Lipid metabolism of plasma-derived small extracellular in COVID-19 convalescent patients (preprint)

The coronavirus disease 2019 (COVID-19), which affects multiple organs, is causing an unprecedented global public health crisis. Most COVID-19 patients recover gradually upon appropriate interventions. Viruses were reported to utilize the small extracellular vesicles (sEVs) to escape the attack from the host’s immune system. This study aimed to examine the lipid profile of plasma small extracellular vesicles of recovered COVID-19 patients (RCs). Plasma sEVs were separated from 83 RCs 3 months after discharge without underlying diseases, including 18 recovered asymptomatic patients (RAs), 32 recovered moderate patients (RMs), and 33 recovered severe and critical patients (RSs), and 19 healthy controls (HCs) by Total Exosome Isolation. Lipids were extracted from sEVs and then subjected to targeted liquid chromatography-mass spectrometry. Size, concentration, and distribution of plasma-derived sEVs from RAs, RMs, RSs, and HCs did not differ in RCs and HCs as validated by transmission electron microscopy, nanoparticle tracking analysis, and immunoblot analysis. Fifteen subclasses of 508 lipids were detected in plasma sEVs from HCs, RAs, RMs, and RSs, such as phosphatidylcholines (PCs) and diacylglycerols (DAGs), etc. Total lipid intensity displayed downregulation in RCs compared with HCs. The relative abundance of DAGs gradually dropped, whereas PCs, lysophosphatidylcholines, and sphingomyelins were higher in RCs relative to HCs, especially RSs. 88 lipids out of 241 were significantly different and a conspicuous increase in lipid profiles of RCs was revealed with disease status. The lipids alternations were found to be significantly correlated with the clinical indices in RCs and HCs, suggesting that the impact of COVID-19 on lipid metabolism lingered for a long time. The lipid abnormalities bore an intimate link with glycerophospholipid metabolism and glycosylphosphatidylinositol anchor biosynthesis. Furthermore, the lipidomic analysis showed that RCs were at higher risk of developing diabetes and sustaining hepatic impairment. The abnormality of immunomodulation in RCs might still exist. The study may offer new insights into the mechanism of organ dysfunction and help identify novel therapeutic targets in the RCs.

Plasma Metabolomic Profiles and Clinical Features in Recovered COVID-19 Patients Without Previous Underlying Diseases 3 Months After Discharge (preprint)

Knowing the residual and future effect of SARS-CoV-2 on recovered COVID-19 patients is critical for optimized long-term patient management. Recent studies focus on the symptoms and clinical indices of recovered patients, but the pathophysiological change is still unclear. To address this question, we examined the metabolomic profiles of recovered asymptomatic (RA), moderate (RM) and severe and critical (RC) patients without previous underlying diseases discharged from the hospital for 3 months, along with laboratory and CT findings. We found that the serum metabolic profiles in recovered COVID-19 patients still conspicuously differed from that in healthy control (HC), especially in the RM, and RC patients. Additionally, these changes bore close relationship with the function of pulmonary, renal, hepatic, microbial and energetic metabolism and inflammation. These findings suggested that RM and RC patients sustained multi-organ and multi-system damage and these patients should be followed up on regular basis for possible organ and system damage.

Predictive Risk Factors at Admission and a "Burning Point" During Hospitalization Serve as Sequential Alerts for Critical Illness in COVID-19 Patients (preprint)

Background In critically ill COVID-19 patients, the crucial turning point before critical illness onset (CIO) remain largely unknown, and the combination of baseline risk factors with the turning point during hospitalization was rarely reported.Methods In this retrospective cohort study, 1150 consecutively admitted patients with confirmed COVID-19 were enrolled, including 296 critical and 854 non-critical patients. We compared the differences of all the clinically tested indicators and their dynamic changes between critical and non-critical patients. Three prediction models were established and validated based on the risk factors at admission, and an online baseline predictive tool was developed. Linear mixed model (LMM) was applied for longitudinal data analysis in 296 critical patients throughout the hospitalization, to predict the likelihood and possible time of critical illness in COVID-19 patients. A crucial turning point, where several indicators will experience a greater and significantly continuous change before CIO, was defined as “burning point” in our study. This point indicates the deterioration of patient’s condition before CIO.Results We established a novel two-checkpoint system to predict critical illness for COVID-19 patients in which the first checkpoint happened at patient admission was assessed by a baseline prediction model to project the likelihood of critical illness based on the variables selected from random forest and LASSO regression analysis, including age, SOFA score, neutrophil-to-lymphocyte ratio (NLR), D-dimer, lactate dehydrogenase (LDH), International Normalized Ratio (INR), and pneumonia area derived from CT images, which yields an AUC of 0.960 (95% confidence interval, 0.941-0.972) and 0.958 (0.936-0.980) in the training and testing sets, respectively. This model has been translated into a public web-based risk calculator. Furthermore, the second checkpoint (designated as “burning point” in our study) could be identified as early as 5 days preceding the CIO, and 12 (IQR, 7-17) days after illness onset. Seven most significant and representative “burning point” indicators were SOFA score, NLR, C-reactive protein (CRP), glucose, D-dimer, LDH, and blood urea nitrogen (BUN).Conclusions With this two-checkpoint prediction system, the deterioration of COVID-19 patients could be early identified and more intensive treatments could be started in advance to reduce the incidence of critical illness.