Metabolic predictors of COVID-19 mortality and severity: A Survival Analysis (preprint)

Background The global healthcare burden of COVID19 pandemic has been unprecedented with a high mortality. Metabolomics, a powerful technique, has been increasingly utilized to study the host response to infections and for understanding the progression of multi-system disorders such as COVID-19. Analysis of the host metabolites in response to SARS-CoV-2 infection can provide a snapshot of the endogenous metabolic landscape of the host and its role in shaping the interaction with SARS-CoV-2. Disease severity and consequently the clinical outcomes may be associated with a metabolic imbalance related to amino acids, lipids, and energy-generating pathways. Hence, the host metabolome can help predict potential clinical risks and outcomes.Methods In this study, using a targeted metabolomics approach, we studied the metabolic signatures of COVID-19 patients and related it to disease severity and mortality. Blood plasma concentrations of metabolites were quantified through LC-MS using MxP Quant 500 kit, which has a coverage of 630 metabolites from 26 biochemical classes including distinct classes of lipids and small organic molecules. We then employed Kaplan-Meier survival analysis to investigate the correlation between various metabolic markers, and disease severity and patient outcomes.Results A comparison of survival rates between individuals with high levels of various metabolites (amino acids, tryptophan, kynurenine, serotonin, creatine, SDMA, ADMA, 1-MH, and indicators of carnitine palmitoyltransferase 1 and 2 enzymes) and those with low levels revealed statistically significant differences in survival outcomes. We further used four key metabolic markers (tryptophan, kynurenine, asymmetric dimethylarginine, and 1-Methylhistidine) to develop a COVID-19 mortality risk model through the application of multiple machine-learning methods.Conclusions In conclusion, these metabolic predictors of COVID19 can be further validated as potential biomarkers to identify patients at risk of poor outcomes. Finally, integrating machine learning models in metabolome analysis of COVID-19 patients can improve our understanding of disease severity and mortality by providing insights into the relationship between metabolites and the survival probability, which can help lead the development of clinical risk models and potential therapeutic strategies.

Complement C5a and Clinical Markers to predict COVID-19 Disease Severity and Mortality in a Multi-ethnic Population (preprint)

Coronavirus disease-2019 (COVID-19) was declared as a pandemic by WHO in March 2020. SARS-CoV-2 causes a wide range of illness from asymptomatic to life-threatening. There is an essential need to identify biomarkers to predict disease severity and mortality during the earlier stages of the disease, aiding treatment and allocation of resources to improve survival. The aim of this study was to identify at the time of SARS-COV-2 infection patients at high risk of developing severe disease associated with low survival using blood parameters, including inflammation and coagulation mediators, vital signs, and pre-existing comorbidities. This cohort included 89 multi-ethnic COVID-19 patients recruited between July 14th and October 20th 2020 in Doha, Qatar. According to clinical severity, patients were grouped into severe (n = 33), mild (n = 33) and asymptomatic (n = 23). Common routine tests such as complete blood count (CBC), glucose, electrolytes, liver and kidney function parameters and markers of inflammation, thrombosis and endothelial dysfunction including complement component split product C5a, Interleukin-6, ferritin and C-reactive protein were measured at the time COVID-19 infection was confirmed. Correlation tests suggest that C5a is a novel predictive marker of disease severity and mortality, in addition to 40 biological and physiological parameters that were found statistically significant between survivors and non-survivors. Survival analysis showed that. high C5a levels, hypoalbuminemia, lymphopenia, elevated procalcitonin, neutrophilic leukocytosis, acute anemia along with increased acute kidney and hepatocellular injury markers were associated with a higher risk of death in COVID-19 patients. Altogether, we created a prognostic classification model, the CAL model (C5a, Albumin, and Lymphocyte count) to predict severity with significant accuracy. Stratification of patients using the CAL model could help the identification of patients likely to develop severe symptoms in advance so that treatments can be targeted accordingly.

Blood snoRNAs and miRNAs as predictors of COVID-19 Severity (preprint)

There is a lack of predictive markers for early and rapid identification of disease progression in COVID-19 patients. Our study aims at identifying non-coding RNAs (ncRNAs) as potential biomarkers of COVID-19 severity. Using differential expression analysis of microarray data (n = 29), we identified hsa-miR-494-3p, hsa-miR-1246, ACA40, hsa-miR-4532, ACA15 as the top five differentially expressed transcripts in severe versus asymptomatic, and ACA40, hsa-miR-3609, hsa-miR-6790-5p, hsa-miR-126-3p, hsa-miR-885-3p as the most significant five in severe versus mild cases. Moreover, we found that WBC count, absolute neutrophil count, neutrophil (%), lymphocyte (%), RBC count, hemoglobin, hematocrit, D-Dimer and albumin were significantly correlated with the identified ncRNAs. Altogether, we present the first comprehensive analysis of COVID-19-associated microRNA (miRNA)/ small nucleolar RNA (snoRNA) signature, highlighting the importance of ncRNAs in SARS-CoV-2 infection. One-Sentence Summary: We show a unique miRNA and snoRNA profile that is associated with a higher risk of severity in SARS-CoV-2 infected patients.