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Updated: Sep 23, 2025

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Laboratory perspectives for Leprosy: Diagnostic, prognostic and predictive tools.

Kiran Preet Malhotra1, Nuzhat Husain1

  • 1Department of Pathology, Dr. Ram Manohar Lohia Institute of Medical Sciences, Gomti Nagar, Lucknow, Uttar Pradesh, India.

Indian Journal of Pathology & Microbiology
|May 13, 2022
PubMed
Summary

Diagnosing leprosy is challenging due to varying host immunity impacting test results. This review analyzes current and novel laboratory tests for accurate leprosy diagnosis, prognostication, and treatment monitoring.

Keywords:
Cytokinesdiagnosisdrug resistance testsleprosypolymerase chain reactionprognosisserologic tests

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Area of Science:

  • Medical Microbiology
  • Immunology
  • Diagnostic Technologies

Background:

  • Leprosy diagnosis presents challenges due to host immune status influencing bacillary load, serology, and tissue response.
  • Current diagnostic tests for leprosy often lack sensitivity for paucibacillary cases and specificity in differentiating contacts from diseased individuals.

Purpose of the Study:

  • To critically analyze available laboratory tests for leprosy diagnosis, prognostication, and prediction of treatment response.
  • To compare the pros and cons of conventional, serologic, and molecular diagnostic methods for leprosy.
  • To suggest an optimal algorithm for leprosy diagnosis utilizing various tests in different clinical settings.

Main Methods:

  • Review of conventional diagnostic tests including slit skin smears and biopsies.
  • Analysis of newer serologic tests measuring antibody titers (e.g., phenolic glycolipid-1).
  • Evaluation of molecular tests like Polymerase Chain Reaction (PCR) for M. leprae DNA detection in various samples.
  • Assessment of cytokine signatures (T helper I/II) for leprosy subtyping and machine learning algorithms for contact prediction.

Main Results:

  • Slit skin smears and biopsies are crucial for excluding mimics, confirming diagnosis, and immunological subtyping.
  • Serologic tests offer noninvasive detection with good specificity.
  • PCR-based methods effectively detect M. leprae DNA in diverse biological samples.
  • Cytokine profiles and machine learning algorithms show potential for subtyping and risk prediction in contacts.

Conclusions:

  • A combination of diagnostic approaches, including serologic and molecular tests, is necessary for comprehensive leprosy management.
  • Newer diagnostic tools enhance the ability to diagnose, subtype, predict treatment response, and monitor drug resistance in leprosy.
  • An integrated diagnostic algorithm is proposed to optimize the utilization of these tests across various clinical scenarios.