Stratification of Sepsis Patients on Admission into the Intensive Care Unit According to Differential Plasma Metabolic Phenotypes.

Delayed diagnosis of patients with sepsis or septic shock is associated with increased mortality and morbidity. UPLC-MS and NMR spectroscopy were used to measure panels of lipoproteins, lipids, biogenic amines, amino acids, and tryptophan pathway metabolites in blood plasma samples collected from 152 patients within 48 h of admission into the Intensive Care Unit (ICU) where 62 patients had no sepsis, 71 patients had sepsis, and 19 patients had septic shock. Patients with sepsis or septic shock had higher concentrations of neopterin and lower levels of HDL cholesterol and phospholipid particles in comparison to nonsepsis patients. Septic shock could be differentiated from sepsis patients based on different concentrations of 10 lipids, including significantly lower concentrations of five phosphatidylcholine species, three cholesterol esters, one dihydroceramide, and one phosphatidylethanolamine. The Supramolecular Phospholipid Composite (SPC) was reduced in all ICU patients, while the composite markers of acute phase glycoproteins were increased in the sepsis and septic shock patients within 48 h admission into ICU. We show that the plasma metabolic phenotype obtained within 48 h of ICU admission is diagnostic for the presence of sepsis and that septic shock can be differentiated from sepsis based on the lipid profile.

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.

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.