Serum metabolomic abnormalities in survivors of non-severe COVID-19

Integration of metabolomics methodologies for the development of predictive models for mortality risk in elderly patients with severe COVID-19

BACKGROUND

The rapid evolution of the COVID-19 pandemic and subsequent global immunization efforts have rendered early metabolomics studies potentially outdated, as they primarily involved non-exposed, non-vaccinated populations. This paper presents a predictive model developed from up-to-date metabolomics data integrated with clinical data to estimate early mortality risk in critically ill COVID-19 patients. Our study addresses the critical gap in current research by utilizing current patient samples, providing fresh insights into the pathophysiology of the disease in a partially immunized global population.

METHODS

One hundred elderly patients with severe COVID-19 infection, including 46 survivors and 54 non-survivors, were recruited in January-February 2023 at the Second Hospital affiliated with Harbin Medical University. A predictive model within 24 h of admission was developed using blood metabolomics and clinical data. Differential metabolite analysis and other techniques were used to identify relevant characteristics. Model performance was assessed by comparing the area under the receiver operating characteristic curve (AUROC). The final prediction model was externally validated in a cohort of 50 COVID-19 elderly critically ill patients at the First Hospital affiliated with Harbin Medical University during the same period.

RESULTS

Significant disparities in blood metabolomics and laboratory parameters were noted between individuals who survived and those who did not. One metabolite indicator, Itaconic acid, and four laboratory tests (LYM, IL-6, PCT, and CRP), were identified as the five variables in all four models. The external validation set demonstrated that the KNN model exhibited the highest AUC of 0.952 among the four models. When considering a 50% risk of mortality threshold, the validation set displayed a sensitivity of 0.963 and a specificity of 0.957.

CONCLUSIONS

The prognostic outcome of COVID-19 elderly patients is significantly influenced by the levels of Itaconic acid, LYM, IL-6, PCT, and CRP upon admission. These five indicators can be utilized to assess the mortality risk in affected individuals.

Sociodemographic factors, clinical characteristics, outcomes and short-term follow-up in COVID-19 patients with new onset hyperglycemia and pre-existing diabetes on admission in a tertiary-care hospital in Bangladesh

BACKGROUND

COVID-19 has been linked to hyperglycemia and diabetes, with noteworthy variation in outcomes. This study aimed to compare the sociodemographic factors, clinical characteristics, and in-hospital and short-term post-discharge outcomes between COVID-19 patients with new onset hyperglycemia and pre-existing diabetes patients in tertiary care hospitals in Bangladesh.

METHODS

A prospective observational study was conducted among adult COVID-19 patients with new onset hyperglycemia or pre-existing diabetes admitted to the COVID-19 unit of Dhaka Medical College Hospital between April 2021 and October 2021. Patients were conveniently selected from indoors. Bivariate analysis was used to compare sociodemographic and clinical characteristics at admission and short-term outcomes. The Cox proportional hazard model was used to examine factors associated with time to death in the hospital. All statistical analyses were performed using Stata Version 17.

RESULTS

A total of 169 patients were included. Of these, 29 died in the hospital, and four left against medical advice. Out of the 136 survivors, 135 came for follow-up two weeks after discharge. At baseline, 30.18% of patients had new onset hyperglycemia, and 69.8% had pre-existing diabetes. The average age of patients was 56.38 ± 14.21 years, and 60.36% were male. A significantly higher proportion of COVID-19 patients with new onset hyperglycemia were smokers than those with pre-existing diabetes (p = 0.003). However, pre-existing diabetes was associated with higher lung involvement (p = 0.047) and comorbidities (p = 0.002). Age, income over 35,000 BDT (USD 335.5$), and a BMI over 25 kg/m2 emerged as significant predictors of prolonged hospital stay and mortality. Post-discharge follow-up indicated that new-onset hyperglycemia resolved in 8.89% of patients, whereas 19.26% continued to exhibit hyperglycemia, with smoking being a significant determinant of its persistence (p = 0.001).

CONCLUSION

In conclusion, our investigation illuminates the clinical trajectory of new-onset hyperglycemia in the context of COVID-19 and reinforces the necessity for diligent monitoring and management post-discharge. Therefore, close monitoring and follow-up of COVID-19 patients is recommended for the early detection and management of hyperglycemia and the prevention of diabetes development in the long run.

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

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.

Unraveling lipid and inflammation interplay in cancer, aging and infection for novel theranostic approaches

The synergistic relationships between Cancer, Aging, and Infection, here referred to as the CAIn Triangle, are significant determinants in numerous health maladies and mortality rates. The CAIn-related pathologies exhibit close correlations with each other and share two common underlying factors: persistent inflammation and anomalous lipid concentration profiles in the membranes of affected cells. This study provides a comprehensive evaluation of the most pertinent interconnections within the CAIn Triangle, in addition to examining the relationship between chronic inflammation and specific lipidic compositions in cellular membranes. To tackle the CAIn-associated diseases, a suite of complementary strategies aimed at diagnosis, prevention, and treatment is proffered. Our holistic approach is expected to augment the understanding of the fundamental mechanisms underlying these diseases and highlight the potential of shared features to facilitate the development of novel theranostic strategies.

High Risk of Heart Tumors after COVID-19

An emergence of evidence suggests that severe COVID-19 is associated with an increased risk of developing breast and gastrointestinal cancers. The aim of this research was to assess the risk of heart tumors development in patients who have had COVID-19.

METHODS

A comparative analysis of 173 heart tumors was conducted between 2016 and 2023. Immunohistochemical examination with antibodies against spike SARS-CoV-2 was performed on 21 heart tumors: 10 myxomas operated before 2020 (the control group), four cardiac myxomas, one proliferating myxoma, three papillary fibroelastomas, two myxofibrosarcomas, one chondrosarcoma resected in 2022-2023. Immunohistochemical analysis with antibodies against CD34 and CD68 was also conducted on the same 11 Post-COVID period heart tumors. Immunofluorescent examination with a cocktail of antibodies against spike SARS-CoV-2/CD34 and spike SARS-CoV-2/CD68 was performed in 2 cases out of 11 (proliferating myxoma and classic myxoma).

RESULTS

A 1.5-fold increase in the number of heart tumors by 2023 was observed, with a statistically significant increase in the number of myxomas. There was no correlation with vaccination, and no significant differences were found between patients from 2016-2019 and 2021-2023 in terms of gender, age, and cardiac rhythm dis-orders. Morphological examination revealed the expression of spike SARS-CoV-2 in tumor cells, endothelial cells, and macrophages in 10 out of 11 heart tumors.

CONCLUSION

The detection of SARS-CoV-2 persistence in endothelium and macrophages as well as in tumor cells of benign and malignant cardiac neoplasms, the increase in the number of these tumors, especially cardiac myxomas, after the pandemic by 2023 may indicate a trend toward an increased risk of cardiac neoplasms in COVID-19 patients, which re-quires further research on this issue and a search for new evidence.

Metabolomics-directed nanotechnology in viral diseases management: COVID-19 a case study

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is currently regarded as the twenty-first century's plague accounting for coronavirus disease 2019 (COVID-19). Besides its reported symptoms affecting the respiratory tract, it was found to alter several metabolic pathways inside the body. Nanoparticles proved to combat viral infections including COVID-19 to demonstrate great success in developing vaccines based on mRNA technology. However, various types of nanoparticles can affect the host metabolome. Considering the increasing proportion of nano-based vaccines, this review compiles and analyses how COVID-19 and nanoparticles affect lipids, amino acids, and carbohydrates metabolism. A search was conducted on PubMed, ScienceDirect, Web of Science for available information on the interrelationship between metabolomics and immunity in the context of SARS-CoV-2 infection and the effect of nanoparticles on metabolite levels. It was clear that SARS-CoV-2 disrupted several pathways to ensure a sufficient supply of its building blocks to facilitate its replication. Such information can help in developing treatment strategies against viral infections and COVID-19 based on interventions that overcome these metabolic changes. Furthermore, it showed that even drug-free nanoparticles can exert an influence on biological systems as evidenced by metabolomics.

Deciphering the Relationship between SARS-CoV-2 and Cancer

Some viruses are known to be associated with the onset of specific cancers. These microorganisms, oncogenic viruses or oncoviruses, can convert normal cells into cancer cells by modulating the central metabolic pathways or hampering genomic integrity mechanisms, consequently inhibiting the apoptotic machinery and/or enhancing cell proliferation. Seven oncogenic viruses are known to promote tumorigenesis in humans: human papillomavirus (HPV), hepatitis B and C viruses (HBV, HCV), Epstein-Barr virus (EBV), human T-cell leukemia virus 1 (HTLV-1), Kaposi sarcoma-associated herpesvirus (KSHV), and Merkel cell polyomavirus (MCPyV). Recent research indicates that SARS-CoV-2 infection and COVID-19 progression may predispose recovered patients to cancer onset and accelerate cancer development. This hypothesis is based on the growing evidence regarding the ability of SARS-CoV-2 to modulate oncogenic pathways, promoting chronic low-grade inflammation and causing tissue damage. Herein, we summarize the main relationships known to date between virus infection and cancer, providing a summary of the proposed biochemical mechanisms behind the cellular transformation. Mechanistically, DNA viruses (such as HPV, HBV, EBV, and MCPyV) encode their virus oncogenes. In contrast, RNA viruses (like HCV, HTLV-1) may encode oncogenes or trigger host oncogenes through cis-/-trans activation leading to different types of cancer. As for SARS-CoV-2, its role as an oncogenic virus seems to occur through the inhibition of oncosuppressors or controlling the metabolic and autophagy pathways in the infected cells. However, these effects could be significant in particular scenarios like those linked to severe COVID-19 or long COVID. On the other hand, looking at the SARS-CoV-2─cancer relationship from an opposite perspective, oncolytic effects and anti-tumor immune response were triggered by SARS-CoV-2 infection in some cases. In summary, our work aims to recall comprehensive attention from the scientific community to elucidate the effects of SARS-CoV-2 and, more in general, β-coronavirus infection on cancer susceptibility for cancer prevention or supporting therapeutic approaches.

Metabolomics as a powerful tool for diagnostic, pronostic and drug intervention analysis in COVID-19

COVID-19 currently represents one of the major health challenges worldwide. Albeit its infectious character, with onset affectation mainly at the respiratory track, it is clear that the pathophysiology of COVID-19 has a systemic character, ultimately affecting many organs. This feature enables the possibility of investigating SARS-CoV-2 infection using multi-omic techniques, including metabolomic studies by chromatography coupled to mass spectrometry or by nuclear magnetic resonance (NMR) spectroscopy. Here we review the extensive literature on metabolomics in COVID-19, that unraveled many aspects of the disease including: a characteristic metabotipic signature associated to COVID-19, discrimination of patients according to severity, effect of drugs and vaccination treatments and the characterization of the natural history of the metabolic evolution associated to the disease, from the infection onset to full recovery or long-term and long sequelae of COVID.

Untargeted analysis in post-COVID-19 patients reveals dysregulated lipid pathways two years after recovery

Introduction: Similar to what it has been reported with preceding viral epidemics (such as MERS, SARS, or influenza), SARS-CoV-2 infection is also affecting the human immunometabolism with long-term consequences. Even with underreporting, an accumulated of almost 650 million people have been infected and 620 million recovered since the start of the pandemic; therefore, the impact of these long-term consequences in the world population could be significant. Recently, the World Health Organization recognized the post-COVID syndrome as a new entity, and guidelines are being established to manage and treat this new condition. However, there is still uncertainty about the molecular mechanisms behind the large number of symptoms reported worldwide. Aims and Methods: In this study we aimed to evaluate the clinical and lipidomic profiles (using non-targeted lipidomics) of recovered patients who had a mild and severe COVID-19 infection (acute phase, first epidemic wave); the assessment was made two years after the initial infection. Results: Fatigue (59%) and musculoskeletal (50%) symptoms as the most relevant and persistent. Functional analyses revealed that sterols, bile acids, isoprenoids, and fatty esters were the predicted metabolic pathways affected in both COVID-19 and post-COVID-19 patients. Principal Component Analysis showed differences between study groups. Several species of phosphatidylcholines and sphingomyelins were identified and expressed in higher levels in post-COVID-19 patients compared to controls. The paired analysis (comparing patients with an active infection and 2 years after recovery) show 170 dysregulated features. The relationship of such metabolic dysregulations with the clinical symptoms, point to the importance of developing diagnostic and therapeuthic markers based on cell signaling pathways.