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Publications

Journal Article Epublish 5 citations

PubMed: 38380250 DOI: 10.1016/j.isci.2024.109135

An integrative multi-omics approach to characterize interactions between tuberculosis and diabetes mellitus.

iScience 27(3):109135

Authors: Caian L Vinhaes, Eduardo R Fukutani, Gabriel C Santana, María B Arriaga, Beatriz Barreto-Duarte, Mariana Araújo-Pereira, Mateus Maggitti-Bezerril, Alice M S Andrade, Marina C Figueiredo, Ginger L Milne, Valeria C Rolla, Afrânio L Kristki, Marcelo Cordeiro-Santos, Timothy R Sterling, Bruno B Andrade, Artur T L Queiroz

Affiliations

  • Laboratório de Pesquisa Clínica e Translacional, Instituto Gonçalo Moniz, Fundação Oswaldo Cruz, Salvador 40296-710, Brazil.
  • Multinational Organization Network Sponsoring Translational and Epidemiological Research (MONSTER) Initiative, Salvador 41810-710, Brazil.
  • Programa de Pós-Graduação em Medicina e Saúde Humana, Escola Bahiana de Medicina e Saúde Pública (EBMSP), Salvador 40290-150, Brazil.
  • Departamento de Infectologia, Hospital Português da Bahia, Salvador 40140-901, Brazil.
  • Instituto de Pesquisa Clínica e Translacional, Faculdade de Tecnologia e Ciências, Salvador 41741-590, Brazil.
  • Centro de Integração de Dados e Conhecimentos para Saúde, Instituto Gonçalo Moniz, Fundação Oswaldo Cruz, Salvador, Brazil.
  • Curso de Medicina, Universidade Salvador, Salvador, Brazil.
  • Division of Infectious Diseases, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA.
  • Programa Acadêmico de Tuberculose. Faculdade de Medicina, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil.
  • Faculdade de Medicina, Univerdidade Federal da Bahia, Salvador, Brazil.
  • Multinational Organization Network Sponsoring Translational and Epidemiological Research (MONSTER) Initiative, Salvador 41810-710, Brazil. Instituto de Pesquisa Clínica e Translacional, Faculdade de Tecnologia e Ciências, Salvador 41741-590, Brazil.
  • Division of Clinical Pharmacology, Department of Medicine, Vanderbilt University School of Medicine, Nashville, TN, USA.
  • Instituto Nacional de Infectologia Evandro Chagas, Fiocruz, Rio de Janeiro, Brazil.
  • Fundação Medicina Tropical Doutor Heitor Vieira Dourado, Manaus, Brazil.
  • Programa de Pós-Graduação em Medicina Tropical, Universidade do Estado do Amazonas, Manaus, Brazil.
  • Universidade Nilton Lins, Manaus, Brazil.

Abstract

Tuberculosis-diabetes mellitus (TB-DM) is linked to a distinct inflammatory profile, which can be assessed using multi-omics analyses. Here, a machine learning algorithm was applied to multi-platform data, including cytokines and gene expression in peripheral blood and eicosanoids in urine, in a Brazilian multi-center TB cohort. There were four clinical groups: TB-DM(n = 24), TB only(n = 28), DM(HbA1c ≥ 6.5%) only(n = 11), and a control group of close TB contacts who did not have TB or DM(n = 13). After cross-validation, baseline expression or abundance of MMP-28, LTE-4, 11-dTxB2, PGDM, FBXO6, SECTM1, and LINCO2009 differentiated the four patient groups. A distinct multi-omic-derived, dimensionally reduced, signature was associated with TB, regardless of glycemic status. SECTM1 and FBXO6 mRNA levels were positively correlated with sputum acid-fast bacilli grade in TB-DM. Values of the biomarkers decreased during the course of anti-TB therapy. Our study identified several markers associated with the pathophysiology of TB-DM that could be evaluated in future mechanistic investigations.

MeSH

Diabetes Mellitus, Multiple Chronic Conditions, Tuberculosis

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