Multi-omics of a pre-clinical model of diabetic cardiomyopathy reveals increased fatty acid supply impacts mitochondrial metabolic selectivity.

The incidence of type 2 diabetes (T2D) is increasing globally, with long-term implications for human health and longevity. Heart disease is the leading cause of death in T2D patients, who display an elevated risk of an acute cardiovascular event and worse outcomes following such an insult. The underlying mechanisms that predispose the diabetic heart to this poor prognosis remain to be defined. This study developed a pre-clinical model (Rattus norvegicus) that complemented caloric excess from a high-fat diet (HFD) and pancreatic ß-cell dysfunction from streptozotocin (STZ) to produce hyperglycaemia, peripheral insulin resistance, hyperlipidaemia and elevated fat mass to mimic the clinical features of T2D. Ex vivo cardiac function was assessed using Langendorff perfusion with systolic and diastolic contractile depression observed in T2D hearts. Cohorts representing untreated, individual HFD- or STZ-treatments and the combined HFD + STZ approach were used to generate ventricular samples (n = 9 per cohort) for sequential and integrated analysis of the proteome, lipidome and metabolome by liquid chromatography-tandem mass spectrometry. This study found that in T2D hearts, HFD treatment primed the metabolome, while STZ treatment was the major driver for changes in the proteome. Both treatments equally impacted the lipidome. Our data suggest that increases in ß-oxidation and early TCA cycle intermediates promoted rerouting via 2-oxaloacetate to glutamate, γ-aminobutyric acid and glutathione. Furthermore, we suggest that the T2D heart activates networks to redistribute excess acetyl-CoA towards ketogenesis and incomplete ß-oxidation through the formation of short-chain acylcarnitine species. Multi-omics provided a global and comprehensive molecular view of the diabetic heart, which distributes substrates and products from excess ß-oxidation, reduces metabolic flexibility and impairs capacity to restore high energy reservoirs needed to respond to and prevent subsequent acute cardiovascular events.

Metabolic Signatures in Coronary Artery Disease: Results from the BioHEART-CT Study.

Despite effective prevention programs targeting cardiovascular risk factors, coronary artery disease (CAD) remains the leading cause of death. Novel biomarkers are needed for improved risk stratification and primary prevention. To assess for independent associations between plasma metabolites and specific CAD plaque phenotypes we performed liquid chromatography mass-spectrometry on plasma from 1002 patients in the BioHEART-CT study. Four metabolites were examined as candidate biomarkers. Dimethylguanidino valerate (DMGV) was associated with presence and amount of CAD (OR) 1.41 (95% Confidence Interval [CI] 1.12-1.79, p = 0.004), calcified plaque, and obstructive CAD (p < 0.05 for both). The association with amount of plaque remained after adjustment for traditional risk factors, ß-coefficient 0.17 (95% CI 0.02-0.32, p = 0.026). Glutamate was associated with the presence of non-calcified plaque, OR 1.48 (95% CI 1.09-2.01, p = 0.011). Phenylalanine was associated with amount of CAD, ß-coefficient 0.33 (95% CI 0.04-0.62, p = 0.025), amount of calcified plaque, (ß-coefficient 0.88, 95% CI 0.23-1.53, p = 0.008), and obstructive CAD, OR 1.84 (95% CI 1.01-3.31, p = 0.046). Trimethylamine N-oxide was negatively associated non-calcified plaque OR 0.72 (95% CI 0.53-0.97, p = 0.029) and the association remained when adjusted for traditional risk factors. In targeted metabolomic analyses including 53 known metabolites and controlling for a 5% false discovery rate, DMGV was strongly associated with the presence of calcified plaque, OR 1.59 (95% CI 1.26-2.01, p = 0.006), obstructive CAD, OR 2.33 (95% CI 1.59-3.43, p = 0.0009), and amount of CAD, ß-coefficient 0.3 (95% CI 0.14-0.45, p = 0.014). In multivariate analyses the lipid and nucleotide metabolic pathways were both associated with the presence of CAD, after adjustment for traditional risk factors. We report novel associations between CAD plaque phenotypes and four metabolites previously associated with CAD. We also identified two metabolic pathways strongly associated with CAD, independent of traditional risk factors. These pathways warrant further investigation at both a biomarker and mechanistic level.