Plasma lipid profiling shows similar associations with prediabetes and type 2 diabetes.

The relationship between lipid metabolism with prediabetes (impaired fasting glucose and impaired glucose tolerance) and type 2 diabetes mellitus is poorly defined. We hypothesized that a lipidomic analysis of plasma lipids might improve the understanding of this relationship. We performed lipidomic analysis measuring 259 individual lipid species, including sphingolipids, phospholipids, glycerolipids and cholesterol esters, on fasting plasma from 117 type 2 diabetes, 64 prediabetes and 170 normal glucose tolerant participants in the Australian Diabetes, Obesity and Lifestyle Study (AusDiab) then validated our findings on 1076 individuals from the San Antonio Family Heart Study (SAFHS). Logistic regression analysis of identified associations with type 2 diabetes (135 lipids) and prediabetes (134 lipids), after adjusting for multiple covariates. In addition to the expected associations with diacylglycerol, triacylglycerol and cholesterol esters, type 2 diabetes and prediabetes were positively associated with ceramide, and its precursor dihydroceramide, along with phosphatidylethanolamine, phosphatidylglycerol and phosphatidylinositol. Significant negative associations were observed with the ether-linked phospholipids alkylphosphatidylcholine and alkenylphosphatidylcholine. Most of the significant associations in the AusDiab cohort (90%) were subsequently validated in the SAFHS cohort. The aberration of the plasma lipidome associated with type 2 diabetes is clearly present in prediabetes, prior to the onset of type 2 diabetes. Lipid classes and species associated with type 2 diabetes provide support for a number of existing paradigms of dyslipidemia and suggest new avenues of investigation.

Plasma lipidomic analysis of stable and unstable coronary artery disease.

OBJECTIVE: Traditional risk factors for coronary artery disease (CAD) fail to adequately distinguish patients who have atherosclerotic plaques susceptible to instability from those who have more benign forms. Using plasma lipid profiling, this study aimed to provide insight into disease pathogenesis and evaluate the potential of lipid profiles to assess risk of future plaque instability. METHODS AND RESULTS: Plasma lipid profiles containing 305 lipids were measured on 220 individuals (matched healthy controls, n=80; stable angina, n=60; unstable coronary syndrome, n=80) using electrospray-ionisation tandem mass spectrometry. ReliefF feature selection coupled with an L2-regularized logistic regression based classifier was used to create multivariate classification models which were verified via 3-fold cross-validation (1000 repeats). Models incorporating both lipids and traditional risk factors provided improved classification of unstable CAD from stable CAD (C-statistic=0.875, 95% CI 0.874-0.877) compared with models containing only traditional risk factors (C-statistic=0.796, 95% CI 0.795-0.798). Many of the lipids identified as discriminatory for unstable CAD displayed an association with disease acuity (severity), suggesting that they are antecedents to the onset of acute coronary syndrome. CONCLUSION: Plasma lipid profiling may contribute to a new approach to risk stratification for unstable CAD.