This prospective study investigated whole blood-based immune cell biomarkers for pulmonary tuberculosis (TB) immunoprofiling. Blood samples from 34 healthy controls and 51 tuberculosis patients were analyzed at three timepoints: Prior to therapy (T0), after 14 days of therapy (T1), and at the end of treatment (Te). Using multiparameter flow cytometry, 386 immune cell populations were analyzed. Predictive models were developed using two machine learning algorithms. A TB5(LF change) score, which was based on five cell populations, effectively distinguished tuberculosis patients from controls (AUC = 0.89) and tuberculosis patients before and at the end of treatment (AUC = 0.92). Similarly, the TB5(Lasso) score distinguished tuberculosis patients from controls (AUC = 0.91) and tuberculosis patients before and at the end of treatment (AUC = 0.93) but was inversely correlated with disease severity (r=-0.43). Both scores included PD-L1(+)CD80(-) neutrophils and PD-L1(+)HLA-DR(+) CD4(+) lymphocytes, highlighting PD-L1-associated and Th1-related immune signatures as candidate biomarkers for TB immunoprofiling. These findings are exploratory and hypothesis-generating, providing a basis for future studies evaluating their potential utility in diagnostic and therapy-monitoring contexts.
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