Medical Student Oregon Health & Science University School of Medicine Portland, California, United States
Background: Mycobacterium tuberculosis (Mtb) causes severe TB disease in children under 5, a population prioritized for tuberculosis-preventive therapy following TB exposure. Interferon-gamma release assays (IGRAs) assess for immune-sensitization to Mtb following exposure to an individual with TB, and a positive response is used as a proxy for Mtb-infection. IGRAs measure production of interferon-gamma (IFNg) and cannot detect alternative Mtb-specific cytokine responses. An alternative cytokine-signature may enhance detection of Mtb-immune sensitization in young children with known TB exposure to identify those most at risk for TB disease and distinguish children with and without TB disease. Exploration of cytokine signatures may also provide insight into the immunobiology of pediatric Mtb infection and TB disease. Objective: To determine if quantifying production of 11 cytokines in response to whole-blood stimulation with Mtb-specific antigens increases identification of young children with presumptive Mtb-infection or TB disease, compared to measurement IFNg alone. Design/Methods: The IRB approval was received at all sponsoring institutions and written informed consent was obtained from a parent or guardian. Ugandan children < 5 yo (n=70) who were household contacts (HHC) of an adult with confirmed TB underwent comprehensive evaluation for Mtb-infection and TB disease, including performance of the QuantiFERON-gold-Plus (QFT) IGRA and TST, at study entry. Following performance of QFT assays in a certified laboratory, residual supernatants from all 4 QFT conditions were cryopreserved and subsequently batched tested for 12 cytokines using a Luminex platform. Individual cytokine levels in each of the 4 QFT conditions will be compared among all children, including those with and without TB disease, and compared to IFNg alone and TST responses. A model capturing composite cytokine biosignatures between children with and without TB will be developed. Modeling will include key biological confounders such as age, sex, and HIV status. Data analysis will be completed by the end of 2023.
