A Metabolomics Study of Cultivated Potato (Solanum tuberosum) Groups Andigena, Phureja, Stenotomum, and Tuberosum Using Gas Chromatography−Mass Spectrometry

2010 ◽  
Vol 58 (2) ◽  
pp. 1214-1223 ◽  
Author(s):  
Gary Dobson ◽  
Tom Shepherd ◽  
Susan R. Verrall ◽  
Wynne D. Griffiths ◽  
Gavin Ramsay ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Gas Chromatography ◽  
Solanum Tuberosum ◽  
Gas Chromatography Mass Spectrometry ◽  
Cultivated Potato ◽  
Chromatography Mass Spectrometry ◽  
Metabolomics Study

scholarly journals Urinary Metabolomics Study of Patients with Gout Using Gas Chromatography-Mass Spectrometry

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Qianqian Li ◽  
Shuangshuang Wei ◽  
Dehong Wu ◽  
Chengping Wen ◽  
Jia Zhou
Keyword(s):  
Mass Spectrometry ◽  
Gas Chromatography ◽  
Logistic Regression ◽  
Roc Curve ◽  
Binary Logistic Regression ◽  
Metabolic Changes ◽  
Gas Chromatography Mass Spectrometry ◽  
Chromatography Mass Spectrometry ◽  
Pathological Mechanism ◽  
Metabolomics Study

Objectives. Gout is a common type of inflammatory arthritis. The aim of this study was to detect urinary metabolic changes in gout patients which may contribute to understanding the pathological mechanism of gout and discovering potential metabolite markers. Methods. Urine samples from 35 gout patients and 29 healthy volunteers were analyzed by gas chromatography–mass spectrometry (GC–MS). Orthogonal partial least-squares discriminant analysis (OPLS-DA) was performed to screen differential metabolites between two groups, and the variable importance for projection (VIP) values and Student’s t-test results were combined to define the significant metabolic changes caused by gout. Further, binary logistic regression analysis was performed to establish a model to distinguish gout patients from healthy people, and receiver operating characteristic (ROC) curve was made to evaluate the potential for diagnosis of gout. Result. A total of 30 characteristic metabolites were significantly different between gout patients and controls, mainly including amino acids, carbohydrates, organic acids, and their derivatives, associated with perturbations in purine nucleotide synthesis, amino acid metabolism, purine metabolism, lipid metabolism, carbohydrate metabolism, and tricarboxylic acid cycle. Binary logistic regression and ROC curve analysis showed the combination of urate and isoxanthopterin can effectively discriminate the gout patients from controls with the area under the curve (AUC) of 0.879. Conclusion. Thus, the urinary metabolomics study is an efficient tool for a better understanding of the metabolic changes of gout, which may support the clinical diagnosis and pathological mechanism study of gout.

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