Marhuenda-Egea F C, Narro-Serrano J
Departamento de Agroquímica y Bioquímica, Universidad de Alicante, Spain.
Departamento de Química Física, Universidad de Alicante, Spain.
Heliyon. 2023 Mar;9(3):e14161. doi: 10.1016/j.heliyon.2023.e14161. Epub 2023 Feb 28.
Since the state of alarm was declared due to the COVID-19 pandemic, hospitals have been the main ones in charge of registering the therapeutic follow-up of affected people. The analysis of these data has allowed those different biochemical markers have been identified as predictors of the severity of the disease, but most of the published studies tend to be eminently descriptive and do not propose a biochemical hypothesis to explain the alteration of the results they are showing. Our objective is to recognize the main metabolic processes that are occurring in COVID-19 patients, as well as the identification of clinical parameters that are decisive to predict the severity of the disease.
A multivariate analysis was carried out from the clinical parameters collected in the database of the HM hospitals in Madrid, to determine the most relevant variables to predict the severity of the disease. Chemometric methods allow these variables to be obtained by applying a classification strategy with PLS-LDA.
The variables that most contribute to separation are age in men and, in both sexes, the concentration of lactate dehydrogenase, urea and C-reactive protein.Oxygen deficiency in the tissues, due to the loss of functionality of the lungs, could be affecting the muscle tissue with special severity. Inflammation and tissue damage is related to increased LDH and CRP. The loss of muscle mass and the increase in the concentration of urea and LDH is explained by the adaptation of muscle metabolism to this oxygen deficiency.
This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profits sectors.
自因新冠疫情宣布进入警戒状态以来，医院一直是负责记录感染者治疗随访情况的主要机构。对这些数据的分析使得不同的生化标志物被确定为疾病严重程度的预测指标，但大多数已发表的研究往往极具描述性，并未提出生化假设来解释其所示结果的变化。我们的目标是识别新冠患者体内正在发生的主要代谢过程，以及确定对预测疾病严重程度起决定性作用的临床参数。
对马德里 HM 医院数据库中收集的临床参数进行多变量分析，以确定预测疾病严重程度最相关的变量。化学计量学方法允许通过应用 PLS-LDA 分类策略来获得这些变量。
对分离贡献最大的变量在男性中是年龄，在男女两性中是乳酸脱氢酶、尿素和 C 反应蛋白的浓度。由于肺部功能丧失导致的组织缺氧可能对肌肉组织造成特别严重的影响。炎症和组织损伤与乳酸脱氢酶和 C 反应蛋白的增加有关。肌肉量的减少以及尿素和乳酸脱氢酶浓度的增加可通过肌肉代谢对这种缺氧的适应来解释。
本研究未获得公共、商业或非营利部门的资助机构提供的任何特定资助。
