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Discriminating between latent and active tuberculosis with multiple biomarker responses.

ABSTRACT: We sought to identify biomarker responses to tuberculosis specific antigens which could 1) improve the diagnosis of tuberculosis infection and 2) allow the differentiation of active and latent infections. Seventy subjects with active tuberculosis (N = 12), latent tuberculosis (N = 32), or no evidence of tuberculosis infection (N = 26) were evaluated. We used the Luminex Multiplexed Bead Array platform to simultaneously evaluate 25 biomarkers in the supernatant of whole blood samples following overnight stimulation using the Quantiferon(®) Gold In-Tube kit. We defined the response to stimulation as the difference (within an individual patient) between the response to the pooled tuberculosis antigens and the negative control. IP-10 response was significantly higher in tuberculosis-infected (active or latent) subjects compared to the uninfected group (p < 0.0001). Among the 25 parameters, expression levels of IL-15 and MCP-1 were found to be significantly higher in the active tuberculosis group compared to the latent tuberculosis group (p = 0.0006 and 0.0030, respectively). When combined, IL-15 and MCP-1 accurately identified 83% of active and 88% of latent infections. The combination of IL-15 and MCP-1 responses was accurate in distinguishing persons with active tuberculosis from persons with latent tuberculosis in this study.

SUBMITTER: Frahm M

PROVIDER: S-EPMC3090479 | biostudies-literature | 2011 May

REPOSITORIES: biostudies-literature

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Discriminating between latent and active tuberculosis with multiple biomarker responses.

Frahm Marc M Goswami Neela D ND Owzar Kouros K Hecker Emily E Mosher Ann A Cadogan Emily E Nahid Payam P Ferrari Guido G Stout Jason E JE

Tuberculosis (Edinburgh, Scotland) 20110310 3

We sought to identify biomarker responses to tuberculosis specific antigens which could 1) improve the diagnosis of tuberculosis infection and 2) allow the differentiation of active and latent infections. Seventy subjects with active tuberculosis (N = 12), latent tuberculosis (N = 32), or no evidence of tuberculosis infection (N = 26) were evaluated. We used the Luminex Multiplexed Bead Array platform to simultaneously evaluate 25 biomarkers in the supernatant of whole blood samples following ov ...[more]

PMID: 21393062

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Project description:BackgroundGiven that there is no rapid and effective method for distinguishing active tuberculosis (ATB) from latent tuberculosis infection (LTBI), the discrimination between these two statuses remains challenging. This study sought to investigate the value of nutritional indexes and tuberculosis-specific antigen/phytohemagglutinin ratio (TBAg/PHA ratio) for distinguishing ATB from LTBI.MethodsParticipants were consecutively recruited based on positive T-SPOT.TB results between January 2018 and January 2020. ATB was diagnosed by positive mycobacterial culture and/or positive GeneXpert MTB/RIF, with clinical symptoms and radiological characteristics suggestive of ATB. Individuals with positive T-SPOT.TB but without the evidence of ATB were defined as LTBI. Patients younger than 17 years and undergoing anti-TB treatment were excluded.ResultsA total of 709 (312 ATB and 397 LTBI) and another 309 (120 ATB and 189 LTBI) subjects were respectively recruited from Tongji Hospital (Qiaokou cohort) and Sino-French New City Hospital (Caidian cohort). The level of prealbumin was significantly lower in ATB than in LTBI. With a cut-off value of 139 mg/L, the sensitivity and specificity of prealbumin in distinguishing ATB from LTBI were 50.96% (45.41%-56.51%) and 91.69% (88.97%-94.40%). Meanwhile, TBAg/PHA ratio was found statistically higher in ATB compared with LTBI. If using the threshold of 0.29, the sensitivity and specificity of TBAg/PHA ratio were 65.71% (60.44%-70.97%) and 90.93% (88.11%-93.76%), respectively. Moreover, the combination of prealbumin and TBAg/PHA ratio (obtaining by diagnostic model) yielded better specificity (90.18%, [87.25%-93.10%]) and sensitivity (87.18%, [83.47%-90.89%]), while the clinical utility index (CUI) positive and CUI negative were respectively 0.76 and 0.81. After anti-TB treatment, TBAg/PHA ratio was declined while the level of prealbumin was restored (Wilcoxon test, P < 0.001). Furthermore, the performance of diagnostic model obtained in Qiaokou cohort was confirmed in Caidian cohort.ConclusionsThe diagnostic model based on combination of prealbumin and TBAg/PHA ratio is a rapid and accurate tool for discriminating ATB from LTBI.

Project description:BACKGROUND: Tuberculosis (TB) continues to cause a high toll of disease and death among children worldwide. The diagnosis of childhood TB is challenged by the paucibacillary nature of the disease and the difficulties in obtaining specimens. Whereas scientific and clinical research efforts to develop novel diagnostic tools have focused on TB in adults, childhood TB has been relatively neglected. Blood transcriptional profiling has improved our understanding of disease pathogenesis of adult TB and may offer future leads for diagnosis and treatment. No studies applying gene expression profiling of children with TB have been published so far. RESULTS: We identified a 116-gene signature set that showed an average prediction error of 11% for TB vs. latent TB infection (LTBI) and for TB vs. LTBI vs. healthy controls (HC) in our dataset. A minimal gene set of only 9 genes showed the same prediction error of 11% for TB vs. LTBI in our dataset. Furthermore, this minimal set showed a significant discriminatory value for TB vs. LTBI for all previously published adult studies using whole blood gene expression, with average prediction errors between 17% and 23%. In order to identify a robust representative gene set that would perform well in populations of different genetic backgrounds, we selected ten genes that were highly discriminative between TB, LTBI and HC in all literature datasets as well as in our dataset. Functional annotation of these genes highlights a possible role for genes involved in calcium signaling and calcium metabolism as biomarkers for active TB. These ten genes were validated by quantitative real-time polymerase chain reaction in an additional cohort of 54 Warao Amerindian children with LTBI, HC and non-TB pneumonia. Decision tree analysis indicated that five of the ten genes were sufficient to classify 78% of the TB cases correctly with no LTBI subjects wrongly classified as TB (100% specificity). CONCLUSIONS: Our data justify the further exploration of our signature set as biomarkers for potential childhood TB diagnosis. We show that, as the identification of different biomarkers in ethnically distinct cohorts is apparent, it is important to cross-validate newly identified markers in all available cohorts.

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Discriminating between latent and active tuberculosis with multiple biomarker responses.

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Discriminating between latent and active tuberculosis with multiple biomarker responses.

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