COVID-19 Diagnostics: Researchers Identify Six Novel Biomarkers To Predict Progression Of Disease Severity In COVID-19 Patients
: Researchers from Lawson Health Research Institute and Western University in a news study have identified six molecules that can be used as biomarkers to predict how severely ill a patient will become.
The research findings were published in the Journal: Critical Care Explorations, the official journal of the Society Of Critical Care Medicine https://journals.lww.com/ccejournal/FullText/2020/09000/Novel_Outcome_Biomarkers_Identified_With_Targeted.2.aspx
The research conducted by analyzing blood samples from critically ill patients at London Health Sciences Centre (LHSC). They build on a growing body of work from the team who were first in the world to profile the body's immune response to the virus by revealing a separate six molecules that could act as potential targets to treat hyperinflammation in critically ill patients.
Dr Douglas Fraser, lead researcher from Lawson and Western's Schulich School of Medicine & Dentistry, and Critical Care Physician at LHSC said, "We have begun answering some of the biggest COVID-19 questions asked by clinicians and health researchers. While the findings need to be validated with larger groups of patients, they could have important implications for treating and studying this disease."
To date with no proven therapies, many COVID-19 patients admitted to intensive care units (ICUs) do not survive.
Dr Fraser explained, "When a patient is admitted to ICU, we normally wait to see if they are going to get worse before we consider any risky interventions. To improve outcomes, we not only need new therapies but also a way to predict prognosis or which patients are going to get worse,"
The study team identified six molecules of importance (CLM-1, IL12RB1, CD83, FAM3B, IGFR1R and OPTC
). They found that these molecules were elevated in COVID-19 patients who would become even more severely ill. They found that when measured on a COVID-19 patient's first day of ICU admission, the molecules could be used to predict which patients will survive following standard ICU treatment.
The details of the six identified protein biomarkers are as follows:
, a type-1 transmembrane glycoprotein with an extracellular immunoglobulin G domain, accurately predicted COVID19 ICU outcome. CLM-1 is expressed predominantly in myeloid cells where it can impair IL-6 production in bone marrow derived mast cells and promotes phagocytosis of dead cells by binding phosphatidylserine, which serves as a common apoptotic cell surface recognition cue. The removal of apoptotic cells by CLM-1 expressing macrophages may prevent the generation of secondary necrosis and the release of potentially toxic or immunogenic components from necrotic cells, reducing the likelihood of an inflammatory reaction.
, one of two subunits within the IL-12 receptor, is expressed on natural killer cells and activated T-cells. Essential for resistance to intracellular pathogens, IL12RB mediates the proinflammatory response to IL-12 that
is released by antigen presenting cells. Individual variability in IL12RB1 function is introduced at the epigenetic, genomic polymorphism, and messenger RNA splicing levels, thereby conferring disease susceptibility and variable outcomes.
, a member of the immunoglobulin superfamily, is expressed on a variety of activated immune cells. Providing selective immunosuppression when membrane bound on antigen presenting cells, soluble CD83 inhibits proliferation and function of T-cells. Viral infection leads to the degradation of dendritic cell CD83, a mechanism described as a viral immune escape mechanism.
expression is induced by glucose and proinflammatory cytokines in the islets of Langerhans of the endocrine pancreas. Under physiologic conditions, FAM3B facilitates insulin secretion; however, it is also a secreted cytokine-like protein that can induce cellular apoptosis. Increased FAM3B secretion is associated with pancreatic β cell dysfunction, hyperglycemia, and insulin resistance, suggesting its role in the regulation of glucose and lipid metabolism.
, a transmembrane tyrosine kinase receptor that is activated by insulin-like growth factor 1 and 2, is expressed on lymphocytes and macrophages where it can cause proliferation, cytokine production, and priming/activation of target cells. In particular, phosphorylation of the IGF1R exaggerates inflammation, and its overexpression increases cytokine levels during influenza infection. Conversely, IGF1R deficiency attenuates the acute inflammatory response in a mouse model of acute lung injury.
is highly expressed in the eye nonpigmented ciliary epithelium that secretes it into the vitreous cavity where it associates with vitreous collagen and adjacent basement membranes. As a small leucine-rich protein, opticin binds collagen fibrils and regulates extracellular matrix adhesiveness to suppress capillary morphogenesis and inhibit endothelial invasion. OPTC is also expressed in lymphocytes and articular joints where it may be degraded by matrix metalloproteinases 1, 2, 3, 7, 8, and 9, and a disintegrin and metalloproteinase with thrombospondin motifs-4 and -5, but its role in infection and inflammation is unknown.
Dr Fraser added, "While further research is needed, we are confident in these biomarkers and suspect these patterns may be present even before ICU admission, such as when a patient first presents to the emergency department. These findings could be incredibly important in determining how severely ill a patient will become."
The study team measured 1,161 plasma proteins from the blood of 30 participants: 10 COVID-19 patients and 10 patients with other infections admitted to LHSC's ICU, as well as 10 healthy control participants. Blood was drawn on set days of ICU admission, processed in a lab and then analyzed using statistical methods and artificial intelligence.
The researchers say that predicting a patient's disease severity can help in a number of ways. It could allow for medical teams to have important conversations with family members, setting goals of care based on the patient's health and personal wishes.
Furthermore medical teams could use the knowledge to mobilize resources more quickly. If they know a patient is at higher risk of death, they may consider intervening sooner despite associated risks. The team also hopes the findings can be used to better design COVID-19 clinical trials by grouping patients based on their risk. This could allow for stronger results when examining potential treatments for the disease.
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