BackgroundWe aimed to define characteristics of TB patients in Puducherry and two districts of Tamil Nadu, India and calculate the population attributable fractions (PAF) of TB from malnutrition and alcohol.MethodsNew smear-positive TB cases were enrolled into the Regional Prospective Observational Research for Tuberculosis (RePORT India) cohort. Census and National Family Health Survey data were used for comparisons.ResultsData were analyzed for 409 participants enrolled between May 2014-June 2016; 307 (75.1%) were male, 60.2% were malnourished (body mass index [BMI] <18.5 kg/m2), and 29.1% severely malnourished (BMI <16). “Hazardous” alcohol use (based on AUDIT-C score) was reported by 155/305 (50.8%) of males. Tuberculosis cases were more likely than the Puducherry population to be malnourished (62.6% v 10.2% males and 71.7% v 11.3% of females; both p<0.001), and male cases were more likely to use alcohol than male non-cases (84.4% v 41%; p < .001). The PAF of malnutrition was 57.4% in males and 61.5% in females; the PAF for alcohol use was 73.8% in males and 1.7% in females.ConclusionsAlcohol use in men and malnutrition are helping drive the TB epidemic in Southern India. Reducing the TB burden in this population will require efforts to mitigate these risk factors.
Background Malnutrition and diabetes are risk factors for active tuberculosis (TB), possible risk factors for latent TB infection (LTBI), and may interact to alter their effect on these outcomes. Studies to date have not investigated this interaction. Methods We enrolled 919 newly diagnosed active TB patients and 1113 household contacts at Primary Health Centres in Puducherry and Tamil Nadu, India from 2014 to 2018. In cross-sectional analyses, we used generalized estimating equations to measure additive and multiplicative interaction of body mass index (BMI) and diabetes on two outcomes, active TB and LTBI. Results Among overweight or obese adults, active TB prevalence was 12-times higher in diabetic compared to non-diabetic participants, 2.5-times higher among normal weight adults, and no different among underweight adults ( P for interaction < 0.0001). Diabetes was associated with 50 additional active TB cases per 100 overweight or obese participants, 56 per 100 normal weight participants, and 17 per 100 underweight participants ( P for interaction < 0.0001). Across BMI categories, screening 2.3–3.8 active TB patients yielded one hyperglycemic patient. LTBI prevalence did not differ by diabetes and BMI*diabetes interaction was not significant. Conclusions BMI and diabetes are associated with newly diagnosed active TB, but not LTBI. Diabetes conferred the greatest risk of active TB in overweight and obese adults whereas the burden of active TB associated with diabetes was similar for normal and overweight or obese adults. Hyperglycemia was common among all active TB patients. These findings highlight the importance of bi-directional diabetes-active TB screening in India. Electronic supplementary material The online version of this article (10.1186/s12879-019-4244-4) contains supplementary material, which is available to authorized users.
Objectives Patients with Covid-19 and obesity have worse clinical outcomes which may be driven by increased inflammation. This study aimed to characterize the association between clinical outcomes in patients with obesity and inflammatory markers. Methods We analyzed data for patients aged ≥18 years admitted with a positive SARS-CoV-2 PCR test. We used multivariate logistic regression to determine the association between BMI and intensive care unit (ICU) transfer and all-cause mortality. Inflammatory markers (C-reactive protein [CRP], lactate dehydrogenase [LDH], ferritin, and D-dimer) were compared between patients with and without obesity (body mass index [BMI] ≥30 kg/m2). Results Of 791 patients with Covid-19, 361 (45.6%) had obesity. In multivariate analyses, BMI ≥35 was associated with a higher odds of ICU transfer (adjusted odds ratio [aOR] 2.388 (95% confidence interval [CI]: 1.074–5.310) and hospital mortality (aOR = 4.3, 95% CI: 1.69–10.82). Compared to those with BMI<30, patients with obesity had lower ferritin (444 vs 637 ng/mL; p<0.001) and lower D-dimer (293 vs 350 mcg/mL; p = 0.009), non-significant differences in CRP (72.8 vs 84.1 mg/L, p = 0.099), and higher LDH (375 vs 340, p = 0.009) on the first hospital day. Conclusions Patients with obesity were more likely to have poor outcomes even without increased inflammation.
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.
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