Changes in Serum Liver Function for Patients with COVID-19: A 1-Year Follow-Up Study.

Objective: Abnormal liver function and liver injury related to COVID-19 during hospitalization has received widespread attention. However, the long-term observation of patients' liver functions after discharge has not been investigated. This study intends to analyze the abnormal liver function in patients one year after they are discharged. Methods: Serum liver function tests were analyzed for the first time immediately after hospitalization (T1), before discharge (T2), a median of 14.0 (14.0, 15.0) days after discharge (T3) and 1 year (356.0 (347.8, 367.0) days) after discharge (T4). Patients with at least one serum parameter (ALT, AST, ALP, GGT and TB) exceeding the upper limit of reference range were defined as having abnormal liver function. Results: For the 118 COVID-19 patients with a median follow-up time of 376.0 (71.5, 385.3) days from onset to the end of the follow-up after discharge, the proportion with abnormal liver function in T1, T2, T3 and T4 were 32.2%, 45.8%, 54.8% and 28.8%, respectively. The proportion of patients with at least once abnormal liver function detected from T1 to T2, T1 to T3, T1 to T4 was 60.2%, 77.4% and 88.9%, respectively. From T1 to T4, the ALT, AST, GGT and BMI at admission were significantly higher in the patients with persistently abnormal liver function than in the patients with persistently normal liver function. Abnormal liver function was mainly manifested in the elevation of GGT and TB levels. Multivariate logistics regression analysis showed that age and gender-adjusted ALT (odds ratio [OR]=2.041, 95% confidence interval [CI]: 1.170-3.561, P=0.012) at admission was a risk factor for abnormal liver function in the T4 stage. Conclusion: Abnormal liver function in patients with COVID-19 can persist from admission to one year after discharge, and therefore, the long-term dynamic monitoring of liver function in patients with COVID-19 is necessary.

Proteomic and metabolomic profiling of urine uncovers immune responses in patients with COVID-19.

The utility of the urinary proteome in infectious diseases remains unclear. Here, we analyzed the proteome and metabolome of urine and serum samples from patients with COVID-19 and healthy controls. Our data show that urinary proteins effectively classify COVID-19 by severity. We detect 197 cytokines and their receptors in urine, but only 124 in serum using TMT-based proteomics. The decrease in urinary ESCRT complex proteins correlates with active SARS-CoV-2 replication. The downregulation of urinary CXCL14 in severe COVID-19 cases positively correlates with blood lymphocyte counts. Integrative multiomics analysis suggests that innate immune activation and inflammation triggered renal injuries in patients with COVID-19. COVID-19-associated modulation of the urinary proteome offers unique insights into the pathogenesis of this disease. This study demonstrates the added value of including the urinary proteome in a suite of multiomics analytes in evaluating the immune pathobiology and clinical course of COVID-19 and, potentially, other infectious diseases.

Proteomic and metabolomic investigation of serum lactate dehydrogenase elevation in COVID-19 patients.

Serum lactate dehydrogenase (LDH) has been established as a prognostic indicator given its differential expression in COVID-19 patients. However, the molecular mechanisms underneath remain poorly understood. In this study, 144 COVID-19 patients were enrolled to monitor the clinical and laboratory parameters over 3 weeks. Serum LDH was shown elevated in the COVID-19 patients on admission and declined throughout disease course, and its ability to classify patient severity outperformed other biochemical indicators. A threshold of 247 U/L serum LDH on admission was determined for severity prognosis. Next, we classified a subset of 14 patients into high- and low-risk groups based on serum LDH expression and compared their quantitative serum proteomic and metabolomic differences. The results showed that COVID-19 patients with high serum LDH exhibited differentially expressed blood coagulation and immune responses including acute inflammatory responses, platelet degranulation, complement cascade, as well as multiple different metabolic responses including lipid metabolism, protein ubiquitination and pyruvate fermentation. Specifically, activation of hypoxia responses was highlighted in patients with high LDH expressions. Taken together, our data showed that serum LDH levels are associated with COVID-19 severity, and that elevated serum LDH might be consequences of hypoxia and tissue injuries induced by inflammation.

Proteomic and Metabolomic Characterization of COVID-19 Patient Sera.

Early detection and effective treatment of severe COVID-19 patients remain major challenges. Here, we performed proteomic and metabolomic profiling of sera from 46 COVID-19 and 53 control individuals. We then trained a machine learning model using proteomic and metabolomic measurements from a training cohort of 18 non-severe and 13 severe patients. The model was validated using 10 independent patients, 7 of which were correctly classified. Targeted proteomics and metabolomics assays were employed to further validate this molecular classifier in a second test cohort of 19 COVID-19 patients, leading to 16 correct assignments. We identified molecular changes in the sera of COVID-19 patients compared to other groups implicating dysregulation of macrophage, platelet degranulation, complement system pathways, and massive metabolic suppression. This study revealed characteristic protein and metabolite changes in the sera of severe COVID-19 patients, which might be used in selection of potential blood biomarkers for severity evaluation.

Prediction of severe illness due to COVID-19 based on an analysis of initial Fibrinogen to Albumin Ratio and Platelet count.

Concomitant coagulation disorder can occur in severe patients withCOVID-19, but in-depth studies are limited. This study aimed to describe the parameters of coagulation function of patients with COVID-19 and reveal the risk factors of developing severe disease. This study retrospectively analyzed 113patients with SARS-CoV-2 infection in Taizhou Public Health Center. Clinical characteristics and indexes of coagulation function were collected. A multivariate Cox analysis was performed to identify potential biomarkers for predicting disease progression. Based on the results of multivariate Cox analysis, a Nomogram was built and the predictive accuracy was evaluated through the calibration curve, decision curve, clinical impact curve, and Kaplan-Meier analysis. Sensitivity, specificity, predictive values were calculated to assess the clinical value. The data showed that Fibrinogen, FAR, and D-dimer were higher in the severe patients, while PLTcount, Alb were much lower. Multivariate Cox analysis revealed that FAR and PLT count were independent risk factors for disease progression. The optimal cutoff values for FAR and PLT count were 0.0883 and 135*109/L, respectively. The C-index [0.712 (95% CI = 0.610-0.814)], decision curve, clinical impact curve showed that Nomogram could be used to predict the disease progression. In addition, the Kaplan-Meier analysis revealed that potential risk decreased in patients with FAR<0.0883 and PLT count>135*109/L.The model showed a good negative predictive value [(0.9474 (95%CI = 0.845-0.986)].This study revealed that FAR and PLT count were independent risk factors for severe illness and the severity of COVID-19 might be excluded when FAR<0.0883 and PLT count>135*109/L.