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. Decrease of urinary ESCRT complex proteins correlates with active SARS-CoV-2 replication. Downregulation of urinary CXCL14 in severe COVID-19 cases positively correlates with blood lymphocyte counts. Integrative multiomic 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 multiomic analytes in evaluating the immune pathobiology and clinical course of COVID-19 and, potentially, other infectious diseases.
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.
Haemorrhagic fever with renal syndrome (HFRS) is a serious zoonotic disease which seriously endangers physical health and mainly occurs in China. To date, there is still a lack of early and novel biomarkers to detect the severity of disease and prognosis of HFRS. This study was aimed to examine the value of the serum Adenosine deaminase (ADA) concentrations in the patients with HFRS. Methods: The clinical and laboratory data of 124 adult patients with HFRS and 131 patients with similar clinical symptoms to HFRS were analyzed. A receiver operating characteristic (ROC) curve was used to analyze the diagnostic value of ADA in HFRS. Results:The ADA levels in the serum of HFRS patients were significantly higher than those in control patients (P < 0.001), and ADA has a strong positive correlation with HFRS (r = 0.785, P < 0.001). The optimal cut-off value of ADA for diagnosis of HFRS was 18 U/L and the area under the curve (AUC) was 0.953 (95% CI: 0.925, 0.981). The sensitivity was 84.8%, the specificity was 93.1%, the positive predictive value was 92.2%, the negative predictive value was 86.5% and the Youden index was 77.9%. Serum ADA levels in patients with HFRS tended to decrease at discharge compared with those at admission. Conclusion: ADA could be a potential molecular marker for diagnosis and prognosis of HFRS patients.
Background: Serum Cholinesterase (CHE) levels have been found to be elevated in individuals with nephrotic syndrome (NS); nevertheless, it is unknown whether CHE can serve as a biomarker for NS diagnosis and what its diagnostic relevance is for NS in minors. Methods: In this study, 138 minors aged 1-17 years with NS were enrolled, including 101 patients with the first episode of NS and 37 patients with relapsing NS. One hundred and four minors suffering from nephritis and 109 healthy minors were included as control groups. The clinical information and laboratory data of all NS patients and the control group were obtained. Logistic regression, correlation analyses and receiver operator characteristic curve were used to examine the value of CHE for NS patients. Results: Compared to patients diagnosed with nephritis and healthy minors in the control group, the serum CHE levels of total/first episode/relapsing NS patients were substantially higher (P < 0.05). The CHE was an independent risk predictor of total (adjusted odds ratio [OR] = 2.23, 95% confidence interval [CI]: 1.57-3.18)/first episode (adjusted OR = 4.02, 95% CI: 1.47-11.08)/relapsing (adjusted OR = 2.04, 95% CI: 1.42-2.93) NS, and was positively correlated with total cholesterol in total/first episode/relapsing NS patients, respectively. The optimal cutoff for total/first episode/relapsing NS all was 11 KU/L, but the diagnostic accuracy in first episode NS (area under the curve [AUC] = 0.96, 95% CI: 0.94-0.98) was higher than the total NS (AUC = 0.93, 95% CI: 0.91-0.96) and relapsing NS (AUC = 0.85, 95% CI: 0.78-0.92). Conclusion: CHE is a possible biomarker for NS and has good diagnostic accuracy for NS in minors, particularly for the first episode of NS in minors.
Coronavirus disease 2019 (COVID-19) patients with liver dysfunction (LD) have a higher chance of developing severe and critical disease. The routine hepatic biochemical parameters ALT, AST, GGT, and TBIL have limitations in reflecting COVID-19–related LD. In this study, we performed proteomic analysis on 397 serum samples from 98 COVID-19 patients to identify new biomarkers for LD. We then established 19 simple machine learning models using proteomic measurements and clinical variables to predict LD in a development cohort of 74 COVID-19 patients with normal hepatic biochemical parameters. The model based on the biomarker ANGL3 and sex (AS) exhibited the best discrimination (time-dependent AUCs: 0.60–0.80), calibration, and net benefit in the development cohort, and the accuracy of this model was 69.0–73.8% in an independent cohort. The AS model exhibits great potential in supporting optimization of therapeutic strategies for COVID-19 patients with a high risk of LD. This model is publicly available athttps://xixihospital-liufang.shinyapps.io/DynNomapp/.
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