Cross-Validation of Metabolic Phenotypes in SARS-CoV-2 Infected Subpopulations Using Targeted Liquid Chromatography-Mass Spectrometry (LC-MS).

To ensure biological validity in metabolic phenotyping, findings must be replicated in independent sample sets. Targeted workflows have long been heralded as ideal platforms for such validation due to their robust quantitative capability. We evaluated the capability of liquid chromatography-mass spectrometry (LC-MS) assays targeting organic acids and bile acids to validate metabolic phenotypes of SARS-CoV-2 infection. Two independent sample sets were collected: (1) Australia: plasma, SARS-CoV-2 positive (n = 20), noninfected healthy controls (n = 22) and COVID-19 disease-like symptoms but negative for SARS-CoV-2 infection (n = 22). (2) Spain: serum, SARS-CoV-2 positive (n = 33) and noninfected healthy controls (n = 39). Multivariate modeling using orthogonal projections to latent structures discriminant analyses (OPLS-DA) classified healthy controls from SARS-CoV-2 positive (Australia; R2 = 0.17, ROC-AUC = 1; Spain R2 = 0.20, ROC-AUC = 1). Univariate analyses revealed 23 significantly different (p < 0.05) metabolites between healthy controls and SARS-CoV-2 positive individuals across both cohorts. Significant metabolites revealed consistent perturbations in cellular energy metabolism (pyruvic acid, and 2-oxoglutaric acid), oxidative stress (lactic acid, 2-hydroxybutyric acid), hypoxia (2-hydroxyglutaric acid, 5-aminolevulinic acid), liver activity (primary bile acids), and host-gut microbial cometabolism (hippuric acid, phenylpropionic acid, indole-3-propionic acid). These data support targeted LC-MS metabolic phenotyping workflows for biological validation in independent sample sets.

Characterization of preovulatory follicular fluid secretome and its effects on equine oocytes during in vitro maturation.

In vitro maturation (IVM) of oocytes is clinically used in horses to produce blastocysts but current conditions used for horses are suboptimal. We analyzed the composition of equine preovulatory follicular fluid (FF) secretome and tested its effects on meiotic competence and gene expression in oocytes subjected to IVM. Preovulatory FF was obtained, concentrated using ultrafiltration with cut-off of 10 kDa, and stored at -80 °C. The metabolic and proteomic composition was analyzed, and its ultrastructural composition was assessed by cryo-transmission microscopy. Oocytes obtained post-mortem or by ovum pick up (OPU) were subjected to IVM in the absence (control) or presence of 20 or 40 µg/ml (S20 or S40) of secretome. Oocytes were then analyzed for chromatin configuration or snap frozen for gene expression analysis. Proteomic analysis detected 255 proteins in the Equus caballus database, mostly related to the complement cascade and cholesterol metabolism. Metabolomic analysis yielded 14 metabolites and cryo-transmission electron microscopy analysis revealed the presence of extracellular vesicles (EVs). No significant differences were detected in maturation rates among treatments. However, the expression of GDF9 and BMP15 significantly increased in OPU-derived oocytes compared to post-mortem oocytes (fold increase ± SEM: 9.4 ± 0.1 vs. 1 ± 0.5 for BMP15 and 9.9 ± 0.3 vs. 1 ± 0.5 for GDF9, respectively; p < 0.05). Secretome addition increased the expression of TNFAIP6 in S40 regardless of the oocyte source. Further research is necessary to fully understand whether secretome addition influences the developmental competence of equine oocytes.

Serum and Urine Metabolomic Profiling of Newly Diagnosed Treatment-Naïve Inflammatory Bowel Disease Patients.

BACKGROUND AND AIMS: Inflammatory bowel disease (IBD) is a prevalent chronic noncurable disease associated with profound metabolic changes. The discovery of novel molecular indicators for unraveling IBD etiopathogenesis and the diagnosis and prognosis of IBD is therefore pivotal. We sought to determine the distinctive metabolic signatures from the different IBD subgroups before treatment initiation. METHODS: Serum and urine samples from newly diagnosed treatment-naïve IBD patients and age and sex-matched healthy control (HC) individuals were investigated using proton nuclear magnetic resonance spectroscopy. Metabolic differences were identified based on univariate and multivariate statistical analyses. RESULTS: A total of 137 Crohn's disease patients, 202 ulcerative colitis patients, and 338 HC individuals were included. In the IBD cohort, several distinguishable metabolites were detected within each subgroup comparison. Most of the differences revealed alterations in energy and amino acid metabolism in IBD patients, with an increased demand of the body for energy mainly through the ketone bodies. As compared with HC individuals, differences in metabolites were more marked and numerous in Crohn's disease than in ulcerative colitis patients, and in serum than in urine. In addition, clustering analysis revealed 3 distinct patient profiles with notable differences among them based on the analysis of their clinical, anthropometric, and metabolomic variables. However, relevant phenotypical differences were not found among these 3 clusters. CONCLUSIONS: This study highlights the molecular alterations present within the different subgroups of newly diagnosed treatment-naïve IBD patients. The metabolomic profile of these patients may provide further understanding of pathogenic mechanisms of IBD subgroups. Serum metabotype seemed to be especially sensitive to the onset of IBD.

Effect of age and dietary habits on Red Blood Cell membrane fatty acids in a Southern Europe population (Basque Country).

The levels of blood eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) are very variable and, in general, low in most of the world population. In this study, the effects of age, sex, COVID-19, and dietary habits on the lipid profile of the erythrocyte membranes were assessed in a sub-cohort of healthy population (N = 203) from a large cohort of individuals from the Basque Country, Spain, (AKRIBEA). Sex did not have an effect on RBC lipid profile. COVID-19 infected participants showed higher levels of DGLA. Oldest participants showed higher oleic acid, EPA and DHA levels. Arachidonic acid in RBC correlated positively with the intake of sunflower oil, butter, eggs, processed and red meat, whereas DHA and EPA correlated positively with oily and lean fish. Basque Country population showed lipid profiles similar to other high fish consuming countries, such as Italy and Japan. Baseline levels of the whole lipidomic profile of the RBC including SFA, MUFA and PUFA should be examined to obtain a better description of the health and nutritional status.

Integrative Plasma Metabolic and Lipidomic Modelling of SARS-CoV-2 Infection in Relation to Clinical Severity and Early Mortality Prediction.

An integrative multi-modal metabolic phenotyping model was developed to assess the systemic plasma sequelae of SARS-CoV-2 (rRT-PCR positive) induced COVID-19 disease in patients with different respiratory severity levels. Plasma samples from 306 unvaccinated COVID-19 patients were collected in 2020 and classified into four levels of severity ranging from mild symptoms to severe ventilated cases. These samples were investigated using a combination of quantitative Nuclear Magnetic Resonance (NMR) spectroscopy and Mass Spectrometry (MS) platforms to give broad lipoprotein, lipidomic and amino acid, tryptophan-kynurenine pathway, and biogenic amine pathway coverage. All platforms revealed highly significant differences in metabolite patterns between patients and controls (n = 89) that had been collected prior to the COVID-19 pandemic. The total number of significant metabolites increased with severity with 344 out of the 1034 quantitative variables being common to all severity classes. Metabolic signatures showed a continuum of changes across the respiratory severity levels with the most significant and extensive changes being in the most severely affected patients. Even mildly affected respiratory patients showed multiple highly significant abnormal biochemical signatures reflecting serious metabolic deficiencies of the type observed in Post-acute COVID-19 syndrome patients. The most severe respiratory patients had a high mortality (56.1%) and we found that we could predict mortality in this patient sub-group with high accuracy in some cases up to 61 days prior to death, based on a separate metabolic model, which highlighted a different set of metabolites to those defining the basic disease. Specifically, hexosylceramides (HCER 16:0, HCER 20:0, HCER 24:1, HCER 26:0, HCER 26:1) were markedly elevated in the non-surviving patient group (Cliff's delta 0.91-0.95) and two phosphoethanolamines (PE.O 18:0/18:1, Cliff's delta = -0.98 and PE.P 16:0/18:1, Cliff's delta = -0.93) were markedly lower in the non-survivors. These results indicate that patient morbidity to mortality trajectories is determined relatively soon after infection, opening the opportunity to select more intensive therapeutic interventions to these "high risk" patients in the early disease stages.

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.

Metabolic Features of Increased Gut Permeability, Inflammation, and Altered Energy Metabolism Distinguish Agricultural Workers at Risk for Mesoamerican Nephropathy.

Mesoamerican nephropathy (MeN) is a form of chronic kidney disease found predominantly in young men in Mesoamerica. Strenuous agricultural labor is a consistent risk factor for MeN, but the pathophysiologic mechanism leading to disease is poorly understood. We compared the urine metabolome among men in Nicaragua engaged in sugarcane harvest and seed cutting (n = 117), a group at high risk for MeN, against three referents: Nicaraguans working less strenuous jobs at the same sugarcane plantations (n = 78); Nicaraguans performing non-agricultural work (n = 102); and agricultural workers in Spain (n = 78). Using proton nuclear magnetic resonance, we identified 136 metabolites among participants. Our non-hypothesis-based approach identified distinguishing urine metabolic features in the high-risk group, revealing increased levels of hippurate and other gut-derived metabolites and decreased metabolites related to central energy metabolism when compared to referent groups. Our complementary hypothesis-based approach, focused on nicotinamide adenine dinucleotide (NAD+) related metabolites, and revealed a higher kynurenate/tryptophan ratio in the high-risk group (p = 0.001), consistent with a heightened inflammatory state. Workers in high-risk occupations are distinguishable by urinary metabolic features that suggest increased gut permeability, inflammation, and altered energy metabolism. Further study is needed to explore the pathophysiologic implications of these findings.

Prospective Metabolomic Studies in Precision Medicine: The AKRIBEA Project.

For a long time, conventional medicine has analysed biomolecules to diagnose diseases. Yet, this approach has proven valid only for a limited number of metabolites and often through a bijective relationship with the disease (i.e. glucose relationship with diabetes), ultimately offering incomplete diagnostic value. Nowadays, precision medicine emerges as an option to improve the prevention and/or treatment of numerous pathologies, focusing on the molecular mechanisms, acting in a patient-specific dimension, and leveraging multiple contributing factors such as genetic, environmental, or lifestyle. Metabolomics grasps the required subcellular complexity while being sensitive to all these factors, which results in a most suitable technique for precision medicine. The aim of this chapter is to describe how NMR-based metabolomics can be integrated in the design of a precision medicine strategy, using the Precision Medicine Initiative of the Basque Country (the AKRIBEA project) as a case study. To that end, we will illustrate the procedures to be followed when conducting an NMR-based metabolomics study with a large cohort of individuals, emphasizing the critical points. The chapter will conclude with the discussion of some relevant biomedical applications.

An NMR-Based Model to Investigate the Metabolic Phenoreversion of COVID-19 Patients throughout a Longitudinal Study.

After SARS-CoV-2 infection, the molecular phenoreversion of the immunological response and its associated metabolic dysregulation are required for a full recovery of the patient. This process is patient-dependent due to the manifold possibilities induced by virus severity, its phylogenic evolution and the vaccination status of the population. We have here investigated the natural history of COVID-19 disease at the molecular level, characterizing the metabolic and immunological phenoreversion over time in large cohorts of hospitalized severe patients (n = 886) and non-hospitalized recovered patients that self-reported having passed the disease (n = 513). Non-hospitalized recovered patients do not show any metabolic fingerprint associated with the disease or immune alterations. Acute patients are characterized by the metabolic and lipidomic dysregulation that accompanies the exacerbated immunological response, resulting in a slow recovery time with a maximum probability of around 62 days. As a manifestation of the heterogeneity in the metabolic phenoreversion, age and severity become factors that modulate their normalization time which, in turn, correlates with changes in the atherogenesis-associated chemokine MCP-1. Our results are consistent with a model where the slow metabolic normalization in acute patients results in enhanced atherosclerotic risk, in line with the recent observation of an elevated number of cardiovascular episodes found in post-COVID-19 cohorts.

A flow cytometry-based neutralization assay for simultaneous evaluation of blocking antibodies against SARS-CoV-2 variants.

Vaccines against SARS-CoV-2 have alleviated infection rates, hospitalization and deaths associated with COVID-19. In order to monitor humoral immunity, several serology tests have been developed, but the recent emergence of variants of concern has revealed the need for assays that predict the neutralizing capacity of antibodies in a fast and adaptable manner. Sensitive and fast neutralization assays would allow a timely evaluation of immunity against emerging variants and support drug and vaccine discovery efforts. Here we describe a simple, fast, and cell-free multiplexed flow cytometry assay to interrogate the ability of antibodies to prevent the interaction of Angiotensin-converting enzyme 2 (ACE2) and the receptor binding domain (RBD) of the original Wuhan-1 SARS-CoV-2 strain and emerging variants simultaneously, as a surrogate neutralization assay. Using this method, we demonstrate that serum antibodies collected from representative individuals at different time-points during the pandemic present variable neutralizing activity against emerging variants, such as Omicron BA.1 and South African B.1.351. Importantly, antibodies present in samples collected during 2021, before the third dose of the vaccine was administered, do not confer complete neutralization against Omicron BA.1, as opposed to samples collected in 2022 which show significant neutralizing activity. The proposed approach has a comparable performance to other established surrogate methods such as cell-based assays using pseudotyped lentiviral particles expressing the spike of SARS-CoV-2, as demonstrated by the assessment of the blocking activity of therapeutic antibodies (i.e. Imdevimab) and serum samples. This method offers a scalable, cost effective and adaptable platform for the dynamic evaluation of antibody protection in affected populations against variants of SARS-CoV-2.

Direct low field J-edited diffusional proton NMR spectroscopic measurement of COVID-19 inflammatory biomarkers in human serum.

A JEDI NMR pulse experiment incorporating relaxational, diffusional and J-modulation peak editing has been implemented for a low field (80 MHz proton resonance frequency) spectrometer system to measure quantitatively two recently discovered plasma markers of SARS-CoV-2 infection and general inflammation. JEDI spectra capture a unique signature of two biomarker signals from acetylated glycoproteins (Glyc) and the supramolecular phospholipid composite (SPC) signals that are relatively enhanced by the combination of relaxation, diffusion and J-editing properties of the JEDI experiment that strongly attenuate contributions from the other molecular species in plasma. The SPC/Glyc ratio data were essentially identical in the 600 MHz and 80 MHz spectra obtained (R2 = 0.97) and showed significantly different ratios for control (n = 28) versus SARS-CoV-2 positive patients (n = 29) (p = 5.2 × 10-8 and 3.7 × 10-8 respectively). Simplification of the sample preparation allows for data acquisition in a similar time frame to high field machines (∼4 min) and a high-throughput version with 1 min experiment time could be feasible. These data show that these newly discovered inflammatory biomarkers can be measured effectively on low field NMR instruments that do not not require housing in a complex laboratory environment, thus lowering the barrier to clinical translation of this diagnostic technology.

Metabolic subtypes of patients with NAFLD exhibit distinctive cardiovascular risk profiles.

BACKGROUND AND AIMS: We previously identified subsets of patients with NAFLD with different metabolic phenotypes. Here we align metabolomic signatures with cardiovascular disease (CVD) and genetic risk factors. APPROACH AND RESULTS: We analyzed serum metabolome from 1154 individuals with biopsy-proven NAFLD, and from four mouse models of NAFLD with impaired VLDL-triglyceride (TG) secretion, and one with normal VLDL-TG secretion. We identified three metabolic subtypes: A (47%), B (27%), and C (26%). Subtype A phenocopied the metabolome of mice with impaired VLDL-TG secretion; subtype C phenocopied the metabolome of mice with normal VLDL-TG; and subtype B showed an intermediate signature. The percent of patients with NASH and fibrosis was comparable among subtypes, although subtypes B and C exhibited higher liver enzymes. Serum VLDL-TG levels and secretion rate were lower among subtype A compared with subtypes B and C. Subtype A VLDL-TG and VLDL-apolipoprotein B concentrations were independent of steatosis, whereas subtypes B and C showed an association with these parameters. Serum TG, cholesterol, VLDL, small dense LDL5,6 , and remnant lipoprotein cholesterol were lower among subtype A compared with subtypes B and C. The 10-year high risk of CVD, measured with the Framingham risk score, and the frequency of patatin-like phospholipase domain-containing protein 3 NAFLD risk allele were lower in subtype A. CONCLUSIONS: Metabolomic signatures identify three NAFLD subgroups, independent of histological disease severity. These signatures align with known CVD and genetic risk factors, with subtype A exhibiting a lower CVD risk profile. This may account for the variation in hepatic versus cardiovascular outcomes, offering clinically relevant risk stratification.

Uneven metabolic and lipidomic profiles in recovered COVID-19 patients as investigated by plasma NMR metabolomics.

COVID-19 is a systemic infectious disease that may affect many organs, accompanied by a measurable metabolic dysregulation. The disease is also associated with significant mortality, particularly among the elderly, patients with comorbidities, and solid organ transplant recipients. Yet, the largest segment of the patient population is asymptomatic, and most other patients develop mild to moderate symptoms after SARS-CoV-2 infection. Here, we have used NMR metabolomics to characterize plasma samples from a cohort of the abovementioned group of COVID-19 patients (n = 69), between 3 and 10 months after diagnosis, and compared them with a set of reference samples from individuals never infected by the virus (n = 71). Our results indicate that half of the patient population show abnormal metabolism including porphyrin levels and altered lipoprotein profiles six months after the infection, while the other half show little molecular record of the disease. Remarkably, most of these patients are asymptomatic or mild COVID-19 patients, and we hypothesize that this is due to a metabolic reflection of the immune response stress.

Diagnostic Potential of the Plasma Lipidome in Infectious Disease: Application to Acute SARS-CoV-2 Infection.

Improved methods are required for investigating the systemic metabolic effects of SARS-CoV-2 infection and patient stratification for precision treatment. We aimed to develop an effective method using lipid profiles for discriminating between SARS-CoV-2 infection, healthy controls, and non-SARS-CoV-2 respiratory infections. Targeted liquid chromatography-mass spectrometry lipid profiling was performed on discovery (20 SARS-CoV-2-positive; 37 healthy controls; 22 COVID-19 symptoms but SARS-CoV-2negative) and validation (312 SARS-CoV-2-positive; 100 healthy controls) cohorts. Orthogonal projection to latent structure-discriminant analysis (OPLS-DA) and Kruskal-Wallis tests were applied to establish discriminant lipids, significance, and effect size, followed by logistic regression to evaluate classification performance. OPLS-DA reported separation of SARS-CoV-2 infection from healthy controls in the discovery cohort, with an area under the curve (AUC) of 1.000. A refined panel of discriminant features consisted of six lipids from different subclasses (PE, PC, LPC, HCER, CER, and DCER). Logistic regression in the discovery cohort returned a training ROC AUC of 1.000 (sensitivity = 1.000, specificity = 1.000) and a test ROC AUC of 1.000. The validation cohort produced a training ROC AUC of 0.977 (sensitivity = 0.855, specificity = 0.948) and a test ROC AUC of 0.978 (sensitivity = 0.948, specificity = 0.922). The lipid panel was also able to differentiate SARS-CoV-2-positive individuals from SARS-CoV-2-negative individuals with COVID-19-like symptoms (specificity = 0.818). Lipid profiling and multivariate modelling revealed a signature offering mechanistic insights into SARS-CoV-2, with strong predictive power, and the potential to facilitate effective diagnosis and clinical management.

Integrative Modeling of Plasma Metabolic and Lipoprotein Biomarkers of SARS-CoV-2 Infection in Spanish and Australian COVID-19 Patient Cohorts.

Quantitative plasma lipoprotein and metabolite profiles were measured on an autonomous community of the Basque Country (Spain) cohort consisting of hospitalized COVID-19 patients (n = 72) and a matched control group (n = 75) and a Western Australian (WA) cohort consisting of (n = 17) SARS-CoV-2 positives and (n = 20) healthy controls using 600 MHz 1H nuclear magnetic resonance (NMR) spectroscopy. Spanish samples were measured in two laboratories using one-dimensional (1D) solvent-suppressed and T2-filtered methods with in vitro diagnostic quantification of lipoproteins and metabolites. SARS-CoV-2 positive patients and healthy controls from both populations were modeled and cross-projected to estimate the biological similarities and validate biomarkers. Using the top 15 most discriminatory variables enabled construction of a cross-predictive model with 100% sensitivity and specificity (within populations) and 100% sensitivity and 82% specificity (between populations). Minor differences were observed between the control metabolic variables in the two cohorts, but the lipoproteins were virtually indistinguishable. We observed highly significant infection-related reductions in high-density lipoprotein (HDL) subfraction 4 phospholipids, apolipoproteins A1 and A2,that have previously been associated with negative regulation of blood coagulation and fibrinolysis. The Spanish and Australian diagnostic SARS-CoV-2 biomarkers were mathematically and biologically equivalent, demonstrating that NMR-based technologies are suitable for the study of the comparative pathology of COVID-19 via plasma phenotyping.

A molecular signature for the metabolic syndrome by urine metabolomics.

BACKGROUND: Metabolic syndrome (MetS) is a multimorbid long-term condition without consensual medical definition and a diagnostic based on compatible symptomatology. Here we have investigated the molecular signature of MetS in urine. METHODS: We used NMR-based metabolomics to investigate a European cohort including urine samples from 11,754 individuals (18-75 years old, 41% females), designed to populate all the intermediate conditions in MetS, from subjects without any risk factor up to individuals with developed MetS (4-5%, depending on the definition). A set of quantified metabolites were integrated from the urine spectra to obtain metabolic models (one for each definition), to discriminate between individuals with MetS. RESULTS: MetS progression produces a continuous and monotonic variation of the urine metabolome, characterized by up- or down-regulation of the pertinent metabolites (17 in total, including glucose, lipids, aromatic amino acids, salicyluric acid, maltitol, trimethylamine N-oxide, and p-cresol sulfate) with some of the metabolites associated to MetS for the first time. This metabolic signature, based solely on information extracted from the urine spectrum, adds a molecular dimension to MetS definition and it was used to generate models that can identify subjects with MetS (AUROC values between 0.83 and 0.87). This signature is particularly suitable to add meaning to the conditions that are in the interface between healthy subjects and MetS patients. Aging and non-alcoholic fatty liver disease are also risk factors that may enhance MetS probability, but they do not directly interfere with the metabolic discrimination of the syndrome. CONCLUSIONS: Urine metabolomics, studied by NMR spectroscopy, unravelled a set of metabolites that concomitantly evolve with MetS progression, that were used to derive and validate a molecular definition of MetS and to discriminate the conditions that are in the interface between healthy individuals and the metabolic syndrome.

Sensitive detection of SARS-CoV-2 seroconversion by flow cytometry reveals the presence of nucleoprotein-reactive antibodies in unexposed individuals.

There is an ongoing need of developing sensitive and specific methods for the determination of SARS-CoV-2 seroconversion. For this purpose, we have developed a multiplexed flow cytometric bead array (C19BA) that allows the identification of IgG and IgM antibodies against three immunogenic proteins simultaneously: the spike receptor-binding domain (RBD), the spike protein subunit 1 (S1) and the nucleoprotein (N). Using different cohorts of samples collected before and after the pandemic, we show that this assay is more sensitive than ELISAs performed in our laboratory. The combination of three viral antigens allows for the interrogation of full seroconversion. Importantly, we have detected N-reactive antibodies in COVID-19-negative individuals. Here we present an immunoassay that can be easily implemented and has superior potential to detect low antibody titers compared to current gold standard serology methods.

SARS-CoV-2 Infection Dysregulates the Metabolomic and Lipidomic Profiles of Serum.

COVID-19 is a systemic infection that exerts significant impact on the metabolism. Yet, there is little information on how SARS-CoV-2 affects metabolism. Using NMR spectroscopy, we measured the metabolomic and lipidomic serum profile from 263 (training cohort) + 135 (validation cohort) symptomatic patients hospitalized after positive PCR testing for SARS-CoV-2 infection. We also established the profiles of 280 persons collected before the coronavirus pandemic started. Principal-component analysis discriminated both cohorts, highlighting the impact that the infection has on overall metabolism. The lipidomic analysis unraveled a pathogenic redistribution of the lipoprotein particle size and composition to increase the atherosclerotic risk. In turn, metabolomic analysis reveals abnormally high levels of ketone bodies (acetoacetic acid, 3-hydroxybutyric acid, and acetone) and 2-hydroxybutyric acid, a readout of hepatic glutathione synthesis and marker of oxidative stress. Our results are consistent with a model in which SARS-CoV-2 infection induces liver damage associated with dyslipidemia and oxidative stress.