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.

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.

J-Edited DIffusional Proton Nuclear Magnetic Resonance Spectroscopic Measurement of Glycoprotein and Supramolecular Phospholipid Biomarkers of Inflammation in Human Serum.

Proton nuclear magnetic resonance (NMR) N-acetyl signals (Glyc) from glycoproteins and supramolecular phospholipids composite peak (SPC) from phospholipid quaternary nitrogen methyls in subcompartments of lipoprotein particles) can give important systemic metabolic information, but their absolute quantification is compromised by overlap with interfering resonances from lipoprotein lipids themselves. We present a J-Edited DIffusional (JEDI) proton NMR spectroscopic approach to selectively augment signals from the inflammatory marker peaks Glyc and SPCs in blood serum NMR spectra, which enables direct integration of peaks associated with molecules found in specific compartments. We explore a range of pulse sequences that allow editing based on peak J-modulation, translational diffusion, and T2 relaxation time and validate them for untreated blood serum samples from SARS-CoV-2 infected patients (n = 116) as well as samples from healthy controls and pregnant women with physiological inflammation and hyperlipidemia (n = 631). The data show that JEDI is an improved approach to selectively investigate inflammatory signals in serum and may have widespread diagnostic applicability to disease states associated with systemic inflammation.

Quantitative In-Vitro Diagnostic NMR Spectroscopy for Lipoprotein and Metabolite Measurements in Plasma and Serum: Recommendations for Analytical Artifact Minimization with Special Reference to COVID-19/SARS-CoV-2 Samples.

Quantitative nuclear magnetic resonance (NMR) spectroscopy of blood plasma is widely used to investigate perturbed metabolic processes in human diseases. The reliability of biochemical data derived from these measurements is dependent on the quality of the sample collection and exact preparation and analysis protocols. Here, we describe systematically, the impact of variations in sample collection and preparation on information recovery from quantitative proton (1H) NMR spectroscopy of human blood plasma and serum. The effects of variation of blood collection tube sizes and preservatives, successive freeze-thaw cycles, sample storage at -80 °C, and short-term storage at 4 and 20 °C on the quantitative lipoprotein and metabolite patterns were investigated. Storage of plasma samples at 4 °C for up to 48 h, freezing at -80 °C and blood sample collection tube choice have few and minor effects on quantitative lipoprotein profiles, and even storage at 4 °C for up to 168 h caused little information loss. In contrast, the impact of heat-treatment (56 °C for 30 min), which has been used for inactivation of SARS-CoV-2 and other viruses, that may be required prior to analytical measurements in low level biosecurity facilities induced marked changes in both lipoprotein and low molecular weight metabolite profiles. It was conclusively demonstrated that this heat inactivation procedure degrades lipoproteins and changes metabolic information in complex ways. Plasma from control individuals and SARS-CoV-2 infected patients are differentially altered resulting in the creation of artifactual pseudo-biomarkers and destruction of real biomarkers to the extent that data from heat-treated samples are largely uninterpretable. We also present several simple blood sample handling recommendations for optimal NMR-based biomarker discovery investigations in SARS CoV-2 studies and general clinical biomarker research.