Trends in Tuberculosis Severity Disease Biomarkers

• DOI: https://doi.org/10.22533/at.ed.813522527021

• Palavras-chave: '

• Keywords: Tuberculosis, heterogeneity, Severity, Biomarkers

• Abstract: INTRODUCTION: Tuberculosis (TB) is a major global health concern primarily caused by the bacterium Mycobacterium tuberculosis (Mtb). The disease exhibits considerable variability in its clinical presentation, pathogenesis, and host response. While some individuals develop effective immune defenses that contain the infection, others fail to do so, resulting in active TB. Even after successful treatment, TB can leave lasting health effects, known as post-TB sequelae, which may include severe lung scarring and cavitation, leading to significant loss of lung function. TB biomarkers play a critical role in enhancing treatment outcomes. Firstly, they enable the stratification of patients into mild, moderate, and severe TB cases. Once severity is determined, additional biomarkers can be used to monitor treatment response, detect early signs of treatment failure, and assess the risk of complications, relapse, or mortality. METHODS: A bibliographic search for this review was conducted using the Google Scholar, PubMed, SciELO, and Virtual Health Library (VHL) databases. Research papers published between 1995 and 2024 were examined, using the following keywords: “Tuberculosis severity,” “Tuberculosis severity biomarkers,” “Tuberculosis sequelae,” and “Tuberculosis post-TB treatment”. RESULTS: TB severity biomarkers for preventing post-TB sequelae are still in the early stages, with serum cytokines and inflammatory markers being the primary tools for patient stratification. However, there is a pressing need to identify more specific biomarkers and validate them for clinical use. Ongoing research is focused on validating these biomarkers across diverse populations, developing multiplex diagnostic platforms, and integrating biomarker data into clinical workflows to enable personalized TB care. CONCLUSION: TB disease severity biomarkers are crucial as they can significantly enhance management of the disease in order to avoid post-TB sequelae. TB severity varies widely, from latent infection to active disease with varying levels of organ damage. Biomarkers can help stratify patients based on the severity of their disease, enabling more personalized treatment plans. Biomarkers can identify patients at risk of severe TB complications, such as extensive lung damage or extra-pulmonary spread. Integrating these biomarkers into predictive models could enhance the management of TB by identifying patients at higher risk for severe disease, thereby guiding therapeutic decisions and potentially improving outcomes.