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Dietary glycemic load and risk of age-related cataract^1,2,3

¹ From the Division of Preventive Medicine (DAS and SL), the Channing Laboratory, Brigham and Women’s Hospital (WCW and SEH), and the Department of Ophthalmology (DAS and JMS), Harvard Medical School, Boston; and the Departments of Epidemiology (DAS, SL, JMS, WCW, and SEH) and Nutrition (WCW), Harvard School of Public Health, Boston

² Supported by grants EY009611, CA87969, and CA55075 from the National Institutes of Health.

³ Reprints not available. Address correspondence to DA Schaumberg, Division of Preventive Medicine, 900 Commonwealth Avenue East, Boston MA 02215. E-mail: dschaumberg{at}rics.bwh.harvard.edu.

	ABSTRACT

TOP ABSTRACT INTRODUCTION SUBJECTS AND METHODS RESULTS DISCUSSION REFERENCES

 
Background: Metabolism of many of the most commonly consumed carbohydrates in the United States results in a high plasma glucose response, which can be quantified by the glycemic load. Although hyperglycemia is a risk factor for cataract, there is no information on the potential effect of a high dietary glycemic load on the incidence of age-related cataract.

Objective: Our objective was to prospectively examine the association between dietary glycemic load and incident age-related cataract.

Design: We studied 2 cohorts—71 919 women and 39 926 men—aged 45 y who had no previous diagnosis of cataract, diabetes mellitus, or cancer and who were followed for 14 and 12 y, respectively, for the occurrence of cataract extraction. We calculated dietary glycemic load from data reported on multiple validated food-frequency questionnaires and used pooled logistic regression models to estimate the association with incident cataract extraction. We performed analyses separately for each cohort and then calculated pooled estimates across cohorts.

Results: During 980 683 person-years of follow-up, we confirmed 4865 incident age-related cataract extractions. After adjustment for age, cigarette smoking, body mass index, total caloric intake, dietary intake of lutein and zeaxanthin, and alcohol consumption, there was no significant relation of dietary glycemic load to risk of cataract extraction (P for trend = 0.10). The pooled relative risk between the highest and lowest quintiles of dietary glycemic load was 0.95 (95% CI: 0.81, 1.11; P for heterogeneity by cohort = 0.1).

Conclusion: These prospective epidemiologic data do not support the hypothesis that a high dietary glycemic load, primarily a result of consumption of refined carbohydrates, increases the risk of cataract extraction.

Key Words: Epidemiology • cataract • cataract surgery • risk factors • glycemic load • glycemic index • diet • carbohydrates

	INTRODUCTION

TOP ABSTRACT INTRODUCTION SUBJECTS AND METHODS RESULTS DISCUSSION REFERENCES

 
Age-related cataract remains the leading cause worldwide of blindness and visual impairment (1). Prevention or delay of this common condition would provide relief not only to those at risk but also to already stressed health care systems by reducing the need for cataract surgery. Available evidence implicates hyperglycemia as a risk factor for the development of age-related cataract (2-4). In the United States, a diet that is low in fat and relatively high in carbohydrates is recommended for the general population (5). However, perhaps in light of the poor dietary choices of many Americans (eg, low consumption of whole grains, an insufficient amount or variety of fruit and vegetables, and frequent consumption of refined carbohydrates), research on the health effects of dietary glycemic load (a variable representing the quantity and quality of carbohydrates in the diet and their interaction) has shed doubt on the healthfulness of a high glycemic load diet, particularly in terms of risk of type 2 diabetes and coronary artery disease, because of its adverse effects on lipid and glucose metabolism (6-8).

Different carbohydrates, as a result of their individual physical and chemical characteristics, induce distinct plasma glucose responses, which can be quantified by the glycemic index (9, 10). The glycemic index is a measure of the comparison of the relative plasma glucose response to a specific food with the response induced by the same amount of carbohydrate from a standard source, such as white bread or pure glucose. Dietary glycemic load is the product of the glycemic index of each specific food and its carbohydrate content, summed over all foods, and thus it represents the quantity and quality of carbohydrates in the overall diet and their interaction (8).

Consumption of foods with lower glycemic indexes reduces glycated hemoglobin concentrations in both diabetic and nondiabetic persons (11-13). This may be relevant to cataract, because hyperglycemia is thought to cause cataract through polyol pathway disruption, lipid peroxidation, glycation, and glycation-mediated oxidation that lead to increased osmotic and oxidative stress in the lens and to eventual opacification (14-16). Animal studies also showed that higher concentrations of plasma glucose are associated with exponential increases in the risk of cataract (17), and data from humans generally support similar relations (2-4, 14). If higher dietary glycemic load were related to cataract, modification of diets to lower the glycemic load might lead to a lower public health burden by reducing the incidence of cataract and the need for cataract extraction. The present study was undertaken to evaluate prospectively whether higher dietary glycemic load is associated with an increased incidence of age-related cataract.

	SUBJECTS AND METHODS

TOP ABSTRACT INTRODUCTION SUBJECTS AND METHODS RESULTS DISCUSSION REFERENCES

 
The Nurses’ Health Study (NHS) is a cohort study of 121 700 female US Registered Nurses who were aged 30–55 y in 1976. Study participants complete a mailed questionnaire every 2 y. Information is obtained on lifestyle factors including diet and on diseases including the occurrence of cataract extraction. The Health Professionals Follow-up Study (HPFS) is a prospective study of 51 529 male US health professionals (including 29 683 dentists, 3743 optometrists, 2218 osteopathic physicians, 4185 pharmacists, 1600 podiatrists, and 10 098 veterinarians) who were aged 40–75 y in 1986. HPFS participants are also followed by means of mailed questionnaires every 2 y to assess lifestyle factors and the occurrence of diseases.

Study population
Given our aim of examining the effect of dietary glycemic load on the risk of cataract, we excluded from the baseline study population all NHS and HPFS participants who did not complete a baseline semiquantitative food-frequency questionnaire (SFFQ), those who had implausible caloric intakes (<600 or >3500 kcal/d for women and <800 or >4200 kcal/d for men), and those who left >70 items blank on the SFFQ; a total of 82 126 women and 49 934 men were thus potentially available for analysis. From these groups, we further excluded all participants who had an existing diagnosis of cataract at baseline (n = 2737) or for whom we were unable to determine the date of cataract diagnosis (n = 492). We also excluded participants who had a preexisting diagnosis of cancer (except nonmelanoma skin cancer) (n = 6339) or diabetes mellitus (n = 3833). Participants for whom information on important covariates at baseline was missing were also excluded from this analysis (n = 4623). In addition, we restricted the population for this study of age-related cataract to women and men who were aged 45 y. Previously included NHS and HPFS participants who were too young for entry into the study at baseline (n = 29 284) were included in the analysis in the 2-y cycle after they reached the age of 45 y.

Assessment of dietary glycemic load
Dietary information was derived from a 126-item SFFQ, which was administered to study participants by mail. A detailed assessment of carbohydrate-containing foods was first undertaken in 1984 for women and 1986 for men, and it formed the baseline assessment for the present study. Measurements of dietary intake were repeated in 1986, 1990, and 1994 in the NHS cohort and in 1990 and 1994 in the HPFS cohort, with the use of virtually identical SFFQs. For each food, a commonly used portion size (eg, one slice of bread) was indicated, and the participant was asked how often during the previous year, on average, he or she had consumed that amount. Participants were given a choice of 9 responses ranging from "never" to "6 times/d."

A detailed description of the SFFQ and of the procedures used for calculating nutrient intake and data on reproducibility and validity was published previously (18, 19). Briefly, we computed nutrient scores by multiplying the frequency of consumption of each unit of food according to the SFFQ by the nutrient content of that specific portion size of the food according to food-composition tables from the US Department of Agriculture and other sources. The performance of the SFFQ in assessing individual foods that are high in carbohydrates has been documented previously and included, for example, correlation coefficients of 0.71 for white bread, 0.77 for dark bread, 0.66 for potatoes, and 0.94 for yogurt (20).

Methods used to calculate the glycemic indexes of individual foods and mixed meals and the assessment of dietary glycemic load have been reported elsewhere (6, 10, 19, 21). Glycemic load was calculated by multiplying the carbohydrate content of each food by its glycemic index, multiplying that value by the frequency of consumption, and summing the values from all foods. Dietary glycemic load thus represents both the quality and the quantity of carbohydrates, as well as the interaction between the two. Each unit of dietary glycemic load represents the equivalent of 1 g carbohydrate from white bread. We also calculated the average dietary glycemic index by dividing the average daily dietary glycemic load by the average daily carbohydrate intake. This variable reflects the overall quality of the carbohydrate intake. We obtained information on potential confounding variables, including age, body weight, height, diagnosis of diabetes mellitus, and cigarette smoking history, from the biennial questionnaires, and we updated these variables for analysis at the beginning of each follow-up period.

Assessment of cataract extraction
Beginning in 1984 for the NHS cohort and in 1988 for the HPFS cohort, participants were asked whether they had undergone a cataract extraction; if so, they were asked for permission to contact the treating ophthalmologist and any other ophthalmologist, optometrist, or other health care providers to obtain medical record information, including the subject’s visual acuity before extraction, the location or locations of the lens opacity, and the actual date or dates of diagnosis and extraction. Of the ophthalmologists contacted, 90% responded, and all confirmed the cataract extraction. We used the information obtained from these doctors to exclude cataracts considered to be congenital or secondary to chronic corticosteroid use, chronic intraocular inflammation, ocular trauma, previous intraocular surgery, or other non-age-related etiologies (n = 552). Participants with cataracts secondary to these other causes contributed person-time to the analysis up to the date on which the cataract was initially diagnosed, which we took as the earlier of the dates provided by the participant and the ophthalmologist. Our primary analysis was based on the presence of any type of cataract. However, we also collected information on the presence of cataract subtypes (nuclear, posterior subcapsular, and cortical), including the presence of each subtype and, when more than one subtype was present, an assessment of the primary subtype. Because the different types of lens opacities may have different risk factors, we also performed analyses of primarily nuclear cataract and primarily posterior subcapsular cataract.

Statistical analysis
We allocated follow-up time in this cohort analysis beginning with the date of return of the 1984 questionnaire for women and the 1986 questionnaire for men or the date at which the participant reached the age of 45 y, whichever was later. Because the diagnosis of cataract might lead to changes in dietary habits, we used the date of diagnosis of the cataract as the end of follow-up for our endpoint of cataract extraction. Beginning in 1990, we excluded any person-time in a given 2-y interval if a participant did not report having had an eye exam during that period, because cataract would be unlikely to be diagnosed under that circumstance. We also excluded person-time after a diagnosis of cancer to avoid any potential bias that might result from changes in both diet and the propensity to undergo cataract extraction related to the illness. Consequently, for purposes of analysis, follow-up was continued until cataract diagnosis, death, cancer diagnosis, loss to follow-up, or end of follow-up (June 1998 for women and January 1998 for men), whichever occurred first.

For our primary analysis, we grouped cases and person-time according to quintiles of glycemic load (and, in other models, glycemic index), by using the cumulative average of exposure based on all available measurements of diet up to the beginning of each 2-y interval. Thus, for example, for the participants from the NHS, we used the 1984 dietary information for follow-up through 1986 and the average of the intakes reported on the 1984 and 1986 questionnaires for follow-up from 1986 through 1990, and so on. To minimize the effect on our analysis of possible changes in dietary habits after the diagnosis of diabetes (a potential intermediate variable), we stopped updating diet if a participant developed diabetes during the follow-up period. In this way, we examined the average glycemic load exposure during the follow-up period. Use of the cumulative average method has the additional advantage of minimizing measurement error (22). We calculated incidence rate ratios as the rate of occurrence of cataract extraction in each quintile group relative to the lowest group after adjustment for age and age-squared. We then used pooled logistic regression analysis with 2-y increments to simultaneously adjust for age and other possible confounders, including cigarette smoking status, lutein and zeaxanthin intake, and body mass index (BMI; in kg/m²). This analysis is asymptotically equivalent to Cox’s proportional hazards regression analysis with time-dependent covariates (23). We also conducted analyses that either controlled for diabetes or excluded those who developed diabetes. We tested for interactions of dietary glycemic load with cigarette smoking, diabetes, and BMI. (Subjects with prevalent diabetes at baseline were included in the model to test for an interaction with diabetes.) In secondary analyses, we also examined the relation of baseline glycemic load with incident cataract extractions. We initially conducted separate analyses for each cohort. We then tested for heterogeneity between the 2 cohorts and, if no significant heterogeneity was present, used a random-effects model to pool the RRs (24).

Finally, because the timing of cataract surgery varies in the United States with regard to the degree of visual impairment, we investigated whether any potential bias may have been introduced by a relation of the visual acuity threshold for cataract surgery to dietary glycemic load. We used analysis of variance to test whether there was any association between visual acuity at the time of surgery and dietary glycemic load. We also fitted an additional set of regression models in which we restricted cases to those in which the person’s visual acuity was reduced to <20/40 at the time of surgery (patients with visual acuity 20/40 were censored at the time of diagnosis).

	RESULTS

TOP ABSTRACT INTRODUCTION SUBJECTS AND METHODS RESULTS DISCUSSION REFERENCES

 
We included in this analysis 53 506 women and 31 246 men aged >45 y at baseline. Because additional participants began to contribute person-time after reaching the age of 45 y, data from a total of 71 919 women and 39 926 men contributed to the overall analysis. Dietary glycemic load varied at baseline in the study population between an average of 107 for women and 131 for men in the lowest quintile and an average of 181 for women and 231 for men in the highest quintile of energy-adjusted glycemic load (Table 1). Women and men with the highest dietary glycemic loads generally consumed more carbohydrates (g/d) but had lower intakes of fats (g/d), proteins (g/d), and alcohol (g/d) and smoked less than did women and men with the lowest dietary glycemic loads.

Table 2

Results from models in which we excluded all person-time after the diagnosis of diabetes did not differ from the main results presented herein (pooled RR for total cataract extraction among subjects in the highest compared with those in the lowest quintile of dietary glycemic load = 0.95; P for trend = 0.07; P for heterogeneity = 0.2). In addition, we observed no significant interaction of dietary glycemic load with diabetes status (pooled P for interaction = 0.5), which suggests that the relations do not differ significantly between persons with diabetes and those without. We observed no overall significant interaction of BMI with dietary glycemic load (pooled P for interaction = 0.3). We observed no significant interaction of dietary glycemic load with cigarette smoking status (pooled P for interaction = 0.4).

In analyses evaluating the association with baseline dietary glycemic load, results were similar to those presented for the cumulative average glycemic load. In this analysis, the test for heterogeneity between women and men was statistically significant for total cataract extraction (P for heterogeneity = 0.03). The RR (95% CI) for the highest compared with the lowest quintile of baseline dietary glycemic load was 0.98 (0.88, 1.10; P for trend = 0.3) among women and 0.83 (0.70, 0.99; P for trend = 0.01) among men.

Finally, because we used cataract extraction as the endpoint for this analysis and because, in the United States, the timing of cataract surgery varies with regard to the degree of visual impairment, we investigated the potential for confounding due to a possible bias that may have been introduced by a relation of the threshold for cataract surgery and glycemic load. However, in these analyses, we observed no significant association between glycemic load and visual acuity at the time of surgery (P = 0.2 in each cohort testing by analysis of variance for differences in mean visual acuity across quintiles of glycemic load). Moreover, in analyses restricted to cases in which the person’s visual acuity was reduced to <20/40 at the time of surgery, results did not differ from those for all extractions. The pooled RR (95% CI) for the highest compared with the lowest quintile of baseline dietary glycemic load was 0.92 (0.75, 1.14; P for trend = 0.09; P for heterogeneity = 0.16).

	DISCUSSION

TOP ABSTRACT INTRODUCTION SUBJECTS AND METHODS RESULTS DISCUSSION REFERENCES

 
In this prospective study of >110 000 female nurses and male health professionals, the risk of cataract extraction was not greater among those with the highest dietary glycemic loads. Similarly, there was no greater risk of cataract extraction in relation to glycemic index. Relations generally did not differ between the sexes.

We observed that, in both men and women, the highest RR of cataract extraction, particularly for nuclear cataract, was in the second quintle of dietary glycemic load. Although we have doubts regarding the importance of this finding, given the overall null results, we nevertheless were struck by the unusual consistency of this finding in the 2 separate large cohorts.

Misclassification of dietary exposure could theoretically affect estimates derived from this study. However, the SFFQ was designed to minimize the possibility of random within-person variation that could attenuate associations of interest. Furthermore, because of the prospective design of the study, measurement errors would be expected to be unrelated to the cataract extraction endpoint. We also updated information on diet in our analyses, thereby accounting for the possibility of dietary changes over time and minimizing measurement error. Although the SFFQ does not provide a perfect assessment of the total glycemic effect of diets, previous studies found significant relations of dietary glycemic load assessed by the SFFQ with other endpoints, including fasting triacylglycerol concentrations, coronary artery disease, diabetes mellitus, and pancreatic cancer in the NHS and HPFS populations (6-8, 25).

Residual confounding of the observed effects is also a concern, because adjustment for potential cataract risk factors, particularly cigarette smoking, did result in an appreciable change in the associations. To the extent that these confounding variables are not perfectly measured, there may be some residual confounding of our estimates by these or other unmeasured or unmeasurable factors.

Yet another potential concern relates to our use of cataract extraction as the endpoint for this analysis. The large size of the cohorts made repeated, standardized lens examinations impossible. Thus, we chose to use cataract extraction as the endpoint for this analysis as a way to minimize misclassification of disease status. In addition, cataract extraction should be a relatively good proxy for the more visually significant cataracts of greatest public health importance. We recognize that the timing of cataract surgery in the United States varies a good deal with respect to the level of visual impairment: ie, some people undergo surgery for "mild" lens opacities, and others opt to wait until the cataract is more "severe." However, because all subjects in the present study were health professionals, their access to medical care and threshold for surgery are likely to be more uniform than those of the general population. Moreover, there was no association between glycemic load and visual acuity at the time of surgery, which makes confounding unlikely. Indeed, in analyses restricted to cases in which the person’s visual acuity was reduced to <20/40 at the time of surgery, results did not differ from those for all extractions.

We excluded subjects with diabetes at baseline because prediagnosis dietary data were not available, and people with diabetes are often given recommendations to alter their diets and reduce carbohydrate consumption. We did not exclude those with an incident diagnosis of diabetes from our primary analysis because incident diabetes has been associated with a history of higher dietary glycemic load in these cohorts (6, 7), and, consequently, we thought it more appropriate to treat diabetes as an intermediate variable. In an effort to minimize the effect on our analysis of possible changes in dietary habits after the diagnosis, however, we did stop updating diets after the diagnosis. In secondary analyses in which we also excluded incident cases of diabetes or controlled for diabetes, results were similar.

Diabetes mellitus has long been recognized as a risk factor for cataract. Prospective epidemiologic data, including those from these same cohorts (26), indicate that the incidence and progression of cataract increased significantly among those with diabetes (27, 28) and that this increase appeared to be related to the overall degree of glycemia (27, 29). Other investigators observed a significant association between glycated hemoglobin concentrations and the risk of cataract (4) or loss of lens transparency (29) among diabetics. Glucose can freely pass into the lens, which is directly exposed to ambient glucose concentrations through the aqueous humor and vitreous (30). Hyperglycemia is thought to cause cataract via osmotic swelling, changes in membrane permeability, and oxidative stress brought about by the build-up of polyol (31), as well as via nonenzymatic glycosylation of lens proteins (32). Diets with a high glycemic load are associated with hyperglycemia and hyperinsulinemia (33, 34). However, our data suggest that, in the absence of diabetes, these metabolic alterations are not sufficient to increase the risk of age-related cataract. These metabolic alterations are exacerbated by underlying insulin resistance, because a larger amount of insulin is needed to compensate for the higher glycemic load (33). In the present study, we were not able specifically to identify those subjects who had insulin resistance, but associations of glycemic load with cataract extraction were not observed in the obese subjects, and those persons have a high probability of insulin resistance. It is possible that there is a threshold for hyperglycemia that must be reached before the risk of cataract is affected, and laboratory data in animals suggest that this threshold may be well within the diabetic range (17). Higher dietary glycemic load has also been associated with higher concentrations of the inflammatory biomarker C-reactive protein (35), which in turn have been associated with an increased risk of cataract in at least one prospective study (36).

In summary, although available evidence implicates hyperglycemia and possibly also low-grade systemic inflammation in the development of cataract, the present study shows that a higher dietary glycemic load, which has been shown to induce hyperglycemia and increase concentrations of the inflammatory biomarker C-reactive protein, is not a risk factor.

	ACKNOWLEDGMENTS

 
DAS conceptualized the study, designed the analytic approach, carried out the analyses, interpreted the data and drafted the manuscript. SL and WCW provided nutritional expertise for interpretation of the data and participated in revision of the manuscript. JMS was involved in designing the collection of cataract extraction data. SHE was involved in designing the analytic approach and interpreting the data and contributed significantly to revision of the manuscript. None of the authors had any conflict of interest associated with this manuscript.

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TOP ABSTRACT INTRODUCTION SUBJECTS AND METHODS RESULTS DISCUSSION REFERENCES

 

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