长期COVID-19血浆蛋白生物标志物：症状严重程度和死亡风险的预测因子

IF 3.2 3区医学 Q2 MEDICAL LABORATORY TECHNOLOGY

Clinica Chimica Acta Pub Date : 2025-05-14 DOI:10.1016/j.cca.2025.120350

Kaleem Maqsood , Shaaf Ahmad , Azeem Saeed , Nabila Roohi

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引用次数: 0

摘要

长期COVID-19的特点是急性期后症状持续存在，需要更深入地了解死亡风险因素以进行有效管理。血浆蛋白有望作为预后标志物，为疾病进展提供见解，并有助于死亡率风险评估。方法共纳入240例患者和89例健康对照。随访2个月后，将患者分为幸存者（n = 212）和非幸存者（n = 28）。血浆样品经二维凝胶电泳（2DE）分离蛋白质，LC-MS/MS鉴定蛋白质，ELISA验证。蛋白质组学数据分析采用Mascot 2.3.02版本和samspot4.5.1软件。采用GraphPad Prism和IBM SPSS进行统计分析。结果hplc -MS/MS鉴定出纤维蛋白原(斑点14；52.15 kDa/5.32 pI；蛋白评分2293)和触珠蛋白(斑点08；45.86 kDa/6.56 pI；蛋白评分453)作为前瞻性预测性生物标志物。ELISA结果证实重症患者血浆纤维蛋白原和触珠蛋白水平升高(P <；0.001)和非幸存者(P <；0.01和P <；分别为0.0086)。ROC分析显示，触珠蛋白对死亡率有中等预测能力(AUC = 0.68；P = 0.0025)，超过纤维蛋白原(AUC = 0.62；p = 0.0374)。结论血浆纤维蛋白原和接触珠蛋白是评估长期COVID患者疾病严重程度和死亡风险的有前景的生物标志物。这些发现突出了它们在预后应用方面的潜力，需要进一步研究它们在COVID-19中的机制作用和更广泛的临床意义。

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Plasma protein biomarkers for long COVID-19: Predictors of symptom severity and mortality risk

Introduction

Long COVID-19 is marked by persistent symptoms beyond the acute phase, necessitating a deeper understanding of mortality risk factors for effective management. Plasma proteins hold promise as prognostic markers, offering insights into disease progression and aiding in mortality risk assessment.

Method

A total of 240 patients and 89 healthy controls were enrolled for this study. Two months post-COVID follow-up, patients were categorized as survivors (n = 212) and non-survivors (n = 28). Plasma samples underwent 2-dimensional Gel Electrophoresis (2DE) for protein separation, followed by LC-MS/MS for protein identification, and validation was performed using ELISA. Proteomic data analysis was conducted using Mascot version 2.3.02 and Samespots software 4.5.1. Statistical analyses were carried out using GraphPad Prism and IBM SPSS.

Results

LC-MS/MS identified fibrinogen (Spot 14; 52.15 kDa/5.32 pI; protein score 2293) and haptoglobin (Spot 08; 45.86 kDa/6.56 pI; protein score 453) as prospective predictive biomarkers. ELISA results confirmed increased plasma levels of fibrinogen and haptoglobin in severe cases (P < 0.001 for both) and non-survivors (P < 0.01 and P < 0.0086, respectively). ROC analysis showed haptoglobin had moderate predictive power for mortality (AUC = 0.68; P = 0.0025), surpassing fibrinogen (AUC = 0.62; P = 0.0374).

Conclusion

Plasma fibrinogen and haptoglobin are promising biomarkers for assessing disease severity and mortality risk in long COVID. These findings highlight their potential for prognostic applications, warranting further research into their mechanistic roles in COVID-19 and broader clinical implications.

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来源期刊

Clinica Chimica Acta 医学-医学实验技术

CiteScore

10.10

自引率

2.00%

发文量

1268

审稿时长

23 days

期刊介绍： The Official Journal of the International Federation of Clinical Chemistry and Laboratory Medicine (IFCC) Clinica Chimica Acta is a high-quality journal which publishes original Research Communications in the field of clinical chemistry and laboratory medicine, defined as the diagnostic application of chemistry, biochemistry, immunochemistry, biochemical aspects of hematology, toxicology, and molecular biology to the study of human disease in body fluids and cells. The objective of the journal is to publish novel information leading to a better understanding of biological mechanisms of human diseases, their prevention, diagnosis, and patient management. Reports of an applied clinical character are also welcome. Papers concerned with normal metabolic processes or with constituents of normal cells or body fluids, such as reports of experimental or clinical studies in animals, are only considered when they are clearly and directly relevant to human disease. Evaluation of commercial products have a low priority for publication, unless they are novel or represent a technological breakthrough. Studies dealing with effects of drugs and natural products and studies dealing with the redox status in various diseases are not within the journal''s scope. Development and evaluation of novel analytical methodologies where applicable to diagnostic clinical chemistry and laboratory medicine, including point-of-care testing, and topics on laboratory management and informatics will also be considered. Studies focused on emerging diagnostic technologies and (big) data analysis procedures including digitalization, mobile Health, and artificial Intelligence applied to Laboratory Medicine are also of interest.