Fakir M Amirul Islam1, Elaine W Chong1, Allison M Hodge2, Robyn H Guymer1, Khin Zaw Aung1, Galina A Makeyeva1, Paul N Baird1, John L Hopper3, Dallas R English3, Graham G Giles3, Liubov D Robman4. 1. Centre for Eye Research Australia, University of Melbourne, Royal Victorian Eye and Ear Hospital, Melbourne, Victoria, Australia. 2. Cancer Epidemiology Centre, Cancer Council Victoria, Melbourne, Victoria, Australia. 3. Cancer Epidemiology Centre, Cancer Council Victoria, Melbourne, Victoria, Australia; Centre for Molecular, Environmental, Genetic and Analytic Epidemiology, School of Public Health, University of Melbourne, Australia. 4. Centre for Eye Research Australia, University of Melbourne, Royal Victorian Eye and Ear Hospital, Melbourne, Victoria, Australia. Electronic address: lrobman@unimelb.edu.au.
Abstract
OBJECTIVE: To evaluate the association between dietary patterns and age-related macular degeneration (AMD). DESIGN: Food frequency data were collected from Melbourne Collaborative Cohort Study (MCCS) participants at the baseline study in 1990-1994. During follow-up in 2003-2007, retinal photographs were taken and evaluated for AMD. PARTICIPANTS: At baseline, 41514 participants aged 40 to 70 years and born in Australia or New Zealand (69%), or who had migrated from the United Kingdom, Italy, Greece, or Malta (31%) were recruited. Of these, 21132 were assessed for AMD prevalence at follow-up. METHODS: Principal component analysis was used to identify dietary patterns (Factors F1-6) among the food items. Logistic regression was used to assess associations of dietary patterns with AMD. MAIN OUTCOME MEASURES: Odds ratios (ORs) for early stages and advanced AMD in association with dietary patterns. RESULTS: A total of 2508 participants (12.8%) had early stages of AMD, and 108 participants (0.6%) had advanced AMD. Six factors characterized by predominant intakes of fruits (F1); vegetables (F2); grains, fish, steamed or boiled chicken, vegetables, and nuts (F3); red meat (F4); processed foods comprising cakes, sweet biscuits, and desserts (F5); and salad (F6) were identified. Higher F3 scores were associated with a lower prevalence of advanced AMD (fourth vs. first quartile) (OR, 0.49; 95% confidence interval [CI], 0.28-0.87), whereas F4 scores greater than the median were associated with a higher prevalence of advanced AMD (OR, 1.46; 95% CI, 1.0-2.17). CONCLUSIONS: Rather than specific individual food items, these factors represent a broader picture of food consumption. A dietary pattern high in fruits, vegetables, chicken, and nuts and a pattern low in red meat seems to be associated with a lower prevalence of advanced AMD. No particular food pattern seemed to be associated with the prevalence of the earliest stages of AMD.
OBJECTIVE: To evaluate the association between dietary patterns and age-related macular degeneration (AMD). DESIGN: Food frequency data were collected from Melbourne Collaborative Cohort Study (MCCS) participants at the baseline study in 1990-1994. During follow-up in 2003-2007, retinal photographs were taken and evaluated for AMD. PARTICIPANTS: At baseline, 41514 participants aged 40 to 70 years and born in Australia or New Zealand (69%), or who had migrated from the United Kingdom, Italy, Greece, or Malta (31%) were recruited. Of these, 21132 were assessed for AMD prevalence at follow-up. METHODS: Principal component analysis was used to identify dietary patterns (Factors F1-6) among the food items. Logistic regression was used to assess associations of dietary patterns with AMD. MAIN OUTCOME MEASURES: Odds ratios (ORs) for early stages and advanced AMD in association with dietary patterns. RESULTS: A total of 2508 participants (12.8%) had early stages of AMD, and 108 participants (0.6%) had advanced AMD. Six factors characterized by predominant intakes of fruits (F1); vegetables (F2); grains, fish, steamed or boiled chicken, vegetables, and nuts (F3); red meat (F4); processed foods comprising cakes, sweet biscuits, and desserts (F5); and salad (F6) were identified. Higher F3 scores were associated with a lower prevalence of advanced AMD (fourth vs. first quartile) (OR, 0.49; 95% confidence interval [CI], 0.28-0.87), whereas F4 scores greater than the median were associated with a higher prevalence of advanced AMD (OR, 1.46; 95% CI, 1.0-2.17). CONCLUSIONS: Rather than specific individual food items, these factors represent a broader picture of food consumption. A dietary pattern high in fruits, vegetables, chicken, and nuts and a pattern low in red meat seems to be associated with a lower prevalence of advanced AMD. No particular food pattern seemed to be associated with the prevalence of the earliest stages of AMD.
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