ArticleMetabolism: clinical and experimental2022
Machine learning and semi-targeted lipidomics identify distinct serum lipid signatures in hospitalized COVID-19-positive and COVID-19-negative patients.
Article in Metabolism: clinical and experimental, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers, 1 of them a synthesis that pooled it.
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Who cites it
33 citing papers in PubMed, 1 synthesis or guideline pooled it, 49 citations in OpenAlex.
- Meta-Analysis of COVID-19 Metabolomics Identifies Variations in Robustness of Biomarkers.International journal of molecular sciences · 2023Pooled it
- Beyond Feature Selection: Interpretable Machine Learning for Mechanistic Insights in Metabolomics.Biology · 2026Review
- COVID-19 is Associated with a Lipid Storm that Worsens in Cases of Severe Pneumonia.Microorganisms · 2025Article
- Role of Lipidomics in Respiratory Tract Infections: A Systematic Review of Emerging Evidence.Microorganisms · 2025Review
- Metabolic Reprogramming in Respiratory Viral Infections: A Focus on SARS-CoV-2, Influenza, and Respiratory Syncytial Virus.Biomolecules · 2025Review
- Severe acute respiratory syndrome coronavirus 2 infection unevenly impacts metabolism in the coronal periphery of the lungs.iScience · 2025Article
- Differentiation of volatile organic compounds in chili powders of different spiciness levels via E-nose, HS-GC-IMS, and chemometrics.Frontiers in nutrition · 2025Article
- Advancements in Mass Spectrometry-Based Targeted Metabolomics and Lipidomics: Implications for Clinical Research.Molecules (Basel, Switzerland) · 2024Review
- Metabolomic profiling of dengue infection: unraveling molecular signatures by LC-MS/MS and machine learning models.Metabolomics : Official journal of the Metabolomic Society · 2024Article
- Sulfated Bile Acids in Serum as Potential Biomarkers of Disease Severity and Mortality in COVID-19.Cells · 2024Article
- Setting Ranges in Potential Biomarkers for Type 2 Diabetes Mellitus Patients Early Detection By Sex-An Approach with Machine Learning Algorithms.Diagnostics (Basel, Switzerland) · 2024Article
- Article
- Contributing to the management of viral infections through simple immunosensing of the arachidonic acid serum level.Mikrochimica acta · 2024Article
- LipidSIM: Inferring mechanistic lipid biosynthesis perturbations from lipidomics with a flexible, low-parameter, Markov modeling framework.Metabolic engineering · 2024Article
- Comparing plasma and skin imprint metabolic profiles in COVID-19 diagnosis and severity assessment.Journal of molecular medicine (Berlin, Germany) · 2024Article
- Article
- Plasma taurine level is linked to symptom burden and clinical outcomes in post-COVID condition.PloS one · 2024Article
- Nucleotide, Phospholipid, and Kynurenine Metabolites Are Robustly Associated with COVID-19 Severity and Time of Plasma Sample Collection in a Prospective Cohort Study.International journal of molecular sciences · 2023Article
- HDL-Related Parameters and COVID-19 Mortality: The Importance of HDL Function.Antioxidants (Basel, Switzerland) · 2023Article
- Quantitative LC-MS study of compounds found predictive of COVID-19 severity and outcome.Metabolomics : Official journal of the Metabolomic Society · 2023Article
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Authors and funding
10 authors at 2 institutions in 1 country.
Funding
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Abstract
backgroundLipids are involved in the interaction between viral infection and the host metabolic and immunological responses. Several studies comparing the lipidome of COVID-19-positive hospitalized patients vs. healthy subjects have already been reported. It is largely unknown, however, whether these differences are specific to this disease. The present study compared the lipidomic signature of hospitalized COVID-19-positive patients with that of healthy subjects, as well as with COVID-19-negative patients hospitalized for other infectious/inflammatory diseases.
methodsWe analyzed the lipidomic signature of 126 COVID-19-positive patients, 45 COVID-19-negative patients hospitalized with other infectious/inflammatory diseases and 50 healthy volunteers. A semi-targeted lipidomics analysis was performed using liquid chromatography coupled to mass spectrometry. Two-hundred and eighty-three lipid species were identified and quantified. Results were interpreted by machine learning tools.
resultsWe identified acylcarnitines, lysophosphatidylethanolamines, arachidonic acid and oxylipins as the most altered species in COVID-19-positive patients compared to healthy volunteers. However, we found similar alterations in COVID-19-negative patients who had other causes of inflammation. Conversely, lysophosphatidylcholine 22:6-sn2, phosphatidylcholine 36:1 and secondary bile acids were the parameters that had the greatest capacity to discriminate between COVID-19-positive and COVID-19-negative patients.
conclusionThis study shows that COVID-19 infection shares many lipid alterations with other infectious/inflammatory diseases, and which differentiate them from the healthy population. The most notable alterations were observed in oxylipins, while alterations in bile acids and glycerophospholipis best distinguished between COVID-19-positive and COVID-19-negative patients. Our results highlight the value of integrating lipidomics with machine learning algorithms to explore the pathophysiology of COVID-19 and, consequently, improve clinical decision making.
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