Profiling metabolites and lipoproteins in COMETA, an Italian cohort of COVID-19 patients

Longitudinal study on immunologic, lipoproteomic, and inflammatory responses indicates the safety of sequential COVID-19 vaccination

COVID-19 vaccines are crucial in reducing SARS-CoV-2 transmission and severe health outcomes. Despite widespread administration, their long-term systemic effects on human metabolism remain inadequately understood. This longitudinal study aims to evaluate IgG responses, 34 cytokines, 112 lipoproteins, and 21 low-molecular-weight metabolites in 33 individuals receiving two to four COVID-19 vaccine doses. Changes in metabolic profiles for the first 16 days post each dose of vaccine, and up to 480 days post-initial dose, were compared to baseline (before vaccination). Additionally, metabolic profiles of vaccinated participants were compared to a reference cohort of unvaccinated individuals without prior exposure to SARS-CoV-2 infection (controls) and SARS-CoV-2 cases. Positive IgG responses were observed in 78.8% (N = 26) of participants after the first dose, reaching 100% with subsequent doses. The most common side effects were localized pain at the injection site and "flu-like" symptoms, reported by > 50% of participants. Systemic side effects, e.g., sore lymph nodes, fatigue, and brain fog, were reported but showed no significant correlations to IgG responses. Transient temporal changes were observed for cytokine IP10 (CXCL10) and glutamic acid around the third vaccine dose. Compared to the reference cohort, 497 vaccinated samples (95.0%) had profiles similar to the controls, while the remaining 26 samples with prior infection exposures were similar to mild cases of SARS-CooV-2 infection. In conclusion, COVID-19 vaccination did not induce lasting changes in inflammatory and metabolic responses, nor did it induce changes similar to mild cases of SARS-CoV-2 infection. This supports the metabolic safety of the vaccine and contributes to increased vaccine confidence. KEY MESSAGES: Minimal changes in inflammatory/metabolic markers up to 480 days post-vaccination. Transient increase in IP10 (CXCL10) and glutamic acid around the third dose. Post-vaccination IgG response did not alter metabolic profiles like SARS-CoV-2 cases. Our findings provide insights into the safety of repeated COVID-19 vaccinations.

Evaluating Cryopreservation Methods in Biobanking: Impacts on Biomarker Integrity and Omics Data Reliability

Objectives: Personalized medicine emphasizes prevention and early diagnosis by developing genetic screening and biomarker assessment tools. Biobanks, including University of Piemonte Orientale (UPO) Biobank, support this effort by providing high-quality biological samples collected, processed, and stored using optimized standardized protocols. To determine the optimal long-term storage conditions for biospecimens used in biomedical research, we evaluated plasma and serum samples cryopreserved using two storage methods, cryovials and straws, across various analytical methodologies with differing sensitivity and robustness. Design and Methods: Plasma and serum samples cryopreserved in liquid nitrogen in vials and straw at the UPO Biobank were subjected to multiple analyses including standard biochemical laboratory analysis, targeted lipidomics, untargeted proteomics, and targeted metabolites quantification through mass spectrometry-based analytical techniques. Results: Our data demonstrate the robustness and applicability of both storage methods for standard laboratory analyses in evaluating clinically relevant markers in plasma and serum. Lipidomic analysis revealed slight disparities in lipid abundance, though these differences were mostly confined to specific lipid species, particularly fatty acids. Conversely, proteomic and metabolomic analyses uncovered variations in abundance in a significant, albeit limited, fraction of analytes between vials and straw-derived samples. Conclusions: By highlighting similarities and differences in samples stored in these conditions, this study provides significant insights into optimizing biobanking practices and understanding the factors that influence the integrity of cryopreserved biospecimens and the reliability of the data derived from them. Both straws and vials are convenient and efficient cryopreservation methods, essentially equivalent for samples dedicated to robust and relatively low-sensitive standardized analyses. However, our findings emphasize the need for caution when interpreting omics data from samples subjected to different cryopreservation methods, as subtle variations can arise even with different types of containers.

Metabolomic and lipoproteomic differences and similarities between COVID-19 and other types of pneumonia

COVID-19 infection has revealed significant effects on the human blood metabolome and lipoproteome, which have been coherently observed in different cohorts worldwide and across the various waves of SARS-CoV-2 pandemic. As one of the main clinical manifestations of COVID-19 is a severe acute respiratory illness, it is pertinent to explore whether this metabolic/lipoproteomic disturbance is associated with the respiratory symptoms. To this purpose we are here reporting comparative1H NMR analyses of the plasma of 252 COVID-19 patients and of patients with non-COVID-19 interstitial (24 individuals) or lobar (21 individuals) pneumonia, all matched by age, gender and disease severity. The analysis is based on 24 metabolites and 114 lipoprotein parameters. Several common traits are observed among the three groups, albeit with some peculiar features characteristic of each group. The main differences were observed between the lobar cases and all the others.

Metabolomic profile of severe COVID-19 and a signature predictive of progression towards severe disease status: a prospective cohort study (METCOVID)

Profound metabolomic alterations occur during COVID-19. Early identification of the subset of hospitalised COVID-19 patients at risk of developing severe disease is critical for optimal resource utilization and prompt treatment. This work explores the metabolomic profile of hospitalised adult COVID-19 patients with severe disease, and establishes a predictive signature for disease progression. Within 48 hours of admission, serum samples were collected from 148 hospitalised patients for nuclear magnetic resonance (NMR) spectroscopy. Lipoprotein profiling was performed using the 1H-NMR-based Liposcale test, while low molecular weight metabolites were analysed using one-dimensional Carr-Purcell-Meiboom-Gill pulse spectroscopy and an adaptation of the Dolphin method for lipophilic extracts. Severe COVID-19, per WHO's Clinical Progression Scale, was characterized by altered lipoprotein distribution, elevated signals of glyc-A and glyc-B, a shift towards a catabolic state with elevated levels of branched-chain amino acids, and accumulation of ketone bodies. Furthermore, COVID-19 patients initially presenting with moderate disease but progressing to severe stages exhibited a distinct metabolic signature. Our multivariate model demonstrated a cross-validated AUC of 0.82 and 72% predictive accuracy for severity progression. NMR spectroscopy-based metabolomic profiling enables the identification of moderate COVID-19 patients at risk of disease progression, aiding in resource allocation and early intervention.

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

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

Metabolomic Insights into COVID-19 Severity: A Scoping Review

BACKGROUND

In 2019, SARS-CoV-2, the novel coronavirus, entered the world scene, presenting a global health crisis with a broad spectrum of clinical manifestations. Recognizing the significance of metabolomics as the omics closest to symptomatology, it has become a useful tool for predicting clinical outcomes. Several metabolomic studies have indicated variations in the metabolome corresponding to different disease severities, highlighting the potential of metabolomics to unravel crucial insights into the pathophysiology of SARS-CoV-2 infection.

METHODS

The PRISMA guidelines were followed for this scoping review. Three major scientific databases were searched: PubMed, the Directory of Open Access Journals (DOAJ), and BioMed Central, from 2020 to 2024. Initially, 2938 articles were identified and vetted with specific inclusion and exclusion criteria. Of these, 42 articles were retrieved for analysis and summary.

RESULTS

Metabolites were identified that were repeatedly noted to change with COVID-19 and its severity. Phenylalanine, glucose, and glutamic acid increased with severity, while tryptophan, proline, and glutamine decreased, highlighting their association with COVID-19 severity. Additionally, pathway analysis revealed that phenylalanine, tyrosine and tryptophan biosynthesis, and arginine biosynthesis were the most significantly impacted pathways in COVID-19 severity.

CONCLUSIONS

COVID-19 severity is intricately linked to significant metabolic alterations that span amino acid metabolism, energy production, immune response modulation, and redox balance.

Alteration of the Gut-Lung Axis After Severe COVID-19 Infection and Modulation Through Probiotics: A Randomized, Controlled Pilot Study

BACKGROUND

The gut-lung axis could be a potential therapeutic target for improving post-acute COVID-19 symptoms, and probiotics have been proposed as possible modulators.

AIM

We conducted a pilot study to understand alterations in the gut-lung axis and to explore the effects of a probiotic in post-acute COVID-19 disease.

METHODS

We included patients after severe COVID-19 disease (sCOV, n = 21) in a randomized, placebo-controlled trial to test the effect of a probiotic (Pro-Vi 5, Institute Allergosan, Graz, Austria) in a six-month intervention and used patients after mild disease (mCOV, n = 10) as controls, to compare the intestinal microbiome, metabolome, and patient-reported outcomes and biomarkers along the gut-lung axis at baseline and throughout probiotic intervention.

RESULTS

Compared to mCOV patients, sCOV patients showed lower microbial richness, which was significantly improved by probiotic intervention. A reorganization of Ruminococcaceae and Lachnospiraceae taxa was observed in sCOV patients but remained unaffected by the intervention. Serum metabolome showed a dysregulation of lipoproteins in accordance with higher BMI and comorbidities in sCOV patients. HDL and LDL fractions/components were temporarily decreased in the probiotic group. Stool metabolome was altered at baseline in sCOV patients and an increase in L-DOPA after 3 months and butyrate after 6 months of intervention could be observed. Probiotics partially improved reduced quality of life and modulated altered immune responses in sCOV patients. Increased intestinal permeability at baseline remained unaffected.

CONCLUSION

The study provides evidence of long-term alterations of the gut-lung axis after severe COVID-19 infection and suggests that probiotics can modulate the biomarkers of the gut-lung axis.

The Impact of COVID-19 Vaccination on Oxidative Stress and Cardiac Fibrosis Biomarkers in Patients with Acute Myocardial Infarction (STEMI), a Single-Center Experience Analysis

The relationship between the classical cardiac biomarker and acute myocardial infarction (STEMI) in patients with COVID-19 is far from being elucidated. Furthermore, superoxide dismutase (SOD), a marker for oxidative stress, was associated with cardiac ischemia. Also, Galectin-3 is significant for defining the relationship between cardiac fibrosis and COVID-19. There are no studies on the effect of SARS-CoV-2 virus infection and vaccination on patients with STEMI and biomarkers above-mentioned.

AIM

our single-center prospective study assesses the relationship between COVID-19 infection with/without vaccination and the value of SOD and Galectin-3 in STEMI patients.

MATERIAL AND METHODS

In total, 93 patients with STEMI and SARS-CoV-2 virus infection were included in the analysis, patients were divided in two groups based on COVID-19 vaccination status. Echocardiographic and laboratory investigations for cardiac ischemia, oxidative stress, and cardiac fibrosis biomarkers were investigated.

RESULTS

In total, 93 patients were included, the majority of which were male (72.0%), 45.2% (n = 42) were vaccinated against SARS-CoV-2; the mean age of vaccinated patients is 62 years, and 57% (n = 53) are smokers; blood pressure is found with a higher frequency in unvaccinated people (62.7%) compared to 28.6% in vaccinated people (p = 0.015), and 90.5% of the vaccinated people presented STEMI, compared with 96.1% of the unvaccinated ones. Revascularization with one stent was achieved in 47.6% of the vaccinated people and 72.5% for the unvaccinated people (p = 0.015). Galectin-3 was slightly more reduced in the vaccinated patients compared to the unvaccinated patients (0.73 vs. 0.99; p = 0.202), and the average level of Cu/ZnSOD was slightly more reduced in vaccinated patients compared to the unvaccinated patients (0.84 vs. 0.91; p = 0.740).

CONCLUSIONS

Regarding patient's SARS-CoV-2 infection functional status, the results from our single-center analysis did not find a statistically significant decrease in oxidative stress and cardiac fibrosis biomarkers along with cardiovascular complication following STEMI treated with percutaneous coronary angioplasty (PCI) in the case of patients with COVID-19 vaccination compared with patients who did not receive COVID-19 vaccine. Anyway, our data suggest that contemporary PCI techniques may offer an alternative revascularization strategy that enables complex CAD COVID-19 patients to be safely discharged from hospital.

Metabolomic profiling of COVID-19 using serum and urine samples in intensive care and medical ward cohorts

The COVID-19 pandemic remains a significant global health threat, with uncertainties persisting regarding the factors determining whether individuals experience mild symptoms, severe conditions, or succumb to the disease. This study presents an NMR metabolomics-based approach, analysing 80 serum and urine samples from COVID-19 patients (34 intensive care patients and 46 hospitalized patients) and 32 from healthy controls. Our research identifies discriminant metabolites and clinical variables relevant to COVID-19 diagnosis and severity. These discriminant metabolites play a role in specific pathways, mainly "Phenylalanine, tyrosine and tryptophan biosynthesis", "Phenylalanine metabolism", "Glycerolipid metabolism" and "Arginine and proline metabolism". We propose a three-metabolite diagnostic panel-comprising isoleucine, TMAO, and glucose-that effectively discriminates COVID-19 patients from healthy individuals, achieving high efficiency. Furthermore, we found an optimal biomarker panel capable of efficiently classify disease severity considering both clinical characteristics (obesity/overweight, dyslipidemia, and lymphocyte count) together with metabolites content (ethanol, TMAO, tyrosine and betaine).

Differential analysis of lipoprotein and glycoprotein profiles in bacterial infections and COVID-19 using proton nuclear magnetic resonance and machine learning

Background

We scrutinized variations in the proton nuclear magnetic resonance (1H NMR) lipoprotein and glycoprotein profiles among hospitalized individuals with infectious diseases.

Methods

We obtained sera from 124 patients with COVID-19, 50 patients with catheter-related bacterial infections, and 50 healthy volunteers. Results were interpreted using machine learning.

Results

COVID-19 patients had bigger and more abundant VLDL particles than the control group and higher VLDL-cholesterol and VLDL-triglyceride concentrations. Patients with bacterial infections showed similar trends, but differences often did not reach statistical significance. Both types of patients showed lower LDL-cholesterol concentrations than the controls. LDL were larger, and the number of particles was lower than that of the healthy individuals. HDL particles had decreased cholesterol and increased triglycerides. Small particles were reduced. Glycoproteins were increased in both groups of patients. All these alterations were more pronounced in COVID-19 patients than those with bacterial infections. The diagnostic accuracy of these profiles exceeded 90 % when distinguishing between healthy individuals and patients, and 85 % when differentiating between the two patient groups.

Conclusion

Our findings highlight the potential of 1H NMR analysis for lipoproteins and glycoproteins as infection biomarkers. Additionally, they reveal differences between viral and bacterial infections, shedding light on an area with promising clinical significance.

Exploring metabolic anomalies in COVID-19 and post-COVID-19: a machine learning approach with explainable artificial intelligence

The COVID-19 pandemic, caused by SARS-CoV-2, has led to significant challenges worldwide, including diverse clinical outcomes and prolonged post-recovery symptoms known as Long COVID or Post-COVID-19 syndrome. Emerging evidence suggests a crucial role of metabolic reprogramming in the infection's long-term consequences. This study employs a novel approach utilizing machine learning (ML) and explainable artificial intelligence (XAI) to analyze metabolic alterations in COVID-19 and Post-COVID-19 patients. Samples were taken from a cohort of 142 COVID-19, 48 Post-COVID-19, and 38 control patients, comprising 111 identified metabolites. Traditional analysis methods, like PCA and PLS-DA, were compared with ML techniques, particularly eXtreme Gradient Boosting (XGBoost) enhanced by SHAP (SHapley Additive exPlanations) values for explainability. XGBoost, combined with SHAP, outperformed traditional methods, demonstrating superior predictive performance and providing new insights into the metabolic basis of the disease's progression and aftermath. The analysis revealed metabolomic subgroups within the COVID-19 and Post-COVID-19 conditions, suggesting heterogeneous metabolic responses to the infection and its long-term impacts. Key metabolic signatures in Post-COVID-19 include taurine, glutamine, alpha-Ketoglutaric acid, and LysoPC a C16:0. This study highlights the potential of integrating ML and XAI for a fine-grained description in metabolomics research, offering a more detailed understanding of metabolic anomalies in COVID-19 and Post-COVID-19 conditions.

Diabetes Mellitus, Energy Metabolism, and COVID-19

Obesity, diabetes mellitus (mostly type 2), and COVID-19 show mutual interactions because they are not only risk factors for both acute and chronic COVID-19 manifestations, but also because COVID-19 alters energy metabolism. Such metabolic alterations can lead to dysglycemia and long-lasting effects. Thus, the COVID-19 pandemic has the potential for a further rise of the diabetes pandemic. This review outlines how preexisting metabolic alterations spanning from excess visceral adipose tissue to hyperglycemia and overt diabetes may exacerbate COVID-19 severity. We also summarize the different effects of SARS-CoV-2 infection on the key organs and tissues orchestrating energy metabolism, including adipose tissue, liver, skeletal muscle, and pancreas. Last, we provide an integrative view of the metabolic derangements that occur during COVID-19. Altogether, this review allows for better understanding of the metabolic derangements occurring when a fire starts from a small flame, and thereby help reducing the impact of the COVID-19 pandemic.

Integrative Molecular Structure Elucidation and Construction of an Extended Metabolic Pathway Associated with an Ancient Innate Immune Response in COVID-19 Patients

We present compelling evidence for the existence of an extended innate viperin-dependent pathway, which provides crucial evidence for an adaptive response to viral agents, such as SARS-CoV-2. We show the in vivo biosynthesis of a family of novel endogenous cytosine metabolites with potential antiviral activities. Two-dimensional nuclear magnetic resonance (NMR) spectroscopy revealed a characteristic spin-system motif, indicating the presence of an extended panel of urinary metabolites during the acute viral replication phase. Mass spectrometry additionally enabled the characterization and quantification of the most abundant serum metabolites, showing the potential diagnostic value of the compounds for viral infections. In total, we unveiled ten nucleoside (cytosine- and uracil-based) analogue structures, eight of which were previously unknown in humans allowing us to propose a new extended viperin pathway for the innate production of antiviral compounds. The molecular structures of the nucleoside analogues and their correlation with an array of serum cytokines, including IFN-α2, IFN-γ, and IL-10, suggest an association with the viperin enzyme contributing to an ancient endogenous innate immune defense mechanism against viral infection.

Comparing plasma and skin imprint metabolic profiles in COVID-19 diagnosis and severity assessment

As SARS-CoV-2 continues to produce new variants, the demand for diagnostics and a better understanding of COVID-19 remain key topics in healthcare. Skin manifestations have been widely reported in cases of COVID-19, but the mechanisms and markers of these symptoms are poorly described. In this cross-sectional study, 101 patients (64 COVID-19 positive patients and 37 controls) were enrolled between April and June 2020, during the first wave of COVID-19, in São Paulo, Brazil. Enrolled patients had skin imprints sampled non-invasively using silica plates; plasma samples were also collected. Samples were used for untargeted lipidomics/metabolomics through high-resolution mass spectrometry. We identified 558 molecular ions, with lipids comprising most of them. We found 245 plasma ions that were significant for COVID-19 diagnosis, compared to 61 from the skin imprints. Plasma samples outperformed skin imprints in distinguishing patients with COVID-19 from controls, with F1-scores of 91.9% and 84.3%, respectively. Skin imprints were excellent for assessing disease severity, exhibiting an F1-score of 93.5% when discriminating between patient hospitalization and home care statuses. Specifically, oleamide and linoleamide were the most discriminative biomarkers for identifying hospitalized patients through skin imprinting, and palmitic amides and N-acylethanolamine 18:0 were also identified as significant biomarkers. These observations underscore the importance of primary fatty acid amides and N-acylethanolamines in immunomodulatory processes and metabolic disorders. These findings confirm the potential utility of skin imprinting as a valuable non-invasive sampling method for COVID-19 screening; a method that may also be applied in the evaluation of other medical conditions. KEY MESSAGES: Skin imprints complement plasma in disease metabolomics. The annotated markers have a role in immunomodulation and metabolic diseases. Skin imprints outperformed plasma samples at assessing disease severity. Skin imprints have potential as non-invasive sampling strategy for COVID-19.

Longitudinal NMR-Based Metabolomics Study Reveals How Hospitalized COVID-19 Patients Recover: Evidence of Dyslipidemia and Energy Metabolism Dysregulation

Long COVID, or post-acute sequelae of SARS-CoV-2 infection (PASC), can manifest as long-term symptoms in multiple organ systems, including respiratory, cardiovascular, neurological, and metabolic systems. In patients with severe COVID-19, immune dysregulation is significant, and the relationship between metabolic regulation and immune response is of great interest in determining the pathophysiological mechanisms. We aimed to characterize the metabolomic footprint of recovering severe COVID-19 patients at three consecutive timepoints and compare metabolite levels to controls. Our findings add proof of dysregulated amino acid metabolism in the acute phase and dyslipidemia, glycoprotein level alterations, and energy metabolism disturbances in severe COVID-19 patients 3-4 months post-hospitalization.

The biomarkers' landscape of post-COVID-19 patients can suggest selective clinical interventions

In COVID-19 clinical symptoms can persist even after negativization also in individuals who have had mild or moderate disease. We here investigated the biomarkers that define the post-COVID-19 clinical state analyzing the exhaled breath condensate (EBC) of 38 post COVID-19 patients and 38 sex and age-matched healthy controls via nuclear magnetic resonance (NMR)-based metabolomics. Predicted gene-modulated microRNAs (miRNAs) related to COVID-19 were quantified from EBC of 10 patients and 10 controls. Finally, clinical parameters from all post-COVID-19 patients were correlated with metabolomic data. Post-COVID-19 patients and controls showed different metabolic phenotype ("metabotype"). From the metabolites, by using enrichment analysis we identified miRNAs that resulted up-regulated (hsa-miR146a-5p) and down-regulated (hsa-miR-126-3p and hsa-miR-223-3p) in post-COVID-19. Taken together, our multiomics data indicate that post-COVID-19 patients before rehabilitation are characterized by persistent inflammation, dysregulation of liver, endovascular thrombotic and pulmonary processes, and physical impairment, which should be the primary clinical targets to contrast the post-acute sequelae of COVID-19.

Fingerprinting and profiling in metabolomics of biosamples

This review focuses on metabolomics from an NMR point of view. It attempts to cover the broad scope of metabolomics and describes the NMR experiments that are most suitable for each sample type. It is addressed not only to NMR specialists, but to all researchers who wish to approach metabolomics with a clear idea of what they wish to achieve but not necessarily with a deep knowledge of NMR. For this reason, some technical parts may seem a bit naïve to the experts. The review starts by describing standard metabolomics procedures, which imply the use of a dedicated 600 MHz instrument and of four properly standardized 1D experiments. Standardization is a must if one wants to directly compare NMR results obtained in different labs. A brief mention is also made of standardized pre-analytical procedures, which are even more essential. Attention is paid to the distinction between fingerprinting and profiling, and the advantages and disadvantages of fingerprinting are clarified. This aspect is often not fully appreciated. Then profiling, and the associated problems of signal assignment and quantitation, are discussed. We also describe less conventional approaches, such as the use of different magnetic fields, the use of signal enhancement techniques to increase sensitivity, and the potential of field-shuttling NMR. A few examples of biomedical applications are also given, again with the focus on NMR techniques that are most suitable to achieve each particular goal, including a description of the most common heteronuclear experiments. Finally, the growing applications of metabolomics to foodstuffs are described.

COVID-19: A complex disease with a unique metabolic signature

Plasma of COVID-19 patients contains a strong metabolomic/lipoproteomic signature, revealed by the NMR analysis of a cohort of >500 patients sampled during various waves of COVID-19 infection, corresponding to the spread of different variants, and having different vaccination status. This composite signature highlights common traits of the SARS-CoV-2 infection. The most dysregulated molecules display concentration trends that scale with disease severity and might serve as prognostic markers for fatal events. Metabolomics evidence is then used as input data for a sex-specific multi-organ metabolic model. This reconstruction provides a comprehensive view of the impact of COVID-19 on the entire human metabolism. The human (male and female) metabolic network is strongly impacted by the disease to an extent dictated by its severity. A marked metabolic reprogramming at the level of many organs indicates an increase in the generic energetic demand of the organism following infection. Sex-specific modulation of immune response is also suggested.

Stratification of hypertension and SARS-CoV-2 infection by quantitative NMR spectroscopy of human blood serum

BACKGROUND

Diagnostic approaches like the nuclear magnetic resonance spectroscopy (NMR) based quantification of metabolites, lipoproteins, and inflammation markers has helped to identify typical alterations in the blood serum of COVID-19 patients. However, confounders such as sex, and comorbidities, which strongly influence the metabolome, were often not considered. Therefore, the aim of this NMR study was to consider sex, as well as arterial hypertension (AHT), when investigating COVID-19-positive serum samples in a large age-and sex matched cohort.

METHODS

NMR serum data from 329 COVID-19 patients were compared with 305 healthy controls. 134 COVID-19 patients were affected by AHT. These were analyzed together with NMR data from 58 hypertensives without COVID-19. In addition to metabolite, lipoprotein, and glycoprotein data from NMR, common laboratory parameters were considered. Sex was considered in detail for all comparisons.

RESULTS

Here, we show that several differences emerge from previous NMR COVID-19 studies when AHT is considered. Especially, the previously described triglyceride-rich lipoprotein profile is no longer observed in COVID-19 patients, nor an increase in ketone bodies. Further alterations are a decrease in glutamine, leucine, isoleucine, and lysine, citric acid, HDL-4 particles, and total cholesterol. Additionally, hypertensive COVID-19 patients show higher inflammatory NMR parameters than normotensive patients.

CONCLUSIONS

We present a more precise picture of COVID-19 blood serum parameters. Accordingly, considering sex and comorbidities should be included in future metabolomics studies for improved and refined patient stratification. Due to metabolic similarities with other viral infections, these results can be applied to other respiratory diseases in the future.

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.

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.

Promising Markers of Inflammatory and Gut Dysbiosis in Patients with Post-COVID-19 Syndrome

Post-COVID-19 syndrome is a complex of different symptoms, which results in a multisystemic impairment after the suffering from COVID-19 infection. The aim of the study was to reveal the clinical, laboratory, and gut disorders in patients with post-COVID-19 syndrome (n = 39) before and after taking part in the 14-day complex program of rehabilitation. A complete blood count, coagulation test, blood chemistry, biomarkers, and metabolites in serum samples, and gut dysbiosis were revealed in patients on the day of admission and after 14-day rehabilitation, in comparison with the variables of healthy volunteers (n = 48) or with reference ranges. On the day of discharge, patients noted an improvement in respiratory function, general well-being, and mood. At the same time, the levels of some metabolic (4-hydroxybenzoic, succinic, fumaric acids) and inflammatory (interleukin-6) variables, which were increased on admission, did not reach the level of healthy people during the rehabilitation program. Taxonomy disbalance was observed in patients' feces, namely, a high level of total bacterial mass, a decrease in the number of Lactobacillus spp., and an increase in pro-inflammatory microorganisms. The authors suggest that the post-COVID-19 rehabilitation program should be personalized, considering the patient's state together with not only the baseline levels of biomarkers, but also with the individual taxonomy of the gut microbiota.

Maintained imbalance of triglycerides, apolipoproteins, energy metabolites and cytokines in long-term COVID-19 syndrome patients

Background

Deep metabolomic, proteomic and immunologic phenotyping of patients suffering from an infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have matched a wide diversity of clinical symptoms with potential biomarkers for coronavirus disease 2019 (COVID-19). Several studies have described the role of small as well as complex molecules such as metabolites, cytokines, chemokines and lipoproteins during infection and in recovered patients. In fact, after an acute SARS-CoV-2 viral infection almost 10-20% of patients experience persistent symptoms post 12 weeks of recovery defined as long-term COVID-19 syndrome (LTCS) or long post-acute COVID-19 syndrome (PACS). Emerging evidence revealed that a dysregulated immune system and persisting inflammation could be one of the key drivers of LTCS. However, how these biomolecules altogether govern pathophysiology is largely underexplored. Thus, a clear understanding of how these parameters within an integrated fashion could predict the disease course would help to stratify LTCS patients from acute COVID-19 or recovered patients. This could even allow to elucidation of a potential mechanistic role of these biomolecules during the disease course.

Methods

This study comprised subjects with acute COVID-19 (n=7; longitudinal), LTCS (n=33), Recov (n=12), and no history of positive testing (n=73). ¹H-NMR-based metabolomics with IVDr standard operating procedures verified and phenotyped all blood samples by quantifying 38 metabolites and 112 lipoprotein properties. Univariate and multivariate statistics identified NMR-based and cytokine changes.

Results

Here, we report on an integrated analysis of serum/plasma by NMR spectroscopy and flow cytometry-based cytokines/chemokines quantification in LTCS patients. We identified that in LTCS patients lactate and pyruvate were significantly different from either healthy controls (HC) or acute COVID-19 patients. Subsequently, correlation analysis in LTCS group only among cytokines and amino acids revealed that histidine and glutamine were uniquely attributed mainly with pro-inflammatory cytokines. Of note, triglycerides and several lipoproteins (apolipoproteins Apo-A1 and A2) in LTCS patients demonstrate COVID-19-like alterations compared with HC. Interestingly, LTCS and acute COVID-19 samples were distinguished mostly by their phenylalanine, 3-hydroxybutyrate (3-HB) and glucose concentrations, illustrating an imbalanced energy metabolism. Most of the cytokines and chemokines were present at low levels in LTCS patients compared with HC except for IL-18 chemokine, which tended to be higher in LTCS patients.

Conclusion

The identification of these persisting plasma metabolites, lipoprotein and inflammation alterations will help to better stratify LTCS patients from other diseases and could help to predict ongoing severity of LTCS patients.

Chewing the fat: How lipidomics is changing our understanding of human health and disease in 2022

Lipids are biological molecules that play vital roles in all living organisms. They perform many cellular functions, such as 1) forming cellular and subcellular membranes, 2) storing and using energy, and 3) serving as chemical messengers during intra- and inter-cellular signal transduction. The large-scale study of the pathways and networks of cellular lipids in biological systems is called "lipidomics" and is one of the fastest-growing omics technologies of the last two decades. With state-of-the-art mass spectrometry instrumentation and sophisticated data handling, clinical studies show how human lipid composition changes in health and disease, thereby making it a valuable medium to collect for clinical applications, such as disease diagnostics, therapeutic decision-making, and drug development. This review gives a comprehensive overview of current workflows used in clinical research, from sample collection and preparation to data and clinical interpretations. This is followed by an appraisal of applications in 2022 and a perspective on the exciting future of clinical lipidomics.

Metabolomic profiling of overnight peritoneal dialysis effluents predicts the peritoneal equilibration test type

This study primarily aimed to evaluate whether peritoneal equilibration test (PET) results can be predicted through the metabolomic analysis of overnight peritoneal dialysis (PD) effluents. From a total of 125 patients, overnight PD effluents on the day of the first PET after PD initiation were analyzed. A modified 4.25% dextrose PET was performed, and the PET type was categorized according to the dialysate-to-plasma creatinine ratio at the 4-h dwell time during the PET as follows: high, high average, low average, or low transporter. Nuclear magnetic resonance (NMR)-based metabolomics was used to analyze the effluents and identify the metabolites. The predictive performances derived from the orthogonal projection to latent structure discriminant analysis (OPLS-DA) modeling of the NMR spectrum were estimated by calculating the area under the curve (AUC) using receiver operating characteristic curve analysis. The OPLS-DA score plot indicated significant metabolite differences between high and low PET types. The relative concentrations of alanine and creatinine were greater in the high transporter type than in the low transporter type. The relative concentrations of glucose and lactate were greater in the low transporter type than in the high transporter type. The AUC of a composite of four metabolites was 0.975 in distinguish between high and low PET types. Measured PET results correlated well with the total NMR metabolic profile of overnight PD effluents.

Integrated Metabolic and Inflammatory Signatures Associated with Severity of, Fatality of, and Recovery from COVID-19

Severe manifestations of coronavirus disease 2019 (COVID-19) and mortality have been associated with physiological alterations that provide insights into the pathogenesis of the disease. Moreover, factors that drive recovery from COVID-19 can be explored to identify correlates of protection. The cellular metabolism represents a potential target to improve survival upon severe disease, but the associations between the metabolism and the inflammatory response during COVID-19 are not well defined. We analyzed blood laboratorial parameters, cytokines, and metabolomes of 150 individuals with mild to severe disease, of which 33 progressed to a fatal outcome. A subset of 20 individuals was followed up after hospital discharge and recovery from acute disease. We used hierarchical community networks to integrate metabolomics profiles with cytokines and markers of inflammation, coagulation, and tissue damage. Infection by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) promotes significant alterations in the plasma metabolome, whose activity varies according to disease severity and correlates with oxygen saturation. Differential metabolism underlying death was marked by amino acids and related metabolites, such as glutamate, glutamyl-glutamate, and oxoproline, and lipids, including progesterone, phosphocholine, and lysophosphatidylcholines (lysoPCs). Individuals who recovered from severe disease displayed persistent alterations enriched for metabolism of purines and phosphatidylinositol phosphate and glycolysis. Recovery of mild disease was associated with vitamin E metabolism. Data integration shows that the metabolic response is a hub connecting other biological features during disease and recovery. Infection by SARS-CoV-2 induces concerted activity of metabolic and inflammatory responses that depend on disease severity and collectively predict clinical outcomes of COVID-19. IMPORTANCE COVID-19 is characterized by diverse clinical outcomes that include asymptomatic to mild manifestations or severe disease and death. Infection by SARS-CoV-2 activates inflammatory and metabolic responses that drive protection or pathology. How inflammation and metabolism communicate during COVID-19 is not well defined. We used high-resolution mass spectrometry to investigate small biochemical compounds (<1,500 Da) in plasma of individuals with COVID-19 and controls. Age, sex, and comorbidities have a profound effect on the plasma metabolites of individuals with COVID-19, but we identified significant activity of pathways and metabolites related to amino acids, lipids, nucleotides, and vitamins determined by disease severity, survival outcome, and recovery. Furthermore, we identified metabolites associated with acute-phase proteins and coagulation factors, which collectively identify individuals with severe disease or individuals who died of severe COVID-19. Our study suggests that manipulating specific metabolic pathways can be explored to prevent hyperinflammation, organ dysfunction, and death.

Phenotypic changes in low-density lipoprotein particles as markers of adverse clinical outcomes in COVID-19

BACKGROUND AND AIMS

Low-density lipoprotein (LDL) plasma concentration decline is a biomarker for acute inflammatory diseases, including coronavirus disease-2019 (COVID-19). Phenotypic changes in LDL during COVID-19 may be equally related to adverse clinical outcomes.

METHODS

Individuals hospitalized due to COVID-19 (n = 40) were enrolled. Blood samples were collected on days 0, 2, 4, 6, and 30 (D0, D2, D4, D6, and D30). Oxidized LDL (ox-LDL), and lipoprotein-associated phospholipase A2 (Lp-PLA2) activity were measured. In a consecutive series of cases (n = 13), LDL was isolated by gradient ultracentrifugation from D0 and D6 and was quantified by lipidomic analysis. Association between clinical outcomes and LDL phenotypic changes was investigated.

RESULTS

In the first 30 days, 42.5% of participants died due to Covid-19. The serum ox-LDL increased from D0 to D6 (p < 0.005) and decreased at D30. Moreover, individuals who had an ox-LDL increase from D0 to D6 to over the 90th percentile died. The plasma Lp-PLA2 activity also increased progressively from D0 to D30 (p < 0.005), and the change from D0 to D6 in Lp-PLA2 and ox-LDL were positively correlated (r = 0.65, p < 0.0001). An exploratory untargeted lipidomic analysis uncovered 308 individual lipids in isolated LDL particles. Paired-test analysis from D0 and D6 revealed higher concentrations of 32 lipid species during disease progression, mainly represented by lysophosphatidyl choline and phosphatidylinositol. In addition, 69 lipid species were exclusively modulated in the LDL particles from non-survivors as compared to survivors.

CONCLUSIONS

Phenotypic changes in LDL particles are associated with disease progression and adverse clinical outcomes in COVID-19 patients and could serve as a potential prognostic biomarker.

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.

SARS-CoV-2 infection induces persistent adipose tissue damage in aged golden Syrian hamsters

Coronavirus disease 2019 (COVID-19, caused by severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2)) is primarily a respiratory illness. However, various extrapulmonary manifestations have been reported in patients with severe forms of COVID-19. Notably, SARS-CoV-2 was shown to directly trigger white adipose tissue (WAT) dysfunction, which in turn drives insulin resistance, dyslipidemia, and other adverse outcomes in patients with COVID-19. Although advanced age is the greatest risk factor for COVID-19 severity, published data on the impact of SARS-CoV-2 infection on WAT in aged individuals are scarce. Here, we characterized the response of subcutaneous and visceral WAT depots to SARS-CoV-2 infection in young adult and aged golden hamsters. In both age groups, infection was associated with a decrease in adipocyte size in the two WAT depots; this effect was partly due to changes in tissue's lipid metabolism and persisted for longer in aged hamsters than in young-adult hamsters. In contrast, only the subcutaneous WAT depot contained crown-like structures (CLSs) in which dead adipocytes were surrounded by SARS-CoV-2-infected macrophages, some of them forming syncytial multinucleated cells. Importantly, older age predisposed to a unique manifestation of viral disease in the subcutaneous WAT depot during SARS-CoV-2 infection; the persistence of very large CLSs was indicative of an age-associated defect in the clearance of dead adipocytes by macrophages. Moreover, we uncovered age-related differences in plasma lipid profiles during SARS-CoV-2 infection. These data suggest that the WAT's abnormal response to SARS-CoV-2 infection may contribute to the greater severity of COVID-19 observed in elderly patients.

NMR-Metabolomics in COVID-19 Research

COVID-19 stands for Corona Virus Disease 2019, which starts as a viral infection that provokes illness with different symptoms and severity. The infected individuals can be asymptomatic or present with mild, moderate, severe, and critical illness with acute respiratory distress syndrome (ARDS), acute cardiac injury, and multiorgan failure. When the virus enters the cells, it replicates and provokes responses. Most diseased individuals resolve the problems in a short time but unfortunately, some may die, and almost 3 years after the first reported cases, COVID-19 still kills thousands per day worldwide. One of the problems in not curing the viral infection is that the virus passes by undetected in cells. This can be caused by the lack of pathogen-associated molecular patterns (PAMPs) that start an orchestrated immune response, such as activation of type 1 interferons (IFNs), inflammatory cytokines, chemokines, and antiviral defenses. Before all of these events can happen, the virus uses the infected cells and numerous small molecules as sources of energy and building blocks for newly synthesized viral nanoparticles that travel to and infect other host cells. Therefore, studying the cell metabolome and metabolomic changes in biofluids might give insights into the state of the viral infection, viral loads, and defense response. NMR-metabolomics can help in solving the real-time host interactions by monitoring concentration changes in metabolites. This chapter addresses the state of the art of COVIDomics by NMR analyses and presents exemplified biomolecules identified in different world regions and gravities of illness as potential biomarkers.

NMR-Based Metabolomics to Evaluate Individual Response to Treatments

The aim of this chapter is to highlight the various aspects of metabolomics in relation to health and diseases, starting from the definition of metabolic space and of how individuals tend to maintain their own position in this space. Physio-pathological stimuli may cause individuals to lose their position and then regain it, or move irreversibly to other positions. By way of examples, mostly selected from our own work using ¹H NMR on biological fluids, we describe the effects on the individual metabolomic fingerprint of mild external interventions, such as diet or probiotic administration. Then we move to pathologies (such as celiac disease, various types of cancer, viral infections, and other diseases), each characterized by a well-defined metabolomic fingerprint. We describe the effects of drugs on the disease fingerprint and on its reversal to a healthy metabolomic status. Drug toxicity can be also monitored by metabolomics. We also show how the individual metabolomic fingerprint at the onset of a disease may discriminate responders from non-responders to a given drug, or how it may be prognostic of e.g., cancer recurrence after many years. In parallel with fingerprinting, profiling (i.e., the identification and quantification of many metabolites and, in the case of selected biofluids, of the lipoprotein components that contribute to the ¹H NMR spectral features) can provide hints on the metabolic pathways that are altered by a disease and assess their restoration after treatment.

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.

Quantitative Serum NMR Spectroscopy Stratifies COVID-19 Patients and Sheds Light on Interfaces of Host Metabolism and the Immune Response with Cytokines and Clinical Parameters

The complex manifestations of COVID-19 are still not fully decoded on the molecular level. We combined quantitative the nuclear magnetic resonance (NMR) spectroscopy serum analysis of metabolites, lipoproteins and inflammation markers with clinical parameters and a targeted cytokine panel to characterize COVID-19 in a large (534 patient samples, 305 controls) outpatient cohort of recently tested PCR-positive patients. The COVID-19 cohort consisted of patients who were predominantly in the initial phase of the disease and mostly exhibited a milder disease course. Concerning the metabolic profiles of SARS-CoV-2-infected patients, we identified markers of oxidative stress and a severe dysregulation of energy metabolism. NMR markers, such as phenylalanine, inflammatory glycoproteins (Glyc) and their ratio with the previously reported supramolecular phospholipid composite (Glyc/SPC), showed a predictive power comparable to laboratory parameters such as C-reactive protein (CRP) or ferritin. We demonstrated interfaces between the metabolism and the immune system, e.g., we could trace an interleukin (IL-6)-induced transformation of a high-density lipoprotein (HDL) to a pro-inflammatory actor. Finally, we showed that metadata such as age, sex and constitution (e.g., body mass index, BMI) need to be considered when exploring new biomarkers and that adding NMR parameters to existing diagnoses expands the diagnostic toolbox for patient stratification and personalized medicine.

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.

Reduced circulating FABP2 in patients with moderate to severe COVID-19 may indicate enterocyte functional change rather than cell death

The gut is of importance in the pathology of COVID-19 both as a route of infection, and gut dysfunction influencing the severity of disease. Systemic changes caused by SARS-CoV-2 gut infection include alterations in circulating levels of metabolites, nutrients and microbial products which alter immune and inflammatory responses. Circulating plasma markers for gut inflammation and damage such as zonulin, lipopolysaccharide and β-glycan increase in plasma along with severity of disease. However, Intestinal Fatty Acid Binding Protein / Fatty Acid Binding Protein 2 (I-FABP/FABP2), a widely used biomarker for gut cell death, has paradoxically been shown to be reduced in moderate to severe COVID-19. We also found this pattern in a pilot cohort of mild (n = 18) and moderately severe (n = 19) COVID-19 patients in Milan from March to June 2020. These patients were part of the first phase of COVID-19 in Europe and were therefore all unvaccinated. After exclusion of outliers, patients with more severe vs milder disease showed reduced FABP2 levels (median [IQR]) (124 [368] vs. 274 [558] pg/mL, P < 0.01). A reduction in NMR measured plasma relative lipid-CH3 levels approached significance (median [IQR]) (0.081 [0.011] vs. 0.073 [0.024], P = 0.06). Changes in circulating lipid levels are another feature commonly observed in severe COVID-19 and a weak positive correlation was observed in the more severe group between reduced FABP2 and reduced relative lipid-CH3 and lipid-CH2 levels. FABP2 is a key regulator of enterocyte lipid import, a process which is inhibited by gut SARS-CoV-2 infection. We propose that the reduced circulating FABP2 in moderate to severe COVID-19 is a marker of infected enterocyte functional change rather than gut damage, which could also contribute to the development of hypolipidemia in patients with more severe disease.

Quantitative NMR-Based Lipoprotein Analysis Identifies Elevated HDL-4 and Triglycerides in the Serum of Alzheimer’s Disease Patients

Alzheimer’s disease (AD) is the most common form of dementia in the elderly and has been associated with changes in lipoprotein metabolism. We performed quantitative lipoprotein analysis in a local cohort of cognitively impaired elderly and control subjects using standardized nuclear magnetic resonance (NMR) spectroscopy. A commercially available quantitative NMR-based assay covering 112 lipoprotein main and subtype variables was used to investigate blood serum samples from a moderate cohort size of 161 persons (71 female, 90 male), including measures of quality control. Additionally, clinical metadata and cerebrospinal fluid AD biomarkers were collected and used for analysis. High-density lipoprotein (HDL) HDL-4 subfraction levels were mostly high in female individuals with mild cognitive impairment (MCI), followed by AD. Low-density lipoprotein (LDL) LDL-2 cholesterol was slightly elevated in male AD patients. HDL-2 apolipoprotein Apo-A1, HDL-2 phospholipids, and HDL-3 triglycerides were highly abundant in AD and MCI women compared to men. When considering clinical biomarkers (Aβ, tau), very low-density lipoprotein (VLDL) VLDL-1 and intermediate-density lipoprotein (IDL) triglycerides were substantially higher in AD compared to MCI. In addition, triglyceride levels correlated positively with dementia. Different lipoprotein serum patterns were identified for AD, MCI, and control subjects. Interestingly, HDL-4 and LDL-2 cholesterol parameters revealed strong gender-specific changes in the context of AD-driven dementia. As gender-based comparisons were based on smaller sub-groups with a low n-number, several statistical findings did not meet the significance threshold for multiple comparisons testing. Still, our finding suggests that serum HDL-4 parameters and various triglycerides correlate positively with AD pathology which could be a read-out of extended lipids traveling through the blood-brain barrier, supporting amyloid plaque formation processes. Thereof, we see herein a proof of concept that this quantitative NMR-based lipoprotein assay can generate important and highly interesting data for refined AD diagnosis and patient stratification, especially when larger cohorts are available.

Metabolomics analysis identifies glutamic acid and cystine imbalances in COVID-19 patients without comorbid conditions. Implications on redox homeostasis and COVID-19 pathophysiology

It is well known that the presence of comorbidities and age-related health issues may hide biochemical and metabolic features triggered by SARS-CoV-2 infection and other diseases associated to hypoxia, as they are by themselves chronic inflammatory conditions that may potentially disturb metabolic homeostasis and thereby negatively impact on COVID-19 progression. To unveil the metabolic abnormalities inherent to hypoxemia caused by COVID-19, we here applied gas chromatography coupled to mass spectrometry to analyze the main metabolic changes exhibited by a population of male patients less than 50 years of age with mild/moderate and severe COVID-19 without pre-existing comorbidities known to predispose to life-threatening complications from this infection. Several differences in serum levels of particular metabolites between normal controls and patients with COVID-19 as well as between mild/moderate and severe COVID-19 were identified. These included increased glutamic acid and reduced glutamine, cystine, threonic acid, and proline levels. In particular, using the entire metabolomic fingerprint obtained, we observed that glutamine/glutamate metabolism was associated with disease severity as patients in the severe COVID-19 group presented the lowest and higher serum levels of these amino acids, respectively. These data highlight the hypoxia-derived metabolic alterations provoked by SARS-CoV-2 infection in the absence of pre-existing co-morbidities as well as the value of amino acid metabolism in determining reactive oxygen species recycling pathways, which when impaired may lead to increased oxidation of proteins and cell damage. They also provide insights on new supportive therapies for COVID-19 and other disorders that involve altered redox homeostasis and lower oxygen levels that may lead to better outcomes of disease severity.