Gabriëlle H S Buitendijk1, Elena Rochtchina2, Chelsea Myers3, Cornelia M van Duijn4, Kristine E Lee3, Barbara E K Klein3, Stacy M Meuer3, Paulus T V M de Jong5, Elizabeth G Holliday6, Ava G Tan2, André G Uitterlinden7, Theru S Sivakumaran8, John Attia9, Albert Hofman10, Paul Mitchell2, Johannes R Vingerling1, Sudha K Iyengar11, A Cecile J W Janssens12, Jie Jin Wang13, Ronald Klein3, Caroline C W Klaver14. 1. Department of Ophthalmology, Erasmus Medical Center, Rotterdam, The Netherlands; Department of Epidemiology, Erasmus Medical Center, Rotterdam, The Netherlands. 2. Centre for Vision Research, Department of Ophthalmology and Westmead Millennium Institute, University of Sydney, Sydney, Australia. 3. Department of Ophthalmology and Visual Sciences, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin. 4. Department of Epidemiology, Erasmus Medical Center, Rotterdam, The Netherlands. 5. Department of Ophthalmogenetics, Netherlands Institute of Neurosciences, An Institute of the Royal Netherlands Academy of Arts and Sciences (KNAW), Amsterdam, The Netherlands; Department of Ophthalmology, Academic Medical Center, Amsterdam, The Netherlands; Department of Ophthalmology, Leiden University Medical Center, Leiden, The Netherlands. 6. Centre for Clinical Epidemiology and Biostatistics, and School of Medicine and Public Health, University of Newcastle, Newcastle, New South Wales, Australia. 7. Department of Epidemiology, Erasmus Medical Center, Rotterdam, The Netherlands; Department of Internal Medicine, Erasmus Medical Center, Rotterdam, The Netherlands; Netherlands Consortium for Healthy Aging, Netherlands Genomics Initiative, the Hague, The Netherlands. 8. Division of Human Genetics, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio; Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, Ohio. 9. Centre for Clinical Epidemiology and Biostatistics, and School of Medicine and Public Health, University of Newcastle, Newcastle, New South Wales, Australia; Department of Medicine, John Hunter Hospital and Hunter Medical Research Institute, Newcastle, New South Wales, Australia. 10. Department of Epidemiology, Erasmus Medical Center, Rotterdam, The Netherlands; Netherlands Consortium for Healthy Aging, Netherlands Genomics Initiative, the Hague, The Netherlands. 11. Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, Ohio. 12. Department of Epidemiology, Erasmus Medical Center, Rotterdam, The Netherlands; Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, Georgia. 13. Centre for Vision Research, Department of Ophthalmology and Westmead Millennium Institute, University of Sydney, Sydney, Australia; Centre for Eye Research Australia, University of Melbourne, Melbourne, Australia. 14. Department of Ophthalmology, Erasmus Medical Center, Rotterdam, The Netherlands; Department of Epidemiology, Erasmus Medical Center, Rotterdam, The Netherlands. Electronic address: c.c.w.klaver@erasmusmc.nl.
Abstract
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.
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.
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