School of Health Sciences, Faculty of Health and Medical Sciences, University of Surrey, Guildford GU2 7XH, UK.
School of Chemistry and Chemical Engineering, Faculty of Engineering and Physical Sciences, University of Surrey, Guildford GU2 7XH, UK.
Int J Mol Sci. 2023 Sep 21;24(18):14371. doi: 10.3390/ijms241814371.
The global COVID-19 pandemic resulted in widespread harms but also rapid advances in vaccine development, diagnostic testing, and treatment. As the disease moves to endemic status, the need to identify characteristic biomarkers of the disease for diagnostics or therapeutics has lessened, but lessons can still be learned to inform biomarker research in dealing with future pathogens. In this work, we test five sets of research-derived biomarkers against an independent targeted and quantitative Liquid Chromatography-Mass Spectrometry metabolomics dataset to evaluate how robustly these proposed panels would distinguish between COVID-19-positive and negative patients in a hospital setting. We further evaluate a crowdsourced panel comprising the COVID-19 metabolomics biomarkers most commonly mentioned in the literature between 2020 and 2023. The best-performing panel in the independent dataset-measured by F1 score (0.76) and AUROC (0.77)-included nine biomarkers: lactic acid, glutamate, aspartate, phenylalanine, β-alanine, ornithine, arachidonic acid, choline, and hypoxanthine. Panels comprising fewer metabolites performed less well, showing weaker statistical significance in the independent cohort than originally reported in their respective discovery studies. Whilst the studies reviewed here were small and may be subject to confounders, it is desirable that biomarker panels be resilient across cohorts if they are to find use in the clinic, highlighting the importance of assessing the robustness and reproducibility of metabolomics analyses in independent populations.
全球 COVID-19 大流行造成了广泛的危害，但也推动了疫苗开发、诊断检测和治疗方面的快速进展。随着该疾病进入地方病状态，用于诊断或治疗的疾病特征性生物标志物的需求已经减少，但仍可以吸取经验教训，为应对未来病原体的生物标志物研究提供信息。在这项工作中，我们针对一组独立的靶向和定量液相色谱-质谱代谢组学数据集，测试了五组研究衍生的生物标志物，以评估这些拟议的生物标志物组合在医院环境中区分 COVID-19 阳性和阴性患者的稳健性。我们还进一步评估了一个由 2020 年至 2023 年文献中最常提到的 COVID-19 代谢组学生物标志物组成的众包生物标志物面板。在独立数据集中表现最佳的生物标志物面板（通过 F1 评分（0.76）和 AUROC（0.77）来衡量）包括九个生物标志物：乳酸、谷氨酸、天冬氨酸、苯丙氨酸、β-丙氨酸、精氨酸、花生四烯酸、胆碱和次黄嘌呤。包含较少代谢物的生物标志物面板表现较差，在独立队列中的统计显著性比其各自的发现研究中最初报道的要弱。虽然这里回顾的研究规模较小，可能受到混杂因素的影响，但如果生物标志物面板要在临床上找到应用，它们在不同队列中具有弹性是可取的，这突出了在独立人群中评估代谢组学分析的稳健性和可重复性的重要性。
