JP2012165716A - Meal support system based on intestinal resident bacterial analysis information - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide technologies for supporting the health promotion by improving intestinal resident bacterial types.SOLUTION: Meals for health promotion are supported by a system including: a step for recognizing the dietary habit of a subject; a step for recognizing intestinal resident bacteria by analyzing the excrement of the subject; a step for finding the deviation from preferable intestinal resident bacteria for the subject based on a preset interrelation between intestinal resident bacteria and the dietary habit, or an interrelation between the intestinal resident bacteria and the dietary habit obtained from the analysis in the past for the subject; a step for finding the dietary habit necessary for achieving the preferable intestinal resident bacteria; and a step for finding and outputting the deviation between the necessary dietary habit and the actual dietary habit of the subject.

Description

  The present invention supports a diet for health promotion based on the determination of the intestinal resident bacteria type based on the estimation of the proportion of enterobacteria and the taxonomic group (family, genus, species) of the intestinal bacteria. About the system.

  In recent years, the aging of the population structure has become a serious problem due to the extension of the average life span and the declining birthrate. In addition, the increase in social security costs due to aging is also a problem. In 2011, medical expenses were 32 trillion yen, welfare related expenses were 18 trillion yen, in 2025 medical expenses were 48 trillion yen, welfare Related costs are expected to increase to 28 trillion yen. For this reason, the reduction of social security costs, especially the reduction of medical costs, due to advance disease prevention and health promotion has become an important issue.

  An increase in the number of patients with lifestyle-related diseases is a factor that greatly affects the increase in medical costs. Lifestyle-related diseases include diseases such as adult diabetes, obesity, hyperlipidemia, colon cancer, hypertension, liver disorders, and periodontal disease. Many lifestyle-related diseases are caused by visceral fat-type obesity caused by unhealthy lifestyles. The onset and progression of such diseases include diet, exercise, rest, smoking, and alcohol consumption. It is said that lifestyle is involved.

  Therefore, specific medical checkups / health guidance began in order to ensure thorough health guidance for affected individuals and reserve groups of metabolic syndrome (visceral fat syndrome), which is a major cause of lifestyle-related diseases. Insurers (National Health Insurance and Employee Insurance) are obliged to conduct health checkup and health guidance projects focusing on visceral fat obesity for insured persons and dependents over 40 years of age. The contents of health guidance include improving lifestyle habits such as diet and exercise aimed at suppressing the onset and progression of lifestyle-related diseases. Above all, the review of eating habits is regarded as important.

  In recent years, the use of foods for specified health purposes for the purpose of promoting health has been increasing, while a review of dietary habits has been called out. In FY2007, the annual number of licenses for specified health foods (Tokuho) reached the highest ever, and the market scale grew to 679.8 billion yen. In particular, it has been reported that intake of lactic acid bacteria, which are permitted as food for specified health use, is effective not only in improving the bowel regulation and intestinal environment, but also in reducing the risk of various diseases such as digestive disorders and allergies. (Non-Patent Documents 1 and 2). In addition, it has also been reported that intestinal environment, especially intestinal resident bacteria are greatly involved in human health maintenance and disease prevention (Non-patent Documents 3 and 4).

  As a method for analyzing indigenous bacteria in the intestine, analysis by a culture method has been generally used in the past, and is positioned as important data when registering food for specified health use. However, the culture method requires expertise and knowledge regarding the handling of microorganisms, and more time and effort are required to analyze one specimen. Therefore, a T-RFLP (Terminal Restriction Fragment Length Polymorphism) analysis method has been proposed as a technique that can easily compare data of multiple samples at once, has high reproducibility, requires a low level of specialized knowledge, and is easily analyzed at low cost. . T-RFLP analysis is a method of amplifying a template DNA using a primer set labeled with a terminal fluorescence, digesting with a restriction enzyme, then performing fragment analysis, and the restriction enzyme cleavage site differs due to the difference in base sequence depending on the bacterial species. This is a fragment polymorphism analysis that is evaluated and compared based on the intensity, position, and number of detected peaks. By this analysis method, the percentage of enterobacteria and taxonomic groups of enterobacteria (family , Genus and species) (Non-patent Document 5).

  The T-RFLP analysis has a short analysis time and has the ability to cope with clinical sites and environmental impact assessments, and is easily reproducible without the need for expertise. It can contribute to the development in the microbial ecology field and the bioindustry field as a method capable of estimating the bacterial species with high sensitivity in the intestinal resident bacteria analysis (Patent Document 1).

Yokokura Teruo: Science and Technology of Lactic Acid Bacteria, p.322-334, Academic Publishing Center, Tokyo, (1996) Kalliomaki M. et al .: The Lancent, 357: 1076-1079. (2001) Shintaro Kotani et al .: Japanese Public Health Journal, 8: 871-895 (1996). Mizutani, T and Mitsuoka T: Cancer Lett., 11: 89-95 (1983) Mitsuo Sakamoto: Journal of Intestinal Bacteriology, 18: 155-159, 2004 JP 2003-265199 A

  However, the relationship between health and probiotics in intestinal resident bacteria (for example, Bifidobacterium genus: bifidobacteria, Lactobacillus genus: microorganisms such as lactic acid bacteria) is not clear. In addition, it is difficult to select dietary habits and balance of ingredients suitable for improvement to individual health, particularly the preferred intestinal resident bacteria type, and the judgment criteria are not established at present.

  The present invention has been made in view of the above circumstances, and an object of the present invention is to elucidate the above-mentioned relationship and provide a technique for supporting health promotion through improvement of intestinal resident bacteria.

  Therefore, the present inventor conducted a large-scale epidemiological survey, compared the intestinal resident bacteria and the average of the actual age, the effect of the resident bacteria on the disease, the relationship between the diet and the resident bacteria. We have earnestly researched about.

  In the process, the ratio of enterobacteria and fecal taxonomic groups (family, genus, species) were estimated by T-RFLP analysis of feces using a combination of several primers and restriction enzymes. And the epidemiological survey were combined to determine the intestinal resident bacteria type and the intestinal age. The results obtained there differed from the individual data evaluations reported so far, and lifestyle-related diseases, metabolic syndrome (hyperglycemia, hypertension, hyperlipidemia, etc.) in the intestinal resident type, intestinal tract-related We have found that it is possible to determine the risk of diseases such as diseases, allergies (food, hay fever), constipation, etc., health check, and evaluation of functional foods. In addition, as a result of analyzing the relationship between eating habits (meal patterns, etc.) and intestinal resident bacteria types (including intestinal resident bacteria clusters), not only specific foods such as yogurt but also daily eating habits We found the fact that it greatly affects the resident bacteria type. Furthermore, the present inventor determines appropriate diets for each dietary type (including dietary clusters) and disease types (including disease clusters) based on the obtained results, and is a diet for promoting health. It came to develop the technology to support.

  In order to solve the problem, the present invention provides a process for grasping the dietary life of the subject, a process for analyzing the feces of the subject and grasping the intestinal resident bacteria, and a preset intestinal resident bacteria. Based on the correlation between the dietary habits or the intestinal resident bacteria previously performed by the subject and the dietary habits, preferred intestinal resident bacteria of the subject intestinal resident type A process of obtaining a deviation from the type or the intestinal resident bacterial type obtained in the past of the subject's intestinal resident bacterial type, and the preferred intestinal resident bacterial type or a more preferred intestinal type Provided is a meal support technique including a process for obtaining a dietary life necessary for realizing a resident bacteria type and a process for outputting the necessary dietary life.

  According to the present invention, it is possible to perform intestinal age determination, disease risk determination, and health check based on determination / classification of intestinal resident bacteria, diet, and disease information. Evaluation of functional foods and determination of effects can be performed.

  Furthermore, according to the present invention, it is possible to perform evaluation / effect determination of drugs and nutrients such as live-bacteria preparations according to dietary and disease types.

  Furthermore, according to the present invention, it is possible to support dietary habits for improving health by eating habits and disease types and reducing the incidence of diseases.

  Embodiments of the present invention will be described below.

  In this embodiment, the process of grasping the eating habits of the subject, the process of analyzing the feces of the subject and grasping the intestinal resident bacteria, and the correlation between the preset intestinal resident bacteria and the eating habits The deviation of the subject's intestinal resident bacteria type from the preferred intestinal resident bacteria type is determined based on the relationship or the correlation between the intestinal resident bacteria and the dietary habits that the subject conducted in the past. A diet support method comprising: a process; a process for obtaining a dietary habit necessary for realizing the preferable intestinal resident bacteria type or a more preferable intestinal resident bacteria type; and a process for outputting the necessary dietary life It is.

  Here, grasping the eating habits type is to grasp the target person's meal pattern, environment, preferences, etc., but it is not necessary to grasp the whole picture of the eating habits. It may be attention and grasp of the intake frequency of the meal item. In the following examples, a questionnaire is conducted on the number of meals of each meal item (for example, pan, meat, fish, vegetables, etc.), and the eating habits are grasped by classifying the eating habits type based on the results. However, depending on the purpose, for example, to grasp the diet type based on the total intake of specific meal items and the proportion of nutrients (proteins, carbohydrates, lipids, vitamins, minerals, dietary fiber) contained in the ingested food You can also.

  In addition, grasping the intestinal resident bacteria type is desirably grasping the intestinal bacterial cluster reflecting the whole image of intestinal bacteria, but based on the results of T-RFLP, several types, or 1 It may be an analysis based on a combination of different types of primers and restriction enzymes, or it may be a probiotic such as bifidobacteria or lactic acid bacteria, or the proportion of spoilage bacteria such as Clostridium: Clostridium or coliforms: Enterococcus. Good.

  Here, the preferable intestinal resident bacteria type means an intestinal resident bacteria type that is considered desirable for the subject. For example, for subjects who need a decrease in intestinal age, it is the average intestinal resident type in young healthy individuals. For subjects who are at high risk for a disease, the intestinal resident type has a low incidence of the disease. In addition, the intestinal resident bacteria type may be a desirable intestinal resident bacteria type for the subject. When the past intestinal resident bacteria type is a desirable intestinal resident bacteria type for the subject, a more preferred intestinal resident bacteria type than the past intestinal resident bacteria type is preferred as the intestinal resident bacteria type. You can also In addition, for the purpose of improving hypertension, lowering blood cholesterol, lowering blood neutral fat, and the like, preferred intestinal resident bacteria types can be determined. In addition, the dietary habits necessary to realize a preferred intestinal resident bacteria type include, for example, the most common dietary type in the group having the preferred intestinal resident bacteria type, and the preferred intestinal resident bacteria type. The characteristic eating habits type of the group can be mentioned. For example, for a subject who needs a decrease in intestinal age, the subject may have a characteristic diet type or an average diet type in a group having an intestinal age younger than the actual age. Furthermore, the process of outputting the necessary dietary life is, for example, a correlation between a preset intestinal resident bacteria and dietary life or a correlation between the intestinal resident bacteria and dietary life conducted by the subject in the past This means a process in which the target person or a third party can recognize the required eating habits required based on the relationship, and the output means is not particularly limited.

  Here, the deviation from the preferred intestinal resident bacteria type indicates a difference between the intestinal resident bacteria type of the subject and the average intestinal resident bacteria type of the preferred intestinal resident bacteria type. For example, it can be represented by a difference in intestinal bacterial clusters or a difference in the ratio of probiotics and spoilage bacteria. Further, the deviation from the intestinal resident bacteria type obtained in the past of the subject's intestinal resident bacteria type refers to the subject's intestinal resident bacteria type and the subject's intestinal resident bacteria type. This shows a difference from the intestinal resident bacteria type obtained in the past, and can be represented by, for example, a difference in intestinal bacterial clusters or a difference in the proportion of probiotics and spoilage bacteria.

  In this embodiment, the process of grasping the eating habits of the subject, the process of analyzing the feces of the subject and grasping the intestinal resident bacteria, and the correlation between the preset intestinal resident bacteria and the eating habits The deviation of the intestinal resident bacteria type of the subject from the preferred intestinal resident bacteria type is determined on the basis of the sex or the correlation between the intestinal resident bacteria and the eating habits performed by the subject in the past. A diet support method comprising: a process; a process for obtaining a dietary habit necessary for realizing the preferred intestinal resident bacteria type; and a process for outputting the necessary dietary life, wherein the dietary habit is grasped The process includes eating habits clustering, and the process of grasping the intestinal resident bacteria type includes intestinal resident bacteria clustering, and the correlation between the preset intestinal resident bacteria type and the eating habits type Is a correlation between the dietary cluster based on the clustering and the intestinal resident bacteria cluster The preferred intestinal flora type than the age of the subject is an average diet support method is indigenous intestinal bacteria types in healthy subjects younger.

  Here, clustering is one of data analysis methods, and classifies given data into several clusters (subsets) according to similarity, and extracts data distribution features. Broadly speaking, there are hierarchical methods and non-hierarchical methods, but here, clustering is performed using the Ward method, which is one of the hierarchical methods, but this is not restrictive. .

  Here, eating habits clustering means classifying the obtained eating habits data according to the degree of similarity and extracting data distribution characteristics. In the following embodiment, the cluster is classified into four clusters. However, the present invention is not limited to this, and may be classified into two, three, five, six, seven, eight, nine, ten or more clusters. Is possible.

  Here, intestinal resident bacteria clustering means classifying the obtained data of intestinal resident bacteria by similarity and extracting the characteristics of data distribution. In the following embodiment, the cluster is classified into five clusters, but the present invention is not limited to this, and the cluster may be classified into two, three, four, six, seven, eight, nine, ten or more clusters. Is possible.

  In this embodiment, the process of grasping the eating habits of the subject, the process of analyzing the feces of the subject and grasping the intestinal resident bacteria, and the correlation between the preset intestinal resident bacteria and the eating habits The deviation of the subject's intestinal resident bacteria type from the preferred intestinal resident bacteria type is determined based on the relationship or the correlation between the intestinal resident bacteria and the dietary habits that the subject conducted in the past. A process for obtaining a diet necessary for realizing the preferred intestinal resident bacteria type, a process for outputting the necessary diet, and an incidence rate of the subject intestinal resident bacteria clustering. The process of grasping high diseases, and the correlation between the intestinal resident bacteria type and the dietary habit type set in advance, or the correlation between the intestinal resident bacteria and the dietary life conducted by the subject in the past, and Based on the correlation between the intestinal resident bacteria type and the incidence of disease, to achieve a preferred intestinal resident bacteria type A method for assisting eating, including a process for obtaining an essential diet type, wherein the process for grasping the diet type includes clustering for diet, and the process for grasping the intestinal resident bacteria type The preferred intestinal resident bacteria type includes resident clustering, the intestinal resident bacteria type having a low onset rate with respect to the disease having a high onset rate, and the preset intestinal resident bacteria type and food The correlation with the life type is a correlation between the dietary life cluster and the intestinal resident bacteria cluster based on the clustering, and the correlation between the preset intestinal resident bacteria type and the incidence of the disease is This is a meal support method, which is a correlation between a disease cluster based on the clustering and an intestinal resident bacteria cluster.

  Here, a disease having a high incidence in the intestinal resident bacterial clustering refers to an intestinal normal in which the subject is classified based on a correlation between a preset intestinal resident bacterial type and the disease incidence. It means a disease that has been determined to have a high incidence in bacterial clustering. Examples of diseases with high incidence in intestinal resident clustering include lifestyle-related diseases, metabolic syndrome (such as hyperglycemia / hypertension / hyperlipidemia), intestinal related diseases, allergies (food, hay fever, etc.), constipation, etc. However, it is not limited to these diseases as long as they can be affected by intestinal bacteria. For example, cancers such as colon cancer, breast cancer, pancreatic cancer, immune diseases, Alzheimer's and stress Sexual diseases and the like can also be included.

  Here, the disease clustering means that the obtained disease data is classified by the similarity and the characteristics of the data distribution are extracted. Disease data may be, for example, health checkup values or biochemical test values (serum blood glucose level, HbA1c test level, urea nitrogen level, uric acid level, creatinine level, total protein level, albumin level, etc.). In the examples below, as a representative example, diabetes is classified into four clusters, but is not limited to this, and is not limited to two, three, five, six, seven, eight, nine, ten or more clusters. It is also possible to classify them.

  In this embodiment, the process of grasping the eating habits of the subject, the process of analyzing the feces of the subject and grasping the intestinal resident bacteria, and the correlation between the preset intestinal resident bacteria and the eating habits The deviation of the subject's intestinal resident bacteria type from the preferred intestinal resident bacteria type is determined based on the relationship or the correlation between the intestinal resident bacteria and the dietary habits that the subject conducted in the past. A diet support method comprising: a process; a process for obtaining a dietary habit necessary for realizing the preferred intestinal resident bacteria type; and a process for outputting the necessary dietary habit, The process of grasping the type is a meal support method including a process of analyzing with several kinds or a combination of one kind of primer and a restriction enzyme.

  Here, the process of analyzing with several types or a combination of one type of primer and a restriction enzyme is, for example, in T-RFLP analysis, 1, 2, 3, 4, 5 or more types of primers as a restriction enzyme. It is a process of estimating intestinal resident bacteria (ratio of enteric bacteria and taxonomic groups (family, genus, species) of intestinal bacteria) in combination. According to this process, it is possible to determine and classify the intestinal resident bacteria type, determine the intestinal age, and provide information such as the intestinal age to the subject. Furthermore, by combining the results of the dietary life cluster, for example, information on meals suitable for rejuvenation of the intestinal age can be provided.

  In addition, the technique relating to the meal support method described above can also be used as a storage medium that can be read by a computer that stores a meal support system, computer software for the meal support system, or meal support system computer software.

  Further, in the present embodiment, the computer receives an input indicating an index indicating the dietary type of the subject and an index indicating the intestinal resident bacteria type of the subject based on the analysis of the subject's stool. Based on the correlation between the intestinal resident bacteria type and the eating habits type, or the correlation between the intestinal resident bacteria and the eating habits performed by the subject in the past, the intestinal resident bacteria type of the subject The computer software for a meal support system for obtaining a deviation from a preferable intestinal resident bacteria type, obtaining a dietary type necessary for realizing the preferred intestinal resident bacteria type, and outputting the necessary dietary type It is.

  Here, the index indicating the eating habits of the subject means the result of classifying the eating habits of the subject based on the eating habits data of the subject, for example, eating habits type or eating habits clustering. Can also be used as an index representing eating habits.

  Here, the index indicating the intestinal resident bacteria type based on the analysis of the subject's stool is a classification of the subject's intestinal resident bacteria based on the data of the subject's intestinal resident bacteria. It can also be used as an index representing intestinal resident bacteria by, for example, intestinal resident bacteria type or intestinal resident bacteria clustering.

  As described above, the computer software can also be used as a computer-readable storage medium that stores the computer software.

  According to the method of the present embodiment, information on the obtained intestinal resident bacteria type (intestinal resident bacteria cluster) and disease type (disease cluster) is used, and lifestyle-related diseases, metabolic syndrome (hyperglycemia, Hypertension, hyperlipidemia, etc.), intestinal related diseases, allergies (food, hay fever, etc.), constipation and diabetes, serum blood glucose, HbA1c test value, renal function test, urea nitrogen, uric acid, creatinine It is possible to provide information on disease risk judgment and health check from total protein, albumin and the like. Furthermore, information regarding risk reduction and health promotion for diseases and the like can be provided as preventive medicine.

  In addition, according to the method of the present embodiment, using the obtained information such as the intestinal resident bacteria type (intestinal resident bacteria cluster) and the determination result of the intestinal age, the eating habit type (dietary life cluster) It is possible to evaluate functional foods according to disease types (disease clusters) and determine the effects, and to evaluate the results of functional foods and determine the effects, and to make functional foods tailor-made and develop new products. Information can be provided.

  Further, according to the method of the present embodiment, using the information such as the obtained intestinal resident bacteria type (intestinal resident bacteria cluster) and the determination result of intestinal age, the dietary life type (food life cluster) ), It is possible to evaluate and evaluate the effects and evaluation of drugs and nutrients such as live bacteria preparations by disease type (disease cluster), Information on the development of new products and tailor-made drugs and nutrients such as viable bacteria preparations can be provided.

  Further, according to the method of the present embodiment, using the information such as the obtained intestinal resident bacteria type (intestinal resident bacteria cluster) and the determination result of intestinal age, the dietary life type (food life cluster) ), Dietary support for health promotion by disease type (disease cluster) can be provided, dietary support for health promotion and nutritional information can be provided .

  As mentioned above, although embodiment of this invention was described, these are only illustrations of this invention.

  The present invention will be described below based on examples, but the examples are described for the purpose of helping understanding of the present invention, and the present invention is not limited to the examples described below. Is self-explanatory.

  First, as a representative example, T-RFLP analysis of about 800 people, biochemical examination (clinical chemistry examination) at the time of health examination, physical fitness measurement, and questionnaire survey on lifestyle etc. for 3 years (total 2,400) Conducted. As shown in the system overview of FIG. 1, the collected data is stored in a database. Based on these data, the proportion of enterobacteria and the taxonomic group (family, genus, species) of the enterobacteria are estimated from several types or a combination of one primer and a restriction enzyme. The type (intestinal resident bacteria cluster) is determined and classified, and the relationship between dietary habits and intestinal resident bacteria and the relationship between intestinal resident bacteria and age, disease, etc. is statistically analyzed. Intestinal age determination model by defining fungal type (intestinal resident bacteria cluster), dietary type (dietary life cluster), disease type (disease cluster) based on test values, etc., and using it as preset data And disease risk assessment model, health check model and diet and intestinal bacteria related model.

  For the above analysis, as shown in the basic database of FIG. 2, a method for handling information considered to be derived from the same fungus group as one unit OTU, including errors of T-RFLP experiments, was developed. Here, OTU means a classification unit in T-RFLP analysis, which corresponds to the type of bacteria. As a result, the obtained experimental result is automatically converted into OTU and becomes a base of statistical analysis for extracting necessary information. Statistical analysis based on this data was carried out, and a database was constructed that serves as basic information on intestinal resident bacteria for evaluating the functions of intestinal resident bacteria clustering, health examinations and health foods.

  Furthermore, by extracting and combining OTUs characteristic of intestinal resident bacteria type (intestinal resident bacteria cluster), dietary type (dietary life cluster), and disease type (disease cluster), intestinal resident bacteria We have developed a system that can output information that suggests advice for improving health from the viewpoint of intestinal bacteria.

  This system applies T-RFLP analysis, clinical chemistry tests, and results of questionnaire surveys related to lifestyle habits, and determination of determining intestinal age and disease risk by applying the results of T-RFLP analysis to the above model. The improvement of intestinal resident bacteria to rejuvenate the intestinal age obtained by the above determination and to reduce the disease risk, as shown in the means and the proposal of health degree determination and health improvement proposal in FIG. Eating habits to promote health through intestinal resident bacteria analysis, which has means for proposing lifestyle improvements including the intended eating habits, and output means for outputting the above determination results and lifestyle improvement plans It is a support system. Table 1 shows the OTUs of several types of restriction enzymes related to intestinal age and the estimated bacterial species. Table 1 shows the OTUs related to intestinal age and the estimated bacterial species, which are divided by restriction enzyme and sex. In the table, “+” means that the age increases as the OTU increases (aging), and “−” means that the age decreases (rejuvenation) as the OTU increases. For example, in BslI, OTU317 (Prevotella), OTU658 (Lactobacillales etc.) are mention | raise | lifted as OTU which leads to male aging. Similar OTUs are shown by gender for four restriction enzymes BslI, AluI, HhaI and MspI.

Table 1

  In addition, using OTU to define the intestinal resident bacteria type (intestinal bacterial cluster), a discrimination model (intestine age model) for discriminating those ages was created, and T-RFLP analysis results of unknown specimens By applying to the discrimination model, it is possible to discriminate the intestine age of the examinee. FIG. 4 shows an example of a result obtained by actually analyzing a sample obtained from a certain recipient and applying it to an intestine age model. In this example, the intestinal age of the examinee is highly likely to be in his 30s, and the probability of being predicted as another age is also displayed.

  Using this model, by analyzing changes in the intestinal resident bacteria type (intestinal resident bacteria cluster), intestinal age and disease risk over time after a pre-meal meal of functional food, for example, lifestyle habits It becomes possible to evaluate the effect of the improved functional food.

  Furthermore, this model is used to analyze changes in the intestinal resident bacteria type (intestinal resident bacteria cluster), intestinal age and disease risk over time before and after ingestion of drugs and nutrients such as viable bacteria preparations. By doing so, it becomes possible to evaluate the effect of a medicine or a nutrient.

  This system is a system that makes intestinal resident bacteria OTU based on the T-RFLP analysis results, performs intestinal resident bacteria clustering, and enables classification into several types. FIG. 5 shows the intestinal resident bacteria clustering results based on the two types of T-RFLP analysis results of restriction enzymes AluI and HhaI. Those having a large amount of DNA in each bacterial species are darkly expressed. As a result of clustering, it was possible to classify into several intestinal resident bacteria clusters. As an example, the classification when classifying into five intestinal resident bacteria clusters is indicated by ◯, and the results of analysis of variance between the intestinal resident bacteria clusters for each OTU are shown in FIG. It was also possible to extract OTUs with significantly different mean values between intestinal resident bacteria clusters.

  Intestinal resident bacteria type (intestinal resident bacteria cluster) and dietary life type (dietary life cluster) are the average value of each OTU in age and gender, and the standard intestinal intestine in age and sex, respectively. It can be defined as a resident type and a standard dietary type.

  FIG. 7 shows changes in the standard intestinal resident bacteria type by age. Intestinal bacterial type (intestinal resident bacteria cluster) that increases or decreases with aging can be determined by creating a contingency table with the intestinal resident bacteria clusters and ages determined above and performing chi-square test. This makes it possible to estimate the intestinal age. It is also possible to narrow down each age by gender.

  Moreover, the eating habits type (eating habits cluster) could be classified into several types by executing clustering based on the questionnaire results on eating habits. FIG. 8 shows dietary life clustering results based on questionnaire results regarding dietary habits. In each meal item, those with a large number of meals per week are shown darkly. As a result of executing clustering, it was possible to classify into several eating habits types (eating habits cluster). As an example, the classification when the food is classified into four types of eating habits (diet life cluster) is indicated by a circle.

  Chi-square test was performed on meal items and eating habits type (eating habits cluster), and changes in meals and eating habits type (eating habits cluster) are shown in FIG. The abscissa indicates the eating habits type (dietary life cluster), and the ordinate indicates the number of people for each cluster as a percentage according to the frequency of eating and drinking of meal items (how many times a week). Furthermore, the result of having extracted the item of a meal with a significant difference by the eating habit type (eating habits cluster) is shown in FIG. The horizontal axis shows meal items, and the vertical axis shows the frequency of meal items. It was possible to confirm that a characteristic meal was taken by eating habits type (dietary life cluster) classification. For example, milk, boiled vegetables, and fruits can be confirmed to be characteristic meals because of the large difference between clusters.

  FIG. 11 shows the results of analysis of variance among the dietary clusters for each OTU. It is possible to extract OTUs that are significantly different from the mean value between dietary clusters. The downward arrows indicate characteristic points, and it can be understood that AluI OTU146, AluI OTU206, AluI OTU242, AluI OTU250, and AluI OTU281 are characteristic.

  FIG. 12 shows the relationship between the intestinal resident bacteria type (intestinal resident bacteria cluster), dietary life type (food life cluster), and age in the case of AluI OTU245.

  AluI OTU245 was extracted as a significant OTU in the intestinal resident bacteria type (intestinal resident bacteria cluster) and dietary type (dietary life cluster). AluI OTU245 is an intestinal resident bacteria type (intestinal resident bacteria cluster) characteristically many in the intestinal resident bacteria cluster 3 ((a) of FIG. 12). It was more common among people with high (Fig. 7). Similarly, when AluI OTU245 was compared with the average for each age, it was revealed that it significantly increased in proportion to the age ((c) of FIG. 12). Thus, it was shown that intestinal age can be lowered by reducing AluI OTU245. On the other hand, when looking at the state of AluI OTU245 in the eating habits type (eating habits cluster), it became clear that there were few in eating habits cluster 2 and eating habits cluster 1 ((b) of FIG. 12). These results show that in order to rejuvenate the intestinal age, AluI OTU245 may be reduced, and in order to reduce AluI OTU245, the eating habits may be improved to eating habits cluster 2 and eating habits cluster 1. It was shown that this system provides information for rejuvenating the intestinal age.

  This result is applied to the examination of intestinal bacteria. First, intestinal resident bacteria are examined, the intestinal age is calculated, and the intestinal resident bacteria type (intestinal resident bacteria cluster) is determined. Combining the results with the dietary habits stored in the database (eating habits cluster) enables us to propose a dietary improvement plan to rejuvenate the intestinal age in addition to evaluating functional foods. Become.

  When it is desired to grasp the intestinal bacteria corresponding to each AluI OTU245, it can be estimated by comparing with the base sequence database. For example, it was speculated that the intestinal bacterium corresponding to AluI OTU245 was Lactococcus lactis subsp. Lactis. Similarly, for other OTUs, it is possible to estimate the intestinal bacteria that constitute the OTUs.

  Furthermore, disease types (disease clusters) can be classified into several types by performing disease clustering based on health check values and biochemical test values required for each test purpose. For example, in the case of diabetes, serum blood glucose, HbA1c test values, and in the case of renal function test, clustering is performed by combining test values for each disease such as urea nitrogen, uric acid, creatinine, total protein, albumin test values. It is possible to classify the degree of the disease according to the degree of similarity of multiple examinations. The target diseases include lifestyle-related diseases, metabolic syndrome (such as hyperglycemia / hypertension / hyperlipidemia), intestinal tract-related diseases By targeting diseases, allergies (food, hay fever, etc.), liver function, constipation, etc., it can be used for the development and evaluation of foods and pharmaceuticals effective in preventing these diseases or promoting health.

  As an example, the analysis result in diabetes is shown. When the diabetic clusters were classified based on the test values of serum blood glucose and HbAlc, they could be classified into four clusters: a low test value cluster, a normal cluster, a high cluster, and a high cluster (FIG. 13). Moreover, the mosaic diagram of a dietary life cluster and a diabetes cluster is shown in FIG.

  When OTU having a significant difference in the amount of bacteria among the four diabetic clusters was extracted (FIG. 15), and OTU having a significant difference in the dietary cluster was also extracted, the presence of multiple restriction enzymes was confirmed ( Table 2). As an example, AluI OTU238 was examined in detail (FIG. 16) and found to be more common in clusters with high diabetes test values. Moreover, when the eating habits cluster is seen, it is understood that the amount of eating habits clusters 1 and 2 is small. From this, the proposal of dietary habits improvement shown in the dietary clusters 1 and 2 for diabetes improvement can be performed through intestinal resident bacteria.

  As described above, a meal support method is provided that can provide a dietary life necessary to realize preferable intestinal resident bacteria corresponding to each purpose such as rejuvenation of intestinal age and reduction of disease incidence. Rukoto has been shown.

Table 2

  Furthermore, it is also conceivable to confirm changes in AluI OTU238 and the like by using intestinal resident bacteria as an index for evaluating the effect of this result when developing a drug for improving diabetes, supplements and the like.

  In the above, this invention was demonstrated based on the Example. It is to be understood by those skilled in the art that this embodiment is merely an example, and that various modifications are possible and that such modifications are within the scope of the present invention.

FIG. 1 shows a system overview of the present invention. FIG. 2 shows a flowchart associated with the basic database of the present invention. FIG. 3 shows a flow chart relating to the health level determination and the proposal of the health level improvement plan of the present invention. FIG. 4 is a diagram showing a result of discriminating the intestinal age of the examinee. FIG. 5 is a diagram showing intestinal resident bacteria clustering (intestinal resident bacteria type) results based on two types of T-RFLP analysis results of restriction enzymes AluI and HhaI. FIG. 6 is a diagram showing the ratio of each OTU in each intestinal resident bacteria cluster and the results of analysis of variance between each intestinal resident bacteria cluster. Here, the result of AluI is illustrated. FIG. 7 is a mosaic diagram of age and intestinal resident bacteria cluster. It shows changes in the standard intestinal resident bacteria type by age. FIG. 8 is a diagram of eating habits clustering based on eating habits. The dietary life clustering result based on the questionnaire result of the number of meals of each meal item is shown. FIG. 9 is a mosaic diagram of meal items and dietary clusters. It shows changes in diet and eating habits type (eating habits cluster). (A) of FIG. 9 has shown the frequency | count and ratio of eating bread in the morning in one week in each dietary life cluster. (B) of FIG. 9 shows the number of times of eating cattle / pigs (with vegetables) and the ratio thereof in one week in each dietary cluster. (C) of FIG. 9 has shown the frequency | count and its ratio of eating grilled fish or scissors in one week in each dietary life cluster. (D) of FIG. 9 has shown the frequency | count and ratio of eating raw vegetables in one week in each dietary life cluster. FIG. 10 is a diagram illustrating dietary life clusters and significant meal items. The vertical axis shows the number of meals for each item in one week, and the horizontal axis shows the meal items. FIG. 11 is a diagram showing the ratio of each OTU in the dietary cluster and the results of analysis of variance between the dietary clusters. Here, the result of AluI is illustrated. FIG. 12 is a diagram showing the relationship between AluI OTU245 and (a) intestinal resident bacteria type (intestinal resident bacteria cluster) or (b) dietary life type (food life cluster). The vertical axis shows the amount of bacteria in percentage, and the horizontal axis shows intestinal resident bacteria clusters or dietary life clusters. (C) is a figure which shows the relationship between AluI OTU245 and (c) age. The vertical axis shows the amount of bacteria in percentage, and the horizontal axis shows the age. FIG. 13 is a diagram illustrating clustering in diabetes based on test values. FIG. 14 is a mosaic diagram of a diabetes cluster and a dietary life cluster. FIG. 15 is a diagram showing the ratio of each OTU in a cluster in diabetes and the results of analysis of variance between diabetes clusters. Here, the result of AluI is illustrated. FIG. 16 is a diagram showing the relationship between AluI OTU238 and (a) the eating habits type (eating habits cluster). The vertical axis shows the amount of bacteria in percentage, and the horizontal axis shows dietary clusters. (B) is a diagram showing the relationship between AluI OTU238 and (c) diabetes cluster. The vertical axis shows the amount of bacteria in percentage, and the horizontal axis shows the diabetes cluster.

Claims (9)

  Based on the process of grasping the dietary habits of the subject, the process of analyzing the feces of the subject and grasping the intestinal resident bacteria, and the correlation between the intestinal resident bacteria and the dietary life, A process for obtaining a deviation from a preferred intestinal resident bacteria type of the subject's intestinal resident bacteria type, a process for obtaining a diet necessary for realizing the preferred intestinal resident bacteria type, A meal support method including a process of outputting a healthy diet.   The process of grasping the eating habits includes eating habits clustering, and the process of grasping the intestinal resident bacteria types includes the intestinal resident bacteria clustering, and the predetermined intestinal resident bacteria types and the foods The correlation with the lifestyle is a correlation between the dietary cluster based on the clustering and the intestinal resident bacteria cluster, and the preferable intestinal resident bacteria type is a healthy person younger than the age of the subject. The meal support method according to claim 1, which is an average intestinal resident bacteria type.   Furthermore, the process of grasping the disease with high incidence in the intestinal resident bacteria clustering of the subject, and the correlation between the preset intestinal resident bacteria type and dietary type, and the intestinal resident bacteria type 2. The meal support method according to claim 1, comprising a step of obtaining a dietary type necessary for realizing a preferable intestinal resident bacteria type based on a correlation between the incidence of the disease and the disease, The process of grasping the eating habit type includes eating habits clustering, the process of grasping the intestinal resident bacteria type includes intestinal resident bacteria clustering, and the preferred intestinal resident bacteria type is the incidence rate. The intestinal resident bacteria type has a low incidence of high-disease diseases, and the correlation between the preset intestinal resident bacteria type and the eating habits type is based on the clustering of eating habits and intestinal It is a correlation with the microbial cluster, the preset The correlation between the incidence of intestinal flora type and disease is a correlation between the disease cluster and intestinal flora cluster based on the clustering, dietary support method.   The meal according to claim 3, wherein the correlation with the incidence of the disease includes a process of grasping a health check value or biochemical test value of the subject and a disease clustering targeted from the test value of the subject. Support method.   5. The meal support method according to any one of claims 1 to 4, wherein the process of grasping the intestinal resident bacteria type includes a process of analyzing with several kinds or a combination of one kind of primer and a restriction enzyme.   An index representing the subject's eating habits and an index representing the subject's intestinal resident bacteria type based on the analysis of the subject's stool, and the preset intestinal resident bacteria type and eating habits type Based on this correlation, the deviation of the intestinal resident bacteria type of the subject from the preferred intestinal resident bacteria type is obtained, and the dietary type necessary to realize the preferred intestinal resident bacteria type is determined. A meal support system for obtaining and outputting the necessary eating habits.   An index representing the subject's eating habits and an index representing the intestinal resident bacteria type of the subject based on the analysis of the subject's feces, and the intestinal resident bacteria obtained as a result of the subject's past implementation Based on the correlation between the type and the dietary type, the deviation of the intestinal resident bacteria type of the subject from the intestinal resident bacteria type obtained in the past is obtained, and more preferable intestinal resident bacteria A meal support system for obtaining a dietary type necessary for realizing a mold and outputting the necessary dietary type.   The computer receives an index representing the subject's dietary type and an index representing the subject's intestinal resident bacteria type based on the analysis of the subject's stool. Based on the correlation with the eating habits type, the deviation of the subject's intestinal resident bacteria type from the preferred intestinal resident bacteria type is determined, and it is necessary to realize the preferred intestinal resident bacteria type Computer software for a meal support system for obtaining a dietary lifestyle type and outputting the necessary dietary lifestyle type.   A computer-readable storage medium storing the computer software according to claim 8.