Using data to improve public health: COVID-19 secondment

由严重急性呼吸系统综合征冠状病毒2 （SARS-CoV-2）引起的2019年冠状病毒病（COVID-19）自2019年12月出现以来，已导致全球住院和死亡人数增加。COVID-19的影响在很大程度上取决于每个患者，从无症状到致命病例不等。症状的持续时间也很不一样，有些患者持续几天，有些患者持续几周，这些患者会出现所谓的“长冠状病毒”。众所周知，年龄较大是严重和长期COVID的风险因素。这与衰老过程引起的免疫反应减弱有关。高血压、糖尿病、心血管疾病或癌症等既往疾病也会增加COVID-19患者严重感染的风险。然而，许多看似健康的中年人也发现了严重的COVID-19。对严重COVID-19风险因素的了解仍然有限，人们对病毒易感性差异如此之大的原因知之甚少。需要更多的研究来揭示严重程度的生物学机制，以便确定高度易感个体和新治疗途径。最近的研究表明，在患有心血管疾病、糖尿病或慢性炎症的人群中，血液、尿液或粪便等生物体液中的分子会发生改变。这些情况代表了严重COVID-19的危险因素，我们假设生物流体分子可以用作代谢生物标志物，以预测感染SARS-CoV-2的患者是否可能受到严重影响。拟议研究的中心思想是使用代谢生物标志物来预测COVID-19的严重程度，以及那些不一定被诊断患有预先存在的健康状况的人长COVID的可能性。为此，我们将使用来自几项队列研究的大流行前数据，这些数据除了有关年龄、性别、种族等的基本信息外，还包含数千人的数百种代谢生物标志物。为了了解这些特征与COVID-19影响之间的联系，我们将使用患有COVID-19的队列研究中个体的症状数据。将用统计方法分析这些数据，以确定大流行前的个体特征与疾病严重程度之间的关联。这一分析将与计算机程序相辅相成，这些程序是为了根据一个人在大流行之前的特征预测他/她的感染是否会产生严重影响而开发的。机器学习技术将用于训练计算机程序，以自动识别代表严重COVID-19风险的代谢特征。该项目在基础科学和应用方面都是有益的。事实上，拟议的研究将增强我们对代谢生物标志物如何解释对严重COVID-19的易感性的理解。从应用的角度来看，使用大量代谢生物标志物编码的信息来训练机器学习模型可以提高我们识别COVID-19可能对其产生严重后果的个体的能力。

The coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has led to a worldwide increase in hospitalisations and deaths since it emerged in December 2019. The effects of COVID-19 depend very much on each patient and range from asymptomatic to fatal cases. The duration of symptoms is also very heterogeneous, lasting between a few days for some patients and several weeks for others that develop the so-called 'long COVID'. Older age is a well-known risk factor for both severe and long COVID. This has been associated with a debilitated immune response caused by ageing processes. Pre-existing diseases such as hypertension, diabetes, cardiovascular disease, or cancer also increase the risk of severe infection in patients with COVID-19. However, severe COVID-19 has also been observed for many seemingly healthy middle-aged individuals. Understanding of the risk factors for severe COVID-19 remains limited and the reasons why susceptibility to the virus varies so widely in the population are poorly understood. More research is needed to unveil the biological mechanisms of severity so that highly susceptible individuals and pathways to novel treatments can be identified.Recent studies have shown that the molecules in biofluids such as blood, urine or faeces are altered in people with cardiovascular disease, diabetes, or chronic inflammation. These conditions represent risk factors for severe COVID-19 and we hypothesise that biofluid molecules can be used as metabolic biomarkers to predict whether a patient infected by SARS-CoV-2 is likely to be seriously affected. The central idea of the proposed research is to use metabolic biomarkers to predict the severity of COVID-19 and the likelihood of long COVID for individuals that have not necessarily been diagnosis with a pre-existing health condition. To this end, we will use pre-pandemic data from several cohort studies which, in addition to basic information on age, sex, ethnicity, etc, contain hundreds of metabolic biomarkers for thousands of individuals. To understand the link between these characteristics and the impact of COVID-19, we will use symptoms data for those individuals in the cohort studies that had COVID-19. The data will be analysed with statistical methods to identify associations between the characteristics of individuals before the pandemic and the severity of the disease. This analysis will be complemented with computer programs developed to predict if the infection of an individual will have serious effects based on his/her characteristics before the pandemic. Machine learning techniques will be used to train computer programs to automatically recognise metabolic features that represent a risk for severe COVID-19.The project can be beneficial both in terms of basic science and applications. Indeed, the proposed research will enhance our understanding of how metabolic biomarkers may explain the susceptibility to severe COVID-19. From an applied viewpoint, using the information encoded by numerous metabolic biomarkers to train machine learning models can improve our ability to identify individuals for whom COVID-19 may have serious consequences.