Yi Jianying, Liu Zhili, Cao Xi, Pi Lili, Zhou Chunlei, Mu Hong
Department of Clinical Laboratory, Tianjin First Central Hospital, School of Medicine, Nankai University, Tianjin, China.
Department of Clinical Laboratory, The Third Central Hospital, Tianjin, China.
Front Cell Infect Microbiol. 2024 Dec 9;14:1477585. doi: 10.3389/fcimb.2024.1477585. eCollection 2024.
COVID-19 has evolved into a seasonal coronavirus disease, characterized by prolonged infection duration and repeated infections, significantly increasing the risk of patients developing long COVID. Our research focused on the immune responses in asymptomatic and mild cases, particularly the critical factors influencing serum immunoglobulin G (IgG) levels and their predictive value.
We conducted a retrospective analysis on data from 1939 asymptomatic or mildly symptomatic COVID-19 patients hospitalized between September 2022 and June 2023. Spearman methods were used to test the correlation between serum IgG and age, immunoglobulin M (IgM), procalcitonin (PCT), interleukin-6 (IL-6), nucleic acid conversion time, and BMI. Univariate and multivariate logistic regression analyses identified independent key factors influencing serum IgG levels, which were integrated and visualized in a nomogram. Finally, receiver operating characteristic (ROC) curves were plotted to predict the model's diagnostic performance by calculating the AUC.
Mild patients showed higher levels of IgG, IgM, and longer nucleic acid conversion times than asymptomatic patients, and a lower proportion of them had received ≥ 3 COVID-19 vaccine doses. Serum IgG was positively correlated with serum IgM and negatively correlated with age, PCT, IL-6, and BMI. Notably, it showed a moderate negative correlation with nucleic acid conversion time (r = -0.578, < 0.001). Logistic regression results showed that younger age, lower IL-6 levels, ≥ 3 doses of vaccine, and no comorbidities were independent predictors of serum IgG levels ≥ 21.08 g/L. We used age, IL-6 levels, vaccine doses, and comorbidities to create a nomogram for predicting serum IgG levels, with the area under the ROC curve reaching 0.772.
Age, IL-6 levels, vaccination status, and comorbidities were independent predictors of serum IgG levels in asymptomatic or mild COVID-19 patients, facilitating risk stratification and clinical decision-making. Notably, receiving ≥3 doses of the COVID-19 vaccine was the most beneficial factor for elevated serum IgG levels.
新型冠状病毒肺炎（COVID-19）已演变成一种季节性冠状病毒疾病，其特点是感染持续时间延长和反复感染，显著增加了患者出现长期COVID的风险。我们的研究聚焦于无症状和轻症病例的免疫反应，特别是影响血清免疫球蛋白G（IgG）水平的关键因素及其预测价值。
我们对2022年9月至2023年6月期间住院的1939例无症状或轻症COVID-19患者的数据进行了回顾性分析。采用Spearman方法检验血清IgG与年龄、免疫球蛋白M（IgM）、降钙素原（PCT）、白细胞介素-6（IL-6）、核酸转阴时间和体重指数（BMI）之间的相关性。单因素和多因素逻辑回归分析确定了影响血清IgG水平的独立关键因素，并将其整合到列线图中进行可视化展示。最后，绘制受试者工作特征（ROC）曲线，通过计算曲线下面积（AUC）来预测模型的诊断性能。
轻症患者的IgG、IgM水平较高，核酸转阴时间较长，且接受≥3剂COVID-19疫苗接种的比例较低。血清IgG与血清IgM呈正相关，与年龄、PCT、IL-6和BMI呈负相关。值得注意的是，它与核酸转阴时间呈中度负相关（r = -0.578，P < 0.001）。逻辑回归结果显示，年龄较小、IL-6水平较低、≥3剂疫苗接种以及无合并症是血清IgG水平≥21.08 g/L的独立预测因素。我们使用年龄、IL-6水平、疫苗接种剂量和合并症创建了一个预测血清IgG水平的列线图，ROC曲线下面积达到0.772。
年龄、IL-6水平、疫苗接种状况和合并症是无症状或轻症COVID-19患者血清IgG水平的独立预测因素，有助于风险分层和临床决策。值得注意的是，接种≥3剂COVID-19疫苗是血清IgG水平升高的最有利因素。
