Combination of prealbumin and tuberculosis‐specific antigen/phytohemagglutinin ratio for discriminating active tuberculosis from latent tuberculosis infection

Luo, Ying; Xue, Ying; Xu, Yuan; Lin, Qun; Tang, Guoqing; L, Mao; Song, Huijuan; Wang, Feng; Sun, Ziyong

doi:10.1111/ijcp.13831

Cited by 17 publications

(16 citation statements)

References 47 publications

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“…Furthermore, we found that lymphocyte non-specific function detection including CD4 + T cell non-specific function, CD8 + T cell nonspecific function, and NK cell non-specific function could be used to distinguish between ATB and LTBI. In view of the fact that the combination of multiple parameters may perform better than single parameter alone in many studies (20,21,40), we further successfully established a three-index diagnostic model that could discriminate ATB from LTBI with good utility based on the combination of lymphocyte non-specific function and T-SPOT assay. The diagnostic model based on the combination of CD4 + T cell non-specific function, NK cell non-specific function, and CFP-10 spot number established in the present study was 90.63% specific and identified 83.67% of ATB cases, indicating that it could be used as a rule-in ATB diagnostic test to allow rapid treatment initiation.…”

Section: Discussionmentioning

confidence: 99%

“…Some studies have shown that poor immune status was one characteristic of ATB patients, suggesting that the evaluation of host immunity could be applied as a potential direction for TB diagnosis and monitoring (13,(20)(21)(22). Unfortunately, difficulties existed in host immunity evaluation of ATB patients due to the limitations of current tests such as lymphocyte subset analysis and the measurement of serum protein (23).…”

Section: Introductionmentioning

confidence: 99%

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Lymphocyte Non-Specific Function Detection Facilitating the Stratification of Mycobacterium tuberculosis Infection

et al. 2021

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BackgroundInadequate tuberculosis (TB) diagnostics, especially for discrimination between active TB (ATB) and latent TB infection (LTBI), are major hurdle in the reduction of the disease burden. The present study aims to investigate the role of lymphocyte non-specific function detection for TB diagnosis in clinical practice.MethodsA total of 208 participants including 49 ATB patients, 64 LTBI individuals, and 95 healthy controls were recruited at Tongji hospital from January 2019 to October 2020. All subjects were tested with lymphocyte non-specific function detection and T-SPOT assay.ResultsSignificantly positive correlation existed between lymphocyte non-specific function and phytohemagglutinin (PHA) spot number. CD4+ T cell non-specific function showed the potential for differentiating patients with negative T-SPOT results from those with positive T-SPOT results with an area under the curve (AUC) of 0.732 (95% CI, 0.572-0.893). The non-specific function of CD4+ T cells, CD8+ T cells, and NK cells was found significantly lower in ATB patients than in LTBI individuals. The AUCs presented by CD4+ T cell non-specific function, CD8+ T cell non-specific function, and NK cell non-specific function for discriminating ATB patients from LTBI individuals were 0.845 (95% CI, 0.767-0.925), 0.770 (95% CI, 0.683-0.857), and 0.691 (95% CI, 0.593-0.789), respectively. Application of multivariable logistic regression resulted in the combination of CD4+ T cell non-specific function, NK cell non-specific function, and culture filtrate protein-10 (CFP-10) spot number as the optimally diagnostic model for differentiating ATB from LTBI. The AUC of the model in distinguishing between ATB and LTBI was 0.939 (95% CI, 0.898-0.981). The sensitivity and specificity were 83.67% (95% CI, 70.96%-91.49%) and 90.63% (95% CI, 81.02%-95.63%) with the threshold as 0.57. Our established model showed superior performance to TB-specific antigen (TBAg)/PHA ratio in stratifying TB infection status.ConclusionsLymphocyte non-specific function detection offers an attractive alternative to facilitate TB diagnosis. The three-index diagnostic model was proved to be a potent tool for the identification of different events involved in TB infection, which is helpful for the treatment and management of patients.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Lymphocyte Non-Specific Function Detection Facilitating the Stratification of Mycobacterium tuberculosis Infection

et al. 2021

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“…TB is generally considered to be a disease with malnutrition. Some previous studies have reported the low level of serum iron ( 55 ) and prealbumin ( 56 ) in ATB patients. Thus, the low level of lymphocyte number and function found in our research echoed these phenomena.…”

Section: Discussionmentioning

confidence: 99%

Lymphocyte-Related Immunological Indicators for Stratifying Mycobacterium tuberculosis Infection

et al. 2021

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BackgroundEasily accessible tools that reliably stratify Mycobacterium tuberculosis (MTB) infection are needed to facilitate the improvement of clinical management. The current study attempts to reveal lymphocyte-related immune characteristics of active tuberculosis (ATB) patients and establish immunodiagnostic model for discriminating ATB from latent tuberculosis infection (LTBI) and healthy controls (HC).MethodsA total of 171 subjects consisted of 54 ATB, 57 LTBI, and 60 HC were consecutively recruited at Tongji hospital from January 2019 to January 2021. All participants were tested for lymphocyte subsets, phenotype, and function. Other examination including T-SPOT and microbiological detection for MTB were performed simultaneously.ResultsCompared with LTBI and HC, ATB patients exhibited significantly lower number and function of lymphocytes including CD4+ T cells, CD8+ T cells and NK cells, and significantly higher T cell activation represented by HLA-DR and proportion of immunosuppressive cells represented by Treg. An immunodiagnostic model based on the combination of NK cell number, HLA-DR+CD3+ T cells, Treg, CD4+ T cell function, and NK cell function was built using logistic regression. Based on receiver operating characteristic curve analysis, the area under the curve (AUC) of the diagnostic model was 0.920 (95% CI, 0.867-0.973) in distinguishing ATB from LTBI, while the cut-off value of 0.676 produced a sensitivity of 81.48% (95% CI, 69.16%-89.62%) and specificity of 91.23% (95% CI, 81.06%-96.20%). Meanwhile, AUC analysis between ATB and HC according to the diagnostic model was 0.911 (95% CI, 0.855-0.967), with a sensitivity of 81.48% (95% CI, 69.16%-89.62%) and a specificity of 90.00% (95% CI, 79.85%-95.34%).ConclusionsOur study demonstrated that the immunodiagnostic model established by the combination of lymphocyte-related indicators could facilitate the status differentiation of MTB infection.

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“…Meanwhile, immunodiagnostics has received considerable attention as an alternative for discrimination of MTB infection status in recent years (63)(64)(65)(66)(67)(68)(69)(70). Nevertheless, the identified biomarkers including proteins and cytokines in serum or plasma for diagnostic aim may not be specific for TB due to the influence brought by other immune related diseases such as infection and autoimmune diseases (71)(72)(73)(74)(75)(76)(77). Thus, an intensified search for suitable host-specific biomarkers targeting TB diagnostic purpose was urgently needed (78).…”

Section: Discussionmentioning

confidence: 99%

Activation Phenotype of Mycobacterium tuberculosis-Specific CD4+ T Cells Promoting the Discrimination Between Active Tuberculosis and Latent Tuberculosis Infection

Luo

Xue

et al. 2021

Front. Immunol.

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BackgroundRapid and effective discrimination between active tuberculosis (ATB) and latent tuberculosis infection (LTBI) remains a challenge. There is an urgent need for developing practical and affordable approaches targeting this issue.MethodsParticipants with ATB and LTBI were recruited at Tongji Hospital (Qiaokou cohort) and Sino-French New City Hospital (Caidian cohort) based on positive T-SPOT results from June 2020 to January 2021. The expression of activation markers including HLA-DR, CD38, CD69, and CD25 was examined on Mycobacterium tuberculosis (MTB)-specific CD4+ T cells defined by IFN-γ, TNF-α, and IL-2 expression upon MTB antigen stimulation.ResultsA total of 90 (40 ATB and 50 LTBI) and another 64 (29 ATB and 35 LTBI) subjects were recruited from the Qiaokou cohort and Caidian cohort, respectively. The expression patterns of Th1 cytokines including IFN-γ, TNF-α, and IL-2 upon MTB antigen stimulation could not differentiate ATB patients from LTBI individuals well. However, both HLA-DR and CD38 on MTB-specific cells showed discriminatory value in distinguishing between ATB patients and LTBI individuals. As for developing a single candidate biomarker, HLA-DR had the advantage over CD38. Moreover, HLA-DR on TNF-α+ or IL-2+ cells had superiority over that on IFN-γ+ cells in differentiating ATB patients from LTBI individuals. Besides, HLA-DR on MTB-specific cells defined by multiple cytokine co-expression had a higher ability to discriminate patients with ATB from LTBI individuals than that of MTB-specific cells defined by one kind of cytokine expression. Specially, HLA-DR on TNF-α+IL-2+ cells produced an AUC of 0.901 (95% CI, 0.833–0.969), with a sensitivity of 93.75% (95% CI, 79.85–98.27%) and specificity of 72.97% (95% CI, 57.02–84.60%) as a threshold of 44% was used. Furthermore, the performance of HLA-DR on TNF-α+IL-2+ cells for differential diagnosis was obtained with validation cohort data: 90.91% (95% CI, 72.19–97.47%) sensitivity and 68.97% (95% CI, 50.77–82.73%) specificity.ConclusionsWe demonstrated that HLA-DR on MTB-specific cells was a potentially useful biomarker for accurate discrimination between ATB and LTBI.

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Combination of prealbumin and tuberculosis‐specific antigen/phytohemagglutinin ratio for discriminating active tuberculosis from latent tuberculosis infection

Cited by 17 publications

References 47 publications

Lymphocyte Non-Specific Function Detection Facilitating the Stratification of Mycobacterium tuberculosis Infection

Lymphocyte Non-Specific Function Detection Facilitating the Stratification of Mycobacterium tuberculosis Infection

Lymphocyte-Related Immunological Indicators for Stratifying Mycobacterium tuberculosis Infection

Activation Phenotype of Mycobacterium tuberculosis-Specific CD4+ T Cells Promoting the Discrimination Between Active Tuberculosis and Latent Tuberculosis Infection

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