Robinson John I, Marks Laura R, Hinton Andrew L, O'Halloran Jane A, Goss Charles W, Mucha Peter J, Henderson Jeffrey P
Division of Infectious Diseases, Department of Internal Medicine, Washington University School of Medicine, St. Louis, Missouri, USA.
Curriculum in Bioinformatics and Computational Biology, University of North Carolina, Chapel Hill, North Carolina, USA.
mBio. 2025 Jan 8;16(1):e0334323. doi: 10.1128/mbio.03343-23. Epub 2024 Nov 22.
In a new respiratory virus pandemic, optimizing allocation of scarce medical resources becomes an urgent challenge. Infection prognosis takes on particular importance when allocating scarce antiviral antibodies and drugs, which are most effective when administered before the onset of severe disease. During arrival of the COVID-19 pandemic to the United States in 2020, we conducted a prognostic biomarker discovery and validation effort based upon metabolomic profiling with a liquid-chromatography-mass spectrometer (LC-MS) type used clinically for rapid toxicology. We obtained urine specimens from 163 patients presenting for evaluation. We obtained LC-MS profiles in the initial cohort and used machine learning methods to define a simplified urine metabolomic signature associated with respiratory failure or death by 90 days. This signature was composed of three metabotypes linked to intestinal microbiome metabolism and anticonvulsant use, with a receiver-operator characteristic area under the curve (ROC AUC) of 89.4%. Blinded application of this signature to the subsequent validation cohort yielded a ROC AUC of 81.2%. A model trained on the two baseline metabotypes present before intubation exhibited similar performance in the validation cohort. This study demonstrates the plausibility and promise of rapid metabolome-based prognostic discovery and validation in the opening wave of a pandemic. The approach used here could be used to inform therapeutic and resource allocation decisions early in a future epidemic.IMPORTANCEIn a new respiratory virus pandemic, the ability to identify patients at greatest risk for severe disease is essential to direct scarce medical resources to those most likely to benefit from them. Tools to predict disease severity are best developed early in a pandemic, but laboratory-based resources to develop these may be limited by available technology and by infection precautions. Here, we show that an accessible metabolic profiling approach could identify a prognostic signature of severe disease in the initial wave of COVID-19, when patients presenting for care often exceeded the available doses of convalescent plasma and remdesivir. In a future pandemic, this approach, alongside efforts to identify clinical disease severity predictors, could improve patient outcomes and facilitate therapeutic trials by identifying individuals at high risk for severe disease.
在新型呼吸道病毒大流行期间，优化稀缺医疗资源的分配成为一项紧迫挑战。在分配稀缺的抗病毒抗体和药物时，感染预后具有特别重要的意义，因为这些药物在严重疾病发作前使用最为有效。2020年新冠疫情蔓延至美国期间，我们基于代谢组学分析开展了一项预后生物标志物发现与验证工作，采用的是临床用于快速毒理学检测的液相色谱 - 质谱仪（LC - MS）。我们从163名前来就诊的患者中获取了尿液样本。我们在初始队列中获得了LC - MS图谱，并使用机器学习方法确定了一种简化的尿液代谢组学特征，该特征与90天内呼吸衰竭或死亡相关。这一特征由三种与肠道微生物群代谢和抗惊厥药物使用相关的代谢型组成，曲线下面积（ROC AUC）为89.4%。将此特征盲目应用于后续验证队列时，ROC AUC为81.2%。在插管前存在的两种基线代谢型基础上训练的模型在验证队列中表现出相似的性能。本研究证明了在大流行初期基于快速代谢组学进行预后发现和验证的可行性和前景。这里所采用的方法可用于为未来疫情早期的治疗和资源分配决策提供依据。重要性在新型呼吸道病毒大流行期间，识别重症风险最高的患者对于将稀缺医疗资源导向最有可能从中受益的人群至关重要。预测疾病严重程度的工具最好在大流行早期开发，但用于开发这些工具的基于实验室的资源可能会受到现有技术和感染预防措施的限制。在此，我们表明，一种易于获得的代谢分析方法能够在新冠疫情初期识别出重症疾病的预后特征，当时前来就诊的患者常常超出了康复血浆和瑞德西韦的可用剂量。在未来的大流行中，这种方法与识别临床疾病严重程度预测指标的努力相结合，通过识别重症高风险个体，可改善患者预后并促进治疗试验。
