1. Biomarkers of iron metabolism facilitate clinical diagnosis in Mycobacterium tuberculosis infection
- Author
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Rihong Zhai, Yi Cai, Lu Tang, Xinchun Chen, Yaoju Tan, Wanshui Shan, Xiaoping Tang, Qianting Yang, Jiubiao Guo, and Youchao Dai
- Subjects
Pulmonary and Respiratory Medicine ,medicine.medical_specialty ,Tuberculosis ,Gastroenterology ,Mycobacterium tuberculosis ,03 medical and health sciences ,Internal medicine ,medicine ,Risk factor ,030304 developmental biology ,chemistry.chemical_classification ,0303 health sciences ,medicine.diagnostic_test ,biology ,030306 microbiology ,business.industry ,biology.organism_classification ,medicine.disease ,Ferritin ,chemistry ,Transferrin ,Cohort ,Serum iron ,biology.protein ,Biomarker (medicine) ,business - Abstract
BackgroundPerturbed 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.MethodsWe 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.ResultsWe 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.ConclusionsThe 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.
- Published
- 2019
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