Pietzner Maik, Denaxas Spiros, Yasmeen Summaira, Ulmer Maria A, Nakanishi Tomoko, Arnold Matthias, Kastenmüller Gabi, Hemingway Harry, Langenberg Claudia
Computational Medicine, Berlin Institute of Health at Charité - Universitatsmedizin Berlin, Berlin, Germany.
Precision Healthcare University Research Institute, Queen Mary University of London, London, UK.
medRxiv. 2024 Jul 1:2023.05.23.23290408. doi: 10.1101/2023.05.23.23290408.
Early evidence that patients with (multiple) pre-existing diseases are at highest risk for severe COVID-19 has been instrumental in the pandemic to allocate critical care resources and later vaccination schemes. However, systematic studies exploring the breadth of medical diagnoses, including common, but non-fatal diseases are scarce, but may help to understand severe COVID-19 among patients at supposedly low risk. Here, we systematically harmonized >12 million primary care and hospitalisation health records from ~500,000 UK Biobank participants into 1448 collated disease terms to systematically identify diseases predisposing to severe COVID-19 (requiring hospitalisation or death) and its post-acute sequalae, Long COVID. We identified a total of 679 diseases associated with an increased risk for severe COVID-19 (n=672) and/or Long COVID (n=72) that spanned almost all clinical specialties and were strongly enriched in clusters of cardio-respiratory and endocrine-renal diseases. For 57 diseases, we established consistent evidence to predispose to severe COVID-19 based on survival and genetic susceptibility analyses. This included a possible role of symptoms of malaise and fatigue as a so far largely overlooked risk factor for severe COVID-19. We finally observed partially opposing risk estimates at known risk loci for severe COVID-19 for etiologically related diseases, such as post-inflammatory pulmonary fibrosis (e.g., and or rheumatoid arthritis (e.g., possibly indicating a segregation of disease mechanisms. Our results provide a unique reference that demonstrates how 1) complex co-occurrence of multiple - including non-fatal - conditions predispose to increased COVID-19 severity and 2) how incorporating the whole breadth of medical diagnosis can guide the interpretation of genetic risk loci.
早期证据表明,患有(多种)基础疾病的患者感染重症 COVID-19 的风险最高,这在大流行期间对于分配重症监护资源和后续疫苗接种计划起到了重要作用。然而,探索包括常见但非致命疾病在内的医学诊断范围的系统性研究很少,但可能有助于了解那些看似低风险患者中的重症 COVID-19 情况。在此,我们将来自约 50 万名英国生物银行参与者的 1200 多万份初级保健和住院健康记录系统地统一为 1448 个整理后的疾病术语,以系统地识别易患重症 COVID-19(需要住院或死亡)及其急性后遗症“长新冠”的疾病。我们共识别出 679 种与重症 COVID-19(n = 672)和/或“长新冠”(n = 72)风险增加相关的疾病,这些疾病几乎涵盖了所有临床专科,并且在心肺和内分泌 - 肾脏疾病集群中高度富集。对于 57 种疾病,我们基于生存和遗传易感性分析建立了易患重症 COVID-19 的一致证据。这包括不适和疲劳症状可能作为重症 COVID-19 一个迄今很大程度上被忽视的风险因素的作用。我们最终在重症 COVID-19 的已知风险位点观察到病因相关疾病(如炎症后肺纤维化(例如 以及 或类风湿关节炎(例如 )的部分相反风险估计,这可能表明疾病机制的分离。我们的结果提供了一个独特的参考,展示了 1)多种(包括非致命)病症的复杂共存如何导致 COVID-19 严重程度增加,以及 2)纳入整个医学诊断范围如何能够指导对遗传风险位点的解释。