Takeda Development Center Americas, Inc., San Diego, California, United States of America.
Takeda Development Center Americas, Inc., Cambridge, Massachusetts, United States of America.
PLoS One. 2023 May 26;18(5):e0285991. doi: 10.1371/journal.pone.0285991. eCollection 2023.
As findings on the epidemiological and genetic risk factors for coronavirus disease-19 (COVID-19) continue to accrue, their joint power and significance for prospective clinical applications remains virtually unexplored. Severity of symptoms in individuals affected by COVID-19 spans a broad spectrum, reflective of heterogeneous host susceptibilities across the population. Here, we assessed the utility of epidemiological risk factors to predict disease severity prospectively, and interrogated genetic information (polygenic scores) to evaluate whether they can provide further insights into symptom heterogeneity. A standard model was trained to predict severe COVID-19 based on principal component analysis and logistic regression based on information from eight known medical risk factors for COVID-19 measured before 2018. In UK Biobank participants of European ancestry, the model achieved a relatively high performance (area under the receiver operating characteristic curve ~90%). Polygenic scores for COVID-19 computed from summary statistics of the Covid19 Host Genetics Initiative displayed significant associations with COVID-19 in the UK Biobank (p-values as low as 3.96e-9, all with R2 under 1%), but were unable to robustly improve predictive performance of the non-genetic factors. However, error analysis of the non-genetic models suggested that affected individuals misclassified by the medical risk factors (predicted low risk but actual high risk) display a small but consistent increase in polygenic scores. Overall, the results indicate that simple models based on health-related epidemiological factors measured years before COVID-19 onset can achieve high predictive power. Associations between COVID-19 and genetic factors were statistically robust, but currently they have limited predictive power for translational settings. Despite that, the outcomes also suggest that severely affected cases with a medical history profile of low risk might be partly explained by polygenic factors, prompting development of boosted COVID-19 polygenic models based on new data and tools to aid risk-prediction.
随着对冠状病毒病 19（COVID-19）的流行病学和遗传风险因素的研究不断增加，其对未来临床应用的综合作用和意义仍未得到充分探索。受 COVID-19 影响的个体的症状严重程度跨度很大，反映了人群中宿主易感性的异质性。在这里，我们评估了流行病学风险因素预测疾病严重程度的效用，并探讨了遗传信息（多基因评分），以评估它们是否可以提供对症状异质性的进一步了解。我们使用主成分分析和逻辑回归基于 2018 年前测量的 8 种已知 COVID-19 医学风险因素的信息，训练了一个标准模型来预测严重 COVID-19。在欧洲血统的英国生物银行参与者中，该模型的表现相对较高（接受者操作特征曲线下面积约为 90%）。从 Covid19 宿主遗传学倡议的汇总统计数据计算得出的 COVID-19 多基因评分与英国生物银行中的 COVID-19 显示出显著关联（p 值低至 3.96e-9，所有 R2 均低于 1%），但无法稳健地提高非遗传因素的预测性能。然而，非遗传因素模型的误差分析表明，被医学风险因素错误分类的受影响个体（预测低风险但实际高风险）的多基因评分略有但一致增加。总体而言，这些结果表明，基于 COVID-19 发病前数年测量的与健康相关的流行病学因素的简单模型可以达到较高的预测能力。COVID-19 与遗传因素之间的关联具有统计学意义，但目前它们对转化环境的预测能力有限。尽管如此，这些结果还表明，具有低风险医疗病史的严重受影响病例可能部分由多基因因素解释，促使基于新数据和工具开发基于新数据和工具的增强型 COVID-19 多基因模型，以帮助进行风险预测。
