Methods for detection, diagnosis, prognosis, treatment, and visualization of periodontal disease or risk thereof

Abstract

Methods that make use of biomolecules, bacteria, and ratios thereof as biological markers for the detection, diagnosis, prognosis, treatment, and / or visualization of periodontal disease using oral fluid obtained from a subject are provided.

Description

METHODS FOR DETECTION, DIAGNOSIS, PROGNOSIS, TREATMENT, AND VISUALIZATION OF PERIODONTAL DISEASE OR RISK THEREOF RELATED APPLICATIONS

[0001] This application claims priority to U.S. Patent Application Serial No. 63 / 622,667 filed onJanuary 19, 2023, the entire disclosure of which is incorporated herein by reference. GOVERNMENT INTEREST

[0002] This invention was made with government support under grant numbers UL1TR001998and P30DK020579 awarded by the National Institute of Health. The government has certain rights in the invention. TECHNICAL FIELD

[0003] The presently disclosed subject matter generally relates to methods that make use ofbiomarkers for the detection, diagnosis, prognosis, treatment, and visualization of periodontal disease. In particular, certain embodiments of the presently disclosed subject matter relate to methods that make use of biomolecules, bacteria, and ratios thereof as biological markers to facilitate the detection, diagnosis, prognosis, treatment, and / or visualization of periodontal disease in a subject using oral fluid obtained from the subject. BACKGROUND

[0004] Periodontal disease, also commonly referred to as gum disease, is an infection of thetissues that hold teeth in place and can contribute to tooth loss. Indeed, more than 60% of tooth 1 13177N:2817WO:1658561:14:LOUISVILLEloss events in adults are singularly attributable to existing periodontal disease and the progression of periodontal disease. Two types of periodontal disease include gingivitis and periodontitis. Gingivitis is a mild form of periodontal disease in which inflammation of the gums occurs as a result of plaque and bacteria buildup on the teeth. If left untreated, gingivitis can lead to a more severe form of periodontal disease known as periodontitis. Periodontitis is a global chronic inflammatory disease characterized by dysbiosis and increased prevalence with age. The effects of this complex disease include destruction to oral soft and hard tissues, functional detriments, tooth loss, and diverse systemic effects. Current methods for the diagnosis of periodontitis rely primarily on clinical and radiographic assessments. This approach together with periodontitis’ non-painful presentation often leads to delays in diagnosis. In turn, these contribute to disease progression and significant economic costs to patients and health care payers. SUMMARY

[0005] The presently disclosed subject matter meets some or all of the above-identified needs, aswill become evident to those of ordinary skill in the art after a study of information provided in this document.

[0006] This summary describes several embodiments of the presently disclosed subject matter,and in many cases lists variations and permutations of these embodiments. This summary is merely exemplary of the numerous and varied embodiments. Mention of one or more representative features of a given embodiment is likewise exemplary. Such an embodiment can typically exist with or without the feature(s) mentioned; likewise, those features can be applied to other embodiments of the presently disclosed subject matter, whether listed in this summary or 2 13177N:2817WO:1658561:14:LOUISVILLEnot. To avoid excessive repetition, this summary does not list or suggest all possible combinations of such features.

[0007] Disclosed is a method for diagnosis or prognosis of periodontal disease in a subject, themethod including: detecting, in an oral fluid sample obtained from the subject, an altered level of a combination of biomarkers including two or more biomarkers selected from Resistin (RETN), Interleukin-lβ (IL-1β), Interleukin-6 (IL-6), B-cell Activating Factor (BAFF), Matrix Metalloproteinase 8 (MMP-8), Matrix Metalloproteinase 9 (MMP-9), Macrophage Inhibitory Protein-1α (MIP-1α), Interferon-α (IFN-α), S100 Calcium Binding Protein A8 (S100A8), Tissue Inhibitor of Metalloproteinases-1 (TIMP-1), Prostaglandin E2 (PGE2), Dialister invisus (Di), Fretibacterium fastidiosum (Ff), Fusobacterium nucleatum (Fn), Mycoplasma faucium (Mf), Prevotella dentalis (Pd), Prevotella denticola (P. denticola), Porphyromonas gingivalis (Pg), Prevotella intermedia (Pi), Prevotella nigrescens (Pn), Prevotella species_HMT_300 (PspHMT300), Selenomonas sputigena (Ss), Treponema denticola (Td), Treponema socranskii (Ts), and ratios thereof relative to a control level of the combination of biomarkers of subjects diagnosed to not have periodontal disease; and diagnosing the subject for periodontal disease or prognosing the subject for periodontal disease based on the detected altered level of the combination of biomarkers in the oral fluid sample relative to the control level of the combination of biomarkers of subjects diagnosed to not have periodontal disease

[0008] Also disclosed is a method for treating periodontal disease, the method including:administering a treatment for periodontal disease in response to detecting an altered level of a combination of biomarkers including two or more biomarkers selected from Resistin (RETN), Interleukin-lβ (IL-1β), Interleukin-6 (IL-6), B-cell Activating Factor (BAFF), Matrix Metalloproteinase 8 (MMP-8), Matrix Metalloproteinase 9 (MMP-9), Macrophage Inhibitory 3 13177N:2817WO:1658561:14:LOUISVILLEProtein-1α (MIP-1α), Interferon-α (IFN-α), S100 Calcium Binding Protein A8 (S100A8), Tissue Inhibitor of Metalloproteinases-1 (TIMP-1), Prostaglandin E2 (PGE2), Dialister invisus (Di), Fretibacterium fastidiosum (Ff), Fusobacterium nucleatum (Fn), Mycoplasma faucium (Mf), Prevotella dentalis (Pd), Prevotella denticola (P. denticola), Porphyromonas gingivalis (Pg), Prevotella intermedia (Pi), Prevotella nigrescens (Pn), Prevotella species_HMT_300 (PspHMT300), Selenomonas sputigena (Ss), Treponema denticola (Td), Treponema socranskii (Ts), and ratios thereof relative to a control level of the combination of biomarkers of subjects diagnosed to not have periodontal disease; wherein treatment includes at least one of administering an antimicrobial agent, a dental cleaning, and a therapeutic mouthwash to the subject.

[0009] Also disclosed is a method for screening for periodontal disease, the method including:assaying an oral fluid sample obtained from a subject to detect a concentration of one or more biomolecule biomarkers selected from Resistin (RETN), Interleukin-lβ (IL-1β), Interleukin-6 (IL-6), B-cell Activating Factor (BAFF), Matrix Metalloproteinase 8 (MMP-8), Matrix Metalloproteinase 9 (MMP-9), Macrophage Inhibitory Protein-1α (MIP-1α), Interferon-α (IFN- α), S100 Calcium Binding Protein A8 (S100A8), Tissue Inhibitor of Metalloproteinases-1 (TIMP-1), and Prostaglandin E2 (PGE2) in the oral fluid sample; sequencing the oral fluid to detect the presence of one or more bacteria biomarkers selected from Dialister invisus (Di), Fretibacterium fastidiosum (Ff), Fusobacterium nucleatum (Fn), Mycoplasma faucium (Mf), Prevotella dentalis (Pd), Prevotella denticola (P.denticola), Porphyromonas gingivalis (Pg), Prevotella intermedia (Pi), Prevotella nigrescens (Pn), Prevotella species_HMT_300 (PspHMT300), Selenomonas sputigena (Ss), Treponema denticola (Td), and Treponema socranskii (Ts); and detecting a concentration of the one or more bacteria biomarkers. 4 13177N:2817WO:1658561:14:LOUISVILLE

[0010] Also disclosed is a method for monitoring periodontal disease progression or regressionin a subject, the method including: detecting, in a first oral fluid sample obtained from the subject at a first time, a first level of two or more biomarkers selected from Resistin (RETN), Interleukin-lβ (IL-1β), Interleukin-6 (IL-6), B-cell Activating Factor (BAFF), Matrix Metalloproteinase 8 (MMP-8), Matrix Metalloproteinase 9 (MMP-9), Macrophage Inhibitory Protein-1α (MIP-1α), Interferon-α (IFN-α), S100 Calcium Binding Protein A8 (S100A8), Tissue Inhibitor of Metalloproteinases-1 (TIMP-1), Prostaglandin E2 (PGE2), Dialister invisus (Di), Fretibacterium fastidiosum (Ff), Fusobacterium nucleatum (Fn), Mycoplasma faucium (Mf), Prevotella dentalis (Pd), Prevotella denticola (P.denticola), Porphyromonas gingivalis (Pg), Prevotella intermedia (Pi), Prevotella nigrescens (Pn), Prevotella species_HMT_300 (PspHMT300), Selenomonas sputigena (Ss), Treponema denticola (Td), Treponema socranskii (Ts), and ratios thereof; detecting, in a second oral fluid sample obtained from the subject at a second time, a second level of the two or more biomarkers; detecting a measurable difference between the level of the two or more biomarkers in the first oral fluid sample and the two or more biomarkers in the second oral fluid sample; and identifying periodontal disease progression or periodontal disease regression in the subject based on the measurable difference between the level of the two or more biomarkers in the first oral fluid sample and the second oral fluid sample.

[0011] Also disclosed is a method for visualizing a state of periodontal disease or risk thereof,the method including detecting a concentration of a plurality of biomarkers in an oral fluid sample obtained from a subject, the plurality of biomarkers including two or more biomarkers selected from Resistin (RETN), Interleukin-lβ (IL-1β), Interleukin-6 (IL-6), B-cell Activating Factor (BAFF), Matrix Metalloproteinase 8 (MMP-8), Matrix Metalloproteinase 9 (MMP-9), 5 13177N:2817WO:1658561:14:LOUISVILLEMacrophage Inhibitory Protein-1α (MIP-1α), Interferon-α (IFN-α), S100 Calcium Binding Protein A8 (S100A8), Tissue Inhibitor of Metalloproteinases-1 (TIMP-1), Prostaglandin E2 (PGE2), Dialister invisus (Di), Fretibacterium fastidiosum (Ff), Fusobacterium nucleatum (Fn), Mycoplasma faucium (Mf), Prevotella dentalis (Pd), Prevotella denticola (P.denticola), Porphyromonas gingivalis (Pg), Prevotella intermedia (Pi), Prevotella nigrescens (Pn), Prevotella species_HMT_300 (PspHMT300), Selenomonas sputigena (Ss), Treponema denticola (Td), Treponema socranskii (Ts), and ratios thereof; generating a score for an oral fluid sample obtained from a subject using a logistic regression model, the logistic regression model including a plurality of variables, with each variable of the plurality of variables corresponding to the concentration of a particular biomarker of the two or more biomarkers; and mapping the score for the oral fluid sample obtained from the subject relative to at least one of scores generated for oral fluid samples of subjects diagnosed to not have periodontal disease using the logistic regression model and scores generated for oral fluid samples of subjects diagnosed with periodontal disease using the logistic regression model.

[0012] Also disclosed is a method for visualizing a state of periodontal disease or risk thereof,the method including: detecting, in an oral fluid sample obtained from a subject, a level of a plurality of biomarkers selected from Resistin (RETN), Interleukin-lβ (IL-1β), Interleukin-6 (IL- 6), B-cell Activating Factor (BAFF), Matrix Metalloproteinase 8 (MMP-8), Matrix Metalloproteinase 9 (MMP-9), Macrophage Inhibitory Protein-1α (MIP-1α), Interferon-α (IFN- α), S100 Calcium Binding Protein A8 (S100A8), Tissue Inhibitor of Metalloproteinases-1 (TIMP-1), Prostaglandin E2 (PGE2), Dialister invisus (Di), Fretibacterium fastidiosum (Ff), Fusobacterium nucleatum (Fn), Mycoplasma faucium (Mf), Prevotella dentalis (Pd), Prevotella denticola (P.denticola), Porphyromonas gingivalis (Pg), Prevotella intermedia (Pi), Prevotella 6 13177N:2817WO:1658561:14:LOUISVILLEnigrescens (Pn), Prevotella species_HMT_300 (PspHMT300), Selenomonas sputigena (Ss), Treponema denticola (Td), Treponema socranskii (Ts), and ratios thereof; and generating a chart for the oral fluid sample obtained from the subject based on the detected level of the plurality of biomarkers relative to a control level of the plurality of biomarkers.

[0013] The above and other aspects and features are described and exemplified by the followingfigures, description of exemplary embodiments, and examples. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] The presently-disclosed subject matter will be better understood, and features, aspectsand advantages other than those set forth above will become apparent when consideration is given to the following detailed description thereof. Such detailed description makes reference to the following drawings, wherein:

[0015] FIG. 1 is a heat map showing the detection of certain protein biomarkers (Matrixmetalloproteinase 9 (MMP-9), S100 calcium binding protein A8 (S100A8), B-cell activating factor (BAFF), Interferon-α (IFN-α), Tissue inhibitor of metalloproteinases-1 (TIMP-1), Interleukin-lβ (IL-1β), Interleukin-6 (IL-6), Macrophage inhibitory protein 1α (MIP-1α), and Matrix metalloproteinase 8 (MMP-8)) and bacteria biomarkers (OTU024, OTU057, OTU146, OTU156, and OTU034) outside (darker cells) or within (lighter cells) a predetermined threshold range for a group of 84 subjects with varying known periodontal statuses (localized gingivitis (LG), general gingivitis (GG), localized periodontitis (LP), general periodontitis (GP), or healthy (H)). Results ranked by value equal to bleeding on probing (BOP) dental index score multiplied by periodontal pocket depth (PD) of subject. Periodontal status indicated within group column corresponds to clinically identified periodontal status of subject. Operational taxonomic units 7 13177N:2817WO:1658561:14:LOUISVILLE(OTUs) proxies for bacteria. OTU024 = Fusobacterium nucleatum; OTU057 = Porphyromonas gingivalis; OTU146 = Treponema denticola; OTU156 = Fretibacterium fastidiosum; and OTU034 = Selenomonas sputigena. Predetermined threshold range of each respective marker based on two standard deviations from the mean of the marker from clinically identified healthy group.

[0016] FIG. 2 is a heat map showing subjects (n=17) from the group of subjects shown in FIG.1 biologically identified as healthy (i.e., without gingivitis or without periodontitis) based on measured protein biomarkers MMP-9, S100A8, BAFF, IFN-α, TIMP-1, IL-1β, IL-6, MIP-1α, and MMP-8 and bacteria biomarkers OTU024, OTU057, OTU146, OTU156, and OTU034. Darker cells indicate biomarker level outside predetermined threshold range, lighter cells indicate biomarker within predetermined threshold range of biomarker. Periodontal status indicated within group column corresponds to clinically identified periodontal status of subject. Predetermined threshold of each respective marker based on two standard deviations from the mean of the marker from clinically identified healthy group.

[0017] FIG. 3 is a heat map showing subjects (n=24) from the group of subjects shown in FIG.1 biologically identified as having gingivitis (i.e., having LG or GG) based on measured protein biomarkers MMP-9, S100A8, BAFF, IFN-α, TIMP-1, IL-1β, IL-6, MIP-1α, and MMP-8 and bacteria biomarkers OTU024, OTU057, OTU146, OTU156, and OTU034. Darker cells indicate biomarker level outside predetermined threshold range, lighter cells indicate biomarker within predetermined threshold range of biomarker. Periodontal status indicated within group column corresponds to clinically identified periodontal status of subject. Predetermined threshold range of each respective marker based on two standard deviations from the mean of the marker from clinically identified healthy group. 8 13177N:2817WO:1658561:14:LOUISVILLE

[0018] FIG. 4 is a heat map showing subjects (n=19) from the group of subjects shown in FIG.1 biologically identified as transitioning from having gingivitis to having periodontitis based on measured protein biomarkers MMP-9, S100A8, BAFF, IFN-α, TIMP-1, IL-1β, IL-6, MIP-1α, and MMP-8 and bacteria biomarkers OTU024, OTU057, OTU146, OTU156, and OTU034. Darker cells indicate biomarker level outside predetermined threshold range, lighter cells indicate biomarker within predetermined threshold range of biomarker. Periodontal status indicated within group column corresponds to clinically identified periodontal status of subject. Predetermined threshold range of each respective marker based on two standard deviations from the mean of the marker from clinically identified healthy group.

[0019] FIG. 5 is a heat map showing subjects (n=24) from the group of subjects shown in FIG.1 biologically identified as having periodontitis (i.e., having either LP or GP) based on measured protein biomarkers MMP-9, S100A8, BAFF, IFN-α, TIMP-1, IL-1β, IL-6, MIP-1α, and MMP-8 and bacteria biomarkers OTU024, OTU057, OTU146, OTU156, and OTU034. Darker cells indicate biomarker level outside predetermined threshold range, lighter cells indicate biomarker within predetermined threshold range of biomarker. Periodontal status indicated within group column corresponds to clinically identified periodontal status of subject. Predetermined threshold range of each respective marker based on two standard deviations from the mean of the marker from clinically identified healthy group.

[0020] FIG. 6 shows the levels of salivary analytes in healthy subjects (left) (n=28) and type 2diabetes mellitus (T2DM) periodontitis (right) (n=28) via raincloud plots that reflect the raw data, mean, distribution, box-plot and density. Significant differences are shown between the groups in each panel (* p ≤ 0.05, ** p ≤ 0.01, *** p ≤ 0.001, **** p ≤ 0.0001). 9 13177N:2817WO:1658561:14:LOUISVILLE

[0021] FIG. 7 is a graph showing receiver operating characteristic analysis: area under curve(AUCs) of the five best-performing salivary panels derived from logistic regression with five- fold cross-validation. M1 = P. Gingivalis; M2 = P. Denticola + MMP-8 / TIMP-1; M3 = P. Gingivalis + Fretibacterium fastidiosum + MIP-1α / TIMP-1; M4 = Fusobacterium nucleatum subsp. vincentii + P. Gingivalis + Fretibacterium fastidiosum + MIP-1α / TIMP-1; M5 = MIP-1α + S. sputigena + P. Gingivalis + P. Nigrescens + Fretibacterium fastidiosum; M6 = S. sputigena+ P. Gingivalis + P. Nigrescens + P. Dentalis + PGE2 / TIMP-1 + MIP-1α / TIMP-1. Note: Somelines corresponding to M1-M6 overlap and have thus been annotated in FIG.7 for further clarity.

[0022] FIG. 8 is a heat map of individual systemically and periodontally healthy (n=28) patientsand T2DM periodontitis (n=28) patients. Healthy (turquoise to white) or elevated (light pink to dark pink) levels above the threshold identified for each patient. Logit score summarizes the number of elevated analytes for each patient. The cutoff of logit score is 0 for discriminating periodontitis from healthy. Color spectrum provided on left-hand side of heat map for reference.

[0023] FIG. 9 is an example chart that may be utilized in the visualization of a state ofperiodontal disease or risk thereof in accordance with the present disclosure.

[0024] FIG. 10 is an example chart that may be utilized in the visualization of a state ofperiodontal disease or risk thereof in accordance with the present disclosure. DESCRIPTION OF EXEMPLARY EMBODIMENTS

[0025] The details of one or more embodiments of the presently-disclosed subject matter are setforth in this document. Modifications to embodiments described in this document, and other embodiments, will be evident to those of ordinary skill in the art after a study of the information provided in this document. The information provided in this document, and particularly the 10 13177N:2817WO:1658561:14:LOUISVILLEspecific details of the described exemplary embodiments, is provided primarily for clearness of understanding and no unnecessary limitations are to be understood therefrom. In case of conflict, the specification of this document, including definitions, will control.

[0026] While the terms used herein are believed to be well understood by those of ordinary skillin the art, certain definitions are set forth to facilitate explanation of the presently-disclosed subject matter.

[0027] Unless defined otherwise, all technical and scientific terms used herein have the samemeaning as is commonly understood by one of skill in the art to which the invention(s) belong.

[0028] All patents, patent applications, published applications and publications, GenBanksequences, databases, websites and other published materials referred to throughout the entire disclosure herein, unless noted otherwise, are incorporated by reference in their entirety.

[0029] Where reference is made to a URL or other such identifier or address, it is understoodthat such identifiers can change and particular information on the internet can come and go, but equivalent information can be found by searching the internet. Reference thereto evidences the availability and public dissemination of such information.

[0030] As used herein, the abbreviations for any protective groups, amino acids and othercompounds, are, unless indicated otherwise, in accord with their common usage, recognized abbreviations, or the IUPAC-IUB Commission on Biochemical Nomenclature (see, Biochem. (1972) 11(9):1726-1732).

[0031] Although any methods, devices, and materials similar or equivalent to those describedherein can be used in the practice or testing of the presently-disclosed subject matter, representative methods, devices, and materials are described herein. 11 13177N:2817WO:1658561:14:LOUISVILLE

[0032] The present application can “comprise” (open ended), “consist of” (closed ended), or“consist essentially of” the components of the present invention as well as other ingredients or elements described herein. As used herein, “comprising” is open ended and means the elements recited, or their equivalent in structure or function, plus any other element or elements which are not recited. The terms “having” and “including” are also to be construed as open ended unless the context suggests otherwise.

[0033] Following long-standing patent law convention, the terms “a”, “an”, and “the” refer to“one or more” when used in this application, including the claims. Thus, for example, reference to “a cell” includes a plurality of such cells, and so forth.

[0034] Unless otherwise indicated, all numbers expressing quantities of ingredients, propertiessuch as reaction conditions, and so forth used in the specification and claims are to be understood as being modified in all instances by the term “about”. Accordingly, unless indicated to the contrary, the numerical parameters set forth in this specification and claims are approximations that can vary depending upon the desired properties sought to be obtained by the presently- disclosed subject matter.

[0035] As used herein, the term “about,” when referring to a value or to an amount of mass,weight, time, volume, concentration or percentage is meant to encompass variations of in some embodiments ±20%, in some embodiments ±10%, in some embodiments ±5%, in some embodiments ±1%, in some embodiments ±0.5%, and in some embodiments ±0.1% from the specified amount, as such variations are appropriate to perform the disclosed method.

[0036] As used herein, ranges can be expressed as from “about” one particular value, and / or to“about” another particular value. It is also understood that there are a number of values disclosed herein, and that each value is also herein disclosed as “about” that particular value in addition to 12 13177N:2817WO:1658561:14:LOUISVILLEthe value itself. For example, if the value “10” is disclosed, then “about 10” is also disclosed. It is also understood that each unit between two particular units are also disclosed. For example, if 10 and 15 are disclosed, then 11, 12, 13, and 14 are also disclosed.

[0037] As used herein, “optional” or “optionally” means that the subsequently described event orcircumstance does or does not occur and that the description includes instances where said event or circumstance occurs and instances where it does not. For example, an optionally variant portion means that the portion is variant or non-variant.

[0038] As used herein, a “biological marker” or “biomarker” is a molecule, bacteria, or ratiothereof that is useful as an indicator of a biologic state in a subject.

[0039] Biomarkers disclosed herein include protein biomolecules (protein biomarkers), lipidmolecules (lipid biomarkers), and bacteria (bacteria biomarkers) that can exhibit a change in level which can be correlated with the risk of developing, the presence of, or the progression of periodontal disease in a subject. In addition, the biomarkers disclosed herein are inclusive of messenger RNAs (mRNAs) which encode the disclosed protein biomarkers, as a change in mRNA can correlate to a change in the level of the protein encoded by the mRNA. As such, detecting a level of a protein biomarker within a sample is inclusive of determining an amount of protein biomarker and / or an amount of an mRNA encoding the protein biomarker, either by direct or indirect (e.g., by measure of a complementary DNA (cDNA) synthesized from the mRNA) measure of the mRNA, in the sample. Furthermore, biomarkers disclosed herein are inclusive of one or more enzymes involved in the production of the disclosed lipid biomarkers, as a change in the level of such enzyme or enzymes can correlate to a change in the level of the lipid resulting from the action of the enzyme or enzymes. As such, detecting a level of a lipid biomarker within a sample is inclusive of determining an amount of lipid biomarker and / or an 13 13177N:2817WO:1658561:14:LOUISVILLEamount of the enzyme or enzymes involved of the production of the lipid biomarker in the sample. For instance, in some embodiments, detection of lipid biomarker Prostaglandin E2 (PGE2) level may involve detecting an amount of one or more cyclooxygenase (COX) enzymes in a sample.

[0040] Biomarkers disclosed herein further include ratio biomarkers. A ratio biomarker iscomprised of the ratio of a first biomarker and a second biomarker, with the first biomarker being the antecedent of the ratio biomarker and the second biomarker being the consequent of the ratio biomarker. Ratio biomarkers disclosed herein include ratio biomarkers comprised of a first biomolecule biomarker and a second as well as ratio biomarkers comprised of a bacteria biomarker and a biomolecule biomarker. Biomolecule biomarkers are inclusive of both protein biomarkers and lipid biomarkers. Accordingly, in some embodiments, the first biomarker of a ratio biomarker may be a protein biomarker, a lipid biomarker, or a bacteria biomarker while the second biomarker of the ratio biomarker is a protein biomarker.

[0041] Ratio biomarkers are generally identified herein and illustrated in the drawings in aquotient format with the first biomarker of the ratio biomarker (i.e., the antecedent of the ratio biomarker) appearing on the left side or above a division operator and the second biomarker of the ratio biomarker (i.e., the consequent of the ratio biomarker) appearing on the right side or beneath the division operator. For instance, a ratio biomarker in which Macrophage Inhibitory Protein-1α (MIP-1α) is the antecedent and Tissue Inhibitor of Metalloproteinases-1 (TIMP-1) is MIPα the consequent may sometimes be indicated as “MIP-1α / TIMP-1” or “ TIMP1”. Detecting a level of a ratio biomarker in a sample is inclusive of detecting an amount of the first biomarker of the ratio biomarker (i.e., the antecedent of the ratio biomarker), detecting an amount of the second biomarker of the ratio biomarker (i.e., the consequent of the ratio biomarker), and obtaining an 14 13177N:2817WO:1658561:14:LOUISVILLEamount based on the ratio of the amount of the first biomarker and the amount of the second biomarker. A ratio biomarker in which a biomolecule biomarker is the antecedent of the ratio biomarker may be characterized as a “biomolecule ratio biomarker.” A ratio biomarker in which a bacteria biomarker is the antecedent of the ratio biomarker may be characterized as a “bacteria ratio biomarker.”

[0042] As used herein “biomarker level” and “biomarker concentration” are usedinterchangeably, except where stated otherwise or context precludes.

[0043] Where reference is made to detecting a level of multiple markers (e.g., “detecting a levelof a combination of biomarkers”), it is appreciated that such references refer to detecting a level of each respective biomarker of the multiple biomarkers. Detection of multiple biomarkers may occur at the same or different times.

[0044] The presently disclosed subject matter is based, in part, on the discovery that certainbiomarker panels including various combinations of biomolecule biomarkers, bacteria biomarkers, biomolecule ratio biomarkers, and / or bacteria ratio biomarkers are effective with respect to biologically distinguishing between subjects having periodontal disease and subjects without periodontal disease (i.e., healthy subjects) using in vitro samples.

[0045] Specifically, it has been discovered that proteins Resistin (RETN), Interleukin-lβ (IL-1β),Interleukin-6 (IL-6), B-cell Activating Factor (BAFF), Matrix Metalloproteinase 8 (MMP-8), Matrix Metalloproteinase 9 (MMP-9), Macrophage Inhibitory Protein-1α (MIP-1α), Interferon-α (IFN-α), S100 Calcium Binding Protein A8 (S100A8), Tissue Inhibitor of Metalloproteinases-1 (TIMP-1), lipid Prostaglandin E2 (PGE2), and bacteria Dialister invisus (Di), Fretibacterium fastidiosum (Ff), Fusobacterium nucleatum (Fn), Mycoplasma faucium (Mf), Prevotella dentalis (Pd), Prevotella denticola (P.denticola), Porphyromonas gingivalis (Pg), Prevotella intermedia 15 13177N:2817WO:1658561:14:LOUISVILLE(Pi), Prevotella nigrescens (Pn), Prevotella species_HMT_300 (PspHMT300), Selenomonas sputigena (Ss), Treponema denticola (Td), and Treponema socranskii (Ts) can be present in the oral fluid of a subject and may be differently expressed and utilized as protein biomarkers, lipid biomarkers, and bacteria biomarkers, respectively, within a biomarker panel to distinguish between healthy subjects and subjects having periodontal disease. It has further been surprisingly discovered that utilizing one or more bacteria biomarkers selected from the foregoing bacteria biomarkers in combination with one or more biomarker ratios using the foregoing protein, lipid, and bacteria biomarkers can improve the accuracy, sensitivity, and specificity of biomarker panels for detecting periodontal disease, and, in particular periodontitis.

[0046] Accordingly, in some embodiments of the presently disclosed subject matter, methods forthe diagnosis or prognosis of periodontal disease in a subject are provided that include: detecting an altered level of a combination of biomarkers selected from two or more biomarkers selected from RETN, IL-1β, IL-6, BAFF, MMP-8, MMP-9, MIP-1α, IFN-α, S100A8, TIMP-1, PGE2, Di, Ff, Fn, Mf, Pd, P. denticola, Pg, Pi, Pn, PspHMT300, Ss, Td, Ts, and ratios thereof relative to a control level of the combination of biomarkers of subject diagnosed to not have periodontal disease; and diagnosing the subject for periodontal disease or prognosing the subject for periodontal disease based on the detected altered level of the combination of biomarkers in the oral fluid sample relative to the control level of the combination of biomarkers of subjects diagnosed to not have periodontal disease.

[0047] Biomarker panels with two or more biomarkers utilizing various combinations of RETN,MIP-1α, TIMP-1, IL-1β, PGE2, IL-6, MMP-8, MMP-9, BAFF, IFN-α, Di, Ff, Fn, Mf, Pd, P. denticola, Pg, Pi, Pn, PspHMT300, Ss, Td, Ts, and ratios thereof have been found to provide accuracies ranging from 84% to 96.7% with respect to the detection of periodontitis in oral fluid 16 13177N:2817WO:1658561:14:LOUISVILLEsamples (see Tables 6 and 9-12 below). Accordingly, in some embodiments, the two or more biomarkers are selected from RETN, MIP-1α, TIMP-1, IL-1β, PGE2, IL-6, MMP-8, MMP-9, BAFF, IFN-α, Di, Ff, Fn, Mf, Pd, P. denticola, Pg, Pi, Pn, PspHMT300, Ss, Td, Ts, and ratios thereof. In some embodiments, Fn may be Fusobacterium nucleatum subspecies vincentii (Fnsv).

[0048] As noted, the inclusion of one or more bacteria biomarkers in combination with one moreratio biomarkers selected from the above-identified protein biomarkers, lipid biomarker, and bacteria biomarkers can serve to improve panel accuracy with respect to the detection of periodontal disease, and, in particular, periodontitis. Accordingly, in some embodiments, the combination of biomarkers in the method for diagnosis or prognosis of periodontal disease includes at least one bacteria biomarker and at least one ratio marker. Bacteria biomarkers Ss, Pg, Pn, and Pd have been found to perform particularly well within oral fluid panels for detecting periodontitis (see Tables 7 and 13). Thus, in some embodiments, the combination of biomarkers includes one or more of Ss, Pg, Pn, and Pd.

[0049] Biomarker ratios which may utilized in the combination of biomarkers include thosehaving a protein biomarker as the consequent of the ratio biomarker and a protein biomarker, lipid biomarker, or bacteria biomarker as the antecedent of the ratio biomarker. In some embodiments, the combination of biomarkers may include a biomarker ratio in which the consequent of the biomarker ratio corresponds to a biomarker whose level decreases in subjects with periodontitis compared to subjects without periodontitis, such as TIMP-1. In some embodiments, the combination of biomarkers can include a plurality of ratio biomarkers, where each respective ratio biomarker has TIMP-1 as the consequent. In various embodiments, the antecedent of each biomarker ratio may be selected from RETN, MIP-1α, IL-1β, PGE2, IL-6, MMP-8, MMP-9, Ff, Mf, Pd, P. denticola, Pg, Pi, and PspHMT300. In some embodiments, the 17 13177N:2817WO:1658561:14:LOUISVILLEcombination of biomarkers includes one or more of MIP-1α / TIMp-1, IL-1β / TIMP-1, PGE2 / TIMP-1, IL-6 / TIMP-1, MMP-8 / TIMP-1, MMP-9 / TIMP-1, BAFF / TIMP-1, P. denticola / TIMP-1, Pg / TIMP-1, Mf / TIMP-1, Pi / TIMP-1, Pd / TIMP-1, Ff / TIMP-1, and RETN / TIMP1. Ratio biomarkers PGE2 / TIMP-1, MIP-1α / TIMP-1, and Ff / TIMP-1 have been found to perform particularly well within oral fluid panels for detecting periodontitis (see Tables 7 and 13). Accordingly, in some embodiments, the combination of biomarkers includes a least one of PGE2 / TIMP-1, MIP-1α / TIMP-1, and Ff / TIMP-1.

[0050] In some embodiments, the combination of biomarkers in the method for diagnosis orprognosis of periodontal disease includes the biomarkers listed in one of the two-biomarker panels provided in Table 9 below. In some embodiments, the combination of biomarkers consists of the two biomarkers listed in one of the two-biomarker panels provided in Table 9 below. In some embodiments, the combination of biomarkers includes at least three biomarkers. In some embodiments, the combination of biomarkers includes at least three biomarkers listed in one of the three-biomarker panels provided in Tables 6 and 10 below. In some embodiments, the combination of biomarkers consists of the three biomarkers listed in one of the three-biomarker panels provided in Tables 6 and 10 below. In some embodiments, the combination of biomarkers includes at least four biomarkers. In some embodiments, the combination of biomarkers includes at least four biomarkers listed in one of the four-biomarker panels provided in Tables 6 and 11 below. In some embodiments, the combination of biomarkers consists of the four biomarkers listed in one of the four-biomarker panels provided Tables 6 and 11 below. In some embodiments, the combination of biomarkers includes at least five biomarkers. In some embodiments, the combination of biomarkers includes at least five biomarkers listed in one of the five-biomarker panels provided in Tables 6 and 12. In some embodiments, the combination 18 13177N:2817WO:1658561:14:LOUISVILLEof biomarkers includes at least six biomarkers. In some embodiments, the combination of biomarkers includes at least six biomarkers listed in one of the six-biomarker panels provided in Table 6. In some embodiments, the combination of biomarkers consists of the six biomarkers listed in one of the six-biomarker panels provided in Table 6. Accordingly, various embodiments of the method for diagnosing or prognosing periodontal disease in which the combination of biomarkers in each respective embodiment includes or consists of a different one of the 40 biomarker panels provided in Tables 6 and 10-12 are expressly contemplated herein. Where reference is made to a multiple biomarkers including or consisting of one of the panels listed in a the foregoing tables, such reference is understood to mean that the multiple biomarkers includes or consists of, respectively, the specific biomarkers listed in a particular panel of the foregoing tables. Various embodiments of the disclosed methods, where each respective embodiment utilizes a different one of the biomarker panels provided in Tables 6 and 9-12 below, are contemplated herein.

[0051] In some embodiments of the method for diagnosis or prognosis of periodontal disease,detecting the altered level of the combination of biomarkers in an oral fluid sample obtained from the subject to the control level of the combination of biomarkers of subjects diagnosed to not have periodontal disease may include comparing the concentration of each respective biomarker of the combination of biomarkers in the oral fluid test sample to a predetermined value or predetermined range of values (predetermined threshold range) for a corresponding biomarker. In this regard, the control level for each respective biomarker of the combination of biomarkers may correspond to a predetermined value or range of values based on the concentrations of such biomarkers detected in subjects without periodontitis. An altered level for a biomarker of the combination of biomarkers in the oral fluid test sample may thus be detected 19 13177N:2817WO:1658561:14:LOUISVILLEin some embodiments when the concentration of that biomarker is detected as exceeding or falling below the predetermined value or falling outside of the predetermined range for the biomarker.

[0052] An oral fluid sample exhibiting an altered level for a particular biomarker may, in someembodiments, be characterized as being “positive” for that biomarker. Conversely, an oral fluid sample not-exhibiting an altered level for a particular biomarker may, in some embodiments, be characterized as being “negative” for that biomarker. In some embodiments, the subject may be diagnosed or prognosed based on the total number of biomarkers of the combination of biomarkers which are positive within the oral fluid sample and / or whether particular biomarkers and / or combination of biomarkers of the combination of biomarkers are positive within the oral fluid sample. In some embodiments, biological characterization criteria relating to the occurrence of positive and negative biomarkers within an oral fluid sample is utilized to characterize the periodontal status of the subject corresponding to the oral fluid test sample. Of course, the biological characterization criteria will vary depending on which biomarkers are included in the combination of biomarkers being assessed. In various embodiments, the periodontal status of a subject based on such criteria may include: healthy (i.e., the subject does not have periodontal disease); periodontal disease (i.e., the subject has periodontal disease); gingivitis (i.e., the subject has gingivitis); localized gingivitis (LG) (i.e., the subject has LG); generalized gingivitis (GG) (i.e., the subject has GG); periodontitis (i.e., the subject has periodontitis); localized periodontitis (LP) (i.e., the subject has LP); generalized periodontitis (GP) (i.e., the subject has GP); and / or transitioning (i.e., the subject is transitioning from one of the foregoing periodontal statuses to another of the foregoing periodontal statuses). 20 13177N:2817WO:1658561:14:LOUISVILLE

[0053] In some embodiments, the detected levels of the biomarkers of the combination ofbiomarkers in the oral fluid sample obtained from the subject may be utilized to generate a visual representation to facilitate subject or clinician understanding of the state of periodontal disease or risk thereof in the subject. In this regard, the visual representation generated may provide information indicating or relating to which biomarkers of the combination of biomarkers were positive within the oral fluid sample, which biomarkers of the combination of biomarkers were negative within the oral fluid sample, whether the subject has or is at risk of periodontal disease, gingivitis, LG, GG, periodontitis, LP, and / or GP, and / or the likelihood to which a subject may transition from one of the foregoing periodontal statuses to another one of the foregoing periodontal statuses. In some embodiments, the visual representation may be in the form of a chart. In some embodiments, the chart may be in the form of a heat map. In some embodiments, the chart may be in the form of a gauge chart, such as that shown in FIGS.9 and 10, that includes indicia (e.g., an arrow) which corresponds to the oral fluid sample obtained from the subject and is mapped relative to one or more indicators relating to oral health. In some embodiments, the mapped placement of the indicia relative to the one or more indicators may indicate the subject has or does not have periodontal disease, has or does not have a particular type of periodontal disease (e.g., gingivitis, LG, GG, periodontitis, LP, GP), and / or is at risk of having periodontal disease or a particular type thereof. Of course, the indicators of oral health and / or the format of the generated visualization may vary depending on the intended application. In some embodiments a score may be generated based on the detected levels of some or all of the biomarkers in the combination of biomarkers in the oral fluid sample and incorporated into the visual representation. Tools, including various software applications and libraries, and computing devices capable of executing such software applications and libraries, and techniques 21 13177N:2817WO:1658561:14:LOUISVILLEfor generating visual representations using numerical data (such as the concentration of biomarkers within oral samples, a score generated for an oral fluid sample, and / or binary values corresponding to an oral sample being positive or negative for a particular biomarker) and mapping such data are readily available and known in the art. Tools which may be utilized in the generation of visual representations and the mapping of numerical data corresponding to biomarker levels disclosed herein include, but are not limited to: R for statistical computing, developed by The R Foundation; ggplot2; Python; Matplotlib; seaborn; Plotly, Tableau, SPSS, and GraphPad Prism.

[0054] Logistic regression models effective for generating a score for an oral fluid sampleindicative of the probability of the subject corresponding to the oral fluid sample having periodontitis were developed utilizing biomarkers found to perform particularly well within oral fluid panels for detecting periodontitis. Specifically two logistic regression models, one utilizing five biomarkers (PGE2, Ss, Pg, Pn, and Ff / TIMP-1) and one utilizing six biomarkers (Ss, Pg, Pn, Pd, PGE2 / TIMP-1, and MIP-1α / TIMP-1), were developed and represented below in Equations (1) and (2):^^^^^^^^^^ = log^^ 1− ^^ = − 8.384 − 2.691 ^^^^^^^^^^^^^^^^^^ ^^^^^^^^^^^^^^^^^^ + 3.723 P.^^^^^^^^^^^^^^^^^^^^+ 6.481 ^^^^.^^^^^^^^^^^^^^^^^^^^ + 1.971 ^^^^. ^^^^^^^^^^^^^^^^ + 4.404PGE2MIP1α TIMP1 + 2.880TIMP1,(1); and^^ logit = ln( 1−^^) = -7.2154 + 4.352 PGE2 −2.967 Ss + 4.815 Pg + 8.458 Pn + 1.116 Ff / TIMP-1, (2),where p is the probability of having periodontitis. Each biomarker identified in Equations (1) and (2) is a variable for which the concentration of the corresponding biomarker within a tested oral fluid sample is inserted. The score for a particular oral fluid test sample using one of the above 22 13177N:2817WO:1658561:14:LOUISVILLElogistic regression models can be compared to the scores generated using such logistic regression model for oral fluid samples of subjects diagnosed to not have periodontal disease (i.e., subjects without periodontal disease) for measurable differences. The measurable differences or lack thereof, between the score of the test sample and the scores of the samples of subjects diagnosed to not have periodontal disease can inform, in whole or in part, the diagnosis or prognosis of the subject corresponding to the test sample with respect to periodontal disease. In various embodiments, a generated score for an oral fluid test sample exceeding a predetermined value or within a predetermined range of values may indicate the subject corresponding to the test sample: is healthy (i.e., does not have periodontal disease); has periodontal disease generally; has gingivitis generally; has localized gingivitis (LG); has generalized gingivitis (GG); has periodontitis generally; has localized periodontitis (LP); has generalized periodontitis (GP); and / or is transitioning from one of the foregoing periodontal statuses to another of the foregoing periodontal statuses. Alternative scoring models, logistic regression or otherwise, may be utilized to a score an oral fluid sample samples in alternative embodiments of the methods disclosed herein involving the scoring of an oral fluid sample. In some embodiments of the methods disclosed herein involving the scoring of an oral fluid sample, a model including a plurality of variables corresponding to additional and / or alternative biomarkers (relative to those in the logistic regression models identified above) disclosed herein may be utilized in scoring oral fluid samples.

[0055] Accordingly, in some embodiments of the method for diagnosis or prognosis ofperiodontal disease, detecting the altered level of the combination of biomarkers in the fluid sample relative to the control level of the combination of subjects diagnosed not to have periodontal disease includes: generating a score for the oral fluid sample obtained from the 23 13177N:2817WO:1658561:14:LOUISVILLEsubject using one of the logistic regression models identified above; and detecting a difference in the score for the oral fluid sample obtained from the subject relative to scores generated for oral fluid samples of subjects diagnosed to not have periodontal disease using the logistic regression model. In some embodiments, the score generated for the oral fluid sample obtained from the subject using a logistic regression model may be mapped relative to the scores generated for oral fluid samples of subjects diagnosed to not have periodontal disease using the logistic regression model and / or scores generated for oral fluid samples of subjects diagnosed with periodontal disease using the logistic regression model. In this way, the mapping of the score of the subject relative to other population groups may help to facilitate subject or clinician understanding of the state of periodontal disease or risk thereof in the subject and / or inform matters such as to whether treatment for a particular condition (e.g., gingivitis or periodontitis) should be commenced, what type of treatment should be administered to the subject, and / or whether a particular treatment is likely to prove effective. In some embodiments, a combination of biological characterization criteria relating to the occurrence of positive and negative biomarkers within an oral fluid sample and a score generated for the oral fluid sample using a logistic regression model may be utilized to diagnose or prognose the subject corresponding to the oral fluid sample.

[0056] The score for the oral fluid sample obtained from the subject and mapping the scores ofsubjects diagnosed as not having periodontal disease and / or the scores of the subjects diagnosed as having periodontal disease may be mapped on a display. In various embodiments, the display may be a digital display (e.g., a computer, tablet, mobile phone, or television screen) or a non- digital display (e.g., a piece of paper, poster bard, or dry erase board).

[0057] Accordingly, as reflected in the discussion above and further discussed below,embodiments of the presently disclosed subject matter include methods for visualizing a state of 24 13177N:2817WO:1658561:14:LOUISVILLEperiodontal disease or risk thereof (i.e., putting a state of periodontal disease or risk thereof in visible form).

[0058] In some embodiments, the method for diagnosis or prognosis of periodontal disease mayfurther include performing a dental examination of the mouth of the subject from which the oral fluid sample was obtained and / or administering treatment to the subject from which the oral fluid sample was obtained, subsequent to detecting the altered level of the combination of biomarkers. In this regard, a dental examination may be performed to clinically verify that the altered levels of the combination of biomarkers detected in the oral fluid sample are in fact indicative of a periodontal diagnosis or prognosis. Dental examinations which may be performed include measuring the amounts of plaque present (plaque index), bleeding on probing assessment, gingival redness and swelling (gingival index) assessment, periodontal pocket depth assessment, clinical attachment loss assessment, furcation involvement and tooth mobility assessment, and combinations thereof. In various embodiments, such dental examination may occur before or after diagnosis or prognosis of the subject. In some embodiments, administering treatment to the subject for periodontal disease may include administering an antimicrobial agent to the subject, administering a dental cleaning to the subject, administering a therapeutic mouthwash to the subject, providing surgical treatment to the subject, providing laser treatment to the subject, bone grafting, and combinations thereof. Antimicrobial agents which may be administered to the subject include antibiotics, such as tetracycline antibiotics (tetracycline hydrochloride, doxycycline, and minocycline), macrolide antibiotics, and metronidazole antibiotics. Dental cleaning may include brushing, scaling, root planning, laser periodontal therapy, ultrasonic scaling, subgingival irrigation, air abrasion, and / or other forms of debridement. Therapeutic mouthwashes which may be utilized include, but are not necessarily limited to, chlorhexidine 25 13177N:2817WO:1658561:14:LOUISVILLEgluconate, essential oil-based mouthwash, cetylpyridinium chloride, hydrogen perioxide-based mouthwash, providone-iodine rinse, antibiotic-based mouthwash, and antimicrobial-based mouth.

[0059] In further embodiments of the presently disclosed subject matter are methods for treatingperiodontal disease. In some embodiments, a method for treating periodontal disease comprises the steps of: administering a treatment for periodontal disease in response to detecting an altered level of a combination of biomarkers including two or more biomarkers selected from RETN, IL-1β, IL-6, BAFF, MMP-8, MMP-9, MIP-1α, IFN-α, S100A8, TIMP-1, PGE2, Di, Ff, Fn, Mf, Pd, P. denticola, Pg, Pi, Pn, PspHMT300, Ss, Td, Ts, and ratios thereof relative to a control level of the combination of biomarkers of subjects diagnosed to not have periodontal disease.

[0060] In some embodiments, administration of the treatment may occur in the same physicaland / or temporal setting as the detection of the altered level of the combination of biomarkers. For example, in some instances, detection of the altered level of the combination of biomarkers may be made by a clinician or occur within a clinician’s office or hospital during a subject’s appointment and treatment may be applied following such detection during the same appointment. In some embodiments, the administration of the treatment may occur in a different physical and / or temporal setting as the detection of the altered level of the combination of biomarkers. For example, in some instances, detection of the altered level of the combination of the biomarkers may occur within a first physical setting (e.g., a laboratory) at a first time and administration of the treatment for periodontal disease may occur at a second, later time (e.g., one day, one week, one month, six months after detection of the altered level of the combination of biomarkers) at a second physical setting (e.g., a clinician’s office). Accordingly, the phrase “in response to” as used within the disclosed methods for treating periodontal disease means that 26 13177N:2817WO:1658561:14:LOUISVILLEtreatment is administered to a subject as a reaction to the prior detection of an altered level of the combination of the biomarkers relative to the control level of the combination of biomarkers.

[0061] Treatments which may be employed in various embodiments of the method for treatingperiodontal disease include the treatment options described above with reference to certain embodiments of the method for diagnosis or prognosis of a subject for periodontal disease.

[0062] The combination of biomarkers for which the detection of an altered level of promptsadministration of treatment for periodontal disease can include any of the biomarker combinations referred to above with reference to various embodiments of the method for diagnosis or prognosis of a subject for periodontal disease. That is, various embodiments of the method for treating periodontal disease, where each respective embodiment employs a different one of the various biomarker combinations or biomarker combination options referred to above with references to various embodiments of the method for diagnosis or prognosis of periodontal disease, are contemplated herein. Accordingly, in some embodiments of the method of treating periodontal disease, the combination of biomarkers prompting treatment includes two or more biomarkers selected from RETN, MIP-1α, TIMP-1, IL-1β, PGE2, IL-6, MMP-8, MMP-9, BAFF, IFN-α, Di, Ff, Fn, Mf, Pd, P. denticola, Pg, Pi, Pn, PspHMT300, Ss, Td, Ts, and ratios thereof. In some embodiments, the combination of biomarkers prompting treatment includes at least one bacteria biomarker and at least one ratio biomarker having a protein biomarker consequent and a protein biomarker, lipid biomarker, or bacteria biomarker antecedent.

[0063] Turning now to detecting the level of various biomarkers disclosed herein, variousmethods known to those skilled in the art can be used to detect the concentration of such biomarkers within a provided biological sample, such as an oral fluid sample. In some embodiments, determining the amount of biomarkers in samples comprises using a RNA 27 13177N:2817WO:1658561:14:LOUISVILLEmeasuring assay to measure mRNA encoding biomarker polypeptides in the sample and / or using a protein measuring assay to measure amounts of biomarker polypeptides in the sample.

[0064] In certain embodiments, the level of some or all of the biomarkers utilized within adisclosed method can be determined by probing for mRNA of each particular biomarker in the sample using any RNA identification assay known to those skilled in the art. Briefly, RNA can be extracted from the sample, amplified, converted to cDNA, labeled, and allowed to hybridize with probes of a known sequence, such as known RNA hybridization probes (selective for mRNAs encoding biomarker polypeptides) immobilized on a substrate, e.g., array, or microarray, or quantitated by real time PCR (e.g., quantitative real-time PCR, such as available from Bio-Rad Laboratories, Hercules, California, U.S.A.). Because the probes to which the nucleic acid molecules of the sample are bound are known, the molecules in the sample can be identified. In this regard, DNA probes for one or more biomarkers can be immobilized on a substrate and provided for use in practicing a method in accordance with the present subject matter.

[0065] In some embodiments, detecting the level of some or all of the biomarkers utilized withina disclosed method comprises the use of mass spectrometry and / or immunoassay devices and methods to measure polypeptides in samples, although other methods are well known to those skilled in the art as well. See, e.g., U.S. Pat. Nos.6,143,576; 6,113,855; 6,019,944; 5,985,579; 5,947,124; 5,939,272; 5,922,615; 5,885,527; 5,851,776; 5,824,799; 5,679,526; 5,525,524; and 5,480,792, each of which is hereby incorporated by reference in its entirety. Immunoassay devices and methods can utilize labeled molecules in various sandwich, competitive, or non- competitive assay formats, to generate a signal that is related to the presence or amount of an analyte of interest. Additionally, certain methods and devices, such as biosensors and optical 28 13177N:2817WO:1658561:14:LOUISVILLEimmunoassays, can be employed to determine the presence or amount of analytes without the need for a labeled molecule. See, e.g., U.S. Pat. Nos.5,631,171; and 5,955,377, each of which is hereby incorporated by reference in its entirety.

[0066] Thus, in certain embodiments of the presently-disclosed subject matter, peptidescorresponding to some or all of the biomarkers are analyzed using an immunoassay. The presence or amount of some or all of the biomarkers utilized in the various embodiments disclosed herein can be determined using antibodies or fragments thereof specific for each marker and detecting specific binding. For example, in some embodiments, one antibody specifically binds to TIMP-1, which is inclusive of antibodies that bind the full-length peptide or a fragment thereof. In some embodiments, the antibody is a monoclonal antibody, such as an anti-TIMP-1 monoclonal antibody. In other embodiments, the antibody is a polyclonal antibody.

[0067] Any suitable immunoassay can be utilized, for example, enzyme-linked immunoassays(ELISA), radioimmunoassays (RIAs), cytokine assays, chemokine assays, competitive binding assays, and the like. Specific immunological binding of the antibody to the marker can be detected directly or indirectly. Direct labels include fluorescent or luminescent tags, metals, dyes, radionuclides, and the like, attached to the antibody. Indirect labels include various enzymes well known in the art, such as alkaline phosphatase, horseradish peroxidase and the like.

[0068] The use of immobilized antibodies or fragments thereof specific for the biomarkers isalso contemplated by the presently-disclosed subject matter. The antibodies can be immobilized onto a variety of solid supports, such as magnetic or chromatographic matrix particles, the surface of an assay plate (such as microtiter wells), pieces of a solid substrate material (such as plastic, nylon, paper), and the like. An assay strip can be prepared by coating the antibody or a plurality of antibodies in an array on a solid support. This strip can then be dipped into the test 29 13177N:2817WO:1658561:14:LOUISVILLEbiological sample and then processed quickly through washes and detection steps to generate a measurable signal, such as for example a colored spot.

[0069] In some embodiments, mass spectrometry (MS) analysis can be used alone or incombination with other methods (e.g., immunoassays) to determine the presence and / or quantity of the one or more biomarkers of interest in a biological sample. In some embodiments, the MS analysis comprises matrix-assisted laser desorption / ionization (MALDI) time-of-flight (TOF) MS analysis, such as for example direct-spot MALDI-TOF or liquid chromatography MALDI- TOF mass spectrometry analysis. In some embodiments, the MS analysis comprises electrospray ionization (ESI) MS, such as for example liquid chromatography (LC) ESI-MS. Mass analysis can be accomplished using commercially-available spectrometers, such as for example triple quadrupole mass spectrometers. Methods for utilizing MS analysis, including MALDI-TOF MS and ESI-MS, to detect the presence and quantity of biomarker peptides in biological samples are known in the art. See for example U.S. Patents 6,925,389; 6,989,100; and 6,890,763 for further guidance, each of which is incorporated herein by this reference. In some embodiments, MS analysis comprises liquid chromatography-mass spectrometry (LC-MS). In some embodiments, LC-MS may be utilized.

[0070] With further respect to the measurement of the biomarkers described herein, in someembodiments, one or more of the biomarkers utilized in the various methods disclosed herein may be detected in the sample using a method selected from the group consisting of ELISA, Luminex, FACs, Western blot, dot blot, immunoprecipitation, immunohistochemistry, immunocytochemistry, immunofluorescence, immunodetection methods, optical spectroscopy, radioimmunoassay, mass spectrometry, HPLC, qPCR, RT-qPCR, multiplex qPCR, SAGE, 30 13177N:2817WO:1658561:14:LOUISVILLERNA-seq, microarray analysis, FISH, MassARRAY technique, liquid chromatograph (LC), gas chromatography (GC) and combinations thereof.

[0071] With respect to the detection and quantification of bacteria biomarkers or bacteria ratiobiomarkers, in some embodiments, detection may include first sequencing the biological sample utilizing known techniques to identify different bacteria present in the sample. Some or all of the identified bacteria may then be quantified using suitable quantification methods known in the art.

[0072] In some embodiments of the disclosed methods, the level of some or all of thebiomolecule biomarkers within the combination of biomarkers may be detected within an oral fluid sample utilizing LUMINEX® human cytokine / chemokine assay kits and / or QUANTIKINE® enzyme-linked immunosorbent assay kits. In some embodiments of the disclosed methods, an oral fluid sample may be sequenced using 16s rRNA sequencing.

[0073] Accordingly, in still yet further embodiments of the presently disclosed subject matter,are methods for screening for periodontal disease. In some embodiments, a method for screening for periodontal disease comprises the steps of: (a) assaying an oral fluid sample obtained from a subject to detect a concentration of one or more biomolecule biomarkers selected from RETN, IL-1β, IL-6, BAFF, MMP-8, MMP-9, MIP-1α, IFN-α, S100A8, TIMP-1, and PGE2 in the oral fluid sample; (b) sequencing the oral fluid sample to detect the presence of one or more bacteria biomarkers selected from Di, Ff, Fn, Mf, Pd, P. denticola, Pg, Pi, Pn, PspHMT300, Ss, Td, and Ts; and (c) detecting a concentration of the one or more bacteria biomarkers. In some embodiments, the method for screening for periodontal disease further includes a step of comparing the detected concentration of the one or more biomolecule biomarkers and the one or more bacteria biomarkers in the oral fluid sample to one or more corresponding biomolecule biomarker controls and one or more corresponding bacteria biomarker controls to determine 31 13177N:2817WO:1658561:14:LOUISVILLEwhether the oral fluid sample exhibits an altered level of the one or more biomolecule biomarkers and / or an altered level of the one or more bacteria biomarkers.

[0074] In some embodiments, the one or more biomolecule biomarkers are selected from RETN,MIP-1α, TIMP-1, IL-1β, PGE2, IL-6, MMP-8, MMP-9, IFN-α, and BAFF and the one or more bacteria biomarkers are selected from Di, Ff, Fn, Mf, Pd, P. denticola, Pg, Pi, Pn, PspHMT300, Ss, Td, and Ts. In some embodiments the one or more biomolecule biomarkers includes each of RETN, MIP-1α, TIMP-1, IL-1β, PGE2, IL-6, MMP-8, MMP-9, IFN-α, and BAFF and the one or more bacteria biomarkers includes each of Di, Ff, Fn, Mf, Pd, P. denticola, Pg, Pi, Pn, PspHMT300, Ss, Td, and Ts.

[0075] In some embodiments, the method for screening for periodontal disease further includes astep of calculating one or more biomarker ratios, where each respective biomarker ratio has an antecedent of a biomolecule biomarker or a bacteria biomarker and a consequent of a biomolecule biomarker. In some embodiments, the one or more biomarker ratios includes a biomarker ratio in which TIMP-1 is the consequent in the biomarker ratio. In some embodiments, the method for screening for periodontal disease further includes generating a score for the oral fluid sample obtained from a subject. Such score may be generated using a logistic regression model and assessed against scores generated for the oral fluid samples of subjects diagnosed to not have periodontal disease using the logistic regression model in the same or similar fashion as discussed above with respect to certain embodiments of the method for diagnosis or prognosis of periodontal disease. In some embodiments, the logistic regression model utilized is selected from those identified above of Equation (1) or Equation (2). In some embodiments, the method for screening for periodontal disease may further include a step of mapping the score of the oral fluid sample obtained from the subject relative to scores generated 32 13177N:2817WO:1658561:14:LOUISVILLEfor oral fluid samples of subjects diagnosed to not have periodontal disease and / or scores generated for oral fluid samples of subjects diagnosed with periodontal disease.

[0076] The one or more biomolecule biomarkers and one or more bacteria biomarkers utilized inthe method for screening for periodontal disease can include any combination of biomarkers referred to above with reference to the various embodiments of the method for diagnosis or prognosis of a subject for periodontal disease which include at least one biomolecule biomarker and at least one bacteria biomarker. That is, various embodiments of the method for screening for periodontal disease, where each respective embodiment employs a different one of the various biomarker combinations or biomarker combination options including at least one biomolecule biomarker and at least one bacteria biomarker referred to above with reference to various embodiments of the method for diagnosis or prognosis of periodontal disease, are contemplated herein.

[0077] The detection of levels of biomarkers consistent with those disclosed herein can also beutilized for the monitoring of subject health over time. Accordingly, still yet further provided in some embodiments of the present disclosure are methods for monitoring periodontal disease progression or regression in a subject. In some embodiments, a method for monitoring periodontal disease progression or regression in a subject comprises the steps of: (a) detecting, in a first oral fluid sample obtained from a subject at a first time, two or more biomarkers selected from RETN, IL-1β, IL-6, BAFF, MMP-8, MMP-9, MIP-1α, IFN-α, S100A8, TIMP-1, PGE2, Di, Ff, Fn, Mf, Pd, P. denticola, Pg, Pi, Pn, PspHMT300, Ss, Td, Ts, and ratios thereof; (b) detecting, in a second oral fluid sample obtained from the subject at a second time, a second level of such two or more biomarkers; (c) detecting a measurable difference between the level of the two or more biomarkers in the first oral fluid sample and the second oral fluid sample; and 33 13177N:2817WO:1658561:14:LOUISVILLE(d) identifying periodontal disease progression or periodontal disease regression in the subject based on the measurable difference between the level of the two or more biomarkers in the first oral fluid sample and the second oral fluid sample.

[0078] In some embodiments, the detection of a measurable difference between the level of thetwo or more biomarkers in the first oral fluid sample and the second oral fluid sample may be facilitated by an indirect comparison of the biomarker levels of the first sample and the second sample. In this regard, and in some embodiments, biological characterization criteria corresponding to biomarker levels indicative of health (the absence of periodontal disease), periodontal disease, gingivitis, LG, GG, periodontitis, LP, and / or GP may be utilized to biologically identify the subject’s oral health at the first time (i.e., when the first oral fluid sample was tested) and at the second time (i.e., when the second oral fluid sample was tested) using the detected biomarker levels in the first oral fluid sample and the second oral fluid sample, respectively. The progression or regression of periodontal disease in the subject can then be identified by comparing the subject’s biologically identified oral health at the first time to the subject’s biologically identified oral health at the second time. In some embodiments, a score may be generated for each of the first oral fluid sample and the second oral fluid sample using a logistic regression model consistent with that described above and the two scores subsequently compared to detect a measurable difference. In some embodiments, biological characterization criteria corresponding to oral fluid sample scores indicative of health (the absence of periodontal disease), periodontal disease, gingivitis, LG, GG, periodontitis, LP, and / or GP may be utilized to biologically identify the subject’s oral health at the first time and at the second time using the scores generated for the first oral fluid sample and the second oral fluid sample, respectively. The progression or regression of periodontal disease in the subject can then be identified by 34 13177N:2817WO:1658561:14:LOUISVILLEcomparing the subject’s biologically identified oral health at the first time to the subject’s biologically identified oral health at the second time.

[0079] To assess the efficacy of a particular treatment for periodontal disease, in someembodiments, treatment for periodontal disease is administered to the subject following the detection of the level of the two or more biomarkers in the first oral fluid sample and before the detection of the level of the two or more biomarkers in the second oral fluid sample. Treatments which may be employed between testing of the first oral fluid sample and the second oral fluid sample from the subject include those described above in certain embodiments of the method for diagnosis or prognosis of periodontal disease.

[0080] The two or more biomarkers for which levels within the first oral fluid sample and thesecond oral fluid sample from the subject are detected can include any of the biomarker combinations referred to above with reference to various embodiments of the method for diagnosis or prognosis of a subject for periodontal disease. That is, various embodiments of the method for monitoring periodontal disease progression or regression in a subject, where each respective embodiment employs a different one of the various biomarker combinations or biomarker combination options referred to above with references to various embodiments of the method for diagnosis or prognosis of periodontal disease, are contemplated herein.

[0081] As noted, in still further embodiments of the presently disclosed subject matter, methodsfor visualizing a state of periodontal disease or risk thereof are provided. In some embodiments, a method for visualizing a state of periodontal disease or risk thereof comprises steps of: detecting, in an oral fluid sample obtained from a subject, a level of a plurality of biomarkers selected from RETN, IL-1β, IL-6, BAFF, MMP-8, MMP-9, MIP-1α, IFN-α, S100A8, TIMP-1, PGE2, Di, Ff, Fn, Mf, Pd, P. denticola, Pg, Pi, Pn, PspHMT300, Ss, Td, Ts, and ratios thereof; 35 13177N:2817WO:1658561:14:LOUISVILLEand generating a chart for the oral fluid sample obtained from the subject based, at least in part, on the detected level of the plurality of biomarkers relative to a control level of the plurality of biomarkers. The control level of each respective biomarker of the plurality of biomarkers examined may, in various embodiments, correspond to a concentration or range of concentrations for the biomarker based on concentrations of the biomarker in oral fluid samples of subjects diagnosed to not have periodontal disease and / or subjects diagnosed as having periodontal disease.

[0082] In some embodiments, the chart may include data indicative of whether the respectivebiomarkers of the plurality of biomarkers tested exceeded a predetermined concentration, fell below a predetermined concentration, or was outside of a predetermined concentration range corresponding to such biomarkers. For instance, in some embodiments, the chart may be in the form of a heat map. In some embodiments, the heat map may be a binary heat map indicating whether each respective biomarker of the plurality of biomarkers detected in the oral fluid sample from the subject did or did not exceed a predetermined concentration, fall below a predetermined concentration, and / or fall outside predetermined range of concentrations. In some embodiments, the heat map may be non-binary and be color coded as to indicate the degree to which each respective biomarker of the plurality of biomarkers detected in the oral fluid sample from the subject exceeded a predetermined concentration, fell below a predetermined concentration, or fell outside of a range of predetermined concentration ranges. In some embodiments, the chart may include on or more indicators relating to oral health. In some embodiments, such indicators may include indicators relating to the absence of periodontal disease, the presence or risk of periodontal disease, the presence or risk of gingivitis, the presence or risk of LG, the presence or risk of GG, the presence or risk of periodontitis, the 36 13177N:2817WO:1658561:14:LOUISVILLEpresence or risk of LP, the presence or risk of GP, and / or the presence or risk of transitioning from one of the foregoing oral health statuses to another one of the foregoing oral health statuses. In some embodiments, the chart is a gauge chart, such as that shown in FIGS.9 and 10, that includes indicia (e.g., an arrow) which corresponds to the oral fluid sample obtained from the subject and is mapped relative to one of the foregoing indicators for oral health. The gauge charts shown in FIGS.9 and 10 can also be characterized as a “dashboards” for oral health. In some embodiments, the indicators relating to oral health may be based on biological characterization criteria which correlates the occurrence of some or all of the plurality of biomarkers falling below a predetermined concentration, exceeding a predetermined concentration, and / or falling outside of a predetermined range of concentrations to a particular oral health status. In some embodiments, the predetermined concentration or predetermined range of concentrations is determined based on detected levels of the plurality of biomarkers in a particular population of subject (e.g., a population of subjects without periodontal disease, a population of subjects with periodontal disease, a population of subjects with gingivitis, a population of subjects with periodontitis, a population of subjects with LG, a population of subjects with GG, a population of subjects with LP, a population of subject with GP). In some embodiments, indicia corresponding to a subject’s oral fluid sample may be mapped as to indicate the subject’s risk of developing periodontal disease or the severity of the subject’s periodontal disease based on the detected levels of the plurality of biomarkers in the subject’s oral fluid sample. The generation of a visual representation of this aspect of the subject’s health may serve to inform matters such as to whether treatment for a particular condition (e.g., gingivitis or periodontitis) should be commenced, what type of treatment should be administered to the subject, and / or whether a particular treatment is likely to prove effective or was effective for the subject. In various 37 13177N:2817WO:1658561:14:LOUISVILLEembodiments, the indicia corresponding to a subject’s oral fluid sample be mapped to a particular location in a chart based on the detected levels of the biomarkers tested relative to the detected levels of such biomarkers in a particular population of subjects.

[0083] In some embodiments, a method for visualizing a state of periodontal disease or riskthereof comprises steps of: (a) detecting, in an oral fluid sample obtained from a subject, a plurality of biomarkers selected from RETN, IL-1β, IL-6, BAFF, MMP-8, MMP-9, MIP-1α, IFN-α, S100A8, TIMP-1, PGE2, Di, Ff, Fn, Mf, Pd, P. denticola, Pg, Pi, Pn, PspHMT300, Ss, Td, Ts, and ratios thereof; (b) generating a score for the oral fluid sample obtained from the subject using a logistic regression model, where the logistic regression model includes a plurality of variables, with each variable of the plurality of variables corresponding to the concentration of a particular biomarker of the plurality of biomarkers; and (c) mapping the score for the oral fluid sample obtained from the subject relative to (i) scores generated for oral fluid samples of subjects diagnosed to not have periodontal disease using the logistic regression model and / or (ii) scores generated for oral fluid samples of subjects diagnosed with periodontal disease using the logistic regression model. In the phrases “scores generated for oral fluid samples of subjects diagnosed to not have periodontal disease using the logistic regression model” and “scores generated for oral fluid samples of subjects diagnosed with periodontal disease using the logistic regression model”, it is appreciated that the logistic regression model is used for the generation of the scores and not the subjects’ diagnosis. In some embodiments, the logistic regression model utilized is one of the logistic regression models corresponding to Equation (1) and Equation (2) described above with reference to certain embodiments of the method for diagnosis or prognosis of periodontal disease, as perhaps best shown in FIG.10. 38 13177N:2817WO:1658561:14:LOUISVILLE

[0084] In some embodiments, mapping the score for the oral fluid sample may include mappingthe score on a display. The display may illustrate (and thus be characterized as including) scores generated for oral fluid samples of subjects diagnosed to not have periodontal disease using the logistic regression model and / or scores generated for oral fluid samples of subjects diagnosed with periodontal disease using the logistic regression model. In some embodiments, such scores may be illustrated as a heat map. In various embodiments, the display may be a digital display (e.g., a computer, tablet, mobile phone, or television screen) or a non-digital display (e.g., a piece of paper, poster board, or dry erase board, etc.). In some embodiments, the display may illustrate one or more indicators relating to oral health. In some embodiments, such indicators may include indicators relating to the absence of periodontal disease, the presence or risk of periodontal disease, the presence or risk of gingivitis, the presence or risk of LG, the presence or risk of GG, the presence or risk of periodontitis, stage of periodontitis, the presence or risk of LP, the presence or risk of GP, and / or the presence or risk of transitioning from one of the foregoing oral health statuses to another one of the foregoing oral health statuses.

[0085] The generation of a visual representation of a subject’s status relative one or moreindicators relating to oral health and / or subjects from various population groups of oral health (e.g., subjects without periodontal disease, subjects with periodontal disease, subjects with gingivitis, and / or subjects with periodontitis) is particularly advantageous as it can enable subjects to readily understand their oral health status and what routines may need to be implemented in order to reach or avoid a particular oral health outcome. Furthermore, such visual representation may find particular utility with clinicians with respect to matters such as determining whether treatment for a particular condition (e.g., gingivitis or periodontitis) should 39 13177N:2817WO:1658561:14:LOUISVILLEbe commenced, what type of treatment should be administered to the subject, and / or whether a particular treatment is likely to prove effective or was effective.

[0086] The plurality of biomarkers utilized in the methods for visualizing a state of periodontaldisease or risk thereof can include any of the biomarker combinations referred to above with reference to various embodiments of the method for diagnosis or prognosis of a subject for periodontal disease. That is, various embodiments of the methods for visualizing a state of periodontal disease or risk thereof, where each respective embodiment employs a different one of the various biomarker combinations or biomarker combination options referred to above with references to various embodiments of the method for diagnosis or prognosis of periodontal disease, are contemplated herein.

[0087] Oral fluid samples which may be utilized in the disclosed methods include salivasamples, gingival crevicular fluid (GCF) samples, oral rinse samples, and oral swab samples as the biomarkers utilized in the disclosed methods can generally be detected from such samples.

[0088] In some embodiments of the disclosed methods, the periodontal disease diagnosed,prognosed, treated, screened, treated, or visualized is periodontitis or a subtype thereof (i.e., LP or GP). In some embodiments of the disclosed methods, the periodontal disease, diagnosed, prognosed, treated, screened, or visualized is gingivitis or a subtype thereof (i.e., LG or GG).

[0089] The terms “diagnosing” and “diagnosis” as used herein refer to methods by which theskilled artisan can estimate and even determine whether or not a subject is suffering from a given disease or condition.

[0090] The terms “making a prognosis” and “prognosing”, as used herein can include predictinga clinical outcome (with or without medical treatment), selecting an appropriate treatment (or whether treatment would be effective), or potentially changing a current retreatment based on the 40 13177N:2817WO:1658561:14:LOUISVILLEmeasure of diagnostic biomarkers disclosed herein. The term "prognosis" does not refer to the ability to predict the course or outcome of a condition with 100% accuracy, or even that a given course or outcome is predictably more or less likely to occur based on the presence, absence or levels of test biomarkers. Instead, the skilled artisan will understand that the term "prognosis" refers to an increased probability that a certain course or outcome will occur; that is, that a course or outcome is more likely to occur in a subject exhibiting a given condition, when compared to those individuals not exhibiting the condition. For example, in individuals not exhibiting the condition (e.g., not expressing the biomarker or expressing it at a reduced level), the chance of a given outcome may be about 3%. In certain embodiments, a prognosis is about a 5% chance of a given outcome, about a 7% chance, about a 10% chance, about a 12% chance, about a 15% chance, about a 20% chance, about a 25% chance, about a 30% chance, about a 40% chance, about a 50% chance, about a 60% chance, about a 75% chance, about a 90% chance, or about a 95% chance.

[0091] The terms “correlated” and "correlating," as used herein in reference to the use ofdiagnostic and prognostic biomarkers, refers to comparing the presence or quantity of the biomarker in a subject to its presence or quantity in subjects known to suffer from, or known to be at risk of, a given condition (e.g., periodontal disease); or in subjects known to be free of a given condition, i.e. "normal individuals” or “healthy individuals”. For example, a biomarker level in a biological sample can be compared to a level known to be associated with periodontal disease or a particular type (gingivitis or periodontitis) or subtype (localized gingivitis, generalized gingivitis, localized periodontitis, or generalized periodontitis). The sample's biomarker level is said to have been correlated with a diagnosis; that is, the skilled artisan can use the biomarker level to determine whether the subject suffers from or is experiencing 41 13177N:2817WO:1658561:14:LOUISVILLEperiodontal disease or a particular type or subtype of periodontal disease, and respond accordingly. Alternatively, the sample's biomarker level can be compared to a control marker level known to be associated with a good outcome (e.g., the absence of periodontal disease), such as an average level found in a population of normal subjects.

[0092] In some embodiments, a diagnostic or prognostic biomarker is correlated to a conditionor disease by merely its presence or absence. In some embodiments, a threshold level of a diagnostic or prognostic biomarker can be established, and the level of the indicator in a subject sample can simply be compared to the threshold level.

[0093] As noted, in some embodiments, multiple determinations diagnostic or prognosticbiomarkers can be made, and a temporal change in the biomarkers can be used to determine a diagnosis, prognosis, progression of disease or regression of disease. For example, diagnostic biomarkers can be determined at an initial time, and again at a second time. In such embodiments, an increase in the biomarkers from the initial time to the second time can be diagnostic of periodontal disease or a particular type or subtype thereof, or a given prognosis. Likewise, a decrease in the biomarkers from the initial time to the second time can be indicative of periodontal disease or a particular type or subtype thereof, or a given prognosis. Furthermore, in some embodiments, the degree of change of biomarkers can be related to the severity of periodontal disease or a particular type or subtype thereof and future adverse events

[0094] The analysis of biomarkers can be carried out separately or simultaneously with multiplebiomarkers within one test sample. For example, several biomarkers can be combined into one test for efficient processing of multiple samples and for potentially providing greater diagnostic and / or prognostic accuracy. In addition, one skilled in the art would recognize the value of testing multiple samples (for example, at successive time points) from the same subject. Such 42 13177N:2817WO:1658561:14:LOUISVILLEtesting of serial samples can allow the identification of changes in marker levels over time. Increases or decreases in biomarker levels, as well as the absence of change in biomarker levels, can provide useful information about the disease status that includes, but is not limited to, identifying the approximate time from onset of the event, the presence and amount of salvageable tissue, the appropriateness of drug therapies, the effectiveness of various therapies, and identification of the subject's outcome, including risk of future events

[0095] The analysis of biomarkers can be carried out in a variety of physical formats as well. Forexample, the use of microtiter plates or automation can be used to facilitate the processing of large numbers of test samples. Alternatively, single sample formats could be developed to facilitate immediate treatment and diagnosis in a timely fashion, for example, in ambulatory transport or emergency room settings.

[0096] The present methods can be used on a wide variety of subjects. Indeed, the term “subject”as used herein is not particularly limited. The term “subject” is inclusive of vertebrates, such as mammals, and the term “subject” can include human and veterinary subjects. Thus, the subject of the herein disclosed methods can be a human, non-human primate, horse, pig, rabbit, dog, sheep, goat, cow, cat, guinea pig, rodent, or the like. The term does not denote a particular age or sex. Thus, adult and newborn subjects, as well as fetuses, whether male or female, are intended to be covered.

[0097] The practice of the presently-disclosed subject matter can employ, unless otherwiseindicated or context precludes, conventional techniques of cell biology, cell culture, molecular biology, transgenic biology, microbiology, recombinant DNA, and immunology, which are within the skill of the art. Such techniques are explained fully in the literature. See e.g., Molecular Cloning A Laboratory Manual (1989), 2nd Ed., ed. by Sambrook, Fritsch and 43 13177N:2817WO:1658561:14:LOUISVILLEManiatis, eds., Cold Spring Harbor Laboratory Press, Chapters 16 and 17; U.S. Pat. No. 4,683,195; DNA Cloning, Volumes I and II, Glover, ed., 1985; Oligonucleotide Synthesis, M. J. Gait, ed., 1984; Nucleic Acid Hybridization, D. Hames & S. J. Higgins, eds., 1984; Transcription and Translation, B. D. Hames & S. J. Higgins, eds., 1984; Culture Of Animal Cells, R. I. Freshney, Alan R. Liss, Inc., 1987; Immobilized Cells And Enzymes, IRL Press, 1986; Perbal (1984), A Practical Guide To Molecular Cloning; See Methods In Enzymology (Academic Press, Inc., N.Y.); Gene Transfer Vectors For Mammalian Cells, J. H. Miller and M. P. Calos, eds., Cold Spring Harbor Laboratory, 1987; Methods In Enzymology, Vols.154 and 155, Wu et al., eds., Academic Press Inc., N.Y.; Immunochemical Methods In Cell And Molecular Biology (Mayer and Walker, eds., Academic Press, London, 1987; Handbook Of Experimental Immunology, Volumes I-IV, D. M. Weir and C. C. Blackwell, eds., 1986

[0098] Although certain embodiments of methods disclosed herein refer to scoring multiplebiomarkers (e.g., a combination of biomarkers being scored) within an oral fluid sample using a logical regression model, in alternative embodiments, alternative means of scoring such biomarkers may be employed. In this regard, biomarker levels detected within an oral fluid sample of a subject, can be scored relative to corresponding biomarker levels in subjects without periodontal disease, subjects with periodontal disease, subjects with gingivitis, and / or subjects with periodontitis utilizing known statistical and scoring methods that facilitate scoring relative to known health and known disease.

[0099] The presently-disclosed subject matter is further illustrated by the following specific butnon-limiting examples. Some of the following examples may include aspects that are prophetic, notwithstanding the numerical values, results and / or data referred to and contained in the examples. Further, the following examples may include compilations of data that are 44 13177N:2817WO:1658561:14:LOUISVILLErepresentative of data gathered at various times during the course of development and experimentation related to the present invention. EXAMPLES

[0100] Periodontitis is a common disease worldwide that has a bidirectional interaction withtype 2 diabetes mellitus (T2DM). The growing prevalence of these two disorders affects over 420 million people worldwide. To prevent or treat periodontal disease in an individual patient, it is important to decide when to initiate lifestyle modification, preventive dental care, and periodontal treatment. However, models that assess or predict risk of periodontal disease for individual patients have not been developed for the general public, and few, if any, exist that are used in private, corporate, or academic dental settings.

[0101] Although factors associated with periodontal disease have previously beenidentified, there are virtually no biomarker panels that have been developed that accurately predict with >95% sensitivity and >95% specificity the presence of periodontal disease, the amount of activity of periodontal disease present, or the likelihood of periodontal disease progression. In this regard, it is noted that current diagnostic technologies use only one or two phase-related biomarkers.

[0102] As chronic inflammation is central to the pathogenesis of T2DM and periodontitis,inflammatory biomolecules are commonly detected in gingival crevicular fluid and saliva of affected patients. These findings suggest that there is potential utility for examining oral fluid biomarkers to detect and elucidate the biology of periodontitis in patients with and without T2DM. However, oral fluid biomarkers, despite their clear relationship to oral disease, have not been adopted in clinical practice. Indeed, there are currently no Food and Drug Administration (FDA) approved salivary 45 13177N:2817WO:1658561:14:LOUISVILLEdiagnostic tests for evaluating the risk of periodontal disease. The adoption of salivary biomarkers as adjuncts in evidence-based clinical decision-making also has lagged due, at least in part, to the lack of demonstrated value, chairside clinical utility, and utility in dental-based reimbursement models.

[0103] Prior clinical studies have demonstrated that specific salivary biomarkers canproduce sensitivities and specificities in the range of 70% to 85% for distinguishing health from periodontitis. Clinical utility, however, requires sensitivities and specificities greater than single biomarkers generally have been able to achieve. Furthermore, the oral microbiome has not been fully utilized with respect to identifying unique biomarkers that could contribute to a panel of salivary analytes that yield improved sensitivity and specificity, e.g., >95%, for detecting periodontitis sufficient as may be required, for instance, for FDA diagnostic approval.

[0104] In view of the foregoing, the studies underlying the current Examples werecarried out to: (i) determine whether an oral-fluid-based panel representing a wide array of immune and host regulatory response mediators together with representatives of the oral microbiome could yield a >95% diagnostic accuracy level for detecting periodontal disease; and (ii) identify various biomarkers and / or biomarker combinations within such panel which may prove useful in various dental or other healthcare applications.

[0105] EXAMPLE 1: 14 Factor Salivary Protein and Bacteria Panel forDistinguishing Health from Periodontal Disease

[0106] Materials and Methods

[0107] Saliva Samples

[0108] In a preliminary study, saliva samples banked from 92 subjects (61 samples fromsubjects who were clinically identified as Type 2 diabetes mellitus (T2DM) and either gingivitis 46 13177N:2817WO:1658561:14:LOUISVILLEor periodontitis and 31 samples from subjects clinically identified as healthy) in a previous study (Miller et al. (2021), Salivary Biomarkers for Discriminating Periodontitis in the Presence of Diabetes. J. Clin. Periodontal, 48(2), 216-225 (Miller et al., 2021)) were accessed and assayed to detect and quantify 14 separate biomarkers within the samples (FIG.1). Of the 92 banked samples from Miller et al., 2021, 84 had sufficient sample amounts for testing and were tested in the study underlying this Example. Saliva samples analyzed were unstimulated whole saliva.

[0109] Collection, management, and storage of saliva as well as the generalcharacteristics of the subjects from which the banked samples were acquired are described in Miller et al., 2021.

[0110] The assay results from each tested sample were correlated to the known, clinicallyidentified periodontal status of the subject from which the sample was sourced and utilized to determine whether the biomarkers could individually or in various combinations prove useful in the assessment, diagnosis, prognosis, risk stratification, staging, monitoring, and / or determination of the efficacy of an administered therapy or treatment in subjects suffering from or at risk of suffering from periodontal disease and injury to oral function, reduced tooth function, and / or tooth loss associated with periodontal disease.

[0111] All subjects from which the samples were acquired were examined clinically andperiodontal measures were recorded. The periodontal status of the subjects from which the saliva samples were acquired were clinically identified as having either periodontal disease or being healthy. Periodontal disease was characterized by the subject being clinically identified as having either: localized gingivitis (LG), where the subject suffers from gingivitis localized to a specific area of the subject’s gums; generalized gingivitis (GG), where the subject suffers from gingivitis that is not localized to a specific area of the subject’s gums; localized periodontitis (LP), where 47 13177N:2817WO:1658561:14:LOUISVILLEthe subject suffers from periodontitis that is localized to a specific area of the subject’s mouth; or generalized periodontitis (GP), where the subject suffers from periodontitis that is not localized to a specific area of the subject’s mouth. Healthy (H) were those subjects who were identified as not having LG, GG, LP, or GP.

[0112] Biomarkers

[0113] As shown in FIGS. 1-5, the 14 biomarker panel included five bacteria biomarkersand nine protein biomarkers. The bacteria biomarkers included bacteria (Bact): Fusobacterium nucleatum (OTU 024); Selenomonas sputigena (OTU 034); Porphyromonas gingivalis (OTU 057); Treponema denticola (OTU 146); and Fretibacterium fastidiosum (OTU 156). The protein biomarkers included: three inflammatory biomarkers (Infl / Bone), including Interleukin-lβ (IL- 1β), Interleukin-6 (IL-6), and Macrophage Inhibitory Protein-1α (MIP-1α); three connective tissue destruction and bone remodeling biomarkers (Tiss), including Matrix Metalloproteinase 8 (MMP-8), Matrix Metalloproteinase 9 (MMP-9), and Tissue Inhibitor of Metalloproteinases-1 (TIMP-1); and three immunomodulatory biomarkers (Imm), including B-cell Activating Factor (BAFF), Interferon-α (IFN-α), and S100 calcium binding protein A8 (S100A8).

[0114] Assays

[0115] IL-1β, IL-6, MIP-1α, BAFF, S100A8, and IFN-α in the samples were measuredusing LUMINEX® technology with human cytokine / chemokine multiplex kits (Millipore, St. Charles, MO, USA). MMP-8, MMP-9, and TIMP-1 were measured using QUANTIKINE® enzyme-linked immunosorbent assay kits (R&D Systems, Minneapolis, MN, USA) as previously described in Miller et al., 2021. Microbiome characteristics of Fusobacterium nucleatum (OTU 024); Selenomonas sputigena (OTU 034); Porphyromonas gingivalis (OTU 057); Treponema denticola (OTU 146); and Fretibacterium fastidiosum (OTU 156) were determined in the 48 13177N:2817WO:1658561:14:LOUISVILLEsamples using 16S rRNA sequencing, as previously described. In this regard, bacterial components in the oral microbiome were determined by molecular sequencing of the gene for 16s rRNA. This providing a total number of individual bacteria “readouts” in the sample from each subject. The proportion of this total for “readout” assigned to each bacterial species was determined and expressed as relative abundance of a particular species within any given sample. Sequences were assigned to their respective taxonomic classification using the Human Oral Microbiome Database (HOMD).

[0116] Data Analysis

[0117] Heat map profiles were constructed for the protein biomarkers and the bacteriabiomarkers. The measured amount of each respective biomarker of each test sample was compared to a predetermined threshold range set for the biomarker to assess whether the biomarker of the sample was within the range of that observed in subjects clinically identified as healthy. The predetermined threshold range for each biomarker was set as two standard deviations from the mean of the measured biomarker of the subjects of the study clinically identified as healthy. Heat maps were generated by color coding cells corresponding to the tested biomarkers for each sample. In this regard, the measured amount of each respective biomarker was compared against the predetermined threshold range for that biomarker and determined as falling within or outside of the predetermined threshold range. In instances, where the measured amount of the biomarker was outside of the predetermined threshold range, the cell corresponding to the biomarker for the sample was shaded red (darker cells in FIGS.1-5). A sample exhibiting a measured biomarker level exceeding or falling below the threshold range for such biomarker may be characterized as being “positive” for that biomarker. Conversely, in instances where the measured amount of the biomarker was within the predetermined threshold 49 13177N:2817WO:1658561:14:LOUISVILLErange, the cell corresponding to the biomarker for sample was shaded green (lighter cells in FIGS.1-5). A sample exhibiting a measured biomarker level within the threshold range for such biomarker may be characterized as being “negative” for that biomarker.

[0118] Samples were biologically characterized into one of four categories (or biologicalphases) based on the comparison of the assay results to the predetermined threshold ranges for each biomarker: Healthy (no periodontal disease), Gingivitis, Transitioning, or Periodontitis.

[0119] Category 1: Health. Samples (and thus the subjects corresponding thereto) werebiologically characterized as healthy (Category 1) if one or more of the following four criteria was met: (1) ≤ 1 total biomarkers positive AND Porphyromonas gingivalis (OTU 57) negative AND IL-1β and MMP-8 negative; or (2) ≤ 1 protein biomarkers positive AND ≤ 1 bacteria biomarker positive; or (3) ≤ 1 bacteria biomarker positive AND Porphyromonas gingivalis (OTU 57) negative AND 3 of 4 of TIMP-1, IL-6, MMP-8, and MMP-9 negative; or (4) IL-1β, MMP-8, and S100A8 negative AND ≤ 1 bacteria biomarker positive.

[0120] Category 2: Gingivitis. Samples (and thus the subjects corresponding thereto)were biologically characterized as having gingivitis (Category 2) if one or more of the following three criteria was met: (1) 2 to 4 total biomarkers positive AND Porphyromonas gingivalis (OTU 57) negative with TIMP-1, IL-β, or MMP-9 positive; or (2) 1 or 2 bacteria biomarkers positive AND BAFF or MMP-9 negative; or (3) Porphyromonas gingivalis (OTU 57) positive AND < 2 protein biomarkers positive. 50 13177N:2817WO:1658561:14:LOUISVILLE

[0121] Category 3: Transitioning. Samples characterized into transitioning group wereconsidered to have localized periodontitis (LP). Samples (and thus the subjects corresponding thereto) were biologically characterized as transitioning (Category 3) if one or more of the following four criteria was met: (1) 5 to 7 total biomarkers positive AND ≤ 1 bacteria biomarkers positive; or (2) 3 to 5 total biomarkers positive AND Porphyromonas gingivalis (OTU 57) positive; or (3) 3 or 4 bacteria biomarkers positive AND ≤ 2 protein biomarkers positive; or (4) 1 to 2 bacteria biomarkers positive AND MIP-1α or BAFF positive.

[0122] Periodontitis. Samples characterized into Periodontitis group were considered tohave generalized periodontitis (GP). Samples (and thus the subjects corresponding thereto) were biologically characterized as having periodontitis (Category 4) if one or more of the following four criteria was met: (1) ≥ 6 total biomarkers positive AND Porphyromonas gingivalis (OTU 57) positive; or (2) >8 total biomarkers positive with TIMP-1, IL-1β, IL-6, BAFF, and MMP-8 or MMP- 9 positive; (3) ≥ 3 bacteria biomarkers positive AND > 3 protein biomarkers positive; or (4) Porphyromonas gingivalis (OTU 57) positive AND TIMP-1 positive.

[0123] Results and Discussion

[0124] As shown in FIG. 2, 17 of the 84 samples tested were biologically characterizedinto Category 1 as healthy utilizing the biological characterization criteria noted above.

[0125] As shown in FIG. 3, 24 of the 84 samples tested were biologically characterizedin the Gingivitis category utilizing the biological characterization criteria noted above. Notably, 51 13177N:2817WO:1658561:14:LOUISVILLEof the 21 samples biologically characterized into Category 2 as having gingivitis, all 24 corresponded to subjects who were clinically identified as healthy or gingivitis.

[0126] As shown in FIG. 4, 19 of the 84 samples tested were biologically characterizedinto Category 3 as transitioning (indicating LP) utilizing the biological characterization criteria noted above. Notably, of the 19 samples biologically characterized into Category 3 as transitioning, three corresponded to subjects clinically identified as being healthy, two corresponded to subjects clinically identified as having LG, five corresponded to subjects clinically identified as having GG, six corresponded to a subject clinically identified as having LP, and three corresponded to a subject clinically identified as having GP.

[0127] As shown in FIG. 5, 24 of the 84 samples tested were biologically characterizedinto the Category 4 for periodontitis (indicating GP) utilizing the biological characterization criteria noted above. Notably, of the 25 samples biologically characterized into Category 4 for periodontitis, three corresponded to subjects clinically identified as being healthy, five corresponded to subjects clinically identified as having some form of gingivitis (either LG or GG), eight corresponded to subjects clinically identified as having LP, and eight corresponded to subjects clinically identified as having GP.

[0128] Accordingly, of the 84 samples tested, 43 were biologically characterized into acategory associated with periodontitis (i.e., Transitioning for LP or Periodontitis for GP) and 41 were biologically characterized into a category not associated with periodontitis (i.e., Health or Gingivitis). All subjects corresponding to the 43 samples biologically characterized into a category associated with periodontitis were found to actually have periodontitis. Conversely, all subjects corresponding to the 41 samples characterized into a category not associated with periodontitis were found not to have periodontitis. Verification of periodontal status with respect 52 13177N:2817WO:1658561:14:LOUISVILLEto having periodontitis or not having periodontitis was achieved as follows: Not periodontitis (np) for a subject from which a sample was taken was defined as having <20% of proximal periodontal probing sites with a bleeding score of >1 (pinpoint bleeding on gentle probing), <5% of sites with PD of ≥4 mm, no pocket depth (PD) ≥5 mm, <2% of sites with CAL of >2 mm with concurrent bleeding on probing (BOP) and PD of ≥4 mm, and did not have diabetes. Conversely, periodontitis for a subject from which a sample was taken was defined as having at least four teeth in two different quadrants with probing depth (PD) of ≥5 mm, clinical attachment loss (AL) of ≥2 mm and bleeding upon probing (BOP).

[0129] Using the above-identified biological characterization criteria for health (Category1) and gingivitis (Category 2), there were 41 samples of persons who did not have periodontitis who were detected as health or gingivitis (i.e., Not Periodontitis). There were no False Negatives. Thus, Sensitivity =41 / (41+0) = 41 / 41 = 100%. All 24 samples biologically characterized for periodontitis (Category 4) subjects were negative for health and gingivitis biological characterization criteria (True Negative 24 + False Positive 0) = 100%. Using the biological characterization criteria for transitioning (Category 3) and periodontitis (Category 4) all subjects biologically characterized into Category 4 for periodontitis (24) were biologically categorized as transitioning or periodontitis, so the sensitivity was 100%. There were, however, 18 patients who were healthy or had gingivitis who were biologically characterized as being in the transition (Category 3) or periodontitis (Category 4) group (i.e., 18 false positives), thus resulting in a specificity of 70% for the biological characterization criteria for transitioning (Category 3) and periodontitis (Category 4).

[0130] Biological indicators (i.e., biomarkers) can provide greater insight into the amountof disease present. The foregoing findings demonstrate that the clinical impression of disease 53 13177N:2817WO:1658561:14:LOUISVILLEmay not truly reflect the amount of disease observed at the biological level when biomarkers are used, and that biological examination of a subject clinically identified as being healthy or having gingivitis may be beneficial with respect to verifying that the subject does not in fact have periodontitis. Gingivitis is often thought to be required to transition to periodontitis, but it is difficult for clinicians to determine who is likely to transition to a more severe condition, biological biomarkers and biological characterization criteria such as that described above may help to make that determination.

[0131] The results of the study suggested that there may be great benefit in using theabove-identified 14 biomarker panel and biological characterization criteria to guide clinicians in decision-making and earlier patient interventions which could improve patient health outcome. In this regard, above-identified biological characterization criteria and the information derived from one or two drops of saliva and the above-identified biomarker panel, could enhance clinicians' decisions regarding prevention, treatment options, and when treatment should be implemented. In some applications, the biomarker panel and / or characterization criteria employed in this study may be utilized to determine which patients should be offered preventive regimens for periodontal disease, the frequency of such regimens, and / or the need for the implementation of methods beyond conventional treatment to achieve adequate patient responses. That is, a person monitored over time (longitudinally) may demonstrate biological changes demonstrating that the person’s oral health is transitioning from one category to another indicating either improving or worsening health that a clinician can act upon by implementing a preventative measure or administering a targeted intervention. The results of this study also provided insight into new potential therapeutic targets for more effective and precise treatments. 54 13177N:2817WO:1658561:14:LOUISVILLE

[0132] EXAMPLE 2: Protein Biomarkers, Bacteria Biomarkers, and Ratios Thereoffor Distinguishing Health from Periodontitis

[0133] Materials and Methods

[0134] Salivary concentrations of protein biomarkers and oral microbiome bacteriaspecies were determined by immunoassays and 16s rRNA sequencing, respectively, from 28 healthy and 28 type 2 diabetic periodontitis adults. Data were analyzed for categorization of health or periodontitis using five-fold cross-validation logistic regression, receiver operator characteristics (ROC), and odds ratios.

[0135] Study and Participants

[0136] The study underlying this Example involved a post-hoc analysis of a salivarydiagnostic investigation, disclosed in Miller, et al. (2021), Salivary Biomarkers for Discriminating Periodontitis in the Presence of Diabetes. J. Clin. Periodontal, 48(2), 216-225, involving two separate cohorts: non-diabetic non-periodontitis (healthy); and type 2 diabetic (T2DM) periodontitis adults.

[0137] Biological Samples and Analysis

[0138] The collection, management, and storage of unstimulated whole saliva sampleswas previously described in Miller, et al.2021. Concentrations of interleukin (IL)-1β, interleukin-6 (IL-6), macrophage inflammatory protein 1α (MIP-1α), B-cell activating factor (BAFF; TNFSF13b), interferon-α (IFN-α), adiponectin, and resistin were measured in duplicate using Luminex technology with human cytokine / chemokine multiplex kits (Millipore, St. Charles, MO, USA). Salivary concentrations of matrix metalloprotease (MMP)-8, MMP-9, prostaglandin E2 (PGE2) and tissue inhibitor of matrix metalloproteinases-1 (TIMP-1) were determined in duplicate for each subject using human QUANTIKINE® enzyme-linked 55 13177N:2817WO:1658561:14:LOUISVILLEimmunosorbent assay kits (R&D Systems, Minneapolis, MN, USA), as previously described.60,72.

[0139] Salivary microbiome characteristics were determined in each subject’s samplesusing 16s rRNA sequencing as previously described.79-81Sequences were assigned to their respective taxonomic classification using the Human Oral Microbiome Database (HOMD V13). The raw data are deposited at BioProject ID PRJNA516659 through the National Institute of Health (NIH) National Center for Biotechnology Information (NCBI).

[0140] Statistical Analysis

[0141] Descriptive statistics were performed for the demographic and clinical features,and compared using Chi-square (for Sex), Fisher’s exact test (for Race), or two sample t-test (for others). Salivary analytes including Operational Taxonomic Unit (OTU) microbiome and the concentration of host response proteins were log transformation and standardized. Logistic regression with five-fold cross-validation was then used to obtain the accuracy, sensitivity, specificity, and precision of the biomarker panels. Panel construction was developed in a step- wise manner. Receiver Operating Characteristic (ROC) curves and measures of prediction for each classification rule were computed using the area under the curve (AUC) to confirm consistency with the reported metrics. Odds ratio (OR) for a predictor in the logistic regression was calculated and it represented the change in the odds of the outcome for a one-unit increase in that predictor, holding all other predictors constant. All computations were conducted in R (version 4.2.2). For all comparisons including the logistic modeling statistical significance was set at 0.05. The sample size was calculated to be 28 participants per group with the desired OR of three and power of 80% for up to six predictors.

[0142] Results56 13177N:2817WO:1658561:14:LOUISVILLE

[0143] Demographic and Clinical Characteristics

[0144] Fifty-six adults (28 healthy and 28 T2DM periodontitis adults) ranging in agefrom 36 to 80 years were evaluated. The demographic details, clinical parameters, and bodymass index (BMI) levels of the participants are summarized in Table 1. Significant differencesbetween groups in race, number of teeth, BMI, and clinical parameters reflecting health or periodontitis were observed (p < 0.05). None of the participants smoked and the HbA1c for the T2DM periodontitis groups was 7.6 (s.d.1.4).

[0145] Table 1. Clinical and Demographic Characteristics of Study Group.Study Group 257 13177N:2817WO:1658561:14:LOUISVILLEStudy Group 2

[0146] Salivary Proteins and Bacteria

[0147] Salivary concentrations of the pro-inflammatory (IL-1β, IL-6, MIP-1α),immunoregulatory (BAFF, IFN-α, S100A8), and tissue destruction (MMP-8, MMP-9, TIMP-1) proteins and lipid PGE2 detected by immunoassay are shown in FIG.6. All mean salivary protein concentrations, except adiponectin and S100A8, were significantly different between the periodontitis and healthy groups. The oral microbiome findings including the top 104 OTUs detected based on levels across the two study groups, the dominant phyla, and demonstration that this portfolio of microorganisms provide 96-98% coverage of the microbiome reads in the samples and that many species were more abundant in the periodontitis group has been reported.

[0148] Receiver Operating Characteristic Curves

[0149] ROC analyses and the corresponding AUC were used to assess the sensitivity andspecificity of individual biomarkers to discriminate periodontal health from periodontitis. Table 2 displays the findings for the best performing (i.e., top six) host response proteins and bacteria detected in saliva. In general, the ROC curves showed a better predictor value for individual bacteria than individual salivary proteins. Two bacteria (Porphyromonas gingivalis and 58 13177N:2817WO:1658561:14:LOUISVILLEMycoplasma faucium) yielded specificities >90%. High sensitivity (86%) was observed with Prevotella sp. The top AUC (0.810) observed was with Treponema socranskii. Table 2. Diagnostic Potential of Salivary Host Response Proteins for Discriminating Periodontitis from Health. Protein Biomarker Accuracy Sensitivity Specificity Precision AUC P-ValueTIMP-1 0.730 0.707 0.753 0.741 0.768 0.0007 of

[0150] Biomarker Ratios and Pairs

[0151] Ratios of salivary protein biomarker were evaluated next using logistic regressionand ROC analyses. The best accuracy, sensitivity and specificity (ranging from 0.71 to 0.82) were observed when TIMP-1 was used as the denominator with MMP-8, MMP-9, BAFF, IFN-α, and IL-1β (Table 3). Table 3. Diagnostic Accuracy of Biomarker Ratios and Biomarker Pairs Biomarker1 Biomarker2 Accuracy Sensitivity Specificity Precision AUC P-Value59 13177N:2817WO:1658561:14:LOUISVILLEMMP- 8 / TIMP-1 0.763 0.707 0.820 0.797 0.828 0.0001MMP-

[0152] Biomarker pairs were evaluated using individual protein biomarkers, proteinratios, and bacteria and the same analytical approach. Table 3 shows that better predictive values were achieved using two biomarkers (i.e., biomarker pairs) with higher accuracies (0.837-0.873), sensitivities (0.773-0.893), and specificities (0.853 to 0.900). The most accurate (0.873) and sensitive (0.893) was Pr. denticola paired with MMP-8 / TIMP-1. PGE2 paired with T. socranskii produced the best AUC. The most specific biomarker pairs were P. gingivalis paired with Mf or MMP-9 / TIMP-1.

[0153] Odds Ratios

[0154] Logistic regression and OR analyses were used to assess the relationship betweenindividual biomarkers / biomarker pairs and the likelihood of detecting periodontitis. This analysis was drawn from the entire pool of biomarkers. Single biomarkers and biomarker ratios that displayed the best ORs are shown in Table 4. Here several bacteria and the ratios MMP-8 / 60 13177N:2817WO:1658561:14:LOUISVILLETIMP1 and MMP-9 / TIMP1 demonstrated high ORs (>3.0). Pairing of PGE2 with T. socranskii yielded the highest ORs (7.1 CI: 2.21- 40.76 and 26.5; CI: 5.36-246.93, respectively).

[0155] Table 4. Odds Ratios for Discriminating Periodontitis from Health.Single Biomarkers (All) Coefficient Odds Ratio 95% CI of Odds Ratio P - Value P. gingivalis0.784 2.189 (1.545, 3.629)0.00020302230757077306142813732454

[0156] Diagnostic Panel

[0157] Logistic regression was next used to determine whether a salivary panel of threeto six biomarker features could yield accuracy of >95%. This analysis was performed with and without resistin to help discriminate the effect of T2DM versus periodontitis on the final panels constructed. The inclusion of resistin produced only minor differences (Table 5) from the analysis without resistin (Table 6). 61 13177N:2817WO:1658561:14:LOUISVILLETable 5. Five-fold Cross-validation Results of Logistic Regression for Various Salivary Biomarker Panels (with Resistin) (Three to Six Biomarker Features). Number of Biomarkers in Accur- Sensi- Speci- Preci- AUC 95%CI of P- biomarker the panel acy tivity ficity sion Accuracy Value s 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 162 13177N:2817WO:1658561:14:LOUISVILLE6 PGE2, Ss, Pg, Pn, Pi, (0.846, < BAFF / TIMP-1 0.943 0.887 1.000 1.000 0.946 1.047) 0.0001 1 yTable 6. Five-fold Cross-validation Results of Logistic Regression for Various Salivary Biomarker Panels (Exclusive of Resistin) (Three to Six Features). Number Biomarkers in Accuracy Sensitivity Specificity Precision AUC 95%CI of P- the panel Accuracy Value 01 01 01 01 01 01 01 01 01 01 01 01 01 0163 13177N:2817WO:1658561:14:LOUISVILLEIL-6 / TIMP-1 5 Ss, Pg, Ff, PspHMT300 (0.836 < 01 01 01 01 01 01

[0158] The results indicate that the derived three-biomarker panel yielded accuracies of0.88 to 0.93 with sensitivities and specificities from 0.81 to 0.97. The four-biomarker panels yielded three combinations with accuracies of 0.927 and high specificities (0.933), and the derived five-biomarker panels achieved 0.93 accuracy with sensitivities (0.927-0.967) and specificities (0.893-0.93). The six-biomarker panel consisting of Selenomonas sputigena, P. gingivalis, Prevotella nigrescens, Pr. dentalis, PGE2 / TIMP-1 and MIP-1α / TIMP-1 yielded the highest accuracy (0.95) and precision 0.966 (FIG.7). The inclusion of P. gingivalis, Pr. nigrescens and PGE2 / TIMP-1 in the salivary panel produced extraordinarily high ORs (>40) indicating a consistently strong association with increased periodontitis risk (Table 7). Table 7. Odds Ratios of Best Performing Salivary Panel Biomarkers. 64 13177N:2817WO:1658561:14:LOUISVILLEBiomarker Coefficient Odds Ratio 95% CI of Odds Ratio P-Value Ss -2.691 0.068 (0.001, 0.375) 0.0519 P 3723 41394 (1096 65693) 04495

[0159] Heat maps and Pattern Expression

[0160] A heat map profile was constructed using the salivary analytes and bacteria withthe best diagnostic performance as shown in Table 6. The heat map is arranged by logit score and categorizes the participant’s data by health and disease (FIG.8). A logistic regression model based on the best six biomarker predictors and having the below formula (equation (1)) was used to generate the logit scores:^^^^^^^^^^ = log^^ 1− ^^ = − 8.384 − 2.691 ^^^^^^^^^^^^^^^^^^ ^^^^^^^^^^^^^^^^^^ + 3.723 P.^^^^^^^^^^^^^^^^^^^^+ 6.481 ^^^^.^^^^^^^^^^^^^^^^^^^^ + 1.971 ^^^^. ^^^^^^^^^^^^^^^^ + 4.404PGE2 + 2.880MIP1α , 1 IMP1(1 TIMP T)where p is the probability of having periodontitis. The concentration of each salivary biomarker was normalized before being applied to the equation above. Specifically, the mean and standard deviation of each biomarker’s concentration are calculated across all subjects. Then, each subject’s biomarker concentration is normalized by subtracting the mean and dividing by the standard deviation.

[0161] The overall pattern indicated that 96% of persons with periodontitis had elevatedlevels of >3 biomarkers and a positive logit score. In contrast, the healthy individuals had negative logit scores and <3 elevated biomarker levels. This modeling produced a sensitivity of 0.964, specificity of 0.964, and accuracy of 0.964 for the discrimination of health from 65 13177N:2817WO:1658561:14:LOUISVILLEperiodontitis. Notably, variation in biomarker levels amongst the individuals across groups was observed. As shown in FIG.8, a subset of clinically healthy subjects with a biological profilemore consistent with periodontitis was observed. This suggests that patients who are “clinicallyhealthy” but show elevated biological measures associated with periodontitis may belong to an “unstable” group who have a higher risk for developing / progressing to periodontitis and would benefit from further assessment.

[0162] Discussion

[0163] The study underlying the current Example identified a salivary panel derived fromthe oral microbiome and an array of immune and host regulatory response mediators that yielded high diagnostic accuracy for periodontitis. The approach utilized a large data pool obtained from the analysis of the oral microbiome, targeted pro-inflammatory cytokines, and mediators of tissue destruction and bone remodeling. Although some studies that have examined the salivary oral microbiome for associations with periodontitis, it is believed that this is the first to combine salivary microbiome findings with differentially expressed salivary proteins to assess multi- biomarker accuracies for the detection of chronic periodontitis. As reflected in the discussion above, the approach taken used logistic regression, ROC analyses, and biomarker ratios. ROC analysis is recommended by the Early Detection Research Network as the preferred approach in biomarker development. The modeling strategy employed allowed for the assessment of high sensitivity, high specificity, and high accuracy using 1,391,841 unique biomarker combinations. This approach demonstrated that high specificity can be achieved using a single bacterial species (i.e., P. gingivalis or M. faucium). However, for both high sensitivity and high specificity, a panel of at least three biomarker features was advantageous in discriminating periodontitis from health. Overall, several biomarker panels yielded accuracies >90%, thus supporting the concept 66 13177N:2817WO:1658561:14:LOUISVILLEthat biological information contained in saliva could help in diagnostic decision-making in dentistry.

[0164] The clinical utility of salivary analytes and biomarkers has not been fully realized,which may relate to an initial scientific focus on individual biomarkers. Our results showed that individual biomarkers generally yielded accuracies of 0.7 to 0.8, with bacteria being generally more sensitive and specific for the detection of periodontitis than individual salivary proteins. Two bacteria (P. gingivalis and M. faucium) detected in saliva yielded specificities >90% for the detection of periodontitis. The high specificity displayed by salivary levels of P. gingivalis and M. faucium for the identification of periodontitis is believed to be a novel finding.

[0165] The step-wise strategy employed in the study showed better sensitivities,specificities, and accuracies as more salivary biomarkers were combined. Biomarker pairs that included protein ratios yielded accuracies, sensitivities, and specificities as high as 0.85-0.89. A three-biomarker panel and several four- and five-biomarker salivary panels yielded accuracies that improved to 0.93. However, it was the six-biomarker panel consisting of S. sputigena, P. gingivalis, Pr. nigrescens, Pr. dentalis, PGE2 / TIMP-1 and MIP-1α / TIMP-1 that achieved accuracy of 0.95. The requirement of <3 biomarkers for high sensitivity and high specificity previously reported in some studies can be explained by differences between such studies and the present study. In prior studies, younger healthier cohorts (i.e., mean age ~ 25-35 years) were compared with an older disease cohort that demonstrated generalized periodontitis or “unstable periodontitis”, thus taking advantage of the wide biological and clinical differences between the groups. In contrast, our periodontal disease cohort had concurrent diabetes and our comparison healthy group had reduced periodontium, a few with BOP / 4 mm PPDs, and was older (i.e., similar in age to the disease cohort). 67 13177N:2817WO:1658561:14:LOUISVILLE

[0166] EXAMPLE 3: Ratio Biomarkers including Bacteria Biomarkers forDistinguishing Health from Periodontitis

[0167] A further study was conducted to assess: (i) the accuracy of other various biomarkerpanels including one to five biomarkers with respect to distinguishing between subjects with and without periodontitis; and (ii) the extent to which ratio biomarkers including various bacteria biomarkers as the antecedent and a protein biomarker as the consequent of the ratio could provide accurate detection of periodontitis. Samples, testing, and analysis was consistent with that described above in Example 2. Logistic regression was used (as before) to determine whether a single biomarker or a panel of two to six biomarker features could yield diagnostic accuracy of >95% for the detection of periodontitis. This analysis was performed by including all bacteria identified using 16s microbiome analysis together with the protein / lipid biomarkers to help construct the final panels for the detection of periodontitis. The inclusion of bacteria created new findings. Results of the study are shown in Tables 8-13. 68 13177N:2817WO:1658561:14:LOUISVILLE0919989796 9 de 0072600000tfsfmeot5.04.2 04.04.03.3.rff0.06.16.10.00.0 0 Bu 0e 7 6 7 5 0 1rFoc83 .8.8.8.8.48S 0 0 0 0 0.0ycar I- uC- - - - -si5cn 95c 582959568 0 92.9.8.8.8.680)178)005)751)201)6eroi0 0 0 0 0.0e yc8.8.8.8 88 -3is y902027 0 dtrcca 0 0 0.0.0.0i8.08.08.6 09.6 08.ofA nev Si0 tsrek 0 87 7raiiC0 72 ksO6.0 7.0 7.6 5.4 6.more1n1- R 0 0 0 0 0ibk 1- Ps -a P1-rc1- P -o ra anMP o PMIE r7836363MIoIsa 635 mT / mT / MIamMI aT / L LIu7 7 7 wtoiallosi T / msT / ll aV ccyc . . .7.7.a 0 0 0 0 0gBetalo Ani cry l ealha8- n pmuetlocSIU do-u vietrneprvig P opo oniMercyicov iutO afer ne PlcP d P g M T M PdL:4 1MI1 n:i165T8 / 01-1- s -P1- Psle s1- s1- P56r 0 P P anMa P IMIe3MIMIonmT / T / a1n a r oMIno eM 1:O mT / mIT W ka rll_TT / T / osa aetr illlalP.krosilallalallalosil / n7 1 moM8-9- yha vietooc9aryha vietocetocr2yha viits82:ovH P P ier_.M Mprgvietnelm oprgovietonvietn EprgisB Ppso 1 M M Pni rg Pedba ioni r e r eGoni eN77 T B P g P d P d P P g R131tfsf 5eo48t44603054 732_tff 4seot619050565 .. .4.5.u4.4.4.4.4.Bu 0 0 0 0 B 0 0 0 0 0 C Ceroi9.09.09.09.09.0 P P -fi 0i606333 3-cey3 3 3 3 3ti3 9 3 9 3 9 3 9 3cy 9eti9.3 3 pci09.09.09.09.0 pc .0.0.0.0.S Sif0 00 0 0 0reP anMmMIP P I I I kIrMouiT / MIsrkrM MT / T / T / aImT / srem aaiaT / eka 4I I a a amT / o oiαr iltcuslldellalram T T 11o- 11- 11-i / / PIPPIPPIP B1y-ha pviaboiietettodiovmrov citmB d d P 1 P 1 M M M M M M PI rg o M Pniegrtse eFaf r tePni rnPe odibrreT auk / lraloc2 a r n nof3c1-i 1- E m a aotP L euiM LrIosMIos gmitPneMIIV kr etm u allT / moilTa all / moiloianiB.nPeddMI .T / f f fSIacase alryvie alryviP F F F U mbooiii ttd o oc toihgocihgduO L:Bei v trtseFaf rnPe1d-proni v tpgeP Ps rnPe1d- ronilgcre14 P Psnisk-rP1:1 a 2 6 s aslaemMI5n oi s-T / 8β gi i561 nsnaB SLI11:r o e maP P D n oPO W krosaryiloam a mnose aryil.a α11kr7 18mh oiprviog h gnoneit1vileuPIpr ig1- a2:oniPIelb m β oi1 vsN7 r- sn g g 7 B P g SpsM P g MaT Be1LIS F P P131e,gf6 9 8 2 9ahpIorc;aMe,g 0 0 0 0 0it uaitarul1- u ua0.0.0.0.0.ncvessit_ 0 0 0 0 0 d. ,PIps csA p P MaPn;sMI;ao ciltematT;oar I-) -) - - - nab nu C47901 339)778)074)0 ene1-nikuelretnI,^1-LI;seicepsmutaelcunmuinrinevegi.0.0.0.0.0;sertc s.sia atSteiceP nit 0 6 9 7p,nboosmnoC 5 u odO9.6 09.3 09.5409.4sP 09.m;10ut27F,ryphosiprRaeEnire7 7 0 3 3lcudn opr F Pfuyc6 9.6 9.5 9.3 9.3 9nn alm,o cca- 0 0 0 0.0 mgaugsPnoAuiretsoi ;dialit- -tcorptsocc -rP P P1ietaM MI -1P -aPb,2aftnemMI IT / osE ed oirT / TMIMIuG muid.rBe f / fd k 5 F 1 F 1 P1- d PT / d PT / F,ProePt f ps;silc;sailsrP n Fa vibeitaternk1erar-ePMI1-1P - P ; mg kMIT / MIM usnig Fdm -rT al IToi s,falFloia / m 2oc / 2T / 2 a EitE Editno;setu os v.ebs e oin G.ne s1 - G G afmo Eia vB 4 P P P d P PverniifmrL uyhLIn Pi,et grireprVrdetPorniaSmtcoIsi;pl^olduoireg nsd n aP b,U O gLa:i1-lat lcBk- 3 P P S P PiteD nrP;4 1:,i eietmniF fsraa,f l.e16 osa5D:orsnporleyoi rotinam Fcii ni 85o rβ: tet 61:B- e k2sg 1 n g S -IsnneorOtoatiabihP.P P Pvmn2oidtp a al olW7erm i 1rai2αv leretla18ote2:baeuelmrE - bvm Nblfssoi eN ber77 AniitbaT Bk- G 1 PsSFI sSsS A Pfo131Table 13. Odds ratios of best performing salivary panel. Biomarker Coefficient Odds Ratio 95% CI of Odds Ratio P-Value PGE2 4.352 77.660 (2.595, 89698.008) 0.077

[0168] The two biomarker panels yielded diagnostic accuracies of 0.84 to 0.88 withsensitivities and specificities from 0.78 to 0.90 (Table 9). The derived three-biomarker panel yielded accuracies of 0.88 to 0.93 with sensitivities and specificities from 0.81 to 0.97 (Table 10). The four-biomarker panels yielded three combinations with accuracies of 0.93 and high specificities (0.93 to 0.96) (Table 11), and the derived five-biomarker panels achieved 0.967 accuracy with sensitivities (0.933-0.967) and specificities (0.93-1.0). The five-biomarker panel consisting of PGE2, S. sputigena (Ss), P. gingivalis (Pg), Pr. nigrescens (Pn), Pr. dentalis (Pd), and F. fastidiosum (Ff) / TIMP-1 yielded the highest diagnostic accuracy (0.967) and precision 0.967 (Table 12). Similarly, the five-biomarker panel consisting of S. sputigena (Ss), P.gingivalis (Pg), Pr. nigrescens (Pn), PGE2 / TIMP-1 and F. fastidiosum (Ff) / TIMP-1 yielded thediagnostic accuracy (0.967) with a precision of 1.0 (Table 12). The inclusion of PGE2, S.sputigena (Ss), P. gingivalis (Pg), Pr. nigrescens (Pn) and F. fastidiosum (Ff) / TIMP-1 in thesalivary panel produced extraordinarily high ORs (several >70) indicating a consistently strong association with increased periodontitis risk (Table 13). Utilizing the best five biomarker predictors, an additional logistic regression model was developed for logit scoring samples: ^^ logit = ln( 1−^^) = -7.2154 + 4.352 PGE2 −2.967 Ss + 4.815 Pg + 8.458 Pn + 1.116 Ff / TIMP-1, (2)72 13177N:2817WO:1658561:14:LOUISVILLEwhere p is the probability of having periodontitis. The concentration of each salivary biomarker was normalized before being applied to the equation above. Specifically, the mean and standard deviation of each biomarker’s concentration are calculated across all subjects.

[0169] This disclosure further encompasses the following aspects.

[0170] Aspect 1. A method for diagnosis or prognosis of periodontal disease in a subject,including the method including detecting, in an oral fluid sample obtained from the subject, an altered level of a combination of biomarkers including two or more biomarkers selected from Resistin (RETN), Interleukin-lβ (IL-1β), Interleukin-6 (IL-6), B-cell Activating Factor (BAFF), Matrix Metalloproteinase 8 (MMP-8), Matrix Metalloproteinase 9 (MMP-9), Macrophage Inhibitory Protein-1α (MIP-1α), Interferon-α (IFN-α), S100 Calcium Binding Protein A8 (S100A8), Tissue Inhibitor of Metalloproteinases-1 (TIMP-1), Prostaglandin E2 (PGE2), Dialister invisus (Di), Fretibacterium fastidiosum (Ff), Fusobacterium nucleatum (Fn), Mycoplasma faucium (Mf), Prevotella dentalis (Pd), Prevotella denticola (P. denticola), Porphyromonas gingivalis (Pg), Prevotella intermedia (Pi), Prevotella nigrescens (Pn), Prevotella species_HMT_300 (PspHMT300), Selenomonas sputigena (Ss), Treponema denticola (Td), Treponema socranskii (Ts), and ratios thereof relative to a control level of the combination of biomarkers of subjects diagnosed to not have periodontal disease; and diagnosing the subject for periodontal disease or prognosing the subject for periodontal disease based on the detected altered level of the combination of biomarkers in the oral fluid sample relative to the control level of the combination of biomarkers of subjects diagnosed to not have periodontal disease.

[0171] Aspect 2. The method of aspect 1, wherein the two or more biomarkers areselected from RETN, MIP-1α, TIMP-1, IL-1β, PGE2, IL-6, MMP-8, MMP-9, BAFF, IFN-α, Di, Ff, Fn, Mf, Pd, P. denticola, Pg, Pi, Pn, PspHMT300, Ss, Td, Ts, and ratios thereof.

[0172] Aspect 3. The method of aspect 1 or 2, wherein the combination of biomarkersincludes at least one bacteria biomarker and at least one ratio biomarker having a protein biomarker consequent and a protein biomarker, lipid biomarker, or bacteria biomarker antecedent.

[0173] Aspect 4. The method of any of aspects 1 to 3, wherein the combination ofbiomarkers include a ratio biomarker in which TIMP-1 is the consequent in the ratio biomarker 73 13177N:2817WO:1658561:14:LOUISVILLE

[0174] Aspect 5. The method of aspect 4, wherein the antecedent of the ratio biomarkeris selected from RETN, MIP-1α, IL-1β, PGE2, IL-6, MMP-8, MMP-9, Ff, Mf, Pd, P. denticola, Pg, Pi, and PspHMT300.

[0175] Aspect 6. The method of any of aspects 1 to 5, wherein the combination ofbiomarkers includes at least one bacteria biomarker.

[0176] Aspect 7. The method of any of aspects 1 to 6, wherein the combination ofbiomarkers includes at least three biomarkers.

[0177] Aspect 8. The method of any of aspects 1 to 6, wherein the combination ofbiomarkers includes at least four biomarkers.

[0178] Aspect 9. The method of any of aspects 1 to 6, wherein the combination ofbiomarkers includes at least five biomarkers.

[0179] Aspect 10. The method of any one of aspects 1 to 6, wherein the combination ofbiomarkers includes at least six biomarkers.

[0180] Aspect 11. The method of any of aspects 1 to 10, wherein the combination ofbiomarkers includes Ff.

[0181] Aspect 12. The method of any of aspects 1 to 11, wherein Fn is Fusobacteriumnucleatum subspecies vincentii (Fnsv).

[0182] Aspect 13. The method of any of aspects 1 to 12, wherein detecting the alteredlevel of the combination of biomarkers in the oral fluid sample relative to the control level of the combination of biomarkers of subjects diagnosed to not have periodontal disease includes generating a score for the oral fluid sample obtained from the subject using a model, and detecting a difference in the score for the oral fluid sample obtained from the subject relative to scores generated for oral fluid samples of subjects diagnosed to not have periodontal disease using the model.

[0183] Aspect 14. The method of aspect 13, wherein the model is a logistic regressionmodel, and wherein the logistic regression model includes plurality of variables, with each variable of the plurality of variables corresponding to a concentration of a different biomarker of the combination of biomarkers. 74 13177N:2817WO:1658561:14:LOUISVILLE

[0184] Aspect 15. The method of 14, wherein the logistic regression model includes atleast one variable corresponding to a bacteria biomarker and at least one variable corresponding to a ratio biomarker.

[0185] Aspect 16. The method of aspect of aspect 15, wherein the logistic regressionmodel is: ^^^^^^^^^^ = log^^ 1−^^= − 8.384 – 2.691 ^^^^ + 3.723 ^^^^ + 6.481 ^^^^ + 1.971 ^^^^ +PGE2 MIP−1 TIMP−1+ 2.88α 4.4040TIMP−1 ; or ^^ logit = ln( 1−^^) = -7.2154 + 4.352 PGE2 −2.967 Ss + 4.815 Pg + 8.458 Pn + 1.116 Ff / TIMP-1.

[0186] Aspect 17. The method of any of aspects 13 to 16, and further comprising:mapping the score for the oral fluid sample obtained from the subject relative to scores generated for oral fluid samples of subjects diagnosed to not have periodontal disease using the model and scores generated for oral fluid samples of subjects diagnosed with periodontal disease using the model.

[0187] Aspect 18. The method of aspect 17, wherein the score for the oral fluid sampleobtained from the subject is mapped relative to both the scores generated for oral fluid samples of subjects diagnosed to not have periodontal disease using the model and to the scores generated for oral fluid samples of subjects diagnosed with periodontal disease using the model.

[0188] Aspect 19. The method of any of aspects 1 to 18, wherein the periodontal diseaseis gingivitis.

[0189] Aspect 20. The method of any one of aspects 1 to 17, wherein the periodontaldisease is periodontitis.

[0190] Aspect 21. The method of any one of aspects 1 to 20, and further comprising atleast one of performing a dental examination of a mouth of the subject and administering a treatment for periodontal disease, subsequent to detecting the altered level of the combination of biomarkers. 75 13177N:2817WO:1658561:14:LOUISVILLE

[0191] Aspect 22. The method of aspect 21, wherein treatment is administered forperiodontal disease, and wherein the treatment includes at least one of administering an antimicrobial agent, a dental cleaning, and a therapeutic mouthwash to the subject.

[0192] Aspect 23. The method of any of aspects 1 to 22, wherein the oral fluid sampleobtained from the subject is a saliva sample.

[0193] Aspect 24. The method of any of aspects 1 to 22, wherein the oral fluid sample isa gingival crevicular fluid (GCF) sample, an oral rinse sample, or an oral swab sample.

[0194] Aspect 25. A method for treating periodontal disease, including administering atreatment for periodontal disease in response to detecting an altered level of a combination of biomarkers including two or more biomarkers selected from Resistin (RETN), Interleukin-lβ (IL- 1β), Interleukin-6 (IL-6), B-cell Activating Factor (BAFF), Matrix Metalloproteinase 8 (MMP- 8), Matrix Metalloproteinase 9 (MMP-9), Macrophage Inhibitory Protein-1α (MIP-1α), Interferon-α (IFN-α), S100 Calcium Binding Protein A8 (S100A8), Tissue Inhibitor of Metalloproteinases-1 (TIMP-1), Prostaglandin E2 (PGE2), Dialister invisus (Di), Fretibacterium fastidiosum (Ff), Fusobacterium nucleatum (Fn), Mycoplasma faucium (Mf), Prevotella dentalis (Pd), Prevotella denticola (P. denticola), Porphyromonas gingivalis (Pg), Prevotella intermedia (Pi), Prevotella nigrescens (Pn), Prevotella species_HMT_300 (PspHMT300), Selenomonas sputigena (Ss), Treponema denticola (Td), Treponema socranskii (Ts), and ratios thereof relative to a control level of the combination of biomarkers of subjects diagnosed to not have periodontal disease; wherein treatment includes at least one of administering an antimicrobial agent, a dental cleaning, and a therapeutic mouthwash to the subject.

[0195] Aspect 26. The method of aspect 25, wherein the two or more biomarkers areselected from RETN, MIP-1α, TIMP-1, IL-1β, PGE2, IL-6, MMP-8, MMP-9, BAFF, IFN-α, Di, Ff, Fn, Mf, Pd, P. denticola, Pg, Pi, Pn, PspHMT300, Ss, Td, Ts, and ratios thereof.

[0196] Aspect 27. The method of aspect 25 or 26, wherein the combination ofbiomarkers includes at least one bacteria biomarker and at least one ratio biomarker having a protein biomarker consequent and a protein biomarker, lipid biomarker, or bacteria biomarker antecedent. 76 13177N:2817WO:1658561:14:LOUISVILLE

[0197] Aspect 28. A method for screening for periodontal disease, including assaying anoral fluid sample obtained from a subject to detect a concentration of one or more biomolecule biomarkers selected from Resistin (RETN), Interleukin-lβ (IL-1β), Interleukin-6 (IL-6), B-cell Activating Factor (BAFF), Matrix Metalloproteinase 8 (MMP-8), Matrix Metalloproteinase 9 (MMP-9), Macrophage Inhibitory Protein-1α (MIP-1α), Interferon-α (IFN-α), S100 Calcium Binding Protein A8 (S100A8), Tissue Inhibitor of Metalloproteinases-1 (TIMP-1), and Prostaglandin E2 (PGE2) in the oral fluid sample; sequencing the oral fluid to detect the presence of one or more bacteria biomarkers selected from Dialister invisus (Di), Fretibacterium fastidiosum (Ff), Fusobacterium nucleatum (Fn), Mycoplasma faucium (Mf), Prevotella dentalis (Pd), Prevotella denticola (P.denticola), Porphyromonas gingivalis (Pg), Prevotella intermedia (Pi), Prevotella nigrescens (Pn), Prevotella species_HMT_300 (PspHMT300), Selenomonas sputigena (Ss), Treponema denticola (Td), and Treponema socranskii (Ts); and detecting a concentration of the one or more bacteria biomarkers.

[0198] Aspect 29. The method of aspect 28, wherein the one or more biomoleculebiomarkers are selected from RETN, MIP-1α, TIMP-1, IL-1β, PGE2, IL-6, MMP-8, MMP-9, IFN-α, and BAFF, and wherein the one or more bacteria biomarkers are selected from Di, Ff, Fn, Mf, Pd, P. denticola, Pg, Pi, Pn, PspHMT300, Ss, Td, and Ts.

[0199] Aspect 30. The method of aspect 29, wherein the one or more biomoleculebiomarkers includes each of RETN, MIP-1α, TIMP-1, IL-1β, PGE2, IL-6, MMP-8, MMP-9, IFN-α, and BAFF, and wherein the one or more bacteria biomarkers includes each of Di, Ff, Fn, Mf, Pd, P. denticola, Pg, Pi, Pn, PspHMT300, Ss, Td, and Ts.

[0200] Aspect 31. The method of any of aspects 28 to 30, and further comprising a stepof: calculating one or more biomarker ratios having an antecedent of a biomolecule biomarker or a bacteria biomarker and a consequent of a biomolecule biomarker.

[0201] Aspect 32. The method of aspect 31, wherein the one or more biomarker ratiosincludes a biomarker ratio in which TIMP-1 is the consequent in the biomarker ratio.

[0202] Aspect 33. The method of aspect 31 or 32, and further comprising: generating ascore for the oral fluid sample obtained from the subject using a model; and detecting a difference in the score for the oral fluid sample obtained from the subject relative to scores 77 13177N:2817WO:1658561:14:LOUISVILLEgenerated for oral fluid samples of subjects diagnosed to not have periodontal disease using the model.

[0203] Aspect 34. The method of any of aspects 28-33, wherein the model is a logisticregression model, and wherein the logistic regression model includes a plurality of variables, with each variable of the plurality of variables corresponding to the concentration of a biomolecule biomarker of the one or more biomolecule biomarkers or the concentration of a bacteria biomarker of the one or more bacteria biomarkers.

[0204] Aspect 35. The method of aspect 34, wherein the logistic regression modelincludes at least one variable corresponding to a bacteria biomarker and at least one variable corresponding to a biomarker ratio.

[0205] Aspect 36. The method of aspect 35, wherein the logistic regression model is:^^^^^^^^^^ = log^^ 1−^^= − 8.384 – 2.691 ^^^^ + 3.723 ^^^^ + 6.481 ^^^^ + 1.971 ^^^^ +PGE2 MI 404 TIMP−1+ 2P−α 4..880TIMP−1 ; or^^ logit = ln( 1−^^) = -7.2154 + 4.352 PGE2 −2.967 Ss + 4.815 Pg + 8.458 Pn + 1.116 Ff / TIMP-1.

[0206] Aspect 37. The method of any of aspects 33 to 36, and further comprising:mapping the score of the oral fluid sample obtained from the subject relative to scores generated for oral fluid samples of subjects diagnosed to not have periodontal disease using the model and scores generated for oral fluid samples of subjects diagnosed with periodontal disease using the model.

[0207] Aspect 38. The method of any of aspects 28 to 37, wherein the periodontal diseaseis gingivitis.

[0208] Aspect 39. The method of any of aspects 28 to 37, wherein the periodontal diseaseis periodontitis. 78 13177N:2817WO:1658561:14:LOUISVILLE

[0209] Aspect 40. The method of any one of aspects 28 to 39, wherein the oral fluidsample is a saliva sample, a gingival crevicular fluid (GCF) sample, an oral rinse sample, or an oral swab sample.

[0210] Aspect 41. A method for monitoring periodontal disease progression or regressionin a subject, comprising: detecting, in a first oral fluid sample obtained from the subject at a first time, a first level of two or more biomarkers selected from Resistin (RETN), Interleukin-lβ (IL- 1β), Interleukin-6 (IL-6), B-cell Activating Factor (BAFF), Matrix Metalloproteinase 8 (MMP- 8), Matrix Metalloproteinase 9 (MMP-9), Macrophage Inhibitory Protein-1α (MIP-1α), Interferon-α (IFN-α), S100 Calcium Binding Protein A8 (S100A8), Tissue Inhibitor of Metalloproteinases-1 (TIMP-1), Prostaglandin E2 (PGE2), Dialister invisus (Di), Fretibacterium fastidiosum (Ff), Fusobacterium nucleatum (Fn), Mycoplasma faucium (Mf), Prevotella dentalis (Pd), Prevotella denticola (P.denticola), Porphyromonas gingivalis (Pg), Prevotella intermedia (Pi), Prevotella nigrescens (Pn), Prevotella species_HMT_300 (PspHMT300), Selenomonas sputigena (Ss), Treponema denticola (Td), Treponema socranskii (Ts), and ratios thereof; and detecting, in a second oral fluid sample obtained from the subject at a second time, a second level of the two or more biomarkers; detecting a measurable difference between the level of the two or more biomarkers in the first oral fluid sample and the two or more biomarkers in the second oral fluid sample; and identifying periodontal disease progression or periodontal disease regression in the subject based on the measurable difference between the level of the two or more biomarkers in the first oral fluid sample and the second oral fluid sample.

[0211] Aspect 42. The method of aspect 41, wherein the two or more biomarkers areselected from RETN, MIP-1α, TIMP-1, IL-1β, PGE2, IL-6, MMP-8, MMP-9, BAFF, IFN-α, Di, Ff, Fn, Mf, Pd, P. denticola, Pg, Pi, Pn, PspHMT300, Ss, Td, Ts, and ratios thereof.

[0212] Aspect 43. The method of aspect 41 or 42, wherein the two or more biomarkersincludes at least one bacteria and at least one ratio biomarker having a protein biomarker consequent and a protein biomarker, lipid biomarker, or bacteria biomarker antecedent.

[0213] Aspect 44. The method of aspect 43, wherein the at least one ratio biomarkerincludes a ratio biomarker in which TIMP-1 is the consequent in the ratio biomarker.

[0214] Aspect 45. The method of any of aspects 41 to 44, wherein the two or morebiomarkers includes at least three biomarkers. 79 13177N:2817WO:1658561:14:LOUISVILLE

[0215] Aspect 46. The method of any of aspects 41 to 44, wherein the two or morebiomarkers includes at least four biomarkers.

[0216] Aspect 47. The method of any of aspects 41 to 44, wherein the two or biomarkersincludes at least five biomarkers.

[0217] Aspect 48. The method of any of aspects 41 to 44, wherein the two or morebiomarkers includes at least six biomarkers.

[0218] Aspect 49. The method of any of aspects 41 to 48, wherein the two or morebiomarkers includes Ff.

[0219] Aspect 50. The method of any of aspects 41 to 49, wherein a treatment forperiodontal disease is administered to the subject following the detection of the level of the two or more biomarkers in the first oral fluid sample and before the detection of the level of the two or more biomarkers in the second oral fluid sample.

[0220] Aspect 51. The method of aspect 50, wherein treatment includes at least one ofadministration of an antimicrobial agent, administration of a dental cleaning, and administration of a therapeutic mouthwash to the subject.

[0221] Aspect 52. The method of any of aspects 41 to 51, wherein the first oral fluidsample and the second oral fluid sample are saliva samples.

[0222] Aspect 53. The method of any of aspects 41 to 52, wherein the first oral fluidsample and the second oral fluid sample are gingival crevicular fluid (GCF) samples, oral rinse samples, or oral swab samples.

[0223] Aspect 54. A method for visualizing a state of periodontal disease or risk thereof,including detecting a concentration of a plurality of biomarkers in an oral fluid sample obtained from a subject, the plurality of biomarkers including two or more biomarkers selected from Resistin (RETN), Interleukin-lβ (IL-1β), Interleukin-6 (IL-6), B-cell Activating Factor (BAFF), Matrix Metalloproteinase 8 (MMP-8), Matrix Metalloproteinase 9 (MMP-9), Macrophage Inhibitory Protein-1α (MIP-1α), Interferon-α (IFN-α), S100 Calcium Binding Protein A8 (S100A8), Tissue Inhibitor of Metalloproteinases-1 (TIMP-1), Prostaglandin E2 (PGE2), Dialister invisus (Di), Fretibacterium fastidiosum (Ff), Fusobacterium nucleatum (Fn), Mycoplasma faucium (Mf), Prevotella dentalis (Pd), Prevotella denticola (P.denticola), 80 13177N:2817WO:1658561:14:LOUISVILLEPorphyromonas gingivalis (Pg), Prevotella intermedia (Pi), Prevotella nigrescens (Pn), Prevotella species_HMT_300 (PspHMT300), Selenomonas sputigena (Ss), Treponema denticola (Td), Treponema socranskii (Ts), and ratios thereof; generating a score for the oral fluid sample obtained from a subject using a model, the model including a plurality of variables, with each variable of the plurality of variables corresponding to the concentration of a particular biomarker of the plurality of biomarkers; and mapping the score for the oral fluid sample obtained from the subject relative to at least one of scores generated for oral fluid samples of subjects diagnosed to not have periodontal disease using the model and scores generated for oral fluid samples of subjects diagnosed with periodontal disease using the model.

[0224] Aspect 55. The method of aspect 54, wherein the plurality of biomarkers areselected from RETN, MIP-1α, TIMP-1, IL-1β, PGE2, IL-6, MMP-8, MMP-9, BAFF, IFN-α, Di, Ff, Fn, Mf, Pd, P. denticola, Pg, Pi, Pn, PspHMT300, Ss, Td, Ts, and ratios thereof.

[0225] Aspect 56. The method of aspect 54 or aspect 55, wherein the plurality ofbiomarkers includes at least one bacteria biomarker and at least one ratio biomarker having a protein biomarker consequent and a protein biomarker, lipid biomarker, or bacteria biomarker antecedent.

[0226] Aspect 57. The method of aspect 56, wherein the plurality of biomarkers includesa ratio biomarker in which TIMP-1 is the consequent in the ratio biomarker.

[0227] Aspect 58. The method of aspect 57, wherein the antecedent of the ratiobiomarker is selected from RETN, MIP-1α, IL-1β, PGE2, IL-6, MM-8, MMP-9, Ff, Mf, Pd, P. denticola, Pg, Pi, and PspHMT300.

[0228] Aspect 59. The method of any of aspects 54 to 58, wherein the model is a logisticregression model, and wherein the logistic regression model is ^^^^^^^^^^ = log^^ 1−^^= − 8.384 – 2.691 ^^^^ + 3.723 ^^^^ + 6.481 ^^^^ + 1.971 ^^^^ +PGE2+ 2MIP−α 4.404 TIMP−1.880TIMP−1 ; or

[0229] logit = ln(^^ 1−^^) = -7.2154 + 4.352 PGE2 −2.967 Ss + 4.815 Pg + 8.458 Pn + 1.116Ff / TIMP-1. 81 13177N:2817WO:1658561:14:LOUISVILLE

[0230] Aspect 60. The method of any of aspects 54 to 59, wherein the periodontal diseaseis gingivitis.

[0231] Aspect 61. The method of any one of aspects 54 to 59, wherein the periodontaldisease is periodontitis.

[0232] Aspect 62. The method of any one of aspects 54 to 61, wherein the oral fluidsample obtained from the subject is a saliva sample.

[0233] Aspect 63. The method of any one of aspects 54 to 61, wherein the oral fluidsample obtained from the subject is a gingival crevicular fluid (GCF) sample, an oral rinse sample, or an oral swab sample.

[0234] Aspect 64. The method of any of aspects 54 to 63, wherein mapping the score forthe oral fluid sample obtained from the subject includes mapping the score on a display.

[0235] Aspect 65. The method of aspect 64, wherein the display includes one or moreindicators relating to oral health.

[0236] Aspect 66. The method of aspect 65, wherein the one or more indicators of thedisplay relate to the absence of periodontal disease, the presence of periodontal disease, gingivitis, localized gingivitis, generalized gingivitis, periodontitis, localized periodontitis, generalized periodontitis, or stage of periodontitis.

[0237] Aspect 67. The method of any of aspects 65 to 66, wherein the display includes aheat map based on at least one of the scores generated for the oral fluid samples of subjects not diagnosed with periodontal disease using the logistic regression model and the scores generated for the oral fluid samples of subjects diagnosed with periodontal disease using the logistic regression model.

[0238] Aspect 68. A method for visualizing a state of periodontal disease or risk thereof,including detecting, in an oral fluid sample obtained from a subject, a level of a plurality of biomarkers selected from Resistin (RETN), Interleukin-lβ (IL-1β), Interleukin-6 (IL-6), B-cell Activating Factor (BAFF), Matrix Metalloproteinase 8 (MMP-8), Matrix Metalloproteinase 9 (MMP-9), Macrophage Inhibitory Protein-1α (MIP-1α), Interferon-α (IFN-α), S100 Calcium Binding Protein A8 (S100A8), Tissue Inhibitor of Metalloproteinases-1 (TIMP-1), Prostaglandin E2 (PGE2), Dialister invisus (Di), Fretibacterium fastidiosum (Ff), Fusobacterium nucleatum 82 13177N:2817WO:1658561:14:LOUISVILLE(Fn), Mycoplasma faucium (Mf), Prevotella dentalis (Pd), Prevotella denticola (P.denticola), Porphyromonas gingivalis (Pg), Prevotella intermedia (Pi), Prevotella nigrescens (Pn), Prevotella species_HMT_300 (PspHMT300), Selenomonas sputigena (Ss), Treponema denticola (Td), Treponema socranskii (Ts), and ratios thereof; and generating a chart for the oral fluid sample obtained from the subject based, at least in part, on the detected level of the plurality of biomarkers relative to a control level of the plurality of biomarkers.

[0239] Aspect 69. The method of aspect 68, wherein the plurality of biomarkers includesat least one bacteria biomarker and at least one ratio biomarker having a protein biomarker consequent and a protein biomarker, lipid biomarker, or bacteria marker antecedent.

[0240] Aspect 70. The method of aspect 69, wherein the plurality of biomarkers includesa ratio biomarker in which TIMP-1 is the consequent in the ratio biomarker.

[0241] Aspect 71. The method of aspect 70, wherein the antecedent of the ratiobiomarker is selected from RETN, MIP-1α, IL-1β, PGE2, IL-6, MMP-8, MMP-9, Ff, Mf, Pd, P. denticola, Pg, Pi, and PspHMT300.

[0242] Aspect 72. The method of any of aspects 68 to 71, wherein the periodontal diseaseis gingivitis.

[0243] Aspect 73. The method of any of aspects 68 to 71, wherein the periodontal diseaseis periodontitis.

[0244] Aspect 74. The method of any of aspects 68 to 74, wherein the oral fluid sampleobtained from the subject is a saliva sample.

[0245] Aspect 75. The method of any of aspects 68 to 74, wherein the oral fluid sampleobtained from the subject is a gingival crevicular fluid (GCF) sample, an oral rinse sample, or an oral swab sample.

[0246] Aspect 76. The method of any of aspects 68 to 75, wherein the chart includes oneor more indicators relating to oral health.

[0247] Aspect 77. The method of any of aspects 68 to 76, wherein the one or moreindicators relate to the absence of periodontal disease, the presence of periodontal disease, gingivitis, localized gingivitis, generalized gingivitis, periodontitis, localized periodontitis, 83 13177N:2817WO:1658561:14:LOUISVILLEgeneralized periodontitis, or the concentration of one or more biomarkers of the plurality of biomarkers in the oral fluid sample.

[0248] Aspect 78. The method of aspect 76 or 77, wherein the chart is a heat map.

[0249] Aspect 79. The method of aspect 76 or 77, wherein the chart is a gauge chart, thegauge chart including indicia corresponding to the oral fluid sample obtained from the subject mapped relative to the one or more indicators relating to oral health.

[0250] Aspect 80. The method of any of aspects 1-24, wherein the combination ofbiomarkers includes one of the three-biomarker panels listed in Table 9, includes one of the three-biomarker panels listed in Tables 6 and 10, includes one of the four-biomarker panels listed in Tables 6 and 11, includes one of the five-biomarkers, includes one of the five-biomarker panels listed in Tables 6 and 12, includes one of the six-biomarker panels listed in Table 6.

[0251] Aspect 81. The method of any of aspects 1-24, wherein the combination ofbiomarkers consists of one of the three-biomarker panels listed in Table 9, consists of one of the three-biomarker panels listed in Tables 6 and 10, consists of one of the four-biomarker panels listed in Tables 6 and 11, consists of one of the five-biomarkers, consists of one of the five- biomarker panels listed in Tables 6 and 12, or consists of one of the six-biomarker panels listed in Table 6.

[0252] Aspect 82. The method of any aspect 25 to 27, wherein the combination ofbiomarkers includes one of the three-biomarker panels listed in Table 9, includes one of the three-biomarker panels listed in Tables 6 and 10, includes one of the four-biomarker panels listed in Tables 6 and 11, includes one of the five-biomarkers, includes one of the five-biomarker panels listed in Tables 6 and 12, includes one of the six-biomarker panels listed in Table 6.

[0253] Aspect 83. The method of any of aspects 25 to 27, wherein the combination ofbiomarkers consists of one of the three-biomarker panels listed in Table 9, consists of one of the three-biomarker panels listed in Tables 6 and 10, consists of one of the four-biomarker panels listed in Tables 6 and 11, consists of one of the five-biomarkers, consists of one of the five- biomarker panels listed in Tables 6 and 12, or consists of one of the six-biomarker panels listed in Table 6. 84 13177N:2817WO:1658561:14:LOUISVILLE

[0254] Aspect 84. The method of any of aspects 28-40, wherein the biomarkers for whicha concentration is detected includes one of the three-biomarker panels listed in Table 9, includes one of the three-biomarker panels listed in Tables 6 and 10, includes one of the four-biomarker panels listed in Tables 6 and 11, includes one of the five-biomarkers, includes one of the five- biomarker panels listed in Tables 6 and 12, includes one of the six-biomarker panels listed in Table 6.

[0255] Aspect 85. The method of any of aspects 28-40, wherein the biomarkers for whicha concentration is detected consists of one of the three-biomarker panels listed in Table 9, consists of one of the three-biomarker panels listed in Tables 6 and 10, consists of one of the four-biomarker panels listed in Tables 6 and 11, consists of one of the five-biomarkers, consists of one of the five-biomarker panels listed in Tables 6 and 12, or consists of one of the six- biomarker panels listed in Table 6.

[0256] Aspect 86. The method of any of aspects 41 to 53, wherein the two or morebiomarkers includes one of the three-biomarker panels listed in Table 9, includes one of the three-biomarker panels listed in Tables 6 and 10, includes one of the four-biomarker panels listed in Tables 6 and 11, includes one of the five-biomarkers, includes one of the five-biomarker panels listed in Tables 6 and 12, includes one of the six-biomarker panels listed in Table 6.

[0257] Aspect 87. The method of any of aspects 41 to 53, wherein the two or morebiomarkers consists of one of the three-biomarker panels listed in Table 9, consists of one of the three-biomarker panels listed in Tables 6 and 10, consists of one of the four-biomarker panels listed in Tables 6 and 11, consists of one of the five-biomarkers, consists of one of the five- biomarker panels listed in Tables 6 and 12, or consists of one of the six-biomarker panels listed in Table 6.

[0258] Aspect 88. The method of any of aspects 54 to 67, wherein the plurality ofbiomarkers includes one of the three-biomarker panels listed in Table 9, includes one of the three-biomarker panels listed in Tables 6 and 10, includes one of the four-biomarker panels listed in Tables 6 and 11, includes one of the five-biomarkers, includes one of the five-biomarker panels listed in Tables 6 and 12, includes one of the six-biomarker panels listed in Table 6.

[0259] Aspect 89. The method of any of aspects 54 to 67, wherein the plurality ofbiomarkers consists of one of the three-biomarker panels listed in Table 8, consists of one of the 85 13177N:2817WO:1658561:14:LOUISVILLEthree-biomarker panels listed in Tables 6 and 10, consists of one of the four-biomarker panels listed in Tables 6 and 11, consists of one of the five-biomarkers, consists of one of the five- biomarker panels listed in Tables 6 and 12, or consists of one of the six-biomarker panels listed in Table 6.

[0260] Aspect 90. The method of any of aspects 68 to 79, wherein the plurality ofbiomarkers includes one of the three-biomarker panels listed in Table 9, includes one of the three-biomarker panels listed in Tables 6 and 10, includes one of the four-biomarker panels listed in Tables 6 and 11, includes one of the five-biomarkers, includes one of the five-biomarker panels listed in Tables 6 and 12, includes one of the six-biomarker panels listed in Table 6.

[0261] Aspect 91. The method of any of aspects 68 to 79, wherein the plurality ofbiomarkers consists of one of the three-biomarker panels listed in Table 9, consists of one of the three-biomarker panels listed in Tables 6 and 10, consists of one of the four-biomarker panels listed in Tables 6 and 11, consists of one of the five-biomarkers, consists of one of the five- biomarker panels listed in Tables 6 and 12, or consists of one of the six-biomarker panels listed in Table 6.

[0262] It will be understood that various details of the presently disclosed subject mattercan be changed without departing from the scope of the subject matter disclosed herein. Furthermore, the foregoing description is for the purpose of illustration only, and not for the purpose of limitation.

[0263] All publications, patents, and patent applications mentioned in this specificationare herein incorporated by reference to the same extent as if each individual publication, patent, or patent application was specifically and individually indicated to be incorporated by reference, including the references set forth in the following references list: REFERENCES1. Lotfy M, Adeghate J, Kalasz H, Singh J, Adeghate E. Chronic Complications of DiabetesMellitus: A Mini Review. Curr Diabetes Rev 2017;13(1):3-10. 86 13177N:2817WO:1658561:14:LOUISVILLE2. World HO Diabetes.2022. "https: / / www.cdc.gov / diabetes / data / statistics-report / index.html".Accessed August 222022.3. Gokhale NH, Acharya AB, Patil VS, et al. Resistin levels in gingival crevicular fluid ofpatients with chronic periodontitis and type 2 diabetes mellitus. J Periodontol 2014;85(4):610-7.4. Mohan M, Jhingran R, Bains VK, et al. 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Claims

CLAIMS What is claimed is:

1. A method for diagnosis or prognosis of periodontal disease in a subject, comprising: detecting, in an oral fluid sample obtained from the subject, an altered level of a combination of biomarkers including two or more biomarkers selected from Resistin (RETN), Interleukin-lβ (IL-1β), Interleukin-6 (IL-6), B-cell Activating Factor (BAFF), Matrix Metalloproteinase 8 (MMP-8), Matrix Metalloproteinase 9 (MMP-9), Macrophage Inhibitory Protein-1α (MIP-1α), Interferon-α (IFN-α), S100 Calcium Binding Protein A8 (S100A8), Tissue Inhibitor of Metalloproteinases-1 (TIMP-1), Prostaglandin E2 (PGE2), Dialister invisus (Di), Fretibacterium fastidiosum (Ff), Fusobacterium nucleatum (Fn), Mycoplasma faucium (Mf), Prevotella dentalis (Pd), Prevotella denticola (P. denticola), Porphyromonas gingivalis (Pg), Prevotella intermedia (Pi), Prevotella nigrescens (Pn), Prevotella species_HMT_300 (PspHMT300), Selenomonas sputigena (Ss), Treponema denticola (Td), Treponema socranskii (Ts), and ratios thereof relative to a control level of the combination of biomarkers of subjects diagnosed to not have periodontal disease; and diagnosing the subject for periodontal disease or prognosing the subject for periodontal disease based on the detected altered level of the combination of biomarkers in the oral fluid sample relative to the control level of the combination of biomarkers of subjects diagnosed to not have periodontal disease.

2. The method of claim 1, wherein the two or more biomarkers are selected from RETN, MIP-1α, TIMP-1, IL-1β, PGE2, IL-6, MMP-8, MMP-9, BAFF, IFN-α, Di, Ff, Fn, Mf, Pd, P. denticola, Pg, Pi, Pn, PspHMT300, Ss, Td, Ts, and ratios thereof. 93 13177N:2817WO:1658561:14:LOUISVILLE3. The method of claim 1 or 2, wherein the combination of biomarkers includes at least one bacteria biomarker and at least one ratio biomarker having a protein biomarker consequent and a protein biomarker, lipid biomarker, or bacteria biomarker antecedent.

4. The method of claim 1, wherein the combination of biomarkers includes a ratio biomarker in which TIMP-1 is the consequent in the ratio biomarker.

5. The method of claim 4, wherein the antecedent of the ratio biomarker is selected from RETN, MIP-1α, IL-1β, PGE2, IL-6, MMP-8, MMP-9, Ff, Mf, Pd, P. denticola, Pg, Pi, and PspHMT300.

6. The method of claim 5, wherein the combination of biomarkers includes at least one bacteria biomarker.

7. The method of claim 1, wherein the combination of biomarkers includes at least three biomarkers.

8. The method of claim 1, wherein the combination of biomarkers includes at least four biomarkers.

9. The method of claim 1, wherein the combination of biomarkers includes at least five biomarkers. 94 13177N:2817WO:1658561:14:LOUISVILLE10. The method of claim 1, wherein the combination of biomarkers includes at least six biomarkers.

11. The method of claim 1, wherein the combination of biomarkers includes Ff.

12. The method of claim 1, wherein Fn is Fusobacterium nucleatum subspecies vincentii (Fnsv).

13. The method of claim 1, wherein detecting the altered level of the combination of biomarkers in the oral fluid sample relative to the control level of the combination of biomarkers of subjects diagnosed to not have periodontal disease includes generating a score for the oral fluid sample obtained from the subject using a model, and detecting a difference in the score for the oral fluid sample obtained from the subject relative to scores generated for oral fluid samples of subjects diagnosed to not have periodontal disease using the model.

14. The method of claim 13, wherein the model is a logistic regression model, and wherein the logistic regression model includes plurality of variables, with each variable of the plurality of variables corresponding to a concentration of a different biomarker of the combination of biomarkers. 95 13177N:2817WO:1658561:14:LOUISVILLE15. The method of 14, wherein the logistic regression model includes at least one variable corresponding to a bacteria biomarker and at least one variable corresponding to a ratio biomarker.

16. The method of claim of claim 15, wherein the logistic regression model is: ^^^^^^^^^^ = log^^ 1−^^= − 8.384 – 2.691 ^^^^ + 3.723 ^^^^ + 6.481 ^^^^ + 1.971 ^^^^ +PGE2 MIP− .404 TIMP−1+ 21α 4.880TIMP−1 ; or ^^ logit = ln( 1−^^) = -−2.967 Ss + 4.815 Pg + 8.458 Pn + 1.116 Ff / TIMP-1.

17. The method of claim 13, and further comprising: mapping the score for the oral fluid sample obtained from the subject relative to scores generated for oral fluid samples of subjects diagnosed to not have periodontal disease using the model and scores generated for oral fluid samples of subjects diagnosed with periodontal disease using the model.

18. The method of claim 17, wherein the score for the oral fluid sample obtained from the subject is mapped relative to relative to both the scores generated for oral fluid samples of subjects diagnosed to not have periodontal disease using the model and to the scores generated for oral fluid samples of subjects diagnosed with periodontal disease using the model.

19. The method of claim 1, wherein the periodontal disease is gingivitis.

20. The method of claim 1, wherein the periodontal disease is periodontitis.

21. The method of claim 1, and further comprising at least one of: 96 13177N:2817WO:1658561:14:LOUISVILLEperforming a dental examination of a mouth of the subject and administering a treatment for periodontal disease, subsequent to detecting the altered level of the combination of biomarkers.

22. The method of claim 21, wherein treatment is administered for periodontal disease, and wherein the treatment includes at least one of administering an antimicrobial agent, a dental cleaning, and a therapeutic mouthwash to the subject.

23. The method of claim 1, wherein the oral fluid sample obtained from the subject is a saliva sample.

24. The method of claim 1, wherein the oral fluid sample is a gingival crevicular fluid (GCF) sample, an oral rinse sample, or an oral swab sample.

25. A method for treating periodontal disease, comprising: administering a treatment for periodontal disease in response to detecting an altered level of a combination of biomarkers including two or more biomarkers selected from Resistin (RETN), Interleukin-lβ (IL-1β), Interleukin-6 (IL-6), B-cell Activating Factor (BAFF), Matrix Metalloproteinase 8 (MMP-8), Matrix Metalloproteinase 9 (MMP-9), Macrophage Inhibitory Protein-1α (MIP-1α), Interferon-α (IFN-α), S100 Calcium Binding Protein A8 (S100A8), Tissue Inhibitor of Metalloproteinases-1 (TIMP-1), Prostaglandin E2 (PGE2), Dialister invisus (Di), Fretibacterium fastidiosum (Ff), Fusobacterium nucleatum (Fn), Mycoplasma faucium (Mf), Prevotella dentalis (Pd), Prevotella denticola (P. denticola), Porphyromonas gingivalis (Pg), 97 13177N:2817WO:1658561:14:LOUISVILLEPrevotella intermedia (Pi), Prevotella nigrescens (Pn), Prevotella species_HMT_300 (PspHMT300), Selenomonas sputigena (Ss), Treponema denticola (Td), Treponema socranskii (Ts), and ratios thereof relative to a control level of the combination of biomarkers of subjects diagnosed to not have periodontal disease; wherein treatment includes at least one of administering an antimicrobial agent, a dental cleaning, and a therapeutic mouthwash to the subject.

26. The method of claim 25, wherein the two or more biomarkers are selected from RETN, MIP-1α, TIMP-1, IL-1β, PGE2, IL-6, MMP-8, MMP-9, BAFF, IFN-α, Di, Ff, Fn, Mf, Pd, P. denticola, Pg, Pi, Pn, PspHMT300, Ss, Td, Ts, and ratios thereof.

27. The method of claim 25, wherein the combination of biomarkers includes at least one bacteria biomarker and at least one ratio biomarker having a protein biomarker consequent and a protein biomarker, lipid biomarker, or bacteria biomarker antecedent.

28. A method for screening for periodontal disease, comprising: assaying an oral fluid sample obtained from a subject to detect a concentration of one or more biomolecule biomarkers selected from Resistin (RETN), Interleukin-lβ (IL-1β), Interleukin-6 (IL-6), B-cell Activating Factor (BAFF), Matrix Metalloproteinase 8 (MMP-8), Matrix Metalloproteinase 9 (MMP-9), Macrophage Inhibitory Protein-1α (MIP-1α), Interferon-α (IFN-α), S100 Calcium Binding Protein A8 (S100A8), Tissue Inhibitor of Metalloproteinases-1 (TIMP-1), and Prostaglandin E2 (PGE2) in the oral fluid sample; 98 13177N:2817WO:1658561:14:LOUISVILLEsequencing the oral fluid to detect the presence of one or more bacteria biomarkers selected from Dialister invisus (Di), Fretibacterium fastidiosum (Ff), Fusobacterium nucleatum (Fn), Mycoplasma faucium (Mf), Prevotella dentalis (Pd), Prevotella denticola (P.denticola), Porphyromonas gingivalis (Pg), Prevotella intermedia (Pi), Prevotella nigrescens (Pn), Prevotella species_HMT_300 (PspHMT300), Selenomonas sputigena (Ss), Treponema denticola (Td), and Treponema socranskii (Ts); and detecting a concentration of the one or more bacteria biomarkers.

29. The method of claim 28, wherein the one or more biomolecule biomarkers are selected from RETN, MIP-1α, TIMP-1, IL-1β, PGE2, IL-6, MMP-8, MMP-9, IFN-α, and BAFF, and wherein the one or more bacteria biomarkers are selected from Di, Ff, Fn, Mf, Pd, P. denticola, Pg, Pi, Pn, PspHMT300, Ss, Td, and Ts.

30. The method of claim 29, wherein the one or more biomolecule biomarkers includes each of RETN, MIP-1α, TIMP-1, IL-1β, PGE2, IL-6, MMP-8, MMP-9, IFN-α, and BAFF, and wherein the one or more bacteria biomarkers includes each of Di, Ff, Fn, Mf, Pd, P. denticola, Pg, Pi, Pn, PspHMT300, Ss, Td, and Ts.

31. The method of claim 28, and further comprising a step of: calculating one or more biomarker ratios having an antecedent of a biomolecule biomarker or a bacteria biomarker and a consequent of a biomolecule biomarker. 99 13177N:2817WO:1658561:14:LOUISVILLE32. The method of claim 31, wherein the one or more biomarker ratios includes a biomarker ratio in which TIMP-1 is the consequent in the biomarker ratio.

33. The method of claim 31, and further comprising: generating a score for the oral fluid sample obtained from the subject using a model; and detecting a difference in the score for the oral fluid sample obtained from the subject relative to scores generated for oral fluid samples of subjects diagnosed to not have periodontal disease using the model.

34. The method of claim 33, wherein the model is a logistic regression model, and wherein the logistic regression model includes a plurality of variables, with each variable of the plurality of variables corresponding to the concentration of a biomolecule biomarker of the one or more biomolecule biomarkers or the concentration of a bacteria biomarker of the one or more bacteria biomarkers.

35. The method of claim 34, wherein the logistic regression model includes at least one variable corresponding to a bacteria biomarker and at least one variable corresponding to a biomarker ratio.

36. The method of claim 35, wherein the logistic regression model is: ^^^^^^^^^^ = log^^ 1−^^= − 8.384 – 2.691 ^^^^ + 3.723 ^^^^ + 6.481 ^^^^ + 1.971 ^^^^ +PGE2 TIMP−1+ 2.MIP−α 4.404880; or ^^logit = ln( 1−^^) = -7.2154 + −2.967 Ss + 4.815 Pg + 8.458 Pn + 1.116 Ff / TIMP-1.100 13177N:2817WO:1658561:14:LOUISVILLE37. The method of claim 33, and further comprising: mapping the score of the oral fluid sample obtained from the subject relative to scores generated for oral fluid samples of subjects diagnosed to not have periodontal disease using the model and scores generated for oral fluid samples of subjects diagnosed with periodontal disease using the model.

38. The method of claim 28, wherein the periodontal disease is gingivitis.

39. The method of claim 28, wherein the periodontal disease is periodontitis.

40. The method of claim 28, wherein the oral fluid sample is a saliva sample, a gingival crevicular fluid (GCF) sample, an oral rinse sample, or an oral swab sample.

41. A method for monitoring periodontal disease progression or regression in a subject, comprising: detecting, in a first oral fluid sample obtained from the subject at a first time, a first level of two or more biomarkers selected from Resistin (RETN), Interleukin-lβ (IL-1β), Interleukin-6 (IL-6), B-cell Activating Factor (BAFF), Matrix Metalloproteinase 8 (MMP-8), Matrix Metalloproteinase 9 (MMP-9), Macrophage Inhibitory Protein-1α (MIP-1α), Interferon-α (IFN- α), S100 Calcium Binding Protein A8 (S100A8), Tissue Inhibitor of Metalloproteinases-1 (TIMP-1), Prostaglandin E2 (PGE2), Dialister invisus (Di), Fretibacterium fastidiosum (Ff), Fusobacterium nucleatum (Fn), Mycoplasma faucium (Mf), Prevotella dentalis (Pd), Prevotella 101 13177N:2817WO:1658561:14:LOUISVILLEdenticola (P.denticola), Porphyromonas gingivalis (Pg), Prevotella intermedia (Pi), Prevotella nigrescens (Pn), Prevotella species_HMT_300 (PspHMT300), Selenomonas sputigena (Ss), Treponema denticola (Td), Treponema socranskii (Ts), and ratios thereof; and detecting, in a second oral fluid sample obtained from the subject at a second time, a second level of the two or more biomarkers; detecting a measurable difference between the level of the two or more biomarkers in the first oral fluid sample and the two or more biomarkers in the second oral fluid sample; and identifying periodontal disease progression or periodontal disease regression in the subject based on the measurable difference between the level of the two or more biomarkers in the first oral fluid sample and the second oral fluid sample.

42. The method of claim 41, wherein the two or more biomarkers are selected from RETN, MIP-1α, TIMP-1, IL-1β, PGE2, IL-6, MMP-8, MMP-9, BAFF, IFN-α, Di, Ff, Fn, Mf, Pd, P. denticola, Pg, Pi, Pn, PspHMT300, Ss, Td, Ts, and ratios thereof.

43. The method of claim 41, wherein the two or more biomarkers includes at least one bacteria and at least one ratio biomarker having a protein biomarker consequent and a protein biomarker, lipid biomarker, or bacteria biomarker antecedent.

44. The method of claim 43, wherein the at least one ratio biomarker includes a ratio biomarker in which TIMP-1 is the consequent in the ratio biomarker. 102 13177N:2817WO:1658561:14:LOUISVILLE45. The method of claim 41, wherein the two or more biomarkers includes at least three biomarkers.

46. The method of claim 41, wherein the two or more biomarkers includes at least four biomarkers.

47. The method of claim 41, wherein the two or biomarkers includes at least five biomarkers.

48. The method of claim 41, wherein the two or more biomarkers includes at least six biomarkers.

49. The method of claim 41, wherein the two or more biomarkers includes Ff.

50. The method of claim 41, wherein a treatment for periodontal disease is administered to the subject following the detection of the level of the two or more biomarkers in the first oral fluid sample and before the detection of the level of the two or more biomarkers in the second oral fluid sample.

51. The method of claim 50, wherein treatment includes at least one of administration of an antimicrobial agent, administration of a dental cleaning, and administration of a therapeutic mouthwash to the subject. 103 13177N:2817WO:1658561:14:LOUISVILLE52. The method of claim 41, wherein the first oral fluid sample and the second oral fluid sample are saliva samples.

53. The method of claim 41, wherein the first oral fluid sample and the second oral fluid sample are gingival crevicular fluid (GCF) samples, oral rinse samples, or oral swab samples.

54. A method for visualizing a state of periodontal disease or risk thereof, comprising: detecting a concentration of a plurality of biomarkers in an oral fluid sample obtained from a subject, the plurality of biomarkers including two or more biomarkers selected from Resistin (RETN), Interleukin-lβ (IL-1β), Interleukin-6 (IL-6), B-cell Activating Factor (BAFF), Matrix Metalloproteinase 8 (MMP-8), Matrix Metalloproteinase 9 (MMP-9), Macrophage Inhibitory Protein-1α (MIP-1α), Interferon-α (IFN-α), S100 Calcium Binding Protein A8 (S100A8), Tissue Inhibitor of Metalloproteinases-1 (TIMP-1), Prostaglandin E2 (PGE2), Dialister invisus (Di), Fretibacterium fastidiosum (Ff), Fusobacterium nucleatum (Fn), Mycoplasma faucium (Mf), Prevotella dentalis (Pd), Prevotella denticola (P.denticola), Porphyromonas gingivalis (Pg), Prevotella intermedia (Pi), Prevotella nigrescens (Pn), Prevotella species_HMT_300 (PspHMT300), Selenomonas sputigena (Ss), Treponema denticola (Td), Treponema socranskii (Ts), and ratios thereof; generating a score for the oral fluid sample obtained from a subject using a model, the model including a plurality of variables, with each variable of the plurality of variables corresponding to the concentration of a particular biomarker of the plurality of biomarkers; and mapping the score for the oral fluid sample obtained from the subject relative to at least one of scores generated for oral fluid samples of subjects diagnosed to not have periodontal 104 13177N:2817WO:1658561:14:LOUISVILLEdisease using the model and scores generated for oral fluid samples of subjects diagnosed with periodontal disease using the model.

55. The method of claim 54, wherein the plurality of biomarkers are selected from RETN, MIP-1α, TIMP-1, IL-1β, PGE2, IL-6, MMP-8, MMP-9, BAFF, IFN-α, Di, Ff, Fn, Mf, Pd, P. denticola, Pg, Pi, Pn, PspHMT300, Ss, Td, Ts, and ratios thereof.

56. The method of claim 54, wherein the plurality of biomarkers includes at least one bacteria biomarker and at least one ratio biomarker having a protein biomarker consequent and a protein biomarker, lipid biomarker, or bacteria biomarker antecedent.

57. The method of claim 56, wherein the plurality of biomarkers includes a ratio biomarker in which TIMP-1 is the consequent in the ratio biomarker.

58. The method of claim 57, wherein the antecedent of the ratio biomarker is selected from RETN, MIP-1α, IL-1β, PGE2, IL-6, MM-8, MMP-9, Ff, Mf, Pd, P. denticola, Pg, Pi, and PspHMT300.

59. The method of claim 54, wherein the model is a logistic regression model, and wherein the logistic regression model is ^^^^^^^^^^ = log^^ 1−^^= − 8.384 – 2.691 ^^^^ + 3.723 ^^^^ + 6.481 ^^^^ + 1.971 ^^^^ +PGE2 MIP IMP−1+ 2.880−α 4.404 T TIMP−1 ; or ^^ logit = ln( 1−^^) = -7.2154 + 4.352 PGE2 −2.967 Ss + 4.815 Pg + 8.458 Pn + 1.116 Ff / TIMP-1.105 13177N:2817WO:1658561:14:LOUISVILLE60. The method of claim 54, wherein the periodontal disease is gingivitis.

61. The method of claim 54, wherein the periodontal disease is periodontitis.

62. The method of claim 54, wherein the oral fluid sample obtained from the subject is a saliva sample.

63. The method of claim 54, wherein the oral fluid sample obtained from the subject is a gingival crevicular fluid (GCF) sample, an oral rinse sample, or an oral swab sample.

64. The method of claim 54, wherein mapping the score for the oral fluid sample obtained from the subject includes mapping the score on a display.

65. The method of claim 64, wherein the display includes one or more indicators relating to oral health.

66. The method of claim 65, wherein the one or more indicators of the display relate to the absence of periodontal disease, the presence of periodontal disease, gingivitis, localized gingivitis, generalized gingivitis, periodontitis, localized periodontitis, generalized periodontitis, or stage of periodontitis. 106 13177N:2817WO:1658561:14:LOUISVILLE67. The method of claim 65, wherein the display includes a heat map based on at least one of the scores generated for the oral fluid samples of subjects not diagnosed with periodontal disease using the logistic regression model and the scores generated for the oral fluid samples of subjects diagnosed with periodontal disease using the logistic regression model.

68. A method for visualizing a state of periodontal disease or risk thereof, comprising: detecting, in an oral fluid sample obtained from a subject, a level of a plurality of biomarkers selected from Resistin (RETN), Interleukin-lβ (IL-1β), Interleukin-6 (IL-6), B-cell Activating Factor (BAFF), Matrix Metalloproteinase 8 (MMP-8), Matrix Metalloproteinase 9 (MMP-9), Macrophage Inhibitory Protein-1α (MIP-1α), Interferon-α (IFN-α), S100 Calcium Binding Protein A8 (S100A8), Tissue Inhibitor of Metalloproteinases-1 (TIMP-1), Prostaglandin E2 (PGE2), Dialister invisus (Di), Fretibacterium fastidiosum (Ff), Fusobacterium nucleatum (Fn), Mycoplasma faucium (Mf), Prevotella dentalis (Pd), Prevotella denticola (P.denticola), Porphyromonas gingivalis (Pg), Prevotella intermedia (Pi), Prevotella nigrescens (Pn), Prevotella species_HMT_300 (PspHMT300), Selenomonas sputigena (Ss), Treponema denticola (Td), Treponema socranskii (Ts), and ratios thereof; and generating a chart for the oral fluid sample obtained from the subject based, at least in part, on the detected level of the plurality of biomarkers relative to a control level of the plurality of biomarkers.

69. The method of claim 68, wherein the plurality of biomarkers includes at least one bacteria biomarker and at least one ratio biomarker having a protein biomarker consequent and a protein biomarker, lipid biomarker, or bacteria marker antecedent. 107 13177N:2817WO:1658561:14:LOUISVILLE70. The method of claim 69, wherein the plurality of biomarkers includes a ratio biomarker in which TIMP-1 is the consequent in the ratio biomarker.

71. The method of claim 70, wherein the antecedent of the ratio biomarker is selected from RETN, MIP-1α, IL-1β, PGE2, IL-6, MMP-8, MMP-9, Ff, Mf, Pd, P. denticola, Pg, Pi, and PspHMT300.

72. The method of claim 68, wherein the periodontal disease is gingivitis.

73. The method of claim 68, wherein the periodontal disease is periodontitis.

74. The method of claim 68, wherein the oral fluid sample obtained from the subject is a saliva sample.

75. The method of claim 68, wherein the oral fluid sample obtained from the subject is a gingival crevicular fluid (GCF) sample, an oral rinse sample, or an oral swab sample.

76. The method of any of claim 68, wherein the chart includes one or more indicators relating to oral health.

77. The method of claim 76, wherein the one or more indicators relate to the absence of periodontal disease, the presence of periodontal disease, gingivitis, localized gingivitis, 108 13177N:2817WO:1658561:14:LOUISVILLEgeneralized gingivitis, periodontitis, localized periodontitis, generalized periodontitis, or the concentration of one or more biomarkers of the plurality of biomarkers in the oral fluid sample.

78. The method of claim 68, wherein the chart is a heat map.

79. The method of claim 68, wherein the chart is a gauge chart, the gauge chart including indicia corresponding to the oral fluid sample obtained from the subject mapped relative to the one or more indicators relating to oral health. 109 13177N:2817WO:1658561:14:LOUISVILLE

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