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.