Metabolomic Biomarkers of Pulmonary Fibrosis in COVID‐19 Patients One Year After Hospital Discharge

Journal of Medical Virology, Journal Year: 2025, Volume and Issue: 97(3)

Published: March 1, 2025

ABSTRACT Coronavirus disease 2019 (COVID‐19) global pandemic has affected more than 600 million people up to date. The symptomatology and severity of COVID‐19 are very broad, there still concerns about the long‐term sequelae that it can have on discharged patients. development pulmonary fibrotic after this infection is especially worrying. Our aim was determine if a metabolomic signature could predict sequelae. It multicenter prospective observation subcohort based COVID‐FIBROTIC study. A analysis performed by nuclear magnetic resonance (NMR) serum samples from patients admitted with bilateral pneumonia collected 2 months hospital discharge. One year admission, clinical, functional radiological data were these same Finally, 109 (mean age 57.68 [DS14.03], 65.13% male) available. Fibrotic 1 found in 33% them. Based NMR samples, possible distinguish 80.82% sensitivity, 72.22% specificity 0.83 an area under curve (AUC) value which would signs pattern sample collection. According metabolites participating discriminative model univariate statistics, glucose, valine, fatty acids (═CH–CH2–CH═) suggested as potential biomarkers COVID‐19. Trial Registration Number clinicaltrials.gov NCT04409275 (June 1, 2020).

Language: Английский

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Enhanced Standard Operating Procedures for ³¹P NMR-Based Metabolomics in Tissue Extracts

JACS Au, Journal Year: 2025, Volume and Issue: unknown

Published: April 13, 2025

Language: Английский

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Convergent Mechanisms in Virus-Induced Cancers: A Perspective on Classical Viruses, SARS-CoV-2, and AI-Driven Solutions

Infectious Disease Reports, Journal Year: 2025, Volume and Issue: 17(2), P. 33 - 33

Published: April 16, 2025

This perspective examines the potential oncogenic mechanisms of SARS-CoV-2 through comparative analysis with established cancer-causing viruses, integrating classical virological approaches artificial intelligence (AI)-driven analysis. The paper explores four key themes: shared between viruses and (including cell cycle dysregulation, inflammatory signaling, immune evasion, metabolic reprogramming); application AI in understanding viral oncogenesis; integration neuroimaging evidence; future research directions. author presents novel hypotheses regarding SARS-CoV-2’s mechanisms, supported by recent PET/FDG imaging studies showing persistent alterations. manuscript emphasizes transformative combining traditional methods advanced technologies for better preventing virus-induced cancers, while highlighting importance long-term monitoring COVID-19 survivors developments.

Language: Английский

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Recommendations for sample selection, collection and preparation for NMR-based metabolomics studies of blood

Metabolomics, Journal Year: 2025, Volume and Issue: 21(3)

Published: May 10, 2025

Metabolic profiling of blood metabolites, particularly in plasma and serum, is vital for studying human diseases, conditions, drug interventions toxicology. The clinical significance arises from its close ties to all cells facile accessibility. However, patient-specific variables such as age, sex, diet, lifestyle health status, along with pre-analytical conditions (sample handling, storage, etc.), can significantly affect metabolomic measurements whole blood, plasma, or serum studies. These factors, referred confounders, must be mitigated reveal genuine metabolic changes due illness intervention onset. This review aims aid metabolomics researchers collecting reliable, standardized datasets NMR-based (whole/serum/plasma) metabolomics. goal reduce the impact confounding factors enhance inter-laboratory comparability, enabling more meaningful outcomes outlines main affecting metabolite levels offers practical suggestions what measure expect, how mitigate properly prepare, handle store biosamples report data targeted studies serum.

Language: Английский

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Early Metabolomic and Immunologic Biomarkers as Prognostic Indicators for COVID-19

Metabolites, Journal Year: 2024, Volume and Issue: 14(7), P. 380 - 380

Published: July 9, 2024

This prospective study in Hong Kong aimed at identifying prognostic metabolomic and immunologic biomarkers for Coronavirus Disease 2019 (COVID-19). We examined 327 patients, mean age 55 (19-89) years, whom 33.6% were infected with Omicron 66.4% earlier variants. The effect size of disease severity on metabolome outweighed others including age, gender, peak C-reactive protein (CRP), vitamin D viral levels. Sixty-five metabolites demonstrated strong associations the majority (54, 83.1%) downregulated severe (z score: -3.30 to -8.61). Ten cytokines/chemokines (

Language: Английский

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Meta-Analysis of COVID-19 Metabolomics Identifies Variations in Robustness of Biomarkers

International Journal of Molecular Sciences, Journal Year: 2023, Volume and Issue: 24(18), P. 14371 - 14371

Published: Sept. 21, 2023

The global COVID-19 pandemic resulted in widespread harms but also rapid advances vaccine development, diagnostic testing, and treatment. As the disease moves to endemic status, need identify characteristic biomarkers of for diagnostics or therapeutics has lessened, lessons can still be learned inform biomarker research dealing with future pathogens. In this work, we test five sets research-derived against an independent targeted quantitative Liquid Chromatography–Mass Spectrometry metabolomics dataset evaluate how robustly these proposed panels would distinguish between COVID-19-positive negative patients a hospital setting. We further crowdsourced panel comprising most commonly mentioned literature 2020 2023. best-performing dataset—measured by F1 score (0.76) AUROC (0.77)—included nine biomarkers: lactic acid, glutamate, aspartate, phenylalanine, β-alanine, ornithine, arachidonic choline, hypoxanthine. Panels fewer metabolites performed less well, showing weaker statistical significance cohort than originally reported their respective discovery studies. Whilst studies reviewed here were small may subject confounders, it is desirable that resilient across cohorts if they are find use clinic, highlighting importance assessing robustness reproducibility analyses populations.

Language: Английский

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Differential abundance of lipids and metabolites related to SARS-CoV-2 infection and susceptibility

Scientific Reports, Journal Year: 2023, Volume and Issue: 13(1)

Published: Sept. 13, 2023

The mechanisms driving SARS-CoV-2 susceptibility remain poorly understood, especially the factors determining why unvaccinated individuals uninfected despite high-risk exposures. To understand lipid and metabolite profiles related with COVID-19 disease progression. We collected samples from an exceptional group of healthcare workers heavily exposed to but not infected ('non-susceptible') subjects who became during follow-up ('susceptible'), including non-hospitalized hospitalized patients different severity providing at early stages. Then, we analyzed their plasma metabolomic using mass spectrometry coupled liquid gas chromatography. show specific lipids metabolites that could explain severity. More importantly, non-susceptible a unique lipidomic pattern characterized by upregulation most lipids, ceramides sphingomyelin, which be interpreted as markers low infection. This study strengthens findings other researchers about importance studying relevant pathogenesis.

Language: Английский

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Urinary phenotyping of SARS-CoV-2 infection connects clinical diagnostics with metabolomics and uncovers impaired NAD⁺ pathway and SIRT1 activation

Clinical Chemistry and Laboratory Medicine (CCLM), Journal Year: 2023, Volume and Issue: 62(4), P. 770 - 788

Published: Nov. 13, 2023

The stratification of individuals suffering from acute and post-acute SARS-CoV-2 infection remains a critical challenge. Notably, biomarkers able to specifically monitor viral progression, providing details about patient clinical status, are still not available. Herein, quantitative metabolomics is progressively recognized as useful tool describe the consequences virus-host interactions considering also metadata.

Language: Английский

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Amino Acid Metabolism in Leukocytes Showing In Vitro IgG Memory from SARS-CoV2-Infected Patients

Diseases, Journal Year: 2024, Volume and Issue: 12(3), P. 43 - 43

Published: Feb. 23, 2024

The immune response to infectious diseases is directly influenced by metabolic activities. COVID-19 a disease that affects the entire body and can significantly impact cellular metabolism. Recent studies have focused their analysis on potential connections between post-infection stages of SARS-CoV2 different pathways. spike S1 antigen was found in vitro IgG antibody memory for PBMCs when obtaining PBMC cultures 60–90 days post infection, significant increase S-adenosyl homocysteine, sarcosine, arginine detected mass spectrometric analysis. involvement these metabolites physiological recovery from viral infections activity well documented, they may provide new simple method better comprehend leukocytes. Moreover, there change metabolism tryptophan urea cycle pathways leukocytes with memory. With data, together results literature, it seems leukocyte reprogrammed after pathogenesis activating certain amino acid pathways, which be related protective immunity against SARS-CoV2.

Language: Английский

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Advantages of Metabolomics-Based Multivariate Machine Learning to Predict Disease Severity: Example of COVID

International Journal of Molecular Sciences, Journal Year: 2024, Volume and Issue: 25(22), P. 12199 - 12199

Published: Nov. 13, 2024

The COVID-19 outbreak caused saturations of hospitals, highlighting the importance early patient triage to optimize resource prioritization. Herein, our objective was test if high definition metabolomics, combined with ML, can improve prognostication and performance over standard clinical parameters using COVID infection as an example. Using resolution mass spectrometry, we obtained metabolomics profiles patients them design machine learning (ML) algorithms predicting severity (herein determined need for mechanical ventilation during care). A total 64 PCR-positive at Poitiers CHU were recruited. Clinical investigations conducted 8 days after onset symptoms. We show that could predict good (AUC ROC curve: 0.85), SpO2, first respiratory rate, Horowitz quotient age most important variables. However, prediction substantially improved by use = 0.92). Our small-scale study demonstrates diagnosis prognosis algorithms, thus be a key player in future discovery new biological signals. This technique is easily deployable clinic, learning, it help mathematical models needed advance towards personalized medicine.

Language: Английский

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