A study published in BMC Infectious Diseases has examined how existing blood samples from a COVID-19 survey can be reused to improve detection of leprosy, a disease that is often diagnosed late due to limited laboratory tools.
Researchers analyzed stored serum samples from a population-based survey in Brazil, combining antibody testing, a digital symptom questionnaire, and geographic mapping. Among individuals who underwent follow-up clinical assessment, 12 previously undiagnosed cases of leprosy were identified, highlighting the presence of missed or “hidden” disease in the community.
A key focus was the evaluation of newer antibody markers targeting the Mce1A protein of Mycobacterium leprae. These markers – particularly IgM anti-Mce1A – performed better than the commonly used PGL-I test in identifying active cases. This is relevant because current serological tests can miss cases, especially in patients with lower bacterial loads.
The study also assessed a machine learning tool that analyses symptom questionnaires. When combined with serology, this approach achieved very high sensitivity, meaning it was able to identify all detected cases in the study while reducing unnecessary clinical evaluations.
Importantly, mapping of cases and antibody positivity showed no clear clustering, suggesting that transmission may be more widespread than expected rather than confined to known high-risk areas. This has implications for screening strategies, indicating that broader surveillance may be needed.
The findings reinforce several points: early leprosy diagnosis remains challenging; no single test is sufficient; and combining serology, digital tools, and clinical assessment may improve case detection. The study also highlights the potential value of using existing sample collections to support surveillance for other infectious diseases.
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