Nathan Lawler

"NmrControlsDiabetesFasted" is a quantified 1H NMR dataset of 198 healthy controls, 208 people with type 2 diabetes mellitus (T2DM) who acted as disease controls, and 13 people with T2DM who participated in a twelve-day exercise intervention. Blood samples were collected cross-sectionally for healthy controls and T2DM-controls, whereas data from two time points (i.e., pre- and post-intervention) are provided for T2DM participants of the exercise intervention. All samples in this dataset were collected in a fasted state.

"NmrLipidsDiabetes" is a dataset of 13 people with T2DM who participated in a twelve-day exercise intervention. As part of the study, participants completed an oral glucose tolerance test (OGTT) before (OGTT1) and after (OGTT2) the twelve-day intervention. Fasted blood samples were collected prior to consuming a drink containing 75g of glucose and at 30-minute intervals until reaching the two-hour mark (i.e., at 30, 60, 90, and 120 minutes post-consumption). Additionally, blood samples were collected before (Pre) and immediately after (Post) exercise training on the first (Ex1) and last day (Ex12) of the intervention. Samples around the exercise sessions were only collected for nine participants. The OGTT and exercise samples were analysed using 1H NMR and lipidomics LC-MS methods.

NMR data were collected on Bruker 600 MHz Avance III HD spectrometers equipped with a BBI probe and with integral Bruker SampleJet robots, and data were automatically processed using Bruker Topspin™ 3.6.2 and ICON NMR to enable phasing, baseline correction, and calibration to TSP (δ = 0) (Dona et al., 2014; Lodge et al., 2021; Lodge et al., 2024).

Liquid chromatography was performed by a SCIEX ExionLC (SCIEX, Concord, CA) where separation was conducted using Waters Acquity BEH C18 1.7 μm, 2.1 × 100 mm column (Waters Corp., MA, USA) at 60°C (Ryan et al., 2023). Mass detection was conducted by a SCIEX QTRAP 6500+ (SCIEX, Concord, CA) with electrospray ionisation and polarity switching (Ryan et al., 2023).

Demographics of the study population and a detailed description of sample preparation and analysis are provided in the journal article associated with this dataset. A reference to this paper will be provided upon publication.

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.