Diagnosis of mild or severe periodontitis

Abstract

An in vitro method is disclosed for assessing whether a human patient with periodontitis has mild or severe periodontitis. This method is based on insights to determine the selection of two biomarker proteins. Therefore, in a saliva sample from a patient with periodontitis, the concentrations of the protein pyruvate kinase (PK) and at least one of matrix metalloproteinase-9 (MMP9), S100 calcium-binding protein A8 (S100A8), and hemoglobin subunit β (Hb-β); or matrix metalloproteinase-9 (MMP9) and at least one of S100 calcium-binding protein A8 (S100A8) and S100 calcium-binding protein A9 (S100A9) are measured. Based on the measured concentrations, a value reflecting the combined concentration of said proteins is determined. This value is compared to a threshold that reflects the combined concentration associated with severe periodontitis in the same manner. This comparison allows assessment of whether the test value indicates the presence of severe or mild periodontitis in the patient. Thus, typically, a test value reflecting a combined concentration below a threshold indicates mild periodontitis in the patient, while a test value reflecting a combined concentration equal to or above a threshold indicates severe periodontitis in the patient.

Description

Technical Field

[0001] This invention belongs to the field of oral care and relates to saliva-based diagnosis of periodontal disease. Specifically, this invention relates to a kit and method for differentiating between mild and severe periodontitis. Background Technology

[0002] Gingivitis, or gum disease, is a non-destructive periodontal disease primarily caused by a biofilm of dental bacteria, or plaque, that adheres to the tooth surface. This adhesion triggers an inflammatory response in the surrounding tissues. Gingivitis is a reversible infection and inflammation of the gum tissue that can be resolved with proper oral hygiene and professional dental intervention. If left undetected or untreated, gingivitis typically leads to inflammation of the tissues surrounding the teeth (i.e., periodontal tissues), a condition defined as periodontitis that results in tissue destruction and alveolar bone loss, ultimately leading to tooth loss. During the development of gum disease, associated clinical signs and symptoms often appear, such as swollen gums, a change in color from pink to dark red, bleeding gums, bad breath, and gums becoming more painful or tender to the touch.

[0003] Periodontitis is a chronic, multifactorial inflammatory disease caused by oral microorganisms, characterized by the progressive destruction of hard (bone) and soft (periodontal ligament) tissues, ultimately leading to tooth loosening and loss. This differs from gingivitis, which is a reversible infection and inflammation of the gum tissue. Inflammatory periodontitis is one of the most common chronic diseases in humans and a leading cause of tooth loss in adults. In addition to its serious negative impact on oral health, there is mounting evidence that periodontitis has systemic consequences and is a risk factor for a variety of systemic diseases, including heart disease (such as atherosclerosis and stroke), diabetes, pregnancy complications, rheumatoid arthritis, and respiratory infections.

[0004] Therefore, early and accurate diagnosis of periodontal disease is very important from the perspective of oral and overall health.

[0005] The diagnosis of periodontal disease in general dental practice remains poor, resulting in relatively low treatment intervention rates and a large number of untreated cases. Current diagnosis relies on imprecise, subjective clinical examinations by dental professionals based on oral tissue conditions (color, swelling, degree of bleeding during probing, pocket depth; and bone loss based on oral X-rays). These routine methods are very time-consuming, and some of the techniques used (pocket depth, X-rays) reflect historical events, such as past disease activity, rather than current disease activity or susceptibility to other diseases. Therefore, there is a need for a more objective, faster, more accurate, and easier-to-use diagnostic method, ideally one that can also be performed by non-professionals. This necessitates measuring current disease activity, and, if possible, the individual's susceptibility to other periodontal diseases.

[0006] For a long time, saliva, or oral fluid, has been advocated as a diagnostic fluid for oral and general diseases. With the advent of miniature biosensors (also known as lab-on-a-chip), point-of-care diagnostics for rapid seat-side testing has gained greater scientific and clinical interest. Particularly for the detection of periodontal disease, inflammatory biomarkers associated with tissue inflammation and breakdown are likely to end up in saliva due to proximity, suggesting a strong potential for saliva in detecting periodontal disease. Indeed, this field has thus received considerable attention and yielded encouraging results. For example, Kido et al. (Jperioont Res 2012; 47: 488-499) identified 104 proteins in gingival crevicular fluid (GCF) samples from healthy and periodontitis sites; 64 were present only in GCF from healthy sites and 63 were present only in GCF from periodontitis sites. However, definitive tests have yet to emerge.

[0007] Biomarkers represent biological indicators that support clinical presentation and are therefore objective measures for diagnosing clinical outcomes of periodontal disease. Ultimately, proven biomarkers can be used to assess the risk of future disease, identify disease at its earliest stages, recognize responses to initial treatment, and allow for the implementation of preventative strategies.

[0008] Limitations in the previous development of salivary biomarker point-of-care testing include a lack of technology suitable for seat-side application and the inability to analyze multiple biomarkers in individual samples. Furthermore, the selection of which biomarkers to include in such testing has not been adequately described in the literature or implemented in practical testing.

[0009] Furthermore, periodontitis can manifest in all degrees of severity, from mild to severe. To facilitate assessment of severity, dentists typically categorize periodontitis patients into two groups—those with mild periodontitis and those with severe periodontitis. However, existing methods for performing this assessment involve a labor-intensive process that dentists do not routinely perform for every patient and / or every visit, and it is not feasible for consumers to perform this process (self-diagnosis).

[0010] The aim is to provide a simpler method, and particularly one that requires only a small saliva sample from the patient (and possibly by the patient himself or herself). The goal is to input this sample into an in vitro diagnostic device that allows for the classification of the saliva sample based on measurements, enabling it to return an indication of whether the patient is classified as having mild or severe periodontitis. Summary of the Invention

[0011] To better address the aforementioned needs, in one aspect, the present invention relates to an in vitro method for assessing whether a human patient has mild or severe periodontitis, the method comprising detecting protein concentrations in a saliva sample from said human patient:

[0012] The test involves: pyruvate kinase (PK) and at least one of matrix metalloproteinase-9 (MMP9), S100 calcium-binding protein A8 (S100A8), and hemoglobin subunit β (Hb-β); determining a test value reflecting a combined concentration of said proteins; and comparing the test value with a threshold reflecting a combined concentration associated with severe periodontitis in the same manner to assess whether the test value indicates mild or severe periodontitis in the patient.

[0013] On the other hand, this invention provides the uses of proteins:

[0014] Pyruvate kinase (PK) and at least one of matrix metalloproteinase-9 (MMP9), S100 calcium-binding protein A8 (S100A8) and hemoglobin subunit β (Hb-β); in saliva samples from human patients, as a biomarker for assessing whether a patient has mild or severe periodontitis.

[0015] Optionally, the patient's age is also used as a biomarker.

[0016] In another aspect, the present invention relates to a system for assessing whether a human patient has mild or severe periodontitis, the system comprising:

[0017] - A detection device that is capable of and suitable for detecting proteins in saliva samples from human patients:

[0018] Pyruvate kinase (PK) and at least one of matrix metalloproteinase-9 (MMP9), S100 calcium-binding protein A8 (S100A8) and hemoglobin subunit β (Hb-β);

[0019] and

[0020] - A processor that is capable of and adapted to determine, based on the determined concentration of the protein, an indication that a patient has mild or severe periodontitis.

[0021] The system may optionally include a data connection to an interface (in particular a graphical user interface) that is capable of presenting information, preferably also capable of inputting information such as the object’s age, and optionally other information such as gender and / or BMI (body mass index), said interface being part of the system or a remote interface.

[0022] Optionally, one or more of the aforementioned items (especially processors) can function “in the cloud,” that is, not on a fixed machine, but through an internet-based application.

[0023] In another aspect, the present invention provides a kit for detecting at least three biomarkers of periodontitis in saliva samples from human patients, the kit comprising detection reagents for detecting:

[0024] Pyruvate kinase (PK) and at least two of matrix metalloproteinase-9 (MMP9), S100 calcium-binding protein A8 (S100A8) and hemoglobin subunit β (Hb-β).

[0025] Three or more assay reagents, such as four assay reagents, are used, each binding to a different biomarker. In one embodiment, a first assay reagent is used to detect PK, a second assay reagent is used to detect one of matrix metalloproteinase-9 (MMP9), S100 calcium-binding protein A8 (S100A8), and hemoglobin subunit β (Hb-β), and a third assay reagent is used to detect one that is different from the group of matrix metalloproteinase-9 (MMP9), S100 calcium-binding protein A8 (S100A8), and hemoglobin subunit β (Hb-β). An optional fourth assay reagent can be used to detect another protein that is different from the group of matrix metalloproteinase-9 (MMP9), S100 calcium-binding protein A8 (S100A8), and hemoglobin subunit β (Hb-β).

[0026] In one embodiment, the present invention provides a kit for detecting at least three biomarkers of periodontitis in saliva samples from human patients, the kit comprising a detection reagent for detecting pyruvate kinase (PK) and a detection reagent for detecting at least two of matrix metalloproteinase-9 (MMP9), S100 calcium-binding protein A8 (S100A8), and hemoglobin subunit β (Hb-β).

[0027] In another aspect, the present invention provides an in vitro method for determining changes in the periodontal status of a human patient with periodontitis over a time interval from a first time point t1 to a second time point t2, the method comprising detecting protein concentrations in at least one saliva sample obtained from the patient at time t1 and at least one saliva sample obtained from the patient at time t2.

[0028] The concentrations of pyruvate kinase (PK) and at least one of matrix metalloproteinase-9 (MMP9), S100 calcium-binding protein A8 (S100A8), and hemoglobin subunit β (Hb-β) are compared, wherein the difference in concentration of any one, two, or all three reflects a change in state.

[0029] On the other hand, the present invention provides a method for diagnosing whether a human patient has mild or severe periodontitis, comprising detecting proteins in a saliva sample of the human patient:

[0030] The method includes at least one of pyruvate kinase (PK), matrix metalloproteinase-9 (MMP9), S100 calcium-binding protein A8 (S100A8), and hemoglobin subunit β (Hb-β); and assesses whether a patient has mild or severe periodontitis based on the concentration of said proteins in the sample. Optionally, this method includes further steps of treating the patient's periodontitis.

[0031] In another aspect, the present invention provides a method for detecting proteins in human patients with mild or severe periodontitis:

[0032] Pyruvate kinase (PK) and at least one of matrix metalloproteinase-9 (MMP9), S100 calcium-binding protein A8 (S100A8), and hemoglobin subunit β (Hb-β); including:

[0033] (a) Obtaining saliva samples from human patients; and

[0034] (b) The presence of the protein in the sample is detected by contacting the sample with one or more detection reagents for binding the protein and detecting the binding between each protein and the one or more detection reagents. Typically, there is a first detection reagent for detecting PK, a second detection reagent for detecting one of matrix metalloproteinase-9 (MMP9), S100 calcium-binding protein A8 (S100A8), and hemoglobin subunit β (Hb-β), and a third detection reagent for detecting a different one of matrix metalloproteinase-9 (MMP9), S100 calcium-binding protein A8 (S100A8), and hemoglobin subunit β (Hb-β). Attached Figure Description

[0035] Figure 1 The diagram illustrates a system used for the methods described in this disclosure. Detailed Implementation

[0036] In summary, this invention is based on the wise insight that only two proteins in saliva samples from human patients with periodontitis can serve as biomarkers for classifying said periodontitis into either of two categories: severe periodontitis or mild or moderate periodontitis (i.e., non-severe; unless otherwise stated, the term "mild periodontitis" above and below will include moderate periodontitis). The latter category is collectively referred to below as mild periodontitis.

[0037] The identified protein biomarkers are pyruvate kinase (PK), matrix metalloproteinase-9 (MMP9), S100 calcium-binding protein A8 (S100A8), and hemoglobin subunit β (Hb-β). S100 calcium-binding protein A9 (S100A9) is also described. According to the present invention, the following combination is used for the diagnosis of gingivitis:

[0038] Pyruvate kinase (PK) and at least one of matrix metalloproteinase-9 (MMP9), S100 calcium-binding protein A8 (S100A8) and hemoglobin subunit β (Hb-β).

[0039] An object's age can optionally be used as an additional identifier.

[0040] Pyruvate kinase (PK) catalyzes the final step of glycolysis. There are four tissue-specific isoenzymes of pyruvate kinase, each with specific kinetic properties required by different tissues.

[0041] MMPs are a family of enzymes responsible for degrading extracellular matrix components such as collagen, proteoglycans, laminins, elastin, and fibronectin. They play a crucial role in periodontal ligament (PDL) remodeling, both under physiological and pathological conditions. MMP-9, also known as 92kDa type IV collagenase, 92kDa gelatinase, or gelatinase B (GELB), is a matrix protein belonging to the zinc metalloproteinase family of enzymes involved in extracellular matrix degradation.

[0042] S100 calcium-binding protein A8 (S100A8) is a calcium and zinc-binding protein that plays a significant role in the regulation of inflammatory processes and immune responses. It can induce neutrophil chemotaxis and adhesion.

[0043] Hemoglobin (Hb) is a ferrometallic transporter found in the red blood cells of almost all vertebrates and in the tissues of some invertebrates. Hemoglobin-β (also known as β-globin, HBB, β-globin, and hemoglobin subunit β) is a globin that, together with α-globin (HBA), constitutes the most common form of hemoglobin in adults, HbA. Hb-β is typically 146 amino acids long and has a molecular weight of 15,867 Da. Normal adult HbA is a heterotetramer composed of two α chains and two β chains. Hb-β is encoded by the HBB gene on human chromosome 11.

[0044] S100 calcium-binding protein A9 (S100A9), also known as calciprotein B, is a calcium and zinc-binding protein that plays a significant role in the regulation of inflammatory processes and immune responses. It can induce neutrophil chemotaxis and adhesion, enhance neutrophil bactericidal activity by activating SYK, PI3K / AKT, and ERK1 / 2 to promote phagocytosis, and induce neutrophil degranulation through a MAPK-dependent mechanism.

[0045] The proteins described above are known in the art. Those skilled in the art are familiar with their structures and methods for detecting them in aqueous samples (e.g., saliva samples). Hereinafter, the aforementioned protein biomarkers are collectively referred to as the "biomarker set of the present invention".

[0046] Table 1 in the examples provides 10 particularly preferred combinations according to the invention.

[0047] In one embodiment, the biomarker set of the present invention comprises or consists of two, three, four, or five protein biomarkers identified in the present invention, namely PK, MMP9, S100A8, Hb-β, and S100A9. Preferably, the biomarker set of the present invention consists of no more than four of the protein biomarkers identified in the present invention, such as PK+Hb-β, S100A8, and / or MMP9. In addition to the biomarker set of the present invention, other biomarkers and / or data, such as demographic data (e.g., age, sex), may be included in a set of data used to determine the type of periodontitis.

[0048] An example of an additional protein biomarker is matrix metalloproteinase-8 (MMP8). Another exemplary additional protein biomarker is an inhibitory protein. Hepatocyte growth factor (HGF) is another possible additional protein biomarker. Free light chain λ (FLC-L) is another optional additional protein biomarker. Another exemplary additional protein biomarker is α-1-acid glycoprotein (A1AGP). These proteins are included in some of the preferred biomarker groups in Table 1 below. All of these proteins are known in the art.

[0049] MMP-8, also known as neutrophil collagenase or PMNL collagenase (MNL-CL), is a collagenase found in the connective tissues of most mammals.

[0050] Inhibitor proteins are actin-binding proteins involved in the dynamic turnover and reorganization of the actin cytoskeleton found in most cells. This is crucial for the spatial and temporal controlled growth of actin microfilaments, a vital process in cell motility and shape changes. Human inhibitor-1 is typically 140 amino acids long when expressed, but is usually further processed into a mature form.

[0051] Hepatocyte growth factor (HGF) is a paracrine cell growth, motility, and morphogenesis factor. Secreted by mesenchymal cells, it primarily targets and acts on epithelial and endothelial cells, but also on hematopoietic progenitor cells. HGF has been shown to play a major role in muscle generation and wound healing. Its ability to stimulate mitosis, cell motility, and matrix invasion makes it central to angiogenesis, tumorigenesis, and tissue regeneration. HGF stimulates epithelial cell growth and inhibits connective tissue attachment and regeneration. HGF is well-known as a serum biomarker indicating disease activity in various diseases.

[0052] Free light chain proteins are immunoglobulin light chains. They are independent of the immunoglobulin heavy chains. Unlike typical intact immunoglobulin molecules, free light chain proteins are not covalently linked to the immunoglobulin heavy chains; for example, free light chains are not linked to heavy chain disulfide bonds. Typically, a free light chain consists of approximately 220 amino acids. Free light chain proteins typically include a variable region (often called the light chain variable region, V...). L ) and the constant region (often called the light chain constant region, C L Humans produce two types of immunoglobulin light chains, named with the letters kappa (κ) and lambda (λ). Each of these can be further subdivided into subgroups based on variations in the variable region: four κ isoforms (Vκ1, Vκ2, Vκ3, and Vκ4) and six λ isoforms (Vλ1, Vλ2, Vλ3, Vλ4, Vλ5, and Vλ6). Free light chains κ are typically monomers. Free light chains λ are typically dimers, linked by disulfide bonds (to another free light chain λ). The polymeric forms of free light chains λ and κ have been determined. Free light chains are produced by bone marrow and lymph node cells, and also locally by diffuse lymphocytes in periodontal tissues, and are rapidly cleared from the bloodstream and metabolized by the kidneys. Monomeric free light chains are cleared within 2–4 hours, while dimer free light chains are cleared within 3–6 hours.

[0053] α-1-Acidic glycoprotein (A1AGP) is a plasma α-globulin glycoprotein primarily synthesized by the liver. It is sometimes also called orotic acid. It acts as a transport protein in the blood, carrying neutrally charged basic lipid compounds. It is also believed to regulate the interaction between blood cells and endothelial cells.

[0054] The preferred extended biomarker set includes or consists of the following:

[0055] MMP8+MMP9+FLCλ+PK

[0056] MMP8+Hb-β+PK+S100A8

[0057] HGF+MMP9+PK+S100A8

[0058] MMP 9+A1AGP+PK+Hb-β

[0059] Inhibitor protein + MMP9 + PK + S100A8

[0060] When other biomarkers are optionally included, the total number of biomarkers (i.e., the biomarker set of the present invention plus other biomarkers) is typically 4, 5 or 6.

[0061] However, a desirable advantage of the present invention is that a patient's periodontitis classification can be determined by measuring preferably no more than four biomarkers, and more preferably only three, wherein the biomarker groups in Table 1 (hereinafter) are preferred. In particular, this determination does not require the use of additional data, which advantageously provides a simple and direct diagnostic test.

[0062] Depending on the need, this method requires only a small saliva sample, such as droplet size, to be extracted from the subject. The sample size is typically 0.1 μl to 2 ml, for example, 1-2 ml, where smaller amounts, such as 0.1 to 100 μl, can be processed using an in vitro device, and therefore larger samples, such as up to 20 ml, can also be collected, for example, 7.5 to 17 ml.

[0063] The sample is fed into an in vitro diagnostic device that measures the concentrations of at least two relevant proteins and returns a diagnostic result, classifying the subject based on the likelihood of having mild or severe periodontitis.

[0064] The ease of use of this invention will make it possible to test most dental patients with or at high risk of developing periodontitis regularly (e.g., as part of a regular dental check-up or even at home). This particularly allows for the detection of mild periodontitis before it progresses to severe periodontitis, enabling more timely oral care interventions to prevent its development. Alternatively, for example, in patients known to be at high risk of periodontitis, and for the first test, the method allows for identification of whether the periodontitis is mild or severe. Furthermore, the method can be applied after treatment of patients previously diagnosed with severe periodontitis to check whether the periodontitis has improved and become mild. In another case, the indication that a patient previously with mild periodontitis has not improved or has actually worsened after the start of a treatment program may lead the dentist or patient to decide to change the treatment plan to help expedite the recovery process. In particular, the method is also suitable for self-diagnosis, whereby the steps of sampling and inputting it into the device can be performed by the patient himself or her.

[0065] When implementing this invention to determine whether periodontitis is mild or severe, it is generally known that a patient has periodontitis. Therefore, in some embodiments, the method is used to assess whether a human patient known to have periodontitis has mild or severe periodontitis.

[0066] The method of the present invention typically involves detecting at least two of the aforementioned proteins constituting the biomarker set of the present invention, as well as optional other biomarker proteins, by using one or more detection reagents.

[0067] The “saliva” tested according to the present invention can be undiluted saliva, which can be obtained by spitting or wiping, or diluted saliva, which can be obtained by rinsing the mouth with a fluid. Diluted saliva can be obtained by a patient rinsing or gargling the mouth with sterile water (e.g., 5 ml or 10 ml) or other suitable fluid for a few seconds and then spitting it into a container. Diluted saliva is sometimes referred to as oral rinsing solution.

[0068] "Detection" refers to the measurement, quantification, scoring, or determination of the concentration of a biomarker protein. Methods for evaluating biological compounds, including biomarker proteins, are known in the art. It is recognized that methods for detecting protein biomarkers include both direct and indirect measurements. Those skilled in the art will be able to select an appropriate method for measuring a specific biomarker protein.

[0069] The term "concentration" associated with protein biomarkers is given its usual meaning: the abundance of protein in a volume. Protein concentration is usually measured by mass / volume, most commonly mg / ml or μg / ml, but sometimes as low as pg / ml. Another measurement method is molar concentration (or molar concentration), mol / L or "M". Concentration can be determined by detecting the amount of protein in a known, defined, or predetermined volume of sample.

[0070] An alternative approach to determining concentration is to determine the absolute amount of the protein biomarker in the sample, or to determine the mass fraction of the biomarker in the sample, such as the amount of the biomarker relative to the total amount of all other proteins in the sample.

[0071] A "detection reagent" is a reagent or compound that specifically (or selectively) binds to, interacts with, or detects a protein biomarker of interest. Such a detection reagent may include, but is not limited to, antibodies, polyclonal antibodies, or monoclonal antibodies that preferentially bind to protein biomarkers.

[0072] When referring to assay reagents, the phrase "specific (or selective) binding" or "specific (or selective) immunoreactivity" refers to a binding reaction that identifies the presence of a protein biomarker in a heterogeneous population of proteins and other biological products. Therefore, under specified immunoassay conditions, a specified assay reagent (e.g., an antibody) binds to a particular protein to at least twice the background level and substantially does not bind to other proteins present in the sample in significant quantities. Specific binding under these conditions may require selection of antibodies specific to a particular protein. Various immunoassay procedures are available for selecting antibodies with specific immunoreactivity to a particular protein. For example, solid-phase ELISA (enzyme-linked immunosorbent assay) is commonly used to select antibodies with specific immunoreactivity to a particular protein (for a description of immunoassay procedures and conditions that can be used to determine specific immunoreactivity, see, for example, Harlow & Lane's *Antibodies, Laboratory Handbook* (1988)). Typically, a specific or selective reaction will be at least twice the background signal or noise, and more typically more than 10 to 100 times the background.

[0073] An "antibody" is a polypeptide ligand essentially encoded by one or more immunoglobulin genes or fragments thereof, which specifically binds to and recognizes epitopes (e.g., antigens). Recognized immunoglobulin genes include κ and λ light chain constant region genes, α, γ, δ, ε, and μ heavy chain constant region genes, and numerous immunoglobulin variable region genes. Antibodies exist, for example, as intact immunoglobulins or as numerous well-defined fragments produced by digestion with various peptidases. This includes, for example, Fab' and F(ab)'2 fragments. The term "antibody" as used herein also includes antibody fragments produced by modifying intact antibodies or antibody fragments resynthesized using recombinant DNA methods. It also includes polyclonal antibodies, monoclonal antibodies, chimeric antibodies, humanized antibodies, or single-chain antibodies. The "Fc" portion of an antibody refers to a portion of the immunoglobulin heavy chain that includes one or more heavy chain constant regions CH1, CH2, and CH3, but does not contain heavy chain variable regions. The antibody can be a bispecific antibody, such as an antibody having a first variable region that specifically binds to a first antigen and a second variable region that specifically binds to a different second antigen. Using at least one bispecific antibody can reduce the number of detection reagents required.

[0074] The sensitivity and specificity of diagnostic methods differ. The "sensitivity" of a diagnostic test refers to the percentage of diseased individuals who test positive (the "true positive" percentage). Diseased individuals who are not detected by the test are called "false negatives." Individuals who are not diseased and test negative are called "true negatives." The "specificity" of a diagnostic test is 1 minus the false positive rate, where the "false positive" rate is defined as the proportion of healthy individuals who test positive.

[0075] One or more biomarker proteins of the present invention can be detected in a sample by any means. Preferred methods for biomarker detection are antibody-based assays, protein array assays, mass spectrometry (MS)-based assays, and (near)infrared spectroscopy-based assays. For example, immunoassays, including but not limited to competitive and non-competitive assay systems using techniques such as Western blots, radioimmunoassays, ELISA, "sandwich" immunoassays, immunoprecipitation assays, precipitation reactions, gel diffusion precipitation reactions, immunodiffusion assays, and fluorescence immunoassays. Such assays are routine and well known in the art. Exemplary immunoassays are briefly described below (but are not intended to be limiting).

[0076] Immunoprecipitation protocols typically involve lysing cell populations in a lysis buffer, such as a RIPA buffer (1% NP-40 or Triton X-100, 1% sodium deoxycholate, 0.1% SDS, 0.15 M NaCl, 0.01 M sodium phosphate at pH 7.2, 1% Trasylol) supplemented with protein phosphate and / or protease inhibitors (e.g., EDTA, PMSF, aprotinin, sodium vanadate), adding the antibody of interest to the cell lysate, incubating at 4°C for a period of time (e.g., 1–4 hours), adding protein A and / or protein G agarose beads to the cell lysate, incubating at 4°C for about one hour or longer, washing the beads in the lysis buffer, and resuspending the beads in SDS / sample buffer. The ability of an antibody to immunoprecipitate a specific antigen can be assessed, for example, by Western blot analysis. Those skilled in the art will recognize parameters that can be modified to increase antibody-antigen binding and reduce background (e.g., pre-cleaning the cell lysate with agarose beads).

[0077] Western blot analysis typically involves preparing a protein sample, performing electrophoresis of the protein sample on a polyacrylamide gel (e.g., 8%–20% SDS-PAGE, depending on the molecular weight of the antigen), transferring the protein sample from the polyacrylamide gel to a membrane, such as nitrocellulose, PVDF, or nylon, blocking the membrane in a blocking solution (e.g., PBS containing 3% BSA or nonfat milk), washing the membrane in a wash buffer (e.g., Wash Buffer (PBS-Tween) 20), blocking the membrane with a primary antibody (antibody of interest) diluted in the blocking buffer, washing the membrane in the wash buffer, blocking the membrane with a secondary antibody (which recognizes the primary antibody, such as an anti-human antibody) that binds to an enzyme substrate (e.g., horseradish peroxidase or alkaline phosphatase) or a radioactive molecule (e.g., 32P or 125I) diluted in the blocking buffer, washing the membrane in the wash buffer, and detecting the presence of the antigen. Those skilled in the art will appreciate parameters that can be modified to increase the detected signal and reduce background noise.

[0078] ELISA typically involves preparing an antigen (i.e., a biomarker protein or fragment thereof of interest), covering the wells of a 96-well microtiter plate with the antigen, adding an antibody of interest that binds to a detectable compound (e.g., an enzyme substrate such as horseradish peroxidase or alkaline phosphatase) to the wells, incubating for a period of time, and detecting the presence of the antigen. In ELISA, the antibody of interest does not necessarily need to bind to the detectable compound; instead, a second antibody that binds to the detectable compound (which recognizes the antibody of interest) can be added to the wells. Furthermore, the wells can be covered with an antibody instead of the antigen. In this case, after the antigen of interest has been added to the covered wells, a second antibody that binds to the detectable compound can be added. Those skilled in the art will appreciate parameters that can be modified to increase the detected signal and other variations of ELISA known in the art.

[0079] Because multiple biomarkers are used, a threshold is determined based on the combined concentration of these biomarkers (and optionally age). This threshold determines whether a patient is classified as having mild or severe periodontitis. This invention reflects the insight that measurements based on the aforementioned combination of biomarkers can sufficiently accurately detect whether periodontitis is mild or severe.

[0080] This insight supports another aspect of the invention, namely the use of proteins:

[0081] Pyruvate kinase (PK) and at least one of matrix metalloproteinase-9 (MMP9), S100 calcium-binding protein A8 (S100A8) and hemoglobin subunit β (Hb-β); as a biomarker in saliva samples of human patients with periodontitis to assess whether the patient has mild or severe periodontitis.

[0082] This application can be carried out in accordance with the methods basically described above and below.

[0083] The method of the present invention includes determining a test value reflecting a combined concentration measured for the protein. The combined concentration value can be any value obtained by inputting the determined concentrations and performing arithmetic operations on these values. For example, this can be a simple addition of concentrations. It also includes multiplying each concentration by a factor reflecting a desired weight for these concentrations and then adding the results. It may also include multiplying concentrations together, or any combination of multiplication, division, subtraction, exponentiation, and addition. This may also include raising the concentrations to a power of several.

[0084] Optionally, the test value reflects the combined concentration of the one or more proteins determined by the age of the subject.

[0085] The resulting combined concentration values ​​are compared to a threshold that reflects the combined concentrations associated with the presence of severe periodontitis in the same way. This comparison allows assessment of whether the test values ​​indicate severe or mild periodontitis in patients undergoing saliva testing.

[0086] The threshold can be, for example, a combined concentration value, which is obtained in the same manner based on the concentration of the same protein determined in a reference sample associated with the presence of severe periodontitis (i.e., in patients diagnosed with severe periodontitis). Thus, typically, a value reflecting the same or higher combined concentration indicates the presence of severe periodontitis in the tested patient. Similarly, a value reflecting a lower combined concentration in the saliva of a patient with periodontitis being tested indicates mild or moderate periodontitis (i.e., non-severe). However, it should be understood that thresholds can also be calculated (e.g., by using a negative multiplier) such that test values ​​indicating severe periodontitis are below the threshold, while test values ​​indicating mild periodontitis are above the threshold.

[0087] The threshold can also be determined based on measuring the concentration of current biomarker proteins in a set of samples, including patients known to be diagnosed with severe periodontitis and those with non-severe (mild and / or moderate) periodontitis. Statistical analysis, potentially including machine learning methods, can then be performed on the measured concentration values ​​to differentiate patients classified as having mild or moderate periodontitis from those classified as having severe periodontitis with the desired sensitivity and specificity. This yields the desired threshold. Based on this threshold, the sample to be tested can undergo the same concentration measurements and then be processed in the same manner as obtaining the threshold to determine a joint concentration value that can be compared to the threshold, thereby allowing the sample to be classified as having mild or severe periodontitis.

[0088] In one interesting embodiment, the combined concentration value is obtained as a fraction. Each measurement is assigned a numerical value (e.g., a protein concentration value in ng / ml), and these values ​​are used in a linear or non-linear combination to calculate a fraction between 0 and 1. When a threshold is determined based on the aforementioned set of objects, a sigmoid function is typically used to calculate the fraction between 0 and 1, taking the combined concentration as input (see below).

[0089] When the score exceeds a certain threshold, the method indicates that the patient has severe periodontitis. The threshold can be selected based on the desired sensitivity and specificity.

[0090] It should be understood that, according to the present invention, when classifying an object as having "mild or severe periodontitis," this refers to an object that can be considered to have periodontitis. This can be known, for example, from a previous diagnosis of periodontitis (although its degree may not be distinguishable), or it can be inferred, for example, from the object's oral health record.

[0091] The generally accepted clinical definition in this field is based on the following:

[0092] Gingival Index (GI)

[0093] The complete oral gingival index will be recorded based on the Robin Modified Gingival Index (MGI), graded on a scale of 0 to 4, where:

[0094] -0 = No inflammation

[0095] -1 = Mild inflammation; slight color change, not a complete change in texture across the entire margin or any part of the papillary gingival unit.

[0096] -2 = Mild inflammation; but involves the entire marginal or papillary unit.

[0097] -3 = Moderate inflammation; shiny, red, swollen, and / or enlarged marginal or papillary units.

[0098] -4 = Severe inflammation; marked redness, swelling and / or hypertrophy, spontaneous bleeding, congestion or ulceration of the marginal or papillary gingival unit.

[0099] Detection depth (PD)

[0100] The probe depth will be recorded to the nearest millimeter using a manual UNC-15 periodontal probe. Probe depth refers to the distance from the probe tip (assuming it is located at the bottom of the pocket) to the free gingival margin.

[0101] Gingival recession (REC)

[0102] Gingival recession will be recorded to the nearest millimeter using a manual UNC-15 periodontal probe. Gingival recession is the distance from the free gingival margin to the cementum-enamel junction. Gingival recession will be shown as a positive value, and gingival hyperplasia will be shown as a negative value.

[0103] Clinical attachment loss (CAL)

[0104] Clinical attachment loss will be calculated based on the detection depth plus the amount of atrophy at each site.

[0105] Bleeding during detection (BOP)

[0106] After the detection is completed, bleeding will be assessed for each site. If bleeding occurs within 30 seconds of detection, the site will be assigned a score of 1 point; otherwise, it will be assigned a score of 0 points.

[0107] The resulting subject group (patient group) is defined as follows, wherein the mild-to-moderate periodontitis group and the severe periodontitis group are relevant to this invention:

[0108] - Healthy group (H): PD ≤ 3 mm in all sites (but pockets up to 4 mm are allowed distal to the last permanent molar), no interproximal attachment loss in any site, GI ≥ 2.0 in ≤ 10% of sites, % BOP score ≤ 10%;

[0109] - Gingivitis group (G): GI≥3.0 in >30% of sites, no sites with interproximal attachment loss, no sites with PD>4mm, and %BOP score>10%;

[0110] - Mild to moderate periodontitis group (MP): Interproximal PD of 5-7 mm at ≥8 teeth (equivalent to approximately 2-4 mm CAL), % BOP score >30%;

[0111] - Severe periodontitis group (AP): Interproximal PD ≥7mm at ≥12 teeth (equivalent to approximately ≥5mm CAL), %BOP score >30%.

[0112] In one embodiment, the method of the present invention utilizes Figure 1 The system is illustrated schematically. The system can be a single device that integrates various equipment components (units). Alternatively, the system can have its various components, or some of these components, as separate devices. Figure 1 The components shown are a measuring device (A), a graphical user interface (B), and a computer processing unit (C).

[0113] As described above, the system of the present invention includes a data connection to an interface, whereby the interface itself may be part of the system or may be a remote interface. The latter refers to the possibility of using different devices (preferably handheld devices, such as smartphones or tablets) to provide the physical interface. In this case, the data connection preferably includes wireless data transmission, such as via Wi-Fi or Bluetooth, or via other technologies or standards.

[0114] The measuring device (A) is configured to receive a saliva sample, for example by placing a drop of saliva onto a cartridge (A1) into which the device (A) can be inserted. The device may be an existing device capable of determining the concentrations of at least the following proteins from the same saliva sample:

[0115] Pyruvate kinase (PK) and at least one of matrix metalloproteinase-9 (MMP9), S100 calcium-binding protein A8 (S100A8) and hemoglobin subunit β (Hb-β).

[0116] The measuring device (A) should be able to receive saliva samples, for example by placing a drop of saliva on a cartridge (A1) into which the device (A) can be inserted. The device may be an existing device capable of determining the concentrations of at least the following proteins from the same saliva sample:

[0117] Pyruvate kinase (PK) and at least one of matrix metalloproteinase-9 (MMP9), S100 calcium-binding protein A8 (S100A8) and hemoglobin subunit β (Hb-β).

[0118] The processing unit (C) receives a numerical value of protein concentration from part (A). Unit (C) is equipped with software (typically embedded software) that allows it to calculate a score (S) between 0 and 1. The software also includes a numerical value for a threshold (T). If the calculated value (S) exceeds (T), unit (C) outputs an indication (I) of "severe periodontitis" to the graphical user interface (GUI) (B); otherwise, it outputs "mild periodontitis". Another embodiment can use a specific value of (S) to indicate the certainty of making the indication (I). This could be a probability score, where 0.5 is a possible threshold; for example, a score S = 0.8 represents the probability of severe periodontitis. Interesting options include:

[0119] Based on the fraction S, the certainty can be directly indicated, that is, S = 0.8 indicates 80% certainty of severe periodontitis;

[0120] -Based on the fraction S, binary or ternary instructions can be made:

[0121] -S <t->Mild periodontitis, S≥T->Severe periodontitis;

[0122] -S <r1->Mild periodontitis, R1≤S <r2->uncertain,

[0123] -S≥R2->Severe periodontitis;

[0124] Additionally, determinism can be attached to this binary or ternary indication. This determinism will be determined by the distance between the fraction S and the selected threshold (T, R1, R2).

[0125] The specific calculation of fractions can be carried out using, for example, the sigmoid function with the following formula:

[0126]

[0127] Where N is the number of proteins / biomarkers used. c0, c1, etc. are coefficients (numerical values) and B1, B2, etc. are the corresponding protein concentrations.

[0128] coefficient c i The determination of can be accomplished through the following training procedure:

[0129] - Select N1 subjects with severe periodontitis (e.g., subjects identified by a dentist using current standards) and N2 subjects with mild periodontitis.

[0130] Subjects without mild periodontitis are considered to have a score of S=0, while subjects with severe periodontitis are considered to have a score of S=1.

[0131] - Saliva samples were collected from each subject, and the protein concentrations of the above-mentioned biomarker combinations were determined.

[0132] - Perform logistic regression on protein concentration and fraction.

[0133] Other regression or machine learning methods (linear regression, neural networks, support vector machines) can be used to train a classifier that predicts whether an object has gingivitis or a healthy oral condition based on protein concentration.

[0134] In particular, this procedure (in the example) has been applied through clinical studies on subjects with mild or severe periodontitis (determined by clinical evaluation by dental professionals according to current standards, such as the American Academy of Periodontology standards). The performance of various biomarker combinations was evaluated using leave-one-out cross-validation to obtain the preferred biomarker combinations of the present invention.

[0135] Referring to the aforementioned system, the present invention also provides, in another aspect, a system for assessing whether a human patient has mild or severe periodontitis, the system comprising:

[0136] - A detection device capable of and adapted to detect proteins in saliva samples from human patients:

[0137] Pyruvate kinase (PK) and at least one of matrix metalloproteinase-9 (MMP9), S100 calcium-binding protein A8 (S100A8), and hemoglobin subunit β (Hb-β). As mentioned above, these devices are known and readily available to those skilled in the art; typically, a container is provided for receiving a sample from the oral cavity of a subject, the container being provided with a detection device;

[0138] - A processor that is capable of and adapted to determine, based on the determined concentration of the protein, an indication that a patient has mild or severe periodontitis.

[0139] Optionally, the system includes a user interface (or a data connection to a remote interface) capable of presenting information, particularly a graphical user interface (GUI); a graphical user interface (GUI) is a type of user interface that allows users to interact with electronic devices through graphical icons and visual indicators (e.g., secondary symbols) rather than text-based user interfaces, typed command labels, or text navigation (the present invention does not exclude any of these interface types); graphical user interfaces (GUIs) are well known and commonly used in handheld mobile devices, such as MP3 players, portable media players, gaming devices, smartphones, and small home, office, and industrial control devices; as mentioned above, the interface may also be optionally selected to allow input of information, such as the object's age, gender, and BMI (body mass index).

[0140] The present invention also provides, alone or as part of the foregoing system, a kit for detecting biomarkers for periodontitis in saliva samples from human patients, the kit comprising detection reagents for detecting the following:

[0141] The kit contains at least two of the following: pyruvate kinase (PK), matrix metalloproteinase-9 (MMP9), S100 calcium-binding protein A8 (S100A8), and hemoglobin subunit β (Hb-β). The kit comprises three assays, each targeting a different biomarker: a first assay for detecting PK; a second assay for detecting one of MMP9, S100 calcium-binding protein A8 (S100A8), and hemoglobin subunit β (Hb-β); and a third assay for detecting a different one of these three biomarkers. As described above with reference to the method of the present invention, the kit may contain additional assays, such as those specifically targeting profilin, A1AGP, FLC-λ, HGF, or MMP8, and / or other proteins. In a preferred embodiment, the detection reagent provided in the kit consists of detection reagents for selecting three proteins constituting the 3-biomarker or 4-biomarker group of the present invention, as described above. In a further embodiment, a separate detection reagent is provided for each biomarker protein present in the combinations illustrated in Table 1 in the following examples.

[0142] Preferably, the kit comprises a solid support, such as a chip, a microtiter plate, or beads or resin comprising the detection reagent. In some embodiments, the kit includes a mass spectrometry probe, such as a protein chip. TM ).

[0143] The kit may also provide a detection reagent for unbound assays or a washing solution and / or detection reagent for the biomarker (sandwich assay).

[0144] In one interesting aspect, the identification of the biomarker set of the present invention is applied to monitor the periodontitis status of human patients over time. Therefore, the present invention also provides an in vitro method for determining changes in the periodontitis status of a human patient with periodontitis over a time interval from a first time point t1 to a second time point t2, the method comprising detecting protein concentrations in at least one saliva sample obtained from the patient at time t1 and in at least one saliva sample obtained from the patient at time t2.

[0145] The study included pyruvate kinase (PK) and at least one of matrix metalloproteinase-9 (MMP9), S100 calcium-binding protein A8 (S100A8), and hemoglobin subunit β (Hb-β); and a comparison of concentrations, wherein the difference between at least two, and more preferably three, concentrations reflects the change in state. This difference can be considered as a concentration difference, thus allowing direct comparison without first generating a number between 0 and 1 or any other classification. It should be understood that measurements received at two time points can also be processed in the same manner as described above for determining mild or severe periodontitis.

[0146] The present invention also provides a method for diagnosing whether a human patient has mild or severe periodontitis, comprising detecting the presence of proteins in the patient's saliva:

[0147] Pyruvate kinase (PK) and at least one of matrix metalloproteinase-9 (MMP9), S100 calcium-binding protein A8 (S100A8) and hemoglobin subunit β (Hb-β).

[0148] The concentration of the protein in the sample is used to assess whether the patient has mild or severe periodontitis. Optionally, this approach includes additional steps to treat the periodontitis in the patient. These optional treatment steps may include administering known therapeutic agents or dental procedures, or a combination of both. Known therapeutic agents include the application of medications containing antimicrobial agents, such as mouthwashes, chips, gels, or microspheres. A typical antimicrobial agent used to treat periodontitis is chlorhexidine. Other therapeutic agents include antibiotics (usually oral antibiotics) and enzyme inhibitors (such as doxycycline). Known non-surgical treatments include scaling and root planing (SRP). Known surgical procedures include pocket reduction, flap surgery, gingival grafts, or bone grafts.

[0149] This invention also provides a method for detecting proteins in patients with mild or severe periodontitis:

[0150] Pyruvate kinase (PK) and at least one of matrix metalloproteinase-9 (MMP9), S100 calcium-binding protein A8 (S100A8), and hemoglobin subunit β (Hb-β); including:

[0151] (a) Obtaining saliva samples from human patients; and

[0152] (b) The presence of a protein in a saliva sample is determined by contacting the saliva sample with first, second, and third detection reagents for detecting proteins and detecting the binding between each protein and the detection reagent.

[0153] The invention will be further illustrated with reference to the following non-limiting examples.

[0154] Example

[0155] In a clinical study of 79 participants, 41 were diagnosed with mild periodontitis (including moderate periodontitis) and 38 were diagnosed with severe periodontitis. The area under the ROC (recipient-operator-characteristic) curve (AUC) was obtained.

[0156] In statistics, the receiver operating characteristic (ROC) curve is a graphical representation of the performance of a binary classifier system as its discrimination threshold varies. This curve is constructed by plotting the true positive rate (TPR) relative to the false positive rate (FPR) at various threshold settings. In machine learning, the true positive rate is also known as sensitivity, recall, or detection probability. 1 The false positive rate, also known as the false detection or false alarm probability, can be calculated as (1 - specificity). Therefore, the ROC curve is a sensitivity function representing the false detection rate. Generally, if the probability distributions of detection and false alarms are known, an ROC curve can be generated for each threshold value by plotting the cumulative distribution function value of the detection probability (the area under the probability distribution from -∞ to the discrimination threshold) on the y-axis, and the cumulative distribution function value of the false alarm probability on the x-axis. The accuracy of a test depends on how well it categorizes the tested population into those with and without the relevant disease. Accuracy is measured by the area under the ROC curve. An area of ​​1 represents a perfect test; an area of ​​0.5 represents a worthless test. A classification guideline for diagnostic test accuracy is the traditional academic scoring system:

[0157] -0.90 - 1 = Excellent (A)

[0158] -0.80 - 0.90 = Good (B)

[0159] -0.70 -0.80 = General (C)

[0160] -0.60 - 0.70 = Difference (D)

[0161] -0.50 - 0.60 = Fail (F)

[0162] Based on the foregoing, in the results of the aforementioned clinical studies, an ROC AUC value greater than 0.75 is considered to represent the ideal accuracy for providing the diagnostic test according to the present invention.

[0163] The protein biomarkers investigated include:

[0164] MMP8

[0165] MMP9

[0166] IL-1β

[0167] HGF

[0168] Free light chain (FLC)κ(kappa)

[0169] Free light chains (FLC)λ(lambda)

[0170] A1AGP

[0171] Hb-β

[0172] HB-δ

[0173] Keratin 4

[0174] Profilin

[0175] Pyruvate kinase

[0176] S100A8

[0177] S100A9

[0178] Furthermore, in the logistic regression employed, we treat κ+λ, κ-λ, and κ / λ as additional predictors.

[0179] In addition, we also used age as a predictor.

[0180] This resulted in a total of 4204 possible non-redundant groups, with a maximum of 4 protein biomarkers (excluding groups containing only age). Here, "non-redundant" means that groups including, for example, κ+λ and κ-λ as predictors are not considered, because in logistic regression, they would give the same results as the corresponding groups including κ and λ as predictors.

[0181] Please note that without limiting the number of protein markers in a group, the predictor described above will generate 98,302 possible non-redundant groups (excluding groups with only age).

[0182] Our study identified 184 groups, each containing up to four protein biomarkers, with an AUCLOOCV value greater than 0.75, suitable for classifying severe and mild periodontitis. The preferred biomarker groups of this invention cover (at least) these 184 identified groups. Furthermore, among these 184 groups:

[0183] Two with only two protein markers

[0184] 27 samples with three protein biomarkers

[0185] 155 samples with four protein biomarkers

[0186] The two groups containing only two protein markers are:

[0187] Pyruvate kinase + MMP9 (AUC LOOCV = 0.759)

[0188] Pyruvate kinase + MMP 9 + age (AUC LOOCV = 0.788)

[0189] These groups are highlighted as preferred embodiments of the invention.

[0190] These data indicate that even two protein biomarkers (in a specific combination) can provide AUC LOOCV values ​​greater than 0.75 for differentiating between mild and severe periodontitis:

[0191] The AUC LOOCV values ​​of the 10 best-performing groups were approximately 0.79, as shown in Table 1 below. Data on the best ROC AUC values ​​representing groups with between two and four protein biomarkers were summarized from all identified biomarkers and biomarker groups.

[0192] Table 1

[0193]

[0194] Each biomarker combination in this table is highlighted as a preferred combination according to the present invention. It can be seen that three of these groups have three protein biomarkers, while seven groups have four protein biomarkers. All groups use age as an additional predictor, which may or may not be used according to the present invention. These top 10 groups can be summarized as showing a preference for pyruvate kinase and at least one of MMP9 or S100A8.

[0195] Although the invention has been described in detail in the accompanying drawings and the foregoing description, such description should be regarded as illustrative or exemplary, and not restrictive; the invention is not limited to the disclosed embodiments.

[0196] For example, detection reagents for different biomarkers can be provided in different units. Alternatively, conveniently, the kit of the present invention may include a fixed set of detection reagents for the protein biomarkers (i.e., PK or MMP9) used in all embodiments, as well as a flexible module including detection reagents for any of other biomarkers (e.g., MMP9, S100A8, Hb-β, and S100A9).

[0197] By studying the accompanying drawings, the disclosure, and the appended claims, those skilled in the art can understand and implement other variations of the disclosed embodiments in practicing the claimed invention. In the claims, the word "comprising" does not exclude other elements or steps, and the indefinite articles "a" or "an" do not exclude a plural. The fact that certain features of the invention are listed in mutually different dependent claims does not in itself imply that combinations of these features cannot be used advantageously. Any reference marks in the claims should not be construed as limiting the scope.

[0198] In summary, we disclose an in vitro method for assessing whether a human patient with periodontitis has mild or severe periodontitis. This method is based on insights to determine the selection of three biomarker proteins. Therefore, in saliva samples from patients with periodontitis, the concentrations of proteins are measured: pyruvate kinase (PK) and at least one of matrix metalloproteinase-9 (MMP9), S100 calcium-binding protein A8 (S100A8), and hemoglobin subunit β (Hb-β); or matrix metalloproteinase-9 (MMP9) and at least one of S100 calcium-binding protein A8 (S100A8) and S100 calcium-binding protein A9 (S100A9). Based on the measured concentrations, a value reflecting the combined concentration of said proteins is determined. This value is compared to a threshold that reflects the combined concentration associated with severe periodontitis in the same manner. This comparison allows assessment of whether the test value indicates the presence of severe or mild periodontitis in the patient. Therefore, typically, a test value reflecting a combined concentration below a threshold indicates mild periodontitis in the patient, while a test value reflecting a combined concentration equal to or above a threshold indicates severe periodontitis in the patient.

Claims

1. A system for assessing whether a human patient has mild or severe periodontitis, the system comprising: - A detection device capable of and adapted to detect proteins in saliva samples from the human patient: Pyruvate kinase (PK), matrix metalloproteinase-9 (MMP9), and S100 calcium-binding protein A8 (S100A8). - A processor capable of and adapted to determine, based on the determined concentration of the protein and the patient's age, whether the patient has mild or severe periodontitis.

2. The system of claim 1 further includes a container for receiving oral fluid samples, the container including the detection device.

3. The system according to claim 1 or 2, further comprising: - A user interface used to present the instructions to the user; and - A data connection between the processor and the user interface is used to transmit the instruction from the processor to the user interface.

4. The system according to any one of claims 1 to 3, wherein the processor is capable of operating via an Internet-based application; and / or The interface is capable of inputting information about the patient's age, and the processor is capable of and adapted to determine, based on a determined concentration, whether the patient has mild or severe periodontitis.

5. A kit for detecting at least three biomarkers of periodontitis in a saliva sample from a human patient and determining whether the patient has mild or severe periodontitis based on the patient's age, the kit comprising at least three assay reagents: a first assay reagent for detecting pyruvate kinase (PK), a second assay reagent for detecting one of S100 calcium-binding protein A8 (S100A8) and matrix metalloproteinase-9 (MMP9), and a third assay reagent for detecting a different one of S100 calcium-binding protein A8 (S100A8) and matrix metalloproteinase-9 (MMP9).

6. The kit according to claim 5, wherein: One or more detection reagents are contained on a solid support; and / or The assay kit consists of assays for the following: MMP9, PK, and S100A8; MMP9, HGF, PK, and S100A8; MMP9, inhibitory protein, PK, and S100A8; or MMP9, PK, S100A8, and S100A9, wherein the inhibitory protein is Profilin.

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