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S. A. Hagstrom, J. Gu, G. J. T. Pauer, X. Yue, U. Narendra, G. M. Sturgill, N. S. Peachey, R. G. Salomon, J. W. Crabb, Clinical Genomic and Proteomic AMD Study Group; Predicting Susceptibility to Age-Related Macular Degeneration With Genomic and Proteomic Biomarkers. Invest. Ophthalmol. Vis. Sci. 2009;50(13):2342.
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Toward developing methods to predict susceptibility to age-related macular degeneration (AMD), we correlated plasma carboxyethylpyrrole (CEP) adducts and autoantibody levels in AMD patients with AMD risk genotypes for Y402H in complement factor H (CFH), R80G in complement C3, A69S in ARMS2 (LOC387715), and rs11200638 in HTRA1.
Plasma CEP and CEP autoantibody levels were determined by ELISA. DNA was genotyped by direct genomic sequencing or restriction analysis. Logistic regression modeling for c-statistics, odds ratios and p values was performed with SAS 9.1. Sensitivity and specificity were calculated to maximize the sum of the two values using receiver operating characteristic (ROC) curves. Odds ratios for elevated CEP adducts and autoantibodies were calculated relative to control donors for homozygous and heterozygous genotypes. The risk for AMD was predicted based on genotype alone or in combination with the CEP markers.
Mean CEP adduct and autoantibody levels in AMD plasma (n = 916) were found to be elevated by ~60% and ~30%, respectively, relative to control plasma (n=488). The odds ratio for both CEP markers to be elevated was at least 3 fold greater in AMD than in control patients. Higher mean CEP marker levels were observed in AMD plasma over a broad age range. The AMD risk predicted for those exhibiting elevated CEP markers and risk genotypes was 2-3 fold greater than the risk based on genotype alone. C-statistics and ROC curves suggest that CEP markers alone can discriminate between AMD and control plasma with ~75% accuracy and, in combination with genomic markers, provide up to ~80% discrimination accuracy.
CEP plasma biomarkers offer a potential early warning system for predicting AMD susceptibility, especially in combination with genomic biomarkers.
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