An integrative multi-omics approach to characterize interactions between tuberculosis and diabetes mellitus

Tuberculosis -diabetes mellitus (TB -DM) is linked to a distinct inflammatory profile, which can be assessed using multi-omics analyses. Here, a machine learning algorithm was applied to multi -platform data, including cytokines and gene expression in peripheral blood and eicosanoids in urine, in a Brazilian multi -center TB cohort. There were four clinical groups: TB-DM(n = 24), TB only(n = 28), DM(HbA1c R 6.5%) only(n = 11), and a control group of close TB contacts who did not have TB or DM(n = 13). After cross -validation, baseline expression or abundance of MMP-28, LTE-4, 11-dTxB2, PGDM, FBXO6, SECTM1, and LINCO2009 differentiated the four patient groups. A distinct multi-omic-derived, dimensionally reduced, signature was associated with TB, regardless of glycemic status. SECTM1 and FBXO6 mRNA levels were positively correlated with sputum acid -fast bacilli grade in TB -DM. Values of the biomarkers decreased during the course of anti -TB therapy. Our study identified several markers associated with the pathophysiology of TB -DM that could be evaluated in future mechanistic investigations.