Classifications

• • G—PHYSICS
  • G01—MEASURING; TESTING
  • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
  • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
  • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
  • G01N33/483—Physical analysis of biological material
  • G01N33/497—Physical analysis of biological material of gaseous biological material, e.g. breath
• • G—PHYSICS
  • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
  • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
  • G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
• • G—PHYSICS
  • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
  • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
  • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
  • G16H50/30—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indices; for individual health risk assessment

Definitions

• the present invention relates to a disease prediction system and a disease prediction method, and more particularly to a disease prediction system and a disease prediction method that provide information on the future possibility of an animal contracting a disease from information on the animal's bad breath, or information on bad breath and intestinal bacteria.
• Pets such as dogs, cats, and rabbits, as well as livestock such as cows and pigs, are irreplaceable to humans.
• Pets such as dogs, cats, and rabbits, as well as livestock such as cows and pigs, are irreplaceable to humans.
• the average lifespan of animals kept by humans has increased significantly, it has become more common for animals to suffer from some kind of disease during their lives, and the rising medical expenses borne by owners has become a problem.
• Patent Document 1 discloses an intestinal flora adjusting or improving composition that has the effect of effectively adjusting or improving the intestinal flora by increasing the number of bacteria in the Bacteroidetes phylum and decreasing the number of bacteria in the Firmicutes phylum in the intestinal flora, but does not disclose a method for predicting whether an animal will become ill based on data regarding the animal's intestinal flora.
• Patent Document 2 also describes a risk assessment system that assesses a patient's risk of systemic disease based on risk information about the oral environment, such as information about the patient's level of halitosis, but this system is intended for humans and requires tests such as periodontal pocket examinations, oral specimen examinations, or nucleic acid amplification tests for periodontal disease bacteria.
• the present invention aims to provide a disease prediction system that can predict the possibility of animals other than humans becoming afflicted with a disease in a simple manner.
• the inventors analyzed and examined a huge amount of data on the bad breath of animals covered by pet insurance and whether or not the animals have filed insurance claims, i.e., whether or not they have contracted a disease. As a result, they discovered that it is possible to use information on an animal's bad breath to predict whether or not the animal will contract a disease in the future, and thus completed the present invention. Furthermore, the inventors also discovered that by combining information on an animal's bad breath with information on the animal's intestinal bacteria, it is possible to more accurately predict whether or not the animal will contract a disease.
• a disease prediction system comprising a prediction unit that uses information about an animal's bad breath to predict whether the animal will develop a disease within a specified period of time or whether the animal is currently suffering from a disease.
• the disease prediction system of [1] further comprising a proposal unit that proposes measures to reduce the possibility of contracting the disease.
• the disease prediction system of [1] wherein the information regarding the animal's bad breath is a questionnaire regarding the presence or absence of bad breath obtained from the animal's owner or manager, or the results of odor measurement using an odor evaluation device.
• the information regarding the animal's intestinal bacteria is information regarding the diversity of the intestinal microbiota.
• a method for predicting disease in an animal in which a computer uses information about the animal's malodor to predict whether the animal will develop a disease within a specified period of time or whether the animal is currently suffering from a disease.
• a health condition prediction system comprising an acquisition unit that acquires halitosis information regarding an animal's halitosis, and a health condition prediction unit that uses the information regarding the halitosis to predict the health condition or future health condition of the animal.
• the health condition prediction system of [10] wherein the health condition prediction unit predicts the health condition or future health condition of the animal using information about the animal's bad breath and information about the animal's intestinal bacteria.
• a health condition prediction program that causes a computer to execute the steps of acquiring halitosis information regarding an animal's halitosis, and predicting the health condition or future health condition of the animal using the information regarding the halitosis.
• An information processing system comprising: an acquisition unit that acquires information regarding an animal's bad breath; and an output unit that outputs information encouraging care of the animal if the animal has bad breath based on the bad breath information.
• An information processing program that causes a computer to execute the steps of acquiring information regarding an animal's bad breath, and encouraging care of the animal if the animal has bad breath based on the bad breath information.
• the present invention makes it possible to provide a disease prediction system and a disease prediction method that predict the possibility that an animal will contract a disease in the future.
• FIG. 1 is a schematic diagram of a disease prediction system of the present invention.
• FIG. 1 is a graph showing the relationship between the number of food types and the diversity of intestinal bacteria.
• FIG. 1 is a flow chart of the disease prediction method of the present invention.
• FIG. 13 is a graph showing the results of a reference example.
• FIG. 13 is a graph showing the results of a reference example.
• FIG. 13 is a graph showing the results of a reference example.
• FIG. 13 is a graph showing the results of a reference example.
• FIG. 13 is a graph showing the results of a reference example.
• FIG. 13 is a graph showing the results of a reference example.
• FIG. 13 is a graph showing the results of a reference example.
• FIG. 13 is a graph showing the results of a reference example.
• FIG. 13 is a graph showing the results of a reference example.
• FIG. 13 is a graph showing the results of a reference example.
• FIG. 13 is a graph showing
• FIG. 13 is a graph showing the results of a reference example.
• FIG. 13 is a graph showing the results of a reference example.
• FIG. 13 is a graph showing the results of a reference example.
• FIG. 13 is a graph showing the results of a reference example.
• FIG. 13 is a graph showing the results of a reference example.
• FIG. 13 is a graph showing the results of a reference example.
• FIG. 13 is a graph showing the results of a reference example.
• FIG. 13 is a graph showing the results of a reference example.
• FIG. 1 is a schematic diagram of a disease prediction system of the present invention.
• FIG. 1 is a flow chart of the disease prediction method of the present invention.
• FIG. 13 is a graph showing the results of a reference example (prevalence of bad breath and periodontal disease in dogs).
• FIG. 1 is a schematic diagram of a disease prediction system of the present invention.
• FIG. 1 is a flow chart of the disease prediction method of the present
• FIG. 1 is a graph showing the results of a reference example (prevalence of halitosis and stomatitis in dogs).
• FIG. 1 is a graph showing the results of a reference example (prevalence of halitosis and oral tumors in dogs).
• FIG. 1 is a graph showing the results of a reference example (prevalence of bad breath and digestive system diseases in dogs).
• FIG. 1 is a graph showing the results of a reference example (bad breath and the prevalence of blood and hematopoietic diseases in dogs). This is a graph showing the results of a reference example (bad breath in dogs and the prevalence of blood and hematopoietic tumors).
• FIG. 1 is a graph showing the results of a reference example (prevalence of halitosis and stomatitis in dogs).
• FIG. 1 is a graph showing the results of a reference example (prevalence of halitosis and oral tumors in dogs).
• FIG. 1 is a
• FIG. 1 is a graph showing the results of a reference example (prevalence of bad breath and neurological diseases in dogs).
• FIG. 13 is a graph showing the results of a reference example (prevalence of bad breath and epilepsy in dogs).
• FIG. 1 is a graph showing the results of a reference example (bad breath and prevalence of brain tumors in dogs).
• FIG. 1 is a graph showing the results of a reference example (prevalence of bad breath and endocrine system diseases in dogs).
• FIG. 1 is a graph showing the results of a reference example (prevalence of bad breath and diabetes in dogs).
• FIG. 1 is a graph showing the results of a reference example (prevalence of halitosis and uveitis in dogs).
• FIG. 13 is a graph showing the results of a reference example (prevalence of bad breath and lethargy in dogs).
• FIG. 13 is a graph showing the results of a reference example (dog bad breath and mortality rate).
• FIG. 13 is a graph showing the results of a reference example (rate of dog breath odor and coat gloss being an issue).
• FIG. 13 is a graph showing the results of a reference example (dogs' bad breath and rate of stranger shyness).
• FIG. 13 is a graph showing the results of a reference example (dog breath odor and rate of animal shyness). This is a graph showing the results of a reference example (prevalence of bad breath and skin tumors in dogs).
• FIG. 13 is a graph showing the results of a reference example (prevalence of cat bad breath and periodontal disease).
• FIG. 1 is a graph showing the results of a reference example (prevalence of cat halitosis and stomatitis).
• FIG. 1 is a graph showing the results of a reference example (prevalence of feline halitosis and oral tumors).
• FIG. 1 is a graph showing the results of a reference example (prevalence of cat breath odor and digestive system diseases).
• FIG. 1 is a graph showing the results of a reference example (cat halitosis and the prevalence of gastritis, gastroenteritis, and enteritis).
• FIG. 1 is a graph showing the results of a reference example (cat breath odor and prevalence of cardiovascular disease).
• FIG. 1 is a graph showing the results of a reference example (cat breath odor and prevalence of cardiovascular disease).
• FIG. 13 is a graph showing the results of a reference example (prevalence of cat breath odor and valvular disease).
• FIG. 1 is a graph showing the results of a reference example (prevalence of cat halitosis and chronic kidney disease).
• FIG. 1 is a graph showing the results of a reference example (prevalence of cat bad breath and atopic dermatitis).
• FIG. 1 is a graph showing the results of a reference example (prevalence of cat breath odor and allergic dermatitis).
• FIG. 1 is a graph showing the results of a reference example (cat breath odor and prevalence of diabetes).
• FIG. 1 is a graph showing the results of a reference example (cat halitosis and prevalence of neoplastic diseases).
• FIG. 1 is a graph showing the results of a reference example (cat breath odor and prevalence of blood and immune system diseases).
• FIG. 1 is a graph showing the results of a reference example (cat breath odor and prevalence of blood and hematopoietic tumors).
• FIG. 1 is a graph showing the results of a reference example (cat breath odor and mortality rate).
• FIG. 13 is a graph showing the results of a reference example (rate of being bothered by cat's bad breath and fur gloss).
• FIG. 13 is a graph showing the results of a reference example (cat's bad breath and rate of stranger shyness).
• FIG. 13 is a graph showing the results of a reference example (cat breath odor and rate of animal shyness).
• the disease prediction system of the present invention is characterized by comprising a prediction unit that predicts whether an animal will develop a disease within a predetermined period of time using information about the animal's bad breath. Examples of animals include dogs, cats, birds, rabbits, ferrets, meerkats, etc. In the disease prediction system of the present invention, it is preferable that the prediction unit uses information regarding the animal's bad breath and information regarding the animal's intestinal bacteria to predict whether the animal will develop a disease within a specified period of time.
• a comprehensive or specific aspect of the disease prediction system of the present invention may be realized by a system, an apparatus, a server, a method, an integrated circuit, a computer program, or a storage medium, or may be realized by any combination of a system, an apparatus, a method, an integrated circuit, a computer program, a server, or a storage medium.
• the disease prediction system of the present invention only needs to have the above configuration, and can be combined with other systems or terminals such as a medical record system of a veterinary clinic, a pet insurance system, or a server for an app for pet insurance subscribers, or can be connected via a network such as the Internet.
• the prediction unit of the present invention is a means for predicting and judging whether an animal will contract a disease within a predetermined period of time or whether the animal is currently contracting a disease based on information about the animal's bad breath.
• the prediction unit is, for example, composed of a processor such as a CPU, and calculates the possibility of contracting a disease using a program for predictive judgment, a trained model or software including the trained model, or a program.
• the program, the trained model, or software including the trained model may be stored in a separate storage device.
• the method of predictive judgment by the prediction unit is not particularly limited.
• a processor predicts and judges whether an animal will contract a disease within a predetermined period of time or whether the animal is currently contracting a disease based on information about the animal's bad breath using a preset program.
• a configuration may be adopted in which a score is calculated according to a preset standard from information about the presence or absence and the degree of bad breath, and the risk of contracting a disease is judged based on a total score obtained by adding up the scores. In other words, if the total score is equal to or greater than a predetermined value, the risk of contracting a disease is high, and if the total score is less than a predetermined value, the risk of contracting a disease is low.
• basic information about the animal such as age, breed, sex, medical history, weight, etc., can also be used.
• the prediction unit of the present invention uses information about the animal's halitosis to predict whether the animal will develop a disease, whether an asymptomatic disease will become manifest, or whether the animal is currently suffering from a disease, preferably within a predetermined period of time, more preferably from the time of reception, the time of collecting the halitosis sample, or the time of determining the presence or absence and severity of halitosis.
• the predetermined period of time is preferably within 3 years, more preferably within 2 years, even more preferably within 1 year, and particularly preferably within 180 days.
• the information on bad breath is information or data related to the strength of bad breath, and may be either a subjective judgment result or an objective numerical value. Preferably, it is the result of a questionnaire regarding the presence or absence and strength of bad breath obtained from the animal's owner or manager, or the result of an odor measurement using an odor judgment device. It may also be the result of a judgment of the presence or absence and strength of bad breath by a veterinarian at an animal hospital.
• the information on bad breath may also be information obtained at multiple different dates and times within a specific period of time, or information that combines such information. For example, information on bad breath is obtained two, three, four, or ten times at different dates and times during a period of two weeks, one month, or three months, and then the information is averaged and combined. In this way, since the information on bad breath is only information on a specific date and time, it is possible to avoid using cases in which a person does not usually have bad breath but happens to have bad breath on that date and time for prediction.
• the prediction unit of the present invention preferably uses information about the animal's intestinal bacteria in addition to information about the animal's halitosis for disease prediction. By using information about halitosis and information about intestinal bacteria, it is expected that disease predictions can be made more accurately than when only one of them is used.
• the information on the intestinal bacteria of an animal may be any information on intestinal bacteria, for example, information or data on the number of intestinal bacterial species, the presence or absence, ratio, content rate, and diversity of specific bacteria or bacteria belonging to specific families or phyla, with data on the diversity of the intestinal flora (also called diversity data) being preferred. It is also preferable that the information on bad breath and the information on intestinal bacteria are obtained close to each other, preferably not more than one year apart, more preferably not more than 180 days apart, and even more preferably not more than 60 days apart. This is because it is expected that the information on bad breath and the information on intestinal bacteria will more accurately reflect the condition of the animal if they are obtained close to each other.
• Diversity data is data related to the diversity of bacteria in the intestinal flora of an animal.
• a high diversity of the intestinal flora means that the intestinal flora contains a wide variety of bacteria.
• diversity indices There are several types of indicators that represent diversity, so-called diversity indices, and any known indices may be used in the present invention. Examples of diversity indices include the Shannon-Wiener diversity index, also known as the Shannon index, and the Simpson diversity index, with the Shannon index being preferred.
• Measurement of data related to intestinal bacteria can be performed using known metagenomic analysis methods such as amplicon sequencing using a sequencer such as NGS, or a method of analyzing the bacterial flora. For example, a method of collecting a sample such as feces from an animal and analyzing the DNA or RNA sequence information of all organisms contained in the sample using a next-generation sequencer to identify the organisms contained in the sample is exemplified.
• composition data of the bacteria in the sample can be processed by software, or by referring to gene databases such as Genbank, Greengenes, and SILVA database, to determine the species of the bacteria contained in the sample, and the occupancy data and diversity data of the intestinal flora of the animal can be measured.
• amplicon analysis bacterial flora analysis
• NGS next-generation sequencer
• DNA is extracted from samples such as feces using a DNA extraction reagent, and the 16SrRNA gene is amplified from the extracted DNA by PCR.
• the amplified DNA fragments are comprehensively sequenced using NGS, low-quality reads and chimeric sequences are removed, and then the sequences are clustered to perform OTU (operational taxonomic unit) analysis.
• OTU operational taxonomic unit that treats sequences with a certain level of similarity (e.g., 96-97% or more homology) as a single bacterial species.
• the number of OTUs represents the number of bacterial species that make up the bacterial flora, and the number of reads belonging to the same OTU represents the relative abundance of that species.
• a representative sequence can be selected from the number of reads belonging to each OTU, and the family name and genus name can be identified by database search. In this way, it is possible to measure data about gut bacteria, such as the occupancy rate of bacteria belonging to specific families and the diversity index of the gut microbiota.
• the type of disease that is the subject of the present invention is not particularly limited, and examples include skin diseases, otological diseases, musculoskeletal diseases, ophthalmic diseases, digestive diseases, systemic diseases, urinary diseases, hepatic, biliary and pancreatic diseases, circulatory diseases, nervous system diseases, respiratory diseases, dental and oral diseases, endocrine diseases, reproductive diseases, and blood and hematopoietic diseases.
• Examples of skin diseases include dermatitis, atopic dermatitis, pyoderma, skin tumors of undetermined pathology, lipoma, histiocytoma (skin), mast cell tumor (skin), melanoma/melanocytoma, cutaneous lymphoma, perianal tumor (including perianal adenocarcinoma), tumors of the skin other than those mentioned above, and allergic dermatitis.
• Examples of otological diseases include otitis externa and otitis media.
• Examples of musculoskeletal disorders include patellar luxation and herniated disc.
• ophthalmological diseases include conjunctivitis, eye discharge, keratitis, corneal ulcer/erosion, epiphora, cataracts, glaucoma, and uveitis.
• Digestive system diseases include, for example, gastritis, enteritis, gastroenteritis, and inflammatory bowel disease (IBD).
• Systemic illnesses include, for example, lethargy and collapse.
• urinary system diseases include cystitis, urolithiasis, and chronic kidney disease.
• diseases of the hepatic, biliary and pancreatic systems include cholesteatosis and chronic renal failure.
• circulatory system diseases include valvular disease and cardiomyopathy.
• Nervous system disorders include, for example, epilepsy, seizures, and brain tumors.
• Respiratory diseases include, for example, coughing, rhinitis, tracheal collapse, and bronchial stenosis.
• dental and oral diseases include periodontal disease, stomatitis, and oral tumors.
• Endocrine diseases include, for example, hypothyroidism and diabetes.
• Reproductive system diseases include, for example, mammary tumors and balanitis.
• Examples of blood and hematopoietic system diseases include lymphoid tissue tumors, thrombocytopenia, multicentric lymphoma, hemangiosarcoma, and other lymphoid/hematopoietic tissue tumors.
• the prediction unit of the present invention may be configured to make a prediction judgment using a trained model.
• a trained model is preferably a trained model that has learned the relationship between the information on the animal's halitosis, or the information on the animal's halitosis and intestinal bacteria, and the information on the halitosis, or the information on whether the animal has suffered from a specific disease within a predetermined period from the time when the sample for the information analysis on the intestinal bacteria was obtained or the intestinal flora was analyzed, or the information on the halitosis, or the information on whether the animal had a disease at the time when the sample for the information analysis on the intestinal bacteria was obtained or the intestinal flora was analyzed.
• a trained model As a trained model, a trained model that has been trained using the information on the animal's halitosis, or the information on the animal's halitosis and intestinal bacteria, and the information on the halitosis, or the information on whether the animal has suffered from a specific disease within a predetermined period from the time when the sample for the information analysis on the intestinal bacteria was obtained or the intestinal flora was analyzed, or the information on the halitosis, or the information on whether the animal had a disease at the time when the sample for the information analysis on the intestinal bacteria was obtained or the intestinal flora was analyzed, as teacher data, is preferable.
• the predetermined period in the information regarding whether or not an animal has contracted a specific disease within such a predetermined period used in the training data is preferably within three years, more preferably within two years, even more preferably within one year, and particularly preferably within 180 days.
• whether or not an animal has contracted a disease can be replaced with a dummy variable.
• Information regarding whether or not an animal has contracted a disease within a predetermined period can be obtained, for example, from a veterinary clinic or an insured owner in connection with an insurance claim (also called an "accident").
• the trained model is preferably artificial intelligence (AI).
• Artificial intelligence is software or a system that uses a computer to imitate the intellectual tasks performed by the human brain, and specifically refers to computer programs that understand natural language used by humans, perform logical inference, and learn from experience.
• the artificial intelligence may be either general-purpose or specialized, and may be any of deep neural networks, convolutional neural networks, etc., and publicly available software may be used.
• Deep learning is an advanced version of machine learning, and is characterized by its ability to automatically find features.
• the learning method for generating a trained model is not particularly limited, and publicly available software can be used.
• DIGITS the Deep Learning GPU Training System
• NVIDIA the Deep Learning GPU Training System
• training may be performed using a known support vector machine method published in, for example, "Introduction to Support Vector Machines" (Kyoritsu Shuppan).
• Machine learning can be either unsupervised learning or supervised learning, but supervised learning is preferred. There are no particular limitations on the supervised learning method, and examples include decision trees, ensemble learning, and gradient boosting. Examples of publicly available machine learning algorithms include XGBoost, CatBoost, and LightGBM.
• a trained model may be generated for each individual disease, or one that can handle multiple diseases may be generated.
• training is performed using training data for an animal suffering from a specific disease, such as information on halitosis or information on halitosis and information on intestinal bacteria acquired a predetermined period before the animal was infected with the disease, the fact that the animal had the disease, and, for comparison, information on halitosis or information on halitosis and information on intestinal bacteria of an animal that has not been infected with the disease for a predetermined period since the information on halitosis or information on intestinal bacteria was acquired, and the fact that the animal has not been infected with the disease for a predetermined period.
• training multiple diseases together for example, training can be performed in a similar manner to that described above without separating the training data by type of disease.
• the prediction system of the present invention preferably includes an acquisition unit.
• the acquisition unit receives input of information about the halitosis of an animal whose disease is to be predicted, and information about intestinal bacteria.
• the configuration of the acquisition unit is not particularly limited, and any known configuration for receiving or acquiring information or data can be adopted.
• the method of receiving information or data may be any method, such as inputting or transmitting data to a terminal.
• the configuration may be such that the transmission or input of diversity data from an external terminal or computer is received via a network such as the Internet.
• the format of the output of the prediction result by the prediction unit of the present invention is not particularly limited.
• a prediction judgment can be output by displaying, for example, "There is a possibility that you will suffer from a disease within the next year,”"There is a low possibility that you will suffer from a disease within the next year,” or "There is a high possibility that you are currently suffering from a disease.”
• a numerical value comparing the possibility of suffering from a disease between a group with bad breath and a group without bad breath, such as "You are twice as likely to suffer from heart disease as a dog without bad breath.”
• a numerical value comparing the possibility of suffering from a disease between a group with bad breath and a group without bad breath, such as "You are twice as likely to suffer
• the disease prediction system of the present invention may further include a suggestion unit that suggests measures to reduce the possibility of contracting a disease depending on the disease prediction result.
• the suggestion unit can receive the prediction result output from the prediction unit, and depending on the prediction result, suggest or recommend a diet for preventing the disease for each predicted disease, a supplement containing bacteria that makes it less likely to cause the disease, a low-salt, low-calorie diet, a low-carbohydrate diet, a diet menu, etc.
• the suggestion unit may have a trained model.
• beverages, meals, and supplements for preventing diseases can be manufactured or customized according to the prediction results output by the disease prediction system or disease prediction method of the present invention.
• Services related to disease prevention can also take the form of prediction by the disease prediction system or disease prediction method of the present invention, provision of the prediction results, manufacturing or customizing beverages, meals, and supplements according to the prediction results, and proposing and recommending the beverages, meals, and supplements.
• the beverages, meals, and supplements include beverages for diet therapy, diet foods, nutritional supplement additives, and the like. In this way, by proposing, manufacturing, and customizing meals and foods based on the prediction results, it is hoped that the onset of the disease can be delayed and symptoms can be improved or alleviated.
• the disease prediction system of the present invention can also be an insurance premium calculation system equipped with a premium calculation unit in addition to the prediction unit.
• the premium calculation unit uses information about the halitosis of the animal to be insured to determine the insurance premium for the animal according to the disease onset predicted and output by the prediction unit, or corrects or adjusts the insurance premium calculated based on basic information such as the animal's age, breed, and medical history according to the disease onset prediction result.
• information such as the animal's facial image, type, breed, age, sex, weight, and medical history may be used to determine the insurance premium.
• the user in addition to applying for pet insurance, the user can send in information about the pet's bad breath and samples such as fecal samples, and at the same time receive a card indicating that the pet has been insured (a health insurance card for pets), and can obtain the pet's insurance premium and a prediction of future disease incidence.
• a health insurance card for pets a health insurance card for pets
• terminal 40 is a terminal used by a user (an owner, a veterinarian, etc.). Examples of terminal 40 include a personal computer, a smartphone, and a tablet terminal. Terminal 40 includes a processing unit such as a CPU, a storage unit such as a hard disk, a ROM, or a RAM, a display unit such as a liquid crystal panel, an input unit such as a mouse, a keyboard, or a touch panel, and a communication unit such as a network adapter. The user accesses the server 1 from terminal 40 and inputs and transmits information regarding the breath of the animal to be predicted, a facial image (photograph), and information such as the animal's type, breed, age at the time of photographing, weight, medical history, etc. In addition, the user can receive the disease prediction results from the server 1 by accessing the server 1 with the terminal 40 .
• a processing unit such as a CPU
• storage unit such as a hard disk, a ROM, or a RAM
• a display unit such as a liquid crystal panel
• the user receives a fecal sample collection kit for investigating the intestinal flora of the animal to be predicted, and sends the fecal sample to a company that measures the intestinal flora (not shown).
• the company measures the intestinal flora of the animal and obtains data on the intestinal flora, for example, the occupancy rate or ratio of a specific bacterium, or data on diversity such as the Shannon index.
• the company may directly input and transmit the data on the intestinal flora of the pet to the acquisition unit 31 of the server via its own terminal, or the company may separately send the data on the intestinal flora of the pet to the user by mail or email, and the user may input and transmit the data on the intestinal flora to the acquisition unit 31 through the terminal 40.
• the prediction system of the present invention receives input, transmission, upload, etc. from the company or user, and obtains information on the intestinal bacteria by the acquisition unit 31.
• the method of analyzing the intestinal flora and the method of generating information about intestinal bacteria are not particularly limited. For example, instead of requesting a company to do the analysis, the user may analyze a fecal sample to obtain information about intestinal bacteria.
• the server is configured by a computer, but it may be any device as long as it has the functions related to the present invention.
• the storage unit 10 is composed of, for example, a ROM, a RAM, or a hard disk.
• the storage unit 10 stores information processing programs for operating each unit of the server, and in particular, a prediction program 11 and, if necessary, an insurance premium calculation program 12.
• the insurance premium calculation program 12 may be omitted.
• the prediction program 11 is software or a program stored in the storage unit, and is loaded into the processing and calculation unit (CPU) 20, which is configured as a prediction unit and receives information about the halitosis of the insured animal input by the user or the company that performed the intestinal flora measurement as described above, and outputs a prediction of whether the animal will contract a disease within a specified period (e.g., within six months) from the time the information about the halitosis was obtained or input, or whether the animal is currently contracting a disease.
• the prediction program is a program for predicting whether the animal will contract a disease within a specified period or whether the animal is currently contracting a disease, based on information about the animal's halitosis.
• the prediction program may also include a trained model.
• the trained model is preferably a trained model that has learned the relationship between information on the animal's halitosis, or information on the animal's halitosis and intestinal bacteria, and information on whether the animal has suffered from a specific disease within a predetermined period from the time of obtaining a sample for information analysis on intestinal bacteria or analyzing the intestinal flora, or information on the halitosis and information on whether the animal had a disease at the time of obtaining a sample for information analysis on intestinal bacteria or analyzing the intestinal flora.
• a trained model that has been trained using as training data information on the animal's halitosis, or information on the animal's halitosis and intestinal bacteria, and information on whether the animal has contracted a specific disease within a specified period from the time when the sample for the intestinal bacteria information analysis was obtained or the intestinal flora was analyzed, or information on the halitosis, or information on whether the animal had a disease at the time when the sample for the intestinal bacteria information analysis was obtained or the intestinal flora was analyzed is preferable.
• the specified period in the information on whether or not the animal has contracted a specific disease within a specified period used in such training data is preferably within three years, more preferably within two years, even more preferably within one year, and particularly preferably within 180 days.
• whether or not the animal has contracted a disease can be replaced with a dummy variable.
• Information on whether or not the animal has contracted a disease within a specified period can be obtained, for example, from a veterinary clinic or an insured owner in connection with the fact of an insurance claim.
• the insurance premium calculation program 12 is software or a program stored in a storage unit, and is loaded into the processing and calculation unit (CPU) 20, which is configured as an insurance premium calculation unit and calculates the insurance premium for the animal from the disease incidence prediction output by the prediction unit and the information such as the type, breed, age, weight, medical history, etc. of the animal input by the user.
• the software is software for classifying insurance premiums according to the type, breed, age, weight, medical history, etc. of the animal, and finally correcting the grade by taking into account the disease incidence prediction output by the prediction unit, thereby calculating the final insurance premium.
• the insurance premium calculation program 12 may also include a trained model.
• An example of a trained model is a model that has learned the relationship between information about the animal's bad breath and the insurance premium set for the animal, and is preferably a model that has been trained using information about the animal's bad breath and the insurance premium set for the animal as training data.
• the trained model may be trained using information on intestinal bacteria, the type, breed, age, weight, medical history, and other information on bad breath.
• the processing calculation unit 20 is, for example, a central processing unit (CPU), and uses a prediction program 11 and an insurance premium calculation program 12 stored in the memory unit to predict the onset of disease and calculate insurance premiums.
• CPU central processing unit
• the interface unit (communication unit) 30 includes an acquisition unit 31 and an output unit 32, and receives information on animal breath odor, intestinal bacterial flora diversity data, and other information from the user's terminal, and outputs disease incidence predictions and insurance premium calculation results to the user's terminal.
• the dog's identification information (information for identifying the individual dog, such as the microchip identification number, a uniquely assigned ID, the owner's name, the dog's name, etc.) is obtained from the dog's owner who visits the veterinary clinic through interviews, etc., and a questionnaire is also conducted regarding the presence or absence of bad breath. For example, if the result of the questionnaire is "bad breath,” the identification information and the questionnaire result "bad breath" are entered into a terminal connected to the Internet, and the questionnaire result is uploaded to the acquisition unit of the disease prediction system of the present invention via the terminal.
• the acquisition unit acquires information on the diversity of the intestinal flora (Shannon index, etc.) from the database based on the identification information of the animal, etc.
• the information on the diversity of the intestinal flora is stored in the database in advance. That is, a veterinarian receives the dog's feces from the owner and sends it to an intestinal bacteria analysis service provider. The analysis results received from the analysis service provider are then entered into the disease prediction terminal of the present invention, and the questionnaire results are uploaded to the acquisition unit of the disease prediction system of the present invention via the terminal. The uploaded information on the diversity of the intestinal flora is linked to the dog's identification information and stored in the database.
• the prediction unit (the processing and calculation unit that reads the prediction program) predicts whether the target dog will develop a disease within a specified period of time based on the information about diversity and the presence or absence of bad breath, outputs this to the output unit, and the output result is displayed on the terminal.
• veterinarians can decide whether to perform tests to diagnose illnesses, such as blood tests or ultrasound scans. They can also use the data to decide whether to suggest health checks to pet owners.
• the owner inputs information about the presence or absence of bad breath and the food currently being fed to the dog into the input screen of the app displayed on the smartphone.
• the input contents of the app are then uploaded to the acquisition unit of the disease prediction system of the present invention via the Internet.
• the information about food can be, for example, the number of types of food.
• Figure 2A is a graph of the information on the number of types of food fed to 38,267 dogs (any breed, 0-3 years old) and the average value of the Shannon index of the intestinal flora.
• Figure 2B is a graph of the information on the number of types of food fed to 39,672 cats (any breed, 0-3 years old) and the average value of the Shannon index of the intestinal flora. From each graph, it can be seen that the Shannon index tends to be higher when the number of types of food is greater. For this reason, if the owner is only feeding their pet one type of food, the suggestion unit will suggest feeding the pet multiple types of food. In addition, by increasing the number of types of food, it is expected that the Shannon index will increase and the incidence rate will decrease, and this prediction will be sent to the owner's mobile device.
• the disease prediction system of the present invention may also include a suggestion unit.
• the suggestion unit may, for example, select an oral care product based on information about bad breath, output it, and send it to the owner's mobile device. This can be achieved by registering oral care products according to the level of bad breath and other information (age, breed, personality, etc.) in a database in advance, and when the acquisition unit receives information about the dog's bad breath and other information, the suggestion unit selects the product best suited to the dog from the database.
• pet owners can learn not only the likelihood of their dog contracting a disease in the following year, but also the measures they can take to reduce the likelihood of their dog contracting that disease.
• the disease prediction method of the present invention is a disease prediction method for animals, in which a computer uses information about the animal's halitosis to predict whether the animal will develop a disease within a predetermined period of time.
• a computer uses information about the animal's halitosis to predict whether the animal will develop a disease within a predetermined period of time.
• Each configuration of the information about the animal's halitosis is the same as that in the disease prediction system described above.
• FIG. 3 is a diagram outlining a disease prediction method according to one embodiment of the present invention.
• the disease prediction method according to this embodiment is a method for predicting disease using information about an animal's bad breath and information about intestinal bacteria. That is, the method includes, in order, step S1 of acquiring information about the animal's bad breath, step S2 of acquiring information about intestinal bacteria, step S3 of predicting the possibility of the animal suffering from a disease from the information about the animal's bad breath and the information about intestinal bacteria, and step S4 of outputting and transmitting the calculated prediction result of the possibility of suffering from a disease.
• FIG. 20 is a diagram outlining a disease prediction method according to another embodiment of the present invention.
• the disease prediction method according to this embodiment is a method for predicting disease using information about an animal's halitosis. That is, the method includes, in order, step S11 of acquiring information about the animal's halitosis, step S12 of predicting the possibility of the animal suffering from a disease from the information about the animal's halitosis, and step S13 of outputting and transmitting the calculated prediction result of the possibility of suffering from a disease.
• the health condition prediction system of the present invention includes an acquisition unit that acquires halitosis information related to the animal's halitosis; and a health condition prediction unit which predicts the health condition or future health condition of the animal using information about the halitosis. It is preferable that the health condition prediction unit of the health condition prediction system of the present invention predicts the health condition or future health condition of the animal using information about the animal's halitosis and information about the animal's intestinal bacteria. It is also preferable that the information about the animal's intestinal bacteria in the health condition prediction system of the present invention is information about the diversity of the intestinal flora.
• the health condition prediction unit of the present invention is a means for predicting and judging whether or not the health condition of an animal will deteriorate within a predetermined period of time or whether or not the health condition has deteriorated based on information about the animal's bad breath.
• the health condition prediction unit is, for example, composed of a processor such as a CPU, and calculates the possibility of a deterioration in the health condition using a program for predicting and judging the health condition, a learned model or software including the learned model, or a program.
• the program, the learned model, or software including the learned model may be stored in a separate storage device.
• the prediction and judgment method by the health condition prediction unit is not particularly limited.
• the processor predicts and judges whether or not the health condition of the animal will deteriorate within a predetermined period of time or whether or not the health condition is currently deteriorated based on information about the animal's bad breath using a preset program.
• a configuration may be adopted in which a score is calculated according to a preset criterion based on information about the presence or absence and the degree of bad breath, and the risk of a deterioration in the health condition is judged based on a total score obtained by adding up the scores.
• the risk of a deterioration in the health condition is high, and if the total score is less than a predetermined value, the risk of a deterioration in the health condition is low.
• basic information about the animal such as age, breed, sex, medical history, weight, etc., can also be used.
• the health condition prediction unit of the present invention uses information about the animal's halitosis to predict whether the animal's health condition will deteriorate, or whether a previously unappeared deterioration in the animal's health will become apparent, or whether the animal's health condition is currently deteriorating, preferably within a predetermined period of time, more preferably from the time of reception, from the time of halitosis collection, or from the time of determining the presence or absence and severity of halitosis.
• the predetermined period of time is preferably within 3 years, more preferably within 2 years, even more preferably within 1 year, and particularly preferably within 180 days.
• the health condition prediction unit of the present invention preferably uses information about the animal's intestinal bacteria in addition to information about the animal's halitosis to predict the health condition.
• information about halitosis and information about intestinal bacteria it is expected that disease predictions can be made more accurately than when only one of them is used.
• the health condition of the animal in this invention refers to, for example, whether the animal is suffering from a disease, whether the animal's mental state is good, or whether the animal's coat is in good condition.
• whether the animal's mental state is good refers to, for example, whether the animal dislikes strangers, dislikes strange animals, or is aggressive.
• the health condition prediction program of the present invention causes a computer to execute the steps of acquiring halitosis information regarding an animal's halitosis, and predicting the health condition or future health condition of the animal using the information regarding the halitosis.
• the health condition prediction program is a program for predicting whether or not an animal's health condition will deteriorate within a specified period of time or whether or not its health condition is currently deteriorating, based on information about the animal's bad breath.
• the program is a program for executing a method in which information about the bad breath of a target animal entered by a user or a company that has measured the intestinal flora is input, and a prediction is output as to whether or not the animal's health condition will deteriorate within a specified period of time (for example, within six months) from the time the information about the bad breath was acquired or input, or whether or not the animal's health condition is currently deteriorating.
• the health condition prediction program is a program for implementing an algorithm that, for example, calculates a score according to a preset standard from information about the presence or absence and the degree of bad breath, and determines the risk of a deterioration in the health condition based on the total score obtained by adding up the scores. In other words, if the total score is equal to or greater than a specified value, the risk of a deterioration in the health condition is high, and if the total score is less than a specified value, the risk of a deterioration in the health condition is low.
• basic information about the animal such as age, breed, sex, medical history, and weight, can also be used.
• the health condition prediction program may also include a trained model.
• the trained model is preferably a trained model that has learned the relationship between the information on the animal's halitosis, or the information on the animal's halitosis and intestinal bacteria, and the information on the halitosis, information on whether the animal's health condition has deteriorated within a predetermined period from the time when the sample for the analysis of information on the intestinal bacteria was obtained or the intestinal flora was analyzed, or the information on the halitosis, or information on whether the health condition had deteriorated at the time when the sample for the analysis of information on the intestinal bacteria was obtained or the intestinal flora was analyzed.
• the trained model is preferably a trained model that has learned the relationship between the information on the animal's halitosis, or the information on the animal's halitosis and intestinal bacteria, and the information on whether the animal's health condition has deteriorated within a predetermined period from the time when the sample for the analysis of information on the intestinal bacteria was obtained or the intestinal flora was analyzed, or the information on the halitosis, or information on whether the health condition had deteriorated at the time when the sample for the analysis of information on the intestinal bacteria was obtained or the intestinal flora was analyzed, as teacher data.
• the predetermined period in the information regarding whether or not the health condition has deteriorated within such a predetermined period used in the training data is preferably within three years, more preferably within two years, even more preferably within one year, and particularly preferably within 180 days.
• whether or not the health condition has deteriorated can be replaced with a dummy variable.
• Information regarding whether or not an animal's health condition has deteriorated within a predetermined period can be obtained, for example, from a veterinary clinic or an insured owner in connection with the fact of an insurance claim.
• the medium on which the health condition prediction program is recorded is not particularly limited, and it may be stored, for example, on a disc such as a DVD or Blu-ray, or on a memory card, or it may be stored on a HDD, SSD, magnetic tape for recording data, or other storage medium.
• the information processing system of the present invention includes an acquisition unit that acquires information regarding an animal's bad breath, and an output unit that outputs information encouraging care of the animal if the animal has bad breath.
• the acquisition section is the same as above.
• the output unit is, for example, a processor such as a CPU, and outputs information based on the prediction result using a program or software for output.
• the program or software may be stored in a separate storage device.
• the format of the output information is not particularly limited.
• the processing by the suggestion unit described above is an example of information based on the prediction result that encourages the care of the animal. For example, a method is given in which a computer issues an instruction to an electronic terminal of the owner, such as a smartphone or tablet, to display a notice to encourage the owner to take a certain care.
• a wording or icon that encourages the owner to take a certain care is displayed on an app in the terminal.
• Specific examples of the display of information that encourages the owner to take a certain care include displays such as "Bad breath has been detected from your dog. For the health of your dog, please take care of your periodontal disease," and "Your cat has bad breath. For the health of your cat, please take care of your intestines," and displaying icons or marks to alert the owner.
• oral care is preferable, and examples of oral care include periodontal care, tartar removal, and disinfection of the oral cavity.
• the information processing program of the present invention causes a computer to execute the steps of acquiring information regarding an animal's bad breath, and encouraging care of the animal if the animal has bad breath based on the information regarding the bad breath.
• the prediction unit may predict whether an animal with halitosis will develop a disease within a specified period of time, or whether it is currently suffering from a disease.
• the prediction unit may also make a prediction based on the difference in incidence between the group with halitosis and the group without halitosis.
• the difference in incidence or prevalence for each disease between the group with halitosis and the group without halitosis may be stored in a database, and the prediction unit may access the database and use information relating to the difference in incidence or prevalence to predict the onset of a disease. Based on these results, the output unit may output the difference in incidence for each disease.
• Figure 4 is a graph showing the incidence rate of heart disease (circulatory system disease). As can be seen, in the group without bad breath, 0.36% of individuals developed heart disease in the year after the questionnaire was taken. In contrast, in the group with bad breath, 0.65% of individuals developed heart disease in the year after the questionnaire was taken. Thus, the group with bad breath had a higher incidence of heart disease.
• Figure 5 is a graph showing the incidence rate of kidney disease. As can be seen, in the group without bad breath, 0.17% of individuals developed kidney disease in the year after the questionnaire was taken. In contrast, in the group with bad breath, 0.42% of individuals developed kidney disease in the year after the questionnaire was taken. In this way, the group with bad breath had a higher incidence of kidney disease.
• Figure 6 is a graph showing the incidence rate of tumor diseases. As can be seen, in the group without bad breath, 0.76% of individuals developed tumor diseases in the year after the questionnaire was taken. In contrast, in the group with bad breath, 1.22% of individuals developed tumor diseases in the year after the questionnaire was taken. In this way, the group with bad breath had a higher incidence of tumor diseases.
• Figure 7 is a graph showing the prevalence of dental and oral diseases. As can be seen, in the group without bad breath, 1.51% of individuals suffered from dental and oral diseases in the year after the questionnaire was taken. In contrast, in the group with bad breath, 4.76% of individuals suffered from dental and oral diseases in the year after the questionnaire was taken. Thus, the group with bad breath had a higher prevalence of dental and oral diseases.
• Figure 8 is a graph showing the proportion of people suffering from respiratory diseases. As can be seen, in the group without bad breath, 1.25% of people suffered from respiratory diseases in the year after the questionnaire was taken. In contrast, in the group with bad breath, 1.48% of people suffered from respiratory diseases in the year after the questionnaire was taken. In this way, the group with bad breath had a higher proportion of people suffering from respiratory diseases.
• Figure 9 is a graph showing the incidence rate of liver, gallbladder, and pancreatic diseases.
• the group without bad breath 1.15% of individuals developed liver, gallbladder, and pancreatic diseases in the year after the questionnaire was taken.
• the group with bad breath 1.56% of individuals developed liver, gallbladder, and pancreatic diseases in the year after the questionnaire was taken. In this way, the group with bad breath had a higher incidence of liver, gallbladder, and pancreatic diseases.
• Fecal samples were collected from each dog and DNA was extracted as follows.
• the dog's owner collected fecal samples from the dog using a fecal collection kit.
• the fecal samples were then received and suspended in water.
• 200 uL of fecal suspension and 810 uL of lysis buffer (containing 224 ug/mL Protenase K) were added to the bead tube, and beads were crushed using a bead homogenizer (6,000 rpm, crushing for 20 seconds, interval for 30 seconds, crushing for 20 seconds).
• the specimen was then left to stand on a 70°C heat block for 10 minutes to treat with Protenase K, and then left to stand on a 95°C heat block for 5 minutes to inactivate Protenase K.
• the specimen that had been subjected to the lysis treatment was subjected to automatic DNA extraction using a chemagic360 (PerkinElmer) using the chemagic kit stool protocol, and 100 uL of DNA extract was obtained.
• Meta 16S sequence analysis was performed using a modified version of illumina 16S Metagenomic Sequencing Library Preparation (version 15044223 B).
• a 460 bp region containing the variable region V3-V4 of the 16S rRNA gene was amplified by PCR using universal primers (Illumina_16S_341F and Illumina_16S_805RPCR).
• the PCR reaction solution was prepared by mixing 10 uL of DNA extract, 0.05 uL of each primer (100 uM), 12.5 uL of 2xKAPA HiFi Hot-Start ReadyMix (F. Hoffmann-LaRoche, Switzerland), and 2.4 uL of PCR grade water.
• PCR was performed by repeating 30 cycles of 95 ° C for 30 seconds, 55 ° C for 30 seconds, and 72 ° C for 30 seconds, and finally by extension reaction at 72 ° C for 5 minutes.
• the amplified product was purified using magnetic beads and eluted with 50 uL of Buffer EB (QIAGEN, Germany).
• the purified amplified product was subjected to PCR using Nextera XT Index Kit v2 (Illumina, CA, US) and indexed.
• the PCR reaction solution was prepared by mixing 2.5 uL of the amplified product, 2.5 uL of each primer, 12.5 uL of 2x KAPA HiFi Hot-Start ReadyMix, and 5 uL of PCR grade water. After thermal denaturation at 95°C for 3 minutes, PCR was repeated 12 times at 95°C for 30 seconds, 55°C for 30 seconds, and 72°C for 30 seconds, and finally an extension reaction was performed at 72°C for 5 minutes.
• the amplified product to which indexing was performed was purified using magnetic beads and eluted with 80-105 uL of Buffer EB.
• the concentration of each amplification product was measured with a NanoPhotometer (Implen, CA, US), adjusted to 1.4 nM, and then mixed in equal amounts to prepare a sequencing library.
• the DNA concentration of the sequencing library and the size of the amplification product were confirmed by electrophoresis and analyzed by MiSeq.
• MiSeq Reagent Kit V3 was used for the analysis, and 2 x 300 bp paired-end sequencing was performed.
• the obtained sequence was analyzed with 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'
• composition data of the intestinal microbiota and measured the Shannon index (Shannon-Wiener diversity index).
• Shannon index Shannon-Wiener diversity index
• the 166,187 dogs were divided into three classes based on their Shannon index values: those with a Shannon index of 0 to less than 3.6 were classified as a low diversity group, those with a Shannon index of 3.6 to less than 4.3 were classified as a medium diversity group, and those with a Shannon index of 4.3 to 7.0 were classified as a high diversity group. Each of the three groups was then further divided into a group with bad breath and a group without bad breath.
• FIG. 10 shows a graph of the incidence rate of kidney disease for each group.
• Figure 11 shows the incidence of kidney disease, plotted in the same manner as above, for only those aged 0-7 years out of the total population of 166,187 animals.
• Figure 12 shows the incidence of kidney disease, plotted in the same way as above, for only those aged 8 to 16 years old from the population of 166,187 cattle.
• the 102,853 cats were divided into three classes based on their Shannon index values: those with a Shannon index between 0 and less than 4.95 were classified as a low diversity group, those with a Shannon index between 4.95 and less than 5.36 were classified as a medium diversity group, and those with a Shannon index between 5.36 and 7.46 were classified as a high diversity group.
• Each of the three groups was then further divided into a group with bad breath and a group without bad breath.
• FIG. 13 shows a graph of the incidence rate of kidney disease for each group.
• Figure 14 shows the incidence of kidney disease, plotted in the same manner as above, for only those aged 0-6 years old from the population of 102,853 cattle.
• Figure 15 shows the incidence of kidney disease, plotted in the same manner as above, for only those aged 7-12 years old from the population of 102,853 cattle.
• FIG. 16 shows a graph of the relationship between the presence or absence of bad breath, data on the diversity of intestinal flora, and the incidence rate of heart disease for 166,137 cattle in the same population as in Reference Example 2.
• Figure 17 shows the incidence of heart disease, plotted in the same way as above, for only those aged 0-7 years old from the population of 166,137 animals.
• Figure 18 shows the incidence of heart disease, plotted in the same way as above, for only those aged 8-16 years old from the population of 166,137 animals.
• Figure 21 shows that the prevalence of periodontal disease is higher in the group with bad breath.
• Figure 22 shows that the prevalence of stomatitis is higher in the group with bad breath.
• Figure 23 shows that the prevalence of oral tumors was higher in the group with bad breath.
• Figure 24 shows that the prevalence of digestive disorders is higher in the group with bad breath.
• the insurance claim rate due to blood and hematopoietic diseases was regarded as the prevalence of blood and hematopoietic diseases, and the relationship between halitosis and the prevalence of blood and hematopoietic diseases was graphed. The results are shown in Figure 25.
• Figure 25 shows that the group with bad breath had a higher prevalence of blood and hematopoietic diseases.
• the insurance claim rate due to tumors of the blood and hematopoietic organs was regarded as the prevalence of tumors of blood and hematopoietic organ diseases, and the relationship between halitosis and the prevalence of tumors of the blood and hematopoietic organs was graphed. The results are shown in Figure 26.
• Figure 26 shows that the group with bad breath had a higher prevalence of blood and hematopoietic tumors.
• Figure 27 shows that the prevalence of neurological disorders is higher in the group with bad breath.
• Figure 28 shows that the prevalence of epilepsy is higher in the group with bad breath.
• Figure 29 shows that the prevalence of brain tumors is higher in the group with bad breath.
• Figure 30 shows that the prevalence of endocrine disorders is higher in the group with bad breath.
• Figure 31 shows that the prevalence of diabetes is higher in the group with bad breath.
• Figure 32 shows that the prevalence of uveitis was higher in the group with bad breath.
• Figure 33 shows that the prevalence of loss of energy was higher in the group with bad breath.
• Figure 34 shows that the group with bad breath had a higher mortality rate.
• Figure 35 shows that the group with bad breath was more likely to have poor coat luster.
• Figure 36 shows that the group with bad breath is more likely to be shy around strangers.
• Figure 37 shows that the group with bad breath was more likely to be animal shy.
• Figure 38 shows that the group with bad breath had a higher prevalence of skin tumors.
• Figure 39 shows that the prevalence of periodontal disease is higher in the group with bad breath.
• Figure 40 shows that the prevalence of stomatitis is higher in the group with bad breath.
• Figure 41 shows that the prevalence of oral tumors was higher in the group with bad breath.
• Figure 42 shows that the prevalence of digestive disorders is higher in the group with bad breath.
• Figure 43 shows that the prevalence of gastritis, gastroenteritis, or enteritis was higher in the group with bad breath.
• Figure 44 shows that the prevalence of cardiovascular disease is higher in the group with bad breath.
• Figure 45 shows that the prevalence of valvular disease is higher in the group with bad breath.
• Figure 46 shows that the prevalence of chronic kidney disease is higher in the group with bad breath.
• Figure 47 shows that the prevalence of atopic dermatitis is higher in the group with bad breath.
• Figure 48 shows that the prevalence of allergic dermatitis is higher in the group with bad breath.
• Figure 49 shows that the prevalence of diabetes is higher in the group with bad breath.
• Figure 50 shows that the group with bad breath had a higher prevalence of neoplastic diseases.
• Figure 51 shows that the prevalence of blood and immune system diseases is higher in the group with bad breath.
• Figure 52 shows that the group with bad breath had a higher prevalence of blood and hematopoietic tumors.
• Figure 53 shows that the group with bad breath had a higher mortality rate.
• Figure 54 shows that the group with bad breath was more likely to have poor coat luster.
• Figure 55 shows that the group with bad breath is more likely to be shy around strangers.
• Figure 56 shows that the group with bad breath was more likely to be animal shy.

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