Metabolic profiling during COVID-19 infection in humans: Identification of potential biomarkers for occurrence, severity and outcomes using machine learning.

BACKGROUND: After its emergence in China, the coronavirus SARS-CoV-2 has swept the world, leading to global health crises with millions of deaths. COVID-19 clinical manifestations differ in severity, ranging from mild symptoms to severe disease. Although perturbation of metabolism has been reported as a part of the host response to COVID-19 infection, scarce data exist that describe stage-specific changes in host metabolites during the infection and how this could stratify patients based on severity. METHODS: Given this knowledge gap, we performed targeted metabolomics profiling and then used machine learning models and biostatistics to characterize the alteration patterns of 50 metabolites and 17 blood parameters measured in a cohort of 295 human subjects. They were categorized into healthy controls, non-severe, severe and critical groups with their outcomes. Subject's demographic and clinical data were also used in the analyses to provide more robust predictive models. RESULTS: The non-severe and severe COVID-19 patients experienced the strongest changes in metabolite repertoire, whereas less intense changes occur during the critical phase. Panels of 15, 14, 2 and 2 key metabolites were identified as predictors for non-severe, severe, critical and dead patients, respectively. Specifically, arginine and malonyl methylmalonyl succinylcarnitine were significant biomarkers for the onset of COVID-19 infection and tauroursodeoxycholic acid were potential biomarkers for disease progression. Measuring blood parameters enhanced the predictive power of metabolic signatures during critical illness. CONCLUSIONS: Metabolomic signatures are distinctive for each stage of COVID-19 infection. This has great translation potential as it opens new therapeutic and diagnostic prospective based on key metabolites.

Plasma osteopontin level as a diagnostic marker of hepatocellular carcinoma in patients with radiological evidence of focal hepatic lesions.

AIMS: Hepatocellular carcinoma is one of the most aggressive malignant tumors and has limited treatment options. Needle-guided biopsies have been utilized as a tool to diagnose malignant focal hepatic lesions. These techniques are discouraged because of their complications. Nowadays, alpha fetoprotein is the most widely used tumor marker for screening and diagnosis of hepatocellular carcinoma. Nevertheless, this marker has limitations. The diagnostic role of plasma osteopontin as an adjuvant or alternative marker to alpha fetoprotein to detect hepatocellular carcinoma in Egyptian patients with focal hepatic lesions was evaluated in this study. SUBJECT AND METHODS: Eighty participants were recruited from the Egyptian National Liver Institute and were self-assigned to three groups, namely, focal hepatic lesions (n = 40), liver cirrhosis (n = 20), and controls (n = 20). Participants' plasma osteopontin and serum alpha fetoprotein levels were determined and were compared across the three groups. RESULTS: The discriminatory ability of plasma osteopontin for hepatocellular carcinoma was lower than that of alpha fetoprotein. Osteopontin and alpha fetoprotein were not correlated with each other. Neither the gender nor the age of the patients showed a significant association with plasma osteopontin level. CONCLUSION: Measuring plasma osteopontin level alone has no advantage over serum alpha fetoprotein in patients with focal hepatic lesions due to chronic liver disease.