Department of Biomedical Sciences, School of Medicine, University of Missouri - Kansas City, 2411 Holmes Street, Kansas City, MO, 64108, USA.
Department of Microbiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.
Sci Rep. 2023 Mar 29;13(1):5145. doi: 10.1038/s41598-023-31683-9.
The novel coronavirus pandemic continues to cause significant morbidity and mortality around the world. Diverse clinical presentations prompted numerous attempts to predict disease severity to improve care and patient outcomes. Equally important is understanding the mechanisms underlying such divergent disease outcomes. Multivariate modeling was used here to define the most distinctive features that separate COVID-19 from healthy controls and severe from moderate disease. Using discriminant analysis and binary logistic regression models we could distinguish between severe disease, moderate disease, and control with rates of correct classifications ranging from 71 to 100%. The distinction of severe and moderate disease was most reliant on the depletion of natural killer cells and activated class-switched memory B cells, increased frequency of neutrophils, and decreased expression of the activation marker HLA-DR on monocytes in patients with severe disease. An increased frequency of activated class-switched memory B cells and activated neutrophils was seen in moderate compared to severe disease and control. Our results suggest that natural killer cells, activated class-switched memory B cells, and activated neutrophils are important for protection against severe disease. We show that binary logistic regression was superior to discriminant analysis by attaining higher rates of correct classification based on immune profiles. We discuss the utility of these multivariate techniques in biomedical sciences, contrast their mathematical basis and limitations, and propose strategies to overcome such limitations.
新型冠状病毒大流行继续在全球范围内造成重大发病率和死亡率。不同的临床表现促使人们多次尝试预测疾病的严重程度，以改善护理和患者的预后。同样重要的是，要了解导致这种不同疾病结果的机制。这里使用多元建模来定义将 COVID-19 与健康对照和严重与中度疾病区分开来的最独特特征。使用判别分析和二项逻辑回归模型，我们可以区分严重疾病、中度疾病和对照，正确分类率从 71%到 100%不等。严重和中度疾病的区别最依赖于自然杀伤细胞和激活的类别转换记忆 B 细胞的耗竭、中性粒细胞频率的增加以及严重疾病患者单核细胞活化标记 HLA-DR 的表达降低。与严重疾病和对照相比，中度疾病中可见激活的类别转换记忆 B 细胞和激活的中性粒细胞的频率增加。我们的结果表明，自然杀伤细胞、激活的类别转换记忆 B 细胞和激活的中性粒细胞对预防严重疾病很重要。我们表明，基于免疫谱，二项逻辑回归比判别分析获得更高的正确分类率，因此更优。我们讨论了这些多元技术在生物医学科学中的应用，对比了它们的数学基础和局限性，并提出了克服这些局限性的策略。
