Alterations in the Immune Response in Individuals with Latent Tuberculosis Infection

TL;DR

This paper reviews immune changes in people with latent tuberculosis infection and explores new methods to predict and prevent progression to active disease.

Contribution

The paper integrates immunological, transcriptomic, and imaging approaches to improve risk stratification for latent tuberculosis reactivation.

Findings

Combining IGRA, TAM tests, and transcriptional signatures improves prediction of LTBI reactivation.

Primate models effectively validate biomarkers and interventions for LTBI.

Multidrug-resistant LTBI requires better immune-based strategies due to limited chemoprophylaxis effectiveness.

Abstract

Latent tuberculosis infection (LTBI) represents a biologically active yet clinically asymptomatic stage of Mycobacterium tuberculosis (Mtb) persistence. This condition is characterized by subtle immunometabolic alterations reflecting the host–pathogen equilibrium. Understanding the mechanisms and biomarkers associated with the preclinical phase of LTBI is crucial for preventing progression to active tuberculosis (ATB). Recent advances have identified multiple immunological, transcriptomic, metabolic, and imaging-based approaches that enable stratification of individuals at increased risk of LTBI reactivation. Quantitative assays such as IGRA, multiplex and T-cell activation marker (TAM) tests, as well as interferon-related transcriptional signatures, demonstrate predictive potential when combined with functional assays (MGIA) and metabolic imaging (PET/CT). Experimental primate models faithfully reproduce the spectrum from latency to reactivation, allowing for validation of biomarkers and vaccine or immunomodulatory strategies. The review also highlights the particular challenges of multidrug-resistant LTBI (MDR-LTBI), where standard chemoprophylaxis is less effective and immune control plays a decisive role. The preclinical phase of LTBI constitutes a key point in the TB control cascade. Integrating immunological, transcriptomic, and radiological data into risk-based screening algorithms could substantially improve early detection and targeted prevention. Translating research-derived signatures into clinically applicable, standardized, and cost-effective diagnostic tools requires coordinated international efforts, technological transfer, and policy-level support to reduce TB reactivation and transmission, including MDR-TB.