Untargeted Metabolomics of Feces Reveals Diagnostic and Prognostic Biomarkers for Active Tuberculosis and Latent Tuberculosis Infection: Potential Application for Precise and Non-Invasive Identification

Dan Luo; Bo-Yi Yang; Kai Qin; Chong-Yu Shi; Nian-Sa Wei; Hai Li; Yi-Xiang Qin; Gang Liu; Xiao-Ling Qin; Shi-Yi Chen; Xiao-Jing Guo; Li Gan; Ruo-Lan Xu; Bai-Qing Dong; Jing Li

doi:10.2147/IDR.S422363

Untargeted Metabolomics of Feces Reveals Diagnostic and Prognostic Biomarkers for Active Tuberculosis and Latent Tuberculosis Infection: Potential Application for Precise and Non-Invasive Identification

Infect Drug Resist. 2023 Sep 12:16:6121-6138. doi: 10.2147/IDR.S422363. eCollection 2023.

Authors

Dan Luo^#^{1

2}, Bo-Yi Yang^#³, Kai Qin⁴, Chong-Yu Shi³, Nian-Sa Wei⁴, Hai Li¹, Yi-Xiang Qin¹, Gang Liu¹, Xiao-Ling Qin¹, Shi-Yi Chen¹, Xiao-Jing Guo¹, Li Gan¹, Ruo-Lan Xu¹, Bai-Qing Dong¹, Jing Li⁵

Affiliations

¹ Department of Biostatistics, School of Public Health and Management of Guangxi University of Chinese Medicine, Nanning, Guangxi, People's Republic of China.
² Guangxi Key Laboratory of Translational Medicine for Treating High-Incidence Infectious Diseases with Integrative Medicine, Guangxi University of Chinese Medicine, Nanning, Guangxi, People's Republic of China.
³ The First Affiliated Hospital, Guangxi Medical University, Nanning, Guangxi, People's Republic of China.
⁴ The Second Affiliated Hospital, Guangxi Medical University, Nanning, Guangxi, People's Republic of China.
⁵ Deparment of Physiology, School of Basic Medical Sciences of Guangxi Medical University, Nanning, Guangxi, People's Republic of China.

^# Contributed equally.

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.

Keywords: active tuberculosis; biomarkers; feces; latent tuberculosis infection; metabolomics; progression.

Grants and funding

This study was supported by the Natural Science Foundation of Guangxi (2017GXNSFAA0198330), Guangxi University of Chinese Medicine−Shenzhen Kangtai Biological Products Co.LTD. Guangxi Hepatitis B Prevention and Control Project (QT022019), and National College Students’ innovation and Entrepreneurship Training Program (202310598006).