NMR-based serum metabolomic signatures distinguish active tuberculosis from latent tuberculosis infection.

BACKGROUND

Tuberculosis (TB) continues to be a major cause of global morbidity and mortality, particularly in low- and middle-income countries. A persistent challenge in TB control is the inability of current immunodiagnostic tools to effectively differentiate active TB from latent tuberculosis infection (LTBI). This diagnostic limitation hampers timely case detection and appropriate treatment, thereby sustaining community transmission.

OBJECTIVES

This study aimed to identify and validate serum metabolomic biomarkers capable of distinguishing active TB from LTBI using high-resolution 1H Nuclear Magnetic Resonance (NMR) spectroscopy coupled with multivariate statistical and machine-learning analyses in an Indian cohort.

METHODS

Serum samples from 52 microbiologically confirmed active TB patients and 51 individuals with LTBI were analyzed using an 800 MHz NMR spectrometer. Spectral data underwent standard pre-processing, followed by Principal Component Analysis (PCA), Partial Least Squares Discriminant Analysis (PLS-DA), and Random Forest (RF) classification. Diagnostic performance was evaluated using univariate statistics and Receiver Operating Characteristic (ROC) curve analysis.

RESULTS

Distinct metabolic differences were observed between TB and LTBI, primarily involving lipid/lipoprotein, energy, and amino acid metabolism. PCA and PLS-DA demonstrated clear group separation (accuracy >90%, Q2 = 0.596), identifying VLDL/LDL, polyunsaturated fatty acids (PUFA), lactate, N-acetyl glycoprotein (NAG), and glucose as key discriminatory metabolites. RF and ROC analyses demonstrated consistent discriminatory performance of these markers within the study cohort (AUC up to 0.934).

CONCLUSIONS

Serum metabolomics using high-resolution 1H NMR offers a promising, non-invasive approach to distinguish active TB from LTBI. The identified metabolic signatures, particularly those related to lipid and energy metabolism, may provide a foundation for future translational development, subject to validation in independent cohorts.