| Summary: | Leprosy is a disease caused by Mycobacterium leprae that carries a high risk of disability, making early diagnosis mandatory. This study aimed to determine the applicability of anti-PGL-1 IgM antibody detection, using the ML FLOW technique, as an assistant tool for the detection of leprosy infection in asymptomatic household contacts (AHHC) of multibacillary leprosy index cases from Midwest Brazil. Serological changes induced by the prophylaxis of these household contacts with Bacillus Calmette-Guérin (BCG) were also verified. A total of 91 AHHC were assessed, among which, 18.68% (n = 17) presented both positive bacilloscopy and positive anti-PGL-1 IgM serology. Positivity concordance between these two laboratorial exams (Kappa Index = 1; p < 0.001) was indicated, however, one case did not demonstrate concordance between the semiquantitative assessment of anti-PGL-1 IgM and the bacilloscopy index (Kappa Index = 0.96; p < 0.001). Among the 17 AHHC with positive bacilloscopy, eight were reassessed after prophylaxis with BCG and two of them presented negative anti-PGL-1 IgM serology, being these patients who had presented a bacilloscopy index of < 2[+] in the initial assessment. This study shows that anti-PGL-1 IgM detection may be used as a tool to determine the bacillary load in AHHC and to detect immune changes related to prophylaxis by nonspecific vaccination. A hanseníase é doença causada pelo Mycobacterium leprae, apresentando elevado potencial incapacitante, o que torna indispensável seu diagnóstico precoce. O estudo visa determinar a aplicabilidade da detecção de anticorpos anti-PGL1-IgM por meio da técnica do ML FLOW como ferramenta adjuvante ao diagnóstico de hanseníase em contatos domiciliares assintomáticos (AHHC) de pacientes multibacilares procedentes da região Centro-Oeste do Brasil, bem como, documentar o comportamento sorológico após a profilaxia com a vacina Bacillus Calmette-Guérin (BCG). Foram avaliados 91 AHHC atendidos no Hospital Universitário de Brasília - Brasil, dos quais 18,68% ...
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