Modulated multi-biomarker disease activity score for the assessment of inflammatory diseases

Abstract

The present teachings provide methods for assessing response to inflammatory disease therapy, including performing an immunoassay to generate a score based on quantitative data about the expression of biomarkers related to inflammatory biomarkers for assessing disease activity in inflammatory diseases such as rheumatoid arthritis. The present teachings also provide methods for adjusting the disease activity score to account for variables that may affect the score.

Description

[Technical Field]

[0001] This application relates to the fields of bioinformatics and inflammatory and autoimmune diseases, and to methods for assessing response to inflammatory disease therapy. Rheumatoid arthritis ("RA") is an example of an inflammatory disease and a chronic, systemic autoimmune disorder. It is the most common systemic autoimmune disease worldwide. The immune system of RA subjects targets the subject's joints and other organs, including the lungs, blood vessels, and pericardium, leading to joint inflammation (arthritis), widespread endothelial inflammation, and ultimately destruction of joint tissue. Erosions and joint space narrowing are largely irreversible, resulting in cumulative damage. [Background technology]

[0002] Although the exact etiology of RA remains uncertain, the underlying disease pathogenesis is multifactorial and involves inflammation and immune dysregulation. The precise mechanisms involved vary among individuals and may change over time within them. Variables such as age, race, biological sex, genetics, body mass index, hormones, and environmental factors can influence the onset and severity of RA disease. Emerging data are also revealing the characteristics of new RA subject subgroups and their complex intertwined relationships with other autoimmune disorders. Disease duration and level of inflammatory activity are also associated with other comorbidities, such as risk of lymphoma, extra-articular manifestations, and cardiovascular disease. See, e.g., S. Banerjee et al., Am. J. Cardiol. 2008, 101(8):1201-1205; E. Baecklund et al., Arth. Rheum. 2006, 54(3):692-701; and N. Goodson et al., Ann. Rheum. Dis. 2005, 64(11):1595-1601.

[0003] The classical model for treating RA is based on the expectation that controlling disease activity (e.g., inflammation) in RA subjects will slow or prevent disease progression in terms of radiographic progression, tissue destruction, cartilage loss, and joint erosion. However, there is evidence that disease activity and disease progression cannot be linked and do not always function perfectly in tandem. In fact, different cell signaling pathways and mediators are involved in these two processes. See W. van den Berg et al., Arth. Rheum. 2005, 52:995-999. The inability to link disease progression and disease activity has been documented in numerous RA clinical trials and animal studies. See, e.g., E Lipsky et al., N. Engl. J. Med. 2003, 343:1594-602; AK Brown et al., Arth. Rheum. 2006, 54:3761-3773; and AR Pettit et al., Am. J. Pathol. 2001, 159:1689-99. Studies of RA subjects show limited correlation between clinical and radiographic response. See Zatarain and V. Strand, Nat. Clin. Pract. Rheum. 2006, 2(11):611-618 (Review). RA subjects have been described as showing radiological benefit from combination treatment with infliximab and methotrexate (MTX), but no clinical benefit was demonstrated as measured by DAS (Disease Activity Score) and CRP (C-reactive protein). See S Smolen et al., Arth. Rheum. 2005, 52(4):1020-30. To explore whether disease activity and remission are unrelated and to analyze the relationship between disease activity, treatment, and progression, RA subjects should be assessed for both disease activity and progression during therapy. Recent advances in inflammatory disease activity and progression assessment are described in U.S. Patent Application No. 2011 / 0137851, which is incorporated herein by reference in its entirety.

[0004] Current clinical management and treatment goals for RA focus on suppressing disease activity, with the goal of improving the subject's functional ability and slowing the progression of joint damage. Clinical assessment of RA disease activity includes measuring the subject's activity difficulty, morning stiffness, pain, inflammation, and tender and swollen joint counts, a physician's global assessment of the subject, a subject's assessment of general well-being, and measuring the subject's erythrocyte sedimentation rate (ESR) and acute phase response levels such as CRP. As a clinical assessment tool for monitoring disease activity, composite indices consisting of multiple variables such as those described above have been developed. The most commonly used are: the American College of Rheumatology (ACR) criteria (D.T. Felson et al., Arth. Rheum. 1993, 36(6):729-740 and, D.T. Felson et al., Arth. Rheum. 1995, 38(6):727-735); the Clinical Disease Activity Index (CDAI) (D. Aletaha et al., Arth. Rheum. 2005, 52(9):2625-2636); the DAS (M.L. Prevoo et al., Arth. Rheum. 1995, 38(1):44-48 and, A.M. van Gestel et al., Arth. Rheum. 1998, 41(10):1845-1850); the Rheumatoid Arthritis Disease Activity Index (RADAI) (G. Stucki et al., Arth. Rheum. 1995, 38(6):795-798); and the Simplified Disease Activity Index (SDAI) (JS Smolen et al., Rheumatology (Oxford) 2003, 42:244-257).

[0005] Current laboratory tests routinely used to monitor disease activity in RA subjects, such as CRP and ESR, are relatively nonspecific (e.g., not RA-specific and cannot be used to diagnose RA) and cannot be used to determine response to treatment or predict future outcome. For example, L. Gossec et al., Ann. Rheum. Dis. 2004, 63(6):675-680; EJA Kroot et al., Arth. Rheum. 2000, 43(8):1831-1835; H. Makinen et al., Ann. Rheum. Dis. 2005, 64(10):1410-1413; Z. Nadareishvili et al., Arth. Rheum. 2008, 59(8):1090-1096; NA Khan et al., Abstract, ACR / ARHP Scientific Meeting 2008; TA Pearson et al., Circulation 2003, 107(3):499-511; MJ Plant et al. al., Arth. Rheum. 2000, 43(7):1473-1477; T. Pincus et al., Clin. Exp. Rheum. 2004, 22(Suppl. 35):S50-S56; and, PM Ridker et al., NEJM 2000, 342(12):836-843. In the case of ESR and CRP, RA subjects will continue to have elevated ESR or CRP levels despite being in clinical remission (and non-RA subjects will exhibit elevated ESR or CRP levels). Some subjects in clinical remission continue to show radiographic progression due to erosions as determined by the DAS. Furthermore, some subjects who do not show clinical benefit still show radiographic benefit from treatment. See, e.g., F.C. Breedveld et al., Arth. Rheum. 2006, 54(1):26-37.Clearly, in order to predict future outcomes and treat RA subjects accordingly, there is a need for clinical assessment tools that accurately assess the level of disease activity in RA subjects and act as predictors of the future course of the disease.

[0006] Clinical assessment of disease activity involves subjective measures of RA, such as signs and symptoms, and subject-reported outcomes, all of which are difficult to consistently quantify. In clinical trials, the DAS is commonly used to assess RA disease activity. The DAS is an index score of disease activity based in part on these subjective parameters. In addition to its subjective component, another drawback to using the DAS as a clinical assessment of RA disease activity is its invasiveness. The physical examination required to obtain a subject's DAS is painful because it requires assessing the amount of tenderness and swelling in the subject's joints, as measured by the level of discomfort the subject feels when pressure is applied to the joint. Assessment of factors involved in DAS scoring is also time-consuming. Furthermore, a skilled assessor is required to accurately determine a subject's DAS. What is needed is a clinical method for assessing disease activity that is less invasive and time-consuming than the DAS, more consistent, objective, and quantitative, yet specific to the disease being assessed (e.g., RA).

[0007] For example, developing a specific biomarker-based test (e.g., measuring cytokines) for the clinical evaluation of RA has proven difficult in practice due to the complexity of RA biology—the various molecular pathways involved and the intersection of autoimmune dysregulation and inflammatory responses. In addition to the difficulty of developing an RA-specific biomarker-based test, there are associated technical challenges: it is necessary to block nonspecific matrix binding in serum or plasma samples, such as rheumatoid factor (RF) in the case of RA. For example, cytokine detection using bead-based immunoassays is unreliable due to RF interference; therefore, RF-positive subjects cannot be tested for RA-associated cytokines using this technology (and attempted RF removal methods have not significantly improved results). See S. Churchman et al., Ann. Rheum. Dis. 2009, 68:A1-A56, Abstract A77. Approximately 70% of RA subjects are RF-positive, so any biomarker-based test that cannot evaluate RF-positive patients is clearly of limited use.

[0008] The MBDA score is a validated tool that quantifies 12 serum protein biomarkers to assess disease activity in adult subjects with rheumatoid arthritis (RA) (Curtis JR, et al., Arthritis Care Res. 64:1794-803 (2012)). The derivation of these 12 biomarkers is fully described in U.S. Patent No. 9,200,324, which is incorporated herein by reference in its entirety.

[0009] Biomarkers are influenced by variables including race, biological sex, genetics, body mass index, hormones, and environmental factors. In particular, variations in age, sociological sex, and adiposity may affect the MBDA score. Inflammation levels generally increase with age, regardless of the presence of any particular clinical condition, and sociological sex differences may affect the interpretation of the MBDA score. Adiposity is associated with low-grade inflammation, with adipose tissue secreting or responding to several components of the MBDA score, such as IL-6 and leptin, or pathway partners such as TNFRI. Thus, adiposity is a potential confounding factor in the relationship between the MBDA score and both disease activity and radiographic progression in RA. Examples of the present teachings provide a way to account for variables that may affect the MBDA score. Summary of the Invention

[0010] The present teachings relate to biomarkers associated with inflammatory and autoimmune diseases, including RA, and methods of modulating such biomarkers to measure disease activity in a subject.

[0011] In one embodiment, a method for assessing inflammatory disease activity in a subject is provided, comprising performing an immunoassay on a blood sample from the subject to generate a test expression score including protein level data for at least two protein markers: chitinase 3-like 1 (cartilage glycoprotein-39) (CHI3L1); C-reactive protein, pentraxin-related (CRP); epidermal growth factor (beta-urogastrone) (EGF); interleukin 6 (interferon, beta 2) (IL6); leptin (LEP); matrix metallopeptidase 1 (interstitial collagenase) (MMP1); matrix metallopeptidase 3 (stromelysin 1, progelatinase) (MMP3); resistin (RETN); serum amyloid A1 (SAA1); tumor necrosis factor receptor superfamily, member 1A (TNFRSF1A); vascular cell adhesion molecule 1 (VCAM1); and vascular endothelial growth factor A. (VEGFA), and the test expression score is generated by (1) weighting the predetermined expression of each protein marker by a predefined coefficient, and then (2) combining the weighted expressions; and providing a disease activity score by combining the test expression score with at least one test clinical score indicative of at least one clinical variable. In a specific example, the at least one clinical score includes at least one clinical variable selected from age, sociological sex, biological sex, smoking status, adiposity, body mass index (BMI), serum leptin, and race / ethnicity. In a specific example, the clinical variable is serum leptin. In a specific example, the inflammatory disease activity is rheumatoid arthritis (RA) disease activity. In a specific example, the disease activity score predicts the likelihood of RA radiographic progression, flare-up, or joint damage in the subject.In certain embodiments, performance of the at least one immunoassay comprises: obtaining a first blood sample, wherein the first blood sample comprises the protein marker; contacting the first blood sample with a plurality of distinct reagents; generating a plurality of distinct complexes between the reagents and the markers; and detecting the complexes to generate the data. In certain embodiments, the at least one immunoassay comprises a multiplex assay. In certain embodiments, the interpretation function is a predictive model. In certain embodiments, the disease activity score is on a scale of 1 to 100; a disease activity score of about 1 to 29 indicates a low level of disease activity, a disease activity score of about 30 to 44 indicates a moderate level of disease activity, and a disease activity score of about 45 to 100 indicates a high level of disease activity. In specific examples, the disease activity score is predictive of radiographic progression; the disease activity score is on a scale of 1 to 100; a disease activity score of about 1 to 29 indicates a low probability of radiographic progression, a disease activity score of about 30 to 44 indicates a moderate probability of radiographic progression, and a disease activity score of about 45 to 100 indicates a high probability of radiographic progression. In specific examples, the at least two biomarkers include IL6, EGF, VEGFA, LEP, SAA1, VCAM1, CRP, MMP1, MMP3, TNFRSF1A, RETN, and CHI3L1.

[0012] In another embodiment, a method for generating quantitative data about a subject is provided, the method comprising performing at least one immunoassay on a first sample from a subject having or suspected of having an inflammatory disease to generate a first dataset comprising the quantitative data, the quantitative data including chitinase 3-like 1 (cartilage glycoprotein-39) (CHI3L1); C-reactive protein, pentraxin-related (CRP); epidermal growth factor (beta-urogastrone) (EGF); interleukin 6 (interferon, beta 2) (IL6); leptin (LEP); matrix metallopeptidase 1 (interstitial collagenase) (MMP1); matrix metallopeptidase 3 (stromelysin 1, progelatinase) (MMP3); resistin (RETN); serum amyloid A1 (SAA1); tumor necrosis factor receptor superfamily, member 1A (TNFRSF1A); vascular cell adhesion molecule 1 (VCAM1); and vascular endothelial growth factor A. (VEGFA), wherein the first dataset is generated by (1) weighting the predetermined representation of each protein marker by a predefined coefficient, and then (2) combining the weighted representations; generating a second dataset including at least one test clinical score indicative of at least one clinical variable; and then generating the quantitative data by combining the first and second datasets. In a specific example, the at least one clinical score includes at least one clinical variable selected from age, sociological sex, biological sex, smoking status, adiposity, body mass index (BMI), serum leptin, and race / ethnicity. In a specific example, the clinical variable is serum leptin. In a specific example, the inflammatory disease activity is rheumatoid arthritis (RA) disease activity.In certain embodiments, performing the at least one immunoassay comprises: obtaining the first blood sample, wherein the first blood sample comprises the protein marker; contacting the first blood sample with a plurality of distinct reagents; forming a plurality of distinct complexes between the reagents and the markers; and detecting the complexes to generate the data. In certain embodiments, the at least one immunoassay comprises a multiplex assay.

[0013] In another embodiment, a method of recommending a therapy regimen in a subject with an inflammatory disorder is provided. The method includes placing the subject on a therapy regimen; determining whether the subject responds to the therapy regimen; and performing an immunoassay on a blood sample from the subject to generate a test expression score including protein level data for at least two protein markers, wherein the at least two protein markers are chitinase 3-like 1 (cartilage glycoprotein-39) (CHI3L1); C-reactive protein, pentraxin-related (CRP); epidermal growth factor (beta-urogastrone) (EGF); interleukin 6 (interferon, beta 2) (IL6); leptin (LEP); matrix metallopeptidase 1 (interstitial collagenase) (MMP1); matrix metallopeptidase 3 (stromelysin 1, progelatinase) (MMP3); resistin (RETN); serum amyloid A1 (SAA1); tumor necrosis factor receptor superfamily, member 1A (TNFRSF1A); vascular cell adhesion molecule 1 (VCAM1); and vascular endothelial growth factor A. (VEGFA), wherein the test expression score comprises at least two markers selected from the group consisting of: (1) weighting the predetermined expression of each protein marker by a predefined coefficient; and (2) combining the weighted expressions; providing a first disease activity score by combining the test expression score with at least one test clinical score indicative of at least one clinical variable; performing a second immunoassay on a second blood sample from the subject to generate a second disease activity score by combining the test expression score with at least one test clinical score indicative of at least one clinical variable; determining a clinically significant change between the first and second disease activity scores based on the difference in scores; and recommending i) reducing the therapy plan when a clinically significant change is determined; or ii) not changing the therapy plan when no clinically significant change is determined.In particular embodiments, the at least one clinical score includes at least one clinical variable selected from age, sociological sex, biological sex, smoking status, adiposity, body mass index (BMI), serum leptin, and race / ethnicity. In particular embodiments, the clinical variable is serum leptin. In particular embodiments, the inflammatory disease activity is rheumatoid arthritis (RA) disease activity. In particular embodiments, the disease activity score predicts the likelihood of RA radiographic progression, flare-up, or joint damage in the subject. In particular embodiments, performance of the at least one immunoassay includes: obtaining the first blood sample, wherein the first blood sample includes the protein marker; contacting the first blood sample with a plurality of distinct reagents; forming a plurality of distinct complexes between the reagents and the markers; and detecting the complexes to generate the data. In particular embodiments, the at least one immunoassay includes a multiplex assay. In particular embodiments, the interpretation function is a predictive model. In particular embodiments, the disease activity score is on a scale of 1 to 100; a disease activity score of about 1 to 29 indicates a low level of disease activity, a disease activity score of about 30 to 44 indicates a moderate level of disease activity, and a disease activity score of about 45 to 100 indicates a high level of disease activity. The disease activity score is predictive of radiographic progression; the disease activity score is on a scale of 1 to 100; a disease activity score of about 1 to 29 indicates a low likelihood of radiographic progression, a disease activity score of about 30 to 44 indicates a moderate likelihood of radiographic progression, and a disease activity score of about 45 to 100 indicates a high likelihood of radiographic progression. In a specific example, the at least two biomarkers include IL6, EGF, VEGFA, LEP, SAA1, VCAM1, CRP, MMP1, MMP3, TNFRSF1A, RETN, and CHI3L1.

[0014] Those skilled in the art will understand that the following drawings are for illustrative purposes only and are not intended to limit the scope of the present teachings in any way. [Brief explanation of the drawings]

[0015] [Figure 1] Figure 1 illustrates the relationship between BMI and serum leptin levels for men and women with RA (N=1411; 1098 women, 313 men) and without RA (N=318; 203 women, 115 men).

[0016] [Figure 2] Figure 2 illustrates the relationship between MBDA and serum leptin. The Y-axis is MBDA score, and the values ​​on the X-axis are leptin concentrations (pg / mL). Age is illustrated in five different age groups, and the age categories, from left to right, are: (15, 30); (30, 45); (45, 60); (60, 75); and (75, 90).

[0017] [Figure 3] Figure 3 illustrates the relationship between MBDA and age and sociological sex. For each representative pair, the bars represent the female pair on the left and the male pair on the right. The Y-axis is MBDA score, and the numbers on the X-axis are age at the time of testing.

[0018] [Figure 4] Figure 4 illustrates the univariate analysis of ΔmTSS for the OPERA and BRASS cohorts on original and leptin-adjusted MBDA (Vectra DA) scores.

[0019] [Figure 5] Figure 5 illustrates the probability of radiographic progression in leptin-adjusted MBDA scores. The Y-axis is the probability of radiographic progression after 1 year (ΔmTSS>3), and the X-axis is the MBDAX radiographic progression score. The upper solid line is ΔmTSS>3, and the lower solid line is ΔmTSS>5.

[0020] [Figure 6] FIG. 6 illustrates the distribution of MBDA by age (decades) and body mass index, as described in Example 2.

[0021] [Figure 7] FIG. 7 illustrates a three-dimensional representation of the degree of regulation by leptin-adjusted MBDA scores versus the original MBDA scores for women and men based on patient age and leptin concentration.

[0022] [Figure 8] FIG. 8 illustrates a schematic block diagram of a computer (1600). It illustrates at least one processor (1602) coupled to a chipset (1604). A memory (1606), a storage device (1608), a keyboard (1610), a graphics adapter (1612), a pointing device (1614), and a network adapter (1616) are also coupled to the chipset (1604). A display (1618) is coupled to the graphics adapter (1612). In one embodiment, the functionality of the chipset (1604) is provided by a memory controller hub (1620) and an I / O controller hub (1622). In another embodiment, the memory (1606) is coupled directly to the processor (1602) instead of the chipset (1604). The storage device (1608) is any device capable of holding data, such as a hard drive, compact disc read-only memory (CD-ROM), DVD, or solid-state memory device. The memory (1606) holds instructions and data used by the processor (1602). The pointing device (1614), which may be a mouse, trackball, or other type of pointing device, can be used in combination with the keyboard (1610) to input data into the computer system (1600). The graphics adapter (1612) displays images and other information on the display (1618). The network adapter (1616) connects the computer system (1600) to a local or wide area network. DETAILED DESCRIPTION OF THE INVENTION

[0023] These and other features of the present teachings will become more apparent from the present specification. While the present teachings are described in connection with various embodiments, it is not intended that the present teachings be limited to those embodiments. On the contrary, the present teachings encompass various alternatives, modifications, and equivalents, as will be recognized by those skilled in the art.

[0024] The present teachings generally relate to biomarkers that are relevant to subjects with inflammatory and / or autoimmune diseases, such as RA, and that are useful for determining and assessing disease activity in response to inflammatory disease therapy to recommend optimal therapy.

[0025] Most terms used herein have the meanings that one of ordinary skill in the art would ascribe to them. Terms specifically defined herein have the meaning provided in the context of the entire teaching and are typically understood by those of ordinary skill in the art. In the event of a conflict between an art-understood definition of a term and a definition specifically taught herein, the present specification controls. It should be noted that, as used in this specification and the appended claims, the singular forms "a," "an," and "the" include plural referents unless otherwise specified.

[0026] definition "Accuracy" refers to the degree to which a measured or calculated value corresponds to its actual value. In clinical trials, "accuracy" relates to the ratio of actual outcomes (true positive or true negative, where the subject is correctly classified as having a disease or as healthy / normal, respectively) to incorrectly classified outcomes (false positive or false negative, where the subject is incorrectly classified as having a disease or as healthy / normal, respectively). Other and / or equivalent terms for "accuracy" include, for example, "sensitivity," "specificity," "positive predictive value (PPV)," "AUC," "negative predictive value (NPV)," "likelihood," and "odds ratio." In the context of the present teachings, "analytical accuracy" refers to the reproducibility and predictability of a measurement. Analytical accuracy is summarized in such measurements, e.g., coefficient of variation (CV), and matching and calibration studies of the same sample or control at different times or with different evaluators, users, instruments, and / or reagents. For a summary of considerations in evaluating new biomarkers, see, e.g., R. Vasan, Circulation 2006, 113(19):2335-2362.

[0027] As used herein, the term "administering" refers to the placement of a composition into a subject by a method or route that results in at least partial localization of the composition at a desired site such that a desired effect occurs. Routes of administration include both local and systemic administration. Generally, local administration delivers more of the composition to a specific site compared to the entire body of the subject, while systemic administration delivers essentially the entire body of the subject.

[0028] The term "algorithm" encompasses any formula, model, mathematical expression, algorithm, analytical or programmed process, or statistical technique or classification analysis, that takes one or more inputs or parameters, whether continuous or categorical, and calculates an output value, index, index value, or score. Examples of algorithms include, but are not limited to, regression operators such as ratios, sums, exponents, or coefficients; biomarker value transformations and normalizations (including, without limitation, normalization schemes based on clinical parameters such as age, sociometric sex, ethnicity, etc.); rules and guidelines; statistical classification models; and neural networks trained on populations. In the context of biomarkers, linear and non-linear equations and statistical classification analyses for determining the relationship between (a) the level of the biomarker detected in a subject sample and (b) the level of disease activity for each subject.

[0029] In the context of the present teachings, the term "analyte" refers to any substance that is measured and may include biomarkers, markers, nucleic acids, electrolytes, metabolites, proteins, sugars, carbohydrates, fats, lipids, cytokines, chemokines, growth factors, proteins, peptides, nucleic acids, oligonucleotides, metabolites, mutants, variants, polymorphisms, modifications, fragments, subunits, degradation products, and other elements. For convenience, standard gene symbols may be used throughout to refer to genes as well as gene products / proteins, rather than standard protein symbols; for example, as used herein, APOA1 also refers to the gene APOA1 and the protein ApoAI. Typically, hyphens are omitted from analyte names and symbols herein (IL-6 = IL6).

[0030] "Analyzing" includes determining a value or set of values ​​associated with a sample by measuring analyte levels in that sample. "Analyzing" also includes comparing levels to constituent levels in a sample or set of samples from the same subject or other subjects. The biomarkers of the present teachings can be analyzed by any of a variety of conventional methods known in the art. Some such methods include, but are not limited to: measuring serum protein or sugar or metabolite or other analyte levels, measuring enzyme activity, and measuring gene expression.

[0031] The term "antibody" refers to any immunoglobulin-like molecule that reversibly binds to another with the required selectivity. Thus, the term includes any such molecule capable of selectively binding to a biomarker of the present teachings. The term includes immunoglobulin molecules capable of binding to an epitope present on an antigen. The term includes intact immunoglobulin molecules such as monoclonal and polyclonal antibodies, as well as antibody isotypes, recombinant antibodies, bispecific antibodies, humanized antibodies, chimeric antibodies, anti-idiopathic (anti-ID) antibodies, single-chain antibodies, Fab fragments, F(ab') fragments, fusion protein antibody fragments, immunoglobulin fragments, Fv fragments, single-chain Fv fragments, and any modification of the above that contains an immunoglobulin sequence and an antigen recognition site with the required selectivity. Chimeras are also intended to be included.

[0032] "Autoimmune disease," as defined herein, includes any disease caused by an immune response against substances and tissues normally present in the body. Examples of suspected or known autoimmune diseases include rheumatoid arthritis, early rheumatoid arthritis, axial spondyloarthritis, juvenile idiopathic arthritis, seronegative arthritis, and rheumatoid arthritis. These include spondyloarthropathies, ankylosing spondylitis, psoriatic arthritis, antiphospholipid syndrome, autoimmune hepatitis, Behçet's disease, bullous pemphigoid, celiac disease, Crohn's disease, dermatomyositis, Goodpasture's syndrome, Graves' disease, Hashimoto's disease, idiopathic thrombocytopenic purpura, IgA nephropathy, Kawasaki disease, systemic lupus erythematosus, mixed connective tissue disease, multiple sclerosis, myasthenia gravis, polymyositis, primary biliary cirrhosis, psoriasis, scleroderma, Sjögren's syndrome, ulcerative colitis, vasculitis, granulomatosis with polyangiitis, temporal arteritis, Takayasu's arteritis, Henoch-Schönlein purpura, vasculitis sclerosing, polyarteritis nodosa, Churg-Strauss syndrome, and mixed cryoglobulinemic vasculitis.

[0033] A "biologic" or "biotherapy" or "biopharmaceutical" is a pharmaceutical therapy product manufactured or extracted from biological materials. Biologics may include vaccines, blood or blood components, allergens, somatic cells, gene therapy, tissues, recombinant proteins, and viable cells; they may consist of sugars, proteins, nucleic acids, viable cells or tissues, or combinations thereof. Examples of biologic drugs include, but are not limited to, biologic agents that target tumor necrosis factor (TNF)-alpha molecules and TNF inhibitors, such as infliximab, adalimumab, etanercept, and golimumab. Other classes of biologic drugs include IL1 inhibitors such as anakinra, T-cell modulators such as abatacept, B-cell modulators such as rituximab, and IL6 inhibitors such as tocilizumab.

[0034] "Biomarker," "biomarker(s)," "marker," or "markers," in the context of the present teachings, includes, without limitation, cytokines, chemokines, growth factors, proteins, peptides, nucleic acids, oligonucleotides, and metabolites, as well as their associated metabolites, mutants, isoforms, variants, polymorphisms, variations, fragments, subunits, degradation products, elements, and other analyte- or sample-derived measures. Biomarkers may also include mutant proteins, mutant nucleic acids, variants in copy number and / or transcriptional variants. Biomarkers also include non-blood-derived and non-analyte physiological markers of health status, and / or other factors or markers that are not measured from a sample (e.g., a biological sample such as a bodily fluid), such as clinical parameters and classical factors for clinical assessment. Biomarkers may be calculated and / or or any mathematically generated index. Biomarkers can include any combination of one or more of the above measurements, including trends and differences over time. When the biomarkers of certain embodiments of the present teachings are proteins, the symbols and names of the genes used herein should be understood to refer to the protein products of these genes, and the protein products of these genes are intended to include any protein isoforms of these genes, regardless of whether the isoform sequences are specifically described herein. When the biomarkers are nucleic acids, the symbols and names of the genes used herein should be understood to refer to the nucleic acids (DNA or RNA) of these genes, and the nucleic acids of these genes are intended to include any transcriptional variants of these genes, regardless of whether the transcriptional variants are specifically described herein.

[0035] "Clinical assessment," or "clinical data point," or "clinical endpoint," in the context of the present teachings, can refer to a measure of disease activity or severity. A clinical assessment can include a score, value, or set of values ​​obtained from assessment of a subject or a sample (or a population of samples) from a subject under a given condition. A clinical assessment can be a questionnaire completed by a subject. A clinical assessment can be predicted by biomarkers and / or other parameters. Those skilled in the art will appreciate that the clinical assessment for RA may include, by way of example and without limitation, one or more of the following: DAS (as defined herein), DAS28, DAS28-ESR, DAS28-CRP, Health Assessment Questionnaire (HAQ), modified HAQ (mHAQ), multidimensional HAQ (MDHAQ), visual analog scale (VAS), Physician Global Assessment VAS, Patient Global Assessment VAS, pain VAS, fatigue VAS, total VAS, sleep VAS, Simplified Disease Activity Index (SDAI), Clinical Disease Activity Index (CDAI), Routine Assessment of Patient Index Data (RAPID), RAPID3, RAPID4, RAPID5, American College of Rheumatology (ACR), ACR20, ACR50, ACR70, SF-36 (a well-validated measure of global health status), RA MRI score (RAMRIS; or RA MRI Scoring System), and total Sharp score. It will be understood that these may include Total Skin Score (TSS), van der Heijde-modified TSS, van der Heijde-modified Sharp score (i.e., Sharp-van der Heijde score (SHS)), Larsen score, Total Joint Count (TJC), Swollen Joint Count (SJC), CRP titer (or level), and erythrocyte sedimentation rate (ESR).

[0036] The term "clinical variables" or "clinical parameters" in the context of the present teachings encompasses all measures of the subject's health status. Clinical parameters can be used to derive a clinical assessment of a subject's disease activity. Clinical parameters can include, without limitation: therapy regimen (including but not limited to DMARDs, steroids, whether conventional or biologics), TJC, SJC, morning stiffness, arthritis in three or more joint areas, arthritis in the wrist, symmetric arthritis, rheumatoid nodules, radiographic changes and other imaging, socio- / biological sex, smoking status, age, race / ethnicity, disease duration, diastolic and systolic blood pressure, resting heart rate, height, weight, adiposity, body mass index, serum leptin, family history, CCP status (i.e., whether the subject is positive or negative for anti-CCP antibodies), CCP titer, RF status, RF titer, ESR, CRP titer, menopausal status, and whether the subject is a smoker or non-smoker.

[0037] "Clinical assessment" and "clinical parameter" are not mutually exclusive terms. There may be overlap between the members of the two categories. For example, CRP concentration can be used as a clinical assessment of disease activity; or it can be used as a measure of the subject's health status, thus functioning as a clinical parameter.

[0038] As used herein, "clinically important change" refers to a clinically important change associated with a clinical benefit in RA compared to a clinical assessment. "Minimal clinically important change" is the smallest clinically important change.

[0039] The term "computer" has its general art-known meaning; i.e., a machine that manipulates data according to a set of instructions. For illustrative purposes only, FIG. 2 is a schematic block diagram of a computer (1600). As is known in the art, a "computer" may have different and / or additional components than those depicted in FIG. 2. Furthermore, the computer 1600 may lack certain components depicted. Furthermore, the storage device (1608) may be local and / or remote from the computer (1600) (e.g., embedded within a storage area network (SAN)). As is known in the art, the computer (1600) is configured to execute computer program modules to provide the functionality described herein. As used herein, the term "module" refers to computer program logic utilized to provide particular functionality. Thus, a module may be implemented in hardware, firmware, and / or software. In one embodiment, a program module is stored in the storage device (1608), loaded into the memory (1606), and executed by the processor (1602). Specific examples of portions described herein may include other and / or different modules than those described herein. Furthermore, the functionality attributed to a module may be performed by other or different modules in other examples. Moreover, the term "module" may be omitted in this specification for clarity and convenience.

[0040] The term "cytokine" in the present teachings refers to any substance secreted by a particular cell, which may be of the immune system, that transmits signals between cells and thus has an effect on other cells. The term "cytokine" encompasses "growth factors." "Chemokines" can also be cytokines. They are a subset of cytokines that induce chemotaxis in cells; thus, they are also known as "chemotactic cytokines."

[0041] "DAS" refers to the Disease Activity Score, a measure of the activity of RA in a subject, and is well known to those skilled in the art. See D. van der Heijde et al., Ann. Rheum. Dis. 1990, 49(11):916-920. As used herein, "DAS" refers to this particular Disease Activity Score. "DAS28" involves the assessment of 28 specific joints. It is the current standard that is well recognized in research and clinical practice. Because DAS28 is a well-recognized standard, it is sometimes referred to as "DAS." Unless otherwise specified, "DAS" can refer to a calculation based on 66 / 68 or 44 joint counts, but "DAS" herein encompasses DAS28. Herein, unless otherwise specified, the term "DAS28" as used in the present teachings can refer to DAS28-ESR or DAS28-CRP obtained by any of the above four formulas; or DAS28 can refer to another reliable DAS28 formula known in the art.

[0042] The DAS28 is calculated for RA subjects according to the standards outlined on the das-score.nl website, maintained by the Department of Rheumatology at the University Medical Center in Nijmegen, The Netherlands. Each subject is assessed for the number of swollen joints or swollen joint counts (SJC28) out of a total of 28, and the number of tender joints or tender joint counts (TJC28) out of a total of 28. In some DAS28 calculations, the subject's general health (GH) is also a factor, and can be measured on a 100-mm visual analog scale (VAS). GH can also be referred to herein as PG or PGA, an abbreviation for "Patient Global Health Assessment" (or simply "Patient Global Assessment"). The "Patient Global Health Assessment VAS" is the GH measurement on the visual analog scale.

[0043] "DAS28-CRP" (or "DAS28CRP") is a DAS28 score calculated using CRP instead of ESR (see below). CRP is produced in the liver. Normally, little or no CRP circulates in an individual's serum; CRP is generally present in the body during situations of acute inflammation or infection, and high or elevated amounts of CRP in serum are associated with acute infection or inflammation. A serum level of CRP above 1 mg / dL is considered normal. Most inflammation and infection results in CRP levels above 10 mg / dL. The amount of CRP in a subject's serum can be quantified, for example, using the DSL-10-42100 ACTIVE® US C-reactive protein enzyme-linked immunosorbent assay (ELISA) developed by Diagnostics Systems Laboratories, Inc. (Webster, TX). CRP production is associated with radiographic progression in RA. See R. Mallya et al., J. Rheum. 1982, 9(2):224-228, and F. Wolfe, J. Rheum. 1997, 24:1477-1485. CRP was thus considered a suitable surrogate for ESR in measuring RA disease activity. See R. Mallya et al., J. Rheum. 1982, 9(2):224-228, and F. Wolfe, J. Rheum. 1997, 24:1477-1485.

[0044] DAS28-CRP can be calculated based on either of the following formulas, with or without the GH factor, where "CRP" represents the amount of this protein present in the subject's serum in mg / L, "sqrt" represents square root, and "ln" represents natural logarithm:

[0045]

number

[0046]

number

[0047] "DAS28-ESR" is the DAS28 assessment, where ESR is also measured for each subject (mm / hr). DAS28-ESR can be calculated as follows:

[0048]

number

[0049]

number

[0050] A "dataset" is a set of numerical values ​​resulting from an assessment of a sample (or population of samples) under desired conditions. The values ​​of the dataset can be obtained, for example, by experimentally obtaining measures from the samples and then constructing a dataset from these measurements; alternatively, by obtaining them from a service provider such as a laboratory, or from a database or server on which the dataset is stored.

[0051] As used herein, "difference" refers to an increase or decrease in the measured value of a biomarker or panel of biomarkers compared to the same biomarker or panel of biomarkers in a second sample.

[0052] The term "disease," in the context of the present teachings, includes any disorder, condition, illness, disease, etc. that manifests, for example, in the unregulated or incorrect functioning of an organ, part, structure, or system of the body, and that results, for example, from a genetic or developmental error, infection, toxin, nutritional malnutrition or imbalance, toxicity, or adverse environmental factors.

[0053] DMARDs can be conventional or biologics. Examples of conventionally considered DMARDs include, but are not limited to, MTX, azathioprine (AZA), bucillamine (BUC), chloroquine (CQ), cyclosporine (CSA, or cyclosporine, or cyclosporine), doxycycline (DOXY), hydroxychloroquine (HCQ), intramuscular gold (IM gold), leflunomide (LEF), levofloxacin (LEV), and sulfasalazine (SSZ). Examples of other conventional DMARDs include, but are not limited to, folinic acid, D-pencilamine, gold auranofin, gold aurothioglucose, gold thiomalate, cyclophosphamide, and chlorambucil. Examples of bioDMARDs (or biologic drugs) include, but are not limited to, biologic agents that target tumor necrosis factor (TNF)-alpha molecules, such as infliximab, adalimumab, etanercept, and golimumab. Other classes of bioDMARDs include IL1 inhibitors, such as anakinra, T-cell modulators, such as abatacept, B-cell modulators, such as rituximab, and IL6 inhibitors, such as tocilizumab.

[0054] As used herein, the term "breakout" refers to a sudden and severe increase in onset and clinical signs, including, but not limited to, an increase in SJC, an increase in TJC, an increase in serological markers of inflammation (e.g., CRP and ESR), a decrease in subjective function (e.g., the ability to perform basic daily activities), an increase in morning stiffness, and a decrease in pain that typically leads to therapy intervention and potentially to therapy enhancement.

[0055] As used herein, an "immunoassay" is a biochemical assay that uses one or more antibodies to measure the presence or concentration of an analyte or biomarker in a biological sample.

[0056] "Inflammatory disease," in the context of the present teachings, includes, without limitation, as defined herein, any disease resulting from the biological response of vascular tissue to harmful stimuli, including, but not limited to, pathogenic stimuli, damaged cells, irritants, antigens, and, in the case of autoimmune diseases, substances and tissues normally present in the body. Non-limiting examples of inflammatory diseases include rheumatoid arthritis (RA), eRA, ankylosing spondylitis, psoriatic arthritis, atherosclerosis, asthma, autoimmune diseases, chronic inflammation, chronic prostatitis, glomerulonephritis, hypersensitivity reactions, inflammatory bowel disease, pelvic inflammatory disease, reperfusion injury, transplant rejection, and vasculitis.

[0057] As used herein, "interpretation function" means the transformation of a set of observed data into a meaningful determination of particular interest; for example, an interpretation function is a predictive model created by utilizing one or more statistical algorithms to convert a dataset of observed biomarker data into a meaningful determination of disease activity or disease status of interest.

[0058] "Measuring" or "measurement," in the context of the present teachings, refers to determining the presence, absence, quantity, amount, or effective amount of a substance in a clinical or subject-derived sample, including the concentration level of the substance, or assessing the value or classification of a clinical parameter of a subject.

[0059] A "multi-biomarker disease activity index score," "MBDA score," or simply "MBDA," in the context of the present teachings, is a score that provides a quantitative measure of inflammatory disease activity or inflammatory disease state in a subject. A set of data from specifically selected biomarkers, such as from a set of disclosed biomarkers, is input into an interpretation function according to the present teachings to derive an MBDA score. The interpretation function, in some embodiments, can be generated from predictive or multivariate modeling based on statistical algorithms. Inputs to the interpretation function include results from testing a set of two or more of the disclosed biomarkers, alone or in combination with clinical parameters and / or clinical assessments, as described herein. In some embodiments of the present teachings, the MBDA score is a quantitative measure of autoimmune disease activity. In some embodiments, the MBDA score is a quantitative measure of RA disease activity. MBDA, as used herein, refers to VECTRA (R) This can be referred to as the DA score.

[0060] "Multiplex assay," as used herein, refers to an assay in which multiple analytes, eg, protein analytes, are measured simultaneously in a single run or cycle of assays.

[0061] "Performance," in the context of the present teachings, relates to, for example, the quality and overall usefulness of a model, algorithm, or diagnostic or prognostic test. Factors to consider in model or test performance include, but are not limited to, the clinical and analytical accuracy of the test, usage characteristics such as stability of reagents and various components, ease of use of the model or test, health or economic value, and the relative cost of the various reagents and components of the test. Execution can mean the act of performing a function.

[0062] A "population" is a grouping of subjects with a particular characteristic. The grouping may be based on, for example, but not limited to, clinical parameters, clinical assessment, therapy plan, disease status (e.g., diseased or healthy), level of disease activity, etc. In the context of using MBDA scores to compare disease activity between populations, an aggregate value can be determined based on the observed MBDA scores of the subjects in the population at a particular time point in a longitudinal study, for example. The aggregate value can be based on, for example, any mathematical or statistical formula useful and known in the art for arriving at a meaningful aggregate value from a collection of individual data points, such as the mean, median, or median of the means.

[0063] A "predictive model," which may be used interchangeably with "multivariate model" or simply "model," is a mathematical construct developed using a statistical algorithm or algorithms to classify sets of data. The term "predict" refers to generating values ​​for data points without actually performing the clinical diagnostic procedures normally or otherwise required to generate the data points; when used in this context, "predict" should not be understood to refer solely to the power of the model to predict a particular outcome. A predictive model can provide an interpretation function; for example, a predictive model can utilize one or more statistical algorithms or methods to convert a dataset of observed data into a meaningful determination of disease activity or disease status of a subject.

[0064] A "prognosis" is a prediction of the likely outcome of a disease. Prognosis estimates are useful, for example, in determining an appropriate therapy plan for a subject.

[0065] "Quantitative dataset" or "quantitative data" as used in the present teachings refers to, for example, the data derived from the detection and combined measurement of the expression of multiple (i.e., two or more) biomarkers in a subject sample.The quantitative dataset can be used to identify, monitor and treat disease conditions, and to generate scores for characterizing the subject's biological state.Various biomarkers may be detected depending on the disease condition or physiological state of interest.

[0066] "Recommend," as used herein, refers to making a recommendation for a therapy plan or to ruling out (i.e., not recommending) a particular therapy plan for a subject. Such a recommendation should optionally serve, together with other information, as a basis for a clinician to apply a particular therapy plan to an individual subject.

[0067] The term "remission" refers to the absence of disease activity in patients known to have a chronic disease that is usually incurable. The term "sustained clinical remission" or "SC-REM" as used herein refers to a state of sustained clinical remission as assessed by clinical assessment, e.g., DAS28, for at least 6 months. The term "functional remission" as used herein refers to a state of remission as assessed using a functional assessment scale, such as, but not limited to, the HAQ. Sustained remission can be used interchangeably with maintained remission.

[0068] "Sample," in the context of the present teachings, refers to any biological sample isolated from a subject. Samples can include, without limitation, single or multiple cells, cell fragments, aliquots of bodily fluids, such as whole blood, platelets, serum, plasma, red blood cells, white blood cells or leukocytes, endothelial cells, tissue biopsies, synovial fluid, lymphatic fluid, peritoneal fluid, and interstitial or extracellular fluid. The term "sample" also encompasses fluids found in the spaces between cells, including synovial fluid, gingival crevicular fluid, bone marrow, cerebrospinal fluid (CSF), saliva, mucus, sputum, semen, sweat, urine, and any other bodily fluid. "Blood sample" refers to whole blood or any fraction thereof, including blood cells, red blood cells, white blood cells or leukocytes, platelets, serum, and plasma. Samples can be obtained from a subject by methods including, but not limited to, venipuncture, ejaculation, ejaculation, massage, biopsy, needle aspiration, lavage, scraping, surgical incision, or intervention or other means known in the art.

[0069] A "score" is a value or set of values ​​selected to provide a quantitative measure of a variable or characteristic of a subject's condition and / or to identify, differentiate, or otherwise characterize the subject's condition. The values ​​comprising the score are based on quantitative data resulting in measurements of one or more sample constituents, e.g., obtained from the subject, or from clinical parameters, or from clinical evaluation, or any combination thereof. In certain embodiments, the score is derived from a single component, parameter, or assessment; in other embodiments, the score is derived from multiple components, parameters, and / or assessments. The score is based on or derived from an interpretation function; e.g., an interpretation function derived from a particular predictive model using any of a variety of statistical algorithms known in the art. A "change in score" refers to the absolute change in the score, e.g., from one time point to the next, or the percent change in score, or the change in score per unit time (e.g., the rate of score change). "Test expression score" refers to a score that includes protein level data, "test clinical score" refers to a score that includes clinical data, and "disease activity score" refers to a combination of test expression and disease activity scores.

[0070] "Multiplex assay," as used herein, refers to an assay in which multiple analytes, eg, protein analytes, are measured simultaneously in a single run or cycle of assays.

[0071] "Statistically significant," in the context of the present teachings, means that the observed change is greater than would be expected to occur by chance alone (e.g., a "false positive"). Statistical significance can be determined by any of a variety of methods well known in the art. An example of a commonly used measure of statistical significance is the p-value. The p-value represents the probability of obtaining a given result equivalent to a particular data point, where said data point is the result of random chance alone. A result is often considered highly significant (not by random chance) with a p-value of 0.05 or less.

[0072] A "subject," in the context of the present teachings, generally refers to a mammal. The subject may be a patient. As used herein, the term "mammal" refers to, but is not limited to, humans, non-human primates, dogs, cats, mice, rats, cows, and pigs. Non-human mammals can be advantageously used as subjects representing animal models of inflammation. The subject may be male or female. The subject may have previously been diagnosed or identified as having an inflammatory disease. The subject may have already undergone or is undergoing therapeutic intervention for an inflammatory disease. The subject may also not have previously been diagnosed with an inflammatory disease; for example, the subject may exhibit one or more symptoms or risk factors for an inflammatory condition, or may exhibit no symptoms or risk factors for an inflammatory condition, or may be asymptomatic for an inflammatory disease.

[0073] , As used herein, "therapeutic regimen," "therapy," or "treatment" includes all clinical management of a subject and interventions intended to maintain, alleviate, improve, or otherwise alter a subject's condition, whether biological, chemical, physical, or a combination thereof. These terms can be used interchangeably herein. Treatment includes, but is not limited to, administration of prophylactic or therapeutic compounds (including conventional DMARDs, bioDMARDs, nonsteroidal anti-inflammatory drugs (NSAIDs) such as COX-2 selective inhibitors, and corticosteroids), exercise regimens, physical therapy, dietary modification and / or supplementation, bariatric surgical intervention, administration of medications (prescription or over-the-counter) and / or anti-inflammatory agents, and any other treatment known in the art to prevent, delay the onset, or alleviate disease. "Response to treatment" includes a subject's response to any of the above-mentioned treatments, whether biological, chemical, physical, or a combination thereof. A "treatment regimen" refers to the dose, duration, intensity, etc., of a particular treatment or therapy regimen. An initial therapy plan, as used herein, is the first choice of therapy.

[0074] "Time point," as used herein, refers to a way of describing time that can be described substantially as a single point. A time point can also be described as the smallest detectable time range. A time point can be a way of describing a state of an aspect of time or a specific period of time. Such time points or ranges include, for example, units from seconds, minutes, to hours or days.

[0075] "Weighting" or "weighted," as used herein, refers to a mathematical function that performs a sum, product, or average so that some elements have a greater influence on the result than other elements in the same set.

[0076] Use of the present teachings in the diagnosis, prognosis, and evaluation of disease The MBDA score is a validated tool that quantifies 12 serum protein biomarkers to assess disease activity in adult patients with rheumatoid arthritis (RA) (Curtis JR, et al., Arthritis Care Res. 64:1794-803 (2012)). The derivation of these 12 biomarkers and the algorithm developed to generate the MBDA score are described in U.S. Patent No. 9,200,324, which is incorporated by reference in its entirety.

[0077] In some embodiments of the present teachings, biomarkers can be used to derive an MBDA score, as described herein, wherein the MBDA score can be used to provide diagnosis, prognosis, and monitoring of disease status and / or disease activity in inflammatory and autoimmune diseases. In certain embodiments, the MBDA score can be used to provide diagnosis, prognosis, and monitoring of disease status and / or disease activity in RA or early RA in response to therapy. In certain embodiments, the MBDA score can be used to recommend discontinuing a therapy plan, or the MBDA score can be used to recommend a change in a therapy plan.

[0078] Biomarkers useful in deriving the MBDA score include chitinase 3-like 1 (cartilage glycoprotein-39) (CHI3L1); C-reactive protein, pentraxin-related (CRP); epidermal growth factor (beta-urogastrone) (EGF); interleukin 6 (interferon, beta 2) (IL6); leptin (LEP); matrix metallopeptidase 1 (interstitial collagenase) (MMP1); matrix metallopeptidase 3 (stromelysin 1, progelatinase) (MMP3); resistin (RETN); serum amyloid A1 (SAA1); tumor necrosis factor receptor superfamily, member 1A (TNFRSF1A); vascular cell adhesion molecule 1 (VCAM1); and vascular endothelial growth factor A (VEGFA), serum amyloid P-component (SAP), cathepsin D (CPSD), chemerin (TIG2), alpha-1-microglobulin (A1M), and haptoglobin. (Hp), pigment epithelium-derived factor (PEDF), clusterin (CLU), tissue-type plasminogen activator (tPA), C-reactive protein (CRP), monocyte chemoattractant protein 4 (MCP-4), alpha-1-acid glycoprotein 1 (AGP-1), cross-linking peptide (C-peptide), complement factor H (CFH), lung and activation-regulated chemokine (PARC), growth regulatory alpha protein (GRO-alpha), sex hormone-binding globulin (SHBG), matrix metalloproteinase-7 (MMP-7), growth / differentiation factor 15 (GDF-15), fibroblast growth factor 21 (FGF-21), angiopoietin-related protein 3 (ANGPTL3), hemopexin (HPX), FASLG receptor (FAS), receptor for advanced glycation end products (RAGE), CD5 antigen-like (CD5L), endoglin (ENG), von Willebrand factor (vWF), apolipoprotein C-III (Apo C-III), interleukin-1 receptor antagonist (IL-1ra), ficolin-3 (FCN3), peroxiredoxin-4 (Prx-IV), ST2 cardiac biomarker (ST2), sortilin (SORT1), tumor necrosis factor ligand superfamily member 12(Tweak), phosphoserine aminotransferase (PSAT), heparin-binding EGF-like growth factor (HB-EGF), interleukin-8 (IL-8), beta-2-microglobulin (B2M), apolipoprotein E (Apo E), urokinase-type plasminogen activator (uPA), adrenomedullin (ADM), urokinase-type plasminogen activator receptor (uPAR), tetranectin (TN), E-selectin (ESEL), monokine induced by gamma interferon (MIG), glucagon-like peptide 1 total (GLP-1 total), interleukin-12 subunit p40 (IL-12p40), cartilage oligomeric matrix protein (COMP), apolipoprotein H (Apo H), factor VII (F7), interferon-inducible T-cell-alpha chemoattractant (ITAC), anti-leukocyte proteinase (ALP), thymus and activation-regulated chemokine (TARC), plasminogen activator inhibitor 1 (PAI-1), interleukin-15 (IL-15), ceruloplasmin (CP), complement factor H-related protein 1 (CFHR1), protein DJ-1 (DJ-1), alpha-fetoprotein (AFP), chemokine CC-4 (HCC-4), ferritin (FRTN), interleukin-15 (IL-15), immunoglobulin A (IgA), thrombin-activated fibrinolysis (TAFI), cystatin-B, alpha-1-antichymotrypsin (AACT), pancreatic polypeptide (PPP), heat shock protein 70 (HSP-70), transferrin receptor protein (TFR1), Tamm-Horsfall urinary glycoprotein (THP), tenascin-C (TN-C), pepsinogen 1 (PG1), hepatocyte growth factor (HGF), T-cell-specific protein RANTES (RANTES), tumor necrosis factor receptor 2 (TNFR2), macrophage colony-stimulating factor 1 (M-CSF), beta-amyloid 1-40 (AB-40), cystatin-C, tissue inhibitor of metalloproteinase 3 (TIMP-3), insulin-like growth factor binding protein 4 (IGFBP4), gastrointestinal inhibitory polypeptide (GIP), and midkine(MDK), angiogenin (ANG), stem cell factor (SCF), myeloid progenitor inhibitory factor 1 (MPIF-1), osteoprotegerin (OPG), CD 40 antigen (CD40), monocyte chemoattractant protein 2 (MCP-2), insulin-like growth factor binding protein 1 (IGFBP-1), vitamin K-dependent protein (VKDPS), hepatocyte growth factor receptor (HGFR), brain-derived neurotrophic factor (BDNF), macrophage-stimulating protein (MSP), or monocyte chemoattractant protein 1 (MCP-1).

[0079] Identifying the state of inflammatory disease in a subject allows for a prognosis of the disease, thus allowing for the informed selection, initiation, adjustment, or augmentation of various therapeutic regimens to delay, reduce, or prevent the subject's progression to a more advanced disease state. In some embodiments, therefore, subjects can be identified as having a particular level of inflammatory disease activity and / or having a particular state of disease or flare based on the determination of their MBDA score, and selected to initiate or accelerate a treatment as provided herein to prevent or slow further progression of the inflammatory disease. In other embodiments, subjects identified by an MBDA score as having a particular level of inflammatory disease activity and / or having a particular state of inflammatory disease can be selected to reduce or discontinue their treatment, resulting in improvement or remission in the subject. In other embodiments, subjects identified by an MBDA score as having a particular level of inflammatory disease activity and / or having a particular state of inflammatory disease can be selected for therapy based on the level of disease activity.

[0080] Regarding the need for early and accurate diagnosis of RA, recent advances in RA treatment have provided a means for deeper disease control and optimal treatment of RA within the first month of onset, resulting in significantly improved outcomes. See F. Wolfe, Arth. Rheum. 2000, 43(12):2751-2761; M. Matucci-Cerinic, Clin. Exp. Rheum. 2002, 20(4):443-444; and V. Nell et al., Lancet 2005, 365(9455):199-200. Unfortunately, most subjects do not receive optimal treatment within this narrow window of opportunity, and some experience poor outcomes and irreversible joint damage due to current diagnostic clinical tests. Diagnosing subjects with RA presents numerous challenges, in part because symptoms are not fully identified in the early stages. This is also because diagnostic tests for RA have been developed based on phenomenological findings rather than the biological basis of the disease. In various embodiments of the present teachings, multi-biomarker algorithms can be derived from the disclosed sets of biomarkers.

[0081] Assessment of disease activity In some embodiments of the present teachings, an MBDA score can be derived as described herein and used to rate inflammatory disease activity; for example, high, moderate, or low. The score can vary based on a set of values ​​selected by a practitioner. For example, scores can be set to range from 0 to 100, with the difference between two scores being at least one point. The practitioner then assigns disease activity based on these values. For example, in some embodiments, a score of about 1 to 29 represents a low level of disease activity, a score of about 30 to 44 represents a moderate level of disease activity, and a score of about 45 to 100 represents a high level of disease activity. In some embodiments, on a 1 to 100 scale, a score of ≦38 represents a low or low-grade score, and a score of >38 represents a high or high-grade score. In some embodiments, on a 1 to 100 scale, a score of ≦30 represents a low or low-grade score, and a score of >30 represents a high or high-grade score. In some embodiments, a BDS score of about ≦25 is remission, about 26-29 is low, about 30-44 is moderate, and about >44 is high. The cutoff values ​​can vary. For example, in some embodiments, a low score can be a score <30, while in other applications, a low score is <29 or <31.

[0082] The disease activity score can be varied based on the range of the score. For example, a score of 1 to 58 can represent a low level of disease activity when using a range of 0 to 200. Differences can be determined based on the probability of the range of scores. For example, using a score range of 0 to 100, a small difference in score is about 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10 points; a medium difference in score is about 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, or 30 points; and a large difference is about 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 35, 40, 45, or 50 points. Thus, by way of example, a practitioner can define a small difference in score as about 6 points, a medium difference in score as about 7 to 20 points, and a large difference in score as about >20 points. The difference can be expressed in any units, e.g., percentage points. For example, a practitioner may define a small difference as about <6 percentage points, a medium difference as about 7-20 percentage points, and a large difference as about >20 percentage points.

[0083] The minimal clinically important change in disease activity score is based on an optimal threshold of score change associated with clinical benefit. For example, a score difference of 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30 or more points is considered clinically important. In a preferred embodiment, a score of ≥ 8 is the minimal clinically important change in disease activity, i.e., MBDA score.

[0084] In some embodiments of the present teachings, autoimmune disease activity can be so assessed. In other embodiments, RA disease activity can be so assessed. Because the MBDA score correlates well with traditional clinical assessments of inflammatory disease activity, for example, in RA, in other embodiments of the present teachings, bone damage and disease progression in a subject or population can be tracked through the use and application of the MBDA score.

[0085] The MBDA score can be used for several purposes. It provides a context for understanding the relative level of disease activity on a subject-specific basis. MBDA assessment of disease activity can be used, for example, by clinicians in making treatment decisions, setting a treatment course, and / or notifying clinicians that a subject has entered remission. It also provides a means to more accurately assess and document the quantitative level of disease activity in a subject. It is also useful in assessing clinical differences between populations of subjects within a practice. For example, this tool can be used to evaluate the relative efficacy of different therapy modalities. It is also useful in assessing clinical differences between different practices. This allows physicians to determine which overall level of disease activity is being achieved by their peers and / or medical management groups to compare results between different practices in terms of cost and relative effectiveness. Because the MBDA score shows strong correlations with established disease activity assessments, such as the DAS28, the MBDA score provides a quantitative measure for monitoring the degree of subject disease activity and response to treatment.

[0086] Target Screening Certain embodiments of the present teachings can be used to screen subject populations in any number of settings. For example, health maintenance organizations, public health organizations, or school wellness programs can screen groups of subjects to identify those requiring intervention, as described above. Other embodiments of these teachings can be used to collect disease activity data for one or more populations of subjects to identify target disease states in the aggregate, for example, to determine the effectiveness of clinical management of the population or to identify gaps in clinical management. Insurance companies (e.g., health, life, or disability) can require screening of applicants in the process of determining coverage for potential interventions. Data collected on such populations, particularly when linked to any clinical course for conditions such as inflammatory diseases and RA, can be valuable for screening and operations of health maintenance organizations, public health programs, and insurance companies, for example.

[0087] Such data arrays or collections can be stored on machine-readable media and used in any number of health-related data management systems to provide improved healthcare services, cost-effective medical care, improved insurance, and the like. See, e.g., U.S. Patent Application Nos. 2002 / 0038227; 2004 / 0122296; 2004 / 0122297, and U.S. Patent No. 5,018,067. Such systems can access the data directly from internal data storage or remotely from one or more data storage sites, as further described herein. Thus, in health-related data management systems, where managing inflammatory disease progression in populations is important to reduce disease-related employment productivity loss, disability, and surgery, and thus reduce overall healthcare costs, various embodiments of the present teachings provide improvements that include the use of data arrays that include biomarker measurements, as defined herein, and / or disease status and activity outcomes derived from those biomarker measurements.

[0088] Calculating the score In some embodiments of the present teachings, inflammatory disease activity in a subject is measured by determining the serum levels of two or more biomarkers in the inflammatory disease subject and then applying an interpretation function to convert the biomarker levels into a single MBDA score, which provides a quantitative measure of inflammatory disease activity in the subject and correlates well with traditional clinical assessments of inflammatory disease activity (e.g., DAS28 or CDAI scores in RA), as demonstrated in the Examples below. In some embodiments, the disease activity so measured is associated with an autoimmune disease. In some embodiments, the disease activity so measured is associated with RA. The biomarkers include chitinase 3-like 1 (cartilage glycoprotein-39) (CHI3L1); C-reactive protein, pentraxin-related (CRP); epidermal growth factor (beta-urogastrone) (EGF); interleukin 6 (interferon, beta 2) (IL6); leptin (LEP); matrix metallopeptidase 1 (interstitial collagenase) (MMP1); matrix metallopeptidase 3 (stromelysin 1, progelatinase) (MMP3); resistin (RETN); serum amyloid A1 (SAA1); tumor necrosis factor receptor superfamily, member 1A (TNFRSF1A); vascular cell adhesion molecule 1 (VCAM1); and vascular endothelial growth factor A (VEGFA), serum amyloid P-component (SAP), cathepsin D (CPSD), chemerin (TIG2), alpha-1-microglobulin (A1M), haptoglobin. (Hp), pigment epithelium-derived factor (PEDF), clusterin (CLU), tissue-type plasminogen activator (tPA), C-reactive protein (CRP), monocyte chemoattractant protein 4 (MCP-4), alpha-1-acid glycoprotein 1 (AGP-1), cross-linking peptide (C-peptide), complement factor H (CFH), lung and activation-regulated chemokine (PARC), growth regulatory alpha protein (GRO-alpha), sex hormone-binding globulin (SHBG), matrix metalloproteinase-7 (MMP-7), growth / differentiation factor 15 (GDF-15), fibroblast growth factor 21 (FGF-21), angiopoietin-related protein 3(ANGPTL3), hemopexin (HPX), FASLG receptor (FAS), receptor for advanced glycation end products (RAGE), CD5 antigen-like (CD5L), endoglin (ENG), von Willebrand factor (vWF), apolipoprotein C-III (Apo C-III), interleukin-1 receptor antagonist (IL-1ra), ficolin-3 (FCN3), peroxiredoxin-4 (Prx-IV), ST2 cardiac biomarker (ST2), sortilin (SORT1), tumor necrosis factor ligand superfamily member 12 (Tweak), phosphoserine aminotransferase (PSAT), heparin-binding EGF-like growth factor (HB-EGF), interleukin-8 (IL-8), beta-2-microglobulin (B2M), apolipoprotein E (Apo E), urokinase-type plasminogen activator (uPA), adrenomedullin (ADM), urokinase-type plasminogen activator receptor (uPAR), tetranectin (TN), E-selectin (ESEL), monokine induced by gamma interferon (MIG), glucagon-like peptide 1 total (GLP-1 total), interleukin-12 subunit p40 (IL-12p40), cartilage oligomeric matrix protein (COMP), apolipoprotein H (Apo H), factor VII (F7), interferon-inducible T-cell-alpha chemoattractant (ITAC), anti-leukocyte proteinase (ALP), thymus and activation-regulated chemokine (TARC), plasminogen activator inhibitor 1 (PAI-1), interleukin-15 (IL-15), ceruloplasmin (CP), complement factor H-related protein 1 (CFHR1), protein DJ-1 (DJ-1), alpha-fetoprotein (AFP), chemokine CC-4 (HCC-4), ferritin (FRTN), interleukin-15 (IL-15), immunoglobulin A (IgA), thrombin-activated fibrinolysis (TAFI), cystatin-B, alpha-1-antichymotrypsin (AACT), pancreatic polypeptide (PPP), heat shock protein 70(HSP-70), transferrin receptor protein (TFR1), Tamm-Horsfall urinary glycoprotein (THP), tenascin-C (TN-C), pepsinogen 1 (PG1), hepatocyte growth factor (HGF), T-cell-specific protein RANTES (RANTES), tumor necrosis factor receptor 2 (TNFR2), macrophage colony-stimulating factor 1 (M-CSF), beta-amyloid 1-40 (AB-40), cystatin-C, tissue inhibitor of metalloproteinase 3 (TIMP-3), insulin-like growth factor binding protein 4 (IGFBP4), gastrointestinal inhibitory polypeptide (GIP), midkine (MDK), angiogenin (ANG), stem cell factor (SCF), myeloid progenitor inhibitory factor 1 (MPIF-1), osteoprotegerin (OPG), CD 40 antigen (CD40), monocyte chemotactic protein 2 (MCP-2), insulin-like growth factor binding protein 1 (IGFBP-1), vitamin K-dependent protein (VKDPS), hepatocyte growth factor receptor (HGFR), brain-derived neurotrophic factor (BDNF), macrophage-stimulating protein (MSP), or monocyte chemotactic protein 1 (MCP-1).

[0089] In some embodiments, the interpretation function is based on a predictive model. Established statistical algorithms and methods known in the art are useful as models or for designing predictive models, including, but not limited to, analysis of manifolds (ANOVA); Bayesian networks; boosting and adaboost; bootstrap aggregating (or bagging) algorithms; decision tree classification techniques such as classification and regression trees (CART), boosted CART, random forests (RF), recursive partitioning trees (RPART), etc.; Carrs and Howey (CW); Carrs and Howey-Lasso; dimension reduction methods such as principal component analysis (PCA) and factor rotation or factor analysis; discriminant function analysis, including linear discriminant analysis (LDA), Eigengene linear discriminant analysis (ELDA), and quadratic discriminant analysis. (DFA); factor rotation or factor analysis; genetic algorithms; hidden Markov models; kernel-based machine algorithms such as kernel density estimation, kernel partial least squares algorithm, kernel matching pursuit algorithm, kernel Fisher's discriminant analysis algorithm, and kernel principal component analysis algorithm; linear regression and generalized linear models including or utilizing forward linear stepwise regression, lasso (or LASSO) shrinkage and selection method, and elastic net regularization and selection method; glmnet (lasso and elastic net-regularized generalized linear models); logistic regression (LogReg); meta-learning algorithms; neighborhood methods for classification or regression, such as K-th nearest neighbors (KNN); nonlinear regression or classification algorithms; neural networks; partial least squares; rule-based classifiers; shrinkage centroids (SC); stratified inverse regression; standard, application-interpreted construction for the exchange of product model data (StepAIC); super principal component (SPC) regression; and support vector machines (SVM) and recursive support vector machines (RSVM), among others. Additionally, clustering algorithms, which are known in the art, can be useful in determining subject subgroups.

[0090] Logistic regression is a traditional predictive modeling method for binary response variables; e.g., treatment 1 vs. treatment 2. It can be used to model both linear and nonlinear aspects of the data variables and provides easily interpretable odds ratios.

[0091] Discriminant function analysis (DFA) uses a set of analytes as variables (roots) to discriminate between two or more naturally occurring groups. DFA is used to test analytes that significantly differ between groups. Forward stepwise DFA can be used to select the set of analytes that best discriminate between the groups studied. Specifically, at each step, all variables can be reexamined to determine which best discriminate between groups. This information is then included in a discriminant function, called a root, which is an equation consisting of a linear combination of analyte concentrations versus predictor of group membership. The discriminant ability of the final equation can be observed as a line plot of the root values ​​obtained for each group. This technique identifies groups of analytes whose changes in concentration levels can be used to delineate profiles, diagnose, and evaluate therapy efficacy. DFA models can generate arbitrary scores by classifying new subjects as either "healthy" or "diseased." To facilitate use of this score in the medical community, the score can be rescaled so that a value of 0 indicates a healthy individual and a score greater than 0 indicates increased disease activity.

[0092] Classification and Regression Trees (CART) perform a logical partitioning (if / then) of data to create a decision tree. All observations entering a particular node are classified according to the most common outcome in that node. CART results are easily interpretable and follow a series of if / then tree branches to a classification result.

[0093] Support vector machines (SVMs) classify objects into two or more classes. Examples of classes include a set of therapy options, a set of diagnostic options, or a set of prognostic options. Each object is assigned to a class based on its similarity to (or distance from) objects in a training dataset where the correct class assignment for each object is known. A measure of similarity of a new object to known objects is determined using support vectors, which define a region in a potentially high-dimensional space (>R6).

[0094] The process of bootstrap aggregating, or "bagging," is computationally simple. In the first step, a given dataset is randomly resampled a certain number of times (e.g., several thousand times) to provide that number of new datasets. These are called "bootstrapped resamples" of the data, and then a model is constructed using each of them. In the classification model example, the number of classification models created in the first step predicts the class of each new observation. The final class decision is based on a "majority vote" of the classification models; that is, the final classification instruction is determined by counting the number of times a new observation falls into a given group and taking the majority classification (33%+ for a three-class system). In a logistic regression model, if the logistic regression is bagged 1,000 times, there will be 1,000 logistic models, each giving the probability of the sample belonging to class 1 or 2.

[0095] Carrs and Whyte (CW) using ordinary least squares (OLS) is another predictive modeling method. See L. Breiman and JH Friedman, J. Royal. Stat. Soc. B 1997, 59(1):3-54. This method improves predictive accuracy compared to the usual procedure of performing separate regressions of each response variable on a common set of predictor variables, X, while accounting for correlations between the response variables. In CW, Y = XB * S, where Y = (ykj), k is the kth patient and j is the jth response (j = 1 for TJC, j = 2 for SJC, etc.), B is obtained using OLS, and S is a shrinkage matrix calculated from the canonical coordinate system. Another method is a combination of Carrs and Whyte and Lasso (CW-Lasso). Instead of obtaining B using OLS as in CW, here we use Lasso and adjust the parameters of the Lasso method accordingly.

[0096] Many of these techniques are useful in combination with biomarker selection techniques (e.g., forward selection, backward selection, or stepwise selection), or for exhaustive enumeration of all possible panels of a given size, or with genetic algorithms, or they may themselves comprise biomarker selection methodologies within their own techniques. These techniques are combined with information criteria such as the Akaike Information Criterion (AIC), the Bayesian Information Criterion (BIC), or cross-validation to quantify the trade-off between the inclusion of additional biomarkers and model improvement, and minimize overfitting. The resulting predictive models are validated in other experiments, or cross-validated with the experiment in which they were originally trained, using techniques such as leave-one-out (LOO) and 10-fold cross-validation (10-fold CV).

[0097] An example of an interpretation function that provides the MBDA scores derived from the above statistical modeling method is given by the following function:

[0098]

number

[0099] The MBDA score obtained for an RA subject with a known clinical assessment (e.g., DAS28 score) can then be compared to the known assessment to determine the level of correlation between the two assessments, and therefore the accuracy of the MBDA score and its underlying predictive model. See specific formulas and constants below.

[0100] An embodiment of the present invention uses clinical variables to adjust the MBDA score. The clinical variables used to adjust the MBDA score may include, but are not limited to, age, sociological sex, biological sex, smoking status, adiposity, body mass index (BMI), serum leptin, and race / ethnicity. As an example, serum leptin can be used to adjust the MBDA score using the following algorithm:

[0101]

number

[0102] In some embodiments of the present teachings, it is not required to compare the MBDA score to any predetermined "reference," "normal," "control," "standard," "healthy," "non-diseased," or other index, for the MBDA score to provide a quantitative measure of inflammatory disease activity in the subject.

[0103] In other embodiments of the present teachings, the amount of a biomarker can be measured in a sample and used to derive an MBDA score, which is then compared to a "normal" or "control" level or value, using techniques such as, for example, reference or discrimination limits or risk-defining thresholds, to define cutoff points and / or outliers for inflammatory disease. The normal level is the level of one or more biomarkers or a combined biomarker index typically found in subjects not affected by the inflammatory disease under assessment. Other terms for "normal" or "control" include, for example, "reference," "index," "baseline," "standard," "healthy," "pre-disease," etc. Such normal levels can vary based on whether a biomarker is used alone or in a formula combined with other biomarkers to generate a score. Alternatively, the normal level can be a database of biomarkers from previously tested subjects who have not converted to the inflammatory disease under assessment over a clinically relevant period of time. The reference (normal, control) value can also be derived, for example, from control subjects or populations with known levels or states of inflammatory disease activity. In some embodiments of the present teachings, the reference value can be derived from one or more subjects who have been treated for an inflammatory disease, or one or more subjects who are at low risk of developing an inflammatory disease, or subjects who have been treated and have shown improvement in inflammatory disease activity factors (e.g., clinical parameters as defined above). In some embodiments, the reference value can be derived from one or more subjects who have not been treated; for example, samples can be collected from (a) subjects who have received initial treatment for an inflammatory disease and (b) subjects who have received subsequent treatment for an inflammatory disease to monitor the progress of treatment. The reference value can also be derived from a disease activity algorithm or a calculated index from a population study.

[0104] Biomarker measurement The quantity of one or more biomarkers of the present teachings is referred to as a value. The value can be one or more numerical values ​​obtained from assessment of a sample, e.g., by measuring the level of the biomarker in the sample by an assay performed in a laboratory, or can be derived from a dataset obtained from a provider such as a laboratory, or from a dataset stored on a server. Biomarker levels can be measured using any of several techniques known in the art. The present teachings encompass such techniques, including all subject fasting and / or serial sampling procedures that measure biomarkers.

[0105] The actual measurement of biomarker levels can be determined at the protein or nucleic acid level using any method known in the art. Protein detection includes detection of full-length proteins, mature proteins, preproteins, polypeptides, isoforms, mutants, variants, post-translationally modified proteins, and their variants, and can be detected in any suitable manner. Biomarker levels can be determined at the protein level, for example, by measuring serum levels of peptides encoded by the gene products described herein or by measuring the enzymatic activity of these protein biomarkers. Such methods are known in the art and include, for example, immunoassays based on antibodies against proteins, aptamers, or molecular imprints encoded by the genes. Any biological material can be used to detect / quantify the proteins or their activity. Alternatively, an appropriate method can be selected to determine the activity of the proteins encoded by the biomarker genes according to the activity of each protein analyzed. For biomarker proteins, polypeptides, isoforms, mutants, and their variants known to have enzymatic activity, activity can be determined in vitro using enzymatic assays known in the art. Such assays include, without limitation, protease assays, kinase assays, phosphatase assays, reductase assays, and many others. The modulation of the kinetics of enzyme activity can be determined by measuring the rate constant, KM, using known algorithms such as Hill plots, Michaelis-Menten equations, linear regression plots such as Lineweaver-Burk analysis, and Scatchard plots.

[0106] Using sequence information provided by public database entries for the biomarker, expression of the biomarker can be detected and measured using techniques well known to those skilled in the art. For example, nucleic acid sequences in sequence databases corresponding to the biomarker nucleic acid can be used to construct primers and probes to detect and / or measure the biomarker nucleic acid. These probes can be used, for example, in Northern or Southern blot hybridization analysis, ribonuclease protection assays, and / or methods for quantitatively amplifying specific nucleic acid sequences. As another example, primers can be constructed to specifically amplify biomarker sequences in amplification-based detection and quantification methods such as reverse transcription-polymerase chain reaction (RT-PCR) and PCR. In cases involving gene amplification, nucleotide deletions, polymorphisms, post-translational modifications, and / or variants, sequence comparisons in test and reference populations can be performed by comparing the relative amounts of the interrogated DNA sequences in the test and reference populations.

[0107] As an example, gene expression can be determined using Northern hybridization analysis using probes that specifically recognize one or more of these sequences. Alternatively, expression can be measured using RT-PCR; for example, polynucleotide primers specific to a differentially expressed biomarker mRNA sequence reverse transcribe the mRNA into DNA, which can then be amplified by PCR and visualized and quantified. Biomarker RNA can also be quantified using other target amplification methods, such as TMA, SDA, and NASBA, or signal amplification methods (e.g., bDNA). Ribonuclease protection assays can also be used to determine gene expression using probes that specifically recognize one or more biomarker mRNA sequences.

[0108] Alternatively, biomarker protein and nucleic acid metabolites can be measured. The term "metabolite" includes any chemical or biochemical product of a metabolic process, such as any compound produced by the processing, cleavage, or consumption of a biomolecule (e.g., protein, nucleic acid, carbohydrate, or lipid). Metabolites can be detected by a variety of methods known to those skilled in the art, including refractive index spectroscopy (RI), ultraviolet spectroscopy (UV), fluorescence spectroscopy, radiochemical analysis, near-infrared spectroscopy (Near-IR), nuclear magnetic resonance spectroscopy (NMR), light scattering spectroscopy (LS), mass spectroscopy, pyrolysis-mass spectroscopy, turbidimetry, Raman scattering spectroscopy, gas chromatography coupled with mass spectrometry, liquid chromatography coupled with mass spectrometry, matrix-assisted laser desorption / ionization-time of flight (MALDI-TOF) coupled with mass spectrometry, ion spray spectrometry coupled with mass spectrometry, capillary electrophoresis, NMR, and IR detection. See WO 04 / 056456 and WO 04 / 088309, which are incorporated by reference in their entireties. In this regard, other biomarker analytes can be measured using the detection methods described above or other methods known to those of skill in the art. For example, circulating calcium ions (Ca 2+ ) can be detected in samples using fluorescent dyes such as the Fluo series, Fura-2A, Rhod-2, etc. Other biomarker metabolites can similarly be detected using reagents specifically designed or tailored to detect such metabolites.

[0109] In some embodiments, biomarkers are detected by contacting a subject sample with a reagent and analyte to form a complex, and then detecting the complex. Examples of "reagents" include, but are not limited to, nucleic acid primers and antibodies.

[0110] In some embodiments of the present teachings, biomarkers are detected using antibody binding assays; for example, a sample from a subject is contacted with an antibody reagent that binds to the biomarker analyte of interest, a reaction product (or complex) comprising the antibody reagent and the analyte is formed, and the presence (or absence) or amount of the complex is determined. Antibody reagents useful for detecting biomarker analytes can be monoclonal, polyclonal, chimeric, recombinant, or fragments of the foregoing, as discussed above, and detecting the reaction product can be carried out by any suitable immunoassay. The sample from the subject is typically a biological fluid, as described above, and can be the same biological fluid sample used to perform the methods described above.

[0111] Immunoassays performed according to the present teachings can be homogeneous or heterogeneous. Immunoassays performed according to the present teachings can be multiplexed. In homogeneous assays, the immunoreaction involves a specific antibody (e.g., an anti-biomarker protein antibody), a labeled analyte, and the sample of interest. The label generates a signal, and the signal derived from the label is altered, directly or indirectly, upon binding of the labeled analyte to the antibody. Both the immunoreaction and detection of the extent of binding can be performed in a homogeneous solution. Immunochemical labels that can be employed include, but are not limited to, free radicals, radioisotopes, fluorescent dyes, enzymes, bacteriophages, and coenzymes. Immunoassays include competitive assays.

[0112] In heterogeneous assays, the reagents can be the sample of interest, an antibody, and a reagent that generates a detectable signal. The above-described samples can be used. The antibody can be immobilized on a support, such as beads (such as protein A and protein G agarose beads), a plate, or a slide, and contacted in liquid phase with a sample suspected of containing a biomarker. The support is separated from the liquid phase, and either the support phase or the liquid phase is examined using a method known in the art to detect a signal. The signal is related to the presence of the analyte in the sample. Methods for generating a detectable signal include, but are not limited to, the use of radiolabels, fluorescent labels, or enzyme labels. For example, when the antigen to be detected contains a second binding site, the antibody that binds to that site is conjugated to a detectable (signal-generating) group and added to the liquid-phase reaction solution before the separation step. The presence of the detectable group on the solid support indicates the presence of the biomarker in the test sample. Examples of suitable immunoassays include, but are not limited to, oligonucleotide, immunoblotting, immunoprecipitation, immunofluorescence, chemiluminescence, electrochemiluminescence (ECL), and / or enzyme-linked immunosorbent assay (ELISA).

[0113] Those of skill in the art are familiar with numerous specific immunoassay formats and variations thereof that may be useful in practicing the methods disclosed herein. See, e.g., E. Maggio, Enzyme-Immunoassay (1980), CRC Press, Inc., Boca Raton, FL. See also U.S. Pat. No. 4,727,022 to C. Skold et al., entitled "Novel Methods for Modulating Ligand-Receptor Interactions and their Application"; U.S. Pat. No. 4,659,678 to G.C. Forrest et al., entitled "Immunoassay of Antigens"; U.S. Pat. No. 4,376,110 to G.S. David et al., entitled "Immunometric Assays Using Monoclonal Antibodies"; U.S. Pat. No. 4,275,149 to D. Litman et al., entitled "Macromolecular Environment Control in Specific Receptor Assays"; U.S. Pat. No. 4,233,402 to E. Maggio et al., entitled "Reagents and Method Employing Channeling"; and U.S. Pat. No. 4,230,797 to R. Boguslaski et al., entitled "Heterogenous Specific Binding Assay Employing a Coenzyme as Label."

[0114] The antibodies can be attached to a solid support suitable for the diagnostic assay (e.g., beads such as microspheres, plates, slides, or wells made of materials such as protein A or protein G agarose, latex, or polystyrene) according to known techniques, such as passive attachment. Antibodies as described herein can be conjugated to a detectable label or group, such as a radiolabel (e.g., 35S, 125I, 131I), an enzyme label (e.g., horseradish peroxidase, alkaline phosphatase), and a fluorescent label (e.g., fluorescein, Alexa, green fluorescent protein, rhodamine), according to known techniques.

[0115] Antibodies are also useful for detecting post-translational modifications of biomarkers. Examples of post-translational modifications include, but are not limited to, tyrosine phosphorylation, threonine phosphorylation, serine phosphorylation, citrullination, and glycosylation (e.g., O-GlcNAc). Such antibodies specifically detect phosphorylated amino acids in a protein or proteins of interest and can be used in the immunoblotting, immunofluorescence, and ELISA assays described herein. These antibodies are well known to those skilled in the art and are commercially available. Post-translational modifications can also be detected using metastable ions in reflector matrix-assisted laser desorption / ionization-time of flight mass spectrometry (MALDI-TOF). See U. Wirth et al., Proteomics 2002, 2(10):1445-1451.

[0116] Therapy Plan The present invention provides methods for recommending a therapy regimen, including discontinuing the therapy regimen, after determining differences in the expression of the biomarkers described herein. Measuring scores derived from biomarker expression levels over time can provide clinicians with a dynamic picture of a subject's biological state. These embodiments of the present teachings thus provide subject-specific bioinformation that can inform therapy decisions, facilitate therapy response monitoring, and result in better management of disease activity and an increased proportion of subjects achieving remission.

[0117] Treatment strategies for autoimmune disorders are confounded by the fact that some autoimmune disorders, such as RA, are classified as groups of subjects with a diverse array of associated symptoms that may flare or go into remission. This suggests that certain RA subtypes are determined by specific cell types or cytokines. As a result, no single therapy has proven optimal for treatment. Given the increasing number of therapeutic options available for RA, the need for individually tailored treatments directed by immunological prognostic factors of treatment outcome is essential. In various embodiments of the present teachings, biomarker-derived algorithms can be used to quantify therapy response in RA subjects. For patients with early RA (eRA), methotrexate (MTX) is sometimes recommended as first-line treatment, and for non-responders, data supports the addition of both traditional non-biologic disease-modifying antirheumatic drug therapy (triple DMARD therapy) and biologic (anti-TNF) therapy. Identification of patients with a high likelihood of responding to one or other of these options will lead to increased availability of more personalized medicines and therapies, which is the primary goal of this invention.

[0118] In some embodiments, prediction of autoimmune disease patients, particularly RA patients, who will be able to successfully discontinue or interrupt therapy is based on the MBDA score. In some embodiments, a high baseline MBDA score as described herein can be an independent predictor of disease progression within a period of time after cessation of therapy. In some embodiments, a moderate baseline MBDA score as described herein can be an independent predictor of disease progression within a period of time after cessation of therapy. In some embodiments, a low baseline MBDA score as described herein can be a predictor of disease progression or remission within a period of time after cessation of therapy.

[0119] Reference Standard for Treatment In many embodiments, the average level of one or more analyte biomarkers or the levels of a particular panel of analyte biomarkers in a sample is compared to a reference standard ("reference standard" or "reference level") to guide treatment decisions. The expression level of one or more biomarkers is associated with a score, which can represent disease activity. A reference standard used in any embodiment disclosed herein comprises the average, mean, or median level of one or more analyte biomarkers or the levels of a particular panel of analyte biomarkers in a control population. The reference standard can further comprise the same subject at an earlier time point. For example, the reference standard can comprise a first time point, and the levels of one or more analyte biomarkers can be investigated at a second, third, fourth, fifth, sixth, etc. time point. Any time point earlier than any particular time point can be considered a reference standard. The reference standard can further comprise a cutoff value or any other statistical attribute of a control population, such as a standard deviation from the mean level of one or more analyte biomarkers or the levels of a particular panel of analyte biomarkers, or an earlier time point of the same subject. In some embodiments, the control population can include healthy individuals or identical subjects prior to the administration of any therapy.

[0120] In some embodiments, a score can be obtained from a reference time point, and a different score can be obtained at a later time point. The first time point can be when an initial therapy regimen is initiated. The first time point can also be when an initial immunoassay is performed. The time point can be an hour, day, month, year, etc. In some embodiments, the time point is 1 month. In some embodiments, the time point is 2 months. In some embodiments, the time point is 3 months. In some embodiments, the time point is 4 months. In some embodiments, the time point is 5 months. In some embodiments, the time point is 6 months. In some embodiments, the time point is 7 months. In some embodiments, the time point is 8 months. In some embodiments, the time point is 9 months. In some embodiments, the time point is 10 months. In some embodiments, the time point is 11 months. In some embodiments, the time point is 12 months. is 1 month. In some embodiments, the time point is 2 years. In some embodiments, the time point is 3 years. In some embodiments, the time point is 4 years. In some embodiments, the time point is 5 years. In some embodiments, the time point is 10 years.

[0121] The difference in score is interpreted as an increase in disease activity.For example, a lower score can represent a lower level of disease activity or remission.In these circumstances, the second score that is lower than the reference score or the first score means that the disease activity of the subject is reduced (improved) or in remission between the first time point and the second time point.Alternatively, a higher score can indicate a lower level of disease activity or remission.In these circumstances, the second score that is higher than the reference score or the first score also means that the disease activity of the subject is improved or in remission between the first time point and the second time point.

[0122] The difference in score can also be interpreted as an increase in disease activity.For example, a lower score can mean a higher level of disease activity or an outbreak.In these circumstances, the second score that is lower than the reference score or the first score means that the disease activity of the subject increases (worsens) between the first time point and the second time point.Alternatively, a higher score can indicate a higher level of disease activity or an outbreak.In these circumstances, the second score that is higher than the reference score or the first score also means that the disease activity of the subject worsens or an outbreak between the first time point and the second time point.

[0123] The difference can vary. For example, when the difference in score is interpreted as a decrease in disease activity, a large difference means a larger decrease in disease activity than a moderate difference or a small difference. Alternatively, when the difference in score is interpreted as an increase in disease activity, a large difference means a larger increase in disease activity than a moderate difference or a slight difference.

[0124] Reference Therapy for Treatment In some embodiments, the patient is treated with more or less aggressiveness than the reference therapy based on the difference in scores. The reference therapy is any therapy that is the standard of care for an autoimmune disorder. Standard of care may change over time or geographically, and one of skill in the art can readily determine the appropriate standard of care by consulting the relevant medical literature.

[0125] In some embodiments, more aggressive therapy than standard therapy includes initiating treatment earlier than standard therapy. In some embodiments, more aggressive therapy than standard therapy includes administering additional treatments than standard therapy. In some embodiments, more aggressive therapy than standard therapy includes administering treatment on an accelerated schedule compared to standard therapy. In some embodiments, more aggressive therapy than standard therapy includes administering additional treatments that are not appropriate for standard therapy.

[0126] In some embodiments, a less aggressive therapy than standard therapy includes delaying treatment relative to standard therapy. In some embodiments, a less aggressive therapy than standard therapy includes administering less treatment than standard therapy. In some embodiments, a less aggressive therapy than standard therapy includes administering treatment on a slowed-down schedule compared to standard therapy. In some embodiments, a less aggressive therapy than standard therapy includes not administering treatment.

[0127] Treating autoimmune disorders In one embodiment, the practitioner discontinues the therapy plan if the score is low. In one embodiment, the practitioner does not change the therapy plan if the score is high. In one embodiment, the practitioner adjusts the therapy based on a comparison between the difference scores or based on the initial predicted score. In one embodiment, the practitioner adjusts the therapy by selecting and administering a different drug. In one embodiment, the practitioner adjusts the therapy by selecting and administering a different combination of drugs. In one embodiment, the practitioner adjusts the therapy by adjusting the drug dose. In one embodiment, the practitioner adjusts the therapy by adjusting the dosing schedule. In one embodiment, the practitioner adjusts the therapy by adjusting the length of the therapy. In one embodiment, the practitioner adjusts the therapy by selecting and administering a different drug combination and adjusting the drug dose. In one embodiment, the practitioner adjusts the therapy by selecting and administering a different drug combination and adjusting the dosing schedule. In one embodiment, the practitioner adjusts the therapy by selecting and administering different drug combinations and adjusting the length of therapy. In one embodiment, the practitioner adjusts the therapy by adjusting the drug dose and dosing schedule. In one embodiment, the practitioner adjusts the therapy by adjusting the drug dose and length of therapy. In one embodiment, the practitioner adjusts the therapy by adjusting the dosing schedule and length of therapy. In one embodiment, the practitioner adjusts the therapy by selecting and administering different drugs, adjusting the drug dose and adjusting the dosing schedule. In one embodiment, the practitioner adjusts the therapy by selecting and administering different drugs, adjusting the drug dose and adjusting the length of therapy. In one embodiment, the practitioner adjusts the therapy by selecting and administering different drugs, adjusting the drug dose and adjusting the length of therapy.In one embodiment, the practitioner adjusts the therapy by adjusting the drug dose, adjusting the dosing schedule, and adjusting the length of therapy. In one embodiment, the practitioner adjusts the therapy by selecting and administering a different drug, adjusting the drug dose, adjusting the dosing schedule, and adjusting the length of therapy.

[0128] In one embodiment, less aggressive therapy includes no change in the therapy plan. In one embodiment, less aggressive therapy includes delaying treatment. In one embodiment, less aggressive therapy includes selecting and administering a less potent medication. In one embodiment, less aggressive therapy includes reducing the frequency of treatment. In one embodiment, less aggressive therapy includes shortening the length of therapy. In one embodiment, less aggressive therapy includes selecting and administering a less potent medication and reducing the medication dose. In one embodiment, less aggressive therapy includes selecting and administering a less potent medication and slowing the dosing schedule. In one embodiment, less aggressive therapy includes selecting and administering a less potent medication and shortening the length of therapy. In one embodiment, less aggressive therapy includes reducing the medication dose and slowing down the dosing schedule. In one embodiment, less aggressive therapy includes reducing the medication dose and shortening the length of therapy. In one embodiment, less aggressive therapy includes slowing down the dosing schedule and shortening the length of therapy. In one embodiment, less aggressive therapy includes selecting and administering a less potent medication, reducing the medication dose and slowing down the dosing schedule. In one embodiment, less aggressive therapy includes selecting and administering a less potent medication, reducing the medication dose and shortening the length of therapy. In one embodiment, less aggressive therapy includes selecting and administering a less potent medication, reducing the medication dose and shortening the length of therapy. In one embodiment, less aggressive therapy includes reducing the drug dose, slowing the dosing schedule, and shortening the length of therapy. In one embodiment, less aggressive therapy includes selecting and administering a less potent drug, reducing the drug dose, slowing the dosing schedule, and shortening the length of therapy. In some embodiments, less aggressive therapy includes administering only non-drug based therapies.

[0129] In another aspect of the application, the treatment includes a more aggressive therapy than the reference therapy. In one embodiment, the more aggressive therapy includes extending the length of the therapy. In one embodiment, the more aggressive therapy includes increasing the frequency of the dosing schedule. In one embodiment, the more aggressive therapy includes selecting and administering a more potent drug and increasing the drug dose. In one embodiment, the more aggressive therapy includes selecting and administering a more potent drug and accelerating the dosing schedule. In one embodiment, the more aggressive therapy includes selecting and administering a more potent drug and extending the length of the therapy. In one embodiment, the more aggressive therapy includes increasing the drug dose and accelerating the dosing schedule. In one embodiment, the more aggressive therapy includes increasing the drug dose and extending the length of the therapy. In one embodiment, a highly aggressive therapy includes accelerating the dosing schedule and extending the length of therapy. In one embodiment, a highly aggressive therapy includes selecting a more potent drug, administering it, increasing the drug dose, and accelerating the dosing schedule. In one embodiment, a highly aggressive therapy includes selecting a more potent drug, administering it, increasing the drug dose, and extending the length of therapy. In one embodiment, a highly aggressive therapy includes selecting a more potent drug, administering it, accelerating the dosing schedule, and extending the length of therapy. In one embodiment, a highly aggressive therapy includes selecting a more potent drug, administering it, increasing the drug dose, accelerating the dosing schedule, and extending the length of therapy. In one embodiment, a highly aggressive therapy includes selecting a more potent drug, administering it, increasing the drug dose, accelerating the dosing schedule, and extending the length of therapy. In one embodiment, a highly aggressive therapy includes selecting a more potent drug, administering it, increasing the drug dose, accelerating the dosing schedule, and extending the length of therapy. In some embodiments, highly aggressive therapy involves administering a combination of drug-based therapies, non-drug-based therapies, or therapies based on a combination of several classes of drugs.

[0130] Therapies can be conventional or biologic. Examples of conventional, commonly considered disease-modifying antirheumatic drugs (DMARDs) include, but are not limited to, MTX, azathioprine (AZA), bucillamine (BUC), chloroquine (CQ), cyclosporine (CSA, or cyclosporine, or cyclosporine), doxycycline (DOXY), hydroxychloroquine (HCQ), intramuscular gold (IM gold), leflunomide (LEF), levofloxacin (LEV), and sulfasalazine (SSZ). Examples of other conventional therapies include, but are not limited to, folinic acid, D-pencilamine, gold auranofin, gold aurothioglucose, gold thiomalate, cyclophosphamide, and chlorambucil. Examples of biologics include, but are not limited to, biologics that target tumor necrosis factor (TNF)-alpha molecules and TNF inhibitors, such as infliximab, adalimumab, etanercept, and golimumab. Other classes of biologics include IL1 inhibitors, such as anakinra, T-cell modulators, such as abatacept, B-cell modulators, such as rituximab, and IL6 inhibitors, such as tocilizumab.

[0131] To identify additional therapeutic agents or drugs suitable for a particular subject, a test sample from the subject is also exposed to the therapeutic agent or drug and the levels of one or more biomarkers are determined. The levels of one or more biomarkers can be compared to samples from the subject before and after treatment, i.e., exposure to the therapeutic agent or drug, or compared to samples from one or more subjects who have shown improvement in inflammatory disease status or activity (e.g., clinical parameters or traditional laboratory risk factors) as a result of the treatment or exposure.

[0132] Clinical Evaluation of the Present Teachings In some embodiments of the present teachings, the MBDA score is tailored to a population, endpoint, or clinical assessment, and / or intended use. For example, the MBDA score can be used to evaluate a subject for primary prevention and diagnosis, and for secondary prevention and management. For primary assessment, the MBDA score can be used for prediction and severity stratification of future conditions or disease sequelae, for diagnosing inflammatory diseases, for prognosis and rate of change of disease activity, and for directing future diagnostic and therapeutic plans. For secondary prevention and clinical management, the MBDA score can be used for prognosis and severity stratification. The MBDA score can be used to support clinical decisions, such as deciding whether to postpone intervention or treatment, whether to recommend preventive screening for at-risk patients, whether to recommend increased frequency of visits, whether to recommend increased testing, and whether to recommend intervention. The MBDA score is also useful for selecting therapy, determining response to therapy, adjusting and administering therapy, monitoring treatment effects during treatment, monitoring therapy discontinuation, and directing the health of therapy plans.

[0133] In some embodiments of the present teachings, the MBDA score can be used to aid in the diagnosis of inflammatory disease and to determine the severity of the disease. The MBDA score can also be used to determine future intervention status in RA, for example, and to determine the prognosis of future joint erosions with or without treatment. Certain embodiments of the present teachings can be tailored to specific treatments or combinations of treatments. X-rays are currently considered the gold standard for assessing disease progression, but their capabilities are limited because subjects may have long-standing active symptomatic disease while their radiographic findings remain normal or show only minor, nonspecific changes. Conversely, subjects with what appears to be quiescent disease (subclinical disease) may progress slowly over time and remain undetected clinically until significant radiographic progression occurs. If subjects with a high likelihood of disease progression could be identified in advance, the opportunity for early, aggressive treatment would result in more effective disease outcomes. See M. Weinblatt et al., N. Engl. J. Med. 1999, 340:253-259.

[0134] Clinical variables that can be used to adjust the MBDA score include, for example, sociological / biological sex, smoking status, age, race / ethnicity, disease duration, diastolic and systolic blood pressure, resting heart rate, height, weight, adiposity, body mass index, serum leptin, family history, CCP status (i.e., whether the subject is positive or negative for anti-CCP antibodies), CCP titer, RF status, RF titer, ESR, CRP titer, menopausal status, and smoker / non-smoker.

[0135] In some embodiments of the present teachings, the minimal clinically important change in the MBDA score can be determined by receiver operating characteristic (ROC) analysis. The optimal threshold of change in the MBDA score can be associated with clinical improvement in any of the clinical assessments described herein, such as, but not limited to, DAS28-ESR or DAS28-CRP.

[0136] System for performing disease activity tests Tests for measuring disease activity according to various embodiments of the present teachings can be performed on a variety of systems typically used to obtain test results, such as results from immunological or nucleic acid detection assays. Such systems may include modules for automating sample preparation, automating tests such as measuring biomarker levels, facilitating testing of multiple samples, and / or programming each sample to perform the same or different tests. In some embodiments, the test system includes one or more of a sample preparation module, a clinical chemistry module, and an immunoassay module on a single platform. Test systems are typically also designed to include modules for collecting, storing, and tracking results by connecting to and utilizing databases residing on the hardware. Examples of these modules include physical and electronic data storage devices well known in the art, such as hard drives, flash memory, and magnetic tape. Test systems also typically include a module for reporting and / or visualizing results. Some examples of reporting modules include a visual display, a graphical user interface that interfaces to a database, a printer, etc. See the Machine-Readable Storage Media section below.

[0137] One embodiment of the present invention includes a system for determining inflammatory disease activity in a subject. In some embodiments, the system includes a module that applies a formula to inputs including measured levels of biomarkers in a panel, as described above, and outputs a score. In some embodiments, the measured biomarker levels are test results that serve as inputs to a computer programmed to apply the formula. The system can include other inputs, in addition to or in combination with one or more clinical parameters, such as therapy regimen, TJC, SJC, morning stiffness, arthritis in three or more joint areas, arthritis in the wrist, symmetric arthritis, rheumatoid nodules, radiographic changes and other imaging, sociological / biological sex, age, race / ethnicity, disease duration, height, weight, body mass index, family history, CCP status, RF status, ESR, smoker / non-smoker, etc., to derive an output score. In some embodiments, the system can apply a formula to biomarker level inputs and then output a disease activity score that can be analyzed with other inputs, such as other clinical parameters. In other embodiments, the system is designed to apply a formula to both biomarker and non-biomarker inputs (such as clinical parameters) and report a composite output disease activity index.

[0138] Many tests are currently available for use in practicing various embodiments of the present teachings, such as the ARCHITECT Series of Integrated Immunochemistry Systems - High-Throughput, Automated, Clinical Chemistry Analyzers (ARCHITECT is a registered trademark of Abbott Laboratories, Abbott Park, Ill. 60064). C. Wilson et al., “Clinical Chemistry Analyzer Sub-System Level Performance,” American Association for Clinical Chemistry Annual Meeting, Chicago, Ill., Jul. 23-27, 2006; and H. J. Kisner, “Product development: the making of the Abbott ARCHITECT,” Clin. Lab. Manage. Rev. 1997 Nov.-Dec., 11(6):419-21; A. Ognibene et al., “A new modular chemiluminescence immunoassay analyzer evaluation,” Clin. Chem. Lab. Med. 2000 March, 38(3):251-60; JW Park et al., “Three-year experience in using total laboratory automation system,” Southeast Asian J. Trop. Med. Public Health 2002, 33 Suppl 2:68-73; D. Pauli et al., “The Abbott Architect c8000: analytical See “Performance and productivity characteristics of a new analyzer applied to general chemistry testing,” Clin. Lab. 2005, 51(1-2):31-41.

[0139] Another testing system useful in embodiments of the present teachings is the VITROS system (VITROS is a registered trademark of Johnson & Johnson Corp., New Brunswick, NJ) - a chemical analysis device used to generate test results from blood and other body fluids for laboratories and clinics. Another testing system is the DIMENSION system (DIMENSION is a registered trademark of Dade Behring Inc., Deerfield Ill.) - a bioanalytical system that includes computer software and hardware to operate the analyzer and analyze data generated by the analyzer.

[0140] Tests required in various embodiments of the present teachings, such as measuring biomarker levels, can be performed by laboratories such as those approved under the Clinical Laboratory Improvement Act (42 U.S.C. Section 263(a)), or any other federal or state law, or any national, state, or county law governing the operation of laboratories that analyze samples for clinical purposes. Such laboratories include, for example, Laboratory Corporation of America, 358 South Main Street, Burlington, NC 27215 (headquarters); Quest Diagnostics, 3 Giralda Farms, Madison, NJ 07940 (headquarters); and other reference and clinical chemistry laboratories.

[0141] kit Other embodiments of the present teachings include biomarker detection reagents packaged together in the form of a kit for performing any of the assays of the present teachings. In certain embodiments, the kit includes oligonucleotides that specifically identify one or more biomarker nucleic acids based on homology and / or complementarity with the biomarker nucleic acids. The oligonucleotide sequences can correspond to fragments of the biomarker nucleic acids. For example, the oligonucleotides are greater than 200, 200, 150, 100, 50, 25, 10, or less than 10 nucleotides in length. In other embodiments, the kit includes an antibody against a protein encoded by the biomarker nucleic acid. The kits of the present teachings can also include an aptamer. The kit may contain, in separate containers, nucleic acids or antibodies (either bound to a solid matrix or packaged separately with reagents for binding to a matrix), control formulations (positive and / or negative), and / or detectable labels such as, but not limited to, fluorescein, green fluorescent protein, rhodamine, cyanine dyes, Alexa dyes, luciferase, and radiolabels. Instructions for performing the assay, optionally including instructions for generating an MBDA score, may be included in the kit; for example, written, on a tape, a VCR, or a CD-ROM. The assay may be in the form of, for example, a Northern hybridization or a sandwich ELISA, as known in the art.

[0142] In some embodiments of the present teachings, biomarker detection reagents are immobilized on a solid matrix, such as a porous strip, to form at least one biomarker detection site. In some embodiments, the measurement or detection region of the porous strip includes multiple sites containing nucleic acids. In some embodiments, the test strip also contains sites for negative and / or positive controls. Alternatively, control sites can be located on a strip separate from the test strip. If desired, different detection sites can contain different amounts of immobilized nucleic acid, e.g., a higher amount in the first detection site and a lower amount in subsequent sites. Upon addition of test sample, the number of sites displaying a detectable signal quantitatively indicates the amount of biomarker present in the sample. The detection sites can be configured in any suitable detectable shape, such as a bar or dot spanning the width of the test strip.

[0143] In other embodiments of the present teachings, the kit can contain a nucleic acid substrate array containing one or more nucleic acid sequences. The nucleic acids on the array specifically identify one or more nucleic acid sequences representing MBDA markers. In various embodiments, expression of one or more of these sequences represented by MBDA markers can be identified by binding to the array. In some embodiments, the substrate array can be on a solid substrate, known as a "chip." See, e.g., U.S. Patent No. 5,744,305. In some embodiments, the substrate array can be a solution array; see, e.g., xMAP (Luminex, Austin, TX), Cyvera (Illumina, San Diego, CA), RayBio Antibody Arrays (RayBiotech, Inc., Norcross, GA), CellCard (Vitra Bioscience, Mountain View, CA), and Quantum Dots' Mosaic (Invitrogen, Carlsbad, CA).

[0144] machine-readable storage medium A machine-readable storage medium can include, for example, a data storage material coated with machine-readable data or a data array. When programmed with instructions for using the data, the data and machine-readable storage medium can be used for a variety of purposes. Such purposes include, without limitation, storing, accessing, and manipulating the inflammatory disease activity of a subject or population over time, or disease activity in response to inflammatory disease treatment, or drug delivery for inflammatory diseases. Data, including measurements of biomarkers of the present teachings, and / or assessment of disease activity or disease status from these biomarkers, can be implemented in a computer program running on a computer, including a processor, a data storage system, one or more input devices, one or more output devices, etc. The program code is applied to the input data to perform the functions described herein and generate output information. This output information can then be applied to one or more output devices, according to methods well known in the art. The computer can be, for example, a personal computer, microcomputer, or workstation of conventional design.

[0145] The computer programs can be implemented in a high-level procedural or object-oriented programming language to communicate with a computer system, such as the computer system illustrated in FIG. 2. The programs can also be implemented in a machine or assembly language. The programming language can also be a compiled or interpreted language. Each computer program can be stored on a storage medium or device, such as a ROM or magnetic disk, and can be readable by a programmed computer to configure and operate the computer when the storage medium or device is read by the computer and executes the procedures described therein. Any health-related data management system of the present teachings can be implemented as a computer-readable storage medium, configured with a computer program, which causes the computer to perform various functions in a specific manner, as described herein.

[0146] The biomarkers disclosed herein can be used to generate a "subject biomarker profile" taken from a subject with an inflammatory disease. The subject biomarker profile can then be compared to a reference biomarker profile to diagnose or identify the subject with an inflammatory disease, monitor the progression or rate of progression of the inflammatory disease, or monitor the effectiveness of a treatment for the inflammatory disease. The biomarker profiles, reference, and subject of embodiments of the present teachings can be contained on a machine-readable medium, such as an analog tape readable by a CD-ROM or USB flash drive, for example. Such machine-readable media can also contain additional test results, such as measurements of clinical parameters and clinical assessments. The machine-readable medium also includes subject information; for example, the subject's medical history or family history. The machine-readable medium also contains information related to other disease activity algorithms and calculated scores or indexes, such as those described herein. [Example]

[0147] Aspects of the present teachings can be further understood in light of the following examples, which should not be construed as limiting the scope of the present teachings in any way.

[0148] The practice of the present teachings will employ, unless otherwise indicated, conventional methods of protein chemistry, biochemistry, recombinant DNA technology, and pharmacology within the skill of the art. Such techniques are fully explained in the literature. See, e.g., T. Creighton, Proteins: Structures and Molecular Properties, 1993, W. Freeman and Co.; A. Lehninger, Biochemistry, Worth Publishers, Inc. (current addition); J. Sambrook et al., Molecular Cloning: A Laboratory Manual, 2nd Edition, 1989; Methods In Enzymology, S. Colowick and N. Kaplan, eds., Academic Press, Inc.; Remington's Pharmaceutical Sciences, 18th Edition, 1990, Mack Publishing Company, Easton, PA; Carey and Sundberg, Advanced Organic Chemistry, Vols. A and B, 3rd Edition, 1992, Plenum Press.

[0149] The practice of the present teachings employs statistical analysis that is conventional within the art unless otherwise indicated, and such techniques are explained fully in the literature. For example, J. Little and D. Rubin, Statistical Analysis with Missing Data, 2nd Edition 2002, John Wiley and Sons, Inc., NJ; M. Pepe, The Statistical Evaluation of Medical Tests for Classification and Prediction (Oxford Statistical Science Series) 2003, Oxford University Press, Oxford, UK; X. Zhoue et al., Statistical Methods in Diagnostic Medicine 2002, John Wiley and Sons, Inc., NJ; See T. Hastie et. al, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition 2009, Springer, NY; W. Cooley and P. Lohnes, Multivariate procedures for the behavioral science 1962, John Wiley and Sons, Inc. NY; E. Jackson, A User's Guide to Principal Components 2003, John Wiley and Sons, Inc., NY.

[0150] Example 1: Adjustment of MBDA scores This example demonstrates the development of an adjusted multi-biomarker disease activity (MBDA) score for rheumatoid arthritis (RA) that accounts for age, biological sex, and adiposity as improved predictors of risk of radiographic damage.

[0151] background The MBDA score is a validated tool that quantifies 12 serum protein biomarkers to assess disease activity in adult patients with rheumatoid arthritis (RA) (Curtis JR, et al., Arthritis Care Res. 64:1794-803 (2012)). The 12 MBDA serum protein biomarkers are VCAM-1, EGF, VEGF-A, IL-6, TNFRI, MMP-1, MMP-3, YKL-40, leptin, resistin, SAA, and CRP. The derivation of these 12 biomarkers is fully described in U.S. Patent No. 9,200,324, which is incorporated by reference in its entirety.

[0152] This example demonstrates the development and validation of an adjusted MBDA score that accounts for the effects of age, socioeconomic sex, and total body fat. Body mass index (BMI) or serum leptin was used as a surrogate for total body fat.

[0153] method MBDA scores were adjusted to account for age and biological sex using data from 325,781 RA patients whose physicians ordered MBDA testing as part of their routine medical care. De-identified serum samples were primarily tested at the Crescendo Bioscience, Inc. (South San Francisco, CA, USA) laboratory. Concentrations of 12 MBDA protein biomarkers, including VCAM-1, EGF, VEGF-A, IL-6, TNFRI, MMP-1, MMP-3, YKL-40, leptin, resistin, serum amyloid A, and CRP, were measured using a multiplexed sandwich immunoassay (Mesoscale Discovery, Rockville, MD, USA). Concentration values ​​were then matched using a validated algorithm that generates integer scores on a 1-100 scale. The immunoassay equipment, reagents, and algorithms used in the VectraDA® commercial test, manufactured by Crescendo Bioscience, Inc. (South San Francisco, CA, USA), were used to measure various samples over the period 2012 to 2016. Results were applied to a separate cohort of 1,411 patients from five studies / registries (CERTAIN, InFoRM, RACER, BRASS, and OPERA) to quantify the effects of BMI and, in separate models, serum leptin, resulting in two adjusted MBDA scores. Both types of adjusted MBDA scores use the same low, moderate, and severe disease activity cutpoints as the original MBDA score.

[0154] cohort INFORM (Index for Rheumatoid Arthritis Measurement) cohort: 459 patients from a multicenter longitudinal observational study of North American RA patients. Clinical characteristics and laboratory details have been previously described.

[0155] RACER (Rheumatoid Arthritis Comparative Effectiveness Research) Cohort: This experimental cohort includes enrolled subjects from the University of Pittsburgh Medical Center (UPMC) registry. Since its inception in February 2010, RACER has enrolled patients aged 18 years and older who were diagnosed with RA by a rheumatologist and followed by UPMC rheumatologists.

[0156] CERTAIN cohort: 105 patients fulfilling ACR criteria for RA with moderate disease activity (CDAI>10) were recruited through a network of private and academic hospitals. Recruitment occurred during routine patient visits that select patients for treatment with biologics.

[0157] OPERA (Optimal Treatment for Patients with Early Arthritis) Cohort: 180 treatment-naive patients with early arthritis were recruited through rheumatology departments in Copenhagen, Herlev, Grosten, Aarhus, Odense, Silkeborg, Vejle, Hjorring, Aalborg, and Vyborg, Denmark. Patients were randomized to stepwise treatment with oral methotrexate and either adalimumab (n=89) or placebo (n=91). Glucocorticoids were injected into up to four swollen joints per visit. Hand and foot radiographs (n=164) at 0 and 12 months were assessed with the modified Sharp-van der Heijde total Sharp score (TSS). A minimal detectable change (1.8 TSS units) defined radiographic progression (i.e., ΔTSS ≥ 2).

[0158] BRASS (Brigham Rheumatoid Arthritis Sequential Study) cohort: 424 RA patients were studied from a prospective observational cohort at the Brigham and Women's Arthritis Center in Boston, Massachusetts. Clinical characteristics and laboratory details have been previously described.

[0159] Serum samples from healthy controls (n=318) were obtained from a commercial source (BioreclamationIVT) and from family members of patients diagnosed with cancer admitted to Fox Chase Cancer Center. Ages ranged from 20 to 80 years, with a mean age of 53 years and 63% women. Control subjects had no history of acute or chronic illness and were not taking any medications other than vitamin supplements and intermittent nonsteroidal anti-inflammatory drugs. All serum samples were processed within 4 hours of phlebotomy and stored at -70°C.

[0160] Crescendo Bioscience Inc. Clinical Trial Cohort: Vectra, marketed prior to June 2017 as part of routine care by US rheumatologists (R) 325,781 patients with confirmed RA were tested with the DA test. Serum samples were obtained from Clinical Laboratory Improvement Amendments (CLIA), New York State, and College of American Pathologists (CAP)-certified laboratories and were de-identified for inclusion in the research study. Patients over 89 years of age were excluded because age over 90 years was considered private information. For patients who were tested more than once, the first test was included.

[0161] The clinical and demographic characteristics of the various health and RA study cohorts are summarized in Table 1 .

[0162] [Table 1]

[0163] Clinical endpoints Disease activity was measured using a scale described as "DAS28*." DAS28* was calculated as DAS28-CRP without the calculated erythrocyte sedimentation rate (ESR) or CRP molecular components. DAS28* was designed to a) allow comparison between MBDA scores and a clinically based composite measure of disease activity that lacks a blood test component; b) avoid overlapping components; and c) avoid the need for physician global assessment, which is unavailable from all five clinical studies / registries. Previously, analogous methods had been used (Bakker et al., 2012; Curtis et al., 2012).

[0164] The OPERA and BRASS studies collected radiographic data. Radiographic progression was measured as the change in mTSS per year. All patients from the OPERA cohort who had radiographs at baseline and 1 year were included in this analysis. The BRASS cohort included all patients who had an x-ray taken within 6 months of the baseline visit and a second x-ray taken between 9 months and 3 years after the first x-ray. Because only the wrists were x-rayed in the BRASS study, mTSS values ​​from the BRASS study were scaled by a factor of 448 / 280 for this analysis. The change in mTSS per year (ΔmTSS) was calculated as the difference in mTSS between follow-up and baseline divided by the time to follow-up.

[0165] Leptin-regulated MBDA score The effects of age, biological sex, and serum leptin concentration on MBDA scores were estimated using a commercial cohort (n = 325,781). A linear model was fitted with MBDA scores as the response variable and age, biological sex, and serum leptin concentrations, as well as their significant (α = 0.01) interaction, as predictors. The relationship between MBDA scores and serum leptin concentrations was observed to be visually nonlinear when leptin levels were left untransformed or transformed using a logarithm. Consequently, power-transformed serum leptin concentrations were included in the linear model, and the exponent of the power transformation was determined by maximizing the likelihood of the linear fit. The estimated effects of age, biological sex, and leptin were subtracted from the MBDA scores in the commercial cohort, and a constant was calculated that ensured the adjusted scores had the same mean as the original MBDA scores. This constant, combined with age, biological sex, and leptin, was used to define the leptin-adjusted MBDA scores.

[0166] BMI-adjusted MBDA score Because BMI was not available for patients in the commercial cohort, the effects of age, biological sex, and BMI were estimated using continuous models in two separate data sets. First, the effects of age and biological sex were estimated using the commercial cohort. A linear model was fitted with MBDA score as the response variable and age, biological sex, and the correlation between age and biological sex as predictors. MBDA scores from the CERTAIN, InFoRM, RACER, OPERA, and BRASS cohorts were adjusted for the effects of age and biological sex by subtracting a linear combination of age, biological sex, and their correlation from the original MBDA score to create an intermediate MBDA score. The effect of BMI on the intermediate MBDA score was then estimated. Each biology was fitted.

[0167] After subtracting the effects of age, sex, and BMI from the MBDA scores of the clinical cohort, constants were calculated to match the mean adjusted scores to the mean original Vectra scores for each sex. These constants, combined with the estimated effects of age, sex, and BMI, were used to define the BMI-adjusted MBDA scores.

[0168] BMI-adjusted MBDA score Validation of the adjusted MBDA score The original and BMI- and leptin-adjusted MBDA scores were compared in terms of their ability to predict disease progression. Univariate regression models were fitted with DAS28* as the response and each MBDA score as a predictor. Additionally, a multiple linear regression model of DAS28* was fitted with paired MBDA scores as simultaneous predictors. Similarly, the association between radiographic progression and each MBDA score was assessed using a univariate linear regression model. To directly compare MBDA scores, they were combined pairwise in a multiple linear regression analysis of ΔmTSS.

[0169] result Leptin as a surrogate for BMI in RA To investigate a suitable surrogate for adiposity, serum leptin levels and BMI were measured in a cohort of healthy patients without RA and a cohort of healthy patients with RA (Figure 1). Leptin showed a significant positive correlation with BMI in healthy men (r = 0.69) and women (r = 0.66) and in RA patients (r = 0.69 and 0.69 for men and women, respectively). Therefore, serum leptin was used as a surrogate for body fat percentage, i.e., BMI, in RA.

[0170] Leptin-regulated MBDA score Because age, biological sex, and adiposity may be confounding factors for MBDA scores, we examined the relationship between these variables and MBDA scores in a well-powered cohort of RA patients representing a wide range of values. As previously described, serum leptin levels were measured as a proxy for fat mass or adiposity. Age, biological sex, serum leptin, and MBDA scores were available for 325,781 patients who underwent commercial testing. MBDA scores tended to be higher in patients with higher serum leptin concentrations, particularly in younger patients (e.g., aged 15–30 years). This association was not prevalent in older age groups and was absent in the oldest age group (aged 75–90 years) (Figure 2). MBDA scores also increased with age in both women and men. However, mean MBDA scores were lower for younger men and increased slightly faster with age than women, with the distribution of scores being similar at older ages (Figure 3).

[0171] The relationship between MBDA score and leptin was nonlinear and best described exponentially; the exponent for leptin maximizing its ability to predict MBDA score was 0.58. In this model, all three two-way interactions between age, biological sex, and serum leptin concentration were significant. Effects are summarized in Table 2.

[0172] [Table 2]

[0173] A model of the leptin-adjusted MBDA score was constructed by adjusting for the direct interactions between MBDA score and age, socio-gender, and leptin concentration, as well as their two-way interactions. It was derived by combining adjustments for these interactions with a constant value (33.9) to maintain the same mean value as the original MBDA score in the commercial cohort, and is given by the following formula:

[0174]

number

[0175] In this algorithm, 1 男性 (Biological sex) is 1 if the patient's biological sex is male, and 0 otherwise.

[0176] BMI-adjusted MBDA score Body mass index (BMI) is a common biometric parameter significantly associated with adiposity. BMI data were not available for the commercial study cohort. To test whether correction for BMI is a comparable alternative to leptin adjustment, MBDA for age and sociological sex was first adjusted for the commercial cohort. The best linear fit of MBDA score as a function of only age, sociological sex, and their interaction in the commercial data, excluding serum leptin concentrations from consideration, is given by the following formula:

[0177]

number

[0178] Next, we have 1,411 patients with MBDA scores, age, biological sex, and BMI from the five clinical cohorts (BRASS, CERTAIN, InFoRM, OPERA, and RACER). The effects of age and biological sex were removed from these patients' MBDA scores to create a mean MBDA score. The effect of BMI on the mean MBDA score was significantly different between biological sexes (interaction p-value = 0.0043). The effect of BMI was -0.203 (-0.575, 0.170) per unit in men and 0.384 (0.233, 0.535) per unit in women.

[0179] Combining the age and sex adjustments from the commercial patient cohort with the BMI adjustments from the clinical study, while maintaining the mean score for each sex, yields an adjusted MBDA score:

[0180]

number

[0181] In this algorithm, 1 男性 (Biological sex) is 1 if the patient's biological sex is male, and 0 otherwise.

[0182] Validation of the adjusted MBDA score To investigate which biomarkers correlate most strongly with DAS28*, the association of DAS28* with each of the MBDA scores and with CRP expression was evaluated in the same set of 1,411 patients, where BMI adjustment was performed. Both CRP, the original MBDA score, and the adjusted MBDA score were separately adjusted for DAS28*. The adjustments were 0.34 (p-value = 1.3 x 10-39) for CRP (logarithmic scale), 0.38 (p-value = 2.7 x 10-48) for the original MBDA score, 0.39 (p-value = 2.3 x 10-52) for the BMI-adjusted MBDA score, and 0.40 (p-value = 4.6 x 10- -54 ) was.

[0183] In a linear regression model in which DAS28* was the response variable and leptin-adjusted MBDA score and either CRP or the base 10 logarithm of the original MBDA score were predictors, leptin-adjusted MBDA score was statistically significant (p = 1.6 × 10, respectively). -16 and 2.3 × 10 -7 ), but not CRP (p=0.18) or original MBDA score (p=0.60). BMI-adjusted MBDA score (p=9.2×10 -15 and 1.6 × 10 -5This was also true when Leptin-adjusted MBDA score (p=0.21) was included with either CRP (p=0.21) or the original MBDA score (p=0.93). However, when the two adjusted MBDA scores were combined in the same model, the leptin-adjusted score (p=0.0048) was a significant predictor of DAS28*, but the BMI-adjusted score (p=0.71) was not.

[0184] Validation of the ability of the leptin-adjusted MBDA score to predict DAS28 and radiographic progression using multivariate models with the original score is shown in Table 3.

[0185] [Table 3]

[0186] Validation of the ability of the leptin-adjusted MBDA score to predict DAS28 and radiographic progression using multivariate models with the BMI-adjusted score is shown in Table 4.

[0187] [Table 4]

[0188] In univariate analysis of the combined OPERA and BRASS cohorts (n = 555), significant variables predicting ΔmTSS were leptin-adjusted MBDA score (Fig. 4), RF or anti-CCP seropositivity, BMI-adjusted MBDA score, MBDA score, BMI, CRP, baseline TSS, disease duration, DAS28-CRP, and DAS28* (Table 5).

[0189] [Table 5]

[0190] The biomarker most highly correlated with radiographic progression was leptin-adjusted MBDA, with the second most highly correlated variable of note being rheumatoid factor and / or anti-cyclic citrullinated peptide positivity (anti-cyclic citrullinated peptide). The three MBDA scores and DAS28-CRP were included as pairs in a regression model predicting ΔmTSS. The BMI-adjusted (p = 0.0027) and leptin-adjusted (p = 0.00066) MBDA scores were significant after adjusting for DAS28-CRP (p = 0.87 and 0.74, respectively), and the leptin-adjusted MBDA score was significant after adjusting for either MBDA (p = 0.34) or the BMI-adjusted MBDA score (p = 0.11, respectively). Of these MBDA score versions, the leptin-adjusted MBDA score was more correlated with radiographic progression in RA. The probability of radiographic progression by leptin regulation score is shown in Figure 5. DAS28* is DAS28 without the CRP or ESR components.

[0191] The multivariate linear regression of ΔmTSS is shown in Table 6. All variables (except BMI) were included in the multivariate linear regression of ΔmTSS, and backward selection was applied, iteratively removing the least significant variables first, until only significant variables remained.

[0192] [Table 6]

[0193] Relevance of leptin-regulated MBDA Three analyses were performed to measure the degree to which a given patient's leptin-adjusted MBDA score differed from the original score and the degree to which the actual patient's score changed numerically and reclassified between MBDA score categories. Figure 7 shows the deviation of the leptin-adjusted MBDA score from the original MBDA score for a wide range of age and serum leptin combinations. The topography of these relationships differs for men and women, and for that reason, biological sex is displayed separately. Young age or low leptin concentrations significantly upweighted the MBDA score, while old age or excessive adiposity significantly downweighted the MBDA score.

[0194] In the commercial cohort, the leptin-adjusted score differed from the original MBDA score by -9 to +11 points for 90% of patients (Figure 2). Table 7 shows the extent to which application of the leptin-adjusted MBDA score resulted in reclassification of RA patients from their original MBDA category using the commercial cohort. From the original low category, 24% of RA patients moved to the moderate group with the use of the leptin-adjusted MBDA score; from the moderate group, 9% moved to the low group and 12% to the severe group; and from the severe group, 21% were reassigned to the moderate category.

[0195] [Table 7]

[0196] conclusion An adjusted MBDA score, combining molecular and biometric variables, taking into account age, biological sex, and adiposity, was developed and significantly outperformed DAS28-CRP and the original MBDA score in predicting the rate of radiographic joint damage progression. Adjusting the MBDA score for leptin instead of BMI resulted in a better predictor of disease activity and radiographic progression. Results suggest that the leptin-adjusted MBDA score may offer improved clinical utility for the personalized management of patients with RA.

[0197] Example 2: Effect of age and BMI on MBDA scores in patients with RA This example demonstrates the association between multi-biomarker disease activity (MBDA) score and age, and between MBDA score and body mass index (BMI) in patients with rheumatoid arthritis (RA).

[0198] method Data for this retrospective study came from CORRONA, a longitudinal RA registry consisting of >625 US rheumatologists across 40 states. Included patients had to have undergone MBDA testing between 1 month before and 7 days after their CORRONA visit, which was the source of patient characteristics and clinical data. MBDA scores were categorized as low (<30), moderate (30-44), and severe (>44). Age was categorized in 10-year increments (<40, 40-49, 50-59, 60-69, 70-79, and >80 years). BMI was ≤25, >25-30, >30-<35, and ≥35 kg / m. 2 The association between age or BMI and MBDA score was assessed using chi-square and trend tests.

[0199] result There were 878 patients: 77.9% were women, with a mean age of 60.9 years, mean weight of 177.6 pounds, and mean RA disease duration of 10.7 years. Approximately half of patients (54%) were using methotrexate or other conventional DMARDs (21%), and nearly half (45%) were using biologics (Table 7). The mean MBDA score was 42.6, with 18% in the low, 38% in the moderate, and 44% in the high MBDA categories. The distribution of patients across low, moderate, and high MBDA categories was significantly associated with age in 10-year increments by both chi-square (p=0.001) and trend (p<0.0001) (Figure 6). MBDA category was also significantly associated with BMI (chi-square p=0.001; trend p<0.0001) (Table 7). Low MBDA scores were observed in 135 of 545 (24.8%) patients with a BMI ≤ 30 and 6 of 142 (4.2%) patients with an MBDA score ≥ 35 (Table 7). Conversely, high MBDA scores were observed in 196 of 545 (36.0%) patients with a BMI ≤ 30 and 91 of 142 (64.1%) patients with a BMI ≥ 35 (Table 8).

[0200] [Table 8]

[0201] Table 8 shows the overall characteristics of the patients, according to their MBDA classification. Percentages reported are column percentages for age and BMI classification (percentages with low, moderate, or high MBDA scores within each age and BMI classification); row percentages elsewhere (each MBDA classification).

[0202] conclusion Age and BMI were each found to have a significant association with MBDA scores. These data suggest that inflammatory biomarkers in RA are influenced by non-RA-related factors.

[0203] All publications and patent applications cited in this specification are herein indicated to be specifically and individually indicated to be incorporated by reference.

[0204] Although the foregoing invention has been described in some detail by way of illustration and example, for purposes of clarity of understanding, those skilled in the art will appreciate that, in light of the teachings of this invention, certain changes and modifications may be made thereto without departing from the spirit and scope of the invention, as set forth in the appended claims.

Claims

1. 1. A method for assessing inflammatory disease activity in a subject, the method comprising: At least one immunoassay is performed on the blood sample obtained from the subject to generate a test expression score including protein expression level data for at least two protein markers, wherein the at least two protein markers are chitinase 3-like 1 (cartilage glycoprotein-39) (CHI3L1); C-reactive protein, pentraxin-related (CRP); epidermal growth factor (beta-urogastrone) (EGF); interleukin 6 (interferon, beta 2) (IL6); leptin (LEP); matrix metallopeptidase 1 (interstitial collagenase) (MMP1); matrix metallopeptidase 3 (stromelysin 1, progelatinase) (MMP3); resistin (RETN); serum amyloid A1 (SAA1); tumor necrosis factor receptor superfamily, member 1A (TNFRSF1A); vascular cell adhesion molecule 1 (VCAM1); and vascular endothelial growth factor A (VEGFA). wherein the test expression score is generated by (1) weighting the protein expression level data for each protein marker by a predefined coefficient, and then (2) combining the weighted expressions; and providing a disease activity score by combining the test expression score with at least one test clinical score representative of clinical variables, wherein the clinical variables include age, biological sex, and serum leptin; wherein said inflammatory disease activity is rheumatoid arthritis (RA) disease activity.

2. The method of claim 1 , wherein the clinical variables further comprise at least one selected from sociometric sex, adiposity, and body mass index (BMI).

3. 3. The method of claim 1 or 2, wherein the disease activity score predicts the likelihood of radiographic progression, flare-ups, or joint damage of RA in the subject.

4. Performing the at least one immunoassay comprises: contacting the obtained blood sample with a plurality of distinct reagents, the reagents comprising the protein markers; generating a plurality of distinct complexes between the reagent and the marker; and detecting said complex and generating said data; The method according to any one of claims 1 to 3, comprising:

5. The method of any one of claims 1 to 4, wherein said at least one immunoassay comprises a multiplex assay.

6. 6. The method of any one of claims 1 to 5, wherein the disease activity score is on a scale of 1 to 100; a disease activity score of 1 to 29 indicates a low level of disease activity, a disease activity score of 30 to 44 indicates a moderate level of disease activity, and a disease activity score of 45 to 100 indicates a high level of disease activity.

7. 7. The method of any one of claims 1 to 6, wherein the disease activity score is predictive of radiographic progression; the disease activity score is on a scale of 1 to 100; a disease activity score of 1 to 29 indicates a low probability of radiographic progression, a disease activity score of 30 to 44 indicates a moderate probability of radiographic progression, and a disease activity score of 45 to 100 indicates a high probability of radiographic progression.

8. 2. The method of claim 1, wherein in the at least one immunoassay, the at least two protein markers include IL6, EGF, VEGFA, LEP, SAA1, VCAM1, CRP, MMP1, MMP3, TNFRSF1A, RETN, and CHI3L1.

9. 1. A method for generating quantitative data about a subject, comprising: At least one immunoassay is performed on a first sample from a subject having or suspected of having an inflammatory disease to detect antibodies against chitinase 3-like 1 (cartilage glycoprotein-39) (CHI3L1); C-reactive protein, pentraxin-related (CRP); epidermal growth factor (beta-urogastrone) (EGF); interleukin 6 (interferon, beta 2) (IL6); leptin (LEP); matrix metallopeptidase 1 (interstitial collagenase) (MMP1); matrix metallopeptidase 3 (stromelysin 1, progelatinase) (MMP3); resistin (RETN); serum amyloid A1 (SAA1); tumor necrosis factor receptor superfamily, member 1A (TNFRSF1A); vascular cell adhesion molecule 1 (VCAM1); and vascular endothelial growth factor A. generating a first dataset comprising data indicative of predetermined expression levels for at least two biomarkers, the predetermined expression levels comprising at least two markers selected from (VEGFA); generating said first dataset by weighting the predetermined expression levels of each protein marker by a predefined coefficient; generating a second dataset comprising at least one test clinical score indicative of clinical variables, wherein said clinical variables comprise age, biological sex, and serum leptin; and generating said quantitative data by combining the first and second data sets; wherein the inflammatory disease is rheumatoid arthritis (RA).

10. 10. The method of claim 9, wherein the clinical variables further comprise at least one selected from socio-gender, adiposity, and body mass index (BMI).

11. Performing the at least one immunoassay comprises: contacting the obtained first blood sample containing the protein marker with a plurality of distinct reagents; generating a plurality of distinct complexes between the reagent and the marker; and detecting said complex and generating said data; 11. The method of claim 9 or 10, comprising:

12. The method of any one of claims 9 to 11, wherein said at least one immunoassay comprises a multiplex assay.

13. 1. A method of recommending a therapy regimen in a subject having an inflammatory disease, the method comprising: At least one immunoassay is performed on the blood sample obtained from the subject to generate a test expression score including protein expression level data for at least two protein markers, wherein the at least two protein markers are chitinase 3-like 1 (cartilage glycoprotein-39) (CHI3L1); C-reactive protein, pentraxin-related (CRP); epidermal growth factor (beta-urogastrone) (EGF); interleukin 6 (interferon, beta 2) (IL6); leptin (LEP); matrix metallopeptidase 1 (interstitial collagenase) (MMP1); matrix metallopeptidase 3 (stromelysin 1, progelatinase) (MMP3); resistin (RETN); serum amyloid A1 (SAA1); tumor necrosis factor receptor superfamily, member 1A (TNFRSF1A); vascular cell adhesion molecule 1 (VCAM1); and vascular endothelial growth factor A. (VEGFA), and the test expression score is generated by (1) weighting the protein expression level data for each protein marker by a predefined coefficient, and then (2) combining the weighted expressions; providing a first disease activity score by combining the test expression score with at least one test clinical score indicative of at least one clinical variable, wherein the clinical variable comprises age, biological sex, and serum leptin; A second immunoassay is performed on a second blood sample from the subject to generate a second test expression score comprising protein expression level data for at least two protein markers, wherein the at least two protein markers are chitinase 3-like 1 (cartilage glycoprotein-39) (CHI3L1); C-reactive protein, pentraxin-related (CRP); epidermal growth factor (beta-urogastrone) (EGF); interleukin 6 (interferon, beta 2) (IL6); leptin (LEP); matrix metallopeptidase 1 (interstitial collagenase) (MMP1); matrix metallopeptidase 3 (stromelysin 1, progelatinase) (MMP3); resistin (RETN); serum amyloid A1 (SAA1); tumor necrosis factor receptor superfamily, member 1A (TNFRSF1A); vascular cell adhesion molecule 1 (VCAM1); and vascular endothelial growth factor A. (VEGFA), and generating a second disease activity score by combining said second test expression score with at least one second test clinical score indicative of clinical variables, wherein said clinical variables include age, biological sex, and serum leptin; determining a clinically important change between the first and second disease activity scores based on a difference between the first and second disease activity scores; and i) reducing the aggressiveness of the therapy plan when a clinically significant change is determined; or ii) not changing the therapy plan when a clinically significant change is determined not to exist. To recommend wherein the inflammatory disease is rheumatoid arthritis (RA).

14. 14. The method of claim 13, wherein the clinical variables further comprise at least one selected from socio-gender, adiposity, and body mass index (BMI).

15. 15. The method of claim 13 or 14, wherein the first and second disease activity scores predict the likelihood of radiographic progression, flare-ups, or joint damage of RA in the subject.

16. Performing the at least one immunoassay comprises: contacting the obtained first blood sample containing the protein marker with a plurality of distinct reagents; generating a plurality of distinct complexes between the reagent and the marker; and detecting said complex and generating said data; The method according to any one of claims 13 to 15, comprising:

17. The method of any one of claims 13 to 16, wherein said at least one immunoassay comprises a multiplex assay.

18. 18. The method of any one of claims 13 to 17, wherein the first and second disease activity scores are on a scale of 1 to 100; a disease activity score of 1 to 29 indicates a low level of disease activity, a disease activity score of 30 to 44 indicates a moderate level of disease activity, and a disease activity score of 45 to 100 indicates a high level of disease activity.

19. 19. The method of any one of claims 13 to 18, wherein the first and second disease activity scores are predictive of radiographic progression; and the disease activity scores are on a scale of 1 to 100; a disease activity score of 1 to 29 indicates a low likelihood of radiographic progression, a disease activity score of 30 to 44 indicates a moderate likelihood of radiographic progression, and a disease activity score of 45 to 100 indicates a high likelihood of radiographic progression.

20. 20. The method of any one of claims 13 to 19, wherein in the at least one immunoassay and the second immunoassay, the at least two protein markers comprise IL6, EGF, VEGFA, LEP, SAA1, VCAM1, CRP, MMP1, MMP3, TNFRSF1A, RETN, and CHI3L1.

21. 14. The method of claim 13, wherein reducing the aggressiveness of the therapy plan comprises delaying treatment, selecting a less effective drug, reducing the frequency of treatment, shortening the length of therapy, reducing drug doses, slowing down the dosing schedule, or administering only non-drug based therapies, or a combination thereof.

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