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

Abstract

The objective is to provide a method for predicting or determining animal diseases, a method for calculating insurance premiums, and an animal disease prediction system. The method for determining or predicting the health status of an animal, characterized by comprising the step of determining the health status of an animal or predicting its future health status using information on whether or not the intestinal microbiota of an animal other than a human contains predetermined bacteria such as Streptococcus constellatus, Streptococcus anginosus, and Slackia exigua.

Description

Technical Field

[0001] The present invention relates to a disease determination or prediction method, an insurance premium calculation method, and a disease prediction system. Specifically, from data related to the intestinal flora of animals, particularly information on whether specific bacteria are included, it relates to a method for determining or predicting whether the animal is suffering from a disease such as periodontal disease or will suffer from it in the future, and a prediction system, as well as a method and a prediction system for predicting the death of animals with chronic kidney disease.

Background Art

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

[0003] To maintain the health of animals, it is important to manage their physical condition through daily diet, exercise, etc. and respond promptly to discomfort. However, since animals cannot express their physical discomfort in their own words, in reality, breeders only notice that the animal has contracted a disease when symptoms progress and some observable signs appear externally.

[0004] Among diseases, periodontal disease, for example, is increasingly recognized as a major problem not only in humans but also in animals. Periodontal disease can lead to tooth loss as it progresses, affecting eating and potentially causing a significant loss of quality of life. Furthermore, while early-stage periodontal disease is treatable, it becomes more difficult to treat as it progresses. Thus, there is a strong need for early detection of periodontal disease, but currently, there are problems in determining whether or not an animal has periodontal disease. In other words, whether or not an animal such as a pet has periodontal disease requires imaging diagnosis, but imaging diagnoses such as dental X-rays and CT scans require general anesthesia, making them complicated and burdensome for animals. Therefore, veterinarians perform a visual inspection of the oral cavity and make a simple decision on whether or not to perform further imaging diagnosis and whether or not to provide treatment based on the findings with the naked eye. However, while veterinarians rely on visual inspection to assess tartar, gums, bite, and tooth alignment, there are limitations to visual diagnosis by veterinarians. For example, even animals that appear not to have periodontal disease at first glance, such as those with little tartar, may be found to have root resorption and be suffering from periodontal disease upon imaging studies. On the other hand, animals are known to suffer from chronic diseases with symptoms that last for a long time. For example, chronic kidney disease (chronic renal failure) is diagnosed when renal failure, a condition in which the kidneys are damaged and no longer function properly, persists for a long period of time. Severe cases of chronic kidney disease can be fatal. The average life expectancy of dogs diagnosed with chronic kidney disease is reported to be, for example, over 400 days in the early stage (stage 1), 200-400 days in the mild stage (stage 2), 110-200 days in the moderate stage (stage 3), and 14-80 days in the terminal stage (stage 4), but there are individual differences and it 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 can be predicted in advance, it is expected that appropriate treatments such as regenerative medicine can be selected.

[0005] Therefore, there is a need for a simple method to determine whether an animal is suffering from periodontal disease or other diseases, whether it is likely to develop them in the future, and what the prognosis is for animals that are suffering from these diseases.

[0006] Patent Document 1 discloses an intestinal microbiota adjustment or improvement composition that effectively adjusts or improves the intestinal microbiota by increasing the number of Bacteroidetes bacteria and decreasing the number of Firmicutes bacteria in the intestinal microbiota. However, it 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 microbiota. [Prior art documents] [Patent Documents]

[0007] [Patent Document 1] International Publication 2017 / 094892 Brochure [Overview of the project] [Problems that the invention aims to solve]

[0008] Therefore, the present invention aims to provide a method and system for determining the health status 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 for predicting its future health status, as well as a system and method for predicting animal mortality. [Means for solving the problem]

[0009] The inventors analyzed and examined a vast amount of data on the gut microbiota of animals insured under pet insurance, as well as data on whether or not insurance claims were filed for those animals, i.e., whether or not they suffered from disease. As a result, they discovered that by determining whether or not a particular bacterium is present in an animal's gut microbiota, it is possible to determine or predict the health status of the animal, such as whether or not it is suffering from a disease like periodontal disease or is likely to suffer from such a disease in the future, or whether or not the animal is likely to die in the near future. This led to the completion of the present invention.

[0010] The inventors examined insurance claim data for tens of thousands of animals accumulated by pet insurance companies and compared separately obtained intestinal microbiota data for each individual between a group of individuals that had filed insurance claims due to periodontal disease within a predetermined period (affected group) and a group of individuals that had not filed insurance claims due to periodontal disease (unaffected 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 unaffected groups. These bacterial species are significantly detected in the intestinal microbiota of animals suffering from periodontal disease, and by examining the presence or absence of these bacterial species in the intestinal microbiota, it is possible to determine or predict whether an individual has periodontal disease or is likely to develop it 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. However, it is surprising that in the periodontal disease group, the bacteria found not in the oral cavity, but in the gut microbiota, which is accessed via the esophagus and stomach, are characteristic. Furthermore, in the case of animals such as dogs and cats, it is difficult to sample tissue around the gums or saliva due to the risk of resistance such as biting. However, according to this invention, it is possible to analyze the gut microbiota by sampling feces, thus reducing the burden on pet owners and veterinary medical professionals.

[0011] Even more surprisingly, the inventors found that when animals possessed bacterial species significantly detected in groups affected by periodontal disease, there was also a significant difference in the incidence of other diseases such as valvular heart disease, as well as a significant difference in the animals' overall health. This means that by examining the presence or absence of specific bacterial species in the gut microbiota, it is possible to determine or predict the health status of an animal, such as whether it is suffering from a disease other than periodontal disease, or whether it is likely to suffer from such a disease in the future. Furthermore, the inventors found that there was a difference in mortality rates depending on whether or not an animal possessed a specific bacterial species. This means that by examining the presence or absence of a specific bacterial species in an animal, it is possible to predict or determine whether or not the animal is likely to die in the near future, or whether the likelihood of death is low or low.

[0012] In other words, the present invention is as follows [1] to

[21] . [1] In the gut microbiota of animals other than humans, Streptococcus constellatus, Streptococcus anginosus, Slackia exigua, Desulfovibrio R446353, Peptostreptococcus canis, Bulleidia moorei, Actinomyces timonensis, Pauljensenia cardiffensis, Nanoperiomorbus, Fusobacterium simiae simiae), Corynebacterium canis, Enterocloster bolteae, Bifidobacterium dentium, Parvimonas micra, Nanosynbacter, Flexilinea sp902786265, CAJPSE01 sp003860125, Campylobacter sp013201975, Fusobacterium russii, Actinomyces weisii weissii), Fusobacterium C canifelinum, Saccharimonas aalborgensis, SDRW01 sp007845485, Corynebacterium freiburgense, Globicatella, Lactobacillus delburgii* palaticanis), Streptococcus fryi, Escherichia ruysiae, Citrobacter gillenii, Pseudomonas guguanensis, Klebsiella pneumoniae, Enterococcus B, Streptococcus minor, Clostridium paraputrificum (Clostridium T paraputrificum 208099, Clostridium T paraputrificum 207370), Clostridium dispolicum (Clostridium T disporicum_203972), Streptococcus oralis_E_351036, Clostridium_P, Bacillus_P_294101 subtilis_291504, Klebsiella barricolavariicola), 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_T isatidis, Canibacter oris, Nanosynbacter lyticus, Proteus mirabilis, Clostridium_AQ innocuum, CCUG-7971 spG000499525, Fusobacterium_A necrogenes, Streptococcus, Anaerostipes cacae Enterococcus caccae, Enterococcus faecalis, Bacteroides fragilis, and Roseburia intestinalis.The process includes determining the health status of an animal or predicting its future health status using information regarding whether or not it contains one or more bacteria selected from the group consisting of (intestinalis), A method for determining or predicting the health status of an animal in which, if one or more bacteria selected from the group are present in the aforementioned step, the animal's health is impaired or at high risk of being impaired in the future. [2] The step of determining the health status of the animal or predicting its future health status is (1) A step of determining whether an animal is suffering from a disease or predicting whether it will suffer from a disease in the future, wherein if the intestinal flora of the animal contains one or more bacteria selected from the group, it is determined that the animal is suffering from a disease or has a high risk of suffering from a disease in the future. (2) A step of determining whether the mental state of an animal is good or predicting whether the mental state of an animal will be good in the future, wherein if the intestinal flora of the animal contains one or more bacteria selected from the group, it is determined that the mental state of the animal is not good or that there is a high risk that the mental state of the animal will not be good in the future. (3) A step to determine whether the animal's coat is in good condition or to predict whether the animal's coat will be in good condition in the future, wherein if the animal's intestinal flora contains one or more bacteria selected from the group, it is determined that the animal's coat is not in good condition or there is a high risk that the animal's coat will not be in good condition in the future. Or, (4) A step to determine whether an animal's breath odor is good or to predict whether an animal's breath odor will be good in the future, wherein if the animal's intestinal flora contains one or more bacteria selected from the group, it is determined that the animal's breath odor is not good or that there is a high risk that the animal's breath odor will not be good in the future. A method for determining or predicting the health status of an animal [1]. [3] A method for determining or predicting the health status of an animal, wherein the step of determining the health status of the animal or predicting its future health status is the step of determining whether the animal is suffering from a disease or predicting whether it will suffer from a disease in the future, and the disease is periodontal disease, valvular heart disease, liver disease, biliary tract disease, pancreatic disease, kidney disease or cancer.[2] [4] A method for determining or predicting the health status of an animal, wherein the step of determining the health status of the animal or predicting its future health status is the 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, and whether the animal's mental state is good is whether the animal is timid or not. [2] [5] A method for determining or predicting the health status of an animal, other than a human, wherein the intestinal microbiota of the animal is derived from a fecal sample, as described in any one of [1] to [4]. [6] Streptococcus constellatus, Streptococcus anginosus, Slackia_A exigua, Desulfovibrio_R_446353, Peptostreptococcus canis, Bulleidia moorei, Actinomyces timonensis, Pauljensenia cardiffensis, Nanoperiomorbus, Fusobacterium_C simiae, Corynebacterium canis Canis), Enterocloster bolteae, Bifidobacterium dentium, Parvimonas micra, Nanosynbacter, Flexilinea sp902786265, CAJPSE01 sp003860125, Campylobacter sp013201975, Fusobacterium C russii, Actinomyces weissii, Fusobacterium caniferinum canifelinum), Saccharimonas aalborgensis, SDRW01 sp007845485, Corynebacterium freiburgense, Globicatella, Lactobacillus delburgii* palaticanis), Streptococcus fryi, Escherichia ruysiae, Citrobacter gillenii, Pseudomonas guguanensis, Klebsiella pneumoniae, Enterococcus B, Streptococcus minor, Clostridium paraputrificum (Clostridium T paraputrificum 208099, Clostridium T paraputrificum 207370), Clostridium dispolicum (Clostridium T disporicum_203972), Streptococcus oralis_E_351036, Clostridium_P, Bacillus_P_294101 subtilis_291504, Klebsiella barricolaClostridioides 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_T isatidis, Canibacter oris, Nanosynbacter lyticus, Proteus mirabilis, Clostridium_AQ innocuum, CCUG-7971 spG000499525, Fusobacterium_A necrogenes, Streptococcus, Anaerostipes cacae Enterococcus caccae, Enterococcus faecalis, Bacteroides fragilis, and Roseburia intestinalis.A method for determining or predicting the health status of an animal, in which the presence of two or more of the fungi (intestinalis) indicates that the animal's health is impaired or at high risk of being impaired in the future.[1] [7] In the gut microbiota of animals other than humans, Streptococcus constellatus, Streptococcus anginosus, Slackia exigua, Desulfovibrio R446353, Peptostreptococcus canis, Bulleidia moorei, Actinomyces timonensis, Pauljensenia cardiffensis, Nanoperiomorbus, Fusobacterium simiae simiae), Corynebacterium canis, Enterocloster bolteae, Bifidobacterium dentium, Parvimonas micra, Nanosynbacter, Flexilinea sp902786265, CAJPSE01 sp003860125, Campylobacter sp013201975, Fusobacterium russii, Actinomyces weisii weissii), Fusobacterium C canifelinum, Saccharimonas aalborgensis, SDRW01 sp007845485, Corynebacterium freiburgense, Globicatella, Lactobacillus delburgii* palaticanis), Streptococcus fryi, Escherichia ruysiae, Citrobacter gillenii, Pseudomonas guguanensis, Klebsiella pneumoniae, Enterococcus B, Streptococcus minor, Clostridium paraputrificum (Clostridium T paraputrificum 208099, Clostridium T paraputrificum 207370), Clostridium dispolicum (Clostridium T disporicum_203972), Streptococcus oralis_E_351036, Clostridium_P, Bacillus_P_294101 subtilis_291504, Klebsiella barricolavariicola), 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_T isatidis, Canibacter oris, Nanosynbacter lyticus, Proteus mirabilis, Clostridium_AQ innocuum, CCUG-7971 spG000499525, Fusobacterium_A necrogenes, Streptococcus, Anaerostipes cacae Enterococcus caccae, Enterococcus faecalis, Bacteroides fragilis, and Roseburia intestinalis.A method for calculating animal insurance premiums, comprising the step of calculating animal insurance premiums using information on whether or not the animal contains one or more species of bacteria selected from the group consisting of (intestinalis). [8] A means for receiving data on the gut microbiota of animals other than humans, Among the aforementioned intestinal microbiota are Streptococcus constellatus, Streptococcus anginosus, Slackia A exigua, Desulfovibrio R446353, Peptostreptococcus canis, Bulleidia moorei, Actinomyces timonensis, Pauljensenia cardiffensis, Nanoperiomorbus, Fusobacterium C simiae, and Corynebacterium canis. canis), Enterocloster bolteae, Bifidobacterium dentium, Parvimonas micra, Nanosynbacter, Flexilinea sp902786265, CAJPSE01 sp003860125, Campylobacter sp013201975, Fusobacterium Crussii, Actinomyces weissii, Fusobacterium C canifelinum, Saccharimonas arbogensis Corynebacterium freiburgense, Globicatella, Lactobacillus delburgii* palaticanis), Streptococcus fryi, Escherichia ruysiae, Citrobacter gillenii, Pseudomonas guguanensis, Klebsiella pneumoniae, Enterococcus B, Streptococcus minor, Clostridium paraputrificum (Clostridium T paraputrificum 208099, Clostridium T paraputrificum 207370), Clostridium dispolicum (Clostridium T disporicum_203972), Streptococcus oralis_E_351036, Clostridium_P, Bacillus_P_294101 subtilis_291504, Klebsiella barricolaClostridioides 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_T isatidis, Canibacter oris, Nanosynbacter lyticus, Proteusmirabilis, Clostridium_AQ innocuum, CCUG-7971 spG000499525, Fusobacterium_A necrogenes, Streptococcus, Anaerostipes cacae Enterococcus caccae, Enterococcus faecalis, Bacteroides fragilis, and Roseburia intestinalis.A determination means for determining or predicting the health status or future health status of an animal using information on whether or not it contains one or more species of bacteria selected from the group consisting of (intestinalis), Equipped with, The determination means is a system for determining or predicting the health status of an animal, which determines that if one or more bacteria selected from the group are present, the animal's health is impaired or at high risk of being impaired in the future. [9] A determination means for determining or predicting the health status or future health status of the animal, (1) A means for determining whether an animal is suffering from a disease or predicting whether it will suffer from a disease in the future, wherein if the intestinal flora of the animal contains one or more bacteria selected from the group, the means for determining that the animal is suffering from a disease or is at high risk of suffering from a disease in the future. (2) A means for determining whether an animal's mental state is good or predicting whether an animal's mental state will be good in the future, and for determining that if the animal's intestinal flora contains one or more bacteria selected from the group, the animal's mental state is not good or there is a high risk that the animal's mental state will not be good in the future. (3) A means for determining whether the condition of an animal's coat is good or predicting whether the condition of an animal's coat will be good in the future, and if the animal's intestinal flora contains one or more bacteria selected from the group, a means for determining that the condition of the animal's coat is not good or that there is a high risk that the condition of the animal's coat will not be good in the future, Or, (4) A means for determining whether an animal's breath odor is good or predicting whether an animal's breath odor will be good in the future, and for determining that if the animal's intestinal flora contains one or more bacteria selected from the group, the animal's breath odor is not good or there is a high risk that the animal's breath odor will not be good in the future. [8] A system for determining or predicting the health status of animals.

[10] A system for determining or predicting the health status or future health status of an animal, wherein the determination means for determining whether an animal has a disease or predicting whether an animal will have a disease in the future, and the disease is periodontal disease, valvular heart disease, liver disease, biliary tract disease, pancreatic disease, kidney disease or cancer.[9]

[11] A system for determining or predicting the health status or future health status of an animal, wherein the determination 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, and whether the animal's mental state is good is whether the animal is timid or not. [9]

[12] A means for receiving data on the gut microbiota of animals other than humans, Among the aforementioned intestinal microbiota are Streptococcus constellatus, Streptococcus anginosus, Slackia A exigua, Desulfovibrio R446353, Peptostreptococcus canis, Bulleidia moorei, Actinomyces timonensis, Pauljensenia cardiffensis, Nanoperiomorbus, Fusobacterium C simiae, and Corynebacterium canis. canis), Enterocloster bolteae, Bifidobacterium dentium, Parvimonas micra, Nanosynbacter, Flexilinea sp902786265, CAJPSE01 sp003860125, Campylobacter sp013201975, Fusobacterium Crussii, Actinomyces weissii, Fusobacterium C canifelinum, Saccharimonas arbogensis Corynebacterium freiburgense, Globicatella, Lactobacillus delburgii* palaticanis), Streptococcus fryi, Escherichia ruysiae, Citrobacter gillenii, Pseudomonas guguanensis, Klebsiella pneumoniae, Enterococcus B, Streptococcus minor, Clostridium paraputrificum (Clostridium T paraputrificum 208099, Clostridium T paraputrificum 207370), Clostridium dispolicum (Clostridium T disporicum_203972), Streptococcus oralis_E_351036, Clostridium_P, Bacillus_P_294101 subtilis_291504, Klebsiella barricolaClostridioides 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_T isatidis, Canibacter oris, Nanosynbacter lyticus, Proteusmirabilis, Clostridium_AQ innocuum, CCUG-7971 spG000499525, Fusobacterium_A necrogenes, Streptococcus, Anaerostipes cacae An insurance premium calculation means for calculating the insurance premium for an animal using information on whether or not it contains one or more fungi selected from the group consisting of caccae, Enterococcus faecalis, Bacteroides fragilis, and Roseburia intestinalis, An insurance premium calculation system equipped with the following features.

[13] In the gut microbiota of animals other than humans, Streptococcus constellatus, Streptococcus anginosus, Slackia exigua, Desulfovibrio R446353, Peptostreptococcus canis, Bulleidia moorei, Actinomyces timonensis, Pauljensenia cardiffensis, Nanoperiomorbus, Fusobacterium simiae simiae), Corynebacterium canis, Enterocloster bolteae, Bifidobacterium dentium, Parvimonas micra, Nanosynbacter, Flexilinea sp902786265, CAJPSE01 sp003860125, Campylobacter sp013201975, Fusobacterium russii, Actinomyces weisii weissii), Fusobacterium C canifelinum, Saccharimonas aalborgensis, SDRW01 sp007845485, Corynebacterium freiburgense, Globicatella, Lactobacillus delburgii* palaticanis), Streptococcus fryi, Escherichia ruysiae, Citrobacter gillenii, Pseudomonas guguanensis, Klebsiella pneumoniae, Enterococcus B, Streptococcus minor, Clostridium paraputrificum (Clostridium T paraputrificum 208099, Clostridium T paraputrificum 207370), Clostridium dispolicum (Clostridium T disporicum_203972), Streptococcus oralis_E_351036, Clostridium_P, Bacillus_P_294101 subtilis_291504, Klebsiella barricolavariicola), 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_T isatidis, Canibacter oris, Nanosynbacter lyticus, Proteus mirabilis, Clostridium_AQ innocuum, CCUG-7971 spG000499525, Fusobacterium_A necrogenes, Streptococcus, Anaerostipes cacae The process includes a step of predicting whether the animal will die within a predetermined period of time, using information on whether or not it contains one or more fungi selected from the group consisting of *Cacae*, *Enterococcus faecalis*, *Bacteroides fragilis*, and *Roseburia intestinalis*. A method for predicting animal mortality, characterized in that, if one or more bacteria selected from the group are present in the aforementioned step, it is determined that the animal has a high risk of dying within a predetermined period.

[14] A method for predicting the death of an animal as described in

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

[15] A means of receiving data on the gut microbiota of animals other than humans, Among the aforementioned intestinal microbiota are Streptococcus constellatus, Streptococcus anginosus, Slackia A exigua, Desulfovibrio R446353, Peptostreptococcus canis, Bulleidia moorei, Actinomyces timonensis, Pauljensenia cardiffensis, Nanoperiomorbus, Fusobacterium C simiae, and Corynebacterium canis. canis), Enterocloster bolteae, Bifidobacterium dentium, Parvimonas micra, Nanosynbacter, Flexilinea sp902786265, CAJPSE01 sp003860125, Campylobacter sp013201975, Fusobacterium Crussii, Actinomyces weissii, Fusobacterium C canifelinum, Saccharimonas arbogensis Corynebacterium freiburgense, Globicatella, Lactobacillus delburgii* palaticanis), Streptococcus fryi, Escherichia ruysiae, Citrobacter gillenii, Pseudomonas guguanensis, Klebsiella pneumoniae, Enterococcus B, Streptococcus minor, Clostridium paraputrificum (Clostridium T paraputrificum 208099, Clostridium T paraputrificum 207370), Clostridium dispolicum (Clostridium T disporicum_203972), Streptococcus oralis_E_351036, Clostridium_P, Bacillus_P_294101 subtilis_291504, Klebsiella barricolaClostridioides 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_T isatidis, Canibacter oris, Nanosynbacter lyticus, Proteusmirabilis, Clostridium_AQ innocuum, CCUG-7971 spG000499525, Fusobacterium_A necrogenes, Streptococcus, Anaerostipes cacae Enterococcus caccae, Enterococcus faecalis, Bacteroides fragilis, and Roseburia intestinalis.prediction means for determining or predicting whether the animal will die within a predetermined period using information on whether one or more bacteria selected from the group consisting of (intestinalis) are included, comprising, The prediction means determines that when one or more bacteria selected from the group are included, the health of the animal is impaired or the risk of future impairment is high. A death prediction system.

[16] The death prediction system according to

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

[17] A method for determining or predicting the health status of an animal, comprising a step of determining or predicting the future health status of the animal using information on whether periodontal disease-related bacteria are included in the intestinal flora of an animal other than a human, The periodontal disease-related bacteria are bacteria whose odds ratio represented by the following formula of 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. A method for determining or predicting the health status of an animal. Odds ratio = Detection rate in animals suffering from periodontal disease / Detection rate in animals of the same species not suffering from periodontal disease

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

[17] .

[19] The step of determining or predicting the health status of the animal is a step of determining or predicting whether the animal is suffering from a disease or will suffer from a future disease, a step of determining or predicting whether the mental state of the animal is good or whether the mental state of the animal in the future will be good, a step of determining or predicting whether the hair condition of the animal is good or whether the hair condition of the animal in the future will be good, or a step of determining or predicting whether the bad breath of the animal is good or whether the bad breath of the animal in the future will be good. The method for determining or predicting the health status of an animal according to

[17] .

[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 simiae), Corynebacterium canis, Enterocloster bolteae, Bifidobacterium dentium, Parvimonas micra, Nanosynbacter, Flexilinea sp902786265, CAJPSE01 sp003860125, Campylobacter sp013201975, Fusobacterium russii, Actinomyces weisii weissii), Fusobacterium C canifelinum, Saccharimonas aalborgensis, SDRW01 sp007845485, Corynebacterium freiburgense, Globicatella, Lactobacillus delburgii* palaticanis), Streptococcus fryi, Escherichia ruysiae, Citrobacter gillenii, Pseudomonas guguanensis, Klebsiella pneumoniae, Enterococcus B, Streptococcus minor, Clostridium paraputrificum (Clostridium T paraputrificum 208099, Clostridium T paraputrificum 207370), Clostridium dispolicum (Clostridium T disporicum_203972), Streptococcus oralis_E_351036, Clostridium_P, Bacillus_P_294101 subtilis_291504, Klebsiella barricolaOne or more bacteria selected from the group consisting of variicola, Clostridioides difficile, Terrisporobacter glycolicus 239331, Clostridium neonatale, Bifidobacterium animalis, Terrisporobacter, Escherichia 710834, Klebsiella 724518, Escherichia fergusonii, Clostridium saudiense, Clostridium tertium, Enterobacter hormaechei 712707, Bilophila wadsworthia, Clostridium isatidis, Canibacter oris, Nanosynbacter lyticus, Proteus mirabilis, Clostridium innocuum, CCUG-7971 spG000499525, Fusobacterium necrogenes, Streptococcus, Anaerostipes caccae, Enterococcus faecalis, Bacteroides fragilis, and Roseburia intestinalis are sterilized, inhibited, inactivated, or removed. A method for preventing a disease comprising the step of

[21] A means for receiving data on the gut microbiota of animals other than humans, Among the aforementioned intestinal microbiota are Streptococcus constellatus, Streptococcus anginosus, Slackia A exigua, Desulfovibrio R446353, Peptostreptococcus canis, Bulleidia moorei, Actinomyces timonensis, Pauljensenia cardiffensis, Nanoperiomorbus, Fusobacterium C simiae, and Corynebacterium canis. canis), Enterocloster bolteae, Bifidobacterium dentium, Parvimonas micra, Nanosynbacter, Flexilinea sp902786265, CAJPSE01 sp003860125, Campylobacter sp013201975, Fusobacterium Crussii, Actinomyces weissii, Fusobacterium C canifelinum, Saccharimonas arbogensis Corynebacterium freiburgense, Globicatella, Lactobacillus delburgii* palaticanis), Streptococcus fryi, Escherichia ruysiae, Citrobacter gillenii, Pseudomonas guguanensis, Klebsiella pneumoniae, Enterococcus B, Streptococcus minor, Clostridium paraputrificum (Clostridium T paraputrificum 208099, Clostridium T paraputrificum 207370), Clostridium dispolicum (Clostridium T disporicum_203972), Streptococcus oralis_E_351036, Clostridium_P, Bacillus_P_294101 subtilis_291504, Klebsiella barricolaClostridioides 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_T isatidis, Canibacter oris, Nanosynbacter lyticus, Proteusmirabilis, Clostridium_AQ innocuum, CCUG-7971 spG000499525, Fusobacterium_A necrogenes, Streptococcus, Anaerostipes cacae An alert system that prompts the user to perform oral care for their animal if one or more bacteria selected from the group consisting of *Cacae*, *Enterococcus faecalis*, *Bacteroides fragilis*, and *Roseburia intestinalis* are present. A disease prevention system for animals equipped with the following features. [Effects of the Invention]

[0013] The present invention makes it possible to provide a method for determining or predicting the health status of animals, a method for determining or predicting diseases, a method for calculating insurance premiums, a health status determination or prediction system, a disease prediction system, and a method and system for predicting death. [Brief explanation of the drawing]

[0014] [Figure 1] This is a schematic diagram of the insurance premium calculation system. [Figure 2] This flowchart illustrates an example of a disease prediction method using a disease prediction system. [Figure 3] This is a graph showing the results of the example. [Figure 4] This table shows the results of the examples. [Figure 5] This is a graph showing the results of the example. [Figure 6] This is a graph showing the results of the example. [Figure 7] This is a graph showing the results of the example. [Figure 8] This is a graph showing the results of the example. [Figure 9] This is a graph showing the results of the example. [Figure 10] This is a graph showing the results of the example. [Figure 11] This is a graph showing the results of the example. [Figure 12] This is a graph showing the results of the example. [Figure 13] This is a graph showing the results of the example. [Figure 14] This is a graph showing the results of the example. [Figure 15] This is a graph showing the results of the example. [Figure 16] This is a graph showing the results of the example. [Figure 17]This is a graph showing the results of the example. [Figure 18] This is a schematic diagram of a mortality prediction system. [Figure 19] This flowchart illustrates an example of the process for predicting death using a death prediction system. [Figure 20] This is a graph showing the results of the example. [Figure 21] This is a graph showing the results of the example. [Figure 22] This is a graph showing the results of the example. [Figure 23] This is a graph showing the results of the example. [Figure 24] This is a graph showing the results of the example. [Figure 25] This is a graph showing the results of the example. [Figure 26] This is a graph showing the results of the example. [Figure 27] This is a graph showing the results of the example. [Figure 28] This is a graph showing the results of the example. [Figure 29] This is a graph showing the results of the example. [Figure 30] This is a graph showing the results of the example. [Figure 31] This is a graph showing the results of the example. [Figure 32] This is a graph showing the results of the example. [Figure 33] This is a graph showing the results of the example. [Figure 34] This is a graph showing the results of the example. [Figure 35] This is a graph showing the results of the example. [Figure 36] This is a graph showing the results of the example. [Figure 37] This is a graph showing the results of the example. [Figure 38] This is a graph showing the results of the example. [Figure 39] This is a graph showing the results of the example. [Figure 40] This is a graph showing the results of the example. [Figure 41] This is a graph showing the results of the example. [Figure 42] This is a graph showing the results of the example. [Figure 43] This is a graph showing the results of the example. [Figure 44] This is a graph showing the results of the example. [Figure 45] This is a graph showing the results of the example. [Figure 46] This is a graph showing the results of the example. [Figure 47] This is a graph showing the results of the example. [Figure 48] This is a graph showing the results of the example. [Figure 49] This is a graph showing the results of the example. [Figure 50] This is a graph showing the results of the example. [Figure 51] This is a graph showing the results of the example. [Figure 52] This is a graph showing the results of the example. [Figure 53] This is a graph showing the results of the example. [Figure 54] This is a graph showing the results of a reference example. [Figure 55] This is a graph showing the results of a reference example. [Figure 56] This is a graph showing the results of the example. [Figure 57] This is a graph showing the results of a reference example. [Figure 58] This is a graph showing the results of a reference example. [Figure 59] This is a graph showing the results of a reference example. [Figure 60] This is a graph showing the results of a reference example. [Figure 61] This is a graph showing the results of the example. [Figure 62] This is a graph showing the results of the example. [Figure 63]This is a graph showing the results of a reference example. [Figure 64] This is a graph showing the results of a reference example. [Modes for carrying out the invention]

[0015] [Methods for determining or predicting the health status of animals] The present invention provides a method for determining or predicting the health status of animals, which includes the following bacteria 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, and Fusobacterium simiae. simiae), Corynebacterium canis, Enterocloster bolteae, Bifidobacterium dentium, Parvimonas micra, Nanosynbacter, Flexilinea sp902786265, CAJPSE01 sp003860125, Campylobacter sp013201975, Fusobacterium russii, Actinomyces weisii weissii), Fusobacterium C canifelinum, Saccharimonas aalborgensis, SDRW01 sp007845485, Corynebacterium freiburgense, Globicatella, Lactobacillus delburgii* palaticanis), Streptococcus fryi, Escherichia ruysiae, Citrobacter gillenii, Pseudomonas guguanensis, Klebsiella pneumoniae, Enterococcus B, Streptococcus minor, Clostridium paraputrificum (Clostridium T paraputrificum 208099, Clostridium T paraputrificum 207370), Clostridium dispolicum (Clostridium T disporicum_203972), Streptococcus oralis_E_351036, Clostridium_P, Bacillus_P_294101 subtilis_291504, Klebsiella barricolaClostridioides 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_T isatidis, Canibacter oris, Nanosynbacter lyticus, Proteus mirabilis, Clostridium_AQ innocuum, CCUG-7971 spG000499525, Fusobacterium_A necrogenes, Streptococcus, Anaerostipes cacae Enterococcus caccae, Enterococcus faecalis, Bacteroides fragilis, and Roseburia intestinalis.The method is characterized by a step of determining the health status of an animal or predicting its future health status using information on whether or not it contains one or more types of bacteria selected from the group consisting of (intestinalis). Hereinafter, these bacteria will be collectively referred to as "specific bacteria".

[0016] The presence of specific bacteria in the gut microbiota can potentially lead to health problems in animals, such as disease or dull coat condition. Conversely, examining whether specific bacteria are present in the gut microbiota can help determine or predict an animal's current health status. One possible reason for this is as follows. The following is merely one hypothetical mechanism and does not limit the present invention. 1. These specific bacteria are detected at a high rate in animals suffering from periodontal disease and are considered to be bacteria associated with periodontal disease. Even if the infection is caused by periodontal disease-related bacteria in the oral cavity, the toxins produced by these bacteria and inflammatory substances caused by bacterial infection can enter the bloodstream from blood vessels near the gums and spread throughout the body, making it easier for the immune balance to be disrupted. 2. Certain bacteria in the oral cavity are constantly carried into the digestive tract by saliva, and can cause infection in the digestive tract as well. 3. When certain pathogenic bacteria or other protein-degrading bacteria evade the host's immune system in the gastrointestinal tract and damage proteins in the walls of the gastrointestinal tract or blood vessels, tiny gaps open up in the walls of the gastrointestinal tract or blood vessels, resulting in a leaky gut condition. 4. Inflammatory substances produced by these bacteria can enter the bloodstream and lymphatic system, leading to a systemic immune deficiency. As a result, this can lead to or exacerbate numerous diseases, including allergies, atopic dermatitis, kidney disease, diabetes, and heart disease.

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

[0018] In the present invention, the health status of an animal is preferably whether the animal is suffering from a disease, and the disease is preferably periodontal disease, valvular heart disease, liver disease, biliary tract disease, pancreatic disease, kidney disease, or cancer. Furthermore, in the present invention, the health status of an animal is preferably whether the animal is in good mental condition, and whether the animal is in good mental condition is preferably whether the animal is timid. Whether an animal is timid can be interpreted, for example, as whether the animal is uncomfortable around other unfamiliar animals or uncomfortable around strangers.

[0019] The present invention provides a method for determining or predicting the health status of animals, preferably a method for determining or predicting diseases in animals, a method for determining or predicting the mental state of animals, a method for determining or predicting the luster of an animal's coat, or a method for determining or predicting bad breath in animals.

[0020] Periodontal disease is one example of a disease. In this invention, the term "periodontal disease" can be defined using a publicly known definition. For example, periodontal disease includes conditions such as gingivitis, where bacteria that have entered through the gap between the tooth and the gum (periodontal pocket) cause inflammation of the gum, and periodontitis, where the bone supporting the tooth (alveolar bone) dissolves and becomes unstable.

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

[0022] The process of determining the health status of an animal includes, for example, determining whether the animal in question is in good health, and evaluating the degree of goodness of the animal's health. Preferably, the timing of the determination is at the time of collection of samples such as fecal samples taken to examine the intestinal microbiota, or at the time of analysis of the intestinal microbiota.

[0023] The process of predicting an animal's future health status refers to predicting whether the animal's health status will be good or bad within a predetermined period, for example, within one year, six months, or three months from the time of sample collection (such as a fecal sample taken to examine the gut microbiota) or the time of gut microbiota analysis, or after the predetermined period has elapsed. The form of the prediction is not particularly limited, and may include predicting that the animal will be in good health or indicating the probability of deterioration.

[0024] In the context of the present invention, if the health status of an animal is defined as whether or not it is suffering from a disease, the prediction refers to whether the animal in question will develop periodontal disease or other diseases 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 taken to examine the intestinal microbiota, or the time of analysis of the intestinal microbiota. The form of the prediction is not particularly limited and may include predicting whether or not the animal will develop a disease or indicating the probability of developing the disease.

[0025] If the health status of an animal is defined as the presence or absence of disease, the determination in this invention is the determination of whether or not the animal in question is suffering from periodontal disease or other diseases. Preferably, the determination is made at the time of collection of a sample such as a fecal sample taken to examine the intestinal microbiota, or at the time of analysis of the intestinal microbiota.

[0026] In the present invention, when an animal's health status is defined as whether its mental state, coat condition, or bad breath is good, the prediction refers to a prediction of whether the animal's mental state is good, whether its coat condition is good, or whether its bad breath is good, within a predetermined period, for example, within one year, six months, or three months from the time of sample collection (such as a fecal sample collected to examine the intestinal microbiota) or the time of intestinal microbiota analysis, or after the predetermined period has elapsed. The form of the prediction is not particularly limited, and examples include a prediction of whether the animal's mental state is good or an indication of the probability that the animal's mental state is good, a prediction of whether the animal's coat condition is good or an indication of the probability that the animal's coat condition is good, or a prediction of whether the animal's bad breath is good or an indication of the probability that the animal's bad breath is good.

[0027] If the health status of an animal is defined as whether the animal's mental state, coat condition, or bad breath is good, then the determination in this invention is the determination of whether the animal's mental state, coat condition, or bad breath is good. The timing of the determination is preferably at the time of collection of a sample, such as a fecal sample taken to examine the intestinal microbiota, or at the time of analysis of the intestinal microbiota.

[0028] Known methods can be used to analyze the gut microbiota of animals, including known metagenomic analysis methods such as amplicon sequencing using sequencers like NGS, and other methods for analyzing the microbiota. For example, one method involves collecting a sample such as feces from an animal and identifying the organisms contained in the sample by analyzing the base sequence information of the DNA and RNA of all organisms contained in the sample using a next-generation sequencer. When performing base sequence analysis of DNA and RNA using a next-generation sequencer, OTU analysis and ASV analysis, which will be described later, can be used.

[0029] The present invention's method for determining or predicting the health status of an animal preferably involves a processor, such as a CPU or GPU in a computer or server, using a pre-set program, code, or software to determine the health status of the target animal or predict its future health status. The method of determination or prediction is not particularly limited, but a preferred example is that the processor detects the sequence of a specific bacterium from data on the gut microbiota of the target animal, for example, from data on 16S rRNA gene amplicon sequencing or shotgun metagenomic sequencing of the gut microbiota. If the sequence of a specific bacterium is detected, the processor determines that the specific bacterium is present in the gut microbiota of the target animal, and that the animal's health status is poor, that it is suffering from a disease, that its mental state is poor, that its coat is not shiny, or that it has bad breath. Alternatively, the processor may analyze the gut microbiota data in advance to obtain information on the presence or absence of a specific bacterium and other bacteria, and then use that information to determine whether the animal's health status is good or to predict its health status. The presence of even one specific bacterium can indicate that an animal may be in poor health or that its health is deteriorating, but the presence of two or more specific bacteria increases the likelihood of poor health or deterioration. Alternatively, a higher score may be assigned to specific bacteria where the difference in detection rates between the affected and unaffected groups is large, and a lower score to those where the difference is small. The scores for each specific bacterium in the gut microbiota are then totaled, and if the total score exceeds a threshold, it can be determined that the animal is in poor health, suffering from a disease, has poor mental health, has poor coat condition, or has bad breath. Alternatively, it can be predicted that the animal may develop poor health, suffer from disease, have poor mental health, have poor coat condition, or have bad breath in the future. Alternatively, a higher score may be assigned to bacteria with a higher odds ratio for insurance claims between populations that carry the bacteria and populations that do not, and a lower score may be assigned to bacteria with a lower odds ratio.

[0030] For diagnosis and prediction, information other than the presence or absence of specific bacteria may be used. Such information includes physical data such as the animal's age, breed, sex, weight, height, and length, as well as sequence data such as genome sequences, SNPs, and specific gene sequences, medical history, and family history. In addition, data on the composition of bacteria in the gut microbiota can be used as other information. Such data on bacterial composition can include occupancy data and diversity data.

[0031] (Occupancy rate data) Occupancy data refers to data related to the occupancy rate of each bacterium in the gut microbiota of animals. Occupancy rate is the relative abundance (detection ratio) of each bacterial species in the gut microbiota, and can be measured as the "hit rate" of detection results in known metagenomic analysis methods such as amplicon sequencing using sequencers such as NGS. In this invention, occupancy rate data can be the occupancy rate of the gut microbiota or labels set based on the occupancy rate.

[0032] The occupancy rate can be measured at any level: phylum, class, order, family, genus, or species, but the occupancy rate by family is preferred. The occupancy rate by family refers to the occupancy rate of all bacteria belonging to a particular family. In other words, when calculating the occupancy rate by family, the occupancy rate of each bacterial species in the intestinal flora is summed up according to the occupancy rate of the species belonging to a particular family. Identification can be performed down to the species or genus level and then summed up for each family, or identification can be performed at the family level without identifying at the species or genus level, and the occupancy rate of the family can be calculated.

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

[0034] As described above, the occupancy rate in the occupancy rate data of the present invention may be the occupancy rate at any level of fungal phylum, class, order, family, genus, or species, but the occupancy rate for each fungal family is preferred. The families are Alcaligenesae, Bacteroidaceae, Bifidobacteriaceae, Clostridaceae, Coprobacillaceae, Coriobacteriaceae, Enterobacteriaceae, and Enterococcusceae. Preferably, one or more families selected from the group consisting of the families Erysipelotrichaceae, Fusobacteriaceae, Lachnospiraceae, Peptostreptococcaceae, Prevotellaceae, Ruminococcusae, and Veillonellaceae.

[0035] Furthermore, in this invention, in addition to the above, the family Streptococcusae is also preferred. Specifically, Alcaligenesae, Bacteroidaceae, Bifidobacteriaceae, Clostridiaceae, Coprobacillaceae, Coriobacteriaceae, Enterobacteriaceae, Enterococcusceae, and Elysipelotricus. It is preferable to have one or more families selected from the group consisting of Erysipelotrichaceae, Fusobacteriaceae, Lachnospiraceae, Peptostreptococcaceae, Prevotellaceae, Ruminococccaceae, Veillonellaceae, and Streptococcusaceae.

[0036] Furthermore, in the present invention, in addition to the above, it is preferable that the family be one or more families selected from the group consisting of Campylobacteraceae, Desulfovibrionaceae, Flavobacteriaceae, Helicobacteraceae, Odoribacteraceae, Paraprevotellaceae, Peptococcaceae, Porphyromonadaceae, and Succinivibrionaceae.That is, Alcaligenaceae, Bacteroidaceae, Bifidobacteriaceae, Clostridiaceae, Coprobacillaceae, Coriobacteriaceae, Enterobacteriaceae, Enterococcaceae, Erysipelotrichaceae, Fusobacteriaceae, Lachnospiraceae, Peptostreptococcaceae, Prebot It is preferable to have one or more families selected from the group consisting of Prevotellaceae, Ruminococccae, Veillonellaceae, Campylobacteraceae, Desulfovibrionaceae, Flavobacteriaceae, Helicobacteraceae, Odoribacteraceae, Paraprevotellaceae, Peptococcusae, Porphyromonadaceae, and Succinivibrionaceae.

[0037] (Diversity data) Diversity data refers to data related to the diversity of bacteria in the gut microbiota of animals. High diversity in the gut microbiota means that the gut microbiota contains a wide variety of different types of bacteria. There are several types of indicators that represent diversity, so-called diversity indices, but in this invention any known one may be used. Examples of diversity indices include the Shannon-Wiener diversity index and the Simpson diversity index.

[0038] (Measurement of occupancy data and diversity data) To measure the occupancy rate and diversity data of the gut microbiota, known metagenomic analysis methods and microbiota analysis methods such as amplicon sequencing using sequencers such as NGS can be used. For example, one method involves collecting a sample such as feces from an animal and identifying the organisms contained in the sample by analyzing the base sequence information of the DNA and RNA of all organisms contained in the sample using a next-generation sequencer. Preferably, one method involves amplifying all or part of the 16S rRNA gene contained in the sample as needed, sequencing it, and analyzing the obtained sequence using software to obtain bacterial composition data in the sample. By processing the bacterial composition data in the sample with software, or by referring to gene databases such as Genbank, Greengenes, and SILVA database, the species of bacteria contained in the sample can be determined, and the occupancy rate and diversity data of the gut microbiota of the animal can be measured.

[0039] This document provides a specific example of amplicon analysis (microbiota analysis) of the 16S rRNA gene using NGS (Next Generation Sequencer). 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. Subsequently, the nucleotide sequence of the amplified DNA fragment is comprehensively determined using NGS, and after removing low-quality reads and chimeric sequences, the sequences are clustered and OTU (Operational Taxonomic Unit) analysis is performed. OTU is an operational taxonomic unit used to treat 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 constituting the microbiota, and the number of reads belonging to the same OTU is thought to represent the relative abundance of that species. Furthermore, representative sequences are selected from the number of reads belonging to each OTU, and the family name, genus name can be identified by database search. In this way, the occupancy rate of bacteria belonging to a specific family and the diversity index of the gut microbiota can be measured. Analysis using ASV (Amplicon Sequence Variant) is also possible. Because ASVs are created after removing erroneous sequences generated during PCR and sequencing, they can distinguish single-nucleotide sequence variations, enabling more precise identification.

[0040] [Methods for diagnosing or predicting animal diseases] The present invention provides a method for determining or predicting animal diseases, characterized by comprising the step of determining whether an animal is suffering from a disease or predicting whether it will suffer from a disease in the future, using information on whether or not one or more specific bacteria are present in the animal's intestinal microbiota. The present invention provides a method for determining whether or not an animal is suffering from a disease or predicting whether or not it will suffer from a disease in the future, but the target animal, determination method, prediction method, intestinal microbiota analysis method, etc., are the same as those for the method for determining or predicting the health status of animals described above.

[0041] [disease] The types of diseases covered by the present invention are not particularly limited, and include, for example, periodontal disease, skin diseases, ear diseases, musculoskeletal diseases, ophthalmic diseases, digestive diseases, systemic diseases, urinary tract diseases, liver, biliary tract and pancreatic diseases, circulatory diseases, nervous system diseases, respiratory diseases, dental and oral diseases, endocrine diseases, reproductive system diseases, blood and hematopoietic system diseases, and cancer. Examples of skin diseases include dermatitis, atopic dermatitis, and pyoderma. Examples of ear-related diseases include otitis externa and otitis media. Examples of musculoskeletal disorders include patellar luxation and herniated discs. Examples of ophthalmic diseases include conjunctivitis, eye discharge, keratitis, corneal ulcers / erosions, epiphora, cataracts, and glaucoma. Examples of digestive system disorders include gastritis and enteritis. Systemic diseases include, for example, lethargy and collapse. Examples of urinary tract diseases include cystitis and urolithiasis. Examples of liver, biliary tract, and pancreatic diseases include biliary sludge and chronic renal failure. Examples of cardiovascular diseases include valvular heart disease and cardiomyopathy. Examples of neurological disorders include epilepsy and seizures. Examples of respiratory diseases include coughing, rhinitis, tracheal collapse, and bronchial stenosis. Examples of dental and oral diseases include periodontal disease and stomatitis. Examples of endocrine disorders include hypothyroidism and diabetes. Examples of reproductive system disorders include mammary gland tumors and balanitis. Examples of blood and hematopoietic system disorders include lymphatic tumors and thrombocytopenia. Examples of cancers include oral cancer, lung cancer, stomach cancer, colorectal cancer, osteosarcoma, and leukemia. In this invention, among the diseases, valvular heart disease, liver, biliary tract, or pancreatic diseases, or cancer are preferred. The inventors believe that the method of the present invention can be used to determine and predict disease onset as follows: In other words, an intestinal microbiota containing specific bacteria cannot be said to be in a healthy state, such as having low diversity, and can be said to be in a state where the individual is more susceptible to other diseases that involve intestinal bacteria or are correlated with the state of the intestinal microbiota.

[0042] [Method for calculating insurance premiums] The present invention provides a method for calculating insurance premiums for animals, characterized by a step of calculating animal insurance premiums using information regarding whether or not one or more specific bacteria are present in the animal's gut microbiota. Information other than the presence or absence of specific bacteria may also be used to calculate insurance premiums. Such information includes physical data such as the animal's age, breed, sex, weight, height, and length; sequence data such as genome sequences, SNPs, and specific gene sequences; medical history; and family history. Furthermore, data regarding the composition of bacteria in the gut microbiota can be used as other information. Such data regarding bacterial composition can include occupancy data and diversity data.

[0043] Specific methods for calculating insurance premiums include, for example, a method in which a processor calculates a provisional insurance premium using a pre-set insurance premium table based on basic information such as the age, breed, sex, and weight of the animal in question, and then modifies the provisional insurance premium using information on the presence or absence of specific bacteria to calculate the final insurance premium. Any publicly known insurance premium table can be used; for example, a table in which grades are determined according to the animal's age, breed, sex, and weight, and insurance premiums are set according to those grades. When using an insurance premium table, it is preferable to lower the grade (increase the insurance premium) by one level if one or more specific bacteria are present. Other methods include, for example, calculating the insurance premium using a pre-set insurance premium table based on basic information and information on the presence or absence of specific bacteria. Yet another method involves calculating the loss rate from the information on the presence or absence of specific bacteria in the animal in question using a trained model that has learned the relationship between information on the presence or absence of specific bacteria, a score calculated based on that information, and the loss rate, and then calculating the insurance premium using that loss rate. Unless otherwise specified, each item of the animal in question is the same as the method for determining or predicting the health status described above.

[0044] [System for determining or predicting the health status of animals] The present invention provides a system for determining or predicting the health status of animals, characterized by comprising a receiving means for receiving input data on the intestinal microbiota of animals other than humans, and a determination means for determining or predicting the health status or future health status of the animal using information on whether or not specific bacteria are present in the intestinal microbiota. Unless otherwise specified, each component is the same as the method for determining or predicting the health status of animals described above.

[0045] [Method of acceptance] The receiving means of the present invention is configured to receive input of data on the intestinal microbiota of an animal whose health status is to be determined or predicted, or information on the presence or absence of specific bacteria. The method for receiving intestinal microbiota data can employ known methods and configurations, such as inputting data into a terminal, transmitting data from the terminal to a server via a network, or uploading the data. Examples of configurations for 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 configuration of the receiving unit include touch panels, buttons, and keys for inputting information.

[0046] [Judgment means] The determination means of the present invention determines or predicts the health status or future health status of an animal other than a human, using information on whether or not a specific bacterium is present in the gut microbiota of that animal. The determination means includes a program, code, or software. Preferably, these programs, codes, or software determine whether the health status of the target animal is good or predict whether its future health status will be good, and are stored in a storage means, read out, and used by a processor to perform determination or prediction. The method of determination or prediction is not particularly limited, but a preferred example is that the processor uses a pre-set program, code, or software to detect the sequence of a specific bacterium from data on the gut microbiota of the target animal, for example, data from 16S rRNA gene amplicon sequencing or shotgun metagenomic sequencing of the gut microbiota. If the sequence of a specific bacterium is detected, it is determined that the specific bacterium is present in the gut microbiota of the target animal, and the determination is made that the target animal's health status is not good, that it is suffering from a disease, that its mental state is not good, that its coat is not shiny, or that it has bad breath. Alternatively, the system could analyze gut microbiota data in advance to obtain information about the presence or absence of specific bacteria and other bacteria, and then use this information to determine whether the animal is in good health or predict whether its future health will be good. The presence of even one specific bacterium can indicate that the animal may not be in good health or may be deteriorating, but the presence of two or more specific bacteria increases the likelihood of poor or deteriorating health. Furthermore, the system could assign high scores to specific bacteria with high odds ratios for detection rates between affected and unaffected groups, and low scores to bacteria with low odds ratios. The scores for each specific bacterium in the gut microbiota are then totaled, and if the total score exceeds a threshold, the system can determine that the animal is in poor health or predict that its future health may deteriorate.

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

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

[0049] The animal health status determination or prediction system of the present invention is preferably an animal disease determination or prediction system, an animal mental state determination or prediction system, an animal coat shine determination or prediction system, or an animal bad breath determination or prediction system.

[0050] For diagnosis and prediction, information other than the presence or absence of specific bacteria may be used. Such information includes physical data such as the animal's age, breed, sex, weight, height, and length, as well as sequence data such as genome sequences, SNPs, and specific gene sequences, medical history, and family history. In addition, data on the composition of bacteria in the gut microbiota can be used as other information. Such data on bacterial composition can include occupancy data and diversity data.

[0051] Furthermore, the processor may perform predictions and decisions using a pre-trained model. An example of a pre-trained model is a model that has learned the relationship between information on the presence or absence of specific bacteria and the quality of health, such as whether or not the animal has a disease. Such a pre-trained model can be obtained by training it with information such as whether or not a specific bacteria is present in an animal's gut microbiota, and how many types are present, as well as information on the animal's health, such as whether or not it had a disease or whether it contracted a disease within a specified period, as training data. Information on health, such as whether or not the animal had a disease, can be replaced with dummy variables. The data on the animal's gut microbiota and information on the presence or absence of specific bacteria used as training data are the same as those used for determining or predicting health status above. Information on whether or not the animal had a disease can be obtained, for example, from a veterinary hospital or an insured pet owner as an insurance claim (also called an "accident"). In other words, if the animal is covered by pet insurance, and the animal is diagnosed with an illness after visiting a hospital, the veterinary hospital or the owner (the pet insurance policyholder) will claim insurance benefits from the insurance company along with the fact that the animal has been diagnosed with an illness, so the insurance company will be able to find out that the animal has been ill. On the other hand, if no claim for insurance benefits is made within a specified time period from the time the gut microbiota data is obtained, it can be concluded that the insured animal was not ill during that period.

[0052] As the aforementioned trained model, artificial intelligence (AI) is preferred. Artificial intelligence (AI) is software or a system that imitates the intellectual tasks performed by the human brain, and specifically refers to computer programs that understand natural language used by humans, perform logical reasoning, and learn from experience. The AI ​​can be general-purpose or specialized, and can be any type of network, such as a deep neural network or a convolutional neural network, and publicly available software can be used.

[0053] To generate a trained model, artificial intelligence is trained using training data. The training method can be either machine learning or deep learning, but machine learning is preferred. Deep learning is an advanced form of machine learning and is characterized by its ability to automatically identify features. In this invention, gut microbiota occupancy data and diversity data are used as features.

[0054] There are no particular restrictions on the training method used to generate the trained model; publicly available software can be used. For example, NVIDIA's DIGITS (the Deep Learning GPU Training System) can be used. Alternatively, the model may be trained using other publicly known support vector machine methods, such as those published in "Introduction to Support Vector Machines" (Kyoritsu Shuppan).

[0055] Machine learning can be either unsupervised or supervised, but supervised learning is preferred. Supervised learning methods are not limited to any specific approach; examples include decision trees, ensemble learning, and gradient boosting. Publicly available machine learning algorithms include XGBoost, CatBoost, and LightGBM.

[0056] When used for disease diagnosis or prediction, trained models may be generated for each individual disease or for multiple diseases together. When generating trained models for each individual disease, the training data consists of gut microbiota data obtained from animals affected by a specific disease a predetermined period before the onset of the disease, and the fact that the animals were affected by the disease. For comparison, the training data consists of gut microbiota data from animals that were not affected by the disease for a predetermined period from the time the gut microbiota data was obtained, and the fact that the animals were not affected by the disease for a predetermined period. When training for multiple diseases together, multiple types of training data can be prepared, such as gut microbiota data obtained from animals affected by a certain disease and a predetermined period before the onset of the disease, gut microbiota data obtained from animals affected by a different disease and a predetermined period before the onset of the disease, and gut microbiota data obtained from animals affected by yet another disease and a predetermined period before the onset of the disease.

[0057] [output] The determination means of the present invention receives data such as data on the intestinal microbiota of an animal or information regarding the presence or absence of specific bacteria as input information, and 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 will be in good health in the future, such as whether it will suffer from a disease, within a predetermined period from a certain point in time, such as when the intestinal microbiota data is acquired, preferably within 3 years, more preferably within 2 years, and even more preferably within 1 year. The output format is not particularly limited. For example, the predictive judgment can be displayed on the screen of a terminal such as a personal computer, with messages such as "There is a high probability that your health is currently deteriorating," "There is a high probability that you currently have periodontal disease," "There is a possibility that you will contract a disease within the next year," or "There is a low probability that you will contract a disease within the next year." The health status determination or prediction system of the present invention may also have a separate output means that receives a determination result from a determination means and outputs the determination 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 microbiota of animals other than humans or information on the presence or absence of specific bacteria, and an insurance premium calculation means for calculating the insurance premium of an animal using information on whether or not one or more specific bacteria are included in the intestinal microbiota.

[0059] The insurance premium calculation system of the present invention is preferably a system capable of performing the above-described method for calculating insurance premiums. For example, data on the intestinal microbiota of an animal covered by insurance or information regarding the presence or absence of specific bacteria is input into the above-described health status determination or prediction system, and the insurance premium for the animal is determined according to the disease incidence determination or prediction output. Information other than disease incidence prediction may also be used to determine the insurance premium. Hereinafter, one embodiment of the insurance premium calculation system of the present invention will be described with reference to Figure 1.

[0060] In Figure 1, terminal 40 is a terminal used by the insurance policyholder (user). Examples of terminal 40 include personal computers and tablet terminals. Terminal 40 consists of 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 the server from terminal 40 and inputs and transmits data on the intestinal microbiota of the insured animal or information regarding the presence or absence of specific bacteria, a facial image (photograph), and information such as the animal's species, breed, age at the time of the photograph, weight, and medical history. Furthermore, insurance policyholders can receive disease prediction results and insurance premium calculation results from the server by having terminal 40 access the server.

[0061] Furthermore, the policyholder receives a fecal sample collection kit to examine the intestinal microbiota of the insured pet and sends the fecal sample to a company that measures the intestinal microbiota (not shown). The company measures the intestinal microbiota of the pet and obtains the intestinal microbiota data. The company may then directly input and transmit the data of the pet's intestinal microbiota to the server's receiving means 31 via its own terminal, or the company may separately send the data of the pet's intestinal microbiota to the policyholder by mail or email, and the policyholder may input and transmit the data of the intestinal microbiota to the receiving means 31 via terminal 40.

[0062] In this embodiment, the server is configured as a computer, but it may be any device as long as it has the functions according to the present invention. The storage unit 10 is composed of, for example, ROM, RAM, or a hard disk. The storage unit 10 stores information processing programs for operating each part of the server, and in particular stores a determination means (in this embodiment, a trained model, but not limited to a trained model; the same applies hereinafter) 11 and, if necessary, an insurance premium calculation means 12. If the system is configured not for the purpose of calculating insurance premiums, but simply as a health status determination or prediction system that outputs a determination or prediction of health status such as the incidence of disease, the insurance premium calculation means 12 may be omitted.

[0063] As described above, the determination means 11 takes data on the intestinal microbiota of the insured animal or information regarding the presence or absence of specific bacteria, input by the insurance policyholder or the company that performed the measurement of the intestinal microbiota, as input, and outputs a prediction of whether the animal's health condition is good or whether it will contract a specific disease within a predetermined period (for example, within six months or one year) from the time the intestinal microbiota data was acquired or the information regarding the presence or absence of specific bacteria was input. In this embodiment, the determination means 11 is composed of, 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 an animal based on the disease incidence prediction output by the determination means 11 and information such as the animal's type, breed, age at the time of obtaining the gut microbiota data, weight, and medical history entered by the policyholder. For example, the software categorizes the insurance premium into grades according to the animal's type, breed, age at the time of obtaining the gut microbiota data, weight, and medical history, and finally adjusts the grade by taking into account the disease incidence prediction output by the determination means 11 to calculate the final insurance premium. The insurance premium calculation means 12 and the determination means 11 may be configured to use a single software. The method for calculating insurance premiums by the insurance premium calculation means 12 can refer to the specific embodiment of the insurance premium calculation method described above.

[0065] The processing unit 20 is composed of processors such as a CPU and a GPU, and uses programs related to the determination means 11 and the insurance premium calculation means 12 stored in the memory unit to predict the incidence of disease and calculate insurance premiums.

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

[0067] With the insurance premium calculation system of this embodiment, in addition to applying for pet insurance, policyholders can send in samples such as animal fecal samples to create a card (pet health insurance card) indicating that they are enrolled in pet insurance, and at the same time obtain predictions of their pet's insurance premiums and future health status, such as the likelihood of developing diseases.

[0068] As one embodiment of a method for predicting a health status using the health status determination or prediction system of the present invention, a flowchart for disease prediction and determination is shown in Figure 2. For the sake of explanation, this embodiment will be described including the acquisition of samples from animals and the acquisition of data on the gut microbiota. The user collects a fecal sample from an animal using a fecal collection kit or the like and sends it to a gut microbiota analysis company (Step S1). The gut microbiota analysis company uses a next-generation sequencer to analyze and acquire data on the presence or absence of each bacterium in the animal's gut microbiota from the fecal sample (Step S2). The gut microbiota analysis company returns the data on the gut microbiota to the user. The user accesses the disease prediction system through a terminal and inputs data on the presence or absence of a predetermined bacterium in the animal's gut microbiota (Step S3). The disease prediction system predicts and determines, from the input data on the presence or absence of a predetermined bacterium in the animal's gut microbiota, how likely the animal is to contract a disease within a predetermined period (e.g., within one year) or is currently suffering from a disease (Step S4). The disease prediction system outputs the prediction judgment and sends it to terminal 40, where the prediction judgment result is displayed (step S5).

[0069] [Method for predicting animal mortality] The present invention provides a method for predicting animal mortality, characterized by comprising the step of predicting whether an animal, excluding humans, will die within a predetermined period of time, using information regarding whether or not one or more specific bacteria are present in the gut microbiota of the animal. Preferably, the animal is suffering from a chronic disease. Unless otherwise specified, the method for analyzing the gut microbiota is the same as the method for predicting health status described above.

[0070] In this invention, mortality prediction refers to a prediction of whether or not a target animal will die, or whether 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 sample collection (such as a fecal sample collected to examine the intestinal microbiota) or the time of analysis of the intestinal microbiota. The form of the prediction is not particularly limited, and examples include predicting whether or not death will occur or indicating the probability of death.

[0071] The mortality prediction method of the present invention preferably involves a processor such as a CPU or GPU in a computer or server using a pre-set program, code, or software to predict the mortality of a target animal. The prediction method is not particularly limited, but a preferred example is that the processor detects the sequence of a specific bacterium from data on the target animal's gut microbiota, for example, data from 16S rRNA gene amplicon sequencing or shotgun metagenomic sequencing of the gut microbiota. If the sequence of a specific bacterium is detected, the processor predicts that the specific bacterium is present in the target animal's gut microbiota and that the target animal has a high probability of dying within a predetermined period. Alternatively, the gut microbiota data may be analyzed in advance to obtain information on the presence or absence of the specific bacterium and other bacteria, and the processor then makes a mortality prediction based on that information. The presence of one specific bacterium indicates a possibility or high probability of death, but the presence of two or more specific bacters increases the probability of death even further. Alternatively, a higher score may be assigned to specific bacteria where the difference in detection rates between the affected and unaffected groups is large, and a lower score to bacteria where the difference is small. The scores for each specific bacterium in the gut microbiota are then totaled, and if the total score exceeds a threshold, it is predicted that the target animal is likely to die. Alternatively, a higher score may be assigned to bacteria with a high odds ratio for insurance claims between populations that carry the bacteria and populations that do not, and a lower score to bacteria with a low odds ratio.

[0072] Prediction may use information other than information about the presence or absence of specific bacteria. Such information may include physical data such as the animal's age, breed, sex, weight, height, and length, as well as sequence data such as genome sequences, SNPs, and specific gene sequences, medical history, and family history. In addition, data on the composition of bacteria in the gut microbiota can be used as other information. Such data on bacterial composition may include occupancy data and diversity data. Furthermore, information such as whether the animal suffers from a chronic disease, and the type, symptoms, and duration of the chronic disease may also be used.

[0073] Animals suffering from chronic diseases include animals diagnosed with chronic diseases, animals suspected of having chronic diseases, and animals exhibiting symptoms of chronic diseases.

[0074] Chronic diseases are illnesses that exhibit chronic symptoms. Examples of chronic diseases include chronic kidney disease, heart disease, diabetes, and autoimmune diseases. Preferably, chronic diseases are those that can lead to death, and more preferably, chronic kidney disease.

[0075] [Animal mortality prediction system] The animal mortality prediction system of the present invention includes a receiving means for receiving data on the gut microbiota of animals other than humans, The system includes a prediction means that uses information on whether or not one or more specific bacteria are present in the aforementioned intestinal microbiota to determine or predict whether the animal will die within a predetermined period. Preferably, the animal is suffering from a chronic disease. Unless otherwise specified, each component is the same as that of the animal disease prediction system described above.

[0076] [Prediction method] The prediction means of the present invention predicts whether an animal other than a human will die within a predetermined period of time, using information on whether or not a specific bacterium is present in the animal's gut microbiota. The prediction means includes a program, code, or software. Preferably, these programs, codes, or software are programs, codes, or software that predict whether the target animal will die within a predetermined period of time, and are stored in a storage means, read out, and used by a processor to perform the prediction. The method of prediction is not particularly limited, but a preferred example is that the processor detects the sequence of a specific bacterium from data on the target animal's gut microbiota, for example, data from 16S rRNA gene amplicon sequencing or shotgun metagenomic sequencing of the gut microbiota, and if the sequence of the specific bacterium is detected, it is assumed that the specific bacterium is present in the target animal's gut microbiota, and the processor predicts that the target animal will die within a predetermined period of time, or that there is a high probability of death. Alternatively, the gut microbiota data may be analyzed in advance to obtain information on the presence or absence of the specific bacterium and other bacteria, and the processor may then perform the death prediction based on that information. The presence of even one specific bacterium can be used to determine that there is a possibility of death, but the presence of two or more specific bacteria increases the likelihood of death. Alternatively, among the specific bacteria, those with a high odds ratio of detection rate between the periodontal disease-affected group and the non-affected group may be assigned a high score, while those with a low odds ratio may be assigned a low score. The scores for each specific bacterium in the gut microbiota are then totaled, and if the total score exceeds a threshold, it is predicted that the target animal will die or is highly likely to die.

[0077] Prediction may use information other than information about the presence or absence of specific bacteria. Such information may include physical data such as the animal's age, breed, sex, weight, height, and length, as well as sequence data such as genome sequences, SNPs, and specific gene sequences, medical history, and family history. In addition, data on the composition of bacteria in the gut microbiota can be used as other information. Such data on bacterial composition may include occupancy data and diversity data. Furthermore, information such as whether the animal suffers from a chronic disease, and the type, symptoms, and duration of the chronic disease may also be used.

[0078] Animals suffering from chronic diseases include animals diagnosed with chronic diseases, animals suspected of having chronic diseases, and animals exhibiting symptoms of chronic diseases.

[0079] Chronic diseases are illnesses that exhibit chronic symptoms. Examples of chronic diseases include chronic kidney disease, heart disease, diabetes, and autoimmune diseases. Preferably, chronic diseases are those that can lead to death, and more preferably, chronic kidney disease.

[0080] The processor may also perform predictions and decisions using a pre-trained model. The pre-trained model is the same as described above.

[0081] [output] The prediction means of the present invention receives data such as data on the intestinal microbiota of an animal or information regarding the presence or absence of specific bacteria as input information, and the processor uses a program to predict whether the animal will die or is highly likely to die within a predetermined period from a certain point in time, such as when the intestinal microbiota data is acquired, preferably within 3 years, more preferably within 2 years, even more preferably within 1 year, and particularly preferably within 6 months. The output format is not particularly limited; for example, the prediction can be displayed on the screen of a terminal such as a personal computer, with messages such as "There is a high probability of death within a specified period," "There is a possibility of death within the next year," or "There is a low probability of death within the next year." The mortality prediction system of the present invention may also have a separate output means that receives a determination result from a prediction means and outputs the prediction result.

[0082] Hereinafter, one embodiment of the mortality prediction system of the present invention will be described with reference to Figure 18.

[0083] In Figure 18, terminal 40 is a terminal used by the user. Examples of terminal 40 include personal computers and tablet terminals. Terminal 40 is composed of 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 microbiota of the target animal or information regarding the presence or absence of specific bacteria, a facial image (photograph), and information such as the animal's species, breed, age, weight, and medical history. Furthermore, the user can receive disease prediction results and insurance premium calculation results from the server by having terminal 40 access the server.

[0084] Furthermore, the user receives a fecal sample collection kit to examine the gut microbiota of the target animal and sends the fecal sample to a company that measures the gut microbiota (not shown). The company measures the gut microbiota of the animal and obtains the gut microbiota data. The company may then directly input and transmit the gut microbiota data of the animal to the server's receiving means 31 via its own terminal, or the company may separately send the gut microbiota data of the animal to the user by mail or email, and the user may input and transmit the gut microbiota data to the receiving means 31 via terminal 40.

[0085] In this embodiment, the server is configured as a computer, but it may be any device as long as it has the functions according to the present invention. The memory unit 10 is composed of, for example, ROM, RAM, or a hard disk. The memory unit 10 stores information processing programs for operating each part of the server, and in particular, it stores the prediction means 13.

[0086] As described above, the prediction means 13 takes as input data on the gut microbiota of an insured animal or information regarding the presence or absence of specific bacteria, input by the user or the company that performed the measurement of the gut microbiota, and outputs a prediction of whether the animal will die or is likely to die within a predetermined period (for example, within one year or within six months) from the time the gut microbiota data was acquired or the information regarding the presence or absence of specific bacteria was input. In this embodiment, the prediction means 13 may include, for example, a trained model such as XGBoost, CatBoost, LightGBM, or a deep neural network or convolutional neural network.

[0087] The processing unit 20 is composed of processors such as a CPU and a GPU, and performs death prediction using the program related to the prediction means 13 stored in the memory 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 microbiota, information on the presence or absence of specific bacteria, and other information from the user's terminal, and outputs a mortality prediction to the user's terminal.

[0089] Figure 19 shows a flowchart of mortality prediction and determination based on one embodiment of the mortality prediction method using the mortality prediction system of the present invention. For the sake of explanation, this embodiment will be described including the acquisition of samples from animals and the acquisition of data on the gut microbiota. The user collects a fecal sample from an animal using a fecal collection kit or the like and sends it to a gut microbiota analysis company (Step S1). The gut microbiota analysis company uses a next-generation sequencer to analyze and acquire data on the presence or absence of each bacterium in the animal's gut microbiota from the fecal sample (Step S2). The gut microbiota analysis company returns the data on the gut microbiota to the user. The user accesses the mortality prediction system via a terminal and inputs data on the presence or absence of a predetermined bacterium in the animal's gut microbiota (Step S3). The mortality prediction system predicts the likelihood that the animal will die within a predetermined period (e.g., within one year) based on the input data on the presence or absence of a predetermined bacterium in the animal's gut microbiota (Step S4). The mortality prediction system outputs the prediction judgment and transmits it to terminal 40, where the prediction judgment result is displayed (step S5).

[0090] (Other methods for determining or predicting health status) Another embodiment of the present invention is a method for determining or predicting the health status of an animal, characterized by comprising the step of determining the health status of an animal other than a human, or predicting its future health status, using information on whether or not periodontal disease-related bacteria are present in the intestinal flora of the animal, The aforementioned periodontal disease-related bacteria are those whose odds ratio, expressed by the following formula, is greater than 1, between the detection rate in animals suffering from periodontal disease and the detection rate in the same species of animals not suffering from periodontal disease. Odds ratio = Detection rate in animals with periodontal disease / Detection rate in the same species of animals without periodontal disease

[0091] In other words, periodontal disease-related bacteria are bacteria that are frequently detected in animals suffering from periodontal disease. The odds ratio expressed 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 an example of periodontal disease-related bacteria identified in the examples and its odds ratio. In Figure 4, the numbers to the right of the bacterial species name indicate the odds ratio expressed by the above formula for each bacterium.

[0092] In another embodiment of the present invention, in a method for determining or predicting the health status of animals, the odds ratio expressed by the above formula is preferably calculated by first examining the intestinal microbiota of multiple animals of the same species to determine the detection rate. Furthermore, it is preferable that the detection rate in animals suffering from periodontal disease is calculated by examining the bacterial composition of the intestinal microbiota of 100 or more animals suffering from periodontal disease, and that the detection rate in animals of the same species not suffering from periodontal disease is calculated by examining the bacterial composition of the intestinal microbiota of 100 or more animals not suffering from periodontal disease. In another aspect of the present invention, a method for determining or predicting the health status of an animal preferably includes the steps of determining whether the animal is suffering from a disease or predicting whether it will suffer from a disease in the future, determining whether the animal's mental state is good or predicting whether the animal's mental state will be good in the future, 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 determining whether the animal's bad breath is good or predicting whether the animal's bad breath will be good in the future.

[0093] [Methods for preventing disease] The present invention provides a method for preventing diseases, comprising the steps of sterilizing, inhibiting, inactivating, or removing one or more specific bacteria or periodontal disease-related bacteria in the oral cavity of animals other than humans. The methods of sterilization, inhibition, inactivation, and removal are not particularly limited and include, for example, periodontal care, tartar removal, and oral disinfection. Periodontal care, tartar removal, and oral disinfection are preferred if they are highly effective in sterilizing, inhibiting, inactivating, or removing specific bacteria or periodontal disease-related bacteria. Examples of diseases are the same as those described above, with tumors being particularly preferred, and oral tumors and tumors of the blood and hematopoietic system 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 microbiota of animals other than humans or information on the presence or absence of specific bacteria; and an alert means for issuing an alert to the user to encourage oral care of the animal when one or more specific bacteria are found in the intestinal microbiota. The alert means can appropriately adopt known configurations, for example, a program that, when specific bacteria are found in the intestinal microbiota of a target animal, displays a warning prompting oral care of the animal on the user's terminal via the network. 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 disinfection of the oral cavity. Furthermore, the system may also include the above-mentioned determination means. If the determination means is included, and it determines that there is a risk of the animal's health being compromised based on the presence or absence of specific bacteria, the alert means will issue an alert according to the determination result. The present invention makes it possible to predict and determine that if specific bacteria are found in the intestinal microbiota of an animal, the animal is at high health risk, such as suffering from disease. This can be used to encourage users to care for animals. [Examples]

[0095] The following are examples of the present invention. 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. The dog owner collected a fecal sample from the dog using a fecal collection kit. The fecal sample was received and suspended in water. Next, 200 μL of fecal suspension and 810 μL of lysis buffer (containing 224 μg / mL of proteinase K) were added to a bead tube, and the beads were homogenized using a bead homogenizer (6,000 rpm, 20 seconds of homogenization, 30 seconds of interval, 20 seconds of homogenization). Subsequently, the samples were treated with proteinase K by standing on a 70°C heat block for 10 minutes, and then the proteinase K was inactivated by standing on a 95°C heat block for 5 minutes. DNA was automatically extracted from the lysed samples using a chemagic360 (PerkinElmer) according to the chemagic kit stool protocol to obtain 100 μL of DNA extract.

[0096] (Meta-16SRNA gene sequencing analysis) Meta-16S sequencing analysis was performed using a modified version of the 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 μL of DNA extract, 0.05 μL of each primer (100 μM), 12.5 μL of 2xKAPA HiFi Hot-Start Ready Mix (F. Hoffmann-LaRoche, Switzerland), and 2.4 μL of PCR-grade water. For PCR, after heat denaturation at 95°C for 3 minutes, a cycle of 95°C for 30 seconds, 55°C for 30 seconds, and 72°C for 30 seconds was repeated 30 times, followed by an extension reaction at 72°C for 5 minutes. The amplified products were purified using magnetic beads and eluted with 50 μL of BufferEB (QIAGEN, Germany). The purified amplified products were subjected to PCR using the Nextera XT Index Kit v2 (Illumina, CA, US) to add an index. The PCR reaction mixture was prepared by mixing 2.5 μL of the amplified product, 2.5 μL of each primer, 12.5 μL of 2x KAPA HiFi Hot-Start Ready Mix, and 5 μL of PCR-grade water. For PCR, after thermal denaturation at 95°C for 3 minutes, the cycle of 95°C for 30 seconds, 55°C for 30 seconds, and 72°C for 30 seconds was repeated 12 times, followed by an extension reaction at 72°C for 5 minutes. The indexed amplified products were purified using magnetic beads and eluted with 80-105 μL of BufferEB. The concentration of each amplified product was measured using a NanoPhotometer (Implen, CA, US), adjusted to 1.4 nM, and then mixed in equal volumes to form a 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 2×300bp paired-end sequencing. The obtained sequences were analyzed using MiSeq Reporter to obtain bacterial composition data. The sequence of the universal primer used above is as follows. This universal primer can be purchased commercially. Illumina_16S_341F 5′-TCGTCGGCAGCGTCAGATGTGTATAAGAGACAGCCTACGGGNGGCWGCAG- 3' llumina_16S_805R 5′-GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAGGACTACHVGGGTATCTAATCC- 3'

[0097] Following the method described above, we obtained data on the composition of the gut microbiota from 3,121 individuals with periodontal disease who filed insurance claims for periodontal disease within 180 days of fecal sample collection, and from 55,750 individuals without periodontal disease who did not file insurance claims for periodontal disease within the same period.

[0098] (Detection of each type of bacteria) Using the gut microbiota composition data obtained above and a publicly known 16 sRNA sequence database (greengenes2-2022.10), we identified the bacteria included in the gut microbiota of each individual. For each bacterium, we examined the presence or absence of insurance claims due to periodontal disease 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, we found that there are bacteria for which there is a significant difference in the insurance claim rate between the detection group and the non-detection group. Whether the difference in insurance claim rates is significant was tested using Fisher's exact test, and a p < 0.0001 was considered significant. Figure 3 shows the top 20 bacteria when the difference in insurance claim rates between the detection group and the non-detection group is ranked in descending order. Figure 3 is a graph including the detection rate obtained by the calculation shown below for the top 20 bacterial species.

[0099] (Detection rate of each bacterial species) Using the gut microbiota composition data obtained above, the detection rate for each bacterial species was calculated. The detection rate is calculated as the number of times a particular bacterium was detected in the affected group or the unaffected group divided by the total number of cases in each group.

[0100] (Odds ratio) Next, for each bacterium, we selected those with an odds ratio of 1 or higher when comparing the insurance claim rate for periodontal disease in individuals in whom the bacterium was detected with the insurance claim rate for periodontal disease in individuals in whom the bacterium was not detected. Figure 4 shows the results for each bacterium, along with its odds ratio. The odds ratio is a statistical indicator that shows how much the odds of a particular outcome differ between a group with a specific exposure (e.g., receiving a certain treatment, being in a specific environment, performing a specific action) and a group without. Fisher's exact test was used to test each odds ratio. 'p' represents the p-value of the exact test. Each bacterium listed in Figure 4 suggests that individuals containing it in their gut microbiota are likely to suffer from or be likely to suffer from periodontal disease.

[0101] (Diseases other than periodontal disease) In addition to periodontal disease, we also compared the insurance claim rates between individuals carrying each bacterium and those not carrying it for valvular heart disease, cancer, liver / biliary tract diseases, and pancreatic diseases, and calculated the odds ratios. The results are shown in Figure 4. As is clear from Figure 4, for diseases other than periodontal disease, individuals carrying the bacteria listed in Figure 4 are either already suffering from the disease or are highly likely to suffer from it.

[0102] (If you have two or more types of bacteria) For 170,886 dogs, intestinal microbiota composition data was obtained from fecal samples 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 the table in Figure 4 were checked. Figure 5 shows a graph illustrating the relationship between age and the insurance claim rate due to periodontal disease for individuals possessing two or more of the first 20 bacteria listed in the table in Figure 4, individuals possessing one type, and individuals not possessing any of these bacteria. As is clear from Figure 5, the group of individuals possessing two or more of the bacteria listed in Figure 4 had a higher incidence of periodontal disease than the group possessing one type.

[0103] (loss ratio) Figure 6 shows a graph of the relationship between the presence or absence of the first 20 bacteria listed in the table in Figure 4 and the rate of injury for the same 170,886 dogs mentioned above. As is clear from Figure 6, individuals carrying the bacteria listed in Figure 4 tend to have a higher rate of injury.

[0104] (Shannon Index) The same 170,886 dogs as above were classified into three groups: those possessing two or more of the bacteria listed in Figure 4 (up to the 20th from the top of the table), those possessing one type of bacteria, and those possessing none of these bacteria. Figure 7 shows 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 possessing the bacteria listed in Figure 4, especially those possessing two or more types, tend to have a lower Shannon index and lower diversity in their gut microbiota.

[0105] (Various diseases) Figures 8 through 14 show graphs illustrating the relationship between the presence or absence of the top 20 bacteria listed in Figure 4 and various diseases for the same 170,886 dogs mentioned above. The results show that individuals possessing even one of the bacteria listed in Figure 4 tend to have a higher incidence of digestive system diseases (including inflammatory bowel disease), respiratory system diseases, and eye diseases across all age groups, and also tend to have a higher incidence of systemic diseases and blood / immune diseases across many age groups.

[0106] For diseases where differences in incidence rates are difficult to discern using only information on the possession of the first 20 bacteria listed in Figure 4, we examined the trends in incidence rates based on information on the possession of all bacteria listed in Figure 4 (up to the 72nd bacteria). Specifically, the same 170,886 dogs were classified into individuals possessing 13 or more of the bacteria listed in Figure 4, individuals possessing 5 to 12 types, and individuals possessing 4 types or less. Figures 15 to 17 show graphs illustrating the relationship between age and the incidence rates of cancer, valvular heart disease, and liver, biliary tract, and pancreatic diseases for each group. Since there were few cases of liver, biliary tract, and pancreatic diseases, these diseases were grouped together and analyzed as a single disease. As is clear from Figures 15 to 17, individuals possessing 13 or more of the bacteria listed in Figure 4 tend to have higher incidence rates 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 described above.

[0108] (Accident rate in the year following fecal sample collection) For 108,058 dogs, the intestinal microbiota 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 first 20 bacteria listed in the table in Figure 4) was examined. Next, from the insurance claim data, the presence or absence of insurance claims one year after the fecal sample collection was examined for each individual, and the accident rate was calculated and graphed for the group of individuals that possessed one or more of the 20 specific bacteria and the group that did not possess any of the 20 specific bacteria. The results are shown in Figure 20. Note that the graphs are shown divided into age groups of 1-3 years, 4-6 years, 7-9 years, and 10-12 years. The accident rate is expressed as (number of dogs for which an insurance claim was made within a certain period) / (number of dogs with insurance contracts during a certain period), and in this example, the total number of dogs in each group is used as the denominator, and the number of dogs within that group that made an insurance claim is used as the numerator.

[0109] Figure 20 shows that the group possessing one or more of the 20 specific bacteria had a higher incidence of illness compared to the group without these bacteria, indicating a higher likelihood of developing the disease within a specified period after fecal sample collection. This supports the idea that the presence or absence of specific bacteria can be used to predict disease development.

[0110] (Relationship with the diversity of the gut microbiota) Next, the Shannon index was examined for the same population, 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 was examined, and the presence or absence of insurance claims after one year was investigated. The accident rate was calculated and graphed. The results are shown in Figure 21. In Figure 21, the leftmost graph represents the low Shannon index group (less than 3.55), the middle graph represents the medium Shannon index group (3.55 or more and less than 4.25), and the rightmost graph represents the high Shannon index group (4.25 or more).

[0111] Figure 21 shows that a higher Shannon index is associated with a lower incidence of illness in the following year. Therefore, it can be seen that using the Shannon index in addition to the presence or absence of specific bacteria in the prediction process makes it possible to predict disease incidence with greater accuracy.

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

[0113] (Periodontal disease incidence rate in the year following fecal sample collection) For 108,058 dogs, the intestinal microbiota was analyzed from fecal samples in the same manner as described above to determine the presence or absence of specific bacteria. Next, using insurance claim data, the presence or absence of periodontal disease one year after fecal sample collection was investigated for each individual. The periodontal disease prevalence rate was calculated and graphed for the group of dogs that possessed one or more of the 20 specific bacteria and the group that did not possess any of the 20 specific bacteria. The results are shown in Figure 22. Note that the graphs are divided into age groups of 1-3 years, 4-6 years, 7-9 years, and 10-12 years. The periodontal disease prevalence rate is calculated as (number of dogs in that group that had insurance claims due to periodontal disease) / (total number of dogs in that group).

[0114] Figure 22 shows that the group possessing the specific bacteria had a higher incidence of periodontal disease compared to the group without the bacteria, indicating a high probability that they developed periodontal disease within a specified period after fecal sample collection. This supports the idea that the presence or absence of specific bacteria can be used to predict the incidence of periodontal disease.

[0115] (Relationship with the diversity of the gut microbiota) Next, the Shannon index was examined for the same individual population, and they 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 was examined, and the presence or absence of insurance claims after one year was examined, and the prevalence of periodontal disease was calculated and graphed. The results are shown in Figure 23. In Figure 23, the leftmost graph represents the low Shannon index group (less than 3.55), the middle graph represents the medium Shannon index group (3.55 or more and less than 4.25), and the rightmost graph represents the high Shannon index group (4.25 or more).

[0116] Figure 23 shows that the group without the specific bacteria had a lower incidence of periodontal disease the following year, and furthermore, among the group without the specific bacteria, those with a higher Shannon index had a lower incidence of periodontal disease the following year. Therefore, it can be seen that using the Shannon index in addition to the presence or absence of the specific bacteria in the prediction makes it possible to predict the incidence of periodontal disease with greater accuracy.

[0117] [Example 4] (DNA extraction from fecal samples) In the same manner as described above, fecal samples were collected from 1156 dogs diagnosed with chronic kidney disease, and DNA was extracted.

[0118] (Mortality rate within one year of fecal sample collection) For the 1156 dogs in question, the intestinal flora was analyzed from fecal samples in the same manner as described above to determine whether they possessed one or more of the 20 specified bacteria. Next, using insurance claim data, the presence or absence of death within one year from the time of fecal sample collection was investigated for each individual. The mortality rate within a specified period was calculated for the group of dogs that possessed one or more of the 20 specified bacteria and the group that did not possess the specified bacteria, and the results were graphed. The results are shown in Figure 24. Note that the graphs are shown divided into age groups: 0-10 years, 11-13 years, 14-15 years, and 16 years and older. The mortality rate is calculated as (number of individuals in that group that died within the specified period) / (total number of individuals in that group).

[0119] Figure 24 shows that in dogs suffering from chronic kidney disease, the group carrying specific bacteria had a higher mortality rate than the group without these bacteria, indicating a higher likelihood of death within a specified 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 described above, fecal samples were collected from 1159 cats diagnosed with chronic kidney disease, and DNA was extracted.

[0121] (Mortality rate within one year of fecal sample collection) For the 1159 cats in question, the intestinal microbiota was analyzed from fecal samples in the same manner as described above to determine whether they possessed one or more of the 20 specified bacteria. Next, using insurance claim data, the presence or absence of death within one year from the time of fecal sample collection was investigated for each individual. The mortality rate within a specified period was calculated for the group of cats that possessed one or more of the 20 specified bacteria and the group that did not possess the specified bacteria, and the results were graphed. The results are shown in Figure 25. Note that the graphs are shown divided into age groups of 0-2 years, 3-5 years, 6-9 years, and 10-13 years. The mortality rate is calculated as (number of individuals in that group that died within the specified period) / (total number of individuals in that group).

[0122] Figure 25 shows that in cats suffering from chronic kidney disease, the group carrying a specific bacterium had a higher mortality rate than the group without the bacterium, indicating a higher likelihood of death within a specified period after fecal sample collection. This supports the idea that the presence or absence of a specific bacterium can be used to predict mortality.

[0123] [Example 6] (DNA extraction from fecal samples) Fecal samples were collected from each dog (unless otherwise specified) and DNA was extracted in the same manner as described above. The composition of the gut microbiota was examined using a next-generation sequencer in the same manner as described above. Each dog was insured, and information identifying each individual dog, as well as whether or not insurance claims were filed, was registered in the pet insurance database (insurance claim database). The gut microbiota data for each dog was then linked to the individual dog's information in the said database. Therefore, by examining the gut microbiota data of a particular dog, it is possible to check for the presence or absence of specific bacteria, and at the same time, to check whether or not an insurance claim was filed for that dog, and for what reason the claim was filed.

[0124] (Prevalence of oral tumors) For 154,888 dogs insured with pet insurance, the intestinal microbiota 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 first 20 bacteria listed in the table in Figure 4) was investigated. Next, the presence or absence of oral tumors was investigated from insurance claim data (a pet insurance database managed and operated by the applicant and its affiliated companies), and the prevalence of oral tumors was calculated for the populations that had one of the 20 specific bacteria, two of them, three or more of them, and none of the 20 specific bacteria. The results are shown in Figure 26.

[0125] Figure 26 shows that the prevalence of oral tumors within a specified 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) For 154,888 dogs insured with pet insurance, the gut microbiota 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 first 20 bacteria listed in the table in Figure 4) was examined. Next, the presence or absence of neoplastic diseases was examined from insurance claim data, and the prevalence of neoplastic diseases was calculated for the groups of dogs that had one of the 20 specific bacteria, two of the 20 specific bacteria, three or more of the 20 specific bacteria, and dogs that did not have any of the 20 specific bacteria. The results are shown in Figure 27.

[0127] Figure 27 shows that the prevalence of neoplastic diseases within a specified period increases if one or more specific bacteria are present.

[0128] (Prevalence of chronic kidney disease) For 154,888 dogs insured with pet insurance, the intestinal microbiota was analyzed from fecal samples in the same manner as described above, and the presence or absence of one or more of the 20 specified bacteria (the first 20 bacteria listed in the table in Figure 4) was examined. Next, the presence or absence of chronic kidney disease was examined from insurance claim data, and the prevalence of chronic kidney disease was calculated for the groups of dogs that had one of the 20 specified bacteria, two of the specified bacteria, three or more of the specified bacteria, and dogs that did not have any of the 20 specified bacteria. The results are shown in Figure 28.

[0129] Figure 28 shows that the prevalence of chronic kidney disease increases when one or more specific types of bacteria are present.

[0130] (mortality rate) For 154,888 dogs insured with pet insurance, the intestinal microbiota was analyzed from fecal samples in the same manner as described above, and the presence or absence of one or more of the 20 specified bacteria (the first 20 bacteria listed in the table in Figure 4) was investigated. Next, from the insurance claim data, the presence or absence of death within one year from the time of fecal sample collection was investigated for each individual, and the mortality rate was calculated for the group of individuals that possessed one of the 20 specified bacteria, the group that possessed two, the group that possessed three or more, and the group that did not possess any of the 20 specified bacteria. The results are shown in Figure 29.

[0131] Figure 29 shows that the mortality rate within a specified period increases if one or more specific bacteria are present.

[0132] (bad breath) For 154,888 dogs insured with pet insurance, the gut microbiota 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 first 20 bacteria listed in the table in Figure 4) was investigated. Next, based on a questionnaire given to insurance policyholders (asking whether they were concerned about the bad breath of their insured dogs), the quality of the bad breath at the time of fecal sample collection was investigated for each individual dog. The percentage of dogs whose bad breath was a concern was calculated for the groups of dogs that possessed one of the 20 specific bacteria, two of the 20 specific bacteria, three or more of the 20 specific bacteria, and dogs that did not possess any of the 20 specific bacteria. The results are shown in Figure 30.

[0133] Figure 30 shows that having one or more specific types of bacteria tends to result in poor breath quality.

[0134] (hair shine) For 154,888 dogs insured with pet insurance, the gut microbiota 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 first 20 bacteria listed in the table in Figure 4) was investigated. Next, based on a questionnaire given to insurance policyholders (asking whether they were concerned about the coat condition of their insured dogs), the quality of the coat condition at the time of fecal sample collection was examined for each individual dog. The percentage of dogs whose coat condition was a concern was calculated for the groups of dogs that possessed one of the 20 specific bacteria, two of the 20 specific bacteria, three or more of the 20 specific bacteria, and dogs that did not possess any of the 20 specific bacteria. The results are shown in Figure 31.

[0135] Figure 31 shows that dogs with one or more specific types of bacteria tend to have poor coat condition.

[0136] (Do you dislike unfamiliar animals?) For 154,888 dogs insured with pet insurance, the gut microbiota 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 first 20 bacteria listed in the table in Figure 4) was investigated. Next, a questionnaire was administered to insurance policyholders (asking whether the insured dogs were afraid of unfamiliar animals) to determine whether each individual dog was afraid of unfamiliar animals. The percentage of dogs that were afraid of unfamiliar animals was then calculated for the groups of dogs that possessed one of the 20 specific bacteria, two of the 20 specific bacteria, three or more of the 20 specific bacteria, and dogs that did not possess any of the 20 specific bacteria. The results are shown in Figure 32.

[0137] Figure 32 shows that individuals possessing one or more specific types of bacteria tend to be afraid of unfamiliar animals. This is thought to be not an innate personality trait, but rather a result of an unhealthy gut microbiome, which leads to a less-than-ideal mental state, causing anxiety and timidity, and thus making them afraid of unfamiliar animals.

[0138] (Do you dislike strangers?) For 154,888 dogs insured with pet insurance, the gut microbiota 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 first 20 bacteria listed in the table in Figure 4) was investigated. Next, a questionnaire was administered to insurance policyholders (asking whether the insured dogs were afraid of strangers) to determine whether each individual dog was afraid of strangers. The percentage of dogs that were afraid of strangers was then calculated for the groups that possessed one of the 20 specific bacteria, two of the 20 specific bacteria, three or more of the 20 specific bacteria, and none of the 20 specific bacteria. The results are shown in Figure 33.

[0139] Figure 33 shows that individuals who possess one or more specific types of bacteria tend to be uncomfortable around strangers. This is thought to be not an inherent personality trait, but rather a result of an unhealthy gut microbiome, which leads to a less-than-ideal mental state, causing anxiety and timidity, and thus making them uncomfortable around 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 described above.

[0141] (Prevalence of vascular and hematopoietic diseases) For 59,627 cats insured with pet insurance, the intestinal microbiota was analyzed from fecal samples in the same manner as described above, and the presence or absence of one or more of the 20 specified bacteria (the first 20 bacteria listed in 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 the groups of cats that had one of the 20 specified bacteria, two of the 20, three or more of the 20, and none of the 20 specified 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 increase in the prevalence of vascular and hematopoietic diseases was significant when three or more specific bacteria were present.

[0143] (Prevalence of chronic kidney disease) For 59,627 cats insured with pet insurance, the intestinal microbiota was analyzed from fecal samples in the same manner as described above, and the presence or absence of one or more of the 20 specified bacteria (the first 20 bacteria listed in the table in Figure 4) was examined. Next, the presence or absence of chronic kidney disease was examined from insurance claim data, and the prevalence of chronic kidney disease was calculated for the groups of cats that had one of the 20 specified bacteria, two of the specified bacteria, three or more of the specified bacteria, and none of the specified bacteria. The results are shown in Figure 35.

[0144] Figure 35 shows that the prevalence of chronic kidney disease increases when one or more specific types of bacteria are present.

[0145] (Prevalence of neoplastic diseases) For 59,627 cats insured with pet insurance, the intestinal microbiota was analyzed from fecal samples in the same manner as described above, and the presence or absence of one or more of the 20 specified bacteria (the first 20 bacteria listed in the table in Figure 4) was examined. Next, the presence or absence of neoplastic diseases was examined from insurance claim data, and the prevalence of neoplastic diseases was calculated for the groups of cats that had one of the 20 specified bacteria, two of the specified bacteria, three or more of the specified bacteria, and none of the specified bacteria. The results are shown in Figure 36.

[0146] Figure 36 shows that the prevalence of neoplastic diseases increases when one or more specific bacteria are present.

[0147] (mortality rate) For 59,627 cats insured with pet insurance, the intestinal microbiota was analyzed from fecal samples in the same manner as described above, and the presence or absence of one or more of the 20 specified bacteria (the first 20 bacteria listed in the table in Figure 4) was investigated. Next, from the insurance claim data, the presence or absence of death within one year from the time of fecal sample collection was investigated for each individual, and the mortality rate was calculated for the group of individuals that possessed one of the 20 specified bacteria, the group that possessed two, the group that possessed three or more, and the group that did not possess any of the 20 specified bacteria. The results are shown in Figure 37.

[0148] Figure 37 shows that the mortality rate within a specified period increases if one or more specific bacteria are present.

[0149] (hair shine) For 59,627 cats insured with pet insurance, 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 first 20 bacteria listed in the table in Figure 4) was investigated. Next, based on a questionnaire to insurance policyholders (asking whether they were concerned about the coat condition of their insured cats), the quality of the coat condition at the time of fecal sample collection was examined for each individual cat. The percentage (rate) of individuals whose coat condition was a concern was calculated for the groups of individuals that possessed one of the 20 specific bacteria, two of the 20 specific bacteria, three or more of the 20 specific bacteria, and none of the 20 specific bacteria. The results are shown in Figure 38.

[0150] Figure 38 shows that dogs with one or more specific types of bacteria tend to have poor coat condition.

[0151] (bad breath) For 59,627 cats insured with pet insurance, the intestinal microbiota 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 first 20 bacteria listed in the table in Figure 4) was investigated. Next, based on a questionnaire given to insurance policyholders (asking whether they were concerned about the bad breath of their insured cats), the quality of the bad breath at the time of fecal sample collection was investigated for each individual cat. The percentage of cats whose bad breath was a concern was calculated for the groups of cats that possessed one of the 20 specific bacteria, two of the 20 specific bacteria, three or more of the 20 specific bacteria, and none of the 20 specific bacteria. The results are shown in Figure 39.

[0152] Figure 39 shows that having one or more specific types of bacteria tends to result in poor breath quality.

[0153] [Example 8] (Oral tumors) For 20,363 dogs insured with pet insurance (selected breeds: American Cocker Spaniel, Corgi Pembroke, 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 dog breeds listed in the "Anicom Household Animal White Paper 2023" and have 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 first 20 bacteria from the table listed in Figure 4) was examined. Next, the presence or absence of insurance claims due to oral tumors was examined from the insurance claim data, and the insurance claim rate for oral tumors was examined for each age group in the group of dogs that had one or more of the specific bacteria and the group that did not. 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 lack of a difference in oral tumor prevalence between 0 and 2 years of age is likely due to the low prevalence of oral tumors in this age group and the limited amount of data available.

[0155] [Example 9] (Numers of the blood and hematopoietic system) For 19,598 dogs insured with pet insurance (breeds selected were American Cocker Spaniel, Corgi Pembroke, 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 in the "Anicom Household Animal White Paper 2023" and have 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 first 20 bacteria from the table listed in Figure 4) was examined. Next, the presence or absence of insurance claims for blood and hematopoietic tumors (multicentric lymphoma, angiosarcoma, lymphoid tissue / hematopoietic tissue tumors) was examined from the insurance claim data, and the insurance claim rates for blood and hematopoietic tumors were examined for each age group for individuals that had one or more specific bacteria and individuals that did not. The results are shown in Figure 41.

[0156] Figure 41 shows that the presence of one or more specific bacteria increases the prevalence of blood and hematopoietic system diseases. The lack of a difference in the incidence of blood and hematopoietic system tumors in infants aged 0-2 years is likely due to the low prevalence of blood and hematopoietic system tumors in this age group and the resulting limited data.

[0157] [Example 10] (Tumors excluding oral tumors) For 20,363 dogs insured with pet insurance (the breeds selected were American Cocker Spaniel, Corgi Pembroke, 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 dog breeds listed in the "Anicom Household Animal White Paper 2023" and have 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 first 20 bacteria from the table listed in Figure 4) was examined. Next, the presence or absence of insurance claims for tumors other than oral tumors was examined from the insurance claim data, and the insurance claim rate for tumors (excluding oral tumors) was examined for each age group for individuals that had one or more of the specific bacteria and individuals that did not. 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 types of bacteria are present.

[0159] [Example 11] (Allergic dermatitis) For 13,582 dogs insured with pet insurance (breeds: Kishu Inu, Kai Inu, Shikoku Inu, Shiba Inu (including Mame Shiba), Akita Inu, Tosa Inu, Hokkaido Inu, and Ryukyu Inu), the intestinal microbiota 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 first 20 bacteria from the table listed in Figure 4) was examined. Next, the presence or absence of insurance claims due to allergic dermatitis was examined from the insurance claim data, and the insurance claim rate for allergic dermatitis was examined for each age group for individuals that had one or more specific bacteria and individuals that did not. The results are shown in Figure 43.

[0160] Figure 43 shows that the prevalence of allergic dermatitis increases when one or more specific types of bacteria are present.

[0161] [Example 12] (Atopic dermatitis) For 16,558 dogs insured with pet insurance (breeds: Kishu Inu, Kai Inu, Shikoku Inu, Shiba Inu (including Mame Shiba), Akita Inu, Tosa Inu, Hokkaido Inu, and Ryukyu Inu), the intestinal microbiota 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 first 20 bacteria listed in the table in Figure 4) was examined. Next, the presence or absence of insurance claims due to atopic dermatitis was examined from the insurance claim data, and the insurance claim rate for atopic dermatitis was examined for each age group for individuals that had one or more specific bacteria and individuals that did not. The results are shown in Figure 44.

[0162] Figure 44 shows that the prevalence of atopic dermatitis increases when one or more specific types of bacteria are present.

[0163] [Example 13] (Gastritis, gastroenteritis, or enteritis) For 170,886 dogs insured with pet insurance, the intestinal microbiota was analyzed from fecal samples in the same manner as described above to determine whether they possessed one or more of the 20 specific bacteria (the first 20 bacteria listed in the table in Figure 4). Next, insurance claim data was examined to determine whether insurance claims were made for gastritis, gastroenteritis, or enteritis. The insurance claim rates for gastritis, gastroenteritis, or enteritis were then examined for each age group in the populations that possessed one or more specific bacteria and those that did not. The results are shown in Figure 45.

[0164] Figure 45 shows that having one or more specific bacteria increases the prevalence of gastritis, gastroenteritis, or enteritis.

[0165] [Example 14] (Pancreatitis) For 186,654 dogs insured with pet insurance, the intestinal microbiota 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 first 20 bacteria listed in the table in Figure 4) was examined. Next, the presence or absence of insurance claims due to pancreatitis was examined from insurance claim data, and the insurance claim rate for pancreatitis was examined for each age group in the population that had one or more specific bacteria and the population that did not. The results are shown in Figure 46.

[0166] Figure 46 shows that the prevalence of pancreatitis increases when one or more specific types of bacteria are present.

[0167] [Example 15] (Biliary sludge disease) For 186,654 dogs insured with pet insurance, the intestinal microbiota 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 first 20 bacteria listed in the table in Figure 4) was examined. Next, the presence or absence of insurance claims due to biliary sludge was examined from insurance claim data, and the insurance claim rate for biliary sludge was examined for each age group in the group of dogs that had one or more specific bacteria and the group that did not. The results are shown in Figure 47.

[0168] Figure 47 shows that the prevalence of biliary sludge increases when one or more specific types of bacteria are present.

[0169] [Example 16] (Epilepsy) For 29,763 dogs (Chihuahuas and Italian Greyhounds) insured with pet insurance, the intestinal microbiota 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 first 20 bacteria listed in the table in Figure 4) was examined. Next, the presence or absence of insurance claims due to epilepsy was examined from insurance claim data, and the insurance claim rate for epilepsy was examined for each age group in the population that had one or more specific bacteria and the population that did not. 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 insured with pet insurance (breeds include Welsh Corgi Penglog, 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 breeds are among the top 30 most popular dog breeds listed in the "Anicom Household Animal White Paper 2023" and have a high rate of insurance claims for urinary tract diseases), the intestinal flora was analyzed from fecal samples in the same manner as described above to determine whether they possessed one or more of the 20 specific bacteria (the first 20 bacteria listed in the table in Figure 4). Next, we examined the insurance claim data to determine whether or not insurance claims were made for chronic kidney disease. We then investigated the insurance claim rate for chronic kidney disease for each age group, separating individuals who carried one or more specific bacteria from those who did not. The results are shown in Figure 49.

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

[0173] [Example 18] (Oral tumors) For 56,011 cats insured with pet insurance, the intestinal microbiota 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 first 20 bacteria listed in the table in Figure 4) was examined. Next, the presence or absence of insurance claims due to oral tumors was examined from insurance claim data, and the insurance claim rate for oral tumors was examined for individuals that had one or more specific bacteria and individuals that did not. The results are shown in Figure 50.

[0174] Figure 50 shows that having one or more specific bacteria increases the incidence of oral tumors.

[0175] [Example 19] (Chronic kidney disease) For 70,733 cats insured with pet insurance, the intestinal microbiota 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 first 20 bacteria listed in the table in Figure 4) was examined. Next, the presence or absence of insurance claims due to chronic kidney disease was examined from insurance claim data, and the insurance claim rate for chronic kidney disease was examined for each age group in the group of cats that had one or more specific bacteria and the group that did not. The results are shown in Figure 51.

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

[0177] [Example 20] (diabetes) For 70,007 cats insured with pet insurance, the intestinal microbiota 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 first 20 bacteria listed in the table in Figure 4) was examined. Next, the presence or absence of insurance claims due to diabetes was examined from insurance claim data, and the insurance claim rate for diabetes was examined for each age group in the group of cats that had one or more specific bacteria and the group that did not. The results are shown in Figure 52.

[0178] Figure 52 shows that the prevalence of diabetes increases when one or more specific types of bacteria are present.

[0179] [Example 21] (Pancreatitis) For 66,044 cats insured with pet insurance, the intestinal microbiota 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 first 20 bacteria listed in the table in Figure 4) was examined. Next, the presence or absence of insurance claims due to pancreatitis was examined from insurance claim data, and the insurance claim rate for pancreatitis was examined for each age group for individuals that had one or more specific bacteria and individuals that did not. The results are shown in Figure 53.

[0180] Figure 53 shows that the prevalence of pancreatitis increases when one or more specific types of bacteria are present.

[0181] [Reference example 1] (Stomatitis and oral tumors) For 668,423 dogs insured with pet insurance, we checked whether there were any claims for oral tumors within one year, two years, and three years of insurance enrollment for each group of dogs that had filed an insurance claim for stomatitis within one year of enrollment, and for dogs that had not filed an insurance claim for stomatitis within one year of enrollment, using the insurance claim database. We then investigated the insurance claim rate for oral tumors. Furthermore, we graphed the insurance claim rate for oral tumors for each group, separated by age group at the time of insurance enrollment. The results are shown in Figure 54.

[0182] As is clear from Figure 54, in each age group, the proportion of individuals suffering from stomatitis was higher in the group suffering from oral tumors. Furthermore, as age increased, the difference in the prevalence of oral tumors between the group suffering from stomatitis and the group not suffering from stomatitis became larger.

[0183] [Reference example 2] (Intraoral tumors and current year mortality rate) For 410,529 dogs insured with pet insurance, we used an insurance claim database to divide them into two groups: those for whom insurance claims were filed due to oral tumors (group with oral tumors) and those for whom insurance claims were filed due to diseases other than oral tumors (group without oral tumors). For each group, we investigated whether the dog died in the year the insurance claim was filed, calculated the mortality rate, and graphed it. The results are shown in Figure 55.

[0184] As is clear from Figure 55, the population suffering from oral tumors had a higher mortality rate than the population suffering from other diseases. This indicates that oral tumors increase the mortality rate in animals.

[0185] [Example 22] (Specific bacteria and mouth ulcers) For 154,888 dogs enrolled in pet insurance, 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 20th bacterium from the top of the table among the bacteria described in FIG. 4) was examined. Next, from the insurance claim data, the presence or absence of insurance claims due to stomatitis was examined, and the insurance claim rate for stomatitis was examined for each age group in the population with one or more specific bacteria and the population without them. The results are shown in FIG. 56.

[0186] From FIG. 56, it was found that the morbidity rate of stomatitis increases when one or more specific bacteria are present. From this, it was found that by performing oral care to sterilize, suppress, inactivate or remove specific bacteria, the onset of stomatitis can be prevented, and ultimately, the prevention of oral tumors and the suppression of death can be achieved.

[0187] [Reference Example 3] (Stomatitis and Oral Tumors) For 668,423 dogs (aged 0 to 11 years) enrolled in pet insurance, using the insurance claim database, they were divided into the population with insurance claims due to stomatitis (with stomatitis) and the population without insurance claims due to stomatitis (without stomatitis), and for each, it was examined whether there was an insurance claim due to oral tumors. The insurance claim rate for oral tumors was examined for each population and graphed. The results are shown in FIG. 57.

[0188] As is clear from FIG. 57, the population suffering from stomatitis had a higher incidence of oral tumors than the population not suffering from it. Regarding the difference in the insurance claim rate for oral tumors between the two groups, a chi-square test was performed and it was confirmed that there was a significant difference (p < 0.0001).

[0189] [Reference Example 4] (Stomatitis and Oral Tumors) For 208,150 cats insured with pet insurance, we checked whether there were any claims for oral tumors within one year, two years, and three years of insurance enrollment for each group of cats that had filed an insurance claim for stomatitis within one year of enrollment, and for those that had not filed an insurance claim for stomatitis within one year of enrollment, using an insurance claim database. We then investigated the insurance claim rate for oral tumors. Furthermore, we graphed the insurance claim rate for oral tumors for each group, separated by age group at the time of insurance enrollment. The results are shown in Figure 58.

[0190] As is clear from Figure 58, in each age group, the proportion of individuals suffering from stomatitis was higher in the group suffering from oral tumors. Furthermore, as age increased, the difference in the prevalence of oral tumors between the group suffering from stomatitis and the group not suffering from stomatitis became larger.

[0191] [Reference example 5] (Stomatitis and current year mortality rate) For 95,236 cats insured with pet insurance, we used an insurance claim database to divide them into two groups: those for whom insurance claims were filed due to stomatitis (stomatitis group) and those for whom insurance claims were filed due to other diseases (no group). Furthermore, we divided them by age group and then examined whether each group died in the year an insurance claim was filed. We calculated the mortality rate and graphed it. The results are shown in Figure 59.

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

[0193] [Reference example 6] (Intraoral tumors and current year mortality rate) For 90,346 cats insured with pet insurance, we used an insurance claim database to divide them into two groups: those for which insurance claims were filed due to oral tumors (group with oral tumors) and those for which insurance claims were filed due to other diseases (group without oral tumors). Furthermore, we divided them by age group and examined whether each group died in the year the insurance claim was filed. We calculated the mortality rate and graphed it. The results are shown in Figure 60.

[0194] As is clear from Figure 60, the population suffering from oral tumors had a higher mortality rate than the population suffering from other diseases. This indicates that oral tumors have a poor prognosis and increase mortality.

[0195] [Example 23] (Specific bacteria and mouth ulcers) For 59,627 cats insured with pet insurance, the intestinal microbiota 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 first 20 bacteria listed in the table in Figure 4) was examined. Next, the presence or absence of insurance claims due to stomatitis was examined from insurance claim data, and the insurance claim rate for stomatitis was examined for each age group for individuals that had one or more specific bacteria and individuals that did not. The results are shown in Figure 61.

[0196] Figure 61 shows that the incidence of stomatitis increases when cats possess one or more specific types of bacteria. This suggests that in cats, oral care can prevent stomatitis by killing, suppressing, inactivating, or removing specific bacteria, which in turn can lead to the prevention of oral tumors and a reduction in mortality.

[0197] Furthermore, for the same 59,627 cats, the insurance claim rate for stomatitis was calculated and graphed based on the presence or absence of specific bacteria and the number of species present, without separating them by age. The results are shown in Figure 62. From Figure 62, it was found that the incidence of stomatitis significantly increased when cats possessed two or more species of specific bacteria.

[0198] [Reference example 7] (Stomatitis and oral tumors) For 218,050 cats (ages 0-11) insured with pet insurance, we used an insurance claim database to divide them into two groups: those with insurance claims due to stomatitis (with stomatitis) and those without (without stomatitis). We then investigated whether there were any insurance claims due to oral tumors in each group. The insurance claim rate for oral tumors for each group was examined and graphed. The results are shown in Figure 63.

[0199] As is clear from Figure 63, the population suffering from stomatitis had a higher incidence of oral tumors than the population without stomatitis. Furthermore, a chi-squared test was performed to confirm a statistically significant difference in the insurance claim rates for oral tumors between the two groups (p<0.001).

[0200] [Reference example 8] (Stomatitis and oral tumors from the previous year) For 220,370 cats (ages 0-16) insured with pet insurance, we used an insurance claim database to divide them into two groups: those with insurance claims due to stomatitis (with stomatitis) and those without (without stomatitis). For each group, we investigated whether there were insurance claims due to oral tumors in the year following the year in which there were claims due to stomatitis. We then examined the insurance claim rate for oral tumors for each group and graphed the results. 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 without stomatitis. Furthermore, a chi-squared test was performed to confirm a statistically significant difference in the insurance claim rates for oral tumors between the two groups (p<0.001). This suggests that stomatitis may worsen and lead to oral tumors.

Claims

1. In the gut microbiota of animals other than humans, the following species are found: Streptococcus constellatus, Streptococcus anginosus, Slackia exigua, Desulfovibrio R446353, Peptostreptococcus canis, Bulleidia moorei, Actinomyces timonensis, Pauljensenia cardiffensis, Nanoperiomorbus, Fusobacterium C simiae, and Corynebacterium canis. Canis), Enterocloster bolteae, Bifidobacterium dentium, Parvimonas micra, Nanosynbacter, Flexilinea sp902786265, CAJPSE01 sp003860125, Campylobacter sp013201975, Fusobacterium C russii, Actinomyces weissii, Fusobacterium C canifelinum, Saccharimonas arbogensis Corynebacterium freiburgense, Globicatella, Lactobacillus delburgii*Clostridium delbrueckii*, *Clostridium disporicum*, *Canibacter sp003859945*, *Pediococcus acidilactici*, *Clostridium sp000821305*, *Pauljensenia canis*, *Nanogingivalis gingivitcus*, *Clostridium baratii*, *Enterococcus gallinarum*, *Cellulosilyticaceae*, *Gemella paraticanis* palaticanis), Streptococcus fryi, Escherichia ruysiae, Citrobacter gillenii, Pseudomonas guguanensis, Klebsiella pneumoniae, Enterococcus B, Streptococcus minor, Clostridium paraputrificum (Clostridium T paraputrificum 208099, Clostridium T paraputrificum 207370), Clostridium dispolicum (Clostridium T disporicum_203972), Streptococcus oralis_E_351036, Clostridium_P, Bacillus_P_294101 subtilis_291504, Klebsiella barricolaClostridioides 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_T isatidis, Canibacter oris, Nanosynbacter lyticus, Proteus mirabilis, Clostridium_AQ innocuum, CCUG-7971 spG000499525, Fusobacterium_A necrogenes, Streptococcus, Anaerostipes cacae Enterococcus caccae, Enterococcus faecalis, Bacteroides fragilis, and Roseburia intestinalis.The process includes determining the health status of an animal or predicting its future health status using information regarding whether or not it contains one or more bacteria selected from the group consisting of (intestinalis). If, in the above process, one or more bacteria selected from the above group are present, it is determined that the animal's health is impaired or at high risk of being impaired in the future. A method for determining or predicting the health status of an animal, wherein the animal is a dog or a cat.

2. The step of determining the health status of the animal or predicting its future health status is, (1) A step of determining whether an animal is suffering from a disease or predicting whether it will suffer from a disease in the future, wherein if the intestinal flora of the animal contains one or more bacteria selected from the group, it is determined that the animal is suffering from a disease or has a high risk of suffering from a disease in the future. (2) A step of determining whether the mental state of an animal is good or predicting whether the mental state of an animal will be good in the future, wherein if the intestinal microbiota of the animal contains one or more bacteria selected from the group, it is determined that the mental state of the animal is not good or that there is a high risk that the mental state of the animal will not be good in the future. (3) A step of determining whether the condition of the animal's coat is good or predicting whether the condition of the animal's coat will be good in the future, wherein if the animal's intestinal flora contains one or more bacteria selected from the group, it is determined that the condition of the animal's coat is not good or there is a high risk that the condition of the animal's coat will not be good in the future. Or, (4) A step of determining whether an animal's breath odor is good or predicting whether an animal's breath odor will be good in the future, wherein if the animal's intestinal flora contains one or more bacteria selected from the group, it is determined that the animal's breath odor is not good or that there is a high risk that the animal's breath odor will not be good in the future. A method for determining or predicting the health status of an animal according to claim 1.

3. The method for determining or predicting the health status of an animal according to claim 2, wherein the step of determining the health status of the animal or predicting its future health status is 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 the disease is periodontal disease, valvular heart disease, liver disease, biliary tract disease, pancreatic disease, kidney disease, or cancer.

4. The method for determining or predicting the health status of an animal according to claim 2, wherein the step of determining the health status of the animal or predicting its future health status is the 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, and whether the animal's mental state is good is whether the animal is 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 microbiota 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 C simiae, Corynebacterium canis, Enterocloster bolteae), Bifidobacterium dentium, Parvimonas micra, Nanosynbacter, Flexilinea sp902786265, CAJPSE01 sp003860125, Campylobacter sp013201975, Fusobacterium C russii, Actinomyces weissii, Fusobacterium C canifelinum, Saccharimonas aalborgensis, SDRW01 sp007845485, Corynebacterium freiburgense, Globicatella, Lactobacillus delbrueckii, Clostridium dyspolicumdisporicum_203974), Canibacter sp003859945, Pediococcus acidilactici, Clostridium sp000821305, Pauljensenia canis, Nanogingivalis gingivitcus, Clostridium baratii, Enterococcus gallinarum, Cellulosilyticaceae, Gemella palaticanis, Streptococcus flii fryi), Escherichia ruysiae, Citrobacter gillenii, Pseudomonas guguanensis, Klebsiella pneumoniae, Enterococcus B, Streptococcus minor, Clostridium paraputrificum (208099, Clostridium paraputrificum 207370), Clostridium disporicum (203972), Streptococcus oralis oralis_E_351036), Clostridium_P, Bacillus_P_294101 subtilis_291504, Klebsiella variicola, Clostridioides_Adifficile), Terrisporobacter glycolicus (Terrisporobacterglycolicus_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 formaechei (Enterobacter_B_713587) hormaechei_712707), Bilophila wadsworthia, Clostridium_T isatidis, Canibacter oris, Nanosynbacter lyticus, Proteus mirabilis, Clostridium_AQ innocuum, CCUG-7971 spG000499525, Fusobacterium_A necrogenes, Streptococcus, Anaerostipes caccae, Enterococcus_H_360604 A method for determining or predicting the health status of an animal according to claim 1, which determines that an animal's health is impaired or at high risk of being impaired in the future if it possesses two or more of the following fungi: Bacteroides faecalis, Bacteroides fragilis, and Roseburia intestinalis.

7. In the gut microbiota of animals other than humans, the following species are found: Streptococcus constellatus, Streptococcus anginosus, Slackia exigua, Desulfovibrio R446353, Peptostreptococcus canis, Bulleidia moorei, Actinomyces timonensis, Pauljensenia cardiffensis, Nanoperiomorbus, Fusobacterium C simiae, and Corynebacterium canis. Canis), Enterocloster bolteae, Bifidobacterium dentium, Parvimonas micra, Nanosynbacter, Flexilinea sp902786265, CAJPSE01 sp003860125, Campylobacter sp013201975, Fusobacterium C russii, Actinomyces weissii, Fusobacterium C canifelinum, Saccharimonas arbogensis Corynebacterium freiburgense, Globicatella, Lactobacillus delburgii*Clostridium delbrueckii*, *Clostridium disporicum*, *Canibacter sp003859945*, *Pediococcus acidilactici*, *Clostridium sp000821305*, *Pauljensenia canis*, *Nanogingivalis gingivitcus*, *Clostridium baratii*, *Enterococcus gallinarum*, *Cellulosilyticaceae*, *Gemella paraticanis* palaticanis), Streptococcus fryi, Escherichia ruysiae, Citrobacter gillenii, Pseudomonas guguanensis, Klebsiella pneumoniae, Enterococcus B, Streptococcus minor, Clostridium paraputrificum (Clostridium T paraputrificum 208099, Clostridium T paraputrificum 207370), Clostridium dispolicum (Clostridium T disporicum_203972), Streptococcus oralis_E_351036, Clostridium_P, Bacillus_P_294101 subtilis_291504, Klebsiella barricolaClostridioides 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_T isatidis, Canibacter oris, Nanosynbacter lyticus, Proteus mirabilis, Clostridium_AQ innocuum, CCUG-7971 spG000499525, Fusobacterium_A necrogenes, Streptococcus, Anaerostipes cacae The process involves calculating animal insurance premiums using information regarding whether or not one or more bacteria selected from the group consisting of *Caccae*, *Enterococcus faecalis*, *Bacteroides fragilis*, and *Roseburia intestinalis* are present. A method for calculating insurance premiums for animals, wherein the animal is a dog or a cat.

8. A means of receiving data on the gut microbiota of animals other than humans, Among the aforementioned intestinal microbiota are Streptococcus constellatus, Streptococcus anginosus, Slackia exigua, Desulfovibrio R446353, Peptostreptococcus canis, Bulleidia moorei, Actinomyces timonensis, Pauljensenia cardiffensis, Nanoperiomorbus, Fusobacterium C simiae, and Corynebacterium canis. canis), Enterocloster bolteae, Bifidobacterium dentium, Parvimonas micra, Nanosynbacter, Flexilinea sp902786265, CAJPSE01 sp003860125, Campylobacter sp013201975, Fusobacterium Crussii, Actinomyces weissii, Fusobacterium C canifelinum, Saccharimonas arbogensis Corynebacterium freiburgense, Globicatella, Lactobacillus delburgii*Clostridium delbrueckii*, *Clostridium disporicum*, *Canibacter sp003859945*, *Pediococcus acidilactici*, *Clostridium sp000821305*, *Pauljensenia canis*, *Nanogingivalis gingivitcus*, *Clostridium baratii*, *Enterococcus gallinarum*, *Cellulosilyticaceae*, *Gemella paraticanis* palaticanis), Streptococcus fryi, Escherichia ruysiae, Citrobacter gillenii, Pseudomonas guguanensis, Klebsiella pneumoniae, Enterococcus B, Streptococcus minor, Clostridium paraputrificum (Clostridium T paraputrificum 208099, Clostridium T paraputrificum 207370), Clostridium dispolicum (Clostridium T disporicum_203972), Streptococcus oralis_E_351036, Clostridium_P, Bacillus_P_294101 subtilis_291504, Klebsiella barricolaClostridioides 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, Canibacter oris, Nanosynbacter lyticus, Proteus mirabilis, Clostridium innocuum (Clostridium_AQ innocuum), CCUG-7971 spG000499525, Fusobacterium necrogenes, Streptococcus, Anaerostipes cacae Enterococcus caccae, Enterococcus faecalis, Bacteroides fragilis, and Roseburia intestinalis.A determination means for determining or predicting the health status or future health status of an animal using information on whether or not it contains one or more species of bacteria selected from the group consisting of (intestinalis), Equipped with, The aforementioned determination means determines that if one or more bacteria selected from the aforementioned group are present, the animal's health is impaired or at high risk of being impaired in the future. The aforementioned animal is either a dog or a cat. A system for determining or predicting the health status of animals.

9. A determination means for determining or predicting the health status or future health status of the animal, (1) A means for determining whether an animal is suffering from a disease or predicting whether it will suffer from a disease in the future, wherein if the intestinal flora of the animal contains one or more bacteria selected from the group, the means for determining that the animal is suffering from a disease or is at high risk of suffering from a disease in the future. (2) A means for determining whether an animal's mental state is good or predicting whether an animal's mental state will be good in the future, and for determining that if the animal's intestinal microbiota contains one or more bacteria selected from the group, the animal's mental state is not good or there is a high risk that the animal's mental state will not be good in the future. (3) A means for determining whether the condition of an animal's coat is good or predicting whether the condition of an animal's coat will be good in the future, and if the animal's intestinal flora contains one or more bacteria selected from the group, a means for determining that the condition of the animal's coat is not good or that there is a high risk that the condition of the animal's coat will not be good in the future, Or, (4) A means for determining whether an animal's breath odor is good or predicting whether an animal's breath odor will be good in the future, and for determining that if the animal's intestinal flora contains one or more bacteria selected from the group, the animal's breath odor is not good or there is a high risk that the animal's breath odor will not be good in the future. The system for determining or predicting the health status of an animal according to claim 8.

10. The system for determining or predicting the health status of an animal according to claim 9, wherein the determination means for determining or predicting the health status or future health status 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 heart disease, liver disease, biliary tract disease, pancreatic disease, kidney disease, or cancer.

11. The animal health status determination or prediction system according to claim 9, wherein the determination means for determining or predicting the health status or future health status of the animal is 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, and whether the animal's mental state is good is whether the animal is timid or not.

12. A means of receiving data on the gut microbiota of animals other than humans, Among the aforementioned intestinal microbiota are Streptococcus constellatus, Streptococcus anginosus, Slackia exigua, Desulfovibrio R446353, Peptostreptococcus canis, Bulleidia moorei, Actinomyces timonensis, Pauljensenia cardiffensis, Nanoperiomorbus, Fusobacterium C simiae, and Corynebacterium canis. canis), Enterocloster bolteae, Bifidobacterium dentium, Parvimonas micra, Nanosynbacter, Flexilinea sp902786265, CAJPSE01 sp003860125, Campylobacter sp013201975, Fusobacterium Crussii, Actinomyces weissii, Fusobacterium C canifelinum, Saccharimonas arbogensis Corynebacterium freiburgense, Globicatella, Lactobacillus delburgii*Clostridium delbrueckii*, *Clostridium disporicum*, *Canibacter sp003859945*, *Pediococcus acidilactici*, *Clostridium sp000821305*, *Pauljensenia canis*, *Nanogingivalis gingivitcus*, *Clostridium baratii*, *Enterococcus gallinarum*, *Cellulosilyticaceae*, *Gemella paraticanis* palaticanis), Streptococcus fryi, Escherichia ruysiae, Citrobacter gillenii, Pseudomonas guguanensis, Klebsiella pneumoniae, Enterococcus B, Streptococcus minor, Clostridium paraputrificum (Clostridium T paraputrificum 208099, Clostridium T paraputrificum 207370), Clostridium dispolicum (Clostridium T disporicum_203972), Streptococcus oralis_E_351036, Clostridium_P, Bacillus_P_294101 subtilis_291504, Klebsiella barricolaClostridioides 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_T isatidis, Canibacter oris, Nanosynbacter lyticus, Proteus mirabilis An insurance premium calculation means for calculating the insurance premium for an animal using information on whether or not it contains one or more fungi selected from the group consisting of mirabilis, Clostridium_AQ innocuum, CCUG-7971 spG000499525, Fusobacterium_A necrogenes, Streptococcus, Anaerostipes caccae, Enterococcus_H_360604 faecalis, Bacteroides_H fragilis, and Roseburia intestinalis, Equipped with, An insurance premium calculation system in which the aforementioned animal is a dog or a cat.

13. In the gut microbiota of animals other than humans, the following species are found: Streptococcus constellatus, Streptococcus anginosus, Slackia exigua, Desulfovibrio R446353, Peptostreptococcus canis, Bulleidia moorei, Actinomyces timonensis, Pauljensenia cardiffensis, Nanoperiomorbus, Fusobacterium C simiae, and Corynebacterium canis. Canis), Enterocloster bolteae, Bifidobacterium dentium, Parvimonas micra, Nanosynbacter, Flexilinea sp902786265, CAJPSE01 sp003860125, Campylobacter sp013201975, Fusobacterium C russii, Actinomyces weissii, Fusobacterium C canifelinum, Saccharimonas arbogensis Corynebacterium freiburgense, Globicatella, Lactobacillus delburgii*Clostridium delbrueckii*, *Clostridium disporicum*, *Canibacter sp003859945*, *Pediococcus acidilactici*, *Clostridium sp000821305*, *Pauljensenia canis*, *Nanogingivalis gingivitcus*, *Clostridium baratii*, *Enterococcus gallinarum*, *Cellulosilyticaceae*, *Gemella paraticanis* palaticanis), Streptococcus fryi, Escherichia ruysiae, Citrobacter gillenii, Pseudomonas guguanensis, Klebsiella pneumoniae, Enterococcus B, Streptococcus minor, Clostridium paraputrificum (Clostridium T paraputrificum 208099, Clostridium T paraputrificum 207370), Clostridium dispolicum (Clostridium T disporicum_203972), Streptococcus oralis_E_351036, Clostridium_P, Bacillus_P_294101 subtilis_291504, Klebsiella barricolaClostridioides 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_T isatidis, Canibacter oris, Nanosynbacter lyticus, Proteus mirabilis, Clostridium_AQ innocuum, CCUG-7971 spG000499525, Fusobacterium_A necrogenes, Streptococcus, Anaerostipes cacae The process includes a step of predicting whether the animal will die within a predetermined period of time, using information on whether or not it contains one or more fungi selected from the group consisting of *Caccae*, *Enterococcus faecalis*, *Bacteroides fragilis*, and *Roseburia intestinalis*. In the above process, if one or more bacteria selected from the above group are present, it is determined that the animal has a high risk of dying within a predetermined period. A method for predicting the death of an animal, characterized in that the animal is a dog or a cat.

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

15. A means of receiving data on the gut microbiota of animals other than humans, Among the aforementioned intestinal microbiota are Streptococcus constellatus, Streptococcus anginosus, Slackia exigua, Desulfovibrio R446353, Peptostreptococcus canis, Bulleidia moorei, Actinomyces timonensis, Pauljensenia cardiffensis, Nanoperiomorbus, Fusobacterium C simiae, and Corynebacterium canis. canis), Enterocloster bolteae, Bifidobacterium dentium, Parvimonas micra, Nanosynbacter, Flexilinea sp902786265, CAJPSE01 sp003860125, Campylobacter sp013201975, Fusobacterium Crussii, Actinomyces weissii, Fusobacterium C canifelinum, Saccharimonas arbogensis Corynebacterium freiburgense, Globicatella, Lactobacillus delburgii*Clostridium delbrueckii*, *Clostridium disporicum*, *Canibacter sp003859945*, *Pediococcus acidilactici*, *Clostridium sp000821305*, *Pauljensenia canis*, *Nanogingivalis gingivitcus*, *Clostridium baratii*, *Enterococcus gallinarum*, *Cellulosilyticaceae*, *Gemella paraticanis* palaticanis), Streptococcus fryi, Escherichia ruysiae, Citrobacter gillenii, Pseudomonas guguanensis, Klebsiella pneumoniae, Enterococcus B, Streptococcus minor, Clostridium paraputrificum (Clostridium T paraputrificum 208099, Clostridium T paraputrificum 207370), Clostridium dispolicum (Clostridium T disporicum_203972), Streptococcus oralis_E_351036, Clostridium_P, Bacillus_P_294101 subtilis_291504, Klebsiella barricolaClostridioides 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, Canibacter oris, Nanosynbacter lyticus, Proteus mirabilis, Clostridium innocuum (Clostridium_AQ innocuum), CCUG-7971 spG000499525, Fusobacterium necrogenes, Streptococcus, Anaerostipes cacae Enterococcus caccae, Enterococcus faecalis, Bacteroides fragilis, and Roseburia intestinalis.A prediction means for determining or predicting whether an animal will die within a predetermined period of time, using information on whether or not it contains one or more species of bacteria selected from the group consisting of (intestinalis), Equipped with, The prediction means determines that if one or more bacteria selected from the group are present, the animal's health is impaired or at high risk of being impaired in the future. A mortality prediction system in which the aforementioned animal is a dog or a cat.

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 status of an animal, characterized by comprising the step of determining the health status of an animal or predicting its future health status using information on whether or not periodontal disease-related bacteria are present in the gut microbiota of an animal other than a human, The aforementioned periodontal disease-related bacteria are those 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 the same species of animals not suffering from periodontal disease, is greater than 1. The aforementioned animal is a dog or a cat. A method for determining or predicting the health status of animals. Odds ratio = Detection rate in animals with periodontal disease / Detection rate in the same species of animals without periodontal disease

18. The method for determining or predicting the health status of an animal according to claim 17, wherein the detection rate in animals suffering from periodontal disease is calculated by examining the bacterial composition of the intestinal microbiota 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 of the intestinal microbiota of 100 or more animals not suffering from periodontal disease.

19. The method for determining or predicting the health status of an animal according to claim 17, wherein the step of determining the health status of the animal or predicting its future health status is 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 the animal's mental state will be good 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 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, the following species were found: Streptococcus constellatus, Streptococcus anginosus, Slackia exigua, Desulfovibrio R446353, Peptostreptococcus canis, Bulleidia moorei, Actinomyces timonensis, Pauljensenia cardiffensis, Nanoperiomorbus, Fusobacterium C simiae, and Corynebacterium canis. Canis), Enterocloster bolteae, Bifidobacterium dentium, Parvimonas micra, Nanosynbacter, Flexilinea sp902786265, CAJPSE01 sp003860125, Campylobacter sp013201975, Fusobacterium C russii, Actinomyces weissii, Fusobacterium C canifelinum, Saccharimonas arbogensis Corynebacterium freiburgense, Globicatella, Lactobacillus delburgii*Clostridium delbrueckii*, *Clostridium disporicum*, *Canibacter sp003859945*, *Pediococcus acidilactici*, *Clostridium sp000821305*, *Pauljensenia canis*, *Nanogingivalis gingivitcus*, *Clostridium baratii*, *Enterococcus gallinarum*, *Cellulosilyticaceae*, *Gemella paraticanis* palaticanis), Streptococcus fryi, Escheritia Escherichia ruysiae, Citrobacter gillenii, Pseudomonas guguanensis, Klebsiella pneumoniae, Enterococcus B, Streptococcus minor, Clostridium paraputrificum (208099, 207370), Clostridium disporicum (203972), Streptococcus oralis oralis_E_351036), Clostridium_P, Bacillus_P_294101 subtilis_291504, Klebsiella variicola, Clostridioides_A difficile, Terrisporobacter glycolicus_239331, Clostridium_T neonatale, Bifidobacterium animalis, Terrisporobacter, Escheritia 710834 (Escherichia_710834), Klebsiella_724518 (Klebsiella_724518), Escherichia fergusonii, Clostridium saudiense (Clostridium_T saudiense), Clostridium tertium (Clostridium_T tertium), Enterobacter formaechey (Enterobacter_B_713587)hormaechei_712707), Bilophila wadsworthia, Clostridium_T isatidis, Canibacter oris, Nanosynbacter lyticus, Proteusmirabilis, Clostridium_AQ innocuum, CCUG-7971 spG000499525, Fusobacterium_A necrogenes, Streptococcus, Anaerostipes caccae, Enterococcus_H_360604 The process includes sterilizing, inhibiting, inactivating, or removing one or more fungi selected from the group consisting of Bacteroides faecalis, Bacteroides fragilis, and Roseburia intestinalis. A method for preventing disease, wherein the animal is a dog or a cat.

21. A means of receiving data on the gut microbiota of animals other than humans, Among the aforementioned intestinal microbiota are Streptococcus constellatus, Streptococcus anginosus, Slackia exigua, Desulfovibrio R446353, Peptostreptococcus canis, Bulleidia moorei, Actinomyces timonensis, Pauljensenia cardiffensis, Nanoperiomorbus, Fusobacterium C simiae, and Corynebacterium canis. canis), Enterocloster bolteae, Bifidobacterium dentium, Parvimonas micra, Nanosynbacter, Flexilinea sp902786265, CAJPSE01 sp003860125, Campylobacter sp013201975, Fusobacterium Crussii, Actinomyces weissii, Fusobacterium C canifelinum, Saccharimonas arbogensis Corynebacterium freiburgense, Globicatella, Lactobacillus delburgii*Clostridium delbrueckii*, *Clostridium disporicum*, *Canibacter sp003859945*, *Pediococcus acidilactici*, *Clostridium sp000821305*, *Pauljensenia canis*, *Nanozingivalis* Nanogingivalis gingivitcus, Clostridium baratii, Enterococcus gallinarum, Cellulosilyticaceae, Gemella palaticanis, Streptococcus fryi, Escherichia ruysiae, Citrobacter gillenii, Pseudomonas guguanensis, Klebsiella pneumoniae pneumoniae_718977), Enterococcus_B, Streptococcus minor, Clostridium_T paraputrificum_208099, Clostridium_T paraputrificum_207370, Clostridium_T disporicum_203972, Streptococcus oralis_E_351036, Clostridium_P, Bacillus_P_294101 subtilis_291504, Klebsiella variicola, Clostridioides_A difficile), Terrisporobacter glycolicus_239331, Clostridium_T neonatale, Bifidobacterium animalis, Terrisporobacter, Escherichia710834 (Escherichia_710834), Klebsiella_724518, Escherichia fergusonii, Clostridium_T saudiense, Clostridium_T tertium, Enterobacter_B_713587 hormaechei_712707, Bilophila wadsworthia, Clostridium_T isatidis, Canibacter oris, Nanosynbacter lyticus An alert system that prompts the user to perform oral care for their animal if one or more bacteria selected from the group consisting of *lyticus*, *Proteus mirabilis*, *Clostridium_AQ innocuum*, CCUG-7971 spG000499525*, *Fusobacterium_A necrogenes*, *Streptococcus*, *Anaerostipes caccae*, *Enterococcus_H_360604 faecalis*, *Bacteroides_H fragilis*, and *Roseburia intestinalis* are present. Equipped with, A disease prevention system for animals, wherein the animal is a dog or a cat.

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