Purpose
We hypothesized that major American dietary patterns are associated with risk for age-related macular degeneration (AMD).
Design
Cross-sectional study.
Methods
We classified 8103 eyes in 4088 eligible participants in the baseline Age-Related Eye Disease Study (AREDS). They were classified into control (n = 2739), early AMD (n = 4599), and advanced AMD (n = 765) by the AREDS AMD Classification System. Food consumption data were collected by using a 90-item food frequency questionnaire.
Results
Two major dietary patterns were identified by factor (principal component) analysis based on 37 food groups and named Oriental and Western patterns. The Oriental pattern was characterized by higher intake of vegetables, legumes, fruit, whole grains, tomatoes, and seafood. The Western pattern was characterized by higher intake of red meat, processed meat, high-fat dairy products, French fries, refined grains, and eggs. We ranked our participants according to how closely their diets line up with the 2 patterns by calculating the 2 factor scores for each participant. For early AMD, multivariate-adjusted odds ratio (OR) from generalized estimating equation logistic analysis comparing the highest to lowest quintile of the Oriental pattern score was OR E5O = 0.74 (95% confidence interval (CI): 0.59–0.91; P trend =0.01), and the OR comparing the highest to lowest quintile of the Western pattern score was OR E5W = 1.56 (1.18–2.06; P trend = 0.01). For advanced AMD, the OR A5O was 0.38 (0.27–0.54; P trend < 0.0001), and the OR A5W was 3.70 (2.31–5.92; P trend < 0.0001).
Conclusions
Our data indicate that overall diet is significantly associated with the odds of AMD and that dietary management as an AMD prevention strategy warrants further study.
Age-related macular degeneration (AMD) is the major cause of blindness in persons 65 years of age and older in developed countries. In the United States, advanced AMD accounts for more than 50% of the legal blindness and, with people living longer, it is estimated that the number of advanced AMD cases will reach 3 million in 2020. Vision impairment due to advanced AMD significantly reduces quality of life and consumes a large portion of the Medicare budget. Currently, clinical treatments for AMD are costly and are limited to arresting the neovascular type of the disease, and they cannot prevent the progression of visual loss. For geographic atrophy, the major cause of visual loss in eyes with late AMD, there is no treatment. Therefore, a high premium is placed on designing strategies that prevent AMD or delay its progress to advanced stages that result in vision loss. Nutritional intervention seems to hold some promise toward these ends, and this was recently corroborated in trials by the Age-Related Eye Disease Study (AREDS) and AREDS2, which show that use of a supplement containing vitamins C and E, lutein/zeaxanthin and zinc delays the progression of advanced AMD in persons with intermediate AMD.
Most of the previous attempts to determine relationships between diet and AMD have focused on how intake of nutrients can be used to prolong retinal function and diminish risk for AMD. It is interesting that nutrient-nutrient interactions were recently observed. For example, we recently found that the benefits of omega-3 fatty acids from foods may depend on the stage of AMD and the status of other nutrients. These findings indicate that intake of one food, with its many nutrients, might affect the bioavailability and nutrition value of another food or nutrient. Our previous studies also suggested that the overall quality of carbohydrate in diet, as measured by a dietary glycemic index, affects the risk for AMD. These data emphasize the importance of studying diet as a whole to understand how it can be optimized to promote health.
In this study, using data from the AREDS, we conducted factor (principal component) analysis to identify major dietary patterns. Factor analysis is a statistical method that can summarize 2 or more food items (groups) into a single factor that represents a major dietary pattern. Thus, we could describe dietary patterns in terms of such factors. Then we related the major dietary patterns to AMD.
Methods
This case-control study is an analysis of preexisting data from the AREDS, and data were analyzed anonymously. The Tufts Health Sciences Campus Institutional Review Board certified the current study as being exempt from institutional review board approval. This study was conducted according to the principles expressed in the Declaration of Helsinki and all federal and state laws in the United States.
The Age-Related Eye Disease Study
The AREDS of the National Eye Institute of the National Institutes of Health is a long-term multicenter, prospective study dedicated to assessing the clinical courses, prognoses, risk factors, and prevention strategies of both AMD and cataract. The protocol was adherent to the Declaration of Helsinki and approved by a Data and Safety Monitoring Committee and by each institutional review board for the 11 participating ophthalmic centers before initiation of the study. Participants were 55–80 years of age at enrollment. A total of 4757 participants were enrolled between November 1992 and January 1998. Informed consent was obtained from participants prior to enrollment.
Data concerning possible risk factors for AMD were obtained from baseline general physical and ophthalmic examinations and detailed questionnaires about basic characteristics and demographic data. Stereoscopic fundus photographs of the macula and slit-lamp and red-reflex lens photographs were taken and graded at a central ophthalmic photograph reading center, where the various lesions associated with AMD and the degree of lens opacities by type were assessed using AREDS grading procedures adapted from the Wisconsin age-related maculopathy grading system and the Wisconsin System for Classifying Cataracts from Photographs, respectively.
For AMD grading, eyes were classified into 1 of 5 groups, according to the size and extent of drusen, the presence of geographic atrophy, and the neovascular changes resulting from AMD. The 5 groups, numbered serially and based on increasing severity of drusen or type of AMD, were defined as follows. The baseline characteristics of the 5 study groups have been published previously.
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Group 1 (control): Eyes had no drusen or nonextensive small drusen.
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Group 2 (intermediate drusen): Eyes had 1 or more intermediate drusen, extensive small drusen or pigment abnormalities associated with AMD.
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Group 3 (large drusen): Eyes had 1 or more large drusen or extensive intermediate drusen.
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Group 4 (geographic atrophy): Eyes had geographic atrophy.
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Group 5 (neovascular): Eyes had choroidal neovascularization or retinal pigment epithelium detachment.
A 90-item modified Block food frequency questionnaire (FFQ) was administered to participants in the AREDS at baseline. The FFQ was validated in relation to 24-hour recall using a subset (n = 192) of the AREDS volunteers. The FFQ collected information about average frequency and serving sizes of consumption over the previous year. For each food item, participants indicated their average frequency of consumption in terms of the specified serving size (small = 0.5 medium; medium or large = 1.5 medium) by checking 1 of 9 frequency categories, ranging from “never or less than once per month” to “two or more times per day.” The medium serving sizes are described by using natural portions (eg, 1 banana and 2 slices of pizza) or standard weight and volume measures of the servings commonly consumed by the American population. The selected frequency categories and serving sizes for each food item were converted into a daily intake in terms of medium servings. For example, a response of “2–4 servings/wk” in large servings was converted to 4.5 (= 3 × 1.5) medium servings/wk (= 4.5/7 = 0.643 medium servings/d).
More details about the AREDS can be found in the AREDS report series.
Statistical Methods
We derived dietary patterns by using food consumption data from the FFQ. We first classified 90 food items in the FFQ into 37 predefined food groups ( Table 1 ) to minimize within-person variations in intakes of individual foods. Individual food items were preserved if they constituted a distinct item on their own (eg eggs, pizza, coffee or tea, and so forth) or if they were thought to represent a particular dietary pattern (eg liquor, wine, beer, and French fries). In order to determine the structure or components of diet and to determine whether the dietary components were associated with AMD, we conducted factor analysis using principal component analysis (PCA) to derive dietary patterns based on the 37 food groups. The goals of PCA are to extract the most important information from a data set and to reduce the number of variables by keeping only important information. PCA computes new variables, called principal components (factors), which are obtained as linear combinations of the original variables (ie food groups). The first principal component is required to have the largest possible variance (ie inertia; therefore, this component will explain or extract the largest part of the inertia of the data set). The second component is computed under the constraint of being orthogonal to the first component and to have the largest possible variance (inertia). In determining the number of factors to retain, we considered their eigenvalues, the scree plot (the principal components as the X axis, and the corresponding eigenvalues as the Y axis), and the interpretability of the factors. The eigenvalues are also known as characteristic values or proper values, and the factor with the largest eigenvalue has the most variance. We did not use the percentage of variance explained by each factor because this criterion depends largely on the total number of variables included in the analyses. The 2 major patterns identified in our factor analysis were similar to those described in many previous American studies, which were often named the prudent pattern and the Western pattern. Here, we named the first factor the Oriental pattern because it was characterized by higher intake of vegetables, legumes, fruit, whole grains, tomatoes, seafood, and so forth. The second factor was named the Western pattern because it was characterized by higher intake of red meat, processed meat, high-fat dairy products, French fries, refined grains, eggs, and so forth. Because of the variability in diets, we did not simply classify our participants as following or not following a given dietary pattern (factor) but instead ranked them according to how closely their diets lined up with the 2 patterns by calculating the 2 factor scores for each participant. The factor score for each pattern was constructed by summing observed intakes of the component food items weighted by factor loadings. The analyses were conducted by using the PROC FACTOR in SAS (v 9.3; SAS Institute, Cary, NC).
| Food group | Food items in the AREDS FFQ |
|---|---|
| 1. Processed meats | Hotdogs, ham, bacon, sausage |
| 2. Red meats | Hamburgers, beef, beef stew, pork or lamb |
| 3. Organ meats | Liver, liverwurst |
| 4. Fish and other seafood | Fried fish, tuna, oysters, shrimp, other fish |
| 5. Poultry | Fried chicken, chicken or turkey |
| 6. Pizza | Pizza |
| 7. Soup | Vegetable and tomato soup, other soup |
| 8. Eggs | Eggs |
| 9. Butter or margarine | Butter added to vegetable, butter on bread, margarine on bread |
| 10. Peanuts | Peanuts or peanut butter |
| 11. Gravies | Gravies |
| 12. Cold breakfast cereal | Milk on cereal, other cold breakfast cereal |
| 13. Whole grains | High-fiber cereals, fortified cereals, cooked cereals, dark bread |
| 14. Refined grains | Biscuits, white bread, corn bread, spaghetti, other noodle |
| 15. Rice | Rice |
| 16. Snacks | Chips |
| 17. High-energy drinks | Regular soft drink, fruit drinks, sugar in coffee or tea |
| 18. Sweets and desserts | Doughnuts, chocolate candy, other candy |
| 19. French fries | French fries |
| 20. Liquor | Liquor |
| 21. Beer | Beer |
| 22. Wine | Wine |
| 23. High-fat dairy products | Whole milk, ice cream, other cheeses, macaroni and cheese, milk in coffee or tea |
| 24. Low-fat dairy products | 2% milk, 1% milk, yogurt, cottage cheese |
| 25. Condiments | Red chili sauce |
| 26. Salad dressings | Salad dressings |
| 27. Fruit | Apples, bananas, peaches, cantaloupe, watermelon, strawberries, oranges, grapefruit, other fruit |
| 28. Fruit juices | Orange or grapefruit juice |
| 29. Cruciferous vegetables | Broccoli, cole slaw, cauliflower, cooked greens |
| 30. Dark-yellow vegetables | Winter squash, carrots, sweet potatoes |
| 31. Tomatoes | Tomatoes |
| 32. Green leafy vegetables | Raw spinach, cooked spinach, green salad |
| 33. Legumes | String beans, peas, other beans, chili with beans |
| 34. Other vegetables | Corn, any other vegetable |
| 35. Potatoes | Other potatoes |
| 36. Coffee or Tea | Coffee or tea |
| 37. Non-dairy creamer in coffee or tea | Non-dairy creamer in coffee or tea |
To evaluate the baseline cross-sectional relationship between the 2 major dietary patterns and AMD, we used eyes with AMD lesions (Groups 2 through 5) as our cases and those in Group 1 as our controls. Odds ratios (ORs) were calculated by dividing the odds of the presence of AMD in eyes in the highest quintile of dietary pattern scores by the odds of its presence in eyes in the lowest quintile of dietary pattern scores. The following baseline characteristics were considered as covariates in our analyses: age; gender; education level (college graduate; high school or less); race (white or others); body mass index (computed from weight and height; kg/m 2 ); alcohol intake (g/d); calorie intake; multivitamin use; smoking status (ever and never); sunlight exposure (h/d) ; hypertension history; lens opacity; and refractive error. Nutrient variables were energy-adjusted by the residual method.
We estimated ORs and 95% confidence intervals (CIs) by logistic regression analysis using SAS PROC GENMOD (v 9.3; SAS Institute). The procedure uses the generalized estimating equation method to estimate the coefficients and adjust the standard errors of the model terms for the correlated data resulting from repeated measurements (both eyes) in the same individual. This accounts for the lack of independence between 2 eyes from the same individual.
We used P < .05 to denote statistical significance, and all tests were 2-sided.
Results
Of the original 4757 subjects in the AREDS, we excluded those with diabetes; with calorie intake for invalids (invalid intakes ranged from 400 to 3000 Kcal/d for women and 600 to 3500 Kcal/d for men); and missing covariate information. This left 4088 persons contributing 8103 eyes available for analysis. The 8103 eyes consisted of 2739 control eyes (Group 1); 4599 eyes with early AMD (1801 eyes with intermediate drusen plus 2798 eyes with large drusen, ie Group 2 plus Group 3); 765 eyes with advanced AMD (164 eyes with geographic atrophy plus 601 eyes with choroidal neovascularization, ie Group 4 plus Group 5).
We entered food consumption data for the 37 predefined food groups ( Table 1 ) into the factor analysis procedure. The Scree plot of eigenvalues indicated 2 major factors with an eigenvalue of 4.01 and 3.29, respectively. They were much higher than the third highest eigenvalue (1.58). Thus, we retained the 2 factors in the final model. Factor-loading matrixes for the 2 major factors are listed in Table 2 . The larger the loading of a given food item or group into the factor, the greater the contribution of that food item or group to a specific factor. The first factor was loaded heavily with the following foods or food groups: vegetables, legumes, fruit, fish, tomatoes, whole grains, poultry, and so forth. The second factor was loaded heavily with red meat, processed meat, butter, high-fat dairy products, French fries, refined grains, eggs, sweets and desserts, potatoes, and so forth. The first factor explained 10.8% of the total variance in food consumption, and the second factor explained 8.9% of the total variance. We named the first factor the Oriental pattern and the second factor the Western pattern.
| Food or food group b | Factor 1 (Oriental pattern) | Factor 2 (Western pattern) |
|---|---|---|
| Dark-yellow vegetables | 0.67 | – |
| Cruciferous vegetables | 0.65 | – |
| Green leafy vegetables | 0.60 | – |
| Legumes | 0.54 | – |
| Fruit | 0.54 | – |
| Other vegetables | 0.54 | – |
| Whole grains | 0.47 | – |
| Tomatoes | 0.46 | – |
| Fish and other seafood | 0.46 | – |
| Rice | 0.45 | – |
| Poultry | 0.45 | – |
| Soup | 0.39 | – |
| Low-fat dairy products | 0.39 | – |
| Red meats | – | 0.65 |
| Processed meats | – | 0.65 |
| Butter or margarine | – | 0.58 |
| High-fat dairy products | – | 0.53 |
| Gravies | – | 0.51 |
| French fries | – | 0.48 |
| Refined grains | – | 0.43 |
| Eggs | – | 0.43 |
| Sweets and desserts | – | 0.40 |
| Potatoes | – | 0.35 |
| High energy drinks | – | 0.32 |
a Foods or food groups with an absolute value for factor loading < 0.30 for both factors were not listed in the table for simplicity.
b See Table 1 for food groupings.
In general, our study subjects who had higher Oriental pattern scores were younger ( P = .03), more likely to have higher levels of education ( P < .0001), less likely to be smokers ( P = .002) and to have hypertension history ( P = .03), and to have higher intakes (all P values <.0001) of dietary intakes of vitamin C, vitamin E, beta-carotene, zinc, lutein/zeaxanthin, docosahexaenoic acid, and eicosapentaenoic acid ( Table 3 ). It is important to note that consumption of vegetables, legumes, fruit, whole-grain products, fish, poultry, low-fat dairy products, and so forth, was positively correlated with the Oriental pattern score, whereas consumption of processed meat, French fries, high-energy drinks, and so forth, was inversely correlated.
| Q1 (n = 817) | Q2 (n = 818) | Q3 (n = 818) | Q4 (n = 818) | Q5 (n = 817) | P value b | |
|---|---|---|---|---|---|---|
| Age a | 68.75 (5.32) | 68.59 (5.18) | 68.58 (5.09) | 68.65 (4.85) | 68.25 (4.98) | .03 |
| Male gender | 0.45 | 0.42 | 0.41 | 0.44 | 0.39 | .13 c |
| College or higher | 0.49 | 0.62 | 0.66 | 0.72 | 0.77 | <.0001 c |
| White | 0.96 | 0.96 | 0.97 | 0.96 | 0.96 | .78 c |
| BMI (kg/m 2 ) | 27.60 (4.76) | 27.38 (4.78) | 26.80 (4.49) | 27.36 (4.63) | 27.25 (5.05) | .25 |
| Sunlight exposure (h/d) | 1.00 (1.08) | 1.00 (1.07) | 1.07 (1.18) | 1.03 (1.12) | 1.10 (1.12) | .21 |
| Ever smoke | 0.59 | 0.58 | 0.53 | 0.54 | 0.50 | .002 c |
| Alcohol intake (g/d) | 5.95 (11.69) | 6.04 (10.92) | 7.42 (14.23) | 6.36 (11.99) | 6.20 (10.41) | .81 |
| Hypertension history | 0.41 | 0.38 | 0.40 | 0.34 | 0.35 | .03 c |
| Daily energy-adjusted nutrient intake | ||||||
| Vitamin C (mg) | 77.85 (42.79) | 95.84 (42.61) | 101.28 (46.77) | 115.11 (50.51) | 134.08 (59.00) | <.0001 |
| Vitamin E (μg) | 8.71 (3.11) | 9.57 (4.60) | 9.78 (5.02) | 10.39 (5.19) | 9.99 (5.29) | <.0001 |
| Beta-carotene (mg) | 1635.35 (787.70) | 2000.15 (963.26) | 2315.48 (1265.80) | 2802.20 (1386.01) | 4131.82 (2394.43) | <.0001 |
| Zinc (mg) | 8.36 (3.24) | 9.33 (3.75) | 9.65 (4.40) | 10.33 (4.69) | 10.95 (5.09) | <.0001 |
| Lutein plus zeaxanthin (μg) | 1113.09 (542.38) | 1362.99 (713.04) | 1511.08 (781.37) | 1844.64 (970.46) | 2493.65 (1554.72) | <.0001 |
| DHA (mg) | 0.03 (0.02) | 0.04 (0.03) | 0.05 (0.03) | 0.05 (0.04) | 0.07 (0.05) | <.0001 |
| EPA (mg) | 0.02 (0.02) | 0.03 (0.02) | 0.03 (0.03) | 0.04 (0.03) | 0.05 (0.04) | <.0001 |
| Calorie intake (Kcal/d) | 1155.63 (486.88) | 1329.22 (505.15) | 1519.07 (505.58) | 1632.48 (524.96) | 1852.98 (546.81) | <.0001 |
| Daily intake of food or food group (medium servings/d) | ||||||
| Dark-yellow vegetables | 0.12 (0.11) | 0.22 (0.18) | 0.29 (0.21) | 0.39 (0.24) | 0.73 (0.48) | <.0001 |
| Cruciferous vegetables | 0.16 (0.14) | 0.26 (0.19) | 0.35 (0.25) | 0.49 (0.32) | 0.81 (0.55) | <.0001 |
| Green leafy vegetables | 0.22 (0.21) | 0.39 (0.28) | 0.53 (0.34) | 0.67 (0.38) | 0.93 (0.49) | <.0001 |
| Legumes | 0.20 (0.15) | 0.32 (0.21) | 0.37 (0.23) | 0.45 (0.27) | 0.66 (0.42) | <.0001 |
| Fruit | 0.85 (0.71) | 1.31 (0.79) | 1.58 (0.98) | 1.97 (1.14) | 2.72 (1.72) | <.0001 |
| Other vegetables | 0.11 (0.10) | 0.16 (0.15) | 0.23 (0.19) | 0.29 (0.23) | 0.51 (0.45) | <.0001 |
| Whole grains | 0.48 (0.48) | 0.78 (0.59) | 0.98 (0.67) | 1.16 (0.72) | 1.52 (0.95) | <.0001 |
| Tomatoes | 0.11 (0.14) | 0.18 (0.19) | 0.24 (0.24) | 0.31 (0.30) | 0.50 (0.39) | <.0001 |
| Fish and other seafood | 0.14 (0.12) | 0.21 (0.15) | 0.26 (0.19) | 0.32 (0.22) | 0.44 (0.33) | <.0001 |
| Rice | 0.06 (0.07) | 0.10 (0.10) | 0.14 (0.14) | 0.19 (0.18) | 0.27 (0.24) | <.0001 |
| Poultry | 0.14 (0.12) | 0.21 (0.17) | 0.28 (0.20) | 0.34 (0.23) | 0.42 (0.27) | <.0001 |
| Soup | 0.12 (0.15) | 0.18 (0.18) | 0.22 (0.24) | 0.29 (0.27) | 0.40 (0.36) | <.0001 |
| Low-fat dairy products | 0.25 (0.30) | 0.38 (0.36) | 0.46 (0.47) | 0.58 (0.48) | 0.82 (0.69) | <.0001 |
b P values are for tests of linear trend, otherwise indicated. Linear regression models were constructed by using continuous characteristics variables as independent variables and continuous Oriental pattern score as the dependent variable.
c Chi-square tests compare the characteristic distributions among Oriental pattern score quintile groups.
Subjects with higher Western pattern scores were younger ( P = .0006) and less educated ( P = .03); were more likely to be male ( P < .0001), to be smokers ( P < .0001), and to be white ( P = .02); they had higher body mass indexes ( P < .0001) and sunlight exposure ( P < .0001), and they drank more alcohol ( P < .0001). They also tended to have lower levels of dietary vitamin C ( P < .0001), beta-carotene ( P < .0001) and lutein/zeaxanthin ( P < .0001) and slightly lower docosahexaenoic acid ( P = .01) and eicosapentaenoic acid ( P < .0001) intakes but had higher intake of vitamin E ( P = .04) ( Table 4 ). Consumption of red meat, processed meat, butter, high-fat dairy products, French fries, refined grain, sweets and dessert, and so forth was positively correlated with the Western pattern score, whereas consumption of whole grains, fruit, low-fat dairy products, vegetables, and so forth was inversely correlated. It is interesting that calorie intake was positively correlated with both pattern scores ( P values <.0001) ( Table 3 and Table 4 ).
