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 Table of Contents  
Year : 2016  |  Volume : 5  |  Issue : 2  |  Page : 111-119

Association of plasma cytokines with radiological recovery in pulmonary tuberculosis patients

1 Department of Pediatrics and Child Health, Aga Khan University; Department of Biological and Biomedical Sciences, Aga Khan University, Karachi, Pakistan
2 Department of Pathology and Laboratory Medicine, Aga Khan University, Karachi, Pakistan
3 Masoomeen General Hospital, Kharadar, Karachi, Pakistan

Date of Web Publication9-Feb-2017

Correspondence Address:
Najeeha Talat Iqbal
Department of Pediatrics and Child Health, Aga Khan University, Stadium Road, P.O. Box 3500, Karachi 74800
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Source of Support: None, Conflict of Interest: None

DOI: 10.1016/j.ijmyco.2015.12.003

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Objective/Background: The characterization of tuberculosis (TB) patients as slow or fast responders post anti-TB treatment has always been a matter of tremendous interest as slow responders are most likely to relapse and/or develop complications. Pulmonary tissue healing as assessed with radiology is the only available tool for tissue recovery but is not predictive at intake. The objective of the current study was to assess biomarkers associated with fast and slow recovery in TB patients at recruitment.
Methods: Pulmonary TB patients (N = 15) were assessed for radiological recovery serially in parallel with clinical signs and symptoms, hematological parameters, and plasma cytokines at 0months, 6months, 12months, and 24months. On the basis of differential radiological healing, patients were characterized into slow (>12 months), intermediate (<12months), and fast (<6months) responders.
Results: Baseline plasma cytokines (interleukin [IL]-2, -4, -6, -10, tumor necrosis factor-α, and interferon-γ) were determined using cytometric bead array. IL-2 and -4 were able to accurately differentiate slow and fast responders into two distinct clusters using hierarchal clustering analysis. Compared with fast responders, slow responders showed significantly high IL-2 and -4 at baseline (p = .001 Mann–Whitney U test).
Conclusion: In-depth analysis of cytokines and its association with radiological recovery in TB patients may be useful in monitoring TB patients postchemotherapy for both clinicians and TB control program.

Keywords: Cytokines, Pulmonary tuberculosis, Radiological recovery

How to cite this article:
Iqbal NT, Hussain R, Shahid F, Dawood G. Association of plasma cytokines with radiological recovery in pulmonary tuberculosis patients. Int J Mycobacteriol 2016;5:111-9

How to cite this URL:
Iqbal NT, Hussain R, Shahid F, Dawood G. Association of plasma cytokines with radiological recovery in pulmonary tuberculosis patients. Int J Mycobacteriol [serial online] 2016 [cited 2022 May 19];5:111-9. Available from: https://www.ijmyco.org/text.asp?2016/5/2/111/199918

  Introduction Top

Pulmonary tuberculosis (TB) is the transmissible form of TB which spreads through the inhalation of droplet nuclei. Mycobacterium tuberculosis is first taken up by alveolar macrophages, which after engulfment activate other immune cells (neutrophils and dendritic cells) for clearance and presentation of mycobacterial antigens to different subsets of T cells to secrete cell activating and differentiating cytokines which orchestrate an adaptive immune response. Containment or localization of infection occurs in case of appropriate immune activation in the form of a granuloma formation and walling off the infection. Generally granulomas are made up of a central core of infected cells surrounded by macrophages and multinucleated giant cells with an outer layer of lymphocytes. Granulomas are further divided into the following three categories: classical caseous, central necrotizing, and fibrotic granuloma [1]. The role of granulomas in TB is not yet clearly defined in terms of its role in protection or progression of infection and disease onset [2]. Radiological findings define the extent of tissue involvement [2] into minimal disease (PMN) which is characterized by slight to moderate lung density (granuloma) with involvement of one or both apices of lungs; moderate disease in addition to the above features presents with a single cavity (PMD); and advance disease (PAD) where disseminated confluent lesions with miliary molting and/or multiple cavities are seen ([Figure S1]). In primary TB infiltration of either the apices, middle, or lower lung zone can occur with or without hilar lymph node involvement and in some cases pleural effusions can also be seen in chest X-rays. The course of radiological recovery in TB patients is variable and may take months to several years to fully resolve with disappearance of infiltration, lung lesions, and/or cavities. As expected, PMN lesions usually show early recovery while PAD shows a slow recovery. However, PMD, which comprise the majority of patients, shows variable radiological recovery. In these patients radiology is not a predictive parameter for the rate of recovery. Therefore, it is important to identify patients who are likely to show slow recovery at the time of intake for purposes of closer monitoring. Often slow responders of TB therapy constitute high risk individuals for relapse and reactivation of TB. This is an important issue for both clinicians as well as TB control program. No clinical or biological markers are available per se to identify the disease resolution process at the time of initiation of chemotherapy. The role of cytokines in TB disease progression and treatment has been studied at length [3], but none of the studies addressed the issue of biomarkers to predict recovery of TB patients at the time of diagnosis and pretreatment. Our current study was therefore unique, as we carried out parallel assessment of various clinical and immune biomarkers including T helper cell TH1 and TH2 cytokines serially at 0months, 6months, 12months, and 24month and correlated them with resolution of clinical signs, symptoms, and lung lesions. On the basis of tissue recovery on a chest radiograph, we divided patients into three categories of response to TB treatment: slow recovery (>12 months), intermediate recovery (<12months), and fast recovery (<6months) and then applied cluster analyses to define fast and slow responders. The characterization of TB patients as slow or fast responders post anti-TB treatment is important as slow responders are most likely to relapse and/or develop complications. Pulmonary tissue healing as assessed with radiology is the only available tool for tissue recovery but this is also not predictive at intake. A single cluster of interleukin (IL)-2 and -4 levels at initiation of therapy was able to group TB patients into fast and slow responders as defined by radiological recovery. These findings may be useful to clinicians in monitoring TB patients postchemotherapy for both clinicians and TB control program.

  Materials and methods Top

Recruitment of TB patients

As part of a larger cohort longitudinal study [4], 20 families each with at least one case of sputum-positive TB (index case) and their exposed household contacts were recruited (from November 2001 to January 2008). Index cases with active TB patients were identified at the Masoomeen General Hospital Karachi located in a low socioeconomic peri-urban area of Karachi, Pakistan. Only newly diagnosed untreated TB patients (N = 15) who were sputum positive for acid-fast bacilli and had <1month of treatment were included in the study. TB patients with relapse and a history of past TB treatment were excluded from the study. Any patient with comorbid conditions such as diabetes, autoimmune disease, and/or on steroids for various TB unrelated conditions were also excluded from study. Study protocol was approved by the Aga Khan University Ethical Committee. As Karachi is highly endemic for TB [5], we also included healthy endemic controls (EC; N = 43) for comparison of baseline cytokine responses. ECs were derived from the same socio-economic background. The age and sex distribution and tuberculin skin test (TST) status of the TB and EC groups is given in [Table 1].
Table 1: Demographics of study groups.

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Cytometric bead array assay

Cytokine assessment was carried out using human TH1/TH2 cytokine kit II (BD Bioscience, San Jose, CA, USA) for simultaneous detection of six cytokines (IL-2, -4, -6, -10, tumor necrosis factor-alpha [TNFα], and interferon-gamma [IFNγ]) in plasma and culture supernatants samples as described previously [5]. All data were acquired on BD fluorescence-activated cell sorting (FACS) array platform. The cytokine measurement was based on the principle of cytometric bead array (CBA) technology. CBA utilizes microparticles or beads labeled with discrete fluorescence intensity. The capture beads were labeled with allophycocyanin and were read at 650nm or on parameters. The detection antibody specific for cytokines was labeled with phycoerythrin fluorochrome which emits at 585nm on yellow parameters. The intensity of fluorescence of the yellow parameter is proportional to the amount of cytokine present in test samples. Briefly, test samples (50μL) and phycoerythrin detection antibody were incubated with capture bead reagent for 3h in the dark at room temperature. All unbound antibodies were washed (1.0mL wash buffer) and resuspended in 300μL before acquisition on BD FACS array bioanalyzer (BD Bioscience). All six cytokines had a single, well separated peak. Six individual cytokine standard curves (range, 0–5000pg/mL) were run in each assay. The range of detection was between 3000pg/mL and 5000pg/mL and inter- and intra-assay coefficient of variation for all cytokines have been reported previously from our laboratory [5] and were in accordance with that provided by the manufacturer.

Statistical analysis

Statistical analyses were performed using SPSS (version 16.0; SPSS Inc., Chicago, IL, USA) for Windows. Cluster analysis was carried out for cytokines and stratification of TB treatment response as fast, slow, and intermediate groups on the basis of radiological recovery in patients. Hierarchical clustering was performed to measure the distance of similar or dissimilar variables. Similarities between cases were assigned as one cluster, whereas dissimilar groups were clustered at a distant in a dendrogram. The Wald cluster method was applied to measure squared Euclidean distance. Numeric values were expressed as mean±standard error of the mean (SEM) and significance between groups was assessed using Mann–Whitney U tests. All p values are two-sided with p < .05 considered as statistically significant.

  Results Top

Characteristics of TB patients and recovery pattern

Pulmonary TB patients were classified into PMN, PMD, and PAD on the basis of lung tissue involvement as per the guidelines of Crofton [6] ([Figure S1]). The demographics of the study group are given in [Table 1] which shows the age, sex, and TST status of TB patients (n = 15) and ECs (n = 43). There was no difference in the mean age of TB patients and ECs; however, the mean TST diameter as expected was significantly higher in the TB patient group compared with the EC group (Student t test; p = .001). The majority of TB patients were women, which could be a selection bias of our cohort study, where index cases and household contacts were recruited in a longitudinal follow up study [7], as women were found to be more reliable in longitudinal follow-up studies in families. TB was confirmed with sputum microscopy, chest X-rays, and signs and symptoms consistent with newly diagnosed TB patients. All patients were microscopically sputum positive, and had received <1month of treatment ([Table S1]). Clinical and radiological follow-up of TB patients was carried out using a standardized questionnaire and reviewed by the chest physicians of Masoomeen General Hospital. To understand the dynamics of recovery in pulmonary patients post-therapy, we analyzed several disease associated parameters which included signs and symptoms, laboratory tests, and radiology ([Table 2]). Detailed signs and symptoms and their recovery are given in [Table S2]. Lung tissue healing was variable and the patients were stratified on this basis, into fast (N = 5) if complete healing occurred in <6months, intermediate (N = 4) <12months, or slow if lesions were present at >12 months (N = 6; [Table S2]). Fever was the first symptom to normalize (14/15 at 6months) in patients, while erythrocyte sedimentation rate (ESR), which is a sign of systemic inflammation showed the slowest recovery and was still raised (>30 mm) in 11/15 patients at 24months ([Table 2]) even in the absence of lung lesions.
Table 2: Recovery rate of tuberculosis patients postchemotherapy.

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Relationship of the extent of lung tissue involvement at intake with radiological recovery of lung lesions post-treatment

We next assessed the radiological recovery in relation to the extent of disease at intake ([Table 3]). Of 15 TB cases, 13 had moderate disease. Furthermore, among those with PMD, eight had only lung tissue involvement, four PMD had hilar lymph node (HLN) involvement in addition, and one PMD had pulmonary effusions (PE). It was interesting to note that HLN involvement in PMD was associated with fast recovery of lung lesions, while PE was associated with a much slower recovery of lung lesions. Recovery in the PMD group with no other site involved was variable and equally distributed between intermediate and slow responders. Therefore it would be important to have predictive biomarkers at initiation of treatment for this particular group of TB patients.
Table 3: Radiological recovery in relation to extent of pulmonary disease at intake.

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There was only one TB patient with advanced disease and had both HLN and PE involvement and as expected showed slow recovery. There was also one patient with minimal disease and had only lung involvement. Unexpectedly, this patient also showed slow resolution of lung lesions.

Plasma cytokines and their relationship with radiological recovery

We first validated CBA methodology by comparing the plasma cytokine levels in the TB (N = 15) and EC (N = 43; [Figure 1]) groups. All cytokines were significantly raised in TB patients compared with healthy controls as previously reported [4],[5]. These results show that the CBA was able to discriminate cytokine levels between patients and control groups. We next assessed the plasma levels at intake within TB patients in relation to radiological recovery. Frequency of cytokine levels in fast, intermediate, and slow recovery groups are shown in [Figure 2] as box plots (TH1: IL-2, IFNγ, TNFα, upper panel A; TH2: IL-4, IL-10, IL-6, lower panel B). A significantly higher level of IL-2 and -4 was associated with slow recovery of lung lesions (p = .027, p = .018) and intermediate recovery (p = .02) of lung lesions compared with fast responders. Interestingly, there was no association of any other cytokines with rate of radiological recovery of lung lesions, although there were trends in other cytokines such as low IFNγ and high TNFα associated with fast recovery, but did not reach statistical significance. To further confirm the ability of these cytokines to predict rate of radiological recovery pretreatment, we also applied cluster analysis as described in the “Materials and methods” Section.
Figure 1: Comparison of circulating plasma cytokines levels in tuberculosis (TB) patients and healthy controls. Comparison of plasma cytokines in healthy community controls (endemic controls [EC] = 43) and pulmonary TB patients (pulmonary TB = 15). Cytokineswere analyzed in plasma using human TH1/TH2 cytokine kit II (BD Bioscience, San Jose, CA, USA). Plasma samples were diluted 1/10 in dilution buffer. All protocols were followed according to manufacturer's instruction and analyzed on a BD fluorescence-activated cell sorting array instrument as described elsewhere [5]. Bar graph shows the plasma cytokine levels diluted 1/10 in assay diluent. All protocols were followed according to manufacturer's instruction and analyzed on a BD fluorescence-activated cell sorting array instrument. The results are shown as mean (bars) and ±standard error (vertical lines). Mann–Whitney U test was applied for significant difference in two groups. All significant p values are indicated. Solid bar indicates TB patient and open bar indicates healthy control. Note: IFN = interferon; Il = interleukin; TNF = tumor necrosis factor.

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Figure 2: Relationship of radiological recovery with pro- and downregulatory cytokines. All details are the same as [Figure 1]. The results are shown as box and whiskers plot indicating 25th, 50th, and 75th percentiles of cytokines in three categories of fast (n = 6), intermediate (n = 4), and slow (n = 5) responders. Mann–Whitney U test was applied for significant difference in two groups. All significant p values are indicated. Note: IFN = interferon; Il = interleukin; TNF = tumor necrosis factor.

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Cluster analysis of cytokines

IL-2/IL-4 cluster analysis

Cluster analysis on plasma cytokines was evaluated at intake. [Figure 3] shows the dendrogram on circulating levels of IL-2 and -4 cytokines in TB patients. The clusters in the dendrogram stratified the fast and slow responders into two distinct clusters with fast and intermediate responders grouping in Cluster 1, and slow responders in Cluster 2. IL-2 and -4 therefore show a dominant association with the rate of recovery of lung lesions. Only one patient with an intermediate response (TB 271) grouped with slow responders. In this patient, although the clinical signs and symptoms resolved at 6months the lung lesion, although localized, was still present at 6months ([Table S2]).
Figure 3: Cluster analysis of interleukin-2 and -4 for discrimination of fast, intermediate, and slow responders. The dendrogram represents individual cases numbered on the vertical axis. The cluster was analyzed using Wards method and data was standardized using Z-score. The horizontal line separates the two clusters (Cluster 1 or Cluster 2). The vertical axis shows the serial number of unique identifier in SPSS database with case identification. Vertical axis also defines the response of individual patients as fast, intermediate, and slow. Note: TB = tuberculosis.

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Cluster analysis on TH1/TH2 combinations

It is likely that additional cytokines may also affect response rate. To address this issue we combined additional cytokines in the cluster analysis as shown in [Table 4]. Adding IFNγ to the existing IL-2 and -4 cluster did not change the existing profile. The addition of IL-10 to existing IL-2 and -4 clusters to some extent stratified slow and fast responders and divided them into Clusters 2 and 3 but an additional cluster with fast and intermediate were also found as Cluster 1. Additional cytokines therefore failed to provide useful information but did suggest that some effect may be exerted by other cytokines on the rate of recovery. However, it is clear that PMD, which forms the majority of TB patients, could be defined as fast and slow responders with IL-2 and -4 clusters pretreatment.
Table 4: Summary of cytokine clustering in relation to rate of radiological recovery.

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Frequency of plasma levels IL-2 and -4 cluster

We next determined if a cut-off level could be defined for slow and fast responders with IL-2 and -4. [Figure 4] shows the bar graph of mean±SEM of IL-2 and -4 cytokines in Clusters 1 and 2. Both cytokines showed three to four fold higher mean levels (p = .001) in the slow responder (Cluster 1) compared with the fast responders group (Cluster 2). A cut off of >50 ng would seem a reasonable cut off for discriminating the two groups. This level is 10-fold above the detection range and therefore can be reliably detected in CBA. These levels, therefore, could be used as predictive markers but further studies are needed with expanded groups to improve the power of prediction for the rate of recovery.
Figure 4: Differential cytokine secretion pattern in Clusters 1 and 2. Bar graph shows the distribution (mean ± standard error of the mean) of plasma cytokines in two clusters (Clusters 1 and 2) as mentioned in [Figure 3]. Results are shown as pg/mL for interleukin-2 and -4. Mann–Whitney U test was applied for comparison of cytokines in Cluster 1 (n = 8) and Cluster 2 (n = 7). Note: IL = interleukin.

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  Discussion Top

This study aimed at defining immune markers at initiation of chemotherapy associated with recovery of radiological lesions which is the gold standard for assessment of successful treatment. Serial radiology over the treatment period has shown considerable variability in the rate of recovery in TB patients, but is not a practical test at the field level. Therefore, a single test at the time of initiation of therapy is necessary which would predict slow and fast recovery. This is particularly important for TB control programs. This is what we attempted to define in the current study.

In the present study we classified TB patients with fast, slow, and intermediate response to therapy, based on the radiological recovery post-treatment in a 2year follow-up. It was surprising that of the six cytokines tested only two cytokines, IL-2 and -4, were dominantly associated with the rate of recovery of lung lesions. The cytokine levels were significantly higher in TB patients compared with the EC groups and considerably above the baseline levels of detection and therefore can be reliably assessed in this assay.

Cluster analyses reliably classified the majority of TB patients with moderate disease into slow and fast responders. Patients with moderate disease form the major group in Bacillus Calmette–Guérin (BCG) vaccinated countries which may be due to the partial protection conferred by BCG vaccination. Therefore, these findings provide important information for countries with a high rate of TB transmission despite BCG vaccination.

Immune markers which discriminate slow and fast response during intensive TB therapy has been attempted previously [8]. However, none of the studies succeeded in identifying predictive immune biomarkers. This is mainly due to logistic challenges and use of nonstandardized assays [9],[10]. Certain statistical models could discriminate slow from fast responders with immune makers at the time of diagnosis and 4-weeks post-treatment [8],[11]. In those studies, mathematical models predicted soluble TNF receptor-1, C-reactive protein [11], granzymes [8],[12], and IP-10 [13] as biomarkers of TB treatment response. In our previous study we attempted to classify immune response according to severity of pulmonary disease using lymphoproliferative assay and purified protein derivative skin test [14]. Our results indicated an association of higher lymphoproliferative assay response in PMN compared with PAD. However, this study did not carry out a serial follow-up of TB patients.

Most studies defined TB patients as slow responders if they remained sputum positive after 2months of intensive treatment to monitor patient progress [15]. The bacterial load also correlated with a higher level of immune markers such as granzyme B, monocytes, and neutrophils after 4-weeks post-treatment [8]. Bacterial load can be deceptive as both dead and live bacteria may be included and this may be the reason as to the extent of disease not relating to the bacterial load. The main strength of our study was the serial assessment of chest X-rays and laboratory tests over a period of 24months. Our classification of slow and fast responders was not based on bacterial load but rather healing and disappearance of lesions which is more likely to be a better assessment of recovery. The baseline assessment of biomarkers to reliably predict outcome of TB disease in terms of slow and fast response to therapy is highly desirable for targeted intervention. However, the low reversion rate of IFNγ release assays post-treatment and inconsistent results in TB treatment [16],[17] directed us toward high throughput multiplex estimation of cytokines to predict recovery of patients before the start of treatment. The majority of studies assessed serum cytokines in TB patients between 8weeks and 12weeks post-treatment; the findings were equivocal to determine the predictive value of each or combined biomarkers. Most studies showed an apparent decrease in cytokine response post-treatment between 0months and 6months [18],[19],[20].

In our study, the radiological recovery was consistent with sputum clearance and no patients had multi-drug resistant TB. All patients had sputum conversion within 6months. One of the limitations of our study was that no follow-up was done at 2months for smear conversion. However, the long follow-up was more critical as this was the main strength of the study. Fever was the earliest to resolve while ESR took the longest time to resolve and was elevated in some patients even at 24months. Fever may therefore be related to antigen clearance while ESR may be related to immune activation of proinflammatory cytokines which takes longer to down regulate and decrease to baseline levels. This may be the reason as to why inflammatory markers have not been useful indicators of resolution of disease. This was also clear in our current study where both predictive markers are involved in either T cell proliferation or differentiation (IL-2) or released from differentiated T cells, while none of the proinflammatory markers such as IFNγ, TNFα and IL-6 showed association with tissue healing. The radiological recovery in a few patients was slow, but none of the patients showed relapse in our group. Interestingly the additional sites involved also had an influence in tissue healing particularly in patients with moderate disease. HLN involvement was predictive of a faster response to treatment while PE usually was associated with a slower rate of recovery in patients with moderate disease. We have not tested drug resistance in the current study group which has been previously reported in this endemic setting [21]. The reason for the high response rate may be due to compliance to the multidrug regimen. Additionally, patients had no prior history of TB treatment or relapse.

The hierarchical clustering for IL-2 and -4 was most discriminatory for classification of TB patients in two distinct clusters and properly assigned 100% of slow responders into Cluster 2 associated with raised IL-2 and -4 levels at baseline [Figure 4]. Similarly high pretreatment levels of other cytokines have been reported in patients showing slow response to treatment, and were labeled as late responders [19],[20]. The definition of late responders in these studies was sputum positive culture after 2months of treatment. We have previously reported a consistent high spontaneous IL-4 secretion in recently exposed household contacts of active TB cases, which progressed to TB disease [22]. These findings indicate that raised IL-4 may be a risk factor even before the establishment of TB disease [22]. Spontaneous IL-2 was also shown to be raised in exposed household contacts progressing to disease but the levels were not significant [22].

Several reports indicate that there is no change in IL-2 and -4 levels after TB treatment [23],[24]. These studies were carried out in human immunodeficiency virus (HIV) infected cohorts [24]. HIV is known to influence the baseline levels of anti-inflammatory cytokines. Pakistan has less than one prevalence of HIV in a TB-infected patient [25] and this may be the reason for discriminatory levels not only in TB patients compared with healthy ECs, but may also be the reason for discrimination in patients with varying severity of TB disease.

One study also reported a higher phenotype of IL-2+ in cured TB patients in the central memory pool and a lower response in the effector memory pool [26]. Higher frequency of IL-2+ T cells was found in the bronchial lavage compared with peripheral blood [27] and an increase in double positive IFNγ+/IL2+ T cells post-treatment [28],[29]. These results may seem discrepant, but the explanation may be linked the different compartments being assessed, i.e., cellular frequency of IL-2+ T cells versus plasma levels. Different compartments in blood are known to show higher or lower frequency in the same patient or control group depending on the compartment being assessed [30]. In addition most of these studies are cross sectional and therefore information on both the classification of TB patients and response to treatment was not available.

  Conclusion Top

In conclusion, we have identified biomarkers in pulmonary TB patients which may predict the rate of recovery at time of initiation of chemotherapy. Radiology, although a gold standard for lung lesion recovery, is difficult to carry out repeatedly in field conditions and also does not predict the rate of recovery at time of initiation of chemotherapy as demonstrated in our study. Therefore identification of these predictive biomarkers is a step forward in improving not only the management of TB patients at the time of initiation of chemotherapy but will also help TB control programs in identifying high risk patients for reactivation and relapse and those who pose a threat to control activities.

  Conflicts of interest Top

All authors have declared no conflict of interest with funding bodies and commercial entities.

  Acknowledgments Top

We are grateful to the Higher Education Commission, Government of Pakistan for providing financial support through Grant 20-796/R&D 07. We also acknowledge Ms. Muniba Islam for technical support. We are grateful to Mr. Mohammad Anwar for collection of blood samples. We are extremely grateful to the Masoomeen Hospital for providing the logistical support for this study.

  Appendix A. Supplementary data Top

Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.ijmyco. 2015.12.003.

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  [Figure 1], [Figure 2], [Figure 3], [Figure 4]

  [Table 1], [Table 2], [Table 3], [Table 4]

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