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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) OPLS-DA two-dimensional model. : TB group; : Healthy control; : Pulmonitis subgroup; : Lung cancer subgroup; : COPD subgroup; : Bronchiectasis subgroup. (b) OPLS-DA three-dimensional model. OPLS-DA scores plot discriminating serum samples of tuberculosis (TB) patients, healthy control and non-TB group based on the metabolite profiling data. : TB group; : Healthy control; : Pulmonitis subgroup; : Lung cancer subgroup; : COPD subgroup; : Bronchiectasis subgroup.
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Figure 4: (a) OPLS-DA two-dimensional model. : TB group; : Healthy control; : Pulmonitis subgroup; : Lung cancer subgroup; : COPD subgroup; : Bronchiectasis subgroup. (b) OPLS-DA three-dimensional model. OPLS-DA scores plot discriminating serum samples of tuberculosis (TB) patients, healthy control and non-TB group based on the metabolite profiling data. : TB group; : Healthy control; : Pulmonitis subgroup; : Lung cancer subgroup; : COPD subgroup; : Bronchiectasis subgroup.

Mentions: Therefore, orthogonal signal correction technology was used to filter out unrelated variable information and retain related variables. Hence, OPLS-DA model was introduced, which is used to determine the maximum separation between different kinds of samples according to the sample classification information. The OPLS-DA model was used to identify biomarkers that accounted for the differences between the three groups, and it clearly distinguished between the TB group and the two control groups (non-TB disease subgroups were combined) [Figure 4]. The results showed that 82.1% of samples were consistent with the discrimination of the model, and the predictive ability of the model was 58.2% (Q2Y = 0.582). These findings indicate that the OPLS-DA model may pave the way for the diagnosis of TB and permit differentiation between other kinds of related 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)

(a) OPLS-DA two-dimensional model. : TB group; : Healthy control; : Pulmonitis subgroup; : Lung cancer subgroup; : COPD subgroup; : Bronchiectasis subgroup. (b) OPLS-DA three-dimensional model. OPLS-DA scores plot discriminating serum samples of tuberculosis (TB) patients, healthy control and non-TB group based on the metabolite profiling data. : TB group; : Healthy control; : Pulmonitis subgroup; : Lung cancer subgroup; : COPD subgroup; : Bronchiectasis subgroup.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 4: (a) OPLS-DA two-dimensional model. : TB group; : Healthy control; : Pulmonitis subgroup; : Lung cancer subgroup; : COPD subgroup; : Bronchiectasis subgroup. (b) OPLS-DA three-dimensional model. OPLS-DA scores plot discriminating serum samples of tuberculosis (TB) patients, healthy control and non-TB group based on the metabolite profiling data. : TB group; : Healthy control; : Pulmonitis subgroup; : Lung cancer subgroup; : COPD subgroup; : Bronchiectasis subgroup.
Mentions: Therefore, orthogonal signal correction technology was used to filter out unrelated variable information and retain related variables. Hence, OPLS-DA model was introduced, which is used to determine the maximum separation between different kinds of samples according to the sample classification information. The OPLS-DA model was used to identify biomarkers that accounted for the differences between the three groups, and it clearly distinguished between the TB group and the two control groups (non-TB disease subgroups were combined) [Figure 4]. The results showed that 82.1% of samples were consistent with the discrimination of the model, and the predictive ability of the model was 58.2% (Q2Y = 0.582). These findings indicate that the OPLS-DA model may pave the way for the diagnosis of TB and permit differentiation between other kinds of related 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