Effect of Smoking on Longitudinal Interferon-ϒ Release Assay Results among Close Contacts of People with Pulmonary Tuberculosis.

Diagnosis of M. tuberculosis (Mtb) infection in close contacts is critical for TB control. Smoking is a risk factor for Mtb infection and TB disease but its effect on longitudinal interferon-gamma release assay (IGRA) results remains unknown. We conducted a multi-site prospective study in Brazil between 2015-2019, among close contacts of adults with culture-confirmed pulmonary TB. IGRA was performed at baseline, month 6 if negative at baseline, and month 24-30 after enrollment. IGRA results were categorized as IGRA-positive (maintained from baseline to last visit), IGRA-conversion (from negative to positive at any time), IGRA-reversion (from positive to negative at any time), and IGRA-negative (maintained from baseline to last visit). Associations between IGRA results and smoking status at baseline (current/former vs never) in contacts were evaluated using propensity score-adjusted logistic regression models. Estimated propensity score was used as a covariate in models, which regressed the outcome (IGRA-positive, IGRA-conversion, IGRA-reversion) on smoking status. Of 430 close contacts, 89 (21%) were IGRA-positive, 30 (7%) were converters, 30 (7%) were reverters and 22 were indeterminate. Smoking frequency was 26 (29%) among IGRA-positive contacts, 7 (23%) in converters, and 3 (10%) in reverters. Smoking in contacts was associated with lower odds of IGRA-reversion (adjusted odds ratio = 0.16; 95% confidence interval = [0.03-0.70]). We did not detect associations between smoking and IGRA-positive or IGRA-conversion. Our findings highlight the importance of smoking on longitudinal IGRA results. This has implications for clinical care and clinical trials in which IGRA status is monitored or used as an outcome.

Prediction Models for Adverse Drug Reactions During Tuberculosis Treatment in Brazil.

BACKGROUND: Tuberculosis (TB) treatment-related adverse drug reactions (TB-ADRs) can negatively affect adherence and treatment success rates. METHODS: We developed prediction models for TB-ADRs, considering participants with drug-susceptible pulmonary TB who initiated standard TB therapy. TB-ADRs were determined by the physician attending the participant, assessing causality to TB drugs, the affected organ system, and grade. Potential baseline predictors of TB-ADR included concomitant medication (CM) use, human immunodeficiency virus (HIV) status, glycated hemoglobin (HbA1c), age, body mass index (BMI), sex, substance use, and TB drug metabolism variables (NAT2 acetylator profiles). The models were developed through bootstrapped backward selection. Cox regression was used to evaluate TB-ADR risk. RESULTS: There were 156 TB-ADRs among 102 of the 945 (11%) participants included. Most TB-ADRs were hepatic (n = 82 [53%]), of moderate severity (grade 2; n = 121 [78%]), and occurred in NAT2 slow acetylators (n = 62 [61%]). The main prediction model included CM use, HbA1c, alcohol use, HIV seropositivity, BMI, and age, with robust performance (c-statistic = 0.79 [95% confidence interval {CI}, .74-.83) and fit (optimism-corrected slope and intercept of -0.09 and 0.94, respectively). An alternative model replacing BMI with NAT2 had similar performance. HIV seropositivity (hazard ratio [HR], 2.68 [95% CI, 1.75-4.09]) and CM use (HR, 5.26 [95% CI, 2.63-10.52]) increased TB-ADR risk. CONCLUSIONS: The models, with clinical variables and with NAT2, were highly predictive of TB-ADRs.

Transcriptomic signatures of progression to TB disease among close contacts in Brazil.

BACKGROUND: Approximately 5% of people infected with Mycobacterium tuberculosis progress to tuberculosis (TB) disease without preventive therapy. There is a need for a prognostic test to identify those at highest risk of incident TB, so that therapy can be targeted. We evaluated host blood transcriptomic signatures for progression to TB disease. METHODS: Close contacts (≥4 hours exposure per week) of adult patients with culture-confirmed pulmonary TB were enrolled in Brazil. Investigation for incident, microbiologically-confirmed or clinically-diagnosed pulmonary or extra-pulmonary TB disease through 24 months of follow-up was symptom-triggered. Twenty previously validated blood TB transcriptomic signatures were measured at baseline by real-time quantitative PCR. Prognostic performance for incident TB was tested using receiver operating characteristic curve (ROC) analysis at 6, 9, 12, and 24 months of follow-up. RESULTS: Between June 2015 and June 2019, 1,854 close contacts were enrolled; Twenty-five progressed to incident TB, of whom 13 had microbiologically-confirmed disease. Baseline transcriptomic signature scores were measured in 1,789 close contacts. Prognostic performance for all signatures was best within 6 months of diagnosis. Seven signatures (Gliddon4, Suliman4, Roe3, Roe1, Penn-Nicholson6, Francisco2, and Rajan5) met the minimum World Health Organization target product profile (TPP) for a prognostic test through 6 months; three (Gliddon4, Rajan5, and Duffy9) through 9 months. None met the TPP threshold through 12 or more months of follow-up. CONCLUSIONS: Blood transcriptomic signatures may be useful for predicting TB risk within 9 months of measurement among TB-exposed contacts, to target preventive therapy administration.

The Xpert® MTB/RIF cycle threshold value predicts M. tuberculosis transmission to close contacts in a Brazilian prospective multicenter cohort.

BACKGROUND: The Xpert® MTB/RIF rapid molecular test provides a quantitative measure of Mycobacterium tuberculosis (Mtb) DNA in the form of cycle threshold (Ct) values. This information can be translated into mycobacterial load and used as a potential risk measure of bacterial spread for tuberculosis cases, which can impact infection control. However, the role of Ct values in assessing Mtb transmission to close contacts has not yet been demonstrated. METHODS: A prospective study was performed to investigate the association between Xpert® MTB/RIF Ct values and Mtb transmission to close contacts of patients with culture-confirmed pulmonary TB in a multi-center Brazilian cohort. We evaluated clinical and laboratory data, such as age, sex, race, smoking habits, drug use, alcohol use, chest radiograph, Xpert® MTB/RIF results among pulmonary tuberculosis cases, and QuantiFERON(QFT)-Plus results at baseline and after six months for close contacts who had a negative result at baseline. RESULTS: A total of 1,055 close contacts of 382 pulmonary tuberculosis cases were included in the study. The median Ct values from pulmonary tuberculosis cases of QFT-Plus positive (at baseline or six months) close contacts were lower compared with those who were QFT-Plus negative. An adjusted logistic regression demonstrated that reduced Ct values from the index cases were independently associated with QFT-Plus conversion from negative to positive (OR: 1.61, 95% CI: 1.12-2.32) after adjusting for clinical characteristics. CONCLUSION: Close contacts of pulmonary TB index cases exhibiting low Xpert MTB/RIF Ct values displayed higher rates of TB infection, reflecting Mtb transmission.

Machine learning algorithms using national registry data to predict loss to follow-up during tuberculosis treatment.

BACKGROUND: Identifying patients at increased risk of loss to follow-up (LTFU) is key to developing strategies to optimize the clinical management of tuberculosis (TB). The use of national registry data in prediction models may be a useful tool to inform healthcare workers about risk of LTFU. Here we developed a score to predict the risk of LTFU during anti-TB treatment (ATT) in a nationwide cohort of cases using clinical data reported to the Brazilian Notifiable Disease Information System (SINAN). METHODS: We performed a retrospective study of all TB cases reported to SINAN between 2015 and 2022; excluding children (< 18 years-old), vulnerable groups or drug-resistant TB. For the score, data before treatment initiation were used. We trained and internally validated three different prediction scoring systems, based on Logistic Regression, Random Forest, and Light Gradient Boosting. Before applying our models we splitted our data into training (~ 80% data) and test (~ 20%) sets, and then compared the model metrics using the test data set. RESULTS: Of the 243,726 cases included, 41,373 experienced LTFU whereas 202,353 were successfully treated. The groups were different with regards to several clinical and sociodemographic characteristics. The directly observed treatment (DOT) was unbalanced between the groups with lower prevalence in those who were LTFU. Three models were developed to predict LTFU using 8 features (prior TB, drug use, age, sex, HIV infection and schooling level) with different score composition approaches. Those prediction scoring systems exhibited an area under the curve (AUC) ranging between 0.71 and 0.72. The Light Gradient Boosting technique resulted in the best prediction performance, weighting specificity and sensitivity. A user-friendly web calculator app was developed ( https://tbprediction.herokuapp.com/ ) to facilitate implementation. CONCLUSIONS: Our nationwide risk score predicts the risk of LTFU during ATT in Brazilian adults prior to treatment commencement utilizing schooling level, sex, age, prior TB status, and substance use (drug, alcohol, and/or tobacco). This is a potential tool to assist in decision-making strategies to guide resource allocation, DOT indications, and improve TB treatment adherence.

The Effect of Diabetes and Prediabetes on Antituberculosis Treatment Outcomes: A Multicenter Prospective Cohort Study.

BACKGROUND: It is unclear whether diabetes or prediabetes affects unfavorable treatment outcomes and death in people with tuberculosis (PWTB). METHODS: Culture-confirmed, drug-susceptible PWTB, enrolled in the Regional Prospective Observational Research in Tuberculosis (RePORT)-Brazil cohort between 2015 and 2019 (N = 643) were stratified based on glycemic status according to baseline glycated hemoglobin. Unfavorable tuberculosis (TB) outcome was defined as treatment failure or modification, recurrence, or death; favorable outcome was cure or treatment completion. We corroborated the findings using data from PWTB reported to the Brazilian National System of Diseases Notification (SINAN) during 2015-2019 (N = 20 989). Logistic regression models evaluated associations between glycemic status and outcomes. RESULTS: In both cohorts, in univariate analysis, unfavorable outcomes were more frequently associated with smoking, illicit drug use, and human immunodeficiency virus infection. Diabetes, but not prediabetes, was associated with unfavorable outcomes in the RePORT-Brazil (adjusted relative risk [aRR], 2.45; P < .001) and SINAN (aRR, 1.76; P < .001) cohorts. Furthermore, diabetes was associated with high risk of death (during TB treatment) in both RePORT-Brazil (aRR, 2.16; P = .040) and SINAN (aRR, 1.93; P = .001). CONCLUSIONS: Diabetes was associated with an increased risk of unfavorable outcomes and mortality in Brazilian PWTB. Interventions to improve TB treatment outcomes in persons with diabetes are needed.

A Clinical Prediction Model for Unsuccessful Pulmonary Tuberculosis Treatment Outcomes.

BACKGROUND: Despite widespread availability of curative therapy, tuberculosis (TB) treatment outcomes remain suboptimal. Clinical prediction models can inform treatment strategies to improve outcomes. Using baseline clinical data, we developed a prediction model for unsuccessful TB treatment outcome and evaluated the incremental value of human immunodeficiency virus (HIV)-related severity and isoniazid acetylator status. METHODS: Data originated from the Regional Prospective Observational Research for Tuberculosis Brazil cohort, which enrolled newly diagnosed TB patients in Brazil from 2015 through 2019. This analysis included participants with culture-confirmed, drug-susceptible pulmonary TB who started first-line anti-TB therapy and had ≥12 months of follow-up. The end point was unsuccessful TB treatment: composite of death, treatment failure, regimen switch, incomplete treatment, or not evaluated. Missing predictors were imputed. Predictors were chosen via bootstrapped backward selection. Discrimination and calibration were evaluated with c-statistics and calibration plots, respectively. Bootstrap internal validation estimated overfitting, and a shrinkage factor was applied to improve out-of-sample prediction. Incremental value was evaluated with likelihood ratio-based measures. RESULTS: Of 944 participants, 191 (20%) had unsuccessful treatment outcomes. The final model included 7 baseline predictors: hemoglobin, HIV infection, drug use, diabetes, age, education, and tobacco use. The model demonstrated good discrimination (c-statistic = 0.77; 95% confidence interval, .73-.80) and was well calibrated (optimism-corrected intercept and slope, -0.12 and 0.89, respectively). HIV-related factors and isoniazid acetylation status did not improve prediction of the final model. CONCLUSIONS: Using information readily available at treatment initiation, the prediction model performed well in this population. The findings may guide future work to allocate resources or inform targeted interventions for high-risk patients.

The Effect of Diabetes and Prediabetes on Mycobacterium tuberculosis Transmission to Close Contacts.

BACKGROUND: It is unknown whether dysglycemia is associated with Mycobacterium tuberculosis transmission. METHODS: We assessed epidemiological and clinical characteristics of patients with culture-confirmed pulmonary tuberculosis and their close contacts, enrolled in a multicenter prospective cohort in Brazil. Contacts were investigated at baseline and 6 months after enrollment. QuantiFERON positivity at baseline and conversion (from negative to positive at month 6) were compared between subgroups of contacts according to glycemic status of persons with tuberculosis (PWTB) as diabetes mellitus (DM) or prediabetes. Multivariable mixed-effects logistic regression models were performed to test independent associations with baseline QuantiFERON positive and QuantiFERON conversion. RESULTS: There were 592 PWTB (153 DM, 141 prediabetes, 211 normoglycemic) and 1784 contacts, of whom 658 were QuantiFERON-positive at baseline and 106 converters. Multivariable analyses demonstrated that tuberculosis-prediabetes cases, acid-fast bacilli-positive, pulmonary cavities, and living with someone who smoked were independently associated with QuantiFERON positive in contacts at baseline. DM, persistent cough, acid-fast bacilli-positive, and pulmonary cavities in tuberculosis source cases were associated with QuantiFERON conversion. CONCLUSIONS: Contacts of persons with pulmonary tuberculosis and dysglycemia were at increased risk of being QuantiFERON positive at baseline or month 6. Increased focus on such close contacts could improve tuberculosis control.

Lack of Weight Gain During the First 2 Months of Treatment and Human Immunodeficiency Virus Independently Predict Unsuccessful Treatment Outcomes in Tuberculosis.

BACKGROUND: Weight change may inform tuberculosis treatment response, but its predictive power may be confounded by human immunodeficiency virus (HIV). METHODS: We prospectively followed up adults with culture-confirmed, drug-susceptible, pulmonary tuberculosis receiving standard 4-drug therapy (isoniazid, rifampin, pyrazinamide, and ethambutol) in Brazil. We examined median weight change 2 months after treatment initiation by HIV status, using quantile regression, and unsuccessful tuberculosis treatment outcome (treatment failure, tuberculosis recurrence, or death) by HIV and weight change status, using Cox regression. RESULTS: Among 547 participants, 102 (19%) were HIV positive, and 35 (6%) had an unsuccessful outcome. After adjustment for confounders, persons living with HIV (PLWH) gained a median of 1.3 kg (95% confidence interval [CI], -2.8 to .1) less than HIV-negative individuals during the first 2 months of tuberculosis treatment. PLWH were at increased risk of an unsuccessful outcome (adjusted hazard ratio, 4.8; 95% CI, 2.1-10.9). Weight change was independently associated with outcome, with risk of unsuccessful outcome decreasing by 12% (95% CI, .81%-.95%) per 1-kg increase. CONCLUSIONS: PLWH gained less weight during the first 2 months of tuberculosis treatment, and lack of weight gain and HIV independently predicted unsuccessful tuberculosis treatment outcomes. Weight, an easily collected biomarker, may identify patients who would benefit from alternative treatment strategies.

ABCB1 gene polymorphism associated with clinical factors can predict drug-resistant tuberculosis.

Polymorphism in the ABCB1 gene encoding P-glycoprotein, a transmembrane drug efflux pump, contributes to drug resistance and has been widely studied. However, their association with rifampicin and ethambutol resistance in tuberculosis (TB) patients is still unclear. Genotype/allele/haplotype frequencies in c.1236C > T (rs1128503), c.2677G > T/A (rs2032582), and c.3435C > T (rs1045642) were obtained from 218 patients. Of these, 80 patients with rifampicin and/or ethambutol resistance were selected as the case group and 138 patients were selected for the control group through the results of their culture and drug-sensitive tests. Patients aged <18 years and HIV-positive serologic tests were excluded. ABCB1 polymorphisms were determined using a PCR direct-sequencing approach, and restriction fragment length polymorphism (RFLP). A nomogram was constructed to simulate a combined prediction of the probability of anti-TB drug resistance, with factors including genotype c.1236C > T (rs1128503) (P=0.02), clinical form (P=0.03), previous treatment (P=0.01), and skin color (P=0.03), contributing up to 90% chance of developing anti-TB drug resistance. Considering genotype analyses, CT (rs1128503) demonstrated an increased chance of anti-TB drug resistance (odds ratio (OR): 2.34, P=0.02), while the analyses for ethambutol resistance revealed an association with a rare A allele (rs2032582) (OR: 12.91, P=0.01), the haplotype TTC (OR: 5.83, P=0.05), and any haplotype containing the rare A allele (OR: 7.17, P=0.04). ABCB1 gene polymorphisms in association with others risk factors contribute to anti-TB drug resistance, mainly ethambutol. The use of the nomogram described in the present study could contribute to clinical decision-making prior to starting TB treatment.

The effect of previous SARS-CoV-2 infection on systemic immune responses in individuals with tuberculosis.

Background: The impact of previous SARS-CoV-2 infection on the systemic immune response during tuberculosis (TB) disease has not been explored. Methods: An observational, cross-sectional cohort was established to evaluate the systemic immune response in persons with pulmonary tuberculosis with or without previous SARS-CoV-2 infection. Those participants were recruited in an outpatient referral clinic in Rio de Janeiro, Brazil. TB was defined as a positive Xpert-MTB/RIF Ultra and/or a positive culture of Mycobacterium tuberculosis from sputum. Stored plasma was used to perform specific serology to identify previous SARS-CoV-2 infection (TB/Prex-SCoV-2 group) and confirm the non- infection of the tuberculosis group (TB group). Plasmatic cytokine/chemokine/growth factor profiling was performed using Luminex technology. Tuberculosis severity was assessed by clinical and laboratory parameters. Participants from TB group (4.55%) and TB/Prex-SCoV-2 (0.00%) received the complete COVID-19 vaccination. Results: Among 35 participants with pulmonary TB, 22 were classified as TB/Prex-SCoV-2. The parameters associated with TB severity, together with hematologic and biochemical data were similar between the TB and TB/Prex-SCoV-2 groups. Among the signs and symptoms, fever and dyspnea were significantly more frequent in the TB group than the TB/Prex-SCoV-2 group (p < 0,05). A signature based on lower amount of plasma EGF, G-CSF, GM-CSF, IFN-α2, IL-12(p70), IL-13, IL-15, IL-17, IL-1ß, IL-5, IL-7, and TNF-ß was observed in the TB/Prex-SCoV-2 group. In contrast, MIP-1ß was significantly higher in the TB/Prex-SCoV-2 group than the TB group. Conclusion: TB patients previously infected with SARS-CoV-2 had an immunomodulation that was associated with lower plasma concentrations of soluble factors associated with systemic inflammation. This signature was associated with a lower frequency of symptoms such as fever and dyspnea but did not reflect significant differences in TB severity parameters observed at baseline.

Isoniazid Monoresistance and Antituberculosis Treatment Outcome in Persons With Pulmonary Tuberculosis in Brazil.

Background: The high burden of drug-resistant tuberculosis (TB) is a problem to achieve the goals of the End TB Strategy by 2035. Whether isoniazid monoresistance (Hr) affects anti-TB treatment (ATT) outcomes remains unknown in high-burden countries. Methods: We evaluated determinants of ATT outcome among pulmonary TB cases reported to the National Notifiable Disease Information System (SINAN) between June 2015 and June 2019, according to drug sensitivity testing (DST) results. Binomial logistic regression models were employed to evaluate whether Hr was associated with an unfavorable ATT outcome: death or failure, compared to cure or treatment completion. Results: Among 60 804 TB cases reported in SINAN, 21 197 (34.9%) were included in the study. In this database, the frequency of unfavorable outcomes was significantly higher in those with Hr in contrast to isoniazid-sensitive persons with pulmonary TB (9.1% vs 3.05%; P < .001). Using a binomial logistic regression model, Hr was independently associated with unfavorable outcomes (odds ratio, 3.34 [95% confidence interval, 2.06-5.40]; P < .001). Conclusions: Hr detected prior to ATT was predictive of unfavorable outcomes at the national level in Brazil. Our data reinforce the need for high-TB-burden countries to prioritize DST to detect Hr. Effective treatment regimens for Hr-TB are needed to improve outcomes.

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 ≥ 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.

Impact of adverse drug reactions on the outcomes of tuberculosis treatment.

BACKGROUND: Adverse drug reactions (ADR) challenge successful anti-tuberculosis treatment (ATT). The aim of this study was to evaluate the impact of ATT-associated ADR and related factors on ATT outcomes. METHODS: A prospective cohort study of persons with tuberculosis (TB) at a referral center in Rio de Janeiro, Brazil, from 2010 to 2016. Baseline information: race, sex, schooling, economic status, tobacco, drugs and alcohol abuse, HIV-infection status and comorbidities were captured during TB screening and diagnosis. Laboratory exams were performed to confirm TB diagnosis and monitor ADRs, favorable (cure and treatment completion) and unfavorable (death, loss to follow up and failure) outcomes were prospectively captured. The Kaplan-Meier curve was used to estimate the probability of ADR-free time. A logistic regression analysis (backward elimination) was performed to identify independent associations with unfavorable outcomes. RESULTS: 550 patients were enrolled, 35.1% were people living with HIV (PLHIV) and ADR occurred in 78.6% of all participants. Smoking (OR: 2.32; 95% CI:1.34-3.99) and illicit-drug use (OR:2.02; 95% CI:1.15-3.55) were independent risk factors for unfavorable outcomes. In PLHIV, alcohol abuse and previous ART use were associated with unfavorable outcomes. In contrast, ADR increased the odds of favorable outcomes in the overall population. PLHIV more frequently experienced grade 3/4-ADR (18.36%), especially "liver and biliary system disorders". Lower CD4 counts (<100 cells/uL) were associated with hepatotoxicity (p = 0.03). ART-naïve participants presented a higher incidence of ADR in comparison with ART-experienced patients. CONCLUSION: Substance use was associated with unfavorable outcomes, highlighting the need for better strategies to reduce this habit. In contrast, ADRs were associated with favorable outcomes. Attention to the occurrence of ADR in PLHIV is essential, especially regarding hepatotoxicity in those with high immunosuppression.

Pharmacogenetics of tuberculosis treatment toxicity and effectiveness in a large Brazilian cohort.

Background: Genetic polymorphisms have been associated with risk of anti-tuberculosis treatment toxicity. We characterized associations with adverse events and treatment failure/recurrence among adults treated for tuberculosis in Brazil. Methods: Participants were followed in Regional Prospective Observational Research in Tuberculosis (RePORT)-Brazil. We included persons with culture-confirmed drug-susceptible pulmonary tuberculosis who started treatment between 2015-2019, and who were evaluable for pharmacogenetics. Treatment included 2 months of isoniazid, rifampin or rifabutin, pyrazinamide, and ethambutol, then 4 months of isoniazid and rifampin or rifabutin, with 24 month follow-up. Analyses included 43 polymorphisms in 20 genes related to anti-tuberculosis drug hepatotoxicity or pharmacokinetics. Whole exome sequencing was done in a case-control toxicity subset. Results: Among 903 participants in multivariable genetic association analyses, NAT2 slow acetylator status was associated with increased risk of treatment-related grade 2 or greater adverse events, including hepatotoxicity. Treatment failure/recurrence was more likely among NAT2 rapid acetylators, but not statistically significant at the 5% level. A GSTM1 polymorphism (rs412543) was associated with increased risk of treatment-related adverse events, including hepatotoxicity. SLCO1B1 polymorphisms were associated with increased risk of treatment- related hepatoxicity and treatment failure/recurrence. Polymorphisms in NR1/2 were associated with decreased risk of adverse events and increased risk of failure/recurrence. In whole exome sequencing, hepatotoxicity was associated with a polymorphism in VTI1A , and the genes METTL17 and PRSS57 , but none achieved genome-wide significance. Conclusions: In a clinical cohort representing three regions of Brazil, NAT2 acetylator status was associated with risk for treatment-related adverse events. Additional significant polymorphisms merit investigation in larger study populations.

Machine learning algorithms using national registry data to predict loss to follow- up during tuberculosis treatment.

Background: Identifying patients at increased risk of loss to follow-up (LTFU) is key to developing strategies to optimize the clinical management of tuberculosis (TB). The use of national registry data in prediction models may be a useful tool to inform healthcare workers about risk of LTFU. Here we developed a score to predict the risk of LTFU during anti-TB treatment (ATT) in a nationwide cohort of cases using clinical data reported to the Brazilian Notifiable Disease Information System (SINAN). Methods: We performed a retrospective study of all TB cases reported to SINAN between 2015-2022; excluding children (<18 years-old), vulnerable groups or drug-resistant TB. For the score, data before treatment initiation were used. We trained and internally validated three different prediction scoring systems, based on Logistic Regression, Random Forest, and Light Gradient Boosting. Before applying our models we split our data into train (~80% data) and test (~20%), and then we compare model metrics using a test data set. Results: Of the 243,726 cases included, 41,373 experienced LTFU whereas 202,353 were successfully treated and cured. The groups were different with regards to several clinical and sociodemographic characteristics. The directly observed treatment (DOT) was unbalanced between the groups with lower prevalence in those who were LTFU. Three models were developed to predict LTFU using 8 features (prior TB, drug use, age, sex, HIV infection and schooling level) with different score composition approaches. Those prediction scoring system exhibited an area under the curve (AUC) ranging between 0.71 and 0.72. The Light Gradient Boosting technique resulted in the best prediction performance, weighting specificity, and sensibility. A user-friendly web calculator app was created (https://tbprediction.herokuapp.com/) to facilitate implementation. Conclusions: Our nationwide risk score predicts the risk of LTFU during ATT in Brazilian adults prior to treatment commencement. This is a potential tool to assist in decision-making strategies to guide resource allocation, DOT indications, and improve TB treatment adherence.

Anemia and anti-tuberculosis treatment outcome in persons with pulmonary tuberculosis: A multi-center prospective cohort study.

BACKGROUND: Tuberculosis (TB) remains a major plague of humanity. People with TB (PWTB) are commonly anemic. Here, we assessed whether the severity of anemia in PWTB prior to anti-TB treatment (ATT) was a risk factor for an unfavorable outcome. METHODS: Patients ≥ 18 years old with culture-confirmed drug-susceptible pulmonary TB enrolled between 2015 and 2019 in a multi-center Brazilian cohort were followed for up to 24 months and classified according to anemia severity (mild, moderate, and severe), based on hemoglobin levels. A multinomial logistic regression model was employed to assess whether anemia was associated with unfavorable outcome (death, failure, loss to follow-up, regimen modification or relapse), compared to treatment success (cure or treatment completion). RESULTS: Among 786 participants who met inclusion criteria, 441 (56 %) were anemic at baseline. Patients with moderate/severe anemia were more HIV-seropositive, as well as more symptomatic and had higher frequencies of unfavorable outcomes compared to the other groups. Moderate/severe anemia (adjusted OR [aOR]: 7.80, 95 %CI:1.34-45.4, p = 0.022) was associated with death independent of sex, age, BMI, HIV and glycemic status. CONCLUSION: Moderate/severe anemia prior to ATT was a significant risk factor for death. Such patients should be closely monitored given the high risk of unfavorable ATT outcomes.

Possible sex difference in latent tuberculosis infection risk among close tuberculosis contacts.

OBJECTIVES: More men than women develop and die of tuberculosis (TB). Fewer data exist on sex differences in latent TB infection (LTBI). We assessed for potential sex differences in LTBI acquisition among close TB contacts. METHODS: Regional Prospective Observational Research for TB-Brazil is an observational multi-center cohort of individuals with culture-confirmed pulmonary TB and their close contacts. Participants were enrolled from five sites in Brazil from June 2015 - June 2019. Close contacts were followed for 24 months after enrollment, with LTBI defined as a positive interferon-γ release assay (IGRA; QuantiFERON 3rd or 4th generation) at baseline or 6 months. We performed univariate, bivariate, and multivariable logistic regression and propensity-score weighted models to assess odds ratios (OR) and 95% confidence intervals (CI) for LTBI acquisition by birth sex among close contacts. RESULTS: Of 1093, 504 (46%) female close contacts were IGRA positive compared to 295 of 745 (40%) men. The unadjusted OR for IGRA positivity among women vs men was 1.31 (95% CI: 1.08-1.58). Bivariate adjustments yielded ORs in women vs men ranging from 1.19 to 1.33 (P-value range: <0.01-0.07). Multivariable regression and weighted models yielded similar ORs in women vs men, of 1.14 (95% CI: 0.92-1.41) and 1.15 (95% CI: 0.94-1.40), respectively. CONCLUSION: The point estimate for LTBI among close TB contacts in Brazil was higher in women, though less pronounced in multivariable models. If the sex difference in LTBI is confirmed in additional settings, studies of possible underlying differences in socio-behavioral factors or TB pathogenesis are warranted.

Effect of Dysglycemia on Urinary Lipid Mediator Profiles in Persons With Pulmonary Tuberculosis.

Background: Oxidized lipid mediators such as eicosanoids play a central role in the inflammatory response associated with tuberculosis (TB) pathogenesis. Diabetes mellitus (DM) leads to marked changes in lipid mediators in persons with TB. However, the associations between diabetes-related changes in lipid mediators and clearance of M. tuberculosis (Mtb) among persons on anti-TB treatment (ATT) are unknown. Quantification of urinary eicosanoid metabolites can provide insights into the circulating lipid mediators involved in Mtb immune responses. Methods: We conducted a multi-site prospective observational study among adults with drug-sensitive pulmonary TB and controls without active TB; both groups had sub-groups with or without dysglycemia at baseline. Participants were enrolled from RePORT-Brazil (Salvador site) and RePORT-South Africa (Durban site) and stratified according to TB status and baseline glycated hemoglobin levels: a) TB-dysglycemia (n=69); b) TB-normoglycemia (n=64); c) non-TB/dysglycemia (n=31); d) non-TB/non-dysglycemia (n=29). We evaluated the following urinary eicosanoid metabolites: 11α-hydroxy-9,15-dioxo-2,3,4,5-tetranor-prostane-1,20-dioic acid (major urinary metabolite of prostaglandin E2, PGE-M), tetranor-PGE1 (metabolite of PGE2, TN-E), 9α-hydroxy-11,15-dioxo-2,3,4,5-tetranor-prostane-1,20-dioic acid (metabolite of PGD2, PGD-M), 11-dehydro-thromboxane B2 (11dTxB2), 2,3-dinor-6-keto-PGF1α (prostaglandin I metabolite, PGI-M), and leukotriene E4 (LTE4). Comparisons between the study groups were performed at three time points: before ATT and 2 and 6 months after initiating therapy. Results: PGE-M and LTE4 values were consistently higher at all three time-points in the TB-dysglycemia group compared to the other groups (p<0.001). In addition, there was a significant decrease in PGI-M and LTE4 levels from baseline to month 6 in the TB-dysglycemia and TB-normoglycemia groups. Finally, TB-dysglycemia was independently associated with increased concentrations of PGD-M, PGI-M, and LTE4 at baseline in a multivariable model adjusting for age, sex, BMI, and study site. These associations were not affected by HIV status. Conclusion: The urinary eicosanoid metabolite profile was associated with TB-dysglycemia before and during ATT. These observations can help identify the mechanisms involved in the pathogenesis of TB-dysglycemia, and potential biomarkers of TB treatment outcomes, including among persons with dysglycemia.

Immunologic Biomarkers in Peripheral Blood of Persons With Tuberculosis and Advanced HIV.

Introduction: Tuberculosis (TB) is a common opportunistic infection among people living with HIV. Diagnostic tests such as culture, Xpert-MTB-RIF, and ULTRA have low sensitivity in paucibacillary TB disease; a blood biomarker could improve TB diagnostic capabilities. We assessed soluble factors to identify biomarkers associated with TB among persons with advanced HIV. Methods: A case-control (1:1) study was conducted, with participants from Rio de Janeiro and Manaus, Brazil. People living with HIV presenting with CD4 count ≤100 cells/mm3 were eligible to participate. Cases had culture-confirmed TB (N=15) (positive for Mycobacterium tuberculosis [Mtb]); controls had HIV-infection only (N=15). Study visits included baseline, month 2 and end of TB therapy, during which samples of peripheral blood were obtained. A panel containing 29 biomarkers including cytokines, chemokines and growth factors was utilized to assess candidate biomarkers using Luminex technology in cryopreserved EDTA plasma samples. We used neural network analysis, based on machine learning, to identify biomarkers (single or in combination) that best distinguished cases from controls. Additional multi-dimensional analyses provided detailed profiling of the systemic inflammatory environment in cases and controls. Results: Median CD4 count and HIV-1 RNA load values were similar between groups at all timepoints. Persons with TB had lower body mass index (BMI) (median=19.6, Interquartile Range [IQR]=18.6-22.3) than controls (23.7; IQR: 21.8 = 25.5, p=0.004). TB coinfection was also associated with increased frequency of other comorbidities. The overall profile of plasma cytokines, chemokines and growth factors were distinct between the study groups at all timepoints. Plasma concentrations of IL-15 and IL-10 were on average lower in TB cases than in controls. When used in combination, such markers were able to discriminate between TB cases and controls with the highest degree of accuracy at each study timepoint. Conclusion: Among persons with advanced HIV, plasma concentrations of IL-15 and IL-10 can be used in combination to identify TB disease regardless of time on anti-TB treatment.