biostudies-literatureKusejko KSwiss National Science FoundationWellcome Truste0158525https://www.ebi.ac.uk/biostudies/studies/S-EPMC12505960biostudies-literatureUnknown16(10)Improved biomarkers for predicting progression to active tuberculosis (TB) are urgently needed, especially in people with HIV, who are at elevated risk. We used high-throughput plasma proteomics and machine learning to identify signatures associated with TB progression in this population. From the Swiss HIV Cohort Study, we analyzed plasma samples collected at least 6 months before TB diagnosis from 91 participants who later developed TB. We selected 293 controls matched for demographic and clinical parameters who remained TB-free to achieve a risk score specific to active TB. In total, 583 samples were analyzed, with 613-1,283 proteins quantified per sample. A random forest classifier predicted a significantly higher median probability of TB progression for cases (33%) than for controls (mBioInflammation and B cell activation define a plasma proteome signature predicting tuberculosis in people with HIV.PMC12505960310030_200407200407310030218408201369Swiss HIV Cohort StudyHaller SZeeb MDolle CCollins BCArefian MGunthard HFDuroux DWandeler GKouyos RDRussenberger DLabhardt NCavassini MKusejko KHoffmann MNemeth JBernasconi EfalseInflammation and B cell activation define a plasma proteome signature predicting tuberculosis in people with HIV.Improved biomarkers for predicting progression to active tuberculosis (TB) are urgently needed, especially in people with HIV, who are at elevated risk. We used high-throughput plasma proteomics and machine learning to identify signatures associated with TB progression in this population. From the Swiss HIV Cohort Study, we analyzed plasma samples collected at least 6 months before TB diagnosis from 91 participants who later developed TB. We selected 293 controls matched for demographic and clinical parameters who remained TB-free to achieve a risk score specific to active TB. In total, 583 samples were analyzed, with 613-1,283 proteins quantified per sample. A random forest classifier predicted a significantly higher median probability of TB progression for cases (33%) than for controls (2025-01-01T00:00:00Z2025 Oct2026-06-04T09:07:44.245Z2026-05-07T03:12:20.057ZS-EPMC125059604087461810.1128/mbio.01585-25