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Tuberculosis
Biomarkers of iron metabolism facilitate clinical diagnosis in M ycobacterium tuberculosis infection
  1. Youchao Dai1,2,
  2. Wanshui Shan3,
  3. Qianting Yang3,
  4. Jiubiao Guo1,
  5. Rihong Zhai4,
  6. Xiaoping Tang2,
  7. Lu Tang5,
  8. Yaoju Tan6,
  9. Yi Cai1,
  10. Xinchun Chen1
  1. 1 Guangdong Key Laboratory of Regional Immunity and Diseases, Department of Pathogen Biology, Shenzhen University School of Medicine, Shenzhen, China
  2. 2 Research Institute of Infectious Diseases, Guangzhou Eighth People's Hospital, Guangzhou Medical University, Guangzhou, China
  3. 3 Shenzhen Key Laboratory of Infection & Immunity, Shenzhen Third People’s Hospital, Shenzhen University School of Medicine, Shenzhen, China
  4. 4 Department of Preventive Medicine, Shenzhen University Health Science Center, Shenzhen, Guangdong, China
  5. 5 Department of Biostatistics, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
  6. 6 State Key Laboratory of Respiratory Disease, Department of Clinical Laboratory, Guangzhou Chest Hospital, Guangzhou, China
  1. Correspondence to Professor Xinchun Chen, Guangdong Key Laboratory of Regional Immunity and Diseases, Department of Pathogen Biology, Shenzhen University School of Medicine, Shenzhen 518054, China; chenxinchun{at}szu.edu.cn

Abstract

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.

  • iron homeostasis
  • diagnostic biomarker
  • neural network model
  • tuberculosis
  • receive operating characteristic (roc)

This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.

https://doi.org/10.1136/thoraxjnl-2018-212557

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    Footnotes

    • Contributors Authors YD and WS contributed equally to this manuscript. XC conceived the study and critically revised the manuscript. YD, WS, QY, JG and YT recruited and assessed the patients and preprocessed the study specimens. RZ, XT, YC and LT were responsible for statistical analysis. YD interpreted the data and wrote the manuscript. All authors contributed to the subsequent drafts and approved the final version.

    • Funding This study was supported by the Twelve-Fifth Mega-Scientific Project on 'prevention and treatment of AIDS, viral hepatitis and other infectious diseases' (grant nos. 2017ZX10201301-001-001/002), the Natural Science Foundation of China Grant (grant nos. 81525016/81671984), the Science and Technology Project of Shenzhen (grant nos. JSGG20160427104724699/JCYJ20160427184123851/JCYJ20160427151540695/JCYJ20150402145015986/JCYJ20150402111430656) and the Jin Qi team of Sanming Project of Medicine in Shenzhen, Sanming Project of Medicine in Shenzhen (grant no. GCZX2015043015340574).

    • Competing interests None declared.

    • Patient consent for publication Not required.

    • Ethics approval The present study was approved by the ethics committees of the Shenzhen Third Hospital and Guangzhou Chest Hospital, China.

    • Provenance and peer review Not commissioned; externally peer reviewed.

    • Data availability statement All data relevant to the study are included in the article or uploaded as supplementary information.