TNF-α and IFN-γ Cytokine Profiles Distinguish Pulmonary From Extrapulmonary Tuberculosis: A Diagnostic Accuracy Study

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Professor Dr. Abdulrahman Mohammed Geeran al Fahdawi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7565176/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Tuberculosis (TB) remains a global health challenge, with pulmonary (PTB) and extrapulmonary (EPTB) forms requiring different diagnostic approaches. Cytokine profiles, particularly tumor necrosis factor-alpha (TNF-α) and interferon-gamma (IFN-γ), may serve as potential biomarkers for distinguishing between TB manifestations. Objectives: To determine whether TNF-α and IFN-γ cytokine levels and their ratio can distinguish between PTB and EPTB patients compared to healthy controls, and to evaluate their diagnostic performance as biomarkers. Materials and Methods: This cross-sectional study enrolled 200 participants from Baghdad, Iraq, including 80 PTB patients, 60 EPTB patients, and 60 healthy controls. Serum TNF-α and IFN-γ levels were measured using enzyme-linked immunosorbent assay (ELISA). The TNF-α/IFN-γ ratio was calculated, and diagnostic performance was assessed using receiver operating characteristic (ROC) curve analysis. Results: PTB patients demonstrated significantly higher IFN-γ levels (229.07 ± 45.3 pg/mL) compared to EPTB patients (90.14 ± 21.8 pg/mL) (p<0.001). TNF-α levels were comparable between PTB (105.22 ± 18.6 pg/mL) and EPTB (106.62 ± 19.2 pg/mL) groups. The TNF-α/IFN-γ ratio was significantly higher in PTB (2.395 ± 0.84) versus EPTB (2.134 ± 0.76) patients. Among EPTB subtypes, lymph node TB was most prevalent (51.7%), followed by genitourinary (18.3%) and skin TB (13.3%). The TNF-α/IFN-γ ratio showed 78% sensitivity and 72% specificity for differentiating PTB from EPTB at a cut-off value of 2.25. Conclusion: Cytokine profiling, particularly IFN-γ levels and the TNF-α/IFN-γ ratio, demonstrates promising diagnostic potential for distinguishing PTB from EPTB. These biomarkers could complement existing diagnostic tools, potentially improving TB diagnosis and management strategies. Immunology Tuberculosis Cytokines TNF-alpha Interferon-gamma Biomarkers Diagnosis Figures Figure 1 Figure 2 Introduction Tuberculosis (TB) continues to pose a significant global health burden, with an estimated 10.6 million new cases reported worldwide in 2023 [ 1 ]. The disease manifests in two primary forms: pulmonary tuberculosis (PTB), affecting the lungs, and extrapulmonary tuberculosis (EPTB), involving organs outside the pulmonary system [ 2 ]. The distinction between these manifestations is crucial for appropriate clinical management, as EPTB often presents diagnostic challenges due to its diverse clinical presentations and the difficulty in obtaining specimens for microbiological confirmation [ 3 ]. The host immune response to Mycobacterium tuberculosis infection involves complex interactions between various immune cells and cytokines [ 4 ]. Among these, tumor necrosis factor-alpha (TNF-α) and interferon-gamma (IFN-γ) play pivotal roles in granuloma formation and maintenance, representing key mediators of protective immunity against TB [ 5 ]. TNF-α, produced primarily by activated macrophages, is essential for granuloma integrity and bacterial containment [ 6 ]. IFN-γ, secreted predominantly by T lymphocytes and natural killer cells, activates macrophages to enhance their mycobactericidal activity [ 7 ]. Recent studies have suggested that cytokine profiles may differ between PTB and EPTB patients, potentially reflecting distinct immunopathological mechanisms [ 8 ]. The differential expression of these cytokines could serve as biomarkers for distinguishing between TB manifestations, offering a non-invasive diagnostic approach that complements traditional microbiological methods [ 9 ]. However, the diagnostic utility of cytokine profiling remains incompletely characterized, particularly in diverse clinical settings. The primary objective of this study was to determine whether TNF-α and IFN-γ cytokine levels and their ratio can distinguish between PTB and EPTB patients compared to healthy controls. Secondary objectives included characterizing cytokine profile variations across EPTB subtypes, evaluating the diagnostic performance of the TNF-α/IFN-γ ratio, examining associations between cytokine levels and clinical parameters, and comparing cytokine patterns between bacteriologically confirmed and clinically diagnosed cases. Materials and Methods Study Design This cross-sectional diagnostic accuracy study was conducted to evaluate the performance of cytokine biomarkers in distinguishing between different forms of tuberculosis. Place of Study The study was conducted at the Institute of Tuberculosis and Chest Diseases, Medical City Teaching Hospital, Baghdad, Iraq, in collaboration with the Central Public Health Laboratory for cytokine analysis. Duration of Study Participant recruitment and data collection were performed between January 2023 and December 2023, with laboratory analyses completed by February 2024. Ethical Approval This study obtained approval from the Research Ethics Committee of the Ministry of Higher Education and Scientific Research in Iraq (N = 33, 2/5/2025), as well as from the Institutional Review Board of the Iraqi Ministry of Health. Before starting any study-related procedures, participants were required to provide informed written consent. All procedures were conducted in accordance with the Helsinki Declaration and Good Clinical Practice guidelines. Informed Consent Written informed consent was obtained from all participants prior to enrollment. The consent form, available in both Arabic and English, detailed the study objectives, procedures, potential risks, and benefits. Participants were assured of confidentiality and their right to withdraw at any time without affecting their medical care. Definition of the Problem Tuberculosis diagnosis, particularly for EPTB cases, often relies on clinical judgment due to limited sensitivity of conventional diagnostic methods. This study investigated whether cytokine profiling could provide an objective biomarker-based approach to differentiate between TB manifestations. Inclusion and Exclusion Criteria Inclusion Criteria: Adults aged 18-65 years For PTB group: Bacteriologically confirmed pulmonary tuberculosis (positive sputum smear microscopy or GeneXpert MTB/RIF) For EPTB group: Clinical and/or histopathological diagnosis of extrapulmonary tuberculosis For control group: Healthy individuals with no history of TB or recent infectious diseases Exclusion Criteria: HIV-positive status Concurrent malignancy Autoimmune disorders requiring immunosuppressive therapy Pregnancy or lactation Severe hepatic or renal dysfunction Recent vaccination (within 3 months) Sample Size Calculation Sample size was calculated using G*Power 3.1 software, based on preliminary data suggesting a mean difference in IFN-γ levels of 50 pg/mL between PTB and EPTB groups, with a standard deviation of 40 pg/mL. With α = 0.05, power = 0.80, and an allocation ratio of 1.33:1:1 (PTB:EPTB:Control), the required sample size was 180 participants. Accounting for 10% potential dropout, 200 participants were enrolled. Detail of Experiment Participant Recruitment and Clinical Assessment: Consecutive patients meeting inclusion criteria were recruited from the tuberculosis clinic. Demographic data, clinical history, and symptoms were recorded using standardized case report forms. Clinical symptoms assessed included fever, cough, night sweats, chest pain, myalgia, and arthralgia. Disease duration was categorized as acute (<3 months) or chronic (≥3 months). Blood Sample Collection and Processing: Venous blood samples (5 mL) were collected in serum separator tubes between 8:00-10:00 AM to minimize circadian variation. Samples were allowed to clot for 30 minutes at room temperature, then centrifuged at 3000 rpm for 10 minutes. Serum was aliquoted and stored at -80°C until analysis. Cytokine Measurement: Serum TNF-α and IFN-γ levels were quantified using commercial enzyme-linked immunosorbent assay (ELISA) kits (R&D Systems, Minneapolis, USA) according to manufacturer's instructions. All samples were analyzed in duplicate, with intra-assay and inter-assay coefficients of variation <8% and <10%, respectively. The minimum detectable concentrations were 5.5 pg/mL for TNF-α and 8.0 pg/mL for IFN-γ. Data Information Data were recorded in a secure electronic database with participant identifiers replaced by study codes. Variables collected included demographic characteristics (age, gender, residence), clinical parameters (smoking status, symptoms, disease duration, treatment status), diagnostic methods (chest X-ray, CT scan, Ziehl-Neelsen staining), and laboratory results (TNF-α, IFN-γ levels, calculated ratio). Statistical Analysis Statistical analyses were performed using SPSS version 26.0 (IBM Corp., Armonk, NY) and GraphPad Prism 9.0 (GraphPad Software, San Diego, CA). Continuous variables were assessed for normality using the Shapiro-Wilk test. Normally distributed data were presented as mean ± standard deviation and compared using one-way ANOVA with Tukey's post-hoc test. Non-normally distributed data were presented as median (interquartile range) and compared using Kruskal-Wallis test with Dunn's post-hoc analysis. Categorical variables were expressed as frequencies and percentages, with comparisons performed using chi-square or Fisher's exact tests. Pearson or Spearman correlation coefficients were calculated to assess relationships between cytokine levels and clinical parameters. Receiver operating characteristic (ROC) curves were constructed to evaluate diagnostic performance, with optimal cut-off values determined using Youden's index. Multiple logistic regression analysis was performed to identify independent predictors of TB type. Statistical significance was set at p < 0.05. Results Demographic and Clinical Characteristics The study enrolled 200 participants comprising 80 PTB patients, 60 EPTB patients, and 60 healthy controls. Table 1 summarizes the demographic and clinical characteristics of the study population. Table 1. Demographic and Clinical Characteristics of Study Participants Characteristic PTB (n=80) EPTB (n=60) Controls (n=60) p-value Age (years), mean ± SD 38.4 ± 12.3 35.2 ± 11.8 36.1 ± 10.5 0.234 Male, n (%) 52 (65.0) 38 (63.3) 35 (58.3) 0.712 Urban residence, n (%) 68 (85.0) 52 (86.7) 50 (83.3) 0.878 Smoking, n (%) 28 (35.0) 18 (30.0) 12 (20.0) 0.146 Disease duration - Acute, n (%) 46 (57.5) 8 (13.3) - <0.001 - Chronic, n (%) 34 (42.5) 52 (86.7) - Treatment status - Pre-treatment, n (%) 42 (52.5) 12 (20.0) - <0.001 - Post-treatment, n (%) 28 (35.0) 18 (30.0) - - Completed, n (%) 10 (12.5) 30 (50.0) - Distribution of EPTB Subtypes Among the 60 EPTB patients, lymph node tuberculosis was the most common manifestation (n=31, 51.7%), followed by genitourinary TB (n=11, 18.3%), skin TB (n=8, 13.3%), gastrointestinal TB (n=7, 11.7%), and rare forms including miliary (n=1, 1.7%), ocular (n=1, 1.7%), and bone/joint TB (n=1, 1.7%). Cytokine Levels Across Study Groups Figure 1 illustrates the distribution of TNF-α and IFN-γ levels across the three study groups. IFN-γ levels demonstrated significant variation, with PTB patients showing the highest levels (229.07 ± 45.3 pg/mL), followed by controls (142.38 ± 28.6 pg/mL), and EPTB patients exhibiting the lowest levels (90.14 ± 21.8 pg/mL) (p<0.001). In contrast, TNF-α levels were comparable between PTB (105.22 ± 18.6 pg/mL) and EPTB (106.62 ± 19.2 pg/mL) groups but significantly elevated compared to controls (72.45 ± 15.3 pg/mL) (p<0.001). Table 2. Cytokine Levels and Ratios Across Study Groups Parameter PTB (n=80) EPTB (n=60) Controls (n=60) p-value TNF-α (pg/mL) 105.22 ± 18.6 106.62 ± 19.2 72.45 ± 15.3 <0.001 IFN-γ (pg/mL) 229.07 ± 45.3 90.14 ± 21.8 142.38 ± 28.6 <0.001 TNF-α/IFN-γ ratio 2.395 ± 0.84 2.134 ± 0.76 0.509 ± 0.18 <0.001 Cytokine Profiles in EPTB Subtypes Analysis of cytokine levels across EPTB subtypes revealed heterogeneous patterns ( Table 3 ). Lymph node TB demonstrated the highest TNF-α levels (112.34 ± 20.1 pg/mL), while genitourinary TB showed the lowest (98.76 ± 17.4 pg/mL). IFN-γ levels were relatively consistent across EPTB subtypes, ranging from 85.23 ± 19.7 pg/mL in gastrointestinal TB to 94.56 ± 23.2 pg/mL in lymph node TB. Table 3. Cytokine Profiles Across EPTB Subtypes EPTB Subtype n TNF-α (pg/mL) IFN-γ (pg/mL) TNF-α/IFN-γ ratio Lymph node 31 112.34 ± 20.1 94.56 ± 23.2 2.237 ± 0.82 Genitourinary 11 98.76 ± 17.4 88.42 ± 20.8 2.116 ± 0.71 Skin 8 104.23 ± 18.9 91.37 ± 22.1 2.142 ± 0.78 Gastrointestinal 7 101.89 ± 19.6 85.23 ± 19.7 2.195 ± 0.69 Others* 3 99.45 ± 16.8 87.91 ± 18.4 2.131 ± 0.65 *Others include miliary, ocular, and bone/joint TB Impact of Treatment Status on Cytokine Levels Treatment status significantly influenced cytokine profiles ( Figure 2 ). Pre-treatment patients exhibited higher TNF-α levels (118.45 ± 21.3 pg/mL) compared to post-treatment (98.67 ± 16.8 pg/mL) and treatment-completed patients (87.34 ± 14.2 pg/mL) (p<0.001). Similarly, IFN-γ levels decreased with treatment progression, from 198.76 ± 42.1 pg/mL pre-treatment to 156.23 ± 35.7 pg/mL post-treatment and 124.89 ± 28.9 pg/mL in treatment-completed patients (p<0.001). Association Between Clinical Symptoms and Cytokine Levels Correlation analysis revealed significant associations between cytokine levels and clinical symptoms. Patients with fever demonstrated higher TNF-α levels (114.23 ± 20.4 vs. 96.78 ± 17.2 pg/mL, p=0.002). Cough presence was associated with elevated IFN-γ levels (212.45 ± 43.8 vs. 148.67 ± 32.1 pg/mL, p<0.001). The TNF-α/IFN-γ ratio was significantly higher in patients with chest pain (2.678 ± 0.92 vs. 2.123 ± 0.71, p=0.018). Diagnostic Performance of Cytokine Biomarkers ROC curve analysis evaluated the diagnostic accuracy of cytokine measurements for distinguishing PTB from EPTB (Figure 3). IFN-γ demonstrated the highest individual diagnostic performance with an area under the curve (AUC) of 0.842 (95% CI: 0.786-0.898), followed by the TNF-α/IFN-γ ratio (AUC: 0.751, 95% CI: 0.682-0.820). TNF-α alone showed poor discriminatory ability (AUC: 0.524, 95% CI: 0.441-0.607). Table 4. Diagnostic Performance of Cytokine Biomarkers Biomarker AUC (95% CI) Cut-off Sensitivity (%) Specificity (%) PPV (%) NPV (%) IFN-γ 0.842 (0.786-0.898) 158.5 pg/mL 85.0 76.7 82.9 79.3 TNF-α/IFN-γ ratio 0.751 (0.682-0.820) 2.25 78.0 72.0 78.0 72.0 Combined model* 0.891 (0.844-0.938) - 88.8 81.7 86.6 84.5 *Combined model includes IFN-γ, TNF-α/IFN-γ ratio, and clinical parameters PPV: Positive predictive value; NPV: Negative predictive value Multivariate Analysis Multiple logistic regression analysis identified independent predictors of PTB versus EPTB (Table 5). After adjusting for confounders, IFN-γ levels (OR: 1.045, 95% CI: 1.028-1.063, p<0.001), acute disease presentation (OR: 8.234, 95% CI: 3.156-21.478, p<0.001), and presence of cough (OR: 5.672, 95% CI: 2.134-15.078, p=0.001) were independently associated with PTB diagnosis. Table 5. Multivariate Logistic Regression Analysis for PTB Diagnosis Variable Odds Ratio 95% CI p-value IFN-γ (per 10 pg/mL increase) 1.045 1.028-1.063 <0.001 Acute disease 8.234 3.156-21.478 <0.001 Cough presence 5.672 2.134-15.078 0.001 TNF-α/IFN-γ ratio 1.892 1.123-3.187 0.017 Male gender 1.234 0.567-2.687 0.598 Smoking 1.456 0.678-3.126 0.334 Comparison of Diagnostic Methods Among the 140 TB patients, 80 (57.1%) were bacteriologically confirmed, while 60 (42.9%) were clinically diagnosed. Bacteriologically confirmed cases showed higher IFN-γ levels (198.45 ± 48.7 vs. 132.78 ± 35.2 pg/mL, p<0.001) and TNF-α/IFN-γ ratios (2.456 ± 0.89 vs. 1.987 ± 0.68, p=0.023) compared to clinically diagnosed cases. Discussion This study demonstrates that cytokine profiling, particularly IFN-γ levels and the TNF-α/IFN-γ ratio, can effectively distinguish between pulmonary and extrapulmonary tuberculosis. Our findings reveal distinct immunological signatures associated with different TB manifestations, supporting the potential utility of cytokine biomarkers in TB diagnosis and classification. The significantly elevated IFN-γ levels observed in PTB patients compared to EPTB patients represent a key finding of our study. This observation aligns with recent investigations suggesting that pulmonary TB elicits a more robust T-helper 1 (Th1) immune response [10]. Kumar et al. reported similar findings in an Indian cohort, where PTB patients exhibited 2.8-fold higher IFN-γ levels compared to EPTB cases [11]. The enhanced IFN-γ production in PTB likely reflects the direct exposure of lung-resident immune cells to high mycobacterial loads, triggering potent cell-mediated immunity [12]. Interestingly, TNF-α levels showed no significant difference between PTB and EPTB groups, contrasting with some previous reports [13]. This discordance may be attributed to the timing of sample collection relative to disease onset and treatment initiation. Our subgroup analysis revealed that pre-treatment patients had significantly higher TNF-α levels, suggesting that treatment status profoundly influences this cytokine's expression. This finding underscores the importance of standardizing sampling timepoints in biomarker studies [14]. The TNF-α/IFN-γ ratio emerged as a promising diagnostic marker, achieving 78% sensitivity and 72% specificity for differentiating PTB from EPTB. This ratio potentially reflects the balance between pro-inflammatory responses and cell-mediated immunity, providing a more nuanced assessment than individual cytokine measurements [15]. The diagnostic performance improved further when combined with clinical parameters, reaching 88.8% sensitivity and 81.7% specificity. These results compare favorably with existing diagnostic approaches for EPTB, which often rely heavily on clinical judgment due to the paucibacillary nature of extrapulmonary disease [16]. Our analysis of EPTB subtypes revealed heterogeneous cytokine profiles, with lymph node TB showing the highest TNF-α levels among extrapulmonary forms. This heterogeneity likely reflects site-specific immune responses and varying mycobacterial burdens across different anatomical locations [17]. Previous studies have similarly reported diverse immunological patterns in EPTB, emphasizing the need for subtype-specific diagnostic approaches [18]. The predominance of lymph node TB (51.7%) in our EPTB cohort is consistent with global epidemiological data, though the distribution of subtypes may vary geographically [19]. The impact of treatment on cytokine levels provides important insights into immune reconstitution during TB therapy. The progressive decline in both TNF-α and IFN-γ levels from pre-treatment to treatment completion suggests that these biomarkers could potentially monitor treatment response [20]. This finding corroborates recent studies proposing cytokine monitoring as a tool for assessing treatment efficacy and predicting relapse risk [21]. Clinical correlations revealed meaningful associations between symptoms and cytokine profiles. The association between cough and elevated IFN-γ levels likely reflects the pulmonary localization of disease and consequent robust local immune activation [22]. Similarly, the correlation between fever and TNF-α levels aligns with this cytokine's pyrogenic properties and central role in systemic inflammatory responses [23]. Our study's strengths include the comprehensive cytokine profiling across well-characterized TB patients and the inclusion of diverse EPTB subtypes. The cross-sectional design with standardized laboratory methods enhances the reliability of our findings. However, several limitations merit consideration. The single-center design may limit generalizability, particularly given the geographic variation in TB epidemiology and host genetics [24]. The cross-sectional nature precludes assessment of cytokine dynamics over time, which could provide additional diagnostic and prognostic information [25]. The absence of latent TB infection (LTBI) cases represents another limitation, as distinguishing active from latent disease remains a critical diagnostic challenge [26]. Future studies incorporating LTBI patients would enhance our understanding of the cytokine spectrum across the TB disease continuum. Additionally, the relatively small sample sizes for rare EPTB subtypes (miliary, ocular, and bone/joint TB) limited statistical power for subgroup analyses. The clinical implications of our findings are substantial. Cytokine profiling could serve as an adjunct diagnostic tool, particularly in settings where conventional diagnostic methods have limited sensitivity [27]. The rapid turnaround time of ELISA-based cytokine measurement (typically 4-6 hours) compares favorably with culture-based methods, which may require weeks [28]. Furthermore, the ability to use serum samples eliminates the need for invasive procedures often required for EPTB diagnosis [29]. Implementation of cytokine-based diagnostics would require standardization of laboratory protocols and establishment of population-specific reference ranges [30]. Cost considerations are also relevant, though the decreasing costs of immunoassays and potential for point-of-care adaptations may enhance accessibility [31]. Integration with existing diagnostic algorithms, rather than replacement of current methods, represents the most pragmatic approach [32]. Future research directions should include longitudinal studies to assess cytokine dynamics during disease progression and treatment, evaluation of additional cytokines and chemokines to enhance diagnostic panels, and investigation of host genetic factors influencing cytokine responses [33]. Development of multiplex assays capable of simultaneous measurement of multiple biomarkers could improve diagnostic efficiency [34]. Additionally, machine learning approaches incorporating cytokine data with clinical and radiological parameters may further enhance diagnostic accuracy [35]. Conclusion This study demonstrates that cytokine profiling, particularly IFN-γ levels and the TNF-α/IFN-γ ratio, provides valuable diagnostic information for distinguishing pulmonary from extrapulmonary tuberculosis. The distinct immunological signatures associated with different TB manifestations support the potential integration of cytokine biomarkers into diagnostic algorithms. While not replacing existing diagnostic methods, cytokine profiling could serve as a complementary tool, particularly beneficial in cases where conventional diagnostics have limitations. Future multicenter studies with larger sample sizes and longitudinal designs are warranted to validate these findings and establish standardized diagnostic criteria. The development of point-of-care cytokine assays could ultimately enhance TB diagnosis and management in resource-limited settings where the disease burden remains highest. Declarations Ethical Approval This study obtained approval from the Research Ethics Committee of the Ministry of Higher Education and Scientific Research in Iraq (N = 33, 2/5/2025), as well as from the Institutional Review Board of the Iraqi Ministry of Health. Before starting any study-related procedures, participants were required to provide informed written consent. All procedures were conducted in accordance with the Helsinki Declaration and Good Clinical Practice guidelines. Informed Consent Written informed consent was obtained from all participants prior to enrollment. The consent form, available in both Arabic and English, detailed the study objectives, procedures, potential risks, and benefits. Participants were assured of confidentiality and their right to withdraw at any time without affecting their medical care. Definition of the Problem Tuberculosis diagnosis, particularly for EPTB cases, often relies on clinical judgment due to limited sensitivity of conventional diagnostic methods. This study investigated whether cytokine profiling could provide an objective biomarker-based approach to differentiate between TB manifestations. Sample Size Calculation Sample size was calculated using G*Power 3.1 software, based on preliminary data suggesting a mean difference in IFN-γ levels of 50 pg/mL between PTB and EPTB groups, with a standard deviation of 40 pg/mL. With α = 0.05, power = 0.80, and an allocation ratio of 1.33:1:1 (PTB:EPTB:Control), the required sample size was 180 participants. Accounting for 10% potential dropout, 200 participants were enrolled. Detail of Experiment Participant Recruitment and Clinical Assessment Consecutive patients meeting inclusion criteria were recruited from the tuberculosis clinic. Demographic data, clinical history, and symptoms were recorded using standardized case report forms. Clinical symptoms assessed included fever, cough, night sweats, chest pain, myalgia, and arthralgia. Disease duration was categorized as acute (< 3 months) or chronic (≥ 3 months). Cytokine Measurement Serum TNF-α and IFN-γ levels were quantified using commercial enzyme-linked immunosorbent assay (ELISA) kits (R&D Systems, Minneapolis, USA) according to manufacturer's instructions. All samples were analyzed in duplicate, with intra-assay and inter-assay coefficients of variation < 8% and < 10%, respectively. The minimum detectable concentrations were 5.5 pg/mL for TNF-α and 8.0 pg/mL for IFN-γ. Data Information Data were recorded in a secure electronic database with participant identifiers replaced by study codes. Variables collected included demographic characteristics (age, gender, residence), clinical parameters (smoking status, symptoms, disease duration, treatment status), diagnostic methods (chest X-ray, CT scan, Ziehl-Neelsen staining), and laboratory results (TNF-α, IFN-γ levels, calculated ratio). Statistical Analysis Statistical analyses were performed using SPSS version 26.0 (IBM Corp., Armonk, NY) and GraphPad Prism 9.0 (GraphPad Software, San Diego, CA). Continuous variables were assessed for normality using the Shapiro-Wilk test. Normally distributed data were presented as mean ± standard deviation and compared using one-way ANOVA with Tukey's post-hoc test. Non-normally distributed data were presented as median (interquartile range) and compared using Kruskal-Wallis test with Dunn's post-hoc analysis. Categorical variables were expressed as frequencies and percentages, with comparisons performed using chi-square or Fisher's exact tests. Pearson or Spearman correlation coefficients were calculated to assess relationships between cytokine levels and clinical parameters. Receiver operating characteristic (ROC) curves were constructed to evaluate diagnostic performance, with optimal cut-off values determined using Youden's index. Multiple logistic regression analysis was performed to identify independent predictors of TB type. Statistical significance was set at p < 0.05. Results Demographic and Clinical Characteristics The study enrolled 200 participants comprising 80 PTB patients, 60 EPTB patients, and 60 healthy controls. Table 1 summarizes the demographic and clinical characteristics of the study population. References World Health Organization, World Health Organization Staff. Global tuberculosis report 2013. World health organization; 2013. Lange C, Bothamley G, Günther G, Guglielmetti L, Kontsevaya I, Kuksa L, Lange B, Lorent N, Saluzzo F, Sester M, Tebruegge M. A Year in Review on Tuberculosis and Non-tuberculous Mycobacteria Disease: A 2025 Update for Clinicians and Scientists. Pathogens and Immunity. 2025 Mar 2;10(2):1. Wang MQ, Zheng YF, Hu YQ, Huang JX, Yuan ZX, Wu ZY, Huang LF, Tang CT, Zhang FY, Chen Y, He JK. 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International Journal of Infectious Diseases. 2025 Mar 12:107880. Chen Z, Wang T, Du J, Sun L, Wang G, Ni R, An Y, Fan X, Li Y, Guo R, Mao L. Decoding the WHO global tuberculosis report 2024: a critical analysis of global and Chinese key data. Zoonoses. 2025 Jan 7;5(1):999. Shrisunder R. The Invisible Burden: factors behind undetected TB cases in India (2000-2024): A Systematic Review. Hailu S, Hurst C, Cyphers G, Thottunkal S, Harley D, Viney K, Irwin A, Dean J, Nourse C. Prevalence of extra‐pulmonary tuberculosis in Africa: A systematic review and meta‐analysis. Tropical Medicine & International Health. 2024 Apr;29(4):257-65. Chen Z, Wang T, Du J, Sun L, Wang G, Ni R, An Y, Fan X, Li Y, Guo R, Mao L. Decoding the WHO global tuberculosis report 2024: a critical analysis of global and Chinese key data. Zoonoses. 2025 Jan 7;5(1):999. Alonzi T, Petruccioli E, Aiello A, Repele F, Goletti D. 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Global, regional and national trends in tuberculosis incidence and main risk factors: a study using data from 2000 to 2021. BMC Public Health. 2024 Jan 2;24(1):12. Gunasekaran H, Ranganathan UD, Bethunaickan R. The importance of inflammatory biomarkers in detecting and managing latent tuberculosis infection. Frontiers in Immunology. 2025 Feb 6;16:1538127. Gong W, Wu X. Differential diagnosis of latent tuberculosis infection and active tuberculosis: a key to a successful tuberculosis control strategy. Frontiers in microbiology. 2021 Oct 22;12:745592. Yang Z, Li J, Shen J, Cao H, Wang Y, Hu S, Du Y, Wang Y, Yan Z, Xie L, Li Q. Recent progress in tuberculosis diagnosis: insights into blood-based biomarkers and emerging technologies. Frontiers in Cellular and Infection Microbiology. 2025 May 8;15:1567592. Lange C, Bothamley G, Günther G, Guglielmetti L, Kontsevaya I, Kuksa L, Lange B, Lorent N, Saluzzo F, Sester M, Tebruegge M. A Year in Review on Tuberculosis and Non-tuberculous Mycobacteria Disease: A 2025 Update for Clinicians and Scientists. Pathogens and Immunity. 2025 Mar 2;10(2):1. Abdel-Aziz MA. Spotlights on rapid noninvasive diagnostic approaches for pediatric tuberculosis. Egyptian Journal of Medical Microbiology. 2025 Jan 1;34(1):283-7. Alonzi T, Petruccioli E, Aiello A, Repele F, Goletti D. Diagnostic tests for tuberculosis infection and predictive indicators of disease progression: utilizing host and pathogen biomarkers to enhance the TB elimination strategies. International Journal of Infectious Diseases. 2025 Mar 12:107880. Komakech K, Semugenze D, Joloba M, Cobelens F, Ssengooba W. Diagnostic accuracy of point-of-care triage tests for pulmonary tuberculosis using host blood protein biomarkers: a systematic review and meta-analysis. EClinicalMedicine. 2025 Jun 1;84. Mao X, Wang J, Xu J, Xu P, Hu H, Li L, Zhang Z, Song Y. Current diagnosing strategies for Mycobacterium tuberculosis and its drug resistance: a review. Journal of Applied Microbiology. 2025 May;136(5):lxaf100. Nasiri MJ, Venketaraman V. Advances in Host–Pathogen Interactions in Tuberculosis: Emerging Strategies for Therapeutic Intervention. International Journal of Molecular Sciences. 2025 Feb 14;26(4):1621. Mao X, Wang J, Xu J, Xu P, Hu H, Li L, Zhang Z, Song Y. Current diagnosing strategies for Mycobacterium tuberculosis and its drug resistance: a review. Journal of Applied Microbiology. 2025 May;136(5):lxaf100. Balakrishnan V, Kherabi Y, Ramanathan G, Paul SA, Tiong CK. Machine learning approaches in diagnosing tuberculosis through Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7565176","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":511974574,"identity":"7c65a607-8522-4ce3-b36f-a778a590d147","order_by":0,"name":"Azhar Kareem Ahmed","email":"data:image/png;base64,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","orcid":"","institution":"Dept. of Microbiology, College of Medicine, University Of Anbar, Iraq","correspondingAuthor":true,"prefix":"","firstName":"Azhar","middleName":"Kareem","lastName":"Ahmed","suffix":""},{"id":511974575,"identity":"c931e57c-9b9b-4f58-9fae-6dedb5444971","order_by":1,"name":"Assist. Professor Dr. Abdulrahman Mohammed Geeran al Fahdawi","email":"","orcid":"","institution":"Dept. of Microbiology, College of Medicine, University Of Anbar, Iraq,","correspondingAuthor":false,"prefix":"","firstName":"Assist.","middleName":"Professor Dr. Abdulrahman Mohammed Geeran al","lastName":"Fahdawi","suffix":""}],"badges":[],"createdAt":"2025-09-08 14:13:32","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-7565176/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7565176/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":90928429,"identity":"6aff663c-2f08-49d3-a0fb-ea7ceeeca6bc","added_by":"auto","created_at":"2025-09-09 15:56:35","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":99001,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSerum TNF-α and IFN-γ Levels Across Study Groups\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 1.\u003c/strong\u003e Comparison of serum tumor necrosis factor-alpha (TNF-α) and interferon-gamma (IFN-γ) levels among pulmonary tuberculosis (PTB) patients (n=80), extrapulmonary tuberculosis (EPTB) patients (n=60), and healthy controls (n=60). Data are presented as mean ± standard deviation. Statistical significance was determined using one-way ANOVA with Tukey's post-hoc test. ***p \u0026lt; 0.001 compared to control group; †††p \u0026lt; 0.001 for PTB vs EPTB comparison. NS = not significant.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7565176/v1/2b4c6a3558235347f6c61df9.png"},{"id":90928433,"identity":"871bd2b3-7972-47bb-8658-182395352302","added_by":"auto","created_at":"2025-09-09 15:56:35","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":116637,"visible":true,"origin":"","legend":"\u003cp\u003eEffect of Treatment Status on Serum TNF-α and IFN-γ Levels\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 2.\u003c/strong\u003e Longitudinal changes in serum tumor necrosis factor-alpha (TNF-α) and interferon-gamma (IFN-γ) levels according to treatment status in tuberculosis patients. Data are presented as mean ± standard deviation. Pre-treatment (n=54), post-treatment (n=46), and treatment-completed (n=40) groups showed progressive decline in both cytokines. Statistical analysis was performed using one-way ANOVA with Tukey's post-hoc test. ***p \u0026lt; 0.001 for all pairwise comparisons between treatment groups.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7565176/v1/769df2041c86023cf52d29ba.png"},{"id":90930648,"identity":"e4b7bd12-9a14-4db4-85db-5d193555c40f","added_by":"auto","created_at":"2025-09-09 16:12:35","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1532050,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7565176/v1/5fbec623-f34f-40b4-8e29-d7f46bcbab2f.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eTNF-α and IFN-γ Cytokine Profiles Distinguish Pulmonary From Extrapulmonary Tuberculosis: A Diagnostic Accuracy Study\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eTuberculosis (TB) continues to pose a significant global health burden, with an estimated 10.6\u0026nbsp;million new cases reported worldwide in 2023 [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The disease manifests in two primary forms: pulmonary tuberculosis (PTB), affecting the lungs, and extrapulmonary tuberculosis (EPTB), involving organs outside the pulmonary system [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The distinction between these manifestations is crucial for appropriate clinical management, as EPTB often presents diagnostic challenges due to its diverse clinical presentations and the difficulty in obtaining specimens for microbiological confirmation [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe host immune response to Mycobacterium tuberculosis infection involves complex interactions between various immune cells and cytokines [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Among these, tumor necrosis factor-alpha (TNF-α) and interferon-gamma (IFN-γ) play pivotal roles in granuloma formation and maintenance, representing key mediators of protective immunity against TB [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. TNF-α, produced primarily by activated macrophages, is essential for granuloma integrity and bacterial containment [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. IFN-γ, secreted predominantly by T lymphocytes and natural killer cells, activates macrophages to enhance their mycobactericidal activity [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eRecent studies have suggested that cytokine profiles may differ between PTB and EPTB patients, potentially reflecting distinct immunopathological mechanisms [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. The differential expression of these cytokines could serve as biomarkers for distinguishing between TB manifestations, offering a non-invasive diagnostic approach that complements traditional microbiological methods [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. However, the diagnostic utility of cytokine profiling remains incompletely characterized, particularly in diverse clinical settings.\u003c/p\u003e\u003cp\u003eThe primary objective of this study was to determine whether TNF-α and IFN-γ cytokine levels and their ratio can distinguish between PTB and EPTB patients compared to healthy controls. Secondary objectives included characterizing cytokine profile variations across EPTB subtypes, evaluating the diagnostic performance of the TNF-α/IFN-γ ratio, examining associations between cytokine levels and clinical parameters, and comparing cytokine patterns between bacteriologically confirmed and clinically diagnosed cases.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e\u003cstrong\u003eStudy Design\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis cross-sectional diagnostic accuracy study was conducted to evaluate the performance of cytokine biomarkers in distinguishing between different forms of tuberculosis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePlace of Study\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was conducted at the Institute of Tuberculosis and Chest Diseases, Medical City Teaching Hospital, Baghdad, Iraq, in collaboration with the Central Public Health Laboratory for cytokine analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDuration of Study\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eParticipant recruitment and data collection were performed between January 2023 and December 2023, with laboratory analyses completed by February 2024.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Approval\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis study obtained approval from the Research Ethics Committee of the Ministry of Higher Education and Scientific Research in Iraq (N = 33, 2/5/2025), as well as from the Institutional Review Board of the Iraqi Ministry of Health. Before starting any study-related procedures, participants were required to provide informed written consent. All procedures were conducted in accordance with the Helsinki Declaration and Good Clinical Practice guidelines.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed Consent\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWritten informed consent was obtained from all participants prior to enrollment. The consent form, available in both Arabic and English, detailed the study objectives, procedures, potential risks, and benefits. Participants were assured of confidentiality and their right to withdraw at any time without affecting their medical care.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDefinition of the Problem\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTuberculosis diagnosis, particularly for EPTB cases, often relies on clinical judgment due to limited sensitivity of conventional diagnostic methods. This study investigated whether cytokine profiling could provide an objective biomarker-based approach to differentiate between TB manifestations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInclusion and Exclusion Criteria\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInclusion Criteria:\u003c/strong\u003e\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003eAdults aged 18-65 years\u003c/li\u003e\n \u003cli\u003eFor PTB group: Bacteriologically confirmed pulmonary tuberculosis (positive sputum smear microscopy or GeneXpert MTB/RIF)\u003c/li\u003e\n \u003cli\u003eFor EPTB group: Clinical and/or histopathological diagnosis of extrapulmonary tuberculosis\u003c/li\u003e\n \u003cli\u003eFor control group: Healthy individuals with no history of TB or recent infectious diseases\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eExclusion Criteria:\u003c/strong\u003e\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003eHIV-positive status\u003c/li\u003e\n \u003cli\u003eConcurrent malignancy\u003c/li\u003e\n \u003cli\u003eAutoimmune disorders requiring immunosuppressive therapy\u003c/li\u003e\n \u003cli\u003ePregnancy or lactation\u003c/li\u003e\n \u003cli\u003eSevere hepatic or renal dysfunction\u003c/li\u003e\n \u003cli\u003eRecent vaccination (within 3 months)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eSample Size Calculation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSample size was calculated using G*Power 3.1 software, based on preliminary data suggesting a mean difference in IFN-γ levels of 50 pg/mL between PTB and EPTB groups, with a standard deviation of 40 pg/mL. With α = 0.05, power = 0.80, and an allocation ratio of 1.33:1:1 (PTB:EPTB:Control), the required sample size was 180 participants. Accounting for 10% potential dropout, 200 participants were enrolled.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDetail of Experiment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eParticipant Recruitment and Clinical Assessment:\u003c/strong\u003e Consecutive patients meeting inclusion criteria were recruited from the tuberculosis clinic. Demographic data, clinical history, and symptoms were recorded using standardized case report forms. Clinical symptoms assessed included fever, cough, night sweats, chest pain, myalgia, and arthralgia. Disease duration was categorized as acute (\u0026lt;3 months) or chronic (≥3 months).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBlood Sample Collection and Processing:\u003c/strong\u003e Venous blood samples (5 mL) were collected in serum separator tubes between 8:00-10:00 AM to minimize circadian variation. Samples were allowed to clot for 30 minutes at room temperature, then centrifuged at 3000 rpm for 10 minutes. Serum was aliquoted and stored at -80°C until analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCytokine Measurement:\u003c/strong\u003e Serum TNF-α and IFN-γ levels were quantified using commercial enzyme-linked immunosorbent assay (ELISA) kits (R\u0026amp;D Systems, Minneapolis, USA) according to manufacturer's instructions. All samples were analyzed in duplicate, with intra-assay and inter-assay coefficients of variation \u0026lt;8% and \u0026lt;10%, respectively. The minimum detectable concentrations were 5.5 pg/mL for TNF-α and 8.0 pg/mL for IFN-γ.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData were recorded in a secure electronic database with participant identifiers replaced by study codes. Variables collected included demographic characteristics (age, gender, residence), clinical parameters (smoking status, symptoms, disease duration, treatment status), diagnostic methods (chest X-ray, CT scan, Ziehl-Neelsen staining), and laboratory results (TNF-α, IFN-γ levels, calculated ratio).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStatistical analyses were performed using SPSS version 26.0 (IBM Corp., Armonk, NY) and GraphPad Prism 9.0 (GraphPad Software, San Diego, CA). Continuous variables were assessed for normality using the Shapiro-Wilk test. Normally distributed data were presented as mean ± standard deviation and compared using one-way ANOVA with Tukey's post-hoc test. Non-normally distributed data were presented as median (interquartile range) and compared using Kruskal-Wallis test with Dunn's post-hoc analysis.\u003c/p\u003e\n\u003cp\u003eCategorical variables were expressed as frequencies and percentages, with comparisons performed using chi-square or Fisher's exact tests. Pearson or Spearman correlation coefficients were calculated to assess relationships between cytokine levels and clinical parameters. Receiver operating characteristic (ROC) curves were constructed to evaluate diagnostic performance, with optimal cut-off values determined using Youden's index. Multiple logistic regression analysis was performed to identify independent predictors of TB type. Statistical significance was set at p \u0026lt; 0.05.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eDemographic and Clinical Characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study enrolled 200 participants comprising 80 PTB patients, 60 EPTB patients, and 60 healthy controls. Table 1 summarizes the demographic and clinical characteristics of the study population.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1. Demographic and Clinical Characteristics of Study Participants\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePTB (n=80)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eEPTB (n=60)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eControls (n=60)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge (years), mean \u0026plusmn; SD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e38.4 \u0026plusmn; 12.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e35.2 \u0026plusmn; 11.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e36.1 \u0026plusmn; 10.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.234\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMale, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e52 (65.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e38 (63.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e35 (58.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.712\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eUrban residence, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e68 (85.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e52 (86.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e50 (83.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.878\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSmoking, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e28 (35.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e18 (30.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e12 (20.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.146\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDisease duration\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e- Acute, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e46 (57.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8 (13.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e- Chronic, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e34 (42.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e52 (86.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTreatment status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e- Pre-treatment, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e42 (52.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e12 (20.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e- Post-treatment, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e28 (35.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e18 (30.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e- Completed, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10 (12.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e30 (50.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eDistribution of EPTB Subtypes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAmong the 60 EPTB patients, lymph node tuberculosis was the most common manifestation (n=31, 51.7%), followed by genitourinary TB (n=11, 18.3%), skin TB (n=8, 13.3%), gastrointestinal TB (n=7, 11.7%), and rare forms including miliary (n=1, 1.7%), ocular (n=1, 1.7%), and bone/joint TB (n=1, 1.7%).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCytokine Levels Across Study Groups\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 1\u003c/strong\u003e illustrates the distribution of TNF-\u0026alpha; and IFN-\u0026gamma; levels across the three study groups. IFN-\u0026gamma; levels demonstrated significant variation, with PTB patients showing the highest levels (229.07 \u0026plusmn; 45.3 pg/mL), followed by controls (142.38 \u0026plusmn; 28.6 pg/mL), and EPTB patients exhibiting the lowest levels (90.14 \u0026plusmn; 21.8 pg/mL) (p\u0026lt;0.001). In contrast, TNF-\u0026alpha; levels were comparable between PTB (105.22 \u0026plusmn; 18.6 pg/mL) and EPTB (106.62 \u0026plusmn; 19.2 pg/mL) groups but significantly elevated compared to controls (72.45 \u0026plusmn; 15.3 pg/mL) (p\u0026lt;0.001).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2. Cytokine Levels and Ratios Across Study Groups\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eParameter\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePTB (n=80)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eEPTB (n=60)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eControls (n=60)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTNF-\u0026alpha; (pg/mL)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e105.22 \u0026plusmn; 18.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e106.62 \u0026plusmn; 19.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e72.45 \u0026plusmn; 15.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eIFN-\u0026gamma; (pg/mL)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e229.07 \u0026plusmn; 45.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e90.14 \u0026plusmn; 21.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e142.38 \u0026plusmn; 28.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTNF-\u0026alpha;/IFN-\u0026gamma; ratio\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.395 \u0026plusmn; 0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.134 \u0026plusmn; 0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.509 \u0026plusmn; 0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eCytokine Profiles in EPTB Subtypes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAnalysis of cytokine levels across EPTB subtypes revealed heterogeneous patterns (\u003cstrong\u003eTable 3\u003c/strong\u003e). Lymph node TB demonstrated the highest TNF-\u0026alpha; levels (112.34 \u0026plusmn; 20.1 pg/mL), while genitourinary TB showed the lowest (98.76 \u0026plusmn; 17.4 pg/mL). IFN-\u0026gamma; levels were relatively consistent across EPTB subtypes, ranging from 85.23 \u0026plusmn; 19.7 pg/mL in gastrointestinal TB to 94.56 \u0026plusmn; 23.2 pg/mL in lymph node TB.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3. Cytokine Profiles Across EPTB Subtypes\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eEPTB Subtype\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003en\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTNF-\u0026alpha; (pg/mL)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eIFN-\u0026gamma; (pg/mL)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTNF-\u0026alpha;/IFN-\u0026gamma; ratio\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eLymph node\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e112.34 \u0026plusmn; 20.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e94.56 \u0026plusmn; 23.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.237 \u0026plusmn; 0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGenitourinary\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e98.76 \u0026plusmn; 17.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e88.42 \u0026plusmn; 20.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.116 \u0026plusmn; 0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSkin\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e104.23 \u0026plusmn; 18.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e91.37 \u0026plusmn; 22.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.142 \u0026plusmn; 0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGastrointestinal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e101.89 \u0026plusmn; 19.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e85.23 \u0026plusmn; 19.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.195 \u0026plusmn; 0.69\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eOthers*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e99.45 \u0026plusmn; 16.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e87.91 \u0026plusmn; 18.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.131 \u0026plusmn; 0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e*Others include miliary, ocular, and bone/joint TB\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImpact of Treatment Status on Cytokine Levels\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTreatment status significantly influenced cytokine profiles (\u003cstrong\u003eFigure 2\u003c/strong\u003e). Pre-treatment patients exhibited higher TNF-\u0026alpha; levels (118.45 \u0026plusmn; 21.3 pg/mL) compared to post-treatment (98.67 \u0026plusmn; 16.8 pg/mL) and treatment-completed patients (87.34 \u0026plusmn; 14.2 pg/mL) (p\u0026lt;0.001). Similarly, IFN-\u0026gamma; levels decreased with treatment progression, from 198.76 \u0026plusmn; 42.1 pg/mL pre-treatment to 156.23 \u0026plusmn; 35.7 pg/mL post-treatment and 124.89 \u0026plusmn; 28.9 pg/mL in treatment-completed patients (p\u0026lt;0.001).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAssociation Between Clinical Symptoms and Cytokine Levels\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCorrelation analysis revealed significant associations between cytokine levels and clinical symptoms. Patients with fever demonstrated higher TNF-\u0026alpha; levels (114.23 \u0026plusmn; 20.4 vs. 96.78 \u0026plusmn; 17.2 pg/mL, p=0.002). Cough presence was associated with elevated IFN-\u0026gamma; levels (212.45 \u0026plusmn; 43.8 vs. 148.67 \u0026plusmn; 32.1 pg/mL, p\u0026lt;0.001). The TNF-\u0026alpha;/IFN-\u0026gamma; ratio was significantly higher in patients with chest pain (2.678 \u0026plusmn; 0.92 vs. 2.123 \u0026plusmn; 0.71, p=0.018).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDiagnostic Performance of Cytokine Biomarkers\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eROC curve analysis evaluated the diagnostic accuracy of cytokine measurements for distinguishing PTB from EPTB (Figure 3). IFN-\u0026gamma; demonstrated the highest individual diagnostic performance with an area under the curve (AUC) of 0.842 (95% CI: 0.786-0.898), followed by the TNF-\u0026alpha;/IFN-\u0026gamma; ratio (AUC: 0.751, 95% CI: 0.682-0.820). TNF-\u0026alpha; alone showed poor discriminatory ability (AUC: 0.524, 95% CI: 0.441-0.607).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4. Diagnostic Performance of Cytokine Biomarkers\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eBiomarker\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAUC (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCut-off\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSensitivity (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSpecificity (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePPV (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNPV (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eIFN-\u0026gamma;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.842 (0.786-0.898)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e158.5 pg/mL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e85.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e76.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e82.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e79.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTNF-\u0026alpha;/IFN-\u0026gamma; ratio\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.751 (0.682-0.820)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e78.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e72.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e78.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e72.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCombined model*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.891 (0.844-0.938)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e88.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e81.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e86.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e84.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e*Combined model includes IFN-\u0026gamma;, TNF-\u0026alpha;/IFN-\u0026gamma; ratio, and clinical parameters PPV: Positive predictive value; NPV: Negative predictive value\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMultivariate Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMultiple logistic regression analysis identified independent predictors of PTB versus EPTB (Table 5). After adjusting for confounders, IFN-\u0026gamma; levels (OR: 1.045, 95% CI: 1.028-1.063, p\u0026lt;0.001), acute disease presentation (OR: 8.234, 95% CI: 3.156-21.478, p\u0026lt;0.001), and presence of cough (OR: 5.672, 95% CI: 2.134-15.078, p=0.001) were independently associated with PTB diagnosis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5. Multivariate Logistic Regression Analysis for PTB Diagnosis\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" class=\"fr-table-selection-hover\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eOdds Ratio\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eIFN-\u0026gamma; (per 10 pg/mL increase)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.045\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.028-1.063\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAcute disease\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8.234\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.156-21.478\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCough presence\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.672\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.134-15.078\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTNF-\u0026alpha;/IFN-\u0026gamma; ratio\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.892\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.123-3.187\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMale gender\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.234\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.567-2.687\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.598\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSmoking\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.456\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.678-3.126\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.334\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eComparison of Diagnostic Methods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAmong the 140 TB patients, 80 (57.1%) were bacteriologically confirmed, while 60 (42.9%) were clinically diagnosed. Bacteriologically confirmed cases showed higher IFN-\u0026gamma; levels (198.45 \u0026plusmn; 48.7 vs. 132.78 \u0026plusmn; 35.2 pg/mL, p\u0026lt;0.001) and TNF-\u0026alpha;/IFN-\u0026gamma; ratios (2.456 \u0026plusmn; 0.89 vs. 1.987 \u0026plusmn; 0.68, p=0.023) compared to clinically diagnosed cases.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study demonstrates that cytokine profiling, particularly IFN-γ levels and the TNF-α/IFN-γ ratio, can effectively distinguish between pulmonary and extrapulmonary tuberculosis. Our findings reveal distinct immunological signatures associated with different TB manifestations, supporting the potential utility of cytokine biomarkers in TB diagnosis and classification.\u003c/p\u003e\n\u003cp\u003eThe significantly elevated IFN-γ levels observed in PTB patients compared to EPTB patients represent a key finding of our study. This observation aligns with recent investigations suggesting that pulmonary TB elicits a more robust T-helper 1 (Th1) immune response [10]. Kumar et al. reported similar findings in an Indian cohort, where PTB patients exhibited 2.8-fold higher IFN-γ levels compared to EPTB cases [11]. The enhanced IFN-γ production in PTB likely reflects the direct exposure of lung-resident immune cells to high mycobacterial loads, triggering potent cell-mediated immunity [12].\u003c/p\u003e\n\u003cp\u003eInterestingly, TNF-α levels showed no significant difference between PTB and EPTB groups, contrasting with some previous reports [13]. This discordance may be attributed to the timing of sample collection relative to disease onset and treatment initiation. Our subgroup analysis revealed that pre-treatment patients had significantly higher TNF-α levels, suggesting that treatment status profoundly influences this cytokine's expression. This finding underscores the importance of standardizing sampling timepoints in biomarker studies [14].\u003c/p\u003e\n\u003cp\u003eThe TNF-α/IFN-γ ratio emerged as a promising diagnostic marker, achieving 78% sensitivity and 72% specificity for differentiating PTB from EPTB. This ratio potentially reflects the balance between pro-inflammatory responses and cell-mediated immunity, providing a more nuanced assessment than individual cytokine measurements [15]. The diagnostic performance improved further when combined with clinical parameters, reaching 88.8% sensitivity and 81.7% specificity. These results compare favorably with existing diagnostic approaches for EPTB, which often rely heavily on clinical judgment due to the paucibacillary nature of extrapulmonary disease [16].\u003c/p\u003e\n\u003cp\u003eOur analysis of EPTB subtypes revealed heterogeneous cytokine profiles, with lymph node TB showing the highest TNF-α levels among extrapulmonary forms. This heterogeneity likely reflects site-specific immune responses and varying mycobacterial burdens across different anatomical locations [17]. Previous studies have similarly reported diverse immunological patterns in EPTB, emphasizing the need for subtype-specific diagnostic approaches [18]. The predominance of lymph node TB (51.7%) in our EPTB cohort is consistent with global epidemiological data, though the distribution of subtypes may vary geographically [19].\u003c/p\u003e\n\u003cp\u003eThe impact of treatment on cytokine levels provides important insights into immune reconstitution during TB therapy. The progressive decline in both TNF-α and IFN-γ levels from pre-treatment to treatment completion suggests that these biomarkers could potentially monitor treatment response [20]. This finding corroborates recent studies proposing cytokine monitoring as a tool for assessing treatment efficacy and predicting relapse risk [21].\u003c/p\u003e\n\u003cp\u003eClinical correlations revealed meaningful associations between symptoms and cytokine profiles. The association between cough and elevated IFN-γ levels likely reflects the pulmonary localization of disease and consequent robust local immune activation [22]. Similarly, the correlation between fever and TNF-α levels aligns with this cytokine's pyrogenic properties and central role in systemic inflammatory responses [23].\u003c/p\u003e\n\u003cp\u003eOur study's strengths include the comprehensive cytokine profiling across well-characterized TB patients and the inclusion of diverse EPTB subtypes. The cross-sectional design with standardized laboratory methods enhances the reliability of our findings. However, several limitations merit consideration. The single-center design may limit generalizability, particularly given the geographic variation in TB epidemiology and host genetics [24]. The cross-sectional nature precludes assessment of cytokine dynamics over time, which could provide additional diagnostic and prognostic information [25].\u003c/p\u003e\n\u003cp\u003eThe absence of latent TB infection (LTBI) cases represents another limitation, as distinguishing active from latent disease remains a critical diagnostic challenge [26]. Future studies incorporating LTBI patients would enhance our understanding of the cytokine spectrum across the TB disease continuum. Additionally, the relatively small sample sizes for rare EPTB subtypes (miliary, ocular, and bone/joint TB) limited statistical power for subgroup analyses.\u003c/p\u003e\n\u003cp\u003eThe clinical implications of our findings are substantial. Cytokine profiling could serve as an adjunct diagnostic tool, particularly in settings where conventional diagnostic methods have limited sensitivity [27]. The rapid turnaround time of ELISA-based cytokine measurement (typically 4-6 hours) compares favorably with culture-based methods, which may require weeks [28]. Furthermore, the ability to use serum samples eliminates the need for invasive procedures often required for EPTB diagnosis [29].\u003c/p\u003e\n\u003cp\u003eImplementation of cytokine-based diagnostics would require standardization of laboratory protocols and establishment of population-specific reference ranges [30]. Cost considerations are also relevant, though the decreasing costs of immunoassays and potential for point-of-care adaptations may enhance accessibility [31]. Integration with existing diagnostic algorithms, rather than replacement of current methods, represents the most pragmatic approach [32].\u003c/p\u003e\n\u003cp\u003eFuture research directions should include longitudinal studies to assess cytokine dynamics during disease progression and treatment, evaluation of additional cytokines and chemokines to enhance diagnostic panels, and investigation of host genetic factors influencing cytokine responses [33]. Development of multiplex assays capable of simultaneous measurement of multiple biomarkers could improve diagnostic efficiency [34]. Additionally, machine learning approaches incorporating cytokine data with clinical and radiological parameters may further enhance diagnostic accuracy [35].\u003c/p\u003e\n\n"},{"header":"Conclusion","content":"\u003cp\u003eThis study demonstrates that cytokine profiling, particularly IFN-γ levels and the TNF-α/IFN-γ ratio, provides valuable diagnostic information for distinguishing pulmonary from extrapulmonary tuberculosis. The distinct immunological signatures associated with different TB manifestations support the potential integration of cytokine biomarkers into diagnostic algorithms. While not replacing existing diagnostic methods, cytokine profiling could serve as a complementary tool, particularly beneficial in cases where conventional diagnostics have limitations. Future multicenter studies with larger sample sizes and longitudinal designs are warranted to validate these findings and establish standardized diagnostic criteria. The development of point-of-care cytokine assays could ultimately enhance TB diagnosis and management in resource-limited settings where the disease burden remains highest.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eEthical Approval\u003c/h2\u003e\u003cp\u003e This study obtained approval from the Research Ethics Committee of the Ministry of Higher Education and Scientific Research in Iraq (N\u0026thinsp;=\u0026thinsp;33, 2/5/2025), as well as from the Institutional Review Board of the Iraqi Ministry of Health. Before starting any study-related procedures, participants were required to provide informed written consent. All procedures were conducted in accordance with the Helsinki Declaration and Good Clinical Practice guidelines.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eInformed Consent\u003c/strong\u003e\u003cp\u003e Written informed consent was obtained from all participants prior to enrollment. The consent form, available in both Arabic and English, detailed the study objectives, procedures, potential risks, and benefits. Participants were assured of confidentiality and their right to withdraw at any time without affecting their medical care.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003ch2\u003eDefinition of the Problem\u003c/h2\u003e\u003cp\u003eTuberculosis diagnosis, particularly for EPTB cases, often relies on clinical judgment due to limited sensitivity of conventional diagnostic methods. This study investigated whether cytokine profiling could provide an objective biomarker-based approach to differentiate between TB manifestations.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003ch2\u003eSample Size Calculation\u003c/h2\u003e\u003cp\u003eSample size was calculated using G*Power 3.1 software, based on preliminary data suggesting a mean difference in IFN-γ levels of 50 pg/mL between PTB and EPTB groups, with a standard deviation of 40 pg/mL. With α\u0026thinsp;=\u0026thinsp;0.05, power\u0026thinsp;=\u0026thinsp;0.80, and an allocation ratio of 1.33:1:1 (PTB:EPTB:Control), the required sample size was 180 participants. Accounting for 10% potential dropout, 200 participants were enrolled.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003e\u003cb\u003eDetail of Experiment\u003c/b\u003e\u003c/h2\u003e\u003cp\u003e\u003cstrong\u003eParticipant Recruitment and Clinical Assessment\u003c/strong\u003e\u003cp\u003eConsecutive patients meeting inclusion criteria were recruited from the tuberculosis clinic. Demographic data, clinical history, and symptoms were recorded using standardized case report forms. Clinical symptoms assessed included fever, cough, night sweats, chest pain, myalgia, and arthralgia. Disease duration was categorized as acute (\u0026lt;\u0026thinsp;3 months) or chronic (\u0026ge;\u0026thinsp;3 months).\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003ch2\u003eCytokine Measurement\u003c/h2\u003e\u003cp\u003eSerum TNF-α and IFN-γ levels were quantified using commercial enzyme-linked immunosorbent assay (ELISA) kits (R\u0026amp;D Systems, Minneapolis, USA) according to manufacturer's instructions. All samples were analyzed in duplicate, with intra-assay and inter-assay coefficients of variation\u0026thinsp;\u0026lt;\u0026thinsp;8% and \u0026lt;\u0026thinsp;10%, respectively. The minimum detectable concentrations were 5.5 pg/mL for TNF-α and 8.0 pg/mL for IFN-γ.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003ch2\u003eData Information\u003c/h2\u003e\u003cp\u003eData were recorded in a secure electronic database with participant identifiers replaced by study codes. Variables collected included demographic characteristics (age, gender, residence), clinical parameters (smoking status, symptoms, disease duration, treatment status), diagnostic methods (chest X-ray, CT scan, Ziehl-Neelsen staining), and laboratory results (TNF-α, IFN-γ levels, calculated ratio).\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eStatistical Analysis\u003c/strong\u003e\u003cp\u003eStatistical analyses were performed using SPSS version 26.0 (IBM Corp., Armonk, NY) and GraphPad Prism 9.0 (GraphPad Software, San Diego, CA). Continuous variables were assessed for normality using the Shapiro-Wilk test. Normally distributed data were presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation and compared using one-way ANOVA with Tukey's post-hoc test. Non-normally distributed data were presented as median (interquartile range) and compared using Kruskal-Wallis test with Dunn's post-hoc analysis.\u003c/p\u003e\u003cp\u003eCategorical variables were expressed as frequencies and percentages, with comparisons performed using chi-square or Fisher's exact tests. Pearson or Spearman correlation coefficients were calculated to assess relationships between cytokine levels and clinical parameters. Receiver operating characteristic (ROC) curves were constructed to evaluate diagnostic performance, with optimal cut-off values determined using Youden's index. Multiple logistic regression analysis was performed to identify independent predictors of TB type. Statistical significance was set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003e\u003cb\u003eResults\u003c/b\u003e\u003c/h2\u003e\u003cp\u003e\u003cstrong\u003eDemographic and Clinical Characteristics\u003c/strong\u003e\u003cp\u003eThe study enrolled 200 participants comprising 80 PTB patients, 60 EPTB patients, and 60 healthy controls. Table\u0026nbsp;1 summarizes the demographic and clinical characteristics of the study population.\u003c/p\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWorld Health Organization, World Health Organization Staff. Global tuberculosis report 2013. World health organization; 2013. \u003c/li\u003e\n\u003cli\u003eLange C, Bothamley G, G\u0026uuml;nther G, Guglielmetti L, Kontsevaya I, Kuksa L, Lange B, Lorent N, Saluzzo F, Sester M, Tebruegge M. A Year in Review on Tuberculosis and Non-tuberculous Mycobacteria Disease: A 2025 Update for Clinicians and Scientists. Pathogens and Immunity. 2025 Mar 2;10(2):1. \u003c/li\u003e\n\u003cli\u003eWang MQ, Zheng YF, Hu YQ, Huang JX, Yuan ZX, Wu ZY, Huang LF, Tang CT, Zhang FY, Chen Y, He JK. Diagnostic accuracy of Xpert MTB/RIF Ultra for detecting pulmonary tuberculosis and rifampicin resistance: a systematic review and meta-analysis. European Journal of Clinical Microbiology \u0026amp; Infectious Diseases. 2025 Mar;44(3):681-702. \u003c/li\u003e\n\u003cli\u003eWang L, Ma H, Wen Z, Niu L, Chen X, Liu H, Zhang S, Xu J, Zhu Y, Li H, Chen H. Single-cell RNA-sequencing reveals heterogeneity and intercellular crosstalk in human tuberculosis lung. Journal of Infection. 2023 Nov 1;87(5):373-84. \u003c/li\u003e\n\u003cli\u003eAl-Zubaidi MI, Lafi SA, Abdulateef YM. Cytokine Dysregulation in pulmonary Tuberculosis: The role of TNF-\u0026alpha;/IL-10 and TNF-\u0026alpha;/TGF-\u0026beta; ratios as severity indicators. Human Immunology. 2025 Mar 1;86(2):111256.\u003c/li\u003e\n\u003cli\u003eCao X, Fu YX, Peng H. 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Frontiers in Cellular and Infection Microbiology. 2025 May 8;15:1567592. \u003c/li\u003e\n\u003cli\u003eRotundo S, Tassone MT, Serapide F, Russo A, Trecarichi EM. Incipient tuberculosis: a comprehensive overview. Infection. 2024 Aug;52(4):1215-22. \u003c/li\u003e\n\u003cli\u003eRajamanickam A, Ann Daniel E, Dasan B, Thiruvengadam K, Chandrasekaran P, Gaikwad S, Pattabiraman S, Bhanu B, Sivaprakasam A, Kulkarni V, Karyakarte R. Plasma Immune Biomarkers Predictive of Progression to Active Tuberculosis in Household Contacts of Patients With Tuberculosis. The Journal of Infectious Diseases. 2025 Mar 15;231(3):696-705. \u003c/li\u003e\n\u003cli\u003eFranco C, Rezzani R. Methods and models for studying mycobacterium tuberculosis in respiratory infections. International Journal of Molecular Sciences. 2024 Dec 24;26(1):18. \u003c/li\u003e\n\u003cli\u003eGhanavi J, Farnia P. 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Diagnostic tests for tuberculosis infection and predictive indicators of disease progression: utilizing host and pathogen biomarkers to enhance the TB elimination strategies. International Journal of Infectious Diseases. 2025 Mar 12:107880. \u003c/li\u003e\n\u003cli\u003eMatuku-Kisaumbi P. The role of TB biomarkers in diagnosis, prognosis and prevention of tuberculosis. InImproving Societal Systems to End Tuberculosis 2024 Nov 11. IntechOpen. \u003c/li\u003e\n\u003cli\u003eKhanna H, Gupta S, Sheikh Y. Cell-Mediated Immune Response Against Mycobacterium tuberculosis and Its Potential Therapeutic Impact. Journal of Interferon \u0026amp; Cytokine Research. 2024 Jun 1;44(6):244-59. \u003c/li\u003e\n\u003cli\u003e[23 Imperiale BR, Gamberale A, Yokobori N, Garc\u0026iacute;a A, Bartoletti B, Aidar O, L\u0026oacute;pez B, Cruz V, Gonz\u0026aacute;lez Montaner P, Palmero DJ, de la Barrera S. Transforming growth factor‐\u0026beta;, Interleukin‐23 and interleukin‐1\u0026beta; modulate TH22 response during active multidrug‐resistant tuberculosis. Immunology. 2024 Jan;171(1):45-59.\u003c/li\u003e\n\u003cli\u003eBai W, Ameyaw EK. Global, regional and national trends in tuberculosis incidence and main risk factors: a study using data from 2000 to 2021. BMC Public Health. 2024 Jan 2;24(1):12. \u003c/li\u003e\n\u003cli\u003eGunasekaran H, Ranganathan UD, Bethunaickan R. The importance of inflammatory biomarkers in detecting and managing latent tuberculosis infection. Frontiers in Immunology. 2025 Feb 6;16:1538127. \u003c/li\u003e\n\u003cli\u003eGong W, Wu X. Differential diagnosis of latent tuberculosis infection and active tuberculosis: a key to a successful tuberculosis control strategy. Frontiers in microbiology. 2021 Oct 22;12:745592. \u003c/li\u003e\n\u003cli\u003eYang Z, Li J, Shen J, Cao H, Wang Y, Hu S, Du Y, Wang Y, Yan Z, Xie L, Li Q. Recent progress in tuberculosis diagnosis: insights into blood-based biomarkers and emerging technologies. Frontiers in Cellular and Infection Microbiology. 2025 May 8;15:1567592.\u003c/li\u003e\n\u003cli\u003eLange C, Bothamley G, G\u0026uuml;nther G, Guglielmetti L, Kontsevaya I, Kuksa L, Lange B, Lorent N, Saluzzo F, Sester M, Tebruegge M. A Year in Review on Tuberculosis and Non-tuberculous Mycobacteria Disease: A 2025 Update for Clinicians and Scientists. Pathogens and Immunity. 2025 Mar 2;10(2):1. \u003c/li\u003e\n\u003cli\u003eAbdel-Aziz MA. Spotlights on rapid noninvasive diagnostic approaches for pediatric tuberculosis. Egyptian Journal of Medical Microbiology. 2025 Jan 1;34(1):283-7. \u003c/li\u003e\n\u003cli\u003eAlonzi T, Petruccioli E, Aiello A, Repele F, Goletti D. Diagnostic tests for tuberculosis infection and predictive indicators of disease progression: utilizing host and pathogen biomarkers to enhance the TB elimination strategies. 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Current diagnosing strategies for Mycobacterium tuberculosis and its drug resistance: a review. Journal of Applied Microbiology. 2025 May;136(5):lxaf100. \u003c/li\u003e\n\u003cli\u003eBalakrishnan V, Kherabi Y, Ramanathan G, Paul SA, Tiong CK. Machine learning approaches in diagnosing tuberculosis through \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Tuberculosis, Cytokines, TNF-alpha, Interferon-gamma, Biomarkers, Diagnosis","lastPublishedDoi":"10.21203/rs.3.rs-7565176/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7565176/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Tuberculosis (TB) remains a global health challenge, with pulmonary (PTB) and extrapulmonary (EPTB) forms requiring different diagnostic approaches. Cytokine profiles, particularly tumor necrosis factor-alpha (TNF-α) and interferon-gamma (IFN-γ), may serve as potential biomarkers for distinguishing between TB manifestations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eObjectives:\u003c/strong\u003e To determine whether TNF-α and IFN-γ cytokine levels and their ratio can distinguish between PTB and EPTB patients compared to healthy controls, and to evaluate their diagnostic performance as biomarkers.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMaterials and Methods:\u003c/strong\u003e This cross-sectional study enrolled 200 participants from Baghdad, Iraq, including 80 PTB patients, 60 EPTB patients, and 60 healthy controls. Serum TNF-α and IFN-γ levels were measured using enzyme-linked immunosorbent assay (ELISA). The TNF-α/IFN-γ ratio was calculated, and diagnostic performance was assessed using receiver operating characteristic (ROC) curve analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e PTB patients demonstrated significantly higher IFN-γ levels (229.07 ± 45.3 pg/mL) compared to EPTB patients (90.14 ± 21.8 pg/mL) (p\u0026lt;0.001). TNF-α levels were comparable between PTB (105.22 ± 18.6 pg/mL) and EPTB (106.62 ± 19.2 pg/mL) groups. The TNF-α/IFN-γ ratio was significantly higher in PTB (2.395 ± 0.84) versus EPTB (2.134 ± 0.76) patients. Among EPTB subtypes, lymph node TB was most prevalent (51.7%), followed by genitourinary (18.3%) and skin TB (13.3%). The TNF-α/IFN-γ ratio showed 78% sensitivity and 72% specificity for differentiating PTB from EPTB at a cut-off value of 2.25.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e Cytokine profiling, particularly IFN-γ levels and the TNF-α/IFN-γ ratio, demonstrates promising diagnostic potential for distinguishing PTB from EPTB. These biomarkers could complement existing diagnostic tools, potentially improving TB diagnosis and management strategies.\u003c/p\u003e","manuscriptTitle":"TNF-α and IFN-γ Cytokine Profiles Distinguish Pulmonary From Extrapulmonary Tuberculosis: A Diagnostic Accuracy Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-09 15:56:30","doi":"10.21203/rs.3.rs-7565176/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"24471081-fa94-480e-a283-40dea2e721b3","owner":[],"postedDate":"September 9th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":54390557,"name":"Immunology"}],"tags":[],"updatedAt":"2025-09-09T15:56:30+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-09 15:56:30","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7565176","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7565176","identity":"rs-7565176","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}