Willette Auriel A, Willette Sara A, Wang Qian, Pappas Colleen, Klinedinst Brandon S, Le Scott, Larsen Brittany, Pollpeter Amy, Li Tianqi, Brenner Nicole, Waterboer Tim
Department of Food Science and Human Nutrition, Iowa State University, Ames, IA, USA.
Department of Neurology, University of Iowa, Iowa City, IA, USA.
medRxiv. 2021 Jan 5:2020.06.09.20127092. doi: 10.1101/2020.06.09.20127092.
Many risk factors have emerged for novel 2019 coronavirus disease (COVID-19). It is relatively unknown how these factors collectively predict COVID-19 infection risk, as well as risk for a severe infection (i.e., hospitalization).
Among aged adults (69.3 ± 8.6 years) in UK Biobank, COVID-19 data was downloaded for 4,510 participants with 7,539 test cases. We downloaded baseline data from 10-14 years ago, including demographics, biochemistry, body mass, and other factors, as well as antibody titers for 20 common to rare infectious diseases. Permutation-based linear discriminant analysis was used to predict COVID-19 risk and hospitalization risk. Probability and threshold metrics included receiver operating characteristic curves to derive area under the curve (AUC), specificity, sensitivity, and quadratic mean.
The "best-fit" model for predicting COVID-19 risk achieved excellent discrimination (AUC=0.969, 95% CI=0.934-1.000). Factors included age, immune markers, lipids, and serology titers to common pathogens like human cytomegalovirus. The hospitalization "best-fit" model was more modest (AUC=0.803, 95% CI=0.663-0.943) and included only serology titers.
Accurate risk profiles can be created using standard self-report and biomedical data collected in public health and medical settings. It is also worthwhile to further investigate if prior host immunity predicts current host immunity to COVID-19.
新型冠状病毒肺炎（COVID-19）已出现许多风险因素。目前尚不清楚这些因素如何共同预测COVID-19感染风险以及严重感染（即住院）风险。
在英国生物银行的老年成年人（69.3±8.6岁）中，下载了4510名参与者7539个检测病例的COVID-19数据。我们下载了10至14年前的基线数据，包括人口统计学、生物化学、体重及其他因素，以及20种常见至罕见传染病的抗体滴度。基于排列的线性判别分析用于预测COVID-19风险和住院风险。概率和阈值指标包括用于得出曲线下面积（AUC）、特异性、敏感性和二次均值的受试者工作特征曲线。
预测COVID-19风险的“最佳拟合”模型具有出色的区分能力（AUC=0.969，95%CI=0.934-1.000）。相关因素包括年龄、免疫标志物、血脂以及针对人类巨细胞病毒等常见病原体的血清学滴度。住院“最佳拟合”模型的区分能力相对较弱（AUC=0.803，95%CI=0.663-0.943），且仅包括血清学滴度。
利用公共卫生和医疗环境中收集的标准自我报告和生物医学数据，可以创建准确的风险概况。进一步研究既往宿主免疫力是否能预测当前宿主对COVID-19的免疫力也很有价值。
