Inclusion of genotype with fundus phenotype improves accuracy of predicting choroidal neovascularization and geographic atrophy.

Abstract

PURPOSE:

The accuracy of predicting conversion from early-stage age-related macular degeneration (AMD) to the advanced stages of choroidal neovascularization (CNV) or geographic atrophy (GA) was evaluated to determine whether inclusion of clinically relevant genetic markers improved accuracy beyond prediction using phenotypic risk factors alone.

DESIGN:

Cohort study.

PARTICIPANTS:

White, non-Hispanic subjects participating in the Age-Related Eye Disease Study (AREDS) sponsored by the National Eye Institute consented to provide a genetic specimen. Of 2415 DNA specimens available, 940 were from disease-free subjects and 1475 were from subjects with early or intermediate AMD.

METHODS:

DNA specimens from study subjects were genotyped for 14 single nucleotide polymorphisms (SNPs) in genes shown previously to associate with CNV: ARMS2, CFH, C3, C2, FB, CFHR4, CFHR5, and F13B. Clinical demographics and established disease associations, including age, sex, smoking status, body mass index (BMI), AREDS treatment category, and educational level, were evaluated. Four multivariate logistic models (phenotype; genotype; phenotype + genotype; and phenotype + genotype + demographic + environmental factors) were tested using 2 end points (CNV, GA). Models were fitted using Cox proportional hazards regression to use time-to-disease onset data.

MAIN OUTCOME MEASURES:

Brier score (measure of accuracy) was used to identify the model with the lowest prediction error in the training set. The most accurate model was subjected to independent statistical validation, and final model performance was described using area under the receiver operator curve (AUC) or C-statistic.

RESULTS:

The CNV prediction models that combined genotype with phenotype with or without age and smoking revealed superior performance (C-statistic = 0.96) compared with the phenotype model based on the simplified severity scale and the presence of CNV in the nonstudy eye (C-statistic = 0.89; P<0.01). For GA, the model that combined genotype with phenotype demonstrated the highest performance (AUC = 0.94). Smoking status and ARMS2 genotype had less of an impact on the prediction of GA compared with CNV.

CONCLUSIONS:

Inclusion of genotype assessment improves CNV prediction beyond that achievable with phenotype alone and may improve patient management. Separate assessments should be used to predict progression to CNV and GA because genetic markers and smoking status do not equally predict both end points.

FINANCIAL DISCLOSURE(S):

Proprietary or commercial disclosure may be found after the references.

• Authors: Lorah T Perlee, Aruna T Bansal, Karen Gehrs, Jeffrey S Heier, Karl Csaky, Rando Allikmets, Paul Oeth, Toni Paladino, Daniel H Farkas, P Lyle Rawlings, Gregory S Hageman
• Journal: Ophthalmology (Ophthalmology) Vol. 120 Issue 9 Pg. 1880-92 (Sep 2013) ISSN: 1549-4713 [Electronic] United States
• PMID: 23523162 (Publication Type: Journal Article, Multicenter Study, Randomized Controlled Trial, Research Support, N.I.H., Extramural)
• Copyright: Copyright © 2013 American Academy of Ophthalmology. Published by Elsevier Inc. All rights reserved.

Chemical References

• ARMS2 protein, human
• Apolipoproteins
• CFH protein, human
• CFHR4 protein, human
• CFHR5 protein, human
• Eye Proteins
• Genetic Markers
• Proteins
• Complement Factor H
• Complement System Proteins

Topics

• Aged
• Aged, 80 and over
• Apolipoproteins (genetics)
• Choroidal Neovascularization (diagnosis, genetics)
• Complement Factor H (genetics)
• Complement System Proteins (genetics)
• Disease Progression
• Eye Proteins (genetics)
• Female
• Fundus Oculi
• Genetic Markers
• Genotype
• Geographic Atrophy (diagnosis, genetics)
• Humans
• Macular Degeneration (diagnosis, genetics)
• Male
• Middle Aged
• Phenotype
• Polymorphism, Single Nucleotide
• Proteins (genetics)
• Reproducibility of Results
• Risk Factors