JP7653693B2 - Method for assessing the risk of developing peri-implantitis - Google Patents

Description

Article 30, paragraph 2 of the Patent Act applies. (1) June 3, 2023: Presented at the 14th Annual Meeting of the Japanese Society for the Study of Halitosis. (2) December 10, 2023: Presented at the 50th Annual General Meeting and Academic Conference of the Fukuoka Dental College Academic Society. (3) January 21, 2024: Presented at the 41st Annual Meeting of the Kyushu Branch of the Japanese Society of Oral Implantology.

The present invention relates to a method for evaluating the risk of developing peri-implantitis.

In dentistry, implant treatment is used clinically as one of the treatment methods for prosthetic replacement. In recent years, the number of patients who have undergone treatment has been increasing. Among these, the number of patients who develop peri-implantitis is on the rise, which is becoming a serious problem that can lead to implant loss. In relation to the above, for example, Non-Patent Document 1 reports on the bacterial flora associated with peri-implantitis.

J. Dent. Res., 2021 Jan; 100(1): 21-28.

Diagnosis of peri-implantitis is commonly performed using visual observation, probing, X-rays, and bacterial testing of the gums around the implant. However, there are issues with accuracy, invasiveness, radiation exposure, bacterial culture, etc.

One aspect of the present invention aims to provide a method for evaluating the risk of developing peri-implantitis in a minimally invasive and simple manner.

Specific means for solving the above problems are as follows.
[1] Contacting a sensor having a plurality of detection elements capable of detecting different odor molecules with a target gas sample containing odor molecules originating from a dental implant to be evaluated, and acquiring odor data of the dental implant;
Transforming the odor data into object coordinates in a coordinate space including a first principal component axis and a second principal component axis in a principal component analysis by a predetermined coordinate transformation;
and evaluating the distance between the subject coordinates and reference coordinates corresponding to odor data of a patient with peri-implantitis.

[2] The evaluation method described in [1], in which the evaluation of the distance between the target coordinates and the reference coordinates is performed by cluster analysis.

[3] Contacting a sensor having a plurality of detection elements capable of detecting different odor molecules with a target gas sample containing odor molecules originating from the dental implant to be evaluated, and acquiring odor data of the dental implant;
Transforming the odor data into object coordinates in a coordinate space including a first principal component axis and a second principal component axis in a principal component analysis by a predetermined coordinate transformation;
A method for evaluating the risk of developing peri-implantitis in an evaluation subject, comprising: evaluating the distance between the subject coordinates and a previously identified area having a high risk of developing peri-implantitis (hereinafter also referred to as an "area specific to peri-implantitis").

[4] The evaluation method according to any one of [1] to [3], wherein the target gas sample is derived from at least one of the implant superstructure and the peri-implant mucosa to be evaluated.

[5] The evaluation method according to any one of [1] to [4], wherein the sensor has 2 to 180 detection elements.

According to one aspect of the present invention, it is possible to provide an evaluation method that can evaluate the risk of developing peri-implantitis in a minimally invasive and simple manner.

FIG. 1 is a diagram showing an example of the results of principal component analysis of odor data of an implant superstructure in Example 1. FIG. 1 is an example of a cluster analysis tree diagram of odor data of the implant superstructure in Example 1. FIG. 13 is a diagram showing an example of the results of principal component analysis of odor data of an implant superstructure in Example 2. 13 is an example of a cluster analysis tree diagram of odor data of the implant superstructure in Example 2. FIG. 13 is a diagram showing an example of the results of principal component analysis of odor data of an implant superstructure in Example 3. 13 is an example of a cluster analysis tree diagram of odor data of the implant superstructure in Example 3. FIG. 11 is a comparison diagram of output values from a detection element of an odor sensor. FIG. 1 is a comparative diagram of bacterial counts for bacterial groups observed in peri-implantitis. FIG. 10. Comparison of bacterial counts for bacterial groups found in healthy implants. FIG. 13 shows the results of an intergroup comparison analysis of bacterial groups in areas specific to peri-implantitis. FIG. 13 shows the results of an intergroup comparison analysis of bacterial groups in areas specific to peri-implantitis.

In this specification, the term "process" includes not only an independent process, but also a process that cannot be clearly distinguished from other processes, as long as the intended purpose of the process is achieved. Furthermore, the content of each component in the composition means the total amount of the multiple substances present in the composition when multiple substances corresponding to each component are present in the composition, unless otherwise specified. Furthermore, the upper and lower limits of the numerical ranges described in this specification can be arbitrarily selected and combined from the numerical values exemplified as the numerical ranges. Hereinafter, the embodiments of the present invention will be described in detail. However, the embodiments shown below are examples of methods for evaluating the risk of developing peri-implantitis in order to embody the technical concept of the present invention, and the present invention is not limited to the methods for evaluating the risk of developing peri-implantitis shown below.

Method for assessing the risk of developing peri-implantitis A method for assessing the risk of developing peri-implantitis in an evaluation subject includes a first step of contacting a sensor having a plurality of detection elements capable of detecting different odor molecules with a target gas sample containing odor molecules derived from the dental implant to be evaluated to acquire odor data of the dental implant to be evaluated, a second step of converting the acquired odor data into object coordinates in a coordinate space including a predetermined first principal component axis and a predetermined second principal component axis in principal component analysis by a predetermined coordinate transformation, and a third step of evaluating the distance between the object coordinates and reference coordinates corresponding to the odor data of a patient with peri-implantitis.

In the method for evaluating the risk of developing peri-implantitis according to this embodiment (hereinafter sometimes simply referred to as the "evaluation method"), the risk of developing peri-implantitis in an evaluation subject is evaluated as follows. First, a principal component analysis is performed on a group of odor data obtained from a population including patients who have been clinically confirmed to have developed peri-implantitis to determine a first principal component and a second principal component in advance. Next, odor data derived from the dental implant of the evaluation subject is transformed into object coordinates in a coordinate space having the predetermined first principal component and second principal component as axes, respectively, by coordinate transformation based on the results of the principal component analysis performed on the odor data group of the population. The risk of developing peri-implantitis in the subject is evaluated by evaluating the distance between the obtained object coordinates and reference coordinates corresponding to the odor data of patients with peri-implantitis in the population.

Here, peri-implantitis refers to inflammation around implants caused by plaque accumulation and the like that has progressed to the bone. The progression of inflammation around implants can be broadly divided into two stages: peri-implant mucositis, in which inflammation occurs only in the mucosa around the implant, and peri-implantitis, in which inflammation spreads from the mucosa around the implant to the alveolar bone. Conventionally, the onset of peri-implantitis has been determined based on clinical findings such as bleeding, purulence, gingival redness, implant mobility, pain, and bone resorption. In addition, the diagnosis of peri-implantitis is generally performed by visual observation, probing, X-ray photography, and bacterial testing of the gingiva around the implant, but there are issues with diagnostic accuracy, invasiveness, radiation exposure, bacterial culture, etc.

In the method for assessing the risk of developing peri-implantitis in a subject, odor data derived from a dental implant is used to assess the risk of developing peri-implantitis. This is an assessment method based on the new knowledge that, for example, inflammatory symptoms caused by bacteria whose presence has been confirmed in peri-implantitis are associated with the odors caused by these phenomena. By analyzing and evaluating odor data derived from a dental implant, the risk of developing peri-implantitis can be assessed in a minimally invasive and simple manner. The method for assessing the risk of developing peri-implantitis according to this embodiment may be a method that assists in the assessment of the risk of developing peri-implantitis in a subject, or a method that assists in the diagnosis of peri-implantitis in a subject.

In the first step, odor data derived from the dental implant to be evaluated is obtained using a sensor (hereinafter also referred to as a "specific sensor") equipped with multiple detection elements capable of detecting different odor molecules. The specific sensor is configured with multiple detection elements, and the detection elements are configured to be able to detect different odor molecules. The detection elements may be, for example, piezoelectric thin films with different sensitive films formed thereon. The piezoelectric thin films may contain, for example, lead zirconate titanate (PZT). The detection elements can identify odors by detecting the resonant frequency, which changes when odor molecules adhere to the sensitive film when a voltage is applied and the sensitive film is in a resonating state. The number of detection elements included in the specific sensor may be, for example, 2 or more and 180 or less, and may be preferably 6 or more, 8 or more, 10 or more, 12 or more, 14 or more, 16 or more, 20 or more, 40 or more, 60 or more, or 80 or more, and may be 160 or less, 140 or less, 120 or less, 100 or less, 80 or less, 40 or less, 20 or less, 16 or less, 12 or less, or 10 or less. Specifically, for example, the odor sensor "nose@MEMS" (registered trademark) by I-PEX can be used as the specific sensor.

The detection elements of the specific sensor may be selected to be those that are highly sensitive to odors originating from patients with peri-implantitis. The selection of detection elements can be performed by comparing the contributions to the reference coordinates corresponding to patients with peri-implantitis through principal component analysis of the odor data group. Specifically, for example, 20 types of detection elements that react more strongly to the target peri-implantitis data group are selected by comparing the principal component score plot and the principal component loading plot obtained based on the analysis of 80 types of detection elements. At this time, it is preferable to use data that has a principal component axis different from other groups or that forms a distant cluster from other groups. Next, the analysis is performed again using the roughly selected detection elements (for example, 20 types), and for example, 8 types of detection elements that react strongly in the same comparison as above are selected. Since all of the detection elements have information for all the plots, it is preferable to repeat the analysis in the selection of the detection elements here. Since data that shows the most separation of groups in the cluster analysis tree diagram is desirable, it is preferable to place importance on cluster analysis in the selection of the detection elements.

When using, for example, the I-PEX odor sensor "nose@MEMS" (registered trademark) as a specific sensor, the detection elements are, for example, 1021, 1022, 1023, 1024, 1025, 1026, 1027, 1028, 1029, 1030, 1031, 1032, 1033, 1034, 1035, 200A, 200B, 200C, 200D, 200F, 200G, 200H, 200I, 200J, 200K, 200M, 200N, 200O, 200P, 200Q, 200S, 200T, 200U, 200V, 200W ... 00X, 200Y, 200Z, 20AA, 20AB, 20BQ, 20BR, 20BS, 20BT, 20BU, 20BV, 20BW, 20BX, 20BY, 20BZ, 20CA, 20CB, 20CC, 20CD, 20CE, 20CF, 20CG, 20CH, 20CI, 20CJ, 20CK, 20CL, 20CM, 20CN, 20CO, 20CP, 20CQ, 20CR, 20CS, 20CT, 20CU, 20CV, 20CW, 20CX, 20CY, 20CZ, 20DA, 20DB, 20DC, 20DD, etc. can be used in combination. Preferably, 1026, 1029, 200K, 200T, 200V, 200W, 200X, 200Y, 20CG, 20CH, 20CI, 20CS, 20CT, 20CU, 20CV, 20CX, 20CY, 20CZ, 20DA, 20DB, etc. can be used in combination as the detector element, and more preferably, 200K, 200V, 200W, 200X, 20CG, 20CH, 20CV, 20DA, etc. can be used in combination.

Odor data derived from dental implants may be obtained by gas chromatography, sensory testing by an evaluator, etc., instead of a specific sensor. Odor data obtained by gas chromatography, sensory testing, etc. may be used alone in the evaluation method, or may be used together with odor data obtained using a specific sensor in the evaluation method.

The odor data derived from the dental implant to be evaluated can be obtained by contacting a target gas sample containing odor molecules derived from the dental implant to be evaluated with a specific sensor. Specifically, for example, a sample containing at least one of the superstructure of the dental implant to be evaluated and the mucosa surrounding the implant is placed in a container for collecting gas samples, and the target gas sample is collected from the container by suction and circulated over a detection element of the specific sensor to bring the odor molecules into contact with the detection element, thereby obtaining odor data. The sample from which odor data is obtained may be only the superstructure of the dental implant, or may include the mucosa surrounding the implant in addition to the superstructure. In addition, the target gas sample may be collected from the container by a suction pump, or may be collected by supplying a gas such as nitrogen gas or air to the container and discharging the target gas sample from the container. Furthermore, the target gas sample may be collected from the container continuously or intermittently.

The container for obtaining the gas sample may be an airtight or sealed container capable of accommodating the superstructure and preventing the intrusion of solids and liquids. The container may be configured to be capable of collecting the target gas sample at a predetermined suction or discharge rate. The collection rate of the target gas sample from the container may be, for example, 0.6 L/min to 2.4 L/min, and preferably 1 L/min to 1.4 L/min. The collection of the target gas sample may be performed, for example, at a temperature of 20°C to 28°C, at 40% RH to 60% RH, and in the atmosphere.

The contact temperature between the target gas sample and the specific sensor may be, for example, 20°C or more and 28°C or less, and preferably 22°C or more or 26°C or less. The contact time between the target gas sample and the specific sensor may be, for example, 10 seconds or more and 1 minute or less, and preferably 20 seconds or more or 40 seconds or less.

Acquisition of odor data is performed by taking a target gas sample and contacting the target gas sample with a specific sensor as one cycle. Odor data may be acquired in one cycle, or multiple cycles may be added after the first cycle as necessary to acquire odor data. When odor data is acquired in multiple cycles, acquisition in the second and subsequent cycles may be performed consecutively for the same sample as in the first cycle. In addition, calibration of the specific sensor may be performed before the start of each cycle. The number of cycles for odor data acquisition may be, for example, 1 to 10, preferably 1 to 5. When odor data is acquired in multiple cycles, all odor data may be subjected to principal component analysis, or only the first cycle, or the first cycle and the second and subsequent cycles may be subjected to principal component analysis. By using odor data from multiple cycles, the risk of developing peri-implantitis can be evaluated with higher accuracy.

In the second step, the acquired odor data is converted into object coordinates in a coordinate space including a first and second principal component axes in the principal component analysis by a predetermined coordinate conversion means. The predetermined coordinate conversion means is determined, for example, as follows. A population is set consisting of subjects with dental implants, including both those who have developed peri-implantitis and those who have not. Here, the subjects included in the population may be classified into three groups, a peri-implantitis group, an odor group, and an odorless group, by determining the gingivitis index (mGI), plaque index (mPI), and gingival bleeding index (mBI) through clinical observation, and also by conducting a questionnaire survey on subjective symptoms in the oral cavity and an odor sensory test of the implant superstructure. From each of the subjects included in the population, odor data derived from a sample including a dental implant superstructure is acquired as described above. For the odor data group acquired from the population, the first and second principal components are determined by principal component analysis. The principal component analysis may be performed, for example, using general-purpose multivariate analysis software, or may be performed using analysis software included with a specific sensor. By determining the first and second principal components for the group of odor data acquired from the population, a coordinate transformation means is determined that transforms the odor data to be evaluated into target coordinates in a coordinate space including an axis corresponding to the first principal component and an axis corresponding to the second principal component.

In the third step, the distance between the object coordinates obtained by coordinate transformation of the odor data of the evaluation target and the reference coordinates corresponding to the odor data of the peri-implantitis patient is evaluated. The risk of developing peri-implantitis in the evaluation target is evaluated based on the evaluated distance. The distance between the object coordinates and the reference coordinates may be evaluated as an absolute value by calculating the distance in a coordinate space including the first and second principal components as axes in a conventional manner. The distance between the object coordinates and the reference coordinates may also be evaluated as a relative value based on the average value of the distance between the coordinates corresponding to the odor data of the target in the population not developing peri-implantitis and the reference coordinates. The distance between the object coordinates and the reference coordinates may also be evaluated using a cluster analysis tree diagram on the principal component axis. The coordinates corresponding to the odor data are coordinates obtained by transforming the odor data by the coordinate transformation determined by the principal component analysis, and refer to coordinates in a coordinate space including the first and second principal component axes in the principal component analysis.

In the method for evaluating the risk of developing peri-implantitis, the risk of developing peri-implantitis in the evaluation target is evaluated based on the distance between the evaluated target coordinates and the reference coordinates. In the method for evaluating the risk of developing, the risk may be evaluated based on the distribution positions of the target coordinates and the reference coordinates, or the risk may be evaluated based on a cluster analysis tree diagram on the principal component axis. When evaluating the risk of developing based on a cluster analysis tree diagram, the risk of developing may be evaluated as high when the target coordinates are regarded as forming a group with the reference coordinates. Also, the risk of developing may be evaluated as high when the distance between the target coordinates and the reference coordinates is smaller than a preset threshold value.

In one aspect of the method for evaluating the risk of developing peri-implantitis, instead of the third step, a fourth step may be included in which the distance between the target coordinates and a pre-specified peri-implantitis specific area (a region with a high risk of developing peri-implantitis) is evaluated. According to the principal component analysis of the odor data, the coordinate group corresponding to the peri-implantitis patient group and the coordinate group corresponding to the no-odor group in the sensory test are considered to be distributed in regions far from each other in relation to the first principal component axis and the second principal component axis. Therefore, the peri-implantitis specific area may be, for example, a region other than the region including the coordinate group corresponding to the odor data classified as the no-odor group in the sensory test (hereinafter simply referred to as the "no-odor region"), and may be a region including the coordinate group corresponding to the odor data classified as the peri-implantitis patient group and a region including an odor region. Here, the odor data classified as the no-odor group in the sensory test may be data whose average value in the sensory test is below a threshold value (for example, 2). The odorous region may be a region including a coordinate group corresponding to odor data classified as an odorous group in a sensory test, or a region including a coordinate group corresponding to odor data whose average value in the sensory test is equal to or greater than a threshold value. Furthermore, the odorless region may be an elliptical region with the major axis being the direction in which the variance of the coordinate group corresponding to the odor data classified as the odorless group in the sensory test is the widest, and the minor axis being the variance in the direction perpendicular to the major axis. Note that the odorless region may include a part of the coordinate group corresponding to the odor data classified as the odorous group and the coordinate group corresponding to the peri-implantitis patient group. It is considered that the odor data included in the odorless region detects an odor derived from a bacterial flora common to the odorless group and the odorous group. Furthermore, the peri-implantitis specific area may include a coordinate group that is considered to be a group with the peri-implantitis patient group based on the cluster analysis tree diagram.

The shape of the peri-implantitis specific area in the coordinate space including the first and second principal component axes in the principal component analysis is not particularly limited as long as it includes a coordinate group corresponding to the odor data of a peri-implantitis patient. The shape of the peri-implantitis specific area may be, for example, a circle, an ellipse, etc., and is preferably an ellipse. The shape of the odorless region is not particularly limited as long as it includes a coordinate group corresponding to odor data classified as the odorless group. The shape of the odorless region may be, for example, a circle, an ellipse, etc., and is preferably an ellipse.

The peri-implantitis specific area may be a region that is identified as a region including a group of coordinates that correspond to odor data derived from a sample in which a specific bacterium is detected in a coordinate space including the first and second principal components in the principal component analysis as axes. It is known that specific bacteria are detected in the bacterial flora in peri-implantitis. (See, for example, G.N.Belibasakis, D.Manoil JDR 2021.) Examples of bacteria that are significantly more frequently detected in the bacterial flora in peri-implantitis include Eubacterium, Campylobacter, Treponema denticola, Fretibacterium fastidiosum, Porphyromonas gingivalis, Porphyromonas endodontalis, Treponema socranskii, Treponema letithinolyticum, Anaeroglobus geminatus, Tannerella forsythia, Mogibacterium, Peptococcus, Actinomyces, Butyvibrio, Filifactor alocis, etc. Among these, Porphyromonas gingivalis, Tannerella forsythia, and Treponema denticola are known as RED COMPLEX bacteria, and are known as particularly virulent periodontal pathogens. In the bacterial flora of a sample belonging to an area specific to peri-implantitis, at least one species, preferably two or more species selected from the group consisting of bacteria belonging to RED COMPLEX may be detected in a significantly greater amount. In addition to bacteria belonging to RED COMPLEX, at least one species, preferably two or more species selected from the group consisting of bacteria other than bacteria belonging to RED COMPLEX may be detected in a significantly greater amount in the bacterial flora of a sample belonging to an area specific to peri-implantitis.

In the bacterial flora corresponding to the area specific to peri-implantitis, at least one species, preferably two or more species selected from the group consisting of Porphyromonas gingivalis, Tannerella forsythia, Porphyromonas endodontalis, and Fusobacterium may be detected in significantly higher amounts.

On the other hand, it is known that certain bacteria are also detected in the bacterial flora of healthy implants. Examples of bacteria detected in significantly higher amounts in the bacterial flora of healthy implants include Leptorrichia, Streptococcus parasanguinis, Streptococcus sanguinis, Streptococcus gordonii, Porphyromonas HMT-277/278, Rothia aeria, Propionibacter, Anaerofilum, Haemophilus, etc. Furthermore, examples of bacteria detected in common between healthy implants and peri-implantitis include Fusobacterium, Veillonella parvula, Veillonella dispar, Neisseria, Campylobacter gracilis, Streptococcus, Anaerococcus, etc.

In the bacterial flora corresponding to the area specific to peri-implantitis, at least one species, preferably two or more species selected from the group consisting of bacteria detected in significantly higher amounts in the bacterial flora of healthy implants, may be detected in significantly lower amounts.

In the fourth step, the risk of developing peri-implantitis is evaluated by evaluating the distance between the target coordinates and a pre-specified peri-implantitis specific area. As a method for evaluating the risk of developing peri-implantitis, the risk of developing peri-implantitis in the subject may be evaluated as high if the target coordinates are present in the peri-implantitis specific area, the risk may be evaluated as high if the distance between the target coordinates and the peri-implantitis specific area is smaller than a pre-set threshold, or the risk may be evaluated based on a cluster analysis tree diagram.

For subjects who are evaluated as being at high risk of developing peri-implantitis by the method for evaluating the risk of developing peri-implantitis, appropriate preventive measures may be administered as necessary. This can effectively suppress the development of peri-implantitis in the subject. That is, the method for evaluating the risk of developing peri-implantitis may be combined with preventive measures to constitute a method for preventing the development of peri-implantitis or a method for assisting in the prevention of development. Examples of preventive measures include probiotics using lactic acid bacteria, local administration of narrow-spectrum antibiotics against bacteria specific to peri-implantitis, application of broad-spectrum Periofeel, shortening the maintenance cycle (for example, shortening the usual three months to one month), and the like. In addition, for subjects who are evaluated as being at low risk of developing peri-implantitis by the method for evaluating the risk of developing peri-implantitis, it is also possible to extend the maintenance cycle.

The present invention will be described in detail below with reference to examples, but the present invention is not limited to these examples.

Example 1
Written informed consent was obtained from nine subjects (four males and five females, average age 62.1 years) undergoing maintenance by a dental hygienist at the Department of Implantology, Medical and Dental General Hospital, Fukuoka Dental College. The subjects were classified into a peri-implantitis group and a non-peri-implantitis group by observing and recording the gingivitis index (mGI), plaque index (mPI), and gingival bleeding index (mBI) in the oral cavity. Observations were recorded at six locations, the mesial papilla, marginal area, and distal papilla, in the buccolingual measurement. In addition, the subjects were asked to complete a questionnaire survey on subjective symptoms in the oral cavity. The questionnaire items were "stickiness in the mouth,""bleeding during brushing,""feeling of loose teeth,""itchy and painful gums,""badbreath,""whitish tongue surface,""slimy feeling on the tooth surface," and "stinging sensation when drinking cold water."

Odor data was acquired from the dental implant superstructure worn by the subject as follows. To keep the measurement conditions constant, the air-conditioning conditions were adjusted to 24°C and 50% RH in the same room. The dental implant superstructure was removed from the patient's mouth and placed in a sealed container connected to a suction hose. A UV-treated beaker (Fressy, Kikutani Co., Ltd.) was used as the container. Gas samples were collected using two air pumps at an air flow rate of 1.2 L/min, and the gas samples were introduced into four odor sensors (nose@MEMS, I-PEX Co., Ltd.), each of which had 20 detection elements, to acquire odor data. Data was acquired in five cycles for 30 seconds, and the odor sensor was calibrated after each cycle. All odor sensors were controlled using I-PEX's software (nose@MEMS Viewer). The odor data acquired was data on temperature, humidity, and 80 types of detection elements.

The detection elements of the odor sensor are 1021, 1022, 1023, 1024, 1025, 1026, 1027, 1028, 1029, 1030, 1031, 1032, 1033, 1034, 1035, 200A, 200B, 200C, 200D, 200F, 200G, 200H, 200I, 200J, 200K, 200M, 200N, 200O, 200P, 200Q, 200S, 200T, 200U, 200V, 200W, 200X, 200Y, 200Z, 20AA ... 0AB, 20BQ, 20BR, 20BS, 20BT, 20BU, 20BV, 20BW, 20BX, 20BY, 20BZ, 20CA, 20CB, 20CC, 20CD, 20CE, 20CF, 20CG, 20CH, 20CI, 20CJ, 20 Eighty types were used: CK, 20CL, 20CM, 20CN, 20CO, 20CP, 20CQ, 20CR, 20CS, 20CT, 20CU, 20CV, 20CW, 20CX, 20CY, 20CZ, 20DA, 20DB, 20DC, and 20DD.

In addition to acquiring odor data using the odor sensor, a sensory test was conducted by four examiners (dentists) to classify the non-peri-implantitis group into an odorous group and an odorless group. In the sensory test, odor intensity was evaluated on a six-point scale from 0 to 5, and comments were surveyed regarding the type of odor. The sensory test was conducted by the four examiners after agreeing on evaluation criteria in advance.

Principal component analysis and cluster analysis were performed on the acquired odor data using the software provided with the odor sensor (nose@MEMS Viewer by I-PEX). The data obtained were contribution rate and cumulative contribution rate, principal component score plot, principal component loading plot, principal component score (free manipulation), principal component loading (free manipulation), principal component score (3D, free manipulation), principal component loading (3D, free manipulation), scatter plot matrix, cluster analysis tree diagram, numerical data of the analysis results, and principal component score plot rotated and overlaid with comparison data. If there were missing values in the data, the missing values were filled with the median value for that sensor. Note that if all data were missing, they were not used in the analysis. Additionally, the variance of none of the data was zero.

The odor data obtained was subjected to a comparative analysis of the peri-implantitis group, the group with odor, the group without odor, and the zirconia crown on titanium group. The results of the principal component analysis are shown in Figure 1, and the cluster analysis tree diagram is shown in Figure 2.

Figures 1 and 2 show that the peri-implantitis groups are distributed together.

When the contribution rate, which is the ratio of the amount of information to the total variables included in each principal component axis, was calculated for the first and second principal components, it was 87.4% for the first principal component and 7.0% for the second principal component, for a total of 94.4%.

Reference Example 1
Based on the analysis results in Example 1, the detection elements that react strongly to the peri-implant inflammation data group were selected as follows. Among the odor data obtained in Example 1, principal component analysis was performed in the same manner as above using only the data from the first cycle. By comparing the principal component score plot and the principal component loading plot, 20 detection elements that reacted more strongly to the target peri-implant inflammation data group were selected. At this time, the data used for the comparison were data that had a principal component axis different from that of the other groups as much as possible, or that formed a distant cluster. The selected detection elements were 1026, 1029, 200K, 200T, 200V, 200W, 200X, 200Y, 20CG, 20CH, 20CI, 20CS, 20CT, 20CU, 20CV, 20CX, 20CY, 20CZ, 20DA, and 20DB.

In the analysis using the above 20 types of detector elements, the contribution rates of the first and second principal components were calculated to be 93.6% for the first principal component and 4.4% for the second principal component, for a total of 98.0%.

Reference Example 2
Based on the analysis results of Reference Example 1, the principal component score plot and the principal component loading plot were compared to select eight types of detector elements that reacted more strongly to the target peri-implantitis data group. The detector elements selected were 200K, 200V, 200W, 200X, 20CG, 20CH, 20CV, and 20DA.

In the analysis using the above eight types of detector elements, the contribution rates of the first and second principal components were calculated to be 82.8% for the first principal component and 14.1% for the second principal component, for a total of 96.9%.

Example 2
The odor data obtained in Example 1 was subjected to principal component analysis and cluster analysis using the software (I-PEX; nose@MEMS Viewer) attached to the odor sensor. However, for the data analysis, only data from eight types of detection elements that had a high contribution rate to the reference coordinates of the odor data derived from the peri-implantitis patient were used, and the principal component analysis and cluster analysis were performed in the same manner as in Example 1 except that the detection elements used were different. The detection elements used were 20CV, 20DA, 20CG, 20CH, 200K, 200V, 200W, and 200X. The results of the principal component analysis are shown in FIG. 3, and the cluster analysis tree diagram is shown in FIG. 4.

Figures 3 and 4 show that by using a specific detector element, the distribution of the peri-implantitis group in the principal component analysis becomes more linear. This makes it easier to compare the odorous group with the odorless group. It can also be seen that the distribution of the peri-implantitis group is in a narrower range.

Example 3
Except for the fact that 29 subjects were subjects (11 males, 18 females, average age 63.59 years), odor data was acquired and a sensory test was carried out in the same manner as in Example 1. For the acquired odor data, principal component analysis and cluster analysis were carried out using data from eight types of sensing elements in the same manner as in Example 2. The results of the principal component analysis are shown in FIG. 5, and the cluster analysis tree diagram is shown in FIG. 6.

Thirteen subjects (4 men, 9 women, average age 64.46 years; average sensory test score 3.19±0.78) were classified into the odor group, whose average sensory test score was above the threshold of 2, and eight subjects (4 men, 4 women, average age 58.63 years; average sensory test score 0.97±0.41) were classified into the odorless group, whose average sensory test score was below the threshold of 2. There were eight patients in the peri-implantitis group (3 men, 5 women, average age 67.1 years; average sensory test score 3.78±1.52). There was a significant difference in the sensory test scores between the odorous and non-odor groups, but no significant difference was observed between the odorous and peri-implantitis groups.

As shown in FIG. 5, an area (odorless area) 20 containing coordinate groups corresponding to odor data classified as the odorless group in the sensory test is separated from an area (peri-implantitis specific area) 10 containing coordinate groups corresponding to odor data classified as the peri-implantitis group. Also, as shown in FIG. 6, the data belonging to the peri-implantitis specific area 10 in FIG. 5 forms one group in the cluster analysis tree diagram.

In Figure 5, the odorless region was defined as an elliptical region with the long axis being the direction in which the variance of the coordinate groups corresponding to the odor data classified as the odorless group in the sensory test was the widest, and the short axis being the variance in the direction perpendicular to the long axis. The circular region outside the odorless region, which includes the coordinate groups corresponding to the odor data classified as the peri-implantitis group and the coordinate groups corresponding to the odor data classified as the odorous group, was defined as the peri-implantitis-specific area. The peri-implantitis-specific area included 11 people (5 men, 6 women, average age 60.09 years) in total, including 6 people from the odorous group (3 men, 3 women, average age 60.17 years) and 5 people from the peri-implantitis group (2 men, 3 women, average age 60.00 years).

The area specific to peri-implantitis includes five pieces of odor data from patients with peri-implantitis, and six pieces of odor data that were classified as having an odor in the sensory test. Therefore, the output values of the detection element of the odor sensor were compared for odor data belonging to the area specific to peri-implantitis and other odor data. The results are shown in Figure 7.

As shown in Figure 7, for all eight types of detection elements used, the output value of the detection element was significantly higher in areas specific to peri-implantitis than in other areas. In other words, the selected detection elements are suitable for assessing the risk of developing peri-implantitis.

Bacterial flora analysis Samples for bacterial flora analysis were collected from subjects from whom odor data was collected, and the bacterial flora contained in the samples was analyzed by amplicon sequence analysis targeting the 16SrDNA partial base sequence. Samples were collected by wiping the inside of the implant access hole in the oral cavity and the surrounding gums with a small cotton ball when the implant superstructure was removed to obtain odor data, and then placing the sample in a microtube and stirring it in a PBS solution. In addition, amplicon sequence analysis was performed by requesting Techno Suruga Lab. The analysis conditions were as follows.

1. DNA extraction/extraction method MORA-EXTRACT kit (Kyokuto Pharmaceutical, Japan)
Cell disruption device: FastPrep 24 5G (MP Biomedicals, USA)
- Absorbance measurement: NanoDrop N D8000 (Thermo Fisher Scientific, USA)
2. PCR
Primers used: Pro341F-Pro805R (bacteria/archaea 16S rDNA V3 to V4 region; approximately 430 bp)
Dual index (8 bp barcode)
・PCR conditions Hell et al. (hemi nested PCR)
(Takahashi S et al. PLoS One 2014;9:e105592.; Hell et al. ISME J 2013;7:1814 1826; Nakayama J. et al. Biosci. Microflora 2010; 29 83 96.)
3. Amplicon sequence analysis: Sequencer MiSeq (Illumina, USA)
・Sequencing kit MiSeq Reagent Kit v3 (600 cycles) (Illumina)
4. Preprocessing of acquired data and removal of primer sequences Software: Cutadapt version 1.18 5) Default values - Joining of paired-end sequences Software: F astq join 6) Default values - Quality filtering Software: FASTX Toolkit version 0.0.14
Sequences with QV20 or higher for 99% or more of each read were used for data analysis. Chimera check software QIIME1.8.0 8)
Detect and remove chimeric sequences using usearch6.1.544_i86 5. Homology search software Metagenome@KIN version 2.3 (World Fusion, Japan)
1) Search and database by Ribosomal Database Project (RDP) RDP Classifier version 2.13 (16S rDNA)
Analysis conditions 16S rDNA
Cut Off ALL: 0.8 1 , Affiliated taxon: Kingdom ~ Genus
2) Search using the microbial identification database: Microorganism identification database NGS DB BA 17.0
(TechnoSuruga Laboratory,
・Analysis conditions: The species with the highest homology rate of 97% or more is output as closely related species. 6. Comparative analysis between samples ・Principal component analysis Drawing and cluster analysis using eigenvalues and eigenvectors of the variance-covariance matrix Clustering method:
Average (UPGMA, Single (single linkage),
Complete (Complete linkage)
Distance matrix:
Canberra distance, Pearson's Correlation coefficients,
Euclidean distance, Manhattan distance, Maximum distance, Minkowski distance
7. Alpha diversity analysis and data analysis software R version 3.1.0
・α diversity index Calculate the Shannon entropy and Simpson index of the samples for each taxonomic rank.

Figure 8 shows the results of a comparison of the bacterial counts in areas specific to peri-implantitis and other areas for the bacterial groups found in peri-implantitis. Figure 9 shows the results of a comparison of the bacterial counts in areas specific to peri-implantitis and other areas for the bacterial groups found in healthy implants and in common areas.

As shown in Figure 8, Porphyromonas gingivalis and Tannerella forsythia, known as RED COMPLEX, and Porphyromonas endodontalis were detected in significantly higher amounts in the areas specific to peri-implantitis. Also, as shown in Figure 9, Rothia aeria, Streptococcus gordonii, and Streptococcus parasanguinis, which are detected in healthy implants, were detected in significantly lower amounts in the areas specific to peri-implantitis.

The Shannon index of alpha diversity in the peri-implantitis specific area and other areas was H = 0.05, p value 0.82, and q value 0.82. This result is thought to indicate that there is no difference in the diversity of the bacterial flora between the peri-implantitis specific area and other areas.

Figures 10 and 11 show the results of the intergroup comparison analysis (LEfSe). As shown in Figures 10 and 11, differences in the bacterial flora were observed between areas specific to peri-implantitis and other areas.

As described above, the risk of developing peri-implantitis can be assessed by analyzing odor data derived from dental implants using an odor sensor equipped with multiple detection elements capable of detecting different odor molecules.

Claims (5)

contacting a sensor having six or more detection elements capable of detecting different odor molecules with a target gas sample containing odor molecules originating from the dental implant to be evaluated, thereby acquiring odor data of the dental implant;
Transforming the odor data into object coordinates in a coordinate space including a first principal component axis and a second principal component axis in a principal component analysis by a predetermined coordinate transformation;
and evaluating a distance between the object coordinates and reference coordinates corresponding to odor data of a patient with peri-implantitis. The method for assisting diagnosis according to claim 1 , wherein the evaluation of the distance between the object coordinates and the reference coordinates is performed by cluster analysis. contacting a sensor having six or more detection elements capable of detecting different odor molecules with a target gas sample containing odor molecules originating from the dental implant to be evaluated, thereby acquiring odor data of the dental implant;
Transforming the odor data into object coordinates in a coordinate space including a first principal component axis and a second principal component axis in a principal component analysis by a predetermined coordinate transformation;
A method for assisting in the diagnosis of peri-implantitis in a subject, comprising: evaluating the distance between the subject coordinates and a pre-identified area at high risk of developing peri-implantitis. A method for assisting diagnosis according to any one of claims 1 to 3, wherein the target gas sample is derived from at least one of the implant superstructure and the peri-implant mucosa to be evaluated. The method for assisting diagnosis according to claim 1 , wherein the sensor comprises between 6 and 180 of the sensing elements.