INTRODUCTION:

Autoimmune rheumatic diseases encompass a diverse range of disorders that present with nearly identical clinical, biochemical, and immunological symptoms, in which the immune system erroneously attacks the body's tissues^1,2. Several studies have shown that the most common are rheumatoid arthritis (RA), spondyloarthritis (SA), and systemic lupus erythematosus (SLE). The global prevalence is 0.46% for RA, 0.2% for SA, and 0.04% for SLE^3-6. Immunosuppressant therapy remains the primary treatment choice in managing patients with autoimmune rheumatic diseases, comprising steroids and disease-modifying antirheumatic drugs (DMARDs), which comprise conventional/synthetic (cDMARDs) and biologic (bDMARDs) forms. Immunosuppressant therapy suppresses the immune system and is used over a prolonged period, increasing the risk of active, latent, and reactivated tuberculosis (TB) infections. Studies have shown that patients receiving steroid therapy equivalent to prednisone at >15 mg/day for at least 1 month are at a 2.8–7.7-fold greater risk of latent TB reactivation than in the general population, while those receiving anti-TNF-α therapy are at a 2–56-fold greater risk^7-13.

Latent TB refers to when an individual is persistently infected with Mycobacterium tuberculosis but shows no TB symptoms. Approximately 5%–10% of individuals with latent TB will progress to active TB if not well managed. Chandrashekara et al. reported a latent TB prevalence of 13% among patients with autoimmune rheumatic diseases in India, consistent with the findings of Callado et al. in Brazil^14-16. Appropriate screening tests and prophylactic therapy can prevent latent TB from transforming into active TB with an efficacy of 60%–90%. The World Health Organization (WHO) recommends that patients receiving immunosuppressant therapy undergo latent TB screening. The recommended tests for latent TB are the conventional tuberculin skin test and/or the recently introduced interferon-gamma (IFN-γ) release assay (IGRA)¹⁷.

The IGRA assesses the levels of T cells IFN-γ in response to stimulation by M. tuberculosis antigens (early secretory antigenic target-6 [ESAT-6] and 10-kDa culture filtrate protein [CFP-10]). The WHO has approved three commercial IGRAs: Quantiferon TB-Gold Plus (Qiagen, Victoria, Australia), T-SPOT.TB (Revvity, Abingdon, UK), and Beijing Wantai (Wantai BioPharm, Beijing, China). IGRAs can produce false positives due to the presence of nontuberculous mycobacteria, such as Mycobacterium kansasii, Mycobacterium szulgai, and Mycobacterium marinum. IGRAs can also produce false negatives due to immunosuppressant therapy, older age (≥60 years), low peripheral lymphocyte count, extrapulmonary TB, HIV, and cancer^18-20. A systematic review by Wong et al. of 17 studies on the effect of immunosuppressant therapy on IGRA results revealed that patients receiving immunosuppressant therapy were less likely to have positive IGRA results (odds ratio [OR]: 0.66, 95% confidence interval [CI]). Research conducted by Özsoy et al. in Turkey and Sauzullo et al. in Italy demonstrated that IGRAs could yield false negative results in patients receiving immunosuppressant therapy, suggesting that flow cytometry be used to confirm the amount of IFN-γ produced by T cells. Research by Dinser et al. in Germany showed that measuring IFN-γ using flow cytometry was useful in patients receiving immunosuppressant Therapy ^21-23. However, flow cytometry has not yet been recognized as a reference method for screening for latent TB.

Therefore, this study aimed to analyze the effect of immunosuppressant therapy on T cells IFN-γ produced in response to ESAT-6 and CFP-10 in patients with autoimmune rheumatic diseases using the T-SPOT.TB IGRA and flow cytometry, as no prior research has been conducted in Indonesia. It also assessed the performance of flow cytometry in screening for latent TB in autoimmune rheumatic disease patients.

MATERIALS AND METHODS:

Study Population:

This analytical, observational study employed a cross-sectional design. It enrolled patients with autoimmune rheumatic diseases at Dr. Soetomo General Academic Hospital in Surabaya, East Java, Indonesia, from June 2024 to November 2024. The study's inclusion criteria were as follows: (i) age >18 years, (ii) confirmed autoimmune rheumatic disease diagnosis (RA, SA, or SLE), and (iii) willingness to participate in this study by providing informed consent. The following conditions were excluded: (i) active TB, HIV, hepatitis B, and/or cancer, as determined from the medical records; (ii) pregnancy; and (iii) age ≥60 years. Eligible patients were stratified by whether they had not received immunosuppressant therapy (i.e., had not received any therapy or had received painkiller therapy, received steroid therapy equivalent to prednisone ≤15 mg/day for ≤1 month, not received any DMARDs therapy, or received cDMARDs for <3 months) or had received immunosuppressant therapy (i.e., received steroids equivalent to prednisone at >15 mg/day for >1 month, received cDMARDs for ≥3 months, or received bDMARDs in the last 3 months). This study received ethical approval from the Ethical Committees in Health Research of Dr. Soetomo General Hospital Surabaya (approval No: 0846/KEPK/XII/2023).

Specimen and data collection:

After the participants provided written informed consent, blood samples were collected via venipuncture into four lithium-heparin tubes (BD Biosciences, Franklin Lakes, NJ, USA), stored at 18°C–25°C (i.e., not refrigerated or frozen), and processed for the T-SPOT.TB IGRA and flow cytometry within 8 hours of collection to ensure cell integrity and viability. Participants’ clinical data were obtained from their medical records.

T-SPOT.TB interferon-gamma release assay:

All tests were conducted according to the T-SPOT.TB IGRA protocol¹⁸. The heparinised peripheral blood samples were used to separate peripheral blood mononuclear cells (PBMCs), which were then cleaned and tallied. The same quantity of PBMCs (2.5×10⁵) was stimulated with Panel A (ESAT-6 antigen), Panel B (CFP-10 antigen), Nil Control (AIM-V media), and Positive Control (phytohemagglutinin [PHA] mitogen) for 18 hours at 37°C with 5% CO₂. Next, the wells were cleaned and developed using a conjugated secondary antibody that binds to IFN-γ captured on the membrane surface. Dark spots were counted for each substrate using an ELISPOT reader. The results were calculated by subtracting the greatest spot count difference between Panels A and B from the Nil Control. A net spot count of ≥8indicated a positive outcome, a net spot count of ≤4 indicated a negative result, and a spot count of 5–7 indicated a borderline result. Dark spot counts of more than 10 in the Nil Control or less than 20 in the Positive Control were deemed invalid

Flow cytometry analysis:

PBMCs were isolated from the heparinized peripheral blood samples using Ficoll-Paque density gradient centrifugation (Cytiva, Marlborough, MA, USA) according to the manufacturer’s instructions. Next, the PBMCs were washed and suspended at a density of 5 × 10⁵ cells/500μL for each test tube in RPMI 1640 medium supplemented with 10% fetal bovine serum, 10.000U/mL penicillin, and 10mg/mL streptomycin (Pan Biotech, Aidenbach, Germany). Four test tubes containing, respectively, RPMI 1640 medium as a Nil Control, PHA mitogen as a Positive Control, ESAT-6 antigen, and CFP-10 antigen (Revvity, Abingdon, UK) were co-stimulated with anti-CD28/49d (BD Biosciences, San Jose, CA, USA) for 2 hours at 37°C with 5% CO₂. Next, protein transport inhibitor GolgiStop (BD Biosciences, San Jose, CA, USA) was added to each tube and incubated for 16 hours. Then, the cells were washed with phosphate-buffered saline (Pan Biotech, Aidenbach, Germany) and stained according to BD Biosciences’ staining protocol. Cell surface staining was performed with allophycocyanin (APC)-conjugated anti-cluster of differentiation 3 (CD3) and phycoerythrin (PE)-conjugated anti-cluster of differentiation 8 (CD8) antibodies (BD Biosciences, San Jose, CA, USA). Then, the cells were fixed and permeabilized with a Cytofix/Cytoperm Kit (BD Biosciences, San Jose, CA, USA) and intracellularly stained with fluorescein isothiocyanate (FITC)-conjugated anti-CD4 molecule (CD4) and Violet 500 (V500)-conjugated anti-IFN-γ antibodies (BD Biosciences, San Jose, CA, USA).

The stained samples were evaluated using a FASCLyric flow cytometer (BD Biosciences, USA) and the BD FACSuite Clinical software. Instrument quality assurance was assessed using BD CS&T Beads (BD Biosciences, San Jose, CA, USA). The negative control used unstained samples and Fluorescence Minus One (FMO) controls for each fluorochrome. The positive control used a single stain for each fluorochrome. The gating strategy for IFN-γ is illustrated in Fig. 1. The lymphocyte population was analyzed using forward versus side scatter (Fig. 1A). This population was used to acquire the gated fluorescence plot of 10,000 events of T cells (CD3⁺; Fig. 1B). The CD4⁺ and CD8⁺ populations were obtained from the CD3⁺ population (Fig. 1C) and used to analyze the IFN-γ⁺ CD4⁺ (Fig. 1D) and IFN-γ⁺ CD8⁺ (Fig. 1E) populations.

Figure 1. The gating strategy. (A) The lymphocyte population was analyzed using forward versus side scatter. (B) The CD3 population was obtained from the lymphocyte population. (C) The CD4 and CD8 populations were obtained from the CD3 population. (D) IFN-γ CD4 population. (E) IFN-γ CD8 population.

Statistical analysis:

Normality was assessed using the Shapiro–Wilk (n < 50) or Kolmogorov–Smirnov (n > 50) tests. Normally distributed variables are presented as the mean ± standard deviation (SD), and non-normally distributed (non-parametric) variables are presented as the median (range). The effects of immunosuppressant therapy on the T-SPOT.TB IGRA and flow cytometry IFN-γ results were assessed using logistic regression. The effects of immunosuppressant therapy on the flow cytometry IFN-γ⁺ CD4⁺ and IFN-γ⁺ CD8⁺ percentages were assessed using the Mann–Whitney U test. Flow cytometry performance was evaluated using positive percent agreement (PPA), negative percent agreement (NPA), overall percent agreement (OPA), and concordance analysis utilizing Cohen’s κ with the T-SPOT.TB IGRA as the reference method. The statistical analyses were conducted using SPSS Statistics (version 25; IBM Corp., Armonk, NY, USA). A P-value of <0.05 was considered statistically significant.

RESULTS:

Study population characteristics:

The study population comprised 57 patients with autoimmune rheumatic diseases, of whom 43 (75%) received immunosuppressant therapy. Most participants were female (89%), and the mean age was 41.4 years. The most common diagnosis was SA (51%), followed by SLE (37%) and RA (12%). The most common immunosuppressant therapy was cDMARDs. The study population’s characteristics are presented in Table 1.

Table 1. Study population characteristics

Characteristic	IST (+) N = 43 (75%)	IS (-) N = 14 (25%)
Male//Female (N/%)	4 (9) / 39 (91)	2 (14) /12 (86)
Age, yrs, average (range)	41.3 (25-59)	41.9 (19-59)
Diagnosis RA (N) SA (N) SLE (N)	5 20 18	2 9 3
Immunosuppressant therapy (N) Steroid cDMARDs MTX SSZ HCQ LEF AZA MMF CsA bDMARDs Anti TNF-α Anti IL-17 IST 1 IST >1	4 11 11 17 2 5 7 5 1 1 23 20

Abbreviations: IST, immunosuppressant therapy; RA, rheumatoid arthritis; SA, spondyloarthritis; SLE, systemic lupus erythematosus; cDMARDs, conventional disease-modifying anti-rheumatic drugs; bDMARDs, biological disease-modifying anti-rheumatic drugs; MTX, methotrexate; SSZ, sulfasalazine; HCQ, hydroxychloroquine; LEF, leflunomide; AZA, azathioprine; MMF, mycophenolate mofetil; CsA, cyclosporine.

T-SPOT.TB IGRA:

The T-SPOT.TB IGRA provided 7 positive (12%), 4 borderline (7%), and 46 negative (81%) results. Positive results were obtained for six patients (14%) in the immunosuppressant therapy group and one patient (7%) in the no immunosuppressant therapy group. Positive results were obtained for four patients (17%) receiving only one type of immunosuppressant therapy and two patients (10%) receiving more than one type of immunosuppressant therapy. Borderline results were obtained for two patients in the immunosuppressant therapy group and two in the non-immunosuppressant therapy group.

The effect of immunosuppressant therapy on the T-SPOT.TB IGRA result:

The logistic regression analyses of the effect of immunosuppressant therapy on the T-SPOT.TB IGRA results included the positive and negative results but excluded the borderline results. The analysis of the effect of immnosuppressant therapy on the T-SPOT.TB IGRA results yielded a nonsignificant OR of 0.53 (P = 0.58; Table 2). The analysis of the effect of the amount of immunosuppressant therapy on the T-SPOT.TB IGRA results yielded a nonsignificant OR of 1.68 (P = 0.58; Table 2). These results indicate that receiving immunosuppressant therapy and the amount of immunosuppressant therapy do not significantly affect the T-SPOT.TB IGRA result.

Flow cytometry:

The percentages of IFN-γ⁺ CD4⁺ and IFN-γ⁺ CD8⁺ T cells in response to ESAT-6 and CFP-10 were derived by subtraction from the Nil Control. The smallest recorded percentage was 0.01% for IFN-γ⁺ CD4⁺ and 0.03% for IFN-γ⁺ CD8⁺. Among all participants, the median percentage was 0.03% (0%–0.87%) for IFN-γ⁺ CD4⁺ and 0.11% (0%–0.87%) for IFN-γ⁺ CD8⁺. In the immunosuppressant therapy group, the median percentage was 0.03% (0%–0.87%) for IFN-γ⁺ CD4⁺ and 0.12% (0%–0.87%) for IFN-γ⁺ CD8⁺. Conversely, in the no immunosuppressant therapy group, the median percentage was 0.01% (0%–0.82%) for IFN-γ⁺ CD4⁺ and 0.05% (0%–0.56%) for IFN-γ⁺ CD8⁺. The full results are shown in Table 3.

The flow cytometry analysis of the IFN-γ⁺ CD4⁺ and IFN-γ⁺ CD8⁺ responses to ESAT-6 and CFP-10 yielded both positive and negative results, determined by the presence or absence of IFN-γ in response to these antigens. Among all participants, the result was positive for 47 (82%) and negative for 10 (18%): 36 (84%) tested positive in the immunosuppressant therapy group, and 11 (79%) tested positive in the no immunosuppressant therapy group. Among those receiving therapy, 19 (83%) receiving a single immunosuppressant therapy tested positive, and 17 (85%) receiving more than one immunosuppressant therapy tested positive.

The effect of immunosuppressant therapy on the ESAT-6 and CFP-10 flow cytometry IFN-γ results:

The median percentages of IFN-γ⁺ CD4⁺ and IFN-γ⁺ CD8⁺ T cells in response to ESAT-6 and CFP-10 did not differ significantly between the immunosuppressant therapy and no immunosuppressant therapy groups (P 0.63 and 0.45). Similarly, the median percentages did not differ significantly between those receiving a single type of immunosuppressant therapy and those receiving more than one type of immunosuppressant therapy (P = 0.15 and 0.22).

The logistic regression analysis of the effect of immunosuppressant therapy on positive and negative flow cytometry results for IFN-γ⁺ CD4⁺ and IFN-γ⁺ CD8⁺ yielded a nonsignificant OR of 0.71 (P = 0.66; Table 4). A similar logistic regression analysis of the effects of the amount of immunosuppressant therapy also yielded a nonsignificant OR of 0.84 (P = 0.83; Table 4). These findings suggest that immunosuppressant therapy and its amount do not significantly affect the flow cytometry IFN-γ⁺ CD4⁺ and IFN-γ⁺ CD8⁺ results.

The potential of flow cytometry for latent TB screening:

Notably, no gold standard test for latent TB currently exists, so the T-SPOT.TB IGRA has become the reference method for flow cytometry performance analysis, excluding borderline results. The agreement between the flow cytometry and T-SPOT.TB IGRA results regarding IFN-γ production in response to ESAT-6 and CFP-10 are presented in Table 5. The PPA was 85.7% (95% CI), the NPA was 17.4% (95% CI), the OPA was 26.4% (95% CI), and Cohen’s κ was nonsignificant (P = 0.84).

Table 5. Agreement of flow cytometry and T-SPOT.TB IGRA

Variable	TSPOT.TB IGRA
	Positive	Negative
Flow cytometry Positive	6	38
Flow cytometry Negative	1	8
Positive agreement (PPA) (%)	85.7
Negative agreement (NPA) (%)	17.4
Overall agreement (OPA) (%)	26.4
Agreement (%)	26.4
Kappa value	0.01
P-value	0.84

*Agreement rates are expressed as the 95% confidence interval.

DISCUSSION:

This study included 57 patients diagnosed with autoimmune rheumatic diseases, of whom 75% had received immunosuppressant therapy. The ratio of patients who had received immunosuppressant therapy to those who had not is consistent with the profile of patients treated at Dr. Soetomo Hospital, a tertiary healthcare facility. Most participants were female (89%), reflecting the higher prevalence of autoimmune rheumatic diseases in women due to the imbalance of sex hormones^24–26. The age range of participants in our study corresponds to the typical age of onset for autoimmune rheumatic diseases. Their most frequently diagnosed condition was SA (51%), followed by SLE (37%) and RA (12%). There are no definitive prevalence statistics for these diagnoses in Indonesia; however, global prevalence rates indicate that RA affected 0.46% of the global population in 2021, SA 0.2% in 2016, and SLE 0.04% in 2022^3–6. The predominant form of immunosuppressant therapy identified in our study was cDMARDs, with hydroxychloroquine the most frequently used. Among our participants, 20 (47%) had received multiple types of immunosuppressant therapy.²⁶ The choice of therapy was tailored according to the level of disease activity and the established treatment protocol.

Our findings indicate that the overall prevalence of latent TB, as determined by the T-SPOT.TB IGRA, is 12%. While no specific data on latent TB prevalence among patients with autoimmune rheumatic diseases in Indonesia exists, the WHO estimated that its prevalence was 25.2% globally in 2019 and 36% in Southeast Asia²⁷. While the prevalence observed in that study aligns with those reported in China (Jiang et al., 2015) and India (Chandrashekara et al., 2023), it is lower than the global prevalence and the estimates provided by the WHO^14,28,29.

Logistic regression analyses indicated that immunosuppressant therapy and its amount do not significantly influence the T-SPOT.TB IGRA result. This finding is consistent with the Constantino et al., Arenas et al., Wong et al., and Park et al.^21,30–32. However, it contrasts with Sauzullo et al., Wong et al., Özsoy et al., and Park et al., who reported that immunosuppressant therapy significantly decreased the positivity rates in general IGRA results (including Quantiferon TB-Gold and T-SPOT.TB), leading to increased false negative results^21–23,31. Nonetheless, Constantino et al. suggested that the T-SPOT.TB IGRA results were largely unaffected by immunosuppressant therapy, potentially due to the analysis being conducted on isolated PBMCs, which may mitigate the influence of immunosuppressant therapy present in whole blood³¹.

The lowest observed percentage of IFN-γ+ CD4+ T cells in response to ESAT-6 and CFP-10 was 0.01%, while the lowest percentage of IFN-γ+ CD8+ T cells in response to the same antigens was 0.03%. This detection threshold aligns with findings from Tesfa et al., Hughes et al., and Won and Park, who reported a limit of 0.01%^32–34. However, the detection limit for IFN-γ+ CD8+ T cells in our study was higher than in other studies. The median percentage of IFN-γ+ CD4+ T cells across all participants was 0.03%, notably lower than the median of 0.2% reported by Sauzullo et al.²². The median percentage of IFN-γ+ CD8+ T cells was 0.11%, surpassing the median of 0.01% reported by Jung et al.³⁵. Our flow cytometry analysis revealed that 82% of all participants were positive for IFN-γ in response to CD4- and CD8-specific ESAT-6 and CFP-10 exposure. This percentage surpasses the findings of Papageorgiou et al., who reported a rate of 49%.³⁶ This discrepancy may be attributed to variations in flow cytometry methodologies, disparities in population demographics, genetic diversity, differences in individual immune responses, and aspects associated with immunosuppressant therapy.

Our study found no significant difference in the median percentages of IFN-γ⁺ CD4⁺ and IFN-γ⁺ CD8⁺ T cells in response to ESAT-6 and CFP-10 between those who had received immunosuppressant therapy and those who had not, as well as between those receiving a single type of immunosuppressant therapy and those on more than one type. The logistic regression analysis revealed that immunosuppressant therapy and its amount did not affect the flow cytometry results for IFN-γ⁺ CD4⁺ and IFN-γ⁺ CD8⁺ T cells in response to ESAT-6 and CFP-10. These results align with Dinser et al., Rovina et al., and Arias et al., who concluded that immunosuppressant therapy did not significantly impact flow cytometry IFN-γ results^37–39.

The agreement analysis indicated a significant lack of concordance between the flow cytometry and T-SPOT.TB IGRA results, exhibiting low NPA and OPA. Since no definitive gold standard exists for latent TB screening, several factors are believed to contribute to the observed discrepancies between IGRA and flow cytometry findings. Firstly, their detection techniques differ. The T-SPOT.TB IGRA identifies effector T cells that respond to ESAT-6 and CFP-10 antigens by capturing IFN-γ near T cells, whereas flow cytometry assesses the percentage of intracellular and extracellular IFN-γ using fluorescence-conjugated monoclonal antibodies analyzed through a laser beam. While flow cytometry provides high resolution, its detection limit for IFN-γ is extremely low. Secondly, neither the T-SPOT.TB IGRA nor flow cytometry has a definite cut-off in patients with autoimmune rheumatic diseases. Thirdly, flow cytometry faces technical challenges, such as a lack of standardization, interuser variability, data analysis complexities, and the risk of erroneous results due to modulation of post-translational protein expression, as detection occurs before cytokine release. Fourthly, genetic variability and individual differences in immune responses can lead to differing activity levels of autoimmune rheumatic diseases. Fifthly, the diverse approaches to immunosuppressant therapy can impact both testing methods, including factors such as type, dosage, combination, and duration^38–42.

The inconsistency between the T-SPOT.TB IGRA and flow cytometry results for IFN-γ production in response to ESAT-6 and CFP-10 indicate that these methodologies are not interchangeable. Patients with autoimmune rheumatic diseases who receive negative IGRA results are not entirely exempt from the risk of latent and reactivation TB, as flow cytometry identified IFN-γ production in CD4⁺ and CD8⁺ T cells in response to ESAT-6 and CFP-10. The flow cytometry results should be interpreted cautiously, given the absence of a definitive cut-off for diagnosing latent TB or initiating TB prophylaxis in patients with autoimmune rheumatic diseases. However, assessing IFN-γ production in response to ESAT-6 and CFP-10 using flow cytometry can serve as a valuable adjunct to latent TB screening in this patient population.

Our study had several limitations. Firstly, there is no established gold standard for latent TB screening, and flow cytometry currently lacks a definitive cut-off for identifying latent TB. Secondly, the characterization of immunosuppressant therapy differs among studies, with variations in its type, dosage, combinations, and duration. Thirdly, the IFN-γ response to ESAT-6 and CFP-10 may be influenced by differences in disease activity, comorbidities, and individual immune responses. Finally, our cross-sectional study was conducted over a limited period.

CONCLUSION:

Our study determined that immunosuppressant therapy does not substantially influence the IFN-γ response to ESAT-6 and CFP-10, as evaluated by the T-SPOT. TB IGRA and flow cytometry in patients with autoimmune rheumatic diseases. Notably, flow cytometry and T-SPOT.TB IGRA results regarding IFN-γ production in response to ESAT-6 and CFP-10 are inconsistent. The T-SPOT.TB IGRA is utilized as a benchmark method for screening latent TB.

CONFLICT OF INTEREST:

The authors have no conflicts of interest regarding this investigation.

ACKNOWLEDGMENTS:

The authors gratefully acknowledge Mrs. Mahmudah, Ph.D., Faculty of Public Health, Universitas Airlangga, for helpful discussions on the statistical analysis. The authors are grateful for the support and cooperation of the staff of the Laboratory of Clinical Pathology, Department of Clinical Pathology, Faculty of Medicine, Dr. Soetomo General Academic Teaching Hospital, Surabaya, East Java, Indonesia.

RESEARCH FUNDING:

This research was funded by the prestigious research grant of Dr. Soetomo General Academic Hospital, Surabaya, East Java, Indonesia (Grant No. 100.3.3//25.1/102.6/2024) and Revvity Inc. for providing T-SPOT.TB kits.

References:

1. Moutsopoulos HM. Autoimmune rheumatic diseases: One or many diseases? Journal of Translational Autoimmunity. 2021; 4: 100129. doi: 10.1016/j.jtauto.2021.100129 Available from: https://linkinghub.elsevier.com/retrieve/pii/S2589909021000496

2. M. Pache M, R. Pangavhane R. Immunotherapy in Autoimmune Diseases: Current Advances and Future Directions. AJPR. 2025 May 5; 183–91. doi: 10.52711/2231-5691.2025.00030 Available from: https://asianjpr.com/AbstractView.aspx?PID=2025-15-2-15

3. Almutairi K, et al. The global prevalence of rheumatoid arthritis: a meta-analysis based on a systematic review. Rheumatol Int. 2021 May; 41(5): 863–77. doi: 10.1007/s00296-020-04731-0 Available from: http://link.springer.com/10.1007/s00296-020-04731-0

4. Stolwijk C, et al. Global Prevalence of Spondyloarthritis: A Systematic Review and Meta‐Regression Analysis. Arthritis Care & Research. 2016 Sep; 68(9): 1320–31. doi: 10.1002/acr.22831 Available from: https://acrjournals.onlinelibrary.wiley.com/doi/10.1002/acr.22831

5. Tian J, et al. Global epidemiology of systemic lupus erythematosus: a comprehensive systematic analysis and modelling study. Ann Rheum Dis. 2023 Mar; 82(3): 351–6. doi:10.1136/ard-2022-223035 Available from: https://ard.bmj.com/lookup/doi/10.1136/ard-2022-223035

6. Gosavi S, et al. Comparative study on treatment of rheumatoid arthritis. Asian Journal of Pharmacy and Technology. 2021; 11(1): 5–12. doi: 10.1136/ard-2022-223035 Available from: http://www.indianjournals.com/ijor.aspx?target=ijor:ajpt&volume=11&issue=1&article=002

7. Perhimpunan Reumatologi Indonesia. Pedoman Penapisan dan Tata Laksana Infeksi Tuberkulosis Laten pada Pasien Penyakit Reumatik yang Akan Mendapatkan Terapi DMARD Biologik. Perhimpunan Reumatologi Indonesia; 2017. Available from: https://reumatologi.or.id/download/pedoman-penapisan-dan-tata-laksana-infeksi-tuberkulosis-laten-pada-pasien-penyakit-reumatik-yang-akan-mendapatkan-terapi-dmard-biologik/

8. Hussain Y, Khan H. Immunosuppressive Drugs. In: Encyclopedia of Infection and Immunity. Elsevier; 2022. p. 726–40. doi: 10.1016/B978-0-12-818731-9.00068-9 Available from: https://linkinghub.elsevier.com/retrieve/pii/B9780128187319000689

9. Jick SS, et al. Glucocorticoid use, other associated factors, and the risk of tuberculosis. Arthritis and Rheumatism. 2006 Feb 15; 55(1): 19–26. doi: 10.1002/art.21705 Available from: https://onlinelibrary.wiley.com/doi/10.1002/art.21705

10. Nandgude TD, et al. Clinical Features and Treatment of Rheumatoid Arthritis: A Review. Rese Jour of Pharm and Technol. 2018; 11(12): 5701. doi: 10.5958/0974-360x.2018.01032.6 Available from: http://www.indianjournals.com/ijor.aspx?target=ijor:rjpt&volume=11&issue=12&article=081

11. Sweta S, et al. Rheumatoid Arthritis, A Laconic Review to understand their Basic Concept and Management Process. AJPR. 2022 Nov 22; 312–22. doi: 10.52711/2231-5691.2022.00051 Available from: https://asianjpr.com/AbstractView.aspx?PID=2022-12-4-10

12. Sonawane TusharN, et al. Application of Steroids in Clinical practice. AJPS. 2022 Mar 5;8–10. doi: 10.52711/2231-5659.2022.00002 Available from: https://ajpsonline.com/AbstractView.aspx?PID=2022-12-1-2

13. Naredla B, et al. Updates on Novel Treatments for Rheumatoid Arthritis. RJST. 2023 Nov 11; 225–32. doi: 10.52711/2349-2988.2023.00039 Available from: https://rjstonline.com/AbstractView.aspx?PID=2023-15-4-11

14. Chandrashekara S, et al. Prevalence of LTBI in patients with autoimmune diseases and accuracy of IGRA in predicting TB relapse. Rheumatology. 2023 Dec 1; 62(12): 3952–6. doi: 10.1093/rheumatology/kead315 Available from: https://academic.oup.com/rheumatology/article/62/12/3952/7205333

15. Anton C, et al. Latent tuberculosis infection in patients with rheumatic diseases. J Bras Pneumol. 2019; 45(2): e20190023. doi: 10.1590/1806-3713/e20190023 Available from: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S1806-37132019000200502&tlng=en

16. Sagavkar SR, Devkar SR. Tuberculosis: A Review. Asian Jour Pharmac Rese. 2018; 8(3): 191. doi: 10.5958/2231-5691.2018.00033.3 Available from: http://www.indianjournals.com/ijor.aspx?target=ijor:ajpr&volume=8&issue=3&article=013

17. World Health Organization. Latent tuberculosis infection: updated and consolidated guidelines for programmatic management. Geneva: World Health Organization; 2018. Available from: https://apps.who.int/iris/handle/10665/260233

18. TB-PI-US-0001-V9.pdf. Available from: https://www.tspot.com/wp-content/uploads/2021/04/TB-PI-US-0001-V9.pdf

19. Use of alternative interferon-gamma release assays for the diagnosis of TB infection - WHO Policy Statement. Available from: https://www.who.int/news/item/28-01-2022-use-of-alternative-interferon-gamma-release-assays-for-the-diagnosis-of-tb-infection---who-policy-statement

20. Yamasue M, et al. Factors associated with false negative interferon-γ release assay results in patients with tuberculosis: A systematic review with meta-analysis. Sci Rep. 2020 Jan 31; 10(1): 1607. doi: 10.1038/s41598-020-58459-9 Available from: https://www.nature.com/articles/s41598-020-58459-9

21. Wong SH, et al. Effect of immunosuppressive therapy on interferon γ release assay for latent tuberculosis screening in patients with autoimmune diseases: a systematic review and meta-analysis. Thorax. 2016 Jan; 71(1): 64–72. doi: 10.1136/thoraxjnl-2015-207811 Available from: https://thorax.bmj.com/lookup/doi/10.1136/thoraxjnl-2015-207811

22. Sauzullo I, et al. Multi-functional flow cytometry analysis of CD4+ T cells as an immune biomarker for latent tuberculosis status in patients treated with tumour necrosis factor (TNF) antagonists. Clinical and Experimental Immunology. 2014 Apr 24;176(3):410–7. doi: 10.1111/cei.12290 Available from: https://academic.oup.com/cei/article/176/3/410/6421167

23. Ozsoy Z, et al. POS1187 DO Immunosuppressive Agents Affect IGRA Tests IN Patients with Rheumatoid Arthritis? Ann Rheum Dis. 2022 Jun; 81(Suppl 1): 921.1-921. doi: 10.1136/annrheumdis-2022-eular.3983 Available from: https://ard.bmj.com/lookup/doi/10.1136/annrheumdis-2022-eular.3983

24. Annavarapu S, et al. A Study on Clinical Manifestations, Treatment Pattern and Outcome in Systemic Lupus Erythematosus Patients in Tertiary Care Hospital. Rese Jour of Pharm and Technol. 2017; 10(5): 1383. doi: 10.5958/0974-360x.2017.00246.3 Available from: http://www.indianjournals.com/ijor.aspx?target=ijor:rjpt&volume=10&issue=5&article=021

25. Oliver JE, Silman AJ. Why are women predisposed to autoimmune rheumatic diseases? Arthritis Res Ther. 2009; 11(5): 252. doi: 10.1186/ar2825 Available from: http://arthritis-research.biomedcentral.com/articles/10.1186/ar2825

26. Srivastava S, et al. Rheumatoid Arthritis: An Autoimmune Disease Prevalent in Females. Rese Jour of Pharm and Technol. 2016; 9(2): 170. doi: 10.5958/0974-360x.2016.00030.5 Available from: http://www.indianjournals.com/ijor.aspx?target=ijor:rjpt&volume=9&issue=2&article=013

27. Kumar VP, et al. A Prospective Study on Comparing the efficacy of Combination Therapy and Monotherapy of DMARDs in Patients with Rheumatoid Arthritis. Rese Jour of Pharm and Technol. 2018; 11(10): 4497. doi: 10.5958/0974-360X.2018.00823.5 Available from: http://www.indianjournals.com/ijor.aspx?target=ijor:rjpt&volume=11&issue=10&article=053

28. Cohen A, et al. The global prevalence of latent tuberculosis: a systematic review and meta-analysis. Eur Respir J. 2019; Sep; 54(3): 1900655. doi: 10.1183/13993003.00655-2019 Available from: http://publications.ersnet.org/lookup/doi/10.1183/13993003.00655-2019

29. Jiang B, et al. Evaluation of interferon‐gamma release assay (T‐SPOT.TBTM ) for diagnosis of tuberculosis infection in rheumatic disease patients. Int J of Rheum Dis. 2016 Jan; 19(1): 38–42. doi: 10.1111/1756-185X.12772 Available from: https://onlinelibrary.wiley.com/doi/10.1111/1756-185X.12772

30. Arenas Miras MDM, et al. Diagnosis of Latent Tuberculosis in Patients with Systemic Lupus Erythematosus: T.SPOT.TB versus Tuberculin Skin Test. BioMed Research International. 2014; 2014: 1–8. doi: 10.1155/2014/291031 Available from: http://www.hindawi.com/journals/bmri/2014/291031/

31. Park CH, et al. Impact of Immunosuppressive Therapy on the Performance of Latent Tuberculosis Screening Tests in Patients with Inflammatory Bowel Disease: A Systematic Review and Meta-Analysis. JPM. 2022 Mar 21; 12(3): 507. doi: 10.3390/jpm12030507 Available from: https://www.mdpi.com/2075-4426/12/3/507

32. Costantino F, et al. Screening for Latent Tuberculosis Infection in Patients with Chronic Inflammatory Arthritis: Discrepancies Between Tuberculin Skin Test and Interferon-γ Release Assay Results. J Rheumatol. 2013 Dec; 40(12): 1986–93. doi: 10.3899/jrheum.130303 Available from: http://www.jrheum.org/lookup/doi/10.3899/jrheum.130303

33. Tesfa L, et al. Confirmation of Mycobacterium tuberculosis infection by flow cytometry after ex vivo incubation of peripheral blood T cells with an ESAT‐6‐derived peptide pool. Cytometry Part B Clinical. 2004 Jul; 60B(1): 47–53. doi: 10.1002/cyto.b.20007 Available from: https://onlinelibrary.wiley.com/doi/10.1002/cyto.b.20007

34. Hughes AJ, et al. Diagnosis of Mycobacterium tuberculosis infection using ESAT-6 and intracellular cytokine cytometry. Clinical and Experimental Immunology. 2005 Jul 28; 142(1): 132–9. doi: 10.1111/j.1365-2249.2005.02884.x Available from: https://academic.oup.com/cei/article/142/1/132/6440445

35. Won DI, Park JR. Flow cytometric measurements of TB‐specific T cells comparing with QuantiFERON‐TB gold. Cytometry Part B Clinical. 2010 Mar; 78B(2): 71–80. doi: 10.1002/cyto.b.20503 Available from: https://onlinelibrary.wiley.com/doi/10.1002/cyto.b.20503

36. Jung J, et al. Is the New Interferon-Gamma Releasing Assay Beneficial for the Diagnosis of Latent and Active Mycobacterium tuberculosis Infections in Tertiary Care Setting? JCM. 2021 Mar 29; 10(7): 1376. doi: 10.3390/jcm10071376 Available from: https://www.mdpi.com/2077-0383/10/7/1376

37. Papageorgiou CV, et al. Flow cytometry analysis of CD4+IFN‐Γ+ T‐ cells for the diagnosis of mycobacterium tuberculosis infection. Cytometry Part B Clinical. 2016 May; 90(3): 303–11. doi: 10.1002/cyto.b.21275 Available from: https://onlinelibrary.wiley.com/doi/10.1002/cyto.b.21275

38. Dinser R, et al. Evaluation of latent tuberculosis infection in patients with inflammatory arthropathies before treatment with TNF- blocking drugs using a novel flow-cytometric interferon- release assay. Rheumatology. 2007 Dec 20; 47(2): 212–8. doi: 10.1093/rheumatology/kem351 Available from: https://academic.oup.com/rheumatology/article-lookup/doi/10.1093/rheumatology/kem351

39. Rovina N, et al. Immune Response to Mycobacterial Infection: Lessons from Flow Cytometry. Clinical and Developmental Immunology. 2013; 2013: 1–9. doi: 10.1155/2013/464039 Available from: http://www.hindawi.com/journals/jir/2013/464039/

40. Arias-Guillén M, et al. T-Cell Profiling and the Immunodiagnosis of Latent Tuberculosis Infection in Patients with Inflammatory Bowel Disease: Inflammatory Bowel Diseases. 2014 Feb; 20(2): 329–38. doi: 10.1097/01.MIB.0000438429.38423.62 Available from: https://academic.oup.com/ibdjournal/article/20/2/329-338/4578970

41. Carranza C, et al. Diagnosis for Latent Tuberculosis Infection: New Alternatives. Front Immunol. 2020 Sep 10; 11: 2006. doi: 10.3389/fimmu.2020.02006 Available from: https://www.frontiersin.org/article/10.3389/fimmu.2020.02006/full

42. Belard E, et al. Prednisolone treatment affects the performance of the QuantiFERON gold in-tube test and the tuberculin skin test in patients with autoimmune disorders screened for latent tuberculosis infection: Inflammatory Bowel Diseases. 2011; Nov; 17(11): 2340–9. doi: 10.1002/ibd.21605 Available from: https://academic.oup.com/ibdjournal/article/17/11/2340-2349/4631016

Received on 22.07.2025 Revised on 28.11.2025

Accepted on 06.02.2026 Published on 01.07.2026

Available online from July 04, 2026

Research J. Pharmacy and Technology. 2026;19(7):3357-3364.

DOI: 10.52711/0974-360X.2026.00477

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. Creative Commons License.

	Mean±SD CD4 (%)	Mean ± SD CD8 (%)	Median (range) IFN-γ CD4 (%)	Median (range) IFN-γ CD8 (%)
All populations	45.7±15.0	45.3±14.1	0.03 (0-0.87)	0.11 (0-0.87)
IST (+)	46.4±15.8	45.6±15.1	0.03 (0-0.87)	0.12 (0-0.87)
IST (-)	43.7±12.6	44.3±11.1	0.01 (0-0.82)	0.05 (0-0.56)
IS 1	47.7±14.3	44.5±12.9	0.02 (0-0.15)	0.10 (0-0.87)
IS >1	44.9±17.6	46.8±17.5	0.04 (0-0.87)	0.19 (0-0.84)
IGRA (+)	45.7±8.1	40.3±5.7	0.02 (0-0.10)	0.20 (0-0.44)
IGRA (-)	45.5±16.0	46.7±15.0	0(0-0.06)	0.06 (0-0.25)