Zhai Xvwen, Feng Min, Guo Hui, Liang Zhaojun, Wang Yanlin, Qin Yan, Wu Yanyao, Zhao Xiangcong, Gao Chong, Luo Jing
Clinical Skills Teaching Simulation Hospital, Shanxi Medical University, Jinzhong, China.
Department of Rheumatology, The Second Hospital of Shanxi Medical University, Taiyuan, China.
Front Cell Infect Microbiol. 2021 Mar 1;11:620372. doi: 10.3389/fcimb.2021.620372. eCollection 2021.
Distinguishing flares from bacterial infections in systemic lupus erythematosus (SLE) patients remains a challenge. This study aimed to build a model, using multiple blood cells and plasma indicators, to improve the identification of bacterial infections in SLE.
Building PLS-DA/OPLS-DA models and a bioscore system to distinguish bacterial infections from lupus flares in SLE.
Department of Rheumatology of the Second Hospital of Shanxi Medical University.
SLE patients with flares (n = 142) or bacterial infections (n = 106) were recruited in this retrospective study.
The peripheral blood of these patients was collected by the experimenter to measure the levels of routine examination indicators, immune cells, and cytokines. PLS-DA/OPLS-DA models and a bioscore system were established.
Both PLS-DA (R2Y = 0.953, Q2 = 0.931) and OPLS-DA (R2Y = 0.953, Q2 = 0.942) models could clearly identify bacterial infections in SLE. The white blood cell (WBC), neutrophile granulocyte (NEUT), erythrocyte sedimentation rate (ESR), C-reactive protein (CRP), procalcitonin (PCT), interleukin-6 (IL-6), IL-10, interferon-γ (IFN-γ), and tumor necrosis factor α (TNF-α) levels were significantly higher in bacteria-infected patients, while regulatory T (Treg) cells obviously decreased. A multivariate analysis using the above 10 dichotomized indicators, based on the cut-off value of their respective ROC curve, was established to screen out the independent predictors and calculate their weights to build a bioscore system, which exhibited a strong diagnosis ability (AUC = 0.842, 95% CI 0.794-0.891). The bioscore system showed that 0 and 100% of SLE patients with scores of 0 and 8-10, respectively, were infected with bacteria. The higher the score, the greater the likelihood of bacterial infections in SLE.
The PLS-DA/OPLS-DA models, including the above biomarkers, showed a strong predictive ability for bacterial infections in SLE. Combining WBC, NEUT, CRP, PCT, IL-6, and IFN-γ in a bioscore system may result in faster prediction of bacterial infections in SLE and may guide toward a more appropriate, timely treatment for SLE.
区分系统性红斑狼疮（SLE）患者的病情活动与细菌感染仍然是一项挑战。本研究旨在建立一个使用多种血细胞和血浆指标的模型，以改善对SLE患者细菌感染的识别。
构建偏最小二乘判别分析（PLS-DA）/正交偏最小二乘判别分析（OPLS-DA）模型和生物评分系统，以区分SLE患者的细菌感染与狼疮病情活动。
山西医科大学第二医院风湿免疫科。
本回顾性研究纳入了病情活动的SLE患者（n = 142）或细菌感染患者（n = 106）。
实验人员采集这些患者的外周血，以测量常规检查指标、免疫细胞和细胞因子水平。建立了PLS-DA/OPLS-DA模型和生物评分系统。
PLS-DA（R2Y = 0.953，Q2 = 0.931）和OPLS-DA（R2Y = 0.953，Q2 = 0.942）模型均能清晰识别SLE患者的细菌感染。细菌感染患者的白细胞（WBC）、中性粒细胞（NEUT）、红细胞沉降率（ESR）、C反应蛋白（CRP）、降钙素原（PCT）、白细胞介素-6（IL-6）、IL-10、干扰素-γ（IFN-γ）和肿瘤坏死因子α（TNF-α）水平显著升高，而调节性T（Treg）细胞明显减少。基于上述10项二分指标各自的ROC曲线临界值进行多因素分析，筛选出独立预测因子并计算其权重，构建生物评分系统，该系统具有较强的诊断能力（AUC = 0.842，95%CI 0.794-0.891）。生物评分系统显示，评分分别为0分和8-10分的SLE患者中，细菌感染率分别为0和100%。评分越高，SLE患者发生细菌感染的可能性越大。
包括上述生物标志物的PLS-DA/OPLS-DA模型对SLE患者的细菌感染具有较强的预测能力。在生物评分系统中结合WBC、NEUT、CRP、PCT、IL-6和IFN-γ，可能会更快地预测SLE患者的细菌感染，并可能有助于指导更恰当、及时地治疗SLE。
