Simple BMI test flags close contacts at higher tuberculosis risk

Tuberculosis (TB) infects millions of people worldwide, but most people exposed to TB never develop active disease. That means public health teams need better ways to tell which contacts of infectious TB patients are most likely to get sick so preventive measures can be focused where they will do the most good. María B. Arriaga and her colleagues used a large, multi-center study to look for simple, practical clues that predict progression from exposure to disease. The team enrolled people who had been in close contact with a person with pulmonary TB into the RePORT-Brazil cohort between 2015 and 2019 and followed them for 24 months. Rather than relying on a single test, the researchers examined a range of clinical and laboratory measurements taken at the study start, then reduced that information to the most informative pieces. Their aim was pragmatic: to find markers that are easy to measure in clinic settings and that improve prediction of who will develop TB, so that prevention efforts like TB preventive therapy can be targeted more effectively.

To identify the most useful predictors, the study team applied dimension reduction approaches, including empirical review and LASSO regression, to baseline clinical and laboratory data. They built prediction models for three groups: all contacts, contacts who were IGRA-positive at baseline, and IGRA-positive contacts who did not receive TB preventive therapy (TPT; defined in the study as fewer than 30 days of isoniazid). The models were internally validated using bootstrapping to assess how reliably they might perform with new data from the same setting. One key finding reported was that Body Mass Index (BMI), a routinely measured and easily obtained marker, helped distinguish which IGRA-positive close contacts were at higher risk of progressing to TB disease. In the groups highlighted by the authors, TB risk was 8.4% versus 2.1%, respectively, suggesting a meaningful difference in short-term progression among identified risk groups.

The implications of these findings are practical and immediate. Because BMI is cheap and simple to measure, it could be incorporated into routine assessments of people who have been exposed to pulmonary TB to help prioritize who receives TPT. The authors conclude that BMI can identify IGRA-positive contacts at higher risk of developing disease, and that TPT should be targeted to that high-risk group to maximize prevention. Using BMI alongside IGRA and other clinical information could make TB prevention programs more efficient by concentrating limited resources—like isoniazid preventive regimens—on those most likely to benefit. The study’s use of LASSO and bootstrapping provides internal evidence that the approach can work in the RePORT-Brazil population, and it points to a straightforward way for clinicians and public health teams to improve the impact of TB prevention efforts.