Background Undernutrition is the leading risk factor for tuberculosis (TB) globally. Its impact on treatment outcomes is poorly defined. Methods We conducted a prospective cohort analysis of adults with drug-sensitive pulmonary TB at five sites in the Regional Prospective Observational Research on Tuberculosis (RePORT) India consortium (2015-2019). Using multivariable Poisson regression, we assessed independent associations between unfavorable outcomes and nutritional status based on body mass index (BMI) nutritional status at treatment initiation, BMI prior to TB disease, stunting, and stagnant or declining BMI after two months of TB treatment. Unfavorable outcome was defined as a composite of treatment failure, death, or relapse within 6 months of treatment completion. Findings Severe undernutrition (BMI < 16 kg/m2) at treatment initiation and severe undernutrition before the onset of TB disease were both associated with unfavorable outcomes (adjusted incidence rate ratio [aIRR]: 2.05; 95% confidence interval [CI]: 1.42-2.91 and 2.20; 95% CI: 1.16-3.94, respectively). Additionally, lack of BMI increase after treatment initiation was associated with increased unfavorable outcomes (aIRR: 1.81; 95% CI: 1.27-2.61). Severe stunting (height-for-age z-score < -3) was associated with unfavorable outcomes (aIRR: 1.52; 95% CI: 1.00-2.24). Severe undernutrition at treatment initiation and lack of BMI increase during treatment were associated with a four and five-fold higher rate of death, respectively. Interpretations Premorbid undernutrition, undernutrition at treatment initiation, lack of BMI increase after intensive therapy, and severe stunting are associated with unfavorable TB treatment outcomes. These data highlight the need for addressing this widely prevalent TB comorbidity. Nutritional assessment should be integrated into standard TB care.
Background Gene expression signatures have been used as biomarkers of tuberculosis (TB) risk and outcomes. Platforms are needed to simplify access to these signatures and determine their validity in the setting of comorbidities. We developed a computational profiling platform of TB signature gene sets and characterized the diagnostic ability of existing signature gene sets to differentiate active TB from LTBI in the setting of malnutrition. Methods We curated 45 existing TB-related signature gene sets and developed our TBSignatureProfiler software toolkit that estimates gene set activity using multiple enrichment methods and allows visualization of single- and multi-pathway results. The TBSignatureProfiler software is available through Bioconductor and on GitHub. For evaluation in malnutrition, we used whole blood gene expression profiling from 23 severely malnourished Indian individuals with TB and 15 severely malnourished household contacts with latent TB infection (LTBI). Severe malnutrition was defined as body mass index (BMI) < 16 kg/m2 in adults and based on weight-for-height Z scores in children < 18 years. Gene expression was measured using RNA-sequencing. Results The comparison and visualization functions from the TBSignatureProfiler showed that TB gene sets performed well in malnourished individuals; 40 gene sets had statistically significant discriminative power for differentiating TB from LTBI, with area under the curve ranging from 0.662–0.989. Three gene sets were not significantly predictive. Conclusion Our TBSignatureProfiler is a highly effective and user-friendly platform for applying and comparing published TB signature gene sets. Using this platform, we found that existing gene sets for TB function effectively in the setting of malnutrition, although differences in gene set applicability exist. RNA-sequencing gene sets should consider comorbidities and potential effects on diagnostic performance.
I N TRODUC T IONTuberculosis (TB) is the leading cause of morbidity and mortality due to a single infectious agent globally. In 2018, almost 10 million TB cases were reported as per WHO [1]. Worldwide, TB incidence is falling at a rate of about 2% per year. However, further efforts are needed to ramp it up to 4-5% annual decline and reach the end TB goal [1]. WHO
The rising geriatric population and the increased susceptibility of this age group to tuberculosis (TB), the deadliest single infectious agent, is bothersome for India. This study tried to explore the demographic and treatment outcome differences between the elderly (aged 60 years and above) and non-elderly TB (<60 years) patients from South India. This study was part of a large ongoing cohort study under the RePORT India consortium. Newly diagnosed TB patients recruited into the cohort between 2014 and 2018 were included in this study. Pretested and standardized questionnaire and tools were used to collect data and were stored securely for the entire cohort. Required demographic, anthropometric and treatment related variables were extracted from this database and analyzed using Stata version 14.0. Prevalence of elderly TB was summarized as percentage with 95% confidence interval (CI). Generalized linear modelling was attempted to find the factors associated with elderly TB. A total of 1,259 eligible TB patients were included into this present study. Mean (SD) of the participants in the elderly and non-elderly group was 65.8 (6.2) and 40.2 (12.0) respectively. Prevalence of elderly TB was 15.6% (95%CI: 13.6%-17.6%) with nearly 71% belonging to 60–69 age category. Male sex, OBC caste, poor education, unemployment, marriage, alcohol consumption and unable to work as per Karnofsky score were found to be significantly associated with an increased prevalence of elderly TB. Unfavorable outcomes (12% vs 6.5%, p value: 0.018), including death (9.3% vs 3.4%, p value: 0.001) were significantly higher among the elderly group when compared to their non-elderly counterparts. The current TB programme should have strategies to maintain follow up with due attention to adverse effects, social support and outcomes. Additional research should focus on predictors for unfavorable outcomes among the elderly TB group and explore ways to handle the same. Rendering adequate social support from the health system side and family side would be a good start.
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