Gingivitis diagnostic method, use and kit

Abstract

An in vitro method for assessing whether a human patient has gingivitis is disclosed. The method is based on the insight of determining biomarker proteins. Therefore, in a saliva sample of a patient suffering from gingivitis, the concentration of a specific protein combination is measured. One such combination is alpha-1-acid glycoprotein (A1AGP) and at least one of the following: matrix metalloproteinase-8 (MMP8), matrix metalloproteinase-9 (MMP9), hepatocyte growth factor (HGF), hemoglobin beta subunit (Hb-β) and S100 calcium binding protein A8 (S100A8). Based on the measured concentration, a value reflecting the combined concentration of the protein is determined. The value is compared with a threshold value, which reflects the combined concentration associated with gingivitis in the same manner. Comparison allows an assessment of whether a test value indicates the presence of gingivitis in the patient. Thus, generally, a test value reflecting a combined concentration lower than the combined concentration reflected by the threshold indicates the absence of gingivitis in the patient, and a test value reflecting a combined concentration equal to or higher than the combined concentration reflected by the threshold indicates that the patient suffers from gingivitis.

Description

Technical Field

[0001] The present invention belongs to the field of oral care and relates to the diagnosis of periodontal diseases based on saliva. In particular, the present invention relates to a kit, use and method for diagnosing gingivitis. Background Art

[0002] Gum inflammation or gingivitis is a non-destructive periodontal disease caused primarily by the adhesion of dental plaque or dental film to the tooth surface. If not detected and treated, reversible gingivitis usually leads to inflammation of the tissues surrounding the teeth (i.e., the periodontium), a condition defined as periodontitis, which is irreversible and leads to tissue destruction and loss of alveolar bone, and ultimately tooth loss. During the progression of gum disease, there are often clinical signs and symptoms associated with it, such as swollen gums, a change in color from pink to dark red, bleeding gums, bad breath, and gums that become increasingly tender or painful to the touch.

[0003] Periodontitis is a chronic, multifactorial inflammatory disease caused by oral microorganisms and is characterized by the progressive destruction of hard (bone) and soft (periodontal ligament) tissues, ultimately leading to loose and falling teeth. This needs to be distinguished from gingivitis, which is a reversible infection and inflammation of the gum tissue. Inflammatory periodontitis is one of the most common chronic human diseases and the main cause of tooth loss in adults. In addition to the substantial negative impact of periodontitis on oral health, there is also a large amount of evidence that periodontitis has systemic effects and is a risk factor for a variety of systemic diseases, including heart disease (e.g., atherosclerosis, stroke), diabetes, pregnancy complications, rheumatoid arthritis, and respiratory infections.

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

[0005] In general dental practice, periodontal disease remains poorly diagnosed, resulting in relatively low rates of therapeutic intervention and a large number of untreated cases. Current diagnosis relies on imprecise, subjective clinical examination of oral tissue condition (color, swelling, degree of bleeding on probing, probing pocket depth; and bone loss from oral x-rays) by dental professionals. These traditional methods are 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 further disease. Therefore, a more objective, faster, accurate, easier to use diagnosis is desired, which preferably can also be performed by non-specialists. Thus, it is desirable to measure current disease activity and possibly the subject's susceptibility to further periodontal disease.

[0006] Saliva or oral fluid has long been advocated as a diagnostic fluid for oral and general diseases, and with the advent of micro-biosensors (also known as labs on a chip), instant diagnostics for rapid chairside testing have attracted greater scientific and clinical interest. In particular, for periodontal disease detection, inflammatory biomarkers associated with tissue inflammation and decomposition may easily end up in saliva due to proximity, suggesting that saliva has a strong potential for periodontal disease detection. Indeed, this field has therefore aroused significant interest and presented encouraging results. For example, Ramseier et al. (JPeriodontol. 2009 Mar; 80 (3): 436-46) identified host and bacteria-derived biomarkers associated with periodontal disease. However, no definitive test has yet emerged.

[0007] Biomarkers represent biological indicators that support clinical manifestations and, therefore, they are objective measures of clinical outcome in diagnosing periodontal disease. Ultimately, validated biomarkers can be used to assess the risk of future disease, identify disease at its earliest stages, identify response to initial treatment and allow implementation of preventive strategies.

[0008] Limitations of previous point-of-care testing for salivary biomarkers include the lack of technology suitable for chairside use and the inability to analyze multiple biomarkers in each sample. Furthermore, the choice of which of the multiple biomarkers to include in such a test has not been adequately addressed in the literature nor implemented in actual testing.

[0009] It would be desirable to provide a simpler process, specifically one that would only require obtaining a small saliva sample from a patient (and possibly by the patient him or herself). It would be desirable to input such a sample into an in vitro diagnostic device that would allow classification of the saliva sample based on measurements such that it could return an indication of the likelihood that the patient would be classified as having gingivitis. Summary of the invention

[0010] In order 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 gingivitis, the method comprising: detecting the concentration of the following proteins in a saliva sample from the human patient:

[0011] (i) alpha-1-acid glycoprotein (A1AGP) and at least one of the following: matrix metalloproteinase-8 (MMP8), matrix metalloproteinase-9 (MMP9), hepatocyte growth factor (HGF), hemoglobin β subunit (Hb-β) and S100 calcium binding protein A8 (S100A8); or

[0012] (ii) hepatocyte growth factor (HGF) and at least one of the following proteins: matrix metalloproteinase-8 (MMP8) and keratin 4 (K-4); or

[0013] (iii) matrix metalloproteinase-8 (MMP8) and at least one of the following proteins: interleukin-1β (IL-1β), keratin 4 (K-4), and arrestin;

[0014] determining a test value reflecting the combined concentration determined for the protein;

[0015] and comparing the test value to a threshold value that reflects the combined concentration associated with gingivitis in the same manner to assess whether the test value indicates that the patient suffers from gingivitis.

[0016] In another aspect, the present invention provides for use of the protein of the first aspect in a saliva sample of a human patient as a biomarker for assessing whether the patient has gingivitis.

[0017] Optionally, the patient's age is also used as a biomarker.

[0018] In yet another aspect, the invention resides in a system for assessing whether a human patient has gingivitis, the system comprising:

[0019] - a detection device capable and adapted to detect proteins in a saliva sample of a human patient:

[0020] (i) alpha-1-acid glycoprotein (A1AGP) and at least one of the following: matrix metalloproteinase-8 (MMP8), matrix metalloproteinase-9 (MMP9), hepatocyte growth factor (HGF), hemoglobin beta subunit (Hb-β), and S100 calcium binding protein A8 (S100A8); or

[0021] (ii) hepatocyte growth factor (HGF) and at least one of the following proteins: matrix metalloproteinase-8 (MMP8) and keratin 4 (K-4); or

[0022] (iii) matrix metalloproteinase-8 (MMP8) and at least one of the following proteins:

[0023] interleukin-1β (IL-1β), keratin 4 (K-4), and arrestin; and

[0024] - a processor capable and adapted to determine, from the determined concentration of said protein, an indication that the patient has gingivitis.

[0025] The system optionally comprises a data connection to an interface (particularly a graphical user interface) capable of presenting information, preferably also capable of inputting information such as the subject'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.

[0026] Optionally, one or more of the aforementioned items (particularly the processor) can be run "in the cloud", ie, not on a fixed machine, but via an Internet-based application.

[0027] In yet another aspect, the present invention provides a kit for detecting at least two biomarkers for gingivitis in a saliva sample of a human patient, the kit comprising a detection reagent for detecting the following proteins:

[0028] (i) alpha-1-acid glycoprotein (A1AGP) and at least one of the following: matrix metalloproteinase-8 (MMP8), matrix metalloproteinase-9 (MMP9), hepatocyte growth factor (HGF), hemoglobin beta subunit (Hb-β), and S100 calcium binding protein A8 (S100A8); or

[0029] (ii) hepatocyte growth factor (HGF) and at least one of the following proteins: matrix metalloproteinase-8 (MMP8) and keratin 4 (K-4); or

[0030] (iii) matrix metalloproteinase-8 (MMP8) and at least one of the following proteins: interleukin-1β (IL-1β), keratin 4 (K-4), and arrestin.

[0031] Typically, two or more detection reagents are used, each detection reagent binding to a different biomarker. In one embodiment, the first detection reagent is capable of binding to A1AGP, and the second detection reagent is capable of binding to MMP8. In another embodiment, the first detection reagent is capable of binding to A1AGP, and the second detection reagent is capable of binding to MMP9. In one other embodiment, the first detection reagent is capable of binding to A1AGP, the second detection reagent is capable of binding to Hb-β, and the third detection reagent is capable of binding to at least one of MMP8, MMP9 and K-4. In another embodiment, the first detection reagent is capable of binding to HGF, the second detection reagent is capable of binding to MMP8, and the third detection reagent is capable of binding to K-4. In yet another embodiment, the first detection reagent is capable of binding to MMP-8, the second detection reagent is capable of binding to IL-1β, the third detection reagent is capable of binding to K-4, and optionally the fourth detection reagent is capable of binding to inhibitory proteins.

[0032] In yet another aspect, the present invention provides an in vitro method for determining a change in the gingivitis status of a human patient during a time interval from a first time point t1 to a second time point t2, the method comprising: detecting the concentration of the following proteins in at least one saliva sample obtained from the patient at t1 and in at least one saliva sample obtained from the patient at t2:

[0033] (i) alpha-1-acid glycoprotein (A1AGP) and at least one of the following: matrix metalloproteinase-8 (MMP8), matrix metalloproteinase-9 (MMP9), hepatocyte growth factor (HGF), hemoglobin beta subunit (Hb-β), and S100 calcium binding protein A8 (S100A8); or

[0034] (ii) hepatocyte growth factor (HGF) and at least one of the following proteins: matrix metalloproteinase-8 (MMP8) and keratin 4 (K-4); or

[0035] (iii) matrix metalloproteinase-8 (MMP8) and at least one of the following proteins: interleukin-1β (IL-1β), keratin 4 (K-4), and arrestin;

[0036] and comparing concentrations, whereby a difference in any one, two, or more of the concentrations reflects a change in state.

[0037] In another aspect, the present invention provides a method for diagnosing whether a human patient has gingivitis, comprising: detecting the following proteins in a saliva sample of the human patient:

[0038] (i) alpha-1-acid glycoprotein (A1AGP) and at least one of the following: matrix metalloproteinase-8 (MMP8), matrix metalloproteinase-9 (MMP9), hepatocyte growth factor (HGF), hemoglobin beta subunit (Hb-β), and S100 calcium binding protein A8 (S100A8); or

[0039] (ii) hepatocyte growth factor (HGF) and at least one of the following proteins: matrix metalloproteinase-8 (MMP8) and keratin 4 (K-4); or

[0040] (iii) matrix metalloproteinase-8 (MMP8) and at least one of the following proteins: interleukin-1β (IL-1β), keratin 4 (K-4), and arrestin;

[0041] and assessing the presence of gingivitis in the patient based on the concentration of said protein in said sample. Optionally, the method of this aspect comprises the further step of treating gingivitis in the patient.

[0042] In yet another aspect, the invention provides a method for detecting the following proteins in a human patient:

[0043] (i) alpha-1-acid glycoprotein (A1AGP) and at least one of the following: matrix metalloproteinase-8 (MMP8), matrix metalloproteinase-9 (MMP9), hepatocyte growth factor (HGF), hemoglobin beta subunit (Hb-β), and S100 calcium binding protein A8 (S100A8); or

[0044] (ii) hepatocyte growth factor (HGF) and at least one of the following proteins: matrix metalloproteinase-8 (MMP8) and keratin 4 (K-4); or

[0045] (iii) matrix metalloproteinase-8 (MMP8) and at least one of the following proteins: interleukin-1β (IL-1β), keratin 4 (K-4), and arrestin;

[0046] Methods include:

[0047] (a) obtaining a saliva sample from a human patient; and

[0048] (b) detecting whether a protein is present in the sample by contacting the sample with one or more detection reagents for the protein and detecting binding between each protein and the one or more detection reagents. Typically, there is at least a first detection reagent and a second detection reagent, and sometimes a third detection reagent and a fourth detection reagent, as set forth elsewhere herein. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 A system for use in the method as described in the present disclosure is schematically represented. DETAILED DESCRIPTION

[0050] In a general sense, the present invention is based on the sensible insight that based on the measurement of a few protein biomarkers, gingivitis can be distinguished from a healthy oral cavity with sufficient accuracy. Specifically, it has been found that in saliva samples of human patients, as few as two proteins can be used as biomarkers to identify the presence or absence of gingivitis.

[0051] The biomarker proteins are alpha-1-acid glycoprotein (A1AGP), matrix metalloproteinase-8 (MMP8), matrix metalloproteinase-9 (MMP9), hepatocyte growth factor (HGF), hemoglobin beta subunit (Hb-β), S100 calcium binding protein A8 (S100A8), keratin 4 (K-4), interleukin-1β (IL-1β) and inhibitory proteins. The following combination of these proteins is used to diagnose gingivitis according to the present invention:

[0052] alpha-1-acid glycoprotein (A1AGP) and at least one of the following: matrix metalloproteinase-8 (MMP8), matrix metalloproteinase-9 (MMP9), hepatocyte growth factor (HGF), hemoglobin beta subunit (Hb-β), and S100 calcium binding protein A8 (S100A8); or

[0053] Hepatocyte growth factor (HGF) and at least one of the following proteins: matrix metalloproteinase-8 (MMP8) and keratin 4 (K-4); or

[0054] Matrix metalloproteinase-8 (MMP8) and at least one of the following proteins: interleukin-1β (IL-1β), keratin 4 (K-4), and arrestin.

[0055] Optionally, the age of the subject may be included as an additional marker.

[0056] Alpha-1-acid glycoprotein (A1AGP) is a plasma alpha-globulin glycoprotein synthesized primarily by the liver. It is also sometimes referred to as serum mucoid. It serves as a transport protein in the blood, acting as a carrier for basic and neutrally charged lipophilic compounds. It is also thought to regulate the interaction between blood cells and endothelial cells.

[0057] MMP is a family of enzymes responsible for degrading extracellular matrix components such as collagen, proteoglycans, laminin, elastin and fibronectin. They play a major role in the remodeling of the periodontal ligament (PDL) under both physiological and pathological conditions. MMP-8, also known as neutrophil collagenase or PMNL collagenase (MNL-CL), is a collagenase present in the connective tissue of most mammals. MMP-9, also known as 92kDa type IV collagenase, 92kDa gelatinase or gelatinase B (GELB), is a matrix, belonging to a class of enzymes of the zinc metalloproteinase family that participates in the degradation of the extracellular matrix.

[0058] Hepatocyte growth factor (HGF) is a paracrine cell growth, motility and morphogenesis factor. It is secreted by mesenchymal cells and targets, and acts primarily on epithelial and endothelial cells, but also on hematopoietic progenitor cells. HGF has been shown to play an important role in myogenesis and wound healing. Its ability to stimulate mitogenesis, cell motility and matrix invasion makes it play a major role in angiogenesis, tumorigenesis and tissue regeneration. HGF stimulates the growth of epithelial cells and prevents the regeneration of connective tissue attachment. HGF is known as a serum marker, indicating disease activity in various diseases.

[0059] Hemoglobin (Hb) is an iron-oxygen transport metalloprotein in the red blood cells of almost all vertebrates and in the tissues of some invertebrates. Hemoglobin-β (also known as β-globulin, HBB, β-globulin and hemoglobin β subunit) is a globulin that, together with α-globulin (HBA), constitutes the most common form of hemoglobin in adults, i.e., HbA. Hb-β is usually 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.

[0060] S100 calcium-binding protein A8 is a calcium- and zinc-binding protein that plays a significant role in regulating inflammatory processes and immune responses. It can induce neutrophil chemotaxis and adhesion.

[0061] Keratin-4 (K4), also known as cytoskeletal keratin 4 (CYK4) or cytokeratin-4 (CK-4), is a protein encoded by the KRT4 gene in humans. It is a member of the keratin gene family. Type II cytokeratins consist of basic or neutral proteins arranged in pairs of heterotypic keratin chains that are co-expressed during differentiation of simple and stratified epithelial tissues. Type II cytokeratin CK4 is specifically expressed in the differentiated layers of the mucosal and esophageal epithelium with family member KRT13. Mutations in these genes are associated with white sponge nevus, which is characterized by white patches of the oral cavity, esophagus, and anus. Type II cytokeratins are clustered in a region of chromosome 12q12-q13.

[0062] Interleukin 1-β (IL-1β) is a member of the interleukin 1 family of cytokines. This cytokine is produced by activated macrophages as a proprotein, which is processed into its active form by caspase 1 (CASP1 / ICE). This cytokine is an important mediator of inflammatory responses and is involved in a variety of cellular activities, including cell proliferation, differentiation, and apoptosis.

[0063] Arrestin is an actin-binding protein that participates in the dynamic turnover and reorganization of the actin cytoskeleton found in most cells. This is important for the spatiotemporal controlled growth of actin microfilaments, a key process for cell motility and cell shape change. Human arrestin-1 is normally 140 amino acids long when expressed, but is often further processed to the mature form.

[0064] The above mentioned proteins are known in the art. Those skilled in the art know their structures and methods of detecting them in aqueous samples (such as saliva samples). Hereinafter, the following protein biomarker combinations are collectively referred to as "biomarker panels of the present invention":

[0065] alpha-1-acid glycoprotein (A1AGP) and at least one of the following: matrix metalloproteinase-8 (MMP8), matrix metalloproteinase-9 (MMP9), hepatocyte growth factor (HGF), hemoglobin beta subunit (Hb-β), and S100 calcium binding protein A8 (S100A8); or

[0066] Hepatocyte growth factor (HGF) and at least one of the following proteins: matrix metalloproteinase-8 (MMP8) and keratin 4 (K-4); or

[0067] Matrix metalloproteinase-8 (MMP8) and at least one of the following proteins: interleukin-1β (IL-1β), keratin 4 (K-4), and arrestin.

[0068] Table 1 in the Examples provides 14 particularly preferred combinations according to the present invention.

[0069] In one embodiment, the biomarker panel of the present invention may consist of the identified protein biomarkers. Preferably, the biomarker panel of the present invention consists of no more than four protein biomarkers identified in the present invention, for example, three or four protein biomarkers of the present invention. In addition to the biomarker panel of the present invention, other biomarkers and / or data, such as demographic data (e.g., age, gender) may also be included in a set of data used to determine the type of gingivitis.

[0070] An example of an additional protein biomarker is free light chain Kappa. This is included in some of the preferred biomarker panels in Table 1 below. Free light chain proteins are immunoglobulin light chains. They are not associated with immunoglobulin heavy chains. Unlike typical complete immunoglobulin molecules, free light chain proteins are not covalently linked to immunoglobulin heavy chains, for example, free light chains are not disulfide bonded to heavy chains. Typically, free light chains include approximately 220 amino acids. Typically, free light chain proteins include a variable region (commonly referred to as a light chain variable region V L ... L ) and constant region (usually called the light chain constant region C L). Humans produce two types of immunoglobulin light chains, named after the letters kappa (κ) and lambda (λ). Each of these can be further divided into subgroups based on changes in the variable region, with four kappa subtypes (Vκ1, Vκ2, Vκ3, and Vκ4) and six lambda subtypes (Vλ1, Vλ2, Vλ3, Vλ4, Vλ5, and Vλ6). Free light chains κ are usually monomeric. Free light chains λ are usually dimers, linked by disulfide bonds (linked to another free light chain λ). Polymeric forms of free light chains λ and free light chains κ have been identified. Free light chains are produced in the periodontium by bone marrow and lymph node cells and diffuse lymphocytes, and are rapidly cleared from the blood and catabolized by the kidneys. Monomeric free light chains are cleared within 2 to 4 hours, and dimeric free light chains are cleared within 3 to 6 hours.

[0071] When additional biomarkers are optionally included, the total number of biomarkers (ie, the biomarker panel of the invention plus the additional biomarkers) is typically 3, 4, 5 or 6.

[0072] However, the desired advantage of the present invention is that the classification of a patient's gingivitis can be determined by preferably measuring no more than four biomarkers (e.g., three or four protein biomarkers). In particular, the determination does not require the use of other data, which advantageously provides a simple and direct diagnostic test.

[0073] As expected, the method requires only a small amount of saliva sample to be obtained from the subject, e.g., the size of a water droplet. Typical sample sizes range from 0.1 μl to 2 ml, such as 1 to 2 ml, however, smaller amounts (e.g., 0.1 to 100 μl) may be used for in vitro device processing, and however, obtaining larger samples is also possible, such as up to 20 ml, such as 7.5 to 17 ml.

[0074] The sample is input into an in vitro diagnostic device which measures the concentration(s) of the protein(s) involved and returns a diagnosis classifying the subject based on the likelihood of having gingivitis.

[0075] The ease of use of the present invention will allow most dental patients who have gingivitis or are at high risk of developing gingivitis to be tested regularly (e.g. as part of a regular dental check-up or even at home). In particular, this allows the presence of gingivitis to be detected soon after it has developed, thus enabling more timely oral care measures to prevent it from developing into periodontitis and to reverse the effects of gingivitis. Alternatively, for example, for a patient known to be at high risk of gingivitis and being tested for the first time, the method allows identification of whether gingivitis has already developed. In particular, the method is also suitable for self-diagnosis, whereby the steps of removing the sample and inputting it into the device are performed by the patient himself or herself.

[0076] When performing the present invention to confirm the presence or absence of gingivitis, the patient may typically be known or suspected to have gingivitis. Thus, in certain embodiments, the method is used to assess whether a human patient known or suspected to have gingivitis has gingivitis. When performing a "healthy or gingivitis classification" on a subject, it is already known or assumed that the subject does not have gingivitis. This may be known, for example, from a previously performed periodontitis detection / classification procedure, or assumed, for example, through a record of the subject's oral health status.

[0077] The methods of the present invention generally comprise detecting the aforementioned proteins and optionally other biomarker proteins constituting the biomarker panel of the present invention by using one or more detection reagents.

[0078] The "saliva" tested according to the present invention can be undiluted saliva that can be obtained by spitting or wiping, or can be diluted saliva that can be obtained by rinsing the mouth with a liquid. Diluted saliva can be obtained by the following method: the patient rinses or gargles with sterile water (e.g., 5ml or 10ml) or other suitable liquid for a few seconds and spits into a container. Diluted saliva can sometimes be called oral rinse.

[0079] "Detection" refers to measuring, quantifying, scoring or determining the concentration of a biomarker protein. Methods for evaluating biological compounds including biomarker proteins are known in the art. It will be appreciated that methods for detecting protein biomarkers include direct and indirect measurements. One skilled in the art will be able to select an appropriate method for determining a particular biomarker protein.

[0080] The term "concentration" with respect to protein biomarkers should be given its usual meaning, i.e., the abundance of a protein in a given volume. Protein concentration is usually measured as mass per volume, most typically mg / ml, μg / ml, or ng / ml, but sometimes as low as pg / ml. An alternative measure is molarity (or molarity), mol / L or "M". Concentration can be determined by measuring the amount of a protein in a sample of a known, determined, or predetermined volume.

[0081] An alternative to determining the 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, e.g., the amount of the biomarker relative to the total amount of all other proteins in the sample.

[0082] A "detection reagent" is a reagent or compound that specifically (or selectively) binds, interacts with, or detects a protein biomarker of interest. Such detection reagents may include, but are not limited to, antibodies, polyclonal antibodies, or monoclonal antibodies that preferentially bind to a protein biomarker.

[0083] When referring to a detection reagent, the phrase "specifically (or selectively) binds" or "specifically (or selectively) immunoreacts with" refers to a binding reaction that determines the presence of a protein biomarker in a heterogeneous population of proteins and other biological preparations. Thus, under specified immunoassay conditions, a specific detection reagent (e.g., an antibody) binds to a specific protein at least twice the background and does not substantially bind to other proteins present in the sample. Specific binding under such conditions may require the selection of antibodies that are specific for a particular protein. A variety of immunoassay formats can be used to select antibodies that specifically immunoreact with a particular protein. For example, solid phase ELISA immunoassays (enzyme-linked immunosorbent assays) are routinely used to select antibodies that specifically immunoreact with proteins (e.g., see Harlow & Lane, Antibodies, 1988, Laboratory Manual, for a description of immunoassay formats and conditions that can be used to determine specific immunoreactions). Typically, the specific or selective reaction is at least twice the background signal or noise, and more typically 10 to 100 times or more of the background.

[0084] "Antibody" refers to a polypeptide ligand substantially encoded by one or more immunoglobulin genes or fragments thereof, which specifically binds to and recognizes an epitope (e.g., an antigen). The recognized immunoglobulin genes include kappa and lambda 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 complete immunoglobulins or as many well-characterized fragments produced by digestion with a variety of peptidases. This includes, for example, Fab' and F(ab)'2 fragments. As used herein, the term "antibody" also includes those antibody fragments produced by modifying complete antibodies or synthesized de novo 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 an immunoglobulin heavy chain that includes one or more heavy chain constant regions CH1, CH2, and CH3, but does not include a heavy chain variable region. The antibody can be a bispecific antibody, for example, an antibody having a first variable region that specifically binds to a first antigen and an antibody having a second variable region that specifically binds to a second different antigen. The use of at least one bispecific antibody can reduce the number of detection reagents required.

[0085] The sensitivity and specificity of a diagnostic method are different. The "sensitivity" of a diagnostic assay refers to the percentage of diseased individuals who test positive (the percentage of "true positives"). Diseased individuals not detected by the assay are "false negatives." Subjects who do not have the disease and test negative in the assay are called "true negatives." The "specificity" of a diagnostic assay is 1 minus the false positive rate, where the "false positive" rate is defined as the proportion of patients who do not have the disease and test positive.

[0086] The (multiple) 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 include, but are not limited to, competitive and non-competitive assay systems using techniques such as Western blots, radioimmunoassays, ELISAs, "sandwich" immunoassays, immunoprecipitation assays, precipitin reactions, gel diffusion precipitin reactions, immunodiffusion assays, fluorescent immunoassays, etc. Such assays are conventional and well known in the art. Exemplary immunoassays are briefly described below (but are not intended to be limiting).

[0087] Immunoprecipitation protocols typically include lysing a cell population in a lysis buffer (such as RIPA buffer (1% NP-40 or Triton X-100, 1% sodium deoxycholate, 0.1% SDS, 0.15 M NaCl, 0.01 M sodium phosphate (pH 7.2), 1% aprotinin) supplemented with protein phosphatase 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 to 4 hours), adding protein A and / or protein G agarose beads to the cell lysate, incubating at 4°C for about an hour or more, washing the beads in lysis buffer, and resuspending the beads in SDS / sample buffer. The ability of an antibody to immunoprecipitate a specific antigen can be assessed by, for example, Western blot analysis. One skilled in the art will appreciate the parameters that can be modified to increase antibody binding to the antigen and reduce background (e.g., pre-clearing the cell lysate with agarose beads).

[0088] Western blot analysis generally includes preparing a protein sample, electrophoresing the protein sample in 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 skim milk), washing the membrane in a wash buffer (e.g., PBS-Tween 20), blocking the membrane with a primary antibody (antibody of interest) diluted in a blocking buffer, washing the membrane in a wash buffer, blocking the membrane with a secondary antibody (which recognizes the primary antibody, e.g., an anti-human antibody) conjugated to an enzyme substrate (e.g., horseradish peroxidase or alkaline phosphatase) or a radioactive molecule (e.g., 32P or 125I) diluted in a blocking buffer, washing the membrane in a wash buffer, and detecting the presence of the antigen. Those skilled in the art will appreciate the parameters that can be modified to increase the detected signal and reduce the background noise.

[0089] ELISA generally involves preparing an antigen (i.e., a biomarker protein of interest or a fragment thereof), coating the wells of a 96-well microtiter plate with the antigen, adding an antibody of interest conjugated to a detectable compound (such as an enzyme substrate (e.g., horseradish peroxidase or alkaline phosphatase)) to the wells and incubating for a period of time, and detecting the presence of the antigen. In an ELISA, the antibody of interest need not be conjugated to the detectable compound; instead, a second antibody (which recognizes the antibody of interest) conjugated to the detectable compound can be added to the wells. Further, instead of coating the wells with the antigen, the antibody can be coated to the wells. In this case, after the antigen of interest is added to the coated wells, a second antibody conjugated to the detectable compound can be added. Those skilled in the art will appreciate the parameters that can be modified to increase the detected signal and other variations of ELISA known in the art.

[0090] Since multiple markers are used, a threshold is determined based on the combined concentrations of these biomarkers (and optionally age). This threshold determines whether a patient is classified as having gingivitis. The insight reflected in the present invention is that gingivitis can be detected with sufficient accuracy based on measuring a combination of the biomarkers indicated above.

[0091] This insight supports another aspect of the present invention, namely, the use of the following proteins in saliva samples of human patients as biomarkers for assessing whether a patient has gingivitis:

[0092] alpha-1-acid glycoprotein (A1AGP) and at least one of the following: matrix metalloproteinase-8 (MMP8), matrix metalloproteinase-9 (MMP9), hepatocyte growth factor (HGF), hemoglobin beta subunit (Hb-β), and S100 calcium binding protein A8 (S100A8); or

[0093] Hepatocyte growth factor (HGF) and at least one of the following proteins: matrix metalloproteinase-8 (MMP8) and keratin 4 (K-4); or

[0094] matrix metalloproteinase-8 (MMP8) and at least one of the following proteins: interleukin-1β (IL-1β), keratin 4 (K-4), and arrestin;

[0095] The use can be carried out in the manner substantially as described above and below.

[0096] The method of the present invention comprises determining a test value reflecting the 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 the concentrations. It can also involve multiplying each concentration by a factor reflecting the desired weight of these concentrations and then adding the results. It can also involve multiplying the concentrations by one another or any combination of multiplication, division, subtraction, exponentiation and addition. It can further involve raising the concentrations to a certain power.

[0097] Optionally, the test value reflects the concentration of the combined concentration determined for the protein(s) in combination with the age of the subject.

[0098] The resulting combined concentration value is compared to a threshold value that reflects in the same manner the combined concentration associated with the presence of gingivitis. This comparison allows an assessment of whether the test value indicates the presence of gingivitis in the patient whose saliva was tested.

[0099] The threshold value may, for example, be based on a joint concentration value obtained in the same manner based on the concentration(s) determined for the same protein(s) in a reference sample that is associated with the presence of gingivitis (i.e., in a patient diagnosed with gingivitis). Typically, therefore, a value reflecting the same or a higher joint concentration indicates the presence of gingivitis in the tested patient. Similarly, a value reflecting a lower joint concentration in the saliva of a tested gingivitis patient indicates the absence of periodontitis. However, it is to be understood that the threshold value may also be calculated (e.g., by using a negative multiplier) such that a test value indicative of gingivitis will be below the threshold value and a test value indicative of the absence of gingivitis will be above the threshold value.

[0100] A threshold value may also be determined based on measuring the concentration(s) of the present(s) biomarker protein(s) in a sample set including patients known to be diagnosed with gingivitis as well as patients who are "not" gingivitis. Thus, a statistical analysis of the measured concentration values ​​may be performed, possibly including machine learning methods, to allow for differentiation of patients classified as having gingivitis from patients classified as not having gingivitis with a desired sensitivity and specificity. Thus, a desired threshold value may be obtained. Based on the threshold value, the same concentration measurements may be performed on the sample to be tested, and the concentration values ​​may then be processed in the same manner as to obtain the threshold value, thereby determining a joint concentration value, which may be compared to the threshold value, thus allowing for classification of the test sample as having gingivitis or not.

[0101] In interesting embodiments, the combined concentration value is obtained in the form of a score as follows. A numerical value (protein concentration value, e.g., in ng / ml) is assigned to each measurement, and these values ​​are used in linear or nonlinear combination to calculate a score between 0 and 1. In the case of the above-mentioned determination of a threshold based on a collection of objects, a sigmoid function with the combined concentration as input is typically used to calculate a score between 0 and 1 (as further shown).

[0102] When the score exceeds a certain threshold, the method indicates that the patient has gingivitis. This threshold can be selected based on the desired sensitivity and specificity.

[0103] It is to be understood that, according to the present invention, when a "gingivitis classification" is performed on a subject, this may be for a subject whose gingivitis status is unknown or known or a subject who may be assumed to be at risk of or suffering from gingivitis. Such prior knowledge may typically be known, for example, from a previously performed gingivitis diagnosis, although the extent may not be distinguishable, or may be assumed, for example, through a record of the subject's oral health status.

[0104] The clinical definition generally accepted in the field is based on the following:

[0105] Gingival Index (GI)

[0106] The full mouth gingival index will be recorded based on the Robin Modified Gingival Index (MGI) scale from 0 to 4, where:

[0107] -0 = no inflammation,

[0108] -1 = mild inflammation; slight change in color of any part, minor change in texture, but not the entire marginal or papillary gingival unit,

[0109] -2 = mild inflammation; but involving the entire marginal or papillary unit,

[0110] -3 = moderate inflammation; glazing, redness, edema and / or hypertrophy of the margin or papillary units,

[0111] -4 = severe inflammation; marked erythema, edema and / or hypertrophy of marginal or papillary gingival units, spontaneous bleeding, hyperemia or ulceration].

[0112] Probing Depth (PD)

[0113] The probing depth was recorded to the nearest millimeter using a manual UNC-15 periodontal probe. The probing depth was measured from the probe tip (assumed to be at the bottom of the pocket) to the free gingival margin.

[0114] Recession of gums (REC)

[0115] Gingival recession was recorded to the nearest millimeter using a manual UNC-15 periodontal probe. Gingival recession is the distance from the free gingival margin to the cementoenamel junction. Gingival recession will be indicated with a positive number and gingival overgrowth will be indicated with a negative number.

[0116] Clinical attachment loss (CAL)

[0117] Clinical attachment loss will be calculated as the sum of the probing depth + amount of atrophy at each site.

[0118] Bleeding on probing (BOP)

[0119] After probing, each site was assessed for bleeding on probing and was assigned a score of 1 if bleeding occurred within 30 s of probing, otherwise a score of 0 was assigned.

[0120] The resulting subject groups (patient groups) were defined as follows, wherein the mild-moderate periodontitis group and the advanced periodontitis group are "periodontitis" relevant to the present invention:

[0121] - Healthy group (H): PD ≤ 3 mm for all sites (but up to four 4 mm pockets were allowed distal to the last retained molar), no sites with interproximal attachment loss, GI ≥ 2.0 in ≤ 10% of sites, % BOP score ≤ 10%;

[0122] - Gingivitis group (G): GI ≥ 3.0 in >30% of sites, no sites with interproximal attachment loss, no sites with PD > 4 mm, % BOP score > 10%;

[0123] - Mild-moderate periodontitis group (MP): interproximal PD of 5 to 7 mm, (equivalent to approximately 2 to 4 mm CAL), ≥ 8 teeth, %BOP score > 30%;

[0124] - Advanced periodontitis group (AP): interproximal PD ≥7 mm, (equivalent to approximately ≥5 mm CAL) ≥12 teeth, %BOP score >30%.

[0125] In an embodiment, the method of the present invention utilizes Figure 1 A schematically represented system. The system may be a single device with various equipment components (units) integrated therein. The system may also have its various components or some of these components as separate devices. Figure 1 The components shown are the measuring device (A), the graphical user interface (B), and the computer processing unit (C).

[0126] As mentioned above, the system of the present invention includes a data connection to an interface, where the interface itself can be part of the system or can be a remote interface. The latter means that different devices can be used to provide the actual interface, preferably a handheld device such as a smartphone or a tablet computer. In this case, the data connection will preferably involve wireless data transmission, such as via Wi-Fi or Bluetooth or via other technologies or standards.

[0127] The measuring device (A) is configured to receive a saliva sample, for example by placing a saliva drop on a cartridge (A1) that can be inserted into the device (A). The device can be an existing device capable of determining the concentration of a protein from the same saliva sample.

[0128] The processing unit (C) receives the numerical value of the protein concentration from part (A). The unit (C) is provided with software (usually embedded software) that allows it to calculate a fraction (S) between 0 and 1. The software further includes a numerical value for a threshold (T). If the calculated value (S) exceeds (T), the unit (C) will output an indication (I) of "gingivitis" to the GUI (B), otherwise it will output "no gingivitis". Another embodiment can use a specific value of (S) to indicate the certainty of making the indication (I). This can be a probability fraction, where 0.5 is a possible threshold, and for example a fraction S = 0.8 will indicate the likelihood of gingivitis. Options of interest are:

[0129] Based on the fraction S, a person can directly indicate the certainty, i.e., S = 0.8 means an 80% certainty of gingivitis; or

[0130] For indication by defining a range R1 to R2 such that when R1 < S < R2, the indication (I) will read "uncertain".

[0131] The specific calculation of the fraction can be implemented, for example, by means of a sigmoid function applying the following formula:

[0132]

[0133] 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.

[0134] The determination of the coefficient c i can be done through a training program:

[0135] - Select N1 subjects with gingivitis (identified by a dentist via current standards) and N2 subjects without gingivitis (with healthy gums).

[0136] - Saliva samples were obtained from each subject and the protein concentration of the biomarker panel was determined as explained above.

[0137] - For gingivitis, the score S is defined as 1, and for no gingivitis (healthy gums), the score S is defined as 0.

[0138] - Fit a sigmoid function to the score and protein concentration values.

[0139] Other regression or machine learning methods (linear regression, neural networks, support vector machines) may be used, where the score S is high for gingivitis patients and low for non-gingivitis / healthy controls.

[0140] Specifically, this procedure is applied (in an example) using a clinical study with subjects with gingivitis or healthy oral conditions identified by dental professionals through clinical assessment via current standards (e.g., American Academy of Periodontology standards). The performance of various biomarker combinations is evaluated by means of leave-one-out cross-validation, resulting in the preferred biomarker combinations of the present invention.

[0141] With reference to the aforementioned system, in yet another aspect, the present invention also provides a system for assessing whether a human patient has gingivitis, the system comprising:

[0142] - a detection device capable and adapted to detect proteins in a saliva sample of a human patient:

[0143] alpha-1-acid glycoprotein (A1AGP) and at least one of the following: matrix metalloproteinase-8 (MMP8), matrix metalloproteinase-9 (MMP9), hepatocyte growth factor (HGF), hemoglobin beta subunit (Hb-β), and S100 calcium binding protein A8 (S100A8); or

[0144] Hepatocyte growth factor (HGF) and at least one of the following proteins: matrix metalloproteinase-8 (MMP8) and keratin 4 (K-4); or

[0145] matrix metalloproteinase-8 (MMP8) and at least one of the following proteins: interleukin-1β (IL-1β), keratin 4 (K-4), and arrestin;

[0146] As explained above, such devices are known and readily available to the skilled person. In general, there is provided a container for receiving therein a subject's oral sample, the container being provided by the detection device;

[0147] - a processor capable and adapted to determine, from the determined concentration of said protein, an indication that the patient has gingivitis.

[0148] 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 GUI is a user interface that allows a user to interact with an electronic device through graphical icons and visual indicators (such as secondary symbols), rather than a text-based user interface, typed command labels or text navigation (any such interface type is not excluded in the present invention); GUIs are generally well known and are commonly used in handheld mobile devices such as MP3 players, portable media players, gaming devices, smartphones, and smaller home, office and industrial controls; as described, the interface can also optionally be selected to enable input of information such as, for example, the subject's age, gender, BMI (body mass index).

[0149] The present invention also provides, either alone or as part of the aforementioned system, a kit for detecting at least two biomarkers for gingivitis in a saliva sample of a human patient, the kit comprising one or more detection reagents for detecting the following proteins:

[0150] alpha-1-acid glycoprotein (A1AGP) and at least one of the following: matrix metalloproteinase-8 (MMP8), matrix metalloproteinase-9 (MMP9), hepatocyte growth factor (HGF), hemoglobin beta subunit (Hb-β), and S100 calcium binding protein A8 (S100A8); or

[0151] Hepatocyte growth factor (HGF) and at least one of the following proteins: matrix metalloproteinase-8 (MMP8) and keratin 4 (K-4); or

[0152] Matrix metalloproteinase-8 (MMP8) and at least one of the following proteins: interleukin-1β (IL-1β), keratin 4 (K-4), and arrestin.

[0153] Typically, the kit includes two or three detection reagents, each for a different biomarker. In one embodiment, the first detection reagent is used to detect A1AGP, the second detection reagent is used to detect MMP8, and the third detection reagent is used to detect MMP9. In another embodiment, the first detection reagent is used to detect A1AGP, the second detection reagent is used to detect HGF, and the third detection reagent is used to detect S100A8. In yet another embodiment, the first detection reagent is used to detect HGF, the second detection reagent is used to detect MMP8, and the optional third detection reagent is used to detect K-4. In yet another embodiment, the first detection reagent is used to detect MMP8, the second detection reagent is used to detect IL-1β or keratin-4, and the third detection reagent is used to detect inhibitory proteins.

[0154] As discussed above with reference to the methods of the invention, the kit may include more detection reagents, such as for other proteins. In a preferred embodiment, the detection reagents available in the kit consist of detection reagents for detecting three or four proteins constituting the biomarker panel of the invention, as mentioned. In other embodiments, a separate detection reagent is provided for each biomarker protein present in the combination illustrated in Table 1 in the examples below.

[0155] Preferably, the kit comprises a solid support, such as a chip, a microtiter plate, or microbeads or resins comprising the detection reagent. In some embodiments, the kit comprises a mass spectrometry probe, such as a ProteinChip TM .

[0156] The kit may also provide a wash solution and / or a detection reagent specific for unbound detection reagent or the biomarker (sandwich type assay).

[0157] In an interesting aspect, the identification of the biomarker panel of the present invention is applied to monitor the gingivitis status of a human patient over time. Therefore, the present invention also provides an in vitro method for determining a change in the gingivitis status of a human patient suffering from gingivitis over a time interval from a first time point t1 to a second time point t2, the method comprising: detecting the concentration of the following proteins in at least one saliva sample obtained from the patient at t1 and in at least one saliva sample obtained from the patient at t2:

[0158] alpha-1-acid glycoprotein (A1AGP) and at least one of the following: matrix metalloproteinase-8 (MMP8), matrix metalloproteinase-9 (MMP9), hepatocyte growth factor (HGF), hemoglobin beta subunit (Hb-β), and S100 calcium binding protein A8 (S100A8); or

[0159] Hepatocyte growth factor (HGF) and at least one of the following proteins: matrix metalloproteinase-8 (MMP8) and keratin 4 (K-4); or

[0160] matrix metalloproteinase-8 (MMP8) and at least one of the following proteins: interleukin-1β (IL-1β), keratin 4 (K-4), and arrestin;

[0161] and comparing the concentrations, whereby the difference of preferably at least two concentrations reflects a change in state. Such a difference can be considered as a concentration difference, thereby allowing a direct comparison without first generating a number between 0 and 1 or any other classification. It is to be understood that the measurements received at two time points can also be processed in exactly the same way as above when determining the gingivitis state.

[0162] The present invention also provides a method for diagnosing whether a human patient has gingivitis, comprising: detecting the following proteins in a saliva sample of the human patient:

[0163] alpha-1-acid glycoprotein (A1AGP) and at least one of the following: matrix metalloproteinase-8 (MMP8), matrix metalloproteinase-9 (MMP9), hepatocyte growth factor (HGF), hemoglobin beta subunit (Hb-β), and S100 calcium binding protein A8 (S100A8); or

[0164] Hepatocyte growth factor (HGF) and at least one of the following proteins: matrix metalloproteinase-8 (MMP8) and keratin 4 (K-4); or

[0165] Matrix metalloproteinase-8 (MMP8) and at least one of the following proteins: interleukin-1β (IL-1β), keratin 4 (K-4), and arrestin.

[0166] The presence of gingivitis in a patient is usually assessed based on the concentration of the protein in the sample. Optionally, the method of this aspect includes a further step of treating the gingivitis in a patient. This optional treatment step may include applying a known therapeutic agent or dental procedure or a combination of a therapeutic agent and a dental procedure. Known therapeutic agents include applying an agent containing an antimicrobial agent, such as a mouthwash, a tablet, a gel or a microsphere. A typical antimicrobial agent for treating gingivitis is chlorhexidine. Other therapeutic agents include antibiotics (usually oral antibiotics) and enzyme inhibitors (such as doxycycline). Known non-surgical treatment procedures include scaling and root planing (SRP). Known surgical procedures include surgical pocket reduction, flap surgery, gum grafts or bone grafts, although these are usually reserved for advanced periodontitis and are not usually used to treat gingivitis.

[0167] The present invention further provides a method for detecting the following protein in a human patient:

[0168] alpha-1-acid glycoprotein (A1AGP) and at least one of the following: matrix metalloproteinase-8 (MMP8), matrix metalloproteinase-9 (MMP9), hepatocyte growth factor (HGF), hemoglobin beta subunit (Hb-β), and S100 calcium binding protein A8 (S100A8); or

[0169] Hepatocyte growth factor (HGF) and at least one of the following proteins: matrix metalloproteinase-8 (MMP8) and keratin 4 (K-4); or

[0170] matrix metalloproteinase-8 (MMP8) and at least one of the following proteins: interleukin-1β (IL-1β), keratin 4 (K-4), and arrestin;

[0171] The method includes:

[0172] (a) obtaining a saliva sample from a human patient; and

[0173] (b) detecting the presence of the protein in the sample by contacting the sample with one or more detection reagents for binding to the protein, and detecting binding between each protein and the one or more detection reagents.

[0174] The present invention will be further described with reference to the following non-limiting examples.

[0175] Example

[0176] A clinical study was performed on 74 subjects, 35 of whom were diagnosed with gingivitis and 39 with healthy gingiva, using a panel containing 2 to 4 protein biomarkers, we obtained receiver-operator-characteristic area under the curve values ​​> 0.75 as stated below.

[0177] Area under the ROC (Receiver-Operator-Characteristic) curve (AUC) values ​​were obtained.The performance of various biomarker combinations was evaluated by means of logistic regression with leave-one-out cross validation (LOOCV) to arrive at the preferred biomarker combinations explained herein.

[0178] In statistics, a receiver operating characteristic curve or ROC curve is a graph that illustrates the performance of a binary classifier system as the discrimination threshold is varied. The curve is created by plotting the true positive rate (TPR) against the false positive rate (FPR) for various threshold settings. The true positive rate is also known in machine learning as sensitivity, reproducibility, or probability of detection. The false positive rate is also known as fall-out or false alarm probability and can be calculated as (1-specificity). Thus, the ROC curve is sensitivity against fall-out. In general, if the probability distributions for detection and false alarms are known, the ROC curve can be generated by plotting the value of the cumulative distribution function of the probability of detection (the area under the probability distribution from -∞ to the discrimination threshold) on the y-axis for each value of the threshold against the value of the cumulative distribution function of the probability of false alarms on the x-axis. The accuracy of a test depends on how well the test classifies the test subjects into those who have or do not have the disease in question. Accuracy is measured by the area under the ROC curve. Area 1 represents a perfect test; area 0.5 represents a worthless test. A guide to classifying the accuracy of a diagnostic test is the traditional academic scoring system:

[0179] -0.90-1=Excellent (A)

[0180] -0.80-0.90 = Good (B)

[0181] -0.70-0.80 = Average (C)

[0182] -0.60-0.70=poor (D)

[0183] -0.50-0.60=Fail (F)

[0184] Based on the foregoing, in the results of the aforementioned clinical studies, a ROC AUC value greater than 0.75 is considered to indicate a desirable accuracy for providing a diagnostic test according to the present invention.

[0185] The protein biomarkers explored were:

[0186] MMP8

[0187] MMP9

[0188] IL-1β

[0189] ·HGF

[0190] Free light chain (FLC) κ (kappa)

[0191] Free light chain (FLC) λ (lambda)

[0192] ·A1AGP

[0193] Hb-β

[0194] Hb-δ

[0195] Keratin 4

[0196] ·Inhibitory proteins

[0197] Pyruvate kinase

[0198] ·S100A8

[0199] ·S100A9

[0200] Furthermore, in the adopted logistic regression, we considered them as additional predictor variables κ+λ, κ-λ, κ / λ.

[0201] Additionally, age can also be included as a predictor variable.

[0202] This yields a total of 4204 possible non-redundant groups with a maximum of 4 protein biomarkers (groups with only age are not considered). Non-redundant here means that groups including, for example, κ+λ and κ-λ as predictors are not considered, because in the logistic regression, they give the same results as the corresponding groups including κ and λ as predictors.

[0203] Note that, given the aforementioned predictor variables, there is no restriction on the number of protein markers in a group, resulting in 98,302 possible non-redundant groups (not considering groups with only age).

[0204] From this study, 407 groups were identified which provided AUC LOOCV>0.75 for classifying gingivitis and oral health.Preferred biomarker panels of the present invention cover (at least) these 407 identified groups.

[0205] In addition, among these 407 groups:

[0206] 6 had only two protein markers

[0207] 65 with three protein markers

[0208] 336 with four protein markers

[0209] The 6 groups that contained only 2 protein markers were:

[0210] ·MMP8+A1AGP (AUC LOOCV=0.788)

[0211] ·MMP9+A1AGP (AUC LOOCV=0.788)

[0212] HGF+keratin 4 (AUC LOOCV=0.775)

[0213] MMP8+A1AGP+age (AUC LOOCV=0.780)

[0214] MMP9+A1AGP+age (AUC LOOCV=0.766)

[0215] HGF+Keratin 4+Age (AUC LOOCV=0.759)

[0216] Each of these groups is highlighted as a preferred embodiment of the present invention.

[0217] (Note that these groups are actually 3 groups, which may additionally include age, and for which age does not improve performance)

[0218] Furthermore, 14 groups were found to have AUC LOOCV > 0.85. These are given in Table 1 below:

[0219]

[0220] Table 1

[0221] Each biomarker combination in the table is highlighted as a preferred combination of the present invention. As can be seen, all of these groups have 4 protein markers. These results can be summarized as showing a preference for at least A1AGP and Hb-β, with an additional preference for at least one (preferably two) of MMP8, MMP9 and keratin 4.

[0222] While the invention has been illustrated and described in detail in the drawings and foregoing description, such illustration and description are to be considered illustrative or exemplary and not restrictive; the invention is not limited to the disclosed embodiments.

[0223] For example, detection reagents for different biomarkers may be presented in different units. Alternatively, conveniently, the kit of the invention may include a fixed set of detection reagents for the protein biomarkers used in all embodiments, e.g., A1AGP, MMP8 or HGF, and optionally a flexible module including detection reagents for other biomarkers (such as, S100A8 and / or K-4).

[0224] Other variations of the disclosed embodiments may be understood and effected by those skilled in the art in practicing the claimed invention by studying the drawings, the disclosure and the appended claims. In the claims, the word "comprising" does not exclude other elements or steps, and the indefinite article "a" or "an" does not exclude a plurality. The fact that certain features of the invention are recited in mutually different dependent claims does not indicate that a combination of these features cannot be used to advantage. Any reference signs in the claims should not be construed as limiting the scope.

[0225] In summary, we disclose here an in vitro method for assessing whether a human patient suffers from gingivitis. The method is based on the insight of determining biomarker proteins. Therefore, in a saliva sample from a patient, the concentration of the protein described herein is measured. Based on the measured concentration, a value reflecting a joint concentration is determined for the protein. This value is compared with a threshold value, which reflects the joint concentration associated with gingivitis in the same way. This comparison allows an assessment of whether a test value indicates the presence of gingivitis in the patient. Thus, typically, a test value reflecting a joint concentration lower than the joint concentration reflected by the threshold value indicates the absence of gingivitis in the patient, and a test value reflecting a joint concentration equal to or higher than the joint concentration reflected by the threshold value indicates that the patient suffers from gingivitis.

Claims

1. A system for assessing whether a human patient has gingivitis, the system comprising: - a detection device configured to detect the following proteins in a saliva sample of said human patient: alpha-1-acid glycoprotein (A1AGP), hemoglobin beta subunit (Hb-β), keratin 4 (K-4), and at least one of the following: matrix metalloproteinase-8 (MMP8), matrix metalloproteinase-9 (MMP9), hepatocyte growth factor (HGF), or S100 calcium binding protein A8 (S100A8); as well as - a processor configured to determine, from the determined concentration of the protein, an indication that the patient has gingivitis.

2. The system of claim 1, further comprising a container for receiving an oral fluid sample, the container comprising the detection device.

3. The system according to claim 1, further comprising: - a user interface for presenting said indication to a user; as well as - a data connection between the processor and the user interface, the data connection being used to transmit the indication from the processor to the user interface.

4. The system of claim 1, wherein the processor is configured to function via an Internet-based application.

5. The system of claim 3, wherein the interface is configured to input information regarding the patient's age, and the processor is configured to determine, via the determined concentration, that the patient has an indication of gingivitis.

6. The system according to any one of claims 1 to 5, wherein the processor is further configured to: - determining a test value reflecting the combined concentration determined for said proteins, said test value being obtained by input of said determined concentrations and an arithmetic operation of said determined concentrations; - comparing the test value with a threshold value reflecting in the same way the joint concentration associated with gingivitis, in order to assess whether the test value is indicative of gingivitis in the patient, the threshold value being obtained by input of the concentration associated with the presence of gingivitis and the same arithmetic operation on the associated concentrations.

7. The system of claim 6, the patient's age is determined, and the test value reflects the combined concentration determined for the protein in combination with the patient's age.

8. The system of claim 6, wherein the processor is configured to determine the threshold based on a concentration determined for the protein in one or more reference samples, each sample being associated with the presence or absence of gingivitis.

9. The system of claim 6, wherein the processor is configured to determine the threshold based on a concentration of the protein in a sample set, the sample set comprising samples from patients with gingivitis and samples from patients without gingivitis.

10. The system according to any one of claims 1 to 5, wherein the protein comprises: MMP8, A1AGP, Hb-β, and K-4; or HGF, A1AGP, Hb-β and K-4.

11. The system according to any one of claims 1 to 5, wherein the protein consists of: MMP8, A1AGP, Hb-β, and K-4; or HGF, A1AGP, Hb-β and K-4. 12 . The system according to claim 1 , wherein the processor is configured to mathematically process the determined concentration value into a number between 0 and 1. 13 .

13. A kit for detecting at least two biomarkers for gingivitis in a saliva sample of a human patient, the kit comprising one or more detection reagents for detecting: alpha-1-acid glycoprotein (A1AGP), hemoglobin beta subunit (Hb-β), keratin 4 (K-4), and at least one of the following: matrix metalloproteinase-8 (MMP8), matrix metalloproteinase-9 (MMP9), hepatocyte growth factor (HGF), or S100 calcium binding protein A8 (S100A8).

14. The kit according to claim 13, wherein the one or more detection reagents include at least two detection reagents, a first detection reagent for detecting A1AGP, a second detection reagent for detecting Hb-β, and a third detection reagent for detecting at least one of MMP8, MMP9 and keratin 4.

15. The kit of claim 13 or 14, wherein the one or more detection reagents are contained on a solid support.

16. The kit according to claim 13 or 14, wherein the one or more detection reagents consist of detection reagents for A1AGP, Hb-β, and detection reagents for one, two or all of the following: MMP8, MMP9 and keratin 4.

17. The kit according to claim 15, wherein the one or more detection reagents consist of detection reagents for A1AGP, Hb-β, and detection reagents for one, two or all of the following: MMP8, MMP9 and keratin 4.

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