Method for predicting weight-loss effect

Abstract

To provide a technique that enables prediction, for each subject, of a weight-loss effect resulting from an increase in physical activity.SOLUTION: A method for predicting a weight-loss effect of a subject resulting from an increase in physical activity according to the present invention comprises an acquisition step of obtaining, as genetic information of the subject, an expression level of at least one gene or an expression product thereof selected from the group consisting of four genes: CCT8, PHF23, C1D, and DEFB4B.SELECTED DRAWING: None

Description

[Technical Field]

[0001] The present invention relates to a technique for predicting the weight loss effect of an increased amount of exercise in a subject. [Background technology]

[0002] Effective weight loss methods are being sought for the purposes of health promotion, obesity relief, beauty, etc. For example, while it is known that increased exercise and dietary management can be effective in weight loss, it is also known that the effectiveness of these measures varies from person to person. Therefore, factors involved in the weight loss effects of increased exercise and dietary management are being investigated.

[0003] For example, Patent Document 1 discloses a combination comprising multiple polynucleotides that are differentially expressed in animals exhibiting a lean phenotype as a result of one or more lean phenotype-promoting treatments, including administration of conjugated linoleic acid (CLA), consumption of a high-protein diet, and increased exercise. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Special Publication No. 2012-501175 Summary of the Invention [Problem to be solved by the invention]

[0005] The above-mentioned Patent Document 1 describes a combination containing multiple polynucleotides whose expression changes as a result of treatments that promote a lean phenotype, such as increased exercise, but does not describe a method for predicting the weight loss effect of increased exercise for each subject.

[0006] The present invention relates to a technique for predicting the weight loss effect of increased exercise for each subject. [Means for solving the problem]

[0007] According to one aspect of the present invention, a method for predicting a weight loss effect of an increased amount of exercise in a subject includes: The method includes an acquisition step of acquiring the expression level of at least one gene selected from the group consisting of four genes, CCT8, PHF23, C1D, and DEFB4B, or its expression product, as the genetic information of the subject.

[0008] According to another aspect of the present invention, a method for predicting a weight loss effect of an increase in the amount of exercise in a subject includes: The method includes a prediction step of predicting the weight loss effect of increasing the amount of exercise on the subject based on the expression level of at least one gene selected from the group consisting of four genes, CCT8, PHF23, C1D, and DEFB4B, or its expression product, obtained as genetic information of the subject.

[0009] A prediction device according to still another aspect of the present invention is a prediction device for predicting a weight loss effect of an increase in the amount of exercise of a subject, and includes a control unit. The control unit The weight loss effect of increasing the amount of exercise on the subject is predicted based on the expression level of at least one gene or its expression product selected from the group consisting of four genes, CCT8, PHF23, C1D, and DEFB4B, obtained as genetic information of the subject.

[0010] A predictive marker according to yet another aspect of the present invention is a predictive marker for predicting a weight loss effect of an increase in an amount of exercise in a subject, comprising: It consists of at least one gene selected from the group consisting of four genes: CCT8, PHF23, C1D, and DEFB4B, or its expression product.

[0011] A prediction kit according to still another embodiment of the present invention is a prediction kit for predicting a weight loss effect of an increase in exercise volume in a subject, comprising: The kit contains a reagent for detecting at least one gene selected from the group consisting of four genes, CCT8, PHF23, C1D, and DEFB4B, or its expression product, from a biological sample collected from the subject. [Effects of the Invention]

[0012] According to the present invention, it is possible to predict the weight loss effect due to an increase in the amount of exercise for each subject. DETAILED DESCRIPTION OF THE INVENTION

[0013] The present invention is described in detail below. All patents, non-patent documents, and other publications cited herein are hereby incorporated by reference in their entirety.

[0014] [Summary of the Invention] The present invention relates to a method for predicting the weight loss effect of an increased amount of exercise on a subject based on the subject's genetic information. The present invention makes it possible to predict and / or assist in the prediction of the weight loss effect of an increased amount of exercise on a subject-by-subject basis. One advantage of the present invention is that it can provide information for determining whether an increased amount of exercise is an effective means of weight loss for each subject based on the subject's genetic information, i.e., biological characteristics that form individual differences between subjects.

[0015] In one embodiment of the present invention, the term "subject" refers to a person who is the subject of the weight loss effect prediction according to the present invention. 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.

[0016] The obesity level of the subject is not particularly limited, but preferably the subject is, for example, of normal weight and / or of high obesity. A normal weight subject is, for example, a subject with a Body Mass Index (BMI) of 18.50 or more and less than 25.00. A high obesity subject is, for example, a subject with a BMI of 25.00 or more. An example of the subject of the present invention is a subject with a BMI of 23.00 or more. In addition to the above-mentioned BMI, "obesity level" can be evaluated using, for example, visceral fat mass, subcutaneous fat mass, body fat mass, body fat percentage, waist circumference, abdominal circumference, weight, body mass index (Rohrer index, Kaup index, etc.), obesity index calculated from the body mass index, subcutaneous fat thickness in the triceps, subcutaneous fat thickness in the subscapularis, basal metabolic rate, blood markers correlated with visceral fat mass (cytokines such as adiponectin, leptin, TNFα, and IL-6, inflammatory markers such as CRP, blood lipids such as LDL-cholesterol and triglycerides, liver function markers, blood glucose levels, etc.).

[0017] In one embodiment of the present invention, the term "fat mass" such as "visceral fat mass" refers to an index representing the amount of fat in a subject, and includes, for example, the mass of fat (in a specific area or in the whole body), the volume of fat (in a specific area or in the whole body), the cross-sectional area of ​​fat in a specific area, the thickness of fat in a specific area, etc.

[0018] In one embodiment of the present invention, the term "amount of exercise" refers to the amount of exercise determined based on the intensity, duration, and frequency of exercise. "Exercise" refers to physical activity that consumes more energy than a resting state, and is intentionally performed for the purpose of weight loss and is sustained. In the present invention, "increasing the amount of exercise" refers to increasing the amount of exercise so as to increase energy expenditure. Specifically, an increase in a subject's amount of exercise can be achieved by, in addition to the subject's current physical activity, for example, increasing the intensity, duration, and frequency of specific types of exercise, such as sports, training, and aerobic exercise, or physical activity such as work or housework. The target amount of exercise can be set appropriately depending on the subject's current amount of exercise, age, health condition, etc., and can be set by referring to the WHO's "Optimal Physical Activity Guidelines for Health Promotion." Examples of increased exercise include exercise that increases energy expenditure to achieve a target weight (a 1-10% reduction from current weight, preferably a 1-5% reduction, and more preferably a 3% reduction), as well as moderate-intensity (3.0-6.0 METs) physical activity (e.g., brisk walking, dancing, mowing the lawn, etc.) for a specified time per week (e.g., at least 150-300 minutes), high-intensity (6.0 METs or higher) physical activity (e.g., running, swimming, climbing stairs, etc.) for a specified time per week (e.g., at least 75-150 minutes), and prescribed strength training (e.g., 15 minutes of multiple strength training exercises such as squats) a specified number of times per week (e.g., about three times).

[0019] In one embodiment of the present invention, "increasing the amount of exercise" preferably refers to a continuous increase in the amount of exercise. A continuous increase in the amount of exercise refers to maintaining an increased state of exercise for a predetermined period of time. The predetermined period is preferably one week or more, more preferably ten days or more, and even more preferably one month or more. Furthermore, during the predetermined period, the frequency of consciously increasing the amount of exercise is preferably three or more days per week, and more preferably every day.

[0020] In one embodiment of the present invention, the "weight loss effect due to increased exercise volume" refers to an effect evaluated by at least one of weight loss (reduction in body weight), changes in physique due to weight loss, and changes in body composition due to weight loss before and after an increase in exercise volume. In one embodiment of the present invention, the prediction of the weight loss effect due to increased exercise volume includes at least one of a qualitative prediction of the weight loss effect and a quantitative prediction of the weight loss effect.

[0021] In one embodiment of the present invention, "weight loss effect prediction" includes at least one of a prediction of the overall weight loss effect or a prediction of a change in an index (hereinafter referred to as a "weight loss evaluation index") that evaluates a specific weight loss effect. A prediction of the overall weight loss effect refers to a qualitative or quantitative prediction of the weight loss effect that does not refer to a specific weight loss evaluation index. A qualitative prediction of the overall weight loss effect is expressed, for example, using expressions such as "high / low weight loss effect" or "easy / difficult to lose weight." Note that the qualitative weight loss effect is not limited to being expressed in two stages, but may be expressed in three or more stages. When the weight loss effect is expressed in three or more stages, it may be expressed using letters, words, symbols, etc., such as numbers (1, 2, 3, etc.) or alphabets (A, B, C, etc.). A quantitative prediction of the overall weight loss effect may be expressed numerically.

[0022] The prediction of a change in a specific weight loss evaluation index may be a qualitative and / or quantitative prediction of a change in the specific weight loss evaluation index. Examples of weight loss evaluation indexes include body weight, visceral fat mass, subcutaneous fat mass, body fat mass, body fat percentage, waist circumference, abdominal circumference, muscle mass (trunk muscle mass, etc.), BMI, body mass index (Rohrer index, Kaup index, etc.), obesity index calculated from the body mass index, triceps subcutaneous fat thickness, subscapular subcutaneous fat thickness, etc. The specific weight loss evaluation index may be one or more. As described below, in one embodiment of the present invention, the weight loss evaluation index preferably includes at least one of body weight and visceral fat mass.

[0023] Qualitative predictions of changes in a weight loss evaluation index include, for example, predictions of how easily the weight loss evaluation index will change (e.g., "easy / difficult to lose weight"), predictions of the magnitude of changes in the weight loss evaluation index (e.g., "large / small amount of weight loss"), etc. The change in a qualitative weight loss evaluation index is not limited to being expressed in two stages, but may be expressed in three or more stages. In one embodiment of the present invention, when the weight loss evaluation index is an index related to "fat mass" such as "visceral fat mass" or "body fat mass," the qualitative predictions may be expressed in terms such as "visceral fat reduction" or "body fat reduction" without the word "quantity."

[0024] Quantitative predictions of changes in weight loss evaluation indexes include, for example, predictions of the amount of change, rate of change, and range thereof of the weight loss evaluation index. In this specification, the "amount of change in the weight loss evaluation index" refers to the difference before and after an increase in the amount of exercise, and the "rate of change in the weight loss evaluation index" refers to the ratio of the difference to the value before the increase in the amount of exercise. Furthermore, the "amount of decrease in the weight loss evaluation index" refers to the amount of change when the value after an increase in the amount of exercise is lower than the value before the increase, and the "negative amount of change in the weight loss evaluation index" refers to the rate of change when the value after an increase in the amount of exercise is lower than the value before the increase.

[0025] In one embodiment of the present invention, the "prediction" of a weight loss effect can be replaced with terms such as "determination," "detection," "measurement," "evaluation," etc. Note that in the present invention, the terms "prediction," "determination," "detection," "measurement," and "evaluation" of a weight loss effect do not include a doctor's diagnosis of a disease such as obesity.

[0026] 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.

[0027] 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.

[0028] 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.

[0029] In one embodiment of the present invention, the expression level of a gene or its expression product in a subject is derived from a biological sample collected from the subject. The phrase "the expression level of a gene or its expression product is derived from a biological sample" means that the expression level is measured or detected from the biological sample. 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), etc., are preferred, and skin surface lipids are particularly preferred. Here, "skin surface lipids (SSL)" refers to the fat-soluble fraction present on the surface of the skin and is sometimes called sebum. Generally, SSL mainly contains secretions from exocrine glands such as sebaceous glands in the skin and exists on the skin surface as a thin layer covering the skin surface. SSL contains RNA expressed in skin cells. The skin from which SSL can be collected can be 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.

[0030] 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.

[0031] 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.

[0032] The method for measuring the expression level of a gene or its expression product from a biological sample 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 to cDNA by reverse transcription, and the expression level of the cDNA or its amplification product is then measured.

[0033] 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.

[0034] In the following description, gene names are based on the official symbols listed in NCBI ([www.ncbi.nlm.nih.gov / ]). Furthermore, genes that can serve as predictive markers in each embodiment of the present invention also include genes having a base sequence substantially identical to the base sequence of the DNA constituting the gene, as long as they have the function of predicting the weight loss effect of increased exercise in a subject. Here, "substantially identical" means, for example, that the base sequence is 90% or more, preferably 95% or more, and even more preferably 98% or more identical to the base sequence of the DNA constituting the gene, when searched using the homology calculation algorithm NCBI BLAST under the following conditions: expectation value = 10; gaps allowed; filtering = ON; match score = 1; mismatch score = -3.

[0035] Specific embodiments of the present invention will be described below: The embodiments of the present invention are not intended to be used for medical purposes on humans, and do not include any therapeutic or diagnostic procedures for medical purposes.

[0036] [First embodiment] (Acquisition process) A method for predicting a weight loss effect according to a first embodiment of the present invention includes at least an acquisition step of acquiring genetic information of a subject, the expression level of at least one gene selected from the group consisting of four genes, CCT8, PHF23, C1D, and DEFB4B, or its expression product. In this embodiment, the genetic information of a subject refers to genetic information derived from a biological sample collected from the subject, and includes the expression level of the gene or its expression product.

[0037] In the acquisition step of this embodiment, "acquiring the expression level of a gene or its expression product as genetic information" refers to acquiring information on the expression level of a gene or its expression product, and includes, for example, at least one of measuring the expression level of a gene or its expression product and acquiring information on the measurement results, or acquiring expression level information from another device, etc. Modes of acquiring expression level information from a device, etc. include, for example, acquiring expression level information by receiving it from a computer, etc. via wireless or wired connections, and acquiring expression level information by reading it from a storage medium, etc. that stores expression level information.

[0038] In this embodiment, the four genes CCT8, PHF23, C1D, and DEFB4B are genes that, in the examples described below, have been shown to correlate, through statistical processing, with both the rate of change in body weight and the amount of change in visceral fat area due to increased exercise, based on data from a group of subjects who were recognized as having complied with the instruction in the program among a group of subjects who had completed a training program for increasing exercise (hereinafter referred to as "homogeneous group of subjects"). Therefore, these four genes can function as predictive markers for predicting the weight loss effect of increased exercise.

[0039] These four genes were extracted from data on a male subject population, and therefore are thought to be particularly useful as predictive markers for predicting the weight loss effects of increased exercise in male subjects.

[0040] Of the four genes, the expression levels of CCT8 and PHF23 or their expression products are negatively correlated with weight loss effects (e.g., body weight loss and visceral fat loss), whereas the expression levels of C1D and DEFB4B or their expression products are positively correlated with weight loss effects (e.g., body weight loss and visceral fat loss).

[0041] These four genes each have a correlation between the mRNA expression level and the rate of change in body weight and visceral fat area due to increased exercise. Therefore, it is possible to predict the weight loss effect using at least one gene, but more accurate predictions can be made by using multiple of the four genes, for example, by combining all of the genes.

[0042] In particular, in the acquisition step of this embodiment, it is preferable to acquire the expression level of CCT8 or its expression product as the genetic information of the subject. In addition to the above-mentioned correlation, CCT8 is a gene that has been evaluated, using the method shown in the Examples below, as having high discriminability for distinguishing between groups with large and small weight loss and between groups with large and small visceral fat loss from data on a homogeneous subject population. In other words, CCT8 can function as a predictive marker for particularly accurately predicting the weight loss effect of increased exercise.

[0043] By obtaining the expression levels of the above four genes or their expression products as genetic information representing the biological characteristics that form the individual differences between subjects through the above acquisition process, it is possible to contribute to predicting the weight loss effect of increasing the amount of exercise for each subject.

[0044] Furthermore, since the genetic information is derived from a non-invasively collected biological sample such as an SSL, the biological sample can be collected more easily and the burden on the subject can be reduced compared to when an invasively collected biological sample such as blood is used.

[0045] (Prediction process) The prediction method in this embodiment preferably further includes a prediction step of predicting the weight loss effect of increasing the amount of exercise of the subject based on the obtained expression levels.

[0046] For example, in this step, the weight loss effect of increasing the amount of exercise of a subject can be predicted using a learning model in which at least one variable including the expression level of the obtained gene or its expression product is used as an explanatory variable and an index showing the weight loss effect of increasing the amount of exercise is used as a response variable. In this case, the prediction process in this step can be performed, for example, by a computer equipped with a control unit.

[0047] The expression levels included in the explanatory variables of the learning model can specifically take the following values. For example, values ​​indicating the expression level when analyzing the expression levels of multiple genes by sequencing include a read count value, which is expression level data; an RPM value obtained by correcting the read count value for differences in the total number of reads between samples; a value obtained by converting the RPM value to a base 2 logarithm (Log2RPM value); a base 2 logarithm obtained by adding an integer 1 to the RPM value (Log2(RPM+1) value); a count value corrected using DESeq2 (Normalized count value); and a base 2 logarithm obtained by adding an integer 1 (Log2(count+1) value). Other examples of values ​​indicating the expression level may include values ​​calculated using common quantitative values ​​in RNA-seq, such as fragments per kilobase of exon per million reads mapped (FPKM), reads per kilobase of exon per million reads mapped (RPKM), and transcripts per million (TPM). Other examples of values ​​indicating the expression level include signal values ​​obtained by microarray analysis and their corrected values. When analyzing the expression level of only a specific gene by RT-PCR or the like, the value indicating the expression level may be a value calculated by a method in which the expression level of the gene to be measured is converted to a relative expression level based on the expression level of a housekeeping gene (relative quantification), or by a method in which the absolute copy number is quantified using a plasmid containing the region of the gene to be measured (absolute quantification). Alternatively, the copy number obtained by digital PCR may be used as the expression level.

[0048] The explanatory variables may also include one or more indicators obtained from biological information other than genetic information related to the subject's biological functions. Examples of such biological information include attribute information related to the subject's biological functions (e.g., gender, age, generation, etc.), physical examination items before the increase in exercise volume, blood test items before the increase in exercise volume, and information on lifestyle habits (e.g., exercise habits, dietary habits, etc.). Examples of the physical examination items include height, weight, visceral fat mass, subcutaneous fat mass, body fat mass, body fat percentage, waist circumference, abdominal circumference, muscle mass, BMI, triceps subcutaneous fat thickness, subscapular subcutaneous fat thickness, etc. Examples of the blood test items include blood lipids such as LDL-cholesterol and triglycerides, liver function markers, blood glucose levels, cytokines (e.g., adiponectin, leptin, TNFα, IL-6, etc.), inflammatory markers (e.g., CRP, etc.), etc. Other biological information may include, for example, basal metabolic rate, body mass index, obesity index, etc.

[0049] The indicator indicating the weight loss effect included in the objective variable of the above learning model is an indicator corresponding to the weight loss effect that is the predicted result, and may be, for example, a number and symbol that gradually indicates the degree of weight loss effect, or a number that indicates the amount and rate of change of the above weight loss evaluation indicator (body weight, visceral fat mass, etc.).

[0050] Such a learning model can be generated by acquiring a large amount of data from a sample population of subjects. Specifically, the learning model can be a model generated by machine learning using values ​​such as expression levels obtained from data from a population of subjects who underwent an exercise intervention test as explanatory variables and an index showing the weight loss effect in the population of subjects as a response variable.

[0051] The algorithms used in the learning model are not particularly limited, and examples 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, and the like.

[0052] Alternatively, in the prediction step of this embodiment, for example, it may be determined whether the expression level of the acquired gene or its expression product satisfies a condition set corresponding to an indicator of a weight loss effect. If it is determined that the condition is satisfied, it can be predicted that the weight loss effect of the subject due to increased exercise amount corresponds to the indicator of a weight loss effect corresponding to the condition. In this case, the above determination process may be performed, for example, by a computer.

[0053] Examples of "conditions corresponding to an index showing a weight loss effect" include a range of values ​​that can be taken by the expression level of each gene or its expression product set corresponding to an index showing a weight loss effect, a value or range calculated by substituting a numerical value showing the expression level into a predetermined mathematical formula, etc. The predetermined mathematical formula may be a mathematical formula that can calculate an index showing a weight loss effect from the expression levels of one or more genes or their expression products.

[0054] When the condition is a range of possible values ​​for the expression level of each gene or its expression product, this step may include comparing the value indicating the expression level of the gene or its expression product with a reference value for the expression level set corresponding to an indicator of weight loss effect. The reference value in this step is a value set for each gene or its expression product, and serves as a reference for determining the indicator of weight loss effect. The reference value can be set, for example, by classifying a group of subjects who have increased their exercise volume according to an indicator of weight loss effect, and using statistical values ​​(such as mean, median, quartile, 95% confidence interval) of the values ​​indicating the expression level of the gene or its expression product calculated for each group.

[0055] Specifically, for a gene or its expression product whose expression level has a positive correlation with weight loss effect, if the value indicating the expression level is greater than or equal to a reference value, it can be determined to be an indicator of a relatively high weight loss effect. On the other hand, for a gene or its expression product whose expression level has a negative correlation with weight loss effect, if the value indicating the expression level is less than or equal to the reference value, it can be determined to be an indicator of a relatively high weight loss effect.

[0056] For example, if the index showing the weight loss effect is expressed in two levels, the index showing the weight loss effect can be determined by comparing the value showing the expression level with one reference value. If the index showing the weight loss effect is expressed in three or more levels, the index showing the weight loss effect can be determined by comparing the value showing the expression level with multiple reference values.

[0057] In the above example, when the expression levels of multiple genes or their expression products are assessed, an index indicating a weight loss effect is assessed for each expression level, and the overall weight loss effect can be assessed based on these multiple prediction results. As a method for assessing the overall weight loss effect, for example, the prediction result corresponding to the index indicating a weight loss effect assessed for the most genes or their expression products may be used as the overall weight loss effect prediction result. As another example, a representative value (such as the mean, median, or mode) of multiple weight loss effect indicators assessed for the expression levels of multiple genes or their expression products may be calculated, and the weight loss effect corresponding to this representative value may be used as the overall weight loss effect prediction result.

[0058] As described above, according to the prediction process, by predicting the weight loss effect of an increased amount of exercise on a subject based on the expression levels of the above four genes or their expression products, it is possible to obtain information for evaluating the effectiveness of increased exercise as a weight loss measure based on the biological characteristics of each subject.

[0059] In this embodiment, the weight loss effect prediction preferably includes at least one of a prediction of a weight loss or a prediction of a visceral fat loss, and may include both of these predictions. In the following description, "a reduction" in weight and visceral fat refers to a "change" in weight and visceral fat in which the value after the increase in exercise amount is smaller than the value before the increase.

[0060] The weight loss prediction may be a qualitative or quantitative prediction of weight loss. Qualitative predictions of weight loss include predictions of how easily weight will be lost (e.g., "easy / difficult to lose weight"), the amount of weight loss (e.g., "large / small amount of weight loss"), etc. Quantitative predictions of weight loss include at least one prediction selected from the amount of weight loss, the rate of weight loss, and a range thereof.

[0061] Similarly, the prediction of visceral fat reduction is preferably a qualitative or quantitative prediction of visceral fat reduction. Qualitative predictions of visceral fat reduction include predictions of the ease of visceral fat reduction (e.g., "visceral fat is easy / difficult to reduce") and predictions of the magnitude of visceral fat reduction (e.g., "large / small amount of visceral fat reduction"). Quantitative predictions of visceral fat reduction include predictions of the amount of visceral fat reduction, reduction rate, and at least one selected from the ranges thereof. As described above, "visceral fat amount" may be any indicator representing the amount of visceral fat, such as the mass of visceral fat, the visceral fat area which is the cross-sectional area of ​​the abdomen, and the volume of visceral fat. Of these, in this embodiment, the visceral fat amount is preferably visceral fat area, and more preferably the visceral fat area of ​​the abdomen.

[0062] Hereinafter, an embodiment will be described in which the weight loss effect prediction includes either a prediction of a weight loss or a prediction of a visceral fat loss. In the following embodiments, the first embodiment will be referred to for terms that overlap or correspond to those in the first embodiment, and explanations thereof will be omitted as appropriate.

[0063] [Second embodiment] In a method for predicting a weight loss effect according to a second embodiment of the present invention, the prediction of the weight loss effect includes a prediction of body weight loss, and the acquisition step acquires the expression level of FAM192A or its expression product as genetic information of the subject. Note that in the acquisition step according to this embodiment, it is preferable to further acquire the expression level of FAM192A or its expression product in addition to the expression level of at least one gene selected from the group consisting of the four genes described in the first embodiment or its expression product. The expression level of FAM192A or its expression product has, for example, a negative correlation with body weight loss.

[0064] In the Examples described below, data from a homogeneous subject population showed that the expression level of FAM192A mRNA correlated with the rate of weight change due to increased physical activity, and the gene was evaluated as having high discriminability for distinguishing between groups with large and small weight loss using the method described in the Examples described below. Therefore, in this embodiment, weight loss due to increased physical activity can be accurately predicted by using FAM192A as a predictive marker.

[0065] Furthermore, FAM192A is a gene extracted from data on a male subject population, and therefore, this gene is thought to be particularly useful as a predictive marker for predicting weight loss due to increased exercise in male subjects.

[0066] Furthermore, in the acquisition step of this embodiment, from the viewpoint of improving prediction accuracy, it is more preferable to acquire the expression levels of two genes, CCT8 and FAM192A, or their expression products. Both CCT8 and FAM192A are genes that have been evaluated as having high discriminative ability for distinguishing between groups with large and small weight loss. Therefore, by using these as predictive markers, weight loss due to increased exercise can be predicted with even greater accuracy.

[0067] [Third embodiment] In a method for predicting a weight loss effect according to a third embodiment of the present invention, the prediction of the weight loss effect includes a prediction of body weight loss, and in the acquisition step, the expression level of at least one gene selected from the group consisting of 13 genes, namely C9orf89, MT2A, P4HA1, HMGB2, LY6E, SNORA70B, ATP2B4, RABIF, GMPS, CRCP, GUSBP3, IRGQ, and ABL1, or its expression product, is acquired as genetic information of the subject. Note that in the acquisition step according to this embodiment, in addition to the expression level of at least one gene selected from the group consisting of the four genes described in the first embodiment or its expression product, it is preferable to acquire the expression level of at least one gene selected from the group consisting of the 13 genes or its expression product. Furthermore, it is more preferable to acquire the expression level of at least one gene selected from the group consisting of the four genes described in the first embodiment or its expression product, and the expression level of FAM192A or its expression product described in the second embodiment, or the expression level of at least one gene selected from the group consisting of the 13 genes.

[0068] Of the 13 genes, the expression levels of six genes, C9orf89, MT2A, P4HA1, HMGB2, LY6E, and SNORA70B, or their expression products, are negatively correlated with, for example, weight loss. Of the 13 genes, the expression levels of seven genes, ATP2B4, RABIF, GMPS, CRCP, GUSBP3, IRGQ, and ABL1, or their expression products, are positively correlated with, for example, weight loss.

[0069] The above 13 genes are genes whose mRNA expression levels have been shown to correlate with the rate of weight change due to increased physical activity from data from a homogeneous subject population in the Examples described below. Therefore, in this embodiment, by using the above 13 genes as predictive markers, weight loss due to increased physical activity can be accurately predicted. Furthermore, since each of these 13 genes has a correlation between the mRNA expression level and the rate of weight change due to increased physical activity, weight loss can be predicted using at least one gene, but more accurate predictions can be made by using multiple genes from the 13 genes, for example, by combining all of the genes.

[0070] Furthermore, the 13 genes were extracted from data on a male subject population, and therefore these genes are thought to be particularly useful as predictive markers for predicting weight loss due to increased exercise in male subjects.

[0071] [Fourth embodiment] In a method for predicting a weight loss effect according to a fourth embodiment of the present invention, the prediction of the weight loss effect includes a prediction of a reduction in visceral fat, and in the acquisition step, the subject's genetic information is obtained by acquiring the expression level of at least one gene selected from the group consisting of 18 genes: ELOF1, KCTD10, CAMSAP1, GTF3C5, TULP4, DUSP11, UBTD1, C18orf21, SNORA50, SNORA7B, BNIP2, SNORA28, EIF4EBP3, STK38L, LYRM1, GPR65, BAX, and SNORA2A. In the acquisition step according to this embodiment, in addition to the expression level of at least one gene selected from the group consisting of the four genes described in the first embodiment or its expression product, it is preferable to further acquire the expression level of at least one gene selected from the group consisting of the above 18 genes or its expression product.

[0072] Of the 18 genes, the expression levels of eight genes, ELOF1, KCTD10, CAMSAP1, GTF3C5, TULP4, DUSP11, UBTD1, and C18orf21, or their expression products, are negatively correlated with, for example, a reduction in visceral fat.Of the 18 genes, the expression levels of ten genes, SNORA50, SNORA7B, BNIP2, SNORA28, EIF4EBP3, STK38L, LYRM1, GPR65, BAX, and SNORA2A, or their expression products, are positively correlated with, for example, a reduction in visceral fat.

[0073] In the examples described below, the mRNA expression levels of the 18 genes were shown to correlate with the change in visceral fat area due to increased physical activity in data from a homogeneous subject population, and the genes were evaluated using the methods described below as having high discriminability for distinguishing between groups with large and small decreases in visceral fat area. Therefore, in this embodiment, the use of the 18 genes as predictive markers allows for accurate prediction of visceral fat loss due to increased physical activity. Furthermore, since each of the 18 genes correlates with the mRNA expression level and the change in visceral fat area due to increased physical activity, it is possible to predict weight loss effects using at least one gene. However, more accurate predictions can be achieved by using multiple genes, such as all of the 18 genes, in combination.

[0074] Furthermore, the 18 genes were extracted from data on a male subject population, and therefore these genes are thought to be particularly useful as predictive markers for predicting visceral fat reduction through increased exercise in male subjects.

[0075] Furthermore, in the acquisition step of this embodiment, from the viewpoint of improving prediction accuracy, it is more preferable to acquire, for example, the expression level of CCT8 or its expression product, and the expression level of at least one gene selected from the group consisting of the above 18 genes or its expression product. CCT8 and the above 18 genes are both genes that have been evaluated as having high discriminative ability for distinguishing between groups with large and small visceral fat reduction, so by using these as predictive markers, it is possible to more accurately predict visceral fat reduction due to increased exercise volume.

[0076] [Fifth embodiment] In a fifth embodiment of the present invention, a method for predicting a weight loss effect includes predicting a reduction in visceral fat. In the obtaining step, genetic information of the subject is obtained, which includes the expression level of at least one gene selected from the group consisting of 36 genes: MRPS15, CCDC85B, ZNF880, HMGXB3, FAM210B, PLSCR3, TUBB6, DUSP8, C2orf47, HBS1L, KCTD12, DCAF10, HIST1H4C, EEF1B2, CENPB, ITPRIPL2, XPOT, ZNF277, NECAP1, PLAGL2, AGTPBP1, PAG1, RAD21, HIST2H3D, FCHSD2, CCRL2, ATP11B, AZIN1, HLA-DPA1, IL18RAP, CLEC16A, MAEA, GPATCH8, SCARF1, GUSB, and SNORA34, or an expression product thereof. In the acquisition step of this embodiment, it is preferable to acquire the expression level of at least one gene or its expression product selected from the group consisting of the above 36 genes in addition to the expression level of at least one gene selected from the group consisting of the 4 genes described in the first embodiment or its expression product, and it is more preferable to acquire the expression level of at least one gene or its expression product selected from the group consisting of the above 36 genes in addition to the expression level of at least one gene or its expression product selected from the group consisting of the 4 genes described in the first embodiment and the expression level of at least one gene or its expression product selected from the group consisting of the 18 genes described in the fourth embodiment.

[0077] Of the 36 genes, the expression levels of 17 genes, namely MRPS15, CCDC85B, ZNF880, HMGXB3, FAM210B, PLSCR3, TUBB6, DUSP8, C2orf47, HBS1L, KCTD12, DCAF10, HIST1H4C, EEF1B2, CENPB, ITPRIPL2, and XPOT, or their expression products, are negatively correlated with, for example, a reduction in visceral fat. Of the 36 genes, the expression levels of 19 genes, namely ZNF277, NECAP1, PLAGL2, AGTPBP1, PAG1, RAD21, HIST2H3D, FCHSD2, CCRL2, ATP11B, AZIN1, HLA-DPA1, IL18RAP, CLEC16A, MAEA, GPATCH8, SCARF1, GUSB, and SNORA34, or their expression products, are positively correlated with, for example, a reduction in visceral fat.

[0078] The above 36 genes are genes whose mRNA expression levels have been shown to correlate with the change in visceral fat area due to increased physical activity in the examples described below, based on data from a homogeneous subject population. Therefore, in this embodiment, by using the above 36 genes as predictive markers, it is possible to accurately predict the reduction in visceral fat due to increased physical activity. Furthermore, since each of these 36 genes has a correlation between the mRNA expression level and the change in visceral fat area due to increased physical activity, it is possible to predict the weight loss effect by using at least one gene, but by using multiple genes from the 36 genes, for example, by combining all of the genes, more accurate prediction is possible.

[0079] Furthermore, the 36 genes were extracted from data on a male subject population, and therefore these genes are considered to be particularly useful as predictive markers for predicting visceral fat reduction through increased exercise in male subjects.

[0080] [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 various modifications can be made without departing from the spirit of the present invention. For example, in addition to the acquisition step, each of the above-described embodiments from the second embodiment onwards may further include a prediction step of predicting the weight loss effect of an increase in the amount of exercise on the subject based on the acquired expression levels.

[0081] In yet another embodiment of the present invention, a method for predicting the weight loss effect of increased exercise in a subject can be provided. The method includes a prediction step of predicting the weight loss effect of increased exercise in a subject based on the expression level of at least one gene selected from the group consisting of four genes: CCT8, PHF23, C1D, and DEFB4B, or its expression product, obtained as genetic information of the subject. This prediction step can be performed in the same manner as the prediction step described in the first embodiment. This can provide information for evaluating the effectiveness of increased exercise as a weight loss measure based on the biological characteristics of each subject.

[0082] As yet another embodiment of the present invention, a prediction device for executing the prediction step can be provided. The prediction device predicts the weight loss effect of an increased amount of exercise in a subject, and includes a control unit that predicts the weight loss effect of an increased amount of exercise in a subject based on the expression level of at least one gene selected from the group consisting of four genes: CCT8, PHF23, C1D, and DEFB4B, or its expression product, obtained as genetic information of the subject. The prediction device may be any device having a control unit capable of performing the prediction process, such as a computer. The control unit may be, for example, a processor such as a CPU.

[0083] Furthermore, as yet another embodiment of the present invention, a prediction system including multiple computers including the prediction device can be provided. The prediction system includes, for example, a prediction device serving as a server on the Internet and at least one terminal connected to the prediction device via the Internet. In this example, the prediction device acquires (receives) the expression level as genetic information of the subject from the terminal and predicts the subject's weight loss effect due to increased exercise volume based on the acquired expression level. Furthermore, in the prediction system, the prediction device may transmit the predicted weight loss effect of the subject to the terminal. Note that the terminal from which the genetic information is acquired may be different from the terminal to which the prediction result is output. The prediction device may also acquire (receive) biological information other than the subject's genetic information from the terminal from which the genetic information is acquired and / or another terminal.

[0084] In yet another embodiment, the present invention provides a predictive marker for predicting the weight loss effect of increased exercise in a subject, which comprises at least one gene selected from the group consisting of CCT8, PHF23, C1D, and DEFB4B, or its expression product.

[0085] In yet another embodiment, the present invention provides a kit for predicting the weight loss effect of increased exercise in a subject, which contains a reagent for detecting at least one gene selected from the group consisting of four genes, CCT8, PHF23, C1D, and DEFB4B, or its expression product, from a biological sample collected from the subject.

[0086] The reagent for detecting the expression level of the gene or its expression product preferably comprises at least one selected from, for example, an oligonucleotide that specifically hybridizes with a nucleic acid derived from the gene and an antibody that recognizes the expression product of the gene. The oligonucleotide comprises at least one selected from, for example, a PCR primer, a hybridization probe, an adapter sequence for sequencing, etc. The antibody may be an antibody that recognizes a protein that is the expression product of the gene.

[0087] In addition to the above-mentioned reagents, the prediction kit may also include, for example, reagents for extracting and purifying RNA from the collected SSL, reagents used to detect the expression level of a gene or its expression product, etc. Examples of such reagents include labeling reagents, buffer solutions, chromogenic substrates, secondary antibodies, blocking agents, and control reagents used as positive or negative controls. Furthermore, the prediction kit may also include indicators or guidance for detecting the expression level of the gene or its expression product, as well as other instruments necessary for testing.

[0088] In addition to the above-mentioned reagents, the prediction kit may also contain tools and reagents necessary for collecting and preserving biological samples such as SSLs. For example, tools and reagents necessary for collecting and preserving SSLs include oil blotting films for collecting SSLs, reagents for preserving the collected SSLs, and storage containers.

[0089] In addition, in the other embodiments described above, in addition to or instead of at least one gene selected from the group consisting of the above four genes, a gene functioning as a predictive marker as described in the second embodiment and subsequent embodiments can be used. For example, when predicting a subject's weight loss due to increased exercise, in addition to or instead of at least one gene selected from the group consisting of the above four genes, FAM192A described in the second embodiment may be used. Furthermore, in addition to or instead of these genes, at least one gene selected from the group consisting of the above 13 genes described in the third embodiment may be used. Furthermore, when predicting a subject's visceral fat loss due to increased exercise, in addition to or instead of at least one gene selected from the group consisting of the above four genes, at least one gene selected from the group consisting of the above 18 genes described in the fourth embodiment may be used. Furthermore, in addition to or instead of these genes, at least one gene selected from the group consisting of the above 36 genes described in the fifth embodiment may be used. [Example]

[0090] 1) Study participants BMI of 23 kg / m 2 A total of 113 adult men (aged 20 to 60 years) who met the eligibility criteria for the study were selected to participate in the study.

[0091] 2) Collection of lipids on the skin surface Before the exercise intervention study described below, RNA-containing skin surface lipids (SSL) were collected from the entire face of the study participants using oil blotting film (5 cm x 8 cm, polypropylene, 3M). The oil blotting film was transferred to a glass vial and stored at -80°C until use for RNA extraction.

[0092] 3) Pre-intervention testing A physical examination was conducted as a pre-intervention test before the exercise intervention test described below. Note that the examination in this test example was conducted with the consent of the test participants.

[0093] Physical examinations were conducted by inviting participants to a designated venue. Physical examination items included height, weight, body composition (fat mass, lean mass, muscle mass, MQP, estimated bone mass, total body water (TBW), total body water percentage (TBW%), body fat percentage, SMI (appendicular muscular index), trunk body fat percentage, trunk body fat mass, trunk lean mass, trunk muscle mass, etc.), visceral fat area, abdominal circumference, blood pressure, pulse, and body temperature. Height was measured using a standard height measuring device. Weight and body composition were measured using a body composition analyzer ("Professional Multi-Frequency Body Composition Analyzer MC-980A-N plus," manufactured by Tanita Corporation). Visceral fat area and abdominal circumference were measured using a Panasonic EW-FA90 visceral fat analyzer (medical device approval number 22500BZX00522000), which was wrapped around the participants' abdomens. Blood pressure and pulse were measured using an upper arm digital blood pressure monitor ("Automatic Blood Pressure Monitor HEM-104", manufactured by Omron Corporation). Body temperature was measured on the forehead using a non-contact thermometer.

[0094] 4) Implementation of exercise intervention Subsequently, the study participants underwent a two-month exercise intervention. Specifically, a fitness instructor provided each study participant with exercise instruction to increase energy expenditure in order to achieve a target weight (a 3% reduction from their current weight). The fitness instructor provided exercise instruction once every two weeks. Each study participant was instructed to keep a daily exercise log (diary) and to measure and record their daily activity using a provided activity monitor. In addition, each study participant was instructed to install an application program (Asuken®) capable of recording dietary information on their personal information terminal, record their daily diet, and not consciously change their diet. After the two-month exercise intervention, the study participants underwent a post-intervention physical examination similar to the pre-intervention examination.

[0095] 5) Selection of subjects Of the 97 study participants who completed the two-month exercise intervention, 77 participants who were judged to have achieved sufficient exercise (more than 50% of the target exercise amount over the entire two months) and not have made significant changes in their dietary intake were selected as a homogeneous study group for analysis. Daily exercise records (diaries) were used primarily to determine exercise intake, with activity measurement results used as secondary evidence. Changes in dietary intake were determined using food records from the above application program.

[0096] 6) 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 for 2 minutes, (99°C for 15 seconds, 62°C for 16 minutes) x 20 cycles, hold at 4°C. 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.

[0097] 7) Data preprocessing The read counts of each read obtained by sequencing the subject's SSL-derived RNA obtained in 6) above were used as expression level data for each RNA, and the count values ​​corrected using DESeq2 (normalized count values) were used for analysis. However, reads with a read count of less than 1 were treated as missing values. Data from 66 subjects in whom 30% or more of all detected gene species (6031 genes) were detected were used for the following analysis.

[0098] 8) Selection of genes correlated with changes in body weight For the genes for which expression level data that was not missing was obtained for more than 90% of the subjects in the data for the 66 subjects, a correlation analysis with the weight change rate ((weight 2 months after exercise intervention - weight on the day exercise intervention began) / weight on the day exercise intervention began) was performed using Spearman's correlation test based on that expression level data, and genes with p<0.05 and r (correlation coefficient)>0.3 or r<-0.3 were selected as genes correlated with weight change. The 18 selected genes are shown in Table 1. Note that in this example, given that most subjects experienced a negative change in weight as a result of the exercise intervention, the selected genes can be said to be genes correlated with weight loss.

[0099] [Table 1]

[0100] 9) Selection of genes that are highly discriminative between responder and non-responder groups in weight loss Of the 66 subjects whose data were analyzed, those with an absolute value of a negative change rate in body weight of 3% or greater were defined as the responder group, and those with an absolute value of a negative change rate in body weight of less than 3% were defined as the nonresponder group. Using the expression level data for each of the 18 genes selected in 8), a binary classification was performed to distinguish between the responder and nonresponder groups. Based on the results, receiver operating characteristic (ROC) curve analysis was performed to calculate the area under the curve (AUC), Youden's index, and cutoff value (see Table 1). The cutoff value was defined as the expression level value corresponding to the point with the smallest distance from the upper left corner (0, 1) of the ROC curve. As a result, two genes, CCT8 and FAM192A, were selected as genes with an AUC of 0.7 or greater. These genes are highly discriminative between responder and non-responder groups in terms of weight loss, and are thought to be highly useful as predictive markers for predicting weight loss due to increased exercise.

[0101] 10) Selection of genes correlated with changes in visceral fat For the genes for which non-missing expression data was obtained for more than 90% of the subjects in the data for the 66 subjects, a correlation analysis with the change in visceral fat area (visceral fat area 2 months after exercise intervention - visceral fat area on the day the exercise intervention began) was performed using Spearman's correlation test based on the expression data. Genes with p<0.05 and r (correlation coefficient) >0.3 or r<-0.3 were selected as genes correlated with changes in visceral fat. The 58 selected genes are shown in Table 2. In this example, given that most subjects experienced a negative change in visceral fat area due to the exercise intervention, the selected genes can be said to be genes correlated with a decrease in visceral fat.

[0102] [Table 2]

[0103] 11) Selection of genes that are highly discriminative between responder and non-responder groups in visceral fat reduction Of the 66 subjects whose data were analyzed, the absolute value of the negative change in visceral fat area was 20 cm 2 Subjects with the above mentioned condition were classified as the Responder group, and subjects with an absolute negative change in visceral fat area of ​​20cm 2 Subjects with a response rate of less than 1 / 2 were defined as the nonresponder group. Using the expression level data for each of the 58 genes selected in 10), a binary classification was performed to distinguish between the responder and nonresponder groups. Similar to 9), ROC curve analysis was performed based on the results, and the AUC, Youden's index, and cutoff value were calculated (see Table 2). As a result, 19 genes with an AUC of 0.7 or higher were selected: ELOF1, CCT8, KCTD10, CAMSAP1, GTF3C5, TULP4, DUSP11, UBTD1, C18orf21, SNORA50, SNORA7B, BNIP2, SNORA28, EIF4EBP3, STK38L, LYRM1, GPR65, BAX, and SNORA2A. These genes are highly discriminative between responder and non-responder groups in terms of visceral fat reduction, and are thought to be highly useful as predictive markers for predicting visceral fat reduction due to increased exercise.

[0104] 12) Selection of genes correlated with both changes in body weight and visceral fat CCT8, PHF23, C1D, and DEFB4B were selected because they overlap with the 18 genes selected as being correlated with the rate of weight change (see Table 1) and the 58 genes selected as being correlated with the amount of change in visceral fat area (see Table 2). These genes are correlated with both the rate of weight change and the amount of change in visceral fat area due to increased exercise, and are considered to be highly useful as predictive markers for predicting the weight loss effect of increased exercise. Furthermore, of the above four genes, CCT8 was also selected as a gene with high discriminability for both weight loss and visceral fat loss, and is considered to be particularly useful as a predictive marker for predicting the weight loss effect of increased exercise.

Claims

1. A method for predicting the weight loss effect of an increased amount of exercise in a subject, comprising: The method includes an acquisition step of acquiring, as the genetic information of the subject, the expression level of at least one gene selected from the group consisting of four genes: CCT8, PHF23, C1D, and DEFB4B, or an expression product thereof; A method for predicting weight loss outcomes.

2. The prediction of the weight loss effect includes at least one of a prediction of a weight loss or a prediction of a visceral fat loss. The method of claim 1.

3. the prediction of the weight loss effect includes a prediction of a weight loss; In the obtaining step, the expression level of FAM192A or its expression product is further obtained as the genetic information. The method of claim 2.

4. In the obtaining step, the gene information further includes obtaining an expression level of at least one gene selected from the group consisting of 13 genes: C9orf89, MT2A, P4HA1, HMGB2, LY6E, SNORA70B, ATP2B4, RABIF, GMPS, CRCP, GUSBP3, IRGQ, and ABL1, or an expression product thereof. The method of claim 3.

5. the prediction of the weight loss effect includes a prediction of a reduction in visceral fat mass, In the obtaining step, the gene information further includes obtaining an expression level of at least one gene or its expression product selected from the group consisting of 18 genes: ELOF1, KCTD10, CAMSAP1, GTF3C5, TULP4, DUSP11, UBTD1, C18orf21, SNORA50, SNORA7B, BNIP2, SNORA28, EIF4EBP3, STK38L, LYRM1, GPR65, BAX, and SNORA2A. The method of claim 2.

6. In the obtaining step, the genetic information may further include MRPS15, CCDC85B, ZNF880, HMGXB3, FAM210B, PLSCR3, TUBB6, DUSP8, C2orf47, HBS1L, KCTD12, DCAF10, HIST1H4C, EEF1B2, CENPB, ITPRIPL2, XPOT, ZNF277, NECAP1, PLAGL 2. Obtaining the expression level of at least one gene or its expression product selected from the group consisting of 36 genes: AGTPBP1, PAG1, RAD21, HIST2H3D, FCHSD2, CCRL2, ATP11B, AZIN1, HLA-DPA1, IL18RAP, CLEC16A, MAEA, GPATCH8, SCARF1, GUSB, and SNORA34; The method of claim 5.

7. The expression level of the gene or its expression product is the expression level of mRNA.

3. The method according to claim 1 or 2.

8. the genetic information is derived from a biological sample non-invasively collected from the subject; 3. The method according to claim 1 or 2.

9. The biological sample is lipids on the skin surface of the subject. The method of claim 8.

10. a prediction step of predicting a weight loss effect of the subject due to an increase in the amount of exercise based on the acquired expression level.

3. The method according to claim 1 or 2.

11. A method for predicting a weight loss effect of an increase in exercise volume in a subject, comprising: a prediction step of predicting the weight loss effect of the subject due to increased exercise volume based on the expression level of at least one gene selected from the group consisting of four genes, CCT8, PHF23, C1D, and DEFB4B, or its expression product, obtained as genetic information of the subject; How to predict weight loss results.

12. A prediction device for predicting a weight loss effect of an increase in exercise volume in a subject, A control unit that predicts the weight loss effect of the subject due to increased exercise volume based on the expression level of at least one gene selected from the group consisting of four genes, CCT8, PHF23, C1D, and DEFB4B, or its expression product, obtained as genetic information of the subject. A prediction device comprising:

13. A predictive marker for predicting the weight loss effect of an increased amount of exercise in a subject, The predictive marker is At least one gene selected from the group consisting of four genes: CCT8, PHF23, C1D, and DEFB4B, or its expression product; Predictive markers.

14. A prediction kit for predicting a weight loss effect of an increased amount of exercise in a subject, comprising: A reagent for detecting at least one gene selected from the group consisting of four genes, CCT8, PHF23, C1D, and DEFB4B, or its expression product, from a biological sample collected from the subject. Predictive kit.

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