Serum geoepidemiology of leprosy biomarkers in a city-wide COVID-19 survey in Brazil

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Abstract Background COVID-19 has created a significant global health emergency and triggered numerous seroepidemiological field tracing initiatives. The use of these samples becomes a timely tool for intensifying active case finding and early diagnosis of leprosy. This study aimed to conduct a seroepidemiological evaluation using leprosy biomarker antibodies against the Mce1A and PGL-I antigens, and to analyze the spatial distribution of both actively detected cases and antibody levels, thereby characterizing the geoepidemiological pattern of bacillary circulation, combined with case-screening strategies. Methods A cross-sectional and geoepidemiological study was carried out using the biorepository of samples from the COVID-19 serosurvey in a municipality in southeastern Brazil. Screening diagnosis using LSQ and the artificial intelligence system MaLeSQs® was applied to investigate neurodermatological signs and symptoms of leprosy (n = 224). IgA, IgM, and IgG anti-Mce1A and IgM anti-PGL-I antibodies were measured using indirect ELISA (n = 195). Georeferencing was employed to create the distribution maps of individuals within the municipality. Global spatial autocorrelation analysis was performed and applied to the serological scores. Results The responses to the clinical questionnaire reported the predominance of neurological signs and symptoms. Twelve new cases were diagnosed (32.4%), and the detection rate in the population sample evaluated was 6.15%. The IgA anti-Mce1A ELISA showed the highest seropositivity (55.3%), the highest rates [median = 0.93 (IQR = 0.59–1.41)]. The IgM and IgG anti-Mce1A serology showed higher rates (P < 0.0001) as compared to the anti-PGL-I serology. The overlap of positivity with the antibodies tested highlights the greater involvement of double or triple positives when Mce1A serology was used. IgM anti-Mce1A serology was positive in 66.7% [8/12 cases; median = 1.25 (IQR = 0.70–1.68)] of new cases detected. IgM anti-Mce1A showed the best serological performance, and its combination with MaLeSQs® (OR condition) achieved 100% sensitivity and NPV, with 68% specificity. Serology with the IgA antibody presented the highest rates in georeferencing analysis and served as an alert for contact with the bacillus. The sociodemographic variables tested did not exhibit statistical difference in spatial autocorrelation (p > 0.05), indicating the absence of a spatially clustered pattern for serological values in the analyzed territory. Conclusion These findings identify IgM anti-Mce1A and MaLeSQs® as key tools to strengthen leprosy case-finding and screening efficiency. The study also revealed a diffuse pattern of transmission and exposure within the municipality and highlights the value of integrating serological biomarkers and digital diagnostic platforms to support earlier detection of leprosy.

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