The diagnostic value of combined detection of GBP1, IFN-γ and IL-2 in differentiating NTM from TB infection.

BACKGROUND

Early differentiation between Mycobacterium tuberculosis (MTB) and non-tuberculous mycobacteria (NTM) is critical due to distinct treatment protocols. Traditional diagnostic methods, including acid-fast staining, bacterial culture, and nucleic acid assays, often face challenges. This study evaluated the diagnostic value of Guanylate-Binding Protein 1 (GBP1), interferon-gamma (IFN-γ), and interleukin-2 (IL-2) in peripheral blood for distinguishing active TB, NTM infections, and cured TB.

METHODS

We recruited patients with active TB (n = 50), NTM disease (n = 46), cured TB (n = 37), and healthy controls (HC, n = 20). GBP1 mRNA in peripheral blood mononuclear cells was quantified by qPCR. MTB-specific IFN-γ and IL-2 levels were measured by ELISA.

RESULTS

The relative expression of GBP1 was significantly higher in active TB (2.764 ± 1.774) and in NTM patients (2.099 ± 0.665) compared to healthy control group (-0.001 ± 1.844; P < 0.0001 and P < 0.01, respectively). Additionally, IL-2 showed prognostic value, as levels in cured TB patients (135.7 ± 332.9) were significantly lower than in active TB patients (362.7 ± 530.7, P < 0.01). For differentiating active TB from healthy controls, the area under the receiver operating characteristic curve (AUC) for GBP1 was 0.899, outperforming IFN-γ (0.846) and IL-2 (0.786). Crucially, a combined three-marker panel demonstrated superior diagnostic performance in all comparisons, notably achieving an AUC of 0.990 for distinguishing active TB from NTM disease, significantly higher than any single marker.

CONCLUSIONS

GBP1 is a robust marker for identifying mycobacterial infections (both MTB and NTM). While IL-2 shows potential for monitoring treatment response, the combined detection of GBP1, IFN-γ, and IL-2 provides the highest diagnostic accuracy, effectively differentiating between NTM and MTB infections. This panel offers a promising tool for improving clinical diagnosis and patient management.