A Model Based on the Combination of IFN-γ, IP-10, Ferritin and 25-Hydroxyvitamin D for Discriminating Latent From Active Tuberculosis in Children

Comella-del-Barrio, Patricia; Abellana, Rosa; Villar-Hernández, Raquel; Coute, Mariette Doresca Jean; Mingels, Beatriz Sallés; Aliaga, Lydia Canales; Narcisse, Margareth; Gautier, Jacqueline; Ascaso, Carlos; Latorre, Irene; Domínguez, José; Perez-Porcuna, Tomas M

doi:10.3389/fmicb.2019.01855

Cited by 19 publications

(16 citation statements)

References 56 publications

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“…It is well known that neither TST nor IGRAs can distinguish between LTBI and active cases 21,22 . In our experience 23 , a model based on the combination of IFN-γ, IP-10, ferritin and 25-hydroxyvitamin D could improve the detection of patients with subclinical TB. The metabolomic approach we described may be useful in detecting the early stages of the disease.…”

Section: Discussionmentioning

confidence: 95%

Discovery and validation of an NMR-based metabolomic profile in urine as TB biomarker

Izquierdo-García

Comella-del-Barrio

Campos-Olivas

et al. 2020

Sci Rep

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Despite efforts to improve tuberculosis (TB) detection, limitations in access, quality and timeliness of diagnostic services in low- and middle-income countries are challenging for current TB diagnostics. This study aimed to identify and characterise a metabolic profile of TB in urine by high-field nuclear magnetic resonance (NMR) spectrometry and assess whether the TB metabolic profile is also detected by a low-field benchtop NMR spectrometer. We included 189 patients with tuberculosis, 42 patients with pneumococcal pneumonia, 61 individuals infected with latent tuberculosis and 40 uninfected individuals. We acquired the urine spectra from high and low-field NMR. We characterised a TB metabolic fingerprint from the Principal Component Analysis. We developed a classification model from the Partial Least Squares-Discriminant Analysis and evaluated its performance. We identified a metabolic fingerprint of 31 chemical shift regions assigned to eight metabolites (aminoadipic acid, citrate, creatine, creatinine, glucose, mannitol, phenylalanine, and hippurate). The model developed using low-field NMR urine spectra correctly classified 87.32%, 85.21% and 100% of the TB patients compared to pneumococcal pneumonia patients, LTBI and uninfected individuals, respectively. The model validation correctly classified 84.10% of the TB patients. We have identified and characterised a metabolic profile of TB in urine from a high-field NMR spectrometer and have also detected it using a low-field benchtop NMR spectrometer. The models developed from the metabolic profile of TB identified by both NMR technologies were able to discriminate TB patients from the rest of the study groups and the results were not influenced by anti-TB treatment or TB location. This provides a new approach in the search for possible biomarkers for the diagnosis of TB.

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Section: Discussionmentioning

confidence: 95%

Discovery and validation of an NMR-based metabolomic profile in urine as TB biomarker

Izquierdo-García

Comella-del-Barrio

Campos-Olivas

et al. 2020

Sci Rep

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“…Monitoring inflammatory status may be useful to predict anti-TB treatment outcomes and combining different immunological biomarkers could increase the predicting accuracy of treatment outcome [ 46 ].…”

Section: Discussionmentioning

confidence: 99%

Prediction of Treatment Outcome with Inflammatory Biomarkers after 2 Months of Therapy in Pulmonary Tuberculosis Patients: Preliminary Results

et al. 2021

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Pro-inflammatory mediators play an important role in the pathogenesis of pulmonary tuberculosis. Consecutively, 26 pulmonary tuberculosis patients were enrolled in our study based on the exclusion criteria. We have used Spearman’s correlation analysis, hierarchical clustering and regression modelling to evaluate the association of 11 biomarkers with culture status after antituberculosis treatment. The results of our study demonstrated that six inflammatory biomarkers of 11, C-reactive protein (CRP), white blood cells (WBC), neutrophils, interferon gamma inducible protein 10, C-reactive protein (CRP) to albumin ratio (CAR) and neutrophil to albumin ratio (NAR), were significantly associated with culture negativity. The predictive ability of a composite model of seven biomarkers was superior to that of any single biomarker based on area under the receiver operating characteristic curve (AUC) analysis, indicating an excellent prediction efficacy (AUC:0.892; 95% CI:0.732-1.0). We also found that the highest significant trends and lower levels of CRP and IP-10 were observed in the two-month treated tuberculosis (TB) patients. We believe that our study may be valuable in providing preliminary results for an additional strategy in monitoring and management of the clinical outcome of pulmonary tuberculosis. Using a panel of predictors added a superior value in predicting culture status after anti-TB therapy.

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“…The serum biosignature consisting of 16 proteins (APRIL/TNFSF13, sCD30/TNFRSF8, chitinase 3-like 1, sIL-6Rα, IL-8, IL-11, IL-12(p70), IL-19, IL-28A/IFN-λ2, LIGHT/TNFSF14, MMP-1, MMP-2, MMP-3, OPN, PTX-3, TWEAK/TNFSF12) was found informative for the differentiation between the TB and HC groups, while the panel of 15 proteins (IL-6, APRIL/TNFSF13, sCD30/TNFRSF8, gp130/sIL-6β, IL-2, sIL-6Rα, IL-8, IL-29/IFNλ1, IL-35, MMP-2, MMP-3, OPN, PTX-3, sTNF-R2, TWEAK/TNFSF12) distinguished the HC group from the LTBI cohort. Currently, a number of studies have demonstrated a potential of blood-derived host protein biomarkers in the diagnosis of active TB [ 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 ]. A 6-marker serum protein biosignature consisting of CRP (c-reactive protein), IFN-γ, IP-10 (human interferon-inducible protein 10), CFH (complement factor H), Apo-AI (apolipoprotein AI), and SAA (serum amyloid A) showed a potential in the diagnosis of childhood tuberculous meningitis48.…”

Section: Discussionmentioning

confidence: 99%

“…It follows from the literature data that different models based on the combination of immune mediators, measured either in serum or M.tb -stimulated cultures, achieved diagnostic performance to discriminate between active and latent pulmonary TB in children. Chegou et al reported that the measurement of the levels of IFN-α2, IL-1Ra, sCD40L, and VEGF (vascular endothelial growth factor) might be a useful method for differentiating between active TB disease and latent M.tb infection, while other authors found the same utility for IFN-γ, IP-10, ferritin, and 25-hydroxyvitamin D [ 50 , 51 ]. Another six-cytokine signature for detecting TB infection and discriminating active from latent TB included M.tb antigen-stimulated levels of IFN-γ, IP-10, and IL-Ra, and unstimulated levels of IP-10, VEGF, and IL-12(p70) [ 52 ].…”

Section: Discussionmentioning

confidence: 99%

Cytokine Biosignature of Active and Latent Mycobacterium Tuberculosis Infection in Children

et al. 2021

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None of the currently used diagnostic tools are efficient enough in diagnosing Mycobacterium tuberculosis (M.tb) infection in children. The study was aimed to identify cytokine biosignatures characterizing active and latent tuberculosis (TB) in children. Using a multiplex bead-based technology, we analyzed the levels of 53 Th17-related cytokines and inflammatory mediators in sera from 216 BCG-vaccinated children diagnosed with active TB (TB) or latent TB (LTBI) as well as uninfected controls (HC). Children with active TB, compared to HC children, showed reduced serum levels of IL-17A, MMP-2, OPN, PTX-3, and markedly elevated concentrations of APRIL/TNFSF13. IL-21, sCD40L, MMP-2, and IL-8 were significantly differentially expressed in the comparisons between groups: (1) HC versus TB and LTBI (jointly), and (2) TB versus LTBI. The panel consisting of APRIL/TNFSF13, sCD30/TNFRSF8, IFN-α2, IFN-γ, IL-2, sIL-6Rα, IL-8, IL-11, IL-29/IFN-λ1, LIGHT/TNFSF14, MMP-1, MMP-2, MMP-3, osteocalcin, osteopontin, TSLP, and TWEAK/TNFSF12 possessed a discriminatory potential for the differentiation between TB and LTBI children. Serum-based host biosignatures carry the potential to aid the diagnosis of childhood M.tb infections. The proposed panels of markers allow distinguishing not only children infected with M.tb from uninfected individuals but also children with active TB from those with latent TB.

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A Model Based on the Combination of IFN-γ, IP-10, Ferritin and 25-Hydroxyvitamin D for Discriminating Latent From Active Tuberculosis in Children

Cited by 19 publications

References 56 publications

Discovery and validation of an NMR-based metabolomic profile in urine as TB biomarker

Discovery and validation of an NMR-based metabolomic profile in urine as TB biomarker

Prediction of Treatment Outcome with Inflammatory Biomarkers after 2 Months of Therapy in Pulmonary Tuberculosis Patients: Preliminary Results

Cytokine Biosignature of Active and Latent Mycobacterium Tuberculosis Infection in Children

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