Novel Lipid Species for Detecting and Predicting Atrial Fibrillation in Patients With Type 2 Diabetes

Abstract

The incidence of atrial fibrillation (AF) is higher in patients with diabetes. The goal of this study was to assess if the addition of plasma lipids to traditional risk factors could improve the ability to detect and predict future AF in patients with type 2 diabetes. Logistic regression models were used to identify lipids associated with AF or future AF from plasma lipids (n = 316) measured from participants in the ADVANCE trial (n = 3,772). To gain mechanistic insight, follow-up lipid analysis was undertaken in a mouse model that has an insulin-resistant heart and is susceptible to AF. Sphingolipids, cholesteryl esters, and phospholipids were associated with AF prevalence, whereas two monosialodihexosylganglioside (GM3) ganglioside species were associated with future AF. For AF detection and prediction, addition of six and three lipids, respectively, to a base model (n = 12 conventional risk factors) increased the C-statistics (detection: from 0.661 to 0.725; prediction: from 0.674 to 0.715) and categorical net reclassification indices. The GM3(d18:1/24:1) level was lower in patients in whom AF developed, improved the C-statistic for the prediction of future AF, and was lower in the plasma of the mouse model susceptible to AF. This study demonstrates that plasma lipids have the potential to improve the detection and prediction of AF in patients with diabetes.