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METHODS AND MATERIALS FOR ASSESSING AND TREATING ARTHRITIS

20230091156 · 2023-03-23

    Inventors

    Cpc classification

    International classification

    Abstract

    This document provides methods and materials for assessing and/or treating mammals (e.g., humans) having arthritis (e.g., rheumatoid arthritis). For example, the presence of distinct metabolite signatures in a sample obtained from a mammal (e.g., a human) having arthritis (e.g., rheumatoid arthritis) can be used to determine the disease activity status of the arthritis. Also provided are materials and methods for treating mammals (e.g., a human) having arthritis (e.g., rheumatoid arthritis).

    Claims

    1-13. (canceled)

    14. A method for treating a mammal having arthritis, wherein said method comprises: (a) determining that a blood sample from said mammal comprises a low disease activity signature, and (b) administering an arthritis drug to said mammal, wherein said low disease activity signature comprises (1a) an increased level of five or more metabolites selected from the group consisting of isoursodeoxycholate, linoleoylcarnitine (C18:2), dihomo-linoleoylcarnitine (C20:2), N-acetyltyrosine, 1-methylhistidine, 4-guanidinobutanoate, lysine, serine, N-acetyltryptophan, 6-bromotryptophan, 1-carboxyethylisoleucine, alpha-ketobutyrate, N2-acetyl,N6-methyllysine, trigonelline (N′-methylnicotinate), 3-phenylpropionate (hydrocinnamate), tryptophan, N-acetylarginine, 1-linoleoyl-GPA (18:2), gulonate, phenol sulfate, branched chain 14:0 dicarboxylic acid, bilirubin, linoleoylcarnitine (C18:3), bilirubin (E,E), eicosenoylcarnitine (C20:1), lanthionine, glycoursodeoxycholate, biliverdin, guanidinoacetate, and myo-inositol or (1b) a decreased level of five or more metabolites selected from the group consisting of (14 or 15)-methylpalmitate (a17:0 or i17:0), 1,6-anhydroglucose, N-acetylneuraminate, hypoxanthine, 1-carboxyethylleucine, ectoine, pyrraline, cysteinylglycine disulfide, erucate (22:1n9), mannose, dimethylguanidino valeric acid (DMGV), 1-carboxyethylvaline, beta-hydroxyisovalerate, stearoyl ethanolamide, trimethylamine N-oxide, 3-hydroxystearate, gluconate, palmitoyl ethanolamide, glucose, and glucoronate.

    15. The method of claim 14, wherein said mammal is a human.

    16. The method of claim 14, wherein said arthritis is a rheumatoid arthritis.

    17. The method of claim 14, wherein said blood sample is a plasma sample.

    18. The method of claim 14, wherein said low disease activity signature comprises (1a) an increased level of 10 or more metabolites selected from the group consisting of isoursodeoxycholate, linoleoylcarnitine (C18:2), dihomo-linoleoylcarnitine (C20:2), N-acetyltyrosine, 1-methylhistidine, 4-guanidinobutanoate, lysine, serine, N-acetyltryptophan, 6-bromotryptophan, 1-carboxyethylisoleucine, alpha-ketobutyrate, N2-acetyl,N6-methyllysine, trigonelline (N′-methylnicotinate), 3-phenylpropionate (hydrocinnamate), tryptophan, N-acetylarginine, 1-linoleoyl-GPA (18:2), gulonate, phenol sulfate, branched chain 14:0 dicarboxylic acid, bilirubin, linoleoylcarnitine (C18:3), bilirubin (E,E), eicosenoylcarnitine (C20:1), lanthionine, glycoursodeoxycholate, biliverdin, guanidinoacetate, and myo-inositol or (1b) a decreased level of 10 or more metabolites selected from the group consisting of (14 or 15)-methylpalmitate (a17:0 or i17:0), 1,6-anhydroglucose, N-acetylneuraminate, hypoxanthine, 1-carboxyethylleucine, ectoine, pyrraline, cysteinylglycine disulfide, erucate (22:1n9), mannose, dimethylguanidino valeric acid (DMGV), 1-carboxyethylvaline, beta-hydroxyisovalerate, stearoyl ethanolamide, trimethylamine N-oxide, 3-hydroxystearate, gluconate, palmitoyl ethanolamide, glucose, and glucoronate.

    19. The method of claim 14, wherein said low disease activity signature comprises (1a) an increased level of 15 or more metabolites selected from the group consisting of isoursodeoxycholate, linoleoylcarnitine (C18:2), dihomo-linoleoylcarnitine (C20:2), N-acetyltyrosine, 1-methylhistidine, 4-guanidinobutanoate, lysine, serine, N-acetyltryptophan, 6-bromotryptophan, 1-carboxyethylisoleucine, alpha-ketobutyrate, N2-acetyl,N6-methyllysine, trigonelline (N′-methylnicotinate), 3-phenylpropionate (hydrocinnamate), tryptophan, N-acetylarginine, 1-linoleoyl-GPA (18:2), gulonate, phenol sulfate, branched chain 14:0 dicarboxylic acid, bilirubin, linoleoylcarnitine (C18:3), bilirubin (E,E), eicosenoylcarnitine (C20:1), lanthionine, glycoursodeoxycholate, biliverdin, guanidinoacetate, and myo-inositol or (1b) a decreased level of 15 or more metabolites selected from the group consisting of (14 or 15)-methylpalmitate (a17:0 or i17:0), 1,6-anhydroglucose, N-acetylneuraminate, hypoxanthine, 1-carboxyethylleucine, ectoine, pyrraline, cysteinylglycine disulfide, erucate (22:1n9), mannose, dimethylguanidino valeric acid (DMGV), 1-carboxyethylvaline, beta-hydroxyisovalerate, stearoyl ethanolamide, trimethylamine N-oxide, 3-hydroxystearate, gluconate, palmitoyl ethanolamide, glucose, and glucoronate.

    20. The method of claim 14, wherein said low disease activity signature comprises (1a) an increased level of the metabolites selected from the group consisting of isoursodeoxycholate, linoleoylcarnitine (C18:2), dihomo-linoleoylcarnitine (C20:2), N-acetyltyrosine, 1-methylhistidine, 4-guanidinobutanoate, lysine, serine, N-acetyltryptophan, 6-bromotryptophan, 1-carboxyethylisoleucine, alpha-ketobutyrate, N2-acetyl,N6-methyllysine, trigonelline (N′-methylnicotinate), 3-phenylpropionate (hydrocinnamate), tryptophan, N-acetylarginine, 1-linoleoyl-GPA (18:2), gulonate, phenol sulfate, branched chain 14:0 dicarboxylic acid, bilirubin, linoleoylcarnitine (C18:3), bilirubin (E,E), eicosenoylcarnitine (C20:1), lanthionine, glycoursodeoxycholate, biliverdin, guanidinoacetate, and myo-inositol or (1b) a decreased level of the metabolites selected from the group consisting of (14 or 15)-methylpalmitate (a17:0 or i17:0), 1,6-anhydroglucose, N-acetylneuraminate, hypoxanthine, 1-carboxyethylleucine, ectoine, pyrraline, cysteinylglycine disulfide, erucate (22:1n9), mannose, dimethylguanidino valeric acid (DMGV), 1-carboxyethylvaline, beta-hydroxyisovalerate, stearoyl ethanolamide, trimethylamine N-oxide, 3-hydroxystearate, gluconate, palmitoyl ethanolamide, glucose, and glucoronate.

    21. The method of claim 14, wherein said low disease activity signature comprises (1a) an increased level of the metabolites selected from the group consisting of isoursodeoxycholate, linoleoylcarnitine (C18:2), dihomo-linoleoylcarnitine (C20:2), N-acetyltyrosine, 1-methylhistidine, 4-guanidinobutanoate, lysine, serine, N-acetyltryptophan, 6-bromotryptophan, 1-carboxyethylisoleucine, alpha-ketobutyrate, N2-acetyl,N6-methyllysine, trigonelline (N′-methylnicotinate), 3-phenylpropionate (hydrocinnamate), tryptophan, N-acetylarginine, 1-linoleoyl-GPA (18:2), gulonate, phenol sulfate, branched chain 14:0 dicarboxylic acid, bilirubin, linoleoylcarnitine (C18:3), bilirubin (E,E), eicosenoylcarnitine (C20:1), lanthionine, glycoursodeoxycholate, biliverdin, guanidinoacetate, and myo-inositol and (1b) a decreased level of the metabolites selected from the group consisting of (14 or 15)-methylpalmitate (a17:0 or i17:0), 1,6-anhydroglucose, N-acetylneuraminate, hypoxanthine, 1-carboxyethylleucine, ectoine, pyrraline, cysteinylglycine disulfide, erucate (22:1n9), mannose, dimethylguanidino valeric acid (DMGV), 1-carboxyethylvaline, beta-hydroxyisovalerate, stearoyl ethanolamide, trimethylamine N-oxide, 3-hydroxystearate, gluconate, palmitoyl ethanolamide, glucose, and glucoronate.

    22. The method of claim 14, wherein said arthritis drug is selected from the group consisting of methotrexate, hydroxychloroquine, sulfasalazine, and leflunomide.

    23-30. (canceled)

    31. A method for treating a mammal having arthritis, wherein said method comprises: (a) determining that a blood sample from said mammal comprises a moderate-to-high disease activity signature, and (b) administering an arthritis drug to said mammal or performing surgery to treat said arthritis, wherein said moderate-to-high disease signature comprises (2a) an increased level of five or more metabolites selected from the group consisting of (14 or 15)-methylpalmitate (a17:0 or i17:0), 1,6-anhydroglucose, N-acetylneuraminate, hypoxanthine, 1-carboxyethylleucine, ectoine, pyrraline, cysteinylglycine disulfide, erucate (22:1n9), 3-methylhistidine, mannose, dimethylguanidino valeric acid (DMGV), 1-carboxyethylvaline, beta-hydroxyisovalerate, stearoyl ethanolamide, trimethylamine N-oxide, 3-hydroxystearate, gluconate, palmitoyl ethanolamide, glucose, and glucoronate or (2b) a decreased level of five or more metabolites selected from the group consisting of isoursodeoxycholate, linoleoylcarnitine (C18:2), dihomo-linoleoylcarnitine (C20:2), N-acetyltyrosine, 1-methylhistidine, 4-guanidinobutanoate, lysine, serine, N-acetyltryptophan, 6-bromotryptophan, 1-carboxyethylisoleucine, alpha-ketobutyrate, N2-acetyl,N6-methyllysine, trigonelline (N′-methylnicotinate), 3-phenylpropionate (hydrocinnamate), tryptophan, N-acetylarginine, 1-linoleoyl-GPA (18:2), gulonate, phenol sulfate, branched chain 14:0 dicarboxylic acid, bilirubin, linoleoylcarnitine (C18:3), bilirubin (E,E), eicosenoylcarnitine (C20:1), lanthionine, glycoursodeoxycholate, biliverdin, guanidinoacetate, and myo-inositol.

    32. The method of claim 31, wherein said mammal is a human.

    33. The method of claim 31, wherein said arthritis is a rheumatoid arthritis.

    34. The method of claim 31, wherein said blood sample is a plasma sample.

    35. The method of claim 31, wherein said moderate-to-high disease signature comprises (2a) an increased level of 10 or more metabolites selected from the group consisting of (14 or 15)-methylpalmitate (a17:0 or i17:0), 1,6-anhydroglucose, N-acetylneuraminate, hypoxanthine, 1-carboxyethylleucine, ectoine, pyrraline, cysteinylglycine disulfide, erucate (22:1n9), 3-methylhistidine, mannose, dimethylguanidino valeric acid (DMGV), 1-carboxyethylvaline, beta-hydroxyisovalerate, stearoyl ethanolamide, trimethylamine N-oxide, 3-hydroxystearate, gluconate, palmitoyl ethanolamide, glucose, and glucoronate or (2b) a decreased level of 10 or more metabolites selected from the group consisting of isoursodeoxycholate, linoleoylcarnitine (C18:2), dihomo-linoleoylcarnitine (C20:2), N-acetyltyrosine, 1-methylhistidine, 4-guanidinobutanoate, lysine, serine, N-acetyltryptophan, 6-bromotryptophan, 1-carboxyethylisoleucine, alpha-ketobutyrate, N2-acetyl,N6-methyllysine, trigonelline (N′-methylnicotinate), 3-phenylpropionate (hydrocinnamate), tryptophan, N-acetylarginine, 1-linoleoyl-GPA (18:2), gulonate, phenol sulfate, branched chain 14:0 dicarboxylic acid, bilirubin, linoleoylcarnitine (C18:3), bilirubin (E,E), eicosenoylcarnitine (C20:1), lanthionine, glycoursodeoxycholate, biliverdin, guanidinoacetate, and myo-inositol.

    36. The method of claim 31, wherein said moderate-to-high disease signature comprises (2a) an increased level of 15 or more metabolites selected from the group consisting of (14 or 15)-methylpalmitate (a17:0 or i17:0), 1,6-anhydroglucose, N-acetylneuraminate, hypoxanthine, 1-carboxyethylleucine, ectoine, pyrraline, cysteinylglycine disulfide, erucate (22:1n9), 3-methylhistidine, mannose, dimethylguanidino valeric acid (DMGV), 1-carboxyethylvaline, beta-hydroxyisovalerate, stearoyl ethanolamide, trimethylamine N-oxide, 3-hydroxystearate, gluconate, palmitoyl ethanolamide, glucose, and glucoronate or (2b) a decreased level of 15 or more metabolites selected from the group consisting of isoursodeoxycholate, linoleoylcarnitine (C18:2), dihomo-linoleoylcarnitine (C20:2), N-acetyltyrosine, 1-methylhistidine, 4-guanidinobutanoate, lysine, serine, N-acetyltryptophan, 6-bromotryptophan, 1-carboxyethylisoleucine, alpha-ketobutyrate, N2-acetyl,N6-methyllysine, trigonelline (N′-methylnicotinate), 3-phenylpropionate (hydrocinnamate), tryptophan, N-acetylarginine, 1-linoleoyl-GPA (18:2), gulonate, phenol sulfate, branched chain 14:0 dicarboxylic acid, bilirubin, linoleoylcarnitine (C18:3), bilirubin (E,E), eicosenoylcarnitine (C20:1), lanthionine, glycoursodeoxycholate, biliverdin, guanidinoacetate, and myo-inositol.

    37. The method of claim 31, wherein said moderate-to-high disease signature comprises (2a) an increased level of the metabolites selected from the group consisting of (14 or 15)-methylpalmitate (a17:0 or i17:0), 1,6-anhydroglucose, N-acetylneuraminate, hypoxanthine, 1-carboxyethylleucine, ectoine, pyrraline, cysteinylglycine disulfide, erucate (22:1n9), 3-methylhistidine, mannose, dimethylguanidino valeric acid (DMGV), 1-carboxyethylvaline, beta-hydroxyisovalerate, stearoyl ethanolamide, trimethylamine N-oxide, 3-hydroxystearate, gluconate, palmitoyl ethanolamide, glucose, and glucoronate or (2b) a decreased level of the metabolites selected from the group consisting of isoursodeoxycholate, linoleoylcarnitine (C18:2), dihomo-linoleoylcarnitine (C20:2), N-acetyltyrosine, 1-methylhistidine, 4-guanidinobutanoate, lysine, serine, N-acetyltryptophan, 6-bromotryptophan, 1-carboxyethylisoleucine, alpha-ketobutyrate, N2-acetyl,N6-methyllysine, trigonelline (N′-methylnicotinate), 3-phenylpropionate (hydrocinnamate), tryptophan, N-acetylarginine, 1-linoleoyl-GPA (18:2), gulonate, phenol sulfate, branched chain 14:0 dicarboxylic acid, bilirubin, linoleoylcarnitine (C18:3), bilirubin (E,E), eicosenoylcarnitine (C20:1), lanthionine, glycoursodeoxycholate, biliverdin, guanidinoacetate, and myo-inositol.

    38. The method of claim 31, wherein said moderate-to-high disease signature comprises (2a) an increased level of the metabolites selected from the group consisting of (14 or 15)-methylpalmitate (a17:0 or i17:0), 1,6-anhydroglucose, N-acetylneuraminate, hypoxanthine, 1-carboxyethylleucine, ectoine, pyrraline, cysteinylglycine disulfide, erucate (22:1n9), 3-methylhistidine, mannose, dimethylguanidino valeric acid (DMGV), 1-carboxyethylvaline, beta-hydroxyisovalerate, stearoyl ethanolamide, trimethylamine N-oxide, 3-hydroxystearate, gluconate, palmitoyl ethanolamide, glucose, and glucoronate and (2b) a decreased level of the metabolites selected from the group consisting of isoursodeoxycholate, linoleoylcarnitine (C18:2), dihomo-linoleoylcarnitine (C20:2), N-acetyltyrosine, 1-methylhistidine, 4-guanidinobutanoate, lysine, serine, N-acetyltryptophan, 6-bromotryptophan, 1-carboxyethylisoleucine, alpha-ketobutyrate, N2-acetyl,N6-methyllysine, trigonelline (N′-methylnicotinate), 3-phenylpropionate (hydrocinnamate), tryptophan, N-acetylarginine, 1-linoleoyl-GPA (18:2), gulonate, phenol sulfate, branched chain 14:0 dicarboxylic acid, bilirubin, linoleoylcarnitine (C18:3), bilirubin (E,E), eicosenoylcarnitine (C20:1), lanthionine, glycoursodeoxycholate, biliverdin, guanidinoacetate, and myo-inositol.

    39. The method of claim 31, wherein said method comprises administering said arthritis drug to said mammal.

    40. The method of claim 39, wherein said arthritis drug is selected from the group consisting of adalimumab, certolizumab, etanercept, golimumab, infliximab, abatacept, tocilizumab, sarilumab, rituximab, tofacitinib, baricitinib, and upadacitinib.

    41. The method of claim 31, wherein said method comprises performing said surgery.

    42-51. (canceled)

    Description

    DESCRIPTION OF THE DRAWINGS

    [0012] FIG. 1. A multi-approach discovery strategy to identify metabolites indicative of RA disease activity. (A) Differentially abundant metabolites between higher and lower disease activity groups were identified using a mixed-effects logistic regression model adjusted for patient age and sex, as well as for Patient ID to control for having multiple samples from the same patient. (B) A selection scheme to identify metabolites associated with DAS28-CRP. Metabolites were selected with mixed-effects linear regression. To further demonstrate their association with DAS28-CRP, these metabolites were used to construct a generalized linear model for predicting DAS28-CRP. Predictive performance of the model was evaluated on the discovery cohort (using a cross-validation technique) and on a validation cohort.

    [0013] FIG. 2. Plasma metabolites differentiating between higher and lower disease activity groups in RA. A total of 2 and 31 metabolites were found to be significantly increased in higher (DAS28-CRP>3.2, n=52) and lower (DAS28-CRP≤3.2, n=76) disease activity groups, respectively. Each point corresponds to a metabolite (686 total). Differentially abundant metabolites were found using a mixed-effects logistic regression model on the discovery cohort (128 samples), for which age and sex were adjusted. Metabolites with P-value <0.05 (based upon the corresponding coefficient of the regression model) were considered as significantly different between groups. P-values and fold-changes for all metabolites are listed in Table 2. Metabolites in bold have been previously described in the literature for their associations with RA.

    [0014] FIG. 3. Evaluation of DAS28-CRP predictive performance in cross-validation. A modified leave-one-out cross-validation approach was used on the samples of the training group (128 samples) to test the performance of a generalized linear model (GLM) in predicting DAS28-CRP scores from metabolite abundances. Distributions of absolute errors from models with and without a feature selection scheme were compared to identify the more robust model. The GLM with the feature selection scheme performed better (MAE±SD: 1.51±1.89) than the model without feature selection (MAE±SD: 2.02±2.52).

    [0015] FIG. 4A-B. GLM with feature selection provides improved DAS28-CRP prediction accuracy in an independent validation group (12 samples). (A) Performance of GLMs in predicting quantitative disease activity were evaluated on samples of an independent validation group. Distributions of absolute errors from models with and without a feature selection scheme were compared to identify the more robust model. (B) Selection of metabolic features prior to model training resulted in higher predictive performance, as evidenced by the stronger correlation between observed and predicted DAS28-CRPs. Three samples predicted to have negative DAS28-CRP values are omitted from the scatter-plot. Dashed violet line indicates ‘y=x’, i.e., an exact match between the observed and predicted values. 95% confidence interval for ρ with feature selection: [0.18, 0.90]; without feature selection: [−0.44, 0.68].

    [0016] FIG. 5. Venn diagram of all plasma metabolites identified through the multi-approach discovery strategy. A total of 67 unique metabolites were identified, among which were found to have no association with the use of treatment. Notably, eight metabolites (6-bromotryptophan, bilirubin (E,E), biliverdin, glucuronate, N-acetyltryptophan, N-acetyltyrosine, serine, and trigonelline) in bold were not only consistently detected across both analytic approaches, but also found to have no association with any treatment use. Colored circles indicate metabolites whose abundances associate with treatment use. Metabolites with red triangles were found to have increasing abundances with worsening disease activity, whereas metabolites with blue triangles were found to have decreasing abundances with worsening disease activity.

    [0017] FIG. 6A-B. Metabolites differentially abundant between two CRP patient groups. Among the 67 total metabolites identified through our multi-approach analysis on the discovery cohort (n=128), eight metabolites were identified to have significant associations with CRP group while controlling for confounding variables (regression coefficient for CRP, P<0.05) (A) Metabolites with higher abundances in the high-CRP group: mannose, beta-hydroxyisovalerate, (14 or 15)-methylpalmitate (a17:0 or i17:0), erucate (22:1n9), 10-undecenoate (11:1n1), and N-acetylcitrulline. (B) Metabolites with higher abundances in the low-CRP group: serine and linoleoylcarnitine (C18:3).

    [0018] FIG. 7. Histogram of DAS28-CRPs corresponding to the 128 samples of the discovery cohort.

    DETAILED DESCRIPTION

    [0019] This document provides methods and materials for assessing and/or treating mammals (e.g., humans) having arthritis (e.g., rheumatoid arthritis). In some cases, this document provides methods and materials for determining the disease activity status of a mammal having arthritis (e.g., RA), and, optionally, treating the mammal. For example, a sample obtained from a mammal having arthritis (e.g., RA) can be assessed to determine the disease activity status of the arthritis based, at least in part, on the presence or absence of an altered level (e.g., an increased level or a decreased level) of 15 or more metabolites (e.g., circulating metabolites) in the sample. As demonstrated herein, a distinct metabolite signature is present in mammals having low disease activity (e.g., having a disease activity−28 using C-reactive protein (DAS28-CRP) score of about 3.2 or less) and in mammals having moderate-to-high disease activity (e.g., having a DAS28-CRP score greater than about 3.2.

    [0020] Any type of mammal can be assessed and/or treated as described herein. Examples of mammals that can have arthritis (e.g., RA) and that can be assessed and/or treated as described herein include, without limitation, primates (e.g., humans and monkeys), dogs, cats, horses, cows, pigs, sheep, rabbits, mice, and rats. In some cases, a human can be assessed and/or treated as described herein.

    [0021] Rheumatoid arthritis, when present, can be in any appropriate joint within a mammal being assessed and/or treated as described herein. Examples of joints that can be arthritic in a mammal (e.g., a human) having rheumatoid arthritis include, without limitation, joints in the feet, joints in the hands, joints in the hips, joints in the knees, joints in the wrist, joints in the elbow, joints in the shoulder, and joints in the ankles.

    [0022] Any appropriate method can be used to identify a mammal as having arthritis (e.g., RA). In some cases, laboratory tests (e.g., analysis of body fluids such as blood, urine, and/or joint fluid for, for example, biomarkers such as rheumatoid factor and anti-cyclic citrullinated protein (CCP) antibodies), imaging techniques (e.g., X-ray, computerized tomography (CT), and magnetic resonance imaging (MRI), and ultrasound) can be used to identify mammals (e.g., humans) as having arthritis (e.g., RA).

    [0023] Once identified as being having arthritis (e.g., RA), a mammal can be assessed to determine the disease activity of the arthritis. For example, a sample (e.g., a blood sample) obtained from the mammal can be assessed for the presence, absence, or level of 15 or more metabolites (e.g., circulating metabolites). As described herein, a distinct metabolite signature in a sample obtained from a mammal having arthritis (e.g., RA) can be used to determine the disease activity of the arthritis.

    [0024] Any appropriate sample from a mammal (e.g., a human) having arthritis (e.g., RA) can be assessed as described herein. In some cases, a sample can be a biological sample. In some cases, a sample can contain metabolites (e.g., amino acids, cofactors, vitamins, nucleotides, lipids, peptides, xenobiotics, and carbohydrates). Examples of samples that can be assessed as described herein include, without limitation, blood samples, whole blood samples, serum samples, and plasma samples. In some cases, plasma samples obtained from a mammal (e.g., a human) having arthritis (e.g., RA) can be assessed as described herein.

    [0025] In some cases, a mammal (e.g., a human) having arthritis (e.g., RA) can be identified as having low disease activity (DAS28-CRP≤3.2) or moderate-to-high disease activity (DAS28-CRP>3.2) based, at least in part, on the presence of an altered level of 15 or more (e.g., 15, 18, 20, 22, 30, 40, 50, 51, or more) metabolites (e.g., circulating metabolites) in a sample (e.g., a plasma sample) obtained from the mammal. In some cases, an altered level of a metabolite can be an increased level of the metabolite. The term “increased level” as used herein with respect to a level of a metabolite refers to any level that is higher than a reference level of the metabolite. In some cases, an altered level of a metabolite can be a decreased level of the metabolite. The term “decreased level” as used herein with respect to a level of a metabolite refers to any level that is lower than a reference level of the metabolite. The term “reference level” for a particular metabolite refers to the median level of that metabolite present in samples obtained from a population of mammals (e.g., a population of 20, 50, 100, or more mammals), where a number of those mammals (e.g., about half of those mammals or about 40-60 percent of those mammals) have arthritis (e.g., RA) with low disease activity and a number of those mammals (e.g., the other about half or about 40-60 percent) have arthritis (e.g., RA) with moderate-to-high disease activity. For example, the term “reference level” for a particular metabolite refers to the median level of that metabolite present in samples obtained from a population of mammals (e.g., a population of 20, 50, 100, or more mammals), where 40 percent of those mammals have arthritis (e.g., RA) with low disease activity and 60 percent of those mammals have arthritis (e.g., RA) with moderate-to-high disease activity. In another example, the term “reference level” for a particular metabolite refers to the median level of that metabolite present in samples obtained from a population of mammals (e.g., a population of 20, 50, 100, or more mammals), where 60 percent of those mammals have arthritis (e.g., RA) with low disease activity and 40 percent of those mammals have arthritis (e.g., RA) with moderate-to-high disease activity. In some cases, abundance values, which are measured from a population of mammals, (or relative abundance values, which are measured and normalized from a population of mammals) that are set to differentiate between low disease activity and moderate-to-high disease activity as described herein can be used as a reference level. Examples of reference levels of particular metabolites for human plasma samples are set forth in Tables A and B. It will be appreciated that levels of metabolites from comparable samples are used when determining whether or not a particular level is an altered level.

    TABLE-US-00001 TABLE A Abundance values of 51 metabolites for human plasma samples (measured by metabolon UPLC-MS/MS). Abundance Abundance differences.sup.β in differences.sup.β in low moderate-high Metabolite Abundance value.sup.α disease activity disease activity isoursodeoxycholate 1340080 Increased (e.g., +9.70%). Decreased (e.g., −13.64%). In some cases, an In some cases, a increase of this decrease of this metabolite of 1, 2, 3, 4, metabolite of 1, 2, 3, 4, 5, 6, 7, 8, 9, or more 5, 6, 7, 8, 9, 10, 11, 12, percent as compared to 13, or more percent as a reference level can compared to a be used to identity low reference level can be activity disease. used to identify moderate to high activity disease. linoleoylcarnitine (C18:2) 10125123.5 Increased (e.g., +5.49%). Decreased (e.g., −3.80%). In some cases, an In some cases, a increase of this decrease of this metabolite of 1, 2, 3, 4, metabolite of 1, 2, 3, 5, or more percent as or more percent as compared to a compared to a reference level can be reference level can be used to identify low used to identify activity disease. moderate to high activity disease. dihomo-linoleoylcarnitine 341755 Increased (e.g., +6.28%). Decreased (e.g., −7.63%) (C20:2) In some cases, an In some cases, a increase of this decrease of this metabolite of 1, 2, 3, 4, metabolite of 1, 2, 3, 4, 5, 6, or more percent 5, 6, 7, or more percent as compared to a as compared to a reference level can be reference level can be used to identify low used to identify activity disease. moderate to high activity disease. N-acetyltyrosine 191301 Increased (e.g., +4.53%). Decreased (e.g., −8.07%). In some cases, an In some cases, a increase of this decrease of this metabolite of 1, 2, 3, 4, metabolite of 1, 2, 3, 4, or more percent as 5, 6, 7, 8, or more compared to a percent as compared to reference level can be a reference level can used to identify low be used to identify activity disease. moderate to high activity disease. (14 or 15)-methylpalmitate 23250337 Decreased (e.g., −2.92%). Increased (e.g., +4.29%). (a17:0 or i17:0) In some cases, a In some cases, an decrease of this increase of this metabolite of 1, 1.5, 2, metabolite of 1, 2, 3, 4, 2.5, or more percent as or more percent as compared to a compared to a reference level can be reference level can be used to identify low used to identify activity disease. moderate to high activity disease. 3-methylhistidine 6881044 0.00% Increased (e.g., +0.02%). In some cases, an increase of this metabolite of 0.005, 0.01, 0.015, or more percent as compared to a reference level can be used to identify moderate to high activity disease. 1,6-anhydroglucose 1731268 Decreased (e.g., −2.40%). Increased (e.g., +1.02%). In some cases, a In some cases, an decrease of this increase of this metabolite of 1, 1.5, 2, metabolite of 0.25, 0.5, 2.3, or more percent as 0.75, or more percent compared to a as compared to a reference level can be reference level can be used to identify low used to identify activity disease. moderate to high activity disease. 1-methylhistidine 648415 Increased (e.g., +1.13%). Decreased (e.g., −0.63%). In some cases, an In some cases, a increase of this decrease of this metabolite of 0.25, 0.5, metabolite of 0.3, 0.4, 0.75, 1, or more 0.5, or more percent as percent as compared to compared to a a reference level can reference level can be be used to identify low used to identify activity disease. moderate to high activity disease. N-acetylneuraminate 1096063.5 Decreased (e.g., −0.12%). Increased (e.g., +0.11%). In some cases, a In some cases, an decrease of this increase of this metabolite of 0.05, metabolite of 0.05, 0.075, 0.085, 0.095, or 0.075, 0.95, or more more percent as percent as compared to compared to a a reference level can reference level can be be used to identify used to identify low moderate to high activity disease. activity disease. 4-guanidinobutanoate 825106 Increased (e.g., +7.15%). Decreased (e.g., −3.63%). In some cases, an In some cases, a increase of this decrease of this metabolite of 1, 2, 3, 4, metabolite of 1, 2, 2.5, 5, 6, 7, or more percent 3, 3.5, or more percent as compared to a as compared to a reference level can be reference level can be used to identify low used to identify activity disease. moderate to high activity disease. hypoxanthine 47766902 Decreased (e.g., −17.11%). Increased (e.g. +14.38%). In some cases, a In some cases, an decrease of this increase of this metabolite of 1, 2, 3, 4, metabolite of 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, or 13, 14, or more percent more percent as as compared to a compared to a reference level can be reference level can be used to identify used to identify low moderate to high activity disease. activity disease. 1-carboxyethylleucine 117997 Decreased (e.g., −6.09%). Increased (e.g., +8.88%). In some cases, a In some cases, an decrease of this increase of this metabolite of 1, 2, 3, 4, metabolite of 1, 2, 3, 4, 5, 6, or more percent 5, 6, 7, 8, or more as compared to a percent as compared to reference level can be a reference level can used to identify low be used to identify activity disease. moderate to high activity disease. ectoine 980789 Decreased (e.g., −6.06%). Increased (e.g., +8.91%). In some cases, a In some cases, an decrease of this increase of this metabolite of 1, 2, 3, 4, metabolite of 1, 2, 3, 4, 5, 6, or more percent 5, 6, 7, 8, or more as compared to a percent as compared to reference level can be a reference level can used to identify low be used to identify activity disease. moderate to high activity disease. lysine 334960560 Increased (e.g., +2.12%). Decreased (e.g., −3.15%). In some cases, an In some cases, a increase of this decrease of this metabolite of 0.5, 1, metabolite of 1, 2, 2.5, 1.5, 2, or more percent 3, or more percent as as compared to a compared to a reference level can be reference level can be used to identify low used to identify activity disease. moderate to high activity disease. pyrraline 263430 Decreased (e.g., −8.77%). Increased (e.g., +5.94%). In some cases, a In some cases, an decrease of this increase of this metabolite of 1, 2, 3, 4, metabolite of 1, 2, 3, 4, 5, 6, 7, 8, or more 5, or more percent as percent as compared to compared to a a reference level can reference level can be be used to identify low used to identify activity disease. moderate to high activity disease. cysteinylglycine disulfide 30506582 Decreased (e.g., −1.76%). Increased (e.g., +0.46%). In some cases, a In some cases, an decrease of this increase of this metabolite of 0.5, 1, metabolite of 0.1, 0.2, 1.5, 1.6, 1.7, or more 0.3, 0.4, or more percent as compared to percent as compared to a reference level can a reference level can be used to identify low be used to identify activity disease. moderate to high activity disease. serine 166907520 Increased (e.g., +1.20%). Decreased (e.g., −2.24%). In some cases, an In some cases, a increase of this decrease of this metabolite of 0.25, 0.5, metabolite of 1, 1.5, 2, 0.75, 1, or more 2.1, or more percent as percent as compared to compared to a a reference level can reference level can be be used to identify low used to identify activity disease. moderate to high activity disease. N-acetyltryptophan 302349 Increased (e.g., +7.46%). Decreased (e.g., −8.01%). In some cases, an In some cases, a increase of this decrease of this metabolite of 1, 2, 3, 4, metabolite of 1, 2, 3, 4, 5, 6, 7, or more percent 5, 6, 7, 8, or more as compared to a percent as compared to reference level can be a reference level can used to identify low be used to identify activity disease. moderate to high activity disease. 6-bromotryptophan 592802.5 Increased (e.g., +7.80%). Decreased (e.g., −10.98%). In some cases, an In some cases, a increase of this decrease of this metabolite of 1, 2, 3, 4, metabolite of 1, 2, 3, 4, 5, 6, 7, or more percent 5, 6, 7, 8, 9, 10, 10.5, as compared to a or more percent as reference level can be compared to a used to identify low reference level can be activity disease. used to identify moderate to high activity disease. 1-carboxyethylisoleucine 88956.5 Increased (e.g., +0.75%). Decreased (e.g., −3.00%). In some cases, an In some cases, a increase of this decrease of this metabolite of 0.5, 0.6, metabolite of 1, 1.5, 2, 0.7, or more percent as 2.5, 2.75, or more compared to a percent as compared to reference level can be a reference level can used to identify low be used to identify activity disease. moderate to high activity disease. erucate (22:1n9) 3775962 Decreased (e.g., −0.56%). Increased (e.g., +8.98%). In some cases, a In some cases, an decrease of this increase of this metabolite of 0.2, 0.3, metabolite of 1, 2, 3, 4, 0.4, 0.5, or more 5, 6, 7, 8, 8.5, or more percent as compared to percent as compared to a reference level can a reference level can be used to identify low be used to identify activity disease. moderate to high activity disease. alpha-ketobutyrate 3890791 Increased (e.g., Decreased (e.g., −1.65%). increased by +0.69%); In some cases, a In some cases, an decrease of this increase of this metabolite of 0.5, 1, metabolite of 0.4, 0.5, 1.5, 1.6, or more 0.6, or more percent as percent as compared to compared to a a reference level can reference level can be be used to identify used to identify low moderate to high activity disease. activity disease. N2-acetyl,N6-methyllysine 955596.5 Increased (e.g., +7.14%). Decreased (e.g., −21.14%). In some cases, an In some cases, a increase of this decrease of this metabolite of 1, 2, 3, 4, metabolite of 5, 10, 15, 5, 6, 7, or more percent 17, 19, 20, 21, or more as compared to a percent as compared to reference level can be a reference level can used to identify low be used to identify activity disease. moderate to high activity disease. trigonelline (N′- 53886060 Increased (e.g., +18.94%). Decreased (e.g., −17.84%). methylnicotinate) In some cases, an In some cases, a increase of this decrease of this metabolite of 1, 2, 3, 4, metabolite of 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 13, 14, 15, 16, 17, or or more percent as more percent as compared to a compared to a reference level can be reference level can be used to identify low used to identify activity disease. moderate to high activity disease. mannose 31007281 Decreased (e.g., −7.42%). Increased (e.g., +10.29%). In some cases, a In some cases, an decrease of this increase of this metabolite of 1, 2, 3, 4, metabolite of 1, 2, 3, 4, 5, 6, 7, or more percent 5, 6, 7, 8, 9, 10, or as compared to a more percent as reference level can be compared to a used to identify low reference level can be activity disease. used to identify moderate to high activity disease. dimethylguanidino valeric 619222 Decreased (e.g., −1.18%). Increased (e.g., +7.38%). acid (DMGV) In some cases, a In some cases, an decrease of this increase of this metabolite of 0.5, 0.75, metabolite of 1, 2, 3, 4, 1, 1.1, or more percent 5, 6, 7, or more percent as compared to a as compared to a reference level can be reference level can be used to identify low used to identify activity disease. moderate to high activity disease. 3-phenylpropionate 783339.5 Increased (e.g., +32.09%). Decreased (e.g., −23.77%). (hydrocinnamate) In some cases, an In some cases, a increase of this decrease of this metabolite of 5, 10, 15, metabolite of 5, 10, 15, 20, 25, 30, 31, 32, or 20, 21, 22, 23, or more more percent as percent as compared to compared to a a reference level can reference level can be be used to identify used to identify low moderate to high activity disease. activity disease. 1-carboxyethylvaline 367668 Decreased (e.g., −0.79%). Increased (e.g., +7.66%). In some cases, a In some cases, an decrease of this increase of this metabolite of 0.5, 0.6, metabolite of 1, 2, 3, 4, 0.7, or more percent as 5, 6, 7, or more percent compared to a as compared to a reference level can be reference level can be used to identify low used to identify activity disease. moderate to high activity disease. tryptophan 195492424 Increased (e.g., +4.66%). Decreased (e.g., −6.07%). In some cases, an In some cases, a increase of this decrease of this metabolite of 1, 2, 3, 4, metabolite of 1, 2, 3, 4, or more percent as 5, 6, or more percent compared to a as compared to a reference level can be reference level can be used to identify low used to identify activity disease. moderate to high activity disease. N-acetylarginine 3040031 Increased (e.g., +9.53%). Decreased (e.g., −12.90%). In some cases, an In some cases, a increase of this decrease of this metabolite of 1, 2, 3, 4, metabolite of 1, 2, 3, 4, 5, 6, 7, 8, 9, or more 5, 6, 7, 8, 9, 10, 11, 12, percent as compared to or more percent as a reference level can compared to a be used to identify low reference level can be activity disease. used to identify moderate to high activity disease. 1-linoleoyl-GPA (18:2) 284195 Increased (e.g., +10.37%). Decreased (e.g., −13.24%). In some cases, an In some cases, a increase of this decrease of this metabolite of 1, 2, 3, 4, metabolite of 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, or 5, 6, 7, 8, 9, 10, 11, 12, more percent as 13, or more percent as compared to a compared to a reference level can be reference level can be used to identify low used to identify activity disease. moderate to high activity disease. beta-hydroxyisovalerate 2821895 Decreased (e.g., −0.88%). Increased (e.g., +0.66%). In some cases, a In some cases, an decrease of this increase of this metabolite of 0.5, 0.6, metabolite of 0.4, 0.5, 0.7, 0.8, or more 0.6, or more percent as percent as compared to compared to a a reference level can reference level can be be used to identify low used to identify activity disease. moderate to high activity disease. stearoyl ethanolamide 1323003.5 Decreased (e.g., −11.03%). Increased (e.g., +8.38%). In some cases, a In some cases, an decrease of this increase of this metabolite of 1, 2, 3, 4, metabolite of 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 10.5, 5, 6, 7, 8, or more or more percent as percent as compared to compared to a a reference level can reference level can be be used to identify used to identify low moderate to high activity disease. activity disease. gulonate 6447782 Increased (e.g., +3.29%). Decreased (e.g., −3.36%). In some cases, an In some cases, a increase of this decrease of this metabolite of 1, 2, 3, metabolite of 1, 1.5, 2, or more percent as 2.5, 3, 3.3, or more compared to a percent as compared to reference level can be a reference level can used to identify low be used to identify activity disease. moderate to high activity disease. phenol sulfate 48825586 Increased (e.g., +7.63%). Decreased (e.g., −22.10%). In some cases, an In some cases, a increase of this decrease of this metabolite of 1, 2, 3, 4, metabolite of 5, 10, 15, 5, 6, 7, or more percent 17, 18, 19, 20, 21, 22, as compared to a or more percent as reference level can be compared to a used to identify low reference level can be activity disease. used to identify moderate to high activity disease. trimethylamine N-oxide 22341643 Decreased (e.g., −2.16%). Increased (e.g., +2.37%). In some cases, a In some cases, an decrease of this increase of this metabolite of 1, 1.5, 2, metabolite of 1, 1.5, 2, 2.1, or more percent as 2.2, or more percent as compared to a compared to a reference level can be reference level can be used to identify low used to identify activity disease. moderate to high activity disease. 3-hydroxystearate 1109475 Decreased (e.g., −9.94%). Increased (e.g., +9.00%). In some cases, a In some cases, an decrease of this increase of this metabolite of 1, 2, 3, 4, metabolite of 1, 2, 3, 4, 5, 6, 7, 8, 9, or more 5, 6, 7, 8, 8.5, or more percent as compared to percent as compared to a reference level can a reference level can be used to identify low be used to identify activity disease. moderate to high activity disease. branched chain 14:0 573385 Increased (e.g., +2.19%). Decreased (e.g., −17.51%). dicarboxylic acid In some cases, an In some cases, a increase of this decrease of this metabolite of 1, 1.5, 2, metabolite of 1, 2, 3, 4, 2.1, or more percent as 5, 6, 7, 8, 9, 10, 11, 12, compared to a 13, 14, 15, 16, 17, or reference level can be more percent as used to identify low compared to a activity disease. reference level can be used to identify moderate to high activity disease. bilirubin 6670012.5 Increased (e.g., +6.59%). Decreased (e.g., −8.73%). In some cases, an In some cases, a increase of this decrease of this metabolite of 1, 2, 3, 4, metabolite of 1, 2, 3, 4, 5, 6, or more percent 5, 6, 7, 8, or more as compared to a percent as compared to reference level can be a reference level can used to identify low be used to identify activity disease. moderate to high activity disease. gluconate 15844674.5 Decreased (e.g., −0.60%). Increased (e.g., +1.48%). In some cases, a In some cases, an decrease of this increase of this metabolite of 0.3, 0.4, metabolite of 0.25, 0.5, 0.5, or more percent as 0.75, 1, 1.25, or more compared to a percent as compared to reference level can be a reference level can used to identify low be used to identify activity disease. moderate to high activity disease. linoleoylcarnitine (C18:3) 830898 Increased (e.g., +11.72%). Decreased (e.g., −14.99%). In some cases, an In some cases, a increase of this decrease of this metabolite of 1, 2, 3, 4, metabolite of 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, or 5, 6, 7, 8, 9, 10, 11, 12, more percent as 13, 14, or more percent compared to a as compared to a reference level can be reference level can be used to identify low used to identify activity disease. moderate to high activity disease. palmitoyl ethanolamide 2865748.5 Decreased (e.g., −21.28%). Increased (e.g., +27.67%). In some cases, a In some cases, an decrease of this increase of this metabolite of 5, 10, 15, metabolite of 5, 10, 15, 17, 18, 19, 20, 21, or 20, 25, 26, 27, or more more percent as percent as compared to compared to a a reference level can reference level can be be used to identify used to identify low moderate to high activity disease. activity disease. glucose 1831276672 Decreased (e.g., −1.40%). Increased (e.g., +2.28%). In some cases, a In some cases, an decrease of this increase of this metabolite of 0.5, 0.75, metabolite of 0.25, 0.5, 1, 1.25, or more 0.75, 1, 1.5, 2, or more percent as compared to percent as compared to a reference level can a reference level can be used to identify low be used to identify activity disease. moderate to high activity disease. bilirubin (E,E) 7864420 Increased (e.g., +6.39%). Decreased (e.g., −10.31%). In some cases, an In some cases, a increase of this decrease of this metabolite of 1, 2, 3, 4, metabolite of 1, 2, 3, 4, 5, 6, or more percent 5, 6, 7, 8, 9, 10, or as compared to a more percent as reference level can be compared to a used to identify low reference level can be activity disease. used to identify moderate to high activity disease. glucuronate 11457360.5 Decreased (e.g., −3.64%). Increased (e.g., +4.97%). In some cases, a In some cases, an decrease of this increase of this metabolite of 1, 1.5, 2, metabolite of 1, 2, 3, 4, 2.5, 3, 3.5, or more 4.5, or more percent as percent as compared to compared to a a reference level can reference level can be be used to identify low used to identify activity disease. moderate to high activity disease. eicosenoylcarnitine (C20:1) 440691.5 Increased (e.g., +19.42%). Decreased (e.g., −7.02%). In some cases, an In some cases, a increase of this decrease of this metabolite of 5, 10, 15, metabolite of 1, 2, 3, 4, 16, 17, 18, 19, or more 5, 6, or more percent percent as compared to as compared to a a reference level can reference level can be be used to identify low used to identify activity disease. moderate to high activity disease. lanthionine 887041 Increased (e.g., +15.01%). Decreased (e.g., −12.40%). In some cases, an In some cases, a increase of this decrease of this metabolite of 1, 2, 3, 4, metabolite of 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, or more percent or more percent as as compared to a compared to a reference level can be reference level can be used to identify low used to identify activity disease. moderate to high activity disease. glycoursodeoxycholate 1865529 Increased (e.g., +18.85%). Decreased (e.g., −20.53%). In some cases, an In some cases, a increase of this decrease of this metabolite of 5, 10, 15, metabolite of 5, 10, 15, 16, 17, 18, or more 16, 17, 18, 19, 20, or percent as compared to more percent as a reference level can compared to a be used to identify low reference level can be activity disease. used to identify moderate to high activity disease. biliverdin 913540 Increased (e.g., +25.30%). Decreased (e.g., −14.36%). In some cases, an In some cases, a increase of this decrease of this metabolite of 5, 10, 15, metabolite of 1, 2, 3, 4, 20, 21, 22, 23, 24, 25, 5, 6, 7, 8, 9, 10, 11, 12, or more percent as 13, 14, or more percent compared to a as compared to a reference level can be reference level can be used to identify low used to identify activity disease. moderate to high activity disease. guanidinoacetate 903718 Increased (e.g., +2.40%). Decreased (e.g., −5.67%). In some cases, an In some cases, a increase of this decrease of this metabolite of 0.25, 0.5, metabolite of 1, 2, 3, 4, 0.75, 1, 1.5, 2, or more 5, or more percent as percent as compared to compared to a a reference level can reference level can be be used to identify low used to identify activity disease. moderate to high activity disease. myo-inositol 25227704 Increased (e.g., +1.62%). Decreased (e.g., −2.17%). In some cases, an In some cases, a increase of this decrease of this metabolite of 0.25, 0.5, metabolite of 1, 1.5, 2, 0.75, 1, 1.5, or more 2.1, or more percent as percent as compared to compared to a a reference level can reference level can be be used to identify low used to identify activity disease. moderate to high activity disease. .sup.αAbundance values were calculated with 76 plasma samples from patients with low disease activity and 52 plasma samples from patients with moderate-high disease activity. Ultra-high-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) was performed by Metabolon Inc.’s Discovery HD4TM platform. .sup.βAbundance differences are differences as compared to the abundance values.

    TABLE-US-00002 TABLE B Relative abundance values (mean ± standard deviation) of 51 metabolites for human plasma samples (measured by metabolon UPLC-MS/MS). The abundance of each metabolite was rescaled to set the median value for each metabolite equal to 1. mean Stdev Min/Max Mean Stdev Min/Max Metabolites (LDA.sup.α) (LDA.sup.α) (LDA.sup.α) (HDA.sup.β) (HDA.sup.β) (HDA.sup.β) Glycoursodeoxycholate 2.448 3.185  0.028/15.312 2.858 7.260  0.014/50.385 Eicosenoylcarnitine (C20:1) 1.203 0.483 0.471/2.861 0.931 0.317 0.417/2.179 Branched chain 14:0 1.391 1.517 0.046/8.492 1.026 1.108 0.046/4.890 dicarboxylic acid Lysine 1.041 0.190 0.634/1.744 0.960 0.168 0.641/1.398 3-methylhistidine 1.413 1.410 0.066/6.667 1.605 1.727 0.056/8.792 1-carboxyethylleucine 1.074 0.567 0.36/3.35 1.198 0.807 0.321/4.121 Biliverdin 1.332 0.720  0.38/3.707 0.952 0.375 0.269/2.134 Trigonelline (N′- 1.584 1.450 0.06/6.48 0.947 0.887 0.034/4.428 methylnicotinate) Bilirubin 1.189 0.565 0.179/3.223 0.990 0.428 0.316/2.513 Isoursodeoxycholate 2.386 3.215 0.109/18.78 3.145 6.411  0.060/34.368 Glucose 1.017 0.174 0.629/1.724 1.118 0.345 0.490/2.475 1-carboxyethylisoleucine 1.109 0.720 0.288/4.394 1.281 1.093 0.288/6.108 3-phenylpropionate 1.577 1.560  0.07/6.914 1.029 1.016 0.070/4.613 (hydrocinnamate) Palmitoyl ethanolamide 2.518 3.611  0.311/20.131 2.678 3.072  0.314/13.862 Tryptophan 1.041 0.184 0.481/1.45  0.938 0.230 0.450/1.649 Dimethylguanidino valeric acid 1.124 0.933 0.072/4.745 1.211 0.969 0.072/6.049 (DMGV) Guanidinoacetate 1.054 0.239 0.569/1.776 0.942 0.260 0.382/1.648 Phenol sulfate 1.422 1.116 0.243/7.888 1.064 1.114 0.087/7.279 Cysteinylglycine disulfide 1.010 0.168 0.618/1.415 1.043 0.164 0.673/1.375 Linoleoylcarnitine (C18:3) 1.202 0.512 0.359/3.092 0.938 0.494 0.266/3.570 1-Linoleoyl-GPA (18:2) 1.108 0.543 0.273/2.976 1.029 0.813 0.273/4.772 Hypoxanthine 0.895 0.517 0.024/2.665 1.132 0.538 0.092/2.632 Mannose 1.046 0.344 0.436/1.984 1.189 0.476 0.553/3.123 Pyrraline 1.184 1.044 0.104/5.588 1.439 1.190 0.104/5.691 Ectoine 1.596 1.645 0.247/9.196 1.747 1.786 0.276/9.152 Trimethylamine N-oxide 1.099 0.576  0.35/4.452 1.217 0.650 0.398/3.350 N2-acetyl,N6-methyllysine 1.761 1.844 0.088/9.117 0.817 0.651 0.088/3.070 beta-hydroxyisovalerate 1.035 0.301  0.37/1.757 1.043 0.382 0.302/2.768 N-acetylarginine 1.076 0.393 0.266/2.005 0.860 0.357 0.150/1.917 Stearoyl ethanolamide 2.267 3.130  0.487/18.608 2.119 2.203  0.518/11.041 Glucuronate 1.062 0.445 0.527/3.379 1.408 1.004 0.524/6.128 6-bromotryptophan 1.121 0.362 0.265/2.12  0.929 0.346 0.218/1.695 Bilirubin (E,E) 1.243 0.613 0.231/3.533 0.921 0.330 0.340/1.931 N-acetyltyrosine 1.185 0.460 0.435/2.723 0.924 0.247 0.340/1.641 Gluconate 1.050 0.459 0.293/3.509 1.187 0.560 0.279/3.298 1-methylhistidine 1.091 0.406 0.462/2.237 1.090 0.622 0.323/4.463 1,6-anhydroglucose 1.189 1.623  0.15/9.931 1.676 2.754  0.150/15.255 (14 or 15)-methylpalmitate 1.077 0.708 0.228/5.194 1.395 1.340 0.223/9.042 (a17:0 or i17:0) 4-guanidinobutanoate 1.130 0.635 0.324/4.371 1.099 0.819 0.277/5.410 N-acetylneuraminate 1.017 0.268 0.541/1.819 1.261 0.978  0.45/6.842 Dihomo-linoleoylcarnitine 1.150 0.400 0.381/2.578 0.915 0.301 0.359/1.924 (C20:2) Erucate (22:ln9) 1.034 0.480  0.36/3.502 1.147 0.667 0.207/3.106 1-carboxyethylvaline 1.067 0.552 0.342/3.308 1.312 0.973 0.263/5.733 Serine 1.029 0.188 0.621/1.571 0.968 0.177 0.502/1.361 Lanthionine 1.358 1.014 0.055/5.311 1.021 0.776 0.055/4.540 alpha-ketobutyrate 1.115 0.577 0.253/3.498 1.248 0.932 0.275/5.625 Myo-inositol 1.040 0.264 0.469/1.801 1.047 0.328 0.588/1.946 N-acetyltryptophan 1.142 0.357 0.578/2.824 0.928 0.271 0.390/1.807 Gulonate 1.087 0.422 0.447/2.751 1.123 0.563 0.447/2.675 Linoleoylcarnitine (C18:2) 1.123 0.330 0.491/1.984 0.948 0.274 0.531/2.029 3-hydroxystearate 0.990 0.525  0.42/4.298 1.251 0.921 0.420/6.967 .sup.αLDA: Low disease activity: DAS28-CRP > 3.2; .sup.βModerate-to-high disease activity: DAS28-CRP ≤ 3.2.

    [0026] A sample (e.g., a plasma sample) obtained from a mammal (e.g., a human) can be assessed for the presence, absence, or level of any appropriate metabolite. A metabolite can be a metabolite that is associated with arthritis (e.g., rheumatoid arthritis). Examples of metabolites (e.g., circulating metabolites) whose presence, absence, or level can be assessed in a sample (e.g., a plasma sample) obtained from a mammal (e.g., a human) as described herein include, without limitation, dihomo-linoleoylcarnitine (C20:2), 6-bromotryptophan, N-acetyltyrosine, eicosenoylcarnitine (C20:1), N-acetylglutamine, bilirubin (E,E), N-acetyltryptophan, N2-acetyl,N6-methyllysine, methionine, biliverdin, hypoxanthine, linoleoylcarnitine (C18:2), glycerophosphorylcholine (GPC), trigonelline (N′-methylnicotinate), tryptophan, linoleoylcarnitine (C18:3), gamma-glutamylmethionine, stearoylcarnitine (C18), N-acetylarginine, 10-undecenoate (11:1n1), N-acetylasparagine, 3-hydroxydecanoylcarnitine, palmitoylcarnitine (C16), glucuronate, glycerophosphoethanolamine, serine, retinal, N-acetyl-2-aminooctanoate, N2,N5-diacetylornithine, carnitine, lysine, N-acetylcitrulline, 3-amino-2-piperidone, 3-hydroxystearate, phenol sulfate, trimethylamine N-oxide, dimethylguanidino valeric acid (DMGV), glycoursodeoxycholate, N-acetylneuraminate, branched chain 14:0 dicarboxylic acid, 1-carboxyethylvaline, (14 or 15)-methylpalmitate (a17:0 or i17:0), isoursodeoxycholate, glucose, 1-methylhistidine, palmitoyl ethanolamide, 3-methylhistidine, 4-guanidinobutanoate, 1-carboxyethylisoleucine, cysteinylglycine disulfide, guanidinoacetate, 1,6-anhydroglucose, pyrraline, mannose, ectoine, 1-linoleoyl-GPA (18:2), erucate (22:1n9), stearoyl ethanolamide, 3-phenylpropionate (hydrocinnamate), beta-hydroxyisovalerate, myo-inositol, gulonate, gluconate, 1-carboxyethylleucine, alpha-ketobutyrate, lanthionine, nonanoylcarnitine (C9), 3-decenoylcarnitine, taurolithocholate 3-sulfate, pyridoxate, 5,6-dihydrouridine, inosine, 2′-O-methyluridine, aconitate [cis or trans], 2-hydroxyphytanate, N-alpha-acetylornithine, creatine, 5-hydroxylysine, N-acetyl-isoputreanine, alpha-ketoglutarate, 1-stearoyl-2-arachidonoyl-GPS (18:0/20:4), hexadecadienoate (16:2n6), S-adenosylhomocysteine (SAH), citraconate/glutaconate, dodecadienoate (12:2), catechol sulfate, octadecanedioylcarnitine (C18-DC), 3-hydroxyadipate, ethylmalonate, 11beta-hydroxyandrosterone glucuronide, bilirubin, isoursodeoxycholate, and glucose.

    [0027] Any appropriate method can be used to determine the presence, absence, or level of or more (e.g., 50 or 51) metabolites (e.g., circulating metabolites) in a sample (e.g., a plasma sample) obtained from a mammal (e.g., a human). In some cases, the presence, absence, or level of 15 or more (e.g., 50 or 51) metabolites (e.g., circulating metabolites) can be identified as described in Example 1.

    [0028] In some cases, the methods and materials provided herein can include determining the presence, absence, or level of dihomo-linoleoylcarnitine (C20:2), 6-bromotryptophan, N-acetyltyrosine, eicosenoylcarnitine (C20:1), N-acetylglutamine, bilirubin (E,E), N-acetyltryptophan, N2-acetyl,N6-methyllysine, methionine, biliverdin, hypoxanthine, linoleoylcarnitine (C18:2), glycerophosphorylcholine (GPC), trigonelline (N′-methylnicotinate), tryptophan, linoleoylcarnitine (C18:3), gamma-glutamylmethionine, stearoylcarnitine (C18), N-acetylarginine, 10-undecenoate (11:1n1), N-acetylasparagine, 3-hydroxydecanoylcarnitine, palmitoylcarnitine (C16), glucuronate, glycerophosphoethanolamine, serine, retinal, N-acetyl-2-aminooctanoate, N2,N5-diacetylornithine, carnitine, lysine, N-acetylcitrulline, and 3-amino-2-piperidone.

    [0029] In some cases, the methods and materials provided herein can include determining the presence, absence, or level of 3-hydroxystearate, phenol sulfate, trimethylamine N-oxide, bilirubin (E,E), serine, dimethylguanidino valeric acid (DMGV), N-acetyltryptophan, glycoursodeoxycholate, N-acetylneuraminate, dihomo-linoleoylcarnitine (C20:2), N-acetyltyrosine, branched chain 14:0 dicarboxylic acid, 1-carboxyethylvaline, (14 or 15)-methylpalmitate (a17:0 or i17:0), isoursodeoxycholate, glucuronate, glucose, linoleoylcarnitine (C18:3), 1-methylhistidine, trigonelline (N′-methylnicotinate), palmitoyl ethanolamide, hypoxanthine, biliverdin, linoleoylcarnitine (C18:2), 3-methylhistidine, N-acetylarginine, 4-guanidinobutanoate, 1-carboxyethylisoleucine, cysteinylglycine disulfide, guanidinoacetate, N2-acetyl,N6-methyllysine, lysine, 1,6-anhydroglucose, pyrraline, mannose, ectoine, 6-bromotryptophan, 1-linoleoyl-GPA (18:2), eicosenoylcarnitine (C20:1), erucate (22:1n9), bilirubin, stearoyl ethanolamide, 3-phenylpropionate (hydrocinnamate), beta-hydroxyisovalerate, myo-inositol, gulonate, gluconate, tryptophan, 1-carboxyethylleucine, alpha-ketobutyrate, and lanthionine.

    [0030] In some cases, the methods and materials provided herein can include determining the presence, absence, or level of nonanoylcarnitine (C9), 3-decenoylcarnitine, taurolithocholate 3-sulfate, pyridoxate, 5,6-dihydrouridine, inosine, 2′-O-methyluridine, 3-amino-2-piperidone, aconitate [cis or trans], 2-hydroxyphytanate, and N-alpha-acetylornithine.

    [0031] In some cases, the methods and materials provided herein can include determining the presence, absence, or level of creatine, 5-hydroxylysine, inosine, N-acetyl-isoputreanine, alpha-ketoglutarate, 1-stearoyl-2-arachidonoyl-GPS (18:0/20:4), hexadecadienoate (16:2n6), S-adenosylhomocysteine (SAH), nonanoylcarnitine (C9), citraconate/glutaconate, dodecadienoate (12:2), catechol sulfate, octadecanedioylcarnitine (C18-DC), 3-hydroxyadipate, ethylmalonate, 11beta-hydroxyandrosterone glucuronide, erucate (22:1n9), gamma-glutamylmethionine, and 2-hydroxyphytanate.

    [0032] In some cases, the methods and materials provided herein can include determining the presence, absence, or level of 6-bromotryptophan, bilirubin (E,E), biliverdin, glucuronate, N-acetyltryptophan, N-acetyltyrosine, serine, and trigonelline (N′-methylnicotinate).

    [0033] In some cases, the presence, absence, or level of 15 or more (e.g., 50 or 51) metabolites (e.g., circulating metabolites) listed in Table A in a sample (e.g., a plasma sample) obtained from a mammal (e.g., a human) having arthritis (e.g., RA) can be used to determine that the mammal has low disease activity of the arthritis. For example, a mammal (e.g., a human) having arthritis (e.g., RA) that is determined to have an increased level of two or more (e.g., 5, 10, 15, 20, 25, or more) of the following metabolites can be identified as having low disease activity: isoursodeoxycholate, linoleoylcarnitine (C18:2), dihomo-linoleoylcarnitine (C20:2), N-acetyltyrosine, 1-methylhistidine, 4-guanidinobutanoate, lysine, serine, N-acetyltryptophan, 6-bromotryptophan, 1-carboxyethylisoleucine, alpha-ketobutyrate, N2-acetyl,N6-methyllysine, trigonelline (N′-methylnicotinate), 3-phenylpropionate (hydrocinnamate), tryptophan, N-acetylarginine, 1-linoleoyl-GPA (18:2), gulonate, phenol sulfate, branched chain 14:0 dicarboxylic acid, bilirubin, linoleoylcarnitine (C18:3), bilirubin (E,E), eicosenoylcarnitine (C20:1), lanthionine, glycoursodeoxycholate, biliverdin, guanidinoacetate, and/or myo-inositol. In some cases, a mammal (e.g., a human) having arthritis (e.g., RA) that is determined to have an increased level of isoursodeoxycholate, linoleoylcarnitine (C18:2), dihomo-linoleoylcarnitine (C20:2), N-acetyltyrosine, 1-methylhistidine, 4-guanidinobutanoate, lysine, serine, N-acetyltryptophan, 6-bromotryptophan, 1-carboxyethylisoleucine, alpha-ketobutyrate, N2-acetyl,N6-methyllysine, trigonelline (N′-methylnicotinate), 3-phenylpropionate (hydrocinnamate), tryptophan, N-acetylarginine, 1-linoleoyl-GPA (18:2), gulonate, phenol sulfate, branched chain 14:0 dicarboxylic acid, bilirubin, linoleoylcarnitine (C18:3), bilirubin (E,E), eicosenoylcarnitine (C20:1), lanthionine, glycoursodeoxycholate, biliverdin, guanidinoacetate, and myo-inositol can be identified as having low disease activity.

    [0034] In another example, a mammal (e.g., a human) having arthritis (e.g., RA) that is determined to have a decreased level of two or more (e.g., 5, 10, 15, or more) of the following metabolites can be identified as having low disease activity: (14 or 15)-methylpalmitate (a17:0 or i17:0), 1,6-anhydroglucose, N-acetylneuraminate, hypoxanthine, 1-carboxyethylleucine, ectoine, pyrraline, cysteinylglycine disulfide, erucate (22:1n9), mannose, dimethylguanidino valeric acid (DMGV), 1-carboxyethylvaline, beta-hydroxyisovalerate, stearoyl ethanolamide, trimethylamine N-oxide, 3-hydroxystearate, gluconate, palmitoyl ethanolamide, glucose, and/or glucoronate. In some cases, a mammal (e.g., a human) having arthritis (e.g., RA) that is determined to have a decreased level of (14 or 15)-methylpalmitate (a17:0 or i17:0), 1,6-anhydroglucose, N-acetylneuraminate, hypoxanthine, 1-carboxyethylleucine, ectoine, pyrraline, cysteinylglycine disulfide, erucate (22:1n9), mannose, dimethylguanidino valeric acid (DMGV), 1-carboxyethylvaline, beta-hydroxyisovalerate, stearoyl ethanolamide, trimethylamine N-oxide, 3-hydroxystearate, gluconate, palmitoyl ethanolamide, glucose, and glucoronate can be identified as having low disease activity.

    [0035] In some cases, a mammal (e.g., a human) having arthritis (e.g., RA) that is determined to have (a) an increased level of two or more (e.g., 5, 10, 15, 20, 25, or more) of isoursodeoxycholate, linoleoylcarnitine (C18:2), dihomo-linoleoylcarnitine (C20:2), N-acetyltyrosine, 1-methylhistidine, 4-guanidinobutanoate, lysine, serine, N-acetyltryptophan, 6-bromotryptophan, 1-carboxyethylisoleucine, alpha-ketobutyrate, N2-acetyl,N6-methyllysine, trigonelline (N′-methylnicotinate), 3-phenylpropionate (hydrocinnamate), tryptophan, N-acetylarginine, 1-linoleoyl-GPA (18:2), gulonate, phenol sulfate, branched chain 14:0 dicarboxylic acid, bilirubin, linoleoylcarnitine (C18:3), bilirubin (E,E), eicosenoylcarnitine (C20:1), lanthionine, glycoursodeoxycholate, biliverdin, guanidinoacetate, and/or myo-inositol and (b) a decreased level of two or more (e.g., 5, 10, 15, or more) of (14 or 15)-methylpalmitate (a17:0 or i17:0), 1,6-anhydroglucose, N-acetylneuraminate, hypoxanthine, 1-carboxyethylleucine, ectoine, pyrraline, cysteinylglycine disulfide, erucate (22:1n9), mannose, dimethylguanidino valeric acid (DMGV), 1-carboxyethylvaline, beta-hydroxyisovalerate, stearoyl ethanolamide, trimethylamine N-oxide, 3-hydroxystearate, gluconate, palmitoyl ethanolamide, glucose, and/or glucoronate can be identified as having low disease activity.

    [0036] In some cases, a mammal (e.g., a human) having arthritis (e.g., RA) that is determined to have (a) an increased level of two or more (e.g., 5, 10, 15, 20, 25, or more) of isoursodeoxycholate, linoleoylcarnitine (C18:2), dihomo-linoleoylcarnitine (C20:2), N-acetyltyrosine, 1-methylhistidine, 4-guanidinobutanoate, lysine, serine, N-acetyltryptophan, 6-bromotryptophan, 1-carboxyethylisoleucine, alpha-ketobutyrate, N2-acetyl,N6-methyllysine, trigonelline (N′-methylnicotinate), 3-phenylpropionate (hydrocinnamate), tryptophan, N-acetylarginine, 1-linoleoyl-GPA (18:2), gulonate, phenol sulfate, branched chain 14:0 dicarboxylic acid, bilirubin, linoleoylcarnitine (C18:3), bilirubin (E,E), eicosenoylcarnitine (C20:1), lanthionine, glycoursodeoxycholate, biliverdin, guanidinoacetate, and myo-inositol and (b) a decreased level of two or more (e.g., 5, 10, 15, or more) of (14 or 15)-methylpalmitate (a17:0 or i17:0), 1,6-anhydroglucose, N-acetylneuraminate, hypoxanthine, 1-carboxyethylleucine, ectoine, pyrraline, cysteinylglycine disulfide, erucate (22:1n9), mannose, dimethylguanidino valeric acid (DMGV), 1-carboxyethylvaline, beta-hydroxyisovalerate, stearoyl ethanolamide, trimethylamine N-oxide, 3-hydroxystearate, gluconate, palmitoyl ethanolamide, glucose, and glucoronate can be identified as having low disease activity.

    [0037] In some cases, the presence, absence, or level of 15 or more (e.g., 50 or 51) metabolites (e.g., circulating metabolites) listed in Table A in a sample (e.g., a plasma sample) obtained from a mammal (e.g., a human) having arthritis (e.g., RA) can be used to determine that the mammal has high-to-moderate disease activity of the arthritis. For example, a mammal (e.g., a human) having arthritis (e.g., RA) that is determined to have a decreased level of two or more (e.g., 5, 10, 15, 20, 25, or more) of the following metabolites can be identified as having high-to-moderate disease activity: isoursodeoxycholate, linoleoylcarnitine (C18:2), dihomo-linoleoylcarnitine (C20:2), N-acetyltyrosine, 1-methylhistidine, 4-guanidinobutanoate, lysine, serine, N-acetyltryptophan, 6-bromotryptophan, 1-carboxyethylisoleucine, alpha-ketobutyrate, N2-acetyl,N6-methyllysine, trigonelline (N′-methylnicotinate), 3-phenylpropionate (hydrocinnamate), tryptophan, N-acetylarginine, 1-linoleoyl-GPA (18:2), gulonate, phenol sulfate, branched chain 14:0 dicarboxylic acid, bilirubin, linoleoylcarnitine (C18:3), bilirubin (E,E), eicosenoylcarnitine (C20:1), lanthionine, glycoursodeoxycholate, biliverdin, guanidinoacetate, and/or myo-inositol. In some cases, a mammal (e.g., a human) having arthritis (e.g., RA) that is determined to have a decreased level of isoursodeoxycholate, linoleoylcarnitine (C18:2), dihomo-linoleoylcarnitine (C20:2), N-acetyltyrosine, 1-methylhistidine, 4-guanidinobutanoate, lysine, serine, N-acetyltryptophan, 6-bromotryptophan, 1-carboxyethylisoleucine, alpha-ketobutyrate, N2-acetyl,N6-methyllysine, trigonelline (N′-methylnicotinate), 3-phenylpropionate (hydrocinnamate), tryptophan, N-acetylarginine, 1-linoleoyl-GPA (18:2), gulonate, phenol sulfate, branched chain 14:0 dicarboxylic acid, bilirubin, linoleoylcarnitine (C18:3), bilirubin (E,E), eicosenoylcarnitine (C20:1), lanthionine, glycoursodeoxycholate, biliverdin, guanidinoacetate, and myo-inositol can be identified as having high-to-moderate disease activity.

    [0038] In another example, a mammal (e.g., a human) having arthritis (e.g., RA) that is determined to have an increased level of two or more (e.g., 5, 10, 15, or more) of the following metabolites can be identified as having high-to-moderate disease activity: (14 or 15)-methylpalmitate (a17:0 or i17:0), 1,6-anhydroglucose, N-acetylneuraminate, hypoxanthine, 1-carboxyethylleucine, ectoine, pyrraline, cysteinylglycine disulfide, erucate (22:1n9), 3-methylhistidine, mannose, dimethylguanidino valeric acid (DMGV), 1-carboxyethylvaline, beta-hydroxyisovalerate, stearoyl ethanolamide, trimethylamine N-oxide, 3-hydroxystearate, gluconate, palmitoyl ethanolamide, glucose, and/or glucoronate. In some cases, a mammal (e.g., a human) having arthritis (e.g., RA) that is determined to have an increased level of (14 or 15)-methylpalmitate (a17:0 or i17:0), 1,6-anhydroglucose, N-acetylneuraminate, hypoxanthine, 1-carboxyethylleucine, ectoine, pyrraline, cysteinylglycine disulfide, erucate (22:1n9), 3-methylhistidine, mannose, dimethylguanidino valeric acid (DMGV), 1-carboxyethylvaline, beta-hydroxyisovalerate, stearoyl ethanolamide, trimethylamine N-oxide, 3-hydroxystearate, gluconate, palmitoyl ethanolamide, glucose, and glucoronate can be identified as having high-to-moderate disease activity.

    [0039] In some cases, a mammal (e.g., a human) having arthritis (e.g., RA) that is determined to have (a) a decreased level of two or more (e.g., 5, 10, 15, 20, 25, or more) of isoursodeoxycholate, linoleoylcarnitine (C18:2), dihomo-linoleoylcarnitine (C20:2), N-acetyltyrosine, 1-methylhistidine, 4-guanidinobutanoate, lysine, serine, N-acetyltryptophan, 6-bromotryptophan, 1-carboxyethylisoleucine, alpha-ketobutyrate, N2-acetyl,N6-methyllysine, trigonelline (N′-methylnicotinate), 3-phenylpropionate (hydrocinnamate), tryptophan, N-acetylarginine, 1-linoleoyl-GPA (18:2), gulonate, phenol sulfate, branched chain 14:0 dicarboxylic acid, bilirubin, linoleoylcarnitine (C18:3), bilirubin (E,E), eicosenoylcarnitine (C20:1), lanthionine, glycoursodeoxycholate, biliverdin, guanidinoacetate, and/or myo-inositol and (b) an increased level of two or more (e.g., 5, 10, 15, or more) of (14 or 15)-methylpalmitate (a17:0 or i17:0), 1,6-anhydroglucose, N-acetylneuraminate, hypoxanthine, 1-carboxyethylleucine, ectoine, pyrraline, cysteinylglycine disulfide, erucate (22:1n9), 3-methylhistidine, mannose, dimethylguanidino valeric acid (DMGV), 1-carboxyethylvaline, beta-hydroxyisovalerate, stearoyl ethanolamide, trimethylamine N-oxide, 3-hydroxystearate, gluconate, palmitoyl ethanolamide, glucose, and/or glucoronate can be identified as having high-to-moderate disease activity.

    [0040] In some cases, a mammal (e.g., a human) having arthritis (e.g., RA) that is determined to have (a) a decreased level of two or more (e.g., 5, 10, 15, 20, 25, or more) of isoursodeoxycholate, linoleoylcarnitine (C18:2), dihomo-linoleoylcarnitine (C20:2), N-acetyltyrosine, 1-methylhistidine, 4-guanidinobutanoate, lysine, serine, N-acetyltryptophan, 6-bromotryptophan, 1-carboxyethylisoleucine, alpha-ketobutyrate, N2-acetyl,N6-methyllysine, trigonelline (N′-methylnicotinate), 3-phenylpropionate (hydrocinnamate), tryptophan, N-acetylarginine, 1-linoleoyl-GPA (18:2), gulonate, phenol sulfate, branched chain 14:0 dicarboxylic acid, bilirubin, linoleoylcarnitine (C18:3), bilirubin (E,E), eicosenoylcarnitine (C20:1), lanthionine, glycoursodeoxycholate, biliverdin, guanidinoacetate, and myo-inositol and (b) an increased level of two or more (e.g., 5, 10, 15, or more) of (14 or 15)-methylpalmitate (a17:0 or i17:0), 1,6-anhydroglucose, N-acetylneuraminate, hypoxanthine, 1-carboxyethylleucine, ectoine, pyrraline, cysteinylglycine disulfide, erucate (22:1n9), 3-methylhistidine, mannose, dimethylguanidino valeric acid (DMGV), 1-carboxyethylvaline, beta-hydroxyisovalerate, stearoyl ethanolamide, trimethylamine N-oxide, 3-hydroxystearate, gluconate, palmitoyl ethanolamide, glucose, and glucoronate can be identified as having high-to-moderate disease activity.

    [0041] In some cases, a disease activity of a mammal (e.g., a human) having arthritis (e.g., RA) identified as described herein (e.g., based, at least in part, on the presence, absence, or level of 15 or more metabolites in a blood sample (e.g., a plasma sample) obtained from the mammal) can be confirmed using one or more other arthritis disease activity assessment methods. Examples of arthritis disease activity assessment methods that can be used in combination with the methods and materials described herein include, without limitation, DAS28-CRP, clinical disease activity index (CDAI), and simple disease activity index (SDAI).

    [0042] This document also provides methods for treating a mammal (e.g., a human) having arthritis (e.g., RA). In some cases, a mammal (e.g., a human) having arthritis (e.g., RA) and assessed as described herein (e.g., for the presence, absence, or level of 15 or more metabolites a blood sample (e.g., a plasma sample) obtained from the mammal) can be administered or instructed to self-administer one or more (e.g., one, two, three, four, five, or more) arthritis treatments, where the one or more arthritis treatments are effective to treat the arthritis within the mammal. For example, a mammal (e.g., a human) having arthritis (e.g., RA) can be administered or instructed to self-administer one or more arthritis treatments selected based, at least in part, on the presence, absence, or level of 15 or more (e.g., 50 or 51) metabolites (e.g., circulating metabolites) assessed in a blood sample (e.g., a plasma sample) obtained from the mammal.

    [0043] When treating a mammal (e.g., a human) having arthritis (e.g., RA) as described herein (e.g., where the treatment is selected based, at least in part, on the presence, absence, or level of 15 or more (e.g., 50 or 51) metabolites in a blood sample (e.g., a plasma sample) obtained from the mammal), the treatment can be effective to reduce or eliminate one of more symptoms of the arthritis. Examples of symptoms of arthritis include, without limitation, pain, stiffness, tenderness, swelling, redness, decreased range of motion, fatigue, fevers, and weight loss. For example, the methods and materials described herein can be used to reduce one or more symptoms within a mammal having arthritis by, for example, 10, 20, 30, 40, 50, 60, 70, 80, 90, 95, or more percent.

    [0044] In some cases, a treatment for arthritis (e.g., RA) can include any appropriate arthritis treatment. In some cases, an arthritis treatment can include administering one or more arthritis drugs to a mammal in need thereof. In some cases, an arthritis drug can be a painkiller. In some cases, an arthritis drug can be an opioid. In some cases, an arthritis drug can be a nonsteroidal anti-inflammatory drug (NSAID). In some cases, an arthritis drug can be a disease-modifying antirheumatic drug (DMARD; e.g., conventional synthetic DMARDs (csDMARDs) and biologic disease-modifying antirheumatic drugs (bDMARDs) such as tumor necrosis factor inhibitors (TNFi) bDMARDs and non-TNFi bDMARDs). In some cases, an arthritis drug can be a corticosteroid. Examples of arthritis drugs that can be administered to a mammal having arthritis (e.g., RA) can include, without limitation, acetaminophen, tramadol, oxycodone, hydrocodone, ibuprofen, naproxen, adalimumab, certolizumab, etanercept, infliximab, abatacept, rituximab, tocilizumab, azathioprine, hydroxychloroquine, leflunomide, sulfasalazine, methotrexate, prednisone, cortisone, sarilumab, anakinra, tofacitinib, upadacitinib, baricitinib, methylprednisolone, and combinations thereof. In some cases, an arthritis treatment can include therapy and/or surgery. Examples of therapies and surgeries that can be performed on a mammal having arthritis (e.g., RA) to treat the mammal include, without limitation, physical therapy and surgery (e.g., joint repair surgery, joint replacement surgery, and joint fusion surgery).

    [0045] When treating a mammal (e.g., a human) having arthritis (e.g., RA) and identified as having low disease activity as described herein (e.g., based, at least in part, on the presence, absence, or level of 15 or more (e.g., 50 or 51) metabolites in a blood sample (e.g., a plasma sample) obtained from the mammal), the mammal can be administered or instructed to self-administer one or more (e.g., one, two, three, four, five, or more) arthritis treatments that are less aggressive and/or less invasive. For example, a mammal (e.g., a human) identified as having low disease activity as described herein may continue on the same drug regimen and/or can be administered one or more arthritis drugs (e.g., adalimumab, certolizumab, etanercept, infliximab, abatacept, rituximab, tocilizumab, sarilumab, azathioprine, hydroxychloroquine, leflunomide, sulfasalazine, methotrexate, prednisone, methylprednisolone, tofacitinib, upadacitinib, baricitinib, and combinations thereof). In some cases, the mammal can continue their prior treatment with one or more of the drugs listed above.

    [0046] When treating a mammal (e.g., a human) having arthritis (e.g., RA) and identified as having moderate-to-high activity as described herein (e.g., based, at least in part, on the presence, absence, or level of 15 or more (e.g., 50 or 51) metabolites in a blood sample (e.g., a plasma sample) obtained from the mammal), the mammal can be administered or instructed to self-administer one or more (e.g., one, two, three, four, five, or more) arthritis treatments that are more aggressive. For example, a mammal (e.g., a human) identified as having moderate-to-high disease activity as described herein (e.g., based, at least in part, on the presence, absence, or level of 15 or more (e.g., 50 or 51) metabolites in a blood sample (e.g., a plasma sample) obtained from the mammal) despite treatment with methotrexate (or other oral conventional synthetic disease-modifying antirheumatic drug) can be administered one or more biological or targeted synthetic disease-modifying antirheumatic drug (e.g., adalimumab, certolizumab, etanercept, infliximab, abatacept, rituximab, tocilizumab, azathioprine, hydroxychloroquine, leflunomide, sulfasalazine, methotrexate, prednisone, cortisone, methylprednisolone, tofacitinib, upadacitinib, baricitinib, and combinations thereof). If a mammal with arthritis (e.g., RA) has moderate-to-high disease activity based on methods described herein despite treatment with a biologic or targeted synthetic disease-modifying antirheumatic drug (DMARD), for example, then the mammal may be switched to an alternative drug in the same or different class based on mechanism of action (e.g., switch from adalimumab to upadacitinib). If drugs are not appropriate, or if preferred based on other characteristics or preferences, the mammal may undergo surgery (e.g., joint repair surgery, joint replacement surgery, and joint fusion surgery).

    [0047] In some cases, a mammal (e.g., a human) having arthritis (e.g., RA) and identified as having low activity as described herein can be administered one or more of methotrexate, hydroxychloroquine, sulfasalazine, and leflunomide, while a mammal (e.g., a human) having arthritis (e.g., RA) and identified as having moderate-to-high activity as described herein can be administered one or more of adalimumab, certolizumab, etanercept, golimumab, infliximab, abatacept, tocilizumab, sarilumab, rituximab, tofacitinib, baricitinib, and upadacitinib.

    [0048] The invention will be further described in the following examples, which do not limit the scope of the invention described in the claims.

    EXAMPLES

    Example 1: Plasma Metabolomic Profiling in Patients with Rheumatoid Arthritis Identifies Biochemical Features Predictive of Quantitative Disease Activity

    [0049] This Example describes the stratification of RA patients of ‘higher’ and ‘lower’ disease activity groups based on their metabolite signatures.

    Materials and Methods

    Study Population, Subject Enrollment, Sample Collection, and Demographic Characteristics

    [0050] The study population consisted of consecutive patients with RA. Eligibility required patients to be adults 18 years of age or older with a clinical diagnosis of RA by a rheumatologist, fulfilling the American College of Rheumatology/European League Against Rheumatism 2010 revised classification criteria for RA (Aletaha et al., Arthritis & Rheumatism, 62(9):2569-2581 (2010)). A total of 76 patients fulfilled the eligibility criteria, who were partitioned into two groups (Table 1): for the discovery cohort of this study, 64 patients with available blood samples from at least two outpatient visits 6-12 months apart were included (128 total samples); for the validation cohort, 12 patients whose blood samples were available from only a single outpatient visit were included (12 total samples). Demographic and clinical data, including the numbers of tender and swollen joints, patient and evaluator global assessments, CRP (mg/L), body mass index (BMI, kg/m.sup.2), smoking status, and results for rheumatoid factor (RF, IU/mL) and anti-cyclic citrullinated peptide antibodies (anti-CCP), were collected from the electronic medical records. The patient samples (140 in total) in the study had established disease with mean age 63.54 (range: 32-86), and 69.7% (53 of 76) were female. Disease activity varied from remission to high disease activity, with a DAS28-CRP mean of 3.0 (range: 1.2-7.0). See FIG. 7 for distribution of DAS28-CRPs corresponding to all study participants.

    TABLE-US-00003 TABLE 1 Demographic characteristics of study participants. Discovery Cohort.sup.α Validation Cohort.sup.β Number of RA patients/samples 64/128 12/12 Sex of RA patients (female/male) 44/20 9/3 Visit 1 Visit 2 — DAS28-CRP Mean ± SD 3.1 ± 1.3 3.0 ± 1.4 2.4 ± 1.3 Range (min-max) 1.5-7.0 1.2-6.6 1.7-5.9 Age (years) Mean ± SD 62.7 ± 10.5 63.5 ± 10.6 67.8 ± 10.6 Range (min-max) 32-85 33-86 54-84 BMI Mean ± SD 30.6 ± 5.7  31.1 ± 6.2  27.0 ± 4.1  Range (min-max) 22.4-45.3 22.8-47.8 19.0-33.3 N/A (n)  6 6 2 Smoking History (n) Current (active within 3 months)  7 5 1 Former 31 32  3 Never 25 27  7 N/A  1 0 1 CRP (mg/L) Mean ± SD 8.91 ± 16.8  8.0 ± 12.7 11.5 ± 21.7 Range (min-max)  0.29-113.0  0.7-84.0 1.0-77.1 RF.sup.γ (n) Positive 36 — 6 Negative 15 — 2 N/A 13 — 4 Anti-CCP.sup.γ (n) Positive 44 — 5 Negative 13 — 1 N/A  7 — 6 Treatment Methotrexate use (n, %) 48 (75.0%) 49 (76.6%) 7 (58.3%) Methotrexate dose (mg/week) median   20.0  20.0  22.5 IQR [Q.sub.1, Q.sub.3] [15.0, 25.0] [15.0, 25.0] [17.5, 25.0] Prednisone use (n, %) 29 (45.3%) 28 (43.8%) 4 (33.3%) Prednisone dose (mg/day) median   5.0   5.0   5.0 IQR [5.0, 7.0] [5.0, 5.0] [5.0, 5.0] TNFi-bDMARDs.sup.δ (n, %) 23 (35.9%) 21 (32.8%) 3 (25.0%) non-TNFi-bDMARDs.sup.ε (n, %) 6 (9.4%)  7 (10.9%) 1 (8.3%)  non-methotrexate csDMARDs.sup.λ (n, %) 20 (31.2%) 27 (42.2%) 1 (8.3%)  .sup.αTraining group. Plasma samples were obtained from patients at two different time-points; .sup.βTest group. Plasma samples were obtained from patients at a single time-point; .sup.γReported only for the first visit; .sup.δadalimumab, certolizumab, etanercept, and infliximab; .sup.εabatacept, rituximab, and tocilizumab; .sup.λazathioprine, hydroxychloroquine, leflunomide, and sulfasalazine; N/A, Not available; RF, rheumatoid factor; Anti-CCP, anti-cyclic citrullinated peptide antibodies; IQR, inter-quartile range; bDMARDs, biologic disease-modifying anti-rheumatic drugs; csDMARDs, conventional synthetic disease-modifying anti-rheumatic drugs; an expanded table with further information on demographic and clinical characteristics are as described in Hur et al. (Arthritis Research & Therapy 23: 164 (2021)).

    Metabolomic Profiling

    [0051] Untargeted metabolomic profiling of plasma samples from both discovery and validation cohorts through ultra-high performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) was performed by Metabolon Inc. (Durham, N.C., USA)'s Discovery HD4™ platform.

    [0052] Sample Accessioning: Following receipt, samples were inventoried and immediately stored at −80° C. Each sample received was accessioned into the Metabolon LIMS system and was assigned by the LIMS a unique identifier that was associated with the original source identifier only. This identifier was used to track all sample handling, tasks, results, etc. The samples (and all derived aliquots) were tracked by the LIMS system. All portions of any sample were automatically assigned their own unique identifiers by the LIMS when a new task was created; the relationship of these samples was also tracked. All samples were maintained at −80° C. until processed.

    [0053] Sample Preparation: Samples were prepared using the automated MicroLab STAR® system from Hamilton Company. Several recovery standards were added prior to the first step in the extraction process for QC purposes. To remove protein, dissociate small molecules bound to protein or trapped in the precipitated protein matrix, and to recover chemically diverse metabolites, proteins were precipitated with methanol under vigorous shaking for 2 minutes (Glen Mills GenoGrinder 2000) followed by centrifugation. The resulting extract was divided into five fractions: two for analysis by two separate reverse phase (RP)/UPLC-MS/MS methods with positive ion mode electrospray ionization (ESI), one for analysis by RP/UPLC-MS/MS with negative ion mode ESI, one for analysis by HILIC/UPLC-MS/MS with negative ion mode ESI, and one sample was reserved for backup. Samples were placed briefly on a TurboVap® (Zymark) to remove the organic solvent. The sample extracts were stored overnight under nitrogen before preparation for analysis.

    [0054] Ultrahigh Performance Liquid Chromatography-Tandem Mass Spectroscopy (UPLC-MS/MS): All methods utilized a Waters ACQUITY ultra-performance liquid chromatography (UPLC) and a Thermo Scientific Q-Exactive high resolution/accurate mass spectrometer interfaced with a heated electrospray ionization (HESI-II) source and Orbitrap mass analyzer operated at 35,000 mass resolution. The sample extract was dried then reconstituted in solvents compatible to each of the four methods. Each reconstitution solvent contained a series of standards at fixed concentrations to ensure injection and chromatographic consistency. One aliquot was analyzed using acidic positive ion conditions, chromatographically optimized for more hydrophilic compounds. In this method, the extract was gradient eluted from a C18 column (Waters UPLC BEH C18-2.1×100 mm, 1.7 μm) using water and methanol, containing 0.05% perfluoropentanoic acid (PFPA) and 0.1% formic acid (FA). Another aliquot was also analyzed using acidic positive ion conditions, however it was chromatographically optimized for more hydrophobic compounds. In this method, the extract was gradient eluted from the same afore mentioned C18 column using methanol, acetonitrile, water, 0.05% PFPA and 0.01% FA and was operated at an overall higher organic content. Another aliquot was analyzed using basic negative ion optimized conditions using a separate dedicated C18 column. The basic extracts were gradient eluted from the column using methanol and water, however with 6.5 mM Ammonium Bicarbonate at pH 8. The fourth aliquot was analyzed via negative ionization following elution from a HILIC column (Waters UPLC BEH Amide 2.1×150 mm, 1.7 μm) using a gradient consisting of water and acetonitrile with 10 mM Ammonium Formate, pH 10.8. The MS analysis alternated between MS and data-dependent MSn scans using dynamic exclusion. The scan range varied slighted between methods but covered 70-1000 m/z. Raw data files were archived and extracted as described below. Metabolites were identified by automated comparison of the ion features in the experimental samples to a reference library of chemical standard entries that included retention time, molecular weight (m/z), preferred adducts, and in-source fragments as well as associated MS spectra, and were curated by visual inspection for quality control using software developed at Metabolon.

    [0055] Data Extraction and Compound Identification: Raw data was extracted, peak-identified and QC processed using Metabolon's hardware and software. These systems are built on a web-service platform utilizing Microsoft's NET technologies, which run on high-performance application servers and fiber-channel storage arrays in clusters to provide active failover and load-balancing. Compounds were identified by comparison to library entries of purified standards or recurrent unknown entities. Metabolon maintains a library based on authenticated standards that contains the retention time/index (RI), mass to charge ratio (m z), and chromatographic data (including MS/MS spectral data) on all molecules present in the library. Furthermore, biochemical identifications are based on three criteria: retention index within a narrow RI window of the proposed identification, accurate mass match to the library +/−10 ppm, and the MS/MS forward and reverse scores between the experimental data and authentic standards. The MS/MS scores are based on a comparison of the ions present in the experimental spectrum to the ions present in the library spectrum. While there may be similarities between these molecules based on one of these factors, the use of all three data points can be utilized to distinguish and differentiate biochemicals. More than 3300 commercially available purified standard compounds have been acquired and registered into LIMS for analysis on all platforms for determination of their analytical characteristics. Additional mass spectral entries have been created for structurally unnamed biochemicals, which have been identified by virtue of their recurrent nature (both chromatographic and mass spectral). These compounds have the potential to be identified by future acquisition of a matching purified standard or by classical structural analysis.

    Analysis Workflow

    [0056] FIG. 1 provides a summary of the analytic strategy used on the 128 plasma samples of the discovery cohort to identify associations between metabolites and RA disease activity. The analysis workflow consists of two complementary approaches: Using mixed-effects logistic regression, the first approach identifies metabolites that are differentially abundant between higher and lower disease activity groups, which were determined by DAS28-CRP scores (FIG. 1A); the second approach uses mixed-effects linear regression to model the relationship between DAS28-CRP and metabolite abundances, allowing the detection of key biochemical features that associate with quantitative disease activity (FIG. 1). To test the predictive accuracy of these selected features when incorporated into a generalized linear model, an additional cohort of twelve plasma metabolomic profiles (from twelve RA patients obtained at single time-points) was collected as an independent validation set.

    Pre-Processing of Metabolomic Profiling Data

    [0057] Statistical analyses on untargeted metabolomic data were performed using scaled imputed data provided by Metabolon, Inc. Briefly, the raw data were normalized to account for inter-day variation, which is a result of UPLC-MS/MS runs over multiple days, then the peak intensities were rescaled to set each metabolite's median equal to 1. Missing values were then imputed with the minimum observed value of the metabolite across all samples, finally yielding the scaled imputed data. In addition, metabolites with missing values in over 20% of the entire samples were removed, resulting in 686 metabolites remaining for further analysis. R (v3.6.1), lme4 package (v1.1.21), Python3 (v3.7.5), and sklearn (v0.22.2) were used to perform all data pre-processing and statistical analyses.

    Delineation of RA Disease Activity Groups

    [0058] Samples from RA patients were divided into two disease activity groups based upon DAS28-CRP: ‘lower’ (DAS28-CRP≤3.2, n=76) and ‘higher’ (DAS28-CRP>3.2, n=52). These pre-defined two disease activity groups were used as the nominal response variable in a mixed-effects logistic regression model to identify differentially abundant metabolites between the two groups. The demographic characteristics of samples (n=128) divided into lower and higher disease activity are as described in Hur et al. (Arthritis Research & Therapy 23:164 (2021)).

    Identification of Differentially Abundant Metabolites while Controlling for Confounding Factors

    [0059] The following patient characteristics were examined to identify potential confounding factors in the association between plasma metabolites and disease activity (i.e., higher or lower disease activity): age, sex, BMI, smoking history, and treatment use (for methotrexate, prednisone, non-methotrexate csDMARDs, TNFi-bDMARDs, and non-TNFi-bDMARDS). Based upon the Fisher's exact test, patient age (age ≤60, age >60) and sex (male, female) were observed to have statistically significant associations with the two disease activity groups; the P-value for age and sex was P=0.01 (odds ratio [OR]=2.74, 95% confidence interval [CI]=1.15-6.73) and P=0.02 (OR=0.37, 95% CI=0.14-0.88), respectively. On the other hand, no statistically significant associations were observed between these two disease activity groups and BMI (BMI≤30, BMI>30; P=0.32), disease duration (duration ≤9 years, duration >9 years; P=0.14), smoking history (smoked at least once, never smoked; P=0.36), or treatment use (user, non-user) for methotrexate (P=0.83), prednisone (P=0.58), TNFi-bDMARDs (e.g., adalimumab, certolizumab, etanercept, and infliximab; P=0.18), non-TNFi-bDMARDs (e.g., abatacept, rituximab, and tocilizumab; P=0.76), or other non-methotrexate csDMARDs (e.g., azathioprine, hydroxychloroquine, leflunomide, and sulfasalazine; P=0.71). In addition, no significant changes in treatment use were observed between the two visits; P-values of the associations between treatment use and time-point based upon McNemar's Chi-squared test for paired nominal data were as follows: methotrexate (P=1), prednisone (P=1), TNFi-bDMARDs (P=0.75), non-TNFi-bDMARDs (P=1), and non-methotrexate csDMARDs (P=0.07). Therefore, the mixed-effects logistic regression model was adjusted for age and sex as fixed effects, but not for all other aforementioned covariates. Patient ID was considered as a random effect in the model to account for intra-subject variance due to having repeated measurements from a single patient. By controlling for patient ID (which are unique to each patient) as a random effect, the non-independence in the data are acknowledged. Leveraging multiple samples from the same patient allows us to compensate for the small number of samples in higher disease activity (DAS28-CRP>3.2) in each visit (visit 1 and visit 2 having 25 and 27 samples, respectively) by maximizing the degree of freedom for the quantitative disease activity measure, and thereby to boost statistical power. No significant difference was observed in DAS28-CRP between visit 1 and visit 2 (P=0.98, Wilcoxon signed-rank test). Metabolites whose corresponding coefficients of the regression model were of P-value <0.05 were considered as differentially abundant, that is, having a statistically significant association with disease activity group.

    Selection of Metabolites Associated with DAS28-CRP

    [0060] Selection of metabolites associated with DAS28-CRP was performed with a mixed-effects linear regression model (DAS28-CRP as the continuous response variable), which controls for fixed effects (scaled metabolite abundances, patients' age and sex) and for random effects (patient ID). Satterthwaite's degrees of freedom method supported by lmerTest (v3.1.1) was applied to test for the statistical significance (P-value) of associations between metabolites and DAS28-CRP. P-values were retrieved from the corresponding regression coefficients of the predictor variables.

    Evaluation of Predictive Performance of DAS28-CRP-Associated Metabolites

    [0061] A generalized linear model (GLM) was used to estimate DAS28-CRP scores using the aforementioned significantly associated metabolites as predictor variables. Predictive performance of the parameterized model was evaluated by two different techniques: First, a modified leave-one-out cross-validation approach was applied to the 128 samples of the training group (discovery cohort). More specifically, in each cross-validation loop, both samples from the same patient were allocated as an internal validation set, while all remaining samples (126 samples from 63 patients) were used to select metabolites significantly associated with DAS28-CRP (P<0.05). These selected biochemical features were then included in a GLM for predicting DAS28-CRP scores of the remaining two samples (of the internal validation group) from their metabolite abundances. The second approach considers testing a GLM, which was composed of the DAS28-CRP-associated metabolites identified from all 128 samples of the training group, on the independent validation group of 12 plasma samples (validation cohort). For both techniques, model performance was reported using mean absolute error (MAE) and standard deviation (SD).

    Identification of Metabolites Associated with Treatment Use

    [0062] A marginal, mixed-effects linear regression model was used to relate metabolite abundance with treatment use. Scaled metabolite abundance, treatment use, and patient ID was set as the response variable, predictor variable (fixed effect), and random effect, respectively. Use of the following treatments was assessed individually: methotrexate, prednisone, non-methotrexate csDMARDs, TNFi-bDMARDs, and non-TNFi-bDMARDs (names of individual drugs in each treatment group are provided in the footnote of Table 1). P-values were retrieved from the corresponding regression coefficient of the predictor variable (i.e., use or non-use), and a significance of P<0.05 was reported as statistically significant.

    Identification of Differentially Abundant Metabolites Between Two CRP Groups

    [0063] Metabolites that are significantly associated with disease activity groups and DAS28-CRP scores were further investigated to find those associated with patient groups delineated by CRP levels. First, all samples were divided into two groups as follows: ‘high-CRP’ (CRP>3.0 mg/L, n=52) and ‘low-CRP’ (CRP≤3.0 mg/L, n=76). Next, a marginal, mixed-effects linear regression model was used to define the abundance of a metabolite based upon the following fixed effects: CRP group, sex, age, smoking history, and treatment with prednisone, methotrexate, non-methotrexate csDMARDs, TNFi-bDMARDs; or non-TNFi-bDMARDs. Additionally, patient ID was treated as a random effect. Any covariates whose association with metabolite abundance was statistically significant (i.e., P-value of the corresponding regression coefficient <0.05) were then included in an adjusted mixed model for metabolite abundance. Finally, metabolites were considered as differentially abundant between the two CRP groups if the association between metabolite abundance and CRP group was still found to be significant in the adjusted model (P<0.05).

    Results

    Differentially Abundant Metabolites Between Higher and Lower Disease Activity Groups

    [0064] As shown in our analysis workflow (FIG. 1), we first sought metabolites that were significantly different in abundance between two major disease activity groups. For this, the 128 metabolomic profiles were divided into two major categories (‘higher’ vs. ‘lower’) based upon the reported disease activity of the corresponding patient at the time of sample collection. Using a mixed-effects logistic regression model, 33 metabolites were identified as differentially abundant between higher (n=52) and lower (n=76) DAS28-CRP groups (FIG. 2). Most of these metabolites (31 of 33) were observed to have significantly increased abundances in lower disease activity, whereas the remaining two (glucuronate and hypoxanthine) were found to be significantly increased in higher disease activity. Of the 31 metabolites increased in lower disease activity, seven metabolites (3-hydroxydecanoylcarnitine, dihomo-linoleoylcarnitine (C20:2), eicosenoylcarnitine (C20:1), linoleoylcarnitine (C18:3), linoleoylcarnitine (C18:2), stearoylcarnitine (C18), palmitoylcarnitine (C16)) are a part of acylcarnitine metabolism, and represent a 3.6-fold enrichment in metabolites involved in this particular pathway (P=1.9×10.sup.−3, hypergeometric test). It is important to note that the differences seen are relatively small in terms of fold-change, with most of the metabolites varying by 1.1-1.3 fold. Despite these subtle differences within RA patients of varying disease activities, statistically significant signal was obtained even after considering and controlling for all known potentially confounding factors (which often leads to reduction in statistical power), while adhering to our cut-offs for statistical significance (P<0.05).

    [0065] N2-acetyl,N6-methyllysine (|log 2(FC)|=1.11, P=1.26×10.sup.−2) and trigonelline (N′-methylnicotinate) (|log 2(FC)|=0.74, P=2.09×10.sup.−2), which were both found to have increased abundance in lower disease activity, were the top two metabolites having the largest fold-changes between the two groups. Biliverdin (|log.sub.2(FC)|=0.48, P=1.38×10.sup.−2) and bilirubin (E,E) (|log 2(FC)|=0.43, P=1.18×10.sup.−2), which are known metabolic products of the heme catabolic pathway, were also observed to have significantly increased abundances in lower disease activity. The full list of differentially abundant metabolites and their associated pathways are shown in Table 2.

    TABLE-US-00004 TABLE 2 Differentially abundant metabolites between lower (DAS28-CRP ≤ 3.2) and higher (DAS28-CRP > 3.2) disease activity groups. Mean Mean (lower (higher disease disease Chemical activity activity log.sub.2 fold Absolute log.sub.2 ID.sup.α P-Value group) group) change.sup.β (fold change) 100015839 0.00740518 1.150059211 0.915419231 −0.329203635 0.329203635 100020414 0.00853385 1.120727632 0.929205769 −0.270365689 0.270365689 100001104 0.00904587 1.184696053 0.923844231 −0.358795442 0.358795442 100015838 0.01050137 1.203475 0.931028846 −0.3703084 0.3703084 100001253 0.01095929 1.364868421 0.9508 −0.521548068 0.521548068 100001950 0.01188551 1.242778947 0.92055 −0.433001718 0.433001718 100001254 0.01254553 1.142303947 0.928190385 −0.299453921 0.299453921 100020546 0.01267987 1.760740789 0.817223077 −1.107380687 1.107380687 415 0.01324431 1.074835526 0.925073077 −0.21647667 0.21647667 250 0.01388466 1.33235 0.952098077 −0.484791019 0.484791019 171 0.01481688 0.894672368 1.132363462 0.339905738 0.339905738 100003151 0.01557361 1.123313158 0.948419231 −0.244163358 0.244163358 100000269 0.01768727 1.088839474 0.988378846 −0.139655236 0.139655236 100001092 0.02099958 1.584206579 0.947273077 −0.741908187 0.741908187 565 0.02305521 1.040543421 0.938330769 −0.14916869 0.14916869 100015831 0.02323304 1.202006579 0.937559615 −0.358462459 0.358462459 100001313 0.02380465 1.202826316 0.960146154 −0.325102402 0.325102402 100001391 0.02389079 1.108465789 0.944884615 −0.230354175 0.230354175 100001266 0.02463805 1.076190789 0.860423077 −0.322815741 0.322815741 100001197 0.02633702 1.215452632 0.952098077 −0.35231157 0.35231157 100001257 0.02908223 1.2572 0.909661538 −0.466812417 0.466812417 100021141 0.03388509 1.260892105 0.965390385 −0.385260467 0.385260467 100000776 0.03528276 1.095951316 0.974332692 −0.169697333 0.169697333 100000257 0.03730717 1.062377632 1.407644231 0.405986076 0.405986076 100001620 0.03774306 1.066026316 0.980257692 −0.12101009 0.12101009 503 0.0381436 1.029375 0.967703846 −0.089131149 0.089131149 100004088 0.03913854 1.114244737 0.884019231 −0.333916487 0.333916487 100020204 0.03969724 1.404751316 1.005826923 −0.481932676 0.481932676 100004575 0.04120342 1.23235 0.895746154 −0.460250205 0.460250205 100000007 0.04185476 1.037297368 0.979798077 −0.082273175 0.082273175 407 0.04472243 1.041238158 0.959955769 −0.117260249 0.117260249 100001577 0.04776566 1.364571053 0.932911538 −0.548635325 0.548635325 100020361 0.04991135 1.041161842 0.910038462 −0.194194919 0.194194919 Metabolic Chemical Super- Metabolic Sub- ID.sup.α Name of Metabolite pathway.sup.γ pathway.sup.g 100015839 Dihomo-linoleoylcarnitine Lipid Fatty Acid Metabolism (C20:2) (Acyl Carnitine, Polyunsaturated) 100020414 6-bromotryptophan Amino Acid Tryptophan Metabolism 100001104 N-acetyltyrosine Amino Acid Tyrosine Metabolism 100015838 Eicosenoylcarnitine Lipid Fatty Acid Metabolism (C20:1) (Acyl Carnitine, Monounsaturated) 100001253 N-acetylglutamine Amino Acid Glutamate Metabolism 100001950 Bilirubin (E,E) Cofactors Hemoglobin and and Vitamins Porphyrin Metabolism 100001254 N-acetyltryptophan Amino Acid Tryptophan Metabolism 100020546 N2-acetyl,N6-methyllysine Amino Acid Lysine Metabolism 415 Methionine Amino Acid Methionine, Cysteine, SAM and Taurine Metabolism 250 Biliverdin Cofactors Hemoglobin and and Vitamins Porphyrin Metabolism 171 Hypoxanthine Nucleotide Purine Metabolism, (Hypo)Xanthine/Inosine containing 100003151 Linoleoylcarnitine (C18:2) Lipid Fatty Acid Metabolism (Acyl Carnitine, Polyunsaturated) 100000269 Glycerophosphorylcholine Lipid Phospholipid (GPC) Metabolism 100001092 Trigonelline (N′- Cofactors Nicotinate and methylnicotinate) and Vitamins Nicotinamide Metabolism 565 Tryptophan Amino Acid Tryptophan Metabolism 100015831 Linoleoylcarnitine (C18:3) Lipid Fatty Acid Metabolism (Acyl Carnitine, Polyunsaturated) 100001313 Gamma-glutamylmethionine Peptide Gamma-glutamyl Amino Acid 100001391 Stearoylcarnitine (C18) Lipid Fatty Acid Metabolism (Acyl Carnitine, Long Chain Saturated) 100001266 N-acetylarginine Amino Acid Urea cycle; Arginine and Proline Metabolism 100001197 10-undecenoate (11:1n1) Lipid Medium Chain Fatty Acid 100001257 N-acetylasparagine Amino Acid Alanine and Aspartate Metabolism 100021141 3-hydroxydecanoylcarnitine Lipid Fatty Acid Metabolism (Acyl Carnitine, Hydroxy) 100000776 Palmitoylcarnitine (C16) Lipid Fatty Acid Metabolism (Acyl Carnitine, Long Chain Saturated) 100000257 Glucuronate Carbohydrate Aminosugar Metabolism 100001620 Glycerophosphoethanolamine Lipid Phospholipid Metabolism 503 Serine Amino Acid Glycine, Serine and Threonine Metabolism 100004088 Retinal Cofactors Vitamin A Metabolism and Vitamins 100020204 N-acetyl-2-aminooctanoate Lipid Fatty Acid, Amino 100004575 N2,N5-diacetylornithine Amino Acid Urea cycle; Arginine and Proline Metabolism 100000007 Carnitine Lipid Carnitine Metabolism 407 Lysine Amino Acid Lysine Metabolism 100001577 N-acetylcitrulline Amino Acid Urea cycle; Arginine and Proline Metabolism 100020361 3-amino-2-piperidone Amino Acid Urea cycle; Arginine and Proline Metabolism .sup.αChemical ID defined by Metabolon’s Discovery HD4 ™ platform .sup.βfold change = mean (higher disease activity group)/mean (lower disease activity group) .sup.γSuper-pathways and sub-pathways were defined by Metabolon's Discovery HD4 ™ platform

    Metabolic Feature Selection Improves DAS28-CRP Prediction Accuracy

    [0066] Having uncovered metabolites demonstrating altered abundance between two major disease activity groups, it was next investigated whether quantitative disease activity can be predicted with plasma metabolomes. Mixed-effects linear regression models were used to select metabolites significantly associated with DAS28-CRP. Afterwards, the abundances of the selected metabolic features were incorporated into a GLM to predict DAS28-CRP. For comparison purposes, a GLM was constructed without metabolic feature selection, and thereby taking into consideration all features of a metabolomic profile.

    [0067] When applying a modified leave-one-out cross-validation technique to the training group samples (n=128), it was found that the GLM incorporating metabolites that were significantly associated with DAS28-CRP outperformed the model without feature selection (i.e., using all metabolites). As shown in FIG. 3, the distribution of absolute errors between the observed and predicted DAS28-CRP scores was smaller (with respect to the cumulative area under the error curve) for the GLM with feature selection than that without feature selection. To this point, the prediction MAE (±SD) of the GLM with and without feature selection was 1.51 (±1.89) and 2.02 (±2.52), respectively.

    [0068] Having confirmed that feature selection can lead to a more accurate prediction model in cross-validation, the same scheme was applied to all metabolome samples of the discovery cohort to obtain a final set of metabolites associated with DAS28-CRP (P<0.05). After adjusting for potential confounding factors, this resulted in a collection of 51 plasma metabolites (Table 3). These metabolites were used to construct a final GLM, whose predictive accuracy was tested on an independent validation cohort (n=12) of plasma metabolomic profiles from twelve RA patients (this additional cohort was not drawn from the same population distribution from which the features were derived). On this previously unseen cohort, the GLM constructed with only the 51 selected metabolites performed considerably better than the model without the feature selection scheme by over two-fold (FIG. 4A); the prediction MAE of the GLM with and without feature selection was 0.97 (+0.47) and 2.01 (+2.18), respectively. Likewise, when the actual and predicted DAS28-CRPs were plotted together for both GLMs (FIG. 4B), it was found that the model with the selection scheme performed more favorably. More specifically, a stronger correlation between the actual and predicted disease activity scores was observed in the model with feature selection (Spearman's ρ=0.69, P=1.40×10.sup.−2, 95% CI: [0.18, 0.90]) compared to the model without (Spearman's ρ=0.18, P=5.72×10.sup.−2, 95% CI: [−0.44, 0.68]).

    TABLE-US-00005 TABLE 3 Plasma metabolites significantly associated with DAS28-CRP. Regression Metabolite Name Super-Pathway.sup.α Sub-Pathway.sup.α HMDB ID.sup.β Coefficient.sup.γ P-value.sup.δ 3-hydroxystearate Lipid Fatty Acid, Monohydroxy N/A 0.418 0.002 Phenol sulfate Amino Acid Tyrosine Metabolism HMDB60015 −0.265 0.003 Trimethylamine N-oxide Lipid Phospholipid Metabolism HMDB00925 0.485 0.004 Bilirubin (E,E) Cofactors and Hemoglobin and Porphyrin Metabolism N/A −0.612 0.007 Vitamins Serine Amino Acid Glycine, Serine and Threonine HMDB00187 −1.594 0.010 Metabolism Dimethylguanidino valeric acid (DMGV) Amino Acid Urea cycle; Arginine and Proline N/A 0.325 0.011 Metabolism N-acetyltryptophan Amino Acid Tryptophan Metabolism HMDB13713 −0.918 0.012 Glycoursodeoxycholate Lipid Secondary Bile Acid Metabolism HMDB00708 0.051 0.012 N-acetylneuraminate Carbohydrate Aminosugar Metabolism HMDB00230 0.470 0.013 Dihomo-linoleoylcarnitine (C20:2) Lipid Fatty Acid Metabolism (Acyl Carnitine, N/A −0.745 0.013 Polyunsaturated) N-acetyltyrosine Amino Acid Tyrosine Metabolism HMDB00866 −0.713 0.014 Branched chain 14:0 dicarboxylic acid Lipid Fatty Acid, Dicarboxylate N/A −0.201 0.014 1-carboxyethylvaline Amino Acid Leucine, Isoleucine and Valine N/A 0.408 0.015 Metabolism (14 or 15)-methylpalmitate Lipid Fatty Acid, Branched N/A 0.227 0.017 (a17:0 or i17:0) Isoursodeoxycholate Lipid Secondary Bile Acid Metabolism HMDB00686 0.059 0.018 Glucuronate Carbohydrate Aminosugar Metabolism HMDB00127 0.396 0.019 Glucose Carbohydrate Glycolysis, Gluconeogenesis, and HMDB00122 1.107 0.019 Pyruvate Metabolism Linoleoylcarnitine (C18:3) Lipid Fatty Acid Metabolism (Acyl Carnitine, N/A −0.534 0.020 Polyunsaturated) 1-methylhistidine Amino Acid Histidine Metabolism HMDB00001 0.580 0.020 Trigonelline (N′-methylnicotinate) Cofactors and Nicotinate and Nicotinamide Metabolism HMDB00875 −0.227 0.020 Vitamins Palmitoyl ethanolamide Lipid Endocannabinoid HMDB02100 0.067 0.020 Hypoxanthine Nucleotide Purine Metabolism, HMDB00157 0.482 0.022 (Hypo)Xanthine/Inosine containing Biliverdin Cofactors and Hemoglobin and Porphyrin Metabolism HMDB01008 −0.436 0.022 Vitamins Linoleoylcarnitine (C18:2) Lipid Fatty Acid Metabolism (Acyl Carnitine, HMDB06469 −0.814 0.023 Polyunsaturated) 3-methylhistidine Amino Acid Histidine Metabolism HMDB00479 0.140 0.025 N-acetylarginine Amino Acid Urea cycle; Arginine and Proline HMDB04620 −0.755 0.026 Metabolism 4-guanidinobutanoate Amino Acid Guanidino and Acetamido Metabolism HMDB03464 0.347 0.026 1-carboxyethylisoleucine Amino Acid Leucine, Isoleucine and Valine N/A 0.307 0.026 Metabolism Cysteinylglycine disulfide Amino Acid Glutathione Metabolism HMDB00709 1.562 0.027 Guanidinoacetate Amino Acid Creatine Metabolism HMDB00128 −1.125 0.027 N2-acetyl,N6-Methyllysine Amino Acid Lysine Metabolism N/A −0.213 0.028 Lysine Amino Acid Lysine Metabolism HMDB00182 −1.395 0.031 1,6-anhydroglucose Xenobiotics Food Component/Plant HMDB00640 0.097 0.032 Pyrraline Xenobiotics Food Component/Plant HMDB33143 0.190 0.032 Mannose Carbohydrate Fructose, Mannose and Galactose HMDB00169 0.633 0.032 Metabolism Ectoine Xenobiotics Chemical N/A 0.123 0.036 6-bromotryptophan Amino Acid Tryptophan Metabolism N/A −0.758 0.037 1-linoleoyl-GPA (18:2) Lipid Lysophospholipid HMDB07856 −0.371 0.039 Eicosenoylcarnitine (C20:1) Lipid Fatty Acid Metabolism (Acyl Carnitine, N/A −0.557 0.039 Monounsaturated) Erucate (22:1n9) Lipid Long Chain Monounsaturated Fatty Acid HMDB02068 0.346 0.040 Bilirubin Cofactors and Hemoglobin and Porphyrin Metabolism HMDB00054 −0.432 0.042 Vitamins Stearoyl ethanolamide Lipid Endocannabinoid HMDB13078 0.070 0.043 3-phenylpropionate (hydrocinnamate) Xenobiotics Benzoate Metabolism HMDB00764 −0.178 0.043 beta-hydroxyisovalerate Amino Acid Leucine, Isoleucine and Valine HMDB00754 0.723 0.045 Metabolism Myo-inositol Lipid Inositol Metabolism HMDB00211 0.944 0.045 Gulonate Cofactors and Ascorbate and Aldarate Metabolism HMDB03290 0.575 0.047 Vitamins Gluconate Xenobiotics Food Component/Plant HMDB00625 0.539 0.047 Tryptophan Amino Acid Tryptophan Metabolism HMDB00929 −1.139 0.048 1-carboxyethylleucine Amino Acid Leucine, Isoleucine and Valine N/A 0.350 0.048 Metabolism alpha-ketobutyrate Amino Acid Methionine, Cysteine, SAM and Taurine HMDB00005 0.268 0.049 Metabolism Lanthionine Amino Acid Methionine, Cysteine, SAM and Taurine N/A −0.229 0.049 Metabolism
    Commonly Identified Metabolites from Two Different Analytic Approaches

    [0069] To summarize the findings above, we found that, from the 686 total detectable metabolites in a metabolomic profile, 33 (4.8%) were differentially abundant between higher and lower disease activity; and 51 (7.4%) were significantly associated with DAS28-CRP (FIG. 5). These separate findings amounted to a total of 67 unique metabolites, among which were found to have no association with the use of prednisone, methotrexate, other non-methotrexate csDMARDs, TNFi-bDMARDs, or non-TNFi-bDMARDs. Eight metabolites (6-bromotryptophan, bilirubin (E,E), biliverdin, glucuronate, N-acetyltryptophan, N-acetyltyrosine, serine, and trigonelline) were not only consistently detected across both analytic approaches, but also found to have no association with any treatment use; these results strongly suggest key metabolic pathways and modules potentially contributing to, or serving as indicators of, RA pathogenesis independent of confounding treatment effects. Consistent with this idea, additional studies into the metabolites found in this study (the majority of which have yet to be linked to RA) may be able to provide new insight into the perturbed physiological metabolic processes-which are then in turn reflected in blood underlying disease progression in RA.

    Metabolites Associated with CRP Patient Groups

    [0070] Elevated levels of C-reactive protein (CRP) in the blood is well known to often indicate increased inflammatory conditions, which may be caused by a wide variety of acute (e.g., infections) and chronic disorders (e.g., rheumatoid arthritis, inflammatory bowel disease). In RA patients, CRP levels have been observed to increase after acute mental stress tasks, and also to be linked to risk of cardiovascular disease. Furthermore, several serum metabolites were found to reflect inflammatory activity in patients with early arthritis.

    [0071] The aforementioned 67 plasma metabolites were further investigated to see whether any were differentially abundant between two CRP patient groups, i.e., ‘high-CRP’ (CRP>3.0 mg/L, n=52) and ‘low-CRP’ (CRP≤3.0 mg/L, n=76). While controlling for potential confounding variables, eight total metabolites were identified that were significantly associated with CRP patient group. More specifically, the abundances of mannose, beta-hydroxyisovalerate, (14 or 15)-methylpalmitate (a17:0 or i17:0), erucate (22:1n9), 10-undecenoate (11:1n1), N-acetylcitrulline were higher in high-CRP, while those of serine and linoleoylcarnitine (C18:3) were lower in high-CRP (FIG. 6). Application of these plasma metabolites, which were found to be connected to both RA disease activity and circulating CRP levels, may lead to the development of new clinical laboratory tests to further enable precision medicine for RA patients.

    Plasma Metabolites Associate with Clinical Improvement in RA

    [0072] Based upon the European League Against Rheumatism (EULAR) response criteria for DAS28-CRP (Wells et al., Annals Rheum. Dis., 68(6):954-960 (2009)), it was found that sixteen of the 64 patients in the discovery cohort showed moderate or good improvement in disease activity from visit 1 to visit 2, while the remaining 48 patients did not show clinical improvement at the time of their second visit. This discovery provided an entry point for the following analysis: For each of these two patient groups, i.e., ‘Improved’ (n=16) and ‘Non-improved’ (n=48) patients, metabolites whose abundances significantly changed from visit 1 to visit 2 were identified while controlling for the same confounding factors (mixed-effects regression model, P<0.05). As a result, eleven metabolites were identified whose abundances significantly changed in the Improved patient group (Table 4), while nineteen metabolites showed significant changes in the Non-improved patient group (Table 5). The following three metabolites, which were discovered in our previous analyses on the 128 plasma metabolome samples of the discovery cohort, were detected once again: erucate (22:1n9), a metabolite identified to be associated with both DAS28-CRP and CRP patient group, was identified to be significantly different between visit 1 and visit 2 in patients who did not show clinical improvement (Non-improved); 3-amino-2-piperidone, a metabolite identified to be differentially abundant between higher and lower disease activity in our study, was identified to be significantly different between visit 1 and visit 2 in patients who showed clinical improvement (Improved); and gamma-glutamylmethionine, a metabolite identified to be differentially abundant between higher and lower disease activity, was identified to be significantly different between visit 1 and visit 2 in the Non-improved group. These results allow us to expand our future direction to investigate metabolites associated with clinical improvement in patients with RA.

    TABLE-US-00006 TABLE 4 Metabolites displaying significant changes in abundances in patients with clinical improvement (n = 16). Chemical mean mean log.sub.2 ID.sup.α P-value BIOCHEMICAL SUPER PATHWAY.sup.β SUB PATHWAY.sup.β (visit 1).sup.γ (visit 2).sup.γ fold-change.sup.δ 100006620 0.0095 nonanoylcarnitine (C9) Lipid Fatty Acid Metabolism (Acyl 0.758 1.525 1.009 Carnitine, Medium Chain) 100021136 0.0126 3-decenoylcarnitine Lipid Fatty Acid Metabolism (Acyl 4.023 2.452 −0.714 Carnitine, Monounsaturated) 100001658 0.0162 taurolithocholate 3-sulfate Lipid Secondary Bile Acid Metabolism 0.972 1.618 0.735 100001121 0.0166 pyridoxate Cofactors and Vitamin B6 Metabolism 0.817 1.348 0.722 Vitamins 100001425 0.0169 5,6-dihydrouridine Nucleotide Pyrimidine Metabolism, Uracil 0.863 1.565 0.859 containing 361 0.0191 inosine Nucleotide Purine Metabolism, 4.470 0.809 −2.466 (Hypo)Xanthine/Inosine containing 100010896 0.0340 2′-O-methyluridine Nucleotide Pyrimidine Metabolism, Uracil 0.907 1.005 0.148 containing 100020361 0.0342 3-amino-2-piperidone Amino Acid Urea cycle; Arginine and Proline 4.499 0.674 −2.739 Metabolism 100001359 0.0366 aconitate [cis or trans] Energy TCA Cycle 1.068 1.393 0.383 100004414 0.0449 2-hydroxyphytanate Lipid Fatty Acid, Branched 0.937 1.088 0.216 100000285 0.0462 N-alpha-acetylornithine Amino Acid Urea cycle; Arginine and Proline 0.979 1.234 0.335 Metabolism .sup.αChemical ID defined by Metabolon’s Discovery HD4 ™ platform .sup.βSuper-pathways and sub-pathways were defined by Metabolon’s Discovery HD4 ™ platform .sup.γMean abundance of the metabolites .sup.δfold change = mean (visit 2)/mean (visit 1)

    TABLE-US-00007 TABLE 5 Metabolites displaying significant changes in abundances in patients without clinical improvement (n = 48). Chemical mean mean log.sub.2 ID.sup.α P-value BIOCHEMICAL SUPER PATHWAY.sup.β SUB PATHWAY.sup.β (visit 1).sup.γ (visit 2).sup.γ fold-change.sup.δ 1221 0.0009 creatine Amino Acid Creatine Metabolism 0.819 1.262 0.624 100000054 0.0049 5-hydroxylysine Amino Acid Lysine Metabolism 0.938 1.184 0.337 361 0.0151 inosine Nucleotide Purine Metabolism, 2.662 1.463 −0.863 (Hypo)Xanthine/Inosine containing 100020487 0.017 N-acetyl-isoputreanine Amino Acid Polyamine Metabolism 0.904 1.166 0.368 93 0.0255 alpha-ketoglutarate Energy TCA Cycle 0.975 1.064 0.126 100001872 0.0275 1-stearoyl-2- Lipid Phosphatidylserine (PS) 1.418 0.886 −0.678 arachidonoyl-GPS (18:0/20:4) 100009394 0.0292 hexadecadienoate Lipid Long Chain Polyunsaturated Fatty 0.955 1.098 0.202 (16:2n6) Acid (n3 and n6) 197 0.0299 S-adenosylhomocysteine Amino Acid Methionine, Cysteine, SAM and 0.828 1.054 0.348 (SAH) Taurine Metabolism 100006620 0.0333 nonanoylcarnitine (C9) Lipid Fatty Acid Metabolism (Acyl 0.968 1.435 0.568 Carnitine, Medium Chain) 100006438 0.0397 citraconate/glutaconate Energy TCA Cycle 1.374 0.998 −0.462 100020478 0.0408 dodecadienoate (12:2) Lipid Fatty Acid, Dicarboxylate 0.972 1.356 0.481 100001605 0.0416 catechol sulfate Xenobiotics Benzoate Metabolism 1.003 1.119 0.158 100005998 0.0423 octadecanedioylcarnitine Lipid Fatty Acid Metabolism (Acyl 1.031 1.189 0.206 (C18-DC) Carnitine, Dicarboxylate) 100004396 0.0457 3-hydroxyadipate Lipid Fatty Acid, Dicarboxylate 1.480 0.906 −0.708 2054 0.0458 ethylmalonate Amino Acid Leucine, Isoleucine and Valine 0.963 1.111 0.206 Metabolism 100020541 0.0472 11beta- Lipid Androgenic Steroids 1.356 0.922 −0.557 hydroxyandrosterone glucuronide 1087 0.0484 erucate (22:1n9) Lipid Long Chain Monounsaturated Fatty 0.869 1.158 0.414 Acid 100001313 0.0490 gamma- Peptide Gamma-glutamyl Amino Acid 1.003 1.097 0.129 glutamylmethionine 100004414 0.0498 2-hydroxyphytanate Lipid Fatty Acid, Branched 0.972 1.073 0.143 .sup.αChemical ID defined by Metabolon’s Discovery HD4 ™ platform .sup.βSuper-pathways and sub-pathways were defined by Metabolon's Discovery HD4 ™ platform .sup.γMean abundance of the metabolites .sup.δfold change = mean (visit 2)/mean (visit 1)

    [0073] Together these results demonstrate that circulating metabolites can be used to identify a mammal (e.g., a human) has having RA. For example, a distinct metabolite signature present in a blood sample obtained from a human can be used to identify that human as having RA. In some cases, the distinct metabolite signature can be used to determine that activity and/or disease stage of the RA.

    Example 2: A Generalized Linear Model Predicts DAS28-CRP Score

    [0074] A mixed-effects linear regression model was used on 128 RA plasma samples to select metabolites that were significantly associated with DAS28-CRP. As a result, 51 metabolites were identified (P<0.05), which were then used as predictor variables for a machine-learning model (i.e., generalized linear model [GLM]) for DAS28-CRP estimation. The main components of the GLM, that is, the coefficients and intercept, were estimated based on the 128 RA plasma samples. Below, Equation (1) summarizes the final constructed GLM for DAS28-CRP prediction based on the abundance of 51 metabolites. M.sub.i represents the abundance of metabolite i in the patient's plasma sample, while β.sub.i is the coefficient corresponding to M.sub.i. For example, M.sub.1 is the measured (scaled) abundance of glycoursodeoxycholate, whose model coefficient β.sub.1 is −0.0137707. Table 6 summarizes the corresponding coefficients (β.sub.i) of each metabolite.

    [00001] DAS28_CRP .Math. i = 1 E ⁢ 1 ⁢ ( β i ⁢ M i ) + 2.641856 ( 1 )

    TABLE-US-00008 TABLE 6 Plasma metabolites significantly associated with DAS28-CRP. Metabolite Coefficient index Metabolite (M.sub.i) index Coefficient (β.sub.i) M.sub.1 Glycoursodeoxycholate β.sub.1 −0.0137707 M.sub.2 Eicosenoylcarnitine (C20:1) β.sub.2 −0.829059 M.sub.3 Branched chain 14:0 β.sub.3 −0.1291121 dicarboxylic acid M.sub.4 Lysine β.sub.4 −0.2994297 M.sub.5 3-methylhistidine β.sub.5 0.12286813 M.sub.6 1-carboxyethylleucine β.sub.6 −0.6156973 M.sub.7 Biliverdin β.sub.7 −0.1344605 M.sub.8 Trigonelline (N′- β.sub.8 −0.1354869 methylnicotinate) M.sub.9 Bilirubin β.sub.9 0.08899905 M.sub.10 Isoursodeoxycholate β.sub.10 0.02911994 M.sub.11 Glucose β.sub.11 −0.5245139 M.sub.12 1-carboxyethylisoleucine β.sub.12 −0.5939626 M.sub.13 3-phenylpropionate β.sub.13 −0.0691645 (hydrocinnamate) M.sub.14 Palmitoyl ethanolamide β.sub.14 0.08090326 M.sub.15 Tryptophan β.sub.15 0.83265799 M.sub.16 Dimethylguanidino valeric β.sub.16 0.24035438 acid (DMGV) M.sub.17 Guanidinoacetate β.sub.17 −0.147475 M.sub.18 Phenol sulfate β.sub.18 −0.1682771 M.sub.19 Cysteinylglycine disulfide β.sub.19 1.23610258 M.sub.20 Linoleoylcarnitine (C18:3) β.sub.20 −1.0951146 M.sub.21 1-linoleoyl-GPA (18:2) β.sub.21 −0.1236492 M.sub.22 Hypoxanthine β.sub.22 0.25248128 M.sub.23 Mannose β.sub.23 −0.0458874 M.sub.24 Pyrraline β.sub.24 0.1463747 M.sub.25 Ectoine β.sub.25 0.03377761 M.sub.26 Trimethylamine N-oxide β.sub.26 −0.1108116 M.sub.27 N2-acetyl,N6-methyllysine β.sub.27 −0.0043281 M.sub.28 beta-hydroxyisovalerate β.sub.28 −0.1330342 M.sub.29 N-acetylarginine β.sub.29 −0.096799 M.sub.30 Stearoyl ethanolamide β.sub.30 −0.0712158 M.sub.31 Glucuronate β.sub.31 0.35355433 M.sub.32 6-bromotryptophan β.sub.32 −0.4218997 M.sub.33 Bilirubin (E,E) β.sub.33 −0.3167004 M.sub.34 N-acetyltyrosine β.sub.34 −0.748858 M.sub.35 Gluconate β.sub.35 0.98991591 M.sub.36 1-methylhistidine β.sub.36 0.67179543 M.sub.37 1,6-anhydroglucose β.sub.37 −0.0045716 M.sub.38 (14 or 15)-methylpalmitate β.sub.38 0.20117096 (a17:0 or i17:0) M.sub.39 4-guanidinobutanoate β.sub.39 0.07228207 M.sub.40 N-acetylneuraminate β.sub.40 0.43856713 M.sub.41 Dihomo-linoleoylcarnitine β.sub.41 −0.4122119 (C20:2) M.sub.42 Erucate (22:1n9) β.sub.42 0.2423382 M.sub.43 1-carboxyethylvaline β.sub.43 1.23402343 M.sub.44 Serine β.sub.44 −0.8581117 M.sub.45 Lanthionine β.sub.45 −0.0125453 M.sub.46 alpha-ketobutyrate β.sub.46 −0.1686771 M.sub.47 Myo-inositol β.sub.47 0.57921768 M.sub.48 N-acetyltryptophan β.sub.48 −0.2090842 M.sub.49 Gulonate β.sub.49 −1.2903587 M.sub.50 Linoleoylcarnitine (C18:2) β.sub.50 2.12785351 M.sub.51 3-hydroxystearate β.sub.51 0.1620114

    Other Embodiments

    [0075] It is to be understood that while the invention has been described in conjunction with the detailed description thereof, the foregoing description is intended to illustrate and not limit the scope of the invention, which is defined by the scope of the appended claims. Other aspects, advantages, and modifications are within the scope of the following claims.