Lipidomics signature in post-COVID patient sera and its influence on the prolonged inflammatory response

Abstract

Background: The ongoing issues with post-COVID conditions (PCC), where symptoms persist long after the initial infection, highlight the need for research into blood lipid changes in these patients. While most studies focus on the acute phase of COVID-19, there's a significant lack of information on the lipidomic changes that occur in the later stages of the disease. Addressing this knowledge gap is critical for understanding the long-term effects of COVID-19 and could be key to developing personalized treatments for those suffering from PCC.

Methods: We employed untargeted lipidomics to analyze plasma samples from 147 PCC patients, assessing nearly 400 polar lipids. Data mining (DM) and machine learning (ML) tools were utilized to decode the results and ascertain significant lipidomic patterns.

Results: The study uncovered substantial changes in various lipid subclasses, presenting a detailed profile of the polar lipid fraction in PCC patients. These alterations correlated with ongoing inflammation and immune response. Notably, there were elevated levels of lysophosphatidylglycerols (LPGs) and phosphatidylethanolamines (PEs), and reduced levels of lysophosphatidylcholines (LPCs), suggesting these as potential lipid biomarkers for PCC. The lipidomic signatures indicated specific anionic lipid changes, implicating antimicrobial peptides (AMPs) in inflammation. Associations between particular medications and symptoms were also suggested. Classification models, such as multinomial regression (MR) and random forest (RF), successfully differentiated between symptomatic and asymptomatic PCC groups using lipidomic profiles.

Conclusions: The study's groundbreaking discovery of specific lipidomic disruptions in PCC patients marks a significant stride in the quest to comprehend and combat this condition. The identified lipid biomarkers not only pave the way for novel diagnostic tools but also hold the promise to tailor individualized therapeutic strategies, potentially revolutionizing the clinical approach to managing PCC and improving patient care.

Keywords: Lipidomic signature; Post-COVID patients; Predictive modeling for inflammatory response; Prolonged inflammatory response.