Mycobacterial heparin-binding hemagglutinin (HBHA)-induced interferon-γ release assay (IGRA) for discrimination of latent and active tuberculosis: A systematic review and meta-analysis

Jinhua Tang; Yuan Huang; Zheng Cai; Yueyun Ma

doi:10.1371/journal.pone.0254571

. 2021 Jul 16;16(7):e0254571. doi: 10.1371/journal.pone.0254571

Mycobacterial heparin-binding hemagglutinin (HBHA)-induced interferon-γ release assay (IGRA) for discrimination of latent and active tuberculosis: A systematic review and meta-analysis

Jinhua Tang ¹, Yuan Huang ¹, Zheng Cai ¹, Yueyun Ma ^1,^*

Editor: Olivier Neyrolles²

PMCID: PMC8284824 PMID: 34270559

Abstract

Background

The Mycobacterial heparin-binding hemagglutinin (HBHA) is an important latency-associated antigen that can be used to distinguish between latent tuberculosis infection (LTBI) and active tuberculosis (ATB). Although many studies were explored the efficiency of the HBHA-induced interferon-γ release assay (IGRA) in different populations, the clinical differential value of HBHA-IGRA is still controversial. Therefore, the aim of this study was to determine whether the HBHA-IGRA can be used as an efficient test for the discrimination of LTBI and ATB by a systematic review and meta-analysis.

Methods

Relevant articles were retrieved from PubMed, Embase, Web of Science, and the Cochrane Library on Oct 18, 2020, with no start date limitation. The quality of each study was evaluated using Review Manager 5.4. The Stata MP v.14.0 software was used to combine sensitivity, specificity, likelihood ratio (LR), diagnostic odds ratio (DOR), summary receiver operating characteristic (SROC) curve, and area under SROC (AUC) to evaluate the diagnostic value of HBHA-IGRA for discrimination of LTBI and ATB. Meta-regression and subgroup analysis were performed for the sources of heterogeneity based on the selection criteria for active TB, the population, the TB burden, the type of antigen, the type of sample, and the time of antigen stimulation.

Results

A total of 13 studies (14 results) were included in this meta-analysis, including 603 ATB patients and 514 LTBI individuals. The pooled sensitivity and specificity of the HBHA-IGRA for discrimination of the LTBI and ATB were 0.70 (95% CI, 0.57~0.80) and 0.78 (95% CI, 0.71~0.84), respectively. The pooled positive likelihood ratio (PLR), negative likelihood ratio (NLR), and diagnostic odds ratio (DOR) were 3.15 (95%CI, 2.43~4.09), 0.39 (95% CI, 0.27~0.56), and 8.11 (95% CI, 4.81~13.67), respectively. The AUC was 0.81 (95% CI, 0.77~0.84). The subgroup analysis showed that the main source of heterogeneity was due to the HIV-infected population incorporated, and the different selection criteria of active TB subjects would also lead to the variation of the pooled sensitivity and specificity. Different TB burdens, HBHA antigen types, sample types, antigen stimulation time and BCG vaccination did not affect the heterogeneity in this analysis.

Conclusion

The HBHA-IGRA is a promising immunodiagnostic test for discrimination of latent and active TB, which can be added in commercial IGRAs to enhance the differential diagnostic performance.

1. Introduction

Tuberculosis (TB) is one of the infectious diseases causing high morbidity and mortality worldwide and remains an important global public health concern. The WHO global tuberculosis report [1] depicts that about a quarter of the world’s population has been infected with Mycobacterium tuberculosis (Mtb). Only about 5–10% of the infected population develops active TB while many of them have asymptomatic “latent tuberculosis infection (LTBI)”. Although many of the LTBIs are not infectious and do not produce active disease, some of the latent infections can become active infections, especially in people with a weak immune system. Therefore, the surveillance and management of latent tuberculosis infections are also critically important to greatly reduce the global burden of TB [2].

The early identification of LTBI and active TB is critical in reducing the global burden of TB. The development of a latent infection or an active case after the entry of Mtb into the body depends on a variety of factors including the most important immune status. The WHO recommends that an interferon (IFN)-γ release assay (IGRA) or a tuberculin skin test (TST) can be used to screen for TB infections [3]. The IGRA which measures IFN-γ secretion stimulated by the Mtb-specific antigens such as ESAT-6 and CFP-10 has a better predictive value than the traditional TST which is based on the purified protein derivative (PPD) in projecting the TB progression [4]. However, neither the IGRAs nor the TST is useful in discriminating the LTBI and the active TB [5, 6]. Therefore, there is currently no efficient test to directly identify the status of Mtb infection in humans.

The mycobacterial heparin-binding hemagglutinin (HBHA) is a major latency antigen associated with the dormancy of the Mtb and LTBI [7, 8]. Many studies [9–11] showed that the HBHA has a discriminatory potential in differentiating the latent and active TB, especially with the release of interferon- γ. There have been many reports of using the HBHA-based IGRAs until now. However, due to the lack of a large sample size and the controversies among different results in previous studies, the clinical use of the HBHA-IGRA to differentiate active TB from the LTBI has not been popularized.

To summarize the current state of the research and evaluate the diagnostic value of the HBHA-IGRA, we performed a systematic review and meta-analysis on previous human studies that used the HBHA as a stimulating antigen in the IGRA tests for the diagnosis of active TB and LTBI. The aim of this study was to identify the efficacy of the HBHA-IGRA as a good differential diagnostic method for active and latent Mtb infection and to provide a basis for its clinical utilization.

2. Materials and methods

The systematic review and the meta-analysis in this study, were conducted strictly following the criteria of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement.

2.1. Search strategy

In this systematic review and meta-analysis, the PubMed, Embase, Web of Science (WOS), and the Cochrane Library databases were searched for the relevant studies in English on Oct 18, 2020, with no start date limitation. The search terms were as follows: (“tuberculosis” OR “tuberculous” OR “tubercular” OR “TB” OR “mycobacterium” OR “mycobacterial”) AND (“interferon-gamma” OR “gamma interferon” OR “IFN gamma” OR “Interferon-γ” OR “IFN-γ” OR “interferon gamma release assays” OR “Interferon-gamma Release Test” OR “IGRA” OR “T cell assay” OR “T cell response” OR “enzyme-linked immunospot” OR “ELISpot”) AND (“heparin-binding hemagglutinin adhesin” OR “heparin-binding hemagglutinin” OR “HBHA”). Additionally, we manually searched the reference list of related articles for the other potentially relevant studies.

2.2. Study selection criteria

All relevant studies included in the meta-analysis must meet the following criteria: (1) the study had the discrimination analysis of the latent tuberculosis infection (LTBI) and active tuberculosis (ATB), (2) subjects in the study included both individuals with the LTBI and patients with the ATB, (3) using mycobacterial heparin-binding hemagglutinin (HBHA) as stimulating antigen and indicators to be evaluated including IFN-γ, (4) studies with a clear diagnostic cut-off value or studies directly or indirectly extracted the true positive (TP), false positive (FP), true negative (TN) and false negative (FN) values of the HBHA-IGRA for the discrimination of LTBI and ATB to construct a diagnostic four-grid table.

The LTBI group was defined as individuals who were selected based on the positive tuberculin skin test (TST) (HIV-uninfected people≥10 mm induration and HIV-infected people≥5 mm induration) or the IGRA tests which had no signs or symptoms of active TB but were at risk for the active TB disease based on the WHO’s recommendation [2]. The ATB group was defined as patients with microbiologically confirmed TB or high clinical suspicion and a positive response to anti-TB treatment, who were untreated or treated within four weeks. The individuals who were infected with the human immunodeficiency virus (HIV) were also included in this study.

The reviews, letters, abstracts, case reports, duplicated studies, studies that did not include the integrated date, studies written in languages other than English, and studies that did not involve humans were excluded from this meta-analysis.

2.3. Data extraction and quality assessment

To compute this systematic review and meta-analysis, two authors independently conducted the data extraction. The quality of the literature was also evaluated by these two authors based on the inclusion and exclusion criteria to include in the meta-analysis. The disagreements between these two individuals’ evaluations were resolved by consensuses. For each study, the basic information and relevant results including the first author and the year of publication; the time when the study performed; country; study design; population; the proportion of individuals who were BCG vaccinated; the total number of cases enrolled; age characteristics; the number of males/females; the number of active TB/LTBI; the definition of active TB; clinical subtype of active TB; antigen type; assay type; stimulation time; cut-off value and sample type in the studies included, were collected. The quality of each study was assessed by the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2). The risk of bias included four parts: Patient selection, Index test, Reference standard, and Flow and timing. The studies with a high risk of bias were determined as poor quality and those with low risk as good quality. The results of the quality assessment were summarized and graphed using Review Manager (RevMan) v.5.4.1. (The Cochrane Collaboration, 2020.)

2.4. Statistical analysis

The meta-analysis was performed using the Stata MP v.14.0 software (StataCorp, LLC, College Station, TX, USA). The pooled sensitivity and specificity, pooled positive likelihood ratio (+LR) and negative likelihood ratio (-LR), diagnostic odds ratio (DOR), and summary receiver operating characteristic (SROC) curve were computed. The heterogeneity caused by the threshold effect was examined by the Spearman correlation analysis. The heterogeneities of sensitivity, specificity, +LR, -LR, and DOR were assessed by the Higgins I² statistic and Cochran’s Q test. If an I² value was>50%, it suggested a significant heterogeneity, and the meta-regression and the subgroup analysis were used to identify the source of the heterogeneity [12]. The Deeks’ funnel plot asymmetry test was used to assess the publication bias and the p<0.05 was considered statistically significant.

3. Results

3.1. Search results and the study characteristics

A total of 168 relevant studies were retrieved from three independent online databases, and 92 duplicated elucidations were removed from the further analysis. Subsequently, after reviewing the title and abstract of these elucidations, only 30 articles directly related to the objective continued to remain. Among these 30 articles, eight articles had no data on patients with ATB or individuals with LTBI, three studies did not mention the use of the IGRA test, five elucidations did not provide sufficient data for meta-analysis, and one study lacked the cut-off value to construct a diagnostic four-grid table despite having sufficient data. After all these filterings, 14 results from 13 studies were eligible for the meta-analysis and included in the current study. The details of the study screening process are shown in Fig 1.

The 13 studies (14 sets of results) were conducted from 1999 to 2019 and consisted of 603 patients with active TB and 514 individuals with LTBI. These studies were mainly performed in three countries: Italy (46%), Belgium (31%), and China (15%). The study subjects mainly were non-HIV infected (87%), from areas with low TB burden (72%), and most adults. The IFN-γ was measured mainly using enzyme-linked immunosorbent assay (ELISA) (86%), all the antigens used in the IGRAs in the studies selected were either natural or recombinant HBHA protein. All selected studies are prospective case-control studies. The detailed characteristics of all the studies are shown in Table 1. Table 2 summarizes the data extraction results from each study (2 × 2 table).

Table 1. Characteristics of studies included in the meta-analysis.

	Time study performed	Country (TB burden)	Study design	Population	BCG vaccinated (%)	Participant number	Age group	Male/female	Active TB/LTBI	Active TB definition	Active TB Type	Antigen type	Assay type	Stimulation time (hour)	Cut-off Value	Sample type
Masungi 2002 [13]	NA	Belgium (low)	Prospective	Contact individuals and patients	NA	49	NA	NA	24/25	microbiologically confirmed TB	81% PTB	nHBHA	IFA	96	100pg/mL	PBMCs
Masungi 2002 [13]	NA	Belgium (low)	Prospective	Contact individuals and patients	NA	49	NA	NA	24/25	microbiologically confirmed TB	19% EPTB	nHBHA	IFA	96	100pg/mL	PBMCs
Temmerman 2004 [14]	NA	Belgium (low)	Prospective	Contact individuals and patients	0%	101	NA	NA	46/55	microbiologically confirmed TB	NA	nHBHA	ELISA	96	100pg/mL	PBMCs
Hougardy 2007 [15]	1999–2007	Belgium (low)	Prospective	Students, household contacts, HCWs and patients	40% LTBI	149	Adults	NA	86/63	clinically confirmed TB	65% PTB	nHBHA	ELISA	96	100pg/mL	PBMCs
Hougardy 2007 [15]	1999–2007	Belgium (low)	Prospective	Students, household contacts, HCWs and patients	40% LTBI	149	Adults	NA	86/63	clinically confirmed TB	35% EPTB	nHBHA	ELISA	96	100pg/mL	PBMCs
Delogu 2011 [a,16]	NA	Italy (low)	Prospective	Contact individuals and patients	37%	87	Adults	53/32	61/26	microbiologically confirmed TB	100% PTB	rHBHAms	ELISA	24	0.25IU/mL	Whole blood
Delogu 2011 [b,16]	NA	Italy (low)	Prospective	Contact individuals and patients	37%	72	Adults	NA	52/20	microbiologically confirmed TB	100% PTB	rHBHAms	ELISA	168	0.75IU/mL	Whole blood
Molicotti 2011 [17]	NA	Italy (low)	Prospective	Contact individuals and patients	NA	63	NA	NA	40/23	microbiologically confirmed TB	NA	rHBHAms	ELISA	24	0.25IU/mL	Whole blood
Wyndham-thomas 2014 [18]	NA	Belgium (low)	Prospective	Contact individuals and patients	45%	49	adults	24/25	17/32	clinically confirmed TB	NA	nHBHA	ELISA	24	50pg/mL	PBMCs
Molicotti 2015 [19]	NA	Italy (low)	Prospective	Contact individuals and patients	NA	83	Mainly adults	NA	27/56	microbiologically confirmed TB	NA	rHBHAms	ELISA	24	0.20IU/mL	Whole blood
Wen 2017 [20]	2016.06–2016.12	China (high)	Prospective	Contact individuals and patients	100%	101	Adults	65/36	86/15	clinically confirmed TB	65% PTB	rHBHAms	ELISPOT	18–20	6 SFCs/10⁶ cells	PBMCs
Wen 2017 [20]	2016.06–2016.12	China (high)	Prospective	Contact individuals and patients	100%	101	Adults	65/36	86/15	clinically confirmed TB	35% EPTB	rHBHAms	ELISPOT	18–20	6 SFCs/10⁶ cells	PBMCs
Chiacchio 2017 [21]	2012–2015	Italy (low)	Prospective	HIV-infected and HIV-uninfected patients	75%	49	Adults	44/5	25/24	microbiologically confirmed TB	100% PTB	rHBHAms	ELISA	16–20	0.25IU/mL	Whole blood
Sali 2018 [22]	NA	Italy (low)	Prospective	Contact individuals and patients	30%	64	Children	36/28	19/45	microbiologically confirmed TB	26% PTB	rHBHAms	ELISA	16–24	0.25IU/mL	Whole blood
Tang 2020 [23]	2019.08–2019.12	China (high)	Prospective	HCWs and patients	80%	62	Adults	36/26	40/22	microbiologically confirmed TB	100% PTB	rHBHAms	ELISA	18	22.4pg/mL	Whole blood
Dirix 2016 [24]	2008.02–2010.05	Uganda (high)	Prospective	HIV-infected and HIV-uninfected patients	NA	147	Adults	NA	62/85	microbiologically confirmed TB	NA	nHBHA	ELISA	72	75pg/mL	PBMCs
Delogu 2016 [25]	2011.12–2014.04	Italy (low)	Prospective	HIV-infected patients	78%	41	Adults	35/6	18/23	microbiologically confirmed TB	NA	rHBHAms	ELISA	72	0.25IU/mL	Whole blood

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Note: In Delogu 2011 [a], the results were from 24-hour antigen stimulation; In Delogu 2011 [b], the results were from 168-hour antigen stimulation.

Abbreviations: NA, not available; HCWs, healthcare workers; BCG, Bacillus Calmette-Guérin; LTBI, latent tuberculosis Infection; TB, tuberculosis; PTB, pulmonary tuberculosis; EPTB, extra-pulmonary tuberculosis; nHBHA, native HBHA; rHBHAms, recombinant HBHA purified from Mycobacterium smegmatis; IFA, immunofluorescence assay; ELISA, enzyme linked immunosorbent assay; ELISPOT, Enzyme Linked Immunospot Assay; IU, international unit; SFCs, spots forming cells; PBMCs, Peripheral blood mononuclear cells.

Table 2. Diagnostic performance of the HBHA-IGRA for discrimination of the LTBI and active TB.

Study	Sample size	TP	FP	FN	TN	Sensitivity (95%CI)	Specificity (95% CI)
Wyndham-thomas 2014	49	24	6	8	11	0.75 (0.57–0.89)	0.65 (0.38–0.86)
Wen 2017	101	10	17	5	69	0.67 (0.38–0.88)	0.80 (0.70–0.88)
Temmerman 2004	101	45	8	10	38	0.82 (0.69–0.91)	0.83 (0.69–0.92)
Tang 2020	62	19	7	3	33	0.86 (0.65–0.97)	0.82 (0.67–0.93)
Sali 2018	64	39	7	6	12	0.87 (0.73–0.95)	0.63 (0.38–0.84)
Molicotti 2015	83	43	4	13	23	0.77 (0.64–0.87)	0.85 (0.66–0.96)
Molicotti 2011	63	19	7	4	33	0.83 (0.61–0.95)	0.82 (0.67–0.93)
Masungi 2002	49	15	1	10	23	0.60 (0.39–0.79)	0.96 (0.79–1.00)
Hougardy 2007	149	58	48	5	38	0.92 (0.82–0.97)	0.44 (0.33–0.55)
Dirix 2016	147	21	14	64	48	0.25 (0.16–0.35)	0.77 (0.65–0.87)
Delogu 2016	41	6	2	17	16	0.26 (0.10–0.48)	0.89 (0.65–0.99)
Delogu 2011 [a]	72	15	13	5	39	0.75 (0.51–0.91)	0.75 (0.61–0.86)
Delogu 2011 [b]	87	13	12	13	49	0.50 (0.30–0.70)	0.80 (0.68–0.89)
Chiacchio 2017	49	13	6	11	19	0.54 (0.33–0.74)	0.76 (0.55–0.91)
Combined						0.70 (0.57–0.80)	0.78 (0.71–0.84)

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Note: In Delogu 2011 [a], the results were from 168-hour antigen stimulation; in Delogu 2011 [b], the results were from 24-hour antigen stimulation.

Abbreviations: TP, true positive; FP, false positive; FN, false negative; TN, true negative; CI, confidence interval.

3.2. Quality assessment and publication bias

Pertinent to the quality assessment, the evaluations from the two independent authors were highly consistent (Fig 2). Since almost all studies (12/13) were case-control studies, the bias in patient selection was judged as “high risk” in most of the studies (8/13). The high risk of bias for the “Index Test” (7/13) largely resulted from the non-pre-specified threshold (cut-off value), and a lack of information on blind testing led to “unclear” results (4/13).

To evaluate the potential publication bias in these studies, the Deeks’ funnel plot asymmetry test was performed (Fig 3). The p-value was 0.28, which indicated that no significant publication bias was found among the included studies.

3.3. Threshold and diagnostic accuracy of the HBHA-IGRA for discrimination of LTBI and active TB

The Spearman correlation coefficient of the 13 selected studies (14 sets of results) in the meta-analysis was 0.381 (p = 0.179), suggesting that there was no significant heterogeneity caused by the diagnostic threshold effect, although different cut-off values were adopted by different research teams.

The diagnostic performance results of the HBHA-IGRA tests are presented in Table 2. The pooled sensitivity and specificity of the HBHA-IGRA for discrimination of the LTBI and ATB were 0.70 (95% CI, 0.57~0.80) and 0.78 (95% CI, 0.71~0.84), respectively (Fig 4A). The pooled estimates for the positive diagnostic LR (DLR), negative DLR, diagnostic score, and DOR were 3.15 (95%CI, 2.43~4.09), 0.39 (95% CI, 0.27~0.56), 2.09 (95% CI, 1.57~2.62), and 8.11 (95% CI, 4.81~13.67), respectively (Fig 4B and 4C). The area under the SROC curve (AUC) was 0.81 (95% CI, 0.77~0.84) (Fig 5). A significant heterogeneity was observed in the above-pooled results, based on I² values of 90.7% for sensitivity, 80.1% for specificity, 64.4% for positive DLR, 90.5% for negative DLR, 68.9% for the diagnostic score, and 100.0% for DOR.

Fig 4 — Forest plots showing the estimates of (a) sensitivity and specificity, (b) positive likelihood ratio and negative likelihood ratio, and (c) diagnostic score and diagnostic odds ratio (DOR) of the HBHA-IGRA for discrimination of the LTBI and active TB. HBHA-IGRA; mycobacterial heparin-binding hemagglutinin-induced interferon-gamma release assay.

Fig 5 — HBHA-IGRA; mycobacterial heparin-binding hemagglutinin-induced interferon-gamma release assay.

3.4. Meta-regression and subgroup analysis

Meta-regression and subgroup analysis were performed to explore the sources of heterogeneity in the studies that used the HBHA-IGRA test for discrimination of LTBI and ATB. Meta-regression suggested that the inclusion of HIV-infected people is the primary factor leading to the heterogeneity (RDOR = 10.05, 95% CI: 2.62~38.53, p = 0.003). The subgroup analysis (Table 3) showed that the studies which enrolled HIV-infected people revealed much lower sensitivity than the studies unenrolled HIV-infected people (I² = 87%, p<0.001), and the studies which ATB group enrolled the microbiologically and clinically confirmed patients also revealed a higher sensitivity and lower specificity result than the studies which only enrolled the microbiologically confirmed TB patients (I² = 62%, p = 0.007). Different HBHA antigens, samples for IGRA test, TB burden, and stimulation time did not significantly affect the discrimination accuracy of the HBHA-IGRAs.

Table 3. Subgroup analysis of the HBHA-IGRA for discrimination of the LTBI and active TB.

Covariate	Subgroup	N		Meta-analytic summary estimate
Covariate	Subgroup	N	Sensitivity (95%CI)	Specificity (95%CI)	I² (%)	p value
Population	HIV-infected enrolled	3	0.32 (0.16–0.47)	0.79 (0.66–0.92)	87	0.00^*
	HIV-infected unenrolled	11	0.78 (0.71–0.85)	0.77 (0.70–0.84)
TB burden	High	3	0.58 (0.29–0.87)	0.80 (0.68–0.92)	0	0.64
	Low	11	0.72 (0.60–0.85)	0.77 (0.70–0.85)
Active TB definition	microbiologically confirmed TB	11	0.66 (0.52–0.80)	0.81 (0.76–0.87)	62	0.07
	clinically confirmed TB	3	0.81 (0.63–0.99)	0.64 (0.51–0.77)
Antigen type	nHBHA	5	0.70 (0.51–0.90)	0.73 (0.62–0.84)	0	0.47
	rHBHAms	9	0.70 (0.55–0.85)	0.80 (0.73–0.87)
Sample type	PBMCs	6	0.70 (0.51–0.88)	0.75 (0.65–0.84)	0	0.61
	whole blood	8	0.70 (0.54–0.86)	0.80 (0.73–0.88)
Stimulation time	>24h	6	0.63 (0.44–0.82)	0.77 (0.68–0.87)	0	0.52
	≤24h	8	0.74 (0.60–0.88)	0.78 (0.70–0.86)

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Abbreviations: TB, tuberculosis; nHBHA, native HBHA; rHBHAms, recombinant HBHA purified from Mycobacterium smegmatis; PBMCs, Peripheral blood mononuclear cells.

*, p<0.05.

3.5. Sensitivity analysis

To further examine the impact of individual study on the pooled results, we performed sensitivity analysis (Fig 6). The results of Hougardy et al (2007) [15] and Dirix et al (2016) [24] greatly affect the pooled results (Fig 6C and 6D). After the exclusion of the two studies, the I² values for heterogeneity were decreased to 76.4% for sensitivity, 13.7% for specificity, 0% for positive DLR, 78.7% for negative DLR, 33.7% for the diagnostic score, and 85.1% for DOR, respectively. Conversely, the sensitivity and specificity had minimal changes, the diagnostic odds ratio (DOR) increased from 8.11 to 9.86. The outcomes of the sequential exclusion of each study (S1 Fig) showed that the DORs did not change significantly in all models, thereby indicating that our results are stable and reliable.

4. Discussion

Accurate and early identification of TB infection status is of great significance for reducing the global TB incidence. Because Mtb infection causes chronic disease and its clinical symptoms are often atypical, immunodiagnostic tests such as TST and IGRAs (QFT and T-SPOT) are commonly used for screening. The culture, nucleic acid amplification testing (NAAT), and GeneXpert can be used for confirmative diagnosis of TB in clinical practice [26, 27]. The whole process is not only expensive and time-consuming, but also affects timely isolation and treatment, and causes the spread of the Mtb infection. Since there are no early and accurate diagnostic tests currently available for detecting active TB and differentiate it from LTBI, immunodiagnostic biomarkers are urgently needed to monitor the progression from LTBI to clinical disease [9, 28, 29]. Studies [9, 10, 11, 30, 31] showed that one of the most promising biomarkers is HBHA. Although the existing studies explored the value of using IGRA with HBHA as a stimulating antigen to differentiate ATB from LTBI, they showed various results and it was difficult to obtain a consensus in deriving an accurate differential diagnosis [15, 16, 20, 22, 32].

This is the first systematic review and meta-analysis on the use of HBHA antigen for the differential diagnosis of ATB and LTBI. No publication bias was detected in any of the studies included in this meta-analysis. This analysis revealed that HBHA-IGRA has acceptable accuracy to differentiate ATB from LTBI in people with a normal T-cell response [sensitivity of 0.78 (95% CI: 0.71–0.85) and specificity of 0.77 (95% CI: 0.70–0.84)], and the Fagan plot also demonstrated satisfactory clinical utility (S2 Fig). However, subgroup analysis showed that the sensitivity of HBHA-induced IFN-γ release in the LTBI subjects was strongly decreased by HIV infection [sensitivity of 0.32 (95% CI: 0.16–0.47) and specificity of 0.79 (95% CI: 0.66–0.92)]. This outcome indicated that low CD4+ T cell number might make it impossible for the HBHA-IGRA to differentiate LTBI from active TB in HIV-infected patients. Interestingly, a current study [33] evaluated the performance of the HBHA-IGRA in HIV-infected individuals living in a low TB incidence country and found that some HIV-infected patients had high responses in contrast to that reported for non-HIV infected subjects; HBHA-IGRA could be more sensitive than both TST and QFT test to identify potentially Mtb-infected people. Several studies [13, 14, 18, 34] also demonstrated that both CD4+ and CD8+ T lymphocytes play major roles in IFN-γ synthesis induced by HBHA. Thus, we considered that the reasons for the low sensitivity of the HBHA-IGRA in immunocompromised people are complex and the mechanism of HIV infection affecting HBHA-induced IFN-γ release needs further investigation.

Furthermore, most of the studies did not provide the information on the number of people inoculated with BCG in the LTBI and ATB groups, resulting in the inability to conduct subgroup analysis and deduce the impact of BCG vaccination on the results of this meta-analysis. However, based on the studies [13, 15, 18, 35] investigating the potential impact of a previous BCG vaccination on the HBHA-IGRA results by testing LTBI subjects and healthy controls who can provide accurate information about their BCG vaccination status and combined with the practice of HBHA-IGRA in high TB burden countries such as China [20, 23], it may be concluded that a BCG vaccination before the HBHA-IGRA has no influence on the results.

Regarding the implementation of HBHA-IGRA, the molecular form and the concentration of HBHA are crucial for the sensitivity of the detection of LTBI subjects based on their positivity in the HBHA-IGRA test [35]. On the one hand, the IFN-γ secretion induced by native HBHA (nHBHA) has a good relationship with the recombinant HBHA purified from Mycobacterium smegmatis (rHBHA-Ms) [14]. On the other hand, T cells from the LTBI subjects who showed no or low IFN-γ response to rHBHA-Ms frequently responded to nHBHA [14, 35]. Therefore, even if no statistically significant difference was detected while using nHBHA or rHBHA-Ms as the stimulating antigen in IGRA format in differentiating the ATB from the LTBI (p = 0.47), it is crucial to use the optimal form and the concentration of the antigen. Moreover, the current results also revealed no statistical difference was observed in HBHA-IGRA with respect to the differential diagnosis of ATB and LTBI between the sample sources [peripheral blood mononuclear cells (PBMCs) or whole blood] and between the durations of the IGRA (within or more than 24 hours) (p = 0.61 and p = 0.52, respectively).

The cut-off values used by all the teams in these studies were different but mainly based on the threshold provided by the two teams from Belgium (100pg/mL) and Italy (0.25 IU/mL). This might be caused by the different monoclonal antibodies, the different reference standards and etc. used in the ELISA process. Nevertheless, upon analysis, no bias caused by the threshold was found in this study. In order to establish a commercial HBHA-IGRA kit, it is important to set an appropriate diagnostic threshold (Cut-off value) and effective reference ranges of people in different TB infection status in the future.

Our meta-analysis has some limitations. First, almost all the included studies were case-control studies. A divergence was noted in the definition of the ATB groups between microbiologically confirmed TB and clinically confirmed TB. The inclusion of the LTBI groups had different selection criteria (TST or IGRA): the TST results are more often positive than the IGRAs, the LTBI groups in different studies exhibited varied risk stratifications [11, 30, 36, 37]. Moreover, some studies specified neither the active TB type (pulmonary or extra-pulmonary TB) of the ATB subjects nor the treatment of the LTBI subjects. Hence, a potential heterogeneity could be present in the target population, resulting in the poor quality of “patient selection”. The variability of the cut-off between different studies might also be due to different demographic characteristics of the populations included in the studies. Next, we only found heterogeneity between normal people and HIV-infected people, but there is still heterogeneity in sensitivity, negative DLR, and DOR among the studies after the exclusion. Therefore, we suspect that the influencing factors included for the subgroup analysis were insufficient. Finally, although a large number of studies were screened for this review, only 13 studies were included in the final analysis. The main reason that led to several exclusions, was the lack of detailed test results using HBHA-IGRA to detect both ATB and LTBI groups. Further, six of 13 included studies were performed in Italy and four of these studies came from Belgium. This may also be a reason that why the conclusion drawn by this meta-analysis is biased.

In conclusion, the results of this meta-analysis suggest that the HBHA-IGRA can be a good diagnostic tool for the discrimination of the latent and active TB, and combination of the results of the HBHA-IGRA with those from other IGRAs (ESAT-6 and CFP-10-based) may allow optimal stratification of Mtb infected patients in different groups with variable risks of reactivation of the infection. Currently, the HBHA-IGRA is the only promising IGRA test discriminating between active TB and LTBI. However, due to the lack of large and high-quality studies in high TB burden countries and immune dysfunction people, the application conditions of HBHA-IGRA need to be clarified further. In order to commercialize the HBHA-based IGRA to efficiently distinguish LTBI from ATB, it is urgent to raise the interest of commercial companies in this test to provide kits with well-defined technical conditions and cut-off values.

Supporting information

S1 Checklist

(DOC)

Click here for additional data file.^{(65.5KB, doc)}

S1 Fig. Meta-analysis estimates of diagnostic odds ratio (DOR) of the HBHA-IGRA for discrimination of the LTBI and active TB, through deleting each study one by one.

(TIF)

Click here for additional data file.^{(332.3KB, tif)}

S2 Fig. Fagan plot to evaluate the clinical utility of the HBHA-IGRA for discrimination of the LTBI and active TB.

Pre-test probability = 50%.

(TIF)

Click here for additional data file.^{(226.9KB, tif)}

Data Availability

All relevant data are within the manuscript and its Supporting Information files.

Funding Statement

This work was supported by a grant from the National Natural Science Foundation of China (81371857).

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PLoS One. doi: 10.1371/journal.pone.0254571.r001

Decision Letter 0

Olivier Neyrolles

28 Apr 2021

PONE-D-21-06709

Mycobacterial heparin-binding hemagglutinin (HBHA)-induced interferon-γ release assay (IGRA) for discrimination of latent and active tuberculosis: a systematic review and meta-analysis

PLOS ONE

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Reviewer #1: This manuscript reports summarized results for HBHA-induced interferon-γ release assay (IGRA) for discrimination of latent and active tuberculosis using a systematic review and meta-analysis. In overall the statistical analysis follows the routine procedures for diagnostic meta-analysis. I have below comments.

The heterogeneity is high, it is not useful to simply pool the results. It will be informative to look widely and deeply about the resources for the variation and provide corresponding discussions. E.g. Data from Dirix 2016 and Delogu 2016 are quite different from other cited studies. Can you investigate the potential reasons for the observed difference?

For each measurement, it will be informative to add sensitivity analysis to find which study would mostly affect the pooled results and provide discussion.

Besides diagnostic odds ratio, please also add analysis to examine publication bias for other measures.

Reviewer #2: The manuscript is interesting and well written.

page 9, row 56: …”recommends that an interferon-γ (IFN-γ) release assay “please change as …recommends that an interferon (IFN)-γ release assay

TABLE 2: please, complete the title as Diagnostic performance of HBHA-IGRA or…LTBI discrimination? Moreover in the footnote, please explain the abbreviations: TP FP FN TN

Reviewer #3: The clinical use of the HBHA-IGRA to differentiate active TB from LTBI has been reported in several different studies. However the sample size in each study is most often limited and the HBHA-IGRA used in different publications present several technical differences, most importantly the nature of the HBHA antigen (native versus recombinant).

In this manuscript, the authors performed a systematic review of the published results to summarize the current state of this research, and they performed a meta-analysis of the published results to determine the efficacy of the HBHA-IGRA to differentiate active from latent TB for its clinical utilization.

Even if the subject is of great interest, several points need to be addressed before publication of this meta-analysis as detailed below.

Introduction

Lines 66-67 the review by Mascart F, Locht C published in 2015 in Exp Rev Vacc should be added as a ref for the discriminatory potential of HBHA between active and latent TB.

Material and methods

Study selection criteria

1. The authors indicate that the LTBI subjects were selected on the basis of a TST> 10 mm or a positive IGRA in absence of symptoms or signs of active TB.

As numerous studies reported a poor correlation between the TST results and those from IGRA, this means that the patients selection is quite different if it is based on TST results or on IGRA results. TST results are most often positive than IGRA so that different authors identify at least two different groups within TST+ LTBI, those with a positive IGRA and those with a negative IGRA (Corbiere C et al Plos One 2012; Mascart F et al Exp Rev Vacc 2015; Serrano CJ et al Clin Immunol 2015). These differences should be mentioned and discussed.

Moreover, most studies did not clearly indicated if both untreated and treated LTBI subjects were included and this point should also be mentioned as a limitation and cause of heterogeneity of the data.

2. The ATB groups is indicated to be defined as microbiologically confirmed TB patients, a definition that is quite restrictive and probably not applied by some authors. In clinical situations, diagnosis of ATB is accepted either in case of microbiological confirmation or in case of high clinical suspicion and objectivated clinical response to treatment. This heterogeneity should be mentioned.

Another heterogeneity among patients with ATB is the presence in most studies of both pulmonary and extra-pulmonary ATB and this heterogeneity should be mentioned when available for instance in Table 1, as results of the HBHA-IGRA are not necessarily similar in both forms of active TB.

Results

3.3 lines 176-177: how was the diagnostic accuracy of the HBHA-IGRA for discrimination between active and latent TB calculated when it is not provided in the original manuscript?

Same question applied for the likehood ratio, Odds ratio and diagnostic scores.

Minor comment: the same order should applied for the different studies in Table 2 and Fig 4

Discussion

Lines 212-213 – studies showed that one of the most promising biomarkers to differentiate active from latent TB is HBHA. Two ref should be added here: Mascart F , Locht C Exp Rev Vacc 2015 and Smits K et al Clin Cell Immunol 2015

Line 222: low sensitivity in immunocompromised patients. Is it not mostly / only HIV infected people? If yes, this should be mentioned.

Line 225: the authors suggest that the release of HBHA-induced IFN-g strongly depends on CD4+ T lymphocytes as its sensitivity is low in immunocompromised patients. In fact, several papers clearly demonstrated that both CD4+ and CD8+ T lymphocytes play a major role in the IFN-g synthesis induced by HBHA and this should be mentioned (ref 12-13-17). This means that the link with a lower sensitivity in immunocompromised people is not so clear for the moment and should be further investigated.

Line 229-230 – possible interference of BCG vaccination. If most studies did not provide detailed information about the BCG status of the patients, this does not mean that we cannot conclude about the possible interference of BCG/ or not on the results of the HBHA-IGRA. It is indeed often difficult to know the BCG status from patients with ATB as most often these patients do not remain themselves if they were vaccinated or not. However, several studies investigated the possible impact of a previous BCG vaccination on the HBHA-IGRA results by testing subjects with LTBI and most importantly healthy controls who can provide more consistent information about their BCG vaccination status. By doing so, it may be clearly concluded that a BCG vaccination administrated at least 10 years before the HBHA-IGRA has no influence on the results (see ref 12- 14-17-31). This point is important to mention in the discussion.

Lines 232-234: “native HBHA (nHBHA) showed better IFN-γ release than the

recombinant HBHA purified from Mycobacterium smegmatis (rHBHA-Ms) upon

peptide stimulation, »

“upon peptide stimulation” should be deleted as this is not correct. It is upon stimulation with either nHBHA or rHBHA-Ms.

Importantly, these differences between the two HBHA preparations were shown to be important for the sensitivity of detection of LTBI subjects by their positivity in the HBHA-IGRA. This paper (ref 31) does not concern differential diagnosis between active and latent TB. It should be indicated that even if the molecular form of HBHA was reported to be important for an accurate detection of LTBI, nHBHA being more sensitive that rHBHA-Ms, this is probably not the case for the differential diagnosis between LTBI and aTB as suggested by the meta-analysis reported here.

Line 243: “the best threshold should be selected”. This is impossible to do as different ELISA tests are used with different monoclonal antibodies, different standards etc….(same comment applies for line 252)

Conclusions

The main conclusion from this meta-analysis is that the HBHA-IGRA is a promising tool to differentiate active from latent TB. However, several publications indicated that an even better discrimination may be obtained by combining the results of the HBHA-IGRA to those of an ESAT-6-IGRA (or QFT ) (see for instance ref 14) and this point should be added to the discussion.

Line 260: we may not conclude that the HBHA-IGRA is a good diagnostic tool in high TB burden countries as only a low number of studies were performed in these countries.

Line 261: “the application of the HBHA-IGRA is restricted by the immune status “ Here again, there are too few studies to draw such a conclusion. Studies on the performance of the HBHA-IGRA to detect LTBI (and not for differential diagnosis between ATB and LTBI) among patients under hemodialysis have shown a high diagnostic accuracy of the HBHA-IGRA test which was more sensitive than the TST and the QFT (Dessein R et al PLoS One 2013).

Lines 262-264. If the HBHA-IGRA tests still has to be standardized for a commercial application, it should however be acknowledged that it is the only IGRA test providing actually such a good discrimination between ATB and LTBI. It is therefore now quite urgent that commercial companies became interested to introduce this test in the commercially available portofolio of standardized IGRA.

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Reviewer #1: No

Reviewer #2: No

Reviewer #3: Yes: Françoise Mascart

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PLoS One. 2021 Jul 16;16(7):e0254571. doi: 10.1371/journal.pone.0254571.r002

Author response to Decision Letter 0

24 May 2021

Dear Dr. Olivier Neyrolles and Reviewers:

Thank you for your letter and for the reviewers’ comments concerning our manuscript entitled “Mycobacterial heparin-binding hemagglutinin (HBHA)-induced interferon-γ release assay (IGRA) for discrimination of latent and active tuberculosis: a systematic review and meta-analysis” (Manuscript Number: PONE-D-21-06709). These comments are all valuable and very helpful for improving the manuscript. We have studied comments carefully and have made corrections which we hope meet with approval. The main revised portion are marked in the Revised Manuscript with Track Changes. The responds to the reviewer’s comments are as follows:

Comments for the author

1. The heterogeneity is high; it is not useful to simply pool the results. It will be informative to look widely and deeply about the resources for the variation and provide corresponding discussions. E.g. Data from Dirix 2016 and Delogu 2016 are quite different from other cited studies. Can you investigate the potential reasons for the observed difference?

Response: Thanks for your comments. Yes, the heterogeneity of the pooled results is high. In order to explore the resources for the variation, we conducted meta regression and subgroup analysis based on the characteristics of the included studies. Through subgroup analysis, we found that the inclusion of HIV-infected people is the major factor leading to the heterogeneity. Dirix 2016 and Delogu 2016 are both clinical studies involving subjects infected with HIV, so data from Dirix 2016 and Delogu 2016 are quite different from other enrolled studies. According to your suggestion, we conducted a thorough analysis and discussion on the potential reasons for the difference, related revisions are marked in the discussion.

2. For each measurement, it will be informative to add sensitivity analysis to find which study would mostly affect the pooled results and provide discussion.

Response: According to your suggestion, we added the “Sensitivity analysis” to the results to verify the robustness of the findings (Fig. 6). In our results, Hougardy 2007 would mostly affect the pooled results. After the exclusion of this study, the I2 for heterogeneity of specificity decreased from 80.05% (p=0.00) to 20.06% (p=0.24). Simultaneously, Dirix 2016 would mostly affect the pooled diagnostic efficacy. After the exclusion of this study, the I2 for heterogeneity of diagnostic score decreased from 68.9% (p=0.00) to 28.7% (p=0.16), the diagnostic odds ratio (DOR) increased from 8.11 to 9.94. However, through omitting each study one by one (Fig. S1), the outcomes indicated that the DORs did not change significantly in all models, showing that our results are stable and reliable. The related revisions are marked in the paper.

3. Besides diagnostic odds ratio, please also add analysis to examine publication bias for other measures.

Response: Thanks for your suggestion. However, separate funnel plots for sensitivity and specificity (after logit transformation) are unlikely to be helpful for detecting sample size effects, because sensitivities and specificities will vary due to both variability of threshold between the studies and random variability. Simultaneous interpretation of two related funnel plots and two tests for funnel plot asymmetry also presents difficulties. At present, formal testing for publication bias may be conducted by a regression of lnDOR (Diagnostic Odds Ratio) against 1/ESS1/2, weighting by ESS (Deeks, 2005), with P <0.05 for the slope coefficient indicating significant asymmetry. For the meta-analysis of the accuracy of diagnostic tests, the examination method of publication bias is limited to Deeks’ test. Other methods such as Egger, Begg, Harbord and Peters tests commonly used in intervention studies are not suitable here due to the high false positive rate.

Reviewer #2: The manuscript is interesting and well written.

Comments for the author

1. Page 9, row 56: “recommends that an interferon-γ (IFN-γ) release assay” please change as “recommends that an interferon (IFN)-γ release assay”.

Response: Thank you for your valuable comments. We changed the words. The related revised portion is marked in red in the paper.

2. TABLE 2: please, complete the title as Diagnostic performance of HBHA-IGRA or…LTBI discrimination? Moreover, in the footnote, please explain the abbreviations: TP FP FN TN.

Response: The imperfect titles of TABLE 2 and TABLE 3 are completed in the paper, and the related abbreviations in the footnote of TABLE 2 is added.

Reviewer #3: The clinical use of the HBHA-IGRA to differentiate active TB from LTBI has been reported in several different studies. However, the sample size in each study is most often limited and the HBHA-IGRA used in different publications present several technical differences, most importantly the nature of the HBHA antigen (native versus recombinant). In this manuscript, the authors performed a systematic review of the published results to summarize the current state of this research, and they performed a meta-analysis of the published results to determine the efficacy of the HBHA-IGRA to differentiate active from latent TB for its clinical utilization. Even if the subject is of great interest, several points need to be addressed before publication of this meta-analysis as detailed below.

Comments for the author

1. Introduction:

Lines 66-67 the review by Mascart F, Locht C published in 2015 in Exp Rev Vacc should be added as a ref for the discriminatory potential of HBHA between active and latent TB.

Response: Thank you very much for your detailed and precious comments. We read this review carefully and benefited a lot. The reference has been added in this part.

2. Material and methods:

Study selection criteria

2.1. The authors indicate that the LTBI subjects were selected on the basis of a TST> 10 mm or a positive IGRA in absence of symptoms or signs of active TB. As numerous studies reported a poor correlation between the TST results and those from IGRA, this means that the patient’s selection is quite different if it is based on TST results or on IGRA results. TST results are most often positive than IGRA so that different authors identify at least two different groups within TST+ LTBI, those with a positive IGRA and those with a negative IGRA (Corbiere V et al Plos One 2012; Mascart F et al Exp Rev Vacc 2015; Serrano CJ et al Clin Immunol 2015). These differences should be mentioned and discussed. Moreover, most studies did not clearly indicate, if both untreated and treated LTBI subjects were included and this point should also be mentioned as a limitation and cause of heterogeneity of the data.

Response: We very much agree with your suggestion that the LTBI subjects should be further stratified based on the results of TST and IGRA results. We reviewed all published studies of the clinical use of the HBHA-IGRA to differentiate active TB from LTBI, most studies used “a positive IGRA result” as the selection criteria for the LTBI subjects, and a small number of early studies used “healthy people with a TST≥10 mm” as the criterion for the LTBI subjects. However, it is not clear from enrolled articles whether the LTBI subjects had both received the TST and IGRA tests and whether they received treatment. At the same time, although the patient’s selection is quite different whether it is based on TST results or on IGRA results, the incidence of active TB is still substantial in numerous at-risk populations after a positive TST or IGRA result (Campbell JR et al BMJ 2020). Therefore, in order to analyze as comprehensively as possible and avoid excessive exclusion, we defined the LTBI subjects as individuals with a TST≥10 mm or a positive IGRA in absence of symptoms or signs of active TB but were at risk for the active TB. Certainly, the difference in the enrollment of LTBI subjects due to the different IGRA screening tests (TST and IGRA) and the difference between untreated and treated LTBI subjects must also be added in the limitation and the resources of heterogeneity of the data. The related revised portion is marked in the paper.

2.2. The ATB groups is indicated to be defined as microbiologically confirmed TB patients, a definition that is quite restrictive and probably not applied by some authors. In clinical situations, diagnosis of ATB is accepted either in case of microbiological confirmation or in case of high clinical suspicion and objectivated clinical response to treatment. This heterogeneity should be mentioned. Another heterogeneity among patients with ATB is the presence in most studies of both pulmonary and extra-pulmonary ATB and this heterogeneity should be mentioned when available for instance in Table 1, as results of the HBHA-IGRA are not necessarily similar in both forms of active TB.

Response: Based on your suggestion, we added the “Active TB definition” part and the “Active TB type” part in Table 1. Through subgroup analysis (added in Table 3), we found the studies which defined ATB groups as microbiologically confirmed TB patients tend to have lower sensitivity and higher specificity than the studies which active TB definition was based either on microbiological proof or on high clinical suspicion with favorable response to anti-TB treatment. The heterogeneity is high (I2=62%). However, although the ATB subjects in most studies included both pulmonary and extra-pulmonary ATB patients, only four of the 13 enrolled studies provided relevant data. Therefore, as a possible source of heterogeneity, it can only be elaborated in the discussion. The related revised portion is marked in the results and the discussion.

3. Results:

lines 176-177: how was the diagnostic accuracy of the HBHA-IGRA for discrimination between active and latent TB calculated when it is not provided in the original manuscript? Same question applied for the likehood ratio, Odds ratio and diagnostic scores.

Minor comment: the same order should be applied for the different studies in Table 2 and Fig 4

Response: We first searched all original research papers that used the HBHA-IGRA to differentiate active TB from LTBI through a specific search strategy in public databases. As there are a few studies in this field at present, 13 of the 30 related literature were selected for further meta-analysis. In order to avoid inappropriate inclusion and exclusion, we conducted the inclusion criteria for the active TB and LTBI subjects, and selected subjects from each article to be enrolled in our study based on these criteria (not all the active TB and LTBI subjects in each article were included in this meta-analysis). According to the summary charts of the IFN-γ levels induced by HBHA in the ATB and LTBI subjects and the cut-off values of HBHA-IGRA provided in each article, we would able to obtain the number of true positives, false positives, false negatives, and true negatives, and then used this data to construct a diagnostic 2x2 table. Using the model estimated coefficients and variance-covariance matrices, the Stata v.14.0 software could calculate the pooled sensitivity and specificity, summary likelihood and odds ratios, diagnostic scores and Summary Receiver Operating Characteristic Curves (SROC), and the global and relevant test performance metric-specific heterogeneity statistics are also provided. In addition, the same order has been used for the different studies in Table 2 and Fig 4, the related revised portion is marked in the paper.

4. Discussion:

4.1. Lines 212-213: studies showed that one of the most promising biomarkers to differentiate active from latent TB is HBHA. Two refs should be added here: Mascart F, Locht C Exp Rev Vacc 2015 and Smits K et al Clin Cell Immunol 2015

Response: “Integrating knowledge of Mycobacterium tuberculosis pathogenesis for the design of better vaccines.” (Mascart F, Locht C Exp Rev Vacc 2015) has been added here as a ref. However, we searched the public databases and did not find the other ref (Smits K et al Clin Cell Immunol 2015). We would appreciate it if you could provide more specific information.

4.2. Line 222: low sensitivity in immunocompromised patients. Is it not mostly / only HIV infected people? If yes, this should be mentioned.

Response: Yes. The paper only conducted the subgroup analysis on HIV-infected and uninfected people, so “immunocompromised patients” here is not appropriate. The portion has been revised and marked in the paper.

4.3. Line 225: the authors suggest that the release of HBHA-induced IFN-g strongly depends on CD4+ T lymphocytes as its sensitivity is low in immunocompromised patients. In fact, several papers clearly demonstrated that both CD4+ and CD8+ T lymphocytes play a major role in the IFN-g synthesis induced by HBHA and this should be mentioned (ref 12-13-17). This means that the link with a lower sensitivity in immunocompromised people is not so clear for the moment and should be further investigated.

Response: After HIV infects the human body, the main target cells are CD4+ T lymphocytes. Simultaneously, the sensitivity of the HBHA-IGRA of the HIV-infected enrolled studies is generally low. Our discussion here was to suppose that whether the decline in IFN-γ release induced by HBHA is mainly caused by HIV damage to CD4+ T lymphocytes. It was not intended to show that the release of HBHA-induced IFN-γ only depends on CD4+ lymphocytes. Because the mechanism by HBHA-induced high levels of IFN-γ in LTBI people may be very complicated and remains unclear. Thank you for pointing out our problem. Based on your suggestions, we modify the discussion here, and emphasize the important role of CD4+ and CD8+ T lymphocytes in the IFN-γ synthesis induced by HBHA.

4.4. Line 229-230: possible interference of BCG vaccination. If most studies did not provide detailed information about the BCG status of the patients, this does not mean that we cannot conclude about the possible interference of BCG/ or not on the results of the HBHA-IGRA. It is indeed often difficult to know the BCG status from patients with ATB as most often these patients do not remain themselves if they were vaccinated or not. However, several studies investigated the possible impact of a previous BCG vaccination on the HBHA-IGRA results by testing subjects with LTBI and most importantly healthy controls who can provide more consistent information about their BCG vaccination status. By doing so, it may be clearly concluded that a BCG vaccination administrated at least 10 years before the HBHA-IGRA has no influence on the results (see ref 12- 14-17-31). This point is important to mention in the discussion.

Response: We agree with your opinion. The inability to conduct subgroup analysis does not mean that the conclusion cannot be drawn from the previous studies. The studies you provided and several studies in China where BCG is commonly vaccinated all showed that a previous BCG vaccination had no influence on the efficacy of the HBHA-IGRA in differentiating active TB from LTBI. This point has been added in the discussion.

4.5. Lines 232-234: “native HBHA (nHBHA) showed better IFN-γ release than the recombinant HBHA purified from Mycobacterium smegmatis (rHBHA-Ms) upon peptide stimulation”-“upon peptide stimulation” should be deleted as this is not correct. It is upon stimulation with either nHBHA or rHBHA-Ms.

Response: As you suggested, it is stimulated by purified nHBHA or rHBHA-Ms antigen protein, not the peptides, “upon peptide stimulation” has been deleted. The expression “although a recent study (ref 31) found that the native HBHA (nHBHA) showed better IFN-γ release than the recombinant HBHA purified from Mycobacterium smegmatis (rHBHA-Ms)” in our paper is inaccurate. Compared with rHBHA-Ms, nHBHA is more easily recognized by T cells from latently-infected humans (about 100-fold better), so it has better sensitivity in detection of LTBI subjects, as shown in Corbière V J Immunol 2020 (ref 31). Therefore, even if there is no statistically significant difference of using nHBHA or rHBHA-Ms as stimulating antigen in IGRA format in differentiating the ATB from the LTBI (p=0.47), it is crucial to use the optimal form and concentration of the antigen. The related portion revised according to your suggestion is indeed more in line with the real situation.

4.6. Line 243: “the best threshold should be selected”. This is impossible to do as different ELISA tests are used with different monoclonal antibodies, different standards etc. (same comment applies for line 252)

Response: For studies with small sample size of different teams in different countries, it is indeed impossible to select the best threshold in the meta-analysis. However, for large multi-centre studies aimed at producing mature commercial HBHA-IGRA kits, it is very important to establish effective reference ranges in people with different TB infection status and select the best diagnostic threshold (Cut-off value) based on the clinical test results. The expression in our paper may have caused confusion, and it has been revised.

5. Conclusions:

5.1. The main conclusion from this meta-analysis is that the HBHA-IGRA is a promising tool to differentiate active from latent TB. However, several publications indicated that an even better discrimination may be obtained by combining the results of the HBHA-IGRA to those of an ESAT-6-IGRA (or QFT) (see for instance ref 14) and this point should be added to the discussion.

Response: We agree with your suggestion. As shown in our previous study (Tang JH et al Tuberculosis 2020) and the recent publications (Hougardy JM et al Plos One 2007, Sali M et al J Infection 2018 and Chedid C et al Front Immunol 2020), the combination of the results of the HBHA-IGRA and the commercial IGRAs based on ESAT-6 or CFP-10 (such as QFT, etc.) may indeed have a better discrimination. This point has been added to the discussion in the revised paper.

5.2. Line 260: we may not conclude that the HBHA-IGRA is a good diagnostic tool in high TB burden countries as only a low number of studies were performed in these countries.

Response: Three of the 13 included studies were conducted in high TB burden countries. Although they are all the studies with small sample size, there is no significant difference between high TB burden countries and low TB burden countries in the differential diagnosis efficacy of HBHA-IGRA through statistical analysis. Perhaps because there are only a low number of studies in high TB burden countries, the pooled results are not as reliable as the results from the countries in low TB burden. Therefore, the conclusion has been softened, the related revised portion is marked in the paper.

5.3. Line 261: “the application of the HBHA-IGRA is restricted by the immune status” Here again, there are too few studies to draw such a conclusion. Studies on the performance of the HBHA-IGRA to detect LTBI (and not for differential diagnosis between ATB and LTBI) among patients under hemodialysis have shown a high diagnostic accuracy of the HBHA-IGRA test which was more sensitive than the TST and the QFT (Dessein R et al PLoS One 2013).

Response: Indeed, the expression “the application of the HBHA-IGRA is restricted by the immune status” here is one-sided and lacks evidence. Therefore, we further checked the relevant literature and revised this part to make the expression more rigorous. The expression “the application of the HBHA-IGRA still need to be further clarified” may be more appropriate here.

5.4. Lines 262-264. If the HBHA-IGRA tests still has to be standardized for a commercial application, it should however be acknowledged that it is the only IGRA test providing actually such a good discrimination between ATB and LTBI. It is therefore now quite urgent that commercial companies became interested to introduce this test in the commercially available portfolio of standardized IGRA.

Response: We are pleased with your comments in the Conclusion. We have revised the relevant expressions in this part, hoping to better express our propose: make HBHA-IGRA get more attention from commercial companies and can be applied to commercial IGRA tests quickly.

Kind regards,

Yueyun Ma

Clinical Laboratory

Air Force Medical Centre

30 Fucheng Road, Beijing, China 100142

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PLoS One. doi: 10.1371/journal.pone.0254571.r003

Decision Letter 1

Olivier Neyrolles

10 Jun 2021

PONE-D-21-06709R1

Mycobacterial heparin-binding hemagglutinin (HBHA)-induced interferon-γ release assay (IGRA) for discrimination of latent and active tuberculosis: a systematic review and meta-analysis

PLOS ONE

Dear Dr. Ma,

Thank you for submitting your revised manuscript to PLOS ONE. After careful consideration, we feel that it has merit but still does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a further revised version of the manuscript that addresses the remaining minor points raised during the review process, before we can proceed for acceptance.

Also, as reviewer #3 pointed out, I strongly encourage you to have the manuscript edited by a native English speaker.

Please submit your revised manuscript by Jul 25 2021 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
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We look forward to receiving your revised manuscript.

Kind regards,

Olivier Neyrolles

Academic Editor

PLOS ONE

Journal Requirements:

Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

Reviewer #2: All comments have been addressed

Reviewer #3: (No Response)

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2. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #1: (No Response)

Reviewer #2: Yes

Reviewer #3: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: (No Response)

Reviewer #2: Yes

Reviewer #3: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #1: (No Response)

Reviewer #2: Yes

Reviewer #3: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #1: (No Response)

Reviewer #2: Yes

Reviewer #3: No

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6. Review Comments to the Author

Reviewer #1: (No Response)

Reviewer #2: please, in table 1, in the column indicating TYPE OF TB, regarding the papers by:

1. Delogu, Plos One 2011

2; Chiacchio, PloS One, 2017:

please indicate that the type of TB was PULMONARY TB in 100% of the TB patients evaluated, as stated in the matherial and method sections where it is written that TB was microbiologically diagnosed on sputum.

Moreover, in the paper by Sali et al, you may see in Table 1, that extrapumonary TB is in 5 over 19 (26%)

Reviewer #3: The authors appropriately answer to my comments and I only have a few remaining comments.

1. Abstract

a. Results – line 40: why did you replace “did” by “”may”? I think “did “ was more appropriate

b. Conclusion. I suggest to delete the last sentence that was added. It is unnecessary and does not correspond to the main message of your meta-analysis. You clearly show that the HBHA-IGRA is very robust and minimally influenced by various technical differences between the studies. Therefore it is not appropriate to conclude that large and high quality studies are further needed. What is necessary now as mentioned at the end of you discussion, is to arise the interest of a commercial company.

2. Discussion

a. I still have a concern concerning the interpretation of the results obtained in studies included in your meta-analysis and comprising HIV-infected patients. You conclude that low responses in these patients may be due to the low CD4+ T cell number in these patients (lines 259-262). However, Wyndham-Thomas et al who were in 2015 the first to evaluate the performance of the HBHA-IGRA in HIV-infected people living in a low TB incidence country, reported that the HBHA-IGRA was more sensitive that both the TST and QFT test to identify potentially Mtb infected people (3 subjects with an isolated HBHA-IGRA had a high Mtb exposure risk). In addition, they showed that the 3 HIV-infected patients with a positive HBHA-IFN-g response had very high responses contrasting to what is reported for non-HIV infected subjects. This observation does not sustain the hypothesis that low CD4+ T cell number account for the low sensitivity of the HBHA IGRA often reported in HIV-infected subjects.

b. Lines 290-291: variability of the cut-off between different studies may also be due to different demographic characteristics of the populations included in the studies.

3. Conclusion

a. Lines 323-324…I suggest “…combination of the results of the HBHA-IGRA with those from other IGRAs (ESAT-6 and CFP-10-based) may allow optimal stratification of Mtb infected patients in different groups with variable risks of reactivation of the infection. Currently, the HBHA-IGRA is the only…..”

b. line 330-331. I do not understand your statement “the procedure of this test needs to be further standardized and optimized” as you clearly showed that technical differences had no impact on the diagnostic performance.

I suggest to modify this sentence as follows: “To commercialize the HBHA-based IGRA to efficiently distinguish LTBI from ATB, it is urgent to rise the interest of commercial companies to this test in order to provide kits with well defined technical conditions and cut off”

4. Table 3

Please correct the subgroups within active TB. I guess the 2nd group is “Clinically confirmed TB”

In the footnote of the table, Mycobacterium smegmatis should be written in italic.

I previously suggest the authors to cite a paper published by Smits K in 2015 and the authors did not find the reference. There was indeed a mistake and I apologize for this. The exact ref is J. Clin. Cell. Immunol. 2015; 6:4 at http://dx.doi.org/10.4172/2155-9899. 1000341

Finally, I strongly suggest the authors to have a final re-lecture and correction by a native English speaking people as the English language should really be improved mostly, but not exclusively, for the modified sentences in the revised version of the manuscript.

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Reviewer #1: No

Reviewer #2: No

Reviewer #3: Yes: Françoise Mascart

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PLoS One. 2021 Jul 16;16(7):e0254571. doi: 10.1371/journal.pone.0254571.r004

Author response to Decision Letter 1

20 Jun 2021

Dear Dr. Olivier Neyrolles and Reviewers:

Thank you for your and the reviewers’ comments concerning our manuscript entitled “Mycobacterial heparin-binding hemagglutinin (HBHA)-induced interferon-γ release assay (IGRA) for discrimination of latent and active tuberculosis: a systematic review and meta-analysis” (Manuscript Number: PONE-D-21-06709R1). Your comments and those of the reviewers were highly insightful and enabled us to greatly improve the quality of our manuscript. We have studied comments carefully and have made corrections which we hope meet with approval. The revised portion are marked in the Revised Manuscript with Track Changes. The responds to the reviewer’s comments are as follows:

Reviewer #1: (No Comments)

Reviewer #2:

Comments for the author

please, in table 1, in the column indicating TYPE OF TB, regarding the papers by:

1. Delogu, Plos One 2011

2; Chiacchio, PloS One, 2017:

please indicate that the type of TB was PULMONRY TB in 100% of the TB patients evaluated, as stated in the material and method sections where it is written that TB was microbiologically diagnosed on sputum.

Moreover, in the paper by Sali et al, you may see in Table 1, that extrapumonary TB is in 5 over 19 (26%)

Response: Thank you very much for your reminder of the omissions in our paper. The related revised portions are marked in the paper.

Reviewer #3: The authors appropriately answer to my comments and I only have a few remaining comments.

Comments for the author

1. Abstract:

a. Results – line 40: why did you replace “did” by “may”? I think “did” was more appropriate

Response: Thank you for your advice. The related revised portion is marked in the paper.

b. Conclusion. I suggest to delete the last sentence that was added. It is unnecessary and does not correspond to the main message of your meta-analysis. You clearly show that the HBHA-IGRA is very robust and minimally influenced by various technical differences between the studies. Therefore, it is not appropriate to conclude that large and high quality studies are further needed. What is necessary now as mentioned at the end of your discussion, is to arise the interest of a commercial company.

Response: Thanks. Your comment is right. The last sentence here may cause misunderstanding and confusion. Therefore, we delete this sentence according to your suggestion.

2. Discussion:

Response: Considering your suggestion, we have substantially elucidated both the outcome of subgroup analysis in HIV-infected people and the observation in the study of Wyndham-Thomas et al in 2015 in the discussion. we consider that the reasons for the low sensitivity of the HBHA-IGRA in HIV-infected patients are complex, and suggest that the mechanism of HIV infection affecting HBHA-induced IFN-γ release still needs further investigation. The specific mechanism is not the main focus of the discussion in this paper.

b. Lines 290-291: variability of the cut-off between different studies may also be due to different demographic characteristics of the populations included in the studies.

Response: We support your suggestion. As you suggested, the related discussion has been added in the limitation.

3. Conclusion

Response: Thank you for your suggestion. The related revised portion is marked in the paper.

Response: Thank you for your suggestion. We have re-written this part according to your suggestion. The related revised portion is marked in the paper.

4. Table 3

Please correct the subgroups within active TB. I guess the 2nd group is “Clinically confirmed TB”

In the footnote of the table, Mycobacterium smegmatis should be written in italic.

Response: We are sorry for our incorrect writing. The related revised portions are marked in the paper.

Response: Thank you very much for providing the specific information of the reference, the reference has been cited in this paper.

Response: Thanks for pointing out our language problem. We employed an English-language editing service, MedSci, to polish our wording. Certification is attached.

Kind regards,

First Author: Jinhua Tang

Corresponding author: Yueyun Ma

E-mail addresses: mayueyun2020@163.com

Clinical Laboratory

Air Force Medical Centre

30 Fucheng Road, Beijing, China 100142

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Submitted filename: Response to Reviewers.docx

Click here for additional data file.^{(1MB, docx)}

PLoS One. doi: 10.1371/journal.pone.0254571.r005

Decision Letter 2

Olivier Neyrolles

30 Jun 2021

Mycobacterial heparin-binding hemagglutinin (HBHA)-induced interferon-γ release assay (IGRA) for discrimination of latent and active tuberculosis: a systematic review and meta-analysis

PONE-D-21-06709R2

Dear Dr. Ma,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

An invoice for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org.

If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

Kind regards,

Olivier Neyrolles

Section Editor

PLOS ONE

Additional Editor Comments (optional):

During the proofreading process, please proceed to the following modifications:

Line 274 « nHBHA frequently responded to the T cells from the LTBI subjects …..should be replaced by “T cells from LTBI subjects who showed …..frequently responded to nHBHA”

Line 296 “the TST results are often positive than the IGRAs” should be replaced by “the TST results are more often positive than the IGRAs”

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

Reviewer #3: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #3: Yes

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3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #3: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #3: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #3: Yes

**********

6. Review Comments to the Author

Reviewer #3: (No Response)

**********

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PLoS One. doi: 10.1371/journal.pone.0254571.r006

Acceptance letter

Olivier Neyrolles

7 Jul 2021

PONE-D-21-06709R2

Mycobacterial heparin-binding hemagglutinin (HBHA)-induced interferon-γ release assay (IGRA) for discrimination of latent and active tuberculosis: a systematic review and meta-analysis

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 Checklist

(DOC)

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S1 Fig. Meta-analysis estimates of diagnostic odds ratio (DOR) of the HBHA-IGRA for discrimination of the LTBI and active TB, through deleting each study one by one.

(TIF)

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S2 Fig. Fagan plot to evaluate the clinical utility of the HBHA-IGRA for discrimination of the LTBI and active TB.

Pre-test probability = 50%.

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Data Availability Statement

All relevant data are within the manuscript and its Supporting Information files.

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PERMALINK

Mycobacterial heparin-binding hemagglutinin (HBHA)-induced interferon-γ release assay (IGRA) for discrimination of latent and active tuberculosis: A systematic review and meta-analysis

Jinhua Tang

Yuan Huang

Zheng Cai

Yueyun Ma

Roles

Abstract

Background

Methods

Results

Conclusion

1. Introduction

2. Materials and methods

2.1. Search strategy

2.2. Study selection criteria

2.3. Data extraction and quality assessment

2.4. Statistical analysis

3. Results

3.1. Search results and the study characteristics

Fig 1. Flow diagram of the study screening process.

Table 1. Characteristics of studies included in the meta-analysis.

Table 2. Diagnostic performance of the HBHA-IGRA for discrimination of the LTBI and active TB.

3.2. Quality assessment and publication bias

Fig 2. Summary of articles included regarding the risk of bias and applicability concerns.

Fig 3. Deeks’ funnel plot asymmetry test.

3.3. Threshold and diagnostic accuracy of the HBHA-IGRA for discrimination of LTBI and active TB

Fig 4.

Fig 5. Summary receiver operating characteristic (ROC) curves of the HBHA-IGRA for discrimination of the LTBI and active TB.

3.4. Meta-regression and subgroup analysis

Table 3. Subgroup analysis of the HBHA-IGRA for discrimination of the LTBI and active TB.

3.5. Sensitivity analysis

Fig 6.

4. Discussion

Supporting information

Data Availability

Funding Statement

References

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Olivier Neyrolles

Roles

Author response to Decision Letter 0

Decision Letter 1

Olivier Neyrolles

Roles

Author response to Decision Letter 1

Decision Letter 2

Olivier Neyrolles

Roles

Acceptance letter

Olivier Neyrolles

Roles

Associated Data

Supplementary Materials

Data Availability Statement

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