Julia P A S Tormin1, Bruno R Nascimento2, Craig A Sable3, Jose Luiz P da Silva4, Camilo Brandao-de-Resende5, Luiz Paulo C Rocha5, Cecília H R Pinto5, Eula Graciele A Neves5, Frederico V B Macedo6, Clara L Fraga6, Kaciane K B Oliveira1, Adriana C Diamantino7, Antônio Luiz P Ribeiro1, Andrea Z Beaton8, Maria Carmo P Nunes1, Walderez O Dutra9. 1. Serviço de Cardiologia e Cirurgia Cardiovascular e Centro de Telessaúde do Hospital das Clínicas da UFMG, Belo Horizonte, MG, Brazil; Faculdade de Medicina da Universidade Federal de Minas Gerais, Belo Horizonte, MG, Brazil. 2. Serviço de Cardiologia e Cirurgia Cardiovascular e Centro de Telessaúde do Hospital das Clínicas da UFMG, Belo Horizonte, MG, Brazil; Faculdade de Medicina da Universidade Federal de Minas Gerais, Belo Horizonte, MG, Brazil. Electronic address: ramosnas@gmail.com. 3. Children's National Health System, Washington, DC, United States. 4. Departamento de Estatística, Universidade Federal do Paraná, Curitiba, PR, Brazil. 5. Laboratory of Cell-Cell Interactions, Institute of Biological Sciences, Department of Morphology, Belo Horizonte, Brazil. 6. Faculdade de Medicina da Universidade Federal de Minas Gerais, Belo Horizonte, MG, Brazil. 7. FIPMoc University Center, Montes Claros, MG, Brazil. 8. The Heart Institute, Cincinnati Childrens Hospital Medical Center, and the University of Cincinnati School of Medicine, Cincinnati, OH, United States. 9. Laboratory of Cell-Cell Interactions, Institute of Biological Sciences, Department of Morphology, Belo Horizonte, Brazil; National Institute of Science and Technology in Tropical Diseases (INCT-DT), Salvador, BA, Brazil.
Abstract
INTRODUCTION: Inflammation associated with rheumatic heart disease (RHD) is influenced by gene polymorphisms and inflammatory cytokines. There are currently no immunologic and genetic markers to discriminate latent versus clinical patients, critical to predict disease evolution. Employing machine-learning, we searched for predictors that could discriminate latent versus clinical RHD, and eventually identify latent patients that may progress to clinical disease. METHODS: A total of 212 individuals were included, 77 with latent, 100 with clinical RHD, and 35 healthy controls. Circulating levels of 27 soluble factors were evaluated using Bio-Plex ProTM® Human Cytokine Standard 27-plex assay. Gene polymorphism analyses were performed using RT-PCR for the following genes: IL2, IL4, IL6, IL10, IL17A, TNF and IL23. RESULTS: Serum levels of all cytokines were higher in clinical as compared to latent RHD patients, and in those groups than in controls. IL-4, IL-8, IL-1RA, IL-9, CCL5 and PDGF emerged in the final multivariate model as predictive factors for clinical, compared with latent RHD. IL-4, IL-8 and IL1RA had the greater power to predict clinical RHD. In univariate analysis, polymorphisms in IL2 and IL4 were associated with clinical RHD and in the logistic analysis, IL6 (GG + CG), IL10 (CT + TT), IL2 (CA + AA) and IL4 (CC) genotypes were associated with RHD. CONCLUSION: Despite higher levels of all cytokines in clinical RHD patients, IL-4, IL-8 and IL-1RA were the best predictors of clinical disease. An association of polymorphisms in IL2, IL4, IL6 and IL10 genes and clinical RHD was observed. Gene polymorphism and phenotypic expression of IL-4 accurately discriminate latent versus clinical RHD, potentially instructing clinical management.
INTRODUCTION: Inflammation associated with rheumatic heart disease (RHD) is influenced by gene polymorphisms and inflammatory cytokines. There are currently no immunologic and genetic markers to discriminate latent versus clinical patients, critical to predict disease evolution. Employing machine-learning, we searched for predictors that could discriminate latent versus clinical RHD, and eventually identify latent patients that may progress to clinical disease. METHODS: A total of 212 individuals were included, 77 with latent, 100 with clinical RHD, and 35 healthy controls. Circulating levels of 27 soluble factors were evaluated using Bio-Plex ProTM® Human Cytokine Standard 27-plex assay. Gene polymorphism analyses were performed using RT-PCR for the following genes: IL2, IL4, IL6, IL10, IL17A, TNF and IL23. RESULTS: Serum levels of all cytokines were higher in clinical as compared to latent RHD patients, and in those groups than in controls. IL-4, IL-8, IL-1RA, IL-9, CCL5 and PDGF emerged in the final multivariate model as predictive factors for clinical, compared with latent RHD. IL-4, IL-8 and IL1RA had the greater power to predict clinical RHD. In univariate analysis, polymorphisms in IL2 and IL4 were associated with clinical RHD and in the logistic analysis, IL6 (GG + CG), IL10 (CT + TT), IL2 (CA + AA) and IL4 (CC) genotypes were associated with RHD. CONCLUSION: Despite higher levels of all cytokines in clinical RHD patients, IL-4, IL-8 and IL-1RA were the best predictors of clinical disease. An association of polymorphisms in IL2, IL4, IL6 and IL10 genes and clinical RHD was observed. Gene polymorphism and phenotypic expression of IL-4 accurately discriminate latent versus clinical RHD, potentially instructing clinical management.
Authors: Gláucia Maria Moraes de Oliveira; Luisa Campos Caldeira Brant; Carisi Anne Polanczyk; Deborah Carvalho Malta; Andreia Biolo; Bruno Ramos Nascimento; Maria de Fatima Marinho de Souza; Andrea Rocha De Lorenzo; Antonio Aurélio de Paiva Fagundes Júnior; Beatriz D Schaan; Fábio Morato de Castilho; Fernando Henpin Yue Cesena; Gabriel Porto Soares; Gesner Francisco Xavier Junior; Jose Augusto Soares Barreto Filho; Luiz Guilherme Passaglia; Marcelo Martins Pinto Filho; M Julia Machline-Carrion; Marcio Sommer Bittencourt; Octavio M Pontes Neto; Paolo Blanco Villela; Renato Azeredo Teixeira; Roney Orismar Sampaio; Thomaz A Gaziano; Pablo Perel; Gregory A Roth; Antonio Luiz Pinho Ribeiro Journal: Arq Bras Cardiol Date: 2022-01 Impact factor: 2.000
Authors: Juliane Franco; Bruno R Nascimento; Andrea Z Beaton; Kaciane K B Oliveira; Marcia M Barbosa; Sanny Cristina C Faria; Nayana F Arantes; Luana A Mello; Maria Cecília L Nassif; Guilherme C Oliveira; Breno C Spolaor; Carolina F Campos; Victor R H Silva; Marcelo Augusto A Nogueira; Antonio L Ribeiro; Craig A Sable; Maria Carmo P Nunes Journal: Pathogens Date: 2022-01-24
Authors: Joselyn Rwebembera; Bruno Ramos Nascimento; Neema W Minja; Sarah de Loizaga; Twalib Aliku; Luiza Pereira Afonso Dos Santos; Bruno Fernandes Galdino; Luiza Silame Corte; Vicente Rezende Silva; Andrew Young Chang; Walderez Ornelas Dutra; Maria Carmo Pereira Nunes; Andrea Zawacki Beaton Journal: Pathogens Date: 2022-01-28