{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Kusejko K"],"funding":["Swiss National Science Foundation","Wellcome Trust"],"pagination":["e0158525"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12505960"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["16(10)"],"pubmed_abstract":["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 ("],"journal":["mBio"],"pubmed_title":["Inflammation and B cell activation define a plasma proteome signature predicting tuberculosis in people with HIV."],"pmcid":["PMC12505960"],"funding_grant_id":["310030_200407","200407","310030","218408","201369"],"pubmed_authors":["Swiss HIV Cohort Study","Haller S","Zeeb M","Dolle C","Collins BC","Arefian M","Gunthard HF","Duroux D","Wandeler G","Kouyos RD","Russenberger D","Labhardt N","Cavassini M","Kusejko K","Hoffmann M","Nemeth J","Bernasconi E"],"additional_accession":[]},"is_claimable":false,"name":"Inflammation and B cell activation define a plasma proteome signature predicting tuberculosis in people with HIV.","description":"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 (","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 Oct","modification":"2026-06-04T09:07:44.245Z","creation":"2026-05-07T03:12:20.057Z"},"accession":"S-EPMC12505960","cross_references":{"pubmed":["40874618"],"doi":["10.1128/mbio.01585-25"]}}