https://orcid.org/0009-0005-7362-3094
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Abstract
Purpose
Distinguishing latent tuberculosis infection (LTBI) from active tuberculosis (ATB) is important to control the prevalence of tuberculosis; however, there is currently no effective method. The aim of this study was to discover specific metabolites through fecal untargeted metabolomics to discriminate ATB, individuals with LTBI, and healthy controls (HC) and to probe the metabolic perturbation associated with the progression of tuberculosis.
Patients and Methods
Liquid chromatography-tandem mass spectrometry (LC-MS/MS) was performed to comprehensively detect compounds in fecal samples from HC, LTBI, and ATB patients. Differential metabolites between the two groups were screened, and their underlying biological functions were explored. Candidate metabolites were selected and enrolled in LASSO regression analysis to construct diagnostic signatures for discriminating between HC, LTBI, and ATB. A receiver operating characteristic (ROC) curve was applied to evaluate diagnostic value. A nomogram was constructed to predict the risk of progression of LTBI.
Results
A total of 35 metabolites were found to exist differentially in HC, LTBI, and ATB, and eight biomarkers were selected. Three diagnostic signatures based on the eight biomarkers were constructed to distinguish between HC, LTBI, and ATB, demonstrating excellent discrimination performance in ROC analysis. A nomogram was successfully constructed to evaluate the risk of progression of LTBI to ATB. Moreover, 3,4-dimethylbenzoic acid has been shown to distinguish ATB patients with different responses to etiological tests.
Conclusion
This study constructed diagnostic signatures based on fecal metabolic biomarkers that effectively discriminated HC, LTBI, and ATB, and established a predictive model to evaluate the risk of progression of LTBI to ATB. The results provide scientific evidence for establishing an accurate, sensitive, and noninvasive differential diagnosis scheme for tuberculosis.
Abbreviations
TB, tuberculosis; HC, healthy controls; LTBI, latent tuberculosis infection; ATB, active tuberculosis; QC, quality control; ESI−, negative ion mode; ESI+, positive ion mode; PCA, principal component analysis; PLS−DA, partial least squares discriminant analysis; ROC curve, receiver operating characteristic curve; AUC, area under curve; 95% CI, 95% confidence interval; LASSO, least absolute shrinkage and selection operator; mTOR, mammalian target of rapamycin; Th17, T helper cell 17; IL-17, interleukin 17; Treg, regulatory cell.
Data Sharing Statement
The datasets are available from one of the corresponding authors on reasonable request (Jing Li, email: [email protected]; Bai-Qing Dong, email: [email protected]).
Ethics Approval and Consent to Participate
This study has been approved by the Ethics Committee on Human Research of Guangxi University of Chinese Medicine. All individuals enrolled in this study gave written informed consent. The study removed all the relevant personal information of the individuals except age and gender. This research based on human fecal samples have been performed in accordance with the principles stated in the Declaration of Helsinki.
Acknowledgments
We thank the relevant staff from the Guigang Center for Disease Control and Prevention and Pingnan People’s Hospital for their support with case enrollment and sample collection. We also thank Novogene Co. Ltd. for their technical support.
Disclosure
The authors report no conflicts of interest in this work.