College of Medicine, University of Illinois at Chicago, Chicago, IL, United States.
Department of Medicine, Rush University Medical Center, Chicago, IL, United States.
Front Immunol. 2024 Apr 30;15:1385858. doi: 10.3389/fimmu.2024.1385858. eCollection 2024.
Mechanisms underlying long COVID remain poorly understood. Patterns of immunological responses in individuals with long COVID may provide insight into clinical phenotypes. Here we aimed to identify these immunological patterns and study the inflammatory processes ongoing in individuals with long COVID. We applied an unsupervised hierarchical clustering approach to analyze plasma levels of 42 biomarkers measured in individuals with long COVID. Logistic regression models were used to explore associations between biomarker clusters, clinical variables, and symptom phenotypes. In 101 individuals, we identified three inflammatory clusters: a limited immune activation cluster, an innate immune activation cluster, and a systemic immune activation cluster. Membership in these inflammatory clusters did not correlate with individual symptoms or symptom phenotypes, but was associated with clinical variables including age, BMI, and vaccination status. Differences in serologic responses between clusters were also observed. Our results indicate that clinical variables of individuals with long COVID are associated with their inflammatory profiles and can provide insight into the ongoing immune responses.
长新冠的发病机制仍知之甚少。长新冠患者的免疫反应模式可能为临床表型提供深入了解。在这里，我们旨在确定这些免疫模式，并研究长新冠患者中持续存在的炎症过程。我们应用无监督分层聚类方法分析了长新冠患者中 42 种生物标志物的血浆水平。逻辑回归模型用于探索生物标志物聚类、临床变量和症状表型之间的关联。在 101 名患者中，我们确定了三个炎症聚类：有限的免疫激活聚类、先天免疫激活聚类和全身免疫激活聚类。这些炎症聚类中的成员身份与个体症状或症状表型无关，但与临床变量相关，包括年龄、BMI 和疫苗接种状态。在聚类之间也观察到了血清学反应的差异。我们的结果表明，长新冠患者的临床变量与他们的炎症特征相关，并能深入了解持续的免疫反应。
