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

Background: Post-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 findings:...

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Autores: Torres Garcia, Ana Maria, Younis, Brima Musa, Tesema, Samuel, Solana, Jose Carlos, Moreno, Javier, Martin-Galiano, Antonio Javier, Musa, Ahmed Mudawi, Alves, Fabiana, Carrillo, Eugenia
Tipo de recurso: artículo
Fecha de publicación:2025
País:España
Institución:Instituto de Salud Carlos III (ISCIII)
Repositorio:Repisalud
Idioma:inglés
OAI Identifier:oai:repisalud.isciii.es:20.500.12105/26655
Acceso en línea:https://hdl.handle.net/20.500.12105/26655
Access Level:acceso abierto
Palabra clave:Adolescent
Adult
Biomarkers
Child
Disease Progression
Female
Humans
Leishmania donovani
Leishmaniasis, Cutaneous
Leishmaniasis, Visceral
Machine Learning
Male
Middle Aged
Sudan
Young Adult
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oai_identifier_str oai:repisalud.isciii.es:20.500.12105/26655
network_acronym_str ES
network_name_str España
repository_id_str
spelling Unsupervised machine learning identifies biomarkers of disease progression in post-kala-azar dermal leishmaniasis in SudanTorres Garcia, Ana MariaYounis, Brima MusaTesema, SamuelSolana, Jose CarlosMoreno, JavierMartin-Galiano, Antonio JavierMusa, Ahmed MudawiAlves, FabianaCarrillo, EugeniaAdolescentAdultBiomarkersChildDisease ProgressionFemaleHumansLeishmania donovaniLeishmaniasis, CutaneousLeishmaniasis, VisceralMachine LearningMaleMiddle AgedSudanYoung AdultBackground: Post-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 findings: This 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. Conclusions: A 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.Public Library of Science (PLOS)Instituto de Salud Carlos IIICentro de Investigación Biomédica en Red - CIBERINFEC (Enfermedades Infecciosas)20252025-05-1320252025-03-0120252025-03-01research articlehttp://purl.org/coar/resource_type/c_2df8fbb1VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfapplication/ziphttps://hdl.handle.net/20.500.12105/26655reponame:Repisaludinstname:Instituto de Salud Carlos III (ISCIII)InglésengESMVP322 19 Not availableESPI22 00009 Not availableESCB21 13 00018open accesshttp://purl.org/coar/access_right/c_abf2Attribution 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:repisalud.isciii.es:20.500.12105/266552026-06-12T12:43:37Z
dc.title.none.fl_str_mv Unsupervised machine learning identifies biomarkers of disease progression in post-kala-azar dermal leishmaniasis in Sudan
title Unsupervised machine learning identifies biomarkers of disease progression in post-kala-azar dermal leishmaniasis in Sudan
spellingShingle Unsupervised machine learning identifies biomarkers of disease progression in post-kala-azar dermal leishmaniasis in Sudan
Torres Garcia, Ana Maria
Adolescent
Adult
Biomarkers
Child
Disease Progression
Female
Humans
Leishmania donovani
Leishmaniasis, Cutaneous
Leishmaniasis, Visceral
Machine Learning
Male
Middle Aged
Sudan
Young Adult
title_short Unsupervised machine learning identifies biomarkers of disease progression in post-kala-azar dermal leishmaniasis in Sudan
title_full Unsupervised machine learning identifies biomarkers of disease progression in post-kala-azar dermal leishmaniasis in Sudan
title_fullStr Unsupervised machine learning identifies biomarkers of disease progression in post-kala-azar dermal leishmaniasis in Sudan
title_full_unstemmed Unsupervised machine learning identifies biomarkers of disease progression in post-kala-azar dermal leishmaniasis in Sudan
title_sort Unsupervised machine learning identifies biomarkers of disease progression in post-kala-azar dermal leishmaniasis in Sudan
dc.creator.none.fl_str_mv Torres Garcia, Ana Maria
Younis, Brima Musa
Tesema, Samuel
Solana, Jose Carlos
Moreno, Javier
Martin-Galiano, Antonio Javier
Musa, Ahmed Mudawi
Alves, Fabiana
Carrillo, Eugenia
author Torres Garcia, Ana Maria
author_facet Torres Garcia, Ana Maria
Younis, Brima Musa
Tesema, Samuel
Solana, Jose Carlos
Moreno, Javier
Martin-Galiano, Antonio Javier
Musa, Ahmed Mudawi
Alves, Fabiana
Carrillo, Eugenia
author_role author
author2 Younis, Brima Musa
Tesema, Samuel
Solana, Jose Carlos
Moreno, Javier
Martin-Galiano, Antonio Javier
Musa, Ahmed Mudawi
Alves, Fabiana
Carrillo, Eugenia
author2_role author
author
author
author
author
author
author
author
dc.contributor.none.fl_str_mv Instituto de Salud Carlos III
Centro de Investigación Biomédica en Red - CIBERINFEC (Enfermedades Infecciosas)
dc.subject.none.fl_str_mv Adolescent
Adult
Biomarkers
Child
Disease Progression
Female
Humans
Leishmania donovani
Leishmaniasis, Cutaneous
Leishmaniasis, Visceral
Machine Learning
Male
Middle Aged
Sudan
Young Adult
topic Adolescent
Adult
Biomarkers
Child
Disease Progression
Female
Humans
Leishmania donovani
Leishmaniasis, Cutaneous
Leishmaniasis, Visceral
Machine Learning
Male
Middle Aged
Sudan
Young Adult
description Background: Post-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 findings: This 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. Conclusions: A 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.
publishDate 2025
dc.date.none.fl_str_mv 2025
2025-05-13
2025
2025-03-01
2025
2025-03-01
dc.type.none.fl_str_mv research article
http://purl.org/coar/resource_type/c_2df8fbb1
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/20.500.12105/26655
url https://hdl.handle.net/20.500.12105/26655
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.relation.none.fl_str_mv ESMVP322 19 Not available
ESPI22 00009 Not available
ESCB21 13 00018
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution 4.0 International
http://creativecommons.org/licenses/by/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution 4.0 International
http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/zip
dc.publisher.none.fl_str_mv Public Library of Science (PLOS)
publisher.none.fl_str_mv Public Library of Science (PLOS)
dc.source.none.fl_str_mv reponame:Repisalud
instname:Instituto de Salud Carlos III (ISCIII)
instname_str Instituto de Salud Carlos III (ISCIII)
reponame_str Repisalud
collection Repisalud
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,81155

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