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

IF 3.4 2区 医学 Q1 PARASITOLOGY
PLoS Neglected Tropical Diseases Pub Date : 2025-03-11 eCollection Date: 2025-03-01 DOI:10.1371/journal.pntd.0012924
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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Abstract

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.

无监督机器学习识别苏丹黑热病后皮肤利什曼病疾病进展的生物标志物。
背景:黑热病后皮肤利什曼病(PKDL)在一些由多诺瓦利什曼原虫引起的内脏利什曼病恢复的个体中表现为皮疹。今天,关于这种被忽视疾病的基本知识以及如何预测其进展在很大程度上仍然未知。方法和发现:本研究解决了使用几个生化、血液学和免疫学变量,独立或通过无监督机器学习(ML)来预测PKDL进展风险的问题。在来自苏丹的110例患者中,在诊断时评估了31个与PKDL疾病状态相关的因素:进展(恶化)与稳定。为了确定与PKDL恶化相关的关键因素,我们使用了传统的统计方法和通过无监督ML进行的多变量分析。在基线检查中,独立使用这些变量预测皮肤病变严重程度的能力有限。相比之下,无监督ML方法确定了一组10个非冗余变量,这些变量与发生进行性PKDL的风险增加3.1倍有关。其中三个聚类因子(利什曼原虫抗原刺激后PBMCs中低白蛋白水平、低红细胞压积和低IFN-γ产生)在进展性疾病患者中显着。维度重建确定了11个进一步显著修改的因素,这对理解恶化的表型也很重要。我们的研究结果表明,贫血和弱Th1免疫反应的结合可能是导致进行性PKDL的主要生理机制。结论:由无监督ML识别的14个生化变量的组合能够检测苏丹患者PKDL状态的恶化。这种方法可能有助于训练基于更大患者队列的未来监督算法,以便更精确地诊断并深入了解这种内脏利什曼病并发症的基本方面。
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来源期刊
PLoS Neglected Tropical Diseases
PLoS Neglected Tropical Diseases PARASITOLOGY-TROPICAL MEDICINE
自引率
10.50%
发文量
723
期刊介绍: PLOS Neglected Tropical Diseases publishes research devoted to the pathology, epidemiology, prevention, treatment and control of the neglected tropical diseases (NTDs), as well as relevant public policy. The NTDs are defined as a group of poverty-promoting chronic infectious diseases, which primarily occur in rural areas and poor urban areas of low-income and middle-income countries. Their impact on child health and development, pregnancy, and worker productivity, as well as their stigmatizing features limit economic stability. All aspects of these diseases are considered, including: Pathogenesis Clinical features Pharmacology and treatment Diagnosis Epidemiology Vector biology Vaccinology and prevention Demographic, ecological and social determinants Public health and policy aspects (including cost-effectiveness analyses).
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