Differences in the serum metabolome and lipidome identify potential biomarkers for seronegative rheumatoid arthritis versus psoriatic arthritis
Differences in the serum metabolome and lipidome identify potential biomarkers for seronegative rheumatoid arthritis versus psoriatic arthritis
复制标题
血清代谢组和脂质组的差异为鉴别血清阴性类风湿关节炎与银屑病关节炎确定了潜在生物标志物。
DOI：
10.1136/annrheumdis-2019-216374
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发表时间：
2020-04-01
影响因子：
27.4
通讯作者：
Lorenz, Hanns-Martin
中科院分区：
文献类型：
--
作者：
Souto-Carneiro, Margarida;Toth, Lilla;Lorenz, Hanns-Martin
目的血清阴性类风湿关节炎（RA）和银屑病关节炎（PsA）由于临床症状相似，缺乏可靠的临床标志物，鉴别诊断困难。由于慢性炎症引起的血清代谢组和脂质组的重大变化，我们测试了血清代谢产物和脂质的差异是否可以帮助提高这些疾病的鉴别诊断。MethodsSera从RA和PsA患者建立诊断收集建立一个生物标志物发现队列和盲验证队列。通过质子核磁共振分析样品。代谢物的浓度计算从光谱和用于选择变量，以建立一个多变量的诊断model.ResultsUnivariate分析表明血清浓度的差异氨基酸：丙氨酸，苏氨酸，亮氨酸，苯丙氨酸和缬氨酸，有机化合物：乙酸，肌酸，乳酸和胆碱;和脂质比L3/L1、L5/L1和L 6/L1，但得到的曲线下面积（AUC）值低于70%，表明特异性和灵敏度差。包括年龄、性别、丙氨酸、琥珀酸和磷酸肌酸浓度以及脂质比L2/L1、L5/L1和L 6/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%。使用这种生物标志物模型，71%的患者从一个盲目的验证队列被正确classificed.ConclusionsPsA和RAP 1 RA有不同的血清代谢和lipidomic签名，可以用作生物标志物来区分它们。在更大的多种族队列验证后，该诊断模型可能成为明确诊断PsRA或PsA患者的有价值的工具。
ObjectivesThe 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.MethodsSera 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.ResultsUnivariate 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.ConclusionsPsA 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.