The highest risk of tuberculosis arises in the first few months after exposure. We reasoned that this risk reflects incipient disease among tuberculosis contacts. Blood transcriptional biomarkers of tuberculosis may predate clinical diagnosis, suggesting they offer improved sensitivity to detect subclinical incipient disease. Therefore, we sought to test the hypothesis that refined blood transcriptional biomarkers of active tuberculosis will improve stratification of short-term disease risk in tuberculosis contacts.

We combined analysis of previously published blood transcriptomic data with new data from a prospective human immunodeficiency virus (HIV)–negative UK cohort of 333 tuberculosis contacts. We used stability selection as an alternative computational approach to identify an optimal signature for short-term risk of active tuberculosis and evaluated its predictive value in independent cohorts.

In a previously published HIV-negative South African case-control study of patients with asymptomatic Mycobacterium tuberculosis infection, a novel 3-gene transcriptional signature comprising BATF2, GBP5, and SCARF1 achieved a positive predictive value (PPV) of 23% for progression to active tuberculosis within 90 days. In a new UK cohort of 333 HIV-negative tuberculosis contacts with a median follow-up of 346 days, this signature achieved a PPV of 50% (95% confidence interval [CI], 15.7–84.3) and negative predictive value of 99.3% (95% CI, 97.5–99.9). By comparison, peripheral blood interferon gamma release assays in the same cohort achieved a PPV of 5.6% (95% CI, 2.1–11.8).

This blood transcriptional signature provides unprecedented opportunities to target therapy among tuberculosis contacts with greatest risk of incident disease.