Medizin 5, Hämatologie, Onkologie und Rheumatologie, UniversitätsKlinikum Heidelberg, Heidelberg, Germany
Medizin 5, Hämatologie, Onkologie und Rheumatologie, UniversitätsKlinikum Heidelberg, Heidelberg, Germany.
Ann Rheum Dis. 2020 Apr;79(4):499-506. doi: 10.1136/annrheumdis-2019-216374. Epub 2020 Feb 20.
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.
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.
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.
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.
由于症状相似且缺乏可靠的临床标志物，血清阴性类风湿关节炎（negRA）和银屑病关节炎（PsA）的鉴别诊断常常较为困难。由于慢性炎症会引起血清代谢组和脂质组的重大变化，我们检测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。
收集确诊的 negRA 和 PsA 患者的血清，建立生物标志物发现队列和盲法验证队列。通过质子核磁共振分析样本。从光谱中计算出代谢物浓度，用于选择变量来构建多变量诊断模型。
单变量分析表明，在氨基酸：丙氨酸、苏氨酸、亮氨酸、苯丙氨酸和缬氨酸；有机化合物：乙酸盐、肌酸、乳酸和胆碱；以及脂质比率 L3/L1、L5/L1 和 L6/L1 的血清浓度存在差异，但曲线下面积（AUC）值均低于 70%，表明特异性和敏感性较差。一个包含年龄、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 的多变量诊断模型提高了诊断的灵敏度和特异性，AUC 为 84.5%。使用该生物标志物模型，验证队列中 71%的患者得到正确分类。
PsA 和 negRA 具有独特的血清代谢组学和脂质组学特征，可以作为鉴别它们的生物标志物。在更大的多民族队列中验证后，该诊断模型可能成为明确诊断 negRA 或 PsA 患者的有价值工具。
