Evidence map›Paper›PMID 40067811›Full record

ArticlePLoS neglected tropical diseases2025

Unsupervised machine learning identifies biomarkers of disease progression in post-kala-azar dermal leishmaniasis in Sudan.

Ana Torres, Brima Musa Younis, Samuel Tesema, Jose Carlos Solana, Javier Moreno, Antonio J Martín-Galiano, Ahmed Mudawi Musa, Fabiana Alves, Eugenia Carrillo

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Article in PLoS neglected tropical diseases, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

9 authors.

Ana TorresWHO Collaborating Centre for Leishmaniasis. Spanish National Centre for Microbiology, Instituto de Salud Carlos III, Majadahonda (Madrid), Spain.
Brima Musa YounisInstitute of Endemic Diseases, University of Khartoum, Khartoum, Sudan.
Samuel TesemaDrugs for Neglected Diseases Initiative, Nairobi, Kenya.
Jose Carlos SolanaWHO Collaborating Centre for Leishmaniasis. Spanish National Centre for Microbiology, Instituto de Salud Carlos III, Majadahonda (Madrid), Spain.ORCID 0000-0002-0485-6127
Javier MorenoWHO Collaborating Centre for Leishmaniasis. Spanish National Centre for Microbiology, Instituto de Salud Carlos III, Majadahonda (Madrid), Spain.
Antonio J Martín-GalianoCore Scientific and Technical Units, Instituto de Salud Carlos III, Madrid, Spain.
Ahmed Mudawi MusaInstitute of Endemic Diseases, University of Khartoum, Khartoum, Sudan.
Fabiana AlvesDrugs for Neglected Diseases Initiative, Geneva, Switzerland.
Eugenia CarrilloWHO Collaborating Centre for Leishmaniasis. Spanish National Centre for Microbiology, Instituto de Salud Carlos III, Majadahonda (Madrid), Spain.

Funding

Instituto de Salud Carlos III
6 · The paper itself

Abstract

backgroundPost-kala-azar dermal leishmaniasis (PKDL) appears as a rash in some individuals who have recovered from visceral leishmaniasis caused by Leishmania donovani. Today, basic knowledge of this neglected disease and how to predict its progression remain largely unknown. METHODS AND

findingsThis study addresses the use of several biochemical, haematological and immunological variables, independently or through unsupervised machine learning (ML), to predict PKDL progression risk. In 110 patients from Sudan, 31 such factors were assessed in relation to PKDL disease state at the time of diagnosis: progressive (worsening) versus stable. To identify key factors associated with PKDL worsening, we used both a conventional statistical approach and multivariate analysis through unsupervised ML. The independent use of these variables had limited power to predict skin lesion severity in a baseline examination. In contrast, the unsupervised ML approach identified a set of 10 non-redundant variables that was linked to a 3.1 times higher risk of developing progressive PKDL. Three of these clustering factors (low albumin level, low haematocrit and low IFN-γ production in PBMCs after Leishmania antigen stimulation) were remarkable in patients with progressive disease. Dimensionality re-establishment identified 11 further significantly modified factors that are also important to understand the worsening phenotype. Our results indicate that the combination of anaemia and a weak Th1 immunological response is likely the main physiological mechanism that leads to progressive PKDL.

conclusionsA combination of 14 biochemical variables identified by unsupervised ML was able to detect a worsening PKDL state in Sudanese patients. This approach could prove instrumental to train future supervised algorithms based on larger patient cohorts both for a more precise diagnosis and to gain insight into fundamental aspects of this complication of visceral leishmaniasis.

Indexed as

BiomarkersLeishmaniasis, CutaneousLeishmaniasis, VisceralMachine LearningAdolescentAdultChildDisease ProgressionFemaleHumansLeishmania donovaniMaleMiddle AgedSudanYoung AdultBiomarkers

Identifiers

PMID40067811
PMCPMC11932480

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