A novel integrated molecular and serological analysis method to predict new cases of leprosy amongst household contacts

Abstract

Background Early detection of Mycobacterium leprae is a key strategy for disrupting the transmission chain of leprosy and preventing the potential onset of physical disabilities. Clinical diagnosis is essential, but some of the presented symptoms may go unnoticed, even by specialists. In areas of greater endemicity, serological and molecular tests have been performed and analyzed separately for the follow-up of household contacts, who are at high risk of developing the disease. The accuracy of these tests is still debated, and it is necessary to make them more reliable, especially for the identification of cases of leprosy between contacts. We proposed an integrated analysis of molecular and serological methods using artificial intelligence by the random forest (RF) algorithm to better diagnose and predict new cases of leprosy. Methods The study was developed in Governador Valadares, Brazil, a hyperendemic region for leprosy. A longitudinal study was performed, including new cases diagnosed in 2011 and their respective household contacts, who were followed in 2011, 2012, and 2016. All contacts were diligently evaluated by clinicians from Reference Center for Endemic Diseases (CREDEN-PES) before being classified as asymptomatic. Samples of slit skin smears (SSS) from the earlobe of the patients and household contacts were collected for quantitative polymerase chain reaction (qPCR) of 16S rRNA, and peripheral blood samples were collected for ELISA assays to detect LID-1 and ND-O-LID. Results The statistical analysis of the tests revealed sensitivity for anti-LID-1 (63.2%), anti-ND-O-LID (57.9%), qPCR SSS (36.8%), and smear microscopy (30.2%). However, the use of RF allowed for an expressive increase in sensitivity in the diagnosis of multibacillary leprosy (90.5%) and especially paucibacillary leprosy (70.6%). It is important to report that the specificity was 92.5%. Conclusion The proposed model using RF allows for the diagnosis of leprosy with high sensitivity and specificity and the early identification of new cases among household contacts.

Detection of new Mycobacterium leprae subtype in Bangladesh by genomic characterization to explore transmission patterns

“…M. leprae DNA detection frequency in HHC from Bangladesh (12.3% in SSS and 18.0% in NS) was in line with previous studies conducted in several hyperendemic areas of Brazil, Colombia and Indonesia (45,(47)(48)(49)(50). In India higher positivity (21%) in SSS of HHC was reported (38) whereas in a Brazilian study from Uberlandia, up to 49% of positivity in NS was observed (12).…”

Functional biomarker signatures of circulating T-cells and its association with distinct clinical status of leprosy patients and their respective household contacts

“…These findings suggest that household contact should be monitored for putative subclinical infection. This hypothesis was further confirmed in a 6 years’ follow-up study demonstrating that a long time some household contacts developed leprosy disease [ 28 ]. Therefore, the significant level of antigen-specific cytokine secretion by peripheral blood cells of household contacts, especially those HHC(MB), described in the present investigation, provides reliable evidence of their higher susceptibility to leprosy disease.…”

Prospects for new leprosy diagnostic tools, a narrative review considering ELISA and PCR assays

“…This is a robust multivariate analysis, able to build classification trees with minimal error rates, which is favorable for the diagnosis of diseases such as leprosy 56,57 . 55 , used the classification model "Sick × Healthy" in the Random Forest analysis based on serological, molecular, and bacilloscopic data, to evaluate the prediction of infection in clinically diagnosed cases and household contacts, accompanying them for five years. This model, Random Forest, highlighted the possibility of early diagnosis of MB (90.5%) and especially PB (70.6%) cases, while separately evaluated tests did not reach the same high rate of diagnostic correctness.…”

“…In areas of greater endemicity, serological and molecular tests have been carried out and analyzed separately. However, Gama et al (2019) 55 proposed the integration of these methods to assist in the diagnosis and monitoring of household contacts using the Random Forest algorithm for data analysis. This is a robust multivariate analysis, able to build classification trees with minimal error rates, which is favorable for the diagnosis of diseases such as leprosy 56 , 57 .…”