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. 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. 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. 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.

中文翻译：

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新冠肺炎后患者血清中的脂质组学特征及其对长期炎症反应的影响

新冠肺炎后病症 (PCC) 的持续问题（即初次感染后症状持续很长时间）凸显了对这些患者血脂变化进行研究的必要性。虽然大多数研究都集中在 COVID-19 的急性期，但严重缺乏有关疾病后期发生的脂质组学变化的信息。解决这一知识差距对于了解 COVID-19 的长期影响至关重要，并且可能是为 PCC 患者开发个性化治疗的关键。我们采用非靶向脂质组学分析了 147 名 PCC 患者的血浆样本，评估了近 400 种极性脂质。利用数据挖掘（DM）和机器学习（ML）工具来解码结果并确定重要的脂质组学模式。该研究发现了各种脂质亚类的显着变化，提供了 PCC 患者极性脂质部分的详细概况。这些改变与持续的炎症和免疫反应相关。值得注意的是，溶血磷脂酰甘油 (LPG) 和磷脂酰乙醇胺 (PE) 的水平升高，而溶血磷脂酰胆碱 (LPC) 的水平降低，表明这些是 PCC 的潜在脂质生物标志物。脂质组学特征表明特定的阴离子脂质变化，表明抗菌肽（AMP）与炎症有关。还提出了特定药物和症状之间的关联。多项回归 (MR) 和随机森林 (RF) 等分类模型使用脂质组学特征成功区分有症状和无症状的 PCC 组。该研究在 PCC 患者中发现了特定的脂质组学破坏的突破性发现，标志着在理解和对抗这种疾病方面迈出了重大一步。鉴定出的脂质生物标志物不仅为新型诊断工具铺平了道路，而且有望定制个体化治疗策略，有可能彻底改变治疗 PCC 和改善患者护理的临床方法。