Patient characteristics were summarized using mean and minimum/maximum or count and percentage, as appropriate. TB disease and household controls were compared using the Mann-Whitney test, Pearson’s chi-square test with Yates Continuity Correction, or Fisher’s exact test, as appropriate.

Both PTB cases (n = 48) and age-matched household controls irrespective of Mtb infection status (n = 49) were randomly divided into a training set (2/3), and a test set (1/3). Signatures were identified by means of a two-step approach previously used for biosignature identification (11). In short, the approach consisted of 1) univariate feature selection analysis using logistic regression, selecting markers by applying stringent p-value (p<0.01), and LASSO regression analysis based on the markers identified in step 1. The resulting LASSO model fits provided estimated coefficients (not reported in the present study, see Sivakumaran et al. (30) for an example). The model fits also enabled prediction of the probability of being a PTB for each participant. A predicted probability of >0.5 resulted in classification as a PTB case and <0.5 resulted in classification as a control. This model-based classification was compared to the actual “true” classification of participants and the number of correctly classified participants could be identified. Specifically, the predictive abilities of the signatures (to classify participants correctly) in both training and test set were summarized by means of receiver operator characteristic (ROC) curves, specifically sensitivity, specificity, and area under the curve (AUC). Analyses were carried out using R (R Core Team) (32) through the graphical user interface RStudio (

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