State Key Laboratory of Complex Severe and Rare Diseases, Department of Cardiology, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China.
State Key Laboratory of Complex Severe and Rare Diseases, Medical Research Center, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China.
Front Immunol. 2021 Apr 16;12:622216. doi: 10.3389/fimmu.2021.622216. eCollection 2021.
Patients with systemic lupus erythematosus (SLE) have a significantly higher incidence of atherosclerosis than the general population. Studies on atherosclerosis prediction models specific for SLE patients are very limited. This study aimed to build a risk prediction model for atherosclerosis in SLE. RNA sequencing was performed on 67 SLE patients. Subsequently, differential expression analysis was carried out on 19 pairs of age-matched SLE patients with (AT group) or without (Non-AT group) atherosclerosis using peripheral venous blood. We used logistic least absolute shrinkage and selection operator regression to select variables among differentially expressed (DE) genes and clinical features and utilized backward stepwise logistic regression to build an atherosclerosis risk prediction model with all 67 patients. The performance of the prediction model was evaluated by area under the curve (AUC), calibration curve, and decision curve analyses. The 67 patients had a median age of 42.7 (Q1-Q3: 36.6-51.2) years, and 20 (29.9%) had atherosclerosis. A total of 106 DE genes were identified between the age-matched AT and Non-AT groups. Pathway analyses revealed that the AT group had upregulated atherosclerosis signaling, oxidative phosphorylation, and interleukin (IL)-17-related pathways but downregulated T cell and B cell receptor signaling. Keratin 10, age, and hyperlipidemia were selected as variables for the risk prediction model. The AUC and Hosmer-Lemeshow test -value of the model were 0.922 and 0.666, respectively, suggesting a relatively high discrimination and calibration performance. The prediction model had a higher net benefit in the decision curve analysis than that when predicting with age or hyperlipidemia only. We built an atherosclerotic risk prediction model with one gene and two clinical factors. This model may greatly assist clinicians to identify SLE patients with atherosclerosis, especially asymptomatic atherosclerosis.
系统性红斑狼疮（SLE）患者发生动脉粥样硬化的风险显著高于一般人群。目前针对 SLE 患者的动脉粥样硬化预测模型研究非常有限。本研究旨在建立 SLE 患者动脉粥样硬化的风险预测模型。
对 67 例 SLE 患者进行 RNA 测序。随后，采用外周静脉血对 19 对年龄匹配的 SLE 患者（AT 组）或无动脉粥样硬化（Non-AT 组）进行差异表达分析。我们使用逻辑最小绝对收缩和选择算子回归（least absolute shrinkage and selection operator regression，LASSO）从差异表达（differentially expressed，DE）基因和临床特征中选择变量，并利用向后逐步逻辑回归（backward stepwise logistic regression）对 67 例患者建立动脉粥样硬化风险预测模型。采用曲线下面积（area under the curve，AUC）、校准曲线和决策曲线分析评估预测模型的性能。
67 例患者的中位年龄为 42.7（Q1-Q3：36.6-51.2）岁，20 例（29.9%）有动脉粥样硬化。年龄匹配的 AT 组和 Non-AT 组之间共鉴定出 106 个 DE 基因。通路分析显示，AT 组动脉粥样硬化信号、氧化磷酸化和白细胞介素（interleukin，IL）-17 相关通路呈上调，而 T 细胞和 B 细胞受体信号通路呈下调。角蛋白 10、年龄和高脂血症被选为风险预测模型的变量。该模型的 AUC 和 Hosmer-Lemeshow 检验 - 值分别为 0.922 和 0.666，提示该模型具有较高的判别和校准性能。与单独预测年龄或高脂血症相比，该预测模型在决策曲线分析中的净获益更高。
我们建立了一个包含一个基因和两个临床因素的动脉粥样硬化风险预测模型。该模型可能极大地帮助临床医生识别有动脉粥样硬化风险的 SLE 患者，尤其是无症状动脉粥样硬化患者。
