Frontiers in Immunology Publishes New Study Data from IncellDx Supporting a Model for Understanding Long COVID’s Cause

Excerpt from the Press Release:

SAN CARLOS, Calif.–(BUSINESS WIRE)–IncellDx, a precision medicine company, today announced the journal Frontiers in Immunology has published a study detailing a framework for understanding the potential mechanism of, and potential treatment for, Post-Acute Sequelae of COVID-19 (PASC), also known as long COVID or long haul COVID.

“Recent analysis confirms long COVID impacts a significant number of people, and its prevalence may increase due to the emergence of the Omicron variant,” said Bruce Patterson, MD, CEO of IncellDx. “We know it’s a highly prevalent and growing problem. What physicians have been lacking is understanding of the underlying cause of long COVID, and a way to objectively diagnose and treat it. We believe this study provides additional evidence for a meaningful path forward for diagnosis and treatment.”

In the study, Persistence of SARS CoV-2 S1 Protein in CD16+ Monocytes in Post-Acute Sequelae of COVID-19 (PASC) Up to 15 Months Post-Infection, patients with previous COVID infection and lingering symptoms were found to have a distinct immunologic profile characterized by differentiated proportions of monocyte subsets. In the study, the presence of SARS-CoV-2 S1 protein was investigated in 46 people. T-cell, B-cell, and monocytic subsets were analyzed in both severe COVID-19 patients and in patients with post-acute sequelae of COVID-19 (PASC). The levels of both intermediate (CD14+, CD16+) and non-classical monocyte (CD14Lo, CD16+) were significantly increased compared with healthy controls. Neither monocyte subset was elevated in cases of severe COVID-19. Additionally, the SARS-CoV-2 protein subunit S1 was present in non-classical monocytes among patients thought to have PASC for up to 16 months following initial infection. Monocytes, a type of white blood cell, are involved in adaptive immunity and are instrumental in attacking viruses and other pathogens.

A previous paper from IncellDx in Frontiers in Immunology proposed the first model for diagnosing, indexing and monitoring long COVID. In that study, Immune-Based Prediction of COVID-19 Severity and Chronicity Decoded Using Machine Learning, 224 individuals, including healthy controls and patients spanning the COVID-19 disease continuum, were assessed using machine learning for severity and chronic symptoms following initial infection. CCL5/RANTES, IL-2, IL-4, CCL3, IL-6, IL-10, IFN-γ, and VEGF were all significantly elevated in long COVID patients compared to healthy controls (P<0.001), while GM-CSF and CCL4 were in significantly lower levels than healthy controls (P=0.01). Data were analyzed to generate objective disease scores for PASC and severe COVID patients.

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