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

Abstract

Dan Luo,1,2,* Bo-Yi Yang,3,* Kai Qin,4 Chong-Yu Shi,3 Nian-Sa Wei,4 Hai Li,1 Yi-Xiang Qin,1 Gang Liu,1 Xiao-Ling Qin,1 Shi-Yi Chen,1 Xiao-Jing Guo,1 Li Gan,1 Ruo-Lan Xu,1 Bai-Qing Dong,1 Jing Li5 1Department of Biostatistics, School of Public Health and Management of Guangxi University of Chinese Medicine, Nanning, Guangxi, People’s Republic of China; 2Guangxi 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; 3The First Affiliated Hospital, Guangxi Medical University, Nanning, Guangxi, People’s Republic of China; 4The Second Affiliated Hospital, Guangxi Medical University, Nanning, Guangxi, People’s Republic of China; 5Deparment of Physiology, School of Basic Medical Sciences of Guangxi Medical University, Nanning, Guangxi, People’s Republic of China*These authors contributed equally to this workCorrespondence: Bai-Qing Dong, Guangxi University of Chinese Medicine, 13 Wuhe Road, Nanning, Guangxi, 530200, People’s Republic of China, Tel/Fax [phone], Email [email protected] Jing Li, Guangxi Medical University, 22 Shuangyong Road, Nanning, Guangxi, 530021, People’s Republic of China, Tel/Fax [phone], Email [email protected]: 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: feces, latent tuberculosis infection, active tuberculosis, biomarkers, progression, metabolomics

Keywords

• feces
• latent tuberculosis infection
• active tuberculosis
• biomarkers
• progression
• metabolomics