Disease determination or prediction method, insurance premium calculation method, and disease prediction system

Abstract

The purpose of the present invention is to provide a method for predicting or determining a disease of an animal, a method for calculating an insurance premium, and a disease prediction system for an animal. This method for determining or predicting a health state of an animal is characterized by comprising a step for determining the health state of an animal, or predicting the future health state of the animal, by using information related to whether a prescribed bacterium such as Streptococcus constellatus, Streptococcus anginosus, and Slackia_A exigua is included in the intestinal flora of the animal, which is not a human.

Description

Disease determination or prediction method, insurance premium calculation method, and disease prediction system

[0001] The present invention relates to a disease determination or prediction method, an insurance premium calculation method, and a disease prediction system, and more particularly to a method and prediction system for determining or predicting whether an animal has or will have a disease such as periodontal disease, based on data on the animal's intestinal flora, particularly information on whether the animal contains specific bacteria, and also to a method and prediction system for predicting the death of an animal with chronic kidney disease.

[0002] Pets such as dogs, cats, and rabbits, as well as livestock such as cows and pigs, are irreplaceable to humans. While the average lifespan of animals kept by humans has increased significantly in recent years, the number of animals suffering from illnesses during their lives has also increased, resulting in an increase in medical expenses borne by owners.

[0003] In order to maintain the health of animals, it is important to manage their physical condition through daily diet and exercise, and to respond quickly to any illnesses. However, because animals cannot verbally express their physical ailments, the reality is that caretakers only become aware that their animals are suffering from an illness when the symptoms progress and some outwardly observable signs appear.

[0004] Among diseases, periodontal disease, for example, is increasingly recognized as a major problem not only in humans but also in animals. As periodontal disease progresses, it can lead to tooth loss and affect eating, potentially resulting in a significant loss of quality of life. Furthermore, while early-stage periodontal disease is treatable, treatment becomes more difficult as the disease progresses. Thus, while there is a strong demand for early detection of periodontal disease, currently, determining whether an animal is suffering from periodontal disease poses a problem. Specifically, imaging diagnostics are required to determine whether an animal, such as a pet, is suffering from periodontal disease. However, imaging diagnostics such as dental X-rays and CT scans require general anesthesia, making them cumbersome and placing a significant burden on the animal. Therefore, veterinarians perform visual examinations of the oral cavity and, based on their findings with the naked eye, make simplified decisions about whether to perform subsequent imaging diagnostics and treatment. However, visual examination by veterinarians involves assessing factors such as tartar, gums, bite alignment, and dentition. However, even in cases where animals appear to be free of periodontal disease, such as those with minimal tartar, imaging diagnostics reveal that the tooth roots have dissolved, indicating periodontal disease. This limits the diagnostic accuracy of visual examination. Animals are also known to suffer from chronic diseases with long-term symptoms. For example, chronic kidney disease (CRD) is diagnosed when renal failure, a condition in which the kidneys are damaged and no longer function properly, continues for a long period of time. Severe cases of CRD can lead to death. The average life expectancy of dogs diagnosed with CRD has been reported to be over 400 days for early stage (stage 1), approximately 200-400 days for mild (stage 2), approximately 110-200 days for moderate (stage 3), and 14-80 days for terminal (stage 4), but this varies from case to case. Other chronic diseases include heart disease, diabetes, and autoimmune diseases. If the severity, mortality rate, and prognosis of such chronic diseases could be predicted in advance, it would be possible to select appropriate treatments, such as regenerative medicine.

[0005] Therefore, there is a need for a simple method to determine whether an animal is suffering from a disease such as periodontal disease, or whether it is likely to suffer from such a disease in the future, and to determine the prognosis of an animal suffering from such a disease.

[0006] Patent Document 1 discloses an intestinal flora adjusting or improving composition that has the effect of effectively adjusting or improving the intestinal flora by increasing bacteria of the Bacteroidetes phylum and reducing bacteria of the Firmicutes phylum in the intestinal flora, but does not disclose a method for determining or predicting whether an animal is suffering from a disease such as periodontal disease based on data regarding the animal's intestinal flora.

[0007] International Publication No. 2017 / 094892 Pamphlet

[0008] Therefore, the present invention aims to provide a method and system for easily determining the health condition of an animal, such as whether the animal is suffering from a disease or is likely to suffer from a disease in the future, or predicting the future health condition of the animal, as well as a system and method for predicting the death of an animal.

[0009] The inventors analyzed and examined data on the intestinal flora of animals that have pet insurance and a huge amount of data on whether or not the animals have filed insurance claims, i.e., whether or not they have a disease. As a result, they discovered that depending on whether or not a specific bacterium is present in an animal's intestinal flora, it is possible to determine or predict the animal's health condition, such as whether or not the animal has or is likely to develop a disease such as periodontal disease, or whether or not the animal is likely to die in the near future, and thus completed the present invention.

[0010] The inventors examined insurance claim data for over tens of thousands of animals stored at a pet insurance company and compared separately obtained gut microbiota data for each animal between a group of animals that had received insurance claims for periodontal disease within a specified period (the affected group) and a group of animals that had not received insurance claims for periodontal disease (the non-affected group). They found that there were bacterial species with significantly different detection rates. For example, the detection rate of Streptococcus constellatus differed by several times between the affected and non-affected groups. These bacterial species were significantly detected in the gut microbiota of animals with periodontal disease. By examining the presence or absence of these bacterial species in the gut microbiota, it is possible to determine or predict whether an individual has periodontal disease or whether it will likely develop periodontal disease in the future. Periodontal disease is an oral disease, and it is natural that bacteria associated with periodontal disease are detected in the oral cavity, but it is surprising that bacteria found in the periodontal disease group are characteristic not in the oral cavity but in the intestinal bacterial flora that have passed through the esophagus, stomach, etc. Furthermore, in the case of animals such as dogs and cats, it is difficult to sample tissues or saliva around the gums because there is a risk of resistance, such as being bitten. However, according to the present invention, it is possible to sample feces and analyze the intestinal bacterial flora, thereby reducing the burden on owners and veterinary medical professionals.

[0011] Even more surprisingly, the inventors have found that when animals possess a bacterial species that is significantly detected in the periodontal disease group, there is a significant difference in the incidence of other diseases, such as valvular heart disease, and there is also a significant difference in the health status of the animals. Thus, by examining the presence or absence of a specific bacterial species in the intestinal bacterial flora, it is possible to determine or predict the health status of an animal, such as whether the individual is suffering from a disease other than periodontal disease, or whether it is likely to suffer from a disease other than periodontal disease in the future. Furthermore, the inventors have found that the presence or absence of a specific bacterial species in an animal leads to a difference in mortality rate. Thus, by examining the presence or absence of a specific bacterial species in an animal, it is possible to predict or determine whether the animal is likely to die in the near future, or whether the likelihood of death is high or low.

[0012] That is, the present invention relates to the following [1] to

[21] . [1] In the intestinal flora of animals other than humans, Streptococcus constellatus, Streptococcus anginosus, Slackia exigua, Desulfovibrio R446353, Peptostreptococcus canis, Bulleidia moorei, Actinomyces timonensis, Pauljensenia cardiffensis, Nanoperiomorbus, Fusobacterium simiae, Corynebacterium canis, and the like are present. canis, Enterocloster bolteae, Bifidobacterium dentium, Parvimonas micra, Nanosynbacter, Flexilinea sp902786265, CAJPSE01 sp003860125, Campylobacter sp013201975, Fusobacterium russii, Actinomyces weissii, Fusobacterium canifelinum, Saccharimonas aalbogensis aalborgensis, SDRW01 sp007845485, Corynebacterium freiburgense, Globicatella, Lactobacillusdelbrueckii, Clostridium disporicum (Clostridium_T disporicum_203974), Canibacter sp003859945, Pediococcus acidilactici, Clostridium sp000821305 (Clostridium_J sp000821305), Pauljensenia canis, Nanogingivalis gingivitcus, Clostridium baratii, Enterococcus gallinarum, Cellulosilyticaceae, Gemella palaticanis, Streptococcus fryi, Escherichia ruysiae, Citrobacter gillenii (Citrobacter_A_692098 gillenii), Pseudomonas guguanensis (Pseudomonas_E_648040 guguanensis), Klebsiella pneumoniae (Klebsiella pneumoniae_718977), Enterococcus B (Enterococcus_B), Streptococcus minor, Clostridium paraputrificum (Clostridium_T paraputrificum_208099, Clostridium_T paraputrificum_207370), Clostridium dispolicum (Clostridium_T disporicum_203972), Streptococcus oralis (Streptococcus oralis_E_351036), Clostridium P (Clostridium_P), Bacillus subtilis (Bacillus_P_294101 subtilis_291504), Klebsiella varicolora (Klebsiellavariicola), Clostridioides A difficile, Terrisporobacter glycolicus 239331, Clostridium T neonatale, Bifidobacterium animalis, Terrisporobacter, Escherichia 710834, Klebsiella 724518, Escherichia fergusonii, Clostridium T saudiense, Clostridium T tertium tertium), Enterobacter hormaechei (Enterobacter_B_713587 hormaechei_712707), Bilophila wadsworthia, Clostridium isatidis (Clostridium_T isatidis), Canibacter oris, Nanosynbacter lyticus, Proteus mirabilis, Clostridium innocuum (Clostridium_AQ innocuum), CCUG-7971 spG000499525, Fusobacterium necrogenes (Fusobacterium_A necrogenes), Streptococcus, Anaerostipes cacae caccae), Enterococcus faecalis (Enterococcus_H_360604 faecalis), Bacteroides fragilis (Bacteroides_H fragilis), and Roseburia intestinalis (Roseburiaa step of determining the health status or predicting the future health status of the animal using information on whether or not the animal contains one or more bacteria selected from the group consisting of Bacillus subtilis (Bacillus intestinalis), and determining that the animal's health is impaired or there is a high risk of future impairment if the animal contains one or more bacteria selected from the group in the step. [2] The step of determining the health state of the animal or predicting its future health state is: (1) a step of determining whether the animal is suffering from a disease or predicting whether it will suffer from a disease in the future, and determining that the animal has a disease or has a high risk of suffering from a disease in the future if the intestinal flora of the animal contains one or more bacteria selected from the group; (2) a step of determining whether the animal's mental state is good or predicting whether the animal's future mental state will be good, and determining that the animal's mental state is not good or there is a high risk that the animal's mental state will be not good in the future if the intestinal flora of the animal contains one or more bacteria selected from the group; (3) a step of determining whether the animal's coat gloss is good or predicting whether the animal's coat gloss is good in the future, and determining that the animal's coat gloss is not good or there is a high risk that the animal's coat gloss will be not good in the future if the intestinal flora of the animal contains one or more bacteria selected from the group; or (4) The method for determining or predicting the health status of an animal according to [1], wherein the step of determining whether the animal has good breath or predicting whether the animal will have good breath in the future is a step of determining whether the animal has bad breath or there is a high risk that the animal will have bad breath in the future if the intestinal bacterial flora of the animal contains one or more bacteria selected from the group. [3] The method for determining or predicting the health status of an animal according to [2], wherein the step of determining whether the animal has a disease or predicts whether the animal will have a disease in the future is a step of determining whether the animal has periodontal disease, valvular disease, liver disease, biliary tract disease, pancreatic disease, kidney disease, or cancer.[4] The method for determining or predicting the health state of an animal according to [2], wherein the step of determining the health state of the animal or predicting the future health state of the animal is a step of determining whether the mental state of the animal is good or predicting whether the future mental state of the animal will be good, and whether the mental state of the animal is good or not means whether the animal is timid. [5] The method for determining or predicting the health state of an animal according to any one of [1] to [4], wherein the intestinal bacterial flora of the non-human animal is derived from a fecal sample. [6] Streptococcus constellatus, Streptococcus anginosus, Slackia exigua, Desulfovibrio R446353, Peptostreptococcus canis, Bulleidia moorei, Actinomyces timonensis, Pauljensenia cardiffensis, Nanoperiomorbus, Fusobacterium simiae, Corynebacterium canis canis, Enterocloster bolteae, Bifidobacterium dentium, Parvimonas micra, Nanosynbacter, Flexilinea sp902786265, CAJPSE01 sp003860125, Campylobacter sp013201975, Fusobacterium russii, Actinomyces weissii, Fusobacterium caniferinumcanifelinum), Saccharimonas aalborgensis, SDRW01 sp007845485, Corynebacterium freiburgense, Globicatella, Lactobacillus delbrueckii, Clostridium disporicum (Clostridium_T disporicum_203974), Canibacter sp003859945, Pediococcus acidilactici, Clostridium sp000821305, Pauljensenia canis canis, Nanogingivalis gingivitcus, Clostridium baratii, Enterococcus D gallinarum, Cellulosilyticaceae, Gemella palaticanis, Streptococcus fryi, Escherichia ruysiae, Citrobacter A692098 gillenii, Pseudomonas E648040 guguanensis, Klebsiella pneumoniae pneumoniae_718977), Enterococcus B (Enterococcus_B), Streptococcus minor (Streptococcus minor), Clostridium paraputrificum (Clostridium_T paraputrificum_208099, Clostridium_T paraputrificum_207370), Clostridium dispolicum (Clostridium_Tdisporicum_203972), Streptococcus oralis (Streptococcus oralis_E_351036), Clostridium P (Clostridium_P), Bacillus subtilis (Bacillus_P_294101 subtilis_291504), Klebsiella variicola, Clostridioides difficile (Clostridioides_A difficile), Terrisporobacter glycolicus (Terrisporobacter glycolicus_239331), Clostridium neonatale (Clostridium_T neonatale), Bifidobacterium animalis, Terrisporobacter, Escherichia 710834 (Escherichia_710834), Klebsiella 724518 (Klebsiella_724518), Escherichia fergusonii, Clostridium saudiense (Clostridium_T saudiense), Clostridium tertium (Clostridium_T tertium), Enterobacter hormaechei (Enterobacter_B_713587 hormaechei_712707), Bilophila wadsworthia, Clostridium isatidis (Clostridium_T isatidis), Canibacter oris, Nanosynbacter lyticus, Proteus mirabilis mirabilis), Clostridium innocuum (Clostridium_AQ innocuum), CCUG-7971 spG000499525, Fusobacterium necrogenes (Fusobacterium_A necrogenes), Streptococcus, Anaerostipes cacaeThe method for determining or predicting the health status of an animal according to [1], wherein the method determines that the animal's health is impaired or that there is a high risk of future impairment when the animal possesses two or more species of bacteria selected from Enterococcus caccae, Enterococcus faecalis (Enterococcus_H_360604 faecalis), Bacteroides_H fragilis, and Roseburia intestinalis. [7] In the intestinal flora of animals other than humans, Streptococcus constellatus, Streptococcus anginosus, Slackia exigua, Desulfovibrio R446353, Peptostreptococcus canis, Bulleidia moorei, Actinomyces timonensis, Pauljensenia cardiffensis, Nanoperiomorbus, Fusobacterium simiae, Corynebacterium canis, and the like are present. canis, Enterocloster bolteae, Bifidobacterium dentium, Parvimonas micra, Nanosynbacter, Flexilinea sp902786265, CAJPSE01 sp003860125, Campylobacter sp013201975, Fusobacterium russii, Actinomycesweissii, Fusobacterium canifelinum, Saccharimonas aalborgensis, SDRW01 sp007845485, Corynebacterium freiburgense, Globicatella, Lactobacillus delbrueckii, Clostridium disporicum, Canibacter sp003859945, Pediococcus acidilactici, Clostridium sp000821305 sp000821305), Pauljensenia canis, Nanogingivalis gingivitcus, Clostridium baratii, Enterococcus gallinarum, Cellulosilyticaceae, Gemella palaticanis, Streptococcus fryi, Escherichia ruysiae, Citrobacter gillenii, Pseudomonas guguanensis, Klebsiella pneumoniae pneumoniae_718977), Enterococcus B (Enterococcus_B), Streptococcus minor (Streptococcus minor), Clostridium paraputrificum (Clostridium_T paraputrificum_208099, Clostridium_Tparaputrificum_207370), Clostridium disporicum (Clostridium_T disporicum_203972), Streptococcus oralis (Streptococcus oralis_E_351036), Clostridium P (Clostridium_P), Bacillus subtilis (Bacillus_P_294101 subtilis_291504), Klebsiella variicola, Clostridioides difficile (Clostridioides_A difficile), Terrisporobacter glycolicus (Terrisporobacter glycolicus_239331), Clostridium neonatale (Clostridium_T neonatale), Bifidobacterium animalis, Terrisporobacter, Escherichia 710834 (Escherichia_710834), Klebsiella 724518 (Klebsiella_724518), Escherichia fergusonii, Clostridium saudiense (Clostridium_T saudiense), Clostridium tertium (Clostridium_T tertium), Enterobacter hormaechei (Enterobacter_B_713587 hormaechei_712707), Bilophila wadsworthia, Clostridium isatidis (Clostridium_T isatidis), Canibacter oris, Nanosynbacter lyticus, Proteus mirabilis mirabilis), Clostridium innocuum (Clostridium_AQ innocuum), CCUG-7971 spG000499525, Fusobacterium necrogenes (Fusobacterium_Anecrogenes), Streptococcus, Anaerostipes caccae, Enterococcus faecalis (Enterococcus_H_360604 faecalis), Bacteroides_H fragilis, and Roseburia intestinalis. [8] A receiving means for receiving data on the intestinal flora of an animal other than a human, and a method for detecting the presence of Streptococcus constellatus, Streptococcus anginosus, Slackia exigua, Desulfovibrio R446353, Peptostreptococcus canis, Bulleidia moorei, Actinomyces timonensis, Pauljensenia cardiffensis, Nanoperiomorbus, Fusobacterium simiae, and the like in the intestinal flora. simiae), Corynebacterium canis, Enterocloster bolteae, Bifidobacterium dentium, Parvimonas micra, Nanosynbacter, Flexilinea sp902786265, CAJPSE01 sp003860125, Campylobacter sp013201975sp013201975, Fusobacterium Crussii, Actinomyces weissii, Fusobacterium canifelinum, Saccharimonas aalborgensis, SDRW01 sp007845485, Corynebacterium freiburgense, Globicatella, Lactobacillus delbrueckii, Clostridium disporicum, Canibacter sp003859945 sp003859945), Pediococcus acidilactici, Clostridium sp000821305 (Clostridium_J sp000821305), Pauljensenia canis, Nanogingivalis gingivitcus, Clostridium baratii (Clostridium_T baratii), Enterococcus gallinarum, Cellulosilyticaceae, Gemella palaticanis, Streptococcus fryi, Escherichia ruysiae), Citrobacter gillenii (Citrobacter_A_692098 gillenii), Pseudomonas guguanensis (Pseudomonas_E_648040 guguanensis), Klebsiella pneumoniae (Klebsiella pneumoniae_718977), Enterococcus B (Enterococcus_B), Streptococcus minor (Streptococcusminor), Clostridium paraputrificum (Clostridium_T paraputrificum_208099, Clostridium_T paraputrificum_207370), Clostridium disporicum (Clostridium_T disporicum_203972), Streptococcus oralis (Streptococcus oralis_E_351036), Clostridium P (Clostridium_P), Bacillus subtilis (Bacillus_P_294101 subtilis_291504), Klebsiella variicola, Clostridioides difficile (Clostridioides_A difficile), Terrisporobacter glycolicus (Terrisporobacter glycolicus_239331), Clostridium neonatale (Clostridium_T neonatale), Bifidobacterium animalis, Terrisporobacter, Escherichia 710834 (Escherichia_710834), Klebsiella 724518 (Klebsiella_724518), Escherichia fergusonii, Clostridium saudiense (Clostridium_T saudiense), Clostridium tertium (Clostridium_T tertium), Enterobacter hormaechei (Enterobacter_B_713587 hormaechei_712707), Bilophila wadsworthia, Clostridium isatidis (Clostridium_T isatidis), Canibacter oris (Canibacter oris), Nanosynbacter lyticus, Proteus mirabilis, Clostridium innocuum, CCUG-7971spG000499525, Fusobacterium A necrogenes, Streptococcus, Anaerostipes caccae, Enterococcus H360604 faecalis, Bacteroides H fragilis, and Roseburia intestinalis, wherein the determination means determines that the animal's health is impaired or that there is a high risk of its health being impaired in the future if the animal contains one or more bacteria selected from the group consisting of: [9] The means for determining or predicting the health state or future health state of the animal is: (1) a means for determining whether an animal is suffering from a disease or for predicting whether an animal will suffer from a disease in the future, and for determining that the animal is suffering from a disease or has a high risk of suffering from a disease in the future if the intestinal flora of the animal contains one or more bacteria selected from the group; (2) a means for determining whether an animal is in a good mental state or for predicting whether the animal's future mental state will be good, and for determining that the animal is not in a good mental state or has a high risk of having a bad mental state in the future if the intestinal flora of the animal contains one or more bacteria selected from the group; (3) a means for determining whether an animal's coat condition is good or for predicting whether the animal's coat condition is good in the future, and for determining that the animal's coat condition is not good or has a high risk of having a bad coat condition in the future if the intestinal flora of the animal contains one or more bacteria selected from the group; or (4) A means for determining whether an animal has good breath odor or predicting whether the animal will have good breath odor in the future, which determines that the animal has bad breath or there is a high risk that the animal will have bad breath in the future if the intestinal bacterial flora of the animal contains one or more bacteria selected from the group.[8] The system for determining or predicting the health state of an animal according to

[10] . The system for determining or predicting the health state of an animal according to [9], wherein the determination means for determining or predicting the health state or future health state of the animal is a means for determining whether the animal is suffering from a disease or for predicting whether the animal will suffer from a disease in the future, the disease being periodontal disease, valvular disease, liver disease, biliary disease, pancreatic disease, renal disease, or cancer.

[11] The system for determining or predicting the health state of an animal according to [9], wherein the determination means for determining or predicting the health state or future health state of the animal is a means for determining whether the animal is in good mental condition or for predicting whether the animal's mental condition will be good in the future, the good or bad mental state being defined as whether the animal is timid.

[12] A receiving means for receiving data on the intestinal flora of an animal other than a human, and a method for detecting the presence of Streptococcus constellatus, Streptococcus anginosus, Slackia exigua, Desulfovibrio R446353, Peptostreptococcus canis, Bulleidia moorei, Actinomyces timonensis, Pauljensenia cardiffensis, Nanoperiomorbus, Fusobacterium simiae, and the like in the intestinal flora. simiae), Corynebacterium canis, Enterocloster bolteae, Bifidobacterium dentium, Parvimonas micra, Nanosynbacter, Flexilinea sp902786265, CAJPSE01sp003860125, Campylobacter sp013201975 (Campylobacter_A sp013201975), Fusobacterium Crussii, Actinomyces weissii, Fusobacterium canifelinum (Fusobacterium_C canifelinum), Saccharimonas aalborgensis, SDRW01 sp007845485, Corynebacterium freiburgense, Globicatella, Lactobacillus delbrueckii, Clostridium disporicum (Clostridium_T disporicum_203974), Canibacter sp003859945 (Canibacter sp003859945), Pediococcus acidilactici, Clostridium sp000821305 (Clostridium_J sp000821305), Pauljensenia canis, Nanogingivalis gingivitcus, Clostridium baratii, Enterococcus gallinarum, Cellulosilyticaceae, Gemella palaticanis, Streptococcus fryi, Escherichia ruysiae), Citrobacter gillenii (Citrobacter_A_692098 gillenii), Pseudomonas guguanensis (Pseudomonas_E_648040 guguanensis), Klebsiella pneumoniaepneumoniae_718977), Enterococcus B (Enterococcus_B), Streptococcus minor (Streptococcus minor), Clostridium paraputrificum (Clostridium_T paraputrificum_208099, Clostridium_T paraputrificum_207370), Clostridium disporicum (Clostridium_T disporicum_203972), Streptococcus oralis (Streptococcus oralis_E_351036), Clostridium P (Clostridium_P), Bacillus subtilis (Bacillus_P_294101 subtilis_291504), Klebsiella variicola, Clostridioides difficile (Clostridioides_A difficile), Terrisporobacter glycolicus_239331, Clostridium neonatale, Bifidobacterium animalis, Terrisporobacter, Escherichia 710834, Klebsiella 724518, Escherichia fergusonii, Clostridium saudiense, Clostridium tertium, Enterobacter hormaekei (Enterobacter B 713587) hormaechei_712707), Bilophila wadsworthia, Clostridium isatidis, Canibacter oris, Nanosynbacter lyticusand an insurance premium calculation means for calculating an insurance premium for the animal using information regarding whether or not the animal contains one or more bacteria selected from the group consisting of Proteus mirabilis, Clostridium AQ innocuum, CCUG-7971 spG000499525, Fusobacterium A necrogenes, Streptococcus, Anaerostipes caccae, Enterococcus H360604 faecalis, Bacteroides H fragilis, and Roseburia intestinalis.

[13] In the intestinal flora of animals other than humans, Streptococcus constellatus, Streptococcus anginosus, Slackia exigua, Desulfovibrio R446353, Peptostreptococcus canis, Bulleidia moorei, Actinomyces timonensis, Pauljensenia cardiffensis, Nanoperiomorbus, Fusobacterium simiae, Corynebacterium canis, and the like are present. canis, Enterocloster bolteae, Bifidobacterium dentium, Parvimonas micramicra), Nanosynbacter, Flexilinea sp902786265, CAJPSE01 sp003860125, Campylobacter sp013201975, Fusobacterium russii, Actinomyces weissii, Fusobacterium canifelinum, Saccharimonas aalborgensis, SDRW01 sp007845485, Corynebacterium freiburgense), Globicatella, Lactobacillus delbrueckii, Clostridium disporicum (Clostridium_T disporicum_203974), Canibacter sp003859945, Pediococcus acidilactici, Clostridium sp000821305 (Clostridium_J sp000821305), Pauljensenia canis, Nanogingivalis gingivitcus, Clostridium baratii, Enterococcus gallinarum (Enterococcus_D gallinarum), Cellulosilyticaceae, Gemella palaticanis, Streptococcus fryi, Escherichia ruysiae, Citrobacter gilenii (Citrobacter_A_692098gillenii), Pseudomonas guguanensis (Pseudomonas_E_648040 guguanensis), Klebsiella pneumoniae (Klebsiella pneumoniae_718977), Enterococcus B (Enterococcus_B), Streptococcus minor, Clostridium paraputrificum (Clostridium_T paraputrificum_208099, Clostridium_T paraputrificum_207370), Clostridium disporicum (Clostridium_T disporicum_203972), Streptococcus oralis (Streptococcus oralis_E_351036), Clostridium P (Clostridium_P), Bacillus subtilis (Bacillus_P_294101) subtilis_291504, Klebsiella variicola, Clostridioides difficile, Terrisporobacter glycolicus_239331, Clostridium neonatale, Bifidobacterium animalis, Terrisporobacter, Escherichia 710834, Klebsiella 724518, Escherichia fergusonii, Clostridium saudiense, Clostridium tertium tertium), Enterobacter hormaechei (Enterobacter_B_713587 hormaechei_712707), Bilophila wadsworthia, Clostridium isatidis (Clostridium_Tisatidis, Canibacter oris, Nanosynbacter lyticus, Proteus mirabilis, Clostridium innocuum (Clostridium_AQ innocuum), CCUG-7971 spG000499525, Fusobacterium necrogenes (Fusobacterium_A necrogenes), Streptococcus, Anaerostipes caccae, Enterococcus faecalis (Enterococcus_H_360604 faecalis), Bacteroides fragilis (Bacteroides_H fragilis), and Roseburia intestinalis

[14] A method for predicting the death of an animal according to

[13] , wherein the animal is an animal suffering from a chronic disease, the method comprising: a step of predicting whether the animal will die within a predetermined period of time using information on whether the animal contains one or more bacteria selected from the group consisting of Pseudomonas intestinalis, and determining that the animal is at high risk of dying within the predetermined period of time if the animal contains one or more bacteria selected from the group consisting of Pseudomonas intestinalis.

[15] A receiving means for receiving data on the intestinal flora of an animal other than a human; and a method for detecting the presence of Streptococcus constellatus, Streptococcus anginosus, Slackia exigua, Desulfovibrio R446353, Peptostreptococcus canis, Bulleidia moorei, Actinomyces timonensis, Pauljensenia caldefensis, or the like in the intestinal flora.cardiffensis, Nanoperiomorbus, Fusobacterium simiae, Corynebacterium canis, Enterocloster bolteae, Bifidobacterium dentium, Parvimonas micra, Nanosynbacter, Flexilinea sp902786265, CAJPSE01 sp003860125, Campylobacter sp013201975 sp013201975, Fusobacterium Crussii, Actinomyces weissii, Fusobacterium canifelinum, Saccharimonas aalborgensis, SDRW01 sp007845485, Corynebacterium freiburgense, Globicatella, Lactobacillus delbrueckii, Clostridium disporicum, Canibacter sp003859945 sp003859945), Pediococcus acidilactici, Clostridium sp000821305 (Clostridium_J sp000821305), Pauljensenia canis, Nanogingivalis gingivitcus, Clostridium_T baratii, Enterococcus gallinarum (Enterococcus_Dgallinarum), Cellulosilyticaceae, Gemella palaticanis, Streptococcus fryi, Escherichia ruysiae, Citrobacter gillenii, Pseudomonas guguanensis, Klebsiella pneumoniae, Enterococcus B, Streptococcus minor, Clostridium paraputrificum, Clostridium T paraputrificum_207370), Clostridium disporicum (Clostridium_T disporicum_203972), Streptococcus oralis (Streptococcus oralis_E_351036), Clostridium P (Clostridium_P), Bacillus subtilis (Bacillus_P_294101 subtilis_291504), Klebsiella variicola, Clostridioides difficile (Clostridioides_A difficile), Terrisporobacter glycolicus (Terrisporobacter glycolicus_239331), Clostridium neonatale (Clostridium_T neonatale), Bifidobacterium animalis, Terrisporobacter, Escherichia 710834 (Escherichia_710834), Klebsiella 724518 (Klebsiella_724518), Escherichia fergusonii, Clostridium saudiens (Clostridium_Tsaudiense), Clostridium tertium (Clostridium_T tertium), Enterobacter hormaechei (Enterobacter_B_713587 hormaechei_712707), Bilophila wadsworthia, Clostridium isatidis (Clostridium_T isatidis), Canibacter oris, Nanosynbacter lyticus, Proteus mirabilis, Clostridium innocuum (Clostridium_AQ innocuum), CCUG-7971 spG000499525, Fusobacterium necrogenes (Fusobacterium_A

[16] A mortality prediction system comprising: a prediction means for determining or predicting whether the animal will die within a predetermined period of time using information on whether the animal contains one or more bacteria selected from the group consisting of: Enterococcus faecalis, Bacteroides fragilis, and Roseburia intestinalis, wherein the prediction means determines that the animal's health is impaired or there is a high risk of future impairment if the animal contains one or more bacteria selected from the group consisting of: Enterococcus faecalis, Bacteroides fragilis, and Roseburia intestinalis.

[17] A mortality prediction system according to

[15] , wherein the animal is suffering from a chronic disease.

[17] A method for determining or predicting the health status of an animal, comprising a step of determining the health status or predicting the future health status of the animal using information regarding whether or not periodontal disease-associated bacteria are present in the intestinal flora of an animal other than humans, wherein the periodontal disease-associated bacteria are bacteria for which the odds ratio, expressed by the following formula, between the detection rate in animals suffering from periodontal disease and the detection rate in animals of the same species not suffering from periodontal disease exceeds 1:Odds ratio = detection rate in animals with periodontal disease / detection rate in animals of the same species not affected by periodontal disease

[18] The method for determining or predicting the health state of an animal according to

[17] , wherein the detection rate in animals with periodontal disease is calculated by examining the bacterial composition of the intestinal flora of 100 or more animals with periodontal disease, and the detection rate in animals of the same species not affected by periodontal disease is calculated by examining the bacterial composition of the intestinal flora of 100 or more animals not affected by periodontal disease.

[19] The method for determining or predicting the health state of an animal according to

[17] , wherein the step of determining the health state of the animal or predicting its future health state is a step of determining whether the animal is affected by a disease or predicting whether it will be affected by a disease in the future, a step of determining whether the animal's mental state is good or predicting whether the animal's mental state will be good in the future, a step of determining whether the animal's coat is shiny or not shiny in the future, or a step of determining whether the animal's bad breath is good or predicting whether the animal's bad breath will be good in the future.

[20] In the oral cavity of animals other than humans, Streptococcus constellatus, Streptococcus anginosus, Slackia exigua, Desulfovibrio R446353, Peptostreptococcus canis, Bulleidia moorei, Actinomyces timonensis, Pauljensenia cardiffensis, Nanoperiomorbus, Fusobacterium simiae, Corynebacterium canis, and the like. canis, Enterocloster bolteae, Bifidobacterium dentiumdentium), Parvimonas micra, Nanosynbacter, Flexilinea sp902786265, CAJPSE01 sp003860125, Campylobacter sp013201975, Fusobacterium russii, Actinomyces weissii, Fusobacterium canifelinum, Saccharimonas aalborgensis, SDRW01 sp007845485, Corynebacterium freiburgense), Globicatella, Lactobacillus delbrueckii, Clostridium disporicum (Clostridium_T disporicum_203974), Canibacter sp003859945, Pediococcus acidilactici, Clostridium sp000821305 (Clostridium_J sp000821305), Pauljensenia canis, Nanogingivalis gingivitcus, Clostridium baratii, Enterococcus gallinarum (Enterococcus_D gallinarum), Cellulosilyticaceae, Gemella palaticanis, Streptococcus fryi, Escherichia ruysiae, Citrobacter gilenii (Citrobacter_A_692098gillenii), Pseudomonas guguanensis (Pseudomonas_E_648040 guguanensis), Klebsiella pneumoniae (Klebsiella pneumoniae_718977), Enterococcus B (Enterococcus_B), Streptococcus minor, Clostridium paraputrificum (Clostridium_T paraputrificum_208099, Clostridium_T paraputrificum_207370), Clostridium disporicum (Clostridium_T disporicum_203972), Streptococcus oralis (Streptococcus oralis_E_351036), Clostridium P (Clostridium_P), Bacillus subtilis (Bacillus_P_294101) subtilis_291504, Klebsiella variicola, Clostridioides difficile, Terrisporobacter glycolicus_239331, Clostridium neonatale, Bifidobacterium animalis, Terrisporobacter, Escherichia 710834, Klebsiella 724518, Escherichia fergusonii, Clostridium saudiense, Clostridium tertium tertium), Enterobacter hormaechei (Enterobacter_B_713587 hormaechei_712707), Bilophila wadsworthia, Clostridium isatidis (Clostridium_Tisatidis, Canibacter oris, Nanosynbacter lyticus, Proteus mirabilis, Clostridium innocuum (Clostridium_AQ innocuum), CCUG-7971 spG000499525, Fusobacterium necrogenes (Fusobacterium_A necrogenes), Streptococcus, Anaerostipes caccae, Enterococcus faecalis (Enterococcus_H_360604 faecalis), Bacteroides fragilis (Bacteroides_H fragilis), and Roseburia intestinalis A method for preventing a disease, comprising a step of killing, inhibiting, inactivating or removing one or more bacteria selected from the group consisting of Bacillus subtilis (Bacillus intestinalis).

[21] A receiving means for receiving data on the intestinal flora of an animal other than a human, and a method for detecting the presence of Streptococcus constellatus, Streptococcus anginosus, Slackia exigua, Desulfovibrio R446353, Peptostreptococcus canis, Bulleidia moorei, Actinomyces timonensis, Pauljensenia cardiffensis, Nanoperiomorbus, Fusobacterium simiae, and the like in the intestinal flora. simiae), Corynebacterium canis, Enterocloster bolteae, Bifidobacterium dentiumdentium), Parvimonas micra, Nanosynbacter, Flexilinea sp902786265, CAJPSE01 sp003860125, Campylobacter sp013201975, Fusobacterium Crussii, Actinomyces weissii, Fusobacterium canifelinum, Saccharimonas aalborgensis, SDRW01 sp007845485, Corynebacterium freiburgense), Globicatella, Lactobacillus delbrueckii, Clostridium disporicum (Clostridium_T disporicum_203974), Canibacter sp003859945, Pediococcus acidilactici, Clostridium sp000821305 (Clostridium_J sp000821305), Pauljensenia canis, Nanogingivalis gingivitcus, Clostridium baratii, Enterococcus gallinarum (Enterococcus_D gallinarum), Cellulosilyticaceae, Gemella palaticanis, Streptococcus fryi, Escherichia ruysiae, Citrobacter gilenii (Citrobacter_A_692098gillenii), Pseudomonas guguanensis (Pseudomonas_E_648040 guguanensis), Klebsiella pneumoniae (Klebsiella pneumoniae_718977), Enterococcus B (Enterococcus_B), Streptococcus minor, Clostridium paraputrificum (Clostridium_T paraputrificum_208099, Clostridium_T paraputrificum_207370), Clostridium disporicum (Clostridium_T disporicum_203972), Streptococcus oralis (Streptococcus oralis_E_351036), Clostridium P (Clostridium_P), Bacillus subtilis (Bacillus_P_294101) subtilis_291504, Klebsiella variicola, Clostridioides difficile, Terrisporobacter glycolicus_239331, Clostridium neonatale, Bifidobacterium animalis, Terrisporobacter, Escherichia 710834, Klebsiella 724518, Escherichia fergusonii, Clostridium saudiense, Clostridium tertium tertium), Enterobacter hormaechei (Enterobacter_B_713587 hormaechei_712707), Bilophila wadsworthia, Clostridium isatidis (Clostridium_Tisatidis, Canibacter oris, Nanosynbacter lyticus, Proteus mirabilis, Clostridium innocuum (Clostridium_AQ innocuum), CCUG-7971 spG000499525, Fusobacterium necrogenes (Fusobacterium_A necrogenes), Streptococcus, Anaerostipes caccae, Enterococcus faecalis (Enterococcus_H_360604 faecalis), Bacteroides fragilis (Bacteroides_H fragilis), and Roseburia intestinalis and an alert means for issuing an alert to a user to encourage oral care of the animal when the animal contains one or more bacteria selected from the group consisting of Bacillus subtilis (Bacillus intestinalis).

[0013] The present invention makes it possible to provide a method for determining or predicting an animal's health condition, a method for determining or predicting a disease, a method for calculating insurance premiums, a health condition determination or prediction system, a disease prediction system, and a method and system for predicting death.

[0014] 1 is a schematic diagram of an insurance premium calculation system; 2 is a flowchart illustrating an example of the flow of a disease prediction method using a disease prediction system; 3 is a graph illustrating the results of an example; 4 is a table illustrating the results of an example; 5 is a graph illustrating the results of an example; 6 is a graph illustrating the results of an example; 7 is a graph illustrating the results of an example; 8 is a graph illustrating the results of an example; 9 is a graph illustrating the results of an example; 10 is a graph illustrating the results of an example; 11 is a graph illustrating the results of an example; 12 is a graph illustrating the results of an example; 13 is a graph illustrating the results of an example; 14 is a graph illustrating the results of an example; 15 is a graph illustrating the results of an example; 16 is a graph illustrating the results of an example; 17 is a graph illustrating the results of an example; 18 is a graph illustrating the results of an example; 19 is a graph illustrating the results of an example; Graph showing the results of Examples. ...FIG. 1 is a graph showing the results of a reference example. FIG. 2 is a graph showing the results of a reference example. FIG. 3 is a graph showing the results of a reference example. FIG. 4 is a graph showing the results of an example. FIG. 5 is a graph showing the results of an example. FIG. 6 is a graph showing the results of a reference example. FIG. 7 is a graph showing the results of a reference example.

[0015] [Method for determining or predicting the health state of an animal] The method for determining or predicting the health state of an animal of the present invention is a method for determining or predicting the health state of an animal by detecting Streptococcus constellatus, Streptococcus anginosus, Slackia exigua, Desulfovibrio R446353, Peptostreptococcus canis, Bulleidia moorei, Actinomyces timonensis, Pauljensenia cardiffensis, Nanoperiomorbus, Fusobacterium simiae, and the like in the intestinal flora of an animal other than humans. simiae), Corynebacterium canis, Enterocloster bolteae, Bifidobacterium dentium, Parvimonas micra, Nanosynbacter, Flexilinea sp902786265, CAJPSE01 sp003860125, Campylobacter sp013201975, Fusobacterium russii, Actinomyces weissii, Fusobacterium caniferinum canifelinum), Saccharimonas aalborgensis, SDRW01 sp007845485, Corynebacterium flavorgensfreiburgense), Globicatella, Lactobacillus delbrueckii, Clostridium disporicum (Clostridium_T disporicum_203974), Canibacter sp003859945, Pediococcus acidilactici, Clostridium sp000821305 (Clostridium_J sp000821305), Pauljensenia canis, Nanogingivalis gingivitcus, Clostridium baratii, Enterococcus gallinarum (Enterococcus_D gallinarum), Cellulosilyticaceae, Gemella palaticanis, Streptococcus fryi, Escherichia ruysiae, Citrobacter gillenii, Pseudomonas guguanensis, Klebsiella pneumoniae, Enterococcus B, Streptococcus minor, Clostridium paraputrificum, Clostridium T paraputrificum_207370), Clostridium disporicum (Clostridium_T disporicum_203972), Streptococcus oralis (Streptococcusoralis_E_351036), Clostridium P, Bacillus subtilis (Bacillus_P_294101 subtilis_291504), Klebsiella variicola, Clostridioides A difficile, Terrisporobacter glycolicus_239331, Clostridium neonatale, Bifidobacterium animalis, Terrisporobacter, Escherichia 710834 (Escherichia_710834), Klebsiella 724518 (Klebsiella_724518), Escherichia fergusonii (Escherichiafergusonii), Clostridium saudiense (Clostridium_T saudiense), Clostridium tertium (Clostridium_T tertium), Enterobacter hormaechei (Enterobacter_B_713587 hormaechei_712707), Bilophila wadsworthia, Clostridium isatidis (Clostridium_T isatidis), Canibacter oris, Nanosynbacter lyticus, Proteus mirabilis mirabilis), Clostridium innocuum (Clostridium_AQ innocuum), CCUG-7971 spG000499525, Fusobacterium necrogenes (Fusobacterium_A necrogenes), Streptococcus, Anaerostipes caccae, Enterococcus faecalis (Enterococcus_H_360604 faecalis), Bacteroides fragilis (Bacteroides_HThe method further comprises a step of determining the health status or predicting the future health status of the animal using information on whether the animal contains one or more bacteria selected from the group consisting of Pseudomonas aeruginosa, ...

[0016] The presence of specific bacteria in the intestinal flora may lead to a deterioration in the health of an animal, such as the risk of contracting disease or losing coat luster. Conversely, the following is one possible reason why examining the presence of specific bacteria in the intestinal flora can determine an animal's health status or predict its future health status. The following is merely one assumed mechanism, and the present invention is not limited thereto. 1. The specific bacteria are bacteria that are detected at a high rate in animals suffering from periodontal disease and are thought to be bacteria associated with periodontal disease. Even in cases of periodontal disease-related bacterial infection in the oral cavity, toxins produced by these bacteria and inflammatory substances caused by bacterial infection flow into the bloodstream from blood vessels near the gums and spread throughout the body, easily disrupting the immune balance. 2. The specific bacteria in the oral cavity are constantly flowing into the digestive tract via saliva, establishing infection in the digestive tract as well. 3. When specific bacteria or other pathogenic bacteria with protein-degrading ability evade the host's immune system in the digestive tract and damage proteins in the digestive tract walls or blood vessel walls, tiny gaps open in the digestive tract walls or blood vessel walls, resulting in a leaky gut. 4. Inflammatory substances caused by these bacteria enter the bloodstream or lymph, causing a systemic immune deficiency. As a result, they can lead to or exacerbate a number of diseases, including allergies, atopic dermatitis, kidney disease, diabetes, and heart disease.

[0017] The health condition of an animal in the present invention refers to, for example, whether the animal is suffering from a disease, whether the animal is in good mental condition, whether the animal's coat is in good condition, and whether the animal has good breath odor.

[0018] The health condition of an animal in the present invention preferably refers to whether the animal is suffering from a disease, and the disease is preferably periodontal disease, valvular disease, liver disease, biliary tract disease, pancreatic disease, renal disease, or cancer. Furthermore, the health condition of an animal in the present invention preferably refers to whether the animal's mental state is good, and whether the animal's mental state is good preferably refers to whether the animal has become timid. Examples of whether an animal has become timid include whether the animal is afraid of other unfamiliar animals or strangers.

[0019] The method for determining or predicting the health status of an animal of the present invention is preferably a method for determining or predicting an animal's disease, a method for determining or predicting an animal's mental state, a method for determining or predicting an animal's coat gloss, or a method for determining or predicting an animal's bad breath.

[0020] One example of such a disease is periodontal disease. The term "periodontal disease" as used herein may be defined as any known disease. For example, periodontal disease includes gingivitis, a condition in which bacteria invade through the gap between the teeth and gums (periodontal pockets) and cause inflammation of the gums, and periodontitis, a condition in which the bone supporting the teeth (alveolar bone) dissolves and becomes unstable.

[0021] In the present invention, the animals are preferably mammals, and dogs and cats are particularly preferred.

[0022] The step of determining the health condition of an animal is, for example, a step of determining whether the health condition of the target animal is good or a step of evaluating the degree of goodness of the animal's health condition. The timing of the determination is preferably the time of collection of a sample such as a fecal sample collected for examining the intestinal bacterial flora or the time of analysis of the intestinal bacterial flora.

[0023] The step of predicting the future health state of an animal refers to predicting whether the health state of the target animal will be good within a predetermined period, for example, within one year, six months, or three months from the time of collection of a sample such as a fecal sample collected to examine the intestinal flora or the time of analysis of the intestinal flora, or after the predetermined period has elapsed. The form of the prediction is not particularly limited, and examples include predicting that the health state will be good or indicating the probability of deterioration.

[0024] When the health condition of an animal is the presence or absence of a disease, the prediction in the present invention refers to a prediction of whether the subject animal will develop periodontal disease or another disease within a predetermined period, for example, within one year, six months, or three months from the time of collection of a sample such as a fecal sample collected to examine the intestinal flora or the time of analysis of the intestinal flora. The form of the prediction is not particularly limited, and examples include a prediction of whether or not the animal will develop a disease or an indication of the probability of development.

[0025] When the health condition of an animal is determined by the presence or absence of a disease, the determination in the present invention refers to a determination of whether the subject animal is suffering from periodontal disease or another disease. The determination is preferably made at the time of collection of a sample such as a fecal sample collected for examining the intestinal bacterial flora or at the time of analysis of the intestinal bacterial flora.

[0026] When the health condition of an animal refers to whether the animal's mental state, coat gloss, or bad breath is good, the prediction in this invention refers to a prediction of whether the animal's mental state, coat gloss, or bad breath is good within a predetermined period, for example, within one year, six months, or three months from the time of collection of a sample such as a fecal sample collected to examine the intestinal flora or the time of analysis of the intestinal flora, or after a predetermined period has passed. The form of the prediction is not particularly limited, and examples include a prediction of whether the animal's mental state is good or a probability that the animal's mental state is good, a prediction of whether the animal's coat gloss is good or a probability that the animal's coat gloss is good, or a prediction of whether the animal's bad breath is good or a probability that the animal's bad breath is good.

[0027] When the health condition of an animal is whether the animal's mental state, coat condition, or breath odor is good, the determination in the present invention refers to determining whether the target animal's mental state, coat condition, or breath odor is good. The determination is preferably made at the time of collection of a sample such as a fecal sample collected to examine the intestinal flora or at the time of analysis of the intestinal flora.

[0028] Known methods for analyzing the intestinal microbiota of animals can be used, including known metagenomic analysis methods and microbiota analysis methods such as amplicon sequencing using a sequencer such as NGS. For example, a sample such as feces is collected from an animal, and the DNA and RNA base sequence information of all organisms contained in the sample is analyzed using a next-generation sequencer to identify the organisms contained in the sample. When analyzing the base sequences of DNA and RNA using a next-generation sequencer, OTU analysis and ASV analysis, which will be described later, can be used.

[0029] In the method for determining or predicting the health status of an animal of the present invention, a processor such as a CPU or GPU in a computer or server preferably uses a preset program, code, or software to determine the health status of a target animal or predict its future health status. The determination or prediction method is not particularly limited, but a suitable example involves the processor detecting the sequence of a specific bacterium from data on the target animal's intestinal flora, such as data from 16S rRNA gene amplicon sequencing or shotgun metagenomic sequencing of the intestinal flora. If the sequence of the specific bacterium is detected, the processor determines that the specific bacterium is contained in the intestinal flora of the target animal and determines that the target animal is in poor health, suffering from a disease, has a poor mental state, has poor coat luster, or has poor breath. Alternatively, the processor may first analyze the intestinal flora data to obtain information on the presence or absence of the specific bacterium and other bacteria, and then use that information to determine whether the health status is good or predict the health status. The presence of one specific bacterium can be used to determine whether the animal's health is poor or has deteriorated, while the presence of two or more specific bacteria increases the likelihood of the animal's health being poor or has deteriorated. Furthermore, among the specific bacteria, bacteria with a large difference in detection rate between the diseased group and the non-diseased group are assigned a high score, and bacteria with a small difference are assigned a low score. The scores for each specific bacterium contained in the intestinal flora are then added up. If the total score exceeds a threshold, the animal is determined to be in poor health, suffering from a disease, in poor mental condition, with poor fur, or with poor breath. Alternatively, it is possible to predict that the animal's health will be poor, that the animal will suffer from a disease, that the animal's mental condition will be poor, that the animal's fur will be poor, or that the animal will have a poor breath in the future. In addition, a higher score may be assigned to bacteria with a higher odds ratio of insurance claim rate between a population that carries the bacteria and a population that does not, and a lower score may be assigned to bacteria with a lower odds ratio.

[0030] Information other than the presence or absence of a specific bacterium may be used for the determination or prediction. Such information may include physical data such as the animal's age, breed, sex, weight, height, and length, sequence data such as genome sequence, SNP, and specific gene sequence, medical history, and family history. Data regarding the composition of bacteria in the intestinal microbiota may also be used as other information. Examples of such data regarding the composition of bacteria include occupancy data and diversity data.

[0031] (Occupancy Data) Occupancy data is data related to the occupancy of each bacterium contained in the intestinal microbiota of an animal. The occupancy is the abundance ratio (detection ratio) of each bacterial species in the intestinal microbiota, and can be measured as a "hit rate," a detection result obtained by a known metagenomic analysis method such as amplicon sequencing using a sequencer such as NGS. In the present invention, the occupancy data can be the occupancy of the intestinal microbiota or a label set based on the occupancy.

[0032] The occupancy rate may be the occupancy rate at any level of bacterial phylum, class, order, family, genus, or species, but is preferably the occupancy rate for each bacterial family. The occupancy rate for each family is the occupancy rate for all bacteria belonging to a certain family. In other words, when calculating the occupancy rate for each family, the occupancy rate for each bacterial species in the intestinal microbiota is calculated by adding up the occupancy rates for each family. Identification to the species or genus level may be performed and then totaled for each family, or identification at the family level may be performed without identification to the species or genus level and the occupancy rate for the family may be calculated.

[0033] As occupancy data, labels or scores set appropriately according to the magnitude of the occupancy value can be used for judgment or prediction. For example, three levels of labels, "large," "medium," and "small," or "large," "medium," and "small," can be set according to the occupancy value. The number of label levels can be set arbitrarily, and multi-level labels such as "0," "1," "2," "3," ... "20" can also be assigned. When using labels, the occupancy rate in the intestinal microflora can be measured, and before inputting the occupancy value into a computer, a specific label can be assigned from a predetermined correspondence table according to the measured occupancy rate, and the label can be input.

[0034] As described above, the occupancy in the occupancy data of the present invention may be the occupancy at any level of phylum, class, order, family, genus or species of fungi, but the occupancy by family of fungi is preferred. The family is preferably one or more families selected from the group consisting of Alcaligenaceae, Bacteroidaceae, Bifidobacteriaceae, Clostridiaceae, Coprobacillaceae, Coriobacteriaceae, Enterobacteriaceae, Enterococcaceae, Erysipelotrichaceae, Fusobacteriaceae, Lachnospiraceae, Peptostreptococcaceae, Prevotellaceae, Ruminococcaceae, and Veillonellaceae.

[0035] In addition to the above, the family Streptococcaceae is also preferred in the present invention, i.e., Alcaligenaceae, Bacteroidaceae, Bifidobacteriaceae, Clostridiaceae, Coprobacillaceae, Coriobacteriaceae, Enterobacteriaceae, Enterococcaceae, Erysipelothrix Preferred are one or more families selected from the group consisting of Erysipelotrichaceae, Fusobacteriaceae, Lachnospiraceae, Peptostreptococcaceae, Prevotellaceae, Ruminococcaceae, Veillonellaceae and Streptococcaceae.

[0036] In addition to the above, the present invention also preferably includes one or more families selected from the group consisting of Campylobacteraceae, Desulfovibrionaceae, Flavobacteriaceae, Helicobacteraceae, Odoribacteraceae, Paraprevotellaceae, Peptococcaceae, Porphyromonadaceae, and Succinivibrionaceae.These include the families Alcaligenaceae, Bacteroidaceae, Bifidobacteriaceae, Clostridiaceae, Coprobacillaceae, Coriobacteriaceae, Enterobacteriaceae, Enterococcaceae, Erysipelotrichaceae, Fusobacteriaceae, Lachnospiraceae, Peptostreptococcaceae, and Prebotea. Preferred are one or more families selected from the group consisting of Prevotellaceae, Ruminococcaceae, Veillonellaceae, Campylobacteraceae, Desulfovibrionaceae, Flavobacteriaceae, Helicobacteraceae, Odoribacteraceae, Paraprevotellaceae, Peptococcaceae, Porphyromonadaceae and Succinivibrionaceae.

[0037] (Diversity Data) Diversity data is data related to the diversity of bacteria in the intestinal microbiota of an animal. A high diversity of the intestinal microbiota means that the intestinal microbiota contains a wide range of different types of bacteria. There are several types of indicators representing diversity, so-called diversity indices, and any known indices may be used in the present invention. Examples of diversity indices include the Shannon-Wiener diversity index and the Simpson diversity index.

[0038] (Measurement of Occupancy Data and Diversity Data) Measurement of intestinal microbiota occupancy data and diversity data can be performed using known metagenomic analysis methods and microbiota analysis methods, such as amplicon sequencing using a sequencer such as NGS. For example, a method is exemplified in which a sample such as feces is collected from an animal and the DNA and RNA base sequence information of all organisms contained in the sample is analyzed using a next-generation sequencer to identify the organisms contained in the sample. A preferred method is to amplify all or part of the 16S rRNA gene contained in the sample as necessary, sequence it, and analyze the obtained sequence using software to obtain bacterial composition data in the sample. The bacterial composition data in the sample can be processed using software or by referring to gene databases such as Genbank, Greengenes, and SILVA databases to determine the species of bacteria contained in the sample, and the occupancy data and diversity data of the animal's intestinal microbiota can be measured.

[0039] An example of amplicon analysis (bacterial flora analysis) of the 16S rRNA gene using next-generation sequencer (NGS) is described below. First, DNA is extracted from a sample such as feces using a DNA extraction reagent, and the 16S rRNA gene is amplified from the extracted DNA by PCR. The amplified DNA fragments are then comprehensively sequenced using NGS. Low-quality reads and chimeric sequences are then removed, and the sequences are clustered to perform operational taxonomic unit (OTU) analysis. An OTU is an operational taxonomic unit that treats sequences with a certain level of similarity (e.g., 96-97% or higher homology) as a single bacterial species. Therefore, the number of OTUs represents the number of bacterial species that make up the bacterial flora, and the number of reads belonging to the same OTU is thought to represent the relative abundance of that species. Furthermore, representative sequences can be selected from the number of reads belonging to each OTU, and the family name, genus, and species name can be identified through a database search. In this way, the occupancy rate of bacteria belonging to a specific family and the diversity index of the intestinal microbiota can be measured. Analysis using ASV (Amplicon Sequence Variant) is also possible. ASV is generated after removing erroneous sequences generated during PCR and sequencing, so it can distinguish sequence variations of single base units, enabling more precise identification.

[0040] [Method for determining or predicting a disease in an animal] The method for determining or predicting a disease in an animal of the present invention is characterized by comprising a step of determining whether the animal has a disease or predicting whether the animal will develop a disease in the future, using information regarding whether the animal's intestinal bacterial flora contains one or more specific bacteria. The method for determining or predicting a disease in an animal of the present invention is a method for determining whether the animal has a disease or predicting whether the animal will develop a disease in the future, and the target animal, the determination method, the prediction method, the method for analyzing the intestinal bacterial flora, etc. are the same as those of the method for determining or predicting the health status of an animal described above.

[0041] [Diseases] The types of diseases that are the subject of the present invention are not particularly limited, and examples thereof include periodontal disease, skin diseases, otological diseases, musculoskeletal diseases, ophthalmic diseases, digestive diseases, systemic diseases, urinary diseases, hepatic, biliary, and pancreatic diseases, circulatory system diseases, nervous system diseases, respiratory diseases, dental and oral diseases, endocrine system diseases, reproductive system diseases, blood and hematopoietic system diseases, and cancer. Skin diseases include dermatitis, atopic dermatitis, and pyoderma. Otologic diseases include otitis externa and otitis media. Musculoskeletal diseases include patellar luxation and intervertebral disc herniation. Ophthalmic diseases include conjunctivitis, eye discharge, keratitis, corneal ulcer / erosion, epiphora, cataracts, and glaucoma. Digestive diseases include gastritis and enteritis. Systemic diseases include loss of energy and collapse. Examples of urinary system diseases include cystitis and urolithiasis. Examples of hepatic, biliary, and pancreatic diseases include bile sludge and chronic renal failure. Examples of circulatory system diseases include valvular disease and cardiomyopathy. Examples of nervous system diseases include epilepsy and seizures. Examples of respiratory system diseases include cough, rhinitis, tracheal collapse, and bronchial stenosis. Examples of dental and oral system diseases include periodontal disease and stomatitis. Examples of endocrine system diseases include hypothyroidism and diabetes. Examples of reproductive system diseases include mammary tumors and balanitis. Examples of blood and hematopoietic system diseases include lymphatic tumors and thrombocytopenia. Examples of cancers include oral cancer, lung cancer, stomach cancer, colon cancer, osteosarcoma, and leukemia. In the present invention, among the diseases, valvular disease, liver, biliary, or pancreatic diseases, and cancer are preferred. The inventors consider the reason why the method of the present invention enables the diagnosis and prediction of disease onset as follows: That is, an intestinal microbiota containing a specific bacterium is not in a good state, for example, it has low diversity, and it can be said that the intestinal microbiota is in a state where it is susceptible to other diseases in which intestinal bacteria are involved or which are correlated with the state of the intestinal microbiota.

[0042] [Insurance Premium Calculation Method] The insurance premium calculation method of the present invention is characterized by comprising a step of calculating an insurance premium for an animal using information regarding whether or not one or more specific bacteria are present in the animal's intestinal microbiota. Information other than information regarding the presence or absence of specific bacteria may also be used to calculate the insurance premium. Examples of such information include physical data such as the animal's age, breed, sex, weight, withers height, and body length; sequence data such as genome sequence, SNP, and specific gene sequence; medical history; and family history. Furthermore, data regarding the composition of bacteria in the intestinal microbiota may be used as other information. Examples of such data regarding the composition of bacteria include occupancy data and diversity data.

[0043] Specific examples of insurance premium calculation methods include a method in which a processor calculates a provisional insurance premium using a preset insurance premium table based on basic information such as the age, breed, sex, and weight of the target animal, and then modifies the provisional insurance premium using information on the presence or absence of specific bacteria to calculate the insurance premium. Any known insurance premium table can be used, such as a table in which a grade is determined based on the animal's age, breed, sex, weight, etc., and insurance premiums are set according to the grade. When using an insurance premium table, it is preferable to lower the grade by one (increase the insurance premium) if one or more specific bacteria are present. Other examples include a method in which an insurance premium is calculated using a preset insurance premium table based on basic information and information on the presence or absence of specific bacteria. Yet another example is a method in which a loss ratio is calculated from information on the presence or absence of specific bacteria on the target animal using a trained model that has learned the relationship between information on the presence or absence of specific bacteria, a score calculated based on the information on the presence or absence of specific bacteria, and the loss ratio, and the insurance premium is calculated using the loss ratio. Unless otherwise defined, each item, such as the target animal, is the same as the health status assessment or prediction method described above.

[0044] [System for determining or predicting the health state of an animal] The system for determining or predicting the health state of an animal of the present invention is characterized by comprising a receiving means for receiving input of data on the intestinal flora of an animal other than humans, and a determining means for determining or predicting the health state or future health state of the animal using information on whether or not the intestinal flora contains a specific bacterium. Unless otherwise specified, each configuration is the same as the method for determining or predicting the health state of an animal described above.

[0045] [Receiving Means] The receiving means of the present invention is configured to receive input of data on the intestinal flora of an animal whose health condition is to be assessed or predicted, or information on the presence or absence of specific bacteria. The method for receiving the intestinal flora data can be any known method or configuration, such as inputting data into a terminal, transmitting data from the terminal to a server via a network, or uploading. Examples of the receiving means include internal / external interfaces such as SCSI and SAS, and network-specific interfaces such as FC (Fibre Channel), iSCSI, FCoE, and NAS. Other examples of the receiving unit configuration include a touch panel, buttons, and keys for inputting information.

[0046] [Determination Means] The determination means of the present invention uses information regarding whether a specific bacterium is present in the intestinal flora of an animal other than humans to determine or predict the health state or future health state of the animal. The determination means includes a program, code, or software. These programs, codes, or software are preferably programs, codes, or software that determine whether the target animal's health state is good or predict whether the target animal's future health state will be good. They are stored in a storage means and read out, and a processor uses them to perform the determination or prediction. The determination or prediction method is not particularly limited, but one preferred example is a processor that uses a preset program, code, or software to detect the sequence of a specific bacterium from data on the intestinal flora of the target animal, such as data from 16S rRNA gene amplicon sequencing or shotgun metagenomic sequencing of the intestinal flora. If the sequence of the specific bacterium is detected, the processor determines that the specific bacterium is present in the intestinal flora of the target animal and determines that the target animal is in poor health, suffering from a disease, has poor mental state, has poor coat luster, or has poor breath. Alternatively, data on the intestinal microbiota may be analyzed in advance to obtain information on the presence or absence of specific bacteria and other bacteria, and the processor may then use that information to determine whether the animal's health is good or predict whether its future health will be good. The presence of one specific bacterium can indicate a possible poor or deteriorating health condition, while the presence of two or more specific bacteria increases the likelihood of a poor or deteriorating health condition. Furthermore, specific bacteria with a high odds ratio of detection rate between the diseased and non-diseased groups may be assigned a high score, and bacteria with a low odds ratio may be assigned a low score. The scores for each specific bacterium contained in the intestinal microbiota may then be summed, and if the sum exceeds a threshold, the animal's health may be determined to be poor or a prediction may be made that its future health condition will likely be poor.

[0047] The judgment means is preferably a means for determining whether an animal is suffering from a disease or predicting whether the animal will suffer from a disease in the future, a means for determining whether the animal's mental state is good or predicting whether the animal's mental state will be good in the future, a means for determining whether the animal's coat condition is good or predicting whether the animal's coat condition will be good in the future, or a means for determining whether the animal's breath smells good or predicting whether the animal's breath smells good in the future.

[0048] The determining means is preferably a means for determining whether an animal is suffering from a disease or for predicting whether the animal will suffer from a disease in the future, and the disease is preferably periodontal disease, valvular disease, liver disease, biliary tract disease, pancreatic disease, kidney disease, or cancer. The determining means is also preferably a means for determining whether the animal's mental state is good or for predicting whether the animal's mental state will be good in the future, and whether the animal's mental state is good preferably means whether the animal has become timid.

[0049] The system for determining or predicting the health state of an animal of the present invention is preferably a system for determining or predicting an animal's disease, a system for determining or predicting an animal's mental state, a system for determining or predicting an animal's coat gloss, or a system for determining or predicting an animal's bad breath.

[0050] Information other than the presence or absence of a specific bacterium may be used for the determination or prediction. Such information may include physical data such as the animal's age, breed, sex, weight, height, and length, sequence data such as genome sequence, SNP, and specific gene sequence, medical history, and family history. Data regarding the composition of bacteria in the intestinal microbiota may also be used as other information. Examples of such data regarding the composition of bacteria include occupancy data and diversity data.

[0051] The processor may also make predictions or determinations using a trained model. Examples of trained models include a model that has learned the relationship between information on the presence or absence of specific bacteria and the health status, such as the presence or absence of a disease. Such a trained model can be obtained by learning information, such as whether or not a certain animal's intestinal flora contains specific bacteria and how many types of bacteria are present, as well as information on the animal's health status, such as whether the animal has a disease or has developed a disease within a predetermined period, as training data. Information on the health status, such as whether or not the animal has a disease, can be replaced with a dummy variable. The training data on the animal's intestinal flora and information on the presence or absence of specific bacteria are similar to those used in the health status determination or prediction method described above. Information on whether the animal has a disease can be obtained, for example, from a veterinary clinic or an insured owner as an insurance claim (also known as an "accident"). In other words, if the animal is insured and the animal is taken to a hospital and diagnosed with a disease, the veterinary hospital or the owner (the pet insurance policyholder) will file a claim for insurance payment with the insurance company along with the fact that the animal has the disease, so the insurance company will know that the animal has the disease. On the other hand, if no claim for insurance is filed within a predetermined period of time from the time the intestinal microbiota data was obtained, it can be determined that the insured animal has not been affected by the disease during that period.

[0052] The trained model is preferably artificial intelligence (AI). Artificial intelligence (AI) refers to software or systems that mimic the intellectual tasks performed by the human brain using a computer, specifically computer programs that understand natural language used by humans, perform logical inference, and learn from experience. AI may be either general-purpose or specialized, and may be a deep neural network, a convolutional neural network, or the like, and publicly available software can be used.

[0053] To generate a trained model, artificial intelligence is trained using training data. The training can be done using either machine learning or deep learning, but machine learning is preferred. Deep learning is an advanced version of machine learning and is characterized by automatically finding features. In the present invention, occupancy data and diversity data of the intestinal microbiota are used as features.

[0054] The 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. Alternatively, the trained model may be trained using a known support vector machine method published in, for example, "Introduction to Support Vector Machines" (Kyoritsu Shuppan).

[0055] Machine learning can be either unsupervised learning or supervised learning, but supervised learning is preferred. The supervised learning method is not particularly limited, and examples thereof include decision trees, ensemble learning, and gradient boosting. Examples of publicly available machine learning algorithms include XGBoost, CatBoost, and LightGBM.

[0056] When used for disease diagnosis or prediction, a trained model may be generated for each individual disease, or may be generated for multiple diseases collectively. When generating a trained model for each individual disease, training is performed using, for an animal suffering from a specific disease, data on the intestinal flora obtained a predetermined period before the onset of the disease, the fact that the animal has the disease, and, for comparison, data on the intestinal flora of an animal that has not suffered from the disease for a predetermined period since the acquisition of the intestinal flora data, and the fact that the animal has not suffered from the disease for the predetermined period as training data. When training multiple diseases collectively, multiple types of training data may be prepared, such as data on the intestinal flora of an animal suffering from a certain disease and data on the intestinal flora of an animal suffering from the disease and data on the intestinal flora of an animal suffering from the disease and data on the intestinal flora of an animal suffering from a different ...

[0057] [Output] When the assessment means of the present invention receives as input information data such as data on the animal's intestinal flora or information regarding the presence or absence of specific bacteria, the processor uses a program to determine whether the animal is in good health, such as whether it is suffering from a disease, or to predict whether the animal's future health will be good, such as whether it will develop a disease within a predetermined period of time, preferably within three years, more preferably within two years, and even more preferably within one year, from a certain point in time, such as the time the intestinal flora data was obtained. The output format is not particularly limited, and the predictive assessment can be output, for example, by displaying on the screen of a terminal such as a personal computer, "There is a high possibility that the animal's health is currently deteriorating," "There is a high possibility that the animal is currently suffering from periodontal disease," "There is a possibility that the animal will develop a disease within the next year," or "There is a low possibility that the animal will develop a disease within the next year." The health status assessment or prediction system of the present invention may also have a separate output means that receives the assessment result from the assessment means and outputs the assessment result.

[0058] [Insurance Premium Calculation System] The insurance premium calculation system of the present invention comprises a receiving means for receiving data on the intestinal flora of an animal other than humans or information on the presence or absence of specific bacteria, and an insurance premium calculation means for calculating the insurance premium for the animal using information on whether the intestinal flora contains one or more specific bacteria.

[0059] The insurance premium calculation system of the present invention is preferably a system capable of executing the above-described insurance premium calculation method. For example, data on the intestinal flora of the insured animal or information on the presence or absence of specific bacteria is input into the above-described health condition assessment or prediction system, and the insurance premium for the animal is determined based on the output disease incidence assessment or prediction. Information other than the disease incidence prediction may also be used to determine the insurance premium. One embodiment of the insurance premium calculation system of the present invention will now be described with reference to FIG. 1.

[0060] In FIG. 1 , terminal 40 is a terminal used by the policyholder (user). Examples of terminal 40 include a personal computer and a tablet terminal. 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 an LCD panel, an input unit such as a mouse, keyboard, or touch panel, and a communication unit such as a network adapter. The policyholder accesses a server from terminal 40 and inputs and transmits data on the insured animal's intestinal flora or information on the presence or absence of specific bacteria, a facial image (photograph), and information such as the animal's type, breed, age at the time of photographing, weight, and medical history. Furthermore, by accessing the server with terminal 40, the policyholder can receive disease prediction results and insurance premium calculation results stored on the server.

[0061] The policyholder receives a fecal sample collection kit for examining the intestinal flora of the insured pet and sends the fecal sample to a service provider (not shown) that measures the intestinal flora of the pet. The service provider measures the intestinal flora of the pet and obtains intestinal flora data. The service provider may then directly input and transmit the intestinal flora data of the pet to the server's reception means 31 via its own terminal. Alternatively, the service provider may separately send the intestinal flora data of the pet to the policyholder by mail, email, or the like, and the policyholder may then input and transmit the intestinal flora data to the reception means 31 via terminal 40.

[0062] 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 memory unit 10 is configured, for example, by a ROM, a RAM, or a hard disk. The memory unit 10 stores information processing programs for operating each unit of the server, and in particular, stores a determination means (which is a trained model in this embodiment, but is not limited to a trained model; the same applies below) 11 and, if necessary, an insurance premium calculation means 12. If the system is configured as a health condition determination or prediction system that simply outputs a determination or prediction of health condition such as the incidence of disease, without the purpose of calculating insurance premiums, the insurance premium calculation means 12 may be omitted.

[0063] As described above, the determination means 11 receives as input data on the intestinal flora of the insured animal or information on the presence or absence of specific bacteria input by the policyholder or the business that measured the intestinal flora, and outputs a prediction of whether the animal is in good health, such as whether the animal will develop a specific disease within a predetermined period (e.g., within six months or one year) from the time the intestinal flora data was acquired or the time the intestinal flora data or the information on the presence or absence of specific bacteria was input. The determination means 11 in this embodiment is configured to include, for example, XGBoost, CatBoost, LightGBM, or a deep neural network or a convolutional neural network.

[0064] The insurance premium calculation means 12 is software that calculates the insurance premium for the animal based on the disease incidence prediction output by the determination means 11 and information input by the policyholder, such as the animal's type, breed, age at the time of obtaining the intestinal flora data, weight, medical history, etc. For example, the software classifies insurance premiums according to the animal's type, breed, age at the time of obtaining the intestinal flora data, weight, medical history, etc., and finally corrects the grade taking into account the disease incidence prediction output by the determination means 11, thereby calculating the final insurance premium. The insurance premium calculation means 12 and the determination means 11 may be configured to use a single piece of software. The method for calculating the insurance premium by the insurance premium calculation means 12 can refer to the specific aspects of the above-mentioned insurance premium calculation method.

[0065] The processing calculation unit 20 is composed of a processor such as a CPU or GPU, and predicts the onset of disease and calculates insurance premiums using programs related to the determination means 11 and insurance premium calculation means 12 stored in the storage unit.

[0066] The interface unit (communication unit) 30 includes a reception means 31 and an output means 32, and receives data on the animal's intestinal flora, information on the presence or absence of specific bacteria, and other information from the policyholder's terminal, and outputs predictions of health conditions such as the onset of disease and calculation results of insurance premiums to the policyholder's terminal.

[0067] With the insurance premium calculation system of this embodiment, in addition to applying for pet insurance, the policyholder can send in a sample such as an animal's feces, and at the same time, a card (pet health insurance card) indicating that the policyholder has pet insurance will be created, and the policyholder can obtain predictions of the pet's health condition, such as the pet's insurance premium and future risk of contracting a disease.

[0068] FIG. 2 shows a flow chart of disease prediction and assessment as one embodiment of a health condition prediction method using the health condition assessment or prediction system of the present invention. For ease of explanation, this embodiment will be described including the acquisition of a sample from an animal and data on the intestinal microbiota. A user collects a fecal sample from an animal using a fecal collection kit or the like and sends it to an intestinal microbiota analysis service provider (Step S1). The intestinal microbiota analysis service provider analyzes and acquires data on the presence or absence of each bacterium in the animal's intestinal microbiota from the fecal sample using a next-generation sequencer (Step S2). The intestinal microbiota analysis service provider returns the data on the intestinal microbiota to the user. The user accesses the disease prediction system via a terminal and inputs data on the presence or absence of a specific bacterium in the animal's intestinal microbiota (Step S3). The disease prediction system predicts and determines the likelihood that the animal will develop a disease within a specified period (e.g., within one year) or is currently suffering from a disease based on the input data on the presence or absence of a specific bacterium in the animal's intestinal microbiota (Step S4). The disease prediction system outputs the prediction judgment and transmits it to the terminal 40, and the prediction judgment result is displayed on the terminal 40 (step S5).

[0069] [Method for predicting animal mortality] The method for predicting animal mortality of the present invention is characterized by comprising a step of predicting whether an animal other than humans will die within a predetermined period of time using information regarding whether the intestinal flora of the animal contains one or more specific bacteria. Preferably, the animal is an animal suffering from a chronic disease. Unless otherwise specified, the method for analyzing the intestinal flora is the same as the method for predicting health status described above.

[0070] In the present invention, the prediction of death refers to a prediction of whether or not a target animal will die, or whether or not there is a high or low probability of death, within a predetermined period, for example, within one year, six months, or three months from the time of collection of a sample such as a fecal sample collected to examine the intestinal bacterial flora, or from the time of analysis of the intestinal bacterial flora. The form of the prediction is not particularly limited, and examples include a prediction of whether or not death will occur, or an indication of the probability of death.

[0071] In the death prediction method of the present invention, a processor such as a CPU or GPU in a computer or server preferably predicts the death of a target animal using a preset program, code, or software. The prediction method is not particularly limited, but in one preferred example, the processor detects the sequence of a specific bacterium from data on the target animal's intestinal flora, such as data from 16S rRNA gene amplicon sequencing or shotgun metagenomic sequencing of the intestinal flora. If the sequence of the specific bacterium is detected, the processor determines that the specific bacterium is contained in the intestinal flora of the target animal and predicts that the target animal has a high probability of death within a predetermined period of time. Alternatively, the processor may analyze the intestinal flora data in advance to obtain information on the presence or absence of the specific bacterium and other bacteria, and then predict death based on that information. The presence of one type of specific bacterium can be used to determine the possibility or high probability of death, while the presence of two or more types of specific bacterium increases the likelihood of death. Alternatively, a high score may be assigned to specific bacteria with a large difference in detection rate between the diseased and non-diseased groups, a low score to bacteria with a small difference, the scores for each specific bacteria contained in the intestinal bacterial flora may be summed, and if the sum of the scores exceeds a threshold, the target animal may be predicted to have a high probability of death.Also, a high score may be assigned to bacteria with a high odds ratio of insurance claim rate between populations that carry the bacteria and populations that do not, and a low score may be assigned to bacteria with a low odds ratio.

[0072] For the prediction, information other than information on the presence or absence of a specific bacterium may be used. Such information includes physical data such as the animal's age, breed, sex, weight, height, and body length, sequence data such as genome sequence, SNP, and specific gene sequence, medical history, and family history. Data on the composition of bacteria in the intestinal microbiota may also be used as other information. Examples of such data on the composition of bacteria include occupancy data and diversity data. Furthermore, information on whether the animal is suffering from a chronic disease, and information on the type, symptoms, and duration of the chronic disease may also be used.

[0073] An animal suffering from a chronic disease includes an animal that has been diagnosed as suffering from a chronic disease, an animal that is suspected of suffering from a chronic disease, or an animal that exhibits symptoms of a chronic disease.

[0074] A chronic disease is a disease that exhibits chronic symptoms, and examples of chronic diseases include chronic kidney disease, heart disease, diabetes, and autoimmune diseases. Preferably, the chronic disease is one that can lead to death, and more preferably, it is chronic kidney disease.

[0075] [Animal Death Prediction System] The animal death prediction system of the present invention comprises: a receiving means for receiving data on the intestinal flora of an animal other than a human; and a prediction means for determining or predicting whether the animal will die within a predetermined period of time using information on whether the intestinal flora contains one or more specific bacteria. Preferably, the animal is an animal suffering from a chronic disease. Unless otherwise specified, each configuration is the same as the above-mentioned animal disease prediction system.

[0076] [Prediction Means] The prediction means of the present invention predicts whether an animal, excluding humans, will die within a predetermined period of time using information regarding whether the animal's intestinal microbiota contains a specific bacterium. The prediction means includes a program, code, or software. These programs, codes, or software are preferably programs, codes, or software that predict whether a target animal will die within a predetermined period of time, and are stored in and read from a storage means, and a processor uses them to perform the prediction. The prediction method is not particularly limited, but in one preferred example, the processor detects the sequence of a specific bacterium from data on the target animal's intestinal microbiota, such as data from 16S rRNA gene amplicon sequencing or shotgun metagenomic sequencing of the intestinal microbiota. If the sequence of the specific bacterium is detected, the processor determines that the specific bacterium is contained in the intestinal microbiota of the target animal and predicts that the target animal will or is likely to die within the predetermined period of time. Alternatively, the processor may analyze the intestinal microbiota data in advance to obtain information regarding the presence or absence of the specific bacterium and other bacteria, and then use that information to predict death. The presence of one specific bacterium can be judged as a possibility of death, but the presence of two or more types increases the possibility of death. Furthermore, among the specific bacteria, a high score may be assigned to bacteria with a high odds ratio of detection rate between the periodontal disease affected group and the non-affected group, and a low score may be assigned to bacteria with a low odds ratio. The scores for each specific bacterium contained in the intestinal microflora may be summed, and if the sum of the scores exceeds a threshold, the target animal may be predicted to die or be highly likely to die.

[0077] For the prediction, information other than information on the presence or absence of a specific bacterium may be used. Such information includes physical data such as the animal's age, breed, sex, weight, height, and body length, sequence data such as genome sequence, SNP, and specific gene sequence, medical history, and family history. Data on the composition of bacteria in the intestinal microbiota may also be used as other information. Examples of such data on the composition of bacteria include occupancy data and diversity data. Furthermore, information on whether the animal is suffering from a chronic disease, and information on the type, symptoms, and duration of the chronic disease may also be used.

[0078] An animal suffering from a chronic disease includes an animal that has been diagnosed as suffering from a chronic disease, an animal that is suspected of suffering from a chronic disease, or an animal that exhibits symptoms of a chronic disease.

[0079] A chronic disease is a disease that exhibits chronic symptoms, and examples of chronic diseases include chronic kidney disease, heart disease, diabetes, and autoimmune diseases. Preferably, the chronic disease is one that can lead to death, and more preferably, it is chronic kidney disease.

[0080] The processor may also make predictions and decisions using a trained model, such as the trained model described above.

[0081] [Output] When the prediction means of the present invention receives as input information data such as data on the animal's intestinal flora or information regarding the presence or absence of specific bacteria, the processor uses a program to predict whether the animal will die or is likely to die within a predetermined period from a certain point in time, such as the time the intestinal flora data was obtained, preferably within three years, more preferably within two years, even more preferably within one year, and particularly preferably within six months. The output format is not particularly limited, and the prediction can be output, for example, by displaying on the screen of a terminal such as a personal computer, "High probability of death within a predetermined period," "Possibility of death within the next year," or "Low probability of death within the next year." The death prediction system of the present invention may also have a separate output means for receiving the determination result from the prediction means and outputting the prediction result.

[0082] Hereinafter, one embodiment of the death prediction system of the present invention will be described with reference to FIG.

[0083] In FIG. 18 , terminal 40 is a terminal used by a user. Examples of terminal 40 include a personal computer and a tablet terminal. 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 an LCD panel, an input unit such as a mouse, keyboard, or touch panel, and a communication unit such as a network adapter. The user accesses the server from terminal 40 and inputs and transmits data on the intestinal flora of the target animal or information regarding the presence or absence of specific bacteria, a facial image (photo), and information such as the animal's type, breed, age, weight, and medical history. Furthermore, by accessing the server with terminal 40, the user can receive disease prediction results and insurance premium calculation results from the server.

[0084] The user also receives a fecal sample collection kit for examining the intestinal flora of the target animal and sends the fecal sample to a business that measures the intestinal flora (not shown). The business then measures the intestinal flora of the animal and obtains intestinal flora data. The business may then directly input and transmit the intestinal flora data of the animal to the server's reception means 31 via its own terminal, or the business may separately send the intestinal flora data of the animal to the user by mail, email, or the like, and the user may then input and transmit the intestinal flora data to the reception means 31 via the terminal 40.

[0085] 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 configured by, for example, a ROM, a RAM, or a hard disk. The storage unit 10 stores information processing programs for operating each part of the server, and in particular, stores the prediction means 13.

[0086] As described above, the prediction means 13 receives as input the intestinal flora data of the insured animal or information regarding the presence or absence of specific bacteria input by the user or the business that measured the intestinal flora, and outputs a prediction as to whether the animal will die within a predetermined period (e.g., within one year or within six months) from the time the intestinal flora data was acquired or the time the intestinal flora data or the information regarding the presence or absence of specific bacteria was input. The prediction means 13 in this embodiment may include a trained model such as XGBoost, CatBoost, LightGBM, or a deep neural network or a convolutional neural network.

[0087] The processing calculation unit 20 is composed of a processor such as a CPU or a GPU, and executes mortality prediction using a program related to the prediction means 13 stored in the storage unit.

[0088] The interface unit (communication unit) 30 includes a receiving means 31 and an output means 32, and receives data on the animal's intestinal flora, information on the presence or absence of specific bacteria, and other information from the user's terminal, and outputs a death prediction to the user's terminal.

[0089] FIG. 19 shows a flowchart of death prediction and determination based on one embodiment of a death prediction method using the death prediction system of the present invention. For ease of explanation, this embodiment will be described including the acquisition of a sample from an animal and data on the intestinal microbiota. A user collects a fecal sample from an animal using a fecal collection kit or the like and sends it to an intestinal microbiota analysis service provider (Step S1). The intestinal microbiota analysis service provider analyzes and obtains data on the presence or absence of each bacterium in the animal's intestinal microbiota from the fecal sample using a next-generation sequencer (Step S2). The intestinal microbiota analysis service provider returns the data on the intestinal microbiota to the user. The user accesses the death prediction system via a terminal and inputs data on the presence or absence of a specific bacterium in the animal's intestinal microbiota (Step S3). The death prediction system predicts the likelihood that the animal will die within a specified period (e.g., within one year) based on the input data on the presence or absence of a specific bacterium in the animal's intestinal microbiota (Step S4). The death prediction system outputs the prediction judgment and transmits it to the terminal 40, and the prediction judgment result is displayed on the terminal 40 (step S5).

[0090] (Another embodiment of the method for determining or predicting the health state of an animal) Another embodiment of the method for determining or predicting the health state of an animal, according to the present invention, comprises a step of determining the health state or predicting the future health state of the animal, using information on whether or not the intestinal bacterial flora of the animal, excluding humans, contains periodontal disease-associated bacteria, wherein the periodontal disease-associated bacteria are bacteria for which the odds ratio, expressed by the following formula, between the detection rate in animals with periodontal disease and the detection rate in animals of the same species not suffering from periodontal disease exceeds 1: Odds ratio = detection rate in animals with periodontal disease / detection rate in animals of the same species not suffering from periodontal disease

[0091] That is, periodontal disease-associated bacteria are bacteria that are detected at a high rate in animals suffering from periodontal disease. The odds ratio represented by the above formula is preferably greater than 1.5, more preferably greater than 2, and even more preferably greater than 2.5. Figure 4 shows examples of periodontal disease-associated bacteria confirmed in the Examples and their odds ratios. In Figure 4, the numerical values ​​written to the right of the bacterial species name represent the odds ratios for each bacterium, as represented by the above formula.

[0092] In another aspect of the method for determining or predicting the health status of an animal of the present invention, the odds ratio represented by the above formula is preferably a detection rate calculated in advance by examining the intestinal flora of a plurality of animals of the same species. Furthermore, the detection rate in animals suffering from periodontal disease is preferably calculated by examining the bacterial composition of the intestinal flora of 100 or more animals suffering from periodontal disease, and the detection rate in animals of the same species that do not suffer from periodontal disease is preferably calculated by examining the bacterial composition of the intestinal flora of 100 or more animals that do not suffer from periodontal disease. In another aspect of the method for determining or predicting the health status of an animal, the step of determining the health status or predicting the future health status of the animal is preferably a step of determining whether the animal is suffering from a disease or predicting whether it will suffer from a disease in the future, a step of determining whether the animal's mental state is good or predicting whether it will be good in the future, a step of determining whether the animal's coat gloss is good or predicting whether it will be good in the future, or a step of determining whether the animal's bad breath is good or predicting whether it will be good in the future.

[0093] [Disease prevention method] The disease prevention method of the present invention comprises a step of sterilizing, inhibiting, inactivating, or removing one or more specific bacteria or periodontal disease-related bacteria in the oral cavity of an animal other than humans. The method of sterilization, inhibition, inactivation, or removal is not particularly limited, and examples include periodontal care, tartar removal, and oral disinfection. Periodontal care, tartar removal, and oral disinfection that are highly effective in sterilizing, inhibiting, inactivating, or removing specific bacteria or periodontal disease-related bacteria are preferred. Diseases include the same as those described above, with tumors being particularly preferred, and oral tumors and tumors of the blood and hematopoietic organs being even more preferred.

[0094] [Disease Prevention System] The disease prevention system of the present invention is an animal disease prevention system comprising: a receiving means for receiving data on the intestinal flora of an animal (excluding humans) or information on the presence or absence of specific bacteria; and an alert means for issuing an alert to a user encouraging oral care of the animal if the intestinal flora contains one or more specific bacteria. The alert means can employ any known configuration, such as a program that displays a warning on a user's terminal via a network when the intestinal flora of the target animal contains specific bacteria, encouraging oral care of the animal. Examples of warnings displayed on the user's terminal include "Dangerous bacteria that may cause disease have been detected" and "There is a risk of illness, so please perform oral care." Examples of oral care include periodontal care, tartar removal, and oral disinfection. The system may further comprise the above-mentioned determination means. When the determination means is included, if the determination means determines that the animal's health is at risk based on the presence or absence of specific bacteria, the alert means issues an alert in accordance with the determination result. According to the present invention, when a specific bacterium is present in the intestinal flora of an animal, it is possible to predict and determine whether the animal is at high risk of contracting a disease or other health issues. This fact can be used to encourage users to take better care of their animals.

[0095] Examples of the present invention are described below. The present invention is not limited to the following examples. [Example 1] (DNA Extraction from Fecal Samples) Fecal samples were collected from each dog and DNA was extracted as follows. Dog owners collected fecal samples using a fecal collection kit. The fecal samples were received and suspended in water. Next, 200 μL of fecal suspension and 810 μL of lysis buffer (containing 224 μg / mL of Proteinase K) were added to a bead tube, and the samples were bead-disrupted using a bead homogenizer (6,000 rpm, 20-second disruption, 30-second interval, 20-second disruption). The samples were then treated with Proteinase K by placing them on a 70°C heat block for 10 minutes, and then inactivated by placing them on a 95°C heat block for 5 minutes. The lysed specimen was subjected to automatic DNA extraction using Chemagic 360 (PerkinElmer) according to the Chemagic kit tool protocol, to obtain 100 μL of DNA extract.

[0096] (Meta-16S RNA gene sequence analysis) Meta-16S sequence analysis was performed using a modified version of 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 mixture was prepared by mixing 10 uL of DNA extract, 0.05 uL of each primer (100 uM), 12.5 uL of 2xKAPA HiFi Hot-Start ReadyMix (F. Hoffmann-LaRoche, Switzerland), and 2.4 uL of PCR-grade water. After heat denaturation at 95 ° C for 3 minutes, PCR was performed by repeating 30 cycles of 95 ° C for 30 seconds, 55 ° C for 30 seconds, and 72 ° C for 30 seconds, followed by a final extension reaction at 72 ° C for 5 minutes. The amplified product was purified using magnetic beads and eluted with 50 uL of Buffer EB (QIAGEN, Germany). The purified amplified product was subjected to PCR using Nextera XT Index Kit v2 (Illumina, CA, US) and indexed. The PCR reaction mixture was prepared by mixing 2.5 uL of the amplified product, 2.5 uL of each primer, 12.5 uL of 2x KAPA HiFi Hot-Start ReadyMix, and 5 uL of PCR-grade water. The PCR involved heat denaturation at 95°C for 3 minutes, followed by 12 cycles of 95°C for 30 seconds, 55°C for 30 seconds, and 72°C for 30 seconds, followed by a final extension reaction at 72°C for 5 minutes. The indexed amplified product was purified using magnetic beads and eluted with 80-105 uL of Buffer EB. The concentration of each amplification product was measured using a NanoPhotometer (Implen, CA, US) and adjusted to 1.4 nM. Equal amounts were mixed to prepare the sequencing library. The DNA concentration and amplification product size of the sequencing library were confirmed by electrophoresis and analyzed using MiSeq. MiSeq Reagent Kit V3 was used for analysis, and 2 x 300 bp paired-end sequencing was performed. The resulting 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′ Illumina_16S_805R 5′-GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAGGACTACHVGGGTATCTAATCC-3′.

[0097] Using the above method, we obtained data on the composition of the intestinal microbiota for 3,121 individuals in the periodontal disease group who filed insurance claims for periodontal disease within 180 days of fecal sample collection, and for 55,750 individuals in the non-periodontal disease group who did not file insurance claims for periodontal disease within the same period.

[0098] (Detection of Each Bacterium) Using the intestinal microbiota composition data obtained above and a publicly known 16sRNA sequence database (greengenes2-2022.10), the bacteria contained in each individual's intestinal microbiota were identified. For a certain bacterium, the presence or absence of insurance claims due to periodontal disease was examined between a group of individuals in which the bacterium was detected (detection group) and a group of individuals in which the bacterium was not detected (non-detection group). As a result, it was found that there were bacteria with a significant difference in insurance claim rates between the detection group and the non-detection group. The significance of the difference in insurance claim rates was tested using Fisher's exact test, and a significant difference was determined when p<0.0001. Figure 3 shows the top 20 bacteria ranked in order of the difference in insurance claim rates between the detection group and the non-detection group. Figure 3 is a graph showing the detection rates for the top 20 bacterial species, calculated using the following calculation.

[0099] (Detection rate of each bacterial species) Using the intestinal bacterial flora composition data obtained above, the detection rate for each bacterial species was calculated. The detection rate is calculated by dividing the number of cases in which a certain bacterium was detected in the diseased or non-diseased group by the total number of cases in each group.

[0100] (Odds Ratio) Next, for each bacterium, the insurance claim rate due to periodontal disease in individuals in whom the bacterium was detected was compared with the insurance claim rate due to periodontal disease in individuals in whom the bacterium was not detected, and bacteria with an odds ratio of 1 or greater were selected. Each bacterium is shown in Figure 4 along with its odds ratio. The odds ratio is a statistical indicator that indicates the degree to which the odds of a particular outcome differ between a group with a particular exposure (e.g., having received a certain treatment, being in a particular environment, engaging in a particular behavior, etc.) and a group without that exposure. Each odds ratio was also tested using Fisher's exact test. p indicates the p-value of the exact test. It can be said that each bacterium listed in Figure 4 is likely to have or be at high risk of having periodontal disease in individuals whose intestinal flora contains that bacterium.

[0101] (Diseases other than periodontal disease) For diseases other than periodontal disease, such as valvular disease, cancer, hepatobiliary system, and pancreatic diseases, the insurance claim rates were also compared between individuals who carried each bacterium and individuals who did not, and odds ratios were calculated. The results are shown in Figure 4. As is clear from Figure 4, individuals who carried the bacteria listed in Figure 4 were either suffering from or had a high probability of suffering from the disease, even for diseases other than periodontal disease.

[0102] (When two or more species of bacteria are present) Composition data on the intestinal microbiota were obtained from fecal samples of 170,886 dogs in the same manner as described above. For each individual, the presence or absence of insurance claims due to periodontal disease and the presence or absence of the first 20 bacteria listed in Figure 4 were confirmed. Figure 5 shows a graph showing the relationship between age and the proportion of insurance claims due to periodontal disease for individuals with two or more species of the first 20 bacteria listed in Figure 4, individuals with one species, and individuals with no species. As is clear from Figure 5, the incidence rate of periodontal disease was higher in individuals with two or more species of the bacteria listed in Figure 4 than in individuals with one species.

[0103] (Loss Rate) Figure 6 shows a graph of the relationship between the presence or absence of the bacteria listed in Figure 4 and the loss rate for the same 170,886 dogs as above, up to the 20th bacteria from the top of the table. As is clear from Figure 6, there is a tendency for the loss rate to be high in dogs carrying the bacteria listed in Figure 4.

[0104] (Shannon Index) The same 170,886 dogs as above were classified into those harboring two or more species of the bacteria listed in Figure 4 (the top 20 of the table), those harboring one species, and those harboring no species. Figure 7 shows a graph of the relationship between age and the Shannon Index for each group. The Shannon-Wiener diversity index was calculated using QIIME2. As is clear from Figure 7, individuals harboring the bacteria listed in Figure 4, especially those harboring two or more species, tended to have a low Shannon Index and a low diversity of intestinal bacterial flora.

[0105] (Various Diseases) For the same 170,886 dogs as above, graphs showing the relationship between the presence or absence of the 20 bacteria from the top of the table listed in Figure 4 and various diseases are shown in Figures 8 to 14. As a result, it can be seen that individuals carrying even one of the bacteria listed in Figure 4 tend to have a high incidence of digestive system diseases (including inflammatory bowel disease), respiratory system diseases, and eye diseases in all age groups, and also a high incidence of systemic diseases and blood / immune diseases in many age groups.

[0106] For diseases for which differences in prevalence are difficult to discern based solely on the information on the presence of the first 20 bacteria listed in Figure 4 , the prevalence trends were confirmed based on the information on the presence of all bacteria listed in Figure 4 (up to 72). Specifically, the same 170,886 dogs as above were classified into those harboring 13 or more species of bacteria listed in Figure 4 , those harboring 5 to 12 species, and those harboring 4 or fewer species. For each group, graphs of the relationship between age and the prevalence of cancer, valvular heart disease, and liver, biliary tract, and pancreatic diseases are shown in Figures 15 to 17 . Because there were few cases of liver, biliary tract, and pancreatic diseases, these diseases were treated as a single disease for analysis. As is clear from Figures 15 to 17 , individuals harboring 13 or more species of bacteria listed in Figure 4 tended to have higher prevalence of cancer, valvular heart disease, and liver, biliary tract, and pancreatic diseases.

[0107] Example 2 (DNA extraction from fecal samples) Fecal samples were collected from each dog and DNA was extracted in the same manner as above.

[0108] (Injury Rate in the Year Following Fecal Sample Collection) The intestinal microbiota of 108,058 dogs was analyzed from fecal samples in the same manner as described above, and the presence or absence of one or more of the 20 specific bacteria (the 20 bacteria listed in Figure 4 from the top of the table) was examined. Next, the insurance claim data was used to examine whether or not each individual had filed an insurance claim one year after the fecal sample was collected. The injury rate was calculated and plotted for groups of dogs that had one or more of the 20 specific bacteria and groups that had none of the 20 specific bacteria. The results are shown in Figure 20. The graph shows each group divided into age groups: 1-3 years, 4-6 years, 7-9 years, and 10-12 years. The injury rate is expressed as (the number of dogs for which insurance claims were filed within a certain period) / (the number of dogs insured during a certain period). In this example, the total number of each group is used as the denominator, and the number of dogs within that group that had insurance claims is used as the numerator.

[0109] 20, the group that had one or more of the 20 specific bacteria had a higher accident rate than the group that did not, indicating a higher probability of contracting a disease within a certain period after collection of the fecal sample. This supports the idea that the presence or absence of specific bacteria can be used to predict the onset of a disease.

[0110] (Relationship with the Diversity of the Gut Microbiota) Next, the Shannon Index of the same population of individuals was examined, and the individuals were divided into high, medium, and low Shannon Index groups. For each age group, and for the presence or absence of one or more of 20 specific bacteria, the presence or absence of insurance claims one year later was examined, and the accident rate was calculated and graphed. The results are shown in Figure 21. In Figure 21, the leftmost graph represents the group with a low Shannon Index (less than 3.55), the middle graph represents the group with a medium Shannon Index (3.55 or more but less than 4.25), and the rightmost graph represents the group with a high Shannon Index (4.25 or more).

[0111] 21, it can be seen that the higher the Shannon Index, the lower the accident rate in the following year. Therefore, it can be seen that by using the Shannon Index in addition to the presence or absence of specific bacteria, it is possible to make more accurate predictions of disease onset.

[0112] Example 3 (DNA extraction from fecal samples) Fecal samples were collected from each dog and DNA was extracted in the same manner as above.

[0113] (Periodontal Disease Incidence Rate in the Year Following Fecal Sample Collection) The intestinal microbiota of 108,058 dogs was analyzed from fecal samples in the same manner as described above to determine whether or not they had specific bacteria. Next, the insurance claim data was used to determine whether or not each individual had periodontal disease one year after fecal sample collection. The periodontal disease incidence rates were calculated and graphed for the groups that had one or more of the 20 specific bacteria and the groups that had none of the 20 specific bacteria. The results are shown in Figure 22. The graph shows each population divided into age groups: 1-3 years, 4-6 years, 7-9 years, and 10-12 years. The periodontal disease incidence rate was calculated as (the number of dogs in the population for which an insurance claim was filed due to periodontal disease) / (the total number of dogs in the population).

[0114] 22, the group that possessed specific bacteria had a higher incidence of periodontal disease than the group that did not, indicating a high probability of developing periodontal disease within a certain period after collection of the fecal sample. This supports the idea that the presence or absence of specific bacteria can be used to predict the onset of periodontal disease.

[0115] (Relationship with the Diversity of the Intestinal Microbiota) Next, the Shannon Index of the same population was examined, and the individuals were divided into high, medium, and low Shannon Index groups. For each age group, the presence or absence of one or more of the 20 specific bacteria species was examined for the presence or absence of insurance claims one year later, and the periodontal disease incidence rate was calculated and graphed. The results are shown in Figure 23. In Figure 23, the leftmost graph represents the group with a low Shannon Index (less than 3.55), the middle graph represents the group with a medium Shannon Index (3.55 or more and less than 4.25), and the rightmost graph represents the group with a high Shannon Index (4.25 or more).

[0116] 23 shows that the group that does not have the specific bacteria has a low incidence of periodontal disease the following year, and furthermore, even among the group that does not have the specific bacteria, those with a high Shannon index have a low incidence of periodontal disease the following year. Therefore, it can be seen that by using the Shannon index in addition to the presence or absence of specific bacteria in the prediction, it is possible to make a more accurate prediction of the incidence of periodontal disease.

[0117] Example 4 (DNA Extraction from Fecal Samples) In the same manner as above, fecal samples were collected from 1,156 dogs diagnosed with chronic kidney disease, and DNA was extracted from them.

[0118] (Mortality Rate Within One Year of Fecal Sample Collection) The intestinal flora of the 1,156 dogs was analyzed from fecal samples in the same manner as described above, and the presence or absence of one or more of the 20 specific bacteria was examined. Next, the insurance claim data was used to examine whether or not each individual had died within one year of fecal sample collection. The mortality rates within a given period were calculated and plotted for the groups of individuals that had one or more of the 20 specific bacteria and the groups that did not have the specific bacteria. The results are shown in Figure 24. The graph shows each population divided into age groups: 0-10 years, 11-13 years, 14-15 years, and 16 years and older. The mortality rate was calculated as (the number of individuals in that population who died within a given period) / (the total number of individuals in that population).

[0119] 24, it can be seen that the mortality rate of dogs with chronic kidney disease that carried specific bacteria was higher than that of dogs that did not, and that there was a high probability of death within a certain period after fecal sample collection. This supports the idea that the presence or absence of specific bacteria can be used to predict mortality.

[0120] Example 5 (DNA extraction from fecal samples) In the same manner as above, fecal samples were collected from 1,159 cats diagnosed with chronic kidney disease, and DNA was extracted from them.

[0121] (Mortality Rate Within One Year of Fecal Sample Collection) The intestinal flora of the 1,159 cats was analyzed from fecal samples in the same manner as described above, and the presence or absence of one or more of the 20 specific bacteria was examined. Next, the insurance claim data was used to examine whether each cat had died within one year of fecal sample collection. The mortality rates within a given period were calculated and plotted for cats that had one or more of the 20 specific bacteria and cats that did not have the specific bacteria. The results are shown in Figure 25. The graph shows each cat population divided into age groups: 0-2 years, 3-5 years, 6-9 years, and 10-13 years. The mortality rate was calculated as (the number of cats that died within a given period within that population) / (the total number of cats in that population).

[0122] 25, it can be seen that the mortality rate of cats with chronic kidney disease that carried specific bacteria was higher than that of cats that did not, and that they were more likely to die within a certain period after fecal sample collection. This supports the idea that the presence or absence of specific bacteria can be used to predict mortality.

[0123] Example 6 (DNA Extraction from Fecal Samples) As described above, fecal samples were collected from each dog (unless otherwise specified, the breed of dog was not limited), and DNA was extracted. As described above, the composition of the intestinal microbiota was examined using a next-generation sequencer. Each dog was covered by pet insurance, and information identifying the individual and whether or not an insurance claim had been filed was registered in the pet insurance database (insurance claim database). The intestinal microbiota data for each dog was linked to the information for each individual in the database. Therefore, by examining the intestinal microbiota data of a certain individual, it is possible to confirm the presence or absence of specific bacteria, and at the same time, it is possible to confirm whether an insurance claim had been filed for that individual and the reason for the claim.

[0124] (Prevalence of Oral Tumors) The intestinal flora of 154,888 dogs enrolled in pet insurance was analyzed from fecal samples in the same manner as described above to determine whether or not they had one or more of the 20 specific bacteria (the bacteria listed in Figure 4, up to the 20th bacteria from the top of the table). Next, the presence or absence of oral tumors was determined from insurance claim data (a pet insurance database managed and operated by the applicant and its affiliates), and the prevalence of oral tumors was calculated for groups that had one, two, or three or more of the 20 specific bacteria, and groups that had none of the 20 specific bacteria. The results are shown in Figure 26.

[0125] 26, it was found that the prevalence of oral tumors within a given period increases when one or more specific bacteria are present. In particular, the increase in the prevalence of oral tumors was significant when two or more specific bacteria were present.

[0126] (Prevalence of Neoplastic Diseases) The intestinal flora of 154,888 dogs enrolled in pet insurance was analyzed from fecal samples in the same manner as described above to determine whether or not they possessed one or more of the 20 specific bacteria (the 20 bacteria listed from the top of the table in Figure 4). Next, the insurance claim data was used to determine whether or not the dogs had a neoplastic disease, and the prevalence of neoplastic diseases was calculated for groups that possessed one of the 20 specific bacteria, groups that possessed two types, groups that possessed three or more types, and groups that possessed none of the 20 specific bacteria. The results are shown in Figure 27.

[0127] From FIG. 27, it was found that the prevalence of neoplastic diseases within a given period increases when one or more specific bacteria are present.

[0128] (Prevalence of Chronic Kidney Disease) The intestinal flora of 154,888 dogs enrolled in pet insurance was analyzed from fecal samples in the same manner as described above to determine whether or not they had one or more of the 20 specific bacteria (the 20 bacteria listed from the top of the table in Figure 4). Next, the insurance claim data was used to determine whether or not the dogs had chronic kidney disease, and the prevalence of chronic kidney disease was calculated for groups that had one, two, or three or more of the 20 specific bacteria, and groups that had none of the 20 specific bacteria. The results are shown in Figure 28.

[0129] From Figure 28, it was found that the prevalence of chronic kidney disease increases when one or more specific bacteria are present.

[0130] (Mortality Rate) The intestinal flora of 154,888 dogs enrolled in pet insurance was analyzed from fecal samples in the same manner as described above to determine whether or not they possessed one or more of the 20 specific bacteria species (the 20 bacteria from the top of the table listed in Figure 4). Next, the insurance claim data was used to determine whether or not each individual died within one year of collection of the fecal sample, and the mortality rate was calculated for groups that possessed one of the 20 specific bacteria species, groups that possessed two species, groups that possessed three or more species, and groups that possessed none of the 20 specific bacteria species. The results are shown in Figure 29.

[0131] From FIG. 29, it was found that the mortality rate within a given period increases when one or more specific bacteria are present.

[0132] (Bad Breath) The intestinal flora of 154,888 dogs enrolled in pet insurance was analyzed from fecal samples in the same manner as described above to determine whether or not they possessed one or more of the 20 specific bacteria (the 20 bacteria listed in Figure 4 from the top of the table). Next, a questionnaire was given to the insured dogs (whether they were concerned about the bad breath of the insured dog) to determine the quality of the bad breath at the time of fecal sample collection for each dog. The proportion of dogs with bad breath that were concerned was calculated for groups with one of the 20 specific bacteria, groups with two, groups with three or more, and groups with none of the 20 specific bacteria. The results are shown in Figure 30.

[0133] From FIG. 30, it was found that possession of one or more specific bacteria tends to result in poor breath quality.

[0134] (Coat Shine) The intestinal flora of 154,888 dogs enrolled in pet insurance was analyzed from fecal samples in the same manner as described above to determine whether or not they possessed one or more of the 20 specific bacteria (the bacteria listed in Figure 4, numbered from the top 20 in the table). Next, based on a questionnaire given to insured dogs (whether or not they were concerned about the coat shine of the insured dog), the quality of coat shine at the time of fecal sample collection for each dog was examined, and the proportion of dogs with coat shine that was a concern was calculated for groups with one, two, or three or more of the 20 specific bacteria, and groups with none of the 20 specific bacteria. The results are shown in Figure 31.

[0135] From FIG. 31, it was found that the presence of one or more specific bacteria tends to result in poor coat gloss.

[0136] (Whether or not dogs are afraid of strange animals) The intestinal flora of 154,888 dogs enrolled in pet insurance was analyzed from fecal samples in the same manner as described above to determine whether or not they possessed one or more of the 20 specific bacteria (the 20 bacteria listed from the top of the table in Figure 4). Next, a questionnaire was given to the insured dogs (whether or not the insured dogs were afraid of strange animals) to determine whether or not each individual was afraid of strange animals. The proportion of dogs that were afraid of strange animals was calculated for groups that possessed one of the 20 specific bacteria, groups that possessed two types, groups that possessed three or more types, and groups that possessed none of the 20 specific bacteria. The results are shown in Figure 32.

[0137] Figure 32 shows that people who have one or more specific bacteria tend to be afraid of strange animals. This is thought to be because, rather than being an innate personality trait, people's mental state is not so good as a result of having a poor intestinal flora, which can cause them to feel anxious or timid, leading to an aversion to strange animals.

[0138] (Whether or not dogs dislike strangers) The intestinal flora of 154,888 dogs enrolled in pet insurance was analyzed from fecal samples in the same manner as above to determine whether or not they possessed one or more of the 20 specific bacteria (the 20 bacteria listed from the top of the table in Figure 4). Next, a questionnaire was sent to policyholders (whether or not the insured dogs disliked strangers), and each individual dog was examined for its dislike of strangers. The proportion of dogs disliked by strangers was calculated for groups that possessed one of the 20 specific bacteria, groups that possessed two, groups that possessed three or more, and groups that possessed none of the 20 specific bacteria. The results are shown in Figure 33.

[0139] Figure 33 shows that people who have one or more specific bacteria tend to dislike strangers. This is thought to be because, rather than being an innate personality trait, a poor state of intestinal flora can lead to a poor mental state, which can cause anxiety and timidity, leading to a dislike of strangers.

[0140] Example 7 (DNA extraction from fecal samples) Fecal samples were collected from each cat and DNA was extracted in the same manner as above.

[0141] (Prevalence of Vascular and Hematopoietic Diseases) The intestinal flora of 59,627 cats enrolled in pet insurance was analyzed from fecal samples in the same manner as described above, and the presence or absence of one or more of the 20 specific bacteria (the 20 bacteria listed from the top of the table in Figure 4) was examined. Next, the presence or absence of vascular and hematopoietic diseases was examined from insurance claim data, and the prevalence of vascular and hematopoietic diseases was calculated for groups that possessed one, two, or three or more of the 20 specific bacteria, and groups that possessed none of the 20 specific bacteria. The results are shown in Figure 34.

[0142] Figure 34 shows that the prevalence of vascular and hematopoietic diseases increases when one or more specific bacteria are present. In particular, the prevalence of vascular and hematopoietic diseases increases significantly when three or more specific bacteria are present.

[0143] (Prevalence of Chronic Kidney Disease) The intestinal flora of 59,627 cats enrolled in pet insurance was analyzed from fecal samples in the same manner as described above to determine whether or not they possessed one or more of the 20 specific bacteria species (the 20 bacteria from the top of the table listed in Figure 4). Next, the insurance claim data was used to determine whether or not the cats had chronic kidney disease, and the prevalence of chronic kidney disease was calculated for groups that possessed one of the 20 specific bacteria species, groups that possessed two species, groups that possessed three or more species, and groups that possessed none of the 20 specific bacteria species. The results are shown in Figure 35.

[0144] From Figure 35, it was found that the prevalence of chronic kidney disease increases when one or more specific bacteria are present.

[0145] (Prevalence of Neoplastic Diseases) The intestinal flora of 59,627 cats enrolled in pet insurance was analyzed from fecal samples in the same manner as described above to determine whether or not they possessed one or more of the 20 specific bacteria (the 20 bacteria listed in Figure 4 from the top of the table). Next, the insurance claim data was used to determine whether or not the cats had a neoplastic disease, and the prevalence of neoplastic diseases was calculated for groups that possessed one of the 20 specific bacteria, groups that possessed two types, groups that possessed three or more types, and groups that possessed none of the 20 specific bacteria. The results are shown in Figure 36.

[0146] From FIG. 36, it was found that the prevalence of tumor diseases increases when one or more specific bacteria are present.

[0147] (Mortality Rate) The intestinal flora of 59,627 cats enrolled in pet insurance was analyzed from fecal samples in the same manner as described above to determine whether or not they possessed one or more of the 20 specific bacteria species (the 20 bacteria from the top of the table listed in Figure 4). Next, the insurance claim data was used to determine whether or not each cat had died within one year of fecal sample collection, and the mortality rate was calculated for groups that possessed one of the 20 specific bacteria species, groups that possessed two species, groups that possessed three or more species, and groups that possessed none of the 20 specific bacteria species. The results are shown in Figure 37.

[0148] From FIG. 37, it was found that the mortality rate within a given period increases when one or more specific bacteria are present.

[0149] (Coat Shine) The intestinal flora of 59,627 cats enrolled in pet insurance was analyzed from fecal samples in the same manner as described above to determine whether or not they possessed one or more of the 20 specific bacteria (the bacteria listed in Figure 4, numbered from the top 20 in the table). Next, based on a questionnaire given to insured cats (whether or not they were concerned about the coat shine of the insured cats), the coat shine of each cat at the time of fecal sample collection was examined, and the proportion (rate) of cats with coat shine concerns was calculated for cats with one, two, or three or more of the 20 specific bacteria. The results are shown in Figure 38.

[0150] From FIG. 38, it was found that the presence of one or more specific bacteria tends to result in poor coat gloss.

[0151] (Bad Breath) The intestinal flora of 59,627 cats enrolled in pet insurance was analyzed from fecal samples in the same manner as above to determine whether or not they possessed one or more of the 20 specific bacteria (the bacteria listed in Figure 4, numbered from the top 20 in the table). Next, a questionnaire was administered to the insured cats (whether or not they were bothered by the bad breath of the insured cats). The good or bad breath of each cat at the time of fecal sample collection was investigated, and the proportion (rate) of cats with bad breath that were bothersome was calculated for cats with one, two, or three or more of the 20 specific bacteria. The results are shown in Figure 39.

[0152] From FIG. 39, it was found that possession of one or more specific bacteria tends to result in poor breath quality.

[0153] Example 8 (Oral Tumors) For 20,363 dogs enrolled in pet insurance (the breeds selected were American Cocker Spaniel, Corgi Penguin, Golden Retriever, Shetland Sheepdog, Bernese Mountain Dog, Beagle, French Bulldog, Boston Terrier, Miniature Schnauzer, and Labrador Retriever. These breeds are among the top 30 most popular breeds listed in the Anicom Household Animal White Paper 2023, and are those with a high rate of insurance claims for neoplastic diseases), the intestinal flora was analyzed from fecal samples in the same manner as described above, and the presence or absence of one or more of the 20 specific bacteria (the bacteria listed in Figure 4, ranked 20 from the top) was examined. Next, the insurance claim data was used to determine whether or not there had been any insurance claims for oral tumors, and the rate of insurance claims for oral tumors was examined for each age group for individuals with and without one or more specific bacteria. The results are shown in Figure 40.

[0154] Figure 40 shows that the prevalence of oral tumors increases when one or more specific bacteria are present. The reason why no difference in the prevalence of oral tumors was observed between the ages of 0 and 2 is thought to be because the prevalence of oral tumors in this age group is low and there is little data.

[0155] [Example 9] (Tumors of the blood and hematopoietic organs) The intestinal flora was analyzed from fecal samples in the same manner as above for 19,598 dogs enrolled in pet insurance (the dog breeds selected were American Cocker Spaniel, Corgi Penn Glove, Golden Retriever, Shetland Sheepdog, Bernese Mountain Dog, Beagle, French Bulldog, Boston Terrier, Miniature Schnauzer, and Labrador Retriever. These dog breeds are among the 30 most popular dog breeds in the Anicom Household Animal White Paper 2023 and have a high rate of insurance claims for neoplastic diseases), and the presence or absence of one or more of 20 specific bacteria (the bacteria listed in Figure 4, ranked up to the 20th from the top of the table) was examined. Next, we investigated the insurance claim data for the presence or absence of claims for tumors of the blood and hematopoietic organs (multicentric lymphoma, angiosarcoma, tumors of lymphoid and hematopoietic tissues), and investigated the insurance claim rates for blood and hematopoietic organs for each age group for individuals with and without one or more specific bacteria. The results are shown in Figure 41.

[0156] Figure 41 shows that the prevalence of blood and hematopoietic organ tumors increases when one or more specific bacteria are present. The reason why no difference in the incidence of blood and hematopoietic organ tumors was observed between the ages of 0 and 2 is likely due to the low prevalence of blood and hematopoietic organ tumors in this age group and the lack of data.

[0157] Example 10 (Tumors Excluding Oral Tumors) 20,363 dogs enrolled in pet insurance (the breeds selected were American Cocker Spaniel, Corgi Penguin, Golden Retriever, Shetland Sheepdog, Bernese Mountain Dog, Beagle, French Bulldog, Boston Terrier, Miniature Schnauzer, and Labrador Retriever. These breeds are among the 30 most popular breeds listed in the Anicom Household Animal White Paper 2023, and are those with a high rate of insurance claims for neoplastic diseases) were analyzed for intestinal flora from fecal samples in the same manner as described above, and the presence or absence of one or more of 20 specific bacteria (the 20 bacteria listed in Figure 4 from the top of the table) was examined. Next, the insurance claim data was examined for the presence or absence of insurance claims for tumors excluding oral tumors, and the insurance claim rate for tumors (excluding oral tumors) was examined for each age group for individuals with and without one or more specific bacteria. The results are shown in Figure 42.

[0158] Figure 42 shows that the prevalence of tumors (excluding oral tumors) increases when one or more specific bacteria are present.

[0159] Example 11 (Allergic Dermatitis) The intestinal flora of 13,582 dogs (dog breeds: Kishu, Kai, Shikoku, Shiba (including Mameshiba), Akita, Tosa, Hokkaido, and Ryukyu) enrolled in pet insurance was analyzed from fecal samples in the same manner as described above, and the presence or absence of one or more of 20 specific bacteria (the 20 bacteria listed from the top of the table in Figure 4) was examined. Next, the insurance claim data was used to examine the presence or absence of insurance claims due to allergic dermatitis, and the insurance claim rate for allergic dermatitis was examined for each age group for individuals with and without one or more specific bacteria. The results are shown in Figure 43.

[0160] From FIG. 43, it was found that the prevalence of allergic dermatitis increases when one or more specific bacteria are present.

[0161] Example 12 (Atopic Dermatitis) The intestinal flora of 16,558 dogs (dog breeds: Kishu, Kai, Shikoku, Shiba (including Mameshiba), Akita, Tosa, Hokkaido, and Ryukyu) enrolled in pet insurance was analyzed from fecal samples in the same manner as described above, and the presence or absence of one or more of the 20 specific bacteria (the 20 bacteria listed from the top of the table in Figure 4) was examined. Next, the insurance claim data was examined for the presence or absence of insurance claims due to atopic dermatitis, and the rate of insurance claims for atopic dermatitis was examined for each age group for individuals with and without one or more specific bacteria. The results are shown in Figure 44.

[0162] From FIG. 44, it was found that the prevalence of atopic dermatitis increases when one or more specific bacteria are present.

[0163] Example 13 (Gastritis, Gastroenteritis, or Enteritis) The intestinal flora of 170,886 dogs enrolled in pet insurance was analyzed from fecal samples in the same manner as described above to determine whether or not they had one or more of the 20 specific bacteria (the 20 bacteria listed from the top of the table in Figure 4). Next, the insurance claim data was used to determine whether or not there had been any insurance claims for gastritis, gastroenteritis, or enteritis, and the insurance claim rates for gastritis, gastroenteritis, or enteritis were determined for each age group for groups of dogs that had one or more specific bacteria and groups that did not have them. The results are shown in Figure 45.

[0164] From FIG. 45, it was found that the prevalence of gastritis, gastroenteritis, or enteritis increases when one or more specific bacteria are present.

[0165] Example 14 (Pancreatitis) The intestinal flora of 186,654 dogs enrolled in pet insurance was analyzed from fecal samples in the same manner as described above to determine whether or not they possessed one or more of the 20 specific bacteria species (the 20 bacteria from the top of the table in Figure 4). Next, the insurance claim data was used to determine whether or not there had been any insurance claims due to pancreatitis, and the rate of insurance claims due to pancreatitis was determined for each age group for groups of dogs that possessed one or more specific bacteria species and groups that did not. The results are shown in Figure 46.

[0166] From Figure 46, it was found that the prevalence of pancreatitis increases when one or more specific bacteria are present.

[0167] Example 15 (Biliary Sludge Disease) The intestinal flora of 186,654 dogs enrolled in pet insurance was analyzed from fecal samples in the same manner as described above to determine whether or not they had one or more of the 20 specific bacteria species (the 20 bacteria from the top of the table in Figure 4). Next, the insurance claim data was used to determine whether or not there had been any insurance claims for biliary sludge disease, and the insurance claim rates for biliary sludge disease were examined for each age group for groups of dogs that had one or more specific bacteria species and groups that did not have them. The results are shown in Figure 47.

[0168] From Figure 47, it was found that the prevalence of biliary sludge increases when one or more specific bacteria are present.

[0169] Example 16 (Epilepsy) The intestinal flora of 29,763 dogs (Chihuahuas and Italian Greyhounds) enrolled in pet insurance was analyzed from fecal samples in the same manner as described above to determine whether or not they possessed one or more of the 20 specific bacteria species (the 20 bacteria listed in Figure 4 from the top of the table). Next, the insurance claim data was used to determine whether or not there had been any insurance claims due to epilepsy, and the epilepsy claim rates were determined for each age group for individuals with and without one or more specific bacteria species. The results are shown in Figure 48.

[0170] Figure 48 shows that the prevalence of epilepsy increases when one or more specific bacteria are present.

[0171] Example 17 (Chronic kidney disease) For 56,987 dogs enrolled in pet insurance (dog breeds: Welsh Corgi Penglow, Cavalier King Charles Spaniel, Golden Retriever, Shih Tzu, Shetland Sheepdog, Bernese Mountain Dog, Jack Russell Terrier, Pug, Papillon, Beagle, Bichon Frise, French Bulldog, Pekingese, Border Collie, Maltese, Miniature Schnauzer, Yorkshire Terrier, Labrador Retriever, Shiba Inu, and Japanese Spitz. These dog breeds are among the 30 most popular dog breeds listed in the Anicom Household Animal White Paper 2023, and have a high rate of insurance claims for urinary system diseases), the intestinal flora was analyzed from fecal samples in the same manner as above, and the presence or absence of one or more of 20 specific bacteria (the bacteria listed in Figure 4, ranked 20 from the top of the table) was examined. Next, we investigated the presence or absence of claims for chronic kidney disease from the insurance claim data, and investigated the claim rates for chronic kidney disease for each age group for individuals with and without one or more specific bacteria. The results are shown in Figure 49.

[0172] Figure 49 shows that the prevalence of chronic kidney disease increases when one or more specific bacteria are present.

[0173] Example 18 (Oral Tumor) The intestinal flora of 56,011 cats enrolled in pet insurance was analyzed from fecal samples in the same manner as described above to determine whether or not they had one or more of the 20 specific bacteria (the 20 bacteria listed in Figure 4 from the top of the table). Next, the insurance claim data was used to determine whether or not there had been any insurance claims due to oral tumors, and the rate of insurance claims for oral tumors was determined for groups of cats that had one or more specific bacteria and groups of cats that did not have them. The results are shown in Figure 50.

[0174] From Figure 50, it was found that the incidence of oral tumors increases when one or more specific bacteria are present.

[0175] Example 19 (Chronic Kidney Disease) The intestinal flora of 70,733 cats enrolled in pet insurance was analyzed from fecal samples in the same manner as described above to determine whether or not they possessed one or more of the 20 specific bacteria species (the 20 bacteria from the top of the table in Figure 4). Next, the insurance claim data was used to determine whether or not there had been any insurance claims due to chronic kidney disease, and the rate of insurance claims for chronic kidney disease was determined for each age group for cats with and without one or more specific bacteria species. The results are shown in Figure 51.

[0176] Figure 51 shows that the prevalence of chronic kidney disease increases when one or more specific bacteria are present.

[0177] [Example 20] (Diabetes) The intestinal flora of 70,007 cats enrolled in pet insurance was analyzed from fecal samples in the same manner as described above to determine whether or not they had one or more of the 20 specific bacteria species (the 20 bacteria from the top of the table in Figure 4). Next, the insurance claim data was used to determine whether or not there had been any insurance claims due to diabetes, and the diabetes insurance claim rates were determined for each age group for populations that had and did not have one or more specific bacteria species. The results are shown in Figure 52.

[0178] From FIG. 52, it was found that the prevalence of diabetes increases when one or more types of specific bacteria are present.

[0179] Example 21 (Pancreatitis) The intestinal flora of 66,044 cats enrolled in pet insurance was analyzed from fecal samples in the same manner as described above to determine whether or not they possessed one or more of the 20 specific bacteria species (the 20 bacteria from the top of the table in Figure 4). Next, the insurance claim data was used to determine whether or not there had been any insurance claims due to pancreatitis, and the rate of insurance claims due to pancreatitis was determined for each age group for cats that possessed one or more specific bacteria species and those that did not. The results are shown in Figure 53.

[0180] From Figure 53, it was found that the prevalence of pancreatitis increases when one or more specific bacteria are present.

[0181] [Reference Example 1] (Stomatitis and Oral Tumors) For 668,423 dogs enrolled in pet insurance, the group that had a claim for stomatitis within one year of enrollment and the group that had not had a claim for stomatitis within one year of enrollment were examined using an insurance claim database to determine whether or not there had been a claim for oral tumors within one year of enrollment, within two years of enrollment, and within three years of enrollment, respectively, to examine the rate of claims for oral tumors. Furthermore, each group was divided by age group at the time of enrollment, and the rate of claims for oral tumors was graphed. The results are shown in Figure 54.

[0182] As is clear from Figure 54, the proportion of individuals with stomatitis who had oral tumors was higher in each age group. Furthermore, as the age group increased, the difference in the prevalence of oral tumors between the stomatitis group and the non-stomatitis group became larger.

[0183] [Reference Example 2] (Oral Tumors and Current Year Mortality Rate) Using an insurance claim database, 410,529 dogs enrolled in pet insurance were divided into a group for which an insurance claim was made due to oral tumors (oral tumor affected group) and a group for which an insurance claim was made due to a disease other than oral tumors (no group). For each group, we investigated whether any of the dogs died in the year in which the insurance claim was made, and calculated the mortality rate, which was then plotted as a graph. The results are shown in Figure 55.

[0184] As is clear from Figure 55, the mortality rate of the group of animals suffering from oral tumors was higher than that of the groups of animals suffering from other diseases. This indicates that oral tumors increase the mortality rate of animals.

[0185] Example 22 (Specific Bacteria and Stomatitis) The intestinal flora of 154,888 dogs enrolled in pet insurance was analyzed from fecal samples in the same manner as described above to determine whether or not they had one or more of 20 specific bacteria (the 20 bacteria listed in Figure 4 from the top of the table). Next, the insurance claim data was used to determine whether or not there had been any insurance claims due to stomatitis, and the insurance claim rates for stomatitis were examined for each age group for groups of dogs that had one or more specific bacteria and groups that did not have them. The results are shown in Figure 56.

[0186] Figure 56 shows that the prevalence of stomatitis increases when one or more specific bacteria are present. This indicates that oral care can prevent the onset of stomatitis by sterilizing, inhibiting, inactivating, or removing specific bacteria, which can ultimately lead to the prevention of oral tumors and mortality.

[0187] Reference Example 3 (Stomatitis and Oral Tumors) Using an insurance claims database, 668,423 dogs (0 to 11 years old) enrolled in pet insurance were divided into a group of dogs that had made insurance claims due to stomatitis (with stomatitis) and a group of dogs that had not made insurance claims due to stomatitis (without stomatitis), and each group was examined to determine whether or not they had made insurance claims due to oral tumors. The rate of insurance claims due to oral tumors for each group was examined and graphed. The results are shown in Figure 57.

[0188] As is clear from Figure 57, the group of individuals with stomatitis had a higher incidence of oral tumors than the group without stomatitis. A chi-square test was performed to confirm the significant difference in the insurance claim rates for oral tumors between the two groups (p<0.0001).

[0189] [Reference Example 4] (Stomatitis and Oral Tumors) For 208,150 cats enrolled in pet insurance, the group that had filed a claim for stomatitis within one year of enrollment and the group that had not filed a claim for stomatitis within one year of enrollment were examined using an insurance claim database to determine whether or not an insurance claim for oral tumors had occurred within one year, two years, or three years of enrollment, respectively, and the rate of insurance claims for oral tumors was examined. Furthermore, each group was divided by age group at the time of enrollment, and the rate of insurance claims for oral tumors was graphed. The results are shown in Figure 58.

[0190] As is clear from Figure 58, the proportion of individuals with stomatitis who had oral tumors was higher in each age group. Furthermore, as the age group increased, the difference in the prevalence of oral tumors between the stomatitis group and the non-stomatitis group became larger.

[0191] [Reference Example 5] (Stomatitis and Current Year Mortality Rate) Using an insurance claims database, 95,236 cats enrolled in pet insurance were divided into a group of cats for which insurance claims were filed due to stomatitis (stomatitis group) and a group of cats for which insurance claims were filed due to diseases other than stomatitis (no group), and further divided into age groups. After investigating whether each group died in the year in which the insurance claim was filed, the mortality rate was calculated and plotted as a graph. The results are shown in Figure 59.

[0192] As is clear from Figure 59, the mortality rate of cats suffering from stomatitis was higher than that of cats suffering from other diseases. This indicates that stomatitis in cats has a poor prognosis and increases mortality. Possible reasons for this include death during treatment of the stomatitis, before it progresses to oral tumors, or before a diagnosis can be made.

[0193] [Reference Example 6] (Oral Tumors and Current-Year Mortality Rate) Using an insurance claim database, 90,346 cats enrolled in pet insurance were divided into a group of cats for which insurance claims were filed due to oral tumors (oral tumor affected group) and a group of cats for which insurance claims were filed due to diseases other than oral tumors (no group), and further divided into age groups. After investigating whether each group died in the year in which the insurance claim was filed, the mortality rate was calculated and plotted as a graph. The results are shown in Figure 60.

[0194] As is clear from Figure 60, the mortality rate of the group of individuals suffering from oral tumors was higher than that of the groups of individuals suffering from other diseases. This shows that oral tumors have a poor prognosis and increase the mortality rate.

[0195] Example 23 (Specific Bacteria and Stomatitis) The intestinal flora of 59,627 cats enrolled in pet insurance was analyzed from fecal samples in the same manner as described above to determine whether or not they had one or more of 20 specific bacteria (the 20 bacteria listed in Figure 4 from the top of the table). Next, the insurance claim data was used to determine whether or not there had been any insurance claims due to stomatitis, and the insurance claim rates for stomatitis were examined for each age group for cats with and without one or more specific bacteria. The results are shown in Figure 61.

[0196] Figure 61 shows that the incidence of stomatitis increases when one or more specific bacteria are present. This indicates that oral care can also prevent the onset of stomatitis by sterilizing, inhibiting, inactivating, or removing specific bacteria, which can ultimately lead to the prevention of oral tumors and mortality in cats.

[0197] In addition, for the same 59,627 cats, without dividing them by age, the insurance claim rate for stomatitis was calculated and graphed for the presence or absence of specific bacteria and the number of species carried. The results are shown in Figure 62. Figure 62 shows that the incidence rate of stomatitis increases significantly when two or more species of specific bacteria are carried.

[0198] Reference Example 7 (Stomatitis and Oral Tumors) Using an insurance claims database, 218,050 cats (0-11 years old) enrolled in pet insurance were divided into a group of cats that had made insurance claims due to stomatitis (with stomatitis) and a group of cats that had not made insurance claims due to stomatitis (without stomatitis), and each group was examined to determine whether or not they had made insurance claims due to oral tumors. The rate of insurance claims due to oral tumors for each group was examined and graphed. The results are shown in Figure 63.

[0199] As is clear from Figure 63, the group with stomatitis had a higher incidence of oral tumors than the group without stomatitis. A chi-square test was performed to confirm the difference in the insurance claim rates for oral tumors between the two groups, and it was confirmed that there was a significant difference (p<0.001).

[0200] Reference Example 8 (Stomatitis and Oral Tumors in the Previous Year) Using an insurance claims database, 220,370 cats (ages 0-16) enrolled in pet insurance were divided into a group of cats that had received an insurance claim due to stomatitis (with stomatitis) and a group of cats that had not received an insurance claim due to stomatitis (without stomatitis). For each group, we investigated whether there had been an insurance claim due to oral tumors in the year following the year in which there had been an insurance claim due to stomatitis. The rate of insurance claims due to oral tumors for each group was investigated and plotted as a graph. The results are shown in Figure 64.

[0201] As is clear from Figure 64, the group of individuals suffering from stomatitis had a higher incidence of oral tumors the following year than the group of individuals without stomatitis. Furthermore, a chi-square test was performed to confirm the difference in the insurance claim rates for oral tumors between the two groups, and a significant difference was found (p<0.001). This suggests that stomatitis may worsen and lead to oral tumors.

Claims

1. In the intestinal flora of animals other than humans, Streptococcus constellatus, Streptococcus anginosus, Slackia exigua, Desulfovibrio R446353, Peptostreptococcus canis, Bulleidia moorei, Actinomyces timonensis, Pauljensenia cardiffensis, Nanoperiomorbus, Fusobacterium simiae, and Corynebacterium canis are present. canis, Enterocloster bolteae, Bifidobacterium dentium, Parvimonas micra, Nanosynbacter, Flexilinea sp902786265, CAJPSE01 sp003860125, Campylobacter sp013201975, Fusobacterium russii, Actinomyces weissii, Fusobacterium canifelinum, Saccharimonas aalbogensis aalborgensis, SDRW01 sp007845485, Corynebacterium freiburgense, Globicatella, Lactobacillusdelbrueckii, Clostridium disporicum (Clostridium_T disporicum_203974), Canibacter sp003859945, Pediococcus acidilactici, Clostridium sp000821305 (Clostridium_J sp000821305), Pauljensenia canis, Nanogingivalis gingivitcus, Clostridium baratii, Enterococcus gallinarum, Cellulosilyticaceae, Gemella palaticanis, Streptococcus fryi, Escherichia ruysiae, Citrobacter gillenii (Citrobacter_A_692098 gillenii), Pseudomonas guguanensis (Pseudomonas_E_648040 guguanensis), Klebsiella pneumoniae (Klebsiella pneumoniae_718977), Enterococcus B (Enterococcus_B), Streptococcus minor, Clostridium paraputrificum (Clostridium_T paraputrificum_208099, Clostridium_T paraputrificum_207370), Clostridium dispolicum (Clostridium_T disporicum_203972), Streptococcus oralis (Streptococcus oralis_E_351036), Clostridium P (Clostridium_P), Bacillus subtilis (Bacillus_P_294101 subtilis_291504), Klebsiella varicolora (Klebsiellavariicola), Clostridioides A difficile, Terrisporobacter glycolicus 239331, Clostridium T neonatale, Bifidobacterium animalis, Terrisporobacter, Escherichia 710834, Klebsiella 724518, Escherichia fergusonii, Clostridium T saudiense, Clostridium T tertium tertium), Enterobacter hormaechei (Enterobacter_B_713587 hormaechei_712707), Bilophila wadsworthia, Clostridium isatidis (Clostridium_T isatidis), Canibacter oris, Nanosynbacter lyticus, Proteus mirabilis, Clostridium innocuum (Clostridium_AQ innocuum), CCUG-7971 spG000499525, Fusobacterium necrogenes (Fusobacterium_A necrogenes), Streptococcus, Anaerostipes cacae caccae), Enterococcus faecalis (Enterococcus_H_360604 faecalis), Bacteroides fragilis (Bacteroides_H fragilis), and Roseburia intestinalis (Roseburiaa step of determining the health status or predicting the future health status of the animal using information on whether or not the animal contains one or more bacteria selected from the group consisting of Bacillus subtilis (Bacillus intestinalis), and determining that the animal's health is impaired or there is a high risk of future impairment if the animal contains one or more bacteria selected from the group in the step.

2. The step of determining the health state of the animal or predicting its future health state is: (1) a step of determining whether the animal is suffering from a disease or predicting whether it will suffer from a disease in the future, and determining that the animal has a disease or has a high risk of suffering from a disease in the future if the intestinal flora of the animal contains one or more bacteria selected from the group; (2) a step of determining whether the animal's mental state is good or predicting whether the animal's future mental state will be good, and determining that the animal's mental state is not good or there is a high risk that the animal's mental state will be not good in the future if the intestinal flora of the animal contains one or more bacteria selected from the group; (3) a step of determining whether the animal's coat condition is good or predicting whether the animal's coat condition will be good in the future, and determining that the animal's coat condition is not good or there is a high risk that the animal's coat condition will be not good in the future if the intestinal flora of the animal contains one or more bacteria selected from the group; or (4) A method for determining or predicting the health status of an animal as described in claim 1, which is a step of determining whether the animal's halitosis is good or predicting whether the animal's halitosis will be good in the future, and if the animal's intestinal bacterial flora contains one or more bacteria selected from the group, it is determined that the animal's halitosis is not good or there is a high risk that the animal's halitosis will be bad in the future.

3. A method for determining or predicting the health state of an animal as described in claim 2, wherein the step of determining the health state of the animal or predicting the future health state of the animal is a step of determining whether the animal is suffering from a disease or predicting whether the animal will suffer from a disease in the future, and the disease is periodontal disease, valvular disease, liver disease, biliary disease, pancreatic disease, renal disease or cancer.

4. A method for determining or predicting the health state of an animal as described in claim 2, wherein the step of determining the health state of the animal or predicting the future health state of the animal is a step of determining whether the animal's mental state is good or predicting whether the animal's future mental state will be good, and whether the animal's mental state is good or not means whether the animal has become timid.

5. A method for determining or predicting the health status of an animal according to any one of claims 1 to 4, wherein the intestinal flora of the animal other than a human is derived from a fecal sample.

6. Streptococcus constellatus, Streptococcus anginosus, Slackia exigua, Desulfovibrio R446353, Peptostreptococcus canis, Bulleidia moorei, Actinomyces timonensis, Pauljensenia cardiffensis, Nanoperiomorbus, Fusobacterium simiae, Corynebacterium canis canis, Enterocloster bolteae, Bifidobacterium dentium, Parvimonas micra, Nanosynbacter, Flexilinea sp902786265, CAJPSE01 sp003860125, Campylobacter sp013201975, Fusobacterium russii, Actinomyces weissii, Fusobacterium canifelinum, Saccharimonas aalbogensis aalborgensis, SDRW01 sp007845485, Corynebacterium freiburgense, Globicatella, Lactobacillusdelbrueckii, Clostridium disporicum (Clostridium_T disporicum_203974), Canibacter sp003859945, Pediococcus acidilactici, Clostridium sp000821305 (Clostridium_J sp000821305), Pauljensenia canis, Nanogingivalis gingivitcus, Clostridium baratii, Enterococcus gallinarum, Cellulosilyticaceae, Gemella palaticanis, Streptococcus fryi, Escherichia ruysiae, Citrobacter gillenii (Citrobacter_A_692098 gillenii), Pseudomonas guguanensis (Pseudomonas_E_648040 guguanensis), Klebsiella pneumoniae (Klebsiella pneumoniae_718977), Enterococcus B (Enterococcus_B), Streptococcus minor, Clostridium paraputrificum (Clostridium_T paraputrificum_208099, Clostridium_T paraputrificum_207370), Clostridium dispolicum (Clostridium_T disporicum_203972), Streptococcus oralis (Streptococcus oralis_E_351036), Clostridium P (Clostridium_P), Bacillus subtilis (Bacillus_P_294101 subtilis_291504), Klebsiella varicolora (Klebsiellavariicola), Clostridioides A difficile, Terrisporobacter glycolicus (Terrisporobacterglycolicus_239331), Clostridium T neonatale, Bifidobacterium animalis, Terrisporobacter, Escherichia 710834 (Escherichia 710834), Klebsiella 724518 (Klebsiella 724518), Escherichia fergusonii, Clostridium T saudiense, Clostridium T tertium tertium), Enterobacter hormaechei (Enterobacter_B_713587 hormaechei_712707), Bilophila wadsworthia, Clostridium isatidis (Clostridium_T isatidis), Canibacter oris, Nanosynbacter lyticus, Proteus mirabilis, Clostridium innocuum (Clostridium_AQ innocuum), CCUG-7971 spG000499525, Fusobacterium necrogenes (Fusobacterium_A necrogenes), Streptococcus, Anaerostipes cacae caccae), Enterococcus faecalis (Enterococcus_H_360604 faecalis), Bacteroides fragilis (Bacteroides_H fragilis), and Roseburia intestinalis (Roseburia2. The method for determining or predicting the health status of an animal according to claim 1, wherein the animal is determined to have impaired health or to be at high risk of being impaired in the future if the animal possesses two or more species of bacteria selected from the group consisting of Pseudomonas aeruginosa, ... and Pseudomonas aeruginosa.

7. In the intestinal flora of animals other than humans, Streptococcus constellatus, Streptococcus anginosus, Slackia exigua, Desulfovibrio R446353, Peptostreptococcus canis, Bulleidia moorei, Actinomyces timonensis, Pauljensenia cardiffensis, Nanoperiomorbus, Fusobacterium simiae, Corynebacterium canis, and canis, Enterocloster bolteae, Bifidobacterium dentium, Parvimonas micra, Nanosynbacter, Flexilinea sp902786265, CAJPSE01 sp003860125, Campylobacter sp013201975, Fusobacterium russii, Actinomyces weissii, Fusobacterium canifelinum, Saccharimonas aalbogensis aalborgensis, SDRW01 sp007845485, Corynebacterium freiburgense, Globicatella, Lactobacillusdelbrueckii, Clostridium disporicum (Clostridium_T disporicum_203974), Canibacter sp003859945, Pediococcus acidilactici, Clostridium sp000821305 (Clostridium_J sp000821305), Pauljensenia canis, Nanogingivalis gingivitcus, Clostridium baratii, Enterococcus gallinarum, Cellulosilyticaceae, Gemella palaticanis, Streptococcus fryi, Escherichia ruysiae, Citrobacter gillenii (Citrobacter_A_692098 gillenii), Pseudomonas guguanensis (Pseudomonas_E_648040 guguanensis), Klebsiella pneumoniae (Klebsiella pneumoniae_718977), Enterococcus B (Enterococcus_B), Streptococcus minor, Clostridium paraputrificum (Clostridium_T paraputrificum_208099, Clostridium_T paraputrificum_207370), Clostridium dispolicum (Clostridium_T disporicum_203972), Streptococcus oralis (Streptococcus oralis_E_351036), Clostridium P (Clostridium_P), Bacillus subtilis (Bacillus_P_294101 subtilis_291504), Klebsiella varicolora (Klebsiellavariicola), Clostridioides A difficile, Terrisporobacter glycolicus 239331, Clostridium T neonatale, Bifidobacterium animalis, Terrisporobacter, Escherichia 710834, Klebsiella 724518, Escherichia fergusonii, Clostridium T saudiense, Clostridium T tertium tertium), Enterobacter hormaechei (Enterobacter_B_713587 hormaechei_712707), Bilophila wadsworthia, Clostridium isatidis (Clostridium_T isatidis), Canibacter oris, Nanosynbacter lyticus, Proteus mirabilis, Clostridium innocuum (Clostridium_AQ innocuum), CCUG-7971 spG000499525, Fusobacterium necrogenes (Fusobacterium_A necrogenes), Streptococcus, Anaerostipes cacae caccae), Enterococcus faecalis (Enterococcus_H_360604 faecalis), Bacteroides fragilis (Bacteroides_H fragilis), and Roseburia intestinalis (Roseburiaand (iii) calculating an insurance premium for an animal using information on whether or not the animal contains one or more bacteria selected from the group consisting of (i) and (ii) (ii) and (iii).

8. A receiving means for receiving data on the intestinal flora of an animal other than a human, and a method for detecting the presence of Streptococcus constellatus, Streptococcus anginosus, Slackia exigua, Desulfovibrio R446353, Peptostreptococcus canis, Bulleidia moorei, Actinomyces timonensis, Pauljensenia cardiffensis, Nanoperiomorbus, Fusobacterium simiae, and the like in the intestinal flora. simiae), Corynebacterium canis, Enterocloster bolteae, Bifidobacterium dentium, Parvimonas micra, Nanosynbacter, Flexilinea sp902786265, CAJPSE01 sp003860125, Campylobacter sp013201975, Fusobacterium Crussii, Actinomyces weissii, Fusobacterium caniferinum canifelinum, Saccharimonas aalborgensis, SDRW01 sp007845485, Corynebacterium freiburgense, Globicatella, Lactobacillusdelbrueckii, Clostridium disporicum (Clostridium_T disporicum_203974), Canibacter sp003859945, Pediococcus acidilactici, Clostridium sp000821305 (Clostridium_J sp000821305), Pauljensenia canis, Nanogingivalis gingivitcus, Clostridium baratii, Enterococcus gallinarum, Cellulosilyticaceae, Gemella palaticanis, Streptococcus fryi, Escherichia ruysiae, Citrobacter gillenii (Citrobacter_A_692098 gillenii), Pseudomonas guguanensis (Pseudomonas_E_648040 guguanensis), Klebsiella pneumoniae (Klebsiella pneumoniae_718977), Enterococcus B (Enterococcus_B), Streptococcus minor, Clostridium paraputrificum (Clostridium_T paraputrificum_208099, Clostridium_T paraputrificum_207370), Clostridium dispolicum (Clostridium_T disporicum_203972), Streptococcus oralis (Streptococcus oralis_E_351036), Clostridium P (Clostridium_P), Bacillus subtilis (Bacillus_P_294101 subtilis_291504), Klebsiella varicolora (Klebsiellavariicola), Clostridioides A difficile, Terrisporobacter glycolicus 239331, Clostridium T neonatale, Bifidobacterium animalis, Terrisporobacter, Escherichia 710834, Klebsiella 724518, Escherichia fergusonii, Clostridium T saudiense, Clostridium T tertium tertium), Enterobacter hormaechei (Enterobacter_B_713587 hormaechei_712707), Bilophila wadsworthia, Clostridium isatidis (Clostridium_T isatidis), Canibacter oris, Nanosynbacter lyticus, Proteus mirabilis, Clostridium innocuum (Clostridium_AQ innocuum), CCUG-7971 spG000499525, Fusobacterium necrogenes (Fusobacterium_A necrogenes), Streptococcus, Anaerostipes cacae caccae), Enterococcus faecalis (Enterococcus_H_360604 faecalis), Bacteroides fragilis (Bacteroides_H fragilis), and Roseburia intestinalis (Roseburiaand a determination means for determining or predicting the health state or future health state of the animal using information on whether or not the animal contains one or more bacteria selected from the group consisting of Bacillus subtilis (Bacillus intestinalis), wherein the determination means determines that the animal's health is impaired or there is a high risk of it being impaired in the future if the animal contains one or more bacteria selected from the group.

9. The means for determining or predicting the health state or future health state of the animal is: (1) a means for determining whether an animal is suffering from a disease or for predicting whether an animal will suffer from a disease in the future, and is a means for determining that the animal is suffering from a disease or has a high risk of suffering from a disease in the future if the intestinal flora of the animal contains one or more bacteria selected from the group; (2) a means for determining whether an animal is in a good mental state or for predicting whether the animal's future mental state will be good, and is a means for determining that the animal is in a poor mental state or has a high risk of having a poor mental state in the future if the intestinal flora of the animal contains one or more bacteria selected from the group; (3) a means for determining whether an animal's coat condition is good or for predicting whether the animal's coat condition is good in the future, and is a means for determining that the animal's coat condition is poor or has a high risk of having a poor coat condition in the future if the intestinal flora of the animal contains one or more bacteria selected from the group; or (4) A system for determining or predicting the health status of an animal as described in claim 8, which is a means for determining whether an animal's bad breath is good or predicting whether the animal's bad breath will be good in the future, and which is a means for determining that the animal's bad breath is not good or that there is a high risk that the animal's bad breath will be bad in the future if the animal's intestinal bacterial flora contains one or more bacteria selected from the group.

10. A system for determining or predicting the health state of an animal as described in claim 9, wherein the determination means for determining or predicting the health state or future health state of the animal is a means for determining whether the animal is suffering from a disease or predicting whether the animal will suffer from a disease in the future, and the disease is periodontal disease, valvular disease, liver disease, biliary disease, pancreatic disease, renal disease or cancer.

11. A system for determining or predicting the health state of an animal as described in claim 9, wherein the determination means for determining or predicting the health state or future health state of the animal is a means for determining whether the mental state of the animal is good or predicting whether the future mental state of the animal will be good, and whether the mental state of the animal is good or not is whether the animal has become timid.

12. A receiving means for receiving data on the intestinal flora of an animal other than a human, and a data set containing data on the intestinal flora of an animal other than a human, the ... simiae), Corynebacterium canis, Enterocloster bolteae, Bifidobacterium dentium, Parvimonas micra, Nanosynbacter, Flexilinea sp902786265, CAJPSE01 sp003860125, Campylobacter sp013201975, Fusobacterium Crussii, Actinomyces weissii, Fusobacterium caniferinum canifelinum), Saccharimonas aalborgensis, SDRW01 sp007845485, Corynebacterium flavorgensfreiburgense), Globicatella, Lactobacillus delbrueckii, Clostridium disporicum (Clostridium_T disporicum_203974), Canibacter sp003859945, Pediococcus acidilactici, Clostridium sp000821305 (Clostridium_J sp000821305), Pauljensenia canis, Nanogingivalis gingivitcus, Clostridium baratii, Enterococcus gallinarum (Enterococcus_D gallinarum), Cellulosilyticaceae, Gemella palaticanis, Streptococcus fryi, Escherichia ruysiae, Citrobacter gillenii, Pseudomonas guguanensis, Klebsiella pneumoniae, Enterococcus B, Streptococcus minor, Clostridium paraputrificum, Clostridium T paraputrificum_207370), Clostridium disporicum (Clostridium_T disporicum_203972), Streptococcus oralis (Streptococcusoralis_E_351036), Clostridium P, Bacillus subtilis (Bacillus_P_294101 subtilis_291504), Klebsiella variicola, Clostridioides A difficile, Terrisporobacter glycolicus_239331, Clostridium neonatale, Bifidobacterium animalis, Terrisporobacter, Escherichia 710834 (Escherichia_710834), Klebsiella 724518 (Klebsiella_724518), Escherichia fergusonii, Clostridium saudiense (Clostridium_T saudiense), Clostridium tertium (Clostridium_T tertium), Enterobacter hormaechei (Enterobacter_B_713587 hormaechei_712707), Bilophila wadsworthia, Clostridium isatidis (Clostridium_T isatidis), Canibacter oris, Nanosynbacter lyticus, Proteus mirabilis, Clostridium innocuum, CCUG-7971 spG000499525, Fusobacterium necrogenes, Streptococcus, Anaerostipes caccae, Enterococcus faecalis, Bacteroides fragilisand an insurance premium calculation means for calculating an insurance premium for the animal using information on whether the animal contains one or more bacteria selected from the group consisting of Bacillus fragilis and Roseburia intestinalis.

13. In the intestinal flora of animals other than humans, Streptococcus constellatus, Streptococcus anginosus, Slackia exigua, Desulfovibrio R446353, Peptostreptococcus canis, Bulleidia moorei, Actinomyces timonensis, Pauljensenia cardiffensis, Nanoperiomorbus, Fusobacterium simiae, Corynebacterium canis, and canis, Enterocloster bolteae, Bifidobacterium dentium, Parvimonas micra, Nanosynbacter, Flexilinea sp902786265, CAJPSE01 sp003860125, Campylobacter sp013201975, Fusobacterium russii, Actinomyces weissii, Fusobacterium canifelinum, Saccharimonas aalbogensis aalborgensis, SDRW01 sp007845485, Corynebacterium freiburgense, Globicatella, Lactobacillusdelbrueckii, Clostridium disporicum (Clostridium_T disporicum_203974), Canibacter sp003859945, Pediococcus acidilactici, Clostridium sp000821305 (Clostridium_J sp000821305), Pauljensenia canis, Nanogingivalis gingivitcus, Clostridium baratii, Enterococcus gallinarum, Cellulosilyticaceae, Gemella palaticanis, Streptococcus fryi, Escherichia ruysiae, Citrobacter gillenii (Citrobacter_A_692098 gillenii), Pseudomonas guguanensis (Pseudomonas_E_648040 guguanensis), Klebsiella pneumoniae (Klebsiella pneumoniae_718977), Enterococcus B (Enterococcus_B), Streptococcus minor, Clostridium paraputrificum (Clostridium_T paraputrificum_208099, Clostridium_T paraputrificum_207370), Clostridium dispolicum (Clostridium_T disporicum_203972), Streptococcus oralis (Streptococcus oralis_E_351036), Clostridium P (Clostridium_P), Bacillus subtilis (Bacillus_P_294101 subtilis_291504), Klebsiella varicolora (Klebsiellavariicola), Clostridioides A difficile, Terrisporobacter glycolicus 239331, Clostridium T neonatale, Bifidobacterium animalis, Terrisporobacter, Escherichia 710834, Klebsiella 724518, Escherichia fergusonii, Clostridium T saudiense, Clostridium T tertium tertium), Enterobacter hormaechei (Enterobacter_B_713587 hormaechei_712707), Bilophila wadsworthia, Clostridium isatidis (Clostridium_T isatidis), Canibacter oris, Nanosynbacter lyticus, Proteus mirabilis, Clostridium innocuum (Clostridium_AQ innocuum), CCUG-7971 spG000499525, Fusobacterium necrogenes (Fusobacterium_A necrogenes), Streptococcus, Anaerostipes cacae caccae), Enterococcus faecalis (Enterococcus_H_360604 faecalis), Bacteroides_H fragilis, and Roseburia intestinalis, and using the information on whether the animal contains one or more bacteria selected from the group consisting of Enterococcus faecalis (Enterococcus_H_360604 faecalis), Bacteroides_H fragilis, and Roseburia intestinalis, predicting whether the animal will die within a predetermined period of time;A method for predicting animal death, characterized in that if one or more types of bacteria selected from the group are present in the above step, the animal is determined to be at high risk of dying within a specified period of time.

14. The method for predicting the death of an animal according to claim 13, wherein the animal is an animal suffering from a chronic disease.

15. A receiving means for receiving data on the intestinal flora of an animal other than a human, and a data set containing data on the intestinal flora of an animal other than a human, the ... simiae), Corynebacterium canis, Enterocloster bolteae, Bifidobacterium dentium, Parvimonas micra, Nanosynbacter, Flexilinea sp902786265, CAJPSE01 sp003860125, Campylobacter sp013201975, Fusobacterium Crussii, Actinomyces weissii, Fusobacterium caniferinum canifelinum), Saccharimonas aalborgensis, SDRW01 sp007845485, Corynebacterium flavorgensfreiburgense), Globicatella, Lactobacillus delbrueckii, Clostridium disporicum (Clostridium_T disporicum_203974), Canibacter sp003859945, Pediococcus acidilactici, Clostridium sp000821305 (Clostridium_J sp000821305), Pauljensenia canis, Nanogingivalis gingivitcus, Clostridium baratii, Enterococcus gallinarum (Enterococcus_D gallinarum), Cellulosilyticaceae, Gemella palaticanis, Streptococcus fryi, Escherichia ruysiae, Citrobacter gillenii, Pseudomonas guguanensis, Klebsiella pneumoniae, Enterococcus B, Streptococcus minor, Clostridium paraputrificum, Clostridium T paraputrificum_207370), Clostridium disporicum (Clostridium_T disporicum_203972), Streptococcus oralis (Streptococcusoralis_E_351036), Clostridium P, Bacillus subtilis (Bacillus_P_294101 subtilis_291504), Klebsiella variicola, Clostridioides A difficile, Terrisporobacter glycolicus_239331, Clostridium neonatale, Bifidobacterium animalis, Terrisporobacter, Escherichia 710834 (Escherichia_710834), Klebsiella 724518 (Klebsiella_724518), Escherichia fergusonii, Clostridium saudiense (Clostridium_T saudiense), Clostridium tertium (Clostridium_T tertium), Enterobacter hormaechei (Enterobacter_B_713587 hormaechei_712707), Bilophila wadsworthia, Clostridium isatidis (Clostridium_T isatidis), Canibacter oris, Nanosynbacter lyticus, Proteus mirabilis, Clostridium innocuum, CCUG-7971 spG000499525, Fusobacterium necrogenes, Streptococcus, Anaerostipes caccae, Enterococcus faecalis, Bacteroides fragilisand a prediction means for determining or predicting whether the animal will die within a predetermined period of time using information about whether the animal contains one or more bacteria selected from the group consisting of Bacillus subtilis, Bacillus fragilis, and Roseburia intestinalis, wherein the prediction means determines that the animal's health is impaired or there is a high risk of it being impaired in the future if the animal contains one or more bacteria selected from the group.

16. The mortality prediction system according to claim 15, wherein the animal is an animal suffering from a chronic disease.

17. A method for determining or predicting the health state of an animal, comprising a step of determining the health state or predicting the future health state of the animal, using information regarding whether or not periodontal disease-associated bacteria are present in the intestinal flora of an animal other than humans, wherein the periodontal disease-associated bacteria are bacteria for which the odds ratio, expressed by the following formula, between the detection rate in animals with periodontal disease and the detection rate in animals of the same species not suffering from periodontal disease exceeds 1: Odds ratio = detection rate in animals with periodontal disease / detection rate in animals of the same species not suffering from periodontal disease 18. A method for determining or predicting the health status of an animal as described in claim 17, wherein the detection rate in animals suffering from periodontal disease is calculated by examining the bacterial composition in the intestinal flora of 100 or more animals suffering from periodontal disease, and the detection rate in animals of the same species not suffering from periodontal disease is calculated by examining the bacterial composition in the intestinal flora of 100 or more animals not suffering from periodontal disease.

19. A method for determining or predicting the health state of an animal as described in claim 17, wherein the step of determining the health state of the animal or predicting the future health state of the animal is a step of determining whether the animal is suffering from a disease or predicting whether the animal will suffer from a disease in the future, a step of determining whether the animal is in a good mental state or predicting whether the animal will be in a good mental state in the future, a step of determining whether the animal's coat is in good condition or predicting whether the animal's coat will be in good condition in the future, or a step of determining whether the animal's breath smells good or predicting whether the animal's breath smells good in the future.

20. Streptococcus constellatus, Streptococcus anginosus, Slackia exigua, Desulfovibrio R446353, Peptostreptococcus canis, Bulleidia moorei, Actinomyces timonensis, Pauljensenia cardiffensis, Nanoperiomorbus, Fusobacterium simiae, and Corynebacterium canis in the oral cavity of animals other than humans. canis, Enterocloster bolteae, Bifidobacterium dentium, Parvimonas micra, Nanosynbacter, Flexilinea sp902786265, CAJPSE01 sp003860125, Campylobacter sp013201975, Fusobacterium russii, Actinomyces weissii, Fusobacterium canifelinum, Saccharimonas aalbogensis aalborgensis, SDRW01 sp007845485, Corynebacterium freiburgense, Globicatella, Lactobacillusdelbrueckii, Clostridium disporicum (Clostridium_T disporicum_203974), Canibacter sp003859945, Pediococcus acidilactici, Clostridium sp000821305 (Clostridium_J sp000821305), Pauljensenia canis, Nanogingivalis gingivitcus, Clostridium baratii, Enterococcus gallinarum, Cellulosilyticaceae, Gemella palaticanis, Streptococcus fryi, Escherichia ruysiae, Citrobacter gillenii (Citrobacter_A_692098 gillenii), Pseudomonas guguanensis (Pseudomonas_E_648040 guguanensis), Klebsiella pneumoniae (Klebsiella pneumoniae_718977), Enterococcus B (Enterococcus_B), Streptococcus minor, Clostridium paraputrificum (Clostridium_T paraputrificum_208099, Clostridium_T paraputrificum_207370), Clostridium dispolicum (Clostridium_T disporicum_203972), Streptococcus oralis (Streptococcus oralis_E_351036), Clostridium P (Clostridium_P), Bacillus subtilis (Bacillus_P_294101 subtilis_291504), Klebsiella varicolora (Klebsiellavariicola), Clostridioides A difficile, Terrisporobacter glycolicus 239331, Clostridium T neonatale, Bifidobacterium animalis, Terrisporobacter, Escherichia 710834, Klebsiella 724518, Escherichia fergusonii, Clostridium T saudiense, Clostridium T tertium tertium), Enterobacter hormaechei (Enterobacter_B_713587 hormaechei_712707), Bilophila wadsworthia, Clostridium isatidis (Clostridium_T isatidis), Canibacter oris, Nanosynbacter lyticus, Proteus mirabilis, Clostridium innocuum (Clostridium_AQ innocuum), CCUG-7971 spG000499525, Fusobacterium necrogenes (Fusobacterium_A necrogenes), Streptococcus, Anaerostipes cacae A method for preventing a disease, comprising a step of killing, inhibiting, inactivating or removing one or more bacteria selected from the group consisting of Enterococcus caccae, Enterococcus faecalis (Enterococcus_H_360604 faecalis), Bacteroides_H fragilis, and Roseburia intestinalis.

21. A receiving means for receiving data on the intestinal flora of an animal other than a human, and a method for detecting the presence of Streptococcus constellatus, Streptococcus anginosus, Slackia exigua, Desulfovibrio R446353, Peptostreptococcus canis, Bulleidia moorei, Actinomyces timonensis, Pauljensenia cardiffensis, Nanoperiomorbus, Fusobacterium simiae, and the like in the intestinal flora. simiae), Corynebacterium canis, Enterocloster bolteae, Bifidobacterium dentium, Parvimonas micra, Nanosynbacter, Flexilinea sp902786265, CAJPSE01 sp003860125, Campylobacter sp013201975, Fusobacterium Crussii, Actinomyces weissii, Fusobacterium caniferinum canifelinum), Saccharimonas aalborgensis, SDRW01 sp007845485, Corynebacterium flavorgensfreiburgense), Globicatella, Lactobacillus delbrueckii, Clostridium disporicum (Clostridium_T disporicum_203974), Canibacter sp003859945, Pediococcus acidilactici, Clostridium sp000821305 (Clostridium_J sp000821305), Pauljensenia canis, Nanogingivalis gingivitcus, Clostridium baratii, Enterococcus gallinarum (Enterococcus_D gallinarum), Cellulosilyticaceae, Gemella palaticanis, Streptococcus fryi, Escherichia ruysiae, Citrobacter gillenii, Pseudomonas guguanensis, Klebsiella pneumoniae, Enterococcus B, Streptococcus minor, Clostridium paraputrificum, Clostridium T paraputrificum_207370), Clostridium disporicum (Clostridium_T disporicum_203972), Streptococcus oralis (Streptococcusoralis_E_351036), Clostridium P, Bacillus subtilis (Bacillus_P_294101 subtilis_291504), Klebsiella variicola, Clostridioides A difficile, Terrisporobacter glycolicus_239331, Clostridium neonatale, Bifidobacterium animalis, Terrisporobacter, Escherichia 710834 (Escherichia_710834), Klebsiella 724518 (Klebsiella_724518), Escherichia fergusonii, Clostridium saudiense (Clostridium_T saudiense), Clostridium tertium (Clostridium_T tertium), Enterobacter hormaechei (Enterobacter_B_713587 hormaechei_712707), Bilophila wadsworthia, Clostridium isatidis (Clostridium_T isatidis), Canibacter oris, Nanosynbacter lyticus, Proteus mirabilis, Clostridium innocuum, CCUG-7971 spG000499525, Fusobacterium necrogenes, Streptococcus, Anaerostipes caccae, Enterococcus faecalis, Bacteroides fragilisand an alert means for issuing an alert to a user to prompt the user to perform oral care of the animal when the oral cavity contains one or more bacteria selected from the group consisting of Bacillus fragilis and Roseburia intestinalis.

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