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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

High performance liquid chromatography-mass spectrometry chromatograms of the serum samples of patients from the tuberculosis (TB) group, the healthy group, and the four subgroups of patients with non-TB diseases. Abscissa: Retention time. Ordinate: Relative abundance. The numbers on the point of the peaks indicate the retention time of each substance. T indicates TB group; N indicates healthy control; P indicates pulmonitis subgroup; L indicates lung cancer subgroup; C indicates chronic obstructive pulmonary disease subgroup; B indicates bronchiectasis subgroup.
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Figure 2: High performance liquid chromatography-mass spectrometry chromatograms of the serum samples of patients from the tuberculosis (TB) group, the healthy group, and the four subgroups of patients with non-TB diseases. Abscissa: Retention time. Ordinate: Relative abundance. The numbers on the point of the peaks indicate the retention time of each substance. T indicates TB group; N indicates healthy control; P indicates pulmonitis subgroup; L indicates lung cancer subgroup; C indicates chronic obstructive pulmonary disease subgroup; B indicates bronchiectasis subgroup.

Mentions: Representative UPLC-MS chromatograms of the serum samples of the patients with active and the subjects with the healthy and non-TB controls were shown in Figure 2. The peaks were very well resolved and were evenly dispersed across the entire retention time domain, showing the high quality of the raw data. There were many significant differences in the areas and heights of peaks among groups, as a matter of fact, it is inferred that differences in peaks resulted from metabolic derangements along with diseases.


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)

High performance liquid chromatography-mass spectrometry chromatograms of the serum samples of patients from the tuberculosis (TB) group, the healthy group, and the four subgroups of patients with non-TB diseases. Abscissa: Retention time. Ordinate: Relative abundance. The numbers on the point of the peaks indicate the retention time of each substance. T indicates TB group; N indicates healthy control; P indicates pulmonitis subgroup; L indicates lung cancer subgroup; C indicates chronic obstructive pulmonary disease subgroup; B indicates bronchiectasis subgroup.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 2: High performance liquid chromatography-mass spectrometry chromatograms of the serum samples of patients from the tuberculosis (TB) group, the healthy group, and the four subgroups of patients with non-TB diseases. Abscissa: Retention time. Ordinate: Relative abundance. The numbers on the point of the peaks indicate the retention time of each substance. T indicates TB group; N indicates healthy control; P indicates pulmonitis subgroup; L indicates lung cancer subgroup; C indicates chronic obstructive pulmonary disease subgroup; B indicates bronchiectasis subgroup.
Mentions: Representative UPLC-MS chromatograms of the serum samples of the patients with active and the subjects with the healthy and non-TB controls were shown in Figure 2. The peaks were very well resolved and were evenly dispersed across the entire retention time domain, showing the high quality of the raw data. There were many significant differences in the areas and heights of peaks among groups, as a matter of fact, it is inferred that differences in peaks resulted from metabolic derangements along with diseases.

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