A combination of iron metabolism indexes and tuberculosis-specific antigen/phytohemagglutinin ratio for distinguishing active tuberculosis from latent tuberculosis infection.


Journal

International journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases
ISSN: 1878-3511
Titre abrégé: Int J Infect Dis
Pays: Canada
ID NLM: 9610933

Informations de publication

Date de publication:
Aug 2020
Historique:
received: 06 04 2020
revised: 18 05 2020
accepted: 23 05 2020
pubmed: 5 6 2020
medline: 6 10 2020
entrez: 5 6 2020
Statut: ppublish

Résumé

Discriminating active tuberculosis (ATB) from latent tuberculosis infection (LTBI) remains challenging. This study aimed to investigate a diagnostic model based on a combination of iron metabolism and the TB-specific antigen/phytohemagglutinin ratio (TBAg/PHA ratio) in T-SPOT.TB assay for differentiation between ATB and LTBI. A total of 345 participants with ATB (n=191) and LTBI (n=154) were recruited based on positive T-SPOT.TB results at Tongji hospital between January 2017 and January 2020. Iron metabolism analysis was performed simultaneously. A diagnostic model for distinguishing ATB from LTBI was established according to multivariate logistic regression. The TBAg/PHA ratio showed 64.00% sensitivity and 90.10% specificity in distinguishing ATB from LTBI when a threshold of 0.22 was used. All iron metabolism biomarkers in the ATB group were significantly different from those in the LTBI group. Specifically, serum ferritin and soluble transferrin receptor in ATB were significantly higher than LTBI. On the contrary, serum iron, transferrin, total iron binding capacity, and unsaturated iron binding capacity in ATB were significantly lower than LTBI. The combination of iron metabolism indicators accurately predicted 60.00% of ATB cases and 91.09% of LTBI subjects, respectively. Moreover, the combination of iron metabolism indexes and TBAg/PHA ratio resulted in a sensitivity of 88.80% and specificity of 90.10%. Furthermore, the performance of models established in the Qiaokou cohort was confirmed in the Caidian cohort. The data suggest that the combination of iron metabolism indexes and TBAg/PHA ratio could serve as a biomarker to distinguish ATB from LTBI in T-SPOT-positive individuals.

Sections du résumé

BACKGROUND BACKGROUND
Discriminating active tuberculosis (ATB) from latent tuberculosis infection (LTBI) remains challenging. This study aimed to investigate a diagnostic model based on a combination of iron metabolism and the TB-specific antigen/phytohemagglutinin ratio (TBAg/PHA ratio) in T-SPOT.TB assay for differentiation between ATB and LTBI.
METHODS METHODS
A total of 345 participants with ATB (n=191) and LTBI (n=154) were recruited based on positive T-SPOT.TB results at Tongji hospital between January 2017 and January 2020. Iron metabolism analysis was performed simultaneously. A diagnostic model for distinguishing ATB from LTBI was established according to multivariate logistic regression.
RESULTS RESULTS
The TBAg/PHA ratio showed 64.00% sensitivity and 90.10% specificity in distinguishing ATB from LTBI when a threshold of 0.22 was used. All iron metabolism biomarkers in the ATB group were significantly different from those in the LTBI group. Specifically, serum ferritin and soluble transferrin receptor in ATB were significantly higher than LTBI. On the contrary, serum iron, transferrin, total iron binding capacity, and unsaturated iron binding capacity in ATB were significantly lower than LTBI. The combination of iron metabolism indicators accurately predicted 60.00% of ATB cases and 91.09% of LTBI subjects, respectively. Moreover, the combination of iron metabolism indexes and TBAg/PHA ratio resulted in a sensitivity of 88.80% and specificity of 90.10%. Furthermore, the performance of models established in the Qiaokou cohort was confirmed in the Caidian cohort.
CONCLUSIONS CONCLUSIONS
The data suggest that the combination of iron metabolism indexes and TBAg/PHA ratio could serve as a biomarker to distinguish ATB from LTBI in T-SPOT-positive individuals.

Identifiants

pubmed: 32497795
pii: S1201-9712(20)30416-1
doi: 10.1016/j.ijid.2020.05.109
pii:
doi:

Substances chimiques

Antigens, Bacterial 0
Biomarkers 0
Phytohemagglutinins 0
Iron E1UOL152H7

Types de publication

Evaluation Study Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

190-196

Informations de copyright

Copyright © 2020 The Author(s). Published by Elsevier Ltd.. All rights reserved.

Auteurs

Ying Luo (Y)

Department of Laboratory Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.

Ying Xue (Y)

Department of Clinical Immunology, Tongji Hospital, Tongji Medical College, Huazhong University of Sciences and Technology, Wuhan, China.

Qun Lin (Q)

Department of Laboratory Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.

Guoxing Tang (G)

Department of Laboratory Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.

Xu Yuan (X)

Department of Laboratory Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.

Liyan Mao (L)

Department of Laboratory Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.

Huijuan Song (H)

Department of Laboratory Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.

Feng Wang (F)

Department of Laboratory Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China. Electronic address: fengwang@tjh.tjmu.edu.cn.

Ziyong Sun (Z)

Department of Laboratory Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China. Electronic address: zysun@tjh.tjmu.edu.cn.

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Classifications MeSH