Oral health support method and oral health support system

Abstract

To provide an oral health support method that allows even ordinary users to easily and efficiently predict their risk of future tooth loss, and to provide a system that utilizes information on the risk of future tooth loss to easily and efficiently support the oral health of users. [Solution] A method for supporting oral health, characterized by predicting the risk of tooth loss using antibody titer test results related to oral bacteria and oral quantity test results, and An oral health support system in which a computer predicts the risk of tooth loss from the results of an antibody titer test related to oral bacteria and the results of an oral bacteria quantity test of the user, using a predetermined classification with variables being antibody titers related to oral bacteria and the amount of oral bacteria in the oral cavity. The system then creates information useful for maintaining and promoting oral health, including the predicted risk of tooth loss, and provides it to the user, medical institution, or healthcare professional.

Description

Technical Field

[0001] Relates to an oral health support method for providing information on the risk of future tooth loss, and a system for supporting the oral health of users that utilizes information on the risk of future tooth loss.

Background Art

[0002] In Japan, in 1989 (the first year of Heisei), an interim report of the Adult Dental Health Care Measures Study Group was released by the Ministry of Health and Welfare, and the "8020 (Hachimaru-Nimaru) Campaign" with the goal of maintaining 20 or more of one's own teeth even at the age of 80 has been promoted. Currently, regular dental check-ups are mandatory for infants aged 1.5 and 3 years old, children and students attending elementary school through high school. However, after graduating from high school, dental check-ups are considered an obligation of effort, and due to factors such as the cost of the check-up, the acceptance rate of dental check-ups has decreased. As a result, it is known that there are many adults suffering from oral diseases such as dental caries and periodontal disease.

[0003] On the other hand, recent research has shown that oral bacteria and oral diseases are associated with systemic diseases such as heart disease, respiratory disease, chronic kidney disease, osteoporosis, rheumatoid arthritis, and cancer. For example, a method for detecting inflammatory bowel disease (Patent Document 1) characterized by randomly determining the base sequence of the 16S ribosomal RNA gene in a saliva sample taken from a subject to obtain a data set of base sequences, and detecting a saliva sample evaluated as inflammatory bowel disease based on the obtained data set of base sequences; a method for diagnosing IgA nephropathy (Patent Document 2) characterized by determining the base sequence of the 16S ribosomal RNA gene of bacteria contained in the microbiota of a saliva sample, and detecting a saliva sample from a subject judged to have IgA nephropathy based on the obtained data set of base sequences; and a method for diagnosing a subject suspected of having epidermoid carcinoma of the oral cavity, pharynx, and larynx in A method for in vitro diagnosis is known, which includes using the concentration of bacteria belonging to the genus Alloprevotella in a saliva or breath sample obtained from the subject as a measure, wherein if the concentration of bacteria belonging to the genus Alloprevotella in such a saliva or breath sample is significantly lower than that in a saliva sample obtained from a healthy subject, or significantly lower than a reference value, this is an indicator that the subject has epidermoid carcinoma of the oral cavity, pharynx, and larynx (Patent Document 3), etc.

[0004] Therefore, in Japan, in order to maintain and improve the health of the public by providing opportunities for regular diagnosis and improvement of the oral environment, the introduction of universal dental checkups is planned from 2025.

[0005] In the aforementioned dental examination, for example, saliva tests are known to measure salivary secretion volume, salivary buffering capacity, salivary occult blood, salivary protein content, bacterial count, etc., all of which are performed to examine the subject's current oral environment. On the other hand, tooth loss (extraction) is known to be an undesirable condition in the oral cavity, and it is thought that in people who have had teeth extracted, there is a possibility of a decline in chewing and swallowing function, as well as a reduced stimulation to the brain, which may increase the risk of neurological disorders such as Alzheimer's disease. Regarding the possibility of such future tooth extraction, for example, it has been disclosed that there is a statistically significant correlation between the number of untreated teeth and the number of lost teeth in univariate analysis (Non-Patent Document 1), and that a person is suffering from severe periodontitis accompanied by the possibility of losing fewer than four teeth, or severe periodontitis accompanied by the possibility of losing four or more teeth, i.e., the loss of the dentition (Patent Document 4).

[0006] However, current dental checkups do not include items that indicate the likelihood of needing tooth extraction in the future, making it difficult for users who have undergone a dental checkup to assess their own risk of tooth loss based on the test results. [Prior art documents] [Patent Documents]

[0007] [Patent Document 1] Japanese Patent Publication No. 2013-183663 [Patent Document 2] Japanese Patent Publication No. 2015-77101 [Patent Document 3] Special Publication No. 2022-529909 [Patent Document 4] Japanese Patent Publication No. 2024-152508 [Non-patent literature]

[0008] [Non-Patent Document 1] Masatoshi Yano, Yuichi Ando, ​​Factor Analysis of Tooth Loss Risk in Adults Receiving Dental Disease Prevention and Management, Journal of the Japanese Society for Oral Health, Vol. 48, 664-677, 1998. [Overview of the project] [Problems that the invention aims to solve]

[0009] In view of the above circumstances, the present invention aims to provide an oral health support method that enables even ordinary users to easily and efficiently predict the risk of future tooth loss, and to provide a system that utilizes information on the risk of future tooth loss to easily and efficiently support the oral health of users. [Means for solving the problem]

[0010] The inventors of this invention conducted diligent research with the aim of developing a method for predicting the risk of future tooth loss. As a result, they discovered that the risk of future tooth loss can be accurately predicted by using antibody titer test results related to oral bacteria and oral bacteria quantity test results, thus completing the present invention.

[0011] In other words, the present invention encompasses the following inventions. (1) A method for supporting oral health, characterized by predicting the risk of tooth loss using the results of antibody titer tests related to oral bacteria and the results of tests on the amount of oral bacteria in the oral cavity. (2) The oral health support method according to (1), wherein the prediction is made using a predetermined classification in which the values ​​relating to antibody titers related to oral bacteria and the values ​​relating to the amount of oral bacteria in the oral cavity are variables. (3) The oral health support method according to (1) or (2) above, wherein the prediction is made using a predetermined mathematical formula in which the value relating to the antibody titer related to oral bacteria and the value relating to the amount of oral bacteria in the oral cavity are variables. (4) The oral health support method according to (3), wherein the predetermined formula is a formula obtained by statistical methods from values ​​relating to multiple antibody titers related to oral bacteria and values ​​relating to multiple oral quantities of oral bacteria, or a formula that includes values ​​relating to antibody titers related to oral bacteria in the numerator and values ​​relating to oral quantities of oral bacteria in the denominator. (5) The oral health support method according to any one of (1) to (4) above, wherein the risk of tooth loss is the presence or absence of the possibility of tooth loss, the degree of the possibility of tooth loss, or the number of teeth that may be lost. (6) The oral health support method according to any one of (1) to (5) above, wherein the oral bacteria include Porphyromonas gingivalis. (7) The oral health support method according to any one of (1) to (6), wherein the antibody titer is the antibody titer against the protease derived from the oral bacteria. (8) An oral health support method according to any of (1) to (7) above, further comprising using one or more other test items selected from the group consisting of age, sex, smoking status, presence or absence of diabetes, blood glucose level, and HbA1c to predict the risk of tooth loss. (9) Computers Using a predetermined classification system with variables related to antibody titers associated with oral bacteria and values ​​related to the amount of oral bacteria in the oral cavity, the risk of tooth loss is predicted from the results of the user's oral bacteria-related antibody titer test and the results of the user's oral bacteria amount test. An oral health support system that creates and provides information useful for maintaining and improving oral health, including tooth loss risk prediction information, to users, medical institutions, or healthcare professionals. (10) The oral health support system described in (9) above, further comprising using one or more other test items selected from the group consisting of age, sex, smoking status, presence or absence of diabetes, blood glucose level, and HbA1c to predict the risk of tooth loss. (11) A computer Using a predetermined classification system with variables representing antibody titers related to oral bacteria and the amount of oral bacteria in the oral cavity, the risk of tooth loss is predicted from the results of the user's oral bacteria-related antibody titer test and the results of the user's oral bacteria amount test. An oral health support system that creates useful information for maintaining and promoting oral health, including predicted tooth loss risk information, stores it in an electronic medical record or a database used for user health management, and utilizes it for the prevention, treatment, and maintenance and promotion of oral diseases in the user. (12) In predicting the risk of tooth loss, the oral health support system according to (11) above further uses one or more other test items selected from the group consisting of age, gender, presence or absence of smoking, presence or absence of diabetes, blood glucose level, and HbA1c. (13) The oral health support system according to any one of (9) to (12) above, wherein Porphyromonas gingivalis is included in the oral bacteria. (14) The oral health support system according to any one of (9) to (13) above, wherein the antibody titer is an antibody titer against a protease derived from the oral bacteria.

Advantages of the Invention

[0012] According to the oral health support method or oral health support system according to the present invention as described above, an ordinary user can easily and efficiently obtain information on their future tooth loss risk. For example, the state of tooth loss makes it easier to grasp the future possibilities of the oral health state than each index such as the severity of periodontal disease obtained in the current dental examination, etc., and also contributes to inspiring the user to perform oral care behaviors, etc., enabling efficient oral health support. Further, the oral health support system according to the present invention creates information useful for maintaining and improving oral health including tooth loss risk prediction information, and appropriately provides it to the user, medical institutions, or medical staff, or utilizes it for disease prevention, treatment, and maintenance and improvement of the user's oral health, thereby realizing a support system that can greatly contribute to the improvement of each user's oral health.

Brief Description of the Drawings

[0013] [ [Figure 1] An explanatory diagram showing the configuration of an oral health support system according to a representative embodiment of the present invention. [Figure 2] Also, a block diagram showing the configuration of a support server in the oral health support system. [Figure 3]A graph showing the analysis results of the Mann-Whitney U test on the relationship between the antibody titer obtained in Example 3, the amount of oral bacteria in the oral cavity, and the presence or absence of future tooth loss. Mode for Carrying Out the Invention

[0014] (Oral Health Support Method) The oral health support method according to the present invention is characterized by predicting the tooth loss risk using the antibody titer test result related to oral bacteria and the test result of the amount of oral bacteria in the oral cavity.

[0015] In the present invention, the tooth loss risk refers to the risk several years later based on the antibody titer test result related to oral bacteria and the test result of the amount of oral bacteria in the oral cavity. Specifically, it indicates the presence or absence of the possibility of tooth loss, the degree of the possibility of tooth loss, or the number of teeth that may be lost. Note that the factors for tooth extraction predicted in the present invention include periodontal disease, dental caries, fracture, etc. In addition, the number of years in which tooth extraction predicted in the present invention may occur is not particularly limited, and may be a specific range of years such as 2 to 4 years later, or a number of years with a specified lower limit such as 5 years later or later.

[0016] In the present invention, oral bacteria refer to bacteria that exist in the oral cavity and throat and may affect the health of the oral cavity, such as the onset and progression of diseases. Bacteria that can negatively affect oral health include, for example, the three bacteria known as the "red complex" associated with periodontal disease: Porphyromonas gingivalis, Treponema denticola, and Tannerella forsythia, as well as Aggregatibacter actinomycetemcomitans (formerly Actinobacillus actinomycetemcomitans), and the "orange complex" consisting of Prevotella intermedia, Eubacterium nodatum, Fusobacterium nucleatum, and Fusobacterium polymorphum. Examples include *Peptococcus polymorphum*, *Prevotella nigrescens*, *Peptostreptococcus micros*, *Campylobacter rectus*, *Campylobacter showae*, *Campylobacter gracilis*, and *Streptococcus constellatus*. Examples of plants known to have a direct or indirect relationship with dental caries include Streptococcus mutans, Streptococcus sobrinus, Actinomyces naeslundii, Actinomyces viscosus, Actinomyces odontolyticus, Actinomyces oris, and Scardovia wiggsiae. Examples of bacteria known to be involved in plaque formation include Streptococcus intermedius and Corynebacterium matruchotii. Examples include Actinomyces israelli, which is known to be associated with actinomycosis of the jaw, and Streptococcus agalactiae, which is known to be associated with invasive infections in the elderly. In particular, Porphyromonas gingivalis is preferred because it allows for highly accurate antibody titer testing and facilitates the measurement of the risk of future tooth loss. The aforementioned oral bacteria may consist of only one type or two or more types.

[0017] In this invention, the antibody titer test result refers to the antibody titer result, which indicates the amount of antibodies related to oral bacteria, measured using a test sample obtained from a subject. The aforementioned antibodies related to oral bacteria refer to proteins that have the activity to specifically bind to antigens produced by antigen stimulation by the bacterial cells themselves, parts of the bacterial cells, and compounds derived from the bacteria. In addition to the bacterial cells themselves, parts of the bacterial cells, and compounds derived from the bacteria, the test antigens that react with the antibodies also include antigenic peptides and combinations thereof that have similar reactivity. When testing for antibodies associated with two or more types of oral bacteria, an antigen-antibody reaction is used, utilizing antigens with distinct antigenic structures corresponding to each antibody. In this case, it is desirable that there is virtually no cross-reactivity between the antigen-antibody reactions themselves, or with antibodies against other oral bacteria present in the sample. In this context, "effectively showing no cross-reactivity" means that even if some cross-reactivity occurs, it is considered not to show any cross-reactivity as long as the presence of antibodies for each oral bacterium being tested can be distinguished. Furthermore, while antibody tests can be qualitative tests that determine the presence or absence of antibodies, tests that can measure antibody titers are preferable because they provide information that can be used to infer the degree and timing of infection. For example, as an antigen that does not exhibit substantial cross-reactivity, proteins present on the surface of oral bacteria or secreted proteins are preferred, from the standpoint of enabling accurate antibody titer testing and facilitating the measurement of future tooth loss risk. For example, if the oral bacterium is Porphyromonas gingivalis, gingipain, the major protease produced by this bacterium, is a suitable candidate.

[0018] The antibody titer related to oral bacteria can be any antibody that indicates the amount of antibodies related to oral bacteria, and examples include neutralizing antibody titers, agglutinating antibody titers, and total antibodies, but there are no particular limitations. Examples of test samples for measuring the antibody titer include blood or saliva collected from the subject. Furthermore, while blood can be collected from veins (commonly used in blood tests during health checkups), other locations such as fingertips and capillaries in the oral cavity are also permitted, there are no particular limitations. Similarly, the method and type of saliva collection are not limited to resting saliva, stimulated saliva, direct collection from salivary glands, mouthwash spit, or gingival crevicular exudate. In the present invention, the value relating to the antibody titer of oral bacteria refers to a value based on measured antibody titers, and examples include quantitative variables such as measured antibody titers, values ​​indicating each classification obtained by dividing antibody titers into predetermined numerical ranges, values ​​indicating each classification obtained by dividing measured values ​​of a certain population or accumulated measured values ​​by median or percentile, and predetermined qualitative variables such as "low," "moderate," and "high."

[0019] Examples of measurement methods used for antibody titer testing related to oral bacteria include known methods for measuring serum IgG antibody titers (Japanese Patent No. 6716241, Japanese Patent Publication No. 2022-096817), periodontal pathogen plasma or serum antibody titer testing kits (Japanese Patent No. 6310631, Japanese Patent No. 6671313, Japanese Patent No. 6144313), etc., but are not particularly limited. For example, methods for measuring antibody titers include immunoassays such as ELISA, EIA, CLEIA, CLIA, and immunochromatography. The method is not particularly limited as long as it uses an antigen to measure antibodies. Antibody titer test results can be obtained by calculating the antibody concentration in a sample from a calibration curve created with antibodies of known concentrations, or by serially diluting the sample and reacting it with the test antigen to determine the dilution ratio at which the reaction occurs. In the case of immunochromatography, the intensity of the resulting line can be measured and classified qualitatively or semi-quantitatively by visual inspection or scanning. The measured values ​​may be absolute or relative.

[0020] In this invention, the result of an oral bacteria quantity test refers to the result of measuring the amount of oral bacteria in a sample obtained from a subject. Furthermore, in the present invention, the oral bacteria targeted by the oral volume test results may be any bacteria targeted by the antibody titer test, and the types may be the same as or different from those in the antibody titer test.

[0021] The term "oral volume" refers to the number of bacteria contained in a sample taken from the oral cavity of a subject. For example, the aforementioned samples include saliva, mouthwash spit, dental plaque, tongue coating, and gingival crevicular exudate. For example, when using saliva, the sample may be the number of bacteria in 1 mL of saliva, or the proportion of a specified oral bacteria to the total number of bacteria in the saliva.

[0022] The oral cavity quantity of the aforementioned oral bacteria can be measured using any method that can be used with the aforementioned sample. For example, methods include detecting the gene sequence specific to the bacteria using PCR or LAMP, or detecting the bacteria by antigen-antibody reaction using antibodies that react with the bacteria (ELISA, EIA, CLEIA, CLIA, immunochromatography, etc.). In any case, a calibration curve can be created using cultured oral bacteria adjusted to known concentrations, and the number of bacteria in 1 mL of saliva can be calculated, for example. In addition to the above, methods that measure the activity of enzymes specific to the bacteria can also be used. For example, the enzyme activity of N-benzoyl-DL-arginyl peptidase produced by periodontal pathogens can be used as a method to measure bacterial concentration because the measurement results reflect the amount of periodontal pathogens. Furthermore, when measuring the proportion of a specific oral bacterium, methods include measuring the total number of bacteria using PCR or LAMP and subtracting a common gene sequence of the bacteria, or determining the proportion of that bacterium from the results of microbiota analysis using a sequencer. In the present invention, the value relating to the amount of oral bacteria in the oral cavity refers to a value based on the measured amount of oral bacteria, and includes quantitative variables such as the amount of oral bacteria in a given saliva, a value indicating each classification obtained by dividing the oral bacterial concentration into a predetermined numerical range, a value indicating each classification obtained by dividing the measured values ​​of a certain group or the median or percentile of accumulated measured values, and predetermined qualitative variables such as "present" or "absent". The measured values ​​may be absolute values ​​or relative values. Furthermore, in the case of immunochromatography or enzyme activity measurements, which yield qualitative or semi-quantitative results, classification can also be performed based on visual inspection or scanner readings.

[0023] In the present invention, the risk of tooth loss is predicted using a predetermined classification system in which the values ​​relating to antibody titers related to oral bacteria and the values ​​relating to the amount of oral bacteria in the oral cavity are variables.

[0024] Examples of the aforementioned predetermined classifications include mathematical formulas obtained by statistical methods from values ​​relating to multiple antibody titers related to oral bacteria and values ​​relating to multiple oral quantities of oral bacteria; mathematical formulas that include values ​​relating to antibody titers related to oral bacteria in the numerator and values ​​relating to oral quantities of oral bacteria in the denominator; and classifications that combine qualitative variables relating to multiple antibody titers related to oral bacteria and qualitative variables relating to multiple oral quantities of oral bacteria using statistical methods.

[0025] Examples of the aforementioned statistical methods include multivariate analyses such as multiple regression analysis and logistic regression analysis, and decision tree analysis. For example, if the risk of tooth loss is the degree of possibility of tooth loss or the number of teeth that may be lost, then this risk of tooth loss can be efficiently measured using a formula based on multiple regression analysis. Furthermore, for example, if the risk of tooth loss is defined as the possibility of tooth loss, the probability of occurrence can be predicted using logistic regression analysis, similar to the multiple regression method described above.

[0026] For example, if the formula obtained by the aforementioned statistical method is for multiple regression analysis, it can be obtained as follows. To obtain the formula, select multiple subjects and, for each subject, compile information such as "whether or not teeth were extracted," "number of teeth extracted," "multiple antibody titers related to oral bacteria," "multiple amounts of oral bacteria in the oral cavity," and, if necessary, "age of the subject," "sex," "smoking status," "presence or absence of diabetes," "blood glucose level," and "HbA1c." By selecting "number of teeth extracted" as the dependent variable and "antibody titer related to oral bacteria" and "amount of oral bacteria in the oral cavity" as independent variables, and performing multiple regression analysis using spreadsheet software capable of multiple regression analysis, the desired formula can be obtained. Next, the number of teeth extracted can be calculated by substituting the user's "multiple antibody titers related to oral bacteria" and "amount of oral bacteria in the oral cavity" into the independence coefficients of the aforementioned formula.

[0027] One characteristic of the present invention is that the formula obtained by the multiple regression analysis shows a negative standardization coefficient for antibody titer and a positive standardization coefficient for the amount of oral bacteria in the oral cavity. In the formula obtained from the aforementioned multiple regression analysis, it is known that items with positive standardized coefficients as independent coefficients have a promoting relationship with the items selected as the dependent variable, while items with negative standardized coefficients as independent coefficients have an inhibiting relationship. On the other hand, antibody titers related to oral bacteria measure the amount of antibodies produced against oral bacteria that have previously entered the body. Therefore, it has been conventionally known that a higher amount of oral bacteria present in the oral cavity results in higher antibody titers. Tooth extraction is thought to be caused in part by a deterioration of the oral environment due to a large number of oral bacteria, such as periodontal disease bacteria. It is believed that the higher the antibody titer, the more the deterioration of the oral environment is accelerated, and thus tooth extraction is more likely to occur. However, in the mathematical formula obtained by the multiple regression analysis in this invention, as mentioned above, the standardization coefficient for antibody titer is shown as negative, indicating that the risk of tooth extraction is reduced as the antibody titer increases. This is completely different from conventional technical knowledge.

[0028] There is no particular limit to the number of subjects used to obtain the aforementioned formula, but increasing the number of subjects can improve the prediction accuracy obtained with the desired formula. From the perspective of improving prediction accuracy, for example, the number of subjects could be 100 or more. Furthermore, the accuracy of the prediction can be improved by selecting a larger number of individuals who are at an age where they are more likely to lose teeth due to periodontal disease, cavities, etc. (for example, 40 years of age or older). Furthermore, by using a formula created by selecting one or more additional independent variables from the group consisting of "age of the subject," "sex," "smoking status," "presence or absence of diabetes," "blood glucose level," and "HbA1c," the accuracy of predictions can be improved according to the characteristics of the subjects. Furthermore, by calculating the significance probability of the selected independent variables, the strength of their association with the likelihood of tooth extraction can be confirmed. For example, an independent variable with a lower significance probability indicates a stronger association with the likelihood of tooth extraction. In this invention, the mathematical formula obtained by the multiple regression analysis shows that the significance probabilities of "multiple antibody titers related to oral bacteria" and "amount of oral bacteria in the oral cavity" are lower than those of "age of the subject" and "sex." Therefore, the possibility of tooth extraction risk can be predicted using a mathematical formula that utilizes "multiple antibody titers related to oral bacteria" and "amount of oral bacteria in the oral cavity."

[0029] The formula that includes a value relating to the antibody titer related to oral bacteria in the numerator and a value relating to the amount of oral bacteria in the oral cavity in the denominator may be a ratio expressed as "antibody titer / amount in the oral cavity" or a ratio expressed as "formula including antibody titer / formula including amount in the oral cavity".

[0030] In particular, the ratio expressed as "antibody titer / oral volume" can be suitably used to predict whether or not a tooth will need to be extracted. Similar to the formula obtained by the aforementioned statistical method, multiple subjects were selected, and for each of these subjects, the "presence or absence of tooth extraction," "antibody titer," and "oral cavity volume" were compiled. Next, ROC analysis can be used to examine the performance of the relationship between "presence or absence of tooth extraction" and "antibody titer," the relationship between "presence or absence of tooth extraction" and "antibody titer / oral volume," and whether they can be used for testing. For example, if the asymptotic significance probability for each is calculated using spreadsheet software capable of ROC analysis, the "antibody titer / oral volume" (0.020) is significantly lower than the "antibody titer" (0.392) and "oral volume" (0.262), as shown in the example described later. Therefore, regarding the risk of tooth extraction, "antibody titer / oral volume" is a more relevant factor than "antibody titer" and "oral volume," and the aforementioned "antibody titer / oral volume" formula provides a higher predictive accuracy. Furthermore, since the asymptotic significance probabilities for antibody titer and oral volume are higher than 0.1, the results obtained when using either test individually are unreliable and therefore unsuitable for predicting the risk of tooth extraction.

[0031] (Oral health support system) The oral health support system according to the present invention is a system capable of implementing the oral health support method, and as a first embodiment, Computers Using a predetermined classification system with antibody titers related to oral bacteria and oral bacterial volume as variables, the risk of tooth loss is predicted from the results of the user's oral bacterial antibody titer test and the results of the user's oral bacterial volume test. This system is characterized by creating information useful for maintaining and improving oral health, including predictive information on the risk of tooth loss, and providing it to users, medical institutions, or healthcare professionals.

[0032] The aforementioned computer functions as a support server for creating and providing information useful for maintaining and improving oral health to users, medical institutions, or healthcare professionals. The aforementioned medical institutions include hospitals, dental clinics, and health insurance organizations such as health insurance associations. The aforementioned medical professionals include dentists, physicians, industrial physicians, and public health nurses.

[0033] In the oral health support system according to the first embodiment, as shown in Figure 1, the support server 1 is connected to individual user terminals 2 via a network N such as the Internet, and various information is exchanged. Furthermore, the support server 1 is connected via the network N to computers owned by medical institutions that hold user information (institutional computer 3a) and computers owned by medical professionals (institutional computer 3b), and various information is exchanged.

[0034] The support server 1 is composed of one or more computers equipped with a processing unit 10 and storage means 11. Specifically, it can use a conventional computer device that is commonly used, centered around the processing unit 10, and including storage means 11, a communication control unit 12, and input operation means (not shown) such as a pointing device, keyboard, display, etc.

[0035] The processing unit 10 is mainly composed of a CPU such as a microprocessor and has a memory unit consisting of RAM and ROM where programs that define the procedures for various processing operations and processing data are stored. The storage means 11 consists of memory and hard disks inside or outside the support server 1. The contents of some or all of the storage unit may be stored in the memory or hard disk of another computer connected to the support server 1 via communication.

[0036] Functionally, the processing unit 10 is broadly divided into an information acquisition unit 10a that acquires information from individual user terminals 2 via the network N regarding the results of antibody titer tests related to the user's oral bacteria and the results regarding the amount of oral bacteria in the user's mouth. An analysis and prediction unit 10b predicts the risk of future tooth loss by statistically analyzing the results of the antibody titer test and the oral volume test using a predetermined classification that uses antibody titers related to oral bacteria and the amount of oral bacteria in the oral cavity as variables. A useful information generation unit 10c creates information useful for maintaining and promoting oral health, including tooth loss risk prediction information obtained by the analysis and prediction unit 10b, The information transmission unit 10d provides this generated useful information to the user terminal 2 via the network N. It has at least the following features, and these processing functions are realized by the above program.

[0037] The information acquired by the information acquisition unit 10a may include, in addition to the antibody titer test results and the oral cavity volume results, one or more other test results selected from the group consisting of age, sex, smoking status, diabetes status, blood glucose level, and HbA1c. Furthermore, the results information of the antibody titer test and the results information of the oral cavity volume may both be numerical values ​​in a predetermined unit, or they may be classified into predetermined numerical ranges (for example, as described in the examples below, there may be three classifications such as "low," "medium," and "high" for antibody titer or oral cavity volume, or two classifications based on the presence or absence of specific oral bacteria instead of oral cavity volume). The number of classifications may be two, or four or more. For example, when classifying antibody titers and oral bacterial counts into three categories based on the proportion of people in each category, the first 50% of people with the lowest antibody titers could be classified as "low," the next 25% as "moderate," and the remaining 25% as "high." Oral bacterial counts can be classified as follows: the lowest 50% of people have a "low" count, the highest 25% have a "moderate" count, and the highest 25% have a "high" count. Regarding the number of oral bacteria, it is acceptable to classify cases as either with or without detection of oral bacteria.

[0038] Specifically, information is transmitted when the user manually enters information such as the antibody titer test result information and the oral cavity volume result information into the user terminal 2, such as a personal computer or smartphone (hereinafter referred to as PC / smartphone), and this information is received by the information acquisition unit 10a of the computer's processing unit 10 via the network N.

[0039] User terminals 2, such as PCs and smartphones, can be widely adopted from known devices, and necessary information can be entered using input devices such as keyboards, microphones, and touch panels.

[0040] In addition to displaying the input items and input completion buttons or send buttons on the screen of the user terminal 2, it also displays tooth loss risk information transmitted from the support server 1 and, if necessary, information useful for maintaining and improving oral health.

[0041] Furthermore, it is preferable that the user terminal 2 has a function to make a phone call to a call center in case the user is unable to input the desired information from the user terminal 2 due to some problem on the user's end.

[0042] Specifically, the aforementioned information transmission involves data transmission between the support server 1 and the system via an internet connection using a wireless LAN such as Wi-Fi or a wired LAN.

[0043] The statistical analysis method used in the analysis and prediction unit 10b is not limited to any particular method, as long as it is appropriate to the classification used. For example, if a predetermined mathematical formula is used, it is not limited to any formula used in the oral health support method. For example, when using a predetermined formula in the analysis and prediction unit 10b, the results of the antibody titer test and the oral cavity volume, transmitted from the user terminal 2, along with one or more other test information selected from the group consisting of age, sex, smoking status, diabetes status, blood glucose level, and HbA1c, are substituted as parameters into the predetermined formula stored in the analysis and prediction unit 10b. The formula is then calculated using the calculation function provided in the support server 1, and predictive information on the risk of future tooth loss can be obtained.

[0044] The analysis and prediction unit 10b may also have a function to calculate the current periodontal disease risk from either the antibody titer test result information or the oral cavity volume result information transmitted from the user terminal 2. In this case, the analysis and prediction unit 10b can also provide two pieces of prediction information: the current periodontal disease risk and the future tooth loss risk. In this case, the analysis and prediction unit 10b can be populated with a calculation formula based on a known method that can calculate the periodontal disease risk from antibody titer or oral cavity volume.

[0045] The useful information generation unit 10c generates information useful for maintaining and improving oral health, including tooth loss risk prediction information obtained by the analysis and prediction unit 10b. The tooth loss risk prediction information shows a prediction for a predetermined number of years from the year in which the results of the antibody titer test and oral volume information were obtained, and specifically includes at least one of the following: whether or not there is a possibility of tooth loss, the degree of the possibility of tooth loss, or the number of teeth that may be lost. The likelihood of tooth loss can be categorized, for example, into "low likelihood," "moderate likelihood," and "high likelihood." The likelihood of tooth loss can be expressed numerically, for example, as the probability of tooth extraction. The number of teeth that may be lost may be expressed numerically. The aforementioned specified period may be 2 to 4 years, or 5 years or later, etc.

[0046] The useful information generation unit 10c can generate more accurate and useful information regarding the user's oral health by adding one or more other test information selected from the group consisting of age, sex, smoking status, diabetes status, blood glucose level, and HbA1c to the antibody titer test and oral volume results information input from the user terminal 2, and analyzing it based on statistical methods.

[0047] Preferably, the above information may be generated by analyzing past information and previously provided useful information using machine learning, and then making inferences. Any machine learning method, such as deep learning using neural networks, can be used for this.

[0048] The information transmission unit 10d transmits the useful information generated by the useful information generation unit 10c to the user terminal 2 of the user. Here, all or part of the useful information may also be transmitted to institutional computers 3a owned by medical institutions or medical professionals of various partner institutions. The useful information transmitted to user terminal 2 and period computer 3 may be viewed or shared with third parties designated in advance by the user, such as dentists, doctors, and family members, via websites or other means. Furthermore, information sharing services via social networking services, such as diary functions, user-to-user messaging functions, and community creation functions, may also be provided.

[0049] The storage means 11 includes at least a user information storage unit 11a that stores various user information acquired by the information acquisition unit 10a, and a useful information storage unit 11b that stores information useful for maintaining and improving oral health, including user tooth loss risk prediction information generated by the useful information generation unit 10c.

[0050] The oral health support system according to the second embodiment is: Computers By using a predetermined classification system with antibody titers related to oral bacteria and oral bacterial volume as variables, the risk of tooth loss is predicted by statistically analyzing the results of the user's oral bacterial antibody titer test and the results of the user's oral bacterial volume test. This system is characterized by creating information useful for maintaining and promoting oral health, including predicted tooth loss risk information, storing it in an electronic medical record or a database used for user health management, and utilizing it for the prevention, treatment, and maintenance and promotion of oral diseases in users.

[0051] The oral health support system according to the second embodiment may have the same configuration as the oral health support system according to the first embodiment until it generates information useful for maintaining and promoting oral health, including information predicting the risk of tooth loss. For example, in the oral health support system according to the second embodiment, as shown in Figure 1, the support server 1 is connected to individual user terminals 2 via a network N such as the Internet, and various information is exchanged. Furthermore, the support server 1 is connected to an external server 3b via the network N by institutional computers 3a owned by medical institutions or medical professionals that hold user information, or by institutions that manage electronic medical record systems, and various information is exchanged. Furthermore, as shown in Figure 2, a support server 1 can be used, which consists of one or more computers equipped with a processing unit 10 having a communication control unit 12 and storage means 11.

[0052] In the oral health support system according to the second embodiment, information useful for maintaining and promoting oral health, including tooth loss risk prediction information produced by the useful information generation unit 10c of the processing unit 10 in the support server 1, is transmitted to an institutional computer 3a owned by a medical institution or medical professional, or to an external server 3b owned by an institution that manages an electronic medical record system, and the information is stored in the electronic medical record or database used for user health management stored in the institutional computer 3a or the external server 3b.

[0053] When a user receives medical treatment, the medical institution or healthcare professional can obtain the user's information from electronic medical records and databases stored on the institution's computer 3a or an external server 3b, which can then be used for the prevention, treatment, and maintenance and promotion of oral diseases in the user. The aforementioned databases include databases of medical institutions, health insurance associations, and databases used by industrial physicians and public health nurses in companies for guidance.

[0054] Although embodiments of the present invention have been described above, the present invention is not limited in any way to these embodiments, and can be implemented in various forms without departing from the spirit of the invention. [Examples]

[0055] (Example 1: Prediction of future tooth loss risk using multiple regression analysis 1) The study targeted 439 employees of the applicant's company, aged 20 to 50 (oldest: 59 years old), who underwent health checkups and dental checkups in 2019, had antibody titers and salivary oral bacterial levels measured in 2019, and remained employed by the company from 2020 onward. Tooth loss was evaluated using dental claims data from April 2019 to March 2023. Specifically, the treatment code (extraction procedure (molar)) from the dental claims was used to compile data on whether or not extractions occurred and the number of extractions during the period. Factors associated with the cumulative number of teeth lost over a four-year period were analyzed using multiple regression analysis. This study examines whether it is possible to predict tooth loss over a four-year period (future tooth loss) that will occur from 2019 onwards, based on examination results from 2019.

[0056] Antibody titers were measured using serum diluted 10-fold. Antibody titers against gingipain produced by Porphyromonas gingivalis were measured. Specifically, the polypeptide described in Japanese Patent No. 6144313 was used as the antigen, and the titer was measured using chemiluminescence immunoassay (CELIA). A calibration curve was created by serially diluting standard positive serum, and the antibody titer was calculated by applying the measured values ​​of the samples to the calibration curve.

[0057] The amount of oral bacteria in saliva was measured using the amount of Porphyromonas gingivalis (hereinafter referred to as Pg amount) in saliva. The amount of Pg was measured by extracting DNA from resting saliva and measuring it by quantitative PCR using a primer for the 16S rRNA gene. The amount of Pg in each saliva sample was calculated from a calibration curve created by serially diluting Pg of known concentration. Microbial analysis was also performed using the extracted DNA. The V3-V4 region of the 16S rRNA gene was amplified and sequenced, microorganisms were identified using QIIME, and the proportion of Pg to the total amount of all bacteria was calculated.

[0058] An Excel spreadsheet was created summarizing the age, sex, antibody titer, salivary Pg level, whether or not teeth were extracted, and the number of teeth extracted for each of the 439 subjects mentioned above. Next, "number of teeth extracted" was selected as the dependent variable (Y), and "age," "sex," "antibody titer," and "salivary Pg level" were selected as independent variables. Multiple regression analysis was then performed using spreadsheet software (SPSS ver27). The results obtained are shown in Table 1.

[0059] [Table 1]

[0060] The results shown in Table 1, along with the significance probabilities, indicate that antibody titers and salivary Pg levels are less than 0.1, suggesting a correlation with the number of teeth extracted over four years. The standardized coefficient beta indicates the magnitude and direction of its influence on the dependent variable "number of teeth extracted." Beta is a number between -1.0 and 1.0, and the further it is from 0, the greater the influence on the dependent variable. The antibody titer of -0.114 suggests an influence toward reducing the number of teeth extracted, while the salivary Pg level of 0.295 suggests an influence toward increasing the number of teeth extracted. Therefore, the number of teeth extracted (Y), antibody titer (X1), and salivary Pg amount (X2) are used. Y = -0.031X1 + 0.000003593X2 ... (Equation 1a) The formula shown can be used to calculate the number of teeth extracted over a four-year period. From the above, it can be seen that the risk of future tooth loss can be predicted based on antibody titers and salivary Pg levels.

[0061] Furthermore, by adding age (X3), gender (X4), and a constant to the formula, as shown in formula 1b below, more accurate predictions can be made that correspond to the subject's condition. Y = -0.031X1 + 0.000003593X2 + 0.002X3 - 0.009X4 - 0.087 ... (Equation 1b) Furthermore, you may create a new formula by removing either age (X3) or gender (X4) from formula 1b.

[0062] In equations 1a and 1b above, the standardization coefficient for antibody titer (X1) is a negative value, indicating that a higher antibody titer results in fewer tooth extractions. Conversely, the standardization coefficient for salivary Pg amount (X2) is a positive value, indicating that a higher salivary Pg amount tends to result in more tooth extractions.

[0063] (Example 2: Prediction of future tooth loss risk using multiple regression analysis 2) In the data used in Example 1, subjects in their 20s and 30s had fewer tooth extractions, and even when extractions occurred, the cause was likely not periodontal disease or cavities, but rather orthodontic extraction or wisdom tooth extraction. Therefore, we selected data from subjects aged 40-59 (233 people), who were more likely to have tooth extractions due to periodontal disease or cavities, and performed multiple regression analysis. The results are shown in Table 2.

[0064] [Table 2]

[0065] Based on the results above, and considering the significance level, both antibody titers and salivary Pg levels are less than 0.05, indicating a significant correlation with the number of teeth extracted over the four-year period. Therefore, the formula obtained using the number of teeth extracted (Y), antibody titer (X1), and salivary Pg amount (X2) is as follows. Y = -0.041X1 + 0.000003605X2 ... (Equation 2a) From the above, it can be seen that the risk of future tooth loss can be predicted based on antibody titers and salivary Pg levels. In particular, the significance probability of the antibody titer in formula 2a is lower at "0.028" compared to "0.053" in formula (1a), indicating that excluding younger individuals leads to higher accuracy in risk prediction.

[0066] Furthermore, by adding age (X3), gender (X4), and a constant to the formula, as shown in formula 2b below, it is possible to make more accurate predictions that correspond to the subject's condition. Y = -0.041X1 + 0.000003605X2 + 0.004X3 - 0.023X4 - 0.008 ... (Equation 2b) Furthermore, you may create a new formula by removing either age (X3) or gender (X4) from formula 2b.

[0067] In equations 2a and 2b above, the standardization coefficient for antibody titer (X1) is a negative value, indicating that a higher antibody titer results in fewer tooth extractions. Conversely, the standardization coefficient for salivary Pg amount (X2) is a positive value, indicating that a higher salivary Pg amount results in more tooth extractions.

[0068] (Example 3: Relationship between antibody titer, the ratio of oral bacteria in the oral cavity, and the likelihood of future tooth loss) Using the subject information of 40-59 year olds from Example 2, we analyzed whether there was a difference in the ratio of antibody titer to salivary Pg amount (antibody titer / salivary Pg amount) between two groups: those with and without cumulative tooth loss over a 4-year period (Mann-Whitney U test). The results obtained are shown in Figure 3. SPSS ver27 was used for the analysis. As shown in Figure 3, the ratio of antibody titer to salivary Pg amount was significantly different between those who lost teeth over a four-year period and those who did not (p=0.017).

[0069] (Example 4: Prediction of future tooth loss risk based on the ratio of antibody titer to the amount of oral bacteria in the oral cavity) Using the subject information of 40-59 year olds from Example 2, ROC analysis was performed to determine whether the ratio of antibody titer to salivary Pg amount (antibody titer / salivary Pg amount) could predict whether a person would not lose teeth within 4 years. SPSS ver27 was used for the analysis. The results obtained are shown in Table 3.

[0070] [Table 3]

[0071] The results in Table 3 show that the asymptotic significance probability for antibody titer / salivary Pg amount is "0.020" and less than 0.05, indicating that the combination of antibody titer and salivary Pg amount can predict whether or not a person will not lose their teeth. On the other hand, the provisional significance probabilities for antibody titer alone and salivary Pg amount alone are "0.262" and "0.392," respectively, indicating that neither can be used to make a prediction on its own. It has been shown that setting a cutoff value for the ratio of antibody titer to salivary Pg amount can predict whether or not future tooth loss will occur.

[0072] (Example 5: Correlation analysis between antibody titer and oral bacterial content in the oral cavity) Using the subject information (overall) from Example 1 and the subject information (ages 40-59) from Example 2, a correlation analysis was performed between antibody titers and the amount of oral bacteria in the oral cavity. Spearman analysis was used for the correlation analysis. SPSS ver27 was used for the analysis. The results obtained are shown in Table 4. The correlation coefficient is expressed as a number in the range of -1 to 1, and the further it is from 0, the stronger the correlation is considered to be.

[0073] [Table 4]

[0074] The results in Table 4 show that, both for the overall group and for the 40-59 age group, the significance level is less than 0.05 and the correlation coefficient is 0.7 or higher, indicating a very strong correlation between antibody titer and salivary Pg levels.

[0075] (Example 6: Prediction of future tooth loss incidence when antibody titer and salivary Pg percentage are classified into three categories) In the subject information for Example 2 (ages 40-59), antibody titers and salivary Pg percentages were classified into three categories based on the following criteria, and the number of subjects belonging to each of the nine groups corresponding to each combination was investigated. The results are shown in Table 5. Antibody titer: The antibody titer measurements of 233 subjects were sorted in ascending order and classified into three categories based on the 50th percentile (values ​​representing 50% of the total) and the 75th percentile (values ​​representing 75% of the total). Low: People whose antibody titer is below the 50th percentile. Moderate: People whose antibody titer is between the 50th and 75th percentiles. High: People whose antibody titer is higher than the 75th percentile. Salivary Pg levels: The salivary Pg percentages of 233 subjects were sorted in ascending order and classified into three categories based on the 50th percentile (value representing 50% of the total) and the 75th percentile (value representing 75% of the total). Low: People whose salivary PG percentage is below the 50th percentile. Moderate: Individuals whose salivary PG content falls between the 50th and 75th percentiles. High: People whose salivary PG content is higher than the 75th percentile.

[0076] [Table 5]

[0077] The results in Table 5 show that a high antibody titer is often associated with a high salivary Pg percentage, and a low antibody titer is often associated with a low salivary Pg percentage, which is consistent with the correlation results in Table 4. However, the classification of antibody titer and salivary Pg percentage does not match for all individuals, and there are people whose results from the two tests do not match, such as those with high antibody titers but low salivary Pg percentages.

[0078] Next, the incidence of tooth loss (probability of tooth extraction) was examined for each of the nine groups, which were combinations of antibody titers and salivary Pg percentages. The results are shown in Table 6. The probability of tooth extraction was calculated as follows: Tooth extraction probability (%) = Number of people in the group who lost teeth / Number of people in the group × 100

[0079] [Table 6]

[0080] The results in Table 6 show that subjects with a high salivary Pg percentage and low antibody titer were at a higher risk of tooth extraction. It can be seen that people with a low salivary Pg percentage and high antibody titers are less likely to have teeth extracted. Therefore, it is possible to predict the risk of future tooth loss by classifying and combining antibody titers and salivary Pg levels. This result shows the tooth extraction rate for each of the nine groups based on combinations of antibody titer and salivary Pg ratio. However, it is also possible to classify the tooth extraction rate, for example, by indicating that less than 10% is a low risk of tooth loss, the 10% to 20% group is a medium risk of tooth loss, and 20% or more is a high risk of tooth loss. Furthermore, it is possible to set a baseline group and indicate how many times more risk a particular group has compared to that group (for example, if the baseline group is defined as having a moderate antibody titer and a low salivary Pg percentage, the group with a moderate antibody titer and a moderate salivary Pg percentage is 3.5 times more likely to lose teeth than the baseline group, and the group with a moderate antibody titer and a high salivary Pg percentage is 6.9 times more likely to lose teeth than the baseline group).

[0081] (Example 7: Prediction of tooth loss incidence when antibody titers are classified into 3 categories and salivary Pg is classified into 2 categories) In the subject information for Example 2 (ages 40-59), antibody titers and salivary Pg were classified according to the following criteria, and the number of subjects belonging to each of the six combinations was investigated. The results are shown in Table 7. Antibody titer: The antibody titer measurements of 233 subjects were sorted in ascending order and classified into three categories based on the 50th percentile (values ​​representing 50% of the total) and the 75th percentile (values ​​representing 75% of the total). Low: People whose antibody titer is below the 50th percentile. Moderate: People whose antibody titer is between the 50th and 75th percentiles. High: People whose antibody titer is higher than the 75th percentile. Salivary Pg: Based on the results of examining the proportion of salivary Pg in 233 subjects, the subjects were classified into two groups based on whether or not salivary Pg was detected. Not detected: People in whom Pg was not detected in their saliva. Detection detected: People in whom Pg was detected in their saliva.

[0082] [Table 7]

[0083] Next, the incidence of tooth loss (probability of tooth extraction) was examined for each of the six groups, which were formed by combining antibody titers and salivary Pg detection. The results are shown in Table 8. The tooth extraction probability was calculated in the same manner as in Example 6.

[0084] [Table 8]

[0085] The results in Table 8 show that subjects with detectable salivary Pg and low antibody titers were at a higher risk of tooth extraction, while those without detectable salivary Pg and high antibody titers were at a lower risk of tooth extraction. This result shows the tooth extraction rate for each of the six groups based on the combination of antibody titer and salivary Pg detection. However, it is also possible to classify the tooth extraction rate, for example, displaying it as follows: less than 10% as low risk of tooth loss, 10% to 20% as medium risk of tooth loss, and 20% or more as high risk of tooth loss. It is also possible to set a baseline group and display how many times higher the risk is compared to that group. Therefore, it is possible to predict the risk of future tooth loss by classifying and combining antibody titers and salivary Pg levels. [Explanation of Symbols]

[0086] 1. Support Server 2 User terminals 3a 3b Engine computer 10 Processing Unit 10a Information Acquisition Unit 10b Analysis and Prediction Section 10c Useful information generation section 10d Information transmission unit 11 Memory means 11a User information storage unit 11b Useful information storage section 12 Communication Control Unit N Network

Claims

1. An oral health support method characterized by predicting the risk of tooth loss using antibody titer test results related to oral bacteria and oral bacteria quantity test results.

2. The oral health support method according to claim 1, wherein the prediction is made using a predetermined classification in which the values ​​relating to antibody titers related to oral bacteria and the values ​​relating to the amount of oral bacteria in the oral cavity are variables.

3. The oral health support method according to claim 1, wherein the risk of tooth loss is the presence or absence of the possibility of tooth loss, the degree of the possibility of tooth loss, or the number of teeth that may be lost.

4. The oral health support method according to claim 1, wherein the oral bacteria include Porphyromonas gingivalis.

5. The oral health support method according to claim 1, wherein the antibody titer is the antibody titer against the protease derived from the oral bacteria.

6. The oral health support method according to any one of claims 1 to 5, further comprising using one or more other test items selected from the group consisting of age, sex, smoking status, presence or absence of diabetes, blood glucose level, and HbA1c to predict the risk of tooth loss.

7. Computers Using a predetermined classification system with variables related to antibody titers associated with oral bacteria and values ​​related to the amount of oral bacteria in the oral cavity, the risk of tooth loss is predicted from the results of the user's oral bacteria-related antibody titer test and the results of the user's oral bacteria amount test. An oral health support system that creates and provides information useful for maintaining and improving oral health, including predictive information on the risk of tooth loss, to users, medical institutions, or healthcare professionals.

8. The oral health support system according to claim 7, further comprising using one or more other test items selected from the group consisting of age, sex, smoking status, presence or absence of diabetes, blood glucose level, and HbA1c to predict the risk of tooth loss.

9. Computers Using a predetermined classification system with variables representing antibody titers related to oral bacteria and the amount of oral bacteria in the oral cavity, the risk of tooth loss is predicted from the results of the user's oral bacteria-related antibody titer test and the results of the user's oral bacteria amount test. An oral health support system that creates useful information for maintaining and promoting oral health, including predicted tooth loss risk information, stores it in an electronic medical record or a database used for user health management, and utilizes it for the prevention, treatment, and maintenance and promotion of oral diseases in users.

10. The oral health support system according to claim 9, further comprising using one or more other test items selected from the group consisting of age, sex, smoking status, presence or absence of diabetes, blood glucose level, and HbA1c to predict the risk of tooth loss.

11. The oral health support system according to any one of claims 7 to 10, wherein the oral bacteria include Porphyromonas gingivalis.

12. The oral health support system according to any one of claims 7 to 10, wherein the antibody titer is the antibody titer against the protease derived from the oral bacteria.

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