BACKGROUND: The identification of the biomarkers of patients with acute anterior uveitis (AAU) may allow for a less invasive and more accurate diagnosis, as well as serving as a predictor in AAU progression and treatment response. The aim of this study was to identify the potential biomarkers and the metabolic pathways from plasma in patients with AAU. METHODS: Both plasma metabolic biomarkers and metabolic pathways in the AAU patients versus healthy volunteers were investigated using ultra-performance liquid chromatography-mass spectrometry (UPLC-MS) and a metabonomics approach. The principal component analysis (PCA) was used to separate AAU patients from healthy volunteers as well as to identify the different biomarkers between the two groups. Metabolic compounds were matched to the KEGG, METLIN, and HMDB databases, and metabolic pathways associated with AAU were identified. RESULTS: The PCA for UPLC-MS data shows that the metabolites in AAU patients were significantly different from those of healthy volunteers. Of the 4,396 total features detected by UPLC-MS, 102 features were significantly different between AAU patients and healthy volunteers according to the variable importance plot (VIP) values (greater than two) of partial least squares discriminate analysis (PLS-DA). Thirty-three metabolic compounds were identified and were considered as potential biomarkers. Meanwhile, ten metabolic pathways were found that were related to the AAU according to the identified biomarkers. CONCLUSIONS: These data suggest that metabolomics study can identify potential metabolites that differ between AAU patients and healthy volunteers. Based on the PCA, PLS-DA, several potential metabolic biomarkers and pathways in AAU patients were found and identified. In addition, the UPLC-MS technique combined with metabonomics could be a suitable systematic biology tool in research in clinical problems in ophthalmology, and can provide further insight into the pathophysiology of AAU.
BACKGROUND: The identification of the biomarkers of patients with acute anterior uveitis (AAU) may allow for a less invasive and more accurate diagnosis, as well as serving as a predictor in AAU progression and treatment response. The aim of this study was to identify the potential biomarkers and the metabolic pathways from plasma in patients with AAU. METHODS: Both plasma metabolic biomarkers and metabolic pathways in the AAUpatients versus healthy volunteers were investigated using ultra-performance liquid chromatography-mass spectrometry (UPLC-MS) and a metabonomics approach. The principal component analysis (PCA) was used to separate AAUpatients from healthy volunteers as well as to identify the different biomarkers between the two groups. Metabolic compounds were matched to the KEGG, METLIN, and HMDB databases, and metabolic pathways associated with AAU were identified. RESULTS: The PCA for UPLC-MS data shows that the metabolites in AAUpatients were significantly different from those of healthy volunteers. Of the 4,396 total features detected by UPLC-MS, 102 features were significantly different between AAUpatients and healthy volunteers according to the variable importance plot (VIP) values (greater than two) of partial least squares discriminate analysis (PLS-DA). Thirty-three metabolic compounds were identified and were considered as potential biomarkers. Meanwhile, ten metabolic pathways were found that were related to the AAU according to the identified biomarkers. CONCLUSIONS: These data suggest that metabolomics study can identify potential metabolites that differ between AAUpatients and healthy volunteers. Based on the PCA, PLS-DA, several potential metabolic biomarkers and pathways in AAUpatients were found and identified. In addition, the UPLC-MS technique combined with metabonomics could be a suitable systematic biology tool in research in clinical problems in ophthalmology, and can provide further insight into the pathophysiology of AAU.
Authors: Maria Dobrinas; Eva Choong; Muriel Noetzli; Jacques Cornuz; Nicolas Ansermot; Chin B Eap Journal: J Chromatogr B Analyt Technol Biomed Life Sci Date: 2011-10-01 Impact factor: 3.205
Authors: Ming-ming Yang; Timothy Y Y Lai; Pancy O S Tam; Sylvia W Y Chiang; Carmen K M Chan; Fiona O J Luk; Tsz Kin Ng; Chi-Pui Pang Journal: Mol Vis Date: 2011-10-13 Impact factor: 2.367
Authors: Melissa P Osborn; Youngja Park; Megan B Parks; L Goodwin Burgess; Karan Uppal; Kichun Lee; Dean P Jones; Milam A Brantley Journal: PLoS One Date: 2013-08-27 Impact factor: 3.240
Authors: Inês Laíns; Daniela Duarte; António S Barros; Ana Sofia Martins; João Gil; John B Miller; Marco Marques; Tânia Mesquita; Ivana K Kim; Maria da Luz Cachulo; Demetrios Vavvas; Isabel M Carreira; Joaquim N Murta; Rufino Silva; Joan W Miller; Deeba Husain; Ana M Gil Journal: PLoS One Date: 2017-05-18 Impact factor: 3.240