Changes in inflammatory protein and lipid mediator profiles persist after antitubercular treatment of pulmonary and extrapulmonary tuberculosis: A prospective cohort study

Abstract

Background: The identification of meaningful biomarkers of tuberculosis (TB) has potential to improve diagnosis, disease staging and prediction of treatment outcomes. It has been shown that active pulmonary TB (PTB) is associated with qualitative and quantitative changes in systemic immune profile, suggesting a chronic inflammatory imbalance. Here we characterized the profile of PTB and extrapulmonary TB (EPTB) in a prospective cohort study. Methods: We measured a panel of 27 inflammatory cytokines, soluble receptors, and lipid mediators in peripheral blood from patients with PTB or EPTB from a prospective clinical study in China. Multidimensional analyses were performed to describe associations between plasma levels of biomarkers and different TB disease presentation profiles. Results: Mycobacterium tuberculosis infection induced changes in both the expression and correlation profiles of plasma mediators of inflammation in patients with PTB compared to those with EPTB. Increases in mycobacterial loads in sputum smears were associated with rises in concentrations of several molecules involved in TB pathogenesis, such as IL-1β, IFN-α, IL-10 and PGF2α. Moreover, PTB patients presenting with severe disease exhibited a distinct inflammatory profile hallmarked by heightened levels of TNF-α, IL-1β, IL17, IL-18 and IL-27. Interestingly, while antitubercular treatment (ATT) resulted in early changes of plasma concentrations of markers in PTB, changes were delayed in EPTB patients. Exploratory analyses of the molecular degree of perturbation (MDP) of the inflammatory mediators before and during ATT suggested the occurrence of infection

Heightened Plasma Levels of Transforming Growth Factor Beta (TGF-β) and Increased Degree of Systemic Biochemical Perturbation Characterizes Hepatic Steatosis in Overweight Pediatric Patients: A Cross-Sectional Study

“…Among all markers, only median values of ALT, GGT, and TGF-β were statistically distinct between the study groups, with higher values being detected in the group of participants with hepatic steatosis (all with adjusted p -values < 0.05). After identifying the consistent changes in the concentrations of biochemical parameters between the clinical groups, we employed a discriminant model using sparse canonical correlation analysis (CCA), as previously described [ 32 , 33 , 34 ] to test whether the statistical relationships between the biomarkers, rather their concentrations/values themselves, could be used to distinguish the groups. Using this approach, we found that the patients with hepatic steatosis could be distinguished from those without steatosis with high degree of accuracy (area under the curve (AUC) of the receiver operator characteristics (ROC) curve = 0.97 (CI: 0.85–1.0), sensitivity 91% (CI: 77.0–96.7), specificity 86% (72.0–94.0); p < 0.0001, Figure 1 B, left panel).…”

“…Given that we observed in the results presented above that the overall profile of correlations was distinct between the study groups, we then tried to visualize the numerous correlations detected. We employed an approach using network analysis in which Spearman correlations are visualized as connections between the parameters [ 33 , 34 , 35 , 36 , 37 ]. This approach helped us to identify the dynamicity, strength and quality of the relationships between values of the biochemical markers and inflammatory proteins in plasma of the different groups of individuals.…”

“…To deepen the analyses in overall disturbances in biochemical profiles, we calculated the Degree of Biochemical Perturbation (DBP, as described in Methods) ( Figure 4 A), which is an adaptation from the molecular degree of perturbation previously published [ 33 , 34 , 35 ]. We found that patients with hepatic steatosis exhibited substantial increase in DBP score values compared to those without steatosis ( p < 0.0001).…”

“…Following this idea, the discriminant model used in the exploration of the data was based on CCA. In this analysis, the way each biomarker correlates with each other, rather than its concentration values, is used to infer differences between the study groups [ 32 , 34 ]. This model is not based on number of correlations, but the quality (whether a correlation is positive or negative) and strength (rho value) are computed to test dissimilarities between the groups.…”

“…The molecular degree of perturbation (MDP) was calculated using values of the biochemical parameters to infer the degree of biochemical perturbation (DBP) associated with hepatic steatosis. This method has been used and detailed previously [ 33 , 34 , 35 ]. In the present study, “No steatosis” was defined as the “reference” group, and the average level and standard deviation of this reference group were calculated for the plasma concentrations of each inflammatory marker.…”