Elsevier

Ophthalmology

Volume 120, Issue 12, December 2013, Pages 2644-2655
Ophthalmology

Original article
Prediction of Age-related Macular Degeneration in the General Population: The Three Continent AMD Consortium

https://doi.org/10.1016/j.ophtha.2013.07.053Get rights and content

Purpose

Prediction models for age-related macular degeneration (AMD) based on case-control studies have a tendency to overestimate risks. The aim of this study is to develop a prediction model for late AMD based on data from population-based studies.

Design

Three population-based studies: the Rotterdam Study (RS), the Beaver Dam Eye Study (BDES), and the Blue Mountains Eye Study (BMES) from the Three Continent AMD Consortium (3CC).

Participants

People (n = 10 106) with gradable fundus photographs, genotype data, and follow-up data without late AMD at baseline.

Methods

Features of AMD were graded on fundus photographs using the 3CC AMD severity scale. Associations with known genetic and environmental AMD risk factors were tested using Cox proportional hazard analysis. In the RS, the prediction of AMD was estimated for multivariate models by area under receiver operating characteristic curves (AUCs). The best model was validated in the BDES and BMES, and associations of variables were re-estimated in the pooled data set. Beta coefficients were used to construct a risk score, and risk of incident late AMD was calculated using Cox proportional hazard analysis. Cumulative incident risks were estimated using Kaplan–Meier product-limit analysis.

Main Outcome Measures

Incident late AMD determined per visit during a median follow-up period of 11.1 years with a total of 4 to 5 visits.

Results

Overall, 363 participants developed incident late AMD, 3378 participants developed early AMD, and 6365 participants remained free of any AMD. The highest AUC was achieved with a model including age, sex, 26 single nucleotide polymorphisms in AMD risk genes, smoking, body mass index, and baseline AMD phenotype. The AUC of this model was 0.88 in the RS, 0.85 in the BDES and BMES at validation, and 0.87 in the pooled analysis. Individuals with low-risk scores had a hazard ratio (HR) of 0.02 (95% confidence interval [CI], 0.01–0.04) to develop late AMD, and individuals with high-risk scores had an HR of 22.0 (95% CI, 15.2–31.8). Cumulative risk of incident late AMD ranged from virtually 0 to more than 65% for those with the highest risk scores.

Conclusions

Our prediction model is robust and distinguishes well between those who will develop late AMD and those who will not. Estimated risks were lower in these population-based studies than in previous case-control studies.

Financial Disclosure(s)

The author(s) have no proprietary or commercial interest in any materials discussed in this article.

Section snippets

Methods

For this article, we followed the guidelines for genetic risk prediction studies.30

Results

In total, 363 participants developed incident late AMD during a median follow-up time of 11.1 years (IQR, 11.0), of whom 132 were in the RS (follow-up 10.7 years; IQR, 12.8), 153 were in the BDES (follow-up 15.6 years; IQR, 10.4), and 78 were in the BMES (follow-up 11.8 years; IQR, 5.6). Incidence rates for the 3 studies were 2.89, 2.96, and 3.66 per 1000 person-years for the RS, BDES, and BMES, respectively. The distribution of demographic characteristics and environmental risk factors

Discussion

In 3 independent population-based studies from 3 continents, we investigated all well-known genetic and nongenetic risk factors for AMD. We found that the best prediction for late AMD was based on age, sex, 26 genetic variants, 2 environmental variables, and early AMD phenotype. The accuracy of a prediction model including all these variables was 0.88 in the RS. Because similar risk estimates were found in the BDES and BMES, the model proved to be well generalizable to people of Caucasian

Acknowledgments

The RS investigators thank Ada Hooghart and Corina Brussee for their effort in data collection and grading of the fundus photographs. The BDES investigators thank Nancy Barrett, MS, Barbara Budig, BS, Holly Cohn, MFA, Shirley Craanen, BS, Andrew Ewen, BS, Ellen Hall, BA, Carol Hoyer, BS, Anne Mosher, BS, and Maria Swift, BS, for their efforts in data collection and grading of fundus photographs. The BMES investigators would like to thank the members of the BMES Genome-Wide Association Study

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    Group members listed online in Appendix 1 (http://aaojournal.org).

    Financial Disclosure(s): The author(s) have no proprietary or commercial interest in any materials discussed in this article.

    Funding/support: The RS was supported by the Netherlands Organization for Scientific Research, the Hague; Swart van Essen, Rotterdam; Bevordering van Volkskracht, Rotterdam; Rotterdamse Blindenbelangen Association, Rotterdam; Algemene Nederlandse Vereniging ter Voorkoming van Blindheid, Doorn, The Netherlands; Oogfonds Nederland, Utrecht; MDFonds, Utrecht; Vereniging Trustfonds Erasmus Universiteit Rotterdam, Rotterdam, The Netherlands; and Lijf en Leven, Krimpen aan de IJssel, The Netherlands. An unrestricted grant was obtained from Topcon Europe BV, Capelle aan den IJssel, The Netherlands. The BDES was supported by National Institutes of Health Grant EY06594 (B.E.K.K. and R.K.) and, in part, by Research to Prevent Blindness (RPB) (B.E.K.K. and R.K., Senior Scientific Investigator Awards), New York, New York. The National Eye Institute provided funding for the entire study, including collection and analyses of data; RPB provided additional support for data analyses. The content of this report is solely the responsibility of the authors and does not necessarily reflect the official views of the National Eye Institute or the National Institutes of Health. The BMES was supported by the Australian National Health & Medical Research Council (NHMRC), Canberra, Australia (NHMRC project Grant IDs 974159, 211069, 302068), and Centre for Clinical Research Excellence in Translational Clinical Research in Eye Diseases (grant ID 529923). The BMES Genome-Wide Association Study and genotyping costs were supported by the Australian NHMRC, Canberra Australia (NHMRC project Grant IDs 512423, 475604, and 529912), and the Wellcome Trust, United Kingdom, as part of the Wellcome Trust Case Control Consortium 2 (A. Viswanathan, P. McGuffin, P. Mitchell, F. Topouzis, P. Foster, grant IDs 085475/B/08/Z and 085475/08/Z). J.J.W. is funded by a National Health & Medical Research Council Senior Research Fellowship (Grant ID 358702, 2005–2009, and ID 632909, 2010–2014). The funding organizations had no role in the design and conduct of the study; the collection, management, analysis, and interpretation of the data; or the preparation, review, or approval of the manuscript.

    Group members of the BMES Genome-Wide Association Study team and the Wellcome Trust Case Control Consortium 2 are listed online in Appendix 1 (available at http://aaojournal.org).

    J.J.W., R.K., and C.C.W.K. contributed equally.

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