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Due to similar symptoms and lack of reliableclinicalmarkers, it is often difficult toidentify seronegative rheumatoid arthritis (
negRA) and psoriasis arthritis (
PsA) to diagnoseSince chronic inflammation causes significant changes in serum metabolites and lipid groups, this study examines whether differences in serum metabolites and lipids can help improve the identification of these diseases
diagnosisresearchers collect edgy from patients with confirmednegRAandPsAto establish biomarker discovery queues and blind-based validation queues Samples are analyzed by proton magnetic resonance The concentration of metabolites is calculated from the spectrum and variables are selected to establish a multi-diagnostic model single-factor analysis showed that amino acid concentrations in the serum: alanine, sucline, leucine, phenylalanine and proline; organic compounds: acetate, creatine, lactate and choline; and lipid ratios L3/L1 , L5/L1 and L6/L1 were different, but the subcurvedial area (
AUC ) was less than 70 Improved diagnostic sensitivity and specificity of multifactorial diagnostic models, including age, sex, alanine, amber acid and creatine phosphate concentrations, and lipid ratios L2/L1 , L5/L1 and L6/L1 , AUC 84.5% % Using this biomarker model, 71 % of patients from the blind verification queue were properly classified PsA and negRA have different serobolic and lipid group characteristics and can be used as biomarkers for differential diagnosis After validation in a large multiracial queue, the diagnostic model can be a valuable tool for identifying patients negRA or PsA