Imatinib metabolite profiling in parallel to imatinib quantification in plasma of treated patients using liquid chromatography–mass spectrometry

2008 ◽  
Vol 43 (6) ◽  
pp. 736-752 ◽  
Author(s):  
Bertrand Rochat ◽  
Aurélie Fayet ◽  
Nicolas Widmer ◽  
Sabine L. Lahrichi ◽  
Benoît Pesse ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Liquid Chromatography ◽  
Metabolite Profiling ◽  
Liquid Chromatography Mass Spectrometry ◽  
Chromatography Mass Spectrometry

High-Performance Liquid Chromatography-Mass Spectrometry-Based Lipid Metabolite Profiling of Acromegaly

2020 ◽  
Vol 105 (4) ◽  
pp. e1075-e1084
Author(s):  
Meng Wang ◽  
Shizhe Guo ◽  
Min He ◽  
Xiaoqing Shao ◽  
Lei Feng ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
High Performance Liquid Chromatography ◽  
Liquid Chromatography ◽  
Glucose Tolerance ◽  
Metabolite Profiling ◽  
High Performance ◽  
Healthy Controls ◽  
Liquid Chromatography Mass Spectrometry ◽  
Chromatography Mass Spectrometry ◽  
Metabolomics Analysis

Abstract Context Metabolic disorders, especially dysregulated lipid metabolism, increase the risk of cardiovascular mortality in acromegaly. Previous studies measuring plasma macromolecular lipids have yielded conflicting results. Purpose To explore the plasma lipid metabolite profiles by metabolomics analysis and identify potential metabolites associated with cardiac function in acromegaly. Methods Plasma was obtained from 80 newly diagnosed, untreated patients with acromegaly and 80 healthy controls. Echocardiography was performed. Based on the results of an oral glucose tolerance test (OGTT), patients were categorized into 2 groups: normal glucose tolerance (NGT, n = 28) and impaired glucose tolerance or diabetes mellitus (IGT/DM, n = 52). High-performance liquid chromatography–mass spectrometry (HPLC–MS)-based metabolomics analysis was conducted. Data were processed by principal components analysis (PCA), orthogonal partial least square-discriminant analysis (OPLS-DA), and MetaboAnalyst 4.0. Associations between metabolic substances and cardiovascular parameters were also explored. Results Metabolomics uncovered a distinct metabolic pattern between acromegaly and healthy controls, and perturbed pathways mainly include glycerophospholipid metabolism, sphingolipid metabolism, as well as linoleic acid metabolism. Collective analysis showed that phosphatidylethanolamine (PE) (22:6/16:0) was positively correlated with LV mass, while lysophosphatidylcholine (LysoPC) (16:0) was positively correlated with fractional shortening (FS) and left ventricle ejection fraction (LVEF). Conclusion Patients with acromegaly have distinct lipid metabolite profiling, while PE (22:6/16:0) and LysoPC (16:0) are correlated with cardiac structure and function, which may contribute to the risk of cardiovascular complications.

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