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Currently diagnosing RA is primarily based on clinical symptoms and the presence of various serum autoantibodies including rheumatoid factor (RF) and anti-citrullinated protein antibody (ACPA). Rheumatoid arthritis (RA) is a complex chronic autoimmune disease with variable presenting symptoms and serum autoantibody test results. Potential markers were also identified in stratifying RA cases based on disease activity. ConclusionsĪ panel of 26 serum markers were selected from omics profiles to build a machine-learning-based prediction model that could aid in diagnosing seronegative RA patients.
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Acyl carnitines (20:3), aspartyl-phenylalanine, pipecolic acid, phosphatidylethanolamine PE (18:1) and lysophosphatidylethanolamine LPE (20:3) were positively correlated with the RA disease activity, while histidine and phosphatidic acid PA (28:0) were negatively correlated with the RA disease activity. A co-occurrence network using serum omics profiles was built and parsed into six modules, showing significant association between the inflammation and immune activity markers and aberrant metabolism of energy metabolism, lipids metabolism and amino acid metabolism. Both seropositive and seronegative patients were identified using this model. The model was subsequently tested on a validation set and achieved accuracy of 90.2%, with sensitivity of 89.7% and specificity of 90.6%. Twenty-six metabolites and lipids were identified from the discovery cohort to construct a RA diagnosis model. A machine-learning-based multivariate classification model was constructed using distinctive metabolites and lipids signals. These samples were divided into a discovery set (n = 243) and a validation set (n = 82). We performed comprehensive metabolomic and lipidomic profiling in serum of 225 RA patients and 100 normal controls. We sought to discover diagnostic biomarkers for seronegative RA cases by studying metabolomic and lipidomic changes in RA patient serum. Diagnosing seronegative rheumatoid arthritis (RA) can be challenging due to complex diagnostic criteria.