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. 2012 Mar 21;53(3):1548-56.
doi: 10.1167/iovs.11-8657. Print 2012 Mar.

Prospective assessment of genetic effects on progression to different stages of age-related macular degeneration using multistate Markov models

Affiliations

Prospective assessment of genetic effects on progression to different stages of age-related macular degeneration using multistate Markov models

Yi Yu et al. Invest Ophthalmol Vis Sci. .

Abstract

Purpose: Understanding the effect of genes on progression to different stages of age-related macular degeneration (AMD) may suggest stage-specific therapeutic targets and more precise prediction of the development of this disease.

Methods: Progression events and time to each stage of AMD were derived from the longitudinal data of 2560 subjects without advanced AMD. SNPs in 12 AMD risk loci were genotyped. A multistate Markov model for progression from normal to intermediate drusen, then to large drusen, and eventually to neovascular disease (NV) or geographic atrophy (GA) was applied to estimate stage-specific hazard ratios for each SNP. The effects of these genetic factors were also estimated by a multivariate multistate Markov model adjusted for baseline age, sex, smoking, body mass index (BMI), education, antioxidant treatment, and the status of AMD in the fellow eye.

Results: Controlling for demographic and behavioral factors and other SNPs, the TT genotype of rs10468017 in LIPC was associated with decreased risk of progression from large drusen to NV (HR = 0.57, P = 0.04) and tended to reduce the risk of progression from normal to intermediate drusen (HR = 0.72, P = 0.07). The SNP rs1883025 (T allele) in ABCA1 was associated with decreased risk of progression from normal to intermediate drusen (HR per allele = 0.82 per allele, P = 9.7 × 10(-3)) and from intermediate drusen to large drusen (HR per allele = 0.77, P = 5.2 × 10(-3)). The genes CFH, C3, CFB, and ARMS2/HTRA1 were associated with progression from intermediate drusen to large drusen and from large drusen to GA or NV.

Conclusions: Genes in different pathways influence progression to different stages of AMD.

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Figures

Figure 1.
Figure 1.
AMD progression through a five-state Markov model among subjects without advanced AMD at baseline. *Included 696 stage 2 subjects at baseline and 494 subjects who progressed from stage 1 to stage 2 during the study. †Includes 1151 stage-3 subjects at baseline and 376 subjects who progressed from stage 2 to stage 3 during the study.
Figure 2.
Figure 2.
Predicted probabilities of progression to the various AMD stages for patients with different genetic risk profiles using the five-state multivariate Markov model. Probabilities of being in each AMD stage for patients with low-, medium- or high-risk profiles are shown separately for 5-year (a, c, e) and 10-year (b, d, f) durations. Three examples for each risk profile are shown for patients, starting with worse eye of normal as stage 1 (a, b), intermediate drusen as stage 2 (c, d), and large drusen as stage 3 (e, f). Stage 4 is GA, stage 5 is NV. Patients were 65 to 74 years old, male, had a normal fellow eye, past smoking history, greater than high school education, and intermediate BMI levels (25–29) at baseline.
Figure 3.
Figure 3.
The ROC curve for progression from normal and nonadvanced stages (stages 1, 2, 3) to advanced stages (stages 4 and 5) of AMD within 5 and 10 years. ROC curves for 5- and 10-year progression from nonadvanced stages (stages 1, 2, and 3) to advanced stages (stages 4, 5) of AMD based on the model in Table 5. The area under the ROC curve (AUC) was 0.883 for 5-year progression from nonadvanced stages to advanced AMD and the AUC was 0.895 for 10-year progression from nonadvanced stages to advanced AMD (dashed line).
Figure 4.
Figure 4.
The genetic effects on different stages of AMD progression. Genes that are significantly associated with the risk of a specific transition in the univariate or the multivariate Markov model are shown. *The SNP in the gene is significantly associated with risk of AMD progression in the multivariate Markov model.

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