JP2023006875A - Death prediction system and death prediction method - Google Patents

Abstract

To provide a death prediction system and a death prediction method for predicting whether an animal is likely to die in near future by a simple method.SOLUTION: A death prediction system comprises reception means for receiving data of presence or absence or occupancy of bacteria belonging to a predetermined fungal family in intestinal flora of an animal; and determination means for predicting and determining whether or not the animal will die within a predetermined period from the data input to the reception means of presence or absence or occupancy of bacteria belonging to a predetermined fungal family in intestinal flora.SELECTED DRAWING: Figure 1

Description

TECHNICAL FIELD The present invention relates to a mortality prediction system and a death prediction method, and more particularly to a death prediction system and a death prediction method that provide information on the mortality probability of an animal from data on the intestinal microflora of the animal.

Pet animals such as dogs, cats, rabbits, and livestock such as cows and pigs are irreplaceable to humans. In recent years, while the average lifespan of animals raised by humans has increased significantly, animals are more likely to suffer from some kind of disease during their lifetime, and the increase in medical expenses borne by animal owners has become a problem. .

In order to maintain the health of animals, it is important to manage their physical condition through daily diet, exercise, etc., and to respond quickly to illness. In the worst case, the animal suddenly dies without the owner noticing the harbinger. There may be situations where it will happen.

If it is known that an animal is nearing the end of life, it will be possible to take measures such as improving diet and lifestyle habits, conducting detailed examinations and treating animals, in order to avoid the worst possible outcome of death.

Therefore, there is a need for a simple means of determining whether an animal is likely to die in the near future.

In Patent Document 1, the effect of effectively adjusting or improving the intestinal flora by increasing the bacteria of the phylum Bacteroidetes and reducing the bacteria of the phylum Firmicutes in the intestinal flora However, no method is disclosed for predicting whether the animal will die in the near future from data on the animal's gut flora.

International publication 2017/094892 pamphlet

Accordingly, an object of the present invention is to provide a death prediction system and a death prediction method for predicting whether an animal is likely to die in the near future by a simple method.

The present inventors have analyzed and examined data on the intestinal flora of animals covered by pet insurance and a huge amount of data on the timing of death of the animal. The inventors have found that it is possible to predict whether or not the animal is likely to die within a predetermined period of time, and have completed the present invention.

That is, the present invention is the following [1] to [8].
[1] Receiving means for receiving data on the presence or absence or occupancy of bacteria belonging to a predetermined fungal family in the intestinal flora of an animal; A death prediction system, comprising a judgment means for predicting and judging whether or not the animal will die within a predetermined period from data on the presence or absence or occupancy of bacteria.
[2] The mortality prediction system of [1], wherein the animal is 10 years or older.
[3] The determination means uses a learned model to determine whether the animal is within a predetermined period from the data on the presence or absence or occupancy of bacteria belonging to a predetermined fungi family in the intestinal flora of the animal input to the reception means. The death prediction system of [1] or [2] for predicting whether or not to die in the future.
[4] The trained model includes data on the presence or absence or occupancy of bacteria belonging to a predetermined fungal family in the intestinal flora, and the animal has a predetermined The mortality prediction system of [3], which is a trained model that has learned the relationship between the data on the presence or absence of bacteria belonging to the family Mycology or the occupancy rate and information on whether or not the person died within a predetermined period of time from the time of acquisition.
[5] The determination means predicts and determines whether or not the animal will die within a predetermined period based on the score obtained by scoring the presence or absence or occupancy of bacteria belonging to a predetermined fungi family [1] Or the mortality prediction system of [2].
[6] the predetermined fungi are Campylobacteraceae, Clostridiaceae, Coprobacillaceae, Desulfovibrioceae, Enterobacteriaceae, Enterococccaceae, Erysipelotrichaceae, Fusobacteriaceae, Lachnospiraceae, Lactobacillaceae, Paraprevotellaceae,
Porphyromonodaceae, Prevotellaceae, Turicibacteraceae, Veillonellaceae, Comamomonadaceae, Leuconostocaceae, Pseudomonadaceae, and Sphingobacterium The mortality prediction system according to any one of [1] to [5], which is one or more selected from the group consisting of Sphingobacteriaceae.
[7] A death prediction method for predicting whether or not an animal will die within a predetermined period from data on the presence or absence or occupancy of bacteria belonging to a predetermined fungi family in the intestinal flora of an animal.
[8] The predetermined family of fungi is Campylobacteraceae, Clostridiaceae, Coprobacillaceae, Desulfovibrioceae, Enterobacteriaceae, Enterococccaceae, Erysipelotrichaceae, Fusobacteriaceae, Lachnospiraceae, Lactobacillaceae, Paraprevotellaceae,
Porphyromonodaceae, Prevotellaceae, Turicibacteraceae, Veillonellaceae, Comamomonadaceae, Leuconostocaceae, Pseudomonadaceae, and Sphingobacterium The mortality prediction method of [7], which is one or more selected from the group consisting of the family Sphingobacteriaceae.

INDUSTRIAL APPLICABILITY According to the present invention, it is possible to provide a death prediction system and a death prediction method for predicting whether an animal is likely to die in the near future by a simple method.

It is a schematic diagram showing one embodiment of the mortality prediction system of the present invention. It is a schematic diagram showing one embodiment of the mortality prediction system of the present invention. It is a flowchart figure showing an example of the flow of the death prediction method by the death prediction system of this invention. It is a graph showing the result of an example.

<Death Prediction System>
The mortality prediction system of the present invention comprises a receiving means for receiving data on the presence or absence or occupancy of bacteria belonging to a predetermined fungal family in the intestinal flora of an animal; determination means for predicting and determining whether or not the animal will die within a predetermined period from data on the presence or absence of fungi belonging to the fungi family or the occupancy rate.

[Receiving means]
The receiving means of the present invention is means for receiving input of data on the presence or absence or occupancy rate of bacteria belonging to a predetermined fungi family in the intestinal flora of an animal whose mortality possibility is to be predicted. Animals include dogs, cats, birds, rabbits, ferrets, and the like. In addition, although the age of the target animal is not limited, it is preferably 6 years old or older, more preferably 9 years old or older, and still more preferably 10 years old or older. The method of receiving data on the presence or absence of bacteria or the occupancy rate may be any method such as inputting or transmitting data to a terminal.

The presence or absence and occupancy of bacteria belonging to a given fungal family in the intestinal flora can be determined by a known metagenomic analysis method such as amplicon sequencing using a sequencer such as NGS, or bacteria It can be obtained by applying the plexus analysis method. For example, there is a method of identifying organisms contained in a sample by analyzing DNA or RNA base sequence information of all organisms contained in the sample using a next-generation sequencer. Preferably, all or part of the 16S rRNA gene contained in the sample is amplified as necessary, sequenced, and the obtained sequence is analyzed using software to obtain composition data of bacteria in the sample. method. By processing the composition data of bacteria in the sample with software or referring to genetic databases such as Genbank, Greengenes, and SILVA database, the attribution of the bacterial species contained in the sample is determined, and the specific family It is possible to measure the presence or absence and occupancy of bacteria belonging to

An example of 16S rRNA gene amplicon analysis (bacterial flora analysis) using NGS (next generation sequencer) will be specifically described. First, DNA is extracted from a sample using a DNA extraction reagent, and the 16S rRNA gene is amplified from the extracted DNA by PCR. After that, the amplified DNA fragments are comprehensively sequenced using NGS, low-quality reads and chimeric sequences are removed, and the sequences are clustered together for OTU (Operational Taxonomic Unit) analysis. An OTU is an operational classification unit for treating sequences having a certain degree of similarity or more (for example, 96-97% or more homology) as if they were a single strain of bacteria. Therefore, it is considered that the number of OTUs represents the number of bacterial species that constitute the bacterial flora, and the number of reads belonging to the same OTU represents the relative abundance of that species. In addition, it is possible to select a representative sequence from among the number of reads belonging to each OTU, and to identify the family name and genus species name by database search. In this way, the presence or absence of bacteria belonging to a specific family and their occupancy can be determined.

The data on the occupancy rate are data related to the occupancy rate of each bacterium contained in the intestinal flora of animals. The occupancy rate is the abundance ratio (detection ratio) of bacteria belonging to each fungal family in the intestinal flora. For example, the detection results of known metagenomic analysis methods such as amplicon sequencing using sequencers such as NGS. It can be measured as "hit rate". In the present invention, the numerical value of the occupancy rate of intestinal microflora may be used as the occupancy rate data, or a label or score set based on the occupancy rate may be used. Also, regarding the presence or absence of bacteria, a label or score set based on the presence or absence of bacteria may be used.

The occupancy rate in the present invention is the occupancy rate for each fungal family. The occupancy rate for each family is the occupancy rate for all fungi belonging to a certain family. That is, when calculating the occupancy rate for each family, the occupancy rate of each family can be calculated by totaling the occupancy rate of the bacterial species belonging to a certain family for each bacterial species in the intestinal flora. You can either identify to the species or genus level and aggregate by family, or you can identify to the family level without identifying to the species or genus level and calculate the family share. may

A label set based on the occupancy rate is a label appropriately set according to the magnitude of the numerical value of the occupancy rate. For example, according to the numerical value of the occupancy, three levels of labels can be set: "large", "medium", "small" or "large", "medium", and "small". Also, the number of levels of labels can be arbitrarily set, and for example, multilevel labels such as "0", "1", "2", "3", . . . "20" can be attached.
When using a label, the occupancy rate in the intestinal flora is measured, and a specific label is selected from a predetermined correspondence table according to the measured occupancy rate before entering the numerical value in the input means. assigned and the label can be entered into the receiving means.
A label set based on the presence or absence of bacteria is a label appropriately set according to the presence or absence of bacteria. For example, if a fungus is present, it can be labeled as "1", if not, it can be labeled as "0".

The score set based on the occupancy rate is a score appropriately set according to the numerical value of the occupancy rate of bacteria. For example, a score of "+1" is given when the occupancy rate is equal to or higher than a certain reference value, and "-1" is given when it is less than the certain reference value.
Also, the score set based on the presence or absence of bacteria is a score appropriately set according to the presence or absence of bacteria. For example, for a particular family, a score of "+1" is given if a fungus belonging to that family exists, and a score of "-1" if not.
Such a score may be calculated for each mycological family and input to the receiving means. In addition, the presence or absence of bacteria belonging to the fungi family and the occupancy rate are input to the reception means, and based on the data on the presence or absence of the entered bacteria and the occupancy rate A configuration in which a score is calculated may be used.
The mortality prediction system of the present invention may be configured such that the judging means totals the scores calculated or input for each fungi family, and predicts and judges the probability of death based on the obtained total score.

[Determination means]
The judging means of the present invention predicts and judges whether or not the animal will die within a prescribed period of time based on the data on the presence or absence or occupancy of bacteria belonging to a given fungal family in the intestinal flora of the animal input to the accepting means. It is a means to A prediction determination method is not particularly limited. For example, a processor predicts and determines whether or not the animal will die within a predetermined period based on the data on the presence or absence or occupancy of bacteria belonging to a predetermined fungal family in the intestinal flora of the animal using a preset program. do. In addition, as described above, a score is calculated according to a preset standard from the presence or absence of bacteria and the occupancy rate for each fungal family, and the death risk is determined based on the total score obtained by summing the scores. . That is, if the total score is greater than or equal to a predetermined value, the risk of death is high, and if the total score is less than the predetermined value, the risk of death is low.

The determining means of the present invention acquires an intestinal microbiota sample within a predetermined period of time, preferably from the time of reception, upon receipt of data on the presence or absence or occupancy of bacteria belonging to a predetermined fungal family in the intestinal microbiota of an animal. From time to time, or within a predetermined period of time, from the data on the presence or absence or occupancy of bacteria belonging to a predetermined fungal family in the intestinal flora of the animal, a prediction determination is made as to whether or not the animal will die. The predetermined period is preferably within 3 years, more preferably within 2 years, and even more preferably within 1 year.

The judging means of the present invention may be configured to carry out predictive judgment using a trained model. Such a trained model includes data on the presence or absence or occupancy of bacteria belonging to a predetermined fungal family in the intestinal flora, and when the animal acquires a sample of the intestinal flora or a predetermined It is preferable to use a trained model that has learned the relationship between data on the presence or absence of fungi belonging to the fungi family or on the occupancy rate and information on whether or not the individual died within a predetermined period of time from the time of acquisition. The trained model further includes data on the presence or absence or occupancy of bacteria belonging to a predetermined fungal family in the intestinal flora, and when the animal acquires the intestinal flora sample or the predetermined bacteria in the intestinal flora It is preferable to use a trained model that has been trained using information on whether or not the individual died within a predetermined period from the acquisition of data on the presence or absence of bacteria belonging to the family or on the occupancy rate, as teacher data. The predetermined period in the information regarding whether or not the person died within the predetermined period used for such training data is preferably within three years, more preferably within two years, and even more preferably within one year.

Artificial intelligence (AI) is preferable as the trained model. Artificial Intelligence (AI) is a software or system that imitates the intellectual work of the human brain on a computer. A computer program, etc., that performs and learns from experience. Artificial intelligence may be general-purpose or specialized, and may be deep neural networks, convolutional neural networks, or the like, and open software can be used.

In order to generate a trained model, AI is trained using teacher data. Learning may be either machine learning or deep learning, but machine learning is preferred. Deep learning is a development of machine learning, and is characterized by automatically finding feature quantities. In the present invention, data on the presence or absence or occupancy of bacteria belonging to a predetermined fungi family in the intestinal flora is used as the feature amount.

A learning method for generating a trained model is not particularly limited, and publicly available software can be used. For example, DIGITS (the Deep Learning GPU Training System) published by NVIDIA can be used. In addition, for example, learning may be performed by a known support vector machine method disclosed in "Introduction to Support Vector Machine" (Kyoritsu Shuppan) or the like.

Machine learning can be either unsupervised learning or supervised learning, but supervised learning is preferred. The method of supervised learning is not particularly limited, and examples thereof include decision tree, ensemble learning, gradient boosting, and the like. Examples of published machine learning algorithms include XGBoost, CatBoost, and LightGBM.

The training data for learning includes, for example, data on the presence or absence or occupancy of bacteria belonging to a predetermined fungal family in the intestinal flora of an animal, and when the animal acquires an intestinal flora sample (e.g., fecal sample). Alternatively, within a predetermined period of time from the acquisition of data on the presence or occupancy of bacteria belonging to a predetermined fungal family in the intestinal flora, preferably within 3 years, more preferably within 2 years, more preferably within 1 year , particularly preferably whether or not the patient died within 180 days. A dummy variable can be substituted for whether or not the character died. The data on the presence or absence and occupancy of bacteria belonging to a predetermined fungi family in the intestinal microbiota of animals as teacher data is the presence or absence of bacteria belonging to a predetermined fungi family in the intestinal microbiota explained in the reception method above and the occupancy rate. similar to the data for Information as to whether the animal has died within a predetermined period of time can be obtained, for example, from a veterinary hospital, an insured owner, or the like in connection with the fact of an insurance claim (also referred to as an "accident").

[output]
The output format of the prediction determination by the determination means of the present invention is not particularly limited. It is possible to output a predictive judgment by displaying a message such as "high possibility of death" or "○% possibility of death within the next year".
The mortality prediction system of the present invention may additionally have output means for receiving the determination results from the determination means and outputting the determination results.

The mortality prediction system of the present invention may further comprise proposal means for suggesting a life improvement method according to the result of the mortality prediction. For example, the proposing means receives the prediction results output from the determination means, and according to the prediction results, diets to avoid the predicted mortality risk, supplements containing bacteria that are less susceptible to diseases, low-salt, low-calorie diets, etc. Meals, low-sugar meals, diet menus, etc. can be proposed and recommended. The suggesting means may have a trained model.

Also, according to the prediction results output by the death prediction system or method of the present invention, beverages, meals, and supplements for preventing death can be manufactured or customized. As services related to mortality prediction, prediction by the death prediction system or death prediction method of the present invention, provision of prediction results, manufacture or customization of beverages, meals, and supplements according to the prediction results, proposal of the beverages, meals, and supplements, It can also take the form of a recommendation. Moreover, after providing such a service, it is also possible to carry out the mortality prediction method of the present invention and present whether or not the probability of death has decreased. The beverages, meals and supplements mentioned above include dietary beverages, diet foods, dietary supplement additives and the like.
In this way, it is expected that the risk of death will be reduced or avoided by proposing, manufacturing, and customizing meals and food according to the prediction results.

[Preferred Family]
As described above, the present invention uses data on the presence or absence or occupancy of bacteria belonging to a given family of fungi. The predetermined fungi family includes, but is not limited to, Campylobacteraceae, Clostridiaceae, Coprobacillaceae, Desulfovibrioceae, Enterobacteriaceae, Enterococcaceae. ), Erysipelotrichaceae, Fusobacteriaceae, Lachnospiraceae, Lactobacillaceae, Paraprevotellaceae, Porphyromonodaceae, Prevotellaceae, one or more selected from the group consisting of Turicibacteraceae, Veillonellaceae, Comamomonadaceae, Leuconostocaceae, Pseudomonadaceae, and Sphingobacteriaceae preferable.

[Campylobacteraceae]
The Campylobacteraceae is a family belonging to the Epsilonproteobacteria order Campylobacter and includes the genus Campylobacter.

[Clostridiaceae]
Clostridiaceae is the family of the order Clostridium and includes the genus Clostridium.

[Coprobacillaceae]
The Coprobacillus family is a family belonging to the order Erysipelotrichidae and includes the genus Coprobacillus.

[Desulfovibrionaceae]
The Desulfovibrioceae is a family belonging to the Deltaproteobacteria order Desulfovibrioles and includes the genus Desulfovibrio.

[Enterobacteriaceae]
Enterobacteriaceae (also called Enterobacteriaceae) is a family belonging to the phylum Proteobacteria, Gammaproteobacteria, order Enterobacteria. The family Enterobacteraceae includes, as genera, for example, the genera Enterobacter, Escherichia, Klebsiella, Salmonella, Serratia, Yersinia, Arsenophonus, Biostraticola, Candidatus Blochmannia, Brenneria, Buchnera, Budvicia, Buttiauxella, Cedecea, Citrobacter, Cosenzaea , Cronobacter, Dickeya, Edwardsiella, Erwinia and others.

[Enterococcaceae]
Enterococciaceae is a family of Gram-positive eubacteria belonging to the order Lactobacilli. Representative genera include Enterococcus, Melissococcus, Pilibacter, Tetragenococcus and Vagococcus.

[Erysipelotrichaceae]
Erysipelotrichaceae is a family belonging to the order Erysipelotrichidae, and genera include, for example, Allobaculum, Bulleidia, Erysipelothrix, and Holdemania.

[Fusobacteriaceae]
Fusobacteriaceae is a family belonging to the order Fusobacterium and includes the genus Fusobacterium.

[Lachnospiraceae]
Lachnospiraceae is a family within the order Clostridium of the class Clostridium and includes the genus Lachnospira.

[Lactobacillaceae]
The Lactobacillus family (also referred to as the Lactobacillus family) is a family belonging to the order Lactobacilli and includes the genus Lactobacillus.

[Paraprevotellaceae]
Paraprevotellaceae is a family belonging to the order Bacteroidetes and includes the genus Paraprevotella. There is also a taxonomy that includes the genus Paraplevotella in the family Prevotellaceae, without making it an independent family.

[Porphyromonodaceae]
The Porphyromonas family is a family belonging to the order Bacteroidetes and includes the genus Porphyromonas.

[Prevotellaceae]
Prevotellaceae is a family belonging to the order Bacteroidetes and includes the genus Prevotella.

[Turicibacteraceae]
The Turicibacteraceae family belongs to the order Turicibacter and includes the genus Turicibacter.

[Veillonellaceae]
Veillonellaceae is a family belonging to the order Clostridiales, and includes genera such as Veillonella, Acidaminococcus, Anaerovibrio, Dialister, Megamonas, Megasphaera, Mitsuokella, Pectinatus, Phascolarctobacterium, Propionispira, Selenomonas, and Succiniclasticum.

[Comamonadaceae]
Comamonasaceae is a family within the order Burkholderia, which includes the genus Comamonas. The genus Comamonas is a genus newly created by reclassifying fungi that belonged to the genus Pseudomonas by gene phylogenetic analysis.

[Leuconostocaceae]
Leuconostocaceae is a family of Gram-positive bacteria belonging to the order Lactobacilli. Representative genera include Fructobacillus, Leuconostoc, Oenococcus and Weissella.
There is also a classification method that includes the genus Leuconostoc in the family Lactobacillus, without making Leuconostocaceae an independent family.

[Pseudomonadaceae]
Pseudomonadaceae is a family belonging to the order Pseudomonads and includes the genus Pseudomonas.

[Sphingobacteriaceae]
The Sphingobacteriaceae is a family belonging to the order Sphingobacteriaceae and includes the genus Sphingobacterium.

The mortality prediction system of the present invention includes data on the presence or absence or occupancy of bacteria belonging to a predetermined fungi family in the intestinal flora of an animal, as well as the facial image of the animal, type, breed, age, sex, weight, medical history , gene sequence information, SNP, the presence or absence of gene mutation, and the like may be used.

An embodiment of the mortality prediction system of the present invention will be described below with reference to FIGS. 1 and 2. FIG.
In FIG. 1, a terminal 40 is used by a person (user) who wants to use the death prediction system. Examples of the terminal 40 include a personal computer, a smart phone, a tablet terminal, and the like. The terminal 40 includes a processing unit such as a CPU, a storage unit such as a hard disk, ROM or RAM, a display unit such as a liquid crystal panel, an input unit such as a mouse, keyboard, touch panel, etc., and a communication unit such as a network adapter. .
The user accesses the server from the terminal 40, and obtains data on the presence or absence or occupancy of bacteria belonging to a predetermined fungi family in the intestinal flora of the target animal, and, if necessary, a facial image (photograph), Enter and transmit information such as the type, breed, age, weight, and medical history of the animal.
Also, the user can receive the death prediction results from the server by accessing the server with the terminal 40 .

In addition, the user receives a stool sample collection kit for examining the intestinal flora of the animal being raised, and sends the stool sample to a company that measures the intestinal flora (not shown). The trader measures the intestinal microflora of the pet, and obtains data on the presence or absence or occupancy of bacteria belonging to a predetermined fungal family in the intestinal microbiota. Then, the trader directly inputs and transmits data on the presence or absence or occupancy rate of bacteria belonging to a predetermined fungi family in the intestinal flora of the animal to the reception means 31 of the server via its own terminal. Alternatively, the trader separately sends data on the presence or occupancy of bacteria belonging to a predetermined fungal family in the intestinal flora of the pet to the user by mail or e-mail, and the user sends the data to the reception means 31 through the terminal 40. Occupancy data and diversity data of intestinal flora may be input and transmitted.

In this embodiment, the server is configured by a computer, but any device may be used as long as it has the functions of the present invention.
The storage unit 10 is composed of, for example, a ROM, a RAM, or a hard disk. The storage unit 10 stores an information processing program for operating each unit of the server, particularly software for the determination unit 11 and the like.

As described above, the determination means 11 collects the data on the presence or absence or occupancy of bacteria belonging to a predetermined fungal family in the intestinal flora of the target animal, which is input by the user or the vendor who performed the measurement of the intestinal flora. It is used as an input and outputs a prediction as to whether or not the animal will die within a predetermined period (for example, within a year), or a percentage of the probability of death. A learned model may be used as the determination means. Such trained models include, for example, XGBoost, CatBoost, LightGBM, or deep neural networks or convolutional neural networks.
In the present embodiment, an embodiment has been described in which the determination means and reception means are stored in the server and connected to the user's terminal via connection means such as the Internet or LAN. A mode in which the means and the interface unit are stored in one server or device, a mode in which a separate terminal for use by the user is not required, or the like may be employed.

The mortality prediction system of the present invention may comprise insurance premium calculation means 12 as shown in FIG. The insurance premium calculation means 12 calculates the death prediction output by the determination means 11 and information such as the type and breed of the animal input by the user, the age at the time of acquisition of the data on the intestinal flora, the weight, and the medical history. Software for calculating animal insurance premiums. For example, the software grades insurance premiums according to the type and breed of the animal, age at the time of acquisition of intestinal microbiota data, weight, medical history, etc. Finally, the determination means 11 outputs This software is for calculating the final insurance premium based on the insurance premium table 13 by correcting the grade in consideration of the mortality prediction.
The insurance premium calculation means 12 and determination means 11 may be one piece of software.

The processing calculation unit 20 uses the determination unit 11 and insurance premium calculation unit 12 stored in the storage unit to calculate death predictions and insurance premiums.

The interface unit (communication unit) 30 includes a reception unit 31 and an output unit 32, and receives data and other information regarding the presence or absence of bacteria belonging to a predetermined fungal family in the intestinal flora of animals or their occupancy from a user's terminal. Accepts and outputs death predictions and insurance premium calculation results to the user's terminal.

<Death Prediction Method>
The mortality prediction method of the present invention is characterized by predicting and judging whether or not the animal will die within a predetermined period from data on the presence or absence or occupancy of bacteria belonging to a predetermined fungal family in the intestinal flora of the animal. It is something to do. Preferably, the step of preparing data on the presence or absence or occupancy of bacteria belonging to a predetermined fungal family in the intestinal flora of an animal, and the presence or absence or occupancy of bacteria belonging to the predetermined fungal family in the intestinal flora of the animal. predicting whether the animal will die within a predetermined period from the data relating to the animal. The method can be performed, for example, using the mortality prediction system described above.
Data on the presence or absence or occupancy of bacteria belonging to a predetermined fungal family in the intestinal flora of animals, and the presence or absence or occupancy of bacteria belonging to a predetermined fungal family in the intestinal flora of animals, in the mortality prediction method of the present invention The method for predicting whether or not the animal will die within a predetermined period from the rate data and the configuration therefor are the same as those described in the death prediction system above.

In the mortality prediction method of the present invention, the predetermined fungi family is Campylobacteraceae, Clostridiaceae, Coprobacillaceae, Desulfovibrioceae, Enterobacteriaceae, Enterococcus Enterococcaceae, Erysipelotrichaceae, Fusobacteriaceae, Lachnospiraceae, Lactobacillaceae, Paraprevotellaceae, Porphyromonodaceae, Prevotellaceae ( Prevotellaceae), Turicibacteraceae, Veillonellaceae, Comamomonadaceae, Leuconostocaceae, Pseudomonadaceae, and Sphingobacteriaceae preferably one or more.

FIG. 3 shows a flow chart of mortality prediction determination based on one embodiment of the death prediction method using the death prediction system of the present invention. This one embodiment, for convenience of explanation, is described including obtaining samples from animals and obtaining data on gut microbiota. A user collects a stool sample from an animal using a stool collection kit or the like, and sends it to an intestinal microflora analysis company (step S1). The intestinal microbiota analysis company uses a next-generation sequencer to analyze and acquire data on the presence or absence or occupancy of bacteria belonging to a predetermined fungal family in the intestinal microbiota of the animal from the stool sample (step S2). . The intestinal microbiota analysis company returns data on the intestinal microbiota to the user. A user accesses the mortality prediction system through a terminal and inputs data on the presence or absence or occupancy of bacteria belonging to a predetermined fungal family in the animal's intestinal flora (step S3). The mortality prediction system predicts the possibility that the animal will die within a predetermined period (for example, within one year) based on the input data on the presence or absence or occupancy of bacteria belonging to a predetermined fungi family in the intestinal flora of the animal. Prediction is made as to whether or not there is only one (step S4). The death prediction system outputs the prediction judgment, transmits it to the terminal 40, and the prediction judgment result is displayed on the terminal 40 (step S5).

Examples of the present invention are shown below. The invention is not limited to the following examples.
(DNA extraction from stool sample)
Fecal samples were collected from each dog and DNA was extracted as follows.
Dog fecal samples were collected by the dog owner using a fecal collection kit. The fecal samples were received and suspended in water.
Next, 200 μL of the fecal suspension and 810 μL of Lysis buffer (containing 224 μg/mL Proteinase K) were added to the bead tube, and the beads were crushed with a bead homogenizer (6,000 rpm, crushing 20 seconds, interval 30 seconds, crushing 20 seconds). seconds). After that, the sample was left on a heat block at 70°C for 10 minutes to be treated with ProteinaseK, and then left on a heat block at 95°C for 5 minutes to inactivate the ProteinaseK. Using chemagic 360 (PerkinElmer), DNA was automatically extracted from the lysed specimen according to the protocol for chemagic kit stool to obtain 100 μL of DNA extract.

(Meta 16S RNA gene sequence analysis)
Meta-16S sequence analysis was performed by modifying illumina 16S Metagenomic Sequencing Library Preparation (version 15044223 B). First, a 460 bp region containing the variable region V3-V4 of the 16S rRNA gene was amplified by PCR using universal primers (Illumina_16S_341F and Illumina_16S_805RPCR). The PCR reaction solution is a mixture of 10 μL of DNA extract, 0.05 μL of each primer (100 μM), 12.5 μL of 2xKAPA HiFi Hot-Start ReadyMix (F. Hoffmann-LaRoche, Switzerland), and 2.4 μL of PCRgrade water. prepared. For PCR, after heat denaturation at 95°C for 3 minutes, a cycle of 95°C for 30 seconds, 55°C for 30 seconds, and 72°C for 30 seconds was repeated 30 times, and finally an extension reaction was performed at 72°C for 5 minutes. Amplified products were purified using magnetic beads and eluted with 50 μL of Buffer EB (QIAGEN, Germany). The amplified product after purification was subjected to PCR using Nextera XT Index Kit v2 (illumina, CA, US) to add an index. A PCR reaction solution was prepared by mixing 2.5 μL of amplified product, 2.5 μL of each primer, 12.5 μL of 2× KAPA HiFi Hot-Start Ready Mix, and 5 μL of PCRgrade water. For PCR, after heat denaturation at 95°C for 3 minutes, a cycle of 95°C for 30 seconds, 55°C for 30 seconds, and 72°C for 30 seconds was repeated 12 times, and finally an extension reaction was performed at 72°C for 5 minutes. The indexed amplification products were purified using magnetic beads and eluted with 80-105 μL of Buffer EB. The concentration of each amplification product was measured with a NanoPhotometer (Implen, Calif., US), adjusted to 1.4 nM, and mixed in equal amounts to prepare a library for sequencing. The DNA concentration of the sequencing library and the size of the amplified product were confirmed by electrophoresis and analyzed by MiSeq. MiSeq Reagent Kit V3 was used for the analysis, and 2×300 bp paired-end sequencing was performed. The resulting sequences were analyzed with MiSeq Reporter to obtain bacterial composition data.
The sequences of the universal primers used above are as follows. This universal primer can be purchased commercially.
Illumina_16S_341F
5′-TCGTCGGCAGCGTCAGATGTGTATAAGAGACAGCCTACGGGNGGCWGCAG-3′
lumina_16S_805R
5′-GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAGGACTACHVGGGTATCTAATCC-3′

According to the above method, compositional data of the intestinal flora of 7018 dogs aged 10 years or older were obtained, and the presence or absence of bacteria belonging to a given family of fungi in the intestinal flora of each dog and their occupancy rates were determined. The number of individuals who died within 1 year from the date of collection of the stool sample (individuals who died within 1 year) was 688 individuals.

(Investigation of death within one year from the time of collection of fecal samples)
All the above dogs were examined to see if they died within one year after collection of fecal samples. Whether or not the animals died was investigated using death cancellation data of pet insurance or reports from veterinary hospitals.

[Example 1]
The relationship between the presence or absence of bacteria belonging to a given fungi family in the intestinal flora and the rate of death within one year (referred to as "mortality rate within one year") was investigated. The mortality rate within 1 year was calculated by the following formula.
Mortality rate within 1 year = number of dead individuals within 1 year included in each group / total number of individuals in each group × 100
The mortality rate within 1 year of each fungal family carrier group was compared with the standard value for dogs aged 10 years or older (this time, the mortality rate within 1 year in the above 7018 dogs). Results for Campylobacteraceae, Coprobacillaceae, Fusobacteriaceae, Comamonadaceae, Leuconostocaceae, Pseudomonadaceae, and Sphingobacteriaceae showed a difference of 1% or more in mortality within one year compared to the overall standard value (see Table 1 below). In addition, a boundary value was set for each fungal family, and the distribution was compared in the same way. As a result, Desulfovibrionaceae, Enterococcaceae, Lachnospiraceae, Lactobacillaceae, Paraprevotellaceae, Porphyromonodaceae, Prevotellaceae, Turicibacteraceae For Turicibacteraceae, Veillonellaceae, and Comamomonadaceae, 1% mortality within 1 year compared to global norms when prevalence is above or below the threshold The above differences were confirmed (see Table 1 below).
In addition, the mortality rate within one year of the non-carrier group of each fungi family was compared to the standard value for 10-year-old dogs (mortality rate within one year in 7018 dogs this time). Results, Clostridiaceae, Coprobacillaceae, Enterobacteriaceae, Enterococcaceae, Erysipelotrichaceae, Fusobacteriaceae, Lachnospiraceae, , a difference of 1% or more was confirmed in the mortality rate within one year compared with the overall standard value (see Table 1 below).

各糞便サンプルを採取し、１年以内に死亡したか否かを調査した全個体のそれぞれについて、合計スコアを算出し、合計スコア（判定スコア）と１年死亡率をプロットしてグラフを作成した。
即ち、表１に記載された各菌科について、保有している／していない、或いは、占有率が高い／低いによって、－２～＋２のスコアを付け、それを合計したスコアを合計スコア（判定スコア）とした。
具体的には、保有群との差分が正の値になっている菌科については、その菌科を保有している場合はプラス（＋１～＋２）のスコアを付与し、保有していない場合は０又はマイナス（－１～－２）のスコアを付与した。一方、保有群との差分が負の値になっている菌科については、その菌科を保有している場合はマイナスのスコアを付与し、保有していない場合は０又はプラスのスコアを付与した。
また、表１中、非保有群との差分が正の値になっている菌科については、その菌科を保有していない場合はプラスのスコアを付与し、保有している場合は０のスコアを付与した。一方、非保有群との差分が負の値になっている菌科については、その菌科を保有している場合はマイナスのスコアを付与し、保有していない場合は０又はプラスのスコアを付与した。
また、表１中、高占有率との差分が正の値になっている菌科についても同様に、境界値よりも高い占有率の場合に高スコアを付与し、低い場合には低スコアを付与した。一方、高占有率との差分が負の値になっている菌科については、境界値よりも高い占有率の場合に低スコアを付与し、低い場合には高スコアを付与した。
また、表１中、低占有率との差分が正の値になっている菌科については、境界値よりも高い占有率の場合に低スコアを付与し、低い場合には高スコアを付与した。一方、低占有率との差分が負の値になっている菌科については、境界値よりも高い占有率の場合に高スコアを付与し、低い場合には低スコアを付与した。
結果を図４に示す。図４から明らかなように、合計スコア（判定スコア）が高いほど、１年死亡率も高くなることが分かった。 [Example 2]
For the fungi listed in Table 1, scores were set according to the predetermined criteria listed in each table. The numbers in parentheses in Table 1 are mortality rates within one year of each group.

Each fecal sample was collected, and for each of all individuals investigated whether they died within one year, the total score was calculated, and a graph was created by plotting the total score (judgment score) and the one-year mortality rate. .
That is, for each fungal family listed in Table 1, a score of -2 to +2 is given depending on whether it has / does not have or the occupancy rate is high / low, and the total score is the total score ( judgment score).
Specifically, for fungi whose difference from the possessing group is a positive value, a score of plus (+1 to +2) is given if they possess that fungus, and if they do not possess it gave a score of 0 or negative (-1 to -2). On the other hand, for fungi whose difference from the possessing group is a negative value, a negative score is given if the fungus is possessed, and a 0 or positive score is given if not possessed. bottom.
In addition, in Table 1, for fungi whose difference from the non-possessing group is a positive value, a positive score is given if they do not possess that fungus, and 0 if they do. given a score. On the other hand, for fungi whose difference from the non-possessing group is a negative value, a negative score is given if the fungus is possessed, and a score of 0 or positive is given if it is not possessed. Granted.
In addition, in Table 1, similarly, for fungi whose difference from the high occupancy rate is a positive value, a high score is given when the occupancy rate is higher than the boundary value, and a low score is given when it is lower. Granted. On the other hand, for fungi whose difference from the high occupancy rate is a negative value, a low score was given when the occupancy rate was higher than the boundary value, and a high score was given when it was lower than the boundary value.
In addition, in Table 1, for fungi whose difference from the low occupancy rate is a positive value, a low score is given when the occupancy rate is higher than the boundary value, and a high score is given when it is lower. . On the other hand, for fungi whose difference from the low occupancy rate is a negative value, a high score was given when the occupancy rate was higher than the boundary value, and a low score was given when it was lower than the boundary value.
The results are shown in FIG. As is clear from FIG. 4, it was found that the higher the total score (determination score), the higher the one-year mortality rate.

Claims (8)

Receiving means for receiving data on the presence or absence or occupancy of bacteria belonging to a predetermined fungal family in the intestinal flora of an animal; Alternatively, a mortality prediction system comprising a determination means for predicting whether or not the animal will die within a predetermined period from data on the occupancy rate. 2. The mortality prediction system of claim 1, wherein said animal is 10 years of age or older. The determining means uses a learned model to determine whether the animal dies within a predetermined period based on the data on the presence or absence or occupancy of bacteria belonging to a predetermined fungal family in the intestinal flora of the animal input to the receiving means. 3. The death prediction system according to claim 1 or 2, which predicts whether or not. The trained model includes data on the presence or absence or occupancy of bacteria belonging to a predetermined fungal family in the intestinal flora, and the animal at the time of acquisition of the intestinal bacterial flora sample or to a predetermined fungal family in the intestinal flora 4. The mortality prediction system according to claim 3, wherein the mortality prediction system is a trained model that has learned the relationship between the data on the presence or absence of the bacteria to which it belongs or the occupancy rate and information on whether or not the individual died within a predetermined period of time from the acquisition of the data. 3. The determining means according to claim 1 or 2, wherein the determining means predicts and determines whether or not the animal will die within a predetermined period based on the score obtained by scoring the presence or absence of bacteria belonging to a predetermined fungi family or the occupancy rate. mortality prediction system. The predetermined fungi family is Campylobacteraceae, Clostridiaceae, Coprobacillaceae, Desulfovibrionaceae, Enterobacteriaceae, Enterococcaceae, Erysipero Erysipelotrichaceae, Fusobacteriaceae, Lachnospiraceae, Lactobacillaceae, Paraprevotellaceae,
Porphyromonodaceae, Prevotellaceae, Turicibacteraceae, Veillonellaceae, Comamomonadaceae, Leuconostocaceae, Pseudomonadaceae, and Sphingobacterium 6. The mortality prediction system according to any one of claims 1 to 5, which is one or more selected from the group consisting of Sphingobacteriaceae. A death prediction method for predicting whether or not an animal will die within a predetermined period from data on the presence or absence of bacteria belonging to a predetermined fungi family in the intestinal flora of an animal or the occupancy rate of the animal. The predetermined fungi family is Campylobacteraceae, Clostridiaceae, Coprobacillaceae, Desulfovibrionaceae, Enterobacteriaceae, Enterococcaceae, Erysipero Erysipelotrichaceae, Fusobacteriaceae, Lachnospiraceae, Lactobacillaceae, Paraprevotellaceae,
Porphyromonodaceae, Prevotellaceae, Turicibacteraceae, Veillonellaceae, Comamomonadaceae, Leuconostocaceae, Pseudomonadaceae, and Sphingobacterium 8. The method for predicting mortality according to claim 7, wherein the number is one or more selected from the group consisting of Sphingobacteriaceae.

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