A method for predicting the proportion of Blautia bacteria in the intestinal flora

Abstract

To provide a method for easily predicting the rate of Blautia bacterial germs in intestinal bacterial flora.SOLUTION: The method for predicting the rate of Blautia bacterial germs in intestinal bacterial flora includes: an acquisition step of acquiring an index value acquired from feces information on feces of a subject; and a prediction step of predicting the rate of Blautia bacterial germs in the intestinal bacterial flora of the subject on the basis of data showing the relationship between the index value of the feces information acquired in the acquisition step and the rate of Blautia bacterial germs in intestinal bacterial flora.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to a method for predicting the proportion of the number of bacteria of the genus Blautia in intestinal bacterial flora. [Background technology]

[0002] One method of checking one's health is to check the state of one's intestinal flora (intestinal flora). For example, Patent Document 1 discloses a method for diagnosing the health state of the intestinal flora of a subject (examinee) by measuring the intestinal bacteria in the feces collected from the subject using a genetic analysis method, characterized in that the bacterial count ratio of at least two or more types of intestinal bacterial groups is used as an index. Furthermore, in Non-Patent Document 1, the present inventors suggest a relationship between the proportion of bacteria belonging to the genus Blautia in the intestinal flora and visceral fat area (VFA). [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Patent Publication No. 2021-45097 [Non-patent literature]

[0004] [Non-Patent Document 1] npj Biofilms and Microbiomes 2019 5:28 Summary of the Invention [Problem to be solved by the invention]

[0005] However, identification and measurement of intestinal bacteria by genetic analysis to confirm the bacterial count ratios and bacterial count proportions of various bacteria in the intestinal bacterial flora may require a great deal of effort and cost. Furthermore, even simply identifying and measuring the bacterial count proportions of specific bacterial species, such as the Blautia genus bacteria, in the intestinal bacterial flora may require a great deal of effort and cost.

[0006] The present invention has been made in view of the above-mentioned problems, and relates to a method capable of simply predicting the proportion of bacteria belonging to the genus Blautia in the intestinal bacterial flora of a subject. [Means for solving the problem]

[0007] The present invention relates to a method for predicting the proportion of the bacterial count of bacteria of the genus Blautia in the intestinal flora, which includes an acquisition step of acquiring an index value obtained from stool information related to the stool of a subject, and a prediction step of predicting the proportion of the bacterial count of bacteria of the genus Blautia in the intestinal flora of a subject based on data showing the relationship between the index value of the stool information acquired in the acquisition step and the proportion of the bacterial count of bacteria of the genus Blautia in the intestinal flora. [Effects of the Invention]

[0008] According to the present invention, a method for simply predicting the proportion of Blautia bacteria in the intestinal bacterial flora of a subject can be provided. [Brief explanation of the drawings]

[0009] [Figure 1] 1 is a flowchart showing a method for predicting the proportion of the bacterial count of Blautia bacteria in intestinal bacterial flora according to the present embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0010] Preferred embodiments of the present invention (hereinafter also referred to as the present embodiment) will be described below. Note that the preferred embodiments described below are merely examples, and the present invention is not limited to the configurations of the following embodiments.

[0011] The method for predicting the proportion of the number of bacteria of the genus Blautia in the intestinal microbiota according to this embodiment (hereinafter also referred to as the present method) includes an acquisition step of acquiring an index value obtained from stool information related to the stool of a subject, and a prediction step of predicting the proportion of the number of bacteria of the genus Blautia in the intestinal microbiota of the subject based on predetermined data from the index value of the stool information acquired in the acquisition step. Furthermore, the method may include a provision step of providing the subject with predicted information on the proportion of the number of bacteria of the genus Blautia in the intestinal microbiota based on the predicted result. Each of these steps will be described in detail below.

[0012] [Acquisition process] The acquisition step of this method is a step of acquiring an index value obtained from stool information relating to at least the stool of the subject (S11 in FIG. 1).

[0013] This method is based on the newly discovered fact that the proportion of Blautia bacteria in a subject's intestinal flora is closely related to stool information about the subject, and the stool information is used as an index for the prediction. Therefore, in the acquisition step, an index value of the stool information is first obtained.

[0014] Examples of stool information related to the subject's stool include information on the defecation status, such as stool properties (color, hardness, etc.), defecation frequency (number of defecations per day or per week), and sensations during defecation. It may also include information on changes in stool properties over a certain period of time, and a history of medical examinations related to defecation. The stool information related to the subject's stool is preferably one or more pieces of information selected from stool properties, defecation frequency, and sensations during defecation, and more preferably includes at least information on defecation frequency. It is even more preferable that the defecation frequency information be defecation frequency (number of defecations) per day or per week. Such stool information can be obtained by, for example, administering a questionnaire to the subject that includes the above questions.

[0015] An index value is then obtained from the stool information of the subject thus obtained. As a specific index value of the stool information, it is preferable to use statistical data (including average values, index values, etc.) obtained by the above-mentioned questionnaire.

[0016] Furthermore, in this acquisition step, in addition to the index value of the stool information, it is preferable to also acquire index values ​​obtained from the subject's physical and habit information, which include at least one or more selected from the subject's age, sex, alcohol consumption, smoking habit, and exercise habit. The index values ​​obtained from the subject's physical and habit information preferably include at least an index value of age, more preferably index values ​​of age and exercise habit, even more preferably index values ​​of age, alcohol consumption, and exercise habit, and even more preferably index values ​​of age, sex, alcohol consumption, and exercise habit. This is because, in the prediction step described below, the proportion of Blautia bacteria in the subject's intestinal flora can be more accurately predicted based on data showing the relationship between the index value of the stool information and the index value of the physical or habit information and the proportion of Blautia bacteria in the intestinal flora.

[0017] The subject's physical and habit information can also be obtained by administering a similar questionnaire to the subject. The amount of alcohol consumed may be, for example, the amount of alcohol consumed per week in the past month, the smoking habit may be, for example, the number of cigarettes smoked per day in the past month (smoking amount), and the exercise habit may be, for example, the amount of exercise (METs: metabolic equivalents). Similarly, statistical data obtained by the above-mentioned questionnaire is also suitable for use as the index values ​​of the subject's physical and habit information.

[0018] Furthermore, if the subject's physical and habit information includes the subject's abdominal circumference, it is more preferable because abdominal circumference is considered to be more closely related to visceral fat area among other physical information, making it easier to more accurately predict the proportion of Blautia bacteria in the subject's intestinal microbiota.For example, it is preferable that the index values ​​obtained from the subject's physical and habit information include at least one index value selected from age, sex, alcohol consumption, smoking habits, and exercise habits, and an index value for abdominal circumference.

[0019] Furthermore, the subject's physical and habit information may further include the subject's blood pressure, muscle mass, dietary energy intake, etc. The dietary energy intake may be, for example, the dietary energy intake per day over the past month.

[0020] [Prediction Process] The prediction step of this method is a step of predicting the proportion of Blautia bacteria in the intestinal flora of a subject based on data showing the relationship between the index value of the stool information acquired in the acquisition step and the proportion of Blautia bacteria in the intestinal flora (S13 in Figure 1). Note that this prediction step is simply a step of predicting the proportion of Blautia bacteria in the intestinal flora, and does not include medical procedures such as diagnosis.

[0021] The data showing the relationship between the index value of the stool information and the proportion of the number of bacteria of the genus Blautia in the intestinal microbiota is not limited to, but can include, for example, a t-value obtained by a t-test of the aforementioned statistical data, a multiple regression equation obtained in advance by multiple regression analysis using statistical data of a population, in which the index value of the stool information is the explanatory variable and the proportion of the number of bacteria of the genus Blautia in the intestinal microbiota is the objective variable, or data obtained from a conversion table in which the index value of the stool information and the proportion of the number of bacteria of the genus Blautia in the intestinal microbiota are correlated in a tabular format. These may also be used in combination, and the proportion of the number of bacteria of the genus Blautia in the intestinal microbiota is predicted from the index value of the stool information of the subject based on data such as the t-value, the multiple regression equation, the conversion table, etc. (e.g., one or more selected from these).

[0022] Furthermore, when index values ​​for the subject's physical and habit information as described above are also obtained along with the index value for the stool information, it is preferable to predict the proportion of Blautia bacteria in the subject's intestinal microbiota based on data showing the relationship between the index values ​​for the stool information and the index values ​​for the physical or habit information and the proportion of Blautia bacteria in the intestinal microbiota. This data is also not limited to, but can be, for example, data obtained from a t-value obtained by a t-test of the statistical data described above, a multiple regression equation obtained in advance by multiple regression analysis using statistical data of a population, in which the index values ​​for the stool information and the index values ​​for the physical and habit information are explanatory variables and the proportion of Blautia bacteria in the intestinal microbiota is the response variable, or a conversion table in which the index values ​​for the stool information, the index values ​​for the physical and habit information, and the proportion of Blautia bacteria in the intestinal microbiota correspond in table form (e.g., one or more selected from these).

[0023] Instead of multiple regression analysis, various other machine learning models can be used to predict the proportion of Blautia bacteria in the subject's intestinal microbiota, such as a logistic regression model, a multilayer perceptron, a neural network such as a convolutional neural network (CNN) or a recurrent neural network (RNN), a support vector machine using an arbitrary kernel function such as a Gaussian kernel, a random forest modeled as a regression tree, a model using a hidden Markov model, a statistical model, or a probabilistic model. A model that combines various models to make a comprehensive judgment can also be used.

[0024] This method includes the above-described acquisition step and prediction step, and thus makes it possible to easily predict the proportion of Blautia bacteria in the intestinal bacterial flora of a subject.

[0025] [Providing process] Preferably, the method further includes a providing step of providing the subject with predicted information on the proportion of Blautia bacteria in the intestinal microbiota based on the results predicted in the prediction step. This is because the subject can be provided with information that can be useful for preventing lifestyle-related diseases, etc. Note that this providing step simply provides potentially useful information to the subject and does not include medical procedures such as diagnosis, treatment, or surgery on the subject.

[0026] For example, based on the results predicted by the prediction step described above, the subject can be provided with predicted information on the proportion of the number of bacteria of the genus Blautia in the intestinal flora, and further, information on the visceral fat area estimated from this predicted information can also be provided. Note that, since the proportion of the number of bacteria of the genus Blautia in the intestinal flora is closely related to the visceral fat area, by obtaining the above information, the subject can easily select, for example, foods optimal for reducing the visceral fat area (such as foods containing catechins) or foods that help increase the proportion of the number of bacteria of the genus Blautia in the intestinal flora, making it easier to reduce the visceral fat area, etc.

[0027] In this provision process, in addition to the predicted information on the proportion of Blautia bacteria in the intestinal flora and information on visceral fat area, more specific information may also be provided, such as the provision of optimal foods, food intake programs, and services for reducing visceral fat area as described above, or the provision of optimal foods, food intake programs, and services for helping to increase the proportion of Blautia bacteria in the intestinal flora.

[0028] This method preferably includes the acquisition step and prediction step as described above, and further includes the provision step as described above, but may further include any step other than those described above as long as it does not affect the effects of the present invention.

[0029] [Device for predicting the proportion of Blautia bacteria in the intestinal flora] The present invention can also provide a prediction device for the proportion of Blautia bacteria in the intestinal flora, which is equipped with an input means for inputting stool information about a subject's stool or an index value of this stool information, and a prediction means for predicting the proportion of Blautia bacteria in the intestinal flora of a subject, based on data showing the relationship between the stool information or its index value input by the input means and the proportion of Blautia bacteria in the intestinal flora.

[0030] This device for predicting the proportion of Blautia bacteria in the intestinal bacterial flora is implemented by a general-purpose computer, which may be, for example, a desktop personal computer (PC), a mobile terminal such as a portable PC, a smartphone, or a tablet, or even a dedicated computer. The input means is not particularly limited as long as it is capable of inputting flight information or its index value. For example, it may be a device that accepts user input, such as a keyboard or mouse, or it may be a configuration in which a display device and an input device are integrated, such as a touch panel. Furthermore, it may include storage means such as a memory that can store the input flight information, perform calculations based on the input flight information, and store the calculations as index values.

[0031] In this method, when the subject's physical and habit information including at least one or more of the subject's age, sex, alcohol consumption, smoking habit, and exercise habit, or further including abdominal circumference, or an index value obtained from this information is acquired in addition to the stool information or index value, any input means capable of inputting this physical and habit information or index value is sufficient. Note that, when the storage means is included, it is preferable that the storage means be capable of storing the input physical and habit information, etc., as with the stool information, or of performing calculations, etc., from the input physical and habit information and storing the results as an index value.

[0032] The prediction means may be connected to the input means and be capable of predicting the proportion of Blautia bacteria in the intestinal flora of a subject from the coefficients of a multiple regression equation, a conversion table, etc., based on data showing the relationship between the index value of the stool information input by the input means (the input index value or the calculated index value) and the proportion of Blautia bacteria in the intestinal flora, and is not particularly limited, but an example is a CPU (Central Processing Unit) capable of performing predetermined calculations, etc., from the information stored in the storage means. As described above, the prediction means may also employ other machine learning models instead of multiple regression analysis.

[0033] Similarly, when the subject's physical and habit information or index values ​​obtained from this information are input in addition to the stool information or index values ​​thereof, the means for predicting the proportion of Blautia bacteria in the subject's intestinal flora can be provided based on data showing the relationship between the input stool information and index values ​​of the physical and habit information (input index values ​​or calculated index values) and the proportion of Blautia bacteria in the intestinal flora.

[0034] Note that this prediction means, rather than the input means, may include a means for calculating stool information and physical and habit information into index values. For example, it may include a means for calculating stool information about the subject's stool input by the input means into index values.

[0035] Furthermore, in addition to the input means and prediction means, this device for predicting the proportion of the bacterial count of bacteria of the genus Blautia in the intestinal microbiota may also include an information providing means for providing the subject with predicted information on the proportion of the bacterial count of bacteria of the genus Blautia in the intestinal microbiota based on the results predicted and output by the prediction means. This information providing means may also provide, in addition to the predicted information on the proportion of the bacterial count of bacteria of the genus Blautia in the intestinal microbiota, information on visceral fat area as described above, optimal foods and food intake programs for reducing visceral fat area, optimal foods and food intake programs for supporting an increase in the proportion of the bacterial count of bacteria of the genus Blautia in the intestinal microbiota, etc. It is more preferable that this information providing means be capable of displaying the above-mentioned information on a display or outputting it on paper, etc.

[0036] Hereinafter, examples of the present invention will be described, but the present invention is not limited to the following examples, and various modifications are possible within the technical concept of the present invention. [Example]

[0037] The following test was conducted on subjects (N=1118) who participated in the 2016 Iwaki Project Health Checkup.

[0038] As an example, with the consent of the subjects, the relationship between the percentage of Blautia bacteria in the intestinal flora and the index values ​​of age, sex, alcohol consumption, smoking habits (amount of cigarettes smoked per day in the past month), exercise habits (activity level (METs)), as well as the index values ​​of bowel movement frequency (number of bowel movements per day or per week) and abdominal circumference was investigated. Specifically, the intestinal flora information for each subject was identified from the stool collected from the subject, and a database was constructed in which the percentage of Blautia bacteria in the intestinal flora of the subject was associated with each index value. On the other hand, as a comparative example, a similar investigation was conducted without considering the index values ​​of bowel movement frequency and abdominal circumference, and a similar database was constructed.

[0039] The intestinal microbiota information of the subjects was determined by collecting stool samples from the subjects using a dedicated stool collection container (manufactured by Techno Suruga Lab). DNA was extracted from the collected stool samples at Techno Suruga Lab. 16S rRNA gene fragments were amplified by PCR using KAPA HiFi Hot Start Ready Mix (manufactured by Kapa Biosystems). A DNA library was prepared using the Nextera XT index kit (manufactured by Illumina) with this gene fragment as a template, and then bacterial flora analysis was performed using the next-generation sequencer MiSeq (manufactured by Illumina).

[0040] As a result, it was revealed that the Examples and Comparative Examples had the relationships shown in the following Table 1. In the following Table 1, * indicates P<0.05, which indicates a statistically significant relationship.

[0041] [Table 1]

[0042] Furthermore, as shown in Table 2 below, even when age, sex, alcohol consumption, smoking, and exercise level were used to predict the percentage of Blautia bacteria in the intestinal flora by simple regression analysis, the R 2 It was also shown that the prediction is not as good as shown in the value. In other words, unless the index value of flight information is combined, the R 2 It also became clear that the values ​​were not high enough to be considered predictable.

[0043] [Table 2] [Explanation of symbols]

[0044] S11 Acquisition process S13 Prediction process

Claims

1. A computer-implemented method, comprising: an acquisition step of receiving input of an index value obtained from information on the subject's bowel movement status and an index value obtained from the subject's physical and habit information; a prediction step of predicting the proportion of the number of bacteria of the genus Blautia in the intestinal flora of the subject based on data indicating a relationship between the index value of the information on the defecation status and the index value of the physical and habit information received in the acquisition step and the proportion of the number of bacteria of the genus Blautia in the intestinal flora; To execute A method for predicting the proportion of Blautia bacteria in the intestinal flora.

2. A method for predicting the proportion of Blautia bacteria in the intestinal flora described in claim 1, wherein the physical and habit information includes at least one or more pieces of information selected from the subject's age, sex, alcohol consumption, smoking habits, and exercise habits.

3. A method for predicting the proportion of the number of Blautia bacteria in the intestinal flora described in claim 1 or 2, wherein the information regarding the subject's bowel movement status includes at least information regarding the subject's bowel movement frequency, and the subject's physical and habit information includes at least information regarding the subject's abdominal circumference.

4. The method for predicting the proportion of the bacterial count of bacteria of the genus Blautia in intestinal bacterial flora according to any one of claims 1 to 3, further comprising a providing step in which the computer provides the subject with predicted information on the proportion of the bacterial count of bacteria of the genus Blautia in the intestinal bacterial flora based on the result predicted by the prediction step.

5. an input means for receiving input of an index value obtained from information on the subject's bowel movement status and an index value obtained from the subject's physical and habit information; a prediction means for predicting the proportion of the number of bacteria of the genus Blautia in the intestinal flora of the subject based on data indicating the relationship between the index value of the information on the defecation status and the index value of the physical and habit information received by the input means and the proportion of the number of bacteria of the genus Blautia in the intestinal flora; Equipped with A device for predicting the proportion of Blautia bacteria in the intestinal flora.

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