The Impact of Age and BMI on the VWF/ADAMTS13 Axis and Simultaneous Thrombin and Plasmin Generation in Hospitalized COVID-19 Patients

Aging and obesity independently contribute toward an endothelial dysfunction that results in an imbalanced VWF to ADAMTS13 ratio. In addition, plasma thrombin and plasmin generation are elevated and reduced, respectively, with increasing age and also with increasing body mass index (BMI). The severity risk of Corona Virus Disease 2019 (COVID-19) increases in adults older than 65 and in individuals with certain pre-existing health conditions, including obesity (>30 kg/m2). The present cross-sectional study focused on an analysis of the VWF/ADAMTS13 axis, including measurements of von Willebrand factor (VWF) antigen (VWF:AG), VWF collagen binding activity (VWF:CBA), Factor VIII antigen, ADAMTS13 antigen, and ADAMTS13 activity, in addition to thrombin and plasmin generation potential, in a demographically diverse population of COVID-19 negative (−) (n = 288) and COVID-19 positive (+) (n = 543) patient plasmas collected at the time of hospital presentation. Data were analyzed as a whole, and then after dividing patients by age (<65 and ≥65) and independently by BMI [<18.5, 18.5–24.9, 25–29.9, >30 (kg/m2)]. These analyses suggest that VWF parameters (i.e., the VWF/ADAMTS13 activity ratio) and thrombin and plasmin generation differed in COVID-19 (+), as compared to COVID-19 (−) patient plasma. Further, age (≥65) more than BMI contributed to aberrant plasma indicators of endothelial coagulopathy. Based on these findings, evaluating both the VWF/ADAMTS13 axis, along with thrombin and plasmin generation, could provide insight into the extent of endothelial dysfunction as well as the plasmatic imbalance in coagulation and fibrinolysis potential, particularly for at-risk patient populations.

Biological and Clinical Factors Contributing to the Metabolic Heterogeneity of Hospitalized Patients with and without COVID-19.

The Corona Virus Disease 2019 (COVID-19) pandemic represents an ongoing worldwide challenge. The present large study sought to understand independent and overlapping metabolic features of samples from acutely ill patients (n = 831) that tested positive (n = 543) or negative (n = 288) for COVID-19. High-throughput metabolomics analyses were complemented with antigen and enzymatic activity assays on plasma from acutely ill patients collected while in the emergency department, at admission, or during hospitalization. Lipidomics analyses were also performed on COVID-19-positive or -negative subjects with the lowest and highest body mass index (n = 60/group). Significant changes in amino acid and fatty acid/acylcarnitine metabolism emerged as highly relevant markers of disease severity, progression, and prognosis as a function of biological and clinical variables in these patients. Further, machine learning models were trained by entering all metabolomics and clinical data from half of the COVID-19 patient cohort and then tested on the other half, yielding ~78% prediction accuracy. Finally, the extensive amount of information accumulated in this large, prospective, observational study provides a foundation for mechanistic follow-up studies and data sharing opportunities, which will advance our understanding of the characteristics of the plasma metabolism in COVID-19 and other acute critical illnesses.

Biological and Clinical Factors contributing to the Metabolic Heterogeneity of Hospitalized Patients with and without COVID-19.

The Corona Virus Disease 2019 (COVID-19) pandemic represents an ongoing worldwide challenge. Exploratory studies evaluating the impact of COVID-19 infection on the plasma metabolome have been performed, often with small numbers of patients, and with or without relevant control data; however, determining the impact of biological and clinical variables remains critical to understanding potential markers of disease severity and progression. The present large study, including relevant controls, sought to understand independent and overlapping metabolic features of samples from acutely ill patients (n = 831), testing positive (n = 543) or negative (n = 288) for COVID-19. High-throughput metabolomics analyses were complemented with antigen and enzymatic activity assays on 831 plasma samples from acutely ill patients while in the emergency department, at admission, and during hospitalization. We then performed additional lipidomics analyses of the 60 subjects with the lowest and highest body mass index, either COVID-19 positive or negative. Omics data were correlated to detailed data on patient characteristics and clinical laboratory assays measuring coagulation, hematology and chemistry analytes. Significant changes in arginine/proline/citrulline, tryptophan/indole/kynurenine, fatty acid and acyl-carnitine metabolism emerged as highly relevant markers of disease severity, progression and prognosis as a function of biological and clinical variables in these patients. Further, machine learning models were trained by entering all metabolomics and clinical data from half of the COVID-19 patient cohort and then tested on the other half yielding ~ 78% prediction accuracy. Finally, the extensive amount of information accumulated in this large, prospective, observational study provides a foundation for follow-up mechanistic studies and data sharing opportunities, which will advance our understanding of the characteristics of the plasma metabolism in COVID-19 and other acute critical illnesses.