Elaine Holmes

Background and aims: Diabetic retinopathy (DR) is a major complication of diabetes mellitus and a leading cause of blindness globally. Early detection and intervention underpin optimal DR management and remain challenging. The underlying biological mechanisms are also poorly understood. This study examined comprehensive plasma lipid profiles using a targeted approach to detect potential DR biomarkers.

Materials and methods: We utilised data and samples from 762 adult participants with type 2 diabetes (mean age 64.5 years, 54.3% males, median diabetes duration 7.0 years) from the community-based longitudinal Fremantle Diabetes Study Phase II. All had DR status (none, mild non-proliferative diabetic retinopathy (NPDR), moderate NPDR, or severe NPDR or worse) assessed by colour fundus photography at baseline and at the Year 4 or 6 review. Ultra-performance liquid chromatography-tandem mass spectrometry was used for plasma lipid profiling using baseline samples. Multiple logistic regression was used to identify baseline associates of i) any new or worsening DR and ii) any incident DR. The likelihood ratio test (LRT) was used to evaluate the incremental contribution of potential new lipid biomarkers. The net reclassification improvement (NRI) was also calculated.

Results: Any new/worsening DR was observed in 121 participants (16%) and 35 of 495 without DR at baseline (7.0%) developed DR during follow-up. We detected five lipids independently associated with one or both DR outcomes, specifically cholesterol ester (20:4), fatty acid (20:3), lysophosphatidylglycerol (18:0), lysophosphatidylglycerol (18:1) and phosphatidylcholine (18:0_20:3). For any new/worsening DR, the inclusion of lipid parameters (cholesterol ester (20:4), fatty acid (20:3) and lysophosphatidylglycerol (18:1)) in addition to conventional risk factors (systolic hypertension, HbA1c, blood glucose-lowering treatment intensity and urinary albumin:creatinine) added significantly to the conventional model (LRT, P=0.00002). The NRI gain for at a moderate cut-off of 10% was 5.7% (SE 2.8%; P=0.043). For incident DR, inclusion of lipid parameters (fatty acid (20:3), lysophosphatidylglycerol (18:0) and phosphatidylcholine (18:0_20:3)) with HbA1c as the only conventional risk factor improved model performance (LRT, P=0.00001). The NRI gain at a moderate cut-off of 5% risk of incident DR was 28.3% (P=0.002).

Conclusion: These data demonstrate that disturbances in lipid metabolism are associated with DR progression in type 2 diabetes. The present five lipid biomarkers have not been identified as determinants of incident DR in limited previous longitudinal studies but have the potential to improve DR risk prediction and provide novel insights into the mechanistic pathways underlying the development and progression of DR.

Background: Metabolic phenotyping is an established tool in systems medicine that captures a profile of one’s individual health status and reflects the interaction between genes and external stressors. It uses analytical platforms such as NMR or MS to acquire molecular profiles, and modelling to extract actionable knowledge. Applying these techniques to a cohort of Australians infected by SARS-CoV-2 revealed a strong alteration in regions of 1D NMR spectra associated with lipoproteins and glycoproteins, and referred as Supramolecular Phospholipid Composite SPC (δ = 3.2 ppm) and Glyc (δ = 2.07 ppm). The latter is an established marker of inflammation. These results were later confirmed using larger cohorts from Spain (n = 525) and the UK (n = 1022). The urgent need for very rapid testing, at the early stage of the pandemic, prompted the development of bespoke NMR experiments able to measure this lipoproteins/glycoproteins signature without requiring complex modelling.

Methods: Physico-chemical properties of lipoprotein particles, such as diffusion, transverse and longitudinal relaxation rates, differ from low molecular weight metabolites. Therefore, an edited experiment JEDI (PGPE) was designed that combines diffusion, relaxation and scalar coupling editing blocks to produce a lipoprotein profile devoid of chemical noise (overlapping peaks) and where peaks give quantitative results.

Results: The SPC peak was further broken down into 3 sub-regions that are related to the main subfraction HDL and LDL (choline headgroups of phospholipids). Interestingly the ratio of SPC3/SPC2 highly correlates with the Apo-B100/Apo-A1 ratio, an established cardiovascular risk marker that is increased in patients with COVID19. Longitudinal data suggest this latter remains elevated even 30 days after onset in some patients. Furthermore, the edited experiment performs equally well at low field using a benchtop NMR.

Conclusion: Translation of a spectral signature relevant for monitoring COVID patients from high to low field NMR is possible using sophisticated editing techniques.

Background
It is estimated that 10% of patients that suffered from severe acute SARS-CoV-2 infection experience symptoms beyond 3 months post-disease onset. Long COVID is a multisystemic condition that comprises more than 200 symptoms. Common new-onset medical conditions include cardiovascular, type-2 diabetes and chronic fatigue syndrome. Whilst progress has been made in characterising the mechanisms underlying long COVID, supported by similarities with other viral infections, available diagnostics are still insufficient. Molecular phenotyping is an established systems medicine tool that provides an integrative profile of an individual’s biological status that results from the cooperative genomic, transcriptomic and proteomic response to environmental stimuli. It exploits spectroscopic platforms based on nuclear magnetic resonance spectroscopy (NMR) and mass spectrometry (MS) to generate precise information on a plethora of molecules that can be modelled to explain specific physiological and pathological conditions.

Methods
A well characterised longitudinal cohort with >200 SARS-CoV-2 positive individuals was collected in Cambridge Hospitals (2020, Wuhan strain). Each individual was classified according to the severity of its respiratory symptoms ranging from A (asymptomatic) to E (external ventilation). For each sample, cellular and immunological parameters, NMR lipoproteins, amino acid, tryptophan and lipidomics assays were measured. Functional PCA models captured latent dynamics in individual trajectories for C-reactive protein, an established marker of inflammation, and metabolic parameters found to be associated with it. Based on these trends, patient heterogeneity was explored using Gaussian mixture modelling and the resulting stratification was used to train personalised predictors of disease outcome using the first time point after onset as input. The addition of a penalty function during training ensured a parsimonious selection of parameters. Patients were asked to complete surveys months (2–6) after infection. Abundance and co-occurrence of symptoms was assessed using latent factor analysis.

Results
Linear mixed model regression revealed strong associations between C-reactive protein abundance and molecular parameters. Quinolinic acid, VLDL triglycerides and phospholipids, VLDL and IDL cholesterol and glycoprotein related GlycA are positively correlated, while tryptophan, taurine, indole-3-acetic acid, HDL cholesterol, and phospholipids and the supramolecular phospholipid composite (SPC) peak are strongly anti-correlated. Those findings are congruent with prior work on cohorts from Western Australia and Spain. In both cases, strong lipoprotein signatures were observed; GlycA is increased during acute phase, while SPC and HDL subfraction 4 are strongly depleted. The analysis of individual trajectories measured by CRP and associated parameters revealed 3 groups, mildly affected, good recovery and poor prospect. Patients from the last category showed profoundly altered metabolic profiles even weeks after onset, and a higher co-occurrence of neurological related symptoms. Furthermore, disease outcome scores were predicted for each patient in the early stage of infection. The training of these predictors also distilled a panel of most relevant parameters for that purpose. NMR lipoproteins featured excellent predictive capabilities.

Conclusion
Broad phenotyping, combined with multi-view multivariate analysis, allowed for robust stratification of COVID-19 patients and accurate personalised prediction of disease outcome. It also confirms the critical role played by lipoprotein metabolism in the immune response that is successfully captured by NMR-base lipoprotein parameters.