Use of interferon-gamma release assay (IGRA) and CXCL-10/IP-10 for screening of latent tuberculosis infection (LTBI) in chronic kidney disease and hemodialysis patients

Use of interferon-gamma release assay (IGRA) and CXCL-10/IP-10 for screening of latent tuberculosis infection (LTBI) in chronic kidney disease and hemodialysis patients | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Use of interferon-gamma release assay (IGRA) and CXCL-10/IP-10 for screening of latent tuberculosis infection (LTBI) in chronic kidney disease and hemodialysis patients Juliana Cristina Borges da Silva, Nathália Barcellos Vieira, Marcelo Ribeiro-Alves, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4725508/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 02 Oct, 2025 Read the published version in BMC Infectious Diseases → Version 1 posted 4 You are reading this latest preprint version Abstract Background There is growing evidence that patients with chronic kidney disease (CKD) show a high risk of acquiring latent tuberculosis infection (LTBI) or of developing active TB. However, diagnosing LTBI is still a challenge based on current tests which reflect the cellular immunity against Mycobacterium tuberculosis infection – that may be compromised in this population. Herein, we evaluated the prevalence of LTBI among patients with CKD and those in routine hemodialysis, and LTBI predictors. Methods A prospective cross-sectional study was conducted in a tertiary nephrology reference hospital, Rio de Janeiro, Brazil. LTBI was diagnosed using interferon-gamma release assay (IGRA; QuantiFERON-TB Gold Plus [QFT-Plus]). CXCL-10/IP-10 release assay was determined from QFT-Plus supernatants. Results A total of 123 patients were enrolled in the study, 111 (90.2%) from the CKD group and 12 (9.8%) from the hemodialysis (HD). IGRA showed 39 (31.7%) positive results in the general study population: 36/111 (32.4%) in CKD and 3/12 (25%) in HD groups. Indeterminate IGRA results were observed in 4/123 (3.3%) patients. CXCL-10/IP-10 positive results were seen in 39/123 (39.8%) patients. Multivariate regression analysis identified that non-vaccination with BCG (aOR 7.41 [CI 2.15–25.48]; p = 0.0014) and the positivity for CXCL-10/IP-10 assay (aOR 4.48 [CI 1.87–10.76]; p = < 0.001) were independent risk factors for LTBI among DRC and HD patients. Conclusion The IGRA QFT-Plus was shown to be a useful method in the surveillance of LTBI in critical stages of CKD and routine hemodialysis. Mtb-specific CXCL-10/IP-10 responses were associated with a positive IGRA and could provide an useful adjuvant LTBI biomarker in this population. chronic kidney disease hemodialysis latent tuberculosis tuberculosis Mycobacterium tuberculosis and renal failure Figures Figure 1 Figure 2 Figure 3 BACKGROUND Tuberculosis (TB), a disease caused by the bacillus Mycobacterium tuberculosis (Mtb), is still an important global public health problem, requiring effective strategies for its control. According to the World Health Organization (WHO), in 2022, approximately 7.5 million people were diagnosed with TB, and it is estimated that around 10.6 million individuals became ill worldwide. Although about a quarter of the world's population is infected with Mtb, some individuals develop a partial immunity, with no signs or symptoms, and unable to spread the infection but in high risk to develop active disease ( 1 , 2 ). In these cases, the bacilli remain in a histological organization called granuloma , in small quiescent foci – a condition known as Latent Tuberculosis Infection (LTBI) ( 3 – 5 ). Several conditions can contribute to the activation of this quiescent infectious focus and its progression to active TB, being the weakening or interference in the immune system the most significant factor. It is known that cellular immunity is the main effective defense mechanism against TB, which is mainly represented by CD4 + and CD8 + T lymphocytes (Th1 response) producing interferon-gamma (IFN-γ) – a key cytokine involved in classical macrophage activation and, consequently, Mtb killing. Other cytokines such as IL-1β, IL-2, IL-6, IL-12, tumor necrosis factor (TNF), and chemokines such as CXCL10/interferon-γ-inducible protein-10 (IP-10) are also essential for containing the bacillus ( 2 , 4 ). However, when the pathogen is not completely eradicated, it persists in a state of latency ( 4 – 6 ) and the evolution of LTBI to active TB can occur depending on a complex and intricated relation between Mtb and host factors ( 3 ). Since the immune system is significant in containing the bacillus, a direct attention to populations at risk for TB is mandatory, such as Diabetes mellitus (DM), rheumatoid arthritis, inflammatory bowel diseases (IBD) and chronic kidney disease (CKD), among others non-communicable diseases ( 7 – 9 ). For instance, CKD patients present 8 to 25 times higher increased risk of developing active TB compared to general population ( 6 , 10 ). Also, mortality rates associated with TB are even higher, especially among those on renal replacement therapy (RRT) ( 11 , 12 ). The uremic state in CKD, characterized by the retention of toxins, disrupts the immune system and results in a decrease of B7-2 co-stimulatory molecule expression in antigen-presenting cells, interfering with the function of polymorphonuclear cells and monocytes/macrophages by altering their phagocytic capacity, low efficiency of chemotactic migration and a decrease in cellular response in the control of intracellular microorganisms ( 13 , 14 ). Screening and prophylactic treatment of LTBI in this population may reduce the risk of developing the active disease, and could reduce the spread of TB. There is not a gold standard test for diagnosing LTBI. Currently, there are two immunological standardized tests in the diagnostical routine: i) the tuberculin skin test (TST) and ii) interferon-gamma release assay (IGRA), such as QuantiFERON-TB Gold Plus (QFT-Plus) and T. SPOT-TB ( 15 ). Both different diagnostic methods depend on the cellular immune response against Mtb, reflecting an indirect measurement of the infection, and have been used to detect LTBI among CKD patients ( 11 , 15 , 16 ). A recent systematic review and metanalysis has found a high pooled prevalence of LTBI in CKD patient, however showing a heterogeneity regarding severity status of renal failure, laboratorial method used to detect LTBI, and TB burden areas ( 17 ). Therefore, despite the recognized relevance of the association between LTBI and CKD, robust investigations are lacking. In this work, we aimed to determine the prevalence of LTBI in CKD patients at different stages of the disease (3b to 5) or on hemodialysis (HD) using QFT-Plus – the fourth and last generation of this IGRA, and identify LTBI predictors. METHODS Participant enrollment and ethical approval This prospective cross-sectional study was conducted in a tertiary referral medical center in Rio de Janeiro, RJ, Brazil, under the approval of the Research Ethics Committee from the Hospital Universitario Pedro Ernesto (HUPE), Rio de Janeiro State University (UERJ), number 4.624.686. All the enrolled participants provided signed informed-consent forms. Between August 2021 and September 2023, adult patients (age ≥ 18 years) with chronic kidney disease (CKD group) in stages 3b (estimated glomerular filtration rate [eGFR] 44 − 30 mL/min/1.73 m²), 4 (eGFR 30 − 15 mL/min), or 5 (eGFR 3 months) hemodialysis (HD group), both from HUPE/UERJ, were diagnosed and recruited based on Kidney Disease Improving Global Outcomes (KDIGO) guidelines ( 10 ). The eGFR was calculated for the CKD group using the CKD-EPI 2021 Eq. (10). Patients with previous TB, pregnant women, and those using immunosuppressants were excluded from the study. Data collection Demographic and clinical data (including age, sex, residence, etiology of CKD, underlying comorbidities, history of TB, history of close contact with an index TB case, history of BCG vaccination, smoking status, and alcoholic drink status) were collected from medical records and/or case report forms, previously approved by Ethics Committee from HUPE/UERJ. Laboratory tests such as hemogram (total blood leucocyte and differential counts) and biochemical analysis (glycated hemoglobin percentage, C-reactive protein, serum albumin concentration, blood urea nitrogen concentration, and creatinine concentration) were carried out by the HUPE/UERJ central laboratory and recovered from hospital’s electronic system. Interferon-gamma release assay (IGRA) LTBI status was determined by interferon-gamma release assay (IGRA) QuantiFERON TB Gold-Plus Kit (QFT-Plus; QIAGEN) according to the manufacturer’s instructions ( 15 ). Briefly, peripheral blood samples were collected by venipuncture using lithium heparin tubes (BD Vacutainer) and transferred for each QFT-Plus tubes: i) negative control (Nil); ii) TB1 Mtb-specific antigens (Mtb-specific antigens to stimulate mainly CD4 + T cells); iii) TB2 (Mtb-specific antigens to stimulate both CD4 + and CD8 + T cells); and, iv) positive control (Mitogen). After overnight incubation at 37°C, tubes were centrifuged at 3,000 x g, 25°C for 15 minutes, plasma supernatants were collected from each tube stored in 150 µL aliquots at -80°C. Samples were then subjected to specific QFT-Plus enzyme-linked immunosorbent assays (ELISA) to measure IFN-γ levels. Results were expressed in UI/mL and categorized as positive when IFN-γ in response to Mtb-specific antigens (TB1 or TB2 minus negative [Nil] control) were ≥ 0.35 IU/mL. Indeterminate results were defined as IFN- γ from Nil tube > 8.0 IU/mL or Mitogen < 0.5 IU/mL. Results were calculated using the QuantiFERON-TB Gold Plus Analysis Software 2.71. The range QFT-Plus ELISA assay was 0.065 IU/mL to 10.0 IU/mL. CXCL-10/IP-10 release assay CXCL-10/IP-10 Mtb-specific responses were measured using plasma supernatants from QFT-Plus, following the manufacturer’s instruction. CXCL-10/IP-10 levels were assessed by ELISA sandwich using human CXCL-10/IP-10 DuoSet ELISA (R&D Systems Inc, MN, USA). The ELISA plate was read using a microplate reader (Thermo Scientific Multiskan™ FC) and the results were analyzed using Skanit 6.0.1 software. The range of these assays was 31.3–10,000 pg/mL. We previously determined the area under the curve (AUC) and cutoff values, in which significant results from AUC analysis were obtained [AUC, 0.8750; 95% confidence interval (CI), 0.744–1.006, p < 0.0001] for the CXCL-10/IP-10 responses to Mtb-antigens into QFT-Plus tubes. For scoring, a cutoff point was chosen to maximize the sum of sensitivity and specificity. A positive result was defined as (Mtb antigens [TB 1 or TB2]-stimulated CXCL-10/IP-10 levels) subtracted by (Nil-stimulated CXCL-10/IP-10 levels)] ≥ 535.9 pg/mL, as previously reported ( 18 ). Statistical analysis For continuous numerical variables, non-parametric Mann-Whitney U tests were used in the comparison of baseline demographic and clinical variables, while for categorical nominal variables, Pearson χ-squared tests were used in the assessment of frequency independence between these variables and the groups of chronic kidney disease (CKD) patients. The comparison between the log-transformed (base 10) production levels of QFT-Plus (Nil, Mitogen, specific TB antigens, and other measures obtained by subtracting the above) and others used the expected/mean marginal values and their 95% confidence intervals obtained from fixed-effects multiple linear (log-linear) regression models with the inclusion of main effects, chronic kidney disease groups and confounding variables (e.g., age, race, hypertension, and diabetes mellitus). Graphical analyses of residuals were carried out for the adjusted models to confirm their randomness. In pairwise comparisons between expected/average marginal values obtained from multiple linear regression models (Student's T-test), confidence level adjustments were made using the Sidak method, and p-value adjustments for multiple comparisons using the Tukey method; the latter only in cases where the main exposure variable had more than 2 levels. To estimate risk factors associated with LTBI or QFT-Plus positivity among CKD patients, multiple fixed-effect binomial models (multivariate logistic analysis) were used, including as main effects variables possibly associated with the QFT-Plus result and confounding variables (variables with at least suggestive levels, p-value < 0.1 (e.g., age, sex, and history of BCG vaccination), in the simple/univariate analyses. The results were presented as OR (Odds-ratio) and aOR (Adjusted Odds-ratio) and their 95% confidence intervals. Whenever necessary, we categorized continuous numerical variables using as cut-offs the round whole number closest to their medians or tertiles (i.e., at 33% and 66%). Two-tailed significance levels less than or equal to 0.01, 0.05, and 0.1 were considered ‘highly significant’, ‘significant,’ and ‘suggestive’, respectively. All statistical analyses were conducted using R version 4.1.2 (R Core Team, 2021), packages ‘lme4’, ‘emmeans”, and their dependencies. RESULTS Overview of the study population A total of 125 patients attended in nephrology outpatient and at the dialysis-unit from Pedro Ernesto University Hospital, Rio de Janeiro, Brazil, were prospectively enrolled in this study. All individuals met the eligibility criteria initially proposed. However, two patients were excluded due to insufficient blood sample collection to perform cytokine release assays. Therefore, the final study population was composed by two groups, as follow: i) CKD group: patients with CKD with eGFR < 45ml/min, classified in stages 3b to 5 (n = 111); and, ii) HD group: patients requiring renal replacement therapy by hemodialysis (n = 12), as depicted in Fig. 1 . A baseline analysis of the clinical and sociodemographic characteristics of the study population is shown in Table 1 . We observed that 51 (45.9%) and 7 (58.3%) were male (p = 0.608), with a mean age of 66 years and 54 years (p = 0.015), in the CKD and HD groups respectively. The predominant CKD stage in this study was: stage 4 with 63 (56.8%) subjects, followed by stage 3 with 29 (26.1%) and stage 5 with 19 (17.1%) (p ≤ 0.001). The commonest causes of CKD were diabetes mellitus, showing 50 (45%) cases in CKD and 2 (16%) in HD, followed by arterial hypertension with 24 (21.6%) CKD and 4 (33.3%) in HD. Among the study population, 21 (18.9%) in the CKD group and 3 (25%) in the HD one had contact with an index TB case. Regarding the history of BCG vaccination in childhood, 95 (85.6%) in the CKD group and 12 (100%) in the HD group showed the vaccination scar. Table 2 shows the laboratory characteristics, where it was possible to observe significantlylowercounts of hemoglobin (p = 0.032), total leukocytes (p = 0.023), and lymphocytes (p = 0.017) in the HD group compared to CKD. Furthermore, anhigher monocyte blood count was seen in HD group (p = 0.044). Table 1 Baseline characteristics of the study population Characteristics CKD (n = 111) HD (n = 12) P- value Age, years (IQR) 66 (12.5) 54 (11.25) 0.015 Male gender 51 (45.9) 7 (58.3) 0.608 BCG vaccination 95 (85.6) 12 (100) 0.338 Contact with TB 21 (18.9) 3 ( 25 ) 0.903 Current smoking 13 (11.9) 2 (16.7) 0.991 Current alcohol drinking 20 (18.3) 3 ( 25 ) 0.865 Locality residence 0.057 Rio de Janeiro 74 (66.7) 12 (100) Metropolitan region 34 (30.6) 0 Others 3 (2.7) 0 Cause of CKD 0.308 Diabetes mellitus 50 (45) 2 (16.7) Hypertension 24 (21.6) 4 (33.3) Glomerulonephritis 7 (6.3) 1 (8.3) Others 30 ( 27 ) 5 (41.7) CKD stage < 0.001 3b 29 (26.1) NA 4 63 (56.8) NA 5 19 (17.1) NA Data are expressed as number (%) or interquartile range (IQR). Pearson χ-squared tests were used in the assessment of frequency independence between variables and the CKD and HD patients’ groups. Abbreviations: CKD: chronic kidney disease; HD: hemodialysis; BCG: bacillus Calmette–Guérin; TB: tuberculosis; NA: not applicable. P-value ≤ 0.05 was considered significant. Table 2 Laboratory characteristics of the study population Characteristics CKD (n = 111) HD (n = 12) P- value Hemoglobin, g/dL 12,06 [11.42–12.70] 10.81 [9.59–12.03] 0.032 Total leukocytes, cells/mm 3 6627.76 [5806.74–7448.79] 4936.54 [3372.32–6500.75] 0.023 Neutrophils, cells/mm 3 4106.49 [3464.12–4748.86] 3299.75 [2073.60–4525.91] 0.164 Monocytes, cells/mm 3 604.64 [447.20–762.08] 891.89 [591.37–1192.42] 0.044 Lymphocytes, cells/mm 3 1742.54 [1508.11–1976.97] 1233.24 [ 785.76–1680.71] 0.017 Glycated hemoglobin, % Diabetics 6.96 [6.72–7.20] 5.23 [4.19–6.28] 0.009 Others 5.41 [5.13–5.68] 5.14 [4.51–5.77] 0.872 C-reactive protein, mg/dL Diabetics 5.42 [3.15–7.68] 5.80 [-0.85–12.45] 0.999 Others 2.38 [-0.48 -5.25] 7.17 [3.23–11.10] 0.208 Albumin, g/dL 4.12 [4.01–4.24] 3.87 [3.27–4.47] 0.388 eGFR, mL/min/1.73 m² < 0.001 3b stage 36.58 [34.47–38.69] NA 4 stage 22.78 [21.24–24.31] NA 5 stage 13.73 [11.40–16.05] NA Data are expressed as marginal mean marginal values [95% confidence interval]. Mean marginal values and their 95% confidence intervals were obtained from fixed-effects multiple linear (log-linear) regression models with the inclusion of main effects, chronic kidney disease groups and confounding variables (e.g., age and race). Abbreviations: CKD: chronic kidney disease; HD: hemodialysis; BCG: bacillus Calmette–Guérin; TB: tuberculosis; eGFR: estimated glomerular filtration rate; NA: not applicable. P value ≤ 0.05 was considered significant. Latent tuberculosis infection prevalence in CKD and HD patients Almost one-third (31.7%; 39/123) of the overall population showed positive results for IGRA, being 36/111 (32.4%) in the CKD and 3/12 (25%) in the HD group. Indeterminate results were found in 4 (3.3%) subjects, one of them from the HD group. Evaluating the IGRA results among the CKD subgroups, we observed 37.9% (11/29) positive and 58.6% (17/29) negative in stage 3b. Meanwhile, in stage 4 we observed 31.7% (20/63) positive and 65.1% (41/63) negative, and in stage 5, 26.3% (5/19) were considered positive and 73.3% (14/19) negative (Fig. 2 ). All patients showed satisfactory responses to positive control (mitogen; data not shown). Since IGRA QFT-Plus provides in vitro IFN-γ blood levels produced mainly by CD4 + (TB1 QFT-tube) or both CD4 + and CD8 + T cells (TB2 QFT-tube) in response to Mtb-specific antigens, we also evaluate the LTBI status regarding the responsiveness by each QFT-tube between CKD and HD groups. Most CKD patient responders, as well as all HD patients, have shown TB1 and TB2 concomitant positivity ( Table 3 ). Table 3: Responses to QFT-TB1 and QFT-TB2 Mtb-specific antigens N (%) of QFT-Plus responders CKD (n = 36) HD (n = 3) TB1 only 4 (11.1) 0 TB2 only 8 (22.2) 0 TB1, TB2 concomitant 24 (66.6) 3 (100) TB1 or TB2 12 (33.3) 0 Abbreviations: QFT-Plus, Quanti-FERON-TB Gold Plus; CKD, chronic kidney disease; HD, hemodialysis; TB1-TB2, Mtb-specific antigens in QFT-Plus tubes. CXCL-10/IP-10 Mtb-specific responses between CKD and HD patients We identified a predominance in the percentage of CXCL-10/IP-10 assay positivity in stage 3b with 48.5% (14/29) and 51.7% (15/29) negative, in stage 4 38.1% (24/63) positive and 61.9% (39/63) negative, while in stage 5 we observed 36.8% (7/19) positive and 63.2% (12/19) negative. Furthermore, 33.3% (4/12) were positive in the HD group ( Figure 3 ). Interestingly, positive responses to IGRA or CXCL-10/IP-10 assays were found in 52.3% (58/111) CKD and 41.7% (5/12) in HD patients (data not shown). Risk factors associated with LTBI status in CKD and HD patients In the multivariable logistic (binomial family) regression analysis, it was possible to evaluate the independent risk factors associated with LTBI by IGRA QFT-Plus positivity ( Table 4 ). We have identified that non-vaccination of BCG in childhood (aOR 7.41 [CI 2.15-25.48]; p = 0.0014) and CXCL-10/IP-10 assay positivity (aOR: 4.48 [1.87-10.76]; p ≤ 0.001) were independent risk factors for test positivity. Although not significant, it is worth of note the tendencies of associations with lower counting of monocytes in the blood (p = 0.075) and the family history of CKD (p = 0.07). Table 4: Independent risk factor associated with LTBI in the study population Risk factors Negative QFT (n=80) Positive QFT (n= 39) OR [CI95%] P- value aOR [CI95%] P- value CKD 72 (90%) 36 (92.31%) 0.75 [0.19-3] 0.68 0.96 [0.23-3.96] 0.95 HD 8 (10%) 3 (7.69%) Gender Male 33 (41.25%) 22 (56.41%) 1.84 [0.85-4] 0.12 1.82 [0.8-4.13] 0.15 Female 47 (58.75%) 17 (43.59%) Family history of DRC 32 (40%) 7 (17.95%) 0.33 [0.13-0.83] 0.012 0.41 [0.15-1.07] 0.07 Non-vaccination with BCG 4 (5%) 11 (28.21%) 7.46 [2.2-25.38] 0.0012 7.41 [2.15-25.48] 0.0014 Contact with index TB case 13 (16.25%) 8 (20.51%) 1.33 [0.5-3.54] 0.56 1.76 [0.61-5.09] 0.3 Current smoking 9 (11.39%) 6 (15.79%) 1.46 [0.48-4.44] 0.50 1.35 [0.4-4.54] 0.62 Current alcohol drinking 13 (16.46%) 9 (23.68%) 1.58 [0.61-4.1] 0.35 1.4 [0.5-3.95] 0.52 CXCL-10/IP-10 assay Negative 57 (71.25%) 14 (35.9%) Positive 23 (28.75%) 25 (64.1%) 4.43 [1.96-9.99] <0.001 4.48 [1.87-10.76] <0.001 Monocytes blood counting NA NA 2.22 [0.99-4.98] 0.054 2.23 [0.92-5.39] 0.075 Data are expressed as OR (Odds-ratio) and aOR (Adjusted Odds-ratio) and their 95% confidence intervals. Risk factors associated with LTBI or QFT-Plus positivity among CKD patients were estimated by multiple fixed-effect binomial models (multivariate logistic analysis) including as main effects variables possibly associated with the QFT-Plus result and confounding variables (variables with at least suggestive levels, p-value < 0.1 (e.g., age, sex, and history of BCG vaccination), in the simple/univariate analyses. Abbreviations: IFN-γ: interferon-gamma; IP-10: interferon-gamma inducible protein of 10-kD.CKD: chronic kidney disease; HD: hemodialysis; BCG: bacillus Calmette–Guérin; TB: tuberculosis; NA: not applicable odds ratio; CI, confidence interval; aOR, adjusted odds ratio. DISCUSSION Although CKD is recognized as a risk factor for the development of active tuberculosis (1,7), the literature evidence on its association with LTBI is still limited, especially based on the diverse cohort study design, the severity of the renal failure (progression stages), and diagnostic methods. To our knowledge, herein we report for the first time, the use of the fourth generation of IGRA QFT-Plus, which is designed to evaluate of Mtb-specific response by both CD4 + and CD8 + T cells, to estimate the prevalence of LTBI in different statuses of CKD and long-term hemodialysis patients in a tertiary reference center from a high TB burden area. We found that one-third of our study population had LTBI, being 32.4% in CKD and 25% in hemodialysis patients – which is higher than the estimated prevalence in the general population, where is considered that around one-fourth of the worldwide is latent infected by Mtb, as reported by WHO (2). Also, a positive CXCL-10/IP-10 assay and lack of BCG vaccination in childhood were associated to LTBI as important independent risk factors. Considering that the screening of vulnerable populations and prophylactic treatment of LTBI are priority strategies to prevent the progression to active disease, its worth of note that patients with CKD are more susceptible to infections in general due to their compromised immune system (19). CKD progression is correlated with a reduction in CD4 + and CD8 + T cells (20), which are essential for cellular immune defense against Mtb (3). In vitro studies have demonstrated that the capacity of T cell proliferation is reduced in the uremic environment (20,21). Additionally, clinical data analysis of our cohort has revealed that 52% of participants had DM as their underlying disease, an important aspect recognized as a factor associated with both LTBI and the development of active tuberculosis (1,9). Its well-recognized that hyperglycemic condition in DM is also associated with an interfering in both innate and adaptative immune system (9). Interestingly, in our cohort, we observed LTBI in 37.9% of patients in stage 3b, followed by 31.7% in stage 4, 26.3% in stage 5, and 25% in the HD group. Based on these findings, we could speculate that, although non-statistically significant, this percentage decrease in IGRA results among the status of severity in CKD patients could be explained by a set of clinical conditions associated with the renal dysfunction and, consequently reduced capacity to build a robust or pronounced in vitro cellular immune response. Previous studies report that high C-reactive protein (CRP) levels are attributed to the degree of pre-existing inflammation in patients with renal dysfunction (13,22,23). In our cohort, it was no different, the mean CRP levels were 6.96 mg/dL in the CKD group and 5.23 mg/dL in the HD group. From this, it is important to highlight that indeterminate results were associated with high levels of basal IFN-γ (Nil tube, data not shown), once all patients were responsive to positive control (mitogen). There were 2.7% and 8.3% indeterminate results in the CKD and HD groups, respectively. Consequently, the underlying inflammation and elevated non-specific IFN-γ levels led to indeterminate diagnostic test results. The interaction between CKD and compromised immune response demonstrates the need for more advanced diagnostic methods to identify LTBI early. The current methods available measure the cellular memory response to the bacillus antigens, as follow: the tuberculin skin test (TST) used for more than a century (1,24) and the newest IGRA (QuantiFERON TB and T-SPOT-TB) (7,15). Studies indicate that QFT-Plus, in its 4 th generation, has greater sensitivity compared to QFT-Gold In Tube (QFT-GIT), this finding can be attributed to the addition of the TB2 tube sensitized to CD8 + T cells responses (15,24). In agreement, our study observed 22.2% positive results for the TB2 tube alone, reinforcing the importance of the CD8 + T cell response in detecting latent Mtb infection. A study conducted in Taiwan, China, using the QFT-GIT to diagnose LTBI, recorded 25% positive results among hemodialysis patients and 11% in individuals with severe CKD with eGFR < 30 mL/min/1.73 (stages 4 and 5) (25). Another study conducted in Taiwan in 2020, carried out on pre-kidney transplant individuals, observed a 20% positivity rate for the QFT-GIT (16). In Thailand, a study using the IGRA T. SPOT-TB, revealed a prevalence of 22.5% in its CKD study population, being: 25% in stage 1, 12.5% in stage 2, 25, 0% in stage 3a, 25.0% in stage 3b and 24.2% in the dialysis group (hemodialysis and peritoneal dialysis) (11). An observational-analytical study conducted in Indonesia, using the QFT-GIT, observed a positivity of 39.2% in individuals on hemodialysis (26). In Brazil, the only study conducted on this thematic, evaluated only patient on hemodialysis and identified a prevalence of LTBI in 8.5% of patients, using the tuberculin skin test as a LTBI diagnostic method (27). It is important to highlight that, Taiwan, Thailand, Indonesia, and Brazil are among the 30 countries with the highest incidence of TB in the world (2). Using multivariate logistic regression analysis (28,29), it was possible to demonstrate the independent risk factors associated with QFT-Plus positivity in our study population. Our results revealed that the lack of BCG vaccination in childhood (adjusted OR: 7.41 [95% CI: 2.15-25.48]; p = 0.0014) was an independent risk factor for a positive result in the diagnostic test. In a meta-analysis evaluating BCG-vaccinated and unvaccinated children who were exposed to tuberculosis, using QFT-GIT test, it was shown that BCG protects against Mtb infection and prevents progression to active disease (28,29). A systematic review demonstrated that BCG vaccination offers protection against pulmonary and extrapulmonary tuberculosis for up to 10 years. However, most of these studies did not follow participants long enough or had a low number of cases after 15 years. These results should not be interpreted as indicating no effect (30). In agreement with our work, a study that investigated the incidence of LTBI in pre-transplant kidney patients found that non-vaccination with BCG was strongly associated as an independent risk factor for the development of LTBI (24). We also found that the positivity in the CXCL-10/IP-10 assay was also considered an independent risk factor for the diagnosis of LTBI (aOR: 4.48 [1.87-10.76]; p = < 0.001). Based on our data, we can observe that high levels of CXCL-10/IP-10 in CKD and HD patients were associated with a significant increase in the probability of developing LTBI, estimated at 4.48 times. IFN-γ-inducible protein 10 (IP-10), a member of CXC chemokine family (XCXC-10), acts recruiting immune cells to the site of infection. This process occurs in synergy with IFN-γ, in response to the recognition of Mtb-specific antigens by specific T cells (31–33). In a recent study, increased levels of IFN-γ and CXCL-10/IP-10 were highlighted in patients with active TB compared to healthy individuals (33). While IFN-γ is produced mainly by T lymphocytes, CXCL-10/IP-10 is produced by numerous cells including the innate immune system such as monocytes and antigen-presenting cells (APCs) (31,33,34). These data point out this chemokine as a potential adjuvant biomarker, in association with IFN-γ for the detection and monitoring of LTBI, which might improve diagnostic accuracy and early intervention. A recent study from our research group reported that individuals with pulmonary TB who had both positive IFN-γ and CXCL-10/IP-10 had increased CD64 expression compared to individuals with LTBI (18). As limitations of this study, we could point out: i) a comparison between QFT-Plus and TST was not analyzed due to instability in the distribution of the test in Brazil in the period of participant recruitment; ii) relatively small number of individuals in the study population due to the criteria of eligibility and infrastructure capacity by the hospital, especially for HD group; iii) because the study was conducted in a reference center and tertiary health unit of health (high complexity), we could not exclude this bias; iv) we did not carried out comparisons with health control group. Despite these observations, our study can be considered a pioneer in Brazil when using the QFT-Plus test to evaluate LTBI infection in this cohort population, which could be well characterized by clinical and laboratory aspects and followed for a long period at HUPE/UERJ. In conclusion, approximately 32% of patients with CKD and 25% of those undergoing HD were diagnosed with LTBI. Our study population demonstrated a positivity rate of 39.1% in the CXCL-10/IP-10 assay, with rates of 40.5% in the CKD group and 33.3% in the HD group, highlighting its potential as a relevant biomarker for LTBI in CKD patients. The absence of BCG vaccination during childhood and elevated levels of the CXCL-10/IP-10 biomarker were identified as higher risk factors for LTBI. Furthermore, our data show that QFT-Plus is a useful tool in LTBI detection among CKD patients in late and terminal stages. Finally, these findings reinforce and base to the need of preventive strategies and continuous surveillance for LTBI, emphasizing the relevance of innovative diagnostic approaches which may contribute to a significant TB control in high-risk populations. Abbreviations BCG bacillus Calmette–Guérin CKD chronic kidney disease CXCL10/IP-10 interferon-g-inducible protein-10 HD hemodialysis HUPE Pedro Ernesto University Hospital IFN-γ interferon-gamma LTBI Latent tuberculosis infection QFT-Plus QuantiFERON-TB Gold Plus. Declarations ACKNOWLEDGMENTS The authors wish to acknowledge all the participants and staff from the Nephrology and Pulmonology Divisions at Pedro Ernesto University Hospital (HUPE/UERJ), such as nurses, physicians, technicians, and multidisciplinary teams. AUTHORS’ CONTRIBUTIONS JCBS, NBV, RSAR, and CLRG contributed to patient recruitment, sample collection, and laboratory routine for the study. RSAR, JCBS, and NBV performed experiments. APS, RM, CCSL, CLRG, JHS, and RB followed the patients. MRA, JCBS, NBV, RB, and LSR were involved in statistical analysis and data interpretation. MRA, JCBS, NBV, APS, RSRA, RB, and LSR wrote, edited, and reviewed the manuscript. RB and LSR conceived the study. LSR was responsible for coordinating the study. All authors contributed to the article and approved the submitted version. FUNDING The authors declare financial support was received for the research, authorship, and/or publication of this article. This study was funded in part by the Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro (FAPERJ) and JCBS was supported by a scholarship from the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES). AVAILABILITY OF DATA AND MATERIALS Clinical and qualitative data from service providers are not publicly available due to privacy and ethical concerns. However, the data used and analyzed in this study are available from the corresponding author upon reasonable request. ETHICS APPROVAL AND CONSENT TO PARTICIPATE All procedures and protocols of this study were previously approved by the Research Ethics Committee of the Pedro Ernesto University Hospital (HUPE), of the Rio de Janeiro State University (UERJ), under opinion number 4.624.686. During recruitment, individuals were presented with the objectives and potential contributions of the study, and upon voluntarily accepting to participate. All the enrolled participants provided signed informed-consent forms. CONSENT FOR PUBLICATION Not applicable. COMPETING INTERESTS The authors declare no potential conflicts of interest. References Ministry of Heath. 2019. Manual of recommendations for tuberculosis control in Brazil [Internet]. . Global tuberculosis report 2023 [Internet]. 2023. https://iris.who.int/ . Ehlers S, Schaible UE. The granuloma in tuberculosis: Dynamics of a host-pathogen collusion. 3, Front Immunol. 2012. Campaniço A, Harjivan SG, Warner DF, Moreira R, Lopes F. Addressing latent tuberculosis: New advances in mimicking the disease, discovering key targets, and designing hit compounds. International Journal of Molecular Sciences. Volume 21. MDPI AG; 2020. pp. 1–25. Cavalcante-Silva LHA, Almeida FS, Andrade AG, de Comberlang FC, Cardoso LL, Vanderley SER, et al. Mycobacterium tuberculosis in a Trap: The Role of Neutrophil Extracellular Traps in Tuberculosis. International Journal of Molecular Sciences. Volume 24. Multidisciplinary Digital Publishing Institute (MDPI); 2023. Domingo-Gonzalez R, Prince O, Cooper A, Khader SA. Cytokines and Chemokines in Mycobacterium tuberculosis Infection. Microbiol Spectr. 2016;4(5). Protocol for latent. Mycobacterium tuberculosis infection in Brazil. Ministry of Heath, 2022 [Internet]. . Amorim RF, Viegas ERC, Carneiro AJV, Esberard BC, Chinem ES, Correa RS, et al. Superiority of Interferon Gamma Assay Over Tuberculin Skin Test for Latent Tuberculosis in Inflammatory Bowel Disease Patients in Brazil. Dig Dis Sci. 2019;64(7):1916–22. Torres AV, Corrêa R, da Bevilacqua S, de F M, do, Prado LCF, Bandeira FMG, de Rodrigues C et al. LS,. Screening of latent tuberculosis infection among patients with diabetes mellitus from a high-burden area in Brazil. Frontiers in Clinical Diabetes and Healthcare. 2022;3. Ugarte-Gil C, Carrillo-Larco RM, Kirwan DE. Latent tuberculosis infection and non-infectious co-morbidities: Diabetes mellitus type 2, chronic kidney disease and rheumatoid arthritis. Int J Infect Dis. 2019;80:S29–31. Hayuk P, Boongird S, Pornsuriyasak P, Bruminhent J. Interferon-gamma release assays for diagnosis of latent TB infection in chronic kidney diseases and dialysis patients. Front Cell Infect Microbiol. 2022;12. Myall K, Milburn HJ. An update on the management of latent tuberculosis infection and active disease in patients with chronic kidney disease. Polish Archives of Internal Medicine. Volume 127. Medycyna Praktyczna; 2017. pp. 681–6. Syed-Ahmed M, Narayanan M. Immune Dysfunction and Risk of Infection in Chronic Kidney Disease. Advances in Chronic Kidney Disease. Volume 26. W.B. Saunders; 2019. pp. 8–15. Girndt M, Sester M, Sester U, Kaul H, Köhler H. Defective expression of B7-2 (CD86) on monocytes of dialysis patients correlates to the uremia-associated immune defect. Kidney Int. 2001;59(4):1382–9. Pourakbari B, Mamishi S, Benvari S, Mahmoudi S. Comparison of the QuantiFERON-TB Gold Plus and QuantiFERON-TB. Gold In-Tube interferon-γ release assays: A systematic review and meta-analysis. Advances in Medical Sciences. Volume 64. Medical University of Bialystok; 2019. pp. 437–43. Shu CC, Tsai MK, Lin SW, Wang JY, Yu CJ, Lee CY. Latent tuberculosis infection increases in kidney transplantation recipients compared with transplantation candidates: A neglected perspective in tuberculosis control. Clin Infect Dis. 2020;71(4):914–23. Alemu A, Bitew ZW, Diriba G, Seid G, Moga S, Abdella S et al. The prevalence of latent tuberculosis infection in patients with chronic kidney disease: A systematic review and meta-analysis. Heliyon. 2023;9 (6). Corrêa RDS, Rodrigues LS, Pereira LHL, Nogueira OC, Leung J, Sousa MDS et al. Neutrophil CD64 expression levels in IGRA-positive individuals distinguish latent tuberculosis from active disease. Mem Inst Oswaldo Cruz. 2019;114 (2). Luczynski P, Holmes T, Romanowski K, Arbiv OA, Cook VJ, Clark EG, et al. Risk of Tuberculosis Disease in People with Chronic Kidney Disease Without Kidney Failure: A Systematic Review and Meta-Analysis. Clin Infect Dis. 2023;77(8):1194–200. Brinkkoetter PT, Marinaki S, Gottmann U, Fleckenstein S, Stump C, Van Der Woude FJ, et al. Altered CD46-mediated T cell co-stimulation in haemodialysis patients. Clin Exp Immunol. 2005;139(3):534–41. Meuer SC, Hauer M, Kurz P, Meyer zum Buschenfelde KH, Kohler H. Selective blockade of the antigen-receptor-mediated pathway of T cell activation in patients with impaired primary immune responses. J Clin Invest. 1987;80(3):743–9. Kawai T, Akira S. The role of pattern-recognition receptors in innate immunity: Update on toll-like receptors. Nat Immunol. 2010;11:373–84. Li J, Chen J, Lan HY, Tang Y. Role of C-Reactive Protein in Kidney Diseases. Vol. 9, kidney diseases. S. Karger AG; 2023. pp. 73–81. Gong W, Wu X. Differential Diagnosis of Latent Tuberculosis Infection and Active Tuberculosis: A Key to a Successful Tuberculosis Control Strategy. Frontiers in Microbiology. Volume 12. Frontiers Media S.A.; 2021. Shu CC, Hsu CL, Lee CY, Wang JY, Wu VC, Yang FJ et al. Comparison of the prevalence of latent tuberculosis infection among non-dialysis patients with severe chronic kidney disease, patients receiving dialysis, and the dialysis-unit staff a cross-sectional study. PLoS ONE. 2015;10(4). Bandiara R, Indrasari A, Dewi Rengganis A, Sukesi L, Afiatin A, Santoso P. Risk factors of latent tuberculosis among chronic kidney disease with routine haemodialysis patients. J Clin Tuberc Other Mycobact Dis. 2022;27. Ferreira V, Da Fonseca CD, Bollela VR, Romão EA, Da Costa JAC, De Sousa AFL et al. Prevalence of latent tuberculosis and associated factors in patients with chronic kidney disease on hemodialysis. Rev Lat Am Enfermagem. 2021;29. Roy A, Eisenhut M, Harris RJ, Rodrigues LC, Sridhar S, Habermann S et al. Effect of BCG vaccination against Mycobacterium tuberculosis infection in children: Systematic review and meta-analysis. BMJ (Online). 2014;349. Syggelou A, Spyridis N, Benetatou K, Kourkouni E, Kourlaba G, Tsagaraki M, et al. BCG vaccine protection against TB infection among children older than 5 years in close contact with an infectious adult TB case. J Clin Med. 2020;9(10):1–10. Abubakar I, Pimpin L, Ariti C, Beynon R, Mangtani P, Sterne J et al. Systematic review, and meta-analysis of the current evidence on the duration of protection by bacillus Calmette-Guérin vaccination against tuberculosis. 17, Health Technology Assessment. 2013. p. 1–4. Santos AP, da Silva Corrêa R, Ribeiro-Alves M, da Silva ACOS, Mafort TT, Leung J et al. Application of Venn’s diagram in the diagnosis of pleural tuberculosis using IFN-γ, IP-10 and adenosine deaminase. PLoS ONE. 2018;13(8). Friedrich R, Rappold E, Bogdan C, Held J. Comparative analysis of the wako Β-glucan test and the fungitell assay for diagnosis of candidemia and Pneumocystis jirovecii pneumonia. J Clin Microbiol. 2018;56(9). Ruhwald M, Dominguez J, Latorre I, Losi M, Richeldi L, Pasticci MB, et al. A multicentre evaluation of the accuracy and performance of IP-10 for the diagnosis of infection with M. tuberculosis. Tuberculosis. 2011;91(3):260–7. Okamoto M, Kawabe T, Iwasaki Y, Hara T, Hashimoto N, Imaizumi K, et al. Evaluation of interferon-γ, interferon-γ-inducing cytokines, and interferon-γ-inducible chemokines in tuberculous pleural effusions. J Lab Clin Med. 2005;145(2):88–93. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 02 Oct, 2025 Read the published version in BMC Infectious Diseases → Version 1 posted Editorial decision: Revision requested 16 Jul, 2024 Editor assigned by journal 15 Jul, 2024 Submission checks completed at journal 12 Jul, 2024 First submitted to journal 11 Jul, 2024 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. 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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-4725508","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":327596449,"identity":"ae7f9e1e-c7b4-4780-820b-964147134c58","order_by":0,"name":"Juliana Cristina Borges da Silva","email":"","orcid":"","institution":"Laboratory of Immunopathology, Medical Sciences Faculty UERJ/FCM","correspondingAuthor":false,"prefix":"","firstName":"Juliana","middleName":"Cristina Borges da","lastName":"Silva","suffix":""},{"id":327596452,"identity":"2b809ea3-8c47-409b-87a0-8eb79f077a4e","order_by":1,"name":"Nathália Barcellos 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16:29:01","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4725508/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4725508/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12879-025-11620-z","type":"published","date":"2025-10-02T15:57:29+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":62190987,"identity":"e9b6f96a-c47e-4497-9315-1c62ac355713","added_by":"auto","created_at":"2024-08-10 12:37:17","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":66277,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFlowchart of the study population: \u003c/strong\u003eStudy design to identify the prevalence of LTBI in patients with chronic kidney disease (CKD) and hemodialysis (HD). QFT-Plus: QuantiFERON-TB Gold Plus; IP-10: interferon-gamma inducible protein 10-kD.\u003c/p\u003e","description":"","filename":"Figure1LTBIamongCKDandHemodialysisBMCInfectiousDiseases.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4725508/v1/388ab1667e8fc61c4726ba94.jpg"},{"id":62190712,"identity":"9e5fc52b-66a5-4611-b02e-e53f387a3a38","added_by":"auto","created_at":"2024-08-10 12:29:17","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":26491,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePrevalence of LTBI obtained by the results of interferon-gamma release assay (IGRA) in patients with chronic kidney disease (CKD), and patients undergoing long-term hemodialysis\u003c/strong\u003e. LTBI was determined by QuantiFERON-TB Gold Plus (QFT-Plus). The results were grouped as positive (black), negative (light gray), and indeterminate (gray) cases.\u003c/p\u003e","description":"","filename":"Figure2LTBIamongCKDandHemodialysisBMCInfectiousDiseases.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4725508/v1/70bd47b0acc349d83d2dcd1e.jpg"},{"id":62190710,"identity":"681415a4-00df-4b12-8d2b-1acf0df7f60c","added_by":"auto","created_at":"2024-08-10 12:29:17","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":25864,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCXCL-10/IP-10 assay results in response to Mtb-specific antigens between chronic kidney disease (CKD), and long-term hemodialysis patients\u003c/strong\u003e. Results of the IP-10/CXCL-10 cytokine measurement in plasma supernatants from QFT-Plus The results were grouped in positive (black) and negative (light gray) cases.\u003c/p\u003e","description":"","filename":"Figure3LTBIamongCKDandHemodialysisBMCInfectiousDiseases.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4725508/v1/48b0fbfdfab5caab660c08f7.jpg"},{"id":92884376,"identity":"d5989928-37f3-4c29-bf56-7a2abd7729be","added_by":"auto","created_at":"2025-10-06 16:12:40","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1275997,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4725508/v1/8c3473bd-50d2-4e74-a66c-19df2f1ef57b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Use of interferon-gamma release assay (IGRA) and CXCL-10/IP-10 for screening of latent tuberculosis infection (LTBI) in chronic kidney disease and hemodialysis patients","fulltext":[{"header":"BACKGROUND","content":"\u003cp\u003eTuberculosis (TB), a disease caused by the bacillus \u003cem\u003eMycobacterium tuberculosis\u003c/em\u003e (Mtb), is still an important global public health problem, requiring effective strategies for its control. According to the World Health Organization (WHO), in 2022, approximately 7.5\u0026nbsp;million people were diagnosed with TB, and it is estimated that around 10.6\u0026nbsp;million individuals became ill worldwide. Although about a quarter of the world's population is infected with Mtb, some individuals develop a partial immunity, with no signs or symptoms, and unable to spread the infection but in high risk to develop active disease (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). In these cases, the bacilli remain in a histological organization called \u003cem\u003egranuloma\u003c/em\u003e, in small quiescent foci \u0026ndash; a condition known as \u003cem\u003eLatent Tuberculosis Infection\u003c/em\u003e (LTBI) (\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSeveral conditions can contribute to the activation of this quiescent infectious focus and its progression to active TB, being the weakening or interference in the immune system the most significant factor. It is known that cellular immunity is the main effective defense mechanism against TB, which is mainly represented by CD4\u003csup\u003e+\u003c/sup\u003e and CD8\u003csup\u003e+\u003c/sup\u003e T lymphocytes (Th1 response) producing interferon-gamma (IFN-γ) \u0026ndash; a key cytokine involved in classical macrophage activation and, consequently, Mtb killing. Other cytokines such as IL-1β, IL-2, IL-6, IL-12, tumor necrosis factor (TNF), and chemokines such as CXCL10/interferon-γ-inducible protein-10 (IP-10) are also essential for containing the bacillus (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). However, when the pathogen is not completely eradicated, it persists in a state of latency (\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e) and the evolution of LTBI to active TB can occur depending on a complex and intricated relation between Mtb and host factors (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSince the immune system is significant in containing the bacillus, a direct attention to populations at risk for TB is mandatory, such as Diabetes mellitus (DM), rheumatoid arthritis, inflammatory bowel diseases (IBD) and chronic kidney disease (CKD), among others non-communicable diseases (\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). For instance, CKD patients present 8 to 25 times higher increased risk of developing active TB compared to general population (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Also, mortality rates associated with TB are even higher, especially among those on renal replacement therapy (RRT) (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). The uremic state in CKD, characterized by the retention of toxins, disrupts the immune system and results in a decrease of B7-2 co-stimulatory molecule expression in antigen-presenting cells, interfering with the function of polymorphonuclear cells and monocytes/macrophages by altering their phagocytic capacity, low efficiency of chemotactic migration and a decrease in cellular response in the control of intracellular microorganisms (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Screening and prophylactic treatment of LTBI in this population may reduce the risk of developing the active disease, and could reduce the spread of TB.\u003c/p\u003e \u003cp\u003eThere is not a gold standard test for diagnosing LTBI. Currently, there are two immunological standardized tests in the diagnostical routine: i) the tuberculin skin test (TST) and ii) interferon-gamma release assay (IGRA), such as QuantiFERON-TB Gold Plus (QFT-Plus) and T. SPOT-TB (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Both different diagnostic methods depend on the cellular immune response against Mtb, reflecting an indirect measurement of the infection, and have been used to detect LTBI among CKD patients (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). A recent systematic review and metanalysis has found a high pooled prevalence of LTBI in CKD patient, however showing a heterogeneity regarding severity status of renal failure, laboratorial method used to detect LTBI, and TB burden areas (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). Therefore, despite the recognized relevance of the association between LTBI and CKD, robust investigations are lacking. In this work, we aimed to determine the prevalence of LTBI in CKD patients at different stages of the disease (3b to 5) or on hemodialysis (HD) using QFT-Plus \u0026ndash; the fourth and last generation of this IGRA, and identify LTBI predictors.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eParticipant enrollment and ethical approval\u003c/h2\u003e \u003cp\u003e This prospective cross-sectional study was conducted in a tertiary referral medical center in Rio de Janeiro, RJ, Brazil, under the approval of the Research Ethics Committee from the Hospital Universitario Pedro Ernesto (HUPE), Rio de Janeiro State University (UERJ), number 4.624.686. All the enrolled participants provided signed informed-consent forms.\u003c/p\u003e \u003cp\u003eBetween August 2021 and September 2023, adult patients (age\u0026thinsp;\u0026ge;\u0026thinsp;18 years) with chronic kidney disease (CKD group) in stages 3b (estimated glomerular filtration rate [eGFR] 44\u0026thinsp;\u0026minus;\u0026thinsp;30 mL/min/1.73 m\u0026sup2;), 4 (eGFR 30\u0026thinsp;\u0026minus;\u0026thinsp;15 mL/min), or 5 (eGFR\u0026thinsp;\u0026lt;\u0026thinsp;15 mL/min/1.73 m\u0026sup2;) from the nephrology outpatient clinic, and patients who received long-term (\u0026gt;\u0026thinsp;3 months) hemodialysis (HD group), both from HUPE/UERJ, were diagnosed and recruited based on Kidney Disease Improving Global Outcomes (KDIGO) guidelines (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). The eGFR was calculated for the CKD group using the CKD-EPI 2021 Eq.\u0026nbsp;(10). Patients with previous TB, pregnant women, and those using immunosuppressants were excluded from the study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eData collection\u003c/h2\u003e \u003cp\u003e Demographic and clinical data (including age, sex, residence, etiology of CKD, underlying comorbidities, history of TB, history of close contact with an index TB case, history of BCG vaccination, smoking status, and alcoholic drink status) were collected from medical records and/or case report forms, previously approved by Ethics Committee from HUPE/UERJ. Laboratory tests such as hemogram (total blood leucocyte and differential counts) and biochemical analysis (glycated hemoglobin percentage, C-reactive protein, serum albumin concentration, blood urea nitrogen concentration, and creatinine concentration) were carried out by the HUPE/UERJ central laboratory and recovered from hospital\u0026rsquo;s electronic system.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eInterferon-gamma release assay (IGRA)\u003c/h2\u003e \u003cp\u003eLTBI status was determined by interferon-gamma release assay (IGRA) QuantiFERON TB Gold-Plus Kit (QFT-Plus; QIAGEN) according to the manufacturer\u0026rsquo;s instructions (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Briefly, peripheral blood samples were collected by venipuncture using lithium heparin tubes (BD Vacutainer) and transferred for each QFT-Plus tubes: i) negative control (Nil); ii) TB1 Mtb-specific antigens (Mtb-specific antigens to stimulate mainly CD4\u003csup\u003e+\u003c/sup\u003e T cells); iii) TB2 (Mtb-specific antigens to stimulate both CD4\u003csup\u003e+\u003c/sup\u003e and CD8\u003csup\u003e+\u003c/sup\u003e T cells); and, iv) positive control (Mitogen). After overnight incubation at 37\u0026deg;C, tubes were centrifuged at 3,000 x g, 25\u0026deg;C for 15 minutes, plasma supernatants were collected from each tube stored in 150 \u0026micro;L aliquots at -80\u0026deg;C. Samples were then subjected to specific QFT-Plus enzyme-linked immunosorbent assays (ELISA) to measure IFN-γ levels. Results were expressed in UI/mL and categorized as positive when IFN-γ in response to Mtb-specific antigens (TB1 or TB2 minus negative [Nil] control) were \u0026ge;\u0026thinsp;0.35 IU/mL. Indeterminate results were defined as IFN- γ from Nil tube\u0026thinsp;\u0026gt;\u0026thinsp;8.0 IU/mL or Mitogen\u0026thinsp;\u0026lt;\u0026thinsp;0.5 IU/mL. Results were calculated using the QuantiFERON-TB Gold Plus Analysis Software 2.71. The range QFT-Plus ELISA assay was 0.065 IU/mL to 10.0 IU/mL.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eCXCL-10/IP-10 release assay\u003c/h2\u003e \u003cp\u003eCXCL-10/IP-10 Mtb-specific responses were measured using plasma supernatants from QFT-Plus, following the manufacturer\u0026rsquo;s instruction. CXCL-10/IP-10 levels were assessed by ELISA sandwich using human CXCL-10/IP-10 DuoSet ELISA (R\u0026amp;D Systems Inc, MN, USA). The ELISA plate was read using a microplate reader (Thermo Scientific Multiskan\u0026trade; FC) and the results were analyzed using Skanit 6.0.1 software. The range of these assays was 31.3\u0026ndash;10,000 pg/mL. We previously determined the area under the curve (AUC) and cutoff values, in which significant results from AUC analysis were obtained [AUC, 0.8750; 95% confidence interval (CI), 0.744\u0026ndash;1.006, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001] for the CXCL-10/IP-10 responses to Mtb-antigens into QFT-Plus tubes. For scoring, a cutoff point was chosen to maximize the sum of sensitivity and specificity. A positive result was defined as (Mtb antigens [TB 1 or TB2]-stimulated CXCL-10/IP-10 levels) subtracted by (Nil-stimulated CXCL-10/IP-10 levels)]\u0026thinsp;\u0026ge;\u0026thinsp;535.9 pg/mL, as previously reported (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eFor continuous numerical variables, non-parametric Mann-Whitney U tests were used in the comparison of baseline demographic and clinical variables, while for categorical nominal variables, Pearson χ-squared tests were used in the assessment of frequency independence between these variables and the groups of chronic kidney disease (CKD) patients. The comparison between the log-transformed (base 10) production levels of QFT-Plus (Nil, Mitogen, specific TB antigens, and other measures obtained by subtracting the above) and others used the expected/mean marginal values and their 95% confidence intervals obtained from fixed-effects multiple linear (log-linear) regression models with the inclusion of main effects, chronic kidney disease groups and confounding variables (e.g., age, race, hypertension, and diabetes mellitus). Graphical analyses of residuals were carried out for the adjusted models to confirm their randomness. In pairwise comparisons between expected/average marginal values obtained from multiple linear regression models (Student's T-test), confidence level adjustments were made using the Sidak method, and p-value adjustments for multiple comparisons using the Tukey method; the latter only in cases where the main exposure variable had more than 2 levels. To estimate risk factors associated with LTBI or QFT-Plus positivity among CKD patients, multiple fixed-effect binomial models (multivariate logistic analysis) were used, including as main effects variables possibly associated with the QFT-Plus result and confounding variables (variables with at least suggestive levels, p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.1 (e.g., age, sex, and history of BCG vaccination), in the simple/univariate analyses. The results were presented as OR (Odds-ratio) and aOR (Adjusted Odds-ratio) and their 95% confidence intervals. Whenever necessary, we categorized continuous numerical variables using as cut-offs the round whole number closest to their medians or tertiles (i.e., at 33% and 66%). Two-tailed significance levels less than or equal to 0.01, 0.05, and 0.1 were considered \u0026lsquo;highly significant\u0026rsquo;, \u0026lsquo;significant,\u0026rsquo; and \u0026lsquo;suggestive\u0026rsquo;, respectively. All statistical analyses were conducted using R version 4.1.2 (R Core Team, 2021), packages \u0026lsquo;lme4\u0026rsquo;, \u0026lsquo;emmeans\u0026rdquo;, and their dependencies.\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003eOverview of the study population\u003c/h2\u003e\n \u003cp\u003eA total of 125 patients attended in nephrology outpatient and at the dialysis-unit from Pedro Ernesto University Hospital, Rio de Janeiro, Brazil, were prospectively enrolled in this study. All individuals met the eligibility criteria initially proposed. However, two patients were excluded due to insufficient blood sample collection to perform cytokine release assays. Therefore, the final study population was composed by two groups, as follow: i) CKD group: patients with CKD with eGFR\u0026thinsp;\u0026lt;\u0026thinsp;45ml/min, classified in stages 3b to 5 (n\u0026thinsp;=\u0026thinsp;111); and, ii) HD group: patients requiring renal replacement therapy by hemodialysis (n\u0026thinsp;=\u0026thinsp;12), as depicted in Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eA baseline analysis of the clinical and sociodemographic characteristics of the study population is shown in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. We observed that 51 (45.9%) and 7 (58.3%) were male (p\u0026thinsp;=\u0026thinsp;0.608), with a mean age of 66 years and 54 years (p\u0026thinsp;=\u0026thinsp;0.015), in the CKD and HD groups respectively. The predominant CKD stage in this study was: stage 4 with 63 (56.8%) subjects, followed by stage 3 with 29 (26.1%) and stage 5 with 19 (17.1%) (p\u0026thinsp;\u0026le;\u0026thinsp;0.001). The commonest causes of CKD were diabetes mellitus, showing 50 (45%) cases in CKD and 2 (16%) in HD, followed by arterial hypertension with 24 (21.6%) CKD and 4 (33.3%) in HD. Among the study population, 21 (18.9%) in the CKD group and 3 (25%) in the HD one had contact with an index TB case. Regarding the history of BCG vaccination in childhood, 95 (85.6%) in the CKD group and 12 (100%) in the HD group showed the vaccination scar.\u003c/p\u003e\n \u003cp\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e shows the laboratory characteristics, where it was possible to observe significantlylowercounts of hemoglobin (p\u0026thinsp;=\u0026thinsp;0.032), total leukocytes (p\u0026thinsp;=\u0026thinsp;0.023), and lymphocytes (p\u0026thinsp;=\u0026thinsp;0.017) in the HD group compared to CKD. Furthermore, anhigher monocyte blood count was seen in HD group (p\u0026thinsp;=\u0026thinsp;0.044).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"char\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eBaseline characteristics of the study population\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCKD (n\u0026thinsp;=\u0026thinsp;111)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHD (n\u0026thinsp;=\u0026thinsp;12)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP-\u003c/em\u003evalue\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge, years (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e66 (12.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e54 (11.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale gender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e51 (45.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7 (58.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.608\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBCG vaccination\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95 (85.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12 (100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.338\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eContact with TB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21 (18.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3 (\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.903\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCurrent smoking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13 (11.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 (16.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.991\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCurrent alcohol drinking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20 (18.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3 (\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.865\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLocality residence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.057\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRio de Janeiro\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e74 (66.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12 (100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMetropolitan region\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34 (30.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOthers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3 (2.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCause of CKD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.308\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiabetes mellitus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50 (45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 (16.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24 (21.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4 (33.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGlomerulonephritis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7 (6.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (8.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOthers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30 (\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5 (41.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCKD stage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd rowspan=\"4\" align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29 (26.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e63 (56.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19 (17.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eData are expressed as number (%) or interquartile range (IQR). Pearson \u0026chi;-squared tests were used in the assessment of frequency independence between variables and the CKD and HD patients\u0026rsquo; groups. Abbreviations: CKD: chronic kidney disease; HD: hemodialysis; BCG: bacillus Calmette\u0026ndash;Gu\u0026eacute;rin; TB: tuberculosis; NA: not applicable. P-value\u0026thinsp;\u0026le;\u0026thinsp;0.05 was considered significant.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"char\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"char\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eLaboratory characteristics of the study population\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCKD (n\u0026thinsp;=\u0026thinsp;111)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHD (n\u0026thinsp;=\u0026thinsp;12)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP-\u003c/em\u003evalue\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHemoglobin, g/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12,06 [11.42\u0026ndash;12.70]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.81 [9.59\u0026ndash;12.03]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal leukocytes, cells/mm\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6627.76 [5806.74\u0026ndash;7448.79]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4936.54 [3372.32\u0026ndash;6500.75]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNeutrophils, cells/mm\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4106.49 [3464.12\u0026ndash;4748.86]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3299.75 [2073.60\u0026ndash;4525.91]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.164\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMonocytes, cells/mm\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e604.64 [447.20\u0026ndash;762.08]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e891.89 [591.37\u0026ndash;1192.42]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.044\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLymphocytes, cells/mm\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1742.54 [1508.11\u0026ndash;1976.97]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1233.24 [ 785.76\u0026ndash;1680.71]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGlycated hemoglobin, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiabetics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.96 [6.72\u0026ndash;7.20]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.23 [4.19\u0026ndash;6.28]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOthers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.41 [5.13\u0026ndash;5.68]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.14 [4.51\u0026ndash;5.77]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.872\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC-reactive protein, mg/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiabetics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.42 [3.15\u0026ndash;7.68]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.80 [-0.85\u0026ndash;12.45]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.999\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOthers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.38 [-0.48 -5.25]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.17 [3.23\u0026ndash;11.10]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.208\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAlbumin, g/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.12 [4.01\u0026ndash;4.24]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.87 [3.27\u0026ndash;4.47]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.388\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eeGFR, mL/min/1.73 m\u0026sup2;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3b stage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e36.58 [34.47\u0026ndash;38.69]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4 stage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22.78 [21.24\u0026ndash;24.31]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5 stage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13.73 [11.40\u0026ndash;16.05]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003eData are expressed as marginal mean marginal values [95% confidence interval]. Mean marginal values and their 95% confidence intervals were obtained from fixed-effects multiple linear (log-linear) regression models with the inclusion of main effects, chronic kidney disease groups and confounding variables (e.g., age and race). Abbreviations: CKD: chronic kidney disease; HD: hemodialysis; BCG: bacillus Calmette\u0026ndash;Gu\u0026eacute;rin; TB: tuberculosis; eGFR: estimated glomerular filtration rate; NA: not applicable. P value\u0026thinsp;\u0026le;\u0026thinsp;0.05 was considered significant.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003eLatent tuberculosis infection prevalence in CKD and HD patients\u003c/h2\u003e\n \u003cp\u003eAlmost one-third (31.7%; 39/123) of the overall population showed positive results for IGRA, being 36/111 (32.4%) in the CKD and 3/12 (25%) in the HD group. Indeterminate results were found in 4 (3.3%) subjects, one of them from the HD group. Evaluating the IGRA results among the CKD subgroups, we observed 37.9% (11/29) positive and 58.6% (17/29) negative in stage 3b. Meanwhile, in stage 4 we observed 31.7% (20/63) positive and 65.1% (41/63) negative, and in stage 5, 26.3% (5/19) were considered positive and 73.3% (14/19) negative (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). All patients showed satisfactory responses to positive control (mitogen; data not shown).\u003c/p\u003e\n \u003cp\u003eSince IGRA QFT-Plus provides \u003cem\u003ein vitro\u003c/em\u003e IFN-\u0026gamma; blood levels produced mainly by CD4\u003csup\u003e+\u003c/sup\u003e (TB1 QFT-tube) or both CD4\u003csup\u003e+\u003c/sup\u003e and CD8\u003csup\u003e+\u003c/sup\u003e T cells (TB2 QFT-tube) in response to Mtb-specific antigens, we also evaluate the LTBI status regarding the responsiveness by each QFT-tube between CKD and HD groups. Most CKD patient responders, as well as all HD patients, have shown TB1 and TB2 concomitant positivity (\u003cstrong\u003eTable\u0026nbsp;3\u003c/strong\u003e).\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;3: Responses to QFT-TB1 and QFT-TB2 Mtb-specific antigens\u003c/strong\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003ctable border=\"1\" width=\"582\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"44.329896907216494%\"\u003e\n \u003cp\u003eN (%) of QFT-Plus responders\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.835051546391753%\"\u003e\n \u003cp\u003eCKD (n = 36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.835051546391753%\"\u003e\n \u003cp\u003eHD (n = 3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"44.329896907216494%\"\u003e\n \u003cp\u003eTB1 \u003cem\u003eonly\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"27.835051546391753%\"\u003e\n \u003cp\u003e4 (11.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"27.835051546391753%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"44.329896907216494%\"\u003e\n \u003cp\u003eTB2 \u003cem\u003eonly\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"27.835051546391753%\"\u003e\n \u003cp\u003e8 (22.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"27.835051546391753%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"44.329896907216494%\"\u003e\n \u003cp\u003eTB1, TB2 \u003cem\u003econcomitant\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"27.835051546391753%\"\u003e\n \u003cp\u003e24 (66.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"27.835051546391753%\"\u003e\n \u003cp\u003e3 (100)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"44.329896907216494%\"\u003e\n \u003cp\u003eTB1 \u003cem\u003eor\u003c/em\u003e TB2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"27.835051546391753%\"\u003e\n \u003cp\u003e12 (33.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"27.835051546391753%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviations: QFT-Plus, Quanti-FERON-TB Gold Plus; CKD, chronic kidney disease; HD, hemodialysis; TB1-TB2, Mtb-specific antigens in QFT-Plus tubes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCXCL-10/IP-10 Mtb-specific responses between CKD and HD patients\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe identified a predominance in the percentage of CXCL-10/IP-10 assay positivity in stage 3b with 48.5% (14/29) and 51.7% (15/29) negative, in stage 4 38.1% (24/63) positive and 61.9% (39/63) negative, while in stage 5 we observed 36.8% (7/19) positive and 63.2% (12/19) negative. Furthermore, 33.3% (4/12) were positive in the HD group (\u003cstrong\u003eFigure 3\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003eInterestingly, positive responses to IGRA \u003cem\u003eor\u003c/em\u003e CXCL-10/IP-10 assays were found in 52.3% (58/111) CKD and 41.7% (5/12) in HD patients (data not shown).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRisk factors associated with LTBI status in CKD and HD patients\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the multivariable logistic (binomial family) regression analysis, it was possible to evaluate the independent risk factors associated with LTBI by IGRA QFT-Plus positivity (\u003cstrong\u003eTable 4\u003c/strong\u003e). We have identified that non-vaccination of BCG in childhood (aOR\u0026nbsp;7.41 [CI 2.15-25.48]; p = 0.0014) and CXCL-10/IP-10 assay positivity (aOR:\u0026nbsp;4.48 [1.87-10.76];\u0026nbsp;p \u0026le; 0.001) were independent risk factors for test positivity. Although not significant, it is\u0026nbsp;worth of note\u0026nbsp;the tendencies of associations with lower counting of\u0026nbsp;monocytes in\u0026nbsp;the blood (p = 0.075) and the family history of CKD (p = 0.07).\u003cbr\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4: Independent risk factor associated with LTBI in the study population\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" width=\"667\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.43778110944528%\"\u003e\n \u003cp\u003e\u003cstrong\u003eRisk factors\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.742128935532234%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNegative QFT\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n=80)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.742128935532234%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePositive QFT\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n= 39)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.694152923538232%\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e[CI95%]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.89505247376312%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP-\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003evalue\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.593703148425787%\"\u003e\n \u003cp\u003e\u003cstrong\u003eaOR\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e[CI95%]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.89505247376312%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP-\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003evalue\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"24.43778110944528%\"\u003e\n \u003cp\u003eCKD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.742128935532234%\"\u003e\n \u003cp\u003e72\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(90%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.742128935532234%\"\u003e\n \u003cp\u003e36\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(92.31%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.694152923538232%\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003cp\u003e[0.19-3]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.89505247376312%\"\u003e\n \u003cp\u003e0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.593703148425787%\"\u003e\n \u003cp\u003e0.96\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e[0.23-3.96]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.89505247376312%\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"24.43778110944528%\"\u003e\n \u003cp\u003eHD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.742128935532234%\"\u003e\n \u003cp\u003e8\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(10%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.742128935532234%\"\u003e\n \u003cp\u003e3\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(7.69%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.694152923538232%\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.89505247376312%\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.593703148425787%\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.89505247376312%\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"24.43778110944528%\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.742128935532234%\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.742128935532234%\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.694152923538232%\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.89505247376312%\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.593703148425787%\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.89505247376312%\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"24.43778110944528%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Male\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.742128935532234%\"\u003e\n \u003cp\u003e33\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(41.25%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.742128935532234%\"\u003e\n \u003cp\u003e22\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(56.41%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.694152923538232%\"\u003e\n \u003cp\u003e1.84\u003c/p\u003e\n \u003cp\u003e[0.85-4]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.89505247376312%\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.593703148425787%\"\u003e\n \u003cp\u003e1.82\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e[0.8-4.13]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.89505247376312%\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"24.43778110944528%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Female\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.742128935532234%\"\u003e\n \u003cp\u003e47\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(58.75%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.742128935532234%\"\u003e\n \u003cp\u003e17\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(43.59%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.694152923538232%\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.89505247376312%\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.593703148425787%\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.89505247376312%\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"24.43778110944528%\"\u003e\n \u003cp\u003eFamily history of DRC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.742128935532234%\"\u003e\n \u003cp\u003e32\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(40%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.742128935532234%\"\u003e\n \u003cp\u003e7\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(17.95%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.694152923538232%\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003cp\u003e[0.13-0.83]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.89505247376312%\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.593703148425787%\"\u003e\n \u003cp\u003e0.41\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e[0.15-1.07]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.89505247376312%\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"24.43778110944528%\"\u003e\n \u003cp\u003eNon-vaccination with BCG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.742128935532234%\"\u003e\n \u003cp\u003e4\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.742128935532234%\"\u003e\n \u003cp\u003e11\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(28.21%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.694152923538232%\"\u003e\n \u003cp\u003e7.46\u003c/p\u003e\n \u003cp\u003e[2.2-25.38]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.89505247376312%\"\u003e\n \u003cp\u003e0.0012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.593703148425787%\"\u003e\n \u003cp\u003e7.41\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e[2.15-25.48]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.89505247376312%\"\u003e\n \u003cp\u003e0.0014\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"24.43778110944528%\"\u003e\n \u003cp\u003eContact with index TB case\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.742128935532234%\"\u003e\n \u003cp\u003e13\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(16.25%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.742128935532234%\"\u003e\n \u003cp\u003e8\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(20.51%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.694152923538232%\"\u003e\n \u003cp\u003e1.33\u003c/p\u003e\n \u003cp\u003e[0.5-3.54]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.89505247376312%\"\u003e\n \u003cp\u003e0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.593703148425787%\"\u003e\n \u003cp\u003e1.76\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e[0.61-5.09]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.89505247376312%\"\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"24.43778110944528%\"\u003e\n \u003cp\u003eCurrent smoking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.742128935532234%\"\u003e\n \u003cp\u003e9\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(11.39%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.742128935532234%\"\u003e\n \u003cp\u003e6\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(15.79%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.694152923538232%\"\u003e\n \u003cp\u003e1.46\u003c/p\u003e\n \u003cp\u003e[0.48-4.44]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.89505247376312%\"\u003e\n \u003cp\u003e0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.593703148425787%\"\u003e\n \u003cp\u003e1.35\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e[0.4-4.54]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.89505247376312%\"\u003e\n \u003cp\u003e0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"24.43778110944528%\"\u003e\n \u003cp\u003eCurrent alcohol drinking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.742128935532234%\"\u003e\n \u003cp\u003e13 (16.46%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.742128935532234%\"\u003e\n \u003cp\u003e9 (23.68%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.694152923538232%\"\u003e\n \u003cp\u003e1.58\u003c/p\u003e\n \u003cp\u003e[0.61-4.1]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.89505247376312%\"\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.593703148425787%\"\u003e\n \u003cp\u003e1.4\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e[0.5-3.95]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.89505247376312%\"\u003e\n \u003cp\u003e0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"24.43778110944528%\"\u003e\n \u003cp\u003eCXCL-10/IP-10 assay\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.742128935532234%\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.742128935532234%\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.694152923538232%\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.89505247376312%\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.593703148425787%\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.89505247376312%\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.43778110944528%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Negative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.742128935532234%\"\u003e\n \u003cp\u003e57 (71.25%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.742128935532234%\"\u003e\n \u003cp\u003e14 (35.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.694152923538232%\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.89505247376312%\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.593703148425787%\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.89505247376312%\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.43778110944528%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Positive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.742128935532234%\"\u003e\n \u003cp\u003e23\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(28.75%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.742128935532234%\"\u003e\n \u003cp\u003e25\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(64.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.694152923538232%\"\u003e\n \u003cp\u003e4.43\u003c/p\u003e\n \u003cp\u003e[1.96-9.99]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.89505247376312%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.593703148425787%\"\u003e\n \u003cp\u003e4.48\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e[1.87-10.76]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.89505247376312%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.43778110944528%\"\u003e\n \u003cp\u003eMonocytes blood counting\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.742128935532234%\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.742128935532234%\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.694152923538232%\"\u003e\n \u003cp\u003e2.22\u003c/p\u003e\n \u003cp\u003e[0.99-4.98]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.89505247376312%\"\u003e\n \u003cp\u003e0.054\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.593703148425787%\"\u003e\n \u003cp\u003e2.23\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e[0.92-5.39]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.89505247376312%\"\u003e\n \u003cp\u003e0.075\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eData are expressed as OR (Odds-ratio) and aOR (Adjusted Odds-ratio) and their 95% confidence intervals. Risk factors associated with LTBI or QFT-Plus positivity among CKD patients were estimated by multiple fixed-effect binomial models (multivariate logistic analysis) including as main effects variables possibly associated with the QFT-Plus result and confounding variables (variables with at least suggestive levels, p-value \u0026lt; 0.1 (e.g., age, sex, and history of BCG vaccination), in the simple/univariate analyses.\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Abbreviations: IFN-\u0026gamma;: interferon-gamma; IP-10: interferon-gamma inducible protein of 10-kD.CKD: chronic kidney disease; HD: hemodialysis; BCG: bacillus Calmette\u0026ndash;Guérin; TB: tuberculosis; NA: not applicable odds ratio; CI, confidence interval; aOR, adjusted odds ratio.\u003cbr\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eAlthough CKD is recognized as a risk factor for the development of active tuberculosis (1,7), the literature evidence on its association with LTBI is still limited,\u0026nbsp;especially based on the diverse cohort study design, the severity of\u0026nbsp;the\u0026nbsp;renal failure (progression stages), and diagnostic methods. To our knowledge, herein we report for the first time, the use of the fourth generation of IGRA QFT-Plus, which is designed to evaluate\u0026nbsp;of Mtb-specific response by both CD4\u003csup\u003e+\u003c/sup\u003e and CD8\u003csup\u003e+\u003c/sup\u003e T cells, to estimate the prevalence of LTBI in different statuses of CKD and long-term hemodialysis patients in a tertiary reference center from a high TB burden area. We found that one-third of our study population had LTBI, being 32.4% in CKD and 25% in hemodialysis patients \u0026ndash; which is higher than the estimated prevalence in the general population,\u0026nbsp;where\u0026nbsp;is considered that\u0026nbsp;around one-fourth of the worldwide is latent infected by Mtb, as reported by WHO (2). Also, a positive CXCL-10/IP-10 assay and lack of BCG vaccination in childhood were associated to LTBI as important independent risk factors.\u003c/p\u003e\n\u003cp\u003eConsidering that the screening of vulnerable populations and prophylactic treatment of LTBI are priority strategies to prevent the progression to active disease, its worth\u0026nbsp;of\u0026nbsp;note that patients with CKD are more susceptible to infections in general due to their compromised immune system (19). CKD progression is correlated with a reduction in CD4\u003csup\u003e+\u003c/sup\u003e and CD8\u003csup\u003e+\u003c/sup\u003e T cells (20), which are essential for cellular immune defense against Mtb (3). \u003cem\u003eIn vitro\u003c/em\u003e studies have demonstrated that the capacity of T cell proliferation is reduced in\u0026nbsp;the\u0026nbsp;uremic environment (20,21). Additionally, clinical data analysis of our cohort has revealed that 52% of participants had DM as their underlying disease, an important aspect recognized as a factor associated with both LTBI and the development of active tuberculosis (1,9). Its well-recognized that hyperglycemic condition in DM is also associated with an interfering in both innate and adaptative immune system (9). Interestingly,\u0026nbsp;in\u0026nbsp;our cohort, we observed LTBI in 37.9%\u0026nbsp;of\u0026nbsp;patients in stage 3b, followed by 31.7% in stage 4,\u0026nbsp;26.3% in stage 5,\u0026nbsp;and 25% in the HD group. Based on these findings, we could speculate that, although non-statistically significant, this percentage decrease\u0026nbsp;in\u0026nbsp;IGRA results among the status of severity in CKD patients could be explained by a set of clinical conditions associated with the renal dysfunction and, consequently reduced capacity to build a robust or pronounced \u003cem\u003ein vitro\u003c/em\u003e cellular immune response.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePrevious studies report that high C-reactive protein (CRP) levels are attributed to the degree of pre-existing inflammation in patients with renal dysfunction (13,22,23). In our cohort,\u0026nbsp;it was no different, the mean CRP levels were 6.96 mg/dL in the CKD group and 5.23 mg/dL in the HD group. From this, it is important to highlight that indeterminate results were associated with high levels of basal IFN-\u0026gamma;\u0026nbsp;(Nil tube, data not shown), once all patients were responsive to positive control (mitogen). There were 2.7% and 8.3% indeterminate results in the CKD and HD groups, respectively. Consequently, the underlying inflammation and elevated non-specific IFN-\u0026gamma; levels led to indeterminate diagnostic test results.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe interaction between CKD and compromised immune response demonstrates the need for more advanced diagnostic methods to identify LTBI early. The current methods available measure the cellular memory response to the bacillus antigens, as follow: the tuberculin skin test (TST) used for more than a century (1,24) and the newest IGRA (QuantiFERON TB and T-SPOT-TB) (7,15). Studies indicate that QFT-Plus, in its 4\u003csup\u003eth\u003c/sup\u003e generation, has greater sensitivity compared to QFT-Gold In Tube (QFT-GIT), this finding can be attributed to the addition of the TB2 tube sensitized to CD8\u003csup\u003e+\u003c/sup\u003e T cells responses (15,24). In agreement, our study observed 22.2% positive results for the TB2 tube alone, reinforcing the importance of the CD8\u003csup\u003e+\u003c/sup\u003e T cell response in detecting latent Mtb infection.\u003c/p\u003e\n\u003cp\u003eA study conducted in Taiwan, China, using the QFT-GIT to diagnose LTBI, recorded 25% positive results among hemodialysis patients and 11% in individuals with severe CKD with eGFR \u0026lt; 30 mL/min/1.73 (stages 4 and 5) (25). Another study conducted in Taiwan in 2020, carried out on pre-kidney transplant individuals, observed\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;a 20% positivity rate for the QFT-GIT (16). In Thailand, a study using the IGRA T. SPOT-TB, revealed a prevalence of 22.5% in its CKD study population, being: 25% in stage 1, 12.5% in stage 2, 25, 0% in stage 3a, 25.0% in stage 3b and 24.2% in the dialysis group (hemodialysis and peritoneal dialysis) (11). An observational-analytical study conducted\u0026nbsp;in Indonesia, using the QFT-GIT, observed a positivity of 39.2% in individuals on hemodialysis (26). In Brazil, the only study conducted\u0026nbsp;on this thematic, evaluated only patient on hemodialysis and identified a prevalence of LTBI in 8.5%\u0026nbsp;of\u0026nbsp;patients, using the tuberculin skin test as a LTBI diagnostic method (27). It is important to highlight that, Taiwan, Thailand, Indonesia, and Brazil\u0026nbsp;are\u0026nbsp;among the 30 countries with the highest incidence of TB in the world (2).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eUsing multivariate logistic regression analysis (28,29), it was possible to demonstrate the independent risk factors associated with QFT-Plus positivity in our study population. Our results revealed that the lack of BCG vaccination in childhood (adjusted OR: 7.41 [95% CI: 2.15-25.48]; p = 0.0014) was an independent risk factor for a positive result in the diagnostic test. In a meta-analysis evaluating BCG-vaccinated and unvaccinated children who were exposed to tuberculosis, using QFT-GIT test, it was shown that BCG protects against Mtb infection and prevents progression to active disease (28,29). A systematic review demonstrated that BCG vaccination offers protection against pulmonary and extrapulmonary tuberculosis for up to 10 years. However, most of these studies did not follow participants long enough or had a low number of cases after 15 years. These results should not be interpreted as indicating no effect (30). In agreement with our work, a study that investigated the incidence of LTBI in pre-transplant kidney patients found that non-vaccination with BCG was strongly associated as an independent risk factor for the development of LTBI (24).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe also found that the positivity in\u0026nbsp;the\u0026nbsp;CXCL-10/IP-10 assay was also considered an independent risk factor for the diagnosis of LTBI (aOR: 4.48 [1.87-10.76]; p = \u0026lt; 0.001). Based on our data, we can observe that high levels of CXCL-10/IP-10 in CKD and HD patients were associated with a significant increase in the probability of developing LTBI, estimated at 4.48 times. IFN-\u0026gamma;-inducible protein 10 (IP-10), a member of\u0026nbsp;CXC chemokine family (XCXC-10), acts recruiting immune cells to the site of infection. This process occurs in synergy with IFN-\u0026gamma;, in response to the recognition of Mtb-specific antigens by specific T cells (31\u0026ndash;33). In a recent study, increased levels of IFN-\u0026gamma; and CXCL-10/IP-10 were highlighted in patients with active TB compared to healthy individuals (33). While IFN-\u0026gamma; is produced mainly by T lymphocytes, CXCL-10/IP-10 is produced by numerous cells including the innate immune system such as monocytes and antigen-presenting cells (APCs) (31,33,34). These data point out this chemokine as a potential adjuvant biomarker, in association with IFN-\u0026gamma;\u0026nbsp;for the detection and monitoring of LTBI, which might improve diagnostic accuracy and early intervention.\u0026nbsp;A recent study from our research group reported that individuals with pulmonary TB who had both positive IFN-\u0026gamma;\u0026nbsp;and CXCL-10/IP-10 had increased CD64 expression compared to individuals with LTBI (18).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAs limitations of this study, we could point out: i) a comparison between QFT-Plus and TST was not analyzed due to instability in the distribution of the test in Brazil in the period of participant recruitment; ii) relatively small number of individuals in the study population due to the criteria of eligibility and infrastructure capacity by the hospital, especially for HD group; iii) because the study was conducted in a reference center and tertiary health unit of health (high complexity), we could not exclude this bias; iv) we did not carried out comparisons with health control group. Despite these observations, our study can be considered\u0026nbsp;a\u0026nbsp;pioneer in Brazil when using the QFT-Plus test to evaluate LTBI infection in this cohort population, which could be well characterized by clinical and laboratory aspects and followed for a long period at HUPE/UERJ.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn conclusion, approximately 32% of patients with CKD and 25% of those undergoing HD were diagnosed with LTBI. Our study population demonstrated a positivity rate of 39.1% in the CXCL-10/IP-10 assay, with rates of 40.5% in the CKD group and 33.3% in the HD group, highlighting its potential as a relevant biomarker for LTBI in CKD patients. The absence of BCG vaccination during childhood and elevated levels of the CXCL-10/IP-10 biomarker were identified as higher risk factors for LTBI. Furthermore, our data show that QFT-Plus is a useful tool in LTBI detection among CKD patients in late and terminal stages. Finally, these findings reinforce and base to the need of preventive strategies and continuous surveillance for LTBI, emphasizing the relevance of innovative diagnostic approaches which may contribute to a significant TB control in high-risk populations.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBCG\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ebacillus Calmette\u0026ndash;Gu\u0026eacute;rin\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCKD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003echronic kidney disease\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCXCL10/IP-10\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003einterferon-g-inducible protein-10\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ehemodialysis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHUPE\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePedro Ernesto University Hospital\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIFN-γ\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003einterferon-gamma\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLTBI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLatent tuberculosis infection\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eQFT-Plus\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eQuantiFERON-TB Gold Plus.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eACKNOWLEDGMENTS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors wish to acknowledge all the participants and staff from\u0026nbsp;the\u0026nbsp;Nephrology and Pulmonology Divisions at Pedro Ernesto University Hospital (HUPE/UERJ), such as nurses, physicians, technicians,\u0026nbsp;and multidisciplinary teams.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAUTHORS\u0026rsquo; CONTRIBUTIONS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJCBS, NBV, RSAR,\u0026nbsp;and CLRG contributed to patient recruitment, sample collection,\u0026nbsp;and laboratory routine for the study. RSAR, JCBS,\u0026nbsp;and NBV performed experiments. APS, RM, CCSL, CLRG, JHS,\u0026nbsp;and RB followed the patients. MRA, JCBS, NBV, RB,\u0026nbsp;and LSR were involved in statistical analysis and data interpretation. MRA, JCBS, NBV, APS, RSRA, RB,\u0026nbsp;and LSR wrote, edited,\u0026nbsp;and reviewed\u0026nbsp;the manuscript. RB and LSR conceived the study. LSR was responsible for coordinating the study. All authors contributed to the article and approved the submitted version.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFUNDING\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe authors declare financial support was received for the research, authorship, and/or\u003c/p\u003e\n\u003cp\u003epublication of this article. This study was funded in part by the Funda\u0026ccedil;\u0026atilde;o Carlos Chagas Filho de Amparo \u0026agrave; Pesquisa do Estado do Rio de Janeiro (FAPERJ) and JCBS was supported by a scholarship from the Coordena\u0026ccedil;\u0026atilde;o de Aperfei\u0026ccedil;oamento de Pessoal de N\u0026iacute;vel Superior (CAPES).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAVAILABILITY OF DATA AND MATERIALS\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eClinical and qualitative data from service providers are not publicly available due to privacy and ethical concerns. However, the data used and analyzed in this study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eETHICS APPROVAL AND CONSENT TO PARTICIPATE\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll procedures and protocols of this study were previously approved by the Research Ethics Committee of the Pedro Ernesto University Hospital (HUPE), of the Rio de Janeiro State University (UERJ), under opinion number 4.624.686. During recruitment, individuals were presented with the objectives and potential contributions of the study, and upon voluntarily accepting to participate. All the enrolled participants provided signed informed-consent forms.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCONSENT FOR PUBLICATION\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCOMPETING INTERESTS\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe authors declare no potential conflicts of interest.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMinistry of Heath. 2019. Manual of recommendations for tuberculosis control in Brazil [Internet]. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e\u003c/span\u003e\u003cspan address=\"http://www.saude.gov.br/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGlobal tuberculosis report 2023 [Internet]. 2023. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://iris.who.int/\u003c/span\u003e\u003cspan address=\"https://iris.who.int/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEhlers S, Schaible UE. The granuloma in tuberculosis: Dynamics of a host-pathogen collusion. 3, Front Immunol. 2012.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCampani\u0026ccedil;o A, Harjivan SG, Warner DF, Moreira R, Lopes F. Addressing latent tuberculosis: New advances in mimicking the disease, discovering key targets, and designing hit compounds. International Journal of Molecular Sciences. Volume 21. MDPI AG; 2020. pp. 1\u0026ndash;25.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCavalcante-Silva LHA, Almeida FS, Andrade AG, de Comberlang FC, Cardoso LL, Vanderley SER, et al. Mycobacterium tuberculosis in a Trap: The Role of Neutrophil Extracellular Traps in Tuberculosis. International Journal of Molecular Sciences. Volume 24. Multidisciplinary Digital Publishing Institute (MDPI); 2023.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDomingo-Gonzalez R, Prince O, Cooper A, Khader SA. Cytokines and Chemokines in Mycobacterium tuberculosis Infection. Microbiol Spectr. 2016;4(5).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eProtocol for latent. \u003cem\u003eMycobacterium tuberculosis\u003c/em\u003e infection in Brazil. Ministry of Heath, 2022 [Internet]. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e\u003c/span\u003e\u003cspan address=\"http://www.saude.gov.br/svs\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAmorim RF, Viegas ERC, Carneiro AJV, Esberard BC, Chinem ES, Correa RS, et al. Superiority of Interferon Gamma Assay Over Tuberculin Skin Test for Latent Tuberculosis in Inflammatory Bowel Disease Patients in Brazil. Dig Dis Sci. 2019;64(7):1916\u0026ndash;22.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTorres AV, Corr\u0026ecirc;a R, da Bevilacqua S, de F M, do, Prado LCF, Bandeira FMG, de Rodrigues C et al. LS,. Screening of latent tuberculosis infection among patients with diabetes mellitus from a high-burden area in Brazil. Frontiers in Clinical Diabetes and Healthcare. 2022;3.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUgarte-Gil C, Carrillo-Larco RM, Kirwan DE. Latent tuberculosis infection and non-infectious co-morbidities: Diabetes mellitus type 2, chronic kidney disease and rheumatoid arthritis. Int J Infect Dis. 2019;80:S29\u0026ndash;31.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHayuk P, Boongird S, Pornsuriyasak P, Bruminhent J. Interferon-gamma release assays for diagnosis of latent TB infection in chronic kidney diseases and dialysis patients. Front Cell Infect Microbiol. 2022;12.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMyall K, Milburn HJ. An update on the management of latent tuberculosis infection and active disease in patients with chronic kidney disease. Polish Archives of Internal Medicine. Volume 127. Medycyna Praktyczna; 2017. pp. 681\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSyed-Ahmed M, Narayanan M. Immune Dysfunction and Risk of Infection in Chronic Kidney Disease. Advances in Chronic Kidney Disease. Volume 26. W.B. Saunders; 2019. pp. 8\u0026ndash;15.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGirndt M, Sester M, Sester U, Kaul H, K\u0026ouml;hler H. Defective expression of B7-2 (CD86) on monocytes of dialysis patients correlates to the uremia-associated immune defect. Kidney Int. 2001;59(4):1382\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePourakbari B, Mamishi S, Benvari S, Mahmoudi S. Comparison of the QuantiFERON-TB Gold Plus and QuantiFERON-TB. Gold In-Tube interferon-γ release assays: A systematic review and meta-analysis. Advances in Medical Sciences. Volume 64. 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J Clin Invest. 1987;80(3):743\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKawai T, Akira S. The role of pattern-recognition receptors in innate immunity: Update on toll-like receptors. Nat Immunol. 2010;11:373\u0026ndash;84.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi J, Chen J, Lan HY, Tang Y. Role of C-Reactive Protein in Kidney Diseases. Vol. 9, kidney diseases. S. Karger AG; 2023. pp. 73\u0026ndash;81.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGong W, Wu X. Differential Diagnosis of Latent Tuberculosis Infection and Active Tuberculosis: A Key to a Successful Tuberculosis Control Strategy. Frontiers in Microbiology. Volume 12. Frontiers Media S.A.; 2021.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShu CC, Hsu CL, Lee CY, Wang JY, Wu VC, Yang FJ et al. 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Application of Venn\u0026rsquo;s diagram in the diagnosis of pleural tuberculosis using IFN-γ, IP-10 and adenosine deaminase. PLoS ONE. 2018;13(8).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFriedrich R, Rappold E, Bogdan C, Held J. Comparative analysis of the wako Β-glucan test and the fungitell assay for diagnosis of candidemia and Pneumocystis jirovecii pneumonia. J Clin Microbiol. 2018;56(9).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRuhwald M, Dominguez J, Latorre I, Losi M, Richeldi L, Pasticci MB, et al. A multicentre evaluation of the accuracy and performance of IP-10 for the diagnosis of infection with M. tuberculosis. Tuberculosis. 2011;91(3):260\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOkamoto M, Kawabe T, Iwasaki Y, Hara T, Hashimoto N, Imaizumi K, et al. Evaluation of interferon-γ, interferon-γ-inducing cytokines, and interferon-γ-inducible chemokines in tuberculous pleural effusions. J Lab Clin Med. 2005;145(2):88\u0026ndash;93.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-infectious-diseases","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"infd","sideBox":"Learn more about [BMC Infectious Diseases](http://bmcinfectdis.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/infd","title":"BMC Infectious Diseases","twitterHandle":"#bmcinfectdis","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"chronic kidney disease, hemodialysis, latent tuberculosis, tuberculosis, Mycobacterium tuberculosis, and renal failure","lastPublishedDoi":"10.21203/rs.3.rs-4725508/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4725508/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThere is growing evidence that patients with chronic kidney disease (CKD) show a high risk of acquiring latent tuberculosis infection (LTBI) or of developing active TB. However, diagnosing LTBI is still a challenge based on current tests which reflect the cellular immunity against \u003cem\u003eMycobacterium tuberculosis\u003c/em\u003e infection \u0026ndash; that may be compromised in this population. Herein, we evaluated the prevalence of LTBI among patients with CKD and those in routine hemodialysis, and LTBI predictors.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA prospective cross-sectional study was conducted in a tertiary nephrology reference hospital, Rio de Janeiro, Brazil. LTBI was diagnosed using interferon-gamma release assay (IGRA; QuantiFERON-TB Gold Plus [QFT-Plus]). CXCL-10/IP-10 release assay was determined from QFT-Plus supernatants.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA total of 123 patients were enrolled in the study, 111 (90.2%) from the CKD group and 12 (9.8%) from the hemodialysis (HD). IGRA showed 39 (31.7%) positive results in the general study population: 36/111 (32.4%) in CKD and 3/12 (25%) in HD groups. Indeterminate IGRA results were observed in 4/123 (3.3%) patients. CXCL-10/IP-10 positive results were seen in 39/123 (39.8%) patients. Multivariate regression analysis identified that non-vaccination with BCG (aOR 7.41 [CI 2.15\u0026ndash;25.48]; p\u0026thinsp;=\u0026thinsp;0.0014) and the positivity for CXCL-10/IP-10 assay (aOR 4.48 [CI 1.87\u0026ndash;10.76]; p\u0026thinsp;=\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were independent risk factors for LTBI among DRC and HD patients.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe IGRA QFT-Plus was shown to be a useful method in the surveillance of LTBI in critical stages of CKD and routine hemodialysis. Mtb-specific CXCL-10/IP-10 responses were associated with a positive IGRA and could provide an useful adjuvant LTBI biomarker in this population.\u003c/p\u003e","manuscriptTitle":"Use of interferon-gamma release assay (IGRA) and CXCL-10/IP-10 for screening of latent tuberculosis infection (LTBI) in chronic kidney disease and hemodialysis patients","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-10 12:29:12","doi":"10.21203/rs.3.rs-4725508/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-07-16T09:34:10+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-07-15T13:32:14+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-07-12T09:36:31+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Infectious Diseases","date":"2024-07-11T16:27:41+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-infectious-diseases","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"infd","sideBox":"Learn more about [BMC Infectious Diseases](http://bmcinfectdis.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/infd","title":"BMC Infectious Diseases","twitterHandle":"#bmcinfectdis","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"bc181394-6c2d-49de-8f21-6d71a2336586","owner":[],"postedDate":"August 10th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-10-06T16:08:45+00:00","versionOfRecord":{"articleIdentity":"rs-4725508","link":"https://doi.org/10.1186/s12879-025-11620-z","journal":{"identity":"bmc-infectious-diseases","isVorOnly":false,"title":"BMC Infectious Diseases"},"publishedOn":"2025-10-02 15:57:29","publishedOnDateReadable":"October 2nd, 2025"},"versionCreatedAt":"2024-08-10 12:29:12","video":"","vorDoi":"10.1186/s12879-025-11620-z","vorDoiUrl":"https://doi.org/10.1186/s12879-025-11620-z","workflowStages":[]},"version":"v1","identity":"rs-4725508","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4725508","identity":"rs-4725508","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}