Biomarker for periodontal disease onset risk determination and determination system

Abstract

To provide a system that determines, in a stage in which a periodontal disease has not yet developed in a patient, the risk of onset of the periodontal disease several years later.SOLUTION: The present invention provides a system for determining the risk of onset of a periodontal disease including: acquiring the amount of collagen in an attached gingiva tissue of a subject, from a biomarker for periodontal disease onset risk determination including collagen in the attached gingiva tissue, a biological sample isolated from the subject, or an ultrasonic image of the attached gingiva tissue of the subject; and comparing the amount of collagen with a cut-off value, and when the amount of collagen is equal to or less than the cut-off value, determining the presence of the risk of onset of a periodontal disease, and a system for determining the risk of onset of a periodontal disease including: acquiring the biological sample isolated from the subject, or the average luminance of the ultrasonic image of the attached gingiva tissue of the subject; and comparing the amount of acquired biomarker or the average luminance of the ultrasonic image with the cut-off value, and when the amount of the biomarker or the average luminance is equal to or less than the cut-off value, determining the presence of the risk of onset of the periodontal disease.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to a biomarker and a determination system for determining the risk of developing periodontal disease. [Background technology]

[0002] Unlike dental caries, periodontal disease has almost no noticeable symptoms and progresses unnoticed, with people often only seeing a dentist once it has worsened, making it the leading cause of tooth loss among the elderly. Furthermore, it has become clear in recent years that periodontal disease is associated with systemic diseases such as metabolic syndrome, coronary artery disease, and diabetes, making periodontal disease a problem that concerns not only dental health but also overall overall health. Therefore, early detection of periodontal disease is important for maintaining the health of not only the oral cavity but also the entire body.

[0003] For example, Patent Document 1 describes a method for measuring the average brightness of the area inside the attached gingiva and determining oral indices (gingival health status) such as the amount of gingival collagen, gingival age, average pocket depth of all teeth, mobility of adjacent teeth, bleeding on probing (BOP) positivity rate, extent of gingival inflammation (PISA), or surface area of ​​periodontal pockets (PESA). [Prior art documents] [Patent documents]

[0004] [Patent Document 1] International Publication No. 2019 / 167963 Summary of the Invention [Problem to be solved by the invention]

[0005] However, Patent Document 1 does not describe at all the relationship between the subsequent risk of periodontal disease and oral indices in subjects who have not developed periodontal disease.

[0006] The present invention aims to provide a system for determining the risk of developing periodontal disease several years from now, when the disease has not yet developed. [Means for solving the problem]

[0007] The present invention provides the following [1] to

[12] . [1] A biomarker for determining the risk of developing periodontal disease, including collagen in the attached gingival tissue. [2] The biomarker described in [1], wherein the collagen is collagen in at least a portion of the attached gingival tissue ranging from just below the epithelium to 1500 μm below the epithelium. [3] The biomarker according to [1] or [2], wherein the risk of developing periodontal disease is the risk of developing periodontal disease within five years. [4] Obtaining the amount of collagen in the attached gingival tissue of the subject from an ultrasound image of a biological sample isolated from the subject or the attached gingival tissue of the subject; and Comparing the collagen amount with a cutoff value, and determining that there is a risk of developing periodontal disease if it is below the cutoff value; A system for assessing the risk of developing periodontal disease, including: [5] The system described in [4], wherein the amount of collagen is determined from the average brightness of an ultrasound image of the subject's attached gingival tissue. [6] A system described in [4] or [5], wherein the ultrasound image of the attached gingival tissue includes at least a portion of the attached gingival tissue in the range from just below the epithelium to 1500 μm below the epithelium. [7] The system according to any one of [4] to [6], wherein the amount of collagen is determined to be equal to or less than a cutoff value when the average brightness is equal to or less than a reference value. [8] The system described in any one of [4] to [7], wherein the risk of developing periodontal disease is the risk of developing periodontal disease within five years. [9] Obtaining an average brightness of an ultrasound image of a biological sample isolated from a subject or the subject's attached gingival tissue; and comparing the amount of biomarker in the biological sample or the average brightness of the ultrasound image of the subject with a cutoff value relating the risk of developing periodontal disease after a period of time has elapsed since the acquisition of the average brightness to the amount of biomarker in the biological sample or the average brightness of the ultrasound image of the attached gingival tissue, and determining that the subject is at risk of developing periodontal disease if the amount of biomarker or the average brightness of the subject is equal to or less than the cutoff value; A system for assessing the risk of developing periodontal disease, including:

[10] The system described in [9], wherein the cutoff value is based on training data correlating the amount of biomarker in a biological sample isolated from a living organism or the average brightness of an ultrasound image of attached gingival tissue with the onset of periodontal disease over a period of time.

[11] The system described in [9] or

[10] , wherein the ultrasound image of the attached gingival tissue includes at least a portion of the attached gingival tissue in the range from just below the epithelium to 1500 μm below the epithelium.

[12] The system according to any one of [9] to

[11] , wherein the period is within five years after the average luminance is acquired. [Effects of the Invention]

[0008] According to the present invention, a periodontal disease determination marker and determination system are provided that can determine whether or not a subject who has not yet developed periodontal disease is likely to develop periodontal disease several years later, and can determine the risk of periodontal disease at a pre-onset stage. [Brief explanation of the drawings]

[0009] [Figure 1] FIG. 1 is a graph showing significant differences in the mean brightness of attached gingiva ultrasound images (range from just below the epithelium to 500 μm) among healthy, non-diseased, moderately severe, and severely severe groups classified by BOP. [Figure 2] FIG. 2 is a graph showing significant differences in the mean brightness of ultrasound images of attached gingiva (in the range from just below the epithelium to 500 μm) among healthy, non-diseased, moderately severe, and severely severe groups classified by PISA. [Figure 3] FIG. 3 is a graph showing significant differences in the mean brightness of ultrasound images of attached gingiva (in the range from just below the epithelium to 1500 μm) among healthy, non-diseased, moderately severe, and severely severe groups classified by PISA. [Figure 4] Figure 4 is a graph showing significant differences in the mean brightness of attached gingiva ultrasound images (range from just below the epithelium to 500 μm) among healthy, non-diseased, moderately severe, and severely severe groups classified by mean periodontal pocket depth. [Figure 5] FIG. 5 is a graph showing significant differences in the diabetes index (HbA1c) among healthy, non-diseased, moderate to severe, and severe groups classified by PISA. [Figure 6] FIG. 6 is a graph showing significant differences in hyperlipidemia (triglyceride) indices among healthy, non-ill, moderate to severe, and severe groups classified by PISA. [Figure 7] FIG. 7 is a graph showing significant differences in the mean values ​​of Pg bacterial load in saliva among healthy, non-ill, moderate to severe, and severe groups classified by PISA. [Figure 8] Figure 8 is a graph showing significant differences in the mean brightness of ultrasound images of attached gingiva (in the range from just below the epithelium to 500 μm) among healthy, non-diseased, moderate, and severe groups classified by mean clinical attachment level (CAL). [Figure 9] FIG. 9 is a graph showing significant differences in the mean brightness of attached gingiva ultrasound images (range from just below the epithelium to 500 μm) among healthy, non-diseased, moderately severe, and severely severe groups classified by periodontal index (GI). DETAILED DESCRIPTION OF THE INVENTION

[0010] [1. Biomarkers for determining the risk of developing periodontal disease] Collagen in the attached gingival tissue can be used to determine the risk of developing periodontal disease, and therefore can be used as a biomarker. Until now, it was thought that gingival collagen was degraded and reduced by the inflammation that accompanies the onset of periodontal disease. However, it was not known that gingival collagen decreases in the pre-disease stage, before the onset of periodontal disease, or that this is a risk factor for the onset of periodontal disease; this finding was the first to be discovered by the present inventors.

[0011] The attached gingiva is a tissue that, together with the alveolar mucosa, forms part of the gingiva. It is distinguished from the alveolar mucosa by the visually identifiable gingival-alveolar mucosa junction, and the area closer to the teeth than the gingival-alveolar mucosa junction is the attached gingiva. The attached gingiva is attached to the alveolar bone and cementum by fibers within the gingiva. On the other hand, the alveolar mucosa is not connected to the internal hard tissues.

[0012] The collagen in the attached gingival tissue may be the amount of collagen in at least a portion of the attached gingival tissue, but typically it is at least a portion of the range from just below the epithelium to 1700 μm below the epithelium, preferably at least a portion of the range from just below the epithelium to 1500 μm below the epithelium, more preferably at least a portion of the range from just below the epithelium to 1000 μm below the epithelium, and even more preferably at least a portion of the range from just below the epithelium to 800 μm below the epithelium. The attached gingiva may be either the upper or lower jaw, but is preferably the upper jaw, more preferably the interdental papillae of maxillary nos. 1 to 5, and even more preferably the attached gingiva of the three interdental papillae between maxillary left and right nos. 1 and 2 and between maxillary right nos. 1 and left nos. 1. This allows for highly reproducible quantitative results. Measurement of collagen as a marker can be performed, for example, by using ultrasound images obtained by ultrasonically imaging the attached gingiva, or by measuring the amount of collagen metabolites. Measurement methods using ultrasound images include those that utilize quantitative values ​​in ultrasound images, such as average brightness. Details of these methods will be explained in the section describing the quantitative system.

[0013] In this specification, the assessment of the risk of developing periodontal disease refers to a preliminary assessment (prediction) prior to a medical diagnosis, and may not necessarily coincide with the assessment of the risk of periodontal disease as a medical procedure. The assessment of the risk of developing periodontal disease in a subject who is not yet afflicted is preferred, and the assessment of the risk of developing periodontal disease within 5 years, 4 years, or 3 years is more preferred.

[0014] [2. Periodontal disease risk assessment system] The risk of developing periodontal disease can be determined by quantifying a biomarker (for example, collagen in the attached gingival tissue). When the biomarker is collagen, for example, information on the amount of collagen in the attached gingival tissue of the subject is obtained, and if the obtained collagen amount is below a cutoff value, it can be determined that there is a risk of developing periodontal disease. On the other hand, if it exceeds the cutoff value, it can be determined that there is no risk of periodontal disease.

[0015] [2.1 Obtaining collagen content information] In risk assessment, first, the amount of biomarkers (for example, collagen amount) in the attached gingival tissue of the subject is obtained.

[0016] -subject- The subject is not particularly limited, and may be any subject for whom the risk of developing periodontal disease is desired to be determined. However, it is preferable that the subject not have periodontal disease. This is advantageous in that even if the subject does not have periodontal disease at the time of collagen measurement, subjects at risk of developing periodontal disease in the future (e.g., within 5 years, 4 years, or 3 years) (non-disease subjects) can be distinguished from subjects at low risk (healthy individuals), and early intervention can be effectively used to prevent the actual onset of periodontal disease. As used herein, "having" periodontal disease means that at least one dental index selected from BOP, periodontal pocket depth, PISA, mean CAL, and GI is classified as early to moderate, or severe periodontal disease in the classification described in the Examples below. On the other hand, "not having" periodontal disease means that none of at least one dental index selected from BOP, periodontal pocket depth, PISA, mean CAL, and GI is classified as early to moderate, or severe periodontal disease in the classification described in the Examples below. The number of subjects may be multiple, preferably 5 or more, more preferably 10 or more, even more preferably 50 or more, and even more preferably 100 or more. The subjects are preferably subjects who have not developed periodontal disease at the time of quantification, as this provides practical benefits for assessing the risk of periodontal disease.

[0017] Information on the amount of a biomarker such as collagen can be obtained, for example, from an ultrasound image of a biological sample isolated from a subject or of the subject's attached gingival tissue.

[0018] -Acquisition of ultrasound images- When ultrasound is applied to the surface of the attached gingiva of a subject, the ultrasound is reflected by the structure inside the attached gingiva and returns to the surface of the attached gingiva of the subject. The reflected ultrasound changes depending on the structural information inside the attached gingiva. Therefore, ultrasound analysis is a technology that can obtain structural information inside the attached gingiva of a subject.

[0019] The frequency band of the ultrasound is usually 10 to 80 MHz, preferably 10 to 50 MHz, and more preferably 15 to 25 MHz. By irradiating ultrasound with a long wavelength, the ultrasound can reach the connective tissue where collagen is present, which is deeper than the epithelium, and can even image the surface of the alveolar bone. By irradiating ultrasound with a short wavelength, it is possible to see the shallow part just below the gingival epithelium.

[0020] Ultrasound images are usually obtained using a measurement probe. The measurement probe is part of an ultrasound measurement device, and is placed against the measurement site of the attached gingiva of the subject, transmits ultrasound waves, and receives ultrasound waves reflected from inside the attached gingiva of the subject. When measuring an ultrasound image, a protective film may be interposed between the measurement probe and the measurement site of the attached gingiva of the subject. The shape of the tip of the measurement probe does not necessarily have to be circular; it may be, for example, elliptical, rectangular, or square.

[0021] The diameter of the tip of the measurement probe used to measure the surface of the subject's attached skin is preferably 0.1 to 5 mm, more preferably 0.3 to 4 mm, and even more preferably 0.5 to 3 mm. When the diameter of the tip of the measurement probe used to measure the surface of the subject's attached skin is 5 mm or less, a good correlation with the intraoral index can be achieved. On the other hand, when the diameter is 0.1 mm or more, the measurement is less susceptible to the influence of structures such as blood vessels in the gingiva, improving reproducibility. Therefore, by keeping the diameter within the above range, it is possible to measure only the attached gingiva portion and to measure as wide an area as possible to reduce variability. In this specification, "diameter" refers to, for example, the diameter of the circle when the shape of the probe tip is a perfect circle, or the minor axis of the ellipse when the shape of the probe tip is elliptical. Furthermore, when the shape of the probe tip is rectangular, it refers to the length of the minor side, and when the shape of the probe tip is square, it refers to the length of one side. When the shape of the probe tip is elliptical, the major axis may be 5 mm or more. When the shape of the probe tip is rectangular, the length of the major side may be 5 mm or more.

[0022] Examples of the measurement probe include a measurement probe that can be connected to an analysis unit and transfer information, as well as a measurement probe that can be connected to communication devices such as smartphones, mobile phones, and personal computers and transfer information.

[0023] Ultrasound images can be taken of the tissue regions described above (at least a portion of the range from just below the epithelium to 1700 μm below the epithelium, preferably at least a portion of the range from just below the epithelium to 1500 μm below the epithelium, more preferably at least a portion of the range from just below the epithelium to 1000 μm below the epithelium, and even more preferably at least a portion of the range from just below the epithelium to 800 μm below the epithelium). While gingival thickness typically varies from subject to subject, specifying the analysis range based on the distance from the epithelium can enhance correlation between variables. Furthermore, analyzing the region from just below the epithelium to within 1500 μm below the epithelium avoids the alveolar bone located deeper than this region, ensuring that the quantitative value (brightness) does not include anything other than connective tissue, improving the reliability of the measurement. Analyzing the region from just below the epithelium to within 800 μm below the epithelium excludes areas with low collagen content from the analysis region, allowing for sufficient coverage of areas with high collagen content, thereby reducing variability and improving the reliability of the measurement.

[0024] Ultrasound images can be directly imported as image data into an analysis unit (software) and used for image analysis, or the ultrasound images can be printed and imported via an image scanner or the like for image analysis. It is preferable that the resolution is sufficient for analysis. In this specification, "sufficient resolution" refers to a level at which the boundary between epithelium and connective tissue can be clearly distinguished.

[0025] The size of the image acquisition range is usually 0.1 mm to 5 mm in diameter, preferably 0.3 mm to 4 mm, and more preferably 0.5 mm to 3 mm. This allows for measurement of only a specific portion of the attached gingiva, and within that, as wide an area as possible, thereby reducing variability. By setting the diameter of the image acquisition range to 5 mm or less, correlation with intraoral clinical indicators is enhanced. On the other hand, by setting it to 0.1 mm or more, it is less susceptible to the influence of structures such as blood vessels in the gingiva, and reproducibility is enhanced.

[0026] A method for determining the risk of developing periodontal disease from ultrasound images preferably uses average brightness, which is useful as information reflecting the amount of biomarkers such as collagen. As a method for obtaining the average brightness, for example, an area to be analyzed in an ultrasound image can be selected and the average value within that area can be calculated using software (e.g., image processing software "ImageJ"). Alternatively, multiple small areas can be selected within the area to be analyzed and the average values ​​of each selected area can be statistically analyzed to calculate the average brightness of the entire area to be analyzed. The function for calculating the average brightness is often built into the software of the analysis unit and can be easily obtained. However, even if the software does not have this function, the average brightness of the area to be analyzed can be calculated by measuring the brightness of enough points.

[0027] - Obtaining biomarker amounts from biological samples isolated from subjects - Examples of biological samples include saliva (e.g., resting saliva, stimulated saliva), blood (e.g., whole blood, serum, plasma), or gingival cells, gingival tissue, and cultures thereof, preferably saliva or blood, more preferably saliva.

[0028] The amount of a biomarker can be obtained from a biological sample by, for example, measuring the amount of collagen metabolites, such as amino acids such as glycine, proline, and hydroxyproline, and peptides such as procollagen type I C-terminal peptide (PIP) and N-terminal propeptide.

[0029] [2.2. Determining periodontal disease risk from collagen content] The risk of periodontal disease is determined based on the amount of biomarkers such as collagen obtained as described above, for example, the average brightness of ultrasound images. In this specification, the determination is a preliminary determination, and does not involve a doctor's judgment. The determination can be made by comparison with a cutoff value. If the collagen amount is equal to or less than the cutoff value, the risk of developing periodontal disease is determined to be high. Examples of analytical methods include machine learning, deep learning, supervised and unsupervised data analysis, and clustering methods (e.g., correlation analysis). Examples of correlation analysis include regression analysis, multiple regression analysis, logistic regression analysis, principal component analysis, independent component analysis, factor analysis, discriminant analysis, quantification theory, cluster analysis, conjoint analysis, multidimensional scaling (MDS), partial least squares discriminant analysis (PLS-DA), random forest, decision tree, support vector machine (SVM), k-nearest neighbors, naive Bayes, linear regression, polynomial regression, SVM for regression, k-means clustering, and hidden Markov model. Multiple regression analysis is preferred. These can be performed using Spearman's method with statistical analysis software (e.g., Pharmaco Basic, manufactured by Scientist Co., Ltd.). For example, this can be done by comparing with information on periodontal disease risk previously created for a test population (for example, a regression model (regression equation) or multiple regression model (multiple regression equation) of collagen levels in individuals whose dental indices (e.g., BOP, periodontal pocket depth, PISA, average CAL, GI) have decreased (they have periodontal disease) three years or more after collagen level measurement). Prior to regression analysis, multiple regression analysis, etc., the explanatory variable (collagen level, for example, average brightness) and the objective variable (periodontal disease risk) may be standardized using an appropriate standardization method. Furthermore, quantitative numerical data may be selected and extracted according to certain criteria (for example, extracting data with little variance) and used in the multiple regression analysis. Machine learning analysis can utilize one or more machine learning algorithms to correlate the amount of a biomarker such as collagen (e.g., mean brightness, collagen metabolites) with information about periodontal disease risk (e.g., collagen levels in individuals who were free of periodontal disease at the time of collagen measurement but who developed periodontal disease within five years). For example, an algorithm can be trained to receive the results of an analysis of collagen amount (preferably mean brightness) and output information about periodontal disease risk.

[0030] According to the determination system and device of the present invention, if a subject does not have periodontal disease at the time of examination, the future risk of developing periodontal disease can be predicted. Furthermore, the above determination system and device can be applied regardless of the condition of the subject at the time of examination, in which case the risk of developing periodontal disease and the presence or absence of periodontal disease can be comprehensively determined, and determinations can be made, for example, as severe periodontal disease, moderate periodontal disease, early periodontal disease, no periodontal disease (but with risk), no periodontal disease (low risk), etc. [Example]

[0031] The present invention will be described below with reference to examples, which are not intended to limit the scope of the present invention.

[0032] Examples 1 to 4 and Comparative Examples 1 to 3 [Dental examination, acquisition of gingival ultrasound images, calculation of average brightness] Ultrasound images of the gingiva were taken for 300 adult men and women, and further image analysis was performed to calculate the average brightness of the connective tissue (attached gingiva (within the range of 500 μm from just below the epithelium (Examples 1, 2, and 4), within the range of 1500 μm from just below the epithelium (Example 3), alveolar mucosa (other than attached gingiva) (Comparative Example 1)). In addition, dental hygienists measured the periodontal pocket depth of all teeth using a 6-point method, gingival bleeding during probing, GI, and tooth mobility, and calculated the average pocket depth, PISA, BOP, average CAL, and GI for each subject (dental checkup in the first year). Furthermore, dental hygienists similarly conducted a dental checkup on the same subjects three years later (dental checkup three years later). In addition, blood tests were performed at the dental checkup in the first year, and HbA1c and blood triglyceride levels were analyzed (Comparative Examples 2 and 3). Furthermore, Phorphyromonas in the saliva was measured at the dental checkup in the first year. The amount of P. gingivalis (Pg) was analyzed by real-time PCR (Comparative Example 4).

[0033] Gingival ultrasound images were acquired as follows. A probe containing a drop of edible gel was applied to the attached gingiva area approximately 3 mm from the gingival margin of the interdental papillae of maxillary teeth Nos. 1 to 5, or to the alveolar mucosal area above the gingival-alveolar mucosal junction approximately 1 cm above this area. 20 MHz ultrasound waves were emitted and received. The reflected waves were visualized by a computer and saved as ultrasound images. These images were analyzed using standard image analysis software (ImageJ (National Institutes of Health)) to calculate the average brightness of the connective tissue area from just below the epithelium to 1500 μm or 500 μm below the epithelium in the ultrasound images (average brightness of attached gingiva ultrasound images: Examples 1 to 4), or the average brightness of the connective tissue area in the alveolar mucosal area above the gingival-alveolar mucosal junction (average brightness of alveolar mucosal ultrasound images: Comparative Example 1). For each subject, measurements were taken three times at each of the nine locations mentioned above, and the average of the 27 data points was used to determine the average brightness for that subject. Ultrasound images were obtained using a skin ultrasound device, DermaLab (manufactured by Cortex Technology), and a probe (manufactured by Integral) with a tip diameter of 2 mm that came into contact with the test site.

[0034] [Subject classification] Based on the results of the dental checkups, the subjects were classified into four groups: "severe periodontal disease" (onset confirmed at the first dental checkup), "early to moderate periodontal disease" (onset confirmed at the first dental checkup), "pre-disease" (onset within three years after the first dental checkup), and "healthy" (healthy for three years after the first dental checkup). The classification criteria are shown below.

[0035] BOP classification: BOP at first year dental checkup Greater than 30%: severe periodontal disease, 5% to 30%: Early to moderate periodontal disease, Less than 5% and an increase of 7.5 points or more by the time of the dental checkup three years later: Pre-illness; Less than 5% and an increase of less than 7.5 points by the time of the dental checkup three years later: healthy.

[0036] Classification by average CAL: The average CAL in the first year of dental examination was Larger than 5mm: severe periodontal disease, 3-4mm: Early to moderate periodontal disease, Less than 3mm and increased by 1.5mm or more by the time of the dental checkup three years later: Pre-disease Less than 3mm and an increase of less than 1.5mm by the time of the dental checkup three years later: healthy.

[0037] GI classification: The average GI of all teeth in the first year of dental examination was Greater than 1.5: severe periodontal disease, 1-1.5: Early to moderate periodontal disease, Less than 1 and increased by 0.4 or more by the time of dental checkup three years later: Pre-illness, Less than 1 and an increase of less than 0.4 by the time of the dental checkup three years later: healthy.

[0038] PISA classification: PISA in the first year of dental examination 650mm 2 Greater: severe periodontal disease, 100~650mm 2 : Early to moderate periodontal disease, 100mm 2 Less than 90mm by the time of the dental checkup in 3 years 2 Increased by more than: Pre-illness, 100mm 2 and the increase by the time of dental checkup after 3 years is 90 mm 2 Under: Healthy.

[0039] Average pocket depth classification: The average pocket depth at the first dental checkup was Larger than 4.5mm: severe periodontal disease, 3-4.5mm: Early to moderate periodontal disease Less than 3mm and increased by 0.5mm or more by the time of dental checkup 3 years later: Pre-disease Less than 3mm and an increase of less than 0.5mm by the time of the dental checkup three years later: healthy.

[0040] [Correspondence between each classification and average brightness] Example 1 (Correspondence between BOP classification and average brightness of attached gingiva) Based on the BOP classification results, the mean brightness of the attached gingiva ultrasound images (range from just below the epithelium to 500 μm) for each group was calculated, and a Steel-Dwass test was performed (Figure 1).

[0041] Example 2 (Correspondence between PISA classification and attached gingival mean brightness (directly below the epithelium to 500 μm)) Based on the PISA classification results, the mean brightness of the attached gingiva ultrasound images (range from just below the epithelium to 500 μm) for each group was calculated, and a Steel-Dwass test was performed (Figure 2).

[0042] Example 3 (Correspondence between PISA classification and attached gingival mean brightness (directly below the epithelium to 1500 μm)) Based on the classification results from PISA, the mean brightness of the attached gingiva ultrasound images (range from just below the epithelium to 1500 μm) for each group was calculated, and a Steel-Dwass test was performed (Figure 3). The average brightness of the attached gingiva ultrasound image was calculated based on the area from just below the epithelium to 500 μm.

[0043] Example 4 (Classification by average pocket depth and correspondence with the average brightness of attached gingiva (just below the epithelium to 500 μm)) Based on the classification results for mean pocket depth, the mean brightness of the attached gingiva ultrasound images (range from just below the epithelium to 1500 μm) for each group was calculated, and a Steel-Dwass test was performed (Figure 4).

[0044] Example 5 (Classification by average CAL and correspondence between average brightness of attached gingiva (directly below the epithelium to 500 μm)) Based on the classification results using the average CAL, the average brightness of the attached gingiva ultrasound images (within the range of just below the epithelium to 500 μm) for each group was calculated, and a Steel-Dwass test was performed (Figure 8).

[0045] Example 6 (Correspondence between GI classification and attached gingival mean brightness (directly below the epithelium to 500 μm)) Based on the GI classification results, the mean brightness of the attached gingiva ultrasound images (range from just below the epithelium to 500 μm) for each group was calculated, and a Steel-Dwass test was performed (Figure 9).

[0046] Example 7 (Confirmation of the direct relationship between each classification and the average brightness of attached gingiva ultrasound images) For classification by each dental index, the correlation coefficient (r value) and p value in Spearman's correlation analysis between the mean brightness of the attached gingiva ultrasound image (range from just below the epithelium to 500 μm) in the "pre-diseased" and "healthy" groups and the change in each dental index over the three years after the first visit were analyzed (Table 1).

[0047] [Table 1]

[0048] Comparative Examples 1 and 2 (Correspondence between PISA classification and indicators of diabetes and hyperlipidemia) Based on the PISA classification results, the mean values ​​of blood HbA1c (Comparative Example 1) and triglycerides (Comparative Example 2) in the first year for each group were calculated, and a Steel-Dwass test was performed (Figures 5 and 6). HbA1c is an index of diabetes and hyperlipidemia, both of which are systemic diseases that have been suggested to be associated with periodontal disease.

[0049] Comparative Example 3 Based on the PISA classification results, the average amount of Pg bacteria in the saliva measured in the first year for each group was calculated and a Steel-Dwass test was performed (Figure 7). Pg bacteria is a species of bacteria that is said to be one of the causes of periodontal disease.

[0050] In Comparative Example 1, there was no difference between the groups, and in Comparative Examples 2 and 3, only a distinction could be made between the "severe periodontal disease group" and the "healthy" and "non-diseased" groups. In contrast, in Examples 1 to 6, significant differences were observed between the groups, including between the "non-diseased" and "healthy" groups. Furthermore, in Example 7, the absolute value of the correlation coefficient between each index and the average brightness was 0.12 or more, and in particular, it exceeded 0.30 for PISA and BOP. These results indicate that there is a correlation between the average brightness of attached gingiva ultrasound images and the progression of periodontal disease, and that it is also possible to identify those at risk of periodontal disease.

Claims

1. A biomarker for determining the risk of developing periodontal disease, including collagen in the attached gingival tissue.

2. The biomarker of claim 1 , wherein the collagen is collagen in at least a portion of the attached gingival tissue ranging from just below the epithelium to 1500 μm below the epithelium.

3. The biomarker according to claim 1 or 2, wherein the risk of developing periodontal disease is the risk of developing periodontal disease within five years.

4. Obtaining the amount of collagen in the attached gingival tissue of the subject from an ultrasound image of a biological sample isolated from the subject or the attached gingival tissue of the subject; and Comparing the collagen amount with a cutoff value, and determining that there is a risk of developing periodontal disease if it is below the cutoff value; A system for assessing the risk of developing periodontal disease, including:

5. The system of claim 4, wherein the amount of collagen is determined from the average brightness of an ultrasound image of the subject's attached gingival tissue.

6. 6. The system of claim 4 or 5, wherein the ultrasound image of the attached gingival tissue includes at least a portion of the attached gingival tissue in the range from just below the epithelium to 1500 μm below the epithelium.

7. The system according to claim 4 or 5, wherein the amount of collagen is determined to be equal to or less than a cutoff value when the average brightness is equal to or less than a reference value.

8. The system according to claim 4 or 5, wherein the risk of developing periodontal disease is the risk of developing periodontal disease within five years.

9. obtaining an average brightness of an ultrasound image of a biological sample isolated from a subject or attached gingival tissue of the subject; and comparing the amount of biomarker in the biological sample or the average brightness of the ultrasound image of the subject with a cutoff value relating the risk of developing periodontal disease after a period of time has elapsed since the acquisition of the average brightness to the amount of biomarker in the biological sample or the average brightness of the ultrasound image of the attached gingival tissue, and determining that the subject is at risk of developing periodontal disease if the amount of biomarker or the average brightness of the subject is equal to or less than the cutoff value; A system for assessing the risk of developing periodontal disease, including:

10. The system of claim 9, wherein the cutoff value is based on training data correlating the amount of a biomarker in a biological sample isolated from a living body or the average brightness of an ultrasound image of attached gingival tissue with the onset of periodontal disease over a period of time.

11. 11. The system of claim 9 or 10, wherein the ultrasound image of the attached gingival tissue includes at least a portion of the attached gingival tissue in the range from just below the epithelium to 1500 μm below the epithelium.

12. The system according to claim 9 or 10, wherein the period is within five years after acquisition of the average luminance.

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