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

doi:10.21203/rs.3.rs-4725508/v1

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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

https://doi.org/10.21203/rs.3.rs-4725508/v1

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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

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.

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 < 15 mL/min/1.73 m²) from the nephrology outpatient clinic, and patients who received long-term (> 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.

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/mm³	6627.76 [5806.74–7448.79]	4936.54 [3372.32–6500.75]	0.023
Neutrophils, cells/mm³	4106.49 [3464.12–4748.86]	3299.75 [2073.60–4525.91]	0.164
Monocytes, cells/mm³	604.64 [447.20–762.08]	891.89 [591.37–1192.42]	0.044
Lymphocytes, cells/mm³	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.

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.

BCG

bacillus Calmette–Guérin

CKD

chronic kidney disease

CXCL10/IP-10

interferon-g-inducible protein-10

hemodialysis

HUPE

Pedro Ernesto University Hospital

IFN-γ

interferon-gamma

LTBI

Latent tuberculosis infection

QFT-Plus

QuantiFERON-TB Gold Plus.

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.

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.

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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

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Abstract

Background

Methods

Results

Conclusion

Figures

BACKGROUND

METHODS

Participant enrollment and ethical approval

Data collection

Interferon-gamma release assay (IGRA)

CXCL-10/IP-10 release assay

Statistical analysis

RESULTS

Overview of the study population

Latent tuberculosis infection prevalence in CKD and HD patients

DISCUSSION

Abbreviations

Declarations

References

Additional Declarations

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Version 1