School of Medicine, Academy for Advanced Interdisciplinary Studies, Southern University of Science and Technology, 1088 Xueyuan Rd., Shenzhen, China.
Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, 210029, Jiangsu, China.
J Transl Med. 2021 Dec 7;19(1):500. doi: 10.1186/s12967-021-03169-7.
Diagnosing seronegative rheumatoid arthritis (RA) can be challenging due to complex diagnostic criteria. We sought to discover diagnostic biomarkers for seronegative RA cases by studying metabolomic and lipidomic changes in RA patient serum.
We performed comprehensive metabolomic and lipidomic profiling in serum of 225 RA patients and 100 normal controls. These samples were divided into a discovery set (n = 243) and a validation set (n = 82). A machine-learning-based multivariate classification model was constructed using distinctive metabolites and lipids signals.
Twenty-six metabolites and lipids were identified from the discovery cohort to construct a RA diagnosis 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%. Both seropositive and seronegative patients were identified using this model. 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. 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 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. Potential markers were also identified in stratifying RA cases based on disease activity.
由于诊断标准复杂，血清阴性类风湿关节炎（RA）的诊断具有挑战性。我们通过研究 RA 患者血清中的代谢组学和脂质组学变化，试图发现血清阴性 RA 病例的诊断生物标志物。
我们对 225 例 RA 患者和 100 例正常对照者的血清进行了全面的代谢组学和脂质组学分析。这些样本分为发现组（n=243）和验证组（n=82）。使用有区别的代谢物和脂质信号构建基于机器学习的多元分类模型。
从发现队列中鉴定出 26 种代谢物和脂质，用于构建 RA 诊断模型。该模型随后在验证组中进行了测试，准确率为 90.2%，敏感性为 89.7%，特异性为 90.6%。该模型可用于鉴定血清阳性和血清阴性患者。使用血清组学谱构建共发生网络，并将其解析为六个模块，显示炎症和免疫活性标志物与能量代谢、脂质代谢和氨基酸代谢异常之间存在显著关联。酰基辅酶 A（20:3）、天冬氨酰苯丙氨酸、哌可酸、磷脂酰乙醇胺 PE（18:1）和溶血磷脂酰乙醇胺 LPE（20:3）与 RA 疾病活动呈正相关，而组氨酸和磷脂酸 PA（28:0）与 RA 疾病活动呈负相关。
从组学谱中选择了一组 26 种血清标志物，构建了一种基于机器学习的预测模型，可辅助诊断血清阴性 RA 患者。还确定了基于疾病活动对 RA 病例进行分层的潜在标志物。
