Systemic inflammatory markers of visceral leishmaniasis treatment response in East Africa

Systemic inflammatory markers of visceral leishmaniasis treatment response in East Africa | medRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-P4HH5NV'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search Systemic inflammatory markers of visceral leishmaniasis treatment response in East Africa Ayenew Addisu , Alice Bayiyana , Joao Cunha , Daniel Matano , Brima M. Younis , Karen Hogg , Rebecca Wiggins , Wilson Biwott , Finnley Osuna , Christine Ichugu , Ayalew Jejaw Zeleke , Eleni Ayele , James Obondo Sande , Eltahir A.G. Khalil , Hussam M.H. Ibrahim , Mahmoud A. Mahmoud , Ahmed I.B. Zakaria , Brenda Adiko , The Immstat@Cure Consortium , Peter O’Toole , Flavia D’Alessio , Charles J.N. Lacey , Jane Mbui , Asrat M. Hailu , View ORCID Profile Paul M. Kaye , Margaret Mbuchi , Ahmed M Musa , Joseph Olobo doi: https://doi.org/10.1101/2025.11.13.25340135 Ayenew Addisu 1 Department of Medical Parasitology and Leishmaniasis Research and Treatment Centre, University of Gondar , Ethiopia Find this author on Google Scholar Find this author on PubMed Search for this author on this site Alice Bayiyana 2 Department of Immunology and Molecular Biology, Makerere University , Kampala, Uganda Find this author on Google Scholar Find this author on PubMed Search for this author on this site Joao Cunha 3 York Biomedical Research Institute, Hull York Medical School, University of York , York, Uk Find this author on Google Scholar Find this author on PubMed Search for this author on this site Daniel Matano 4 Center for Clinical Research, Kenya Medical Research Institute , Nairobi, Kenya Find this author on Google Scholar Find this author on PubMed Search for this author on this site Brima M. Younis 5 Institute of Endemic Diseases , Khartoum, Sudan Find this author on Google Scholar Find this author on PubMed Search for this author on this site Karen Hogg 6 BioSciences Technology Facility, Dept of Biology, University of York , York, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site Rebecca Wiggins 3 York Biomedical Research Institute, Hull York Medical School, University of York , York, Uk Find this author on Google Scholar Find this author on PubMed Search for this author on this site Wilson Biwott 4 Center for Clinical Research, Kenya Medical Research Institute , Nairobi, Kenya Find this author on Google Scholar Find this author on PubMed Search for this author on this site Finnley Osuna 4 Center for Clinical Research, Kenya Medical Research Institute , Nairobi, Kenya Find this author on Google Scholar Find this author on PubMed Search for this author on this site Christine Ichugu 4 Center for Clinical Research, Kenya Medical Research Institute , Nairobi, Kenya Find this author on Google Scholar Find this author on PubMed Search for this author on this site Ayalew Jejaw Zeleke 1 Department of Medical Parasitology and Leishmaniasis Research and Treatment Centre, University of Gondar , Ethiopia Find this author on Google Scholar Find this author on PubMed Search for this author on this site Eleni Ayele 1 Department of Medical Parasitology and Leishmaniasis Research and Treatment Centre, University of Gondar , Ethiopia Find this author on Google Scholar Find this author on PubMed Search for this author on this site James Obondo Sande 2 Department of Immunology and Molecular Biology, Makerere University , Kampala, Uganda Find this author on Google Scholar Find this author on PubMed Search for this author on this site Eltahir A.G. Khalil 5 Institute of Endemic Diseases , Khartoum, Sudan Find this author on Google Scholar Find this author on PubMed Search for this author on this site Hussam M.H. Ibrahim 5 Institute of Endemic Diseases , Khartoum, Sudan Find this author on Google Scholar Find this author on PubMed Search for this author on this site Mahmoud A. Mahmoud 5 Institute of Endemic Diseases , Khartoum, Sudan Find this author on Google Scholar Find this author on PubMed Search for this author on this site Ahmed I.B. Zakaria 5 Institute of Endemic Diseases , Khartoum, Sudan Find this author on Google Scholar Find this author on PubMed Search for this author on this site Brenda Adiko 2 Department of Immunology and Molecular Biology, Makerere University , Kampala, Uganda Find this author on Google Scholar Find this author on PubMed Search for this author on this site Peter O’Toole 6 BioSciences Technology Facility, Dept of Biology, University of York , York, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site Flavia D’Alessio 7 European Vaccine Initiative , Heidelberg Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site Charles J.N. Lacey 3 York Biomedical Research Institute, Hull York Medical School, University of York , York, Uk Find this author on Google Scholar Find this author on PubMed Search for this author on this site Jane Mbui 4 Center for Clinical Research, Kenya Medical Research Institute , Nairobi, Kenya Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: paul.kaye{at}york.ac.uk hailu_a2004{at}yahoo.com jmbui{at}kemri.go.ke Mmbuchi{at}kemri.go.ke musaam2003{at}yahoo.co.uk oloboj{at}yahoo.co.uk Asrat M. Hailu 1 Department of Medical Parasitology and Leishmaniasis Research and Treatment Centre, University of Gondar , Ethiopia Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: paul.kaye{at}york.ac.uk hailu_a2004{at}yahoo.com jmbui{at}kemri.go.ke Mmbuchi{at}kemri.go.ke musaam2003{at}yahoo.co.uk oloboj{at}yahoo.co.uk Paul M. Kaye 3 York Biomedical Research Institute, Hull York Medical School, University of York , York, Uk Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Paul M. Kaye For correspondence: paul.kaye{at}york.ac.uk hailu_a2004{at}yahoo.com jmbui{at}kemri.go.ke Mmbuchi{at}kemri.go.ke musaam2003{at}yahoo.co.uk oloboj{at}yahoo.co.uk Margaret Mbuchi 4 Center for Clinical Research, Kenya Medical Research Institute , Nairobi, Kenya Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: paul.kaye{at}york.ac.uk hailu_a2004{at}yahoo.com jmbui{at}kemri.go.ke Mmbuchi{at}kemri.go.ke musaam2003{at}yahoo.co.uk oloboj{at}yahoo.co.uk Ahmed M Musa 5 Institute of Endemic Diseases , Khartoum, Sudan Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: paul.kaye{at}york.ac.uk hailu_a2004{at}yahoo.com jmbui{at}kemri.go.ke Mmbuchi{at}kemri.go.ke musaam2003{at}yahoo.co.uk oloboj{at}yahoo.co.uk Joseph Olobo 2 Department of Immunology and Molecular Biology, Makerere University , Kampala, Uganda Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: paul.kaye{at}york.ac.uk hailu_a2004{at}yahoo.com jmbui{at}kemri.go.ke Mmbuchi{at}kemri.go.ke musaam2003{at}yahoo.co.uk oloboj{at}yahoo.co.uk Abstract Full Text Info/History Metrics Data/Code Preview PDF Abstract Background Visceral leishmaniasis (VL) is the most severe form of leishmaniasis, with East Africa accounting for ∼70% of global burden. It primarily affects malnourished children, young adults, and HIV co-infected individuals. Clinical outcomes range from asymptomatic to fatal, with relapse mostly linked to HIV co-infection, splenomegaly, high parasite load, poor immune responses, and elevated IgG1 levels. In rodent VL models, systemic immune and metabolic abnormalities persist at the end of the drug treatment regime. However, the immune status of VL patients in East Africa at the end of treatment is not fully understood. Methodology/Principal Findings We conducted ImmStat@cure, a multicentre clinical study to assess clinical and immune profiles of VL patients at admission and end of treatment (EoT) in East African countries. Clinical, haematological and inflammatory markers data were collected from patients from Ethiopia, Kenya, Sudan and Uganda on both time points and from convenience controls at a single time point. By integrating clinical data with haematological and inflammation markers, we have shown that patient clinical and inflammatory profiles varied at admission and partially reverted to healthy levels at EoT. Partial least squares determination and logistic regression showed that levels of inflammatory markers, including soluble TNF receptors and sCD40L, consistently changed between admission and EoT in all four countries, and were associated with increased odds of hepatomegaly and splenomegaly. Conclusions/Significance. The recovery of haematological parameters, alongside a reduction in systemic inflammatory markers may be indicative of successful treatment of VL in East Africa. The biomarker dynamics suggest a partial resolution of inflammation and restoration of immune homeostasis during treatment. To confirm their predictive value, these markers should be evaluated in cohorts with a larger number of patients who experience treatment failure. Author summary Visceral leishmaniasis (VL) is a life-threatening disease caused by infection with Leishmania parasites. It mainly affects vulnerable groups such as malnourished children, young adults, and people with HIV. It is endemic to South America, Asia and Africa, and most cases now occur in East Africa. While some people recover , others relapse or die, especially if they have weakened immune systems. Although treatment exists, little is known about how inflammatory markers change during and shortly after therapy. ImmStat@cure studied patients from Ethiopia, Kenya, Sudan, and Uganda to understand how their health and immune responses change from hospital admission to the end of treatment. By analysing blood samples and clinical data, we found that while some signs of illness improved by the end of treatment, others did not fully return to normal levels. Variations in certain blood markers linked to inflammation, such as soluble TNF receptors and sCD40L, changed after treatment and were linked to symptoms like enlarged liver and spleen. These findings suggest that tracking these markers may help doctors understand how well a patient is recovering. Future research should test whether these indicators can predict which patients are at risk of not fully recovering or relapsing. 1 Introduction Visceral Leishmaniasis (VL), also known as Kala-azar, is the most severe form of leishmaniasis [ 1 , 2 ]. It is mainly caused by parasites from the Leishmania donovani complex, L. donovani and L. infantum , and transmitted by female sand fly vectors. VL is endemic to more than 60 countries, and around 90% of the reported cases occur in Brazil, Ethiopia, India, Kenya, Somalia, South Sudan, and Sudan [ 2 , 3 ]. Following the success of the tri-national VL control initiative in India, Nepal and Bangladesh [ 4 – 6 ], East Africa (EA) has emerged as the primary global hotspot of VL. As of 2022, EA accounted for 73% of the global human VL burden, with an estimated annual incidence of 8,000-13,000 cases and average case fatality rate of 4-11% [ 7 , 8 ]. Given this significant epidemiological burden, there is a pressing need to prioritize research and control efforts in East Africa. Most L. donovani infected individuals are asymptomatic carriers [ 2 , 9 , 10 ]. When symptomatic, VL is characterized by splenomegaly, hepatomegaly, weight loss, pancytopenia, hypergammaglobulinemia, irregular fever, high parasitaemia and parasite burden, with consequent chronic inflammation in the liver, lymph nodes, spleen and bone marrow. VL has a mortality of ∼95% in symptomatic patients who do not receive treatment [ 2 , 11 ]. There is substantial variation among symptomatic cases regarding disease progression, response to treatment, relapse and occurrence of post-kala-azar dermal leishmaniasis (PKDL) [ 12 – 14 ]. Host and parasite genetics are likely important factors for disease severity [ 12 – 14 ], including polymorphisms in the HLA locus [ 13 , 15 , 16 ]. In EA, the highest incidence of symptomatic VL is observed among malnourished children and young adults, and HIV co-infected patients, as a consequence of both immunological naivety/suppression and behaviours that increase exposure to Phlebotomus vectors in endemic settings [ 17 – 20 ]. There is also evidence of gender disparity in VL cases, which might relate to behaviour and/or biological differences between sexes [ 21 – 23 ]. However, the underlying causes for this heterogeneity are not fully understood. VL relapses are often associated with HIV co-infection. In Ethiopia, more than 50% of the VL-HIV coinfected patients relapse within 9 months of the end of the treatment [ 24 ], maintaining high parasite loads, hepatosplenomegaly and pancytopenia at the end of treatment cycle [ 25 ]. VL-HIV relapses were associated with failure to restore antigen-specific production of IFN-γ, persistent lower CD4+ T cell counts and higher expression of PD1 on CD4+ and CD8+ T cells [ 25 ]. In HIV negative VL patients in EA, relapse rates were estimated as 5-13% depending on the treatment regime, and often occur within 6 months after the end of the treatment [ 26 – 29 ]. In South Sudan, spleen size on admission and persistent splenomegaly at the end of treatment with SSG/PM were associated with a higher risk of relapse, while no associations were observed with age, sex, malnutrition and treatment complications [ 30 ]. Similarly, spleen size at discharge was associated with relapse risk in patients treated with liposomal amphotericin B [ 31 ], but not with Miltefosine in India [ 32 ]. Evidence of parasite persistence or poor anti-leishmania immune responses after the end of treatment were also associated with treatment failure in VL patients. Higher levels of IgG1 six months after the end of treatment were associated with a higher relapse in Indian patients [ 33 ], while parasite load after 56 days of the start of the treatment, measured by qPCR, may be a sensitive VL relapse marker in EA [ 34 ]. In rodent models of VL drug treatment, splenomegaly and hepatomegaly were still evident at the end of treatment, and transcriptomic analyses have shown that indicators of systemic abnormalities of immune, metabolic and tissue remodelling processes had not been fully restored to homeostasis [ 35 – 37 ]. In Kenya, severe VL was associated with higher circulating levels of IL-10 and lower levels of IFN-γ. Following 17 days of treatment with SSG/PM, patients exhibited a return to immune homeostasis and undetectable parasite levels; however, anaemia persisted. Post-treatment analyses also revealed a decrease in IL-10 and IL-6 levels, while IL-17 levels remained unchanged [ 38 ]. However, the systemic inflammatory status of patients across EA countries at the end of treatment with SSG/PM is not fully understood. To address these shortcomings and further understand the links between hepatosplenomegaly and systemic inflammatory markers, we conducted ImmStat@cure, the most comprehensive multicentre clinical study in EA to investigate the clinical and immune response status in VL patients on admission and at the end of treatment (EoT) after treatment with SSG/PM. Here, we describe the ImmStat@cure study and report how clinical and serologic inflammatory markers reveal both common and yet country-specific changes in inflammatory profile at EoT. We also provide preliminary evidence to support sCD40L and sTNR receptors as prognostic markers of, and contributors to the pathophysiology of hepatomegaly and splenomegaly. 2 Material and methods 2.1 Ethical and regulatory approval This multicentre clinical study was approved by ethical and regulatory agencies in the UK (Department of Biology Ethics Committee reference PK202001, Hull York Medical School Ethics Committee reference 22-23.58), Ethiopia (University of Gondar Ethics Committee reference V/P/RCS/05/495/2019; Ministry of Education reference 7/2-324/M259/35), Kenya (National Commission for Science, Technology and Innovation references NACOSTI/P/21/11096/P6958/25/415974; KEMRI Scientific and Ethics Review Unit reference KEMRI/SERU/CCR/0169/4113), Sudan (University of Khartoum Ethics Committee reference FM/DO/EC; Sudan Ministry of Health approval 7-12-20) and Uganda (Makerere University Ethics Committee reference SBS-REC-751;Uganda National Council for Science and Technology reference HS1266ES). All studies were conducted in accord with the Declaration of Helsinki and with participant informed consent / assent. Formal consent to participate in the study was obtained from the patients or their legal guardians. The study did not involve modification of existing standards of care provided to patients. 2.2 Study sites and recruitment Immstat@cure was an observational multicentre clinical study that sought to recruit up to 160 VL patients to assess the clinical and immunological status before and at EoT after treatment with SSG/PM/MG across four countries in EA (40 per site). Up to 120 healthy convenience controls (HV; 30 per site) were also collected to provide indicative baseline data. The study was prospectively registered on ClinicalTrials.gov ( ClinicalTrials.gov ID: NCT04342715 , link: https://clinicaltrials.gov/study/NCT04342715?cond=NCT04342715&rank=1 ). The protocol is provided as supplementary information (S1 Supplementary Information file). In Ethiopia, patient recruitment and study visits as well as all haematological and biochemical screening were conducted at University of Gondar (UoG). Healthy endemic controls were recruited from the Gondar region. In Kenya, VL patient recruitment and study visits were conducted at Chemolingot Sub-County Hospital in Baringo County, and healthy non-endemic controls were recruited at the Center for Clinical Research, Nairobi County, Kenya. Immunological assays were performed at the Kenya Medical Research Institute (KEMRI). In Sudan, patient recruitment, study visits, haematological and biochemical screening and safety tests were conducted at Soba Hospital, Khartoum, the Institute for Endemic Diseases (IEND), the El Hassan Centre for Tropical Medicine, Doka, Gedarif State and the University of Khartoum, Sudan. Healthy endemic controls were recruited from the same region. In Uganda, patient recruitment, study visits, haematological and biochemical screening were conducted at Amudat Hospital, and at the Makerere University (MU) College of Health Sciences. Healthy controls were recruited from the Kampala region. VL diagnosis was initially performed using the rK39 rapid diagnostic test [ 39 ]. Confirmatory diagnostics was performed by a parasitologist through microscopic examination of aspirates (spleen, bone marrow or lymph nodes) stained with Giemsa and observed at 10x eyepiece and x100 magnification, to identify amastigotes. Aspirates were graded from 1+ to 6+, based on the WHO TRS 949 guidelines [ 40 ]. Inclusion criteria are fully described in the Protocol (S1 Supplementary Information file) and included: Aged 12 to 50 years; either sex; confirmed VL diagnostics; suitable for treatment using SSG/PM; negative for malaria, tuberculosis, leprosy, HIV, HBV, HCV; no previous diagnosis of leishmaniasis; not pregnant or lactating; and not severely malnourished, based on country-specific metrics. The occurrence of relapse and PKDL was assessed at 6 months follow up after completing treatment. 2.3 Sample and data collection Clinical, haematological and immunological data were collected on admission and at the end of treatment. Demographic data, sex, age, height, weight; and clinical data: sitting systolic blood pressure (SSBP), sitting diastolic blood pressure (SDBP), temperature, pulse; skin, cardiovascular or respiratory abnormalities; the presence of hepatomegaly, splenomegaly and lymphadenopathy were collected by clinicians. Blood was also collected on both time points, for biochemical and immunological assays. Details of standard reference ranges for blood biochemistry are included in the Protocol (S1 Supplementary Information file). Blood samples for bulk transcriptomics and proteomics, as well as for CD8+ T-cell and B-cell phenotyping by flow cytometry were also collected. These will be evaluated and reported in future publications. 2.4 Plasma inflammation markers Inflammatory markers were quantified by flow cytometry, using the LEGENDplex tm Inflammation Panel 2 (BioLegend) kit on a Beckman Coulter CytoflexⓇ flow cytometer, following manufacturer instructions, using plasma obtained from peripheral blood. The evaluated markers were: CX3CL1, CXCL12, PTX3, TGF.B1, sCD25, sCD40L, sRAGE, sST2, sTNF.RI, sTNF.RII and sTREM.1. The LEGENDplexTM Data Analysis Software Suite (Qognit, v2024-06-15, San Carlos, CA, USA) was used for initial data processing and transformation from median fluorescence intensity (MFI) to predicted concentration. For samples from each country, the highest value of the Limit of Quantification (LOQ) across experimental plates for each marker was used as a lower cutoff value for the measurements across all plates. Measurements that were below this cutoff were imputed as this lower LOQ cutoff. Samples were analysed in duplicate, and the mean of the duplicates was used for downstream analysis. To account for inter-plate variation, pooled control VL patient samples were included in each plate from the same country. Marker values were standardized by scaling each measurement to the mean value of the corresponding markers across VL control pools. Statistical analysis was done in R (see below). 2.5 Identification of markers for hepatomegaly and splenomegaly Potential markers for hepatomegaly and splenomegaly were identified using a combination of logistic regression and partial least squares discriminant analysis (PLS-DA). Four binary clinical outcome groups were evaluated: i) Hepatomegaly at admission (V1); ii) Persistent splenomegaly after treatment (V2); iii) Splenomegaly at admission (V1); and iv) the accuracy of pre-treatment clinical/immunological markers (V1) to predict persistent splenomegaly after the end of treatment (V2). Due to data heterogeneity, each country was analysed independently with comparisons as reported in the Results. Patient data was log-transformed log10(x+0.001) and scaled, based only on the dataset used. Only patients where all the assessed clinical, haematological and inflammatory markers were present were evaluated. Markers that increased the odds of the clinical outcomes were identified by logistic regression, optimized to unbalanced and low sample sizes, with the logistf function in R [ 41 ], using the family “binomial” (as in presence or absence of hepatomegaly) and the method “logistf” (Firth’s bias-reducing penalized likelihood to reduce small-sample bias). Representative markers were selected based on p-value < 0.05 and a confidence interval for the regression coefficient that did not cross zero, as this would indicate a reversal of the direction of the association (positive vs. negative odds). Prediction uncertainty was assessed using 1000 stratified bootstrap replicates, maintaining the original case-control ratio by sampling the same number of cases and controls as in the original dataset. Logistic regression evaluations were also performed using age as a covariate. In parallel, the predictive power of each marker for the clinical outcomes was assessed using partial least squares discriminant analysis (PLS-DA), implemented via the mixOmics and caret packages in R [ 42 ]. Initially, Variable Importance in Projection (VIP) scores were calculated to rank markers according to their contribution to discrimination of the outcomes. Subsequently, models incorporating the top 2, 3, 4, 5, 6, 10, or 15 markers (based on VIP scores) were evaluated using PLS-DA with leave-one-patient-out (LOPO) cross-validation to assess their predictive performance. The pre-selection of markers might lead to data leakage and an overestimate of the model predictive value, but ensures that each LOPO iteration uses the same markers. Concordance between the markers identified by logistic regression and those highlighted by PLS-DA increases confidence in their relevance to the clinical outcomes under investigation. 2.6 Statistical analysis and data representation All comparative statistical analysis and data representation were generated in R [ 43 ]. Differences between VL patients pre and post treatment, and for the clinical, haematological immunological markers were assessed using non-parametric paired Wilcoxon signed rank test, while comparisons between VL patients and convenience controls were performed using the Wilcoxon rank sum test (equivalent to Mann-Whitney test). P-values were adjusted for multiple comparisons using Benjamini-Hochberg (BH) false discovery rate (FDR) method. All patients that had data for a given marker were included in the evaluation, even if there were missing data for other analytes. The number of patients used in each comparison and the min-values for each clinical trait from each country can be seen in S1 and S2 tables. For radar plots, we first estimated the median value for each marker in each condition (pre-treatment V1, post-treatment V2 and convenience control HV). For each marker, the highest median value for the three conditions was set to 100%, and the remaining values were scaled proportionally. The spearman correlation between and within clinical, haematological and immunological markers was estimated in R using cor.test, and the FDR was corrected using BH. For the principal component analysis (PCA) and Uniform Manifold Approximation and Projection (UMAP), the data was log-transformed log10(x+0.001), centred (subtracting the mean) and scaled (divided by the standard deviation). Only samples with the complete set of markers were used. Then, the prcomp and umap [ 44 ] functions were respectively used to generate the PCA and UMAP. Graphical representations were generated using ggplot and factoextra. For the country specific Inflammation data PCA/UMAPs, the used markers were: CX3CL1, CXCL12, PTX3, TGF.B1, sCD25, sCD40L, sRAGE, sST2, sTNF.RI, sTNF.RII and sTREM.1. For the PCA/UMAP with the full combined dataset, the data for each country was log-transformed log10(x+0.001) and scaled independently, and combined in a single dataset. In this analysis, the clinical/haematological markers haemoglobin, WBC, neutrophil counts, lymphocyte counts, platelets counts, ALT, creatinine, albumin, and bilirubin levels were used in combination with the previously listed inflammatory markers. In this PCA there are no Uganda V2 data, as all samples were missing the clinical/haematological data. 3 Results 3.1 Study overview and patient demographics ImmStat@cure was a observational multicentre clinical two time-point study to assess the immune response status in VL patients on admission before treatment (V1), and ∼17 days after treatment (V2; EoT). Clinical, haematological and inflammatory markers data were collected from patients on both time points and from convenience controls at a single time point. A total of 212 patients were enrolled, corresponding to 109 VL patients, and 103 convenience controls (HV) across the four countries. Patients were recruited during the course of 3 years, where the first and last recruitment for each country were: Ethiopia, 01/2024 and 06/2024; Kenya, 11/2022 and 04/2024; Sudan, 01/2023 and 05/2024; Uganda, 02/2022 and 06/2024. Recruitment is shown in the flow diagram ( Figure 1 ). Download figure Open in new tab Figure 1: Flow diagram summarizing the Immstat@cure study. A summary of the patient demographics and clinical state can be seen in S1 table. Ethiopia (University of Gondar), Kenya (Kenya Medical Research Institute - KEMRI), Sudan (Institute of Endemic Diseases) and Uganda (Makerere University). The occurrence of relapses or PKDL in VL patients was assessed 6 months after the end of the treatment. Only 3 relapses were reported. The patient retention was ∼98%, with variable levels of data collection completeness ( Figure 1 , S1 table and S1 Figure). Patients in Ethiopia, Kenya, Uganda and Sudan had a median age of respectively 21.5, 15, 20 and 16 years, and respectivelly 100%, 88%, 72% and 77% were male. All patients were serologically negative for malaria, HIV, Hepatitis B, Hepatitis C, HBV, HCV. All VL patients were treated for leishmaniasis as per standard of care using (SSG/PM). Five patients in Ethiopia switched drug treatment to Ambisome after clinical abnormalities related to SSG/PM were noted. One patient from Ethiopia died during treatment due to visceral leishmaniasis complications. Only patients with missing data were not included in each downstream comparison (see methods). 3.2 Clinical and haematological markers before and at the end of VL treatment in East Africa An evaluation of hepatomegaly, splenomegaly and 18 clinical and haematological traits across East Africa showed that VL patient clinical markers recovered towards normal reference range (NRR) by the end of treatment, with the extent of improvement varying across countries. Splenomegaly was observed in approximately 70–100% of patients at admission, but decreased to 0–5% in Sudan and Uganda, and to 31% and 88% respectively in Kenya and Ethiopia. Hepatomegaly was reported at admission in Ethiopia (26%), Sudan (38%) and Kenya (∼3%), but by the end of treatment, only one case persisted in Ethiopia ( Figure 2A ). Similar improvements were observed for the majority of the haematological markers, with recoveries in albumin and haemoglobin levels; and platelets, white blood cell (WBC), neutrophils and lymphocytes counts after treatment ( Figure 2 B , S2 table). Download figure Open in new tab Figure 2: Patient clinical symptoms and haematological markers before and after treatment for VL. A) Proportion of the male patients, and patients with Hepatomegaly, Splenomegaly or detectable axillary lymph nodes. The columns represent convenience controls (HV), patients pre-treatment (V1) and post-treatment (V2). At the top of each bar, the number on the right corresponds to the total of the samples that had non-missing data, while the number on the right corresponds to the number of samples that were positive for the clinical trait. B) dot and whiskers plot representing the median, 25 and 75 quantiles for each continuous clinical trait/haematological marker assessed in the project. Each box represents a different clinical trait/haematological marker, each colour represents a different country and the number corresponds to the number of evaluated samples. Relative difference (Post-treatment - Pre-treatment)/Pre-treatment) of C) Albumin and D) Creatinine levels. The dashed red line represents a relative difference of zero. Values to the right or left of the dashed line represent respectively higher or lower values post treatment. E) Proportion of patients’ clinical traits that were below (blue) within range (green) or above the normal range (gold) for each haematological marker accessed. Missing data is represented in gray. Statistical comparisons between V1, V2 and HV results separated by sex can be seen in S2 Figure and S2 table. Interval ranges for each patient group can be seen in the S1 table. Statistically significant recovery was observed for most traits in males from Ethiopia and Kenya, and for a subset of markers in males from Sudan (S2 Figure A-D, S2 table). Some of the markers, such as albumin, showed a consistent recovery trend among patients, while others, such as creatinine levels, varied among countries and patients ( Figure 2 C – D ; males S3 Figure and females S4 Figure). This may reflect recovery toward the normal reference range (NRR), as albumin levels were below the NRR in most patients before treatment, while creatinine levels mostly remained within the normal range ( Figure 2 E , S5 Figure, S3 Table). Ethiopia had the lowest average proportion of patients with markers within NRR on admission (∼30%), when compared to Kenya (∼54%), Sudan (∼70%) and Uganda (∼55%). This suggests that the patients were in different stages of VL when hospitalized, with more severe cases in Ethiopia. Ethiopia also had the largest recovery after treatment, with an average of ∼70% of the patients within NRR, which was comparable to the ∼76% in Kenya and ∼84% in Sudan. Some of the largest recoveries to NRR were for albumin levels (∼6% V1 to ∼60% V2), platelets (∼8% V1 to ∼88% in V2) and WBC counts (∼6% V1 to ∼70% in V2) in Ethiopia; Albumin (∼28% V1 to ∼66% V2) and Haemoglobin levels (∼9% V1 to ∼51% in V2) in Kenya and platelet counts (∼39% to ∼83%) in Sudan ( Figure 2 E , S5 Figure, S3 Table). This likely reflects liver, spleen and bone marrow recovery and immune control after effective VL treatment in the four countries. The lack of post-treatment clinical and haematological data for Uganda prevented most of the V1-V2 comparisons in this country. Due to the limited sample size, the female dataset also lacked sufficient statistical power to detect significant differences before and after treatment, although recovery trends similar to the male dataset were observed (S2 Figure E-G, S2 table). The limited female sample size also reduced the statistical power for male–female comparisons, where only higher creatinine levels in males pre-treatment (V1) from Sudan reached statistical significance (S6 Figure). 3.3 Changes to inflammatory markers pre and post treatment To evaluate the inflammation status of each patient before and after VL treatment, we used a panel of 11 inflammatory markers. This includes soluble decoy receptors for TNF (sTNF-RI and II) [ 45 , 46 ]; sRAGE, the soluble decoy for RAGE [ 47 – 49 ]; potential markers for T-cell activation (sCD25 - IL-2Ra) [ 50 , 51 ]; myeloid cell activation and septic shock (sTREM-1) [ 52 , 53 ]; cell trafficking (CXCL12 - SDF1); [ 54 ] acute inflammation (PTX3) [ 55 ]; and chronic inflammation with macrophage and B-cell activation (sCD40L) [ 56 , 57 ]. Most of the analytes were above the limit of detection (S6 Figure), with the exception of TGF β1 in all countries, and sTREM1 and CX3CL1 in Kenya. The standard curves were similar between plates, and the Limit of Quantification (LOQ) used for each country can be seen on S7 Figure. The combined analysis of 11 inflammatory markers suggests that post-treatment patients are in an intermediate state between pre-treatment and convenience controls for the four countries ( Figure 3 and S8 Figure), consistent with the clinical traits. For male patients, Principal components 1 and 2 collectively accounted for around 50% of the overall data variability, indicating that they effectively captured the main patterns in the data (S8 Figure). Comparable tendencies were observed for female patients (S9 Figure), although with lower statistical support. Download figure Open in new tab Figure 3: Inflammatory markers in VL male patients across East Africa. Left panel: UMAP of the inflammatory markers values for each patient. Each patient is represented by a dot, coloured in yellow (V1) blue (V2) and red (HV). The UMAP was generated with samples that had the complete set of inflammatory markers (Ethiopia: HV:29, V1:34, V2:34; Kenya HV:29, V1:31, V2:31; Sudan HV:14, V1:13, V2:13; Uganda HV:14, V1:13, V2:13). PCAs with the same data can be seen in S8 Figure. Middle panel: is a radar plot, where each segment corresponds to the median of patients in a group (V1, V2 or HV), scaled to the highest median in the three groups of one inflammatory marker. Markers that were significantly (p <0.05) higher in V1 or V2 are respectively coloured in yellow or blue. Markers that were not significantly different are in black. Violin plots showing the range and statistical support for the variations in inflammatory markers pre and post treatment. The numbers of asterisks represent p-values, where 1 to 4 corresponds respectively values below 0,05, 0.01, 0.001 and 0.0001. A) Ethiopia, B) Kenya, C) Sudan and D) Uganda. The statistical results and number of samples used in each comparison can be seen in S2 table. Levels of the inflammatory markers sTNF-RI, sTNF-RII, sST2, and sCD25 were consistently higher prior to treatment, suggesting robust inflammatory activity characterized by TNF production, myeloid and T cell activation during active VL. These levels declined following treatment, concomitantly with an increase in sCD40L levels and to a lower extent sRAGE levels. This suggests a decrease in the immune activation status, but with persistent chronic inflammation after the end of treatment ( Figure 3 , S10 figure, and S2 table). Comparisons between males and females were again limited by the small female sample size. However, PTX3 levels were significantly higher in males after treatment in Sudan, whereas sRAGE levels were higher in females following treatment in Uganda (S6 Figure), When each stage, pre- or post-treatment, was evaluated independently, few significant correlations after FDR correction were observed between clinical traits, haematological and inflammatory markers, aside the expected weight-height and white blood cells - neutrophils associations ( Figure 5 , S11 figure, and S4 table). sTREM-1/CX3CL1 and sTNF.RI/sCD25 were correlated in Ethiopia and Uganda pre-treatment, and in Kenya and Uganda post-treatment. sTNF.RI/CX3CL1 was correlated in Ethiopia and Uganda pre-treatment, while sTNF.RI/sTNF.RII was correlated in Kenya and Uganda post-treatment . Within the cohort, two patients from Ethiopia, VL006 and VL016 , and one patient from Sudan, VL017, relapsed ( Figure 4 ). From those, VL006 relapsed just after treatment, while VL017 relapsed within 6 months and VL016 relapsed 6 months after the treatment. The small number of relapsed cases in each site limits the statistical power to identify associations between clinical/immunological markers and relapse. However, some indicative patterns were observed. VL006 and VL016 were the only Ethiopian patients with a grade 6+ spleen aspirate at admission ( Figure 4 A ), supporting a potential association between the parasite burden before treatment and increased risk of relapse. There was no aspirate grade data for Sudan samples. Patient VL006 did not exhibit reductions in the levels of the inflammation markers sTNF-RI, sTNF-RII, and sCD25, neither a large recovery in albumin levels following treatment, in contrast to the majority of cured visceral leishmaniasis (VL) patients in Ethiopia. In fact, VL006 had the highest values of both sCD25 and sTNF.RII at the EoT. Patient VL017 from Sudan also had low albumin levels, the highest level of sCD25 and one of the highest levels of sTNF-RI at EoT. Similar results were not observed for VL016, suggesting that there were differences in the immune state of relapsed patients. The difference in immune response is also supported by the fact that hepatomegaly at admission was only observed in VL006. Both Ethiopian patients exhibited some of the lowest values for sRAGE and the highest values for CX3CL1 and sTREM1 on both pre and post treatment time points, suggesting that these patients still have persistent inflammation, with signs of myeloid activation and recruitment to inflamed tissues at the EoT. Download figure Open in new tab Figure 4: Clinical and immunological features of relapsed patients. Only three patients relapsed. Two from Ethiopia, VL006 and VL016, and one from Sudan VL017. A) Spleen aspirate grade of all patients from Ethiopia. B) and C) Slopegraph showing values for clinical and inflammatory traits before (V1) and after (V2) treatment among patients from Ethiopia. VL006, VL016 and VL017 are represented respectively in red, orange and blue, while other VL patients from the same country are represented by gray circles. For the clinical traits, normal reference ranges are represented by green (upper normal limit) and red (lower normal limit) horizontal dashed lines. Download figure Open in new tab Figure 5: Overview of the inflammatory markers and clinical data across the four countries. A) PCA biplot and B) UMAP shows the distribution of patients before (V1) and after (V2) treatment, as well as convenience controls (HV), using clinical, immune and haematological data. C) The Relative difference was estimated as (Post-treatment - Pre- treatment)/Pre-treatment). Each panel corresponds to a different trait/marker. The dashed red line represents the expected value if there are no differences before and after treatment. Values to the right or left of the dashed line represent respectively higher values post or pre-treatment. Each country is represented by a different colour. There is no data for the clinical/haematological samples from Uganda. D) UMAP separated by patient stage, (HV, V1 and V2), where individuals with Splenomegaly or Hepatosplenomegaly are highlighted. This analysis used 208 samples, that had full clinical, haematological and inflammation data (Ethiopia: HV:26, V1:31, V2:26; Kenya HV:28, V1:31, V2:21; Sudan HV:10, V1:13, V2:13; Uganda HV:0, V1:9, V2:0). 3.4 Identification of markers for hepatomegaly or persistent splenomegaly in each country To provide an overview of the variation of the clinical, haematological and inflammatory marker variations among countries, trait measurements were log-normalized and scaled individually for each country, then combined to be visualized using PCA and UMAP, confirming the pre-post treatment variations previously observed in individual countries ( Figure 5 A and 5B ). This is further supported by the individual evaluation of each clinical/haematological/inflammatory marker ( Figure 5 C ). No distinct clustering was observed for patients with hepatomegaly pre-treatment or persistent splenomegaly after treatment ( Figure 5D ), suggesting that the overall clinical, haematological, and inflammatory profiles do not markedly differ between patients in these groups. Hence, potential differences have to be evaluated individually for each marker or for a subset of markers. 3.5 Prognostic markers of hepatomegaly and splenomegaly To identify prognostic markers of hepatomegaly and splenomegaly and their change associated with treatment, we used logistic regression and partial least squares discriminant analysis (PLS-DA). Potential markers associated with pre-treatment hepatomegaly and early markers for persistent splenomegaly in Ethiopia and with persistent splenomegaly in Kenya were identified ( Figure 6 , S5 table). Given the small and unbalanced sample sizes, these markers should be interpreted with caution, rather than definitive predictors of these outcomes. Download figure Open in new tab Figure 6: Hepatomegaly and Splenomegaly (traits) marker assessment. Left panel : sPLS-DA loading plot, showing the loading weight of the Inflammatory/Clinical markers influence on the assessed trait, ordered from bottom (highest impact) to top (lowest impact). Values in orange and blue are respectively associated with the presence or absence of the trait. Inflammatory/Clinical markers below the red line had Variable Importance in Projection (VIP) Values higher than 1.3. Middle panel : Logistic Regression coefficient with 95% confidence intervals for the association of each marker to the evaluated trait. Increases in values of markers that are above or below zero respectively increases or decreases the Odds Ratio of the trait. Values with p-value <0.05 are presented by triangles. Bootstrap validation of these predictions can be seen in the S13 figure. Right panel : Model prediction scores result for the PLS-DA leave one patient out (LOPO) using increasing numbers of markers, selected based on highest VIP scores. PPV: Positive Predictive Value; NPV: Negative Predictive Value. A) Ethiopia hepatomegaly in pre-treatment patients; B) Ethiopia, prediction of persistent splenomegaly based on pre-treatment data; C) Kenya persistent splenomegaly post-treatment. For Ethiopia, from the 31 patients with complete data, 9 had hepatomegaly. Logistic regression analysis identified sTNF.RII and albumin levels respectively associated with higher and lower odds of having hepatomegaly. sTNF.RII association was supported even when using age as a covariate (S12 Figure). PLS-DA analysis identified five markers with VIP values above 1.3, albumin, sTNF.RII, sCD40L, hemoglobin and ALT levels, in accordance with the logistic regression results. LOPO validation of the predictive model using the top 3 VIPs, Albumin, sTNF.RII and sCD40L resulted in the highest balanced accuracy of 79% and Negative Predictive Value (NPV) of 53% ( Figure 6 A , S13 Figure A, S5 table, Man Whitney U test p-values on S6 table). Next, to evaluate the value of pre-treatment data to predict the persistent splenomegaly (splenomegaly after treatment), a total of 31 patients, 27 with persistent splenomegaly from Ethiopia, were used. The logistic regression analysis identified lower sCD40L levels as associated with higher odds of developing persistent splenomegaly. sCD40L association was significant even when using age as a covariate (S12 Figure). The PLS-DA analysis identified four markers with VIP higher than 1.3, sCD40L, CXCL12 levels, sST2 and platelet counts. LOPO validation of a model with the top two VIPs, sCD40L and CXCL12, resulted in the highest balanced accuracy of 94%, and a PPV of 83% ( Figure 6 B , S13 figure and D, S5 table Man Whitney U test p-values on S6 table). Finally, for persistent splenomegaly in Kenya, from the 29 patients with complete data, 10 had splenomegaly after treatment. The logistic regression analysis identified albumin levels, WBC and neutrophil counts as associated with lower odds and sTNF.RI as associated with higher odds of developing persistent splenomegaly. Albumin and sTNF.RII associations were still significant even when using age as a covariate (S12 Figure). PLS-DA analysis identified five markers with VIP higher than 1.3: albumin, WBC, sTNF.RI, neutrophils and sCD25. LOPO validation of a model with the top 4 VIPs, albumin, WBC, sTNF.RI and neutrophil counts resulted in the highest accuracy of 89% and a PPV of 86% ( Figure 6 C , S13 figure E and F, Man Whitney U test p-values on S6 table, S5 table). Results for hepatomegaly in Sudan, early splenomegaly in Uganda and Sudan, persistent splenomegaly in Ethiopia and the predictive power of early markers to identify late splenomegaly in Kenya can be seen in S14 figure and S5 table. For the Sudan and Uganda sets, the sample number was low, which might result in lower precision and inflated PLS-DA predictive power and logistic regression odds ratios. Taken together, these results suggest that higher levels of sCD40L and a reduction in the levels of sTNF receptors might be important for a better prognosis of VL after treatment, with reduced odds for hepatomegaly and persistent splenomegaly. Discussion Immstat@cure is a multi-institute project that aims to characterize the clinical and immunological status of VL patients at admission and at the EoT, across EA. In this initial phase of analysis, by integrating detailed clinical data with serological and inflammation markers from VL patients across four East African countries, we have shown that: (1) patient clinical and inflammatory profiles varied at admission and partially revert to healthy levels at EoT, with different levels of recovery across countries; (2) levels of inflammatory markers, including soluble TNF receptors and sCD40L, consistently changed between admission and EoT in all four countries; and (3) these markers are also associated with increased odds of hepatomegaly and splenomegaly in visceral leishmaniasis (VL). This is the most comprehensive multi-country assessment of VL patient clinical and inflammatory state in EA to date. At admission, splenomegaly, hypoalbuminemia, anaemia, and pancytopenia were widely observed in VL patients from the four countries, while hepatomegaly was observed in cases from Ethiopia and Sudan. In VL, albumin levels are typically low due to chronic inflammation and liver dysfunction, while decreased haemoglobin and white blood cell counts can be caused by systemic and splenic inflammation [ 58 ] and bone marrow dysfunction [ 59 ]. This observation is consistent with the elevated levels of inflammatory markers, such as sTNF-RI, sTNF-RII, sCD25, and sST2, detected in EA VL patients at admission. These markers collectively indicate a systemic immune / inflammatory response characterized by a pronounced pro-inflammatory profile, including activation of T cells, with suppression of the Th2 response and a concomitant shift toward a Th1-dominant immune profile at patient admission. After 17 days of treatment, substantial improvements were observed in the patients’ clinical condition, with splenomegaly only persisting in Ethiopia and to a lower extent in Kenya, while hepatomegaly was no longer evident except for one patient in Ethiopia. This was concomitant with a decline in sTNFR, sCD25 and sST2 levels; and an increase in sCD40L and sRAGE levels, suggesting a decrease in the immune activation status at EoT, but with persistent chronic inflammation. This partial homeostasis restoration is in agreement with what was observed in a transcriptomics analysis of spleen and liver from L. donovani infected mice before and after treatment with AmBisome [ 37 ]; in microarrays from blood from VL patients from India treated with 15 lower doses of amphotericin B over 30 days, or a single high dose of liposomal amphotericin B at admission [ 60 ]; and in microarrays from VL patients from Brazil, treated with pentavalent antimony [ 61 ]. Taken together, these results indicate that, regardless of the treatment administered for visceral leishmaniasis (VL), patients often do not achieve full homeostatic restoration at EoT. From the eleven evaluated inflammation markers, six have potential to be used to assess treatment efficacy: high levels of sTNF-RI, sTNF-RII, sCD25 and sST2 at admission and high levels of sCD40L and sRAGE at EoT. Soluble TNF receptors I and II (sTNF-RI and sTNF-RII) function as decoy receptors that modulate TNF activity, and reflect TNF-driven systemic inflammatory responses. They persist longer in the circulation than TNF, making them reliable markers for TNF-mediated immune activity [ 45 , 46 , 62 ]. In the present study, sTNF receptors were identified as potential markers for treatment success in VL across EA, and also potential markers for hepatomegaly in Ethiopia and persistent splenomegaly in Kenya. These findings are consistent with reports of sTNF receptor levels in VL patients treated for 30 days with SSG in Sudan [ 63 ]; or meglumine antimoniate in Brazil [ 46 ]. A reduction in serum levels of sTNF receptors is also a marker for treatment success in tuberculosis infections [ 64 ]. High levels of sTNF receptors were associated with severity, acute kidney injury and mortality in patients with COVID-19 [ 65 ], and with active infection and liver damage in schistosomiasis japonica [ 66 ]. Similarly, a reduction in levels of sCD25 and sST2 could also indicate VL treatment success in EA. Soluble CD25 (sCD25 - IL-2Ra), the alpha chain of the IL-2 receptor, is shed from activated T cells, being a marker of T-cell activation [ 50 , 51 ]. Reduction in sCD25 levels was observed after treatment in VL patients from Bangladesh [ 67 ], as well as in patients with brucellosis [ 68 ]. sST2 (IL1RL1) is a decoy receptor for IL-33, which reduces Th2 responses by competing for ligation with IL-33 [ 69 ]. It is increased in patients with pathogenic inflammation [ 70 – 72 ] and sepsis [ 73 – 75 ]. At EoT, we observed a sharp decrease in sTNF-RI, sTNF-RII, sCD25, sST2 levels, with concomitant increases in sCD40L and sRAGE levels. sCD40L (soluble CD40 ligand) is the cleaved circulating form of CD40L, a key molecule involved in adaptive immune response. CD40L is expressed in many cell types, but is mainly observed on activated CD4+ cells and platelets [ 76 – 78 ], playing a crucial role in the activation of myeloid cells and B cells [ 77 – 79 ]. In the context of VL, elevated sCD40L levels were observed in VL patients after antimony treatment, with levels inversely correlating with spleen size and parasite burden [ 80 ]. Moreover, in vitro studies have demonstrated that recombinant CD40L, as well as human sera with high concentrations of sCD40L, reduced both the number of infected macrophages and the intracellular load of Leishmania amastigotes [ 57 ]. Similarly, lower sCD40L levels have been observed in HIV–VL coinfected patients compared to HIV-VL asymptomatic patients [ 81 ], further supporting a protective role for sCD40L in VL. sRAGE levels were higher at EoT in Kenya, and had a tendency of increasing in the other countries at ToC. sRAGE is the soluble decoy for RAGE (receptor for advanced glycation end products). RAGE binds to Advanced Glycation End-products (AGEs) and damage associated molecular patterns (DAMPs), triggering pro-inflammatory processes. sRAGE plays a dual role in inflammation. On one hand, it competes with RAGE receptors for ligands, preventing inflammatory activation and buffering against chronic inflammation and oxidative stress [ 47 ]. On the other hand, it can exert pro-inflammatory effects, such as promoting leukocyte recruitment to sites of infection and inflammation, and inducing macrophage activation [ 48 ]. Serum sRAGE levels increase during host response to infection [ 49 ], and might reflect overstimulation of membrane-bound RAGE, being a marker for inflammation [ 47 ]. We only observed three cases of VL relapse in our cohort, two in Ethiopia and one in Sudan. Both patients from Ethiopia had high parasite burden at admission, evidenced by a splenic aspirate grade 6+, in agreement with previous studies that reported parasite burden as a predictor of treatment failure in HIV and no-HIV coinfected VL patients [ 82 , 83 ]. Both patients from Ethiopia, but not the relapsed patient from Sudan had persistent splenomegaly at the end of treatment, which is previously reported as a marker for relapse risk in Sudan and India [ 30 , 31 ], and in VL-HIV co-infected patients from Ethiopia [ 84 ] and Brazil [ 85 ]. It is important to note that ∼81% of the VL patients from Ethiopia had splenomegaly at EoT, and most did not relapse within 6 months of infection. Patient VL006 had the highest levels of sTNF.RI and II, and sCD25 after treatment. This is in agreement with previous data from Sudan, where patients that relapsed had higher levels of both sTNFRI and II at end of the treatment cycle, which were still high 4-6 months after treatment [ 63 ]. Splenomegaly, and to a lesser extent, hepatomegaly, are hallmarks of active visceral leishmaniasis (55). To investigate the association between 20 predictor variables and three clinical outcomes: hepatomegaly at admission, splenomegaly at EoT, and early markers predictive of persistent splenomegaly; we applied both PLS-DA and logistic regression (LR). PLS-DA was chosen for its robustness in handling multicollinearity and its capacity to identify predictive markers in multi-dimensional data settings (42, 85, 86). Logistic regression was employed as an interpretable reference model, providing estimates of individual variable effects with adjustments for small and non-balanced sampling. Model building was challenging due to the limited sample size, imbalanced case/control distribution, and significant heterogeneity across patients. The datasets from Ethiopia and Kenya each included approximately 30 samples with complete datasets, while Sudan and Uganda had around 15. As a result, reliable predictive modeling was only feasible for Ethiopia and Kenya. With Ethiopia and Kenya samples, both PLS-DA and LR approaches yielded consistent results, reinforcing the reliability of the identified predictive markers and the overall robustness of the classification framework. In line with the treatment response patterns observed, we identified sTNF receptors as potential markers for hepatomegaly, and as potential early markers to predict persistent splenomegaly in Ethiopia, while elevated levels of sCD40L could be in persistent splenomegaly in Kenya. These markers should however only be considered as indicators of the predicted outcomes and interpreted with caution. This study has some limitations. As a multicountry project conducted in four EA nations, it faced logistical, data collection and standardization challenges. While patient retention was high, there were differences in data collection protocols between sites. For instance, there was no collection of post-treatment haematological and biochemical data from patients in Uganda. Another challenge was the low number of female patients and relapses, which precluded statistical support and conclusions of directly comparing these with their counterparts. Similarly, imbalanced group sizes affected the accuracy of prognostic models for splenomegaly and hepatomegaly. Despite these constraints, the combination of clinical/haematological and immunological data from a large cohort of patients across EA provided valuable insights into patient status before and after treatment for VL. The recovery of hematological parameters, such as albumin, hemoglobin, white blood cell (WBC) count, and platelet count; alongside a reduction in systemic inflammatory markers including sTNF-RI, sTNF-RII, sCD25, and sST2, and an increase in sCD40L levels, may be indicative of successful treatment of visceral leishmaniasis (VL). These biomarker dynamics suggest a partial resolution of inflammation and restoration of immune homeostasis during recovery. To confirm their predictive value, these markers should be evaluated in cohorts with a larger number of patients who experience treatment failure. Data Availability The R script used for data analysis can be obtained from GitHub: https://github.com/jaumlrc/Immstat_cure.git. Summaries and descriptions of average patient clinical and immunological data can be seen in S1 table. Individualized Anonymized patient data tables with patient clinical and immunological data are available upon request. https://github.com/jaumlrc/Immstat_cure.git Author contributions Conceptualization: AMH, PMK, CJNL, AMM, MM, JM, POT Data curation: AA, AB, JC, KH, RW, BMY Data analysis: JC, KH, RW Funding acquisition: AMH, PMK, CJNL, AMM, MM, JM, POT Investigation: all authors Methodology: AMH, KH, PMK, CJNL, AMM, MM, JM, POT, JC Project administration: RW Resources: AMH, PMK, CJNL, AMM, MM, JM, POT Supervision: AMH, KH, PMK, CJNL, AMM, MM, JM, POT, JC, KH Validation: PMK, RW Visualization: JC Writing – original draft: JC Writing – review & editing: all authors. RESOURCE AVAILABILITY Lead contact Further information and requests for resources and reagents should be directed to and will be fulfilled by the lead contacts Paul Kaye ( paul.kaye{at}york.ac.uk ). Materials availability Materials and reagents generated in this study are available upon a reasonable request from the lead contacts and may require a completed Material Transfer Agreement. Data and code availability The R script used for data analysis can be obtained from GitHub: https://github.com/jaumlrc/Immstat_cure.git . Summaries and descriptions of average patient clinical and immunological data, as well as Individualized Anonymized patient data tables with patient clinical and immunological data can be seen in S1 table. Supporting information S1 Supplementary Information file: Protocol for the project. Supplementary Figures S1 Figure : Missing data across patients. Each row corresponds to a clinical/Haematological/Immune marker, and each column corresponds to a patient. Red cells represent missing data. The colour-strip at the bottom represents the country of origin of each sample. S2 Figure : Clinical data changes with VL patient treatment. Violin plots showing the range and statistical support for the variations in clinical, haematological and inflammation markers pre and post treatment. The numbers of asterisks represent Wilcoxon signed-rank test (V1 x V2 comparison) or Mann-Whitney U test (HV comparing to V1 or V2) p-values, where 1 to 4 corresponds respectively values below 0,05, 0.01, 0.001 and 0.0001. A) Males Ethiopia, B) Males Kenya, C) Males Sudan, D) Males Uganda, E) Females Kenya, F) Females Sudan, G) Females Uganda. Most of the after treatment clinical data for Uganda is missing. There is only one Female HV in Kenya. S3 Figure : Slopegraph showing male patient’s trait levels before (V1) and after (V2) treatment. Each panel corresponds to a different trait. The normal reference ranges for the traits are represented by green (upper normal limit) and red (lower normal limit) dashed lines. Patients are identified by a combination of colour and shape. A) Males Ethiopia, B) Males Kenya, C) Males Sudan, D) Males Uganda. S4 Figure : Slopegraph showing female patient’s trait levels before (V1) and after (V2) treatment. Each panel corresponds to a different trait. The normal reference ranges for the traits are represented by green (upper normal limit) and red (lower normal limit) lines. Patients are identified by a combination of colour and shape. A) Females Ethiopia, B) Females Kenya, C) Females Sudan, D) Females Uganda. S5 Figure : Proportion of patients’ clinical traits that were below (low) within range (green) or above the normal range for each assessed haematological marker. Missing data is represented in gray (NA). Interval ranges for each patient group can be seen in S1 table. Statistical comparisons between V1, V2 and HV can be seen in S2 table. A) All data. B) Male patient data. C) Female patient data. D) Convenience controls data. S6 Figure : Male-Female clinical, haematological and inflammatory markers comparison. Violin plots showing the range and statistical support for the variations in clinical, haematological and inflammation markers in males (red) and females (blue). The numbers of asterisks represent Mann-Whitney U test p-values, where 1 to 4 corresponds respectively values below 0,05, 0.01, 0.001 and 0.0001. S7 Figure : LEGENDplex standard curves. Each panel represents a different marker, and each different plate used in the experiment is shown by different colour. The red line represents the Limit of Quantification (LOQ) used for each country, which was based on the higher LOQ for a given marker for all plates from a given country. A) Ethiopia, B) Kenya, C) Sudan, D) Uganda. S8 Figure : PCA, Scree plot, loading plot and patient group biplot for each country. A) Ethiopia. Kenya. C) Sudan. D) Uganda. S9 Figure : Inflammation immune markers in females across the four countries. The left panel is a PCA biplot of the inflammation markers values for each patient. Each patient is represented by a dot, coloured in yellow (V1) blue (V2) and red (HV). The arrows represent how much each immune marker contributes to the principal components. The middle panel is a radar plot, where each segment corresponds to the median of patients in a group (V1, V2 or HV), scaled to the highest median in the three groups (ex: if V1, V2 and HV were respectively 20, 10 and 5, they would be represented as 100, 50 and 25) of one inflammation marker. Markers that were significantly (p <0.05) higher in V1 or V2 are respectively coloured in yellow or blue. Markers that were not significantly different are in black. Violin plots showing the range and statistical support for the variations in inflammation markers pre and post treatment. The numbers of asterisks represent p-values, where 1 to 4 corresponds respectively values below 0,05, 0.01, 0.001 and 0.0001. The letters correspond to the results from A) Ethiopia, B) Kenya, Sudan and D) Uganda. The statistical results and number of samples used in each comparison can be seen in S2 table. The PCA and UMAP were generated with samples that had the complete set of inflammatory markers. S10 figure : Slope graph showing male patient’s trait levels before (V1) and after (V2) treatment. Each panel corresponds to a different trait. Patients are identified by a combination of colour and shape. A) Males Ethiopia, B) Males Kenya, C) Males Sudan, D) Males Uganda. S11 figure : Correlation between clinical, haematological and inflammation markers. The left panel shows only correlations with FDR adjusted p-values < 0.05. The right panel shows all of the identified correlations, irrespective of the p-values. A) Ethiopia pre-treatment; B) Ethiopia post-treatment; C) Kenya pre-treatment; D) Kenya post-treatment; E) Sudan pre-treatment; F) Sudan post-treatment; G) Uganda pre-treatment; H) Uganda pos-treatment. S12 Figure : Logistic regression using Age as covariate. Logistic Regression coefficient with 95% confidence intervals for the association of each marker to the evaluated trait, using Age as a covariate. Increases in values of markers that are above or below zero respectively increases or decreases the Odds Ratio of the trait. Values with p-value <0.05 are presented by triangles. A) Ethiopia hepatomegaly in pre-treatment patients; B) Ethiopia, prediction of persistent splenomegaly based on pre-treatment data; C) Kenya persistent splenomegaly post-treatment. S13 Figure : Hepatomegaly and splenomegaly marker assessment. The left panel corresponds to boxplots showing the logistic regression coefficient values of the 1000 stratified bootstrap replicates. The Y axis represents the markers, and the X axis the logistic regression coefficient. The middle panel corresponds to the result of the logistic regression bootstrap replicates. Each dot corresponds to the result of one of the 1000 replicates, where the X axis represents -log10(p-value) and the Y axis represents the logistic regression coefficient, as a representation of the Odds Ratio. In both left and middle panels, positive and negative values correspond respectively to increased or decreased odds of having the evaluated the clinical outcome. In the middle panel, the vertical and horizontal red lines correspond respectively to the p-value of 0.05 and to the logistic regression coefficient of zero. The right panel corresponds to violin plots comparing hepatomegaly or splenomegaly patients in red (1), with patients without those symptoms in blue (2), using Mann Whitney u test. A) Hepatomegaly in Ethiopia; B) pre-treatment markers prediction of late (post-treatment) splenomegaly in Ethiopia; C) persistent splenomegaly in Kenya. ns: non-significant. S14 figure : Hepatomegaly and splenomegaly marker assessment. Top left panel: sPLS-DA loading plot, showing the loading weight of the Inflammation/Clinical markers influence on the assessed trait, ordered from bottom (highest impact) to top (lowest impact). Values in yellow and blue are respectively associated with the presence or absence of the trait. Bottom left panel: Variable Importance in Projection (VIP) of the evaluated Inflammation/Clinical markers for a given trait. Values higher than 1.3 are highlighted in red. Left middle panel: Logistic Regression coefficient with 95% confidence intervals for the association of each marker to the evaluated trait. Increases in values of markers that are above or below zero respectively increases or decreases the Odds Ratio of the trait. Values with p-value <0.05 are presented by triangles. Middle right panel: corresponds to the result of the logistic regression bootstrap replicates. The X axis represents the logistic regression coefficient, as a representation of the Odds Ratio. Positive and negative values correspond respectively to increased or decreased odds of having the evaluated the clinical outcome. Right panel: Model prediction scores results for the PLS-DA leave one patient out (LOPO) cross validation, using increasing numbers of markers, selected based on highest VIP scores. PPV: Positive Predictive Value; NPV: Negative Predictive Value. A) Ethiopia persistent splenomegaly; B) Early hepatomegaly in Sudan; C) predictive power of early markers to identify late splenomegaly in Kenya; D) Early splenomegaly in Sudan; E) Early splenomegaly in Uganda. Supplementary tables S1 Table : Patient’s Clinical, haematological and inflammatory markers measurements. Summary of the Males and females are represented in the first two sheets. The third sheet represents the individual anonymized patient data. S2 Table : Statistical evaluation of the difference of haematological and inflammatory markers values among pre treatment (V1), post treatment (V2) patients and healthy volunteers (HV). Each sheet represented a different combination of sex and country. S3 Table : Assessment of the number of patients in V1, V2 and HV that were within the reference range for the clinical and haematological traits. Sheets 1, to 4 represent s respectively all patients, males, females and healthy volunteers. S4 table : Correlation between Patient’s Clinical, haematological and inflammatory markers, in V1 and V2. Each sheet corresponds to a different country. S5 table : Summary of the diagnostic metrics performance of the PLS-DA, with LOPO cross validation. Each sheet corresponds to a patient stage (as V1, and V2) and clinical outcome (as splenomegaly). S6 table : Statistical evaluation of the difference between patients with different clinical outcomes (as hepatomegaly and no hepatomegaly) in different clinical stages and countries. Acknowledgments This project is part of the EDCTP2 programme supported by the European Union (grant number RIA2016V-1640-PREV_PKDL). JC, RW and PMK were also supported by a Wellcome Trust Investigator Award (#224290 to PMK). We thank all patients and their families for agreeing to participate and their collaboration. The authors thank all members of the field teams at the study sites of Chemolingot, Amudat, Doka and Gondar, including clinicians, nurses, laboratory technicians, and the local communities for their contributions to the study. We also acknowledge the Ministries of Health of Kenya and Sudan; the Ministry of Education in Ethiopia and the National Council for Science and Technology in Uganda for their support. The authors thank the African Centre for Community Investment in Health (ACCIH), a partner to the Chemolingot Sub County Hospital, Kenya, and the County Government of Baringo for allowing them to undertake the study at the Chemolingot hospital. Disclaimer. This manuscript is published with permission from the Director of Kenya Medical Research Institute. Footnotes ↵ ^ The Immstat@cure consortium members are listed in Supplementary Immstat@Cure Consortium References 1. ↵ Global leishmaniasis surveillance updates 2023: 3 years of the NTD road map . World Health Organization ; 8 Nov 2024 [cited 3 Jul 2025]. Available: https://www.who.int/publications/i/item/who-wer-9945-653-669 2. ↵ Burza S , Croft SL , Boelaert M. Leishmaniasis . Lancet (London, England) . 2018 ; 392 . doi: 10.1016/S0140-6736(18)31204-2 OpenUrl CrossRef PubMed 3. ↵ Alvar J , Vélez ID , Bern C , Herrero M , Desjeux P , Cano J , et al. Leishmaniasis worldwide and global estimates of its incidence . PloS one . 2012 ; 7 . doi: 10.1371/journal.pone.0035671 OpenUrl CrossRef PubMed 4. ↵ World Health Organization . Regional Office for South-East Asia. Regional strategic framework for accelerating and sustaining elimination of kala-azar in the South-East Asia Region: 2022–2026 . World Health Organization. Regional Office for South-East Asia ; 2022 . 5. Nagi N . Bangladesh eliminates visceral leishmaniasis . Lancet Microbe . 2024 ; 5 : e420 . OpenUrl 6. ↵ Pandey DK , Alvar J , den Boer M , Jain S , Gill N , Argaw D , et al. Kala-azar elimination in India: reflections on success and sustainability . Int Health . 2025 . doi: 10.1093/inthealth/ihaf013 OpenUrl CrossRef 7. ↵ Strategic framework for the elimination of visceral leishmaniasis as a public health problem in eastern Africa 2023–2030: web annex: the Nairobi Declaration . World Health Organization ; 10 Jun 2024 [cited 30 Jun 2025 ]. Available: https://www.who.int/publications/i/item/B09041 8. ↵ Abongomera C , van Henten S , Vogt F , Buyze J , Verdonck K , van Griensven J . Prognostic factors for mortality among patients with visceral leishmaniasis in East Africa: Systematic review and meta-analysis . PLoS Neglected Tropical Diseases . 2020 ; 14 : e0008319 . OpenUrl 9. ↵ Bern C , Haque R , Chowdhury R , Ali M , Kurkjian KM , Vaz L , et al. The epidemiology of visceral leishmaniasis and asymptomatic leishmanial infection in a highly endemic Bangladeshi village . The American journal of tropical medicine and hygiene . 2007 ; 76 . Available: https://pubmed.ncbi.nlm.nih.gov/17488915/ 10. ↵ Mannan SB , Elhadad H , Loc TTH , Sadik M , Mohamed MYF , Nam NH , et al. Prevalence and associated factors of asymptomatic leishmaniasis: a systematic review and meta-analysis . Parasitology international . 2021 ; 81 . doi: 10.1016/j.parint.2020.102229 OpenUrl CrossRef 11. ↵ Belo VS , Struchiner CJ , Barbosa DS , Nascimento BWL , Horta MAP, da Silva ES , et al. Risk factors for adverse prognosis and death in American visceral leishmaniasis: a meta-analysis. PLoS Negl Trop Dis . 2014 ; 8 : e2982 . OpenUrl PubMed 12. ↵ Bucheton B , Kheir MM , El-Safi SH , Hammad A , Mergani A , Mary C , et al. The interplay between environmental and host factors during an outbreak of visceral leishmaniasis in eastern Sudan . Microbes Infect . 2002 ; 4 : 1449 – 1457 . OpenUrl CrossRef PubMed Web of Science 13. ↵ Blackwell JM , Fakiola M , Ibrahim ME , Jamieson SE , Jeronimo SB , Miller EN , et al. Genetics and visceral leishmaniasis: of mice and man . Parasite Immunol . 2009 ; 31 : 254 – 266 . OpenUrl CrossRef PubMed Web of Science 14. ↵ Grace CA , Sousa Carvalho KS , Sousa Lima MI , Costa Silva V , Reis-Cunha JL , Brune MJ , et al. Parasite Genotype Is a Major Predictor of Mortality from Visceral Leishmaniasis . mBio . 2022 ; 13 : e0206822 . OpenUrl CrossRef PubMed 15. ↵ Blackwell JM , Fakiola M , Castellucci LC . Human genetics of leishmania infections . Hum Genet . 2020 ; 139 : 813 – 819 . OpenUrl CrossRef PubMed 16. ↵ Fakiola M , Strange A , Cordell HJ , Miller EN , Pirinen M , Su Z , et al. Common variants in the HLA-DRB1-HLA-DQA1 HLA class II region are associated with susceptibility to visceral leishmaniasis . Nature genetics . 2013 ; 45 . doi: 10.1038/ng.2518 OpenUrl CrossRef PubMed 17. ↵ Jones CM , Welburn SC . Leishmaniasis Beyond East Africa . Front Vet Sci . 2021 ; 8 : 618766 . OpenUrl PubMed 18. Bekele F , Belay T , Zeynudin A , Hailu A . Visceral leishmaniasis in selected communities of Hamar and Banna-Tsamai districts in Lower Omo Valley, South West Ethiopia: Sero-epidemological and Leishmanin Skin Test Surveys . PLoS One . 2018 ; 13 : e0197430 . OpenUrl PubMed 19. Ahmed M , Abdullah AA , Bello I , Hamad S , Bashir A . Prevalence of human leishmaniasis in Sudan: A systematic review and meta-analysis . World Journal of Methodology . 2022 ; 12 : 305 . OpenUrl PubMed 20. ↵ Leta S , Dao THT , Mesele F , Alemayehu G . Visceral Leishmaniasis in Ethiopia: An Evolving Disease . PLoS Neglected Tropical Diseases . 2014 ; 8 : e3131 . OpenUrl PubMed 21. ↵ Dahal P , Singh-Phulgenda S , Olliaro PL , Guerin PJ . Gender disparity in cases enrolled in clinical trials of visceral leishmaniasis: A systematic review and meta-analysis . PLOS Neglected Tropical Diseases . 2021 ; 15 : e0009204 . OpenUrl PubMed 22. Jayakumar B , Murthy N , Misra K , Burza S . “It’s just a fever”: Gender based barriers to care-seeking for visceral leishmaniasis in highly endemic districts of India: A qualitative study . PLoS neglected tropical diseases . 2019 ; 13 . doi: 10.1371/journal.pntd.0007457 OpenUrl CrossRef PubMed 23. ↵ Tarekegn B , Tamene A . Clinical and laboratory profiles of visceral leishmaniasis among adult patients admitted to Felege Hiwot Hospital, Bahir Dar, Ethiopia . SAGE Open Medicine . 2021 [cited 30 Jun 2025]. doi: 10.1177/20503121211036787 OpenUrl CrossRef 24. ↵ Diro E , Edwards T , Ritmeijer K , Fikre H , Abongomera C , Kibret A , et al. Long term outcomes and prognostics of visceral leishmaniasis in HIV infected patients with use of pentamidine as secondary prophylaxis based on CD4 level: a prospective cohort study in Ethiopia . PLoS neglected tropical diseases . 2019 ; 13 . doi: 10.1371/journal.pntd.0007132 OpenUrl CrossRef 25. ↵ Takele Y , Mulaw T , Adem E , Shaw CJ , Franssen SU , Womersley R , et al. Immunological factors, but not clinical features, predict visceral leishmaniasis relapse in patients co-infected with HIV . Cell reports Medicine . 2021 ; 3 . doi: 10.1016/j.xcrm.2021.100487 OpenUrl CrossRef PubMed 26. ↵ Tamiru A , Mohammed R , Atnafu S , Medhin G , Hailu A . Efficacy and safety of a combined treatment of sodium stibogluconate at 20mg/kg/day with upper maximum daily dose limit of 850mg and Paromomycin 15mg/kg/day in HIV negative visceral leishmaniasis patients. A retrospective study, northwest Ethiopia . PLoS Negl Trop Dis . 2021 ; 15 : e0009713 . OpenUrl PubMed 27. Melaku Y , Collin SM , Keus K , Gatluak F , Ritmeijer K , Davidson RN . Treatment of kala-azar in southern Sudan using a 17-day regimen of sodium stibogluconate combined with paromomycin: a retrospective comparison with 30-day sodium stibogluconate monotherapy . Am J Trop Med Hyg . 2007 ; 77 : 89 – 94 . OpenUrl Abstract / FREE Full Text 28. Musa A , Khalil E , Hailu A , Olobo J , Balasegaram M , Omollo R , et al. Sodium stibogluconate (SSG) & paromomycin combination compared to SSG for visceral leishmaniasis in East Africa: a randomised controlled trial . PLoS Negl Trop Dis . 2012 ; 6 : e1674 . OpenUrl CrossRef PubMed 29. ↵ Chhajed R , Dahal P , Singh-Phulgenda S , Brack M , Naylor C , Sundar S , et al. Estimating the proportion of relapse following treatment of Visceral Leishmaniasis: meta-analysis using Infectious Diseases Data Observatory (IDDO) systematic review . Lancet Reg Health Southeast Asia . 2024 ; 22 : 100317 . OpenUrl PubMed 30. ↵ Gorski S , Collin SM , Ritmeijer K , Keus K , Gatluak F , Mueller M , et al. Visceral Leishmaniasis Relapse in Southern Sudan (1999–2007): A Retrospective Study of Risk Factors and Trends . PLOS Neglected Tropical Diseases . 2010 ; 4 : e705 . OpenUrl PubMed 31. ↵ Burza S , Sinha PK , Mahajan R , Lima MA , Mitra G , Verma N , et al. Risk factors for visceral leishmaniasis relapse in immunocompetent patients following treatment with 20 mg/kg liposomal amphotericin B (Ambisome) in Bihar, India . PLoS Negl Trop Dis . 2014 ; 8 : e2536 . OpenUrl CrossRef PubMed 32. ↵ Ostyn B , Hasker E , Dorlo TP , Rijal S , Sundar S , Dujardin JC , et al. Failure of miltefosine treatment for visceral leishmaniasis in children and men in South-East Asia . PloS one . 2014 ; 9 . doi: 10.1371/journal.pone.0100220 OpenUrl CrossRef PubMed 33. ↵ Bhattacharyya T , Ayandeh A , Falconar AK , Sundar S , El-Safi S , Gripenberg MA , et al. IgG1 as a potential biomarker of post-chemotherapeutic relapse in visceral leishmaniasis, and adaptation to a rapid diagnostic test . PLoS Negl Trop Dis . 2014 ; 8 : e3273 . OpenUrl CrossRef PubMed 34. ↵ Verrest L , Kip AE , Musa AM , Schoone GJ , Hdfh S , Mbui J , et al. Blood Parasite Load as an Early Marker to Predict Treatment Response in Visceral Leishmaniasis in Eastern Africa . Clinical infectious diseases : an official publication of the Infectious Diseases Society of America . 2021 ; 73 . doi: 10.1093/cid/ciab124 OpenUrl CrossRef 35. ↵ Ashwin H , Seifert K , Forrester S , Brown N , MacDonald S , James S , et al. Tissue and host species-specific transcriptional changes in models of experimental visceral leishmaniasis . Wellcome open research . 2019 ; 3 . doi: 10.12688/wellcomeopenres.14867.2 OpenUrl CrossRef PubMed 36. Forrester S , Goundry A , Dias BT , Leal-Calvo T , Moraes MO , Kaye PM , et al. Tissue Specific Dual RNA-Seq Defines Host-Parasite Interplay in Murine Visceral Leishmaniasis Caused by Leishmania donovani and Leishmania infantum . Microbiology spectrum . 2022 ; 10 . doi: 10.1128/spectrum.00679-22 OpenUrl CrossRef 37. ↵ Forrester S , Siefert K , Ashwin H , Brown N , Zelmar A , James S , et al. Tissue-specific transcriptomic changes associated with AmBisome® treatment of BALB/c mice with experimental visceral leishmaniasis . Wellcome open research . 2019 ; 4 . doi: 10.12688/wellcomeopenres.15606.1 OpenUrl CrossRef PubMed 38. ↵ van Dijk N , Carter J , Kiptanui D , Pinelli E , Schallig H . Cytokine profiles, blood parasite load and clinical features of visceral leishmaniasis in West Pokot County, Kenya . Parasitology . 2024 ; 151 : 753 – 761 . OpenUrl PubMed 39. ↵ Burns JM Jr , Shreffler WG , Benson DR , Ghalib HW , Badaro R , Reed SG . Molecular characterization of a kinesin-related antigen of Leishmania chagasi that detects specific antibody in African and American visceral leishmaniasis . Proc Natl Acad Sci U S A . 1993 ; 90 : 775 – 779 . OpenUrl Abstract / FREE Full Text 40. ↵ Control of the leishmaniases WHO TRS n° 949 . World Health Organization ; 10 Sep 2010 [cited 30 Jun 2025]. Available: https://www.google.com/url?q= https://www.who.int/publications/i/item/WHO-TRS-949&sa=D&source=docs&ust=1751291698299029&usg=AOvVaw2Fc840K88r55Ilc1z9aHBv 41. ↵ Firth’s Bias-Reduced Logistic Regression [R package logistf version 1.26.1] . 2025 [cited 30 Jun 2025]. Available: https://CRAN.R-project.org/package=logistf 42. ↵ Rohart F , Gautier B , Singh A , Cao K-AL . mixOmics: An R package for ‘omics feature selection and multiple data integration . PLOS Computational Biology . 2017 ; 13 : e1005752 . OpenUrl CrossRef PubMed 43. ↵ Website. Available: R Core Team ( 2024 ). R: A language and environment for statistical computing . R Foundation for Statistical Computing, Vienna, Austria . URL https://www.R-project.org/ . 44. ↵ Konopka T. Uniform Manifold Approximation and Projection [R package umap version 0.2.10.0] . 2023 [cited 30 Jun 2025]. Available: https://CRAN.R-project.org/package=umap 45. ↵ Soluble receptors for tumour necrosis factor in clinical laboratory diagnosis . [cited 23 Jun 2025 ]. doi: 10.1111/j.1600-0609.1995.tb01618.x OpenUrl CrossRef PubMed Web of Science 46. ↵ Medeiros IM , Reed S , Castelo A , Salomão R . Circulating levels of sTNFR and discrepancy between cytotoxicity and immunoreactivity of TNF-alpha in patients with visceral leishmaniasis . Clinical microbiology and infection : the official publication of the European Society of Clinical Microbiology and Infectious Diseases . 2000 ; 6 . doi: 10.1046/j.1469-0691.2000.00011.x OpenUrl CrossRef PubMed 47. ↵ Erusalimsky JD . The use of the soluble receptor for advanced glycation-end products (sRAGE) as a potential biomarker of disease risk and adverse outcomes . Redox Biol . 2021 ; 42 : 101958 . OpenUrl PubMed 48. ↵ Wang Y , Wang H , Piper MG , McMaken S , Mo X , Opalek J , et al. sRAGE induces human monocyte survival and differentiation . Journal of immunology (Baltimore, Md : 1950) . 2010 ; 185 . doi: 10.4049/jimmunol.0903398 OpenUrl Abstract / FREE Full Text 49. ↵ The role of RAGE in host pathology and crosstalk between RAGE and TLR4 in innate immune signal transduction pathways . [cited 10 Jul 2025 ]. doi: 10.1096/fj.202002136R OpenUrl CrossRef PubMed 50. ↵ Brusko TM , Wasserfall CH , Hulme MA , Cabrera R , Schatz D , Atkinson MA . Influence of membrane CD25 stability on T lymphocyte activity: implications for immunoregulation . PloS one . 2009 ; 4 . doi: 10.1371/journal.pone.0007980 OpenUrl CrossRef PubMed 51. ↵ Rubin LA , Kurman CC , Fritz ME , Biddison WE , Boutin B , Yarchoan R , et al. Soluble interleukin 2 receptors are released from activated human lymphoid cells in vitro . Journal of immunology (Baltimore, Md : 1950) . 1985 ; 135 . Available: https://pubmed.ncbi.nlm.nih.gov/3930598/ 52. ↵ François B , Wittebole X , Ferrer R , Mira J-P , Dugernier T , Gibot S , et al. Nangibotide in patients with septic shock: a Phase 2a randomized controlled clinical trial . Intensive Care Medicine . 2020 ; 46 : 1425 – 1437 . OpenUrl PubMed 53. ↵ Jolly L , Carrasco K , Salcedo-Magguilli M , Garaud JJ , Lambden S , van der Poll T , et al. sTREM-1 is a specific biomarker of TREM-1 pathway activation . Cellular & molecular immunology . 2021 ; 18 . doi: 10.1038/s41423-021-00733-5 OpenUrl CrossRef 54. ↵ Karin N . The multiple faces of CXCL12 (SDF-1alpha) in the regulation of immunity during health and disease . J Leukoc Biol . 2010 ; 88 : 463 – 473 . OpenUrl CrossRef PubMed Web of Science 55. ↵ Kunes P , Holubcova Z , Kolackova M , Krejsek J . Pentraxin 3(PTX 3): An Endogenous Modulator of the Inflammatory Response . Mediators of Inflammation . 2012 ;2012: 920517 . 56. ↵ Liu Z , Fan Y , Zhou A , Liu J , Jiao Q . Assessment of serum soluble CD40 ligand levels in patients with chronic rhinosinusitis . The World Allergy Organization journal . 2024 ; 17 . doi: 10.1016/j.waojou.2024.100880 OpenUrl CrossRef 57. ↵ de Oliveira FA , Barreto AS , Bomfim LG , Leite TR , Dos Santos PL , de Almeida RP , et al. Soluble CD40 Ligand in Sera of Subjects Exposed to Leishmania infantum Infection Reduces the Parasite Load in Macrophages . PloS one . 2015 ; 10 . doi: 10.1371/journal.pone.0141265 OpenUrl CrossRef PubMed 58. ↵ Costa CHN , Chang K-P , Costa DL , Cunha FVM . From Infection to Death: An Overview of the Pathogenesis of Visceral Leishmaniasis . Pathogens . 2023 ; 12 . doi: 10.3390/pathogens12070969 OpenUrl CrossRef PubMed 59. ↵ Marwaha N , Sarode R , Gupta RK , Garewal G , Dash S . Clinico-hematological characteristics in patients with kala azar. A study from north-west India . Tropical and geographical medicine . 1991 ; 43 . Available: https://pubmed.ncbi.nlm.nih.gov/1812600/ 60. ↵ Fakiola M , Singh OP , Syn G , Singh T , Singh B , Chakravarty J , et al. Transcriptional blood signatures for active and amphotericin B treated visceral leishmaniasis in India . PLoS neglected tropical diseases . 2019 ; 13 . doi: 10.1371/journal.pntd.0007673 OpenUrl CrossRef 61. ↵ Gardinassi LG , Garcia GR , Costa CHN , Silva VC , de Miranda Santos IKF . Blood Transcriptional Profiling Reveals Immunological Signatures of Distinct States of Infection of Humans with Leishmania infantum . PLOS Neglected Tropical Diseases . 2016 ; 10 : e0005123 . OpenUrl PubMed 62. ↵ Mwatha JK , Kimani G , Kamau T , Mbugua GG , Ouma JH , Mumo J , et al. High levels of TNF, soluble TNF receptors, soluble ICAM-1, and IFN-gamma, but low levels of IL-5, are associated with hepatosplenic disease in human schistosomiasis mansoni . Journal of immunology (Baltimore, Md : 1950) . 1998 ; 160 . Available: https://pubmed.ncbi.nlm.nih.gov/9469463/ 63. ↵ Zijlstra EE , van der Poll T , Mevissen M . Soluble receptors for tumor necrosis factor as markers of disease activity in visceral leishmaniasis . The Journal of infectious diseases . 1995 ; 171 . doi: 10.1093/infdis/171.2.498 OpenUrl CrossRef PubMed 64. ↵ Juffermans NP , Verbon A , van Deventer SJ , van Deutekom H , Speelman P , van der Poll T . Tumor necrosis factor and interleukin-1 inhibitors as markers of disease activity of tuberculosis . Am J Respir Crit Care Med . 1998 ; 157 : 1328 – 1331 . OpenUrl CrossRef PubMed Web of Science 65. ↵ Sancho Ferrando E , Hanslin K , Hultström M , Larsson A , Frithiof R , Lipcsey M , et al. Soluble TNF receptors predict acute kidney injury and mortality in critically ill COVID-19 patients: A prospective observational study . Cytokine . 2022 ; 149 : 155727 . OpenUrl PubMed 66. ↵ Ellis MK , Li Y , Hou X , Chen H , McManus DP . sTNFR-II and sICAM-1 are associated with acute disease and hepatic inflammation in schistosomiasis japonica . Int J Parasitol . 2008 ; 38 : 717 – 723 . OpenUrl CrossRef PubMed Web of Science 67. ↵ Duthie MS , Guderian J , Vallur A , Bhatia A , Lima dos Santos P , Vieira de Melo E , et al. Alteration of the serum biomarker profiles of visceral leishmaniasis during treatment . Eur J Clin Microbiol Infect Dis . 2014 ; 33 : 639 – 649 . OpenUrl CrossRef PubMed 68. ↵ Sun H-L , Ma C-J , Du X-F , Yang S-Y , Lv X , Zhao H , et al. Soluble IL-2Rα correlates with imbalances of Th1/Th2 and Tc1/Tc2 cells in patients with acute brucellosis . Infect Dis Poverty . 2020 ; 9 : 92 . OpenUrl PubMed 69. ↵ Kakkar R , Lee RT . The IL-33/ST2 pathway: therapeutic target and novel biomarker . Nature reviews Drug discovery . 2008 ; 7 : 827 . OpenUrl CrossRef PubMed Web of Science 70. ↵ Kuroiwa K , Arai T , Okazaki H , Minota S , Tominaga S . Identification of human ST2 protein in the sera of patients with autoimmune diseases . Biochemical and biophysical research communications . 2001 ; 284 . doi: 10.1006/bbrc.2001.5090 OpenUrl CrossRef PubMed Web of Science 71. Oshikawa K , Kuroiwa K , Tago K , Iwahana H , Yanagisawa K , Ohno S , et al. Elevated soluble ST2 protein levels in sera of patients with asthma with an acute exacerbation . Am J Respir Crit Care Med . 2001 ; 164 : 277 – 281 . OpenUrl CrossRef PubMed Web of Science 72. ↵ Weinberg EO , Shimpo M , Hurwitz S , Tominaga S-I , Rouleau J-L , Lee RT . Identification of serum soluble ST2 receptor as a novel heart failure biomarker . Circulation . 2003 ; 107 : 721 – 726 . OpenUrl Abstract / FREE Full Text 73. ↵ Brunner M , Krenn C , Roth G , Moser B , Dworschak M , Jensen-Jarolim E , et al. Increased levels of soluble ST2 protein and IgG1 production in patients with sepsis and trauma . Intensive Care Med . 2004 ; 30 : 1468 – 1473 . OpenUrl CrossRef PubMed Web of Science 74. Hoogerwerf JJ , Tanck MWT , van Zoelen MAD , Wittebole X , Laterre P-F , van der Poll T . Soluble ST2 plasma concentrations predict mortality in severe sepsis . Intensive Care Med . 2010 ; 36 : 630 – 637 . OpenUrl CrossRef PubMed Web of Science 75. ↵ Hur M , Kim H , Kim HJ , Yang HS , Magrini L , Marino R , et al. Soluble ST2 has a prognostic role in patients with suspected sepsis . Annals of laboratory medicine . 2015 ; 35 . doi: 10.3343/alm.2015.35.6.570 OpenUrl CrossRef 76. ↵ Cognasse F , Duchez AC , Audoux E , Ebermeyer T , Arthaud CA , Prier A , et al. Platelets as Key Factors in Inflammation: Focus on CD40L/CD40 . Front Immunol . 2022 ; 13 : 825892 . OpenUrl PubMed 77. ↵ Laman JD , Molloy M , Noelle RJ . Switching off autoimmunity . Science . 2024 [cited 9 Jul 2025]. doi: 10.1126/science.ade6949 OpenUrl CrossRef PubMed 78. ↵ Yacoub D , Hachem A , Théorêt J-F , Gillis M-A , Mourad W , Merhi Y . Enhanced levels of soluble CD40 ligand exacerbate platelet aggregation and thrombus formation through a CD40-dependent tumor necrosis factor receptor-associated factor-2/Rac1/p38 mitogen-activated protein kinase signaling pathway . Arterioscler Thromb Vasc Biol . 2010 ; 30 : 2424 – 2433 . OpenUrl Abstract / FREE Full Text 79. ↵ Laman JD , Claassen E , Noelle RJ . Functions of CD40 and Its Ligand, gp39 (CD40L) . Critical reviews in immunology . 2017 ; 37 . doi: 10.1615/CritRevImmunol.v37.i2-6.100 OpenUrl CrossRef PubMed 80. ↵ de Oliveira FA , Vanessa OSC , Damascena NP , Passos RO , Duthie MS , Guderian JA , et al. High levels of soluble CD40 ligand and matrix metalloproteinase-9 in serum are associated with favorable clinical evolution in human visceral leishmaniasis . BMC infectious diseases . 2013 ; 13 . doi: 10.1186/1471-2334-13-331 OpenUrl CrossRef PubMed 81. ↵ Adriaensen W , Abdellati S , van Henten S , Gedamu Y , Diro E , Vogt F , et al. Serum Levels of Soluble CD40 Ligand and Neopterin in HIV Coinfected Asymptomatic and Symptomatic Visceral Leishmaniasis Patients . Front Cell Infect Microbiol . 2018 ; 8 : 428 . OpenUrl PubMed 82. ↵ Chulay JD , Bryceson AD . Quantitation of amastigotes of Leishmania donovani in smears of splenic aspirates from patients with visceral leishmaniasis . Am J Trop Med Hyg . 1983 ; 32 : 475 – 479 . OpenUrl Abstract / FREE Full Text 83. ↵ Abongomera C , Diro E , Vogt F , Tsoumanis A , Mekonnen Z , Admassu H , et al. The Risk and Predictors of Visceral Leishmaniasis Relapse in Human Immunodeficiency Virus-Coinfected Patients in Ethiopia: A Retrospective Cohort Study . Clinical Infectious Diseases: An Official Publication of the Infectious Diseases Society of America . 2017 ; 65 : 1703 . OpenUrl CrossRef PubMed 84. ↵ Mohammed R , Fikre H , Schuster A , Mekonnen T , van Griensven J , Diro E . Multiple Relapses of Visceral Leishmaniasis in HIV Co-Infected Patients: A Case Series from Ethiopia . Current therapeutic research, clinical and experimental . 2020 ; 92 . doi: 10.1016/j.curtheres.2020.100583 OpenUrl CrossRef 85. ↵ Ldln C , Lima US , Rodrigues V , Lima MIS , Silva LA , Ithamar J , et al. Factors associated with relapse and hospital death in patients coinfected with visceral leishmaniasis and HIV: a longitudinal study . BMC infectious diseases . 2023 ; 23 . doi: 10.1186/s12879-023-08009-1 OpenUrl CrossRef View the discussion thread. Back to top Previous Next Posted November 15, 2025. Download PDF Data/Code Email Thank you for your interest in spreading the word about medRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. You are going to email the following Systemic inflammatory markers of visceral leishmaniasis treatment response in East Africa Message Subject (Your Name) has forwarded a page to you from medRxiv Message Body (Your Name) thought you would like to see this page from the medRxiv website. Your Personal Message CAPTCHA This question is for testing whether or not you are a human visitor and to prevent automated spam submissions. Share Systemic inflammatory markers of visceral leishmaniasis treatment response in East Africa Ayenew Addisu , Alice Bayiyana , Joao Cunha , Daniel Matano , Brima M. Younis , Karen Hogg , Rebecca Wiggins , Wilson Biwott , Finnley Osuna , Christine Ichugu , Ayalew Jejaw Zeleke , Eleni Ayele , James Obondo Sande , Eltahir A.G. Khalil , Hussam M.H. Ibrahim , Mahmoud A. Mahmoud , Ahmed I.B. Zakaria , Brenda Adiko , The Immstat@Cure Consortium , Peter O’Toole , Flavia D’Alessio , Charles J.N. Lacey , Jane Mbui , Asrat M. Hailu , Paul M. Kaye , Margaret Mbuchi , Ahmed M Musa , Joseph Olobo medRxiv 2025.11.13.25340135; doi: https://doi.org/10.1101/2025.11.13.25340135 Share This Article: Copy Citation Tools Systemic inflammatory markers of visceral leishmaniasis treatment response in East Africa Ayenew Addisu , Alice Bayiyana , Joao Cunha , Daniel Matano , Brima M. Younis , Karen Hogg , Rebecca Wiggins , Wilson Biwott , Finnley Osuna , Christine Ichugu , Ayalew Jejaw Zeleke , Eleni Ayele , James Obondo Sande , Eltahir A.G. Khalil , Hussam M.H. Ibrahim , Mahmoud A. Mahmoud , Ahmed I.B. Zakaria , Brenda Adiko , The Immstat@Cure Consortium , Peter O’Toole , Flavia D’Alessio , Charles J.N. Lacey , Jane Mbui , Asrat M. Hailu , Paul M. Kaye , Margaret Mbuchi , Ahmed M Musa , Joseph Olobo medRxiv 2025.11.13.25340135; doi: https://doi.org/10.1101/2025.11.13.25340135 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area Infectious Diseases (except HIV/AIDS) Subject Areas All Articles Addiction Medicine (572) Allergy and Immunology (864) Anesthesia (302) Cardiovascular Medicine (4448) Dentistry and Oral Medicine (444) Dermatology (383) Emergency Medicine (609) Endocrinology (including Diabetes Mellitus and Metabolic Disease) (1515) Epidemiology (15240) Forensic Medicine (30) Gastroenterology (1130) Genetic and Genomic Medicine (6614) Geriatric Medicine (669) Health Economics (1000) Health Informatics (4550) Health Policy (1372) Health Systems and Quality Improvement (1613) Hematology (543) HIV/AIDS (1268) Infectious Diseases (except HIV/AIDS) (15928) Intensive Care and Critical Care Medicine (1106) Medical Education (624) Medical Ethics (147) Nephrology (668) Neurology (6621) Nursing (346) Nutrition (999) Obstetrics and Gynecology (1147) Occupational and Environmental Health (957) Oncology (3343) Ophthalmology (977) Orthopedics (369) Otolaryngology (421) Pain Medicine (436) Palliative Medicine (130) Pathology (665) Pediatrics (1695) Pharmacology and Therapeutics (693) Primary Care Research (714) Psychiatry and Clinical Psychology (5459) Public and Global Health (9248) Radiology and Imaging (2205) Rehabilitation Medicine and Physical Therapy (1371) Respiratory Medicine (1197) Rheumatology (597) Sexual and Reproductive Health (715) Sports Medicine (530) Surgery (714) Toxicology (99) Transplantation (289) Urology (265) (function(){function c(){var b=a.contentDocument||a.contentWindow.document;if(b){var d=b.createElement('script');d.innerHTML="window.__CF$cv$params={r:'a028a0b14adc8e2e',t:'MTc3OTkyMjc0MQ=='};var a=document.createElement('script');a.src='/cdn-cgi/challenge-platform/scripts/jsd/main.js';document.getElementsByTagName('head')[0].appendChild(a);";b.getElementsByTagName('head')[0].appendChild(d)}}if(document.body){var a=document.createElement('iframe');a.height=1;a.width=1;a.style.position='absolute';a.style.top=0;a.style.left=0;a.style.border='none';a.style.visibility='hidden';document.body.appendChild(a);if('loading'!==document.readyState)c();else if(window.addEventListener)document.addEventListener('DOMContentLoaded',c);else{var e=document.onreadystatechange||function(){};document.onreadystatechange=function(b){e(b);'loading'!==document.readyState&&(document.onreadystatechange=e,c())}}}})();