ArticleChinese medical journal2015
Analysis of serum metabolic profile by ultra-performance liquid chromatography-mass spectrometry for biomarkers discovery: application in a pilot study to discriminate patients with tuberculosis.
Article in Chinese medical journal, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.
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Who cites it
36 citing papers in PubMed.
- Novel therapeutic evaluation biomarkers of lipid metabolism targets in uncomplicated pulmonary tuberculosis patients.Signal transduction and targeted therapy · 2021Trial
- Integrated metabolomic and transcriptomic analysis reveals novel plasma biomarkers and metabolic pathway dysregulation in latent tuberculosis infection.Microbiology spectrum · 2026Article
- NMR based human gut metabolomic profiling reveals altered metabolites associated with pulmonary and extra-pulmonary tuberculosis.Metabolomics : Official journal of the Metabolomic Society · 2025Article
- Lipidomics-Based Identification of Plasma Lipid Biomarkers in Tuberculosis-Coronary Artery Disease Comorbidity.Infection and drug resistance · 2025Article
- Identifying the Interaction Between Tuberculosis and SARS-CoV-2 Infections via Bioinformatics Analysis and Machine Learning.Biochemical genetics · 2024Article
- Variations in Quinolinic Acid Levels in Tuberculosis Patients with Diabetes Comorbidity: A Pilot Prospective Cohort Study.Infection and drug resistance · 2024Article
- A novel humanized mouse model for HIV and tuberculosis co-infection studies.Frontiers in immunology · 2024Article
- The metabolic consequences of HIV/TB co-infection.BMC infectious diseases · 2023Article
- Tuberculosis is associated with sputum metabolome variations, irrespective of patient sex or HIV status: an untargeted GCxGC-TOFMS study.Metabolomics : Official journal of the Metabolomic Society · 2023Article
- Decoding the mechanism of hypertension through multiomics profiling.Journal of human hypertension · 2023Review
- Metabolites enhance innate resistance to human Mycobacterium tuberculosis infection.JCI insight · 2022Article
- Multi-block data integration analysis for identifying and validating targeted N-glycans as biomarkers for type II diabetes mellitus.Scientific reports · 2022Article
- Non-volatile organic compounds in exhaled breath particles correspond to active tuberculosis.Scientific reports · 2022Article
- Targeted metabolomics analysis of serum and Mycobacterium tuberculosis antigen-stimulated blood cultures of pediatric patients with active and latent tuberculosis.Scientific reports · 2022Article
- Pathological and protective roles of dendritic cells inFrontiers in cellular and infection microbiology · 2022Review
- Host lipidome and tuberculosis treatment failure.The European respiratory journal · 2022Article
- Metabolomics Strategy Assisted by Transcriptomics Analysis to Identify Potential Biomarkers Associated with Tuberculosis.Infection and drug resistance · 2021Article
- Understanding Metabolic Regulation Between Host and Pathogens: New Opportunities for the Development of Improved Therapeutic Strategies AgainstFrontiers in cellular and infection microbiology · 2021Review
- Differential Diagnosis of Latent Tuberculosis Infection and Active Tuberculosis: A Key to a Successful Tuberculosis Control Strategy.Frontiers in microbiology · 2021Review
- Discovery and validation of an NMR-based metabolomic profile in urine as TB biomarker.Scientific reports · 2020Article
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6 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundTuberculosis (TB) is a chronic wasting inflammatory disease characterized by multisystem involvement, which can cause metabolic derangements in afflicted patients. Metabolic signatures have been exploited in the study of several diseases. However, the serum that is successfully used in TB diagnosis on the basis of metabolic profiling is not by much.
methodsOrthogonal partial least-squares discriminant analysis was capable of distinguishing TB patients from both healthy subjects and patients with conditions other than TB. Therefore, TB-specific metabolic profiling was established. Clusters of potential biomarkers for differentiating TB active from non-TB diseases were identified using Mann-Whitney U-test. Multiple logistic regression analysis of metabolites was calculated to determine the suitable biomarker group that allows the efficient differentiation of patients with TB active from the control subjects.
resultsFrom among 271 participants, 12 metabolites were found to contribute to the distinction between the TB active group and the control groups. These metabolites were mainly involved in the metabolic pathways of the following three biomolecules: Fatty acids, amino acids, and lipids. The receiver operating characteristic curves of 3D, 7D, and 11D-phytanic acid, behenic acid, and threoninyl-γ-glutamate exhibited excellent efficiency with area under the curve (AUC) values of 0.904 (95% confidence interval [CI]: 0863-0.944), 0.93 (95% CI: 0.893-0.966), and 0.964 (95% CI: 00.941-0.988), respectively. The largest and smallest resulting AUCs were 0.964 and 0.720, indicating that these biomarkers may be involved in the disease mechanisms. The combination of lysophosphatidylcholine (18:0), behenic acid, threoninyl-γ-glutamate, and presqualene diphosphate was used to represent the most suitable biomarker group for the differentiation of patients with TB active from the control subjects, with an AUC value of 0.991.
conclusionThe metabolic analysis results identified new serum biomarkers that can distinguish TB from non-TB diseases. The metabolomics-based analysis provides specific insights into the biology of TB and may offer new avenues for TB diagnosis.
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