Julien Wist

Understanding the distribution and variation in NMR-based inflammatory markers is crucial in the evaluation of their clinical utility in disease prognosis and diagnosis. We applied high resolution 1H NMR spectroscopy of blood plasma and serum to measure the acute phase reactive glycoprotein signals (GlycA and GlycB) and the subregions of the lipoprotein based Supramolecular Phospholipid Composite signals (SPC1, SPC2 and SPC3) in a large multi-cohort population study. A total of 5702 samples were measured to determine the signal variations in a range of chronic and acute inflammatory conditions. We found that while the GlycA and GlycB were increased in inflammation, the SPC regions behaved independently of Glyc signals, with SPC2 and SPC3 being reduced in chronic inflammation in comparison to healthy controls (p-value SPC2=2.9x10-10, p-value SPC3=2.2x10-3) and SPC1 (p-value=0.29) being unchanged. SPC1 was decreased in acute inflammation indicating a link to the immune response (p-value=2.5x10-11). These findings confirm the independent biological relevance of all 3 SPC subregions and contraindicate the use of aggregate SPC values as general inflammatory markers.

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 are determined relatively soon after infection, opening the opportunity to select more intensive therapeutic interventions to these “high risk” patients in the early disease stages.

Between 2012 and 2014, 715 green coffee samples were gathered by Almacafé S.A. (Bogotá, Colombia) from 27 countries. These were analysed at the nuclear magnetic resonance (NMR) laboratory at Universidad del Valle (Cali, Colombia). Over 1000 methanolic coffee extracts were prepared and 4563 spectra were acquired in a fully automatic manner using a 400 MHz NMR spectrometer (Bruker Biospin, Germany). The dataset spans the variance that could be expected for an industrial application of origin monitoring, including samples from different harvest times, collected over several years, and processed by at least two distinct operators. The resulting 1D and 2D spectra can be used to develop and evaluate feature extraction methods, multivariate algorithms, and automation monitoring techniques. They can also be used as datasets for teaching, or as a reference for new studies of similar samples and approaches.