Objectives The differential diagnosis of seronegative rheumatoid arthritis (negRA) and psoriasis arthritis (PsA) is often difficult due to the similarity of symptoms and the unavailability of reliable clinical markers. Since chronic inflammation induces major changes in the serum metabolome and lipidome, we tested whether differences in serum metabolites and lipids could aid in improving the differential diagnosis of these diseases. Methods Sera from negRA and PsA patients with established diagnosis were collected to build a biomarker-discovery cohort and a blinded validation cohort. Samples were analysed by proton nuclear magnetic resonance. Metabolite concentrations were calculated from the spectra and used to select the variables to build a multivariate diagnostic model. Results Univariate analysis demonstrated differences in serological concentrations of amino acids: alanine, threonine, leucine, phenylalanine and valine; organic compounds: acetate, creatine, lactate and choline; and lipid ratios L3/L1, L5/L1 and L6/L1, but yielded area under the curve (AUC) values lower than 70%, indicating poor specificity and sensitivity. A multivariate diagnostic model that included age, gender, the concentrations of alanine, succinate and creatine phosphate and the lipid ratios L2/L1, L5/L1 and L6/L1 improved the sensitivity and specificity of the diagnosis with an AUC of 84.5%. Using this biomarker model, 71% of patients from a blinded validation cohort were correctly classified. Conclusions PsA and negRA have distinct serum metabolomic and lipidomic signatures that can be used as biomarkers to discriminate between them. After validation in larger multiethnic cohorts this diagnostic model may become a valuable tool for a definite diagnosis of negRA or PsA patients.

中文翻译：

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血清代谢组和脂质组的差异确定了血清阴性类风湿性关节炎与银屑病关节炎的潜在生物标志物

目的 血清阴性类风湿性关节炎 (negRA) 和银屑病关节炎 (PsA) 的鉴别诊断通常很困难，因为症状相似且缺乏可靠的临床标志物。由于慢性炎症会引起血清代谢组和脂质组的重大变化，我们测试了血清代谢物和脂质的差异是否有助于改善这些疾病的鉴别诊断。方法 收集来自已确诊的 negRA 和 PsA 患者的血清，以建立一个生物标志物发现队列和一个盲法验证队列。通过质子核磁共振分析样品。根据光谱计算代谢物浓度并用于选择变量以构建多变量诊断模型。结果 单变量分析表明氨基酸的血清学浓度存在差异：丙氨酸、苏氨酸、亮氨酸、苯丙氨酸和缬氨酸；有机化合物：醋酸盐、肌酸、乳酸盐和胆碱；和脂质比 L3/L1、L5/L1 和 L6/L1，但曲线下面积 (AUC) 值低于 70%，表明特异性和敏感性较差。包括年龄、性别、丙氨酸、琥珀酸和磷酸肌酸浓度以及脂质比率 L2/L1、L5/L1 和 L6/L1 在内的多变量诊断模型提高了诊断的敏感性和特异性，AUC 为 84.5%。使用该生物标志物模型，盲法验证队列中 71% 的患者被正确分类。结论 PsA 和 negRA 具有不同的血清代谢组学和脂质组学特征，可用作区分它们的生物标志物。