Identifying Cardiac Autoantibody Biomarkers that Predict the Severity of Chagas Cardiomyopathy
Abstract
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.
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Authors: Hériks Gomes Ribeiro, Anton Dmitriev, Antonia Pereira Rosa Neta, Meena Fatah, Diptendu Chatterjee, Léa Campos de Oliveira, Fabiano Castro Albrecht, Hui Tzu Lin Wang, Juliana de Assis S. Gomes, Olgierd Woźniak, Karolina Borowiec, Elżbieta Katarzyna Biernacka, Antonia Claudia Jácome da Câmara, Luciana Sacilotto, Francisco Darrieux, Maurício Scanavacca, José Eduardo Krieger, Lúcia Maria da Cunha Galvão, Walderez Dutra, Antônio Luiz Pinho Ribeiro, Edécio Cunha‐Neto, Maria Carmo Pereira Nunes, Ester Cerdeira Sabino, Cestari Igor, Andre Luchessi, Vivian Nogueira Silbiger, Robert M. Hamilton
Institutions: Universidade de São Paulo, Universidade Federal do Rio Grande do Norte, Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo, Hospital for Sick Children, SickKids Foundation, McGill University, Universidade Federal de Minas Gerais, Institute of Cardiology, Hospital das Clínicas da Universidade Federal de Minas Gerais, Instituto de Medicina Tropical, Instituto Dante Pazzanese de Cardiologia, Ted Rogers Centre for Heart Research, Instituto do Coração - HCFMUSP