Disease Incidence Prediction System, Insurance Premium Calculation System, Disease Incidence Prediction Method, and Insurance Premium Calculation Method

Abstract

To provide a disease incidence prediction system and a disease incidence prediction method for predicting a possibility of incidence of a specific disease of an animal by a simple method.SOLUTION: Provided is a disease incidence prediction system comprising reception means for receiving information on presence or absence of bacteria belonging to family Fusobacteriaceae and / or presence or absence of bacteria belonging to family Veillonellaceae in a sample obtained from an animal; and determination means for predicting and determining a possibility of incidence of the animal with keratoconjunctivitis sicca from the information input to the reception means of the presence or absence of bacteria belonging to family Fusobacteriaceae and / or the presence or absence of bacteria belonging to family Veillonellaceae.SELECTED DRAWING: Figure 1

Description

Technical Field

[0001] The present invention relates to a disease incidence prediction system, an insurance premium calculation system, a disease incidence prediction method, and an insurance premium calculation method. Specifically, from data regarding whether an animal carries bacteria belonging to a specific bacterial family, the present invention provides a disease incidence prediction system and a disease incidence prediction method for providing prediction information on whether the animal will contract a specific disease, and an insurance premium calculation system and an insurance premium calculation method for calculating or modifying the insurance premium of an animal from data regarding whether the animal carries bacteria belonging to a specific bacterial family.

Background Art

[0002] Pets such as dogs, cats, and rabbits, and livestock such as cows and pigs are invaluable to humans. In recent years, while the average lifespan of animals raised by humans has increased significantly, animals are more likely to contract some disease during their lifetime, and the increasing medical costs borne by breeders have become a problem.

[0003] To maintain the health of animals, it is important to manage their physical condition through daily diet, exercise, etc., and to respond promptly to discomfort. However, since animals cannot communicate their physical discomfort in their own language, in reality, breeders only notice that an animal has contracted a disease when the symptoms progress and some observable symptom appears externally. If it were possible to grasp the possibility that an animal may contract a certain disease, measures could be taken to avoid such a disease.

[0004] Therefore, there is a need for a means to know, by a simple method, whether an animal may contract a specific disease.

[0005] Keratoconjunctivitis sicca (KCS) is an ophthalmic disease that causes damage to the cornea and conjunctiva due to a decrease in the amount of tears. When the surface of the eye dries out, it causes conjunctival congestion, corneal inflammation, wounds, pigmentation, etc. Allergic dermatitis refers to dermatitis caused by an allergen, which is a substance that causes allergic symptoms, due to an excessive reaction of the immune system in the animal body. Types of allergens include fleas, house dust, pollen, food, etc. Inflammatory bowel disease (IBD) is an idiopathic chronic digestive disease characterized by the infiltration of inflammatory cells into the intestinal mucosa. IBD is classified into "lymphocytic plasma cell enteritis", "lymphocytic plasma cell colitis", "eosinophilic gastroenteritis", "granulomatous enteritis", "histiocytic ulcerative enteritis", etc. according to the type and location of the infiltrating inflammatory cells.

[0006] Patent Document 1 discloses an intestinal flora regulating or improving composition having an effect of effectively regulating or improving the intestinal flora by growing bacteria of the phylum Bacteroidetes and decreasing bacteria of the phylum Firmicutes in the intestinal flora, but does not disclose a method for predicting whether an animal is likely to suffer from a specific disease based on data regarding whether the animal harbors bacteria belonging to a specific family.

Prior Art Documents

Patent Documents

[0007]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0008] Therefore, an object of the present invention is to provide a disease susceptibility prediction system and a disease susceptibility prediction method for predicting, by a simple method, whether an animal is likely to suffer from a specific disease.

Means for Solving the Problems

[0009] The inventors analyzed and studied a vast amount of data on the bacteria carried by animals insured under pet insurance, particularly data on the gut microbiota, as well as data on whether the animal had contracted a specific disease and whether an insurance claim had been made for the animal. As a result, they found that it was possible to predict whether the animal was likely to contract a specific disease using the data on the bacteria carried by the animal, and thus completed the present invention.

[0010] That is, the present invention is as follows in [1] to

[10] . [1] A disease susceptibility prediction system comprising: a receiving means for receiving information on the presence or absence of bacteria belonging to the family Fusobacteriaceae and / or the presence or absence of bacteria belonging to the family Veillonellaceae in a sample collected from an animal; and a determination means for predicting and determining the susceptibility of the animal to dry keratoconjunctivitis from the information on the presence or absence of bacteria belonging to the family Fusobacteriaceae and / or the presence or absence of bacteria belonging to the family Veillonellaceae input to the receiving means. [2] A disease susceptibility prediction system comprising: a receiving means for receiving information on the presence or absence of bacteria belonging to the family Fusobacteriaceae and / or the presence or absence of bacteria belonging to the family Veillonellaceae in a sample collected from an animal; and a determination means for predicting and determining the susceptibility of the animal to allergic dermatitis from the information on the presence or absence of bacteria belonging to the family Fusobacteriaceae and / or the presence or absence of bacteria belonging to the family Veillonellaceae input to the receiving means. [3] A disease susceptibility prediction system comprising: a receiving means for receiving information on the presence or absence of bacteria belonging to the family Fusobacteriaceae and / or the presence or absence of bacteria belonging to the family Veillonellaceae in a sample collected from an animal; and a determination means for predicting and determining the susceptibility of the animal to inflammatory bowel disease from the information on the presence or absence of bacteria belonging to the family Fusobacteriaceae and / or the presence or absence of bacteria belonging to the family Veillonellaceae input to the receiving means. [4] The disease susceptibility prediction system according to any one of [1] to [3], wherein the sample is feces. [5] An insurance premium calculation system comprising: a receiving means for receiving information on the presence or absence of bacteria belonging to the family Fusobacteriaceae and / or the presence or absence of bacteria belonging to the family Veillonellaceae in a sample collected from an animal; and an insurance premium calculation means for calculating or modifying an insurance premium using the information on the presence or absence of bacteria belonging to the family Fusobacteriaceae and / or the presence or absence of bacteria belonging to the family Veillonellaceae input to the receiving means. [6] A disease susceptibility prediction method comprising the step of predicting and determining the susceptibility of an animal to dry keratoconjunctivitis from information on the presence or absence of bacteria belonging to the family Fusobacteriaceae and / or the presence or absence of bacteria belonging to the family Veillonellaceae in a sample collected from the animal. [7] A disease susceptibility prediction method comprising the step of predicting and determining the susceptibility of an animal to allergic dermatitis from information on the presence or absence of bacteria belonging to the family Fusobacteriaceae and / or the presence or absence of bacteria belonging to the family Veillonellaceae in a sample collected from the animal. [8]A disease susceptibility prediction method comprising the step of predicting and determining the susceptibility of an animal to inflammatory bowel disease based on information on the presence or absence of bacteria belonging to the family Fusobacteriaceae and / or the presence or absence of bacteria belonging to the family Veillonellaceae in a sample collected from the animal. [9]The disease susceptibility prediction method according to any one of [6] to [8], wherein the sample is feces.

[10] An insurance premium calculation method comprising the step of calculating or modifying an insurance premium using information on the presence or absence of bacteria belonging to the family Fusobacteriaceae and / or the presence or absence of bacteria belonging to the family Veillonellaceae in a sample collected from an animal. [Advantages of the Invention]

[0011] According to the present invention, it is possible to provide a disease susceptibility prediction system and a disease susceptibility prediction method for predicting, by a simple method, whether an animal is likely to suffer from a specific disease. Further, since the presence or absence of bacteria belonging to the family Fusobacteriaceae and / or the presence or absence of bacteria belonging to the family Veillonellaceae in a sample collected from an animal is related to the possibility of future insurance claims of the animal, it is possible to provide an insurance premium calculation system and an insurance premium calculation method capable of calculating or modifying the insurance premium of an animal using information on the presence or absence of bacteria belonging to the family Fusobacteriaceae and / or the presence or absence of bacteria belonging to the family Veillonellaceae. [Brief Description of the Drawings]

[0012]

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[0013] [Disease Susceptibility Prediction System] The disease susceptibility prediction system of the present invention includes a reception means for receiving information regarding the presence or absence of bacteria belonging to the family Fusobacteriaceae and / or the presence or absence of bacteria belonging to the family Veillonellaceae in a sample collected from an animal, and from the information regarding the presence or absence of bacteria belonging to the family Fusobacteriaceae and / or the presence or absence of bacteria belonging to the family Veillonellaceae input to the reception means, a determination means for predicting and determining the likelihood of the animal developing keratoconjunctivitis sicca, allergic dermatitis, or inflammatory bowel disease (hereinafter, also collectively referred to as "specific diseases").

[0014] [Reception Means] The reception means of the present invention is a means for receiving input of information regarding the presence or absence of bacteria belonging to the family Fusobacteriaceae and / or the presence or absence of bacteria belonging to the family Veillonellaceae in a sample collected from an animal for which the likelihood of developing a specific disease is to be predicted. Examples of animals include dogs, cats, birds, rabbits, ferrets, etc. The age of the target animal is not limited. The method of receiving data regarding the presence or absence or occupancy rate of bacteria may be any method such as data input to a terminal, transmission, etc. Examples of samples collected from animals include saliva, feces, etc., and feces are preferred. By analyzing feces, it is possible to confirm the presence or absence of bacteria belonging to the family Fusobacteriaceae and / or the presence or absence of bacteria belonging to the family Veillonellaceae in the intestinal flora of the animal.

[0015] The presence or absence of bacteria belonging to the family Fusobacteriaceae and / or the family Veillonellaceae in a sample collected from an animal can be obtained by applying known metagenomic analysis methods such as amplicon sequencing using a sequencer such as NGS or bacterial flora analysis methods to samples collected from animals such as saliva samples and fecal samples. For example, a method of identifying organisms contained in the sample by analyzing the base sequence information of DNA and RNA of all organisms contained in the sample using a next-generation sequencer can be mentioned. 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 bacterial composition data in the sample. By processing the bacterial composition data in the sample with software or referring to gene databases such as Genbank, Greengenes, and SILVA database, the attribution of the bacterial species contained in the sample can be determined, and the presence or absence and occupancy rate of bacteria belonging to a specific family can be measured.

[0016] An example of amplicon analysis (bacterial flora analysis) of the 16S rRNA gene using NGS (next-generation sequencer) will be specifically described. First, DNA is extracted from the sample using a DNA extraction reagent, and the 16S rRNA gene is amplified from the extracted DNA by PCR. Then, the nucleotide sequence of the amplified DNA fragment is comprehensively determined using NGS. After removing low-quality reads and chimeric sequences, the sequences are clustered to perform OTU (Operational Taxonomic Unit) analysis. OTU is an operational taxonomic unit for treating sequences having a certain degree of similarity (for example, homology of 96-97% or more) as if they were of the same bacterial species. Therefore, the number of OTUs represents the number of bacterial species constituting the bacterial flora, and the number of reads belonging to the same OTU is considered to represent the relative abundance of that species. In addition, a representative sequence is selected from among the number of reads belonging to each OTU, and the family name and genus-species name can be identified by database search. In this way, the presence or absence and occupancy rate of bacteria belonging to a specific family can be measured.

[0017] In the present invention, as information regarding the presence or absence of bacteria belonging to the Fusobacteriaceae family and / or the presence or absence of bacteria belonging to the Veillonellaceae family, labels or scores set based on the presence or absence of bacteria belonging to the Fusobacteriaceae family and / or the presence or absence of bacteria belonging to the Veillonellaceae family may be used.

[0018] The 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, a label such as "1" when bacteria are present and "0" when they are absent can be attached.

[0019] 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 specific family, a score of "+1" is given if bacteria belonging to that family are present, and a score of "-1" is given if they are absent. Such scores may be calculated for each bacterial family and input to the receiving means. Alternatively, a configuration may be adopted in which the presence or absence of bacteria belonging to a bacterial family is input to the receiving means, and scores are calculated for each bacterial family based on score assignment criteria preset based on the data regarding the presence or absence of the input bacteria. The disease susceptibility prediction system of the present invention may be configured such that the determination means sums up the scores calculated or input for each bacterial family, and predicts and determines the susceptibility to a specific disease based on the obtained total score.

[0020] [Determination means] The determination means of the present invention is a means for predicting and determining whether an animal will contract a specific disease within a predetermined period based on information regarding the presence or absence of bacteria belonging to a predetermined bacterial family in a sample collected from the animal input to the reception means. The prediction and determination method is not particularly limited. For example, a processor uses a preset program to predict and determine whether an animal will contract a specific disease within a predetermined period based on information regarding the presence or absence of bacteria belonging to a predetermined bacterial family in a sample collected from the animal. Also, as described above, a configuration may be adopted in which a score is calculated according to a preset standard based on the presence or absence of bacteria for each bacterial family, and the disease susceptibility risk is determined based on the total score obtained by summing up the scores. That is, if the total score is equal to or higher than a predetermined value, the disease susceptibility risk is high, and if the total score is less than the predetermined value, the disease susceptibility risk is low.

[0021] When the determination means of the present invention receives information regarding the presence or absence of bacteria belonging to a predetermined bacterial family in a sample collected from an animal, it preferably predicts and determines whether the animal will contract a specific disease within a predetermined period, more preferably within a predetermined period from the time of reception, from the time of sample collection, or from the time of obtaining data regarding the presence or absence of bacteria belonging to a predetermined bacterial family. The predetermined period is preferably within 3 years, more preferably within 2 years, still more preferably within 1 year, and particularly preferably within 180 days.

[0022] The determination means of the present invention may be configured to perform prediction determination using a learned model. Such a learned model preferably learns the relationship between data on the presence or absence of bacteria belonging to the family Fusobacteriaceae and / or the family Veillonellaceae in a sample collected from an animal or in the gut microbiota, and information on whether or not the animal has developed a specific disease within a predetermined period from the time of sample acquisition or the time of acquisition of data on the presence or absence of bacteria belonging to a predetermined bacterial family. The learned model further preferably learns, as teacher data, data on the presence or absence of bacteria belonging to a predetermined bacterial family in a sample collected from an animal, for example, in the gut microbiota, and information on whether or not the animal has developed a specific disease within a predetermined period from the time of sample acquisition or the time of acquisition of data on the presence or absence of bacteria belonging to a predetermined bacterial family. The predetermined period in the information on whether or not the animal has developed a specific disease within a predetermined period used for such teacher data is preferably within 3 years, more preferably within 2 years, still more preferably within 1 year, and particularly preferably within 180 days.

[0023] The learned model is preferably artificial intelligence (AI). Artificial intelligence (AI) is software or a system that mimics the intellectual work performed by the human brain using a computer. Specifically, it refers to computer programs that understand natural languages used by humans, perform logical inferences, and learn from experience. The artificial intelligence may be either general-purpose or specialized, and may be any of a deep neural network, a convolutional neural network, etc., and publicly available software can be used.

[0024] To generate a learned model, artificial intelligence is trained using teacher data. The training may be either machine learning or deep learning, but machine learning is preferred. Deep learning is an evolution of machine learning and is characterized by automatically finding features. In the present invention, as features, data regarding the presence or absence of bacteria belonging to the family Fusobacteriaceae and / or the presence or absence of bacteria belonging to the family Veillonellaceae in a sample collected from an animal, for example, the gut microbiota, is used.

[0025] The learning method for generating a learned 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. Additionally, for example, it may be trained by a known support vector machine method (Support Vector Machine method) published in "Introduction to Support Vector Machines" (Kyoritsu Shuppan), etc.

[0026] The 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 trees, ensemble learning, gradient boosting, etc. Examples of publicly available machine learning algorithms include XGBoost, CatBoost, and LightGBM.

[0027] Teacher data for learning is, for example, data on the presence or absence of bacteria belonging to the family Fusobacteriaceae and / or the family Veillonellaceae in the gut microbiota in a sample collected from an animal, and whether the animal has developed a specific disease or not within a predetermined period, preferably within 3 years, more preferably within 2 years, still more preferably within 1 year, and particularly preferably within 180 days, from the time of sample collection or the time of obtaining data on the presence or absence of bacteria belonging to a predetermined bacterial family. Whether the animal has developed a specific disease can be replaced with a dummy variable. Data on the presence or absence of bacteria belonging to a predetermined bacterial family in a sample collected from an animal as teacher data is the same as the data on the presence or absence of bacteria belonging to a predetermined bacterial family in the gut microbiota described in the above reception method. Information on whether the animal has developed a specific disease within a predetermined period can be obtained, for example, from an animal hospital or an insured owner in relation to the fact of insurance claims (also referred to as "accidents").

[0028] [Output] The output format of the prediction determination by the determination means of the present invention is not particularly limited. For example, on the screen of a terminal such as a personal computer or a smartphone, a prediction determination can be output by displaying "There is a possibility of developing dry keratoconjunctivitis within 1 year", "The possibility of developing allergic dermatitis within 1 year is high", or "The possibility of developing inflammatory bowel disease within 1 year is ○%". The disease development prediction system of the present invention may separately have an output means that receives the determination result from the determination means and outputs the determination result.

[0029] The disease development prediction system of the present invention may further include a proposal means for proposing a life improvement method according to the result of the disease development prediction. For example, the proposal means receives the prediction result output from the determination means and can propose or recommend a diet for avoiding the predicted disease development risk, a supplement containing bacteria that are less likely to cause disease, a low-salt, low-calorie diet, a low-carbohydrate diet, a diet menu, etc. according to the prediction result. The proposal means may have a learned model.

[0030] In addition, according to the prediction results output by the disease susceptibility prediction system or disease susceptibility prediction method of the present invention, beverages, foods, and supplements for preventing susceptibility to specific diseases can also be manufactured or customized. As services related to disease susceptibility prediction, forms such as prediction by the disease susceptibility prediction system or disease susceptibility prediction method of the present invention, provision of prediction results, manufacturing or customizing beverages, foods, and supplements according to the prediction results, and proposal and recommendation of such beverages, foods, and supplements can be taken. Further, after providing such services, it is also possible to implement the disease susceptibility prediction method of the present invention and present whether the susceptibility to a specific disease has decreased. The above-mentioned beverages, foods, and supplements include diet beverages, diet foods, nutritional supplements, and the like. Thus, by proposing, manufacturing, and customizing foods and food products according to the prediction results, reduction and avoidance of the risk of susceptibility to specific diseases are expected.

[0031] [Family Fusobacteriaceae] The family Fusobacteriaceae belongs to the order Fusobacteriales and includes the genus Fusobacterium.

[0032] [Family Veillonellaceae] The family Veillonellaceae belongs to the order Clostridiales and, as genera, for example, includes the genus Veillonella, Acidaminococcus, Anaeroarcus, Anaerovibrio, Dialister, Megamonas, Megasphaera, Mitsuokella, Pectinatus, Phascolarctobacterium, Propionispira, Selenomonas Succiniclasticum.

[0033] [Other bacterial families] The disease prediction system of the present invention may use data related to the presence or absence and hit rate of bacteria belonging to bacterial families other than Fusobacteriaceae and Veillonellaceae for prediction and determination. Such other bacterial families are not particularly limited, and examples include Campylobacteraceae, Clostridiaceae, Coprobacillaceae, Desulfovibrionaceae, Enterobacteriaceae, Enterococcaceae, Erysipelotrichaceae, Lachnospiraceae, Lactobacillaceae, Paraprevotellaceae, Porphyromonodaceae, Prevotellaceae, Turicibacteraceae, Comamonadaceae, Leuconostocaceae, Pseudomonadaceae, and Sphingobacteriaceae.

[0034] [Campylobacteraceae] Campylobacteraceae is a family belonging to the order Campylobacterales in the class Epsilonproteobacteria and includes the genus Campylobacter.

[0035] [Clostridiaceae] Clostridiaceae is a family of the order Clostridiales and includes the genus Clostridium.

[0036] [Coprobacillaceae] Coprobacillaceae is a family belonging to the order Erysipelotrichales and includes the genus Coprobacillus.

[0037] [Family Desulfovibrionaceae] The family Desulfovibrionaceae belongs to the order Desulfovibrionales of the class Deltaproteobacteria and includes the genus Desulfovibrion.

[0038] [Family Enterobacteriaceae] The family Enterobacteriaceae (also known as the Enterobacter - family) belongs to the order Enterobacterales of the class Gammaproteobacteria in the phylum Proteobacteria. The genus Enterobacteriaceae includes, 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, etc.

[0039] [Family Enterococcaceae] The family Enterococcaceae is a family of Gram - positive eubacteria belonging to the order Lactobacillales. Representative genera include Enterococcus, Melissococcus, Pilibacter, Tetragenococcus, and Vagococcus.

[0040] [Family Erysipelotrichaceae] The family Erysipelotrichaceae belongs to the order Erysipelotrichales and includes genera such as Allobaculum, Bulleidia, Erysipelothrix, Holdemania, etc.

[0041] [Family Lachnospiraceae] The family Ruminococcaceae is a family included in the order Clostridiales of the class Clostridia and includes the genus Ruminococcus.

[0042] [Lactobacillaceae] The family Lactobacillaceae (also called the Lactobacillus family) is a family belonging to the order Lactobacillales and includes the genus Lactobacillus.

[0043] [Paraprevotellaceae] The family Paraprevotellaceae is a family belonging to the order Bacteroidales and includes the genus Paraprevotella. There is also a classification method in which the genus Paraprevotella is included in the family Prevotellaceae instead of being an independent family.

[0044] [Porphyromonodaceae] The family Porphyromonodaceae is a family belonging to the order Bacteroidales and includes the genus Porphyromonas.

[0045] [Prevotellaceae] The family Prevotellaceae is a family belonging to the order Bacteroidales and includes the genus Prevotella.

[0046] [Turicibacteraceae] The family Turicibacteraceae is a family belonging to the order Turicibacterales and includes the genus Turicibacter.

[0047] [Comamonadaceae] The family Comamonadaceae is a family included in the order Burkholderiales and includes the genus Comamonas. The genus Comamonas is a newly established genus to which bacteria that originally belonged to the genus Pseudomonas were reclassified by phylogenetic analysis of genes.

[0048] [Leuconostocaceae] The Leuconostocaceae is a family of Gram-positive bacteria belonging to the order Lactobacillales. Representative genera include Fructobacillus, Leuconostoc, Oenococcus, and Weissella. There is also a classification method that includes the genus Leuconostoc in the family Lactobacillaceae without treating the Leuconostocaceae as an independent family.

[0049] [Pseudomonadaceae] The Pseudomonadaceae is a family belonging to the order Pseudomonadales and includes the genus Pseudomonas.

[0050] [Sphingobacteriaceae] The Sphingobacteriaceae is a family belonging to the order Sphingobacteriales and includes the genus Sphingobacterium.

[0051] In addition to data on the presence or occupancy rate of bacteria belonging to a predetermined bacterial family, the disease susceptibility prediction system of the present invention may also use information such as the facial image, species, breed, age, sex, weight, medical history, gene sequence information, SNP, and presence or absence of gene mutations of an animal.

[0052] Hereinafter, an embodiment of the disease susceptibility prediction system of the present invention will be described with reference to FIG. 1. In FIG. 1, the terminal 40 is a terminal used by a person (user) who wants to use the disease susceptibility prediction system. The terminal 40 includes, for example, a personal computer, a smartphone, or a tablet terminal. The terminal 40 is configured to include a processing unit such as a CPU, a storage unit such as a hard disk, a ROM, or a RAM, a display unit such as a liquid crystal panel, an input unit such as a mouse, a keyboard, or a touch panel, and a communication unit such as a network adapter. The user accesses the server from the terminal 40 and inputs and transmits information on the presence or absence of bacteria belonging to the Fusobacteriaceae and / or the presence or absence of bacteria belonging to the Veillonellaceae in a sample collected from the target animal, and, if necessary, information such as the facial image (photo), species, breed, age, weight, and medical history of the animal. In addition, the user can receive the disease susceptibility prediction results on the server by accessing the server with the terminal 40.

[0053] In addition, the user receives a sample collected from the animal being raised, for example, a fecal sample collection kit for examining the bacterial flora in feces, and sends the fecal sample to a contractor who measures the bacterial flora (not shown). The contractor measures the bacterial flora in the fecal sample of the animal and obtains data regarding the presence or absence of bacteria belonging to the family Fusobacteriaceae and / or the presence or absence of bacteria belonging to the family Veillonellaceae in the sample. Then, the contractor may directly input and transmit data regarding the presence or absence of bacteria belonging to the family Fusobacteriaceae and / or the presence or absence of bacteria belonging to the family Veillonellaceae in the fecal sample of the animal to the receiving means 31 of the server via his / her own terminal, or the contractor may separately send data regarding the presence or absence of bacteria belonging to the family Fusobacteriaceae and / or the presence or absence of bacteria belonging to the family Veillonellaceae in the fecal sample of the animal to the user by mail, email, etc., and the user may input and transmit the data to the receiving means 31 through the terminal 40. When the contractor receives the feces, analyzes the bacterial flora, and inputs the analysis results into the disease susceptibility prediction system of the present invention, the user can know the likelihood of the animal being susceptible to a specific disease by simply collecting and mailing the feces of the animal being raised.

[0054] In this embodiment, the server is constituted by a computer, but any device may be used as long as it has the functions according to the present invention. The storage unit 10 is constituted of, for example, a ROM, a RAM, or a hard disk. The storage unit 10 stores an information processing program for operating each part of the server, and in particular, software for the determination means 11 is stored.

[0055] As described above, the determination means 11 takes as input data on the presence or absence of bacteria belonging to the Fusobacteriaceae family and / or the presence or absence of bacteria belonging to the Veillonellaceae family in the microbiota of the target animal input by the user or the operator who measured the microbiota, and outputs a prediction as to whether the animal will develop a specific disease within a predetermined period (e.g., within one year), or what percentage of the possibility of developing the disease is. The determination means may be a learned model. Such a learned model is composed of, for example, XGBoost, CatBoost, LightGBM, or a deep neural network or a convolutional neural network.

[0056] In this embodiment, an aspect has been described in which the determination means and the reception means are stored in a server and connected to the user's terminal by a connection means such as the Internet or a LAN. However, the present invention is not limited to this, and an aspect in which the determination means, the reception means, and the interface unit are stored in one server or device, or an aspect in which a terminal used by the user is not separately required may also be acceptable.

[0057] <Disease Susceptibility Prediction Method> The disease susceptibility prediction method of the present invention includes a step of predicting and determining the susceptibility of the animal to dry keratoconjunctivitis, allergic dermatitis, or inflammatory bowel disease from information on the presence or absence of bacteria belonging to the Fusobacteriaceae family and / or the presence or absence of bacteria belonging to the Veillonellaceae family in a sample collected from the animal. Preferably, it includes a step of preparing information on the presence or absence of bacteria belonging to the Fusobacteriaceae family and / or the presence or absence of bacteria belonging to the Veillonellaceae family in a sample collected from the animal, and a step of predicting and determining the susceptibility of the animal to dry keratoconjunctivitis, allergic dermatitis, or inflammatory bowel disease from the information. The method can be performed, for example, using the above-described disease susceptibility prediction system.

[0058] Regarding the information on the presence or absence of bacteria belonging to the Fusobacteriaceae family and / or the presence or absence of bacteria belonging to the Veillonellaceae family in a sample collected from an animal in the method for predicting disease susceptibility of the present invention, and a method for predicting and determining whether the animal will develop a specific disease within a predetermined period based on the information on the presence or absence of bacteria belonging to the Fusobacteriaceae family and / or the presence or absence of bacteria belonging to the Veillonellaceae family in a sample collected from an animal, and the configuration therefor, are the same as those described in the above disease susceptibility prediction system.

[0059] A flowchart of disease susceptibility prediction determination based on an embodiment of the disease susceptibility prediction method using the disease susceptibility prediction system of the present invention is shown in FIG. 2. This embodiment will be described including the acquisition of a sample from an animal and the acquisition of data on the microbiota in the sample, for example, the gut microbiota, for convenience of explanation. The user collects a fecal sample from the animal using a fecal collection kit or the like and sends it to a gut microbiota analysis provider (step S1). The gut microbiota analysis provider analyzes and acquires data on the presence or absence of bacteria belonging to a predetermined bacterial family in the gut microbiota of the animal from the fecal sample using a next-generation sequencer (step S2). The gut microbiota analysis provider returns the data on the gut microbiota to the user. The user accesses the disease susceptibility prediction system through a terminal and inputs data on the presence or absence of bacteria belonging to the Fusobacteriaceae family and / or the presence or absence of bacteria belonging to the Veillonellaceae family in the gut microbiota of the animal (step S3). The disease susceptibility prediction system predicts and determines how likely the animal is to develop a specific disease within a predetermined period (for example, within 1 year) based on the input data on the presence or absence of bacteria belonging to the Fusobacteriaceae family and / or the presence or absence of bacteria belonging to the Veillonellaceae family in the gut microbiota of the animal (step S4). The disease susceptibility prediction system outputs the prediction determination, transmits it to the terminal 40, and the prediction determination result is displayed on the terminal 40 (step S5).

[0060] <Insurance premium calculation system> The insurance premium calculation system of the present invention includes a reception means for receiving information on the presence or absence of bacteria belonging to the family Fusobacteriaceae and / or the presence or absence of bacteria belonging to the family Veillonellaceae in a sample collected from an animal, and an insurance premium calculation means for calculating or correcting an insurance premium using the information on the presence or absence of bacteria belonging to the family Fusobacteriaceae and / or the presence or absence of bacteria belonging to the family Veillonellaceae input to the reception means.

[0061] As a result of analyzing a vast amount of data on the bacterial flora in samples collected from animals, particularly fecal samples, of the inventors who are enrolled in pet insurance and the presence or absence of insurance claims for those animals, a correlation was found between the presence or absence of bacteria belonging to the family Fusobacteriaceae and / or the presence or absence of bacteria belonging to the family Veillonellaceae in the samples collected from animals and the amount of insurance claims and the number of insurance claims. Therefore, by using whether an animal seeking to enroll in pet insurance has bacteria belonging to the family Fusobacteriaceae and / or the family Veillonellaceae as a basis for calculating the insurance premium, it becomes possible to calculate an insurance premium taking into account the possibility of future insurance claims and to correct the insurance premium calculated using other factors such as breed, age, gender, weight, and medical history.

[0062] [Reception means] The reception means in the insurance premium calculation system is the same as the reception means of the above-mentioned disease prediction system.

[0063] [Insurance premium calculation means] The premium calculation means is a means for calculating an appropriate premium for the animal from information on the presence or absence of bacteria belonging to a predetermined bacterial family in a sample collected from the animal input by the reception means, and, if necessary, information such as the breed, age, sex, weight, and medical history of the animal. The means for calculating the premium is not particularly limited. For example, a processor calculates an appropriate premium for the animal from information on the presence or absence of bacteria belonging to a predetermined bacterial family in a sample collected from the animal using preset software or a program. When calculating the premium, a pre-stored premium table may be referred to. Further, for example, the software constituting the premium calculation means may perform grading of the premium according to the type, breed, sex, weight, medical history, etc. of the animal, and finally, modify the grade in consideration of the presence or absence of bacteria belonging to the family Fusobacteriaceae and / or the presence or absence of bacteria belonging to the family Veillonellaceae to calculate the final premium.

[0064] <Premium Calculation Method> The premium calculation method of the present invention is characterized by comprising a step of calculating or modifying a premium using information on the presence or absence of bacteria belonging to the family Fusobacteriaceae and / or the presence or absence of bacteria belonging to the family Veillonellaceae in a sample collected from an animal. Preferably, it preferably comprises a step of preparing information on the presence or absence of bacteria belonging to the family Fusobacteriaceae and / or the presence or absence of bacteria belonging to the family Veillonellaceae in a sample collected from the animal, and a step of calculating or modifying the premium for the animal based on the information. The method can be performed, for example, using the above-described premium calculation system.

[0065] Hereinafter, an embodiment of the premium calculation system of the present invention will be described with reference to FIG. 3. Parts common to FIG. 1 will be omitted as appropriate.

[0066] The user receives the delivery of a fecal sample collection kit for examining the flora in a sample collected from the animal being raised, such as feces, and sends the fecal sample to a vendor who measures the flora (not shown). The vendor measures the flora in the fecal sample of the animal and obtains data regarding the presence or absence of bacteria belonging to the family Fusobacteriaceae and / or the presence or absence of bacteria belonging to the family Veillonellaceae in the sample. Then, the vendor may directly input and transmit, via their own terminal, data regarding the presence or absence of bacteria belonging to the family Fusobacteriaceae and / or the presence or absence of bacteria belonging to the family Veillonellaceae in the fecal sample of the animal to the reception means 31 of the server, or the vendor may separately send, by mail, email, etc., data regarding the presence or absence of bacteria belonging to the family Fusobacteriaceae and / or the presence or absence of bacteria belonging to the family Veillonellaceae in the fecal sample of the animal to the user, and the user may input and transmit the data to the reception means 31 through the terminal 40. When the vendor receives the feces, analyzes the flora, and inputs the analysis results into the insurance premium calculation system of the present invention, the user can know the insurance premium of the animal just by collecting and mailing the feces of the animal being raised.

[0067] In FIG. 3, the insurance premium calculation means 12 is software that calculates the insurance premium of the animal from information such as the type, breed, age, weight, and medical history of the animal input by the user at the time of obtaining the flora data. For example, the software classifies the insurance premium according to the type, breed, age, weight, medical history, etc. of the animal at the time of obtaining the intestinal flora data, and finally modifies the class considering the presence or absence of bacteria belonging to the family Fusobacteriaceae and / or the presence or absence of bacteria belonging to the family Veillonellaceae, and is software for calculating the final insurance premium based on the insurance premium table 13. Separate from the insurance premium calculation means 12, a determination means 11 for predicting the onset of a specific disease may be provided, and the insurance premium calculation means 12 and the determination means 11 may be one software.

[0068] The processing and calculation unit 20 uses the determination means 11 and the insurance premium calculation means 12 stored in the storage unit to predict the onset of a disease and calculate the insurance premium.

[0069] The interface unit (communication unit) 30 includes a reception means 31 and an output means 32, receives data and other information regarding the presence or absence of bacteria belonging to a predetermined bacterial family in the flora of an animal, for example, the intestinal flora, from the user's terminal, and outputs a disease susceptibility prediction and an insurance premium calculation result to the user's terminal.

[0070] A flowchart of insurance premium calculation based on an embodiment of an insurance premium calculation method using the insurance premium calculation system of the present invention is shown in FIG. 4. This embodiment will be described including the acquisition of a sample from an animal and the acquisition of data regarding the flora in the sample, for example, the intestinal flora, for convenience of explanation. The user collects a fecal sample from the animal using a fecal collection kit or the like and sends it to an intestinal flora analysis provider (step S1). The intestinal flora analysis provider analyzes and acquires data regarding the presence or absence of bacteria belonging to a predetermined bacterial family in the intestinal flora of the animal from the fecal sample using a next-generation sequencer (step S2). The intestinal flora analysis provider returns the data regarding the intestinal flora to the user. The user accesses the insurance premium calculation system through the terminal and inputs data regarding the presence or absence of bacteria belonging to the Fusobacteriaceae family and / or the presence or absence of bacteria belonging to the Veillonellaceae family in the intestinal flora of the animal (step S3). The insurance premium calculation system calculates the insurance premium of the animal from the input data regarding the presence or absence of bacteria belonging to the Fusobacteriaceae family and / or the presence or absence of bacteria belonging to the Veillonellaceae family in the intestinal flora of the animal (step S4). The insurance premium calculation system outputs the prediction determination, transmits it to the terminal 40, and the calculation result is displayed on the terminal 40 (step S5).

Example

[0071] Examples of the present invention are shown below. The present invention is not limited to the following examples. (DNA Extraction from Fecal Samples) Fecal samples were collected from each dog and DNA was extracted as follows. The dog breeder collected a fecal sample of the dog using a fecal collection kit. The fecal sample was received and suspended in water. Next, 200 μL of fecal suspension and 810 μL of Lysis buffer (containing 224 μg / mL of Protenase K) were added to a bead tube, and bead disruption (6,000 rpm, disruption for 20 seconds, interval of 30 seconds, disruption for 20 seconds) was performed using a bead homogenizer. Subsequently, the sample was allowed to stand on a heat block at 70°C for 10 minutes for treatment with Protenase K, and then allowed to stand on a heat block at 95°C for 5 minutes to inactivate Protenase K. For the sample subjected to lysis treatment, DNA was automatically extracted using chemagic360 (PerkinElmer) according to the chemagic kit protocol for stool, and 100 μL of DNA extract was obtained.

[0072] (Meta 16S RNA gene sequence analysis) The meta 16S sequence analysis was performed by modifying the Illumina 16S Metagenomic Sequencing Library Preparation (version 15044223 B). First, a 460 bp region containing the variable regions V3-V4 of the 16S rRNA gene was amplified by PCR using universal primers (Illumina_16S_341F and Illumina_16S_805R PCR). The PCR reaction mixture was prepared by mixing 10 μL of DNA extract, 0.05 μL of each primer (100 μM), 12.5 μL of 2x KAPA HiFi Hot-Start ReadyMix (F. Hoffmann-LaRoche, Switzerland), and 2.4 μL of PCR-grade water. For PCR, after heat denaturation at 95°C for 3 minutes, cycles of 95°C for 30 seconds, 55°C for 30 seconds, and 72°C for 30 seconds were repeated 30 times, and finally an extension reaction at 72°C for 5 minutes was performed. The amplification products were purified using magnetic beads and eluted with 50 μL of Buffer EB (QIAGEN, Germany). The purified amplification products were subjected to PCR using the Nextera XT Index Kit v2 (Illumina, CA, US) to add indexes. The PCR reaction mixture was prepared by mixing 2.5 μL of the amplification product, 2.5 μL of each primer, 12.5 μL of 2x KAPA HiFi Hot-Start ReadyMix, and 5 μL of PCR-grade water. For PCR, after heat denaturation at 95°C for 3 minutes, cycles of 95°C for 30 seconds, 55°C for 30 seconds, and 72°C for 30 seconds were repeated 12 times, and finally an extension reaction at 72°C for 5 minutes was performed. The amplification products with added indexes were purified using magnetic beads and eluted with 80 - 105 μL of Buffer EB. The concentration of each amplification product was measured using a NanoPhotometer (Implen, CA, US), adjusted to 1.4 nM, and then mixed in equal amounts to obtain a sequencing library. The DNA concentration of the sequencing library and the size of the amplification products were confirmed by electrophoresis and analyzed using MiSeq. For the analysis, the MiSeq Reagent Kit V3 was used to perform 2×300 bp paired-end sequencing. The obtained sequences were analyzed using MiSeq Reporter to obtain bacterial composition data. The sequences of the universal primers used above are as follows. These universal primers can be purchased commercially. Illumina_16S_341F 5′-TCGTCGGCAGCGTCAGATGTGTATAAGAGACAGCCTACGGGNGGCWGCAG- 3’ llumina_16S_805R 5′-GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAGGACTACHVGGGTATCTAATCC- 3’

[0073] According to the above method, for 19,040 toy poodles aged 0 years and above (average age: 3.4 years), compositional data of the intestinal flora were obtained, and the presence or absence of bacteria belonging to a predetermined bacterial family in the intestinal flora of each dog was measured. Among the 19,040 individuals, 7,805 individuals (accident group) had insurance claims within 180 days (total 360 days) before and after fecal sample collection, and 11,235 individuals (non-accident group) had no insurance claims during this period.

[0074] [Example 1] Among the above 19,040 individuals, for the toy poodles in the accident group, the presence or absence of bacteria belonging to the Fusobacteriaceae family and the presence or absence of bacteria belonging to the Veillonellaceae family were examined, and the relationship between the presence or absence of these bacteria and the annual insurance payment amount was investigated. The results are shown in Figure 5. In Figure 5, "F negative V negative" refers to the group of individuals who do not possess any bacteria belonging to the Fusobacteriaceae family and the Veillonellaceae family. "F negative V positive" refers to the group of individuals who do not possess bacteria belonging to the Fusobacteriaceae family but possess bacteria belonging to the Veillonellaceae family. "F positive V negative" refers to the group of individuals who possess bacteria belonging to the Fusobacteriaceae family but do not possess bacteria belonging to the Veillonellaceae family. "F positive V positive" refers to the group of individuals who possess both bacteria belonging to the Fusobacteriaceae family and bacteria belonging to the Veillonellaceae family. As is clear from Fig. 5, it can be seen that in all age groups, the group of individuals without Fusobacterium and without Veillonella has a large total annual insurance benefit amount and a large amount of insurance benefits are paid. Also, the group of individuals with Fusobacterium and with Veillonella has a small total annual insurance benefit amount and tends to have few insurance benefit payments. (The amount at 0 years old for those with Fusobacterium and with Veillonella was taken as the reference amount.) Thus, it was confirmed that there is a certain correlation between the presence or absence of bacteria belonging to the family Fusobacteriaceae and the presence or absence of bacteria belonging to the family Veillonellaceae in the fecal samples of animals, and the amount of insurance benefits paid.

[0075] [Example 2] Among the above 19,040 individuals, for the Toy Poodles in the accident group, the presence or absence of bacteria belonging to the family Fusobacteriaceae and the presence or absence of bacteria belonging to the family Veillonellaceae were examined, and the relationship between the presence or absence of these bacteria and the annual insurance utilization frequency was investigated. The results are shown in Fig. 6. As is clear from Fig. 6, it can be seen that in all age groups, the group of individuals without Fusobacterium and without Veillonella has a high annual insurance utilization frequency and a high number of times of using pet insurance by receiving treatment at an animal hospital. Also, the group of individuals with Fusobacterium and with Veillonella has a low annual insurance utilization frequency and tends to have few times of using pet insurance. (The frequency at 0 years old for those with Fusobacterium and with Veillonella was taken as the reference frequency 1.) Thus, it was confirmed that there is a certain correlation between the presence or absence of bacteria belonging to the family Fusobacteriaceae and the presence or absence of bacteria belonging to the family Veillonellaceae in the fecal samples of animals, and the insurance utilization frequency.

[0076] [Example 3] For the above 19,040 individuals, the presence or absence of bacteria belonging to the family Fusobacteriaceae and the presence or absence of bacteria belonging to the family Veillonellaceae were examined, and the relationship between the presence or absence of these bacteria and the presence or absence of suffering from dry keratoconjunctivitis was investigated. Among the entire 19,040 individuals, 22 individuals were confirmed to have suffered from dry keratoconjunctivitis within 180 days (a total of 360 days) before and after fecal sample collection. For each of the individual groups of without Fusobacterium and without Veillonella, without Fusobacterium and with Veillonella, with Fusobacterium and without Veillonella, and with Fusobacterium and with Veillonella, the incidence rate of dry keratoconjunctivitis (the ratio of individuals suffering from dry keratoconjunctivitis in each individual group) was calculated. The results are shown in Fig. 7. As is clear from Fig. 7, since the prevalence of dry keratoconjunctivitis decreased in the order of no Fusobacterium and no Veillonella, no Fusobacterium and with Veillonella, with Fusobacterium and no Veillonella, and with Fusobacterium and with Veillonella, there was a possibility that the relationship between the presence or absence of bacteria belonging to the family Fusobacteriaceae and / or the presence or absence of bacteria belonging to the family Veillonellaceae and dry keratoconjunctivitis could be shown. As a result, it was found that by examining the presence or absence of bacteria belonging to the family Fusobacteriaceae and / or the presence or absence of bacteria belonging to the family Veillonellaceae, it was possible to predict whether an animal would develop dry keratoconjunctivitis.

[0077] [Example 4] Regarding the above 19,040 individuals, the presence or absence of bacteria belonging to the family Fusobacteriaceae and the presence or absence of bacteria belonging to the family Veillonellaceae were examined, and the relationship between the presence or absence of these bacteria and the presence or absence of allergic dermatitis was examined. Among the entire 19,040 individuals, 444 individuals were confirmed to have developed allergic dermatitis within 180 days (a total of 360 days) before and after the fecal sample collection. For each group of individuals of no Fusobacterium and no Veillonella, no Fusobacterium and with Veillonella, with Fusobacterium and no Veillonella, and with Fusobacterium and with Veillonella, the prevalence of allergic dermatitis (the proportion of individuals who developed allergic dermatitis in each group of individuals) was calculated. The results are shown in Fig. 8. As is clear from Fig. 8, since the prevalence of allergic dermatitis decreased in the order of no Fusobacterium and no Veillonella, with Fusobacterium and no Veillonella, no Fusobacterium and with Veillonella, and with Fusobacterium and with Veillonella, there was a possibility that the relationship between the presence or absence of bacteria belonging to the family Fusobacteriaceae and / or the presence or absence of bacteria belonging to the family Veillonellaceae and allergic dermatitis could be shown. As a result, it was found that by examining the presence or absence of bacteria belonging to the family Fusobacteriaceae and / or the presence or absence of bacteria belonging to the family Veillonellaceae, it was possible to predict whether an animal would develop allergic dermatitis.

[0078] [Example 5] Regarding the above 19,040 individuals, the presence or absence of bacteria belonging to the family Fusobacteriaceae and the presence or absence of bacteria belonging to the family Veillonellaceae were examined, and the relationship between the presence or absence of these bacteria and the presence or absence of inflammatory bowel disease was examined. Among the entire 19,040 individuals, 12 individuals were confirmed to have developed inflammatory bowel disease within 180 days (a total of 360 days) before and after the fecal sample collection. For each of the populations of F-negative V-negative, F-negative V-positive, F-positive V-negative, and F-positive V-positive, the incidence rate of inflammatory bowel disease (the proportion of individuals in each population who developed inflammatory bowel disease) was calculated. The results are shown in Fig. 9. As is clear from Fig. 9, since the incidence rate of inflammatory bowel disease decreased in the order of F-negative V-positive, F-negative V-negative, F-positive V-positive, and F-positive V-negative, there was a possibility that the presence or absence of bacteria belonging to the family Fusobacteriaceae and / or the presence or absence of bacteria belonging to the family Veillonellaceae could be shown to be related to inflammatory bowel disease. Consequently, it was found that by examining the presence or absence of bacteria belonging to the family Fusobacteriaceae and / or the presence or absence of bacteria belonging to the family Veillonellaceae, it was possible to predict and determine whether an animal would develop inflammatory bowel disease.

Claims

1. Receiving means for receiving information on the presence or absence of bacteria belonging to the family Fusobacteriaceae and / or the presence or absence of bacteria belonging to the family Veillonellaceae in feces collected from a dog, and from the information on the presence or absence of bacteria belonging to the family Fusobacteriaceae and / or the presence or absence of bacteria belonging to the family Veillonellaceae input to the receiving means, determination means for predicting and determining the likelihood of the dog suffering from keratoconjunctivitis sicca. A disease susceptibility prediction system comprising:

2. Receiving means for receiving information on the presence or absence of bacteria belonging to the family Fusobacteriaceae and / or the presence or absence of bacteria belonging to the family Veillonellaceae in feces collected from a dog, and from the information on the presence or absence of bacteria belonging to the family Fusobacteriaceae and / or the presence or absence of bacteria belonging to the family Veillonellaceae input to the receiving means, determination means for predicting and determining the likelihood of the dog suffering from allergic dermatitis. A disease susceptibility prediction system comprising:

3. Receiving means for receiving information on the presence or absence of bacteria belonging to the family Fusobacteriaceae and / or the presence or absence of bacteria belonging to the family Veillonellaceae in feces collected from a dog, and from the information on the presence or absence of bacteria belonging to the family Fusobacteriaceae and / or the presence or absence of bacteria belonging to the family Veillonellaceae input to the receiving means, determination means for predicting and determining the likelihood of the dog suffering from inflammatory bowel disease. A disease susceptibility prediction system comprising:

4. A disease susceptibility prediction method comprising the step of predicting and determining the likelihood of a dog suffering from keratoconjunctivitis sicca from information on the presence or absence of bacteria belonging to the family Fusobacteriaceae and / or the presence or absence of bacteria belonging to the family Veillonellaceae in feces collected from the dog.

5. A disease susceptibility prediction method comprising the step of predicting and determining the likelihood of a dog developing allergic dermatitis from information on the presence or absence of bacteria belonging to the family Fusobacteriaceae and / or the presence or absence of bacteria belonging to the family Veillonellaceae in feces collected from the dog.

6. A disease susceptibility prediction method comprising the step of predicting and determining the likelihood of a dog developing inflammatory bowel disease from information on the presence or absence of bacteria belonging to the family Fusobacteriaceae and / or the presence or absence of bacteria belonging to the family Veillonellaceae in feces collected from the dog.

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