Identifying Cardiac Autoantibody Biomarkers that Predict the Severity of Chagas Cardiomyopathy.

Chagas cardiomyopathy (ChCM) affects up to 30% of patients with chronic Chagas disease and lacks reliable biomarkers for severity and conduction abnormalities. We profiled cardiac autoantibodies using 2-dimensional gel electrophoresis and mass spectrometry, followed by enzyme-linked immunosorbent assay validation and machine learning analysis. Six autoantibodies linked to glycolysis, adenosine triphosphate metabolism, and sarcomere structure distinguished ChCM stages (area under the curve: 0.87; 95% CI: 0.777-0.977) and a mean classification accuracy (0.84; 95% CI: 0.705-0.935). These findings suggest that an antibody-based panel may enable cost-effective risk stratification and prognostication in ChCM, with prospective validation before clinical application.