Diagnostic value of nanopore-based targeted sequencing technology for subclinical tuberculosis

Abstract

Background

Subclinical Tuberculosis (TB), with low bacterial loads and non-specific symptoms, is difficult to diagnose and promotes transmission. This study aimed to evaluate the diagnostic performance of nanopore-based targeted sequencing (NTS) for subclinical TB in comparison with Mycobacteria Growth Indicator Tube (MGIT) culture and GeneXpert MTB/RIF (Xpert MTB/RIF) assays.

Methods

This retrospective research included 103 subclinical TB suspects. We tested sputum, bronchoalveolar lavage fluid, or pleural effusion from each patient using NTS, MGIT culture, and Xpert. Diagnostic performance was assessed using a composite standard.

Results

NTS exhibited higher sensitivity (90.9%) than MGIT culture (40.9%) and Xpert MTB/RIF (50.0%). It had a larger area under the curve (AUC) of 0.923 than Xpert (0.731) and MGIT culture (0.667), as well as an impressive negative predictive value (97.4%) and accuracy (93.2%).

Conclusion

NTS efficiently identifies subclinical TB cases with low bacterial burdens using the composite diagnostic criteria, outperforming conventional methods. It could detect TB early, closing the gap in TB control.

Introduction

Subclinical Tuberculosis (TB) denotes a condition where a person carries viable Mycobacterium TB (MTB) without exhibiting any symptoms indicative of TB during screening. However, the disease can be detected through bacteriological tests (such as culture, PCR, and rapid diagnostics) and/or radiographic assessments (like chest X-rays) [1]. It lies between latent infection and clinically active TB. Although there is currently no consensus on the definition of subclinical TB, most national programs define it as “a simple symptom screening of presumed TB patients, regardless of other symptoms, provided they have no cough lasting ≥ 2 weeks.” [2].

The global burden of subclinical TB is currently significant. The World Health Organization (WHO) estimates indicate that around 7 million individuals globally experience a subclinical state of TB annually [3]. Recent studies revealed that subclinical TB patients constituted around 50% of all active TB cases worldwide [2, 4]. However, the current focus of TB prevention and control efforts remains primarily on symptomatic TB patients, with a significant lack of research on subclinical TB [5, 6].

MTB is transmitted through respiratory droplets expelled from the lungs. Coughing is generally considered the primary mode of transmission for TB [7]. Although subclinical TB patients lack obvious clinical symptoms such as persistent coughing, respiratory droplets can still be expelled through activities like singing, speaking, and breathing. Therefore, their contribution to community transmission remains substantial [8]. A recent study on TB transmission found that 40–79% of patients likely transmitted the disease before symptoms appeared [9]. This confirms that while transmission rates during the asymptomatic stage are lower than in the exacerbation phase, they can lead to widespread transmission [10]. However, subclinical TB patients have limited sputum, negative bacteriological tests, and are often misdiagnosed due to the absence of TB symptoms. Four main pathways lead to their detection: (1) incidental detection of abnormal chest radiographs during routine physical examinations or occupational health screenings; (2) active case finding among close contacts of confirmed TB patients; (3) incidental findings during medical evaluations for other unrelated conditions; and (4) regular screening in high-risk populations like HIV-positive people, prisoners, and the elderly [11].

Early diagnosis of subclinical TB is of utmost clinical and public health importance. First, almost half of the untreated subclinical TB infections can progress to symptomatic active tuberculosis, which can lead to lung damage, treatment difficulties and death. Second, delayed detection of this “hidden reservoir” leads to disease progression and TB transmission in the community, impeding the WHO’s End TB Strategy goals. Thus, enhancing diagnostic efficiency for subclinical TB patients is a critical issue.

Currently, TB patients lacking pathogenetic evidence and TB-related symptoms often fail to receive early diagnosis [8, 12]. Smear microscopy, mycobacterial liquid culture, and the molecular diagnostic technique GeneXpert MTB/RIF (Xpert MTB/RIF) are widely used. However, the sensitivity of smear microscopy is about 30% [13]. Mycobacterial culture is considered the gold standard method, but it is time-consuming, with positive results taking an average of approximately two weeks [14]. This delays treatment and increases the risk of MTB transmission. The Xpert MTB/RIF method is relatively rapid and exhibits acceptable specificity, but its sensitivity remains insufficient to meet clinical needs, primarily because subclinical TB patients typically have scant sputum and low bacterial loads [15]. In cases of negative pathogen detection, clinicians face challenges in diagnosing TB based solely on imaging and immunological test results.

Instead of sequencing the genome, nanopore-based targeted sequencing (NTS) analyzes specific DNA or RNA regions and genes related to infection or treatment resistance [16]. Using nanopore sequencing, single-stranded DNA or RNA passes through nanoscale pores, altering ionic currents. These enable NTS to rapidly identify pathogens or genetic markers in clinical samples, including direct detection of MTB without culture [16]. NTS detects low-concentration infections through targeted enrichment, making it ideal for subclinical diseases with low bacterial loads [17]. Its portable design suits resource-constrained environments and requires low sample volumes [18]. However, most NTS studies have focused on symptomatic TB, with few examining low-bacterial-load, asymptomatic subclinical TB [19–21]. This study focuses on the “hidden population” within the TB spectrum: subclinical TB patients. By analyzing and comparing the diagnostic performance of NTS, MGIT culture, and Xpert MTB/RIF technology, we aim to address gaps in TB prevention and control, offering new insights for early screening and intervention.

Materials and methods

Study design

This retrospective study was conducted at Chun’an First People’s Hospital (a designated TB hospital in Hangzhou City). We reviewed clinical data from patients suspected of MTB infection without typical TB symptoms between March 2024 and December 2025. Inclusion criteria: (1) Patients who underwent acid-fast bacilli smear microscopy, MTB culture, and Xpert MTB/RIF testing; (2) Radiological findings of pulmonary nodules or patchy shadows, or immunological tests (e.g., tuberculin skin test, gamma-interferon release assay, anti-TB antibody detection) suggesting Mycobacterium TB infection. Exclusion criteria: (1) Persistent cough lasting ≥ 2 weeks; (2) Concurrent HIV infection. This study was approved by the Ethics Committee of the Chun’an First People’s Hospital. Given the retrospective nature of this study and the anonymity of all data, patient-informed consent was waived.

The diagnostic criteria for TB are based on the Health Industry Standard of the People’s Republic of China—Diagnosis for Pulmonary TB (WS 288–2017). The gold standard is a combination of MTB culture and clinical diagnosis, which must meet one or more of the following criteria: (1) Positive mycobacterial culture (species identified as MTB complex) or positive molecular biology test; (2) Pulmonary tissue biopsy demonstrating pathological features consistent with TB; (3) Clinically diagnosed pulmonary TB in culture-negative patients, with reduction or disappearance of pulmonary lesions after 3 months of preventive anti-TB therapy. All culture-negative cases underwent comprehensive differential diagnosis to rule out alternative causes of pulmonary lesions before initiating anti-TB therapy.

Clinical specimens

Samples analyzed were sputum, bronchoalveolar lavage fluid (BALF), and pleural effusion of patients. Sputum specimens included both nocturnal and morning sputum. All sputum specimens were non-putrid, non-dried, and uncontaminated; BALF samples were obtained via bronchoscopy by instilling 50–100 mL of sterile saline into the suspected lesion segment and retrieving 5–20 mL of fluid. Pleural fluid samples were collected under sterile conditions via thoracentesis to aspirate pleural effusion. For each patient’s clinical specimen, the sample was vortexed vigorously at maximum speed for 20 s to ensure uniform distribution of mycobacteria within the specimen. For viscous purulent specimens, 1–3 times the volume of PBS was added to liquefy the sample before vortexing. Equally divided samples were analyzed by MGIT culture, Xpert MTB/RIF assay, and NTS. All samples were collected under strict aseptic technique to avoid contamination by oral, skin, and environmental bacteria.

MGIT culture and species identification

Following NALC-NaOH processing, the specimens were rinsed twice with sterile PBS. Subsequently, 0.5 milliliters of the suspension were inoculated into MGIT culture tubes containing 0.8 milliliters of BACTEC MGIT culture supplement and a mixture of antimicrobial agents. All procedures were conducted within a Class II biosafety cabinet. The MGIT tube was scanned into the BACTEC MGIT instrument and incubated at 37 °C. The system automatically recorded fluorescence signals every 60 min and reported positive results. When a positive signal was detected, a smear from 0.2 mL of culture was heat-fixed and Ziehl–Neelsen stained to confirm acid-fast bacilli. After confirmation of a positive MGIT culture, the sample was sent to the municipal center for disease control and prevention for mycobacterial identification. The procedure involved mixing 0.4 mL of the positive broth with 4% saline, allowing it to stand for 15 min, and adjusting to McFarland 1.0 turbidity. One-hundred-microliter samples were inoculated into three MGIT tubes: a control, one with 500 µg/mL p-nitrobenzoic acid (PNB), and another with 5 µg/mL thiophene-2-carboxylic acid hydrazide (TCH). Tubes were re-incubated and monitored daily for time-to-positivity. Isolates delayed by PNB (> 5 days) were identified as MTB complex; growth within 5 days indicated non-tuberculous mycobacteria (NTM). Within MTBC, growth in TCH within 5 days suggested M. bovis, while no growth indicated MTB.

Xpert test

Xpert MTB/RIF testing was performed following the manufacturer’s instructions (Cepheid, Sunnyvale, CA, USA). Briefly, sputum specimens were mixed with the sample processing solution at a 1:1 to 1:2 ratio, while centrifuged BALF and pleural effusion specimens were mixed with 2 mL of the sample processing solution. The mixture was vortexed vigorously for 20 s and incubated at room temperature for 15 min to liquefy and decontaminate the samples. Then, 2 mL of the processed sample was carefully added to an Xpert MTB/RIF reaction cartridge, which was then inserted into the GeneXpert instrument module for fully automated nucleic acid extraction, amplification, and detection. Results for the detection of MTB complex were automatically generated by the system within 2 h.

NTS

Specimen pretreatment

Viscous specimens (sputum and viscous BALF): The specimen was mixed with liquefaction buffer in a ratio of 1:1 (minimum 1:9), followed by the addition of 20 µL of XC reagent. XC is an external standard included in the kit, used to monitor whether the testing process is proceeding correctly. The mixture was vortexed thoroughly and incubated at 37 °C for 10 min. After centrifugation at 12,000 rpm for 2 min, the supernatant was discarded, and 30–40 µL of residual liquid containing the bacterial pellet was reserved. Pleural effusion specimens: Take 5 mL of pleural effusion, centrifuge at 12,000 rpm for 2 min, discard the supernatant, and reserve 1 mL of sediment. Transfer to a new tube, add 20 µL of XC reagent, centrifuge at 12,000 rpm for 2 min, discard the supernatant, and reserve 30–40 µL of residual liquid. Non-viscous specimens: Add 20 µL of XC reagent to 1 mL of specimen, centrifuge at 12,000 r/min for 2 min, discard the supernatant, and reserve 30–40 µL of residual liquid.

Lysis and disruption

Add 200 µL of lysis buffer LS and 200 mg of glass beads to the pretreated sample. Incubate at 37 °C for 10 min and then disrupt mechanically at high speed using a bead beater in S4 mode for 5 min.

Automated DNA extraction

Add 400 µL of lysis buffer LE and 20 µL of proteinase K to the disrupted sample, mix thoroughly. Transfer approximately 600 µL of the supernatant to a 96-well deep-well plate. Total genomic DNA was extracted using the Nucleic Acid Extraction or Purification Reagent Kit (InvitroPro, Zhejiang, China) on an automated nucleic acid extractor (Hema E96-II, Zhuhai, China) according to the preset program, which included 95 °C high-temperature lysis, three washing steps, and 80 °C elution.

DNA concentration was quantified using the Qubit dsDNA High Sensitivity Kit (Yisheng, China). The quality of extracted DNA was verified using a commercial MTB nucleic acid detection kit (PCR fluorescent probe method), and nucleic acid extraction was considered qualified based on the external standard Ct value.

Targeted multiplex PCR enrichment

Four MTB-specific genomic regions (IS6110, rpoB, hsp65, and gyrB) were amplified simultaneously using a PCR Amplifier (Hema 9600, Zhuhai, China). The primer sequences draw upon the work of Yan et al. [23] and are supplied by Hangzhou Dian Diagnostic Co., Ltd. Each PCR reaction was performed in a final volume of 20 µL consisting of 13 µL of premix (8 µL PCR mix + 5 µL primer pool) and 7 µL of template DNA. The amplification program consisted of an initial denaturation step at 95 °C for 10 min; 35 cycles of 95 °C for 10 s, 60 °C for 2 min; and a final extension step at 60 °C for 10 min. The PCR products were pooled for each sample and purified using magnetic beads.

Library construction

End repair and barcode ligation

The purified PCR products were processed with the end repair enzyme at 20 °C for 20 min, heat-inactivated at 65 °C for 10 min, then ligated to the Native Barcode (EXP-NBD196, Oxford Nanopore Technologies) at 20 °C for 15 min, heat-inactivated at 65 °C for 10 min. The ligation products were pooled and purified with magnetic beads.

Adapter ligation

The pooled barcoded library was ligated with Sequencing Adapter Mix II (EXP-AMII001, Oxford Nanopore Technologies) at 20 °C for 20 min. The ligation product was again purified with magnetic beads. The library concentration was measured.

Sequencing

Sequencing was performed on a MinION Mk1C or GridION Nanopore Sequencer (Oxford Nanopore Technologies) using an R9.4.1 Flow Cell (FLO-MIN106D). The flow cell was pretreated with FB/FLT buffer first.

A sequencing mix was prepared by mixing 17.75 µL Sequencing Buffer, 12.75 µL Loading Beads, and 9.5 µL of the final DNA library to a total of 40 µL. The sequencing mix was loaded onto the flow cell through the SpotON port. MinKnow software was set up according to the manufacturer’s instructions, and the planned data volume was calculated as “total mass of end-repaired DNA (ng)/2” (in Mb).

Bioinformatics analysis

Data quality control: Raw FASTQ data were filtered using NanoFilt to remove low-quality sequences. Sequence alignment: The quality-controlled reads were aligned to the in-house drug resistance gene reference database and species identification database using Minimap2. generating alignment files in PAF/SAM format. Variant calling: Bcftools was used for variant identification, and the detected variants were annotated by comparison with the in-house reference database. A sample was considered MTB-positive by NTS if at least one MTB-specific genomic region was detected with ≥ 1 unique read.

Statistical analysis

Results were analyzed using SPSS 24 and Origin 2024 software to calculate the sensitivity, specificity, positive predictive value, and negative predictive value for each detection method. Cochran’s Q test was used to compare the three testing methods. Paired McNemar’s chi-square test was employed to compare the positivity rates between nanopore sequencing technology and either MGIT culture or Xpert methods, with significance indicated as P < 0.05 (*) and high significance as P < 0.01 (**). The Kappa test was used to assess the consistency among the three methods. Origin software was utilized to plot ROC curves and Venn diagrams for comparing the diagnostic performance of the three methods.

Results

Specimen data

As shown in Fig. 1, initially, we screened a total of 280 patients for NTS. Subsequently, 177 patients were excluded, including 9 who did not complete MGIT culture and GeneXpert testing and 159 who presented with typical TB symptoms (cough with sputum production lasting 2 weeks). Ultimately, 103 suspected subclinical TB patients were enrolled. Each patient provided one sample (sputum, BALF, or pleural fluid), yielding a total of 21 sputum samples, 76 BALF samples, and 6 pleural fluid samples. No patients tested HIV-positive. Table 1 presents the clinical characteristics and demographic information of the enrolled patient samples, and Fig. 2 shows boxplots of clinical data statistics. The average age of 21 TB patients was 62, showing no significant difference compared to the 63-year-old average for non-TB patients. There was also no significant difference in the prevalence rate between males and females. Among laboratory blood test indicators, although the average white blood cell and neutrophil counts in subclinical TB patients were slightly higher than those in non-TB patients, the difference was not statistically significant. Similarly, no significant differences were observed in the average platelet and hemoglobin counts. However, subclinical TB patients exhibited lower lymphocyte levels (Fig. 2d). This may result from the activation of the immune system following MTB infection, while the pathogen likely suppresses lymphocyte proliferation and activation or induces apoptosis through multiple mechanisms. For example, macrophages may secrete cytokines (such as TNF-α) after phagocytosing MTB, thereby inhibiting T cell proliferation; MTB antigens may also directly induce lymphocyte apoptosis.

Fig. 1.

Screening flow chart of enrolled patients

Table 1.

Clinical characteristics of patients enrolled

Characteristics	PTB (n = 21)	Non-PTB (n = 82)				P value
Age (years) (mean ± SD)	62 ± 12.7	63 ± 11.8			0.69
Gender					0.80
Male (n, %)	14 (64%)	58 (72%)
Female (n, %)	8(36%)	23(28%)
Laboratory examinations
Leukocyte (∗109/L) (Median, IQR)	5.5 (4.6–5.9)		5.3 (3.9–6.4)	0.86
Neutrophil(∗109/L) (Median, IQR)	3.6 (2.8-4.0)		3.5 (2.4–4.4)	0.98
lymphocyte(∗109/L) (Median, IQR)	1.1 (0.5–1.5)		1.4 (1.1–1.6)	0.02
Platelet(∗109/L) (Median, IQR)	185 (128–225)		183 (143–228)	0.89
Hemoglobin(g/L) (Median, IQR)	112 (101–125)		114 (104–123)	0.58

Fig. 2.

Statistical boxplots of age distribution and clinical hematology laboratory data for enrolled patients. a Age data; (b) Leukocyte counts; (c) Neutrophil counts; (d) Lymphocyte counts; (e) Platelet counts; and (f) Haemoglobin counts for TB and non-TB patients

The presence of positive findings for subclinical TB according to the three methods

As shown in Fig. 3, the positive detection cases for NTS, Xpert MTB/RIF testing, and MGIT culture were 25, 14, and 15, respectively. Cochran’s Q test revealed significant differences in the positive detection rate among the three methods (P = 0.016).

Fig. 3.

Positive cases detected by NTS, Xpert MTB/RIF, and MGIT culture

Positive samples from NTS produced reads between 34 and 523,333. The average read count is 55,318.16, with a median of 5,208, an interquartile range of 9,615, and a maximum of 523,333. Figure 4 shows the distribution of read counts. Five patients were diagnosed with non-tuberculous mycobacterial (NTM) disease. NTS identified four cases (one patient tested as MTB via NTS), while MGIT culture detected three cases. All NTM patients tested negative by Xpert. Additionally, we compared the three detection methods using a Venn diagram (Fig. 5). Figure 5a showed that NTS and Xpert combined yielded 10 positive results, Xpert and MGIT culture combined yielded 7 positive results, and all three methods together yielded 6 positive results. Figure 5b showed that the three methods yielded a total of 76 identical results; NTS and Xpert had 84, while NTS and MGIT culture had 83.

Fig. 4.

Sequence numbers of MTB-positive results obtained through NTS

Fig. 5.

Venn diagram showing the results detected by NTS, Xpert MTB/RIF, and MGIT culture. a Venn diagram of positive results. b Venn diagram of overall results

Detection performance of the three methods

Based on the final clinical diagnosis, the sensitivities of NTS, MGIT culture, and Xpert testing were 90.9%, 40.9%, and 50.0%, respectively. The specificities were 92.6%, 93.8%, and 96.3%, and the accuracies were 93.2%, 86.4%, and 81.5%, respectively. The Youden indices were calculated as 0.84, 0.35, and 0.46, while the positive predictive values (PPV) were 80.0, 60.0, and 78.6, and the negative predictive values (NPV) were 97.4, 85.2, and 87.6, respectively (see Table 2). The Receiver Operating Characteristic (ROC) curves presented in Fig. 6 indicate that the area under the curve (AUC) value for NTS (0.923, 95% CI: 0.854–0.993) is higher than that of the Xpert assay (0.731, 95% CI: 0.630–0.832) and the MGIT culture assay (0.667, 95% CI: 0.553–0.781). Further McNemar chi-square test revealed a significant difference in the positive detection rate between NTS and Xpert (0.01) (Table 3) or MGIT cultures (0.03) (Table 4). The Kappa values were 0.41 and 0.39, respectively.

Table 2.

Analysis of diagnostic indicators for NTS, MGIT culture, and Xpert testing

Test	Sensitivity (%)	Specificity (%)	Accuracy (%)	Youden’s index	PPV (%)	NPV (%)	AUC
Xpert MTB/RIF	50.0	96.3	86.4	0.46	78.6	87.6	73.1
MGIT culture	40.9	93.8	81.5	0.35	60.0	85.2	66.7
NTS	90.9	92.6	93.2	0.84	80.0	97.4	92.3

Sensitivity = [True positives / (True positives + False negatives)]×100%; Specificity = [False negatives / (False positives + True negatives)] × 100%; PPV = [True positives / (True positives + False positives)]×100%; NPV = [False negatives / (False positives + True negatives)]×100%; Youden’s index = Sensitivity + Specificity-1; Accuracy = [(True positives + False negatives)/ Total patients]×100%

Fig. 6.

ROC curves of NTS, MGIT culture, and Xpert MTB/RIF

Table 3.

Comparison of detection performance between NTS and Xpert

Nanopore	Xpert MTB/RIF		Number	χ2	P value	kappa value
	Positive	Negative
Positive	10	15	25	6.4	0.01	0.41
Negative	4	74	78
Number	14	89	103

Table 4.

Comparison of detection performance between NTS and MGIT Culture

Nanopore	MGIT culture		Number	χ2	P value	kappa value
	Positive	Negative
Positive	10	15	25	5.0	0.03	0.39
Negative	5	73	78
Number	15	88	103

Discussion

Subclinical TB, a challenging phase between latent infection and active disease, is hard to diagnose due to low bacterial presence and lack of symptoms. This study shows that NTS technology is more effective in diagnosing subclinical TB than traditional methods, with a sensitivity of 90.9% and an AUC of 0.923, outperforming Xpert MTB/RIF (50.0% sensitivity, AUC 0.731) and MGIT culture (40.9% sensitivity, AUC 0.667). The notable performance advantage (P = 0.01 compared to Xpert, P = 0.03 compared to MGIT) is crucial due to the diagnostic difficulties of subclinical TB, where patients often show few or no symptoms and have low bacterial levels. NTS’s improved detection fills a vital gap in TB prevention and control, which has typically overlooked this significant “hidden population” that plays a major role in spreading the disease.

Our findings support and extend nanopore sequencing investigations for low-bacterial burden TB. For general PTB diagnosis, Yu et al. (2022) showed 94.8% sensitivity using respiratory samples, [22] and Yan et al. (2024) found 83.33% utilizing BALF samples from smear-negative PTB patients [23]. Our 90.9% sensitivity for subclinical TB matches these results and targets populations previously neglected in studies. We also found good specificity (92.6%) and PPV (80.0%), answering concerns about false positives in low-prevalence subclinical screens, a common issue with sensitive molecular tests. Xpert MTB/RIF, a key tool for rapid TB diagnosis, relies on a single-copy gene (rpoB) and has moderate sensitivity, especially in low-bacterial-load cases [24]. NTS trumps it. NTS’s targeted enrichment and long-read sequencing, averaging 55,318 bases (Fig. 4), allow it to detect MTB at much lower concentrations than conventional methods. Notably, NTS identified 11 additional cases missed by both Xpert and culture (Fig. 5a), underscoring its ability to reveal hidden infectious reservoirs.

The technical benefits of NTS explain its improved subclinical TB diagnosis performance. Multiplex PCR for specific MTB genomic areas enhances signals needed to detect low pathogen levels in samples dominated by host and ambient DNA [17]. The depth of untargeted metagenomic approaches is sometimes insufficient for MTB detection. Long-read nanopore technology can identify MTB-specific sequences from non-tuberculous mycobacteria, simplifying diagnosis [25]. NTS correctly recognized 4 of 5 NTM cases in our investigation, unlike Xpert MTB/RIF, which only detects MTB complexes. Platforms like MinION are portable and have a turnaround in 24–48 h, making NTS realistic for resource-limited settings and overcoming a significant sequencing diagnostics challenge [26]. The median read count of 5,208 in NTS-positive samples suggests it can quantify bacterial load and disease activity.

Our results show that NTS may be an accurate diagnostic technique for subclinical TB, which is crucial for transmission control but underserved by present methods. Including NTS in clinical protocols for asymptomatic TB patients with radiological or immunological evidence could improve detection and curb transmission. NTS can also identify species and test drug resistance, making it appropriate for TB diagnosis and precision treatment in one assay. Notably, NTS enables quantitative assessment of bacterial load, which is a core indicator reflecting disease activity and infectiousness.

Methodological flaws in this study need to be explained. In accordance with the Chinese national standard WS 288–2017, this study employed a composite reference standard (CRS) combining mycobacterial culture with clinical diagnosis; this standard incorporates molecular biological test results into the clinical diagnostic criteria. Currently, studies on tuberculosis diagnosis generally adopt this method of assessment, which avoids serious selection bias caused by the exclusion of culture-negative cases; however, this approach may result in a certain degree of confounding bias. Nevertheless, this bias does not overestimate the sensitivity of the test, and all clinical diagnostic results were independently reviewed and confirmed by at least two senior respiratory or tuberculosis specialists, thereby minimising the occurrence of false positives. It should be clearly stated that the conclusion drawn in this study regarding the superior diagnostic performance of the NTS merely reflects a higher degree of alignment between this testing method and the CRS used in the study; it does not imply that it is directly superior to mycobacterium culture and species identification, which remains the microbiological gold standard in the field of tuberculosis diagnosis.

Second, as a retrospective study, our research did not collect long-term follow-up data and thus cannot analyze the correlation between NTS detection results and the clinical outcomes of subclinical TB patients. A systematic review by Teo et al. has demonstrated that approximately half of patients with subclinical TB will progress to clinically active tuberculosis [1]. Kendall et al. further point out that the natural history of subclinical TB is highly heterogeneous, with a subset of cases experiencing spontaneous resolution without treatment [3].

Therefore, future studies should not only test NTS in multiple settings, examine cost-effectiveness for policy guidance, and simplify automated operations in peripheral laboratories, but also conduct multicenter prospective cohort studies to follow up NTS-positive subclinical TB patients for at least 2 years. By integrating bacterial load quantified by NTS, immune biomarkers, and radiological features, we can construct a disease progression risk prediction model to provide a scientific basis for stratified management and precise intervention of subclinical TB. Collectively, these applications of NTS technology provide robust support for the WHO’s End TB Strategy.

Conclusions

This study found that NTS detects low-bacterial-load subclinical TB better than MGIT culture and Xpert MTB/RIF. It effectively diagnoses this “hidden” group, which contributes to community transmission but escapes symptom-based screening. NTS is ideal for high-burden, resource-limited scenarios due to its portability, speed, and cost. To maximize its public health impact, future work should validate its usage in multiple settings, improve cost-effectiveness, and integrate it into routine screening programs.

Authors’ contributions

KZ was responsible for the overall planning of the article and conducted the literature search. JZ drafted the core content, provided valuable insights, and assisted in refining the text. SS engaged in in-depth analysis and discussion of the literature, enhancing the comprehensiveness and accuracy of the article. LC performed supplementary literature searches and verification work. Additionally, JZ and LC jointly verified the authenticity of relevant data points within the literature. All authors reviewed and approved the final manuscript.

Funding

This research was supported by the Medical and Health Science and Technology Programme of Hangzhou (B20252555, B20262506), Key Projects of Chun'an Medical and Health Science and Technology Programme (2025CAYY006), Medicine and Health Research Foundation of Zhejiang Province (2025KY1246), Hangzhou Municipal Bureau of Science and Technology Guided Project (20241029Y192) and Traditional Chinese Medicine Science and Technology Programme Project of Zhejiang Province (2026ZF80).

Data availability

The data are clinical diagnostic reports; they do not require deposition in a public repository. Data will be made available on reasonable request.

Declarations

Ethics approval and consent to participate

This study was approved by the Ethics Committee of Chun’an First People’s Hospital and conducted in strict accordance with the Declaration of Helsinki. Given the retrospective design of the study and the complete anonymization of all clinical data, the need for written informed consent from participants was waived by the Ethics Committee of Chun’an First People’s Hospital.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.