNA. The normalized counts (normalized counts) were then used for analysis. Reads with a read count of less than 1 were treated as missing values. The 62 samples were divided into training data (80% for 49 subjects) for building a predictive model using machine learning and test data (20% for 13 subjects) for verifying the accuracy of the predictive model. For genes in the training data with non-missing expression data in over 90% of the subjects, a correlation analysis with the change in visceral fat area (visceral fat area after 10 weeks of intake minus visceral fat area on the day of intake initiation) was performed using Spearman's correlation test. Genes with p<0.05 and a correlation coefficient of <0.4 or p<0.05 and a correlation coefficient of >0.4 were extracted. As a result, 103 genes whose expression levels correlated with the change in visceral fat area were identified, as shown in Table 1. These genes were selected as relevant genes to be used in the following prediction model construction.

[0137] [Table 1]

[0138] 7) Model construction The expression data of the related genes selected in step 6) were converted from RPM values, which follow a negative binomial distribution, to logarithmic values ​​(Log2(RPM+1) values) with the addition of an integer 1 to approximate a normal distribution. A regression model was constructed to predict changes in visceral fat area using the Log2(RPM+1) values ​​of the expression data of the 103 related genes as explanatory variables and the change in visceral fat area as the objective variable. A linear kernel support vector machine algorithm was specified as a method in Python, and the optimal values ​​of the hyperparameters were tuned using 10-fold cross-validation. The linear kernel support vector machine algorithm was run using the hyperparameters determined by tuning, and the root mean square error (RMSE) of the difference between the predicted and measured values ​​was calculated. A smaller RMSE indicates higher accuracy of the prediction model. The RMSE for the model using the expression data of the 103 genes as variables was 0.984. As a result, as shown in Figure 8, it was demonstrated that it is possible to predict changes in visceral fat due to the intake of ALA-DAG by a subject using the genes shown in Table 1 as related genes and the above prediction model as correlation information.

[0139] <Test Example 2: Construction of a regression model predicting changes in visceral fat in subjects due to ALA-DAG intake using other related genes> 1) Data analysis and selection of relevant genes Five division patterns were created by changing the breakdown of the training data (49 individuals) and test data (13 individuals) in 6) of Test Example 1. For each of the five training data (49 or 50 individuals), a correlation analysis with the change in visceral fat area was performed using Spearman's correlation test based on gene expression information, as in 6). Genes with p<0.05 and a correlation coefficient of <0.4, or p<0.05 and a correlation coefficient of >0.4, were extracted. Of the genes extracted from the five training data, the five genes shown in Table 2 below were extracted as genes commonly extracted from the five. These five genes were selected as related genes to be used in building the following prediction model.

[0140] [Table 2]

[0141] 2) Model construction The expression data of the related genes selected in 1) were converted from RPM values, which follow a negative binomial distribution, to logarithmic values ​​(Log2(RPM+1) values) with the addition of an integer 1 to approximate a normal distribution. A regression model was constructed to predict changes in visceral fat area using the Log2(RPM+1) values ​​of the expression data of the five related genes obtained as explanatory variables and the change in visceral fat area as the objective variable. A linear kernel support vector machine algorithm was specified as a method in Python, and the optimal values ​​of the hyperparameters were tuned using 10-fold cross-validation. The linear kernel support vector machine algorithm was executed using the hyperparameters determined by tuning, and the root mean square error (RMSE) of the difference between the predicted and measured values ​​was calculated. The RMSE for the model using the expression data of the five genes as variables was 0.767. As shown in Figure 9, this demonstrates that changes in visceral fat due to ALA-DAG intake in subjects can be predicted using the genes listed in Table 2 as related genes and the above prediction model as correlation information. Furthermore, it was shown that the prediction model had higher accuracy than the prediction model shown in Test Example 1.

[0142] <Test Example 3: Construction of a regression model for predicting changes in visceral fat in subjects due to ALA-DAG intake using expression levels of related genes and other biological information> 1) Selection of relevant genes and data to be used The five genes shown in Table 2 above, which were extracted in Test Example 2, were selected as relevant genes to be used in constructing the following prediction model. In addition, data on the visceral fat area of ​​the test participants on the day they started taking ALA-DAG (hereinafter referred to as "initial visceral fat area"), measured in 3) of Test Example 1, and data on the age of the test participants were selected as biological information to be used as explanatory variables for the prediction model.

[0143] 2) Model construction The expression data of the related genes selected in 1) were converted from RPM values, which follow a negative binomial distribution, to logarithmic values ​​(Log2(RPM+1) values) with the addition of an integer 1 to approximate a normal distribution. A regression model was constructed to predict changes in visceral fat area using the Log2(RPM+1) values ​​of the expression data of the five related genes obtained, initial visceral fat area, and age as explanatory variables, and the change in visceral fat area as the objective variable. A linear kernel support vector machine algorithm was specified as a method in Python, and the optimal values ​​of the hyperparameters were tuned using 10-fold cross-validation. The linear kernel support vector machine algorithm was run using the hyperparameters determined by tuning, and the root mean square error (RMSE) of the difference between the predicted and measured values ​​was calculated. The RMSE for the model using the expression data of the five genes, initial visceral fat area, and age as variables was 0.708. As a result, as shown in Figure 10, it was demonstrated that the change in visceral fat of a subject due to the intake of ALA-DAG can be predicted using the genes shown in Table 2 as related genes, the initial visceral fat area and age as biological information, and the above prediction model as correlation information. Furthermore, it was demonstrated that the prediction model has higher accuracy than the prediction model shown in Test Example 2. [Explanation of symbols]

[0144] 11...CPU (control unit) 18...Storage section 19…Communications Department 31...Subject information database 32...Correlation information database 100...Server 200...Subject terminal 300...Measuring terminal

Claims

1. A computer-implemented method for predicting a change in body fat in a subject due to intake of lipids containing α-linolenic acid as a constituent fatty acid, comprising: Acquire biological information of the subject, including gene expression information that is information on the expression level of at least one related gene or its expression product associated with a change in body fat due to intake of the lipid, in association with subject identification information that identifies the subject; The change in body fat in a plurality of sample subjects who ingested the lipids; At least one variable obtained from the biological information of the plurality of sample subjects, the variable including an expression level of the relevant gene or its expression product associated with changes in body fat in the plurality of sample subjects; and predicting a change in body fat of the subject due to the intake of the lipids from the biological information based on correlation information indicating the correlation between the above. How to predict body fat changes.

2. The correlation information is a learning model in which the at least one variable including the expression level of the associated gene or its expression product in the plurality of sample subjects is used as an explanatory variable, and the change in body fat in the plurality of sample subjects is used as a response variable; The method for predicting a change in body fat according to claim 1 .

3. the correlation information is information indicating a correlation between the amount of change in body fat in the plurality of sample subjects and the at least one variable including an expression level of the associated gene or an expression product thereof in the plurality of sample subjects; predicting a change in body fat of the subject due to intake of the lipid from the biological information based on the correlation information; The method for predicting a change in body fat according to claim 1 or 2.

4. the biological information further includes at least one of information regarding the body fat mass of the subject and information regarding the age of the subject; The correlation information is The change in body fat in a plurality of sample subjects who ingested the lipids; A plurality of variables obtained from the biological information of the plurality of sample subjects, the plurality of variables including at least one of expression levels of the relevant genes or expression products thereof related to changes in body fat in the plurality of sample subjects, and variables obtained from information on the body fat mass of the plurality of sample subjects, or variables obtained from information on the ages of the plurality of sample subjects; Showing the correlation of The method for predicting a change in body fat according to claim 1 or 2.

5. The related gene is At least one gene selected from the group consisting of KDELR1, KLC1, PRCC, SEC24C, and TRAPPC5; The method for predicting a change in body fat according to claim 1 or 2.

6. The related gene is ACBD5, EYA3, KRT34, PFKFB4, SPRR1B, CARD16, AFF1, FABP9, KRT72, STXBP3, SLED1, ANKFY1, FAM13B, KRTA P1-5, PRR9, TARDBP, SUZ12P1, ARF3, FAM160B1, KRTAP17-1, PSMB3, TCHH, ARPC5, FAM83A, KRTAP19-1, PSM E1, TCHHL1, B4GALT1, FBXO32, KRTAP19-3, PSORS1C2, TMED4, B4GALT5, FOXN3, KRTAP2-2, PYGL, TMEM66, B DH1, FOXQ1, KRTAP3-1, RAC1, TNIP2, APMAP, GSR, KRTAP3-3, RHCG, TRAPPC12, C6orf132, HSPA4, KRTAP7-1, RPS6KA3, INIP, IDO1, LOC100288432, RSF1, TUSC3, CCAR1, JAK2, LOC100505839, SCAF11, UBXN4, CHD4, JM Y, MARCHF8, USP53, CNN3, MAT2B, SF3A1, VPS26A, CRYAB, KIAA1033, MED1, SLC25A44, WFDC5, CYB5R4, KIAA 1429, NBN, SLC38A10, ZC3HAV1, DIS3, KIAA1432, NFYA, SLC5A1, ZDHHC7, DNAJB12, NOSIP, SMG5, ZNF124, DYNLL1, KLC3, NUBP1, SNX20, ZNF622, ELMO2, KRT25, ORC3, SORT1, and ARPC4; The method for predicting a change in body fat according to claim 5.

7. outputting prediction result information including the predicted change in body fat and the subject identification information; The method for predicting a change in body fat according to claim 1 or 2.

8. The gene expression information includes information on the expression level of mRNA of the related gene. The method for predicting a change in body fat according to claim 1 or 2.

9. The prediction of a change in body fat includes a prediction of a change in visceral fat. The method for predicting a change in body fat according to claim 1 or 2.

10. The lipid contains diacylglycerol containing α-linolenic acid as a constituent fatty acid. The method for predicting a change in body fat according to claim 1 or 2.

11. A body fat change prediction system for predicting a change in body fat of a subject due to intake of lipids containing α-linolenic acid as a constituent fatty acid, an acquisition unit that acquires biological information of the subject, including gene expression information that is information on the expression level of at least one related gene or its expression product that is related to a change in body fat due to the intake of the lipid, in association with subject identification information that identifies the subject; The change in body fat in a plurality of sample subjects who ingested the lipids; At least one variable obtained from the biological information of the plurality of sample subjects, the variable including an expression level of the relevant gene or its expression product associated with changes in body fat in the plurality of sample subjects; a storage unit that stores correlation information indicating the correlation between a prediction unit that predicts a change in body fat due to the intake of the lipids of the subject from the biological information based on the correlation information; A body fat change prediction system comprising:

12. A program for predicting a change in body fat of a subject due to intake of lipids containing α-linolenic acid as a constituent fatty acid, the program comprising: acquiring biological information of the subject, including gene expression information that is information on the expression level of at least one related gene or its expression product associated with a change in body fat due to the intake of lipids, in association with subject identification information that identifies the subject; The change in body fat in a plurality of sample subjects who ingested the lipids; At least one variable obtained from the biological information of the plurality of sample subjects, the variable including an expression level of the relevant gene or its expression product associated with changes in body fat in the plurality of sample subjects; predicting a change in body fat due to the intake of the lipids of the subject from the biological information based on correlation information indicating the correlation between the above-mentioned A program that executes the following.

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