2026-04-17T03:38:49Zhttps://recercat.cat/oai/request

oai:recercat.cat:11351/126672025-10-24T10:24:04Zcom_2072_378070com_2072_378040col_2072_378092

2025-10-24T10:24:04Z urn:hdl:11351/12667 Identification of Chagas disease biomarkers using untargeted metabolomics Bosch-Nicolau, Pau Perez-Molina, Jose Antonio Monge-Maillo, Begoña Herreros-Cabello, Alfonso Salvador, Fernando Rodríguez Palomares, José F SÁNCHEZ-MONTALVÁ, ADRIÁN Molina Romero, Israel Marcadors bioquímics Chagas, Malaltia de Miocardi - Malalties Metabolòmica CHEMICALS AND DRUGS::Biological Factors::Biomarkers DISCIPLINES AND OCCUPATIONS::Natural Science Disciplines::Biological Science Disciplines::Biochemistry::Metabolomics DISEASES::Parasitic Diseases::Protozoan Infections::Euglenozoa Infections::Trypanosomiasis::Chagas Disease::Chagas Cardiomyopathy COMPUESTOS QUÍMICOS Y DROGAS::factores biológicos::biomarcadores DISCIPLINAS Y OCUPACIONES::disciplinas de las ciencias naturales::disciplinas de las ciencias biológicas::bioquímica::metabolómica ENFERMEDADES::enfermedades parasitarias::infecciones por protozoos::infecciones por Euglenozoa::tripanosomiasis::enfermedad de Chagas::miocardiopatía chagásica Biomarkers; Chagas disease; Untargeted metabolomics Biomarcadores; Enfermedad de Chagas; Metabolómica no dirigida Biomarcadors; Malaltia de Chagas; Metabolòmica no dirigida Untargeted metabolomic analysis is a powerful tool used for the discovery of novel biomarkers. Chagas disease (CD), caused by Trypanosoma cruzi, is a neglected tropical disease that affects 6–7 million people with approximately 30% developing cardiac manifestations. The most significant clinical challenge lies in its long latency period after acute infection, and the lack of surrogate markers to predict disease progression or cure. In this cross-sectional study, we analyzed sera from 120 individuals divided into four groups: 31 indeterminate CD, 41 chronic chagasic cardiomyopathy (CCC), 18 Latin Americans with other cardiomyopathies and 30 healthy volunteers. Using a high-throughput panel of 986 metabolites, we identified three distinct profiles among individuals with cardiomyopathy, indeterminate CD and healthy volunteers. After a more stringent analysis, we identified some potential biomarkers. Among peptides, phenylacetylglutamine and fibrinopeptide B (1–13) exhibited an increasing trend from controls to ICD and CCC. Conversely, reduced levels of bilirubin and biliverdin alongside elevated urobilin correlated with disease progression. Finally, elevated levels of cystathionine, phenol glucuronide and vanillactate among amino acids distinguished CCC individuals from ICD and controls. Our novel exploratory study using metabolomics identified potential biomarker candidates, either alone or in combination that if confirmed, can be translated into clinical practice. This project was funded by “Proyectos de investigación en salud” program of the Instituto Carlos III, Ministry of Science, Innovation and Universities of the Spanish Government. Grant number: PI19/01807. Dr Ribeiro is supported in part by CNPq (465518/2014-1 e 310790/2021-2) and FAPEMIG (RED 00192-23). Dr. Sabino is supported in part by NIH grant: 1U01AI168383-01. 2025-10-24T10:24:04Z 2025-10-24T10:24:04Z 2025-02-28T09:13:12Z 2025-02-28T09:13:12Z 2024-08-13 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion http://hdl.handle.net/11351/12667 Scientific Reports;14 https://doi.org/10.1038/s41598-024-69205-w info:eu-repo/grantAgreement/ES/PE2017-2020/PI19%2F01807 Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/ info:eu-repo/semantics/openAccess Nature Portfolio Scientia