Plasma metabolomic profiling in patients with rheumatoid arthritis identifies biochemical features predictive of quantitative disease activity

• Published in: Arthritis research & therapy 2021-06, Vol.23 (1), p.1-164, Article 164
• Main Authors: Hur, Benjamin, Gupta, Vinod K, Huang, Harvey, Wright, Kerry A, Warrington, Kenneth J, Taneja, Veena, Davis, John M, Sung, Jaeyun
• Format: Article
• Language: English
• Subjects: Age; Arthritis; Biomarker; Biomarkers; Cytokines; DAS28-CRP; Development and progression; Diagnosis; Generalized linear models; Health aspects; Identification and classification; Inflammation; Machine learning; Metabolism; Metabolites; Metabolomics; Patients; Plasma; Plasma metabolites; Precision medicine; Rheumatoid arthritis; Rheumatology
• ISSN: 1478-6362; 1478-6354; 1478-6362
• DOI: 10.1186/s13075-021-02537-4

Background Rheumatoid arthritis (RA) is a chronic, autoimmune disorder characterized by joint inflammation and pain. In patients with RA, metabolomic approaches, i.e., high-throughput profiling of small-molecule metabolites, on plasma or serum has thus far enabled the discovery of biomarkers for clinical subgroups, risk factors, and predictors of treatment response. Despite these recent advancements, the identification of blood metabolites that reflect quantitative disease activity remains an important challenge in precision medicine for RA. Herein, we use global plasma metabolomic profiling analyses to detect metabolites associated with, and predictive of, quantitative disease activity in patients with RA. Methods Ultra-high-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) was performed on a discovery cohort consisting of 128 plasma samples from 64 RA patients and on a validation cohort of 12 samples from 12 patients. The resulting metabolomic profiles were analyzed with two different strategies to find metabolites associated with RA disease activity defined by the Disease Activity Score-28 using C-reactive protein (DAS28-CRP). More specifically, mixed-effects regression models were used to identify metabolites differentially abundant between two disease activity groups ("lower", DAS28-CRP [less than or equai to] 3.2; and "higher", DAS28-CRP > 3.2) and to identify metabolites significantly associated with DAS28-CRP scores. A generalized linear model (GLM) was then constructed for estimating DAS28-CRP using plasma metabolite abundances. Finally, for associating metabolites with CRP (an indicator of inflammation), metabolites differentially abundant between two patient groups ("low-CRP", CRP [less than or equai to] 3.0 mg/L; "high-CRP", CRP > 3.0 mg/L) were investigated. Results We identified 33 metabolites differentially abundant between the lower and higher disease activity groups (P < 0.05). Additionally, we identified 51 metabolites associated with DAS28-CRP (P < 0.05). A GLM based upon these 51 metabolites resulted in higher prediction accuracy (mean absolute error [MAE] [+ or -] SD: 1.51 [+ or -] 1.77) compared to a GLM without feature selection (MAE [+ or -] SD: 2.02 [+ or -] 2.21). The predictive value of this feature set was further demonstrated on a validation cohort of twelve plasma samples, wherein we observed a stronger correlation between predicted and actual DAS28-CRP (with feature selection: Spearman's [rho] = 0.69, 95