Method of predicting weight loss

Abstract

To provide a technique that enables easy prediction of weight loss of a subject through dietary management.SOLUTION: A method of the present invention for quantitatively predicting weight loss of a subject through dietary management comprises an acquisition step of acquiring at least one indicator selected from a group consisting of: an indicator computed from physical indicators consisting of the height and pulse rate, and answer indicators consisting of answers to questions about awareness and / or behavior regarding health-consciousness; an indicator regarding recognition of the physical constitution and weight computed from answers to an eating behavior questionnaire; an indicator computed from answers to questions regarding mindset about dieting; and an indicator computed from answers to questions regarding motivation for dietary management.SELECTED DRAWING: None

Description

[Technical Field]

[0001] The present invention relates to a technique for predicting the weight loss effect of a subject through dietary management. [Background technology]

[0002] Methods to enhance the effectiveness of weight loss are being sought for the purposes of health promotion, obesity reduction, beauty, etc. For example, while dietary management is known to be effective in weight loss, it is also known that the effectiveness of such measures varies from person to person. Therefore, factors involved in the weight loss effects of dietary management, etc. are being investigated.

[0003] For example, Patent Document 1 describes a method for predicting the degree of weight loss that can be achieved by applying one or more dietary interventions to a subject, which predicts body mass index (BMI2) by substituting measured values ​​of fructosamine, factor VII, adiponectin, and fasting insulin into a predetermined formula. Patent Document 2 also describes a method for predicting the degree of weight loss that can be achieved by applying one or more dietary interventions to a subject, which predicts body mass index (BMI2) by substituting 21 biomarkers including vitamin K-dependent protein C into a predetermined formula.

[0004] For example, Non-Patent Document 1 describes a study of individual predisposing characteristics for weight loss through dietary therapy and a model for predicting weight loss. The document also describes that a prediction model was created using a data set consisting of age, sex, BMI, blood CRP, IL-6, HbA1c, HOMA-IR, and zonulin, and the prediction accuracy was verified. [Prior art documents] [Patent documents]

[0005] [Patent Document 1] Special Publication No. 2017-525953 [Patent Document 2] Special Publication No. 2019-516095 [Non-patent literature]

[0006] [Non-Patent Document 1] Data integration for prediction of weight loss in randomized controlled dietary trials. Nielsen RL, et al. Sci Rep. 2020 Nov 18;10(1): 20103. Summary of the Invention [Problem to be solved by the invention]

[0007] The prediction models described in the above-mentioned documents require highly invasive blood samples and are not very convenient for both the subject and the person conducting the prediction. Therefore, a simpler method is needed to predict the weight loss effect of dietary management in a subject.

[0008] The present invention relates to a technique for simply predicting the weight loss effect of a subject through dietary management. [Means for solving the problem]

[0009] A method for predicting a weight loss effect according to one embodiment of the present invention is a method for predicting a weight loss effect of dietary management in a subject, comprising: The method includes at least one step selected from the group consisting of steps (A), (B), and (C). Step (A) is a step of measuring at least one physical index representing at least one selected from the body size, body composition, and physical function of the subject, and is selected from the group consisting of pulse rate, height, and MQP (Muscle Quality Point). The step (B) is a step of measuring at least one blood indicator selected from the group consisting of albumin concentration, potassium concentration, uric acid concentration, chloride concentration, white blood cell count, glutamine concentration, valine concentration, leucine concentration, isoleucine concentration, 3-methylhistidine concentration, red blood cell count, calcium concentration, free testosterone concentration, and tryptophan concentration from a blood sample of the subject. Step (C) is a step of calculating at least one response index calculated from the subject's responses to the questionnaire, selected from the group consisting of an index calculated from responses to questions asking about awareness and / or behavior regarding irregular mealtimes, an index calculated from responses to an eating behavior self-efficacy scale, an index calculated from responses to a rest behavior self-efficacy scale, an index of eating habits regularity calculated from responses to an eating behavior questionnaire, an index of eating motivation calculated from responses to the eating behavior questionnaire, and an index calculated from responses to questions asking about motivation for dietary management.

[0010] A method for predicting a weight loss effect according to another aspect of the present invention is a method for predicting a weight loss effect of dietary management in a subject, comprising: a physical index representing at least one selected from the group consisting of the subject's physique, body composition, and physical function; a blood index measured from a blood sample of the subject; and a response index calculated from the subject's responses to the questionnaire; The method includes a step of determining whether at least one index selected from satisfies a condition set for the index and corresponding to the level of weight loss effect. The physical index includes at least one index selected from the group consisting of pulse rate, height, and MQP (Muscle Quality Point). The blood indicators include at least one indicator selected from the group consisting of albumin concentration, potassium concentration, uric acid concentration, chloride concentration, glutamine concentration, valine concentration, leucine concentration, isoleucine concentration, 3-methylhistidine concentration, red blood cell count, calcium concentration, free testosterone concentration, and tryptophan concentration. The response index includes at least one index selected from the group consisting of an index calculated from responses to questions asking about awareness and / or behavior regarding irregular mealtimes, an index calculated from responses to an eating behavior self-efficacy scale, an index calculated from responses to a rest behavior self-efficacy scale, an index of regularity of eating habits calculated from responses to an eating behavior questionnaire, an index of eating motivation calculated from responses to the eating behavior questionnaire, and an index calculated from responses to questions asking about motivation for dietary management.

[0011] A method for quantitatively predicting a weight loss effect of a subject through dietary management according to another aspect of the present invention includes: Height and pulse as physical indicators representing at least one of the subject's physique or body composition, and Response indices calculated from the subject's responses to the questionnaire include an index calculated from the responses to questions asking about health-consciousness and / or behavior, an index about awareness of physical constitution and weight calculated from the responses to the eating behavior questionnaire, an index calculated from the responses to questions asking about awareness of dieting, and an index calculated from the responses to questions asking about motivation for dietary management. The method includes an acquisition step of acquiring at least one index selected from the group consisting of:

[0012] A method for quantitatively predicting a weight loss effect of a subject through dietary management according to another aspect of the present invention includes: Height and pulse as physical indicators representing at least one of the subject's physique or body composition, and Response indices calculated from the subject's responses to the questionnaire include an index calculated from the responses to questions asking about awareness and / or behavior regarding health orientation, an index about awareness regarding physical constitution and weight calculated from the responses to the eating behavior questionnaire, an index calculated from the responses to questions asking about awareness of dieting, and an index calculated from the responses to questions asking about motivation for dietary management. and a prediction step of quantitatively predicting the weight loss effect of the subject by applying at least one index selected from the group consisting of: to a prediction model for deriving a predicted value indicating the weight loss effect from the acquired index. [Effects of the Invention]

[0013] According to the present invention, it is possible to simply predict the weight loss effect of dietary management on a subject. [Brief explanation of the drawings]

[0014] [Figure 1] FIG. 1 shows a dietary and lifestyle questionnaire relating to obesity (35-question dietary questionnaire) used in an example of the present invention. [Figure 2] FIG. 1 is a diagram showing the eating behavior self-efficacy scale used in an example of the present invention. [Figure 3] FIG. 1 is a diagram showing the rest behavior self-efficacy scale and the exercise behavior self-efficacy scale used in an example of the present invention. [Figure 4] FIG. 1 is a diagram showing questions included in an eating behavior questionnaire used in an example of the present invention. [Figure 5] FIG. 1 shows the Chalder Fatigue Scale questionnaire used in the examples of the present invention. [Figure 6] FIG. 1 is a diagram showing the first half of a questionnaire for the Big Five scale used in an embodiment of the present invention. [Figure 7] FIG. 1 is a diagram showing the second half of the Big Five scale questionnaire used in an embodiment of the present invention. [Figure 8] FIG. 1 is a diagram showing a simple occupational stress questionnaire used in an example of the present invention. [Figure 9] FIG. 1 is a diagram showing a simple occupational stress questionnaire used in an example of the present invention. [Figure 10] FIG. 1 shows a questionnaire for assessing the stages of health behavior change used in an example of the present invention. [Figure 11]FIG. 1 is a diagram showing the Athens Insomnia Scale used in an example of the present invention. [Figure 12] FIG. 1 is a diagram showing the WHO-5 (Mental Health Status Questionnaire) used in an example of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0015] 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.

[0016] [Summary of the Invention] The present invention relates to a method for predicting the weight loss effect of dietary management in a subject, and a determination method for predicting the weight loss effect. The present invention makes it possible to predict and / or assist in the prediction of the weight loss effect of dietary management in a subject. An advantage of the present invention is that it allows for the easy preparation of predictive markers and the prediction of weight loss effect. Another advantage of the present invention is that it makes it possible to predict the weight loss effect of dietary management alone, thereby providing information for determining whether dietary management is an effective means of weight loss for each subject.

[0017] In 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.

[0018] 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.).

[0019] In the present invention, the term "fat mass" such as "visceral fat mass" refers to a numerical value that quantitatively represents the amount of fat in the body of a subject, and includes, for example, the mass of fat (at a specific site or the whole body), the volume of fat (at a specific site or the whole body), the cross-sectional area of ​​fat at a specific site, etc. For example, "visceral fat mass" is an expression that includes the mass of visceral fat, the visceral fat area which is the cross-sectional area of ​​the abdomen, and the volume of visceral fat.

[0020] In the present invention, "dietary management" refers to managing dietary content by adjusting energy intake and / or nutritional balance to achieve a weight loss effect. Specifically, dietary management preferably includes a reduction in energy intake and may further include at least one selected from the group consisting of a reduction in carbohydrate intake, a reduction in lipid intake, an increase in protein intake, an increase in B vitamin intake, an increase in dietary fiber intake, and the intake of other nutrients known to have a weight loss effect. Furthermore, the reduction in energy intake is preferably a reduction in daily energy intake. The daily energy intake in dietary management can be set based on the subject's gender, age, physique, health condition, basal metabolic rate, physical activity level, etc., and can be set, for example, with reference to the estimated energy requirements by gender and age set forth in the "Dietary Reference Intakes for Japanese (2020 Edition)" published by the Ministry of Health, Labor, and Welfare. The reduction in energy intake in dietary management can be, for example, a reduction in energy intake aimed at achieving a target weight (a 1-10% reduction, preferably a 1-5% reduction, more preferably a 3% reduction from current weight).

[0021] In the present invention, "dietary management" is preferably continuous dietary management. The period of continuous dietary management is preferably one week or more, more preferably ten days or more, and even more preferably one month or more. Furthermore, the frequency of consciously adjusted dietary intake is preferably one or more times a day, and more preferably every meal.

[0022] In the present invention, the "weight loss effect of dietary management" refers to an effect evaluated by weight loss (body weight loss), changes in physique associated with weight loss, or changes in body composition associated with weight loss before and after dietary management. In the present invention, indicators for evaluating the weight loss effect (hereinafter referred to as "weight loss evaluation indicators") include, for example, body weight, visceral fat mass, subcutaneous fat mass, body fat mass, body fat percentage, waist circumference, abdominal circumference (trunk muscle mass, etc.), muscle mass, BMI, body mass index (Rohrer index, Kaup index, etc.), obesity index calculated from the body mass index, triceps subcutaneous fat thickness, and subscapular subcutaneous fat thickness. For example, the "weight loss effect of dietary management" can be evaluated by the amount and / or rate of change in a weight loss evaluation indicator before and after dietary management. In this specification, the "amount of change in a weight loss evaluation indicator" refers to the difference between before and after dietary management, and the "rate of change" refers to the ratio of the difference to the value before dietary management.

[0023] In the present invention, "prediction of weight loss effect" includes at least one of prediction of overall weight loss effect or prediction of changes in a specific weight loss evaluation index. Prediction of overall weight loss effect refers to a qualitative or quantitative prediction of weight loss effect without referring to a specific weight loss evaluation index. Qualitative prediction of overall weight loss effect is expressed, for example, by expressions such as "high / low weight loss effect" or "easy / difficult to lose weight." Note that qualitative weight loss effect is not limited to an example expressed in two stages, but may be expressed in three or more stages. When weight loss effect is expressed in three or more stages, it may be expressed by letters, words, symbols, etc., such as numbers (1, 2, 3, etc.) or alphabets (A, B, C, etc.). Quantitative prediction of overall weight loss effect includes an example of expressing weight loss effect numerically.

[0024] The prediction of the change in the specific weight loss evaluation index may be a qualitative and / or quantitative prediction of the change in the specific weight loss evaluation index. The specific weight loss evaluation index may be one or more. As described below, the weight loss evaluation index used in the present invention preferably includes at least one of body weight and visceral fat mass.

[0025] Qualitative predictions of changes in weight loss evaluation indexes include, for example, predictions of how easily the weight loss evaluation index will change (e.g., "it is easy / difficult to lose weight"), predictions of the magnitude of changes in the weight loss evaluation index (e.g., "the amount of weight loss is large / small"), etc. Note that changes in qualitative weight loss evaluation indexes are not limited to examples expressed in two stages, but may be expressed in three or more stages. Quantitative predictions of changes in weight loss evaluation indexes include, for example, predictions of the amount of change, rate of change, and ranges thereof of the weight loss evaluation index.

[0026] In the present invention, the prediction of the weight loss effect preferably includes at least one of a prediction of body weight loss or a prediction of visceral fat mass reduction. In one embodiment of the present invention, the prediction of body weight loss may be a qualitative or quantitative prediction of body weight loss. Qualitative predictions of body weight loss include predictions of the ease of weight loss (e.g., "easy / difficult to lose weight"), the amount and / or rate of weight loss (e.g., "large / small amount of weight loss"), etc. Quantitative predictions of weight loss include predictions of at least one selected from the amount of weight loss, the rate of weight loss, and ranges thereof. Similarly, in one embodiment of the present invention, the prediction of visceral fat mass reduction may be a qualitative or quantitative prediction of visceral fat mass reduction. Qualitative predictions of visceral fat mass reduction include predictions of the ease of visceral fat mass reduction (e.g., "easy / difficult to lose weight"), the amount and / or rate of visceral fat mass reduction (e.g., "large / small amount of visceral fat mass reduction"), etc. The quantitative prediction of the reduction in visceral fat mass includes at least one prediction selected from the amount of visceral fat reduction, the reduction rate, and the range thereof.

[0027] In this specification, the "amount of reduction" of body weight and visceral fat refers to the difference obtained by subtracting the value after dietary management from the value before dietary management, and the "rate of reduction" refers to the ratio of the above difference to the value before dietary management.

[0028] In the present invention, the "prediction" of a weight loss effect can also be expressed by 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.

[0029] [Methods for predicting weight loss effects] A method for predicting a weight loss effect according to one embodiment of the present invention includes at least one step selected from the group consisting of steps (A), (B), and (C) below. The method of the present invention may include one step selected from steps (A), (B), and (C), or may include multiple steps selected from these. When the method includes multiple steps, the order in which the steps are performed is not particularly limited.

[0030] The indices listed in steps (A), (B), and (C) of the present invention are collectively referred to as "predictive markers" for predicting weight loss effects. Predictive markers are indices used to predict weight loss effects, and specifically include at least one indice selected from the group consisting of physical indices, blood indices, and response indices described below. As shown in the Examples described below, the predictive markers of the present invention are indices selected based on the results of physical examinations, blood tests, and questionnaire tests conducted before and after a dietary management intervention for a group of subjects. More specifically, the predictive markers are indices that showed significant differences between a responder group and a non-responder group selected from the group of subjects in Test Example 1 described below based on the presence or absence of a weight loss effect. The gender of the subjects to be predicted by the predictive markers obtained by steps (A), (B), and (C) is not particularly limited, but males are preferred from the viewpoint of improving prediction accuracy.

[0031] Step (A) is a step of measuring at least one physical index representing at least one selected from the group consisting of pulse rate, height, and MQP (Muscle Quality Point), which is a physical index representing at least one selected from the body size, body composition, and physical function of the subject. Of these physical indexes, height shows a positive correlation with weight loss effect, and MQP shows a negative correlation with weight loss effect.

[0032] In the present invention, the physical indices may be measurements obtained by general physical measurements. Furthermore, indices representing physique, such as height, may be measurements obtained by analyzing image information of the subject's body. Measurement of each physical indices is performed by a person performing the measurement in order to predict the weight loss effect, but all or part of the measurement may be outsourced to a testing institution, research institution, medical institution, or the like. When measuring multiple physical indices, the timing of these measurements may be different.

[0033] Among physical indices, pulse is the number of pulse beats per minute (for example, unit: / min) and can be measured by touch or with a pulse meter. Height can be measured using a standard height measuring device, and units such as "cm" or "m" can be used.

[0034] MQP is an index of muscle quality obtained by applying alternating current at two frequencies, high frequency (e.g., 250 kHz) and low frequency (e.g., 5 kHz), to a subject's body using bioelectrical impedance analysis. Specifically, MQP can be an index corresponding to the ratio of muscle fiber to interstitial tissue in muscle tissue. MQP can be calculated using the "Muscle Quality Score (registered trademark)" (1 to 100) measured using a body composition analyzer manufactured by Tanita Corporation (e.g., the "Innerscan Dual" series, "Professional Multi-Frequency Body Composition Analyzer MC-980A-N plus," etc.). MQP can be calculated, for example, as the "muscle quality score" described in JP 2015-29832 A. The muscle quality score according to this document is calculated by acquiring at least one of a first parameter expressed as the ratio between the resistance component and the reactance component of bioelectrical impedance, and a second parameter expressed as the ratio between the first impedance measured by supplying a low-frequency alternating current and a second impedance measured by supplying a predetermined high-frequency alternating current to a living organism, and a physique parameter, multiplying the statistically calculated contribution rate of each parameter to muscle quality by the corresponding parameter, and adding up all the items (see paragraphs

[0075] to

[0086] of the above document, etc.).

[0035] Step (B) is a step of measuring at least one blood index selected from the group consisting of albumin concentration, potassium concentration, uric acid concentration, chloride concentration, white blood cell count, glutamine concentration, valine concentration, leucine concentration, isoleucine concentration, 3-methylhistidine level, red blood cell count, calcium concentration, free testosterone concentration, and tryptophan concentration from a blood sample of the subject. Among these blood indexes, chloride concentration and valine concentration show a positive correlation with weight loss effects. Albumin concentration, potassium concentration, uric acid concentration, white blood cell count, glutamine concentration, leucine concentration, isoleucine concentration, 3-methylhistidine level, red blood cell count, calcium concentration, free testosterone concentration, and tryptophan concentration show a negative correlation with weight loss effects.

[0036] In the present invention, the blood sample for measuring blood indicators is selected from whole blood, serum, and plasma, and an appropriate sample can be used depending on the indicator. Each blood indicator is measured by a person who measures it to predict the weight loss effect, but in this case, all or part of the measurement process may be outsourced to a testing institution, research institution, medical institution, etc. Note that when multiple blood indicators are measured, the timing of these measurements may be different.

[0037] The white blood cell count and red blood cell count are indicators related to blood cell components in blood, and can be measured, for example, by a blood cell count test using the whole blood of a subject. The white blood cell count can be calculated, for example, by multiplying the white blood cell count by 10. 2 The red blood cell count is expressed in units such as " / μL" or " / μL". 4 It is expressed in units such as " / μL" or " / μL".

[0038] Albumin concentration (e.g., g / dL) is a protein test item and can be measured using established methods such as the modified BCP method using serum samples. Potassium concentration (e.g., mEq / L), chloride concentration (e.g., mEq / L), and calcium concentration (e.g., mEq / L) are electrolyte test items and can be measured using established methods such as the electrode method using serum samples. Calcium concentration (e.g., mg / dL) is an electrolyte test item and can be measured using established methods such as the Arsenazo III method using serum samples. Uric acid concentration (e.g., mg / dL) can be measured using established methods such as the uricase POD method using serum samples. Glutamine concentration (e.g., unit: nmol / mL), valine concentration (e.g., unit: nmol / mL), leucine concentration (e.g., unit: nmol / mL), isoleucine concentration (e.g., unit: nmol / mL), and tryptophan concentration (e.g., unit: nmol / mL) are values ​​measured in amino acid analysis of plasma samples and can be measured, for example, by HPLC. 3-Methylhistidine concentration (e.g., unit: nmol / mL) is a value used as an indicator of muscle protein catabolism and can be measured, for example, by HPLC. Free testosterone concentration (e.g., unit: pg / mL) is one of the items in endocrinological tests and can be measured, for example, by established methods such as solid-phase radioimmunoassay (RIA) using serum samples. Note that the units of each indicator described above are merely examples and are not limiting.

[0039] Step (C) is a step of calculating at least one response index calculated from the subject's responses to the questionnaire, which is selected from the group consisting of an index calculated from responses to questions asking about awareness and / or behavior regarding irregular mealtimes, an index calculated from responses to an eating behavior self-efficacy scale, an index calculated from responses to a rest behavior self-efficacy scale, an index of eating habits regularity calculated from responses to an eating behavior questionnaire, an index of eating motivation calculated from responses to an eating behavior questionnaire, and an index calculated from responses to questions asking about motivation for dietary management. Among these response indexes, the index calculated from responses to questions asking about awareness and / or behavior regarding irregular mealtimes, the index calculated from responses to an eating behavior self-efficacy scale, and the index calculated from responses to questions about motivation for dietary management show a positive correlation with weight loss effect. The index calculated from the responses to the rest behavior self-efficacy scale, the index of regularity of eating habits calculated from the responses to the eating behavior questionnaire, and the index of eating motivation calculated from the responses to the eating behavior questionnaire show a negative correlation with the weight loss effect.

[0040] In the present invention, the questionnaire for obtaining the response indicators may be provided in paper form or electronic form. The response format is not particularly limited, and examples thereof include handwritten responses, responses input using a computer input device, and oral responses in interviews.

[0041] The calculation of the response index may be performed using a calculation device such as a calculator or computer as a calculation means, as necessary. The calculation of each response index is performed by a person who performs the calculation in order to predict the weight loss effect, but in this case, all or part of the calculation process may be outsourced to a testing institution, research institution, medical institution, etc. Note that when multiple response indexes are calculated, the calculations may be performed at different times.

[0042] The questions inquiring about awareness and / or behavior regarding irregular mealtimes preferably include at least one question selected from the group consisting of a question about late-night snacks after dinner, a question about irregular mealtimes, a question about the interval between dinner and bedtime, and a question about lifestyle rhythms (night owl or morning person), and more preferably form a group of questions including two or more questions. Specific examples of such questions include Question 13 "I often eat late-night snacks after dinner," Question 17 "My meal times are irregular," Question 19 "I eat late-night snacks," Question 23 "I often have late dinners because of work," Question 25 "I finish dinner more than two hours before going to bed," and Question 30 "I'm a night owl and have trouble catching up in the mornings" in the dietary and lifestyle questionnaire related to obesity (hereinafter also referred to as the "35-question dietary questionnaire") illustrated in FIG. 1. The index calculated from the responses to questions inquiring about perceptions and behaviors regarding mealtime irregularity can be a numerical value selected from, for example, the average score, total score, and score percentage, which is the ratio of the total score to the highest score, corresponding to the answers to the six questions. The questionnaire shown in Figure 1 was disclosed in "Objective Creation of a Dietary Habits Questionnaire and the Relationship Between Evaluation in Occupational Surveys and Visceral Fat Accumulation" (Yoshiko Yanagisawa et al., Ningen Dock 32:611-617, 2017) (hereinafter referred to as "Yanagisawa et al., 2017"). Specifically, the questionnaire was created by comprehensively collecting a wide range of information on dieting, creating questions, conducting a nationwide online survey, and using statistical methods to create the questionnaire from the response data. Furthermore, the six questions correspond to the third factor (mealtime) of five factors (diet, health awareness, mealtime, dietary restrictions, and exercise) derived from factor analysis (maximum likelihood method, oblique rotation) of all 35 questions, and can be used as a measure of mealtime irregularity.

[0043] The self-efficacy scale for eating behavior and the self-efficacy scale for resting behavior were the same as those described in "Study of Self-Efficacy Scales and Social Support Scales Useful for Supporting Health Behavioral Change" (Kiyoshi Moriya and Mari Shimizu, Bulletin of Tenshi University, Vol. 9, June 30, 2009) (hereinafter referred to as "Moritani et al., 2009"). Figure 2 shows the self-efficacy scale for eating behavior taken from the same reference, and Figure 3 shows the self-efficacy scale for resting behavior and the self-efficacy scale for exercise behavior taken from the same reference. For example, the index calculated from the responses to these scales can be calculated by referring to the reference and calculating the sum of the scores for each scale selected from a range from "I think I can do it well" (-3 points) to "I think I can't do it at all" (+3 points). Note that these indices may be total scores or percentage scores, which are the ratio of the total score to the average or highest score.

[0044] The Eating Behavior Questionnaire, cited in the "Obesity Treatment Guidelines 2006" (Japan Society for the Study of Obesity), is a questionnaire provided by Sakata et al. (Reference: Hironobu Yoshimatsu (1996) Initial Operation "Obesity Treatment Manual," edited by Toshiie Sakata, Ishiyaku Shuppan, Tokyo, pp. 17-38). The questions in this questionnaire are categorized into seven areas (perceptions of constitution and weight, eating motivation, vicarious eating, hunger, satiety, eating habits, meal content, and regularity of eating habits), as illustrated in Figure 4. The indices of regularity of eating habits and eating motivation calculated from this questionnaire can be numerical values ​​selected from the total score, average score, and percentage score (the ratio of the total score to the highest score) of the questions in each category. For example, the response options for each question include four levels: "Not at all," "Sometimes this happens," "Tends to be like that," and "Exactly true," and each response option can be assigned a predetermined score (e.g., 0 to 3 points).

[0045] The question asking about motivation for dietary management, for example, asks about the subject's motivation (motivation) for dietary management before implementing dietary management. For example, the answer options for motivation are expressed on an 11-point scale from 0 to 10 (very motivated to not motivated at all). The index calculated from the question can be the score corresponding to the answer.

[0046] Furthermore, the prediction method according to one embodiment of the present invention may further include step (D) after steps (A), (B), and / or (C). Step (D) is a step of determining whether at least one index selected from the group consisting of a physical index, a blood index, and a response index satisfies a condition corresponding to the level of weight loss effect set for that index. In step (D), the weight loss effect may be predicted from one predictive marker, or the weight loss effect may be predicted from multiple predictive markers. Note that the determination of whether the condition in this step is satisfied may be performed using a computer or the like, as necessary.

[0047] In step (D), the "level of weight loss effect" is a level that qualitatively or quantitatively represents the weight loss effect, and is expressed, for example, as an indication (words, numbers, letters, symbols, etc.) that indicates the degree of the weight loss effect in stages, a numerical value that indicates the degree of the weight loss effect, etc. Specific examples of weight loss effect levels include an indication that qualitatively represents the overall weight loss effect (e.g., "high / low weight loss effect," "easy / difficult to lose weight," etc.), a numerical value that quantitatively represents the overall weight loss effect, an indication that qualitatively represents the degree of change in a specific weight loss evaluation index (e.g., "large / small amount of weight loss," etc.), a numerical value that indicates the amount and / or rate of change in a specific weight loss evaluation index, etc.

[0048] Examples of "conditions corresponding to the level of weight loss effect" include the range of values ​​that can be taken by each index set corresponding to the level of weight loss effect, or the numerical value and range of a value calculated by substituting the numerical value of an index into a predetermined mathematical formula. The predetermined mathematical formula may be a mathematical formula that can calculate a numerical value indicating the level of weight loss effect from the numerical values ​​of one or more indexes.

[0049] When the condition is a range of values ​​that each index can take, step (D) may include comparing at least one index selected from the physical index, blood index, and response index with a reference value of the index set corresponding to the level of weight loss effect. The reference value in step (D) is a threshold value set for each index (predictive marker) for determining the level of weight loss effect. The reference value can be set, for example, by classifying a group of subjects who have undergone dietary management intervention according to the level of weight loss effect, and using statistical values ​​(such as mean, median, quartile, 95% confidence interval) of the predictive marker calculated for each group.

[0050] Specifically, for an index in which a predictive marker has a positive correlation with weight loss effect, if the value of the predictive marker is greater than or equal to the reference value, it can be determined that the level of weight loss effect is relatively high. On the other hand, for an index in which a predictive marker has a negative correlation with weight loss effect, if the value of the predictive marker is less than or equal to the reference value, it can be determined that the level of weight loss effect is relatively high.

[0051] For example, if the weight loss effect is expressed in two levels, the level of the weight loss effect can be determined by comparing the value of the predictive marker with one reference value. If the weight loss effect is expressed in three or more levels, the level of the weight loss effect can be determined by comparing the value of the predictive marker with multiple reference values.

[0052] In the above example, when a determination is made for multiple predictive markers, the weight loss effect level is determined for each indicator, and the overall weight loss effect level can be determined based on these multiple prediction results. As a method for determining the overall weight loss effect level, for example, the weight loss effect level predicted for the most indicators may be used as the predicted weight loss effect result. As another example, the multiple weight loss effect levels determined for multiple predictive markers may each be converted into numerical values ​​to calculate a representative value (such as the average, median, or mode), and the weight loss effect level corresponding to this representative value may be used as the predicted overall weight loss effect result.

[0053] As another example of predicting the weight loss effect, a computer may be used to predict the weight loss effect using a prediction model for predicting the weight loss effect from the predictive markers. In this case, the weight loss effect may be either a qualitative weight loss effect or a predicted value indicating the weight loss effect. Prediction using a prediction model is suitable for predicting the predicted value of a weight loss evaluation index or for predicting the weight loss effect from multiple predictive markers.

[0054] Hereinafter, specific examples of steps (A), (B), and (C) when the weight loss effect prediction is a prediction of a weight loss or a prediction of a visceral fat mass will be described. Note that the present invention is not limited to the following embodiments.

[0055] [Example of a method for predicting weight loss] (Embodiment 1) In the method according to the first embodiment of the present invention, the prediction of the weight loss effect is a prediction of body weight loss, and includes at least one step selected from the group consisting of the following steps (A1), (B1), and (C1): The predictive marker in this embodiment is an index that showed a significant difference between a responder group and a non-responder group, selected based on the weight loss rate from a group of subjects who underwent a dietary management instruction program in Test Example 1 of the Examples described below.

[0056] Step (A1) is a step of measuring at least one physical index selected from the group consisting of pulse rate and height. Among these physical indexes, height shows a positive correlation with the amount and / or rate of weight loss, and pulse rate shows a negative correlation with the amount and / or rate of weight loss.

[0057] Step (B1) is a step of measuring at least one blood index selected from the group consisting of albumin concentration, potassium concentration, uric acid concentration, chloride concentration, white blood cell count, glutamine concentration, valine concentration, leucine concentration, isoleucine concentration, and 3-methylhistidine concentration. Among these blood indexes, chloride concentration and valine concentration show a positive correlation with the amount and / or rate of weight loss. Albumin concentration, potassium concentration, uric acid concentration, white blood cell count, glutamine concentration, leucine concentration, isoleucine concentration, and 3-methylhistidine concentration show a negative correlation with the amount and / or rate of weight loss.

[0058] Step (C1) is a step of calculating at least one response index selected from the group consisting of an index calculated from responses to questions asking about awareness and / or behavior regarding mealtime irregularity, an index calculated from responses to an eating behavior self-efficacy scale, an index calculated from responses to a rest behavior self-efficacy scale, an index of eating habits regularity calculated from responses to an eating behavior questionnaire, and an index of eating motivation calculated from responses to an eating behavior questionnaire. Among these response indexes, the index calculated from responses to questions asking about awareness and / or behavior regarding mealtime irregularity and the index calculated from responses to the eating behavior self-efficacy scale show a positive correlation with the amount and / or rate of weight loss. The index calculated from responses to the rest behavior self-efficacy scale, the index of eating habits regularity calculated from responses to an eating behavior questionnaire, and the index of eating motivation calculated from responses to an eating behavior questionnaire show a negative correlation with the amount and / or rate of weight loss.

[0059] (Embodiment 1-1) As a specific example of the above-mentioned embodiment 1, the method according to embodiment 1-1 preferably includes at least one step selected from the group consisting of the following steps (A11) and (B11). The predictive marker in this embodiment is an index that showed a significant difference between a responder group and a non-responder group selected based on the weight loss rate from all of the subjects who completed a dietary management instruction program in Test Example 1 of the Examples described below. The predictive marker according to this embodiment can be used to predict weight loss due to dietary management for a variety of subjects.

[0060] Step (A11) is a step of measuring a physical index consisting of pulse rate.

[0061] Step (B11) is a step of measuring at least one blood index selected from the group consisting of albumin concentration, potassium concentration, uric acid concentration, and chloride concentration.

[0062] Step (C11) is a step of calculating at least one response index selected from the group consisting of an index calculated from answers to questions asking about awareness and / or behavior regarding irregular mealtimes, an index calculated from answers to an eating behavior self-efficacy scale, and an index calculated from answers to a rest behavior self-efficacy scale.

[0063] In this embodiment, the above-mentioned predictive markers preferably include at least one selected from pulse, potassium concentration, albumin concentration, uric acid concentration, and chloride concentration, from the viewpoint that they can be obtained by a general test, and more preferably include at least one selected from pulse and potassium concentration.

[0064] When qualitatively predicting weight loss using each predictive marker in this embodiment, examples of reference values ​​for each predictive marker for predicting a responder, i.e., "large amount and / or rate of weight loss," are shown below. Note that each reference value is the numerical value of each predictive marker measured or calculated by the method shown in the Examples.

[0065] The pulse rate is preferably 78.5 / min or less, more preferably 73.5 / min or less, and even more preferably 68.5 / min or less.

[0066] The albumin concentration is preferably 4.9 g / dL or less, more preferably 4.8 g / dL or less, and even more preferably 4.6 g / dL or less. The potassium concentration is preferably 4.2 mEq / L or less, more preferably 4.1 mEq / L or less, and even more preferably 4.0 mEq / L or less. The uric acid concentration is preferably 6.9 mg / dL or less, more preferably 6.3 mg / dL or less, and even more preferably 5.6 mg / dL or less. The chloride concentration is preferably 102.1 mEq / L or more, more preferably 103.0 mEq / L or more, and even more preferably 103.8 mEq / L or more.

[0067] The index calculated from the answers to questions asking about awareness and / or behavior regarding irregular mealtimes is preferably 3.10 or higher, more preferably 3.45 or higher, and even more preferably 3.80 or higher. The index calculated from the answers to the eating behavior self-efficacy scale is preferably 0.5 or higher, more preferably 13.9 or higher, and even more preferably 27.2 or higher. The index calculated from the answers to the rest behavior self-efficacy scale is preferably 4.9 or lower, more preferably 4.1 or lower, and even more preferably 3.3 or lower.

[0068] (Embodiment 1-2) As another specific example of the above-mentioned embodiment 1, the method according to embodiment 1-2 preferably includes the following steps (A12), (B12), and (C12). The predictive marker in this embodiment is an index that shows a significant difference between a responder group and a non-responder group selected based on the weight loss rate from a group of subjects who completed a dietary management instruction program and were certified as having complied with the instruction in the program in Test Example 1 of the Examples described below. The predictive marker according to this embodiment can be used, for example, to predict weight loss due to dietary management for subjects who are able to comply with a set dietary management program.

[0069] Step (A12) is a step of measuring at least one physical index selected from the group consisting of pulse rate and height.

[0070] Step (B12) is a step of measuring at least one blood index selected from the group consisting of uric acid concentration, white blood cell count, glutamine concentration, valine concentration, leucine concentration, isoleucine concentration, and 3-methylhistidine concentration.

[0071] Step (C12) is a step of calculating at least one response index selected from the group consisting of an index of eating habits regularity calculated from the responses to the eating behavior questionnaire and an index of eating motivation calculated from the responses to the eating behavior questionnaire.

[0072] In this embodiment, it is preferable that the predictive markers include at least one selected from pulse rate, height, uric acid concentration, white blood cell count, valine concentration, leucine concentration, isoleucine concentration, and 3-methylhistidine concentration, and from the viewpoint that the predictive markers can be obtained by a general test, it is more preferable that the predictive markers include at least one selected from pulse rate, height, uric acid concentration, and white blood cell count.

[0073] When qualitatively predicting weight loss using each predictive marker in this embodiment, examples of reference values ​​for each predictive marker for predicting a responder, i.e., "large amount and / or rate of weight loss," are shown below. Note that each reference value is the numerical value of each predictive marker measured or calculated by the method shown in the Examples.

[0074] The pulse rate is preferably 86.6 / min or less, more preferably 74.1 / min or less, and even more preferably 61.6 / min or less. The height is preferably 164.8 cm or more, more preferably 169.5 cm or more, and even more preferably 174.2 cm or more.

[0075] The uric acid concentration is preferably 7.7 mg / dL or less, more preferably 6.6 mg / dL or less, and even more preferably 5.4 mg / dL or less. The white blood cell count is preferably 7440.0 / μL or less, more preferably 6190.0 / μL or less, and even more preferably 4940.0 / μL or less. The glutamine concentration is preferably 556.2 nmol / mL or less, more preferably 594.3 nmol / mL or less, and even more preferably 632.3 nmol / mL or less. The valine concentration is preferably 196.7 nmol / mL or more, more preferably 205.5 nmol / mL or more, and even more preferably 214.3 nmol / mL or more. The leucine concentration is preferably 160.7 nmol / mL or less, preferably 138.25 nmol / mL or less, and even more preferably 115.8 nmol / mL or less. The isoleucine concentration is preferably 99.6 nmol / mL or less, more preferably 83.0 nmol / mL or less, and even more preferably 66.3 nmol / mL or less. The 3-methylhistidine concentration is preferably 6.0 nmol / mL or less, more preferably 5.6 nmol / mL or less, and even more preferably 5.2 nmol / mL or less.

[0076] The index of regularity of eating habits calculated from the answers to the eating behavior questionnaire is preferably 68.8 or less, more preferably 56.9 or less, and even more preferably 45.0 or less. The index of eating motivation calculated from the answers to the eating behavior questionnaire is preferably 70.5 or less, more preferably 60.8 or less, and even more preferably 51.0 or less.

[0077] [Example of a method for predicting the reduction of visceral fat mass] (Embodiment 2) In the method according to the second embodiment of the present invention, the prediction of the weight loss effect is a prediction of a reduction in visceral fat mass, and includes at least one step selected from the group consisting of the following steps (A2), (B2), and (C2): The predictive marker in this embodiment is an index that showed a significant difference between a responder group and a non-responder group, selected based on the amount of reduction in visceral fat mass from a group of subjects who underwent a dietary management instruction program in Test Example 1 of the Examples described below.

[0078] Step (A2) is a step of measuring physical indices including at least one selected from the group consisting of pulse rate and MQP. These physical indices all show a negative correlation with the amount and / or rate of reduction in visceral fat mass.

[0079] Step (B2) is a step of measuring at least one blood index selected from the group consisting of albumin concentration, potassium concentration, red blood cell count, calcium concentration, free testosterone concentration, and tryptophan concentration. All of these blood indexes show a negative correlation with the amount and / or rate of reduction in visceral fat mass.

[0080] Step (C2) is a step of calculating a response index consisting of an index calculated from the response to a question asking about motivation for dietary management. This response index shows a positive correlation with the amount and / or rate of reduction of visceral fat mass.

[0081] (Embodiment 2-1) As a specific example of the above-mentioned embodiment 2, the method according to embodiment 2-1 preferably includes at least one step selected from the group consisting of the following steps (A21), (B21), and (C21). Note that the predictive marker in this embodiment is an index that showed a significant difference between a responder group and a non-responder group selected from all subjects who underwent a dietary management instruction program in Test Example 1 of the Examples described below, based on the amount and / or rate of visceral fat mass reduction. The predictive marker of this embodiment can be used to predict visceral fat mass reduction due to dietary management for a variety of subjects.

[0082] Step (A21) is a step of measuring at least one physical index selected from the group consisting of pulse rate and MQP.

[0083] Step (B21) is a step of measuring at least one blood index selected from the group consisting of albumin concentration, potassium concentration, red blood cell count, and calcium concentration.

[0084] Step (C21) is a step of calculating response indices that are calculated from responses to questions asking about motivation for dietary management.

[0085] In this embodiment, it is preferable that the above-mentioned predictive markers include at least one selected from pulse rate, MQP, albumin concentration, potassium concentration, red blood cell count, and calcium concentration, and from the viewpoint of being obtainable by a general test, it is more preferable that the predictive markers include at least one selected from pulse rate, albumin concentration, potassium concentration, red blood cell count, and calcium concentration.

[0086] When qualitatively predicting the reduction of visceral fat mass using the above-mentioned predictive markers, examples of reference values ​​for each predictive marker for predicting a responder, i.e., "a large reduction in visceral fat mass and / or a large reduction rate," are shown below. Note that each reference value is the numerical value of each predictive marker measured or calculated by the method shown in the Examples.

[0087] The pulse rate is preferably 82.3 / min or less, more preferably 74.1 / min or less, and even more preferably 65.8 / min or less. The MQP is preferably 63.8 or less, more preferably 58.6 or less, and even more preferably 53.4 or less.

[0088] The albumin concentration is preferably 4.9 g / dL or less, more preferably 4.75 g / dL or less, and even more preferably 4.6 g / dL or less. The potassium concentration is preferably 4.2 mEq / L or less, more preferably 4.1 mEq / L or less, and even more preferably 4.0 mEq / L or less. The red blood cell count is preferably 538.3 x 10 4 / μL or less, preferably 517.6 × 10 4 / μL or less, more preferably 496.9 × 10 4 The calcium concentration is preferably 9.8 mEq / L or less, more preferably 9.6 mEq / L or less, and even more preferably 9.4 mEq / L or less.

[0089] The index calculated from the answers to the questions asking about motivation for dietary management is preferably 1.8 or more, more preferably 2.5 or more, and even more preferably 3.2 or more.

[0090] (Embodiment 2-2) As a specific example of the above-mentioned embodiment 2, the method according to embodiment 2-2 preferably includes the following step (B22). The predictive marker in this embodiment is an index that shows a significant difference between a responder group and a non-responder group selected from a group of subjects who completed a dietary management instruction program and were certified as having complied with the instruction in the program in Test Example 1 of the Examples described below, and that shows a significant difference between these groups based on the amount and / or rate of visceral fat mass reduction. The predictive marker in this embodiment can be used, for example, to predict the reduction in visceral fat mass due to dietary management for subjects who can comply with a set dietary management program. The prediction of the reduction in visceral fat mass in this embodiment is preferably a qualitative prediction of the reduction in visceral fat mass.

[0091] Step (B22) is a step of measuring at least one blood index selected from the group consisting of free testosterone concentration and tryptophan concentration.

[0092] In this embodiment, the above-mentioned predictive markers preferably include tryptophan concentration in particular.

[0093] When qualitatively predicting a reduction in visceral fat mass using the above-mentioned predictive markers, examples of reference values ​​for each predictive marker for predicting a responder, i.e., a "high effect of reducing visceral fat mass," are shown below. Note that each reference value is the numerical value of each predictive marker measured by the method shown in the Examples.

[0094] The free testosterone concentration is preferably 14.9 pg / mL or less, more preferably 12.6 pg / mL or less, and even more preferably 10.2 pg / mL or less. The tryptophan concentration is preferably 79.7 nmol / mL or less, more preferably 71.4 nmol / mL or less, and even more preferably 63.1 nmol / mL or less.

[0095] [Method for predicting weight loss effect] Furthermore, the present invention provides a method for predicting the weight loss effect of dietary management in a subject. The method includes the above-mentioned step (D). That is, step (D) is a step of determining whether at least one index selected from the group consisting of the physical index, the blood index, and the response index satisfies a condition corresponding to the level of weight loss effect set for that index. As described above, step (D) preferably includes comparing at least one index selected from the physical index, the blood index, and the response index with a reference value for that index set corresponding to the level of weight loss effect. Note that step (D) of this embodiment is similar to step (D) in the above-mentioned "method for prediction," and therefore a detailed description thereof will be omitted.

[0096] The physical indicators include at least one indicator selected from the group consisting of pulse rate, height, and MQP, which are listed in step (A).

[0097] The blood indicators include at least one indicator selected from the group consisting of albumin concentration, potassium concentration, uric acid concentration, chloride concentration, white blood cell count, glutamine concentration, valine concentration, leucine concentration, isoleucine concentration, 3-methylhistidine concentration, red blood cell count, calcium concentration, free testosterone concentration, and tryptophan concentration, which are listed in step (B).

[0098] The response index includes at least one index selected from the group consisting of an index calculated from responses to questions asking about awareness and / or behavior regarding irregular mealtimes, as listed in step (C), an index calculated from responses to an eating behavior self-efficacy scale, an index calculated from responses to a rest behavior self-efficacy scale, an index of regularity of eating habits calculated from responses to an eating behavior questionnaire, an index of eating motivation calculated from responses to an eating behavior questionnaire, and an index calculated from responses to questions asking about motivation for dietary management.

[0099] Furthermore, when the weight loss effect is predicted based on body weight loss, it is preferable to use at least one index selected from the predictive markers described in the above-mentioned embodiment 1. Specific examples of the predictive markers include a physical index of at least one index selected from the group consisting of pulse rate and height, a blood index of at least one index selected from the group consisting of albumin concentration, potassium concentration, uric acid concentration, chloride concentration, white blood cell count, glutamine concentration, valine concentration, leucine concentration, isoleucine concentration, and 3-methylhistidine concentration, and a response index of at least one index selected from the group consisting of an index calculated from responses to questions asking about awareness and / or behavior regarding irregular mealtimes, an index calculated from responses to an eating behavior self-efficacy scale, an index calculated from responses to a rest behavior self-efficacy scale, an index of eating habits regularity calculated from responses to an eating behavior questionnaire, and an index of eating motivation calculated from responses to an eating behavior questionnaire.

[0100] Furthermore, it is more preferable to use at least one indicator selected from the predictive markers described in embodiment 1-1 above, or at least one indicator selected from the predictive markers described in embodiment 1-2 among these predictive markers.

[0101] Furthermore, when the prediction of the weight loss effect is a prediction of a reduction in visceral fat mass, it is preferable to use at least one index selected from the predictive markers described above in the second embodiment. Specific examples of predictive markers include a physical index of at least one index selected from the group consisting of pulse rate and MQP, a blood index of at least one index selected from the group consisting of albumin concentration, potassium concentration, red blood cell count, calcium concentration, free testosterone concentration, and tryptophan concentration, and a response index calculated from responses to questions asking about motivation for dietary management.

[0102] Furthermore, it is more preferable to use at least one indicator selected from the predictive markers described in embodiment 2-1 above, or at least one indicator selected from the predictive markers described in embodiment 2-2, among these predictive markers.

[0103] [Method for quantitatively predicting weight loss effects] (Embodiment 3) Next, a method for quantitatively predicting the weight loss effect of dietary management will be described as a third embodiment of the present invention. The predictive markers used in the method according to this embodiment are predictive markers selected from Test Example 2, which had a larger number of subjects than Test Example 1, from which the predictive markers of the above-described embodiment were selected. In the following description, explanations of terms and predictive markers that overlap with those of the above-described embodiment will be omitted as appropriate.

[0104] As described above, "quantitatively predicting the weight loss effect" includes quantitatively predicting the overall weight loss effect and quantitatively predicting changes in specific weight loss evaluation indexes. Quantitative predictions of changes in specific weight loss evaluation indexes preferably include at least one of quantitative predictions of body weight loss or quantitative predictions of visceral fat mass reduction. Quantitative predictions of weight loss include at least one prediction selected from the amount of weight loss, the rate of weight loss, and ranges thereof, and are preferably predictions of the rate of weight loss. Quantitative predictions of visceral fat mass reduction include at least one prediction selected from the amount of visceral fat reduction, the rate of visceral fat reduction, and ranges thereof, and are preferably predictions of the amount of visceral fat area reduction.

[0105] The method for quantitatively predicting the weight loss effect of dietary management according to this embodiment includes an acquisition step (E1) of acquiring at least one index selected from the group consisting of height and pulse rate as physical indices representing at least one of the subject's physique or body composition, and response indices calculated from the subject's answers to a questionnaire, including indices calculated from answers to questions asking about health-consciousness and / or behavior, indices about physical constitution and weight awareness calculated from answers to an eating behavior questionnaire, indices calculated from answers to questions asking about dieting awareness, and indices calculated from answers to questions asking about motivation for dietary management. The indices acquired in the acquisition step (E1) each function as a "prediction marker" for quantitatively predicting the weight loss effect. The acquisition step (E1) is a step of acquiring at least one predictive marker listed as a physical indices and a response indices.

[0106] The above-mentioned predictive markers are high-priority indicators classified as "Priority 1" among the indicators commonly used in constructing a linear regression model for calculating the weight loss rate and a linear regression model for calculating the amount of visceral fat area reduction from the data of all subjects in a group of subjects who underwent the dietary management instruction program in Test Example 2 of the Examples described below. Indicators classified as Priority 1 are indicators that are minimally invasive and can be easily obtained, specifically, physical indicators as body measurement values ​​and indicators belonging to response indicators. These predictive markers can be used to predict the weight loss effect of dietary management for a variety of subjects. The gender of the subject to be predicted by the predictive marker obtained by the acquisition step (E1) is not particularly limited, but from the viewpoint of improving prediction accuracy, it is preferable that the subject be male.

[0107] In this embodiment, "acquisition" of a predictive marker refers to acquisition of information about the predictive marker. Here, "acquisition of information" includes not only acquisition of computer-processable data but also acquisition of information via paper media. Specifically, "acquisition of physical indices" refers to both measuring physical indices and acquiring information about their measurement values, as described in step (A), and acquiring information about physical indices from other devices. The "acquisition of blood indices" described below refers to both measuring blood indices from a subject's blood sample and acquiring information about their measurement values, as described in step (B), and acquiring information about blood indices from other devices. The "acquisition of response indices" refers to both calculating response indices from the subject's responses to the questionnaire and acquiring information about the calculated values, as described in step (C), and acquiring information about response indices from other devices. In other words, the "acquisition" in the acquisition step (E1) refers to a concept that includes the measurement in step (A), the measurement in step (B), and the calculation in step (C).

[0108] More specifically, "acquiring physical information" preferably includes measuring a physical indicator and storing information on the measurement value in a measuring device, and / or receiving information on the measurement value. "acquiring blood indicators" preferably includes measuring a blood indicator and storing information on the measurement value in a measuring device, and / or receiving information on the measurement value. "acquiring answer indicators" preferably includes calculating answer indicators and storing information on the calculated values ​​in a computer, and / or receiving information on the calculated values.

[0109] Hereinafter, predictive markers not described in the above embodiments will be described.

[0110] The questions inquiring about health-consciousness and / or behavior preferably include at least one question selected from the following: dietary fiber intake, green and yellow vegetable intake, vegetable fat and fish fat intake, protein source preference, proactiveness, cooperativeness, and insight (such as sharp intuition). Specifically, such questions may include, for example, questions 9 ("I actively eat green and yellow vegetables"), 11 ("I actively choose foods high in dietary fiber"), 14 ("I try to limit animal fat and consume vegetable fat and fish fat"), 12 ("I prefer fish to meat"), 31 ("I'm lazy"), 32 ("I'm insightful"), 33 ("I'm cooperative"), and 34 ("I'm proactive") in the "35-Question Dietary Questionnaire" described in Yanagisawa et al., 2017, as illustrated in Figure 1. The index calculated from the answers to the questions asking about health-consciousness and / or behavior may be a numerical value selected from, for example, the average score, total score, and score rate, which is the ratio of the total score to the highest score, corresponding to the answers to the above eight questions. Furthermore, the above eight questions correspond to the second factor (health-consciousness) of five factors (diet, health consciousness, mealtime, dietary restrictions, and exercise) derived as a result of factor analysis (maximum likelihood method, oblique rotation) of all 35 questions using the method described in "Yanagisawa et al., 2017" mentioned above, and can be used as a measure of health-consciousness.

[0111] The eating behavior questionnaire is the questionnaire exemplified in FIG. 4 described above. The index for the perception of constitution and weight calculated from the questionnaire can be a numerical value selected from the total score, the average score, and the score rate, which is the ratio of the total score to the highest score, of the questions included in "perception of constitution and weight" out of the seven areas exemplified in FIG. 4. For example, the options for each question include four levels: "Not true," "Sometimes true," "Tends to be true," and "Exactly true," and each option can be assigned a predetermined score (for example, 0 to 3 points).

[0112] A question asking about attitudes toward dieting, for example, asks about the subject's attitude toward dieting (weight loss and obesity reduction) before implementing dietary management. A specific question might be, "Please rate your current attitude toward dieting (weight loss and obesity reduction) on a scale of 1 to 10." The options are, for example, expressed as 11 levels from 0 to 10 (very high, none to very low, none). The index calculated from the question can be the score corresponding to this answer.

[0113] Furthermore, the prediction method according to this embodiment may include a prediction step (F) after the acquisition step (E1). The prediction step (F) is a step of quantitatively predicting the weight loss effect of the subject by applying at least one index acquired in the acquisition step (E1) to a prediction model for deriving a predicted value indicating the weight loss effect from the acquired index. The prediction step (F) may be executed, for example, by a computer having a control unit. The control unit may be composed of a processor such as a CPU (Central Processing Unit).

[0114] The predictive model can be generated by acquiring a large amount of data from a sample population of subjects. For example, the predictive model can be generated by machine learning using the values ​​of indicators in the population of subjects as explanatory variables and values ​​indicating the weight loss effect in the population of subjects as objective variables. The explanatory variables are selected from the training data using an appropriate explanatory variable selection algorithm, such as lasso regression or elastic net. Algorithms used in machine learning models are preferably linear regression models, such as linear regression models, regularized linear regression models (lasso regression, elastic net, etc.), generalized linear models, etc., and nonlinear algorithms are also possible.

[0115] The optimal prediction model can be determined by inputting verification data into a prediction model constructed using the algorithm described above to calculate predicted values, and then selecting the model whose predicted values ​​best match the actual measured values, for example, the model with the highest accuracy rate or the model with the smallest prediction error.

[0116] As described above, the predictive markers of this embodiment are all minimally invasive and can be easily obtained, which can reduce the burden on the subject associated with obtaining the indicators. Therefore, according to this embodiment, the weight loss effect can be quantitatively and accurately predicted using easily obtainable indicators.

[0117] (Embodiment 3-1) When the quantitative prediction of the weight loss effect is a quantitative prediction of weight loss, in addition to the indicators described in embodiment 3, the acquisition process (E1) preferably acquires at least one indicator selected from the group consisting of an indicator calculated from answers to questions asking about awareness and / or behavior regarding irregular mealtimes, an indicator calculated from answers to questions asking about awareness and / or behavior regarding dietary restrictions, an indicator about eating habits calculated from answers to an eating behavior questionnaire, an indicator calculated from answers to questions about stress responses in the Occupational Stress Brief Questionnaire, an indicator of conscientious personality calculated from answers to the Big Five scale, an indicator calculated from answers to a questionnaire evaluating the exercise behavior change stage, an indicator calculated from answers to a question in the WHO-5 (Mental Health Status Questionnaire) asking about "staying motivated and active", and an indicator calculated from answers to a questionnaire evaluating the rest behavior change stage.

[0118] The above-mentioned predictive markers are indices classified as "Priority 1" among indices selected by an appropriate explanatory variable selection algorithm for predicting the weight loss rate from the data of all subjects in a group of subjects who underwent the dietary management instruction program in Test Example 2 of the Examples described below, and are not listed in Embodiment 3. By using these indices in addition to the indices listed in Embodiment 3, it is possible to quantitatively predict weight loss.

[0119] Hereinafter, predictive markers not described in the above embodiments will be described.

[0120] The questions inquiring about awareness and / or behavior regarding dietary restriction preferably include at least one question selected from, for example, a question about food portion size restriction, a question about calorie restriction, and a question about awareness of one's own tendency to gain weight, and more preferably form a group of questions including two or more questions. Specifically, such questions include, for example, question 4 of the "35-Question Dietary Habits Questionnaire" described in "Yanagisawa et al., 2017" illustrated in FIG. 1, "I consciously limit my food intake to avoid gaining weight," question 6, "I eat less because I feel guilty about overeating," question 7, "I try to buy low-calorie foods," and question 15, "I think I am more prone to gaining weight than other people." The index calculated from the responses to the questions inquiring about awareness and / or behavior regarding dietary restriction may be, for example, a numerical value selected from the average score, total score, and score percentage, which is the ratio of the total score to the highest score, corresponding to the answers to the above four questions. Furthermore, the above four questions correspond to the fourth factor (dietary restrictions) out of the five factors (diet, health awareness, mealtimes, dietary restrictions, and exercise) derived from a factor analysis (maximum likelihood method, oblique rotation) of all 35 questions using the method described in Yanagisawa et al., 2017, and can be used as a measure of one's own dietary restrictions.

[0121] The eating behavior questionnaire is the questionnaire exemplified in FIG. 4 above. The index for eating habits calculated from the questionnaire can be a numerical value selected from the total score, average score, and score rate, which is the ratio of the total score to the highest score, of the questions included in "eating habits" out of the seven areas exemplified in FIG. 4. For example, the options for each question include four levels: "Not at all," "Sometimes this happens," "Tends to be like that," and "Exactly true," and each option can be assigned a predetermined score (for example, 0 to 3 points).

[0122] The Simple Occupational Stress Questionnaire, shown in Figures 8 and 9, is a questionnaire provided by the Ministry of Health, Labor and Welfare to investigate occupational stress levels. The questions about stress reactions are a group of 29 questions in Area B. The index calculated from the answers to the questions can be, for example, the total score of the answers to the questions. These indices may be the total score, or the score percentage, which is the ratio of the total score to the average score or the highest score.

[0123] The Big Five Scale (BFS) is a questionnaire for calculating five indicators of personality traits based on the Big Five personality traits: openness, conscientiousness, extraversion, agreeableness, and emotional stability. The Big Five personality traits are a method for classifying an individual's personality traits based on five factors: openness, conscientiousness, extraversion, agreeableness, and emotional stability. The Big Five Scale can be implemented, for example, as a questionnaire including 60 questions, as shown in Figures 6 and 7. In this case, the subject selects the degree to which each question on the Big Five Scale applies to their perceived personality traits from a seven-point scale (1: not at all applicable, 2: hardly applicable, 3: not very applicable, 4: neither applicable nor unrelated, 5: somewhat applicable, 6: very applicable, and 7: very applicable). As shown in "Subjective Fulfillment and the Big Five" (Kazuya Horike, Journal of Contemporary Behavioral Science, Vol. 15, pp. 1-8 (1999)) (hereafter referred to as "Horike, 1999"), an index of conscientious personality can be calculated based on the scores of the three items with the highest loadings on each factor extracted by factor analysis (items marked with a solid line in Table 4 of the same reference). Referring to the same reference, the three items used to calculate an index of conscientious personality can be "careless," "responsible," and "thoughtless." Specifically, the scale scores can be reversed as necessary so that higher scores on the three items related to each index indicate stronger conscientiousness, and then the total score can be calculated. The average score divided by the number of items is then used to calculate each index.

[0124] "Behavior change stages" refer to stages of behavioral change based on the theory of stages of behavioral change (see Prochaska, J.O. & Velicer, W.F.: The transtheoretical model of health behavior change, American Journal of Health Promotion, 12, 38-48, 1997). The questionnaire used to assess the exercise behavior change stage was the "Health Behavior Change Stage Test" shown in Figure 1 of "Study of Self-Efficacy Scales and Social Support Scales Useful for Supporting Health Behavior Change" (Kiyoshi Moriya and Mari Shimizu, Bulletin of Tenshi University, Vol. 9, June 30, 2009) (hereafter referred to as "Moritani et al., 2009"). The questionnaire used to assess the rest behavior change stage can be the same as the "Health Behavior Change Stage Test," but with "activity and exercise behavior" replaced with "stress management and rest behavior." The "Health Behavior Change Stage Test" described in the same publication is shown in Figure 10. The index calculated from the answers to this test paper can be calculated by referring to the same literature, and the numbers of the options for the question I. about the awareness of change and stage of health behavior can be used as the index.

[0125] The index calculated from the response to the question item "I spent my time motivated and active" in the WHO-5 (Mental Health Status Questionnaire) is the index calculated from the response to the question item "I spent my time motivated and active" in question 3 of the five questions shown in Figure 11. This index can be the number assigned to each option.

[0126] (Embodiment 3-2) When the quantitative prediction of the weight loss effect is a quantitative prediction of body weight loss, in the acquisition step (E1), in addition to the indicators described in embodiments 3 and 3-1, it is preferable to acquire at least one indicator selected from the group consisting of phenylalanine concentration, 3-methylhistidine concentration, tryptophan concentration, hydroxyproline concentration, and ornithine concentration as blood indicators measured from the subject's blood sample.

[0127] The above-mentioned predictive markers are indices classified as "Priority 3" among indices selected by an appropriate explanatory variable selection algorithm for predicting weight loss rates from the data of all subjects in a group of subjects who underwent the dietary management instruction program in Test Example 2 of the Examples described below. Indicators classified as Priority 3 are values ​​measured in amino acid analysis (amino acid fractions) of plasma samples. The blood indices of this embodiment are all expressed in units such as "nmol / mL" and can be measured, for example, by HPLC or the like. In this embodiment, weight loss can be quantitatively predicted with greater accuracy by using Priority 3 indices that can be measured as amino acid fractions in addition to the Priority 1 indices described in Embodiments 3 and 3-1.

[0128] Among the indices selected from the data of all subjects who underwent the dietary management instruction program in Test Example 2 of the Examples described below using an appropriate explanatory variable selection algorithm for predicting the weight loss rate, no indices classified as Priority 2 were obtained. The indices classified as Priority 2 are blood indices included in the test items of a general health checkup.

[0129] (Embodiment 3-3) When the quantitative prediction of the weight loss effect is a quantitative prediction of body weight loss, in the acquisition step (E1), in addition to the indicators described in embodiments 3, 3-1, and 3-2, it is preferable to further acquire at least one indicator selected from the group consisting of MQP (Muscle Quality Point) as a physical indicator, and creatine kinase value, urea nitrogen concentration, potassium concentration, magnesium concentration, cortisol concentration, and tumor necrosis factor-α concentration as a blood indicator.

[0130] The predictive markers are those classified as "Priority 4" among those selected by an appropriate explanatory variable selection algorithm for predicting weight loss rates from the data of all subjects in a group who underwent the dietary management instruction program in Test Example 2 of the Examples described below. The indicators classified as Priority 4 are those selected by the algorithm, excluding those classified as Priorities 1 to 3, and are more specialized than those classified as Priorities 1 to 3. Examples of indicators classified as Priority 4 include those measured using specific equipment, test items with higher precision than those in general periodic health checkups (such as test items performed in comprehensive medical checkups), and specialized blood test items. In this embodiment, weight loss can be predicted with even greater accuracy by using a Priority 4 indicator in addition to the Priority 1 and 3 indicators described in Embodiments 3, 3-1, and 3-2.

[0131] Predictive markers not described in the above embodiments will now be described. Creatine kinase (CK) levels (e.g., units: U / L) can be measured using serum samples by established methods such as the JSCC standardized method. Urea nitrogen concentrations (e.g., units: mg / dL) can be measured using serum samples by established methods such as the urease GLDH ammonia elimination method. Magnesium concentrations (e.g., units: mg / dL) can be measured using serum samples by established methods such as the xylidyl blue method. Cortisol concentrations (e.g., units: μg / dL) can be measured using serum samples by established methods such as the ECLIA method. Tumor necrosis factor-α (TNF-α) concentrations (e.g., units: pg / mL) can be measured using serum samples by established methods such as the EIA method.

[0132] (Embodiments 3-4) When the quantitative prediction of the weight loss effect is a quantitative prediction of the reduction in visceral fat mass, in addition to the indicators described in embodiment 3, the acquisition step (E1) preferably acquires at least one index selected from the group consisting of an index of awareness of the regularity of eating habits calculated from the answers to the eating behavior questionnaire, an index of mental fatigue calculated from the answers to the Chalder Fatigue Scale, an index calculated from the answers to questions on work stressors and questions on modifying factors including support from those around you and satisfaction with work or family life in the Occupational Stress Brief Questionnaire, an index of extroverted personality calculated from the answers to the Big Five scale, and an index calculated from the answers to the eating behavior self-efficacy scale.

[0133] The above-mentioned predictive markers are indices classified as "Priority 1" among indices selected by an appropriate explanatory variable selection algorithm for predicting the amount of visceral fat area reduction from the data of all subjects in a group of subjects who underwent the dietary management instruction program in Test Example 2 of the Examples described later, and are not listed in Embodiment 3. By using the indices listed in this embodiment in addition to the indices listed in Embodiment 3, it is possible to quantitatively and accurately predict the reduction in visceral fat mass.

[0134] Hereinafter, predictive markers not described in the above embodiments will be described.

[0135] The Chalder Fatigue Scale is a measure of mental fatigue and is a questionnaire used in the treatment of conditions such as chronic fatigue syndrome. This questionnaire includes, for example, the 14 questions shown in Figure 5. The index of mental fatigue calculated from the responses to the Chalder Fatigue Scale can be one of the following: a total score (0-18) calculated from the subject's answers to six of the 14 questions (questions 9-14) that inquire about mental fatigue; an average score; or a score percentage, which is the ratio of the total score to the highest score. For example, it can be the total score.

[0136] The questions about work stressors and the questions about modifying factors including support from those around you and satisfaction with work or family life in the Simple Occupational Stress Questionnaire are the questions in Area A (questions about work stressors) and Area C (modifying factors including support from those around you and satisfaction with work or family life) in the Simple Occupational Stress Questionnaire shown in Figures 8 and 9. The index calculated from the answers to these questions can be, for example, the total score of the answers to these two question groups. Note that these indexes may be the total score or the score percentage, which is the ratio of the total score to the average score or the highest score.

[0137] The index of extroversion personality calculated from responses to the Big Five scale can be calculated based on the scores of the three items (items marked with a solid line in Table 4 of Horike, 1999) that load highly on the "extroversion" factor extracted by factor analysis of the Big Five scale. The three items used to calculate the index of extroversion personality can be "active," "extroverted," and "talkative." Specifically, the scale scores can be reversed as necessary so that higher scores on the three items related to each index indicate stronger extroversion, and then the total score can be calculated. The average score divided by the number of items is then calculated as the average score for each index.

[0138] (Embodiments 3-5) When the quantitative prediction of the weight loss effect is a quantitative prediction of the reduction in visceral fat mass, in the acquisition step (E1), in addition to the indicators described in embodiments 3 and 3-4, it is preferable to acquire at least one indicator selected from the group consisting of glutamic acid concentration, sarcosine concentration, α-amino-n-butyric acid concentration, leucine concentration, β-alanine concentration, ornithine concentration, lysine concentration, monoethanolamine concentration, alanine concentration, and serine concentration as a blood indicator.

[0139] The predictive markers are those classified as "Priority 3" from among the indices selected by an appropriate explanatory variable selection algorithm for predicting the amount of visceral fat area reduction from all of the subjects who underwent the dietary management instruction program in Test Example 2 of the Examples described below. Indicators classified as Priority 3 are values ​​measured in amino acid analysis (amino acid fractions) of plasma samples. The blood indices of this embodiment are all expressed in units such as "nmol / mL" and can be measured, for example, by HPLC or the like. In this embodiment, by using Priority 3 indices that can be measured as amino acid fractions in addition to the Priority 1 indices described in Embodiments 3 and 3-4, it is possible to quantitatively predict the reduction in visceral fat mass with greater accuracy.

[0140] Furthermore, from the data of all subjects who underwent the dietary management instruction program in Test Example 2 of the Examples described below, no indicators classified as priority 2 were obtained among the indicators selected using an appropriate explanatory variable selection algorithm for predicting the amount of visceral fat area reduction.

[0141] (Embodiments 3-6) When the quantitative prediction of the weight loss effect is a quantitative prediction of the reduction in visceral fat mass, in addition to the indicators described in embodiments 3, 3-4, and 3-5, the acquisition step (E1) preferably further acquires MQP as a physical indicator, and at least one indicator selected from the group consisting of total bilirubin concentration, creatine kinase value, uric acid concentration, calcium concentration, inorganic phosphorus concentration, magnesium concentration, mean corpuscular hemoglobin concentration (MCHC), platelet count, free testosterone concentration, blood ammonia concentration, high-sensitivity CRP concentration, and somatomedin-C (IGF-1) concentration as a blood indicator.

[0142] The above-mentioned predictive markers are indices classified as "Priority 4" as in Example 3-3, among indices selected by an appropriate explanatory variable selection algorithm for predicting the amount of visceral fat area reduction from all of the subjects who underwent the dietary management instruction program in Test Example 2 of the Examples described later. In this embodiment, by using such indices in addition to the indices described in Embodiments 3, 3-4, and 3-5, it is possible to quantitatively predict the reduction in visceral fat mass with higher accuracy.

[0143] Predictive markers not described in the above embodiments will now be described.

[0144] Total bilirubin concentration (e.g., in mg / dL) can be measured by established methods such as the vanadate oxidation method using serum samples. Inorganic phosphorus concentration (e.g., in mg / dL) can be measured by established methods such as the enzymatic method using serum samples. MCHC (e.g., in %), platelet count (e.g., in × 10 4The blood ammonia concentration (e.g., unit: μg / dL) can be measured, for example, by a blood cell count test using the subject's whole blood. The blood ammonia concentration (e.g., unit: μg / dL) can be measured, for example, by an established method such as the modified Fujii-Okuda method using deproteinized supernatant. The high-sensitivity CRP (C-reactive protein) concentration (e.g., unit: mg / dL) can be measured, for example, by an established method such as the latex agglutination method using serum samples. The somatomedin-C (IGF-1) concentration (e.g., unit: ng / mL) can be measured, for example, by an established method such as the ECLIA method using serum samples.

[0145] (Embodiment 4) In the above-mentioned embodiment 3, a method was described in which an index selected from the data of all the subjects in the subject group who underwent the dietary management instruction program in Test Example 2 of the Examples described later, but in the following embodiment 4, a method is described in which an index (predictive marker) selected from the subject group who were certified as having complied with the instruction in the program, among the subjects in the subject group who underwent the dietary management instruction program in Test Example 2. Hereinafter, terms and explanations that overlap with those in the above-mentioned embodiments will be omitted as appropriate.

[0146] The method for quantitatively predicting the weight loss effect of dietary management in this embodiment includes an acquisition step (E2) of acquiring at least one index selected from the group consisting of height as a physical index and, as response indexes, an index calculated from answers to questions asking about awareness and / or behavior regarding health-consciousness, an index calculated from answers to questions asking about awareness and / or behavior regarding irregular mealtimes, an index regarding awareness of constitution and weight calculated from answers to an eating behavior questionnaire, an index calculated from answers to questions about stress responses in an occupational stress brief questionnaire, an index calculated from answers to an exercise behavior self-efficacy scale, and an index calculated from answers to questions asking about awareness of dieting.

[0147] The predictive markers are indices classified as priority 1, as in embodiment 3, among the indices commonly used in constructing a linear regression model for calculating a weight loss rate and a linear regression model for calculating a visceral fat area reduction amount from data of a group of subjects who completed a dietary management instruction program in Test Example 2 of the Examples described below and who were certified as having complied with the instruction in the program. The predictive markers can be used, for example, to predict the weight loss effect of dietary management for subjects who are able to comply with a set dietary management program.

[0148] Predictive markers not described in the above embodiments will now be described.

[0149] The exercise behavior self-efficacy scale used was the exercise behavior self-efficacy scale (Figure 3) described in Moriya et al., 2009. The index calculated from the responses to this scale can be calculated by referring to the same literature, and the total score of the answers selected for each scale from "I think I can do it well" (-3 points) to "I think I can't do it at all" (+3 points) can be used as the index for each scale. This index can be the total score or the score percentage, which is the ratio of the total score to the average score or the highest score.

[0150] The predictive markers of this embodiment are all minimally invasive and easily obtainable indicators, which can reduce the burden on subjects associated with obtaining the indicators. Therefore, according to this embodiment, it is possible to quantitatively and accurately predict the weight loss effect using easily obtainable indicators, particularly for subjects who are able to comply with a dietary management program.

[0151] Furthermore, the method of this embodiment may also include the above-mentioned prediction step (F) in addition to the acquisition step (E2).

[0152] (Embodiment 4-1) When the quantitative prediction of the weight loss effect is a quantitative prediction of body weight loss, in the acquisition process (E2), in addition to the indicators described in embodiment 4, it is preferable to acquire at least one indicator selected from the group consisting of age as a physical indicator, an indicator calculated from answers to questions asking about awareness and behavior regarding lack of exercise as an answer indicator, an indicator about eating habits calculated from answers to an eating behavior questionnaire, an indicator calculated from answers to the Athens Insomnia Scale, and an indicator of conscientiousness personality calculated from answers to the Big Five scale.

[0153] The predictive markers are indices classified as "Priority 1" among indices selected by an appropriate explanatory variable selection algorithm for predicting the weight loss rate from data of a group of subjects who underwent a dietary management instruction program in Test Example 2 of the Examples described below and who were certified as having complied with the instructions in the program, and are not listed in Embodiment 4. By using these indices in addition to the indices listed in Embodiment 4, weight loss can be quantitatively predicted with higher accuracy.

[0154] Predictive markers not described in the above embodiments will now be described.

[0155] The questions inquiring about perceptions and behaviors related to lack of exercise include questions about the subject's own perceptions of lack of exercise, their preference for exercise, and behaviors and preferences related to lack of exercise. The questions inquiring about perceptions and behaviors related to lack of exercise preferably include at least one question selected from the group consisting of a question about preference for exercise, a question about the frequency of exercise, and a question about the subject's perception of lack of exercise, and more preferably a group of two or more questions. Specifically, such questions include, for example, question 24 of the "35-Question Diet Questionnaire" illustrated in FIG. 1 , "I would use an elevator or escalator if available," question 26, "I don't like walking or cycling," question 27, "I don't exercise much," and question 29, "I think it's more about lack of exercise than overeating." The index calculated from the responses to the questions inquiring about perceptions and behaviors related to lack of exercise can be, for example, one selected from the average score, total score, and score percentage, which is the ratio of the total score to the highest score, corresponding to the answers to the above four questions, and can be, for example, the average score. Furthermore, the above four questions correspond to the fifth factor (exercise) out of the five factors (diet, health awareness, mealtimes, dietary restrictions, and exercise) derived from a factor analysis (maximum likelihood method, oblique rotation) of all 35 questions in the "35-Question Diet Questionnaire" in the aforementioned "Yanagisawa et al., 2017," and can be used as a measure to suggest lack of exercise.

[0156] The Athens Insomnia Scale is a commonly used questionnaire for assessing insomnia tendency, such as the questionnaire shown in FIG. 11 in a document on the Japanese version of the Athens Insomnia Scale (Okajima I, Nakajima S, Kobayashi M, Inoue Y. Psychiatry Clin Neurosci 2013; 67: 420-425.). The index calculated from the responses to the Athens Insomnia Scale can be, for example, the total score calculated from all responses. Note that this index may be the total score or the score ratio, which is the ratio of the total score to the average score or the highest score.

[0157] (Embodiment 4-2) When the quantitative prediction of the weight loss effect is a quantitative prediction of body weight loss, in addition to the indicators described in Embodiments 4 and 4-1, the acquisition step (E2) preferably further acquires at least one indicator selected from the group consisting of total cholesterol concentration and ALT (GPT) activity as a blood indicator measured from a blood sample of the subject. Total cholesterol concentration (e.g., unit: mg / dL) can be measured by an established method such as an enzymatic method using a serum sample. ALT (alanine aminotransferase, GPT: glutamic pyruvic transaminase) activity (e.g., unit: U / L) can be measured by an established method such as a JSCC standardized method using a serum sample.

[0158] The predictive markers are high-priority indicators classified as "Priority 2" above, selected by an appropriate explanatory variable selection algorithm for predicting weight loss rates from data on a group of subjects who completed the dietary management instruction program in Test Example 2 of the Examples described below and who were certified as having complied with the instructions in the program. In this embodiment, in addition to the Priority 1 indicators described in Embodiments 4 and 4-1, Priority 2 indicators that can be measured at general medical institutions are used, making it possible to quantitatively predict weight loss with greater accuracy.

[0159] (Embodiment 4-3) When the quantitative prediction of the weight loss effect is a quantitative prediction of body weight loss, in the acquisition step (E2), in addition to the indicators described in embodiments 4, 4-1, and 4-2, it is preferable to further acquire at least one indicator selected from the group consisting of hydroxyproline concentration, threonine concentration, proline concentration, isoleucine concentration, leucine concentration, and phenylalanine concentration as a blood indicator.

[0160] The above-mentioned predictive markers are indices classified as "Priority 3" among indices selected by an appropriate explanatory variable selection algorithm for predicting weight loss rate from all of the subjects who completed the dietary management instruction program in Test Example 2 of the Examples described below. Indicators classified as Priority 3 are values ​​measured in amino acid analysis (amino acid fractions) of plasma samples. The blood indices of this embodiment are all expressed in units such as "nmol / mL" and can be measured, for example, by HPLC or the like. In this embodiment, weight loss can be quantitatively predicted with greater accuracy by using a Priority 3 indicator that can be measured as an amino acid fraction in addition to the Priority 1 and 2 indicators described in Embodiments 4, 4-1, and 4-2.

[0161] (Embodiment 4-4) When the quantitative prediction of the weight loss effect is a quantitative prediction of body weight loss, in the acquisition step (E2), in addition to the indicators described in embodiments 4, 4-1, 4-2, and 4-3, it is preferable to further acquire MQP and basal metabolic rate as physical indicators, and at least one indicator selected from the group consisting of magnesium concentration, dehydroepiandrosterone sulfate (DHEA-S) concentration, thyroxine (T4) concentration, urea nitrogen concentration, and insulin activity as blood indicators.

[0162] The predictive markers are indices classified as "Priority 4" above, selected by an appropriate explanatory variable selection algorithm for predicting the weight loss rate from data on a group of subjects who were certified as having complied with the instruction in the dietary management instruction program of Test Example 2. In this embodiment, by using an index of Priority 4 in addition to the indices of Priorities 1 to 3 described in Embodiments 4, 4-1, 4-2, and 4-3, weight loss can be quantitatively predicted with higher accuracy.

[0163] Predictive markers not described in the above embodiments will now be described.

[0164] Basal metabolic rate (unit: kcal) refers to the minimum daily energy expenditure required to maintain life, and can be calculated by substituting values ​​such as height, weight, and age into a known estimation formula (e.g., the Harris-Benedict formula, the Mifflin Saint Joe formula, or the National Institute of Health and Nutrition formula). Basal metabolic rate can also be measured using a body composition monitor manufactured by Tanita Corporation (e.g., the "Innerscan Dual" series, the "Commercial Multi-Frequency Body Composition Monitor MC-980A-N plus," etc.).

[0165] DHEA-S (dehydroepiandrosterone sulfate) concentration (e.g., in μg / dL) can be measured using established methods such as the CLEIA method using serum samples. Thyroxine (T4) concentration (e.g., in ng / dL) is a value measured in thyroid function tests and can be measured using established methods such as the ECLIA method using serum samples. Insulin activity (e.g., in μu / mL) can be measured using established methods such as the CLIA method using serum samples.

[0166] (Embodiments 4-5) When the quantitative prediction of the weight loss effect is a quantitative prediction of the reduction in visceral fat mass, in addition to the indicators described in embodiment 4, the acquisition process (E2) preferably acquires at least one indicator selected from the group consisting of an indicator calculated from the answer to the question "I felt calm and relaxed" in the WHO-5 (Mental Health Questionnaire) as a response indicator, and an indicator calculated from the answer to the question asking about motivation for dietary management.

[0167] The predictive markers are indices classified as "Priority 1" among indices selected by an appropriate explanatory variable selection algorithm for predicting the rate of visceral fat mass reduction from data of a group of subjects who underwent a dietary management instruction program in Test Example 2 of the Examples described below and who were certified as having complied with the instruction in the program, and are not listed in Embodiment 4. By using these indices in addition to the indices listed in Embodiment 4, it is possible to quantitatively predict the reduction in visceral fat mass with higher accuracy.

[0168] Predictive markers not described in the above embodiments will now be described.

[0169] The WHO-5 (Mental Health Questionnaire) is a commonly used questionnaire for investigating mental health status, and is a questionnaire consisting of five questions as shown in FIG. 8. In this embodiment, the response index is calculated from the response to question 2 of these five questions, which asks, "I felt calm and relaxed." This index can be a number assigned to the selected option.

[0170] (Embodiments 4-6) When the quantitative prediction of the weight loss effect is a quantitative prediction of the reduction in visceral fat mass, in addition to the indicators described in embodiment 4 and embodiments 4-5, the acquisition step (E2) preferably further acquires MQP as a physical indicator, and at least one indicator selected from the group consisting of total bilirubin concentration, albumin / globulin ratio (A / G), sodium concentration, potassium concentration, calcium concentration, inorganic phosphorus concentration, magnesium concentration, MCHC concentration, platelet count, free testosterone concentration, free fatty acid concentration, lactate dehydrogenase (LD) activity, growth hormone (GH) concentration, free thyroxine (FT4) concentration, and somatomedin-C (IGF-1) concentration as blood indicators.

[0171] The predictive markers are the indices classified as "Priority 4" among the indices selected by an appropriate explanatory variable selection algorithm for predicting the rate of visceral fat mass reduction from data of a group of subjects who completed the dietary management instruction program in Test Example 2 and were certified as having complied with the instruction in the program. In this embodiment, by using an index of Priority 4 in addition to the index of Priority 1 described in Embodiments 4 and 4-5, it is possible to quantitatively predict the reduction in visceral fat mass with higher accuracy. Note that the indices selected by an appropriate explanatory variable selection algorithm for predicting the amount of visceral fat area reduction from data of a group of subjects who complied with the dietary management instruction program in Test Example 2 of the Examples described below did not include indices classified as Priority 2 or 3.

[0172] Predictive markers not described in the above embodiments will now be described. The albumin / globulin ratio can be the ratio of albumin concentration to globulin concentration measured using, for example, a serum sample. The free fatty acid concentration (e.g., unit: mEq / L) can be measured using, for example, a serum sample by an established method such as an enzymatic method. The lactate dehydrogenase (LD) activity (e.g., unit: U / L) can be measured using, for example, an established method that complies with the IFCC standard. The growth hormone concentration (e.g., unit: ng / mL) can be measured using, for example, a serum sample by an established method such as the ECLIA method.

[0173] [Additional remarks] 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 the acquisition step of Embodiment 3 and / or Embodiment 4, each predictive marker used in Embodiments 1 and 2 may be further acquired.

[0174] While the above embodiments have described methods for predicting weight loss effects, other embodiments of the present invention may provide methods for predicting weight loss effects. For example, a prediction method according to one embodiment of the present invention quantitatively predicts the weight loss effect of a subject through dietary management, and includes a prediction step of quantitatively predicting the weight loss effect of the subject by applying, to a prediction model, a predicted value indicating the weight loss effect from at least one of the following physical indices representing at least one of the subject's physique or body composition: height and pulse rate; and response indices calculated from the subject's responses to a questionnaire: an index calculated from answers to questions asking about health-consciousness and / or behavior; an index regarding awareness of constitution and weight calculated from answers to an eating behavior questionnaire; an index calculated from answers to questions asking about dieting attitudes; and an index calculated from answers to questions asking about motivation for dietary management. The prediction step may further use at least one of the predictive markers described in embodiments 3-1 to 3-6, or may use other predictive markers.

[0175] Alternatively, a prediction method according to another embodiment of the present invention is a prediction method for quantitatively predicting the weight loss effect of a subject through dietary management, and includes a prediction step of quantitatively predicting the weight loss effect of the subject by applying to a prediction model a predicted value indicating the weight loss effect a physical indicator, such as height, and at least one index selected from the group consisting of an index calculated from answers to questions inquiring about health consciousness and / or behavior, an index calculated from answers to questions inquiring about irregular mealtimes and / or behavior, an index regarding awareness of constitution and weight calculated from answers to an eating behavior questionnaire, an index calculated from answers to questions about stress response in a brief occupational stress questionnaire, an index calculated from answers to an exercise behavior self-efficacy scale, and an index calculated from answers to questions inquiring about dieting consciousness. The prediction step may further use at least one of the predictive markers described in embodiments 4-1 to 4-6, or may further use other predictive markers.

[0176] The prediction step can be carried out in the same manner as the prediction step (F) described above.

[0177] In addition, in the present invention, for example, a plurality of the above-mentioned predictive markers may be combined to predict the weight loss effect, or the above-mentioned predictive markers may be combined with other indicators to predict the weight loss effect.

[0178] In another embodiment, the present invention may provide a predictive marker for predicting weight loss effects, the predictive marker including at least one of the physical indicators, blood indicators, and response indicators described above. Furthermore, the present invention may provide a use of the predictive marker. [Example]

[0179] [Dietary intervention trial] BMI of 23 kg / m 2 Sixty adult men (aged 20 to 60 years) who were over 60 years old and met the eligibility criteria for the study were selected as the subject group. Before the dietary intervention, the subject group underwent a physical examination, blood tests, and questionnaire tests. The tests in this study were conducted with the consent of the subjects.

[0180] Physical examinations were conducted by gathering a group of subjects at 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) wrapped around the subject's abdomen. 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.

[0181] For blood tests, venous blood was drawn from the subjects, and the following items were measured from the blood samples obtained. Measurement of each item was outsourced to a testing institution. The general blood test items were white blood cell count, red blood cell count, hemoglobin content, hematocrit value, platelet count, mean corpuscular volume, mean corpuscular hemoglobin content (hemoglobin value), and mean corpuscular hemoglobin concentration. The blood biochemistry test items were creatinine, uric acid, urea nitrogen, aspartate aminotransferase, alanine aminotransferase, γ-glutamyltranspeptidase, alkaline phosphatase, lactate dehydrogenase, creatine kinase, total cholesterol, HDL cholesterol, non-HDL cholesterol, LDL cholesterol, triglycerides, total bilirubin, total bile acids, sodium, potassium, chloride, calcium, magnesium, inorganic phosphorus, iron, total protein, albumin, albumin / globulin ratio, and Free fatty acids, blood glucose, HbA1c, insulin, NEFA, total ketone bodies, acetoacetate, 3-hydroxybutyrate, high-sensitivity CRP (hsCRP), leptin, growth hormone, cortisol, dehydroepiandrosterone (DHEA), dehydroepiandrosterone sulfate (DHEA-S), testosterone, androgens, TNF-α, IGF-1, triiodothyronine (T3), thyroxine (T4), TSH, free triiodothyronine (FT3), free thyroxine (FT4), ammonia, and amino acid fractions.

[0182] The questionnaire test was conducted by distributing questionnaires to the subjects, who completed them themselves. The questionnaires included the BDHQ (Brief Self-Administered Diet History Questionnaire), the International Standardized Physical Activity Questionnaire, the Obesity-Related Diet and Lifestyle Questionnaire (35-question dietary questionnaire), the WHO-5 Mental Health Questionnaire, the Brief Occupational Stress Questionnaire, the Chalder Fatigue Scale, the Athens Insomnia Scale, the Big Five Scale, self-efficacy scales (eating behavior self-efficacy scale, exercise behavior self-efficacy scale, and rest behavior self-efficacy scale), the Exercise Behavior Stage of Change Questionnaire, the Rest Behavior Stage of Change Questionnaire, the Eating Behavior Stage of Change Questionnaire, the Eating Behavior Questionnaire, a questionnaire on motivation for dietary management, and a questionnaire on attitudes toward dieting.

[0183] The BDHQ (Brief Self-Administered Dietary History Questionnaire) is a dietary survey developed by the Department of Social Preventive Epidemiology (Laboratory), Department of Public Health / Department of Health Sciences and Nursing, Graduate School of Medicine, The University of Tokyo, and the BDHQ questionnaire was used with permission from the lab. Analysis of the responses was requested from the lab. As a result of the analysis, estimates of nutrient intake by food group, such as energy intake, water intake, protein, lipids, saturated fatty acids, carbohydrates, dietary fiber, alcohol, salt, potassium, calcium, vitamin C, and folic acid, were obtained.

[0184] The International Standardized Physical Activity Questionnaire is a commonly used questionnaire on physical activity, and the short version was used. Calculation of indices from the scores was performed with reference to the literature (Murase et al., "International Standardization of Physical Activity - Evaluation of the Reliability and Validity of the Japanese Version of the IPAQ," Welfare Indicators, 49(11), 1-9, 2002).

[0185] The 35-question dietary questionnaire was created using the questionnaire shown in Figure 2 in Yanagisawa et al., 2017. The index was calculated using the average scores for each question group comprising each of the following factors: Factor 1 (appetite), Factor 2 (health consciousness), Factor 3 (meal time), Factor 4 (dietary restrictions), and Factor 5 (exercise). The questions comprising Factor 2 (health consciousness) correspond to the aforementioned "questions asking about health consciousness and / or behavior." The questions comprising Factor 3 (meal time) correspond to the aforementioned "questions asking about awareness and / or behavior regarding irregular mealtimes." The questions comprising Factor 4 (dietary restrictions) correspond to the aforementioned "questions asking about awareness and / or behavior regarding dietary restrictions." The questions comprising Factor 5 (exercise) correspond to the aforementioned "questions asking about awareness and behavior regarding lack of exercise."

[0186] The WHO-5 Mental Health Questionnaire is a commonly used questionnaire for investigating mental health status, as shown in Figure 12, and the sum of the scores of all responses was calculated as an index. In addition, the scores of the responses to each question shown in Figure 12 were also calculated as an index.

[0187] The Simple Occupational Stress Questionnaire is a questionnaire provided by the Ministry of Health, Labor and Welfare that investigates occupational stress levels, and the sum of the scores of all responses was calculated as an index. This questionnaire is shown in Figures 8 and 9. In addition, the sum of the scores of the responses to each of the questions in Area A (questions about work stressors), Area B (questions about stress reactions), and Area C (questions about modifying factors, including support from those around you and satisfaction with work or family life) as well as the sum of the scores of the responses to the questions in Areas A and C were calculated.

[0188] The Chalder Fatigue Scale is one of the common scales for investigating mental fatigue, and a questionnaire containing the 14 questions shown in Figure 5 was used. Of these, the total score of the eight items asking about physical fatigue was calculated as an index of physical fatigue, and the total score of the six items asking about mental fatigue was calculated as an index of mental fatigue.

[0189] The Big Five Scale (BFS) is a questionnaire for calculating five indices of openness, conscientiousness, extraversion, agreeableness, and emotional stability based on the Big Five personality traits. Specifically, a questionnaire containing 60 questions as shown in Figures 6 and 7 was used as the Big Five Scale, and indices of agreeableness, openness, extraversion, emotional stability, and conscientiousness were calculated from the scores of the responses. Specifically, referring to "Subjective Fulfillment and the Big Five" (Horigome Kazuya, Journal of Contemporary Behavioral Science, No. 15, pp. 1-8 (1999)), for the three items that loaded highly on each factor (items marked with a solid line in Table 4 of the same reference), the scale scores were reversed as necessary so that the higher the score, the stronger the extroversion, openness, emotional stability, conscientiousness, and agreeableness were indicated, and then the total score was calculated, and the average score divided by the number of items was used as the individual personality score.

[0190] The Athens Insomnia Scale is a commonly used questionnaire on insomnia tendency, and the total score calculated from all responses was used as an index. Specifically, we used the questionnaire shown in Figure 11, referring to the literature on the Japanese version of the Athens Insomnia Scale (Okajima I, Nakajima S, Kobayashi M, Inoue Y. Psychiatry Clin Neurosci 2013; 67: 420-425.).

[0191] The eating behavior questionnaire used a questionnaire containing the questions shown in Figure 4, and categorized responses into seven areas (perceptions of body type and weight, motivation for eating, vicarious eating, hunger, feeling of fullness, eating habits, meal content, and regularity of eating habits) as "That's not true," "Sometimes that happens," "Tends to be like that," and "Exactly true," each of which was rated on a four-point scale from 0 to 3. The total score for each area was added up, and the percentage of the maximum score was calculated as an index (%).

[0192] The self-efficacy scale for eating behavior, the self-efficacy scale for exercise behavior, and the self-efficacy scale for rest behavior were calculated using the questionnaires shown in Figures 2 and 3, with reference to Moriya et al., 2009, in which participants were asked to select one answer on a seven-point scale, and the sum of the scores for each scale was calculated as the index for each scale.

[0193] The exercise behavior stage of change questionnaire used the "Health Behavior Change Stage Test Paper" shown in Figure 1 of "Moritani et al., 2009" mentioned above. Similarly, the rest behavior change stage questionnaire and the eating behavior change stage questionnaire were prepared by replacing "activity and exercise behavior" with "stress management and rest behavior" and "dietary habits and eating behavior," respectively, from the "Health Behavior Change Stage Test Paper." The "Health Behavior Change Stage Test Paper" taken from the same publication is shown in Figure 10. The indices calculated from the responses to these tests were calculated by referring to Figure 10, and the numbers of the options for questions regarding I. health behavior change awareness and stage were used as indices.

[0194] The questionnaire about motivation for dietary management consisted of the question, "Please rate your current motivation for this program (calorie restriction through dietary guidance) on a scale of 1 to 10." Participants were asked to respond on an 11-point scale from 0 to 10 (very motivated to not motivated at all). The score corresponding to this response was used as an "index calculated from the answers to the motivation questions."

[0195] The questionnaire about attitudes toward dieting consisted of the question, "Please rate your current attitude toward dieting (weight loss and obesity reduction) on a scale of 1 to 10." Participants were asked to respond on an 11-point scale from 0 to 10 (very high to very low to none). The score corresponding to this response was used as an "index calculated from the responses to questions about attitudes toward dieting."

[0196] Subsequently, each subject in the subject group underwent a two-month dietary intervention. Specifically, a registered dietitian provided each subject with dietary advice to reduce their calorie intake in order to achieve a target weight (a weight loss of 3% of their current weight). Dietary advice from the registered dietitian was given once every two weeks. Each subject was instructed to install an application program ("Asuken (registered trademark)") capable of recording their dietary information on their personal information terminal and to record the contents of each meal using the application program. In addition, each subject was instructed to record their daily activity level using a loaned activity monitor and not to consciously increase their exercise level.

[0197] After two months of dietary intervention, the subjects underwent a post-intervention test, which included the same physical examination, blood tests, and questionnaire tests as in the pre-intervention test (excluding the self-efficacy scale, polyphenol questionnaire, and motivation questionnaire). After that, the subjects were given no dietary intervention for one month, and then the same tests as in the post-intervention test were administered as a follow-up test.

[0198] [Selection of subjects for overall analysis and homogeneous analysis] All 52 subjects who completed the two-month dietary intervention were selected as subjects for the overall analysis. Furthermore, 20 subjects who were judged to have achieved adequate dietary management (50% or more of the daily target energy reduction over the entire two-month period) and not have significantly changed their physical activity were selected as subjects for the homogeneous analysis, which is an analysis of a homogeneous group. Changes in dietary intake were determined using the food records in the application program, and physical activity was determined based on the results of activity measurements.

[0199] [Overall analysis of weight loss rate] The weight loss rate was calculated from the data of the subjects (52 people) in the overall analysis using the following formula. Weight loss rate (%) = (weight before intervention (kg) - weight after intervention (kg)) / weight before intervention × 100 The analysis subjects whose weight loss rate was in the top 25% were defined as the responder group (13 subjects), and the analysis subjects whose weight loss rate was in the bottom 25% were defined as the non-responder group (13 subjects).

[0200] For each of the responder and non-responder groups, the average values ​​and standard deviations of the results of the physical examination, blood test, and questionnaire test in the pre-test were calculated. Student's t-tests were performed on the average values ​​of the responder and non-responder groups for each item, and items with p<0.05 were extracted as items with significant differences between the groups. The extracted items correspond to the indicators in embodiment 1-1. The average values ​​(ave) and standard deviations (sd) of the extracted items for the responder and non-responder groups are shown in Table 1.

[0201] [Table 1]

[0202] [Homogenous analysis of weight loss rate] The weight loss rate was calculated from the data of the subjects (20 people) in the homogeneous analysis, as in the overall analysis. The group of subjects whose weight loss rate was in the top 25% was defined as the responder group (5 people), and the group of subjects whose weight loss rate was in the bottom 25% was defined as the non-responder group (5 people). Next, as in the overall analysis, the average value and standard deviation of the results for each item were calculated for the responder group and the non-responder group, and items with significant differences in average value between the groups were extracted. The extracted items correspond to the indicators in embodiments 1-2. The average value (ave) and standard deviation (sd) of the extracted items for the responder group and the non-responder group are shown in Table 2.

[0203] [Table 2]

[0204] [Overall analysis of visceral fat area reduction] The visceral fat area reduction was calculated from the data of the subjects (52 subjects) in the overall analysis by subtracting the visceral fat area value after the intervention from the visceral fat area value before the intervention. The analysis subjects in the top 25% of the visceral fat area reduction amount were defined as the Responder group (13 subjects), and the analysis subjects in the bottom 25% were defined as the Non-Responder group (13 subjects). Next, as in the overall analysis of weight loss rate, the mean and standard deviation of the results for each item were calculated for the Responder group and the Non-Responder group, and items with significant differences in mean values ​​between the groups were extracted. The extracted items correspond to the indicators in embodiment 2-1. The mean (ave) and standard deviation (sd) of the extracted items for the Responder group and the Non-Responder group are shown in Table 3.

[0205] [Table 3]

[0206] [Homogenous analysis of visceral fat area reduction] The data from the subjects (20 subjects) in the homogeneous analysis described above were used to calculate the reduction in visceral fat area, as in the overall analysis. The analysis subjects with the highest 25% reduction in visceral fat area were defined as the Responder group (5 subjects), and the analysis subjects with the lowest 25% reduction in visceral fat area were defined as the Non-Responder group (5 subjects). Next, as in the overall analysis, the average and standard deviation of each item were calculated for the Responder group and the Non-Responder group, and items with significantly different average values ​​were extracted between the groups. The extracted items correspond to the indicators in embodiment 2-2. The average (ave) and standard deviation (sd) of the extracted items for the Responder group and the Non-Responder group are shown in Table 4.

[0207] [Table 4]

[0208] <Test Example 2> [Dietary intervention trial] Next, BMI is 23 kg / m 2 Sixty adult males (aged 20 to 60 years) who met the same eligibility criteria as in Test Example 1 were selected, and a physical examination, blood test, and questionnaire test were conducted as pre-intervention tests before the dietary intervention. These tests were the same as those in Test Example 1. The tests in this test example were conducted with the consent of the subjects.

[0209] Subsequently, each newly selected subject underwent a two-month dietary intervention identical to that in Test Example 1. After the two-month dietary intervention, the subjects underwent post-intervention tests similar to those in the pre-intervention tests, including physical examinations, blood tests, and questionnaire tests (excluding the self-efficacy scale and polyphenol questionnaire tests).

[0210] [Selection of subjects for overall analysis and homogeneous analysis] The 52 subjects selected in Test Example 1 and the 60 subjects who completed the two-month dietary intervention in Test Example 2 were combined to form the subject group for Test Example 2. All of this subject group were selected as subjects for the overall analysis in Test Example 2. Furthermore, from the subject group for the overall analysis, 65 subjects who were judged to have achieved sufficient dietary management (50% or more of the daily target energy reduction over the entire two-month period) and not have significantly changed their exercise volume were selected as subjects for a homogeneous analysis, which is an analysis of a homogeneous group. Note that changes in dietary volume were determined using the food records in the application program, and the results of activity measurement were used as the basis for determining the amount of exercise.

[0211] [Overall analysis of weight loss rate] Weight loss rates were calculated from the data of the subjects (112 people) in the overall analysis, as in Test Example 1. Using the data from the results of each test item as explanatory variables and the calculated weight loss rate data as the objective variable, an explanatory variable selection algorithm was constructed to generate a machine learning model that predicts weight loss rates. The Elastic Net algorithm was specified as the method and constructed, and the optimal values ​​of the hyperparameters were tuned using 5-fold cross-validation to minimize the error between the predicted value and the actual measured value (e.g., root mean square error (RMSE)). The explanatory variables (prediction markers) of the prediction model selected in this way are shown in Table 5.

[0212] [Table 5]

[0213] The selected indicators were classified into Priorities 1 to 4 as shown in Table 5. Priority 1 was assigned to indicators that belong to the category of physical indicators and response indicators as physical measurements that are minimally invasive and easily obtainable. Priority 2 was assigned to blood indicators included in test items in regular health checkups. Priority 3 was assigned to values ​​measured in amino acid analysis (amino acid fractions) of plasma samples. Priority 4 was assigned to indicators classified as "other," such as items measured using specific equipment, test items with higher precision than those in general regular health checkups (such as test items performed in comprehensive medical checkups), and special blood test items. However, there were no indicators that fell into Priority 2.

[0214] Next, indicators were selected according to priority, and linear regression models 1 to 4 were constructed in the same manner, with the selected indicators as explanatory variables. Note that model 5 was a model that used all of the indicators shown in Table 5 above as explanatory variables. For Model 1, from Priority 1 shown in Table 5, height, "Dietary 35 Questions Factor 2: Health Orientation" (an index calculated from responses to questions asking about awareness and / or behavior regarding health orientation), "Dietary 35 Questions Factor 3: Mealtime" (an index calculated from responses to questions asking about awareness and / or behavior regarding irregular mealtimes), and "Dietary 35 Questions Factor 4: Dietary Restrictions" (an index calculated from responses to questions asking about awareness and / or behavior regarding dietary restrictions) were selected as explanatory variables. In addition to the explanatory variables selected in Model 1, Model 2 included the following explanatory variables: "Big 5: Conscientiousness personality score" (an index of conscientiousness personality calculated from responses to the Big Five scale), "Exercise behavior stage of change" (an index calculated from responses to a questionnaire assessing the exercise behavior stage of change), and "Rest behavior stage of change" (an index calculated from responses to a questionnaire assessing the rest behavior stage of change). Model 3 selected all indicators of priority 1 shown in Table 5 as explanatory variables. Model 4 selected all indicators of priorities 1, 2, and 3 shown in Table 5 as explanatory variables.

[0215] A correlation analysis of the predicted and measured weight loss rates using each model was performed using Spearman's correlation test. The correlation coefficients were 0.296 for Model 1, 0.316 for Model 2, 0.417 for Model 3, 0.534 for Model 4, and 0.733 for Model 5, all of which had absolute values ​​of 0.2 or greater. These results demonstrate that weight loss rates can be accurately predicted by generating a prediction model using the indicators shown in Table 5.

[0216] [Overall analysis of visceral fat area reduction] The reduction in visceral fat area was calculated from the data of the subjects (112 people) in the overall analysis, as in Test Example 1. Using the data from the results of each test item as explanatory variables and the calculated data on the reduction in visceral fat area as the objective variable, an explanatory variable selection algorithm was constructed to generate a machine learning model that predicts the reduction in visceral fat area. The Elastic Net algorithm was specified as the method and constructed, and the optimal values ​​of the hyperparameters were tuned using 5-fold cross-validation to minimize the error between the predicted value and the actual measured value (e.g., root mean square error (RMSE)). The explanatory variables (prediction markers) of the prediction model selected in this way are shown in Table 6.

[0217] [Table 6]

[0218] The selected indicators were classified into priority levels 1 to 4 as shown in Table 6. However, there were no indicators that fell into priority level 2. Next, indicators were selected according to the priority level, and linear regression models 6 to 9 were similarly constructed using the selected indicators as explanatory variables. Model 10 was a model that used all of the indicators shown in Table 6 above as explanatory variables. For Model 6, height, pulse, "Eating Behavior Questionnaire: Regularity of eating habits" (an index of regularity of eating habits calculated from responses to the eating behavior questionnaire), "Eating Behavior Questionnaire: Awareness of constitution and weight" (an index of awareness of constitution and weight calculated from responses to the eating behavior questionnaire), and "Dietary 35 Factor 2: Health orientation" were selected as explanatory variables from Priority 1 shown in Table 6. In Model 7, from Priority 1 shown in Table 6, in addition to the explanatory variables selected in Model 6, the following were selected as explanatory variables: "Chalda: Mental fatigue" (an index calculated from Chalda's mental fatigue scale), "Occupational stress: A+C" (an index calculated from answers to questions on work-related stress factors in the Occupational Stress Brief Questionnaire and questions on modifying factors including support from those around you and satisfaction with work or family life), and "Big 5: Extroversion personality score" (an index of extroversion personality calculated from answers to the Big Five scale). Model 8 selected all indicators of priority 1 shown in Table 6 as explanatory variables. Model 9 selected all indicators of priority 1 and 3 shown in Table 6 as explanatory variables.

[0219] A correlation analysis of the predicted and measured values ​​of visceral fat area reduction predicted by each model was performed using Spearman's correlation test. The correlation coefficient values ​​were 0.254 for Model 6, 0.281 for Model 7, 0.364 for Model 8, 0.557 for Model 9, and 0.674 for Model 10, all of which had absolute values ​​of 0.2 or greater. From these results, it was found that by generating a prediction model using the indicators shown in Table 6, it is possible to accurately predict the amount of visceral fat area reduction.

[0220] [Homogenous analysis of weight loss rate] Weight loss rates were calculated from the data of the subjects (65 people) in the homogeneous analysis described above, in the same way as in the overall analysis. Using the data on the results of each test item as explanatory variables and the calculated weight loss rate data as the objective variable, an explanatory variable selection algorithm was constructed to generate a machine learning model to predict weight loss rates. The Elastic Net algorithm was specified as the method and constructed, and the optimal values ​​of the hyperparameters were tuned using five-fold cross-validation to minimize the error between the predicted value and the actual measured value (e.g., root mean square error (RMSE)). The explanatory variables (predictive markers) of the prediction model selected in this way are shown in Table 7.

[0221] [Table 7]

[0222] The selected indicators were classified into priorities 1 to 4 as shown in Table 7. Next, indicators were selected according to the priority, and linear regression models 11 to 16 were similarly constructed using the selected indicators as explanatory variables. Note that model 17 was a model using all of the indicators shown in Table 7 above as explanatory variables. For Model 11, from Priority 1 shown in Table 7, "Factor 3 of 35 Dietary Questions: Mealtime" and "Food Behavior Questionnaire: Eating Method" were selected as explanatory variables. For Model 12, age, height, "Dietary 35 Questions Factor 2: Health Orientation," "Dietary 35 Questions Factor 3: Mealtime," and "Dietary 35 Questions Factor 4: Dietary Restrictions" were selected from Priority 1 shown in Table 7 as explanatory variables. For Model 13, from Priority 1 shown in Table 7, age, height, "Dietary Questions 35 Factor 2: Health Orientation," "Dietary Questions 35 Factor 3: Mealtime," "Dietary Questions 35 Factor 5: Exercise" (an index calculated from responses to questions asking about awareness and behavior regarding lack of exercise), "Eating Behavior Questionnaire: Awareness of physical constitution and weight," "Eating Behavior Questionnaire: How to eat," and "Big 5: Conscientiousness personality score" (an index of conscientiousness personality calculated from responses to the Big Five scale) were selected as explanatory variables. Model 14 selected all indicators of priority 1 shown in Table 7 as explanatory variables. Model 15 selected all indicators of Priority 1 and 2 shown in Table 7 as explanatory variables. Model 16 selected all indicators of priorities 1, 2, and 3 shown in Table 7 as explanatory variables.

[0223] A correlation analysis of the predicted and measured weight loss rates predicted by each model was performed using Spearman's correlation test. The correlation coefficients were 0.430 for Model 11, 0.453 for Model 12, 0.564 for Model 13, 0.594 for Model 14, 0.660 for Model 15, 0.702 for Model 16, and 0.752 for Model 17, all of which had absolute values ​​of 0.2 or greater. These results demonstrate that weight loss rates can be accurately predicted by generating a predictive model using the indicators shown in Table 7.

[0224] [Homogenous analysis of visceral fat area reduction] The reduction in visceral fat area was calculated from the data of the subjects (65 people) in the homogeneous analysis described above, in the same way as in the overall analysis. Using the data from the results of each test item as explanatory variables and the calculated data on the reduction in visceral fat area as the objective variable, an explanatory variable selection algorithm was constructed to generate a machine learning model to predict the reduction in visceral fat area. The Elastic Net algorithm was specified as the method and constructed, and the optimal values ​​of the hyperparameters were tuned using five-fold cross-validation to minimize the error between the predicted value and the actual measured value (e.g., root mean square error (RMSE)). The explanatory variables (predictive markers) of the prediction model selected in this way are shown in Table 8.

[0225] [Table 8]

[0226] The selected indicators shown in Table 8 were classified into priorities 1 to 4, similarly to Table 5. However, there were no indicators that fell into priorities 2 and 3. Next, indicators were selected according to the priority, and linear regression models 18 and 19 were similarly constructed using the selected indicators as explanatory variables. Note that a model using all of the indicators shown in Table 8 above as explanatory variables was designated Model 20. For Model 18, height, "Dietary Questions 35 Factor 2: Health Orientation" and "Dietary Questions 35 Factor 3: Meal Time" were selected from Priority 1 shown in Table 8 as explanatory variables. Model 19 selected all indicators of priority 1 shown in Table 8 as explanatory variables.

[0227] A correlation analysis was performed using Spearman's correlation test to compare the predicted and measured values ​​of visceral fat area reduction predicted by each model. The correlation coefficients were 0.275 for model 18, 0.462 for model 19, and 0.799 for model 20, all of which had absolute values ​​of 0.2 or greater. These results demonstrate that the amount of visceral fat reduction can be accurately predicted by generating a prediction model using the indicators shown in Table 8.

[0228] [Summary] From the above, it was found that the above physical indices, blood indices, and response indices shown in Test Example 1 were indices in which significant differences were confirmed between the responder group and the non-responder group, and that they can be used as predictive markers for predicting the weight loss effect of dietary intervention. Furthermore, since these indices can predict the weight loss effect of dietary intervention individually, it is possible to make a simple prediction, and it is thought that the weight loss effect can be predicted by combining these indices or by combining them with other indices related to the weight loss effect. Furthermore, it was found that the above physical indices, blood indices, and response indices shown in Test Example 2 function as predictive markers that can quantitatively predict the weight loss effect. Furthermore, it was found that the prediction accuracy can be improved by combining these predictive markers.

Claims

1. A method for quantitatively predicting a weight loss effect of a subject through dietary management, comprising: Height and pulse as physical indicators representing at least one of the subject's physique or body composition, and Response indices calculated from the subject's responses to the questionnaire include an index calculated from the responses to questions asking about health-consciousness and / or behavior, an index about awareness of physical constitution and weight calculated from the responses to the eating behavior questionnaire, an index calculated from the responses to questions asking about awareness of dieting, and an index calculated from the responses to questions asking about motivation for dietary management. An acquisition step of acquiring at least one index selected from the group consisting of: A method for quantitatively predicting weight loss effects.

2. The quantitative prediction of the weight loss effect includes at least one of a quantitative prediction of a weight loss or a quantitative prediction of a visceral fat mass loss, The method for quantitatively predicting the weight loss effect according to claim 1.

3. The quantitative prediction of the weight loss effect is a quantitative prediction of weight loss, The obtaining step further comprises: The response indicators include an indicator calculated from responses to questions inquiring about awareness and / or behavior regarding irregular mealtimes, an indicator calculated from responses to questions inquiring about awareness and / or behavior regarding dietary restrictions, an indicator about eating habits calculated from responses to the eating behavior questionnaire, an indicator calculated from responses to questions about stress reactions in the Occupational Stress Brief Questionnaire, an indicator of conscientious personality calculated from responses to the Big Five scale, an indicator calculated from responses to a questionnaire evaluating the exercise behavior stage of change, an indicator calculated from responses to a question in the WHO-5 (Mental Health Status Questionnaire) asking about "staying motivated and active," and an indicator calculated from responses to a questionnaire evaluating the rest behavior stage of change. obtaining at least one indicator selected from the group consisting of: The method for quantitatively predicting the weight loss effect according to claim 2.

4. The obtaining step further comprises: blood indices measured from the subject's blood sample, including phenylalanine concentration, 3-methylhistidine concentration, tryptophan concentration, hydroxyproline concentration, and ornithine concentration; obtaining at least one indicator selected from the group consisting of: The method for quantitatively predicting the weight loss effect according to claim 3.

5. The obtaining step further comprises: MQP (Muscle Quality Point) as the physical index, and the blood indices, creatine kinase level, urea nitrogen concentration, potassium concentration, magnesium concentration, cortisol concentration, and tumor necrosis factor-α concentration; obtaining at least one indicator selected from the group consisting of: The method for quantitatively predicting the weight loss effect according to claim 4.

6. The quantitative prediction of the weight loss effect is a quantitative prediction of a reduction in visceral fat mass, The obtaining step further comprises: The response indicators include an index of awareness of regularity of eating habits calculated from responses to an eating behavior questionnaire, an index of mental fatigue calculated from responses to the Chalder Fatigue Scale, an index calculated from responses to questions on work stressors and modifying factors including support from those around them and satisfaction with work or family life in the Occupational Stress Brief Questionnaire, an index of extroverted personality calculated from responses to the Big Five scale, and an index calculated from responses to the eating behavior self-efficacy scale. obtaining at least one indicator selected from the group consisting of: The method for quantitatively predicting the weight loss effect according to claim 2.

7. The obtaining step further comprises: blood indices measured from a blood sample of the subject, including glutamic acid concentration, sarcosine concentration, α-amino-n-butyric acid concentration, leucine concentration, β-alanine concentration, ornithine concentration, lysine concentration, monoethanolamine concentration, alanine concentration, and serine concentration; obtaining at least one indicator selected from the group consisting of: The method for quantitatively predicting the weight loss effect according to claim 6.

8. The obtaining step further comprises: MQP as the physical index, and as the blood indicators, total bilirubin concentration, creatine kinase level, uric acid concentration, calcium concentration, inorganic phosphorus concentration, magnesium concentration, mean corpuscular hemoglobin concentration (MCHC), platelet count, free testosterone concentration, blood ammonia concentration, high-sensitivity CRP concentration, and somatomedin-C (IGF-1) concentration; obtaining at least one indicator selected from the group consisting of: The method for quantitatively predicting the weight loss effect according to claim 7.

9. The method further includes a prediction step of quantitatively predicting the weight loss effect of the subject by applying at least one index acquired in the acquisition step to a prediction model for deriving a predicted value indicating the weight loss effect from the acquired index. A method for quantitatively predicting a weight loss effect according to any one of claims 1 to 8.

10. A method for quantitatively predicting a weight loss effect of a subject through dietary management, comprising: Height and pulse as physical indicators representing at least one of the subject's physique or body composition, and Response indices calculated from the subject's responses to the questionnaire include an index calculated from the responses to questions asking about awareness and / or behavior regarding health orientation, an index about awareness regarding physical constitution and weight calculated from the responses to the eating behavior questionnaire, an index calculated from the responses to questions asking about awareness of dieting, and an index calculated from the responses to questions asking about motivation for dietary management. and a prediction step of quantitatively predicting the weight loss effect of the subject by applying at least one index selected from the group consisting of: How to predict weight loss results.

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