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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.

Feng S, Du YQ, Zhang L, Zhang L, Feng RR, Liu SY - Chin. Med. J. (2015)

Bottom Line: From among 271 participants, 12 metabolites were found to contribute to the distinction between the TB active group and the control groups.The largest and smallest resulting AUCs were 0.964 and 0.720, indicating that these biomarkers may be involved in the disease mechanisms.The metabolic analysis results identified new serum biomarkers that can distinguish TB from non-TB diseases.

View Article: PubMed Central - PubMed

Affiliation: Department of Clinical Laboratory, Haihe Hospital, Respiratory Disease Research Institute, Tianjin 300350, China.

ABSTRACT

Background: Tuberculosis (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.

Methods: Orthogonal 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.

Results: From 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.

Conclusion: The 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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Related in: MedlinePlus

(a) The receiver operating characteristic (ROC) curve of metabolites that were decreased in the active tuberculosis (TB) group compared with controls. ROC curves of metabolites for which the serum concentrations were significantly decreased in the active TB group compared with controls. The ROC curves of each metabolite that was decreased in concentration in the TB group sera showed a moderate distinguishing efficiency. (b) The receiver operating characteristic (ROC) curve of metabolites that were increased in the active tuberculosis (TB) group compared with controls. ROC curves of metabolites for which the serum concentrations were significantly increased in the active TB group compared with controls. The ROC curves of each metabolite that was increased in concentration in the TB group sera showed a moderate distinguishing efficiency. (c) The receiver operating characteristic (ROC) curve of the biomarker combination identified as a putative serum signature of active tuberculosis (TB). The ROC curve of the combination of lysophosphatidylcholine (18:0), behenic acid, threoninyl-γ-glutamate, and presqualene diphosphate showed excellent distinguishing efficiency between patients with active TB and controls.
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Figure 7: (a) The receiver operating characteristic (ROC) curve of metabolites that were decreased in the active tuberculosis (TB) group compared with controls. ROC curves of metabolites for which the serum concentrations were significantly decreased in the active TB group compared with controls. The ROC curves of each metabolite that was decreased in concentration in the TB group sera showed a moderate distinguishing efficiency. (b) The receiver operating characteristic (ROC) curve of metabolites that were increased in the active tuberculosis (TB) group compared with controls. ROC curves of metabolites for which the serum concentrations were significantly increased in the active TB group compared with controls. The ROC curves of each metabolite that was increased in concentration in the TB group sera showed a moderate distinguishing efficiency. (c) The receiver operating characteristic (ROC) curve of the biomarker combination identified as a putative serum signature of active tuberculosis (TB). The ROC curve of the combination of lysophosphatidylcholine (18:0), behenic acid, threoninyl-γ-glutamate, and presqualene diphosphate showed excellent distinguishing efficiency between patients with active TB and controls.

Mentions: To investigate whether the characteristics of the metabolites that significantly differed among the three groups could be efficiently exploited for building a sensitive biosignature of TB status, ROC curves, which have been conventionally used to evaluate diagnostic performance in clinical research, were calculated. In the specific metabolites that were decreased in active TB patients, the ROC curves of 3D, 7D, 11D-phytanic acid, behenic acid and threoninyl-γ-glutamate exhibited excellent efficiency with a AUC values of 0.904 (95% CI: 0863–0.944), 0.93 (95% CI: 0.893–0.966) and 0.964 (95% CI: 00.941–0.988), respectively [Table 3 and Figure 7a]. Kynurenine, quinolinic acid (QUIN), and presqualene diphosphate (PSDP) showed significant up-regulation in patients with active TB (P < 0.05). The AUC values for these metabolites were more than 0.8 [Table 3 and Figure 7b], showing moderate performance of diagnostic value. The largest and smallest resulting AUC values were 0.964 and 0.720 [Table 3, Figure 7a and b], which indicated that these biomarkers may potentially be involved in the disease mechanisms. Using multiple logistic regression analysis of these 12 metabolites, the combination of lysoPC (18:0), behenic acid, threoninyl-γ-glutamate, and PSDP was used to represent a suitable biomarker group that allowed efficient differentiation of active TB from the controls. The resulting ROC curve of the biomarker combination had an AUC value of 0.991 (95% CI: 0.982–1.000) [Table 3 and Figure 7c], which reflects strong significant difference between active TB and the control patient groups.


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.

Feng S, Du YQ, Zhang L, Zhang L, Feng RR, Liu SY - Chin. Med. J. (2015)

(a) The receiver operating characteristic (ROC) curve of metabolites that were decreased in the active tuberculosis (TB) group compared with controls. ROC curves of metabolites for which the serum concentrations were significantly decreased in the active TB group compared with controls. The ROC curves of each metabolite that was decreased in concentration in the TB group sera showed a moderate distinguishing efficiency. (b) The receiver operating characteristic (ROC) curve of metabolites that were increased in the active tuberculosis (TB) group compared with controls. ROC curves of metabolites for which the serum concentrations were significantly increased in the active TB group compared with controls. The ROC curves of each metabolite that was increased in concentration in the TB group sera showed a moderate distinguishing efficiency. (c) The receiver operating characteristic (ROC) curve of the biomarker combination identified as a putative serum signature of active tuberculosis (TB). The ROC curve of the combination of lysophosphatidylcholine (18:0), behenic acid, threoninyl-γ-glutamate, and presqualene diphosphate showed excellent distinguishing efficiency between patients with active TB and controls.
© Copyright Policy - open-access
Related In: Results  -  Collection

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Show All Figures
getmorefigures.php?uid=PMC4837832&req=5

Figure 7: (a) The receiver operating characteristic (ROC) curve of metabolites that were decreased in the active tuberculosis (TB) group compared with controls. ROC curves of metabolites for which the serum concentrations were significantly decreased in the active TB group compared with controls. The ROC curves of each metabolite that was decreased in concentration in the TB group sera showed a moderate distinguishing efficiency. (b) The receiver operating characteristic (ROC) curve of metabolites that were increased in the active tuberculosis (TB) group compared with controls. ROC curves of metabolites for which the serum concentrations were significantly increased in the active TB group compared with controls. The ROC curves of each metabolite that was increased in concentration in the TB group sera showed a moderate distinguishing efficiency. (c) The receiver operating characteristic (ROC) curve of the biomarker combination identified as a putative serum signature of active tuberculosis (TB). The ROC curve of the combination of lysophosphatidylcholine (18:0), behenic acid, threoninyl-γ-glutamate, and presqualene diphosphate showed excellent distinguishing efficiency between patients with active TB and controls.
Mentions: To investigate whether the characteristics of the metabolites that significantly differed among the three groups could be efficiently exploited for building a sensitive biosignature of TB status, ROC curves, which have been conventionally used to evaluate diagnostic performance in clinical research, were calculated. In the specific metabolites that were decreased in active TB patients, the ROC curves of 3D, 7D, 11D-phytanic acid, behenic acid and threoninyl-γ-glutamate exhibited excellent efficiency with a AUC values of 0.904 (95% CI: 0863–0.944), 0.93 (95% CI: 0.893–0.966) and 0.964 (95% CI: 00.941–0.988), respectively [Table 3 and Figure 7a]. Kynurenine, quinolinic acid (QUIN), and presqualene diphosphate (PSDP) showed significant up-regulation in patients with active TB (P < 0.05). The AUC values for these metabolites were more than 0.8 [Table 3 and Figure 7b], showing moderate performance of diagnostic value. The largest and smallest resulting AUC values were 0.964 and 0.720 [Table 3, Figure 7a and b], which indicated that these biomarkers may potentially be involved in the disease mechanisms. Using multiple logistic regression analysis of these 12 metabolites, the combination of lysoPC (18:0), behenic acid, threoninyl-γ-glutamate, and PSDP was used to represent a suitable biomarker group that allowed efficient differentiation of active TB from the controls. The resulting ROC curve of the biomarker combination had an AUC value of 0.991 (95% CI: 0.982–1.000) [Table 3 and Figure 7c], which reflects strong significant difference between active TB and the control patient groups.

Bottom Line: From among 271 participants, 12 metabolites were found to contribute to the distinction between the TB active group and the control groups.The largest and smallest resulting AUCs were 0.964 and 0.720, indicating that these biomarkers may be involved in the disease mechanisms.The metabolic analysis results identified new serum biomarkers that can distinguish TB from non-TB diseases.

View Article: PubMed Central - PubMed

Affiliation: Department of Clinical Laboratory, Haihe Hospital, Respiratory Disease Research Institute, Tianjin 300350, China.

ABSTRACT

Background: Tuberculosis (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.

Methods: Orthogonal 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.

Results: From 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.

Conclusion: The 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.

Show MeSH
Related in: MedlinePlus