Abstract 204: Detection and Determination of Protein Network Associated With Atrial Fibrillation Subtypes

Atrial fibrillation (AF) is associated with increased risks of stroke, cardiac failure, and mortality. The underlying mechanisms and pathology of AF remain elusive. The aim of this study is to proteomically analyze the left atrial appendage tissue obtained from patients suffering from subtypes (paroxysmal, persistent, and long-standing persistent) AF. MALDI Imaging mass spectrometry (MALDI-IMS) was applied to differentiate in classification of pathophysiological AF subtypes, through the direct (in situ) analysis of formalin-fixed paraffin embedded (FFPE) left atrial appendage (LAA) tissue. FFPE LAA tissue were collected from patients with predisposed paroxysmal (n = 9, mean age 69.0±3.1 years), persistent (n = 18, mean age 67.0±2.7 years), and long-standing persistent AF (n = 19, mean age 71.0±2.0 years). Sections were dewaxed and thereupon soused by trypsin solutions using an automated spraying device. Spectra were acquired at a mass range of m/z 800-3500Da and lateral resolution of 80 μm. Two hundred laser shots were acquired per pixel and random walk of 50/position. Data analyses were performed using SCiLS Lab software. Component analysis of MALDI Imaging data through probabilistic latent semantic analysis results in a clear discrimination in the first 3 components of atrial fibrillation. Employing receiver operating characteristic analysis (AUC > 0.7), characteristic intensity distribution in given m/z values, which are discriminative for the considered cluster, was determined to distinguish between paroxysmal vs. persistent AF, and persistent vs. long-persistent AF, m/z values were determined between persistent vs long-persistent AF (1.59±0.12 vs 6.85±3.02, p = 0.02). Follow-up of neurological events in case-controlled assessment presented 13±12% in paroxysmal, 56±12% in persistent and 42±12% long-persistent AF. The tissue-based proteomic approach provides clinically relevant beneficial information in improving risk stratification for AF patients. In the future, this obtained information might be considered new biomarker to support the diagnosis of the severity of AF status. They also suggest a new criterion to determine the most appropriate procedure for each AF subtype to improve postoperative outcomes.