JP6643815B2 - Intestinal condition determination method and intestinal condition determination device - Google Patents

Description

  The present invention relates to a method for determining an intestinal condition and a device for determining an intestinal condition.

  In recent years, with increasing interest in health, attention has been focused on intestinal age. The intestinal age is an age (or age) determined based on intestinal conditions. Many bacteria are present in the intestinal tract of humans and animals, forming intestinal flora. The types of bacteria constituting the intestinal flora vary depending on physiology, diseases, drugs, food, lifestyle, stress, aging, interaction of intestinal bacteria, and the like.

  To examine such an intestinal condition, phylogenetic microarrays (phylogenetic microarray = Principal Component Analysis (PCA) is performed using a Human Intestinal Tract Chip (HITChip) for examining 16S rRNA gene amplicon to analyze the intestinal flora. Then, the composition distribution of the elderly group over 100 years old and the intestinal flora of their offspring is compared. In addition, analysis has been performed on the relationship between the inflammatory state of the subject and the composition of the intestinal microflora (for example, see Non-Patent Document 1).

  In addition, analysis of the base sequences of bacteria contained in feces of each of the young and the elderly, and analysis of the composition distribution of the intestinal flora of each of the young and the elderly (for example, non- See Patent Document 2).

“Through Aging, and Beyond: Gut Microbiota and Inflammatory Status in Seniors and Centenarians”, Elena Biagi, Lotta Nylund, et al., PLoS ONE, May 2010 Volume 5 Isseu 5 e10667, 2010 “Composition, variability, and temporal stability of the intestinal microbiota of the elderly”, Marcus J. Claesson, Siobhan Cusack, et al., PNAS, www.pnas.org/cgi/doi/10.1073/pnas.1000097107, 2010

  However, the techniques described in Non-Patent Literature 1 and Non-Patent Literature 2 cannot determine the intestinal condition of the subject.

  The present invention has been made in view of the above points, and an object of the present invention is to provide an intestinal state determination method and an intestinal state determination device capable of determining or estimating an intestinal state.

(1) In order to achieve the above object, a method for determining an intestinal condition according to one embodiment of the present invention provides a method for determining an intestinal condition, comprising: an identifier for identifying each of a plurality of subjects; and a bacterium including all types extracted from the plurality of subjects. For each of the plurality of intestinal bacteria at the classification level, a value representing the ratio to all the intestinal bacteria at the bacterial classification level extracted for each of the plurality of subjects, and information representing the attribute of each of the plurality of subjects. Using the intestinal microflora database associated with, the cluster number calculating step of calculating the number of clusters, and the result of analyzing the intestinal microflora database by a multidimensional scaling method, the cluster number calculation Classified into clusters of the number of clusters calculated by the step, and based on the age of the subject having information on the intestinal microflora included in each of the clusters, A labeling step of applying a label Te, for each of a plurality of enteric bacteria in the bacterial classification level including all types extracted from the plurality of subjects, in all intestinal in bacteria classification level extracted from the subject By analyzing the value representing the ratio to bacteria and the gut microbiota database by multidimensional scaling, it is determined whether the intestinal condition of the subject belongs to any of two or more types based on age. includes a determination step of, the, the two or more types in the determining step is the label that has been applied by the labeling process.

( 2 ) In the method for determining an intestinal condition according to one embodiment of the present invention, information on intestinal bacterial flora extracted from stool of the subject is extracted from the plurality of subjects and only from the plurality of subjects. A subject information updating step of adding information on the extracted intestinal flora, and inserting a composition ratio of 0% into the added information of the intestinal flora, wherein the determining step includes: The information on the intestinal flora of the plurality of subjects stored in the intestinal flora database and the updated information on the intestinal flora of the subject are integrated, and the integrated intestinal flora is integrated. May be analyzed by a multidimensional scaling method to determine whether the intestinal condition of the subject belongs to any of the two or more types based on the age .

(3) Further, in the determination method of the intestinal state according to one aspect of the present invention, the age of the plurality of subjects included before Kichonai flora database, age classification step of classifying a plurality of age groups, the Further, the labeling step may be such that a label is preliminarily provided to each cluster based on the age group of the subject having information on the intestinal microflora included in each cluster.

( 4 ) In the method for determining an intestinal condition according to one embodiment of the present invention, the multidimensional scaling method may be principal coordinate analysis or principal component analysis.
( 5 ) Further, in the method for determining an intestinal condition according to one aspect of the present invention, the plurality of subjects may include a plurality of subjects in each of age groups from before weaning to 104 years of age.

( 7 ) Also, in the method for determining an intestinal condition according to one embodiment of the present invention, the intestinal bacterial flora database includes information on intestinal bacteria extracted from the subject with respect to all intestinal bacteria extracted from the subject. An acquisition step of acquiring each ratio for each subject, and a total number m of the subjects (m is an integer of 2 or more) using each ratio of intestinal bacteria for each subject acquired in the acquisition step; An extraction step of extracting a total number n (n is an integer of 2 or more) of a plurality of types of intestinal bacteria at a bacterial classification level including all types extracted from the plurality of subjects, and Using the total number m of the subjects, the total number n of the types of the plurality of intestinal bacteria, and a value representing the ratio to all the intestinal bacteria at the bacterial classification level extracted for each of the plurality of subjects. hand a configuration data construction step of constructing configuration data of m rows × n rows, acquiring information indicating the age of each of the plurality of subjects, and acquiring the information indicating the age of each subject, in the configuration data construction step The database may be constructed by a database construction step of constructing an intestinal flora database in association with the constructed configuration data.

( 7 ) In order to achieve the above object, the intestinal condition determining apparatus according to one aspect of the present invention provides an intestinal condition identification device that identifies each of a plurality of subjects and a bacterium including all types extracted from the plurality of subjects. For each of the plurality of intestinal bacteria at the classification level, a value representing the ratio to all the intestinal bacteria at the bacterial classification level extracted for each of the plurality of subjects, and information representing the attribute of each of the plurality of subjects. And the intestinal flora database associated with the intestinal flora database, using the intestinal flora database to calculate the number of clusters, the intestinal flora database was analyzed by multidimensional scaling, the result was calculated. and wherein classifying the cluster number of clusters, based on the age of the subjects with information of the intestinal flora contained in each of the clusters, the label assigned to each cluster, before For each of a plurality of enteric bacteria in the bacterial classification level including all types extracted from the plurality of subjects, and a value representing the ratio of all the intestinal bacteria in bacterial classification level extracted from the subject, the intestine A bacterial flora database, an analysis unit that analyzes the multidimensional scaling method, and, based on the result analyzed by the analysis unit, classifies the intestinal condition of the subject into one of two or more types based on age. And a determination unit that performs the determination.

According to (1) and ( 7 ), the subject was analyzed using multi-dimensional scaling analysis of intestinal flora information composed of a plurality of intestinal bacteria stored in the intestinal flora database. Information of the intestinal flora of the cats is classified into types based on attributes. As a result, according to (1), the intestinal condition of the subject can be classified into two or more types. The classification levels of bacteria are classification levels of phylum, class, order, family, genus, species, and the like.

According to (1) and (7) , the intestinal condition of the subject can be classified into any of the types of clusters calculated in advance.
According to ( 2 ), it is possible to align the columns of the configuration data composed of the composition ratios of the intestinal flora of a plurality of subjects and the configuration data composed of the composition ratios of the intestinal flora of the subjects. Therefore, the two configuration data can be integrated to perform the multidimensional scaling. As a result, according to ( 2 ), the intestinal condition, which is the composition ratio of the intestinal flora of the subject, can be determined or estimated based on the composition ratio of the intestinal flora of a plurality of subjects.

According to ( 3 ), the intestinal condition of the subject can be classified into clusters having labels assigned based on age groups. Thereby, according to ( 3 ), since the determination result is a name based on the age group, the person receiving the determination result can easily understand the determination result.

According to ( 4 ) or ( 5 ), the intestinal state of the subject is analyzed by analyzing the intestinal flora information stored in the intestinal flora database by principal coordinate analysis and principal component analysis, and the clustered cluster is obtained. Can be classified into any one of two or more types based on the age included in. As a result, in ( 4 ) or ( 5 ), the intestinal condition of the subject can be classified into one of the types that are labeled according to the age.
According to ( 7 ), an intestinal flora database can be constructed using intestinal bacteria extracted from a subject or a person other than the subject. As a result, in the present embodiment, the intestinal flora database constructed in this way and the intestinal flora information of the subject are analyzed by a multidimensional scaling method, so that the intestinal state of the subject can be accurately determined. Can be estimated well.

It is a block diagram of an intestinal state judging device concerning an embodiment. It is a flowchart of the process which the determination apparatus of the intestinal state which concerns on embodiment performs. It is a figure showing an example of the construction procedure of the intestinal flora database concerning an embodiment. FIG. 7 is a diagram illustrating an example of a clustering process and a label processing procedure performed by an analysis unit according to the embodiment. It is a figure showing the result of having performed the main coordinate analysis concerning an embodiment, and having classified into three clusters. FIG. 9 is a diagram illustrating a result of performing principal component analysis according to the embodiment and classifying the cluster into three clusters. FIG. 7 is a diagram illustrating an example of an age group of each cluster in FIGS. 5 and 6. FIG. 7 is a diagram showing a ratio of each cluster in an age group in the analysis results shown in FIGS. 5 and 6. It is a flowchart of the determination processing of the intestinal state of a subject. It is a flowchart of the extraction process of the intestinal bacteria which the intestinal bacteria extraction device which concerns on embodiment performs. It is a figure which shows the result of the main coordinate analysis of the intestinal flora of 380 people including a 40-year-old male subject in the first test example. It is a figure which shows the result of the principal component analysis of the intestinal flora of 380 people including a 40-year-old male subject in the second test example.

  Hereinafter, embodiments of the present invention will be described with reference to the drawings. It should be noted that the terms “determination” and “estimation” described in the specification and the claims are used as being substantially equivalent in the present disclosure.

<Configuration of intestinal state determination device 1>
First, the configuration of the intestinal state determination device 1 will be described.
FIG. 1 is a block diagram of an intestinal state determination device 1 according to the present embodiment. As shown in FIG. 1, the intestinal state determination device 1 includes an intestinal bacteria extraction device 11, a database generation unit 12, a database 13, an analysis unit 14, and a determination unit 15.

  The intestinal bacteria extraction device 11 extracts intestinal bacteria from samples of a subject and a subject. Here, the subject is a person who has provided a sample in order to construct a database of intestinal flora. The target person is a person whose intestinal condition is estimated. After the database is constructed, the subject may be the subject. The extracted result includes an identifier for identifying the subject or the subject, the name of the extracted bacterium, and information indicating the composition ratio of each bacterium. The intestinal bacteria extraction device 11 outputs the extracted subject or subject extraction results to the database generation unit 12 and the analysis unit 14. The method for extracting intestinal bacteria will be described later. Further, as described later, the intestinal bacteria extraction device 11 may include a plurality of devices, a plurality of applications, and the like.

  The database generation unit 12 constructs an intestinal flora database by associating the extraction result output by the intestinal bacterial extraction device 11 with the input attribute of the subject, and stores the constructed intestinal flora database in the database 13. I do. Here, the subject's attributes include the subject's age, gender, weight, height, place of residence, place of origin, nationality, and the like. In the following example, the case of the age as the attribute of the subject will be described. The attribute may be the age group of the subject. Here, the age group may be one of a set of predetermined ages, for example, 0 to 10 years, 10 to 19 years, ..., 90 to 99 years, 100 years or more. For example, if the subject's age is 42, the attribute may be 40-49.

  As shown in Table 1, the database 13 stores the identifiers of the subjects in association with the age (attribute) and the composition ratio of the intestinal microflora. In addition, the composition ratio of the intestinal microflora was extracted from each of all the bacterial types n (n is an integer of 2 or more) extracted from m (m is an integer of 2 or more) subjects. This is the occupation ratio of each intestinal bacterium. For this reason, the ratio to the intestinal bacteria extracted only from other subjects is 0%.

The classification level of bacteria in Table 1 is at least one of phylum, class, order, family, genus, and species. For example, when the identifier is “1”, the age (attribute) is “weaning”, the composition ratio of the first bacterium is “0.057 (%)”, and the composition ratio of the second bacterium is “1.23 (%)”. ,..., And the composition ratio of the nth bacterium is associated with “0 (%)”. The age of the subject stored in the database 13 is, for example, information on a plurality of intestinal flora (hereinafter referred to as intestinal flora information) for each age group during weaning (0 years old) to 104 years old. Have been. The database 13 may store an intestinal flora database based on intestinal flora information for each bacterial classification level.
The database 13 stores P as the number of clusters as shown in Table 2, and stores the first label name,..., P-th label name as the label name for each cluster as shown in Table 3. Have been.

  In addition, intestinal flora information of subjects of each age group is stored in the database 13 for, for example, 30 persons. The number of clusters and the label name will be described later.

  The analysis unit 14 reads the intestinal flora information for m persons stored in the database 13 and calculates the optimal number of clusters for the read intestinal flora information. The analysis unit 14 calculates the Calinski-Harabasz (CH) Index using, for example, the Calinski-Harabasz criterion. The analysis unit 14 stores the highest value among the calculated values as the number of clusters P in the database 13. Note that the analysis unit 14 may calculate the optimum number of clusters by using another method such as Cubic Clustering Criterion, Dunn's Index, DB's Index, and Pseudo T-square.

The analysis unit 14 performs an analysis process on the read intestinal microflora information by, for example, a main coordinate analysis using a cluster package of R (version 3.0.3) software. The analysis unit 14 may perform analysis on the intestinal flora information by other multidimensional scaling methods such as principal component analysis, metric multidimensional scaling, and non-metric multidimensional scaling. Based on the result of the principal coordinate analysis, the analysis unit 14 assigns m persons to any of the calculated number of clusters P using the shortest distance method, the longest distance method group averaging method, the Ward method, the K-means method, or the like. Classify. The analysis unit 14 assigns a cluster name to each cluster based on the configuration of the age included in each classified cluster.
The analysis unit 14 integrates the intestinal flora information of the subject output by the intestinal bacteria extraction device 11 with the intestinal flora information of m persons stored in the database 13 and integrates (m + 1) persons Coordinate analysis is performed on the intestinal microflora information of. The analysis unit 14 outputs the analyzed result to the determination unit 15.

  The determining unit 15 determines which of the classified clusters the intestinal flora information of the subject belongs to in the analysis result output by the analyzing unit 14. The determination unit 15 outputs a result of the determination to a printer, a display unit (not shown), or the like. The intestinal condition determination device 1 or the external device may generate an advice on health or a meal on the subject based on the determination result. In this case, the intestinal condition determination device 1 or the external device may be connected to a database in which advice on health, advice on meals, and the like are associated with each other for each age estimated to be the actual age.

<Outline of the processing procedure performed by the intestinal state determination device 1>
Next, an outline of a processing procedure performed by the intestinal state determination device 1 will be described.
FIG. 2 is a flowchart of a process performed by the intestinal state determination device 1 according to the present embodiment.
(Step S1) The intestinal bacteria extraction device 11 performs an intestinal bacteria extraction process from each of the samples of the subjects in a plurality of age groups.
(Step S2) The database generation unit 12 performs a database construction process by associating the extraction result extracted by the intestinal bacteria extraction apparatus 11 with the attribute of the input subject.

(Step S3) The analysis unit 14 calculates the optimal number of clusters P for the intestinal bacterial flora information for m persons read from the database 13, and stores the calculated number of clusters P in the database 13.

(Step S4) The analysis unit 14 performs an analysis process by multidimensional scaling analysis on the intestinal flora information of the m persons read out in step S3, etc., and assigns m persons to any of the cluster numbers P. Classify. Subsequently, the analysis unit 14 performs a labeling process of assigning a cluster name to each cluster based on the configuration of the age included in each of the classified clusters or the percentage of an age group described later. Subsequently, the analysis unit 14 stores the assigned label name in the database 13.

(Step S5) The intestinal bacteria extraction device 11 performs an intestinal bacteria extraction process on the subject.
(Step S6) The analysis unit 14 integrates the intestinal flora information of the subject output by the intestinal bacteria extraction device 11 with the intestinal flora information of m persons read from the database 13. Subsequently, the analysis unit 14 performs an analysis process on the intestinal microflora information of (m + 1) persons by multidimensional scaling analysis such as main coordinate analysis.

(Step S7) The determination unit 15 determines which of the classified clusters the intestinal microflora information of the subject belongs to based on the result analyzed by the analysis unit 14, thereby determining the intestinal state. Perform determination processing.
The intestinal condition determining apparatus 1 repeats the processing of steps S5 to S7 for each subject.

  As described above, in the present embodiment, the intestinal state of the subject is not estimated based on only the specific intestinal bacteria, but based on the composition ratio of the extracted intestinal bacterial flora, the intestinal state of the subject. Is classified into which cluster. In this embodiment, after the database 13 is constructed, it is preferable not to recalculate the optimal number of clusters each time the intestinal state of the subject is determined. Furthermore, in the present embodiment, after the database 13 is constructed, it is preferable that the label name of each cluster is not reassigned every time the intestinal state of the subject is determined.

<Building the intestinal flora database>
Next, an example of a method of constructing an intestinal flora database performed by the database generation unit 12 in step S2 of FIG. 2 will be described. The database generation unit 12 constructs an intestinal flora database for at least one of the phylum, class, order, family, genus, and the like.
FIG. 3 is a diagram illustrating an example of a procedure for constructing the intestinal flora database according to the present embodiment.
(Step S11) The database generation unit 12 acquires the composition ratio of the intestinal flora of all subjects. Table h101 in FIG. 3 shows the composition ratio of the intestinal flora of the first subject, table h102 in FIG. 3 shows the composition ratio of the intestinal flora of the second subject, and table h103 in FIG. , The composition ratio of the intestinal flora of the m-th subject. Here, the composition ratio of the intestinal flora is the ratio of each of the intestinal bacteria to all of the intestinal bacteria in one subject. In addition, the extracted intestinal bacteria and the number of types of intestinal bacteria may differ between subjects. For example, first to 100th intestinal bacteria may be detected from a first subject, and first, third,..., 100, 101, and 102 intestinal bacteria may be extracted from a second subject.

(Step S12) The database generation unit 12 extracts the total number m of subjects and the total number n of types of intestinal bacteria using the information on the composition ratio of the intestinal bacterial flora of all subjects acquired in step S11. . Note that the total number n of the types of bacteria to be extracted is at least one of the classification levels of bacteria (phylum, class, order, family, genus, species, etc.). For example, if the number of types of intestinal bacteria extracted from the first subject is 100, and if 80 types of intestinal bacteria different from the 100 types are extracted from other subjects, the total number of types of intestinal bacteria is calculated. n is 180 types. The database generation unit 12 stores the total number m of the extracted subjects and the total number n of the types of intestinal bacteria in the database 13.

(Step S13) The database generation unit 12 uses the information on the total number of subjects, the total number of types of intestinal bacteria, and the composition ratio of the intestinal bacterial flora of all subjects acquired in step S11, extracted in step S12. As shown in a table h111 in FIG. 3, m × n configuration data is constructed. In the mxn configuration data, as shown in Table h111 in FIG. 3, the database generation unit 12 inserts 0% into the composition ratio of the intestinal bacteria extracted from other subjects.

(Step S14) The database generation unit 12 acquires the age (attribute) information of each subject.
(Step S15) The database generation unit 12 associates the age information acquired in Step S14 with the m × n configuration data constructed in Step S13, and as shown in Table h121 of FIG. 3, m rows × (n + 1). Build a gut microbiota database of rows.

<Clustering processing, label processing>
Next, an example of clustering processing and label processing performed by the analysis unit 14 in steps S3 and S4 of FIG. 2 will be described.
FIG. 4 is a diagram illustrating an example of a clustering process and a label processing procedure performed by the analysis unit 14 according to the present embodiment. The database 13 contains intestinal flora information (structure data at the bacterial classification level (at least one of phylum, class, order, family, genus, etc.) of the intestinal flora) for m persons. It is assumed that it is stored.

(Step S21) The analysis unit 14 reads intestinal flora information for m persons from the database 13.
(Step S22) The analysis unit 14 calculates the CH Index using the Calinski-Harabasz criterion, for example, in order to calculate the optimal number of clusters for the read intestinal microflora information for m persons. Subsequently, the analysis unit 14 stores the highest value among the calculated values as the number of clusters P in the database 13. FIG. 4 is an example in which the optimum number of clusters P is calculated as 3.

(Step S23) The analysis unit 14 analyzes the read intestinal bacterial flora information by a multidimensional scaling method such as principal coordinate analysis using, for example, a cluster package of R (version 3.0.3) software. . The diagram indicated by the symbol g1 is a diagram of the result of performing the main coordinate analysis. When performing the main coordinate analysis, first, the first main coordinate and the second main coordinate are obtained, and the analysis is performed using the obtained first main coordinate and the second main coordinate. When performing the principal component analysis, first, the first principal component and the second principal component are obtained, and the analysis is performed using the obtained first principal component and the second principal component.

(Step S24) The analysis unit 14 classifies the result of the multidimensional scaling analysis into the number of clusters P calculated in step S22, for example, by the Ward method. The diagram indicated by the symbol g2 is a diagram in which the result of the main coordinate analysis is classified into three clusters.
(Step S25) The analysis unit 14 reads information indicating the age of each subject included in the intestinal flora information database. The analysis unit 14 classifies the read age information into a plurality of age groups as shown in Table 4, for example. Subsequently, the analysis unit 14 checks the ratio of the age group of the subject included in each cluster classified in step S24.

(Step S26) The analysis unit 14 assigns a cluster name to each cluster based on the ratio of the age group included in each cluster, for example, as shown in Table 5 (reference numeral g6 in FIG. 4). Note that a pie chart g3 represents the ratio of each of the age groups classified into the first cluster, and a pie chart g4 represents a ratio of each of the age groups classified into the second cluster. g5 represents the ratio of each age group classified into the third cluster. Subsequently, the analysis unit 14 stores the assigned cluster name in the database 13.

Thus, the clustering process and the label process performed by the analysis unit 14 are completed.
That is, in the present embodiment, the results analyzed by the multidimensional scaling method are classified into the optimal number of clusters P, and the clusters are classified into the clusters based on the ratio of the age (attribute) of the subject included in each classified cluster. Labels are given. This label is also used for the determination result of the intestinal state of the subject.

<Example of the result of the main coordinate analysis and classification into clusters>
Next, an example of the result of performing the main coordinate analysis and classifying the data into clusters will be described.
FIG. 5 is a diagram illustrating a result of performing the main coordinate analysis according to the present embodiment and classifying the cluster into three clusters. In FIG. 5, the horizontal axis is the first main coordinate, and the vertical axis is the second main coordinate. The example shown in FIG. 5 is a result of main coordinate analysis of intestinal flora information of m = 379 people, ages 0 to 104, and 187 kinds of bacteria. In addition, as shown in Table 4, the examples shown in FIG. 5 are before weaning, during weaning, and after weaning to 9 years old, 10-19 years old, 20-29 years old, 30-39 years old, 40-49 years old, 50 years old. These are examples divided into 13 age groups of -59, 60-69, 70-79, 80-89, 90-99, and 100 years or older. Further, the highest value of the calculated CH Index values is an example of 3.

  In FIG. 5, a range surrounded by a circle g101 indicates intestinal flora information by the subject classified into the first cluster, and a range surrounded by a circle g102 indicates intestinal flora information by the subject classified into the second cluster. And a range surrounded by a circle g103 indicates intestinal flora information by the subject classified into the third cluster. Each of the white squares 1 to 3 represents the center of gravity of each cluster. A line segment connected from the center of gravity to the intestinal flora information of each subject indicates which of the first to third clusters each intestinal flora information belongs to.

<Example of principal component analysis result>
Next, an example of a result of performing principal component analysis and classifying into clusters will be described.
FIG. 6 is a diagram illustrating a result of performing principal component analysis according to the present embodiment and classifying the cluster into three clusters. In FIG. 6, the horizontal axis is the first principal component, and the vertical axis is the second principal component. The intestinal flora information used for the analysis is the same as the main coordinate analysis shown in FIG.

  In FIG. 6, a range surrounded by a circle g201 indicates intestinal flora information by the subject classified into the first cluster, and a range surrounded by a circle g202 indicates intestinal flora information by the subject classified into the second cluster. And a range surrounded by a circle g203 indicates intestinal flora information by the subject classified into the third cluster. Lines connected from the centroids 1 to 3 to the intestinal flora information of each subject are the same as those in FIG.

<Example of age group included in each cluster>
Next, an example of the age group of each cluster in FIGS. 5 and 6 will be described.
FIG. 7 is a diagram illustrating an example of the age group of each cluster in FIGS. 5 and 6.
7, a pie chart g301 shows the proportion of the age group of the first cluster, a pie chart g302 shows a proportion of the age group of the second cluster, and a pie chart g303 shows the proportion of the age group of the third cluster. .

  As shown by the circle graph g301, in the first cluster, the total of the proportion of the subjects of the age of 9 years old before, during and after weaning is about 50%. For this reason, the analysis unit 14 assigns a label name of “infant” to the first cluster as an enterotype (enterotype). The enterotype is a pattern of a human intestinal symbiotic bacterial flora, and in the present embodiment, is an age type that is classified based on a human intestinal symbiotic bacterial flora pattern.

Further, as indicated by the pie chart g302, in the second cluster, the total ratio of the subjects of the ages of 10-19, 20-29 and 30-39 is about 50%. Therefore, the analysis unit 14 assigns a label name of “adult” to the second cluster.
Further, as indicated by the pie chart g303, in the third cluster, the total ratio of subjects aged 80-89, 90-99, and 100 years or older is 2/3 or more. Therefore, the analysis unit 14 assigns a label name of “elderly person” to the second cluster.
Note that the above-described label name is an example, and the present invention is not limited to this. Any name may be used according to the age group included in each cluster.

<Ratio of each cluster in age group>
Next, the ratio of each cluster in the age group in the analysis results shown in FIGS. 5 and 6 will be described.
FIG. 8 is a diagram showing the ratio of each cluster in the age group in the analysis results shown in FIG. 5 and FIG.
In FIG. 8, the horizontal axis represents the age group, and the vertical axis represents the ratio. In FIG. 8, a rectangle g401 represents “infant” (first cluster), a rectangle g402 represents “adult” (second cluster), and a rectangle g403 represents “elderly person” (third cluster).

In FIG. 8, for example, about 70% of subjects in the weaning age group are classified as “baby” and about 30% are classified as “elderly”.
In addition, for example, among subjects in the age group of 80-89, about 80% are classified as "elderly", about 16% are classified as "adult", and about 4% are classified as "infant".
As described above, many subjects belong to the age-appropriate enterotype, but some subjects belong to the adult enterotype even before weaning, or belong to the infant enterotype even in their 80s. Therefore, the actual age does not always match the age of the intestinal flora.

<Determination of the intestinal condition of the subject>
Next, an example of the procedure for determining the intestinal state of the subject will be described.
FIG. 9 is a flowchart of a process of determining the intestinal state of the subject according to the present embodiment.
(Step S51) The analysis unit 14 reads intestinal microflora information for m persons from the database 13.
(Step S52) The analysis unit 14 reads the number of clusters and the label name for each cluster from the database 13.

(Step S53) The intestinal bacteria extraction apparatus 11 analyzes the subject's feces by the processing described below (steps S101 to S116 in FIG. 10) and extracts the subject's intestinal flora information. Subsequently, the analysis unit 14 acquires the intestinal bacterial flora information of the subject output by the intestinal bacterial extractor 11.

(Step S54) The analysis unit 14 adds the information of the intestinal bacteria extracted only from the subject without being extracted from the subject to the obtained intestinal flora information of the subject, and Update bacterial flora information. For example, when the number of types of intestinal bacteria extracted from the subject is 100, the intestinal bacterial flora information of the subject output by the intestinal bacteria extraction device 11 is 1 as indicated by reference numeral g451 in FIG. It is information of a row × 100 columns. The analysis unit 14 adds 87 (= 187-100) types of information on intestinal bacteria to the column information, as indicated by reference numerals g452 and g453 in FIG. Further, the analysis unit 14 inserts 0% into each of the composition ratios (composition ratios) of the information on the added intestinal bacteria, as indicated by reference numeral g453. The analysis unit 14 updates the subject's intestinal flora information at the same classification level as that of the bacteria stored in the intestinal flora database.

(Step S55) The analysis unit 14 integrates the read intestinal flora information for m persons and the subject's intestinal flora information updated in step S54. Subsequently, the analysis unit 14 performs multidimensional scaling analysis such as principal coordinate analysis on the integrated intestinal flora information of the (m + 1) persons using, for example, a cluster package of R software. Subsequently, the analysis unit 14 classifies the intestinal flora information into the read cluster number by, for example, the Ward method.

(Step S56) The determining unit 15 determines to which cluster among the classified classes the composition ratio of the intestinal flora of the subject belongs.
(Step S57) The determining unit 15 determines to which cluster the constituent ratio of the intestinal flora of the subject belongs. When the determining unit 15 determines that the composition ratio of the intestinal microflora of the subject belongs to the first cluster (step S57; first cluster), the process proceeds to step S58, and the determination unit 15 belongs to the second cluster. (Step S57; second cluster), the process proceeds to step S59, and if it is determined that the pixel belongs to the third cluster (step S57; third cluster), the process proceeds to step S60.

(Step S58) The determining unit 15 determines that the intestinal condition of the subject is the first label name, for example, “infant”, and outputs the determined result to the external device. After outputting the determination result, the determination unit 15 ends the process.
(Step S59) The determining unit 15 determines that the intestinal condition of the subject is the second label name, for example, “adult”, and outputs the determined result to the external device. After outputting the determination result, the determination unit 15 ends the process.
(Step S60) The determining unit 15 determines that the intestinal condition of the subject is a third label name, for example, “elderly person”, and outputs the determined result to the external device. After outputting the determination result, the determination unit 15 ends the process. In addition, based on steps S58 to S60, the external device notifies the subject, for example, "Your intestinal condition has been classified as" the composition ratio of intestinal bacteria in adults "".
Thus, the determination processing of the intestinal state in the subject is completed.

  In the example described above, an example in which the intestinal state is determined for the subject is described, but the present invention is not limited to this. After constructing the database, the intestinal flora information of one of the m subjects may be extracted, and the intestinal state of the extracted subject may be determined.

  Further, even after the database 13 is constructed, the database generating unit 12 sequentially stores the intestinal microflora information of the subject and the age in the database 13 in association with each other. Then, when the intestinal flora information of the subject and the subject stored in the database 13 exceeds a predetermined number, the analysis unit 14 recalculates the optimal number of clusters and changes the label name of each cluster. You may make it give again.

<Method of extracting intestinal bacteria>
Next, an example of a method for extracting intestinal bacteria performed by the intestinal bacteria extraction device 11 will be described.
FIG. 10 is a flowchart of the intestinal bacteria extraction process performed by the intestinal bacteria extraction device 11 according to the present embodiment.

(Step S101) The intestinal bacteria extraction device 11 measures about 20 mg of feces for each subject or each subject, and suspends them in 450 ul of an extract (100 mM Tris / HCl, 4 mM EDTA, pH 9.0).
(Step S102) The intestinal bacteria extraction apparatus 11 mixes the extract suspended in step S101 with 50ul of 10% SDS solution, 300mg of 0.1mm diameter glass beads, and 500ul of TE-saturated phenol (for example, Wako Pure Chemical Industries). Then, for example, crushing is performed at a power level of 5, 30 seconds using FastPrep FP 100A (manufactured by Funakoshi).

(Step S103) The intestinal bacteria extraction apparatus 11 obtains 400 ul of supernatant after centrifuging the solution subjected to the crushing treatment in step S102 at 14,000 g for 5 minutes. Subsequently, the intestinal bacterial extraction device 11 added and mixed 250 ul of a phenol / chloroform solution (for example, Wako Pure Chemical Industries) to the obtained supernatant, and centrifuged at 14,000 g for 5 minutes to obtain 250 ul of the supernatant. .

(Step S104) The intestinal bacteria extraction apparatus 11 adds 250 ul of 2-propanol to the supernatant obtained in step S103, dissolves the precipitate in isopropanol with 200 ul of Tris-EDTA buffer (pH 8.0), Form a solution.

(Step S105) The intestinal bacteria extraction device 11 uses the first primer set for amplifying the third to fourth variable regions of the bacterial 16S rRNA gene, Tru357F [(5′-CGCTCTTCCCGATCTCTGTACGGRAGGCAGCAG-3 ′ (SEQ ID NO: 1)). And Tru806R [5'-CGCTCTCCGATCTGGACGACTACHVGGTWTTCTAAT-3 '(SEQ ID NO: 2)], and Fwd [5'-X1NNNNNNNN-X2-, which is a 2nd primer set required for analysis by, for example, a next-generation sequencer Miseq (manufactured by Illumina). 3 '] and Rev [5'-X3-NNNNNNNNNN-X4-3'], and combine the primers using, for example, an oligo primer production service of Life Technologies. To.
In addition, X1, X2, X3, X4, and N represent the following sequences.
X1: AATGATACGGCGACCACCGAGATCTACAC (SEQ ID NO: 3)
X2: ACACTCTTTTCCTACACGACGCTCTTCCGATCCTCTG (SEQ ID NO: 4)
X3: CAAGCAGAAGACGGCATACGAGAT (SEQ ID NO: 5)
X4: GTGACTGGAGTTCAGACGTGTGCCTTCCCGATCTGAC (SEQ ID NO: 6)
N is a base sequence for identifying n subjects consisting of arbitrary bases

(Step S106) The intestinal bacteria extraction device 11 prepares a reaction solution having a total volume of 25 μl including the template DNA solution and the first primer set, using, for example, TaKaRa Ex Taq HS kit (manufactured by Takara Bio Inc.). Subsequently, the intestinal bacteria extraction device 11 is, for example, Verity 200 (manufactured by Life Technologies), for example, after 94 ° C. for 3 minutes, 94 ° C. for 30 seconds, 50 ° C. for 30 seconds, and 72 ° C. for 30 seconds 20 times at 72 ° C. Perform a PCR (Polymerase Chain Reaction) reaction for 10 minutes.

(Step S107) The intestinal bacteria extraction device 11 electrophoreses the PCR product obtained in step S106 on a 1% agarose gel, and confirms a band pattern.
(Step S108) The intestinal bacteria extraction apparatus 11 performs a PCR reaction process using 1 ul of the PCR product obtained in step S106 as a template and a 2nd primer set under the same conditions as described above. However, the number of PCR cycles is eight instead of twenty.

(Step S109) The intestinal bacteria extraction apparatus 11 electrophoreses the PCR product obtained in step S108 on a 1% agarose gel, and confirms a band pattern.
(Step S110) After the treatment in step S109, the intestinal bacteria extraction device 11 performs purification using, for example, a QIAquick 96 PCR Purification Kit (manufactured by Qiagen), and, for example, Quant-iT PicoGreen dsDNA Assay kit (manufactured by Life Technologies). And measure the concentration. Subsequently, the intestinal bacteria extraction device 11 supplies the mixed DNA solution having the same concentration to, for example, a Miseq v3 Reagent kit (manufactured by Illumina), and performs a sequence analysis using Miseq.

(Step S111) The intestinal bacteria extraction apparatus 11 removes Pix (control sequence at the time of the sequence) and the human genome sequence from the fastq file obtained in step S110.
(Step S112) After the processing of step S111, the intestinal bacteria extraction apparatus 11 removes bases of Q17 or less from the base sequence 3 'side.

(Step S113) After the processing of step S112, the intestinal bacteria extraction device 11 removes the lead that has become 150 bp or less. Subsequently, the intestinal bacteria extraction apparatus 11 removes the leads of Q25 or less as a whole and the unpaired leads.
(Step S114) After the processing of step S113, the intestinal bacteria extraction apparatus 11 compares the obtained paired end sequence with, for example, fastq-join (version 1.1.1.2-301) (http://code.google.com). / P / ea-utils / wiki / FastqJoin).

(Step S115) After the processing of step S114, the intestinal bacteria extraction apparatus 11 removes the chimeric sequence by, for example, UCHIME.
(Step S116) Finally, for example, 4,979 ± 1,800 sequences are obtained per sample, and the intestinal bacteria extraction apparatus 11 converts these into, for example, QIIME software (version 1.8.0) (http: // Each sequence having 97% homology in qime.org/) is defined as OTU (Operational Taxonomy Unit). Subsequently, the intestinal bacteria extraction device 11 compares the representative sequence of each OTU with, for example, Greengenes database 12_10 (http://greengenes.secondgenome.com/downloads/database/12_10), for example, by blasting (BLAST; basicAnalysisLast). By using a search tool (Search Tool), constituent data at the bacterial classification level (phylum, class, order, family, genus, species, etc.) of the intestinal flora is generated. The configuration data is a table of m rows × o columns when the type of intestinal bacteria detected from m persons is o. BLAST is, for example, an algorithm for finding a similar sequence group with a score equal to or higher than a predetermined threshold, and a program in which the algorithm is implemented. Subsequently, the database generation unit 12 stores the configuration data of the intestinal flora generated by the intestinal bacteria extraction device 11 at the bacterial classification level in the database 13 in association with the age of the subject.

  This is the end of the intestinal bacteria extraction processing procedure. The procedure of the intestinal bacteria extraction process described above, the solution to be used, the volume of the solution to be used, the application to be used, and the like are merely examples, and are not limited thereto.

[First test example]
Next, a first test example will be described.
(Procedure 1) The intestinal bacteria extraction device 11 uses the feces of the 379 subjects for the database to perform processing in steps S101 to S116 shown in FIG. Generate. Subsequently, the database generation unit 12 stores the data of the intestinal flora at the bacterial classification level generated by the intestinal bacteria extraction device 11 in the database 13 in association with the age of each of the 379 subjects. That is, the database 13 stores configuration data of 379 (number of subjects) rows × 187 (number of types of bacteria) columns in association with identifiers and ages. The intestinal bacteria extracted from the 379 subjects were the bacteria shown in the first columns of Tables 6 to 15. As shown in Tables 6 to 15, the levels of the intestinal bacteria extracted are at the genus level. In Tables 6 to 15, "[...]" represents a name that was used once because the classification is currently unclear. “Gen.” Indicates that the classification at the genus level is unknown or unidentified, “fan.” Indicates that the classification at the family level is unknown or unidentified, and “ord.” Indicates that the eye-level classification is unknown or unidentified.

(Procedure 2) The analysis unit 14 calculates that the optimal number of clusters is 3 by calculating the CH Index for the intestinal flora database constructed in Procedure 1. Subsequently, the analysis unit 14 performs a main coordinate analysis on the intestinal microflora database constructed in the procedure 1 using a cluster package of R software, and classifies the data into three clusters. Subsequently, the analysis unit 14 assigns the label names “infant”, “adult”, and “elderly” to each of the three clusters based on the ages of the subjects included in the three clusters.

(Procedure 3) The intestinal bacteria extraction device 11 extracts the intestinal bacterial flora by using the feces of a 40-year-old male subject through the processing of steps S101 to S116 shown in FIG. Next, the analysis unit 14 adds information of the intestinal bacteria extracted only from the subject without being extracted from the subject to the acquired intestinal flora information of the subject, and Update the plexus information. Further, the analysis unit 14 inserts 0% into each of the composition ratios (composition ratios) of the information on the added intestinal bacteria, as indicated by reference numeral g453 in FIG. The second column of Tables 6 to 15 shows the composition ratio of the extracted enterobacteria and the enterobacteria extracted only from the subject. The data obtained after the update is the construction data of 1 row × 187 columns.

(Procedure 4) The analysis unit 14 integrates the 1-row × 187-column construction data obtained in the procedure 3 and the 379-row × 187-column configuration data constructed in the procedure 1 to obtain 380 rows × 187 columns. Generate configuration data for Subsequently, the analysis unit 14 performs a main coordinate analysis on the generated configuration data of 380 rows × 187 columns using a cluster package of R software. At this time, it is preferable that the analysis unit 14 does not recalculate the optimum number of clusters. That is, the number of clusters may be kept at three.

(Procedure 5) As a result of the analysis performed in Procedure 4, the determination unit 15 determines that the 40-year-old male subject belongs to the “adult” enterotype, as shown in FIG. It is to be noted that the determination of which cluster belongs to is performed by a process for cluster classification after the main coordinate analysis by the analysis unit 14. FIG. 11 is a diagram showing the results of main coordinate analysis of intestinal flora of 380 persons including a 40-year-old male subject in the first test example. 11, the horizontal axis and the vertical axis are the same as in FIG. A circle g501 is a cluster of “infant” (enterotype), a circle g502 is a cluster of “adult” (enterotype), and a circle g503 is a cluster of “elderly person” (enterotype). In addition, x g511 represents the intestinal flora of a 40-year-old male subject.

[Second test example]
Next, a second test example will be described.
By the procedure 1 of the first test example, the construction of the database is completed, that is, the configuration data of 379 rows × 187 columns are stored in the database 13 in association with the identifier and the age. Further, the optimal number of clusters is calculated as 3 by the procedure 2 of the first test example.

(Procedure 2 ') The analysis unit 14 performs principal component analysis on the database constructed in Procedure 1 using an R software cluster package, and classifies the database into three clusters. Subsequently, the analysis unit 14 assigns the label names “infant”, “adult”, and “elderly” to each of the three clusters based on the ages of the subjects included in the three clusters.

(Procedure 3 ′) The intestinal bacteria extraction device 11 performs the same processing as the procedure 3 of the first test example, and generates 1 row × 187 columns of configuration data of the subject.

(Procedure 4 ′) The analysis unit 14 constructs the subject's 1-row × 187-column construction data obtained in Procedure 3 ′ and the subject's 379-row × 187-column constructed by Procedure 1 of the first test example. The configuration data is integrated with the configuration data to generate 380 rows × 187 columns of configuration data. Subsequently, the analysis unit 14 performs a principal component analysis on the generated configuration data of 380 rows × 187 columns by using an R software cluster package. At this time, it is preferable that the analysis unit 14 does not recalculate the optimum number of clusters. That is, the number of clusters may be kept at three.

(Step 5 ′) As a result of the analysis performed in Step 4 ′, the determination unit 15 determines that the 40-year-old male belongs to the “adult” enterotype, as shown in FIG. FIG. 12 is a diagram showing the results of a principal component analysis of intestinal flora of 380 persons including a 40-year-old male subject in the second test example. 12, the horizontal axis and the vertical axis are the same as in FIG. Circle g601 is a cluster of "infant" (enterotype), circle g602 is a cluster of "adult" (enterotype), and circle g603 is a cluster of "elderly" (enterotype). The star g611 represents the intestinal flora of a 40-year-old male subject.

  As shown in FIGS. 11 and 12, the intestinal condition of a 40-year-old male subject was determined to belong to the “adult” enterotype, regardless of the principal coordinate analysis or the principal component analysis. Was.

Note that, in the above-described example, an example is described in which an example of age is used as the attribute, but the present invention is not limited to this. Attributes may be gender, weight, height, place of residence, place of origin, nationality, and the like. The attributes associated with the intestinal flora may be plural (for example, age and nationality). For example, when the attribute is weight, the weight is classified into several groups (for example, less than 10 kg, 10-19 kg, 20-29 kg, ..., 90-99 kg, 100 kg or more), and the weight included in the clustering result is included. Labeling may be performed based on groups.
Further, the information stored in the database 13 may be a plurality of data for each nationality, each region, or the like.

  As described above, in the method for determining an intestinal condition according to the present embodiment, the intestinal condition determination device 1 uses the bacterium including the identifier for identifying each of the plurality of subjects and all the types extracted from the plurality of subjects. For each of the plurality of enterobacteria at the classification level (eg, phylum, class, order, family, genus, species, etc.), all the enterobacteria at the bacterial classification level extracted for each of the plurality of subjects (the classification level) Is a genus level, for example, a value representing a ratio to the genus Metanoblewibacter, Actinomyces,..., And attributes (eg, age, gender, weight, height, residence, And the intestinal flora database (database 13), which is associated with information indicating the location, hometown, nationality, etc., and a plurality of intestinal microbiota at the bacterial classification level including all types extracted from a plurality of subjects. Bacterial For each case, a value representing the ratio of all the intestinal bacteria at the bacterial classification level extracted from the subject (for example, 1-row × 187-column configuration data) and a multidimensional scaling method (for example, principal coordinate analysis, principal coordinate analysis, By performing component analysis (step S6 in FIG. 2), it is determined whether the intestinal condition of the subject belongs to any of two or more types (for example, infants, adults, and the elderly) based on attributes. Step (Step S7 in FIG. 2).

  Thus, in the present embodiment, the intestinal flora information of the subject is analyzed using the result of analyzing the intestinal flora information composed of a plurality of intestinal bacteria stored in the database 13 by the multidimensional scaling method. Classify information into types based on attributes. As a result, according to the present embodiment, the intestinal condition of the subject can be classified into two or more types.

Further, in the intestinal state determination method of the present embodiment, the intestinal state determination device 1 uses the intestinal microflora database (database 13) to calculate the number of clusters (step of FIG. 2). S3) and classifying the result of analyzing the intestinal microflora database by multidimensional scaling (for example, principal coordinate analysis, principal component analysis) into clusters of the number of clusters calculated in the cluster number calculation step, and Labels (eg, infants, adults) for each cluster based on the attributes of subjects (eg, age, gender, weight, height, place of residence, hometown, nationality, etc.) that have information on intestinal flora contained in (A step S4 in FIG. 2) for giving a label for the elderly. A.
Note that the intestinal flora is a set of intestinal bacteria (for example, genus Methanoblewibacter, Actinomyces,...) At the bacterial classification level.

  As a result, in the present embodiment, the intestinal condition of the subject can be classified into any of the types of clusters calculated in advance.

  In the intestinal state determination method of the present embodiment, the information on the intestinal bacterial flora extracted from the stool of the subject, the intestinal bacteria extracted from the plurality of subjects and extracted only from the plurality of subjects A subject information updating step (step S54 in FIG. 9) of adding flora information and inserting a 0% composition ratio into the added intestinal bacterial flora information. The information on the intestinal flora of a plurality of subjects stored in the flora database (database 13) and the updated information on the intestinal flora of the subject are integrated, and the integrated information on the intestinal flora is obtained. Analyzed by multidimensional scaling (eg, principal coordinate analysis, principal component analysis), the intestinal condition of the subject can be attributed to any of two or more types based on attributes (eg, infant, adult, elderly) Is determined to belong to .

  Thereby, in the present embodiment, the columns of the configuration data composed of the composition ratio of the intestinal flora of a plurality of subjects and the configuration data composed of the composition ratio of the intestinal flora of the subject are aligned. Therefore, the two configuration data can be integrated to perform the multidimensional scaling. As a result, according to the present embodiment, the intestinal condition, which is the composition ratio of the intestinal flora of the subject, can be determined based on the composition ratio of the intestinal flora of a plurality of subjects.

  In the intestinal condition determination method of the present embodiment, the attribute is age, and the ages of a plurality (m) of subjects included in the intestinal microflora database (database 13) are classified into a plurality of age groups. The labeling step further includes an age classification step (step S25 in FIG. 4), and the labeling step preliminarily assigns a label to each cluster based on the age group of the subject having information on the intestinal microflora included in each cluster. I do.

  As a result, in the present embodiment, the intestinal condition of the subject can be classified into clusters having a label assigned based on the age group. Accordingly, since the determination result is a name based on the age group, the person receiving the determination result can easily understand the determination result.

In the method for determining an intestinal state according to the present embodiment, the multidimensional scaling method is principal coordinate analysis or principal component analysis.
In the method for determining an intestinal condition according to the present embodiment, the plurality of subjects include a plurality of subjects in each of age groups from before weaning to 104 years of age.

  Thus, in the present embodiment, the intestinal condition of the subject is analyzed by analyzing the intestinal microflora information stored in the database 13 by principal coordinate analysis and principal component analysis, and based on the age included in the clustered cluster. Can be classified into any of two or more types. As a result, in the present embodiment, the intestinal condition of the subject can be classified into one of the types that are labeled according to the age.

  In the intestinal condition determination method of the present embodiment, the intestinal flora database acquires, for each subject, a ratio of each of the intestinal bacteria extracted from the subject to all the intestinal bacteria extracted from the subject. Includes the total number m of subjects (m is an integer of 2 or more) and all types extracted from a plurality of subjects by using the ratio of the intestinal bacteria for each subject acquired in the acquiring process and the acquiring process An extraction step of extracting a total number n (n is an integer of 2 or more) of a plurality of types of intestinal bacteria at a bacterial classification level; a total number m of subjects extracted by the extraction step; A configuration data construction step of constructing m rows × n rows of configuration data using the total number n and a value representing the ratio of all the intestinal bacteria at the bacterial classification level extracted for each of the plurality of subjects; , Multiple Acquisition information indicating the age of each person, the acquired information indicating the age of each subject, the database construction step of building the intestinal flora database in association with the configuration data constructed in the configuration data construction step, It was built.

  Thus, in the present embodiment, an intestinal flora database can be constructed using intestinal bacteria extracted from a subject or a person other than the subject. As a result, in the present embodiment, the intestinal flora database constructed in this way and the intestinal flora information of the subject are analyzed by a multidimensional scaling method, so that the intestinal state of the subject can be accurately determined. Can be estimated well.

  In the above-described example, an example in which the intestinal state determination device 1 estimates the intestinal state of a human as the intestinal age will be described, but the present invention is not limited to this. The intestinal condition determination device 1 may estimate the intestinal condition of the animal as the intestinal age.

  In addition, the CPU (Central Processing Unit), the DSP (Digital Signal Processor), and the computer may perform some or all of the intestinal condition determination device 1 of the present embodiment using an installed application.

DESCRIPTION OF SYMBOLS 1 ... Intestinal state determination apparatus, 11 ... Intestinal bacteria extraction apparatus, 12 ... Database generation part, 13 ... Database, 14 ... Analysis part, 15 ... Determination part

Claims (7)

An identifier for identifying each of the plurality of subjects, and a bacterial classification extracted for each of the plurality of subjects, for each of the plurality of intestinal bacteria at the bacterial classification level including all types extracted from the plurality of subjects. Cluster number calculation for calculating the number of clusters using an intestinal flora database in which a value representing a ratio to all intestinal bacteria at the level and information representing the attributes of each of the plurality of subjects are associated. Process and
The results obtained by analyzing the intestinal flora database by a multidimensional scaling method are classified into clusters of the number of clusters calculated in the cluster number calculation step, and have information on intestinal flora included in each cluster. A labeling step of labeling each cluster based on the age of the subject;
For each of a plurality of enteric bacteria in the bacterial classification level including all types extracted from the plurality of subjects, and a value representing the ratio of all the intestinal bacteria in bacterial classification level extracted from the subject, the intestine An internal flora database, by analyzing the multidimensional scaling method , to determine whether the subject's intestinal status belongs to any of two or more types based on age ;
It includes,
The method for determining an intestinal condition, wherein the two or more types in the determining step are the labels provided in the label providing step . The information on the intestinal flora extracted from the subject's feces is added to the information on the intestinal flora extracted from the plurality of subjects and extracted only from the plurality of subjects, and the added intestinal flora is added. A subject information updating step of inserting a composition ratio of 0% into the information of the bacterial flora,
In the determining step,
The information on the intestinal flora of the plurality of subjects stored in the intestinal flora database and the updated information on the intestinal flora of the subject are integrated, and the integrated intestinal bacteria information flora, that is analyzed by a multidimensional scaling, intestinal states of the subject is determined whether they belong to one of two or more types based on the age, according to claim 1 Method for determining intestinal condition. The age of the plurality of subjects included in the prior Kichonai flora database, including age classification step of classifying a plurality of age groups, a further,
3. The labeling method according to claim 1 , wherein the labeling step applies a label to each cluster in advance based on the age group of a subject having information on intestinal microflora included in each cluster. 4. Method for determining intestinal condition. The intestinal state determination method according to any one of claims 1 to 3 , wherein the multidimensional scaling method is principal coordinate analysis or principal component analysis. The method for determining an intestinal condition according to any one of claims 1 to 4 , wherein the plurality of subjects include a plurality of subjects in each of age groups from before weaning to 104 years of age. The intestinal flora database,
An acquisition step of acquiring, for each subject, a ratio of each of the intestinal bacteria extracted from the subject to all intestinal bacteria extracted from the subject,
Using the ratio of each intestinal bacterium for each subject obtained in the obtaining step, includes the total number m of the subjects (m is an integer of 2 or more) and all types extracted from the plurality of subjects. An extraction step of extracting a total number n (n is an integer of 2 or more) of a plurality of types of intestinal bacteria at a bacterial classification level;
The total number m of the subjects extracted in the extraction step, the total number n of the types of the plurality of intestinal bacteria, and the ratio of all the intestinal bacteria at the bacterial classification level extracted for each of the plurality of subjects, A configuration data constructing step of constructing m rows × n rows of configuration data using
The information indicating the age of each of the plurality of subjects is acquired, and the acquired information indicating the age of each subject is associated with the configuration data constructed in the configuration data construction step to construct an intestinal flora database. Database construction process,
The method for judging an intestinal condition according to any one of claims 1 to 5 , wherein the method is a database constructed by: An identifier for identifying each of the plurality of subjects, and a bacterial classification extracted for each of the plurality of subjects, for each of the plurality of intestinal bacteria at the bacterial classification level including all types extracted from the plurality of subjects. A value representing the ratio to all intestinal bacteria at the level, and information representing the attributes of each of the plurality of subjects, the intestinal flora database is associated,
Using the intestinal flora database, calculate the number of clusters, the intestinal flora database is analyzed by multidimensional scaling, the results are classified into clusters of the calculated number of clusters, and included for each cluster A label is assigned to each cluster based on the age of the subject having information on the intestinal flora to be obtained, and each of the plurality of enterobacteria at the bacterial classification level including all types extracted from the plurality of subjects is provided. For, a value representing the ratio to all intestinal bacteria at the bacterial classification level extracted from the subject, and the intestinal flora database, an analysis unit that analyzes by multidimensional scaling,
A determination unit that classifies the intestinal condition of the subject into one of two or more types based on age , based on a result analyzed by the analysis unit;
An intestinal condition determination device comprising: