PURPOSE: To determine if primary open-angle glaucoma (POAG) patients can be differentiated from controls based on metabolic characteristics. METHODS: We used ultra-high resolution mass spectrometry with C18 liquid chromatography for metabolomic analysis on frozen plasma samples from 72 POAG patients and 72 controls. Metabolome-wide Spearman correlation was performed to select differentially expressed metabolites (DEM) correlated with POAG. We corrected P values for multiple testing using Benjamini and Hochberg false discovery rate (FDR). Hierarchical cluster analysis (HCA) was used to depict the relationship between participants and DEM. Differentially expressed metabolites were matched to the METLIN metabolomics database; both DEM and metabolites significantly correlating with DEM were analyzed using MetaboAnalyst to identify metabolic pathways altered in POAG. RESULTS: Of the 2440 m/z (mass/charge) features recovered after filtering, 41 differed between POAG cases and controls at FDR = 0.05. Hierarchical cluster analysis revealed these DEM to associate into eight clusters; three of these clusters contained the majority of the DEM and included palmitoylcarnitine, hydroxyergocalciferol, and high-resolution METLIN matches to sphingolipids, other vitamin D-related metabolites, and terpenes. MetaboAnalyst also indicated likely alteration in steroid biosynthesis pathways. CONCLUSIONS: Global ultrahigh resolution metabolomics emphasized the importance of altered lipid metabolism in POAG. The results suggest specific metabolic processes, such as those involving palmitoylcarnitine, sphingolipids, vitamin D-related compounds, and steroid precursors, may contribute to POAG status and merit more detailed study with targeted methods.
PURPOSE: To determine if primary open-angle glaucoma (POAG) patients can be differentiated from controls based on metabolic characteristics. METHODS: We used ultra-high resolution mass spectrometry with C18 liquid chromatography for metabolomic analysis on frozen plasma samples from 72 POAG patients and 72 controls. Metabolome-wide Spearman correlation was performed to select differentially expressed metabolites (DEM) correlated with POAG. We corrected P values for multiple testing using Benjamini and Hochberg false discovery rate (FDR). Hierarchical cluster analysis (HCA) was used to depict the relationship between participants and DEM. Differentially expressed metabolites were matched to the METLIN metabolomics database; both DEM and metabolites significantly correlating with DEM were analyzed using MetaboAnalyst to identify metabolic pathways altered in POAG. RESULTS: Of the 2440 m/z (mass/charge) features recovered after filtering, 41 differed between POAG cases and controls at FDR = 0.05. Hierarchical cluster analysis revealed these DEM to associate into eight clusters; three of these clusters contained the majority of the DEM and included palmitoylcarnitine, hydroxyergocalciferol, and high-resolution METLIN matches to sphingolipids, other vitamin D-related metabolites, and terpenes. MetaboAnalyst also indicated likely alteration in steroid biosynthesis pathways. CONCLUSIONS: Global ultrahigh resolution metabolomics emphasized the importance of altered lipid metabolism in POAG. The results suggest specific metabolic processes, such as those involving palmitoylcarnitine, sphingolipids, vitamin D-related compounds, and steroid precursors, may contribute to POAG status and merit more detailed study with targeted methods.
Authors: Kathryn P Burdon; Stuart Macgregor; Alex W Hewitt; Shiwani Sharma; Glyn Chidlow; Richard A Mills; Patrick Danoy; Robert Casson; Ananth C Viswanathan; Jimmy Z Liu; John Landers; Anjali K Henders; John Wood; Emmanuelle Souzeau; April Crawford; Paul Leo; Jie Jin Wang; Elena Rochtchina; Dale R Nyholt; Nicholas G Martin; Grant W Montgomery; Paul Mitchell; Matthew A Brown; David A Mackey; Jamie E Craig Journal: Nat Genet Date: 2011-05-01 Impact factor: 38.330
Authors: Vincent M Asiago; Leiddy Z Alvarado; Narasimhamurthy Shanaiah; G A Nagana Gowda; Kwadwo Owusu-Sarfo; Robert A Ballas; Daniel Raftery Journal: Cancer Res Date: 2010-10-19 Impact factor: 12.701
Authors: Puya Gharahkhani; Kathryn P Burdon; Rhys Fogarty; Shiwani Sharma; Alex W Hewitt; Sarah Martin; Matthew H Law; Katie Cremin; Jessica N Cooke Bailey; Stephanie J Loomis; Louis R Pasquale; Jonathan L Haines; Michael A Hauser; Ananth C Viswanathan; Peter McGuffin; Fotis Topouzis; Paul J Foster; Stuart L Graham; Robert J Casson; Mark Chehade; Andrew J White; Tiger Zhou; Emmanuelle Souzeau; John Landers; Jude T Fitzgerald; Sonja Klebe; Jonathan B Ruddle; Ivan Goldberg; Paul R Healey; Richard A Mills; Jie Jin Wang; Grant W Montgomery; Nicholas G Martin; Graham RadfordSmith; David C Whiteman; Matthew A Brown; Janey L Wiggs; David A Mackey; Paul Mitchell; Stuart MacGregor; Jamie E Craig Journal: Nat Genet Date: 2014-08-31 Impact factor: 38.330
Authors: Douglas I Walker; Karan Uppal; Luoping Zhang; Roel Vermeulen; Martyn Smith; Wei Hu; Mark P Purdue; Xiaojiang Tang; Boris Reiss; Sungkyoon Kim; Laiyu Li; Hanlin Huang; Kurt D Pennell; Dean P Jones; Nathaniel Rothman; Qing Lan Journal: Int J Epidemiol Date: 2016-10-05 Impact factor: 7.196
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
Authors: Sabrina L Mitchell; Karan Uppal; Samantha M Williamson; Ken Liu; L Goodwin Burgess; ViLinh Tran; Allison C Umfress; Kelli L Jarrell; Jessica N Cooke Bailey; Anita Agarwal; Margaret Pericak-Vance; Jonathan L Haines; William K Scott; Dean P Jones; Milam A Brantley Journal: Invest Ophthalmol Vis Sci Date: 2018-10-01 Impact factor: 4.799
Authors: Muhammad Zain Chauhan; Ann-Katrin Valencia; Maria Carmen Piqueras; Mabel Enriquez-Algeciras; Sanjoy K Bhattacharya Journal: Invest Ophthalmol Vis Sci Date: 2019-04-01 Impact factor: 4.799