Increasing the complexity of lipoprotein characterization for cardiovascular risk in type 2 diabetes.

The burden of cardiovascular disease is particularly high among individuals with diabetes, even when LDL cholesterol is normal or within the therapeutic target. Despite this, cholesterol accumulates in their arteries, in part, due to persistent atherogenic dyslipidaemia characterized by elevated triglycerides, remnant cholesterol, smaller LDL particles and reduced HDL cholesterol. The causal link between dyslipidaemia and atherosclerosis in T2DM is complex, and our contention is that a deeper understanding of lipoprotein composition and functionality, the vehicle that delivers cholesterol to the artery, will provide insight for improving our understanding of the hidden cardiovascular risk of diabetes. This narrative review covers three levels of complexity in lipoprotein characterization: 1-the information provided by routine clinical biochemistry, 2-advanced nuclear magnetic resonance (NMR)-based lipoprotein profiling and 3-the identification of minor components or physical properties of lipoproteins that can help explain arterial accumulation in individuals with normal LDLc levels, which is typically the case in individuals with T2DM. This document highlights the importance of incorporating these three layers of lipoprotein-related information into population-based studies on ASCVD in T2DM. Such an attempt should inevitably run in parallel with biotechnological solutions that allow large-scale determination of these sets of methodologically diverse parameters.

Lipidomics Reveals Myocardial Lipid Composition in a Murine Model of Insulin Resistance Induced by a High-Fat Diet.

Ectopic fat accumulation in non-adipose tissues is closely related to diabetes-related myocardial dysfunction. Nevertheless, the complete picture of the lipid metabolites involved in the metabolic-related myocardial alterations is not fully characterized. The aim of this study was to characterize the specific lipid profile in hearts in an animal model of obesity/insulin resistance induced by a high-fat diet (HFD). The cardiac lipidome profiles were assessed via liquid chromatography-mass spectrometry (LC-MS)/MS-MS and laser desorption/ionization-mass spectrometry (LDI-MS) tissue imaging in hearts from C57BL/6J mice fed with an HFD or standard-diet (STD) for 12 weeks. Targeted lipidome analysis identified a total of 63 lipids (i.e., 48 triacylglycerols (TG), 5 diacylglycerols (DG), 1 sphingomyelin (SM), 3 phosphatidylcholines (PC), 1 DihydroPC, and 5 carnitines) modified in hearts from HFD-fed mice compared to animals fed with STD. Whereas most of the TG were up-regulated in hearts from animals fed with an HFD, most of the carnitines were down-regulated, thereby suggesting a reduction in the mitochondrial ß-oxidation. Roughly 30% of the identified metabolites were oxidated, pointing to an increase in lipid peroxidation. Cardiac lipidome was associated with a specific biochemical profile and a specific liver TG pattern. Overall, our study reveals a specific cardiac lipid fingerprint associated with metabolic alterations induced by HFD.

The Lipoprotein Profile Evaluated by 1H-NMR Improves the Performance of Genetic Testing in Familial Hypercholesterolemia.

BACKGROUND: The familial hypercholesterolemia (FH) diagnosis is based on clinical and genetic criteria. A relevant proportion of FH patients fulfilling the criteria for definite FH have negative genetic testing. Increasing the identification of true genetic-based FH is a clinical challenge. Deepening the analysis of lipoprotein alterations could help increase the yield of genetic testing. We evaluated whether the number, size, and composition of lipoproteins assessed by 1H-NMR could increase the identification of FH patients with pathogenic gene variants. METHODS: We studied 294 clinically definite FH patients, 222 (75.5%) with positive genetic testing, as the discovery cohort. As an external validation cohort, we studied 88 children with FH, 72 (81%) with positive genetic testing. The advanced lipoprotein test based on 1H-NMR (Liposcale®) was performed at baseline after a lipid-lowering drug wash-out of at least 6 weeks. The association of variables with genetic variants was evaluated by random forest and logistic regression. Areas under the curve (AUCs) were calculated. A predictive formula was developed and applied to the validation cohort. RESULTS: A formula derived from NMR lipoprotein analyses improved the identification of genetically positive FH patients beyond LDL-C levels (AUC=0.87). The parameters contributing the most to the identification formula were LDL particle number, HDL size and remnant cholesterol. The formula also increases the classification of FH children with a pathogenic genetic variation. CONCLUSIONS: NMR lipoprotein profile analysis identifies differences beyond standard lipid parameters that help identify FH with a positive pathogenic gene variant, increasing the yield of genetic testing in FH patients.