Oxidative stress in primary open angle glaucoma relevance of genetic polymorphisms of antioxidant genes and differential metabolite profiles

• Main Author: Zakaria Mohamed Bannur (Author)
• Format: Thesis Book
• Language: English
• Published: Sungai Buloh, Selangor Universiti Teknologi MARA. Faculty of Medicine 2014
• Subjects: Blindness > prevention & control; Glaucoma, Open-Angle; Polymorphism, Genetic > analysis
• Online Access: Click Here to View Status and Holdings.
• Item Description: UiTM Digitized
• Physical Description: xv, 136 pages illustrations, charts, (some color) 30 cm 1 computer optical disc (4 ¾ inch)
• Bibliography: Includes bibliographical references (page 101-116)

Glaucoma is a common optic neuropathy. It is the second common cause of blindness. The molecular basis of primary open-angle glaucoma (POAG) is not fully understood. Thus, this study aims to examine the polymorphisms of antioxidant genes and to establish a predictive model based on the metabolite profiles using both genomics and metabolomics approaches. A total of 252 healthy volunteers and 23 POAG patients were recruited. Blood and serum samples were collected. Allele specific-PCR was developed and validated. The technique was efficient to detect genetic variations of antioxidant genes. Manganese superoxide dismutase polymorphism (Val16Ala) showed higher frequency in POAG patients. Val genotype/allele was found to be significantly higher among Chinese. No polymorphisms of catalase (C262T) and glutathione peroxidase (P200L) were detected. Thirty eight different metabolites were detected by global metabolomics. Molecular feature extraction, data filtering, and statistical analysis [/?-value <0.01 (unpaired t-test) and 2-folds change, ANOVA and PCA were able to differentiate metabolites expression between the two groups. Glycine-conjugated bile acids, palmitoylcamitine, inosine, and Pureidoisobutyric acid were increased in POAG patients. These metabolites indicate oxidative stress states. A predictive model using metabolites profiles was developed and validated. It discriminated each group in separate clustering. An overall predictive accuracy of 96.7% was obtained by this model. Ten features model was the best with highest sensitivity and specificity (AUC =0.999, Cl 0.991-1). The model testing resulted in predictive accuracy around 0.95 (AUC= 0.985 95% Cl: 0.944-1), and empirical P< 0.002 at 1000 times permutation test. This pilot study provides further evidence on the involvement of oxidative stress in POAG. The predictive model would be applicable after further validation. This generates new hypothesis for designing medicine and substantial improvements in patients' clinical outcomes