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Mares JA, Voland RP, Sondel SA, et al. Healthy Lifestyles Related to Subsequent Prevalence of Age-Related Macular Degeneration. Arch Ophthalmol. 2011;129(4):470–480. doi:10.1001/archophthalmol.2010.314
The macula of the human eye progressively degenerates with age, more quickly in some people than in others. This can lead to advanced age-related macular degeneration (AMD), which involves the loss of photoreceptors in the macula of the eye. Treatment for advanced AMD is of limited effectiveness, is costly,1 and will become even more costly as the number of older Americans increases in the coming decades.2 Moreover, it profoundly limits the ability of older adults to function independently. The loss of central vision associated with advanced AMD diminishes the ability to see and recognize other people's faces and to read fine print such as that in newspapers and on pill bottles and food packages.
Early and advanced stages of AMD are consistently more common in people who have specific genotypes (many of which function to mediate response to inflammation, oxidative stress, lipid metabolism, and angiogenesis, as recently reviewed by Ding et al3). Several modifiable aspects of lifestyle have been related to a lower occurrence of AMD, including not smoking, physical activity, and certain aspects of diet. Also, AMD is sometimes observed to be more common in people with a history of chronic diseases or conditions that can also be modified by lifestyle choices,4 such as cardiovascular disease,5,6 diabetes,7 hypertension,8 obesity,9-11 and diseases of inflammation or elevated markers of inflammation.12
Smoking has been the most consistently reported risk factor for AMD.13 However, these associations may reflect other unhealthy lifestyle habits, which are more common in smokers; the associations of smoking and AMD are not often adjusted for other aspects of healthy lifestyles, which are less common in smokers. Physical activity has been only recently studied in relation to AMD and was related to lower risk for advanced AMD in 3 past studies.14-16
Age-related macular degeneration has often been associated with diets that are poor in 1 or more ways; it has been more common among people with low levels of carotenoids in the diet, serum, or macula, with diets or serum low in 1 or more other nutrients, or with diets high in fat (reviewed by Mares and Millen17). However, associations of single nutrients and AMD are often inconsistent across studies and impossible to totally disentangle from other aspects of diet. Moreover, they do not account for synergistic relationships of food components. Recently, combinations of nutrients18,19 or a diet score that reflects 1990 US Dietary Guidelines have been related to lower risk of AMD.20 To our knowledge, relationships of AMD to 2 currently recommended diet patterns (the 2005 US Dietary Guidelines or a Mediterranean diet pattern) have not been previously reported.
The common approaches to assessing relationships of healthy lifestyles to AMD in observational studies may give erroneous estimates of single aspects of healthy lifestyles. This is, in part, because single aspects of diet or lifestyle are difficult to disentangle from each other. We cannot measure the levels of these individual aspects of a healthy lifestyle perfectly across several decades of adult life when they are likely to influence AMD; any attempt to simply adjust one in consideration of the other(s) risks the likelihood of incomplete adjustment or residual confounding.
Moreover, adjustment of one healthy behavior for another may lead to misleading interpretations. This is because the mechanisms of protection of some healthy behaviors are related. For example, the energy expenditure of physical activity permits a higher daily nutrient intake and may be protective in this way. Also, both physical activity and diet can contribute to better vitamin D status,21 which has been related to lower risk for AMD.22
If several aspects of lifestyle all protect through a common mechanism (such as reducing inflammation), then examination of risk associated with 1 healthy behavior can be underestimated. Single studies have not previously considered these risk factors, together with diet, concurrently. The objective of this article is to describe relationships of AMD to a combination of healthy behaviors, including diet, physical activity, and smoking history. This was possible in the Carotenoids in Age-Related Eye Disease Study (CAREDS) because participants were recruited from a sample of women who provided detailed dietary and lifestyle habit histories as part of the Women's Health Initiative (WHI) an average of 6 years before AMD was assessed.
Women aged 50 to 79 years were recruited into the CAREDS from those who were enrolled in the WHI Observational Study (WHIOS) cohort23 at 3 of 40 sites: the University of Wisconsin, Madison; the University of Iowa, Iowa City; and the Kaiser Center for Health Research, Portland, Oregon. Women who had intakes of lutein plus zeaxanthin that were above the 78th and below the 28th percentiles (n = 3143) as assessed at WHIOS baseline (1994-1998) were sent letters inviting them to participate in the eye study. Sampling women at the extremes of dietary intake maximized the statistical power available to detect associations between AMD and levels of lutein and zeaxanthin in the diet and serum. Because the intake of lutein and zeaxanthin is also correlated with intakes of many vitamins and minerals from foods (range of Spearman rank correlation coefficients, 0.27 for vitamin D to 0.77 for folate) and negatively correlated with fat intake (r = −0.36), this design would also be expected to maximize extremes in intake of other aspects of healthy diets and enhance the power available to detect associations with these related aspects of diet, relative to samples with similar ranges of intake of comparable sizes.
Of the 3143 women recruited, 2005 (64%) were enrolled and photographic evidence of AMD was determined in CAREDS examinations from May 1, 2001, through January 30, 2004, 4 to 7 years (mean, 6.3 years) after WHIOS baseline. Gradable fundus eye photography was completed for 1853 eligible women, of whom 1787 provided full detail regarding covariates used in regression models of AMD. Based on evidence for selective mortality bias in associations of diet to AMD in women older than 75 years24,25 and similar findings in relation to the independent variables that are evaluated in the present analysis (data not shown), the present data set includes only the 75% of women in this sample who were younger than 75 years at the time of eye photography (n = 1325 women). All procedures conformed to the Declaration of Helsinki and were approved by the institutional review board at each university.
A comparison of CAREDS participants and nonrespondents in the full data set has been previously described.24,25 Further, we compared CAREDS participants younger than 75 years (n = 1325) with WHIOS participants of the same ages who were recruited but did not participate or were excluded from our analysis data set because of missing covariate data (n = 922). Four percent of women in this analysis reported having physician-diagnosed AMD at WHIOS 3-year follow-up visits vs 2% of the nonparticipating women (P = .13). Women in this analysis compared with nonparticipating women were (after age adjustment) more likely to have never smoked (53% vs 49%, respectively; P < .001), had diets that were slightly lower in fat (32% vs 33% of energy, respectively; P = .001) and higher in lutein and zeaxanthin (1.8 vs 1.6 mg/d, respectively; P < .001), reported higher levels of physical activity (15 vs 12 metabolic energy task [MET] hours per week, respectively; P < .001), and had lower body mass index (BMI) (calculated as weight in kilograms divided by height in meters squared) (median, 27.7 vs 28.4, respectively).
Measures of macular pigment density26 and dilated fundus photography27 taken at CAREDS baseline (2001-2004) have been described. Iris color was determined from retinal photographs. Stereoscopic fundus photographs were graded for AMD by the University of Wisconsin Fundus Photography Reading Center using methods based on those used in the Age-Related Eye Disease Study.27
The primary outcome was the presence of early AMD in at least 1 eye. Early AMD was defined as the presence of either (1) large drusen (≥1 large drusen [≥125 μm] or extensive intermediate drusen [area ≥360 μm when soft indistinct drusen are present or ≥650 μm when soft indistinct drusen are absent]) or (2) pigmentary abnormalities of the retinal pigment epithelium (an increase or decrease in pigmentation accompanied with ≥1 drusen [≥63 μm]), consistent with previously established definitions.28 This corresponds to stage 3 of the Age-Related Eye Disease Study original AMD definitions, with the exception that, like other population-based studies, it includes having pigmentary abnormalities (with drusen) in the definition of early AMD. All analyses were also performed separately for 2 components of early AMD (large drusen and pigmentary abnormalities).
Only 12 women in the CAREDS sample who were younger than 75 years had advanced AMD (defined as geographic atrophy, neovascularization, or exudation in the center subfield or receiving a physician's diagnosis of advanced AMD, confirmed subsequently in writing by a physician). Because this was too small of a number to analyze this end point separately and because we aimed to reduce the possibility of temporal bias influencing the estimates of relationships of healthy lifestyles to early AMD, we excluded these women from the main analyses.
Daily levels of nutrients in diets were estimated from responses to a previously validated, semiquantitative food frequency questionnaire29 at WHIOS baseline. For this report, we primarily evaluated diets using the 2005 Healthy Eating Index (HEI-2005), which reflects adherence to the 2005 US Dietary Guidelines.30
The HEI-2005 assigns scores for the intake of specific food components (per 1000 kilocalories) (Table 1). This score represents different aspects of a healthy diet, including the abundant intake of fruits and vegetables (including those that are particularly nutrient rich) and whole grains as well as low intake of discretionary calories from sugar, fat, and alcohol and of saturated fat. We made 1 modification to the original 100-point HEI-2005 scoring system, herein referred to as the modified HEI-2005 (mHEI), by omitting the 10 points assigned for the intake of oils (nonhydrogenated vegetable oils and oils in fish, nuts, and seeds). The original intent for including points for oils in the scoring system was to ensure adequate intake of vitamin E and essential fatty acids. However, using this component in evaluation of diets from Americans who otherwise consume adequate sources of these nutrients may lead to higher scores for diets that are less nutrient dense. Indeed, omitting this score component (which largely reflected vegetable oil intake in this sample) improved correlations with several vitamins and minerals, while not influencing associations with blood levels of α-tocopherol (data not shown).
We also explored whether a similar association of AMD to another healthy diet pattern existed by assigning scores for adherence to an alternative Mediterranean diet (aMED) adapted for use by American people.31 In a 9-point scoring system, a point was assigned for the following: (1) servings of each of the following food components greater than the 75th percentile within the sample: fruits, vegetables, whole grains, legumes, nuts, fish, and the ratio of monounsaturated to saturated fat; (2) less than the 25th percentile for servings of red meat; and (3) alcohol intake of 5 to 25 g/d. These scores were associated with a wider distribution of intakes of many vitamins and minerals than scores on the mHEI (data not shown).
While results are reported by both scoring systems, for the opportunity of greater insights we focus on the mHEI and use the aMED scoring system for further exploratory analyses. This is because scores are spread more widely on the mHEI (90 possible points) than on the aMED (9 possible points). Moreover, high scores on the aMED were uncommon in this sample; only 53 women had scores of 6 to 9. The mHEI has the added advantages of being based on established recommendations for reduction of chronic disease risk in Americans32 and being easier for the comparison of results across American study samples.
At WHIOS baseline, women were asked about participation in total and recreational physical activity, including household and yard work, walking, and strenuous, moderate, and intensive activities.33 Responses to these questions were used to estimate energy expenditure in MET hours per week. These reflect the sum of METs multiplied by the duration and frequency of activity in a week. The MET values are based on estimates of the intensity of the physical activity; for example, 1 hour of strenuous activity requires 7 METs, 1 hour of moderate activity or walking very quickly requires 4 to 4.5 METs, and 1 hour of low-intensity activity or walking slowly requires 3 METs.
At WHIOS baseline, women were weighed and measured, their BMIs were computed, and their blood pressure was measured. At the time of entry into the WHIOS, women provided information about their smoking history. This was updated in CAREDS questionnaires, permitting the creation of a summary variable of lifetime smoking (pack-years), further categorized as the following: (1) never smoked; (2) smoked 0 to 7 pack-years; or (3) smoked more than 7 pack-years. Additional demographic, lifestyle, and health history data were available from questionnaires, including education, hormone replacement therapy, alcohol use, pulse pressure, and history of diabetes, hypertension, and cardiovascular disease. At CAREDS visits, we also queried family history of macular degeneration (immediate family member aged ≥65 years when diagnosed) and obtained updated histories of alcohol use and diabetes. The WHIOS baseline serum samples, collected after a fast for 10 hours or longer, were stored at −80°C. In 2004 and 2005, they were analyzed for serum levels of lutein, zeaxanthin, and tocopherols by reverse-phase high-performance liquid chromatography.34 In 2007 and 2008, they were analyzed for serum 25-hydroxyvitamin D by the DiaSorin Liaison chemiluminescence method and for high-sensitivity C-reactive protein by a high-sensitivity C-reactive protein assay kit (DiaSorin Inc, Stillwater, Minnesota) at Heartland Assays Inc, Ames, Iowa.
We constructed a 6-point healthy lifestyle score (HLS) that gave equal weight to 3 levels of each of 3 health habits (diet, physical activity, and smoking) based on our knowledge of the distribution of these variables. We assigned 2 possible points for healthy levels of each behavior: diet (mHEI score in the lowest 20% = 0, 21%-80% = 1, and the highest 20% = 2), physical activity (MET hours per week) (lowest tertile = 0, second tertile = 1, and third tertile = 2), and smoking (>7 pack-years = 0, >0 to ≤7 pack-years = 1, and 0 pack-years or never smoked = 2).
We evaluated the relationships of quintile ranks for scores on the mHEI and physical activity to other risk and protective factors for AMD using analysis of covariance and the Cochran-Mantel-Haenszel test. Next, logistic regression (PROC LOGISTIC in SAS version 9.2 statistical software; SAS Institute, Inc, Cary, North Carolina) was used to compute odds ratios (ORs) and 95% confidence intervals (CIs) for specific AMD end points (early AMD, large drusen, pigmentary abnormalities, excluding women with advanced AMD from the reference groups [n = 12], and any AMD). The ORs are described by quintile or categories, and P values for trend across the continuous range of score values were also computed.
The ORs for AMD were first adjusted for age. Multivariate models were further adjusted for other risk factors that were not explanatory or intermediary variables. Previous multivariate models in this sample24,25 included age, pack-years smoked, history of diabetes, family history of AMD, iris color, history of cardiovascular disease, and hormone replacement therapy. These were included in the multivariate-adjusted models unless they were independent variables. Additional variables that were statistically significantly related (P ≤ .10) to both AMD and healthy diet pattern or healthy lifestyle scores in the CAREDS or that were previously suspected to be biologically plausible confounders were tested by adding them singly to age-adjusted models. (For mHEI score, only nondietary risk factors were tested.) Those covariates that changed the AMD OR by 10% or more were retained in the final model. We tested for interactions to determine whether mHEI score or physical activity associations with AMD differed (P for interaction < .10) by age, study site, family history of AMD, level of smoking, or BMI.
Women whose mHEI diet scores were ranked in the highest quintile compared with the lowest quintile had lower rates of early AMD (11% vs 19%, respectively) (Table 2), diets significantly lower in fat (percentage of energy), and diets higher in median servings of several food groups (fruits, vegetables, dairy, grains, and meats or alternatives [including poultry, meat, fish, beans, and eggs]) (Table 3). Supplement use was fairly common: 56% of women in the highest quintile vs 37% of those in the lowest quintile for mHEI score used multivitamins. Being in high quintiles compared with low quintiles for mHEI score was also associated with reporting more physical activity, fewer pack-years of smoking, a lower likelihood of having a history of hypertension, lower systolic blood pressure, lower BMI, and lower level of serum C-reactive protein (Table 3). Associations between aMED scores and nonnutritional AMD risk factors were generally similar (data not shown).
Women in the highest quintile compared with the lowest quintile for mHEI score had 46% lower odds for early AMD (multivariate-adjusted OR = 0.54; 95% CI, 0.33-0.88) (Table 4). The 53 women with aMED scores of 6 to 9 had 66% lower odds for AMD, compared with many more women (n = 490) in this sample who scored 0 or 1 on this pattern (Table 4). Further adjusting for physical activity attenuated ORs for early AMD in women with high scores compared with those with low scores for both diet patterns (for early AMD among women in high vs low quintiles for mHEI: multivariate-adjusted OR = 0.64; 95% CI, 0.38-1.07; for early AMD among women with aMED scores of 6-9 vs 0-1: multivariate-adjusted OR = 0.44; 95% CI, 0.10-1.27), indicating that association with diet is not totally independent from association with physical activity.
The multivariate-adjusted ORs for large drusen and pigmentary abnormalities among women in the highest quintile compared with those in the lowest quintile of the mHEI score were similar to those for overall early AMD (large drusen: multivariate-adjusted OR = 0.56; 95% CI, 0.31-0.97; P for trend = .049; and pigmentary abnormalities: multivariate-adjusted OR = 0.58; 95% CI, 0.29-1.13; P for trend = .07). Associations of mHEI score to total AMD (early plus advanced AMD) were also similar (multivariate-adjusted OR = 0.55; 95% CI, 0.33-0.88; P for trend = .01).
The associations between mHEI score and early AMD were significantly different across study sites (P for interaction = .08), with stronger inverse associations between mHEI score and AMD in Portland (n = 425) and Madison (n = 436) than in Iowa City (n = 452), but all associations were inverse (data not shown). The inverse associations of mHEI score to AMD did not significantly differ (P > .20) by BMI, physical activity level, smoking history, macular pigment density level, or having a family history of AMD (data not shown).
Women in the highest quintile vs those in the lowest quintile for physical activity had greater than 2-fold lower multivariate-adjusted odds for AMD (OR = 0.46; 95% CI, 0.27-0.78; P for trend = .002). Associations with drusen, pigmentary abnormalities, and any AMD were similar (data not shown). Despite a significant correlation between level of physical activity and mHEI score (Spearman rank correlation coefficient = 0.30; P < .001), adjusting for mHEI score only slightly attenuated this association (OR = 0.52; 95% CI, 0.30-0.89; P for trend = .009). The association also remained consistently inverse in women with mHEI scores above and below the median (P for interaction = .90). Physical activity was also correlated with BMI (r = −0.26; P < .001). However, the association of physical activity to early AMD was also consistently inverse across all levels of BMI (BMI <25, 25-29, and ≥30; P for interaction = .33).
The association of physical activity to early AMD appeared to reflect the weekly time spent in physical activity rather than any specific type or intensity of physical activity. For example, multivariate-adjusted ORs for women in the highest tertile vs those in the lowest tertile (minutes per week) were 0.56 (95% CI, 0.37-0.84; P for linear trend = .004) for total recreational activity, 0.78 (95% CI, 0.52-1.16; P for linear trend = .01) for moderately strenuous activity, and 0.67 (95% CI, 0.46-0.96; P for linear trend = .004) for strenuous activity.
The ORs for early AMD by smoking history and BMI are also given in Table 4. Women who smoked more than 7 pack-years had 45% increased odds for AMD in multivariate-adjusted models, but the association across all levels of smoking was only marginally significant (P = .07) and was attenuated after adjusting for mHEI score and physical activity. Only the 10% of women who were extremely obese had 58% higher odds for early AMD; after further adjustment for multiple variables, mHEI score, and physical activity, BMI was no longer associated with AMD (Table 3).
Women who had an HLS of 6, which reflects the healthiest (lowest risk levels) of all 3 score components (diet, physical activity, and smoking), had 71% lower odds for early AMD compared with women who had scores of 0 to 2 (Table 4). Obesity (BMI >30) was much less common in women with an HLS of 6 (9%) vs 0 to 2 (43%) (P < .001). However, adjusting for BMI did not influence associations. We explored the consistency of the HLS associations with early AMD in women who were obese (BMI >30) vs those who were not obese (BMI ≤30). (In these analyses, we grouped women with scores of 5-6 because too few women with an HLS of 6 were obese.) The associations were somewhat but not statistically significantly stronger in obese women (P for interaction = .17). The multivariate-adjusted ORs were 0.26 (95% CI, 0.06-0.78; P for trend = .004) in women who were obese and 0.60 (95% CI, 0.34-1.06; P for trend = .02) in women who were not.
The results of this study indicate that broadly healthy diets and lifestyles in women aged 50 to 69 years were associated with a lower prevalence of early AMD an average of 6 years later. A combination of healthy lifestyles, which included healthy diets, physical activity, and not smoking, resulted in 3-fold lowered odds for early AMD. Specifically, in this particular sample, the 5% of women with the highest HLS (score of 6) never smoked (<0.1 pack-year), reported the equivalent of about 10 hours of low-intensity physical activity per week (such as light housework, walking, or gardening) or 8 hours of moderate activity per week, and had the following diet qualities: daily servings of fruits (3.5/day) and vegetables (about 5/day, 2 of which were dark green, orange, or legumes), dairy (2.3/day), meat or alternatives (meat, poultry, fish, beans, or eggs) (2.7 oz/day), and grains (3.5 servings/day, of which 1 serving/day is whole grain). These lifestyle habits are interrelated in practice and in biological effect (discussed later) so that the degree to which they might contribute independently to associations cannot be accurately assessed in this study.
These associations, like those generated from data in prospective studies, are not likely to reflect temporal biases, which are possible in studies in which lifestyle and AMD are assessed at the same point in time. This is because (1) lifestyle was assessed an average of 6 years before photographs documented AMD, (2) most women who had AMD at this point were in early stages (77% of women who had not been previously told that they had AMD), and (3) the women who had AMD were also not likely to have had AMD for many years owing to their young ages.
To our knowledge, these analyses provide the first estimate of associations between AMD and the dietary patterns35 recommended by the 2005 US Dietary Guidelines.32 Associations with mHEI score were stronger than associations for individual aspects of diet previously studied in this sample.24,25 Results are consistent with a recently reported association between an alternative version of the 1990 Dietary Guidelines and advanced AMD in a case-control study.20 Further, results extend the protective nature of broadly healthy diet patterns to early AMD, which dramatically increases risk for eventually developing advanced AMD in white populations.36-38
The shift in OR toward unity after adjusting for physical activity suggests that some of this effect could be due to physical activity. Both healthy diets and physical activity improve nutritional status. (Recommendations for both are included in the 2005 US Dietary Guidelines.32) Physical activity might contribute to better nutritional health by (1) increasing energy expenditure, allowing a larger absolute intake of phytochemicals and micronutrients, and (2) increasing moderate exposure to sunlight when outdoors as was common in this sample. Recreational activities, such as walking, were predictors of blood vitamin D levels in this sample (data not shown) and in a separate sample of WHI participants.21 Low levels of vitamin D were associated with higher odds for AMD in a subsample of the CAREDS39 and in a separate sample.22
Having a high score (6-9) on the aMED diet pattern, which is more plant-food focused than the US Dietary Guidelines and predicts somewhat higher intake of several nutrients in the diet and serum than the mHEI score (data not shown), was associated with lower odds for AMD than scores in the top quintile for the mHEI (66% vs 46% reduction, respectively, in odds). Few women in this sample (4%) had aMED scores in this range. Intakes of 3 food groups that contribute to higher scores on the aMED, whole grains (Table 3), nuts, and fish (previously described by Parekh et al25), were limited in this sample. These foods could contribute to the intake of short-chain (grains and nuts) and long-chain (fatty fish from cold water) ω-3 polyunsaturated fatty acids. The intake of fish or ω-3 polyunsaturated fatty acids has been associated with lower risk for AMD in many previous studies.40,41
The protective association of the mHEI score to AMD in this sample is likely to reflect the fact that high scores were associated with an intake of high levels of several single nutrients that have been related to low prevalence or progression of AMD in previous studies (antioxidants,18,42 B vitamins,43 zinc,18,44,45 and lutein plus zeaxanthin24,45-49). Previous investigators have found that combinations of nutrients from food are more strongly associated with AMD risk than single nutrients.18,19 The protective association of the mHEI score to early AMD is also likely to reflect direct relationships of dietary fat to AMD as previously reported in this sample25 and other samples.50-53
To our knowledge, this was the first observation of a relationship between physical activity and early AMD. Protective associations with physical activity were reported in relation to the incidence of diagnosed AMD or photographically evident advanced AMD in 3 previous studies.14-16 Evidence from our study indicates that the association of physical activity is independent of diet. The degree to which associations of physical activity to AMD might have reflected better diets in people with higher levels of physical activity had not been previously assessed, except in 1 study with limited dietary data.15
Smoking has been the one risk factor most consistently associated with a higher risk for AMD.13 The association between lifelong smoking and AMD in our study was only marginal and further attenuated after additionally adjusting for diet and physical activity. Obesity has been associated with a higher prevalence or incidence of AMD less consistently,9 but weight loss has been associated with reduced AMD presence.10 Only extreme obesity was associated with AMD in our sample; this was not significant after multivariate adjustment and was almost completely attenuated after adjusting for the potential explanatory variables of mHEI score and physical activity. It is unclear how much of the associations of these risk factors in past studies may have been attributed to that fact that smoking and obesity are more common among people with poor diets and people who exercise less. Previous studies did not adjust for both diet and physical activity. Moreover, even when adjusted for, some residual confounding can be expected owing to imperfect measurement and failure to capture these exposures over long periods in adult life.
In this study, the 3-fold lower odds for AMD among women with a combination of healthy lifestyles compared with those with unhealthy lifestyles suggest that a combination of healthy lifestyle practices might be more important in reducing AMD risk than a focus on one. These changes collectively may contribute to lowering oxidative stress, inflammation, and blood pressure and improving blood lipid levels, all of which are thought to be pathogenic mechanisms that promote AMD. It is well known that smoking increases oxidative stress,54 and it is expected that stopping smoking lessens it. Physical activity can also upregulate antioxidant protection enzyme systems so that it reduces oxidative stress, despite the fact that bouts of physical activity can increase oxidative stress in the short term (reviewed by Ji et al55). Improvements in diet and physical activity alone or in conjunction with a reduction in obesity can lessen oxidative stress as well (reviewed by Vincent et al56).
Healthy lifestyles may lower AMD risk by lowering systemic inflammation, which is widely thought to contribute to AMD pathogenesis. Healthy diet patterns and physical activity have been related to lower blood levels of C-reactive protein, a marker of systemic inflammation in other samples,31,57 as they were in our sample (Table 3).
Healthy lifestyles may also lower AMD risk by reducing blood pressure (related to AMD risk in some past studies, previously reviewed by Klein et al8). Intervention trials have demonstrated that reductions in blood pressure can result from healthy diets,58 physical activity,59 and weight loss.59,60 A history of hypertension was less common in women in the highest quintiles for mHEI score, physical activity, and HLS (Table 3).
In addition to these mechanisms, we speculate that healthy diets and physical activity might lower risk for AMD by improving the status of macular pigment. Macular pigment density was associated with healthy diets, physical activity, and HLS (Table 3). The carotenoids composing macular pigment can block the frequencies of blue light that are known to damage the retina directly; they may also quench reactive oxygen species that form as a result of the light- and oxygen-rich environment (previously reviewed by Landrum and Bone61). They could reduce the formation of a toxic metabolite of retinal recycling (A2E), which is stimulated by blue light,62 by blocking blue light from reaching the retina. Lutein and zeaxanthin supplementation from foods can clearly increase macular pigment density, but the ability to increase macular pigment varies considerably among persons.63-65 As we have previously discussed,66,67 several aspects of diet such as the overall intake of fruits, vegetables, whole grains, and fat may contribute to the uptake and turnover of these carotenoids. Physical activity might contribute to greater macular pigment density by reducing inflammation and oxidative stress directly or by reducing obesity. Obesity is related to lower macular pigment density in this and other samples66,68,69 and may increase oxidative stress and carotenoid turnover as well.56
Confirmation of these associations of healthy diets and lifestyles to AMD in intervention studies and long-term population-based studies that include men and a broader sample of ethnic backgrounds would provide additional evidence and more reliable risk estimates for the strong associations we observed among women. Our estimates in this primarily white sample may overestimate the overall effect in people from Hispanic, African, and Asian origins, who seem to be at lower risk for developing advanced stages of AMD despite similar levels of early AMD.70 Conversely, because women with less healthy lifestyles were less likely to participate in this study, the power to estimate AMD rates among those with unhealthy lifestyle habits was weakened.
The HLS was not constructed a priori and needs to be further studied in separate samples. Healthy diets or lifestyles that we evaluated might reflect other unknown and unmeasured aspects of lifestyle. Socioeconomic status can be a surrogate for some such unknown factors. The socioeconomic status of women in this sample is high, limiting the extent to which this may be a confounder. For example, 78% of women had more than a high school education. Further adjustment for educational or income levels did not influence associations in this sample (data not shown).
This sample and any single sample available today are not large enough, nor are the samples diverse enough, to evaluate associations of each healthy lifestyle behavior to AMD independently. For example, we could not evaluate the potential benefit of healthy diets in women who were overweight compared with those who were lean. Exploratory analyses indicated stronger associations of mHEI score to AMD among women with BMIs greater than 30 compared with those with BMIs less than 25, but these associations and the interactions between diet and BMI were not significant. Larger studies or pooled samples across many studies might be useful to estimate interactions between these healthy habits and their influence on the occurrence of AMD.
Finally, it may be that the effect of healthy habits is more or less important in people who have high-risk genotypes for AMD. In several recent studies, healthy lifestyles were more strongly associated with lower risk for AMD among people with high variants of CFH Y402H71-75 and ARMS2 A69S.71 In our study, having a family history of AMD did not modify the association of a healthy diet or lifestyle to AMD, but genotyping will better characterize a person's susceptibility for the disease and improve the ability to examine the possibility that diet and lifestyle modify genetic risk.
A combination of healthy lifestyle behaviors that includes healthy diet, physical activity, and not smoking was associated with markedly lowered prevalence of early AMD an average of 6 years later in postmenopausal women. Adopting these healthy habits may markedly lower the prevalence of early AMD, the number of people who develop advanced AMD in their lifetime, and health care costs associated with treatment for this condition.
These results also serve to remind us that risk for AMD is passed to subsequent generations not only through genes but also possibly through the lifestyle habits we model and encourage. Specifically, we believe that these results, together with current scientific evidence for chronic disease prevention, support recommendations to exercise (move at least at a low intensity for 1-2 hours per day; outside when possible), avoid smoking, and follow a healthy diet pattern that meets the following criteria: (1) is abundant in plant foods (vegetables [including dark leafy green and orange vegetables], fruits, and whole grains); (2) contains daily protein sources in moderation and variety (beans, nuts, fish, dairy, eggs, meat, and poultry); and (3) limits foods high in sugar, fat, alcohol, refined starches, and oils.
Correspondence: Julie A. Mares, PhD, Department of Ophthalmology and Visual Sciences, University of Wisconsin, Madison, 610 N Walnut St, 1063 WARF Bldg, Madison, WI 53726-2336 (firstname.lastname@example.org).
Submitted for Publication: March 30, 2010; final revision received October 18, 2010; accepted October 18, 2010.
Published Online: December 13, 2010. doi:10.1001/archophthalmol.2010.314
Financial Disclosure: Dr Gehrs has been a consultant for the EyeTech advisory board, the US Department of Energy review panel, and the Abbott advisory board and has received research sponsorship from Alcon, Novartis, Ophtherion, Genentech, Alimera, Sirion, EyeTech, Regeneron, Pfizer, Thrombogenics, and Occulogix.
Funding/Support: This research was supported by grants EY013018 and EY016886 from the National Eye Institute, National Institutes of Health, and by Research to Prevent Blindness. The WHI program is funded by the National Heart, Lung, and Blood Institute, National Institutes of Health through contracts N01WH22110, 24152, 32100-2, 32105-6, 32108-9, 32111-13, 32115, 32118-32119, 32122, 42107-26, 42129-32, and 44221.
National Heart, Lung, and Blood Institute, Bethesda, Maryland: Jacques Rossouw, Shari Ludlam, Joan McGowan, Leslie Ford, and Nancy Geller.
Clinical Coordinating Center
Fred Hutchinson Cancer Research Center, Seattle, Washington: Ross Prentice, Garnet Anderson, Andrea LaCroix, and Charles L. Kooperberg. Medical Research Labs, Highland Heights, Kentucky: Evan Stein. University of California at San Francisco: Steven Cummings.
Albert Einstein College of Medicine, Bronx, New York: Sylvia Wassertheil-Smoller. Baylor College of Medicine, Houston, Texas: Haleh Sangi-Haghpeykar. Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts: JoAnn E. Manson. Brown University, Providence, Rhode Island: Charles B. Eaton. Emory University, Atlanta, Georgia: Lawrence S. Phillips. Fred Hutchinson Cancer Research Center: Shirley Beresford. George Washington University Medical Center, Washington, DC: Lisa Martin. Los Angeles Biomedical Research Institute at Harbor–UCLA Medical Center, Torrance, California: Rowan Chlebowski. Kaiser Permanente Center for Health Research, Portland, Oregon: Erin LeBlanc. Kaiser Permanente Division of Research, Oakland, California: Bette Caan. Medical College of Wisconsin, Milwaukee: Jane Morley Kotchen. MedStar Research Institute/Howard University, Washington, DC: Barbara V. Howard. Northwestern University, Chicago/Evanston, Illinois: Linda Van Horn. Rush Medical Center, Chicago: Henry Black. Stanford Prevention Research Center, Stanford, California: Marcia L. Stefanick. State University of New York at Stony Brook: Dorothy Lane. The Ohio State University, Columbus: Rebecca Jackson. University of Alabama at Birmingham: Cora E. Lewis. University of Arizona, Tucson/Phoenix: Cynthia A. Thomson. University at Buffalo, Buffalo, New York: Jean Wactawski-Wende. University of California at Davis, Sacramento: John Robbins. University of California at Irvine: F. Allan Hubbell. University of California at Los Angeles: Lauren Nathan. University of California at San Diego, La Jolla/Chula Vista: Robert D. Langer. University of Cincinnati, Cincinnati, Ohio: Margery Gass. University of Florida, Gainesville/Jacksonville: Marian Limacher. University of Hawaii, Honolulu: J. David Curb. University of Iowa, Iowa City/Davenport: Robert B. Wallace. University of Massachusetts/Fallon Clinic, Worcester: Judith Ockene. University of Medicine and Dentistry of New Jersey, Newark: Norman Lasser. University of Miami, Miami, Florida: Mary Jo O’Sullivan. University of Minnesota, Minneapolis: Karen Margolis. University of Nevada, Reno: Robert Brunner. University of North Carolina, Chapel Hill: Gerardo Heiss. University of Pittsburgh, Pittsburgh, Pennsylvania: Lewis Kuller. University of Tennessee Health Science Center, Memphis: Karen C. Johnson. University of Texas Health Science Center, San Antonio: Robert Brzyski. University of Wisconsin, Madison: Gloria E. Sarto. Wake Forest University School of Medicine, Winston-Salem, North Carolina: Mara Vitolins. Wayne State University School of Medicine/Hutzel Hospital, Detroit, Michigan: Michael S. Simon.
WHI Memory Study
Wake Forest University School of Medicine: Sally Shumaker.
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