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Background: Perturbed iron homeostasis is a risk factor for tuberculosis (TB) progression and an indicator of TB treatment failure and mortality. Few studies have evaluated iron homeostasis as a TB diagnostic biomarker.Methods: We recruited participants with TB, latent TB infection (LTBI), cured TB (RxTB), pneumonia (PN) and healthy controls (HCs). We measured serum levels of three iron biomarkers including serum iron, ferritin and transferrin, then established and validated our prediction model.Results: We observed and verified that the three iron biomarker levels correlated with patient status (TB, HC, LTBI, RxTB or PN) and with the degree of lung damage and bacillary load in patients with TB. We then built a TB prediction model, neural network (NNET), incorporating the data of the three iron biomarkers. The model showed good performance for diagnosis of TB, with 83% (95% CI 77 to 87) sensitivity and 86% (95% CI 83 to 89) specificity in the training data set (n=663) and 70% (95% CI 58 to 79) sensitivity and 92% (95% CI 86 to 96) specificity in the test data set (n=220). The area under the curves (AUCs) of the NNET model to discriminate TB from HC, LTBI, RxTB and PN were all >0.83. Independent validation of the NNET model in a separate cohort (n=967) produced an AUC of 0.88 (95% CI 0.85 to 0.91) with 74% (95% CI 71 to 77) sensitivity and 92% (95% CI 87 to 96) specificity.Conclusions: The established NNET TB prediction model discriminated TB from HC, LTBI, RxTB and PN in a large cohort of patients. This diagnostic assay may augment current TB diagnostics. [ABSTRACT FROM AUTHOR]

The present cross-sectional study consisted of 18,265 Chinese patients not previously diagnosed with diabetes mellitus, and who underwent physical examination at the Third People's Hospital of Shenzhen between June 2014 and May 2015 (mean patient age, 51.312±15.252 years). The study was composed of 11,770 males and 6,495 females. The aim was to investigate the association between glycated hemoglobin A1c (HbA1c) levels, gender and age. HbA1c values were measured using a Bio-Rad VARIANT? II HbA1c Reorder Pack. All data was collected for analysis of the HbA1c levels in different gender and age groups, in order to investigate the association between HbA1c levels and age. Analysis of the 18,265 total cases and 16,734 cases with HbA1c levels <6.5%, demonstrated a positive correlation between levels of HbA1c and patient age. Linear regression for patient age and HbA1c levels demonstrated that HbA1c (%) = 0.020 x age (years) + 4.523 (r=0.369, P<0.0001) and HbA1c (%) = 0.014 x age (years) + 4.659 (r=0.485, P<0.0001), respectively. HbA1c levels of the male group were significantly higher than those of the female group (P<0.0001). Furthermore, in different gender groups, HbA1c levels gradually rose with increasing age. Therefore, HbA1c levels are associated with age and gender in Chinese populations, and this should be considered when selecting HbA1c as a criterion for future diabetes screening. [ABSTRACT FROM AUTHOR]