Peer-reviewedCC BYJACC. Basic to translational science
Identifying Cardiac Autoantibody Biomarkers that Predict the Severity of Chagas Cardiomyopathy.
15 affiliations
- 1Department of Clinical Analysis and Toxicology, Federal University of Rio Grande do Norte, Natal, RN, Brazil; Department of Pediatrics, The Labatt Family Heart Centre and Translational Medicine, The Hospital for Sick Children, Toronto, Ontario, Canada.
- 2Department of Pediatrics, The Labatt Family Heart Centre and Translational Medicine, The Hospital for Sick Children, Toronto, Ontario, Canada; Institute of Medical Science, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada.
- 3Department of Clinical Analysis and Toxicology, Federal University of Rio Grande do Norte, Natal, RN, Brazil.
- 4Department of Pediatrics, The Labatt Family Heart Centre and Translational Medicine, The Hospital for Sick Children, Toronto, Ontario, Canada.
- 5Department of Infectious Disease, Tropical Medicine, University of São Paulo, São Paulo, SP, Brazil.
- 6Dante Pazzanese Institute of Cardiology, São Paulo, SP, Brazil.
- 7Department of Morphology, Institute of Biological Sciences, Federal University of Minas Gerais, Belo Horizonte, MG, Brazil.
- 8Department of Congenital Heart Diseases, Cardinal Stefan Wyszyński National Institute of Cardiology, Warsaw, Poland.
- 9Arrhythmia Unit, Heart Institute (Institute Coração), University of São Paulo Medical School, São Paulo, Brazil.
- 10Laboratory of Genetics and Molecular Cardiology (LGMC), Heart Institute (Institute Coração), University of São Paulo Medical School, São Paulo, Brazil.
- 11Department of Internal Medicine, School of Medicine, Telehealth Center, Cardiology Service, Hospital das Clínicas, Federal University of Minas Gerais, Belo Horizonte, Minas Gerais, Brazil.
- 12Laboratory of Clinical Immunology and Allergy, Heart Institute (InCor), Hospital das Clínicas, Faculdade de Medicina da Universidade de São Paulo, São Paulo, Brazil; Institute of Investigation in Immunology, National Institute of Science and Technology (iii-INCT), São Paulo, Brazil.
- 13Institute of Parasitology, McGill University, Sainte-Anne-de-Bellevue, Quebec, Canada; Division of Clinical and Translational Research, Department of Medicine, McGill University, Montreal, Quebec, Canada.
- 14Department of Clinical Analysis and Toxicology, Federal University of Rio Grande do Norte, Natal, RN, Brazil; Department of Pediatrics, The Labatt Family Heart Centre and Translational Medicine, The Hospital for Sick Children, Toronto, Ontario, Canada. Electronic address: [email protected].
- 15Department of Pediatrics, The Labatt Family Heart Centre and Translational Medicine, The Hospital for Sick Children, Toronto, Ontario, Canada. Electronic address: [email protected].
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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BibTeX
@article{ribeiro2026identifying,
title = {{Identifying Cardiac Autoantibody Biomarkers that Predict the Severity of Chagas Cardiomyopathy.}},
author = {Heriks Ribeiro and Anton Dmitriev and Antonia Pereira Rosa Neta and Meena Fatah and Diptendu Chatterjee and Lea Campos de Oliveira and Fabiano Castro Albrecht and Hui Tzu Lin Wang and Juliana de Assis S Gomes and Olgierd Woźniak and Karolina Borowiec and Elżbieta Katarzyna Biernacka and Antonia Claudia Jácome da Câmara and Luciana Sacilotto and Francisco Carlos da Costa Darrieux and Maurício Ibrahim Scanavacca and José Eduardo Krieger and Lúcia Maria da Cunha Galvão and Walderez Dutra and Antonio Luiz Pinho Ribeiro and Edecio Cunha-Neto and Maria Carmo Pereira Nunes and Ester Cerdeira Sabino and Igor Cestari and Andre Ducati Luchessi and Vivian Nogueira Silbiger and Robert M Hamilton},
journal = {JACC. Basic to translational science},
year = {2026},
month = oct,
doi = {10.1016/j.jacbts.2026.101709},
url = {https://europepmc.org/article/MED/42854667},
note = {CC BY},
}
RIS
TY - JOUR
TI - Identifying Cardiac Autoantibody Biomarkers that Predict the Severity of Chagas Cardiomyopathy.
AU - Heriks Ribeiro
AU - Anton Dmitriev
AU - Antonia Pereira Rosa Neta
AU - Meena Fatah
AU - Diptendu Chatterjee
AU - Lea Campos de Oliveira
AU - Fabiano Castro Albrecht
AU - Hui Tzu Lin Wang
AU - Juliana de Assis S Gomes
AU - Olgierd Woźniak
AU - Karolina Borowiec
AU - Elżbieta Katarzyna Biernacka
AU - Antonia Claudia Jácome da Câmara
AU - Luciana Sacilotto
AU - Francisco Carlos da Costa Darrieux
AU - Maurício Ibrahim Scanavacca
AU - José Eduardo Krieger
AU - Lúcia Maria da Cunha Galvão
AU - Walderez Dutra
AU - Antonio Luiz Pinho Ribeiro
AU - Edecio Cunha-Neto
AU - Maria Carmo Pereira Nunes
AU - Ester Cerdeira Sabino
AU - Igor Cestari
AU - Andre Ducati Luchessi
AU - Vivian Nogueira Silbiger
AU - Robert M Hamilton
PY - 2026
DA - 2026/10/09
JO - JACC. Basic to translational science
DO - 10.1016/j.jacbts.2026.101709
UR - https://europepmc.org/article/MED/42854667
AB - 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.
SN - 2452-302X
ER -