8 research outputs found
Metabolomics Reveals Metabolite Changes in Acute Pulmonary Embolism
Pulmonary embolism (PE) is a common
cardiovascular emergency which
can lead to pulmonary hypertension (PH) and right ventricular failure
as a consequence of pulmonary arterial bed occlusion. The diagnosis
of PE is challenging due to nonspecific clinical presentation, which
results in relatively high mortality. Moreover, the pathological factors
associated with PE are poorly understood. Metabolomics can provide
new highlights which can help in the understanding of the processes
and even propose biomarkers for its diagnosis. In order to obtain
more information about PE and PH, acute PE was induced in large white
pigs and plasma was obtained before and after induction of PE. Metabolic
fingerprints from plasma were obtained with LC–QTOF-MS (positive
and negative ionization) and GC–Q-MS. Data pretreatment and
statistical analysis (uni- and multivariate) were performed in order
to compare metabolic fingerprints and to select the metabolites that
showed higher loading for the classification (28 from LC and 19 from
GC). The metabolites found differentially distributed among groups
are mainly related to energy imbalance in hypoxic conditions, such
as glycolysis-derived metabolites, ketone bodies, and TCA cycle intermediates,
as well as a group of lipidic mediators that could be involved in
the transduction of the signals to the cells such as sphingolipids
and lysophospholipids, among others. Results presented in this report
reveal that combination of LC–MS- and GC–MS-based metabolomics
could be a powerful tool for diagnosis and understanding pathophysiological
processes due to acute PE
Metabolomics Reveals Metabolite Changes in Acute Pulmonary Embolism
Pulmonary embolism (PE) is a common
cardiovascular emergency which
can lead to pulmonary hypertension (PH) and right ventricular failure
as a consequence of pulmonary arterial bed occlusion. The diagnosis
of PE is challenging due to nonspecific clinical presentation, which
results in relatively high mortality. Moreover, the pathological factors
associated with PE are poorly understood. Metabolomics can provide
new highlights which can help in the understanding of the processes
and even propose biomarkers for its diagnosis. In order to obtain
more information about PE and PH, acute PE was induced in large white
pigs and plasma was obtained before and after induction of PE. Metabolic
fingerprints from plasma were obtained with LC–QTOF-MS (positive
and negative ionization) and GC–Q-MS. Data pretreatment and
statistical analysis (uni- and multivariate) were performed in order
to compare metabolic fingerprints and to select the metabolites that
showed higher loading for the classification (28 from LC and 19 from
GC). The metabolites found differentially distributed among groups
are mainly related to energy imbalance in hypoxic conditions, such
as glycolysis-derived metabolites, ketone bodies, and TCA cycle intermediates,
as well as a group of lipidic mediators that could be involved in
the transduction of the signals to the cells such as sphingolipids
and lysophospholipids, among others. Results presented in this report
reveal that combination of LC–MS- and GC–MS-based metabolomics
could be a powerful tool for diagnosis and understanding pathophysiological
processes due to acute PE
Metabolomics Reveals Metabolite Changes in Acute Pulmonary Embolism
Pulmonary embolism (PE) is a common
cardiovascular emergency which
can lead to pulmonary hypertension (PH) and right ventricular failure
as a consequence of pulmonary arterial bed occlusion. The diagnosis
of PE is challenging due to nonspecific clinical presentation, which
results in relatively high mortality. Moreover, the pathological factors
associated with PE are poorly understood. Metabolomics can provide
new highlights which can help in the understanding of the processes
and even propose biomarkers for its diagnosis. In order to obtain
more information about PE and PH, acute PE was induced in large white
pigs and plasma was obtained before and after induction of PE. Metabolic
fingerprints from plasma were obtained with LC–QTOF-MS (positive
and negative ionization) and GC–Q-MS. Data pretreatment and
statistical analysis (uni- and multivariate) were performed in order
to compare metabolic fingerprints and to select the metabolites that
showed higher loading for the classification (28 from LC and 19 from
GC). The metabolites found differentially distributed among groups
are mainly related to energy imbalance in hypoxic conditions, such
as glycolysis-derived metabolites, ketone bodies, and TCA cycle intermediates,
as well as a group of lipidic mediators that could be involved in
the transduction of the signals to the cells such as sphingolipids
and lysophospholipids, among others. Results presented in this report
reveal that combination of LC–MS- and GC–MS-based metabolomics
could be a powerful tool for diagnosis and understanding pathophysiological
processes due to acute PE
Identified metabolites that significantly change in human plasma from PAH patients <i>vs</i>. Control group, detected in LC-MS.
<p>Identified metabolites that significantly change in human plasma from PAH patients <i>vs</i>. Control group, detected in LC-MS.</p
Identified metabolites that significantly change in human plasma from PAH patients <i>vs</i>. Control group, detected in GC-Q-MS.
<p>Identified metabolites that significantly change in human plasma from PAH patients <i>vs</i>. Control group, detected in GC-Q-MS.</p
The connections between metabolic pathways altered in plasma of PAH patients as compared to control group.
<p>TCA: tricarboxylic acid.</p
OPLS-DA plots for plasma metabolic fingerprints obtained from C and PAH groups.
<p>(A) OPLS-DA model (R<sup>2</sup> = 0.844, Q<sup>2</sup> = 0.653) for LC-MS data in positive ionization mode. (B) OPLS-DA model (R<sup>2</sup> = 0.897, Q<sup>2</sup> = 0.618) for LC-MS data in negative ionization mode. (C) OPLS-DA model (R<sup>2</sup> = 0.825, Q<sup>2</sup> = 0.649) for GC-MS data. Pulmonary hypertensive group (PAH) and control (C) have been marked as red triangles and green circles, respectively. OPLS-DA: orthogonal partial least squares discriminant analysis.</p
Results of univariate statistical analysis in the independent validation study.
<p>Results of univariate statistical analysis in the independent validation study.</p