CFH and ARMS2 genetic risk determines progression to neovascular age-related macular degeneration after antioxidant and zinc supplementation

Abstract

We evaluated the influence of an antioxidant and zinc nutritional supplement [the Age-Related Eye Disease Study (AREDS) formulation] on delaying or preventing progression to neovascular AMD (NV) in persons with age-related macular degeneration (AMD). AREDS subjects (n = 802) with category 3 or 4 AMD at baseline who had been treated with placebo or the AREDS formulation were evaluated for differences in the risk of progression to NV as a function of complement factor H (CFH) and age-related maculopathy susceptibility 2 (ARMS2) genotype groups. We used published genetic grouping: a two-SNP haplotype risk-calling algorithm to assess CFH, and either the single SNP rs10490924 or 372_815del443ins54 to mark ARMS2 risk. Progression risk was determined using the Cox proportional hazard model. Genetics-treatment interaction on NV risk was assessed using a multiiterative bootstrap validation analysis. We identified strong interaction of genetics with AREDS formulation treatment on the development of NV. Individuals with high CFH and no ARMS2 risk alleles and taking the AREDS formulation had increased progression to NV compared with placebo. Those with low CFH risk and high ARMS2 risk had decreased progression risk. Analysis of CFH and ARMS2 genotype groups from a validation dataset reinforces this conclusion. Bootstrapping analysis confirms the presence of a genetics-treatment interaction and suggests that individual treatment response to the AREDS formulation is largely determined by genetics. The AREDS formulation modifies the risk of progression to NV based on individual genetics. Its use should be based on patient-specific genotype.

Keywords: bootstrap validation; genetic effect modification; macular degeneration; ophthalmology; statistical interaction.

Fig. 1.

Distribution of subjects by treatment and genotype group in discovery, validation, and combined sets. PBO, placebo.

Fig. 2.

Bootstrapping schema involving GTG2 and GTG3 subjects from the combined dataset. The number of selections from the expanded set of subjects is the same as the number of available subjects in this series of analyses (412). This selection forms an iteration specific bootstrap discovery set, which contains duplicates because each selected case is returned to the pool before the subsequent case is selected (“replacement”). Subjects not selected at least once for the discovery set are then assigned to the bootstrap validation set (152 on average). A Cox prediction model is generated from the discovery set and tested against the validation set, generating a concordance index. This entire procedure of selection, model derivation from the bootstrap discovery set, and bootstrap set validation is repeated 100 times and the C indices for models built on the same covariates are averaged. To generate 95% confidence intervals, the bootstrap process based on 100 resamplings was repeated 1,000 times. The red arrow shows the order of the process. The C index reflects the predictive power of the Cox covariates. Models including covariates with superior predictive ability will have a higher C index. The addition of uninformative covariates will not result in higher C indices, and will often lower predictive accuracy.

Fig. 3.

Cox-derived survival curves using expanded datasets for NV-free survival for subjects with high CFH and no ARMS2 risk alleles (GTG2; n = 107) (Left) and for individuals with low CFH and high ARMS2 risk (GTG3; n = 305) (Right). Subjects in both panels were treated with either placebo or the AREDS formulation.

Fig. 4.

A 0.632 bootstrap analysis of GTG2 and GTG3 subjects from the expanded dataset (n = 412) showing superior prediction of NV progression for the interaction model. Somers’ Dxy (a calibrated C-index measure) for three different Cox proportional hazards models was generated across follow-up time points (x) with approximate pointwise 95% confidence intervals. The interaction model includes sex, smoking, age, AREDS formulation, genetics, and interaction between AREDS formulation treatment and genetics. The additive model includes all of the covariates in the interaction model, but does not allow for interaction. The base model considers only sex, smoking, age, and genetics as covariates.

Fig. 5.

Cox model-derived survival curves of NV-free survival for unique set subjects with high CFH risk alleles and no ARMS2 risk alleles (GTG2; n = 39) (Left) and low CFH risk alleles and high ARMS2 risk alleles (GTG3; n = 121) (Right). Subjects were treated with placebo or the AREDS formulation.